{"data":[{"id":"10.6086/d1709n","type":"dois","attributes":{"doi":"10.6086/d1709n","identifiers":[],"creators":[{"name":"Hwang, Kristy","nameType":"Personal","givenName":"Kristy","familyName":"Hwang","affiliation":["University of California San Diego"],"nameIdentifiers":[]},{"name":"Langley, Jason","nameType":"Personal","givenName":"Jason","familyName":"Langley","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9412-1265","nameIdentifierScheme":"ORCID"}]},{"name":"Tripath, Richa","nameType":"Personal","givenName":"Richa","familyName":"Tripath","affiliation":["West Virginia University"],"nameIdentifiers":[]},{"name":"Hu, Xiaoping","nameType":"Personal","givenName":"Xiaoping","familyName":"Hu","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Huddleston, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Huddleston","affiliation":["Emory University"],"nameIdentifiers":[]}],"titles":[{"title":"Replication of imaging metrics in Parkinson's disease"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Basic medicine","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Basic medicine","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[{"name":"University, Emory","nameType":"Personal","givenName":"Emory","familyName":"University","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2023-02-28T21:41:38Z","dateType":"Submitted"},{"date":"2023-03-01T00:00:00Z","dateType":"Issued"},{"date":"2023-03-01T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2022.02.23.22271356","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.pone.0282684","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["5666516137 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This data contains imaging data from two cohorts of Parkinson's\n disease and control subjects, recruited from the Emory Movement Disorders\n Clinic and scanned on two different MRI scanners. In Cohort 1, imaging\n data from 19 controls and 22 Parkinson’s disease patients were acquired\n with a Siemens Trio 3 Tesla scanner using a 2D gradient echo sequence with\n magnetization transfer preparation pulse. Cohort 2 consisted of 33\n controls and 39 Parkinson’s disease patients who were scanned on a Siemens\n Prisma 3 Tesla scanner with a similar imaging protocol.","descriptionType":"Abstract"},{"description":"Cohort 1 data was collected from 2012–2014 and included 19\n controls and 22 PD patients. Cohort 2 consisted of 33 healthy controls\n (HC) and 39 PD patients with data collected from 2015–2016. Controls were\n recruited from the community and the Emory Alzheimer’s Disease Research\n Center control population. MRI data for Cohort 1 were\n acquired with a Siemens Trio 3 Tesla scanner (Siemens Medical Solutions,\n Malvern, PA, USA) at Emory University with a 12-channel receive-only head\n coil. NM-MRI data was acquired using a 2D magnetization transfer (MT)\n prepared gradient echo (GRE) sequence [13, 14]: echo time (TE) /\n repetition time (TR) = 2.68 ms / 337 ms, slice thickness 3 mm, in plane\n resolution 0.39x0.39 mm\u003csup\u003e2\u003c/sup\u003e, field of view (FOV) =\n 162–200 mm\u003csup\u003e2\u003c/sup\u003e, flip angle (FA) = 40°, 470 Hz/pixel\n bandwidth, 15 contiguous slices, and magnetization transfer preparation\n pulse (300°, 1.2 kHz off resonance, 10 ms duration), 7 measurements, scan\n time 16 minutes 17 seconds. For registration from subject space to common\n space, a T1 magnetization-prepared rapid gradient echo (MP-RAGE) sequence\n was acquired with the following parameters: TE/TR= 3.02 ms/2600 ms,\n inversion time = 800 ms, FA=8°, voxel size = 1.0 x 1.0 x 1.0\n mm\u003csup\u003e3\u003c/sup\u003e. Cohort 2 was scanned with a\n Siemens Prisma 3 Tesla scanner using a 64-channel receive-only coil.\n NM-MRI data were acquired using a 2D GRE sequence with a MT preparation\n pulse: TE/TR = 3.10 ms /354 ms, 15 contiguous slices, FOV = 162–200\n mm\u003csup\u003e2\u003c/sup\u003e, in plane resolution = 0.39 x 0.39\n mm\u003csup\u003e2\u003c/sup\u003e), slice thickness = 3mm, 7 measurements, FA =\n 40°, 470 Hz/pixel receiver bandwidth, and MT pulse (300°, 1.2 kHz off\n resonance, 10 ms duration), scan time 17 minutes 12 seconds. For\n registration, structural images were acquired with an MP-RAGE sequence:\n TE/TR = 2.46 ms/1900 ms, inversion time = 900 ms, FA = 9°, voxel size =\n 0.8 x 0.8 x 0.8\n mm\u003csup\u003e3\u003csub\u003e.\u003c/sub\u003e\u003c/sup\u003e Data\n are raw and have not been processed.","descriptionType":"Methods"},{"description":"Any image processing software (i.e. SPM, FSL, AFNI, ImageJ).","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"Michael J. Fox Foundation","awardNumber":"MJFF-010556","funderIdentifier":"https://ror.org/03arq3225","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Michael J. Fox Foundation","awardNumber":"MJFF-010854","funderIdentifier":"https://ror.org/03arq3225","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1709N","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":194,"downloadCount":27,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-03-01T23:17:55Z","registered":"2023-03-01T23:17:56Z","published":null,"updated":"2026-03-17T15:57:33Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d19q37","type":"dois","attributes":{"doi":"10.6086/d19q37","identifiers":[],"creators":[{"name":"Close, Timothy","nameType":"Personal","givenName":"Timothy","familyName":"Close","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9759-3775","nameIdentifierScheme":"ORCID"}]},{"name":"Oyatomi, Olaniyi","nameType":"Personal","givenName":"Olaniyi","familyName":"Oyatomi","affiliation":["International Institute of Tropical Agriculture"],"nameIdentifiers":[]},{"name":"Guo, Yi-Ning","nameType":"Personal","givenName":"Yi-Ning","familyName":"Guo","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Paliwal, Rajneesh","nameType":"Personal","givenName":"Rajneesh","familyName":"Paliwal","affiliation":["International Institute of Tropical Agriculture"],"nameIdentifiers":[]},{"name":"Muñoz-Amatriaín, María","nameType":"Personal","givenName":"María","familyName":"Muñoz-Amatriaín","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Roberts, Philip","nameType":"Personal","givenName":"Philip","familyName":"Roberts","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Abberton, Michael","nameType":"Personal","givenName":"Michael","familyName":"Abberton","affiliation":["International Institute of Tropical Agriculture"],"nameIdentifiers":[]},{"name":"Marimagne, Tchamba","nameType":"Personal","givenName":"Tchamba","familyName":"Marimagne","affiliation":["International Institute of Tropical Agriculture"],"nameIdentifiers":[]},{"name":"Boukar, Ousmane","nameType":"Personal","givenName":"Ousmane","familyName":"Boukar","affiliation":["International Institute of Tropical Agriculture"],"nameIdentifiers":[]},{"name":"Fatokun, Christian","nameType":"Personal","givenName":"Christian","familyName":"Fatokun","affiliation":["International Institute of Tropical Agriculture"],"nameIdentifiers":[]},{"name":"Huynh, Bao Lam","nameType":"Personal","givenName":"Bao Lam","familyName":"Huynh","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6845-125X","nameIdentifierScheme":"ORCID"}]},{"name":"Lonardi, Stefano","nameType":"Personal","givenName":"Stefano","familyName":"Lonardi","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Lucas, Mitchell R.","nameType":"Personal","givenName":"Mitchell R.","familyName":"Lucas","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Lo, Sassoum","nameType":"Personal","givenName":"Sassoum","familyName":"Lo","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Senn, Savanah","nameType":"Personal","givenName":"Savanah","familyName":"Senn","affiliation":["University of California, Riverside"],"nameIdentifiers":[]}],"titles":[{"title":"SNP genotypes of the international institute of tropical agriculture Cowpea Core"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"Vigna unguiculata"},{"subject":"Cowpea"},{"subject":"gentoyping"},{"subject":"SNPs"},{"subject":"IITA Core"},{"subject":"Plant science","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Genetics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Other agricultural sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Other agricultural sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2023-09-21T17:04:19Z","dateType":"Created"},{"date":"2023-09-21T17:06:34Z","dateType":"Submitted"},{"date":"2023-09-26T00:00:00Z","dateType":"Issued"},{"date":"2023-09-26T00:00:00Z","dateType":"Available"},{"date":"2023-10-19T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1017/s1479262107837166","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/tpj.13404","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/tpj.14349","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/leg3.95","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/tpg2.20319","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/g3journal/jkae071","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2023.12.21.572659","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["718356597 bytes"],"formats":[],"version":"6","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Cowpea (Vigna unguiculata [L.], Walp.) is a member of the family Fabaceae,\n subfamily Faboideae (a.k.a. Papilionoideae) and tribe Phaseoleae, along\n with other “warm season” legumes such as soybean, common bean, mung bean,\n adzuki bean, Bambara groundnut and others. The International Institute of\n Tropical Agriculture (IITA) in Ibadan, Nigeria maintains the world’s\n largest collection of cowpea germplasm with a collection size of 16,460\n accessions as of September 2023. Information on these accessions is\n available online (https://my.iita.org/accession2/collection.jspx?id=1),\n including passport data, characterization descriptors and images. A subset\n known as the “IITA Cowpea Core” is comprised of nearly 2,100 accessions\n (Mahalakshmi et al. 2007; doi: 10.1017/S1479262107837166). Here we present\n single nucleotide polymorphism (SNP) data for most of the accessions in\n the IITA Cowpea Core, along with a few simple observations resulting from\n analyses of the SNP data. The SNP data were generated using the Illumina\n iSelect Cowpea Consortium Array, described in Muñoz-Amatriaín et al. 2017\n (doi: 10.1111/tpj.13404). A characteristic of this platform is that\n missing data (a.k.a. “nocall”) is nearly always either attributable to a\n SNP assay that failed technically and is excluded from all samples\n (“zeroed”) or a result of either the absence of a DNA segment in the\n genome or a sequence difference near the SNP position that precludes a\n successful assay. As a consequence, the frequency of “nocall” provides a\n broad indicator of whether or not a given accession is in the same species\n as cowpea. A few accessions in the IITA Cowpea Core are outliers, as\n follows. Three accessions (TVu-14726, TVu-16409, TVu-8383) had more than\n 31,000 nocalls (after excluding 1,863 zeroed SNPs) and fairly low (181 or\n 246) or moderate (1,337) heterozygous calls, indicating that these are not\n in the same species as cowpea. The passport data from IITA notes TVu-8383\n as “wild” and TVu-14726 as “landrace”. There is no additional passport\n information on TVu-16409, but based on its SNP characteristics TVu-16409\n clearly also is not in the same species as cowpea. Three other accessions\n (TVu-5540, TVu-6968, TVu-14935) have from 8,985 to 10,025 nocalls (after\n excluding 1,863 zeroed SNPs), which indicates that these accessions are\n more closely related to cowpea, but also are from a different species.\n Among these three accessions, TVu-14935 also had 18,134 heterozygous SNPs,\n which indicates that the plant representing this accession was not highly\n inbred. Residual heterozygosity is a common characteristic among single\n plant representatives of germplasm accessions, which begin as one or more\n seeds collected from their original open-pollenating location and then\n proceed through a variable number of selfed generations to become more\n inbred in germplasm collections. Five accessions that are stated in the\n passport data to be from three V. unguiculata subspecies other than\n subspecies unguiculata had the same range of nocalls (708 to 943) as\n accessions reported to be subspecies unguiculata, consistent with the\n expectation that the cultivated subspecies all are closely related to each\n other. Two of these (TVu-3661 and TVu-3662) are stated in the passport\n data to be subspecies dekindtiana, which is generally considered to be the\n reservoir of variation for subspecies unguiculata, and one (TVu-3657) is\n stated to be subspecies cylindrica. The other two (TVu-3652, TVu-3656) of\n these five accessions are stated to be subspecies sesquipedalis, which has\n been well documented to be readily crossable with subspecies unguiculata.\n It should be noted also that there are several other accessions from Asia\n that are not specifically marked as sesquipedalis. Analysis of the overall\n population structure of the IITA Cowpea Core places these Asian accessions\n within the same sub-population as the accessions stated to be sub-species\n sesquipedalis. Based on principle component analysis, five sub-populations\n are evident among the IITA Cowpea Core, one from West Africa represented\n by Sanzi, another from West Africa represented by Suvita-2, one from Asia\n represented by TZ30 and ZN016, one from Northeast Africa, Europe and\n California represented by CB5-2, and one from South and East Africa\n represented by UCR779. It is anticipated that the IITA Cowpea Core SNP\n dataset can provide a useful resource for a number of genome-wide\n association studies (GWAS) and decisions related to germplasm management.","descriptionType":"Abstract"},{"description":"Young, tender leaf tissue was excised from one plant of each\n accession of the IITA Cowpea Core collection, then dried prior to DNA\n extraction. A total of 1,789 plants that provided leaf tissue were grown\n at the International Institute of Tropical Agriculture (IITA) in Ibadan,\n Nigeria and 15 at the IITA in Kano, Nigeria. Leaves from 1,655 of these\n plants were dried inside sealable plastic bags containing packets of\n silica gel, then shipped at ambient temperature from IITA (Ibadan and\n Kano) to the University of California in Riverside (UCR), California, USA.\n An additional 232 tissue samples from IITA Cowpea Core accessions that\n were included in the “UCR Minicore” (Muñoz-Amatriaín et al. 2021; doi:\n 10.1002/leg3.95) were prepared the same way (young leaves, dried with\n silica gel packets) from individual plants grown in greenhouses at UCR.\n DNA was extracted at UCR from each of these 1,887 dried leaf samples using\n either Qiagen (https://www.qiagen.com/us) DNeasy Plant or Machery-Nagel\n (https://www.mn-net.com/us/) NucleoMag kits. DNA was prepared at IITA from\n desiccated leaf tissue of an additional 149 accessions using a CETAB\n method, then these DNA solutions were sent to UCR at ambient temperature.\n All of these 2,036 DNA samples (10 µL each) were arranged in 96-well\n plates at UCR at concentrations ranging from 50 to 450 ng/µL, then\n transported to the University of Southern California Molecular Genomics\n Core facility (https://uscnorriscancer.usc.edu/molecular-genomics-core/) for single nucleotide polymorphism (SNP) genotyping using the Illumina (https://www.illumina.com/) iSelect Cowpea Consortium Array, which was described in Muñoz-Amatriaín et al. 2017. The tissue production, DNA extraction and genotyping occurred incrementally over a period of 6.5 years from May 2014 through November 2020. Raw SNP data and sample sheets were transferred to UCR by FTP, then imported into the Illumina GenomeStudio software using a cluster file developed in 2014 to 2015 at UCR for broad cowpea germplasm, then exported as “Forward Strand” in a tab-delimited text file. SNP data from seven diverse cowpea accessions (CB5-2, IT97K-499-35, Sanzi, Suvita-2, TZ30, UCR779, ZN016) that have been assembled as described in Liang et al. 2023 (doi: 10.1002/tpg2.20319) also were included, taking the total number of accessions in this dataset to 2,043. There was no apparent difference in the quality or completeness of the SNP data, regardless of the DNA extraction method or DNA concentration in this range. The orientation of the cowpea iSelect “Forward Strand” is arbitrary relative to the “Watson” strand of the assembled genome sequence of accession IT97K-499-35 (Lonardi et al. 2019; doi: 10.1111/tpj.14349). So, in addition to providing the SNP data as iSelect “Forward Strand”, which has been used for numerous publications, here we provide the SNP data in a spreadsheet containing two sheets, with the first sheet being the SNPs according to the “Watson” strand, and the second sheet being the SNPs as iSelect “Forward Strand”. Additional information in the spreadsheet includes chromosome (or contig if unmapped), nucleotide position in IT97K-499-35, and other information indicating confidence to use or exclude the data for a given SNP, as described in Liang et al. 2023 Table S03. \u003cstrong\u003eUsage notes:\u003c/strong\u003e The two-sheet SNP dataset is provided as a .xlsx file, which is a zipped, XML-based file format that can be opened with Microsoft Excel (Office 2007 or later), LibreOffice Calc, Google Sheets, Apache OpenOffice and others.","descriptionType":"Methods"},{"description":"# SNP Genotypes of the International Institute of Tropical Agriculture\n Cowpea Core\n [https://doi.org/10.6086/D19Q37](https://doi.org/10.6086/D19Q37) The\n two-sheet SNP dataset is provided as a .xlsx file, which is a zipped,\n XML-based file format that can be opened with Microsoft Excel (Office 2007\n or later), LibreOffice Calc, Google Sheets, Apache OpenOffice and others.\n The URLs for the cited references are as follows. Liang et al. 2023;\n https://doi.org/10.1002/tpg2.20319 Lonardi et al. 2019;\n https://doi.org/10.1111/tpj.14349 Mahalakshmi et al. 2007;\n https://doi.org/10.1017/S1479262107837166 Muñoz-Amatriaín et al. 2017;\n https://doi.org/10.1111/tpj.13404 Muñoz-Amatriaín et al. 2021;\n https://doi.org/10.1002/leg3.95 ## Description of the data and file\n structure Missing data is indicated as \"--\". Of the 51,128\n attempted SNP assays using the Illumina iSelect Cowpea Consortium Array, a\n total of 1,863 SNP assays that failed technically were \"zeroed\n out\" in the Illumina GenomeStudio workspace as missing data\n (\"--\") for all samples, leaving a total of 49,265 SNP assays\n whose data were further considered. As noted in the methods, and in\n reference to Liang et al. 2023 Table S03, 677 additional SNPs were\n excluded. Most of these 677 excluded SNP assays provided frequencies of\n heterozygous calls that are impossible given the highly inbred nature of\n the accessions, leaving a total of 48,588 SNPs with data deemed to be\n reliable for further analyses. For these filtered 48,588 SNPs, missing\n data is most often indicative of one of two situations. One is\n presence/absence variation (PAV), which for many SNPs involves two or more\n adjacent SNPs (when sorted by position) that are absent in unison across a\n large portion of accessions. The other is a mismatch between the target\n sequence abutting a SNP position and the SNP-interrogating\n oligonucleotide, a situation that precludes determination of the SNP\n allele. The frequency of this latter situation parallels the genetic\n distance between a given accession and the set of subspecies *unguiculata*\n accessions from which the SNP assays were designed. The first sheet in the\n SNP data table (Watson strand for the sheet named “v1.0\\_Watson”) has\n column organization as follows. Column A: SNP name, sorted\n alphanumerically by name. Column B: chromsome number (or unmapped contig\n name) as per Liang et al. 2023. Column C: nucleotide position within\n chromosome or unmapped contig as per Liang et al. 2023. Column D: the\n allele call on the Watson strand of the assembled genome of IT97K-499-35,\n as per Liang et al. 2023. Column E: the alternate allele on the Watson\n strand at the SNP position, as per Liang et al. 2023. Column F: the two\n possible SNP alleles on the Watson strand. Column G: the two possible SNP\n alleles considering the iSelect Forward Strand. Column H: the orientation\n of the iSelect Forward Strand for SNP calls relative to the Watson strand.\n Column I: recommendation to exclude the SNP from data analyses. Columns J\n through BZX the set of SNP calls for each of 2,043 accessions, which\n includes 2,036 IITA Cowpea Core accessions and the 7 assembled genomes\n described in Lonardi et al. 2019 (IT997K-499-35 only) and Liang et al.\n 2023. The second sheet in the SNP data table (iSelect Forward Strand for\n the sheet named “iSelect\\_FWD”) has column organization as follows. Column\n A: SNP name, sorted alphanumerically by name. Columns B through BZP the\n set of SNP calls for each of 2,043 accessions, which includes 2,036 IITA\n Cowpea Core accessions and the 7 assembled genomes described in Lonardi et\n al. 2019 (IT997K-499-35 only) and Liang et al. 2023. ## Sharing/Access\n information No additional Sharing/Access information. ## Code/Software No\n Code/Software.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"IOS-1543963","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"United States Agency for International Development","awardNumber":"AID-OAA-A-13-00070","funderIdentifier":"https://ror.org/01n6e6j62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"United States Agency for International Development","awardNumber":"EDH-A-00-07-00005-00","funderIdentifier":"https://ror.org/01n6e6j62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Bill \u0026 Melinda Gates Foundation","awardNumber":"OPP1114827","funderIdentifier":"https://ror.org/0456r8d26","funderIdentifierType":"ROR"},{"schemeUri":"https://www.crossref.org/services/funder-registry/","funderName":"Crop Trust","funderIdentifier":"https://doi.org/10.13039/501100018774","funderIdentifierType":"Crossref Funder ID"}],"url":"https://datadryad.org/dataset/doi:10.6086/D19Q37","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":318,"downloadCount":59,"referenceCount":0,"citationCount":6,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-09-27T06:47:15Z","registered":"2023-09-27T06:47:16Z","published":null,"updated":"2026-03-14T22:05:50Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1t10v","type":"dois","attributes":{"doi":"10.6086/d1t10v","identifiers":[],"creators":[{"name":"Clark, Chris","nameType":"Personal","givenName":"Chris","familyName":"Clark","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7943-9291","nameIdentifierScheme":"ORCID"}]},{"name":"Myers, Brian","nameType":"Personal","givenName":"Brian","familyName":"Myers","affiliation":["Cal Poly Ponoma"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6485-8127","nameIdentifierScheme":"ORCID"}]},{"name":"Rankin, David","nameType":"Personal","givenName":"David","familyName":"Rankin","affiliation":["University of California, Riverside"],"nameIdentifiers":[]}],"titles":[{"title":"Sound recordings of courtship displays of Allen's (Selasphorus sasin), Rufous (S. rufus), and Hybrid (S. sasin x S. rufus) hummingbirds recorded between 2014 and 2021 in California, Oregon, and Alaska"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2022-01-27T19:13:05Z","dateType":"Submitted"},{"date":"2022-03-15T00:00:00Z","dateType":"Issued"},{"date":"2022-03-15T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/auk/ukz049","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/ece3.7174","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.anbehav.2022.01.018","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["56758400059 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"These are sound recordings of courtship displays that are associated with\n the 386 individual Selasphorus hummingbirds that are the focus of analyses\n presented in Myers et al 2022. A subset of these birds were also used in\n Myers et al. (2019), and 159 of them have been deposited as\n specimens in the San Diego State University Museum of Biodiversity or the\n San Diego Natural History Museum.  Many recordings will include a\n verbal tag from CJC, BMM or DTR stating relevant recording conditions\n (e.g. date, locality). Each subfolder contains the recordings for 1 bird.\n Unfortunately, recordings for 15 birds were lost in a hard-drive\n crash.  Moreover, we recorded many additional birds which we were\n thereafter unable to catch. Some of those not-analyzed birds are also\n included here, even though they were not included in the associated\n studies, since we lacked a DNA sample, morphometrics, or feathers.","descriptionType":"Abstract"},{"description":"Sound recordings of\n courtship displays of wild hummingbirds including Allen's\n (\u003cem\u003eSelasphorus sasin\u003c/em\u003e), Rufous (\u003cem\u003eSelasphrous\n rufus\u003c/em\u003e) and their hybirds (\u003cem\u003eSelasphorus sasin\n \u003c/em\u003ex \u003cem\u003eSelasphorus rufus\u003c/em\u003e). Sound recordings\n have been placed in folders by recording year, then according to a\n field identifier (such as a field ID, or a locality). All files are the\n original .wav.  Generally the recordings are un-cropped, except in\n rare instances in which the recorder was accidentally left running for a\n long time. The\n full dataset has been split into 13 different zipped folders:\n \"2014\", \"2015\", \"hybrid localities\n 2016-2018\" (6 parts), \"Allen's localities 2016-2018\",\n \"Rufous localities 2016-2018\", \"2019 Brian\" \"2019\n David and Chris\" and \"2021\".  Please refer to Myers et al 2022\n for field recording methods. ","descriptionType":"Methods"},{"description":"The recordings are 16 or 24 bit .wav files, recorded at 44.1, 48,\n or 96 kHz. CJC generally recorded with a Sound Devices 702 recorder and a\n Telinga pro parabola with a Sennheiser MKH 20 microphone. BMM recorded\n with combinations of a Sennheiser MKH 70 shotgun microphone, an\n Audio-Technica AT875R shotgun microphone, a Tascam DR-05 portable\n recorder, a Tascam DR-60DmkII audio recorder. DTR recorded with any of the\n above. CJC recordings from Ketchikan, Alaska in 2018 also include voice of\n Alan Brelsford, while BMM recordings from 2018 include Zachary Williams\n and in 2019 include voice of Kevin Burns. Some\n sub-folders contain recordings as initially named by the recorder (name is\n unedited); others the file name has been edited (but retains original name\n given by the recorder). For birds only identified by a field ID, an\n associated table \"bird locality \u0026amp; lookup table.xls\"\n should provide enough information to associate each individual folder with\n the \"Main ID\" provided in the paper.  Also note that,\n occasionally, some display bouts will span multiple recordings, if (for\n example) the 'rec' button on a recorder got pressed multiple\n times during a display bout.  Note that, as the\n recordings were verbally annotated in real time, it was somewhat common\n for the recordist mis-speak in the recorded annotations. This was\n particularly true for repetitions pendulum display counts, because it was\n easy to mis-count the number of pendulum displays in real-time.  Therefore\n the digitized sequences reflect our analysis of the sounds themselves\n rather than the verbatim verbal tag. Also note: the definitions of\n behaviors are as given in Myers et al 2022. See the supplemental\n materials, which includes figure S1 that shows example sequences with\n multiple possible mappings from sounds onto defined behaviors. \n Allen's type dives begin with what sounds like a pendulum display\n (the 'incorporated pendulum') but was coded as part of the dive,\n and not a pendulum. ","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"IOS-1656867","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"IOS-1656708","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1T10V","contentUrl":null,"metadataVersion":10,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":118,"downloadCount":9,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-03-15T23:15:25Z","registered":"2022-03-15T23:15:26Z","published":null,"updated":"2026-03-13T21:50:32Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d14t2b","type":"dois","attributes":{"doi":"10.6086/d14t2b","identifiers":[],"creators":[{"name":"Schreiner-McGraw, Adam","nameType":"Personal","givenName":"Adam","familyName":"Schreiner-McGraw","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3424-9202","nameIdentifierScheme":"ORCID"}]},{"name":"Ajami, Hoori","nameType":"Personal","givenName":"Hoori","familyName":"Ajami","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6883-7630","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Impact of uncertainty in precipitation forcing datasets on the hydrologic budget of an integrated hydrologic model in mountainous terrain"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[],"contributors":[],"dates":[{"date":"2021-02-16T23:57:05Z","dateType":"Submitted"},{"date":"2021-02-22T00:00:00Z","dateType":"Issued"},{"date":"2021-02-22T00:00:00Z","dateType":"Available"},{"date":"2021-02-26T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2020wr027639","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["9525163744 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Precipitation is a key input variable in distributed surface\n water-groundwater models, and its spatial variability is expected to\n impact watershed hydrologic response via changes in subsurface flow\n dynamics. Gridded precipitation datasets based on gauge observations,\n however, are plagued by uncertainty, especially in mountainous terrain\n where gauge networks are sparse. To examine the mechanisms via which\n uncertainty in precipitation data propagates through a watershed, we\n perform a series of numerical experiments using an integrated surface\n water-groundwater hydrologic model, ParFlow.CLM. The Kaweah River\n watershed in California, USA is used as our virtual catchment laboratory\n to characterize watershed response to variable precipitation forcing from\n headwaters to groundwaters. By applying the three cornered hat method, we\n quantify the spatially distributed uncertainty in four publically\n available precipitation forcing datasets and their simulated hydrology.\n Simulations demonstrate that uncertainty in the simulated groundwater\n storage is primarily a result of topographic redistribution of uncertainty\n in precipitation forcing. Soil water redistribution is the primary pathway\n that redistributes uncertainty downslope. We also find that topography\n exerts a larger impact than variable subsurface parameters on propagating\n uncertainty in simulated fluxes. Finally, we find that improvement in\n model performance metrics is higher for a single simulation forced with\n the mean precipitation from the available datasets than the averaged\n simulated results of separate simulations forced with each dataset.\n Results from this study highlight the importance of topography-moderated\n flow through the critical zone in shaping the groundwater response to\n climate variability.","descriptionType":"Abstract"},{"description":"See README for Usage Notes","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6086/D14T2B","contentUrl":null,"metadataVersion":10,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":144,"downloadCount":16,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-02-22T19:30:46Z","registered":"2021-02-22T19:30:48Z","published":null,"updated":"2026-03-11T20:58:34Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d10m3d","type":"dois","attributes":{"doi":"10.6086/d10m3d","identifiers":[],"creators":[{"name":"Wu, Guoyuan","nameType":"Personal","givenName":"Guoyuan","familyName":"Wu","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6707-6366","nameIdentifierScheme":"ORCID"}]},{"name":"Zhao, Zhouqiao","nameType":"Personal","givenName":"Zhouqiao","familyName":"Zhao","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5286-3807","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"PTV VISSIM simulation data for efficient eco-ramp control project funded by NCST 18-19"}],"publisher":"Dryad","container":{},"publicationYear":2019,"subjects":[],"contributors":[],"dates":[{"date":"2019-10-04T09:40:17Z","dateType":"Submitted"},{"date":"2019-10-20T00:00:00Z","dateType":"Issued"},{"date":"2019-10-20T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"https://rosap.ntl.bts.gov/view/dot/55700","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["45354731 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Our current transportation system faces a variety of issues in terms of\n safety, mobility, and environmental sustainability. The emergence of\n innovative intelligent transportation system (ITS) technologies such as\n connected and automated vehicles (CAVs) and transportation electrification\n unfold unprecedented opportunities to address aforementioned issues. In\n this project, we propose a hierarchical ramp control system that\n not only allows microscopic cooperative maneuvers for connected and\n automated electric vehicles (CAEVs) on the ramp to merge into mainline\n traffic flow under certain controlled ramp inflow rate,  but\n also enables macroscopic corridor-level traffic flow control\n (i.e., coordinated ramp metering rate determination). A centralized\n optimal control-based approach is proposed to both smooth the merging flow\n and improve the system-wide mobility of the network. Linear quadratic\n trackers in both finite horizon and receding horizon forms are developed\n to solve the optimization problem in terms of path planning and sequence\n determination, and a microscopic electric vehicle (EV) energy consumption\n model is applied to estimate the energy consumption. Finally, traffic\n simulation is conducted through PTV VISSIM to evaluate the impact of the\n proposed system on a highway segment. The results confirm that under the\n regulated inflow rate, the proposed system can avoid potential traffic\n congestion and improve mobility significantly up to 102% compared to the\n conventional ramp metering and the ramp without any control approach.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6086/D10M3D","contentUrl":null,"metadataVersion":16,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":347,"downloadCount":44,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2019-10-20T23:47:11Z","registered":"2019-10-20T23:47:12Z","published":null,"updated":"2026-03-11T17:17:18Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d11h3p","type":"dois","attributes":{"doi":"10.6086/d11h3p","identifiers":[],"creators":[{"name":"Hao, Peng","nameType":"Personal","givenName":"Peng","familyName":"Hao","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5864-7358","nameIdentifierScheme":"ORCID"}]},{"name":"Wei, Zhensong","nameType":"Personal","givenName":"Zhensong","familyName":"Wei","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3523-5689","nameIdentifierScheme":"ORCID"}]},{"name":"Barth, Matthew","nameType":"Personal","givenName":"Matthew","familyName":"Barth","affiliation":["University of California, Riverside"],"nameIdentifiers":[]}],"titles":[{"title":"Speed trajectory data from adaptive eco-driving applications"}],"publisher":"Dryad","container":{},"publicationYear":2019,"subjects":[],"contributors":[],"dates":[{"date":"2019-10-02T17:00:23Z","dateType":"Submitted"},{"date":"2019-10-17T00:00:00Z","dateType":"Issued"},{"date":"2019-10-17T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"https://rosap.ntl.bts.gov/view/dot/54634","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["25029600 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The eco-approach and departure (EAD) application for signalized\n intersections has been proved to be environmentally efficient in a\n Connected and Automated Vehicles (CAVs) system. In the real-world traffic,\n the traffic-related information received from sensing or communication\n devices is highly uncertain due to the limited sensing range and varying\n driving behaviors of other vehicles. This uncertainty increases the\n difficulty to predict the actual queue length of the downstream\n intersection. It further brings great challenge to derive an energy\n efficient speed profile for vehicles to follow. This research\n proposes an adaptive strategy for connected eco-driving towards a\n signalized intersection under real world conditions including uncertain\n traffic condition. A graph-based model is created with nodes representing\n dynamic states of the host vehicle (distance to intersection and current\n speed) and indicator of queue status and directed edges with weight\n representing expected energy consumption between two connected states.\n Then a dynamic programing approach is applied to identify the optimal\n speed for each vehicle-queue-signal state iteratively from downstream to\n the upstream. The uncertainty can be addressed by formulating stochastic\n models when describing the transition of queue-signal state. For uncertain\n traffic conditions, numerical simulation results show an average energy\n saving of 9%. It also indicates that energy consumption of a vehicle\n equipped with adaptive EAD strategy and a 100m-range sensor is equivalent\n to a vehicle with conventional EAD strategy and a 190m-range sensor. To\n some extent, the proposed strategy could double the effective detection\n range in eco-driving.","descriptionType":"Abstract"},{"description":"The trajectory data was collected from numerical simulation using\n three types of methods including the proposed method in this research, the\n ideal method and other baseline EAD\n methods.  The proposed method corresponds\n to the adaptive strategy for connected eco-driving with known\n historical queue distribution. The ideal trajectory for\n absolute minimum energy consumption can be derived when the actual queue\n length is known (i.e. perfect information) at the beginning of the\n simulation. This strategy can only be achieved if all vehicles are\n connected to share their positions to the study vehicle.  Besides the\n ideal method, couple of baseline EAD methods\n (Baseline\u003csub\u003ek\u003c/sub\u003e) are setup for comparison: Assuming the\n queue length to be \u003ci\u003eQ\u003csub\u003ek\u003c/sub\u003e\u003c/i\u003e, the\n vehicle first follows the ideal trajectory of the assumed\n \u003ci\u003eQ\u003csub\u003ek\u003c/sub\u003e\u003c/i\u003e length, then change to the\n corresponding strategy after detecting the real queue length. These\n baselines are the methods given the same information as the proposed\n method except the historical queue distribution is missing. Note that if\n \u003ci\u003ek\u003c/i\u003e is 0, Baseline0 corresponds to the scenario when the\n vehicle follows the existing EAD strategy with no-queue assumption until\n the sensor detects preceding traffic.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://www.crossref.org/services/funder-registry/","funderName":"National Center for Sustainable Transportation Technology","awardNumber":"UCR-DOT-510","funderIdentifier":"https://doi.org/10.13039/501100018880","funderIdentifierType":"Crossref Funder ID"}],"url":"https://datadryad.org/dataset/doi:10.6086/D11H3P","contentUrl":null,"metadataVersion":17,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":331,"downloadCount":62,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2019-10-17T15:02:38Z","registered":"2019-10-17T15:02:38Z","published":null,"updated":"2026-03-11T17:13:49Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d16h37","type":"dois","attributes":{"doi":"10.6086/d16h37","identifiers":[],"creators":[{"name":"Rotenberry, John","nameType":"Personal","givenName":"John","familyName":"Rotenberry","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0864-1676","nameIdentifierScheme":"ORCID"}]},{"name":"Balasubramaniam, Priya","nameType":"Personal","givenName":"Priya","familyName":"Balasubramaniam","affiliation":["none"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Estimating egg mass-body mass relationships in birds"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[{"subject":"Egg mass"},{"subject":"avian eggs"},{"subject":"avian life-history"}],"contributors":[],"dates":[{"date":"2020-04-13T19:10:04Z","dateType":"Submitted"},{"date":"2021-04-20T00:00:00Z","dateType":"Issued"},{"date":"2021-04-20T00:00:00Z","dateType":"Available"},{"date":"2020-09-18T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"https://www.esapubs.org/archive/ecol/E095/178/","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"https://www.esapubs.org/archive/ecol/E096/269/","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/auk/ukaa019","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["910817 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The mass of a bird’s egg is a critical attribute of the species’ life\n history and represents a fundamental component of reproductive\n effort.  Indeed, the trade-off between the number of eggs in a\n clutch and clutch mass lies at the heart of understanding how\n environmental attributes such as nest predation or adult mortality\n influence reproductive investment.  However, egg masses have not\n been reported for the majority of avian species.  We capitalized\n on the strong allometric relationship between avian body mass and egg mass\n to produce egg mass estimates for over 5,500 species previously lacking\n such information.  These estimates are accompanied by measures of\n the robustness of the regressions used to produce them (e.g., sample size,\n root mean square error of estimation, coefficient of determination, degree\n of extrapolation), thus allowing independent evaluation of the suitability\n of any estimate to address a particular research question relating to\n avian life history.  Most estimates (~5,000) were based on family\n level egg mass-body mass regressions, with the remainder derived from\n other relationships such as ordinal regressions.  We compared\n estimating regressions based on adult vs. female body masses, and after\n finding little difference between the two based our final estimates on\n adult masses as those were more numerous in the literature.  What\n small differences between adult- and female-based regressions that did\n occur were not related to sexual size dimorphism across\n families.  These new estimates, coupled with ~5,000 egg masses\n reported in the literature, provide a foundation of over 10,000 species\n for wider investigations assessing variation in reproductive effort in\n birds over a broad array of ecological and evolutionary\n contexts. ","descriptionType":"Abstract"},{"description":"Avian egg mass and body mass data downloaded from various\n published sources as indicated in Methods.  For the large majority of\n species for which egg masses had not been reported we estimated them based\n on the ordinary least squares regression relationship derived from other\n species with known values in the same family.","descriptionType":"Methods"},{"description":"See ReadMe file for contents of each dataset.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6086/D16H37","contentUrl":null,"metadataVersion":20,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":507,"downloadCount":80,"referenceCount":0,"citationCount":3,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-04-20T20:24:01Z","registered":"2020-04-20T20:24:02Z","published":null,"updated":"2026-03-05T00:29:25Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d11974","type":"dois","attributes":{"doi":"10.6086/d11974","identifiers":[],"creators":[{"name":"Peng, Dongbo","nameType":"Personal","givenName":"Dongbo","familyName":"Peng","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9857-3303","nameIdentifierScheme":"ORCID"}]},{"name":"Wu, Guoyuan","nameType":"Personal","givenName":"Guoyuan","familyName":"Wu","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Boriboonsomsin, Kanok","nameType":"Personal","givenName":"Kanok","familyName":"Boriboonsomsin","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2558-5343","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Developing an efficient dispatching strategy to support commercial fleet electrification"}],"publisher":"Dryad","container":{},"publicationYear":2024,"subjects":[{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Battery eletric truck dispatching"},{"subject":"Fleet operation"},{"subject":"Vehicle routing problem with backhauls"}],"contributors":[],"dates":[{"date":"2023-06-29T06:24:07Z","dateType":"Created"},{"date":"2023-06-29T06:25:10Z","dateType":"Submitted"},{"date":"2023-07-05T00:00:00Z","dateType":"Issued"},{"date":"2023-07-05T00:00:00Z","dateType":"Available"},{"date":"2024-01-02T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.3390/su15020925","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["450126 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This is a real-world dataset in a full-service supply chain company to\n evaluate the performance of our proposed battery electric truck\n dispatching strategy. We generated four instances ranging from 47 to 90\n customers based on the real-world dataset, the typical one-day historical\n movements of a heavy-duty diesel truck fleet that operated in the\n Riverside and San Bernardino County regions of California.","descriptionType":"Abstract"},{"description":"## Developing an Efficient Dispatching Strategy to Support Commercial\n Fleet Electrification \\[dataset] The generated four fleet operation\n instances ranging from 47 to 90 customers are based on a real-world\n dataset, the typical one-day historical movements of a heavy-duty diesel\n truck fleet that operated in the Riverside and San Bernardino County\n regions of California. ## Description of the data and file structure There\n are three types of CSV files: problem instance (BETVRPB), related distance\n matrix (BETVRPB_dist), and time matrix (BETVRPB_time). The distance matrix\n and time matrix files are used for the calculation. The detailed\n information is illustrated in the following. For the problem instances,\n the generated battery electric truck (vehicle) routing problem with\n backhauls (denoted BETVRPB) instances in our case study are described in\n the CSV files, i.e., BETVRPB1-BETVRPB4. It contains delivery ID, tractor\n ID, delivery types (i.e., pickups and deliveries), service time duration,\n required demands, and city name. That information is shown in the columns\n of the file BETVRPB from left to right, respectively. The depot time\n window section is shown in the last row of each problem instance. For each\n dispatching instance, the distance [in meter] and travel duration [in\n second] matrices are generated by OpenRouteService (openrouteservice.org)\n for the truck routes between node-to-node locations, which can be used to\n estimate the trip energy consumption. There is an example illustrating how\n to use the generated instances. Take the problem instance BETVRPB1 as an\n example. It contains 47 nodes, where the depot ID is 0, and others are\n customers (from 1 to 46). The related distance and trip time information\n is illustrated in files 'BETVRPB1_dist' and\n 'BETVRPB1_time' for calculation steps. For example, the truck\n travel distance from the depot (0) to the customer ID1 is the element\n (0,1) in the distance matrix file (i.e., BETVRPB1_dist). The calculation\n of travel time is similar to the travel distance.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of Transportation","awardNumber":"69A3551747114","funderIdentifier":"https://ror.org/02xfw2e90","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D11974","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":190,"downloadCount":25,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-07-05T17:59:51Z","registered":"2023-07-05T17:59:52Z","published":null,"updated":"2026-01-28T15:17:27Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d15091","type":"dois","attributes":{"doi":"10.6086/d15091","identifiers":[],"creators":[{"name":"Duro, Alyssa","nameType":"Personal","givenName":"Alyssa","familyName":"Duro","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7006-5219","nameIdentifierScheme":"ORCID"}]},{"name":"Hirmas, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Hirmas","affiliation":["Texas Tech University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1204-0638","nameIdentifierScheme":"ORCID"}]},{"name":"Ajami, Hoori","nameType":"Personal","givenName":"Hoori","familyName":"Ajami","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6883-7630","nameIdentifierScheme":"ORCID"}]},{"name":"Billings, Sharon","nameType":"Personal","givenName":"Sharon","familyName":"Billings","affiliation":["University of Kansas"],"nameIdentifiers":[]},{"name":"Li, Li","nameType":"Personal","givenName":"Li","familyName":"Li","affiliation":["Pennsylvania State University"],"nameIdentifiers":[]},{"name":"Flores, Alejandro","nameType":"Personal","givenName":"Alejandro","familyName":"Flores","affiliation":["Boise State University"],"nameIdentifiers":[]},{"name":"Guilinger, James","nameType":"Personal","givenName":"James","familyName":"Guilinger","affiliation":["California State University, Monterey Bay"],"nameIdentifiers":[]},{"name":"Oleghe, Ewan","nameType":"Personal","givenName":"Ewan","familyName":"Oleghe","affiliation":["Rutgers, The State University of New Jersey"],"nameIdentifiers":[]},{"name":"Gimenez, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Gimenez","affiliation":["Rutgers, The State University of New Jersey"],"nameIdentifiers":[]},{"name":"Gray, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Gray","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Sullivan, Pamela","nameType":"Personal","givenName":"Pamela","familyName":"Sullivan","affiliation":["Oregon State University"],"nameIdentifiers":[]}],"titles":[{"title":"Laboratory-based hyperspectral visible near-infrared reflectance spectral dataset of soil samples across a range of surface orientations"}],"publisher":"Dryad","container":{},"publicationYear":2024,"subjects":[{"subject":"FOS: Earth and related environmental sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Soil carbon","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Imaging techniques","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Soil science","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"name":"University of California, Riverside","nameType":"Personal","givenName":"Riverside","familyName":"University of California","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2023-12-09T17:10:23Z","dateType":"Created"},{"date":"2023-12-09T17:13:56Z","dateType":"Submitted"},{"date":"2024-03-19T00:00:00Z","dateType":"Issued"},{"date":"2024-03-19T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/saj2.20612","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.10324288","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2161279175 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"A custom-designed, 3-D printed sample array was used to present 681\n homogenized soil samples packed into sample wells to a laboratory-based\n visible near-infrared (VNIR) hyperspectral imaging (HSI) reflectance\n spectrometer in a total of 91 different configurations of slope and\n aspect. Hyperspectral imaging was performed with a high-sensitivity sCMOS\n VNIR hyperspectral camera (MSV 500, Middleton Spectral Vision, Middleton,\n WI). Raw reflectance data were collected with FastFrame data acquisition\n software (Middleton Spectral Vision, Middleton, WI). After data were\n collected using FastFrame, data processing and analysis were performed in\n R using the R Scripts found on GitHub at\n github.com/aduro005/HSITopographicCorrectionRScripts. The design for this\n sample array can be found on GitHub at\n github.com/aduro005/HSITopographicCorrectionSampleWellArray.","descriptionType":"Abstract"},{"description":"Hyperspectral imaging (HSI) was performed with a high-sensitivity\n sCMOS VNIR hyperspectral camera (MSV 500, Middleton Spectral Vision,\n Middleton, WI). A custom-designed, 3-D printed sample array was used to\n present homogenized soil samples packed into sample wells to a\n laboratory-based HSI reflectance spectrometer at 91 configurations of\n slope and aspect. Pixels representing each soil sample's reflectance\n spectra were isolated from hyperspectral images and averaged to obtain a\n single reflectance spectrum for each slope and aspect configuration per\n soil sample.","descriptionType":"Methods"},{"description":"Raw data were collected with FastFrame data acquisition software\n (Middleton Spectral Vision, Middleton, WI). Data processing and analysis\n were performed in R using the R Scripts found on GitHub at\n github.com/aduro005/HSITopographicCorrectionRScripts.","descriptionType":"Other"},{"description":"# Laboratory-based hyperspectral visible near-infrared reflectance\n spectral dataset of soil samples across a range of surface orientations\n This README file was generated on 03/02/2024 by Alyssa Duro This README\n file describes the R scripts available on GitHub at[\n https://github.com/aduro005/HSITopographicCorrectionRscripts](https://github.com/aduro005/HSITopographicCorrectionRScripts) and the .RData files (available on Dryad Data Repository UCR at [https://doi.org/10.6086/D15091](https://doi.org/10.6086/D15091). These R scripts and .RData files are associated with the HSI topographic correction method described in the article titled *Topographic correction of visible near-infrared reflectance spectra for horizon-scale soil organic carbon mapping* available at[ https://doi.org/10.1002/saj2.20612](https://doi.org/10.1002/saj2.20612). \\# ---------- \\# Some notes on this dataset: This project began with the intention of calibrating several empirical regression equations to predict soil chemical properties from visible near-infrared (VNIR) reflectance spectra of soil surfaces positioned at different angles relative to a VNIR hyperspectral imaging (HSI) reflectance spectrometer. This is why the prefix “HSICalLib'' is used in file names and why this project is referred to in some places as the “HSI Calibration Library”. Later, the focus of the project narrowed, and the goal became developing a method for removing the influence of surface orientation from VNIR reflectance spectra. As a result, this project is also sometimes referred to as the “HSI Topographic Correction”. The FastFrame software used to operate the HSI camera and scan stage outputs 2 data files after each scan. Together, these two files are sometimes referred to as the “raw data” for each scan. These two files have the same name, but one has the extension .hdr and the other is .raw. The file name and output location are input into the FastFrame software before a scan is performed. The .hdr file can be opened using Notepad. The .raw file is a 3-dimensional matrix with dimensions 471 (number of wavebands) x number of pixels in the lateral spatial dimension (also called “columns” and “samples”) x number of pixels in the direction of the scan stage movement (also called “rows” and “lines”).  \\ The raw (unprocessed) data for this project is not included in this dataset, but the authors are happy to share it upon request. This raw dataset consists of 3,125 hyperspectral images (2 files per image) and is about 2 TB in size. The most raw version of the soil VNIR reflectance spectra (observed, uncorrected) obtained at each slope, aspect configuration along with selected soil properties is called HSICalLib_b1_b30_p.RData and can be found on the UCR Dryad Data Repository. The soil reflectance spectra included here are averaged across all the pixels within each sample well. This file is output from the Step 7 of HSI Data Processing R script called HSICalLib_4_intmean_to_masterintmean_to_p.R (which can also be found on GitHub). Starting with this file, you can follow Steps 8-14 of HSI Data Processing (also on GitHub) to obtain all of the input files you need for ALL of the HSI Data Analysis workflow (Steps 1-4 and Final Plots) which was used for the HSI Topographic Correction. Even so, all of the files used during the HSI Topographic Correction that are output after HSICalLib_b1_b30_p.RData in the Data Processing and HSI Data Analysis workflows are included in the UCR Dryad Data Repository dataset. So, you can open any R script AFTER Step 7 of HSI Data Processing (also called HSICalLib_4_intmean_to_masterintmean_to_p.R), and the necessary input files are included on the UCR Dryad Data Repository dataset. \\# ---------- \\# File folder structure: Data Analysis \\\u0026gt; Data \\\u0026gt; Output Files \\\u0026gt; Plots \\\u0026gt; R scripts \\# --------------------------------------------------------------------------- \\# Description of the data \\# --------------------------------------------------------------------------- Hyperspectral imaging of 1178 homogenized soil samples was performed with a high-sensitivity sCMOS VNIR hyperspectral camera (MSV 500, Middleton Spectral Vision, Middleton, WI) in the Department of Environmental Sciences at University of California, Riverside.  Each soil sample was packed into sample wells, positioned under the hyperspectral camera, and imaged at 98 orientations (i.e., 7 slope x 14 aspect angles) using a custom designed and 3-D printed sample array. The sample array was designed such that sample wells could be presented to the spectrometer at 7 slope angles (0°, 10°, 20°, 30°, 40°, 50°, 60°). The design for this sample array is available on GitHub at[ https://github.com/aduro005/HSITopographicCorrectionSampleWellArray](https://github.com/aduro005/HSITopographicCorrectionSampleWellArray). The sample array was aligned to an arbitrarily defined 0° N aspect and rotated at 15° intervals from 0° N to 90° E, and 180° S to 225° W using a protractor affixed to the scan stage under the spectrometer. The 195° to 270° W aspect values were converted to 165° to 90° E aspects during data processing (see HSICalLib_6a_aspect_correction.R). This was done so the aspect angle values only took on values from 0° to 180° in the development of the topographic correction method. 574 soil samples were provided by the NEON Initial Characterization Soils Archive at the University of Michigan Biological Station Sample Archive Facility in Ehlers (UMBS-SAFE) ([https://mfield.umich.edu/soil_archive_request](https://mfield.umich.edu/soil_archive_request)) and accompanying soil properties data were obtained from the NEON Data Portal ([https://data.neonscience.org/](https://data.neonscience.org/)).  450 soil samples were collected from Duke Farms in Hillsborough Township, New Jersey ([https://www.dukefarms.org/](https://www.dukefarms.org/)) and accompanying soil properties data were provided by the Department of Environmental Sciences at Rutgers University.  57 soil samples were collected from locations in the Santa Ana Mountains, California that were affected by wildfire and accompanying soil properties data was provided by the Department of Environmental Sciences at University of California, Riverside ([http://www.thegraylab.org/](http://www.thegraylab.org/)). 97 soil samples were a laboratory standard soil from the Pedology Laboratory in the Department of Environmental Sciences at University of California, Riverside.  \\# ---------- \\# List of files in the Output Data folder: HSICalLib_wavevec.RData HSICalLib_b1_b30_p.RData HSICalLib_b1-b30_20230223_p_gold.RData HSICalLib_20230223_s0-s60_cosIL_all.RData HSICalLib_20230223_b1-b30_pga.RData HSICalLib_20230223_b1-b30_pga_melt.RData HSICalLib_20230223_b1-b30_pga_refI_melt.RData HSICalLib_20230223_b1-b30_pga_dI_melt.RData HSICalLib_20230310_rutgerssamples_rand1.RData HSICalLib_20230613_globaldI_predict_lm_pga_dI_refI_melt_slm_dIp_dIc_2.RData HSICalLib_20230613_globaldI_spectralstats_dIc_1.RData HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData HSICalLib_20230613_globaldI_spectralstats_dIc_w_a_2.RData HSICalLib_20230613_globaldI_spectralstats_dIc_w_s_2.RData HSICalLib_20230613_pga_slm_cosIL.RData HSICalLib_20230613_spectralstats_coscor.RData HSICalLib_20230613_spectralstats_ccor.RData HSICalLib_20230613_globaldI_OCpredict_refI_p.RData HSICalLib_20230613_globaldI_OCpredict_obsI_p.RData HSICalLib_20230613_globaldI_OCpredict_dIc_p.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_dIc.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_obs.RData HSICalLib_20230613_globaldI_PLSR_log10volC_summarystats_OC.RData \\# ---------- \\# List of abbreviations: obsI = observed, uncorrected reflectance intensity refI = reference reflectance intensity, mean of all reflectance spectra observed for each soil sample across all aspects at zero slope, represents a soil sample’s expected reflectance spectrum when the effect of surface orientation on reflectance is absent dI = delta intensity, obsI - refI dIp = predicted dI, this value is predicted by a multiple linear regression model trained to predict dI from slope, aspect, wavelength, and their interaction terms dIc = dI-corrected VNIR reflectance intensities, obsI - dI  coscorI = cosine corrected reflectance intensities ccorI = C corrected reflectance intensities \\# ---------- \\# Specific information for data file:  HSICalLib_wavevec.RData \\# Name and type of R object: wavevec (vector, numeric) \\# Number of observations (rows): 471 \\# Description of observations:  Each observation is a wavelength (λ) in units of (nm). Wavelengths corresponds to reflectance intensity observations (measurements) made at each waveband for reflectance spectra collected using the high-sensitivity sCMOS VNIR hyperspectral camera (MSV 500, Middleton Spectral Vision, Middleton, WI) in the Department of Environmental Sciences at University of California, Riverside. This spectrometer measures 471 reflectance intensities (i.e., reflectance intensities are measured at 471 wavebands) between 400 and 1000 nm wavelengths. The 471 observations in this vector (“wavevec”) are the wavelengths corresponding to each waveband.  \\# Missing data values (NA): None \\# Number of variables (columns): 1 \\# Description of variables:  wavelength (nm) \\# Related data files:  Any .hdr file resulting from a scan with this spectrometer contains the information contained in HSICalLib_wavevec.RData. However, the .hdr files also contain other information, so this vector was made in R by Alyssa Duro so the wavelength values corresponding to each waveband were accessible on their own in a .RData file. \\# R script that outputs this file:  HSICalLib_3_intensities_to_intmean_intsd.R \\# ---------- \\# Specific information for data file:  HSICalLib_b1_b30_p.RData \\# Name and type of R object: p (data frame) \\# Number of observations (rows): 115,444 reflectance spectra  \\# Description of observations:  Mean reflectance spectra for each soil sample at each orientation along with selected soil properties data and sample identifiers. This is the most raw form of the data included in this dataset. 1178 soil samples * 98 configurations = 115,444  \\# Missing data values (NA): Some soil properties data are not available for all soil samples resulting in NA’s. \\# Number of variables (columns): 486 \\# Description of variables:  obsI*[wavelength]* (numeric): observed (uncorrected) reflectance intensities measured at 471 wavebands, together, these 471 values represent the average reflectance spectrum for a single soil sample at a single orientation slope (integer, degrees): angle between the scan stage and the soil surface aspect (integer, degrees): angle clockwise from N  batch (integer, 1-30): soil samples were imaged in groups of 40 at a time well (integer, 1-40): indexed location of the soil sample in the sample well array HSInumber (integer, 1-1141): unique soil sample identifier  HSIPackedDensity (numeric, g/cm3): mass soil sample per volume sample well  sandTotal (numeric, %): sand (only available for samples from the NEON archive) siltTotal (numeric, %): silt (only available for samples from the NEON archive) clayTotal (numeric, %): clay (only available for samples from the NEON archive) OC (numeric, %): soil organic carbon (by weight) archive (character): source of the soil sample and soil properties data adod (numeric, unitless): air dried soil mass / oven dried soil mass volC (numeric, %): soil organic carbon (by volume) log10volC (numeric): log10(volC) batchwellID (character): unique reflectance spectra identifier \\# R script that outputs this file:  HSICalLib_4_intmean_to_masterintmean_to_p.R \\# ---------- \\# Specific information for data file:  HSICalLib_b1-b30_20230223_p_gold.RData \\# Name and type of R object: p_gold (data frame) \\# Number of observations (rows): 107,486 reflectance spectra \\# Description of observations:  Same as HSICalLib_b1_b30_p.RData (output from R script 4) except the reflectance spectra (rows) with unusually large or small obsI OR dI values have been identified and removed as imaging errors.  \\# Missing data values (NA): Missing values occur when soil properties data are not available for some soil samples. There is spectral data for all soil samples, but some soil properties were not measured for all soil samples.  \\# Number of variables (columns): 486 \\# Description of variables:  Same as HSICalLib_b1_b30_p.RData (output from R script 4) \\# Related data files:  HSICalLib_b1_b30_p.RData \\# R script that outputs this file:  HSICalLib_5b_p_dI_cleaning.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230223_s0-s60_cosIL_all.RData  \\# Name and type of R object: cosIL (numeric, data frame) \\# Number of observations (rows): 91 orientations (7 slopes * 13 aspects) \\# Description of observations:  Each row contains the constants needed to perform the cosine correction and C correction for 1 of 91 possible combinations of slope and aspect.  \\# Missing data values (NA): None \\# Number of variables (columns): 16 \\# Description of variables:  slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData z1: zenith angle (degrees) between light bank 1 and the HSI camera, varies with slope, light bank 1 = N = 0 azimuth z2: zenith angle (degrees) between light bank 2 (S) and the HSI camera, varies with slope, light bank 2 = S = 180 azimuth meanz: average of z1 and z2 (this is the one used for the paper) cosz1: cosine of z1 cosz2: cosine of z2 cosmeanz: cosine of (meanz) meancosz: average of cos(z1) and cos(z2) cosIL1: cos( illumination angle light bank 1 (IL1) ) = cos(z1)*cos(slope) + sin(z1)*sin(slope)*cos(azimuth-aspect) cosIL2: cos( illumination angle light bank 2 (IL2) ) = cos(z2)*cos(slope) + sin(z2)*sin(slope)*cos(azimuth-aspect) meancosIL: average of cosIL1 and cosIL2 r1: cos(z1) / cosIL1 r2: cos(z2) / cosIL2 rmeans: ( cos(meanz) ) / (meancosIL) rcosmeans: (meancosz) / (meancosIL) (this is the one used for the paper) \\# Related data files:  HSICalLib_20230613_pga_slm_cosIL.RData \\# R script that outputs this file:  HSICalLib_6b_cosIL_calculation.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230223_b1-b30_pga.RData \\# Name and type of R object: pga (data frame) \\# Number of observations (rows): 99,537 reflectance spectra \\# Description of observations:  Same as HSICalLib_b1_b30_p.RData (output from R script 4) except the number of spectra was reduced during the “aspect correction” (HSICalLib_6a_aspect_correction.R) \\# Missing data values (NA): None \\# Number of variables (columns): 486 \\# Description of variables:  Same as HSICalLib_b1_b30_p.RData  \\# Related data files:  HSICalLib_b1_b30_p.RData \\# R script that outputs this file:  HSICalLib_7a_observedI \\# ---------- \\# Specific information for data file:  HSICalLib_20230223_b1-b30_pga_melt.RData \\# Name and type of R object: pga_melt (data frame) \\# Number of observations (rows): 46,881,927 reflectance intensities \\# Description of observations:  “Long” version of HSICalLib_20230223_b1-b30_pga.RData where wavelength is a variable. 99,537 reflectance spectra (from HSICalLib_20230223_b1-b30_pga.RData) * 471 wavebands = 46,881,927 reflectance intensities \\# Missing data values (NA): None \\# Number of variables (columns): 5 \\# Description of variables:  slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData wavelength: see HSICalLib_wavevec.RData obsI: Same values reported in HSICalLib_b1_b30_p.RData except now wavelength is a variable. These values have not been “corrected”.  \\# Related data files:  HSICalLib_20230223_b1-b30_pga.RData \\# R script that outputs this file:  HSICalLib_7a_observedI \\# ---------- \\# Specific information for data file:  HSICalLib_20230223_b1-b30_pga_refI_melt.RData \\# Name and type of R object: pga_refI_melt (data frame) \\# Number of observations (rows): 46,881,927 reflectance intensities \\# Description of observations:  Same as HSICalLib_20230223_b1-b30_pga_melt.RData except reference reflectance intensities (refI) are reported instead of obsI.  \\# Missing data values (NA): None \\# Number of variables (columns): 5 \\# Description of variables:  Same as  slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData wavelength: see HSICalLib_wavevec.RData refI: These values represent the average reflectance spectrum measured for each soil sample across all aspect positions at zero slope. There is only 1 reference spectrum per soil sample.  \\# Related data files:  HSICalLib_20230223_b1-b30_pga_melt.RData \\# R script that outputs this file:  HSICalLib_7b_referenceI \\# ---------- \\# Specific information for data file:  HSICalLib_20230223_b1-b30_pga_dI_melt.RData \\# Name and type of R object: pga_dI_melt (data frame) \\# Number of observations (rows): 46,881,927 reflectance intensities \\# Description of observations:  Same as HSICalLib_20230223_b1-b30_pga_melt.RData except dI values are reported instead of obsI  \\# Missing data values (NA): None \\# Number of variables (columns): 5 \\# Description of variables:  slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData wavelength: see HSICalLib_wavevec.RData dI: actual (measured) delta (“change in”) reflectance intensity = obsI - refI \\# Related data files:  HSICalLib_20230223_b1-b30_pga_melt.RData HSICalLib_20230223_b1-b30_pga_refI_melt.RData \\# R script that outputs this file:  HSICalLib_7c_dI_calculation \\# ---------- \\# Specific information for data file:  HSICalLib_20230310_rutgerssamples_rand1.RData \\# Name and type of R object: rand1 (numeric, vector) \\# Number of observations (rows): 50 \\# Description of observations:  A randomly chosen subset of 50 soil samples (out of the 450 samples collected from Duke Farms and imaged using HSI) were included in the topographic correction study due to these soil sample properties all being very similar while making up a large portion of the training data. This vector contains the HSI numbers for these 50 randomly chosen soil samples (all from the Rutgers archive). \\# Missing data values (NA): None \\# Number of variables (columns): 1 \\# Description of variables:  HSInumber: see HSICalLib_b1_b30_p.RData \\# Related data files: HSICalLib_20230613_globaldI_predict_lm_pga_dI_refI_melt_slm_dIp_dIc_2.RData \\# R script that outputs this file:  HSICalLib_8b_dI_predict_global_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_predict_lm_pga_dI_refI_melt_slm_dIp_dIc_2.RData \\# Name and type of R object: pga_dI_refI_melt_slm_dIp_dIc_2 (data frame) \\# Number of observations (rows): 22,678,179 \\# Description of observations:  Each row is a reflectance intensity for a single soil sample at a single orientation at a single wavelength. Same as HSICalLib_20230223_b1-b30_pga_melt.RData except the number of observations was reduced by selecting ONLY the HSInumbers (spectra) for the 681 soil samples included in this study. This data frame is output AFTER training and evaluating the dI+ correction wherein a multiple linear regression model was trained to predict dI using slope, aspect, wavelength, and their interactions as predictor variables. This model was evaluated to get predicted dI (dIp), then dIp was used to adjust (aka “correct”) obsI resulting in dI-corrected intensities (dIc). If the model was a perfect predictor, then dIp would equal dI AND dIc would equal refI. \\# Missing data values (NA): None \\# Number of variables (columns): 9 \\# Description of variables: Same as HSICalLib_20230223_b1-b30_pga_melt.RData except dI, refI, dIp, and dIc columns have been added.  slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData wavelength: see HSICalLib_wavevec.RData obsI: see HSICalLib_20230223_b1-b30_pga_melt.RData dI: see HSICalLib_20230223_b1-b30_pga_dI_melt.RData refI: see HSICalLib_20230223_b1-b30_pga_refI_melt.RData dIp: predicted dI resulting from evaluation of the dI+ multiple linear regression model, if the model was a perfect predictor, dIp would equal dI  dIc: dI-corrected reflectance intensities = obsI - dIp, if the dI correction was successful, dIc should equal refI  \\# Related data files:  HSICalLib_20230223_b1-b30_pga_melt.RData \\# R script that outputs this file:  HSICalLib_8b_dI_predict_global_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData \\# Name and type of R object: spectralstats_dIc_2 (data frame) \\# Number of observations (rows): 48,149 \\# Description of observations:  Number of spectra compared when reflectance intensities are grouped this way before calculating objective functions. RMSE, NSE, and KGE are calculated for every soil sample at every configuration across all wavelengths. These dIc values were obtained by evaluating the dI+ multiple linear regression model (i.e., the one that includes slope, aspect, wavelength, and all their interactions as predictor variables).  \\# Missing data values (NA): None \\# Number of variables (columns): 15 \\# Description of variables:  slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData RMSE_obs: Root mean squared error (obsI vs refI) tells how far obsI is from refI, RMSE=0 suggests surface orientation has no effect RMSE_dIc: Root mean squared error (dIc vs refI) tells how far dIc is from refI, RMSE=0 suggests dI correction was successful NSE_obs: Nash-Sutcliffe efficiency (obsI vs refI) tells if obsI is closer to mean refI or refI, NSE=1 suggests surface orientation has no effect  NSE_dIc: Nash-Sutcliffe efficiency (dIc vs refI) tells if dIc is closer to mean refI or refI, NSE=1 suggests dI correction was successful KGE_obs: Kling-Gupta efficiency (obsI vs refI), same as NSE KGE_obs_r: Pearson correlation coefficient (obsI vs refI), component of KGE KGE_obs_beta: mean obsI / mean refI (obsI vs refI), component of KGE, ratio of the means of obsI and refI, beta=1 suggests surface orientation has no effect  KGE_obs_alpha: standard deviation(obsI) /  standard deviation(refI), component of KGE, ratio of the standard deviations of obsI and refI, alpha=1 suggests surface orientation has no effect KGE_dIc: Kling-Gupta efficiency (dIc vs refI), same as NSE KGE_dIc_r: Pearson correlation coefficient (dIc vs refI), component of KGE KGE_dIc_beta: mean dIc / mean refI (dIc vs refI), component of KGE, ratio of the means of dIc and refI, beta=1 suggests dI correction was successful KGE_dIc_alpha: standard deviation(dIc) /  standard deviation(refI), component of KGE, ratio of the standard deviations of dIc and refI, alpha=1 suggests dI correction was successful \\# Related data files:  HSICalLib_20230613_globaldI_spectralstats_dIc_w_s_2.RData HSICalLib_20230613_globaldI_spectralstats_dIc_w_a_2.RData HSICalLib_20230613_spectralstats_coscor.RData HSICalLib_20230613_spectralstats_ccor.RData \\# R script that outputs this file:  HSICalLib_8b_dI_predict_global_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_spectralstats_dIc_1.RData \\# Name and type of R object: spectralstats_dIc_1 (numeric, data frame) \\# Number of observations (rows): 48,149 \\# Description of observations:  Number of spectra compared when reflectance intensities are grouped this way before calculating objective functions. RMSE, NSE, and KGE are calculated for every soil sample at every configuration across all wavelengths. These dIc values were obtained by evaluating the dI multiple linear regression model (i.e., the one that includes ONLY slope, aspect, and wavelength as predictor variables).  \\# Missing data values (NA): None \\# Number of variables (columns): 15 \\# Description of variables:  Same as HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData \\# Related data files:  HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData \\# R script that outputs this file:  HSICalLib_8b_dI_predict_global_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_spectralstats_dIc_w_s_2.RData \\# Name and type of R object: spectralstats_dIc_w_s_2 (data frame) \\# Number of observations (rows): 2,826 \\# Description of observations:  Number of sample groups compared when reflectance intensities are grouped this way before calculating objective functions. RMSE, NSE, and KGE are calculated across all aspects and soil samples at each wavelength, slope combination (471 wavelengths * 6 slopes = 2,826 results for each objective function). Same as HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData BUT reflectance intensities were grouped in a different way before calculating objective functions. \\# Missing data values (NA): None \\# Number of variables (columns): 14 \\# Description of variables:  slope: see HSICalLib_b1_b30_p.RData wavelength: see HSICalLib_wavevec.RData RMSE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData RMSE_dIc: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData NSE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData NSE_dIc: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_r: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_beta: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_alpha: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_dIc: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_dIc_r: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_dIc_beta: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_dIc_alpha: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData \\# Related data files:  HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData HSICalLib_20230613_globaldI_spectralstats_dIc_w_a_2.RData \\# R script that outputs this file:  HSICalLib_8b_dI_predict_global_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_spectralstats_dIc_w_a_2.RData \\# Name and type of R object: spectralstats_dIc_w_a_2 (data frame) \\# Number of observations (rows): 6,123 \\# Description of observations:  Number of sample groups compared when reflectance intensities are grouped this way before calculating objective functions. RMSE, NSE, and KGE are calculated across all slopes and soil samples at each wavelength, aspect combination (471 wavelengths * 13 aspects = 6,123 results for each objective function). Same as HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData BUT reflectance intensities were grouped in a different way before calculating objective functions. \\# Missing data values (NA): None \\# Number of variables (columns): 14 \\# Description of variables:  aspect: see HSICalLib_b1_b30_p.RData wavelength: see HSICalLib_wavevec.RData RMSE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData RMSE_dIc: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData NSE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData NSE_dIc: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_r: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_beta: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_alpha: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_dIc: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_dIc_r: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_dIc_beta: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_dIc_alpha: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData \\# Related data files:  HSICalLib_20230613_globaldI_spectralstats_dIc_w_s_2.RData \\# R script that outputs this file:  HSICalLib_8b_dI_predict_global_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_pga_slm_cosIL.RData \\# Name and type of R object: pga_slm_cosIL (data frame) \\# Number of observations (rows): obsI spectra = 48,149 \\# Description of observations:  Same as HSICalLib_20230223_b1-b30_pga.RData except the number of spectra was reduced by selecting spectra (using HSInumbers) from ONLY the 681 soil samples used in this study AND spectra collected at non-zero slopes. \\# Missing data values (NA): None \\# Number of variables (columns): 500 \\# Description of variables:  See HSICalLib_b1_b30_p.RData (486 columns) and HSICalLib_20230223_s0-s60_cosIL_all.RData (16 columns, slope and aspect are redundant). HSICalLib_20230223_b1-b30_pga.RData was subset by soil sample and slope, then merged with HSICalLib_20230223_s0-s60_cosIL_all.RData resulting in a “wide” data frame with all the same variables as HSICalLib_b1_b30_p.RData AND the constants needed for the cosine correction (from HSICalLib_20230223_s0-s60_cosIL_all.RData). \\# Related data files:  HSICalLib_20230223_b1-b30_pga.RData HSICalLib_20230223_s0-s60_cosIL_all.RData \\# R script that outputs this file:  HSICalLib_9a_cosine_correction_final \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_spectralstats_coscor.RData \\# Name and type of R object: spectralstats_coscor (numeric, data frame) \\# Number of observations (rows): 48,149 \\# Description of observations:  Number of spectra compared when reflectance intensities are grouped this way before calculating objective functions. RMSE, NSE, and KGE are calculated for every soil sample at every configuration across all wavelengths. Same as HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData except corrected spectra result from cosine correction instead of dI correction.  \\# Missing data values (NA): None \\# Number of variables (columns): 15 \\# Description of variables: slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData RMSE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData RMSE_coscor: Root mean squared error (coscorI vs refI) tells how far coscorI is from refI, RMSE=0 suggests cosine correction was successful NSE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData NSE_coscor: Nash-Sutcliffe efficiency (coscorI vs refI) tells if coscorI is closer to mean refI or refI, NSE=1 suggests cosine correction was successful KGE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_r: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_beta: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_alpha: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_coscor: Kling-Gupta efficiency (coscorI vs refI), same as NSE KGE_coscor_r: Pearson correlation coefficient (coscorI vs refI), component of KGE KGE_coscor_beta: mean coscorI / mean refI (coscorI vs refI), component of KGE, ratio of the means of coscorI and refI, beta=1 suggests cosine correction was successful KGE_coscor_alpha: standard deviation(coscorI) /  standard deviation(refI), component of KGE, ratio of the standard deviations of coscorI and refI, alpha=1 suggests cosine correction was successful \\# Related data files:  HSICalLib_20230613_spectralstats_ccor.RData \\# R script that outputs this file:  HSICalLib_9a_cosine_correction_final \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_spectralstats_ccor.RData \\# Name and type of R object: spectralstats_ccor.RData (numeric, data frame) \\# Number of observations (rows): 48,149 \\# Description of observations:  Number of spectra compared when reflectance intensities are grouped this way before calculating objective functions. RMSE, NSE, and KGE are calculated for every soil sample at every configuration across all wavelengths. Same as HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData except corrected spectra result from C correction instead of dI correction.  \\# Missing data values (NA): None \\# Number of variables (columns): 15 \\# Description of variables:  slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData RMSE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData RMSE_ccor: Root mean squared error (ccorI vs refI) tells how far ccorI is from refI, RMSE=0 suggests C correction was successful NSE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData NSE_ccor: Nash-Sutcliffe efficiency (ccorI vs refI) tells if ccorI is closer to mean refI or refI, NSE=1 suggests C correction was successful KGE_obs: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_r: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_beta: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_obs_alpha: see HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData KGE_ccor: Kling-Gupta efficiency (ccorI vs refI), same as NSE KGE_ccor_r: Pearson correlation coefficient (ccorI vs refI), component of KGE KGE_ccor_beta: mean ccorI / mean refI (ccorI vs refI), component of KGE, ratio of the means of ccorI and refI, beta=1 suggests C correction was successful KGE_ccor_alpha: standard deviation(ccorI) /  standard deviation(refI), component of KGE, ratio of the standard deviations of ccorI and refI, alpha=1 suggests C correction was successful \\# Related data files:  HSICalLib_20230613_spectralstats_coscor.RData \\# R script that outputs this file:  HSICalLib_10a_C_correction_final \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_OCpredict_obsI_p.RData \\# Name and type of R object: obsI_p (“wide” data frame) \\# Number of observations (rows): 48,149 \\# Description of observations:  Same observations as HSICalLib_20230613_pga_slm_cosIL.RData except different columns are included along with the 471 observed (uncorrected) reflectance intensities (obsI) for each soil sample at each orientation.  \\# Missing data values (NA): None \\# Number of variables (columns): 475 \\# Description of variables:  obsI*[wavelength]*: see HSICalLib_b1_b30_p.RData slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData log10volC: see HSICalLib_b1_b30_p.RData \\# Related data files:  HSICalLib_20230613_globaldI_OCpredict_refI_p.RData HSICalLib_20230613_globaldI_OCpredict_dIc_p.RData \\# R script that outputs this file:  HSICalLib_12a_OC_predict_pls_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_OCpredict_refI_p.RData \\# Name and type of R object: refI_p (“wide” data frame) \\# Number of observations (rows):  48,149 \\# Description of observations:  Same observations as HSICalLib_20230613_pga_slm_cosIL.RData except different columns are included along with the 471 reference reflectance intensities (refI) for each soil sample at each orientation.  \\# Missing data values (NA): None \\# Number of variables (columns): 475 \\# Description of variables:  refI*[wavelength]*: same as HSICalLib_b1_b30_p.RData except these are reference reflectance intensities rather than obsI  slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData log10volC: see HSICalLib_b1_b30_p.RData \\# Related data files:  HSICalLib_20230613_globaldI_OCpredict_obsI_p.RData HSICalLib_20230613_globaldI_OCpredict_dIc_p.RData \\# R script that outputs this file:  HSICalLib_12a_OC_predict_pls_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_OCpredict_dIc_p.RData \\# Name and type of R object: dIc_p (“wide” data frame) \\# Number of observations (rows): 48,149 \\# Description of observations:  Same observations as HSICalLib_20230613_pga_slm_cosIL.RData except different columns are included along with the 471 dI corrected reflectance intensities (dIc) for each soil sample at each orientation.  \\# Missing data values (NA): None \\# Number of variables (columns):  \\# Description of variables:  Number of columns/variables = 475 dIc*[wavelength]*: same HSICalLib_b1_b30_p.RData except these reflectance intensities have been dI corrected using the dI+ multiple linear regression model to predict dI  slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData log10volC: see HSICalLib_b1_b30_p.RData \\# Related data files:  HSICalLib_20230613_globaldI_OCpredict_refI_p.RData HSICalLib_20230613_globaldI_OCpredict_obsI_p.RData \\# R script that outputs this file:  HSICalLib_12a_OC_predict_pls_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData \\# Name and type of R object: sstatdf_tr_ref (numeric, data frame) \\# Number of observations (rows): 681 \\# Description of observations:  Each row contains error metrics for log10volC predictions made from the reference spectrum for each soil sample. This partial least squares regression model was trained on these same 681 reference spectra (1 for each soil sample).  \\# Missing data values (NA): None \\# Number of variables (columns): 12 \\# Description of variables:  predicted: log10volC predicted by the “reference” partial least squares regression model using 471 reference reflectance intensities (refI) as predictor variables observed: log10volC observed based on laboratory measurements of SOC slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData NOTE: slope and aspect are the same for all samples (rows) in this data frame because refI is the same regardless of orientation HSInumber: see HSICalLib_b1_b30_p.RData RMSE: Root mean squared error (observed log10volC vs predicted log10volC) tells how far predicted is from observed, smaller RMSE means better prediction, ideal RMSE=0 NSE: Nash-Sutcliffe efficiency (observed log10volC vs predicted log10volC) tells if predicted is closer to mean observed or observed, NSE=1 means the model is a perfect predictor, NSE\u0026lt;0 means predicted is closer to mean observed than observed R2: Coefficient of determination (observed log10volC vs predicted log10volC) KGE: Kling-Gupta efficiency (observed log10volC vs predicted log10volC), same as NSE KGE_r: Pearson correlation coefficient (observed log10volC vs predicted log10volC) KGE_beta: mean predicted / mean observed (observed log10volC vs predicted log10volC), component of KGE, ideal beta=1 KGE_alpha: standard deviation(predicted) /  standard deviation(observed), component of KGE, ideal alpha=1 \\# Related data files: HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_obs.RData \\# R script that outputs this file:  HSICalLib_12a_OC_predict_pls_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_obs.RData \\# Name and type of R object: sstatdf_obs \\# Number of observations (rows): 48,149 \\# Description of observations:  Rows contain error metrics for log10volC predictions made from all of the observed (uncorrected) spectra collected at non zero slopes for each soil sample (1 prediction per spectrum means multiple log10volC predictions for each soil sample). This partial least squares regression model was trained on 681 reference spectra (1 for each soil sample).  \\# Missing data values (NA): None \\# Number of variables (columns): 12 \\# Description of variables:  predicted: log10volC predicted by the “reference” partial least squares regression model using 471 observed (uncorrected) reflectance intensities (obsI) as predictor variables observed: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData RMSE: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData NSE: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData R2: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE_r: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE_beta: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE_alpha: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData \\# Related data files:  HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData \\# R script that outputs this file:  HSICalLib_12a_OC_predict_pls_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData \\# Name and type of R object: sstatdf_tr_dIc (data frame) \\# Number of observations (rows): 681 \\# Description of observations:  Rows contain error metrics for log10volC predictions made from the training dIc spectra (1 prediction and 1 spectrum per sample from a randomly chosen orientation). This partial least squares regression model was trained on the same 681 dIc spectra. \\# Missing data values (NA): None \\# Number of variables (columns): 12 \\# Description of variables:  Same as HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData except this model was trained and evaluated using the “corrected” PLSR model and dI corrected training spectra. predicted: log10volC predicted by evaluating the “corrected” partial least squares regression model using 471 dI corrected reflectance intensities (dIc) as predictor variables. Only spectra from the “corrected” PLSR model training set were evaluated.  observed: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData RMSE: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData NSE: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData R2: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE_r: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE_beta: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE_alpha: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData \\# Related data files: HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_dIc.RData \\# R script that outputs this file:  HSICalLib_12a_OC_predict_pls_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_dIc.RData \\# Name and type of R object: sstatdf_dIc (numeric, data frame) \\# Number of observations (rows): 48,149  \\# Description of observations:  Rows contain error metrics for log10volC predictions made from dI corrected reflectance spectra from all orientations for all soil samples using the “corrected” partial least squares regression model that was trained on 681 dI corrected spectra (1 per soil sample from a randomly chosen orientation). Multiple predictions are made for each soil sample since more than 1 spectrum per sample is evaluated.  \\# Missing data values (NA): None \\# Number of variables (columns): 12 \\# Description of variables:  Same as HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_obs.RData except this model was trained and evaluated using the “corrected” PLSR model and dI corrected spectra. predicted: log10volC predicted by evaluating the “corrected” partial least squares regression model using 471 dI corrected reflectance intensities (dIc) from all soil samples at all orientations as predictor variables. More than 1 spectrum per soil sample is evaluated so multiple predictions are made for each soil sample. observed: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData HSInumber: see HSICalLib_b1_b30_p.RData RMSE: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData NSE: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData R2: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE_r: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE_beta: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData KGE_alpha: see HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData \\# Related data files:  HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_obs.RData \\# R script that outputs this file:  HSICalLib_12a_OC_predict_pls_final.R \\# ---------- \\# Specific information for data file:  HSICalLib_202300613_globaldI_PLSR_log10volC_summarystats_OC.RData \\# Name and type of R object: summarystats_OC (data frame, numeric) \\# Number of observations (rows): 78 \\# Description of observations:  Error metrics for all 78 (6 slopes * 13 aspects) non-zero orientations across all predictions made by 1) evaluating the “reference” PLSR model on all observed (obsI) spectra (same results used in HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_obs.RData), and 2) evaluating the “corrected” PLSR model on all dI corrected spectra (same results used in HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_dIc.RData). \\# Missing data values (NA): None \\# Number of variables (columns): 16 \\# Description of variables: Same as HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData except error metrics for this data frame were calculated across all predictions made at each orientation from either 1) all obsI vs training refI spectra using the reference PLSR model, or 2) all dIc vs training dIc spectra using the corrected PLSR model. slope: see HSICalLib_b1_b30_p.RData aspect: see HSICalLib_b1_b30_p.RData RMSE_obs: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData except calculated across all soil samples at each orientation, predicted = “reference” PLSR model evaluated using all obsI spectra, observed = “reference” PLSR model evaluated using training refI spectra RMSE_dIc: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData except calculated across all soil samples at each orientation, predicted = “corrected” PLSR model evaluated using all dIc spectra, observed = “corrected” PLSR model evaluated using training dIc spectra NSE_obs: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData except calculated across all soil samples at each orientation, predicted = “reference” PLSR model evaluated using all obsI spectra, observed = “reference” PLSR model evaluated using training refI spectra NSE_dIc: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData except calculated across all soil samples at each orientation, predicted = “corrected” PLSR model evaluated using all dIc spectra, observed = “corrected” PLSR model evaluated using training dIc spectra R2_obs: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData except calculated across all soil samples at each orientation, predicted = “reference” PLSR model evaluated using all obsI spectra, observed = “reference” PLSR model evaluated using training refI spectra R2_dIc: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData except calculated across all soil samples at each orientation, predicted = “corrected” PLSR model evaluated using all dIc spectra, observed = “corrected” PLSR model evaluated using training dIc spectra KGE_obs: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData except calculated across all soil samples at each orientation, predicted = “reference” PLSR model evaluated using all obsI spectra, observed = “reference” PLSR model evaluated using training refI spectra KGE_dIc: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData except calculated across all soil samples at each orientation, predicted = “corrected” PLSR model evaluated using all dIc spectra, observed = “corrected” PLSR model evaluated using training dIc spectra KGE_r_obs: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData except calculated across all soil samples at each orientation, predicted = “reference” PLSR model evaluated using all obsI spectra, observed = “reference” PLSR model evaluated using training refI spectra KGE_r_dIc: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData except calculated across all soil samples at each orientation, predicted = “corrected” PLSR model evaluated using all dIc spectra, observed = “corrected” PLSR model evaluated using training dIc spectra KGE_beta_obs: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData except calculated across all soil samples at each orientation, predicted = “reference” PLSR model evaluated using all obsI spectra, observed = “reference” PLSR model evaluated using training refI spectra KGE_beta_dIc: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData except calculated across all soil samples at each orientation, predicted = “corrected” PLSR model evaluated using all dIc spectra, observed = “corrected” PLSR model evaluated using training dIc spectra KGE_alpha_obs: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData except calculated across all soil samples at each orientation, predicted = “reference” PLSR model evaluated using all obsI spectra, observed = “reference” PLSR model evaluated using training refI spectra KGE_alpha_dIc: See HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData except calculated across all soil samples at each orientation, predicted = “corrected” PLSR model evaluated using all dIc spectra, observed = “corrected” PLSR model evaluated using training dIc spectra \\# Related data files:  HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_obs.RData HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_dIc.RData \\# R script that outputs this file:  HSICalLib_12a_OC_predict_pls_final.R \\# --------------------------------------------------------------------------- \\# Sharing/Access information \\# --------------------------------------------------------------------------- \\# ---------- Links to other publicly accessible locations of the data: github.com/aduro005/HSITopographicCorrectionSampleWellArray (custom designed and 3-D printed sample array) github.com/aduro005/HSITopographicCorrectionRscripts (R scripts used to manipulate the data found on the UCR Dryad Data Repository) \\# ---------- Data was derived from the following sources: data.neonscience.org/home (574 soil samples obtained from NEON Initial Characterization Soils Archive at the University of Michigan Biological StationSample Archive Facility in Ehlers (UMBS-SAFE) with accompanying soil properties data obtained from the NEON Data Archive) \\# --------------------------------------------------------------------------- \\# Description of the R scripts \\# --------------------------------------------------------------------------- The titles of the R scripts indicate the order in which they are meant to be used. The only exception to this convention is the HSICalLib_0_FinalPlots.R script which could be used at different points during the workflow, but is intended to be used last.  \\# ---------- List of files in the R scripts folder: HSI Data Processing: HSICalLib_1a_hdr_raw_to_hsi_rgbmat.R HSICalLib_1b_rgbmat_to_soilindices.R HSICalLib_1c_rgbmat_soilindices_to_Tsoilindices.R HSICalLib_1d_rgbmat_Tsoilindices_to_adjustedTsoilindices.R HSICalLib_2_soilindices_hsi_to_intensities.R HSICalLib_3_intensities_to_intmean_intsd.R HSICalLib_4_intmean_to_masterintmean_to_p.R HSICalLib_5a_p_obsI_cleaning.R HSICalLib_5b_p_dI_cleaning.R HSICalLib_6a_aspect_correction.R HSICalLib_6b_cosIL_calculation.R HSICalLib_7a_observedI.R HSICalLib_7b_referenceI.R HSICalLib_7c_dI_calculation.R HSI Data Analysis: HSICalLib_8b_dI_predict_global_final.R HSICalLib_9a_cosine_correction_final.R HSICalLib_10a_C_correction_final.R HSICalLib_12a_OC_predict_pls_final.R HSICalLib_0_FinalPlots.R \\# ---------- \\# Specific information for R script:  HSICalLib_1a_hdr_raw_to_hsi_rgbmat.R \\# Description of script:  Read in raw data, dark calibration, and white calibration files from the Data folder (3 .hdr and 3 .raw files), perform white and dark correction, then output .RData and .tiff files to the Output Files folder for each scan. Output files contain the hyperspectral image of the scan (reflectance intensity for 471 wavebands between 400 - 1000 nm in 2 spatial dimensions), an RGB image of the scan (reflectance intensity for red, green, and blue wavebands for each pixel in the image), and a .tiff of the RGB image (can be opened in a photo viewer). \\# Input files: 2 raw data files (.hdr and .raw) per HSI scan (not included in this dataset) hsi_HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*.hdr hsi_HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*.raw 2 raw data files (.hdr and .raw) for white calibration (not included in this dataset) hsi_HSICalLib_b*[batch]*_*[date]*_white.hdr hsi_HSICalLib_b*[batch]*_*[date]*_white.raw 2 raw data files (.hdr and .raw) for dark calibration (not included in this dataset) hsi_HSICalLib_b*[batch]*_*[date]*_dark.hdr hsi_HSICalLib_b*[batch]*_*[date]*_dark.raw \\# Output files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_hsi.RData (not included in this dataset) Array with the same dimensions as the .raw file, but the raw reflectance intensities have been scaled between 0 (dark, minimum reflectance) and 1 (white, maximum reflectance) using white and dark calibration scan data. HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_rgbmat.RData (not included in this dataset) Array with the same spatial dimensions as the .raw file but the spectral dimension only contains data for 3 wavebands corresponding to the red, green, and blue color wavelengths. This array is used to generate the RGB.tiff file. HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_mmResolution_RGB.tiff (not included in this dataset) RGB image of the scan \\# ---------- \\# Specific information for R script:  HSICalLib_1b_rgbmat_to_soilindices.R  \\# Description of script:  Make a “template” with the row and column coordinates for every pixel occurring within the 40 sample wells at all 98 slope, aspect configurations. This script can be used to manually identify the location (row, column coordinates) of pixels occurring within sample wells from images. The result of this is a “template” which can be used to automatically identify the location (row, column coordinates) of pixels occurring within sample wells from any image given the slope and aspect of the sample well array in the image AND isolate the reflectance spectra from those pixels.  \\# Input files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_mmResolution_RGB_rgbmat.RData (not included in this dataset) Output from R script 1a. \\# Output files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_soilindices.RData (not included in this dataset) List containing 40 elements (1 for each sample well/soil sample). Each element of the list contains a data frame with 2 columns “soilrows” and “soilcols”. These are the row, column coordinates (i.e., the 2 spatial dimensions of “rgbmat” or “hsi” arrays) of pixels occurring within each of the 40 sample wells (i.e., the 40 elements in this list) at all 98 orientations. There is a separate file for each configuration, and the configuration (i.e., slope and aspect) is indicated in the file name. These coordinates are later used to isolate spectra from ONLY the areas of the images that correspond to sample wells AND to match reflectance spectra from sample wells to the correct soil sample and its properties. HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_mmResolution_RGB_soilindices.tiff (not included in this dataset) RGB image of the scan with pixels corresponding to sample wells turned some color. These images were used to visually check that the row, column coordinates (indicated by HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_soilindices.RData) were correctly aligned with actual locations of sample wells in images.  \\# ---------- \\# Specific information for R script:  HSICalLib_1c_rgbmat_soilindices_to_Tsoilindices.R \\# Description of script:  Use “soilindices” list (created in R script 1b) along with “rgbmat” arrays to identify pixels occurring in sample wells and turn those pixels a certain color using the row, column indices (pixels) indicated by soilindices.RData or Tsoilindices.RData (i.e., the “soilindices” list). Then output the updated “soilindices” list as “_Tsoilindices.RData” and an image called “_Tsoilindices.tiff” where sample well pixels are turned some color (i.e., certain values are manually assigned to the red, green, and blue wavebands for pixels occurring in sample wells).\\ \\# Input files: HSICalLib_b*[1]*_*date[1]*_s*[slope]*_a*[aspect]*_soilindices.RData (not included in this dataset) OR HSICalLib_b*[4]*_*date[4]*_s*[slope]*_a*[aspect]*_Tsoilindices.RData (not included in this dataset) Output from R script 1a HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_rgbmat.RData (not included in this dataset) Output from R script 1a \\# Output files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_mmResolution_RGB_Tsoilindices.tiff (not included in this dataset) Same as \\_soilindices.tiff (output from 1b) except the row and column indices occurring in sample wells have been automatically selected based on the “master” templates that were manually created for each orientation using batch 1 and 4 (R scripts not included in this dataset). HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_Tsoilindices.RData (not included in this dataset) Same as \\_soilindices.RData (output from 1b) except the row and column indices corresponding to sample wells have been automatically selected based on the “master” templates that were manually created for each orientation using batch 1 and 4 (R scripts not included in this dataset). \\# ---------- \\# Specific information for R script:  HSICalLib_1d_rgbmat_Tsoilindices_to_adjustedTsoilindices \\# Description of script:  Use “_Tsoilindices.RData” (“soilindices” list) (created in R script 1c) along with “rgbmat” arrays to MANUALLY adjust the location of pixels occurring in sample wells and turn those pixels some color using the row, column indices (pixels) indicated by “_Tsoilindices.RData” AND visual inspection by a user in R. Then output the updated “soilindices” list as “_Tsoilindices.RData” and “_Tsoilindices.tiff”. \\# Input files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_rgbmat.RData (not included in this dataset) Output from R script 1a HSICalLib_b*[4]*_*date[4]*_s*[slope]*_a*[aspect]*_Tsoilindices.RData (not included in this dataset) Output from R script 1c \\# Output files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_Tsoilindices.RData (not included in this dataset) Same as the “soilindices” list (output from R script 1b and 1c) except the locations of pixels occurring within sample wells have been adjusted based on visual inspection by a user. HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_mmResolution_RGB_Tsoilindices.tiff (not included in this dataset) Same as the RGB image output from R script 1b and 1c except the locations of pixels occurring within sample wells have been adjusted based on visual inspection by a user. \\# ---------- \\# Specific information for R script:  HSICalLib_2_soilindices_hsi_to_intensities.R \\# Description of script:  Use “_Tsoilindices.RData” and “_hsi.RData” to isolate reflectance spectra (i.e., reflectance intensities measured at 471 wavebands) from pixels (i.e., row, column coordinates indicated by “_Tsoilindices.RData”) corresponding to sample wells (soil samples) in “_hsi.RData” (output from R script 1a).  \\# Input files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_Tsoilindices.RData (not included in this dataset) Output from R script 1d HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_hsi.RData (not included in this dataset) Output from R script 1a \\# Output files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_hsi_intensities.RData (not included in this dataset) List containing 40 elements (1 for each sample well/soil sample). Each element of the list contains a data frame where each row is a reflectance spectrum from 1 pixel occurring within a sample well. For example, the first element of the list contains reflectance spectra from all the pixels occurring within the first sample well.  Number of rows = number of pixels occurring within this sample well Number of columns = 471 reflectance intensities  NOTE: the wavelengths corresponding to these 471 reflectance intensities can be found in wavevec.RData (). \\# ---------- \\# Specific information for R script:  HSICalLib_3_intensities_to_intmean_intsd.R \\# Description of script:  Use “_intensities.RData” (output from R script 2) to get the average reflectance spectrum of each soil sample. In other words, get the mean and sd of reflectance intensities measured at each waveband across all pixels occurring within each sample well/soil sample.  \\# Input files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_hsi_intensities.RData (not included in this dataset) Output from R script 2 \\# Output files: HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_intmean.RData (not included in this dataset) Data frame where each row contains the average reflectance spectrum for a soil sample/sample well for this batch at this configuration (also see \\_intensities.RData file name for batch, slope, and aspect info). Each row is the average reflectance spectrum for a sample/well. Each file corresponds to a single scan (total number of scans = 30 batches x 98 configurations). Number of rows/samples = 40  Number of columns/variables = 475 471 reflectance intensities: See Description of the data slope: See Description of the data aspect: See Description of the data batch: See Description of the data well: See Description of the data NOTE: Batch and Well were used together as a key to merge soil sample properties data with reflectance data in R script 4. Each soil sample has 98 reflectance spectra (1 obtained at each slope, aspect configuration) but only 1 set of properties data. The chemical and physical properties of a soil sample don’t change as the sample orientation changes, but reflectance does (as shown in this study).  HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_intsd.RData (not included in this dataset) Same as \\_intmean.RData, but standard deviation of reflectance at each waveband is reported rather than the mean. \\# ---------- \\# Specific information for R script:  HSICalLib_4_intmean_to_masterintmean_to_p.R \\# Description of script:  Bring in soil properties data from 4 separate sources, then merge these data frames, and output a single data frame called HSICalLib_b1_b30_prep_rutgers_neon_fire.RData which contains soil properties data for all soil samples in the study. Bring in \\_intmean.RData (output from R script 3) for each scan (98 orientations/scans per batch), then output a single data frame containing the mean reflectance spectra for each soil sample at 98 configurations for ONLY this batch. This file is similar to \\_intmean.RData except the separate \\_intmean.RData files for each scan are combined into a single \\_intmean.RData file for each batch.  Bring in \\_intmean.RData for each batch (output from this script) and merge into a single data frame called “_masterintmean.RData” containing reflectance spectra for all soil samples at all orientations. Merge \\_masterintmean.RData (output from this script) with \\_prep_rutgers_neon_fire.RData (output from this script) resulting in a data frame called “p” with reflectance spectra from all soil samples at all orientations along with selected soil properties data.  \\# Input files: HSICalLib_20230418_SamplePrepData_R.csv (not included in this dataset) Soil sample properties data provided by the Pedology Lab at UC Riverside Created in Google Sheets by Alyssa Duro HSICalLib_20230418_Fire_R.csv (not included in this dataset) Soil sample properties data provided by the Gray Lab at UC Riverside Created in Google Sheets by Alyssa Duro HSICalLib_20230418_NEON_R.csv (not included in this dataset) Soil sample properties data provided by NEON (NRCS performed lab analysis) Created in Google Sheets by Alyssa Duro HSICalLib_20230418_Rutgers_R.csv (not included in this dataset) Soil sample properties data provided by Rutgers (samples are from Duke Farms) Created in Google Sheets by Alyssa Duro HSICalLib_b*[batch]*_*[date]*_s*[slope]*_a*[aspect]*_intmean.RData (not included in this dataset) Output from R script 3 \\# Output files: HSICalLib_b1_b30_prep_rutgers_neon_fire.RData (not included in this dataset) Merged \\_SamplePrepData_R.RData,  \\_Fire_R.RData, \\_NEON_R.RData, and \\_Rutgers_R.RData resulting in a single data frame with 1180 rows (soil samples) and 58 columns/variables (measured soil properties and sample identifiers). Many soil properties data available for some soil samples were not available for all soil samples resulting in 32,727 NA’s. Number of rows/soil samples = 1180 Number of columns/variables = 58 HSICalLib_b*[batch]*_*[date]*_intmean.RData (not included in this dataset) Same as \\_intmean.RData (output from R script 3) except now each soil sample (i.e., each batch, well combination) is associated with 98 different reflectance spectra, each with a different combination of slope and aspect). Each file corresponds to a single batch (total number of batches = 30). Number of rows/soil samples/reflectance spectra = 3920 40 sample wells (soil samples) * 98 configurations Number of columns/variables = 475 Same variables as \\_intmean.RData (output from R script 3) HSICalLib_b1_b30_masterintmean.RData (not included in this dataset) Same as \\_intmean.RData (output from R script 3) except now each soil sample is associated with 98 reflectance spectra collected at different slope, aspect combinations.  Number of rows/soil samples/reflectance spectra = 117,600  40 soil samples * 30 batches * 98 configurations Number of columns/variables = 475  Same as \\_intmean.RData (outputs from R script 3 and 4) HSICalLib_b1_b30_p.RData A data frame with mean reflectance spectra for each soil sample at each orientation along with selected soil properties data and sample identifiers. NA’s occur where soil properties data are not available for a soil sample.  This is the most raw form of the data included in this dataset. Number of rows/spectra = 115,444 reflectance spectra 1178 soil samples * 98 configurations Number of columns/variables = 486 471 observed (uncorrected) reflectance intensities slope: See Description of the data aspect: See Description of the data batch: Soil samples were imaged in groups of 40 at a time well: Indexed location of the soil sample in the sample well array HSInumber: unique soil sample identifier  HSIPackedDensity: mass of soil sample per volume of sample well  sandTotal: % sand (only available for samples from the NEON archive) siltTotal: % silt (only available for samples from the NEON archive) clayTotal: % clay (only available for samples from the NEON archive) OC: % soil organic carbon (by weight) archive: source of the soil sample and soil properties data adod: air dried soil mass / oven dried soil mass volC: % soil organic carbon (by volume) log10volC: log10(volC) batchwellID: unique reflectance spectra identifier \\# ---------- \\# Specific information for R script:  HSICalLib_5a_p_obsI_cleaning.R \\# Description of script:  Remove reflectance spectra reflectance spectra that are not truly representative of soil samples based on visual identification of imaging errors then output as \\_p_clean.RData (not included in this dataset) OR output this data frame as a “long” version (where wavelength is a variable) called \\_p_clean_melt.RData (not included in this dataset). Then remove reflectance spectra that are not truly representative of soil samples by removing spectra containing unusually large or small observed intensities (obsI) and output this data frame as \\_p_clean_obsI.RData (). \\# Input files: HSICalLib_wavevec.RData HSICalLib_b1_b30_p.RData (output from R script 4) \\# Output files: HSICalLib_b1-b30_20230223_p_clean.RData (not included in this dataset) Same as \\_p.RData (output from R script 4) except some known (visually identified) imaging mistakes (rows/spectra) have been removed. Details are provided as comments in the R script. Number of rows/spectra = 114,764 Number of columns/variables = 486 Same as \\_p.RData (output from R script 4) HSICalLib_20230223_b1-b30_p_clean_melt.RData (not included in this dataset) “Long” version of \\_p_clean.RData where wavelength is a variable Number of rows/observed reflectance intensities (obsI) = 54,053,844 114,764 spectra * 471 wavebands Number of columns/variables = 8 slope: See Description of the data aspect: See Description of the data batch: Soil samples were imaged in groups of 40 at a time well: Indexed location of the soil sample in the sample well array HSInumber: unique soil sample identifier  wavelength: wavelength corresponding to each obsI reflectance intensity obsI: observed reflectance intensities ()  batchwellID: unique reflectance spectra identifier HSICalLib_20230223_b1-b30_obsIoutliers.RData (not included in this dataset) Character vector containing the “batchwellID” (a sample identifier unique to each reflectance spectrum) for the reflectance spectra identified as imaging errors using the observed intensities (obsI) approach. Length/number of spectra to be removed based on obsI values = 115 HSICalLib_b1-b30_20230223_p_clean_obsI.RData  Same as \\_p_clean.RData (output from this script) except image mistakes have been identified (see \\_obsIoutliers.RData) and removed based on unusually large or small observed intensities (obsI).  Number of rows/obsI spectra = 114,649 Number of columns/variables = 486 Same as \\_p.RData (output from R script 4) HSICalLib_20230223_b1-b30_p_clean_obsI_melt.RData (not included in this dataset) “Long” version of \\_p_clean_obsI.RData (output from this script) where wavelength is a variable Number of rows/observed reflectance intensities (obsI) = 53,999,679 114,649 spectra * 471 wavebands Number of columns/variables = 8 Same as \\_p_clean_melt.RData (output from this script) \\# ---------- \\# Specific information for R script:  HSICalLib_5b_p_dI_cleaning.R \\# Description of script:  Remove reflectance spectra reflectance spectra that are not truly representative of soil samples (due to imaging errors) if they contain unusually large or small change in intensities (dI or ΔI) values. These dI values () are the difference between the obsI and reference intensities (refI). \\# Input files: HSICalLib_20230223_b1-b30_p_clean_obsI.RData (output from R script 5a) HSICalLib_wavevec.RData \\# Output files: HSICalLib_20230223_b1-b30_p_clean_obsI_dI.RData (not included in this dataset) Same as \\_p_clean_obsI.RData (output from R script 5a) except 471 dI values (1 per wavelength) are reported rather than 471 observed reflectance intensities (obsI). Number of rows/dI spectra = 114,649 (same as \\_p_clean_obsI.RData) Number of columns/variables = 486 Same as \\_p.RData (output from R script 4).  NOTE: These are 471 dI reflectance intensities NOT obsI ()  HSICalLib_20230223_b1-b30_p_clean_obsI_dI_melt.RData (not included in this dataset) “Long” version of \\_p_clean_obsI_dI.RData where wavelength is a variable Number of rows/dI reflectance intensities = 53,999,679 114,649 spectra * 471 wavebands Number of columns/variables = 8 slope: See Description of the data aspect: See Description of the data batch: Soil samples were imaged in groups of 40 at a time well: Indexed location of the soil sample in the sample well array HSInumber: unique soil sample identifier  wavelength: wavelength corresponding to each dI reflectance intensity dI: the difference between obsI and refI  batchwellID: unique reflectance spectra identifier HSICalLib_20230223_b1-b30_dIoutliers.RData (not included in this dataset) Character vector containing the “batchwellID” (a sample identifier unique to each reflectance spectrum) for the reflectance spectra identified as imaging errors using the change/difference in intensities (dI) approach. Length/number of spectra to be removed based on dI values = 7188 HSICalLib_b1-b30_20230223_p_gold.RData Same as \\_p.RData (output from R script 4) except the reflectance spectra with unusually large or small obsI OR dI values have been identified (see \\_dIoutliers.RData and \\_obsIoutliers.RData) and removed as imaging errors.  Number of rows/obsI reflectance spectra = 107,486  Number of columns/variables = 486 Same as \\_p.RData (output from R script 4) \\# ---------- \\# Specific information for R script:  HSICalLib_6a_aspect_correction.R \\# Description of script:  Convert aspect (column) values 195, 210, 225, 240, 255, and 270 in \\_p_gold.RData (output from R script 5b) to 165, 150, 135, 120, 105, and 90 then output as \\_p_gold_acor.RData (wide version) and \\_p_gold_acor_melt.RData (long version). \\# Input files: HSICalLib_wavevec.RData HSICalLib_b1-b30_20230223_p_gold.RData (output from R script 5b) \\# Output files: HSICalLib_20230223_b1-b30_p_gold_acor.RData Same as \\_p_gold.RData (output from R script 5b) except some of the aspect values have been converted. Number of rows/obsI reflectance spectra = 99,804 Number of columns/variables = 486 Same as \\_p.RData (output from R script 4) HSICalLib_20230223_b1-b30_p_gold_acor_melt.RData “Long” version of \\_p_gold_acor.RData where wavelength is a variable Number of rows/dI reflectance intensities = 47,007,684 107,486 spectra * 471 wavebands Number of columns/variables = 5 slope:  aspect:  HSInumber: unique soil sample identifier  wavelength: wavelength corresponding to each obsI reflectance intensity obsI: observed reflectance intensities ()  \\# ---------- \\# Specific information for R script:  HSICalLib_6b_cosIL_calculation.R \\# Description of script:  Calculate values needed for the theoretical “cosine correction” based on measurements of the HSI setup used in this study. Details are provided as comments in the R script.  \\# Input files: None \\# Output files: HSICalLib_20230223_s0-s60_cosIL_all.RData  Number of rows/orientations = 91 Number of columns/variables = 16 slope: See Description of the data aspect: See Description of the data z1: zenith angle (degrees) between light bank 1 and the HSI camera,  NOTE: zenith varies with slope, light bank 1 = N = 0 azimuth z2: zenith angle (degrees) between light bank 2 (S) and the HSI camera,  NOTE: zenith varies with slope, light bank 2 = S = 180 azimuth meanz: average of z1 and z2 cosz1: cos(z1) cosz2: cos(z2) cosmeanz: cos(meanz) meancosz: average of cos(z1) and cos(z2) cosIL1: cos( illumination angle (IL) ) light bank 1  \\= cos(z1)*cos(slope) + sin(z1)*sin(slope)*cos(azimuth-aspect) cosIL2: cos( illumination angle (IL) ) light bank 2  \\= cos(z2)*cos(slope) + sin(z2)*sin(slope)*cos(azimuth-aspect) meancosIL: average of cosIL1 and cosIL2 r1: cos(z1) / cosIL1 r2: cos(z2) / cosIL2 rmeans: cos(meanz) / meancosIL rcosmeans: meancosz / meancosIL \\# ---------- \\# Specific information for R script:  HSICalLib_7a_observedI.R \\# Description of script:  Remove any remaining NA’s introduced during aspect correction in R script 6a, then output a final wide and long version of the obsI () spectra for the soil samples used in this study.  \\# Input files: HSICalLib_wavevec.RData HSICalLib_20230223_b1-b30_p_gold_acor.RData (output from R script 6a) \\# Output files: HSICalLib_20230223_b1-b30_pga.RData Number of rows/obsI spectra = 99,537 Number of columns/variables = 486  Same as \\_p.RData (output from R script 4) HSICalLib_20230223_b1-b30_pga_melt.RData “Long” version of \\_pga.RData where wavelength is a variable Number of rows/obsI reflectance intensities = 46,881,927 99,537 spectra * 471 wavebands Number of columns/variables = 5 Same as \\_p_gold_acor_melt.RData (output from R script 6a) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  wavelength: wavelength corresponding to each obsI reflectance intensity obsI: observed reflectance intensities ()  \\# ---------- \\# Specific information for R script:  HSICalLib_7b_referenceI.R \\# Description of script:  Bring in \\_pga.RData (output from R script 7a), then calculate and output a final wide and long version of the reference intensities (refI) () spectra for the soil samples used in this study. NOTE: refI spectra are the same for each configuration. \\# Input files: HSICalLib_wavevec.RData HSICalLib_20230223_b1-b30_pga.RData (output from R script 7a) \\# Output files: HSICalLib_20230223_b1-b30_pga_refI.RData Data frame with the same dimensions as \\_pga.RData (output from R script 7a) except reference intensities (refI) () are reported instead of obsI.  Number of rows/refI spectra = 99,537 Number of columns/variables = 486  Same as \\_p.RData (output from R script 4) NOTE: These are 471 refI reflectance intensities NOT obsI ()  HSICalLib_20230223_b1-b30_pga_refI_melt.RData “Long” version of \\_pga_refI.RData where wavelength is a variable Number of rows/refI reflectance intensities = 46,881,927 99,537 spectra * 471 wavebands Number of columns/variables = 5 Same as \\_p_gold_acor_melt.RData (output from R script 6a) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  wavelength: wavelength corresponding to each obsI reflectance intensity refI: reference reflectance intensities ()  \\# ---------- \\# Specific information for R script:  HSICalLib_7c_dI_calculation.R \\# Description of script:  Bring in \\_pga.RData (output from R script 7a), then calculate and output a final wide and long version of the delta (aka “change in”) intensities (dI) () spectra for the soil samples used in this study. \\# Input files: HSICalLib_wavevec.RData HSICalLib_20230223_b1-b30_pga.RData (output from R script 7a) \\# Output files: HSICalLib_20230223_b1-b30_pga_dI.RData Data frame with the same dimensions as \\_pga.RData (output from R script 7a) except delta (aka “change in”) intensities (dI) values () are reported instead of obsI.  Number of rows/refI spectra = 99,537 Number of columns/variables = 486  Same as \\_p.RData (output from R script 4) NOTE: These are 471 dI reflectance intensities NOT obsI ()  HSICalLib_20230223_b1-b30_pga_dI_melt.RData “Long” version of \\_pga_dI.RData where wavelength is a variable Number of rows/dI reflectance intensities = 46,881,927 99,537 spectra * 471 wavebands Number of columns/variables = 5 Same as \\_p_gold_acor_melt.RData (output from R script 6a) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  wavelength: wavelength corresponding to each obsI reflectance intensity dI: change in (aka “delta”) intensities ()  \\# ---------- \\# Specific information for R script:  HSICalLib_8b_dI_predict_global_final.R \\# Description of script:  Calibrate and evaluate a multiple linear regression model to predict dI using slope, aspect, and wavelength as predictor variables. Calculate error metrics (RMSE, NSE, and KGE) to quantify whether dI-corrected intensities (dIc) are closer to reference intensities (refI) than observed intensities (obsI).  \\# Input files: HSICalLib_wavevec.RData See Description of the data HSICalLib_20230223_b1-b30_pga_melt.RData (output from R script 7a) HSICalLib_20230223_b1-b30_pga_refI_melt.RData (output from R script 7b) HSICalLib_20230223_b1-b30_pga_dI_melt.RData (output from R script 7c) \\# Output files: HSICalLib_20230224_pga_dI_refI_melt.RData Merged (long) form of \\_pga_melt.RData (output from R script 7a), \\_pga_refI_melt.RData (output from R script 7b), \\_pga_dI_melt.RData (output from R script 7c).  Number of rows/reflectance intensities = 46,881,927 99,537 spectra * 471 wavebands Number of columns/variables = 7 slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  wavelength: wavelength corresponding to each obsI reflectance intensity obsI: observed reflectance intensities ()  refI: reference reflectance intensities ()  dI: change in (aka “delta”) intensities ()  HSICalLib_20230310_rutgerssamples_rand1.RData A randomly chosen subset of 50 soil samples (out of the 450 samples collected from Duke Farms and imaged using HSI) were ultimately included in the topographic correction study due to these soil sample properties all being very similar while making up a large portion of the training data. This vector contains the HSI numbers for these 50 randomly chosen soil samples (all from the Rutgers archive).  Length/number of soil samples (HSInumbers) = 50 HSICalLib_20230613_pga_dI_refI_melt_slm.RData Data frame with the same dimensions as Same as \\_pga_dI_refI_melt.RData (output from this R script) except it ONLY contains obsI, refI, and dI spectra collected at all non-zero slope orientations for the 681 soil samples used in this study.  Number of rows/reflectance intensities = 22,678,179 22,678,179 intensities / 471 wavebands = 48,149 spectra Number of columns/variables = 7  Same as \\_pga_dI_refI_melt.RData (output from this R script) HSICalLib_20230613_finalHSInums.RData A vector containing the HSI numbers corresponding to the 681 soil samples used in this study. Length/number of soil samples (HSInumbers) = 681 HSICalLib_20230613_globaldI_predict_lm_pga_dI_refI_melt_slm_dIp_dIc_2.RData Same as \\_pga_dI_refI_melt_slm.RData (output from this R script) except the columns dIp and dIc have been added. A multiple linear regression model was trained to predict dI using slope, aspect, wavelength, and their interactions as predictor variables. This model was evaluated to get predicted dI (dIp), then dIp was used to adjust (aka “correct”) obsI resulting in corrected dI (dIc). If the model was a perfect predictor, then dIp would equal dI AND dIc would equal refI. Number of rows/reflectance intensities = 22,678,179 Number of columns/variables = 7  slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  wavelength: wavelength corresponding to each obsI reflectance intensity obsI: observed reflectance intensities ()  refI: reference reflectance intensities ()  dI: change in (aka “delta”) intensities ()  dIp: predicted dI intensities () dIc: dI-corrected intensities () NOTE: These dIc values are compared to obsI and refI to get summary stats that quantify how well the dI correction worked (i.e., how much closer dIc was to refI than obsI was to refI).  HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData Summary stats for dI corrected spectra at each configuration across all wavelengths. Calculate RMSE, NSE, and KGE for every soil sample at every configuration. In other words, compare obsI to refI AND dIc to refI using these RMSE, NSE, and KGE metrics.  Number of rows = 48,149 Number of spectra compared to their reference using this approach Number of columns/variables = 15 slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  RMSE_obs: Root mean squared error (obsI vs refI) RMSE_dIc: Root mean squared error (dIc vs refI) NSE_obs: Nash-Sutcliffe efficiency (obsI vs refI) NSE_dIc: Nash-Sutcliffe efficiency (dIc vs refI) KGE_obs: Kling-Gupta efficiency (obsI vs refI) KGE_obs_r: Pearson correlation coefficient (obsI vs refI) KGE_obs_beta: mean obsI / mean refI (obsI vs refI) KGE_obs_alpha: standard deviation(obsI) /  standard deviation(refI) KGE_dIc: Kling-Gupta efficiency (dIc vs refI) KGE_dIc_r: Pearson correlation coefficient (dIc vs refI) KGE_dIc_beta: mean dIc / mean refI (dIc vs refI) KGE_dIc_alpha: standard deviation(dIc) /  standard deviation(refI) HSICalLib_20230613_globaldI_spectralstats_dIc_w_s_2.RData Summary stats for spectra at each wavelength \u0026amp; slope across all aspects. Same as \\_spectralstats_dIc_2.RData (output from this script) BUT spectra were grouped in a different way before calculating RMSE, NSE, and KGE.  Number of rows = 2,826 Number of intensities compared to their reference using this approach Number of columns/variables = 14 slope: See Description of the data wavelength: wavelength corresponding to each reflectance intensity RMSE_obs: Root mean squared error (obsI vs refI) RMSE_dIc: Root mean squared error (dIc vs refI) NSE_obs: Nash-Sutcliffe efficiency (obsI vs refI) NSE_dIc: Nash-Sutcliffe efficiency (dIc vs refI) KGE_obs: Kling-Gupta efficiency (obsI vs refI) KGE_obs_r: Pearson correlation coefficient (obsI vs refI) KGE_obs_beta: mean obsI / mean refI (obsI vs refI) KGE_obs_alpha: standard deviation(obsI) /  standard deviation(refI) KGE_dIc: Kling-Gupta efficiency (dIc vs refI) KGE_dIc_r: Pearson correlation coefficient (dIc vs refI) KGE_dIc_beta: mean dIc / mean refI (dIc vs refI) KGE_dIc_alpha: standard deviation(dIc) /  standard deviation(refI) HSICalLib_20230613_globaldI_spectralstats_dIc_w_a_2.RData Summary stats for spectra at each wavelength \u0026amp; aspect across all slopes. Same as before but aspect (rather than slope) \u0026amp; wavelength. Same as \\_spectralstats_dIc_2.RData (output from this script) BUT spectra were grouped in a different way before calculating RMSE, NSE, and KGE.  Number of rows = 6,123 Number of intensities compared to their reference using this approach Number of columns/variables = 14 aspect: See Description of the data wavelength: wavelength corresponding to each reflectance intensity RMSE_obs: Root mean squared error (obsI vs refI) RMSE_dIc: Root mean squared error (dIc vs refI) NSE_obs: Nash-Sutcliffe efficiency (obsI vs refI) NSE_dIc: Nash-Sutcliffe efficiency (dIc vs refI) KGE_obs: Kling-Gupta efficiency (obsI vs refI) KGE_obs_r: Pearson correlation coefficient (obsI vs refI) KGE_obs_beta: mean obsI / mean refI (obsI vs refI) KGE_obs_alpha: standard deviation(obsI) /  standard deviation(refI) KGE_dIc: Kling-Gupta efficiency (dIc vs refI) KGE_dIc_r: Pearson correlation coefficient (dIc vs refI) KGE_dIc_beta: mean dIc / mean refI (dIc vs refI) KGE_dIc_alpha: standard deviation(dIc) /  standard deviation(refI) \\# ---------- \\# Specific information for R script:  HSICalLib_9a_cosine_correction_final.R \\# Description of script:  Correct spectra using the theoretical “cosine correction”. Calculate error metrics (RMSE, NSE, and KGE) to quantify whether cosine corrected intensities (coscorI) are closer to reference intensities (refI) than observed intensities (obsI) or delta I corrected (dIc).  \\# Input files: HSICalLib_wavevec.RData See Description of the data HSICalLib_20230223_b1-b30_pga.RData  (output from R script 7a) HSICalLib_20230223_s0-s60_cosIL_all.RData (output from R script 6b) HSICalLib_20230310_rutgerssamples_rand1.RData (output from R script 8b) HSICalLib_20230223_b1-b30_pga_melt.RData (output from R script 7a) HSICalLib_20230223_b1-b30_pga_refI_melt.RData (output from R script 7b) \\# Output files: HSICalLib_20230613_pga_slm_cosIL.RData Select the rows in \\_pga.RData (output from R script 7a) corresponding to the 681 soil samples used in this study (see \\_finalHSInums.RData output from R script 8b), then merge this data frame with \\_cosIL_all.RData (output from R script 6b) resulting in a wide data frame with all reflectance spectra for the soil samples used in this study AND the constants needed for the cosine correction (calculated in R script 6b based on measurements of the HSI setup used in this study).  Number of rows/obsI spectra = 48,149 Number of columns = 500 slope: See Description of the data aspect: See Description of the data batch: Soil samples were imaged in groups of 40 at a time well: Indexed location of the soil sample in the sample well array HSInumber: unique soil sample identifier  HSIPackedDensity: mass of soil sample per volume of sample well  sandTotal: % sand (only available for samples from the NEON archive) siltTotal: % silt (only available for samples from the NEON archive) clayTotal: % clay (only available for samples from the NEON archive) OC: % soil organic carbon (by weight) archive: source of the soil sample and soil properties data adod: air dried soil mass / oven dried soil mass volC: % soil organic carbon (by volume) log10volC: log10(volC) 471 observed (uncorrected) reflectance intensities (obsI*[wavelength]*) batchwellID: unique reflectance spectra identifier z1: zenith angle (degrees) between light bank 1 and the HSI camera,  NOTE: zenith varies with slope, light bank 1 = N = 0 azimuth z2: zenith angle (degrees) between light bank 2 (S) and the HSI camera,  NOTE: zenith varies with slope, light bank 2 = S = 180 azimuth meanz: average of z1 and z2 cosz1: cos(z1) cosz2: cos(z2) cosmeanz: cos(meanz) meancosz: average of cos(z1) and cos(z2) NOTE: This is the way we decided to combine z1 and z2. cosIL1: cos( illumination angle (IL) ) light bank 1  \\= cos(z1)*cos(slope) + sin(z1)*sin(slope)*cos(azimuth-aspect) cosIL2: cos( illumination angle (IL) ) light bank 2  \\= cos(z2)*cos(slope) + sin(z2)*sin(slope)*cos(azimuth-aspect) meancosIL: average of cosIL1 and cosIL2 r1: cos(z1) / cosIL1 r2: cos(z2) / cosIL2 rmeans: cos(meanz) / meancosIL rcosmeans: meancosz / meancosIL NOTE: This is the ratio used in the final cosine correction. HSICalLib_20230613_pga_slm_coscorI.RData Same as \\_pga_slm_cosIL.RData (output from this script) except intensities reported are cosine corrected intensities (coscorI) rather than obsI. Number of rows/cosine corrected (coscorI) spectra = 48,149 Number of columns/variables = 500 slope: See Description of the data aspect: See Description of the data batch: Soil samples were imaged in groups of 40 at a time well: Indexed location of the soil sample in the sample well array HSInumber: unique soil sample identifier  HSIPackedDensity: mass of soil sample per volume of sample well  sandTotal: % sand (only available for samples from the NEON archive) siltTotal: % silt (only available for samples from the NEON archive) clayTotal: % clay (only available for samples from the NEON archive) OC: % soil organic carbon (by weight) archive: source of the soil sample and soil properties data adod: air dried soil mass / oven dried soil mass volC: % soil organic carbon (by volume) log10volC: log10(volC) 471 cosine corrected reflectance intensities (coscorI*[wavelength]*) batchwellID: unique reflectance spectra identifier z1: zenith angle (degrees) between light bank 1 and the HSI camera,  NOTE: zenith varies with slope, light bank 1 = N = 0 azimuth z2: zenith angle (degrees) between light bank 2 (S) and the HSI camera,  NOTE: zenith varies with slope, light bank 2 = S = 180 azimuth meanz: average of z1 and z2 cosz1: cos(z1) cosz2: cos(z2) cosmeanz: cos(meanz) meancosz: average of cos(z1) and cos(z2) NOTE: This is the way we decided to combine z1 and z2. cosIL1: cos( illumination angle (IL) ) light bank 1  \\= cos(z1)*cos(slope) + sin(z1)*sin(slope)*cos(azimuth-aspect) cosIL2: cos( illumination angle (IL) ) light bank 2  \\= cos(z2)*cos(slope) + sin(z2)*sin(slope)*cos(azimuth-aspect) meancosIL: average of cosIL1 and cosIL2 r1: cos(z1) / cosIL1 r2: cos(z2) / cosIL2 rmeans: cos(meanz) / meancosIL rcosmeans: meancosz / meancosIL NOTE: This is the ratio used in the final cosine correction. HSICalLib_20230613_pga_slm_coscorI_melt.RData “Long” version of \\_pga_slm_coscorI.RData (output from this script) where wavelength is a variable. Number of rows/cosine corrected intensities (coscorI) = 22,678,179 Number of columns/variables = 5 slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier wavelength: wavelength corresponding to each reflectance intensity coscorI: cosine corrected reflectance intensity  HSICalLib_20230613_pga_coscorI_refI_melt_slm.RData Same as \\_pga_slm_coscorI_melt.RData (output from this script) except reference intensity (refI) and observed intensity (obsI) have been added as a columns by merging \\_pga_slm_coscorI_melt.RData (output from this script) with \\_pga_melt.RData (output from R script 7a) AND \\_pga_refI_melt.RData (output from R script 7b). Number of rows/cosine corrected intensities (coscorI) = 22,678,179 Number of columns/variables = 5 slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier wavelength: wavelength corresponding to each reflectance intensity coscorI: cosine corrected reflectance intensity  obsI: observed reflectance intensities  refI: reference reflectance intensities HSICalLib_20230613_spectralstats_coscor.RData Summary stats for spectra at each configuration across all wavelengths. Number of rows = 48,149 Number of spectra compared to their reference using this approach Number of columns/variables = 15 slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier RMSE_obs: Root mean squared error (obsI vs refI) RMSE_coscor: Root mean squared error (coscor vs refI) NSE_obs: Nash-Sutcliffe efficiency (obsI vs refI) NSE_coscor: Nash-Sutcliffe efficiency (coscor vs refI) KGE_obs: Kling-Gupta efficiency (obsI vs refI) KGE_obs_r: Pearson correlation coefficient (obsI vs refI) KGE_obs_beta: mean obsI / mean refI (obsI vs refI) KGE_obs_alpha: standard deviation(obsI) /  standard deviation(refI) KGE_coscor: Kling-Gupta efficiency (coscor vs refI) KGE_coscor_r: Pearson correlation coefficient (coscor vs refI) KGE_coscor_beta: mean coscor / mean refI (coscor vs refI) KGE_coscor_alpha: standard deviation(coscor) /  standard deviation(refI) \\# ---------- \\# Specific information for R script:  HSICalLib_10a_C_correction_final.R \\# Description of script:  Correct spectra using the semi-empirical “C correction”. Calculate error metrics (RMSE, NSE, and KGE) to quantify whether C corrected intensities (ccorI) are closer to reference intensities (refI) than observed intensities (obsI). \\# Input files: HSICalLib_wavevec.RData HSICalLib_20230223_s0-s60_cosIL_all.RData (output from R script 6b) HSICalLib_20230613_pga_slm_cosIL.RData  (output from R script 9a) HSICalLib_20230310_rutgerssamples_rand1.RData (output from R script 8b) HSICalLib_20230223_b1-b30_pga_melt.RData (output from R script 7a) HSICalLib_20230223_b1-b30_pga_refI_melt.RData (output from R script 7b) \\# Output files: HSICalLib_20230613_C-coefficient_ccdf.RData Data frame containing the semi-empirically determined C coefficients for every waveband. These are calculated using obsI from \\_pga_melt.RData (output from R script 7a) and cosIL_all.RData (output from R script 6b). Number of rows/wavelengths = 471 Number of columns/variables = 5 wavelength: wavelength corresponding to each reflectance intensity slope: slope of the best fit line between obsI and cosIL NOTE: Different than “slope angle” used everywhere else intercept: intercept of the best fit line between obsI and cosIL ccoef: C coefficient = intercept / slope HSICalLib_20230613_pga_slm_cosIL_ccorI.RData Data frame containing C corrected spectra along with selected soil properties, imaging orientation, theoretically calculated constants, and soil sample identifiers. Same as \\_pga_slm_cosIL.RData and \\_pga_slm_coscorI.RData (output from R script 9a) except intensities reported are C corrected intensities (ccorI) rather than obsI or coscorI.  Number of rows/C corrected (ccorI) spectra = 48,149 Number of columns/variables = 500 slope: See Description of the data aspect: See Description of the data batch: Soil samples were imaged in groups of 40 at a time well: Indexed location of the soil sample in the sample well array HSInumber: unique soil sample identifier  HSIPackedDensity: mass of soil sample per volume of sample well  sandTotal: % sand (only available for samples from the NEON archive) siltTotal: % silt (only available for samples from the NEON archive) clayTotal: % clay (only available for samples from the NEON archive) OC: % soil organic carbon (by weight) archive: source of the soil sample and soil properties data adod: air dried soil mass / oven dried soil mass volC: % soil organic carbon (by volume) log10volC: log10(volC) 471 C corrected reflectance intensities (ccorI*[wavelength]*) batchwellID: unique reflectance spectra identifier z1: zenith angle (degrees) between light bank 1 and the HSI camera,  NOTE: zenith varies with slope, light bank 1 = N = 0 azimuth z2: zenith angle (degrees) between light bank 2 (S) and the HSI camera,  NOTE: zenith varies with slope, light bank 2 = S = 180 azimuth meanz: average of z1 and z2 cosz1: cos(z1) cosz2: cos(z2) cosmeanz: cos(meanz) meancosz: average of cos(z1) and cos(z2) NOTE: This is the way we decided to combine z1 and z2. cosIL1: cos( illumination angle (IL) ) light bank 1  \\= cos(z1)*cos(slope) + sin(z1)*sin(slope)*cos(azimuth-aspect) cosIL2: cos( illumination angle (IL) ) light bank 2  \\= cos(z2)*cos(slope) + sin(z2)*sin(slope)*cos(azimuth-aspect) meancosIL: average of cosIL1 and cosIL2 r1: cos(z1) / cosIL1 r2: cos(z2) / cosIL2 rmeans: cos(meanz) / meancosIL rcosmeans: meancosz / meancosIL NOTE: This is the ratio used in the final cosine correction. HSICalLib_20230613_pga_slm_ccorI_melt.RData “Long” version of \\_pga_slm_ccorI.RData (output from this script) where wavelength is a variable. Number of rows/C corrected intensities (ccorI) = 22,678,179 Number of columns/variables = 5 slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier wavelength: wavelength corresponding to each reflectance intensity ccorI: C corrected reflectance intensity  HSICalLib_20230613_pga_ccorI_refI_melt_slm.RData Same as \\_pga_slm_ccorI_melt.RData (output from this script) except reference intensity (refI) and observed intensity (obsI) have been added as a columns by merging \\_pga_slm_ccorI_melt.RData (output from this script) with \\_pga_melt.RData (output from R script 7a) AND \\_pga_refI_melt.RData (output from R script 7b). Number of rows/C corrected intensities (ccorI) = 22,678,179 Number of columns/variables = 5 slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier wavelength: wavelength corresponding to each reflectance intensity ccorI: C corrected reflectance intensity  obsI: observed reflectance intensities  refI: reference reflectance intensities HSICalLib_20230613_spectralstats_ccor.RData Summary stats for spectra at each configuration across all wavelengths. Number of rows = 48,149 Number of spectra compared to their reference using this approach Number of columns/variables = 15 slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier RMSE_obs: Root mean squared error (obsI vs refI) RMSE_ccor: Root mean squared error (ccor vs refI) NSE_obs: Nash-Sutcliffe efficiency (obsI vs refI) NSE_ccor: Nash-Sutcliffe efficiency (ccor vs refI) KGE_obs: Kling-Gupta efficiency (obsI vs refI) KGE_obs_r: Pearson correlation coefficient (obsI vs refI) KGE_obs_beta: mean obsI / mean refI (obsI vs refI) KGE_obs_alpha: standard deviation(obsI) /  standard deviation(refI) KGE_ccor: Kling-Gupta efficiency (ccor vs refI) KGE_ccor_r: Pearson correlation coefficient (ccor vs refI) KGE_ccor_beta: mean ccor / mean refI (ccor vs refI) KGE_ccor_alpha: standard deviation(ccor) /  standard deviation(refI) \\# ---------- \\# Specific information for R script:  HSICalLib_12a_OC_predict_pls_final.R \\# Description of script:  Train and evaluate a model to predict soil organic carbon (SOC) from reference spectra (refI), observed (non-zero slope, measured, uncorrected) spectra, and delta I corrected (dIc) to see whether dIC provide a better prediction of SOC than obsI. \\# Input files: HSICalLib_wavevec.RData See Description of the data HSICalLib_b1-b30_20230223_p_gold.RData (for soil properties) Output from R script 5b HSICalLib_20230613_globaldI_predict_lm_pga_dI_refI_melt_slm_dIp_dIc_2.RData Output from R script 8b HSICalLib_20230310_rutgerssamples_rand1.RData Output from R script 8b \\# Output files: HSICalLib_20230613_p_plots.RData Selected soil properties data for the 681 soil samples used in this study Number of rows/soil samples = 681 Number of columns/variables = 15 slope: See Description of the data aspect: See Description of the data batch: Soil samples were imaged in groups of 40 at a time well: Indexed location of the soil sample in the sample well array HSInumber: unique soil sample identifier  HSIPackedDensity: mass of soil sample per volume of sample well  sandTotal: % sand (only available for samples from the NEON archive) siltTotal: % silt (only available for samples from the NEON archive) clayTotal: % clay (only available for samples from the NEON archive) OC: % soil organic carbon (by weight) archive: source of the soil sample and soil properties data adod: air dried soil mass / oven dried soil mass volC: % soil organic carbon (by volume) log10volC: log10(volC) batchwellID: unique reflectance spectra identifier HSICalLib_20230613_globaldI_OCpredict_dIc.RData “Wide” version of \\_globaldI_predict_lm_pga_dI_refI_melt_slm_dIp_dIc_2.RData (output from R script 8b) where dIc (delta I corrected) intensities are reported as spectra (471 variables) for each soil sample at each orientation. Number of rows/dIc spectra = 48,149 Number of columns/variables = 474 471 wavebands = dIc*[wavelength]* NOTE: These reflectance intensities are dIc (dI corrected) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  HSICalLib_20230613_globaldI_OCpredict_refI.RData “Wide” version of \\_globaldI_predict_lm_pga_dI_refI_melt_slm_dIp_dIc_2.RData (output from R script 8b) where refI (reference) intensities are reported as spectra (471 variables) for each soil sample at each orientation. Number of rows/refI spectra = 48,149 Number of columns/variables = 474 471 wavebands = refI*[wavelength]* NOTE: These reflectance intensities are refI (reference) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  HSICalLib_20230613_globaldI_OCpredict_obsI.RData “Wide” version of \\_globaldI_predict_lm_pga_dI_refI_melt_slm_dIp_dIc_2.RData (output from R script 8b) where obsI (observed, uncorrected) intensities are reported as spectra (471 variables) for each soil sample at each orientation. Number of rows/obsI spectra = 48,149 Number of columns/variables = 474 471 wavebands = obsI*[wavelength]* NOTE: These reflectance intensities are obsI (observed) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  HSICalLib_20230613_globaldI_OCpredict_refI_p.RData Merge \\_globaldI_OCpredict_refI.RData (output from this script) with the soil properties data in \\_p_gold.RData resulting in a data frame with refI (reference) intensities reported as spectra (471 variables) for each soil sample at each orientation. Number of rows/refI spectra = 48,149 Number of columns/variables = 475 471 wavebands (predictor variables for PLSR) = refI*[wavelength]* NOTE: These reflectance intensities are refI (reference) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  log10volC: log base 10 of % SOC by volume (outcome variable for PLSR) HSICalLib_20230613_globaldI_OCpredict_obsI_p.RData Merge \\_globaldI_OCpredict_obsI.RData (output from this script) with the soil properties data in \\_p_gold.RData resulting in a data frame with obsI (observed, uncorrected) intensities reported as spectra (471 variables) for each soil sample at each orientation. Number of rows/obsI spectra = 48,149 Number of columns/variables = 474 471 wavebands = obsI*[wavelength]* NOTE: These reflectance intensities are obsI (observed) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier log10volC: log base 10 of % SOC by volume (outcome variable for PLSR) HSICalLib_20230613_globaldI_OCpredict_dIc_p.RData Merge \\_globaldI_OCpredict_dIc.RData (output from this script) with the soil properties data in \\_p_gold.RData resulting in a data frame with dIc (delta I corrected) intensities reported as spectra (471 variables) for each soil sample at each orientation. Number of rows/dIc spectra = 48,149 Number of columns/variables = 475 471 wavebands = dIc*[wavelength]* NOTE: These reflectance intensities are dIc (dI corrected) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  log10volC: log base 10 of % SOC by volume (outcome variable for PLSR) HSICalLib_20230613_globaldI_plsmodel_log10volC_refI_train.RData\" Partial least squares regression model trained with \\_refI_p.RData (output from this script) to predict OC from 471 reflectance intensities. HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData Evaluate pls on refI training data, then calculate RMSE, NSE, and KGE for observed (laboratory measured) SOC vs SOC predicted by the PLSR model trained and evaluated on refI. Number of rows/SOC predictions made from refI = 681 Number of columns/variables = 12 predicted: SOC predicted by this PLSR model observed: laboratory measured SOC slope: See Description of the data aspect: See Description of the data NOTE: slope and aspect are the same for all samples (rows) in this data frame because refI is the same regardless of orientation HSInumber: unique soil sample identifier RMSE: Root mean squared error  NSE: Nash-Sutcliffe efficiency (observed vs refI predicted SOC) R2: Coefficient of determination (observed vs refI predicted SOC) KGE: Kling-Gupta efficiency(observed vs refI predicted SOC) KGE_r: Pearson correlation coefficient (observed vs refI predicted SOC) KGE_beta: mean refI predicted / mean observed SOC KGE_alpha: sd(refI predicted) /  sd(observed) SOC HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_obs.RData Evaluate \\_globaldI_plsmodel_log10volC_refI_train.RData (output from this script) on \\_obsI_p.RData (output from this script) to predict OC from 471 obsI intensities. Then quantify the PLSR model performance using RMSE, NSE, and KGE (compare laboratory measured SOC to PLSR model predicted SOC). Number of rows/SOC predictions made from obsI = 48,149 Number of columns/variables = 12 predicted: SOC predicted by this PLSR model observed: laboratory measured SOC slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier RMSE: Root mean squared error  NSE: Nash-Sutcliffe efficiency (observed vs refI predicted SOC) R2: Coefficient of determination (observed vs refI predicted SOC) KGE: Kling-Gupta efficiency(observed vs refI predicted SOC) KGE_r: Pearson correlation coefficient (observed vs refI predicted SOC) KGE_beta: mean refI predicted / mean observed SOC KGE_alpha: sd(refI predicted) /  sd(observed) SOC HSICalLib_20230613_globaldI_dIc_train.RData This data frame contains the reflectance spectra which were randomly selected for each soil sample to train the NEXT PLSR model along with sample identifiers and log10volC (outcome variable). Number of rows/dIc spectra = 681 Number of columns/variables = 475 471 wavebands = dIc*[wavelength]* NOTE: These reflectance intensities are dIc (dI corrected) slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier  log10volC: log base 10 of % SOC by volume (outcome variable for PLSR) HSICalLib_20230613_globaldI_plsmodel_log10volC_dIc_train.RData PLSR model trained to predict SOC using 1 delta I corrected reflectance spectra per soil sample from a randomly chosen orientation (see \\_globaldI_dIc_train.RData, output from this script).  HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData Evaluate PLSR trained using dIc spectra from 1 randomly chosen orientation per sample on its training data, then quantify performance using RMSE, NSE, and KGE. Number of rows/SOC predictions made from dIc = 681 Number of columns/variables = 12 predicted: SOC predicted by this PLSR model observed: laboratory measured SOC slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier RMSE: Root mean squared error (observed vs dIc predicted SOC) NSE: Nash-Sutcliffe efficiency (observed vs dIc predicted SOC) R2: Coefficient of determination (observed vs dIc predicted SOC) KGE: Kling-Gupta efficiency(observed vs dIc predicted SOC) KGE_r: Pearson correlation coefficient (observed vs dIc predicted SOC) KGE_beta: mean dIc predicted / mean observed SOC KGE_alpha: sd(dIc predicted) /  sd(observed) SOC HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_dIc.RData Evaluate \\_globaldI_plsmodel_log10volC_dIc_train.RData (output from this script) on \\_dIc_p.RData (output from this script) to predict OC from 471 dIc intensities (using dIc spectra from all soil samples at all orientations). Then quantify the PLSR model performance using RMSE, NSE, and KGE (compare laboratory measured SOC to PLSR model predicted SOC). Number of rows/SOC predictions made from dIc = 48,149 Number of columns/variables = 12 predicted: SOC predicted by this PLSR model observed: laboratory measured SOC slope: See Description of the data aspect: See Description of the data HSInumber: unique soil sample identifier RMSE: Root mean squared error (observed vs dIc predicted SOC) NSE: Nash-Sutcliffe efficiency (observed vs dIc predicted SOC) R2: Coefficient of determination (observed vs dIc predicted SOC) KGE: Kling-Gupta efficiency(observed vs dIc predicted SOC) KGE_r: Pearson correlation coefficient (observed vs dIc predicted SOC) KGE_beta: mean dIc predicted / mean observed SOC KGE_alpha: sd(dIc predicted) /  sd(observed) SOC HSICalLib_202300613_globaldI_PLSR_log10volC_summarystats_OC.RData Summary stats for spectra at each configuration across all wavelengths \\# ---------- \\# Specific information for R script:  HSICalLib_0_FinalPlots.R \\# Description of script:  Create all the final plots for the paper. \\# Input files: HSICalLib_wavevec.RData See Description of the data HSICalLib_20230613_globaldI_predict_lm_pga_dI_refI_melt_slm_dIp_dIc_2.RData Output from R script 8b HSICalLib_20230613_globaldI_spectralstats_dIc_1.RData Output from R script 8b HSICalLib_20230613_globaldI_spectralstats_dIc_2.RData Output from R script 8b HSICalLib_20230613_spectralstats_coscor.RData Output from R script 9a HSICalLib_20230613_spectralstats_ccor.RData Output from R script 10a HSICalLib_20230613_globaldI_spectralstats_dIc_w_s_2.RData Output from R script 8b HSICalLib_20230613_globaldI_spectralstats_dIc_w_a_2.RData Output from R script 8b HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_ref.RData Output from R script 12a HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_obs.RData Output from R script 12a HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_tr_dIc.RData Output from R script 12a HSICalLib_20230613_globaldI_PLSR_log10volC_sstatdf_dIc.RData Output from R script 12a HSICalLib_20230613_globaldI_PLSR_log10volC_summarystats_OC.RData Output from R script 12a \\# Output files: HSICalLib_20231014_spectralstats_boxplots_final.pdf Figure 6 RMSE and NSE vs slope and aspect (box plots) NOTE: Different than Figure 10 because these are for dIc predictions HSICalLib_20231014_KGE_slope_boxplots_final.pdf KGE, alpha, and beta vs slope (box plots) HSICalLib_20231014_KGE_aspect_boxplots_final.pdf KGE, alpha, and beta vs aspect (box plots) HSICalLib_20231014_obsI_dIc_refI_spectra_final.pdf Figure 8 RI vs wavelength (refI, obsI, and dIc), dIc colored by slope and aspect HSICalLib_20231014_obsI_spectra_final.pdf Figure 5 RI vs wavelength (refI and obsI), obsI colored by slope and aspect HSICalLib_20231014_dIc_RMSE_spectra_final.pdf Figure 7 RMSE vs wavelength (obsI and dIc), dIc colored by slope and aspect HSICalLib_20230622_OCvalidationplots_final.pdf\" Figure 9 Observed vs predicted SOC (colored by slope) HSICalLib_20231014_globaldI_summarystats_OC_boxplots_final.pdf Figure 10 RMSE and NSE vs slope and aspect (box plots) NOTE: Different than Figure 6 because these are for SOC predictions","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"2034232 (PLS)","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Food and Agriculture","awardNumber":"DRH-no. 2021-67019-34341","funderIdentifier":"https://ror.org/05qx3fv49","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"2034214 (LL)","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Food and Agriculture","awardNumber":"SAB-no. 2021-67019-34338","funderIdentifier":"https://ror.org/05qx3fv49","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Food and Agriculture","awardNumber":"AF-no. 2021-67019-343340","funderIdentifier":"https://ror.org/05qx3fv49","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Food and Agriculture","awardNumber":"CA-R-ENS-5195-H","funderIdentifier":"https://ror.org/05qx3fv49","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Food and Agriculture","awardNumber":"CA-R-ENS-5147-H","funderIdentifier":"https://ror.org/05qx3fv49","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DBI-1624205","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Battelle","awardNumber":"US001-0000757206","funderIdentifier":"https://ror.org/01h5tnr73","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D15091","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":145,"downloadCount":43,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:45:02Z","registered":"2023-10-04T22:45:03Z","published":null,"updated":"2026-01-28T15:00:15Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d18t16","type":"dois","attributes":{"doi":"10.6086/d18t16","identifiers":[],"creators":[{"name":"Standring, Samantha","nameType":"Personal","givenName":"Samantha","familyName":"Standring","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5906-5488","nameIdentifierScheme":"ORCID"}]},{"name":"Forero, Dimitri","nameType":"Personal","givenName":"Dimitri","familyName":"Forero","affiliation":["Universidad Nacional de Colombia"],"nameIdentifiers":[]},{"name":"Weirauch, Christiane","nameType":"Personal","givenName":"Christiane","familyName":"Weirauch","affiliation":["University of California, Riverside"],"nameIdentifiers":[]}],"titles":[{"title":"Dataset for: Untangling the assassin’s web: Phylogeny and classification of the spider-associated Emesine complex (Hemiptera: Reduviidae)"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Emesinae"},{"subject":"Saicinae"},{"subject":"Visayanocorinae"},{"subject":"Systematics"}],"contributors":[{"name":"University of California, Riverside","nameType":"Personal","givenName":"Riverside","familyName":"University of California","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2023-05-30T19:12:34Z","dateType":"Submitted"},{"date":"2023-06-07T00:00:00Z","dateType":"Issued"},{"date":"2023-06-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/syen.12603","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["56308886 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Web-building spiders are formidable predators, yet assassin bugs in the\n Emesine Complex (Hemiptera: Reduviidae: Emesinae, Saicinae, and\n Visayanocorinae) prey on spiders. The Emesine Complex comprises\n \u0026gt;1,000 species and these web-associated predatory strategies may\n have driven their diversification. However, lack of natural history data\n and a robust phylogenetic framework currently preclude tests of this\n hypothesis. We combine Sanger (207 taxa, 3,865 bp) and high-throughput\n sequencing data (15 taxa, 381 loci) to generate the first taxon- and\n data-rich phylogeny for this group. We discover rampant paraphyly among\n subfamilies and tribes, necessitating revisions to the classification. We\n use ancestral character state reconstructions for 40 morphological\n characters to identify diagnostic features for a revised classification.\n Our new classification treats Saicinae Stål and Visayanocorinae Miller as\n junior synonyms of Emesinae Amyot and Serville, synonymizes the emesine\n tribes Ploiariolini Van Duzee and Metapterini Stål with Emesini Amyot and\n Serville, and recognizes six tribes within Emesinae (Collartidini\n Wygodzinsky, Emesini, Leistarchini Stål, Oncerotrachelini trib. nov.,\n Saicini Stål stat. nov., and Visayanocorini Miller stat. nov.). We show\n that a pretarsal structure putatively involved in web-associated behaviors\n evolved in the last common ancestor of Emesini, the most species-rich\n clade within Emesinae, suggesting that web-associations could be\n widespread in Emesinae.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DEB – 1655769","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DGE – 1840991","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D18T16","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":131,"downloadCount":16,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-06-07T11:37:32Z","registered":"2023-06-07T11:37:33Z","published":null,"updated":"2026-01-28T14:51:10Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1p09p","type":"dois","attributes":{"doi":"10.6086/d1p09p","identifiers":[],"creators":[{"name":"Fairbairn, Daphne","nameType":"Personal","givenName":"Daphne","familyName":"Fairbairn","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Roff, Derek","nameType":"Personal","givenName":"Derek","familyName":"Roff","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9337-7303","nameIdentifierScheme":"ORCID"}]},{"name":"Wolak, Matthew","nameType":"Personal","givenName":"Matthew","familyName":"Wolak","affiliation":["Auburn University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7962-0071","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Aquarius remigis measurements"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Quantitative genetics"},{"subject":"Body size"},{"subject":"sexual dimorphism."},{"subject":"Sex-linkage"},{"subject":"dominance variance"},{"subject":"genetic correlation"},{"subject":"water strider"},{"subject":"Aquarius remigis"},{"subject":"animal model"}],"contributors":[],"dates":[{"date":"2023-07-04T19:20:26Z","dateType":"Created"},{"date":"2023-05-15T22:17:17Z","dateType":"Submitted"},{"date":"2023-05-19T00:00:00Z","dateType":"Issued"},{"date":"2023-05-19T00:00:00Z","dateType":"Available"},{"date":"2023-05-18T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1038/s41437-023-00626-5","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["669091 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Excel file containing measurements in mm of all water striders (Aquarius\n remigis) from the pedigree experiment described in Fairbairn DJ, Roff DA,\n Wolak ME (2023) Tests for associations between sexual dimorphism and\n patterns of quantitative genetic variation in the water strider, Aquarius\n remigis. Heredity The evolution of sexual dimorphisms requires divergence\n between sexes in the evolutionary trajectories of the traits involved.\n Discerning how genetic architecture could facilitate such divergence has\n proven challenging because of the difficulty in extimating non-additive\n and sex-linked genetic variances using traditional quantitative genetic\n designs.  Here we use a 3-generation, double-first-cousin\n pedigree design to estimate additive, sex-linked and dominance\n (co)variances for 12 traits in the water strider, Aquarius remigis.\n Comparisons among these traits, which have size ratios ranging fron 1 to 5\n (larger/smaller) allow us to ask if sexual dimorphisms are associated with\n characteristic patterns of quantitative genetic variation.  We\n frame our analysis around three main questions, derived from existing\n theory and empirical evidence: Are sexual dimorphisms associated with (1)\n lower additive inter-sex genetic correlations, (2) higher proportions of\n sex-linked variance, or (3) differences between sexes in autosomal\n additive and dominance genetic variances. For questions (1) and (2), we\n find weak and non-significant trends in the predicted directions, which\n preclude definitive conclusions.  However, in answer to question\n (3), we find strng evidence for a positive relationship between sexual\n dimorphism and differences between sexes in proportions of autosomal\n dominance variance.  We also find strong interactions among the\n three genetic components indicating that their relative influence differs\n among traits and between sexes.  These results highlight the need\n to include all three components of genetic (co)variance in both\n theoretical evolutionary models and empirical estimations of the genetic\n architecture of dimorphic traits.","descriptionType":"Abstract"},{"description":"Results of a three generation rearing experiment in the\n laboratory under controlled conditions. For details of rearing and\n measurement protocols and landmarks used, see Supplementary Information in\n the above manuscript.","descriptionType":"Methods"},{"description":"Excel","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DEB-0743166","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1P09P","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":128,"downloadCount":9,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-05-19T19:13:20Z","registered":"2023-05-19T19:13:21Z","published":null,"updated":"2026-01-28T14:39:35Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1sq4z","type":"dois","attributes":{"doi":"10.6086/d1sq4z","identifiers":[],"creators":[{"name":"Hanscom, Ryan","nameType":"Personal","givenName":"Ryan","familyName":"Hanscom","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8106-1304","nameIdentifierScheme":"ORCID"}]},{"name":"Hill, Jessica","nameType":"Personal","givenName":"Jessica","familyName":"Hill","affiliation":["San Diego State University"],"nameIdentifiers":[]},{"name":"Patterson, Charlotte","nameType":"Personal","givenName":"Charlotte","familyName":"Patterson","affiliation":["San Diego State University"],"nameIdentifiers":[]},{"name":"Marbach, Tyler","nameType":"Personal","givenName":"Tyler","familyName":"Marbach","affiliation":["San Diego State University"],"nameIdentifiers":[]},{"name":"Sukumaran, Jeet","nameType":"Personal","givenName":"Jeet","familyName":"Sukumaran","affiliation":["San Diego State University"],"nameIdentifiers":[]},{"name":"Higham, Timothy","nameType":"Personal","givenName":"Timothy","familyName":"Higham","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Clark, Rulon","nameType":"Personal","givenName":"Rulon","familyName":"Clark","affiliation":["San Diego State University"],"nameIdentifiers":[]}],"titles":[{"title":"Cryptic behaviour and activity cycles of a small mammal keystone species revealed through accelerometry: a case study of Merriam’s kangaroo rats"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"biologging"},{"subject":"ecosystem engineer"},{"subject":"Foraging","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"moonlight"}],"contributors":[],"dates":[{"date":"2023-05-14T17:52:01Z","dateType":"Submitted"},{"date":"2023-05-17T00:00:00Z","dateType":"Issued"},{"date":"2023-05-17T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1186/s40462-023-00433-x","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2761888661 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Kangaroo rats are among the most abundant vertebrates in many terrestrial\n ecosystems in Western North America and are considered both keystone\n species and ecosystem engineers, providing linkages to other species as\n both consumers and resources. We developed an integrative miniaturized\n animal-borne accelerometry/radiotelemetry approach to quantify the cryptic\n behaviour and activity cycles of kangaroo rats. Our study highlights a\n method of attachment and retrieval for deploying accelerometers, a\n non-disruptive method of gathering observational validation datasets for\n acceleration data on free-ranging nocturnal small mammals, and a case\n study for analyzing how behavioural patterns relate to abiotic factors. We\n found that Merriam’s kangaroo rats are only active during later light\n phases of the night, show no reduction in activity or foraging associated\n with moonlight (indicating that kangaroo rats are actually more\n lunarphilic than lunarphobic) and increased foraging effort on more humid\n nights (most likely as a mechanism to avoid cutaneous water loss). Our\n work represents the first continuous detailed quantitative description of\n fine-scale behavioural activity budgets in kangaroo rats and lays out a\n general framework of how to use miniaturized biologging devices to examine\n behavioural responses of nocturnal small mammals to environmental factors.","descriptionType":"Abstract"},{"description":"We used these files to create our machine-learning models in the\n open-access software Accelerater. ","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"1856404","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1SQ4Z","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":148,"downloadCount":10,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-05-17T22:48:20Z","registered":"2023-05-17T22:48:21Z","published":null,"updated":"2026-01-28T14:38:24Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1xh5h","type":"dois","attributes":{"doi":"10.6086/d1xh5h","identifiers":[],"creators":[{"name":"Zhang, Weiyi","nameType":"Personal","givenName":"Weiyi","familyName":"Zhang","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Giang, Chau Minh","nameType":"Personal","givenName":"Chau Minh","familyName":"Giang","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Cai, Qingan","nameType":"Personal","givenName":"Qingan","familyName":"Cai","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Badie, Behnam","nameType":"Personal","givenName":"Behnam","familyName":"Badie","affiliation":["City of Hope"],"nameIdentifiers":[]},{"name":"Sheng, Jun","nameType":"Personal","givenName":"Jun","familyName":"Sheng","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Li, Chen","nameType":"Personal","givenName":"Chen","familyName":"Li","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3724-3893","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Dataset of: Using random forest for brain tissue identification by Raman spectroscopy"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Mechanical engineering","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Mechanical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"random forest"},{"subject":"Raman spectroscopy","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"brain tissues"}],"contributors":[],"dates":[{"date":"2023-05-12T09:04:44Z","dateType":"Submitted"},{"date":"2023-06-02T00:00:00Z","dateType":"Issued"},{"date":"2023-06-02T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1088/2632-2153/ad1349","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1536565493 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The traditional definitive diagnosis of brain tumors is performed by\n needle biopsy under the guidance of imaging-based exams. This\n paradigm is based on the experience of radiogolists, and accuracy\n could be affected by uncertainty in imaging interpretation and\n needle placement. Raman spectroscopy has the potential to improve\n needle biopsy by providing fingerprints of different materials\n and performing in situ tissue identification. In this paper, we\n present the development of a supervised machine learning algorithm using\n random forest to distinguish the Raman spectrum of different\n types of tissue. An integral process from raw data collection and\n preprocessing to model training and evaluation is presented. To\n illustrate the feasibility of this approach, viable animal\n tissues were used, including ectocinerea (grey matter), alba\n (white matter) and blood vessels. Raman spectra were acquired using a\n custom-built Raman spectrometer. The hyperparameters of the random forest\n model were determined by combining a cross-validation-based\n algorithm and manually adjusting. The experimental results show\n the ability of our approach to discriminate different types of tissues\n with high accuracy.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6086/D1XH5H","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":279,"downloadCount":62,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-06-02T22:34:33Z","registered":"2023-06-02T22:34:34Z","published":null,"updated":"2026-01-28T14:37:33Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1267d","type":"dois","attributes":{"doi":"10.6086/d1267d","identifiers":[],"creators":[{"name":"Spasojevic, Marko","nameType":"Personal","givenName":"Marko","familyName":"Spasojevic","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1808-0048","nameIdentifierScheme":"ORCID"}]},{"name":"Ramachandran, Advyth","nameType":"Personal","givenName":"Advyth","familyName":"Ramachandran","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Huxley, Jared D.","nameType":"Personal","givenName":"Jared D.","familyName":"Huxley","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8921-6235","nameIdentifierScheme":"ORCID"}]},{"name":"McFaul, Shane","nameType":"Personal","givenName":"Shane","familyName":"McFaul","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Schauer, Lisa","nameType":"Personal","givenName":"Lisa","familyName":"Schauer","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Diez, Jeff","nameType":"Personal","givenName":"Jeff","familyName":"Diez","affiliation":["University of Oregon"],"nameIdentifiers":[]},{"name":"Boone, Rohan","nameType":"Personal","givenName":"Rohan","familyName":"Boone","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Madsen‐Hepp, Tesa","nameType":"Personal","givenName":"Tesa","familyName":"Madsen‐Hepp","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3288-8943","nameIdentifierScheme":"ORCID"}]},{"name":"McCann, Erin","nameType":"Personal","givenName":"Erin","familyName":"McCann","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Franklin, Janet","nameType":"Personal","givenName":"Janet","familyName":"Franklin","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Logan, Danielle","nameType":"Personal","givenName":"Danielle","familyName":"Logan","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Rose, M. Brooke","nameType":"Personal","givenName":"M. Brooke","familyName":"Rose","affiliation":["University of California, Riverside"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Integrating ontogeny and ontogenetic dependency into community assembly"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Plant science","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Ecology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Ecology, Evolution, Behavior and Systematics"}],"contributors":[],"dates":[{"date":"2023-08-17T19:26:14Z","dateType":"Created"},{"date":"2023-04-27T20:38:27Z","dateType":"Submitted"},{"date":"2023-05-05T00:00:00Z","dateType":"Issued"},{"date":"2023-05-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/1365-2745.14132","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2476716 bytes"],"formats":[],"version":"8","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"To gain more insights into the process influencing forest dynamics in\n Southern California, we have established the 4ha San Jacinto Forest\n Dynamics Plot (SJFDP) on the west slope of Mt. San Jacinto in Southern\n California. This area is adjacent to the University of California James\n Reserve and is in the U.S. Forest Service (USFS) Hall Canyon research\n natural area. This dataset include three files. Census data is data on\n every free-standing woody stem (live or dead) greater than 1cm in diameter\n which has identified to species, mapped, measured, and tagged for\n long-term monitoring. Seedling data includes data from 256 1m2 plots where\n every seedling woody stem less than 1m tall has been identified to\n species, mapped, measured, and tagged for long-term monitoring.\n Envrionmental data includes soil and topographic variables that were\n collected within each of 100 20x20m quadrats withint he SJFDP.","descriptionType":"Abstract"},{"description":"To quantify woody species adult composition across the SJFDP, we\n subdivided the SJFDP into 100 20x20 all free-standing stems of woody\n species greater than 1cm diameter at breast height (DBH) that have been\n tagged, identified, measured, and mapped following CTFS-ForestGEO\n protocols (Condit, 1998). To quantify woody species seedling composition\n across the SJFDP, we surveyed the central 64 20x20m quadrats (out of 100\n total quadrats) in 2021, leaving a 20-meter buffer between seedling plots\n and the edge of the SJFDP. The purpose of the buffer was to ensure that\n adult composition is quantified for all of the 8 quadrats surrounding\n every quadrat in which seedling composition was quantified so that we can\n more accurately assess the influence of adult composition on seedling\n composition. To estimate seedling composition within each of the central\n 64 quadrats, we established four 1m\u003csup\u003e2\u003c/sup\u003e subplots\n (n=256 subplots). Each subplot was positioned 7m from the corner of each\n quadrat and aligned on a 45-degree angle relative to the x-y axes of the\n SJFDP grid (65º, 155º, 245º, 335º). Subplot locations were moved to the\n nearest suitable location if the initial subplot location was completely\n occupied by a log or rock. In each subplot, we identified all seedlings\n (defined as individuals under 1m tall following the CTFS-ForestGEO\n protocol (Condit, 1998)) to species, mapped their locations in a 100-cell\n grid, measured their height, and added an identification tag unique to\n each individual. To quantify environmental variation\n among quadrats we measured 8 soil variables and 6 topographic variables.\n In the center of each 20x20m quadrat, we collected a sample of ~500g of\n soil (0- to 10-cm depth) excluding the top organic horizon and analyzed\n organic matter (OM, by loss on ignition), phosphorus (P; Weak Bray and\n Sodium Bicarbonate), potassium (K), magnesium (Mg), calcium (Ca), sodium\n (Na), and cation exchange capacity (CEC, cations and CEC by ammonium\n acetate method) and pH (analysis was done by A \u0026amp; L Western\n Laboratories, Modesto, CA., USA). For each 20x20 m quadrat, we\n additionally calculated 6 topographic variables: mean elevation, slope,\n convexity, aspect, topographic position index, topographic ruggedness\n index, and flow direction. Mean elevation above sea level was quantified\n as the mean elevation of the four corners of each quadrat. Slope was\n quantified using the slope tool in ArcGIS 10.1. To quantify the remaining\n terrain characteristics,  we used a 1-m digital elevation model (DEM) from\n the USGS 3D Elevation Program (3DEP) and the raster package in R (Hijmans\n et al., 2013). Because aspect is a circular variable, we used\n cosine(aspect) in our analyses (Legendre et al., 2009). South aspect was\n measured as cos(aspect)*sin(slope), where higher values correspond to\n south-facing slopes that are associated with warmer and/or drier site\n conditions (Ackerly et al., 2020). Finally, we calculated the topographic\n position index (TPI) as the difference between the elevation of a quadrat\n and the mean elevation of the eight surrounding quadrats; topographic\n ruggedness index (TRI) as the mean absolute difference between the\n elevation of a quadrat and the elevation of the eight surrounding\n quadrats; and flow direction (flowdir) as the direction of the greatest\n drop in elevation for a given quadrat.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of Agriculture","awardNumber":"Hatch Funds 1023123","funderIdentifier":"https://ror.org/01na82s61","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"United States Department of Agriculture","awardNumber":"Hatch Funds 1021558","funderIdentifier":"https://ror.org/01na82s61","funderIdentifierType":"ROR"},{"funderName":"California Native Plant Society San Gabriel Mountains Chapter*"},{"schemeUri":"https://ror.org","funderName":"United States Department of Agriculture","awardNumber":"McIntire Stennis 7002251","funderIdentifier":"https://ror.org/01na82s61","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1267D","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":199,"downloadCount":29,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-05-06T00:04:46Z","registered":"2023-05-06T00:04:46Z","published":null,"updated":"2026-01-28T14:24:53Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d16108","type":"dois","attributes":{"doi":"10.6086/d16108","identifiers":[],"creators":[{"name":"Schwab, Stuart","nameType":"Personal","givenName":"Stuart","familyName":"Schwab","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8155-4464","nameIdentifierScheme":"ORCID"}]},{"name":"Jenerette, G. Darrel","nameType":"Personal","givenName":"G. Darrel","familyName":"Jenerette","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Larios, Loralee","nameType":"Personal","givenName":"Loralee","familyName":"Larios","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9740-8111","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Prescribed burning may produce refugia for invasive forb, Oncosiphon pilulifer"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"grassland management"},{"subject":"litter"},{"subject":"microclimate"},{"subject":"postburn heterogeneity"},{"subject":"seed addition"},{"subject":"Seed availability"},{"subject":"seedbank"},{"subject":"stinknet"}],"contributors":[],"dates":[{"date":"2023-04-18T20:18:06Z","dateType":"Submitted"},{"date":"2023-04-24T00:00:00Z","dateType":"Issued"},{"date":"2023-04-24T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/rec.13922","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["178051 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Prescribed burning is a common management technique to reduce non‐native\n grass cover and seed availability in temperate forests and grasslands;\n however, its effectiveness in reducing non‐native forbs is unclear. Litter\n of invasive forbs like Oncosiphon pilulifer is not consumed by\n fire like invasive grass litter is, resulting in residual singed stands\n and high heterogeneity in the postburn landscape. We investigated the\n potential for this incomplete burning to alter postfire establishment of\n native and non‐native plant species by conducting a field experiment in a\n prescribed burn in Lake Perris State Park, CA. We investigated the role of\n microclimate and seed availability on establishment for 2 years following\n a prescribed burn in both singed stands and completely burned patches by\n adding or removing litter and adding native seed in a factorial design.\n Litter presence reduced soil temperatures and light availability, while\n singed stands had lower soil moisture and temperature. Litter present\n treatments had 5.6 ± 5.9% (mean ± SE) greater Oncosiphon cover\n yet doubled Oncosiphon viable seeds in the seedbank.\n Singed stands had 22.6 ± 4.9% greater Oncosiphon cover\n and more than doubled Oncosiphon viable seeds. Native\n seed addition did not influence native\n or Oncosiphon cover. These results suggest that residual\n singed stands within the prescribed burn landscape can create a favorable\n microclimate and allow Oncosiphon to retain seed,\n increasing reinvasion. Our experiment suggests that litter increased\n establishment of non-native species as these species may better\n utilize postburn establishment opportunities impacting overall community\n recovery. Management of invasive forbs with prescribed burns may require\n secondary treatments to reduce reinvasion.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"funderName":"Riverside County Habitat Conservation Agency*"}],"url":"https://datadryad.org/dataset/doi:10.6086/D16108","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":120,"downloadCount":4,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-04-24T17:42:31Z","registered":"2023-04-24T17:42:32Z","published":null,"updated":"2026-01-28T14:19:17Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1fh6j","type":"dois","attributes":{"doi":"10.6086/d1fh6j","identifiers":[],"creators":[{"name":"Huynh, Bao-Lam","nameType":"Personal","givenName":"Bao-Lam","familyName":"Huynh","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6845-125X","nameIdentifierScheme":"ORCID"}]},{"name":"Stangoulis, James","nameType":"Personal","givenName":"James","familyName":"Stangoulis","affiliation":["Flinders University"],"nameIdentifiers":[]},{"name":"Vuong, Tri","nameType":"Personal","givenName":"Tri","familyName":"Vuong","affiliation":["University of Missouri"],"nameIdentifiers":[]},{"name":"Nguyen, Henry","nameType":"Personal","givenName":"Henry","familyName":"Nguyen","affiliation":["University of Missouri"],"nameIdentifiers":[]},{"name":"Duong, Tra","nameType":"Personal","givenName":"Tra","familyName":"Duong","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Boukar, Ousmane","nameType":"Personal","givenName":"Ousmane","familyName":"Boukar","affiliation":["International Institute of Tropical Agriculture"],"nameIdentifiers":[]},{"name":"Kusi, Francis","nameType":"Personal","givenName":"Francis","familyName":"Kusi","affiliation":["Council of Scientific and Industrial Research"],"nameIdentifiers":[]},{"name":"Batieno, Benoit Joseph","nameType":"Personal","givenName":"Benoit Joseph","familyName":"Batieno","affiliation":["Institut de l’Environnement et de Recherches Agricoles"],"nameIdentifiers":[]},{"name":"Cisse, Ndiaga","nameType":"Personal","givenName":"Ndiaga","familyName":"Cisse","affiliation":["Institut Senegalais De Recherches Agricoles"],"nameIdentifiers":[]},{"name":"Diangar, Mouhamadou Moussa","nameType":"Personal","givenName":"Mouhamadou Moussa","familyName":"Diangar","affiliation":["Institut Senegalais De Recherches Agricoles"],"nameIdentifiers":[]},{"name":"Awuku, Frederick Justice","nameType":"Personal","givenName":"Frederick Justice","familyName":"Awuku","affiliation":["Council of Scientific and Industrial Research"],"nameIdentifiers":[]},{"name":"Attamah, Patrick","nameType":"Personal","givenName":"Patrick","familyName":"Attamah","affiliation":["Council of Scientific and Industrial Research"],"nameIdentifiers":[]},{"name":"Crossa, José","nameType":"Personal","givenName":"José","familyName":"Crossa","affiliation":["Centro Internacional de Mejoramiento de Maíz Y Trigo"],"nameIdentifiers":[]},{"name":"Pérez-Rodríguez, Paulino","nameType":"Personal","givenName":"Paulino","familyName":"Pérez-Rodríguez","affiliation":["Colegio de Postgraduados"],"nameIdentifiers":[]},{"name":"Ehlers, Jeffrey","nameType":"Personal","givenName":"Jeffrey","familyName":"Ehlers","affiliation":["Gates Foundation"],"nameIdentifiers":[]},{"name":"Roberts, Philip","nameType":"Personal","givenName":"Philip","familyName":"Roberts","affiliation":["University of California, Riverside"],"nameIdentifiers":[]}],"titles":[{"title":"Grain nutritional profiles of a cowpea MAGIC population"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Agricultural sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Agricultural sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2023-03-25T06:06:54Z","dateType":"Submitted"},{"date":"2023-03-29T00:00:00Z","dateType":"Issued"},{"date":"2023-03-29T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1038/s41598-024-55214-2","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["348051 bytes"],"formats":[],"version":"8","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This dataset includes nutritional and agronomic traits collected from a\n multi-parent advanced generation intercross (MAGIC) population of cowpea\n grown in California during 2016–2017. The data were used in QTL mapping\n and genomic prediction as part of the manuscript entitled\n \"Quantitative trait loci and genomic prediction for grain sugar and\n mineral  concentrations of cowpea [Vigna unguiculata (L.)\n Walp.]\"","descriptionType":"Abstract"},{"description":"Grain sugar concentrations were assayed using high-performance\n liquid chromatography. Grain mineral concentrations were determined by\n inductively coupled plasma–optical emission spectrometry and\n combustion.","descriptionType":"Methods"},{"description":"Microsoft Excel is required to open this data file.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Agency for International Development","awardNumber":"7200AA18LE00003","funderIdentifier":"https://ror.org/01n6e6j62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"United States Agency for International Development","awardNumber":"EDH-A-00-07-00005","funderIdentifier":"https://ror.org/01n6e6j62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"United States Agency for International Development","awardNumber":"AID-OAA-A-13-00070","funderIdentifier":"https://ror.org/01n6e6j62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1FH6J","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":169,"downloadCount":19,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-03-29T20:27:51Z","registered":"2023-03-29T20:27:52Z","published":null,"updated":"2026-01-28T14:00:25Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1q10x","type":"dois","attributes":{"doi":"10.6086/d1q10x","identifiers":[],"creators":[{"name":"Scora, George","nameType":"Personal","givenName":"George","familyName":"Scora","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8595-3913","nameIdentifierScheme":"ORCID"}]},{"name":"Barth, Matthew","nameType":"Personal","givenName":"Matthew","familyName":"Barth","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Vu, Alex","nameType":"Personal","givenName":"Alex","familyName":"Vu","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Oswald, David","nameType":"Personal","givenName":"David","familyName":"Oswald","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2307-1437","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Evaluating the effectiveness of “Smart Pedal” systems for vehicle fleets"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"fuel consumption"},{"subject":"SmartPedal"},{"subject":"Eco Pedal"},{"subject":"throttle controller"},{"subject":"FOS: Environmental engineering","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Environmental engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Vehicle activity"}],"contributors":[],"dates":[{"date":"2023-05-17T06:52:41Z","dateType":"Submitted"},{"date":"2023-05-21T00:00:00Z","dateType":"Issued"},{"date":"2023-05-21T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["159701173 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"California has major initiatives for reducing greenhouse gas (GHG)\n emissions by 40% below 1990 levels by 2030, and 80% reduction below 1990\n levels by 2050. In recent years, there have been a number of “Smart Pedal”\n systems that emerged, both as automotive OEM equipment and as third-party\n hardware. These “Smart Pedal” systems can be installed in vehicles with\n the potential to reduce fuel consumption and GHG emissions by smoothing a\n driver’s acceleration and deceleration patterns, with little effect on\n travel time or safety. This research investigates the effectiveness of a\n select “Smart Pedal” system in reducing fuel consumption and GHG\n emissions. The SmartPedalTM technology was evaluated using six Caltrans\n vehicles, each monitored for two data collection periods: 1) without the\n SmartPedalTM device, to collect the baseline data sets,  and 2)\n with the SmartPedalTM device, to collect a comparison data set with the\n “Smart Pedal” technology. The collected data is presented here.","descriptionType":"Abstract"},{"description":"Data were collected using Global Positioning Systems (GPS)\n enabled Engine Control Unit (ECU) data loggers from the HEM corporation.\n The raw data files were converted to .csv files using HEM software. The\n HEM software also provides fuel economy based on the Mass Air Flow (MAF)\n sensor which was available for each test vehicle.  Recorded GPS data was\n used for map matching to determine road grade and road type.  This\n information is provided here, when available, however, GPS data is not\n provided due to privacy-related restrictions.  ","descriptionType":"Methods"},{"description":"The README file provides information on the data file name\n structure, test vehicle information, and a variable guide for the vehicle\n data files. The data files are in standard .csv format.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"California Department of Transportation","funderIdentifier":"https://ror.org/04d46jp54","funderIdentifierType":"ROR"},{"schemeUri":"https://www.crossref.org/services/funder-registry/","funderName":"National Center for Sustainable Transportation Technology","funderIdentifier":"https://doi.org/10.13039/501100018880","funderIdentifierType":"Crossref Funder ID"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1Q10X","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":146,"downloadCount":12,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-05-21T22:52:59Z","registered":"2023-05-21T22:52:59Z","published":null,"updated":"2026-01-28T13:59:37Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1tq2h","type":"dois","attributes":{"doi":"10.6086/d1tq2h","identifiers":[],"creators":[{"name":"Rankin, Erin","nameType":"Personal","givenName":"Erin","familyName":"Rankin","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7741-113X","nameIdentifierScheme":"ORCID"}]},{"name":"Knowlton, Jessie","nameType":"Personal","givenName":"Jessie","familyName":"Knowlton","affiliation":["Wheaton College - Massachusetts"],"nameIdentifiers":[]}],"titles":[{"title":"Dataset for: Diets of two non-native praying mantids (Tenodera sinensis and Mantis religiosa) show consumption of arthropods across all ecological roles"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Natural sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Natural sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Mantid"},{"subject":"Diet overlap"},{"subject":"food web"},{"subject":"Introduced"}],"contributors":[],"dates":[{"date":"2023-03-22T18:08:50Z","dateType":"Submitted"},{"date":"2023-03-27T00:00:00Z","dateType":"Issued"},{"date":"2023-03-27T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.fooweb.2023.e00280","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["307230 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"These data are from a comparative diet analysis which reveals many\n ecological guilds are consumed by nonnative mantids in old-field\n ecosystems. We observed low diet overlap between mantid species and\n overall high diet variability across samples. ","descriptionType":"Abstract"},{"description":"Mantids were collected in the field. We dissected their gut\n contents and used metabarcoding approaches to identify major diet\n components.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"funderName":"Wheaton College*"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1TQ2H","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":164,"downloadCount":22,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-03-27T22:35:12Z","registered":"2023-03-27T22:35:13Z","published":null,"updated":"2026-01-28T13:57:19Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1367q","type":"dois","attributes":{"doi":"10.6086/d1367q","identifiers":[],"creators":[{"name":"Hao, Peng","nameType":"Personal","givenName":"Peng","familyName":"Hao","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5864-7358","nameIdentifierScheme":"ORCID"}]},{"name":"Oswald, David","nameType":"Personal","givenName":"David","familyName":"Oswald","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2307-1437","nameIdentifierScheme":"ORCID"}]},{"name":"Barth, Matthew","nameType":"Personal","givenName":"Matthew","familyName":"Barth","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Wu, Guoyuan","nameType":"Personal","givenName":"Guoyuan","familyName":"Wu","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6707-6366","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Vehicle trajectory data in Eco-friendly Cooperative Traffic Optimization (EcoTOp) system at signalized intersections"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Eco-Approach and Departure"},{"subject":"Cooperative Traffic Optimization"},{"subject":"vehicle-to-infrastructure communications"},{"subject":"SUMO"},{"subject":"traffic simulation"}],"contributors":[{"name":"University of California, Riverside","nameType":"Personal","givenName":"Riverside","familyName":"University of California","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2023-01-31T20:31:12Z","dateType":"Submitted"},{"date":"2023-03-01T00:00:00Z","dateType":"Issued"},{"date":"2023-03-01T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["213499213 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Surface transportation systems (e.g., arterial roadways with signalized\n intersections) are inherently inefficient, particularly at higher traffic\n volumes. In general, both the infrastructure (e.g., traffic signals) and\n the vehicles operate independently, with little coordination between them.\n Previous research has shown that implementing strategies that take\n advantage of infrastructure-to-vehicle communication can improve overall\n mobility and reduce environmental impacts, e.g., the Eco-Approach and\n Departure (EAD) application that takes advantage of communicating signal\n phase and timing information to the vehicles. In this research, we will\n build upon this past research to develop a new cooperative traffic\n operation approach that takes advantage of not only\n infrastructure-to-vehicle communications, but also\n vehicle-to-infrastructure communications. This effort integrates a dynamic\n traffic signalization algorithm together with EAD algorithm to achieve\n even greater traffic efficiency. The research was carried out in a\n high-fidelity simulation environment and shows upwards of 15% fuel savings\n and 85% reductions in waiting time. The dataset contain the vehicle\n trajectory of all CAVs and non-CAVs in the SUMO-based traffic simulation\n from all approaches, with vary penetratin rate at 0% (baseline), 20%, 50%,\n 80% and 100%.","descriptionType":"Abstract"},{"description":"The dataset was collected from the\n trajectory output of SUMO, a micro-scopic traffic simulation software.\n Different scenarios under varying penetrations are tested, e.g. penetratin\n rate at 0% (baseline), 20%, 50%, 80% and 100%. The trajectory data are then archived as\n txt files.","descriptionType":"Methods"},{"description":"The data were saved in txt files in\n the format of second-by-second trajectories. One can use any text editor\n to open it.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"funderName":"National Center for Sustainable Transportation*","awardNumber":"DOT 69A3551747114"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1367Q","contentUrl":null,"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":169,"downloadCount":20,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-03-01T20:10:32Z","registered":"2023-03-01T20:10:33Z","published":null,"updated":"2026-01-28T13:17:05Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1m67c","type":"dois","attributes":{"doi":"10.6086/d1m67c","identifiers":[],"creators":[{"name":"Boan, Phillip","nameType":"Personal","givenName":"Phillip","familyName":"Boan","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7704-5328","nameIdentifierScheme":"ORCID"}]},{"name":"Evans, Scott","nameType":"Personal","givenName":"Scott","familyName":"Evans","affiliation":["Florida State University"],"nameIdentifiers":[]},{"name":"Hall, Christine","nameType":"Personal","givenName":"Christine","familyName":"Hall","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Droser, Mary","nameType":"Personal","givenName":"Mary","familyName":"Droser","affiliation":["University of California, Riverside"],"nameIdentifiers":[]}],"titles":[{"title":"Spatial distributions of Tribrachidium, Rugoconites, and Obamus from the Ediacara Member (Rawnsley Quartzite), South Australia"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Earth and related environmental sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"spatial ecology"},{"subject":"Ediacara Biota"},{"subject":"Australia","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Paleontology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2023-01-19T21:00:29Z","dateType":"Submitted"},{"date":"2023-01-27T00:00:00Z","dateType":"Issued"},{"date":"2023-01-27T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.7552585","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1017/pab.2023.9","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["116890 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The spatial distribution of in situ sessile organisms, including those\n from the fossil record, provides information about life histories, such as\n possible dispersal and/or settlement mechanisms, and how taxa are\n interacting with each other and their local environments. At Nilpena\n Ediacara National Park (NENP), South Australia, the exquisite preservation\n and excavation of 33 fossiliferous bedding planes from the Ediacara Member\n of the Rawnsley Quartzite reveals in situ communities of the Ediacara\n Biota. Here, the spatial distributions of three relatively common taxa,\n Tribrachidium, Rugoconites, and Obamus, occurring on excavated surfaces\n were analyzed using spatial point pattern analysis. Tribrachidium have a\n variable spatial distribution, implying that settlement or post-settlement\n conditions/preferences had an effect on populations. Rugoconites display\n aggregation, possibly related to their reproductive methods in combination\n with settlement location availability at the time of dispersal and/or\n settlement. Additionally, post-settlement environmental controls could be\n affecting Rugoconites on other surfaces, resulting in lower populations\n and densities. Both Tribrachidium and Rugoconites also commonly occur as\n individuals on a number of beds constraining possible reproductive\n strategies and environmental/substrate preferences. The distribution of\n Obamus is consistent with selective settlement, aggregating near\n conspecifics and on substrates of mature microbial mat. This dispersal\n process is the first example of substrate selective dispersal amongst the\n Ediacara Biota, thus making Obamus similar to numerous modern sessile\n invertebrates with similar dispersal and settlement strategies.","descriptionType":"Abstract"},{"description":"Please see the README document\n (\"README_TribRugoObam.txt\") and the accompanying published\n article: Boan, Evans, Hall, and Droser (in press). Spatial distributions\n of Tribrachidium, Rugoconites, and Obamus from the Ediacara Member\n (Rawnsley Quartzite), South Australia","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNG04GJ42G","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"American Museum of Natural History","funderIdentifier":"https://ror.org/03thb3e06","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Geological Society of America","awardNumber":"Graduate Student Geoscience Grant # 13053-21","funderIdentifier":"https://ror.org/0029f7m05","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Paleontological Society","funderIdentifier":"https://ror.org/01f9cfx50","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1M67C","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":133,"downloadCount":9,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-01-27T23:21:16Z","registered":"2023-01-27T23:21:17Z","published":null,"updated":"2026-01-28T13:09:39Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1vq36","type":"dois","attributes":{"doi":"10.6086/d1vq36","identifiers":[],"creators":[{"name":"Larios, Loralee","nameType":"Personal","givenName":"Loralee","familyName":"Larios","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9740-8111","nameIdentifierScheme":"ORCID"}]},{"name":"Glassman, Sydney I.","nameType":"Personal","givenName":"Sydney I.","familyName":"Glassman","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Randolph, James W. J.","nameType":"Personal","givenName":"James W. J.","familyName":"Randolph","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Saroa, Sameer S.","nameType":"Personal","givenName":"Sameer S.","familyName":"Saroa","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Capocchi, Joia K.","nameType":"Personal","givenName":"Joia K.","familyName":"Capocchi","affiliation":["University of California, Irvine"],"nameIdentifiers":[]},{"name":"Walters, Kendra E.","nameType":"Personal","givenName":"Kendra E.","familyName":"Walters","affiliation":["University of California, Irvine"],"nameIdentifiers":[]},{"name":"Pulido-Chavez, M. Fabiola","nameType":"Personal","givenName":"M. Fabiola","familyName":"Pulido-Chavez","affiliation":["University of California, Riverside"],"nameIdentifiers":[]}],"titles":[{"title":"Data for: Prescribed versus wildfire impacts on exotic plants and soil microbes in California grasslands"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"California grasslands"},{"subject":"prescribed burns"},{"subject":"Tenaja fire"},{"subject":"Bacteria","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Fungi","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"invasive plants"},{"subject":"microbial succession"},{"subject":"FOS: Natural sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Natural sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2023-01-08T18:52:21Z","dateType":"Submitted"},{"date":"2023-02-01T00:00:00Z","dateType":"Issued"},{"date":"2023-02-01T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2021.09.22.461426","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.apsoil.2022.104795","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["13683959 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Prescribed burns are often used as a management tool to decrease exotic\n plant cover and increase native plant cover in grasslands. These changes\n may also be mediated by fire impacts on soil microbial communities, which\n drive plant productivity and function. Yet, the ecological effects of\n prescribed burns compared to wildfires on either plant or soil microbial\n composition remain unclear. Grassland fires account for roughly 80% of\n global annual fires, but only roughly 12% of research on belowground\n impacts of fires occurs in grasslands, limiting our understanding of\n aboveground belowground connections in these important habitats. Here, we\n took advantage of the serendipitous opportunity of a wildfire burning\n through the same reserve where we had previously sampled a prescribed\n burn. This enabled us to investigate the impacts of a spring prescribed\n burn versus a fall wildfire on plant cover and community composition and\n bacterial and fungal richness, abundance, and composition. Our California\n grassland sites were thus within the same reserve, limiting environmental,\n vegetation, or climate variation between the sites. We used qPCR of 16S\n and 18S to assess impacts on bacterial and fungal abundance and Illumina\n MiSeq of 16S and ITS2 to assess impacts on bacterial and fungal richness\n and composition. Wildfire had stronger impacts than prescribed burns on\n microbial communities and both fires had similar impacts on plants with\n both prescribed and wildfire reducing exotic plant cover but neither\n reducing exotic plant richness. Fungal richness declined after the\n wildfire but not prescribed burn, but bacterial richness was unaffected by\n either. Yet, fire exposure in both fire types resulted in reduced\n bacterial and fungal abundance and altered bacterial and fungal\n composition. Plant diversity differentially impacted soil microbial\n diversity, with exotic plant diversity positively impacting bacterial\n richness and having no effect on arbuscular mycorrhizal richness. However,\n the remainder of the soil microbial communities were more related to\n aspects of soil chemistry including cation exchange capacity, organic\n matter, pH and phosphorous. Our coupled plant and soil community sampling\n allowed us to capture the sensitivity to fire of the fungal community and\n highlights the importance of potentially incorporating management actions\n such as soil or fungal amendments to promote this critical community that\n mediates native plant performance.","descriptionType":"Abstract"},{"description":"\u003cstrong\u003eAPSoil_file column header\n metadata.txt\u003c/strong\u003e This is a central file that\n has column header descriptions for the Dryad data files.\n \u003cstrong\u003eAPSoil_SpringPlantdata.csv\u003c/strong\u003e\n Plant species composition data for grassland plots in prescribed\n and wildfire study sites. It includes plots that were exposed to fire\n (i.e. burned) and those not (i.e. unburned). Data were collected in the\n first spring after the respective fire. Six letter species codes are used\n for column headers.  Full species names for species codes can be found in\n APSoil_Plant Species List.csv Columns for species diversity response\n variables are also included in that are stratified across the entire\n community or based on species provenance (i.e. native or\n exotic). \u003cstrong\u003eAPSoil_Plant Species\n List.csv\u003c/strong\u003e Provides full species names for\n any plant species observed during the study. A \"NA\" value was\n given for any species that had no field observation notes.\n \u003cstrong\u003eAPSoil_Bacterial-Metadata-withsoilPCA_final.csv\u003c/strong\u003e Bacterial richness, diversity and abundance response variables for grassland plots sampled in prescribed and wildfire study sites that were either exposed to fire or not. Data are reported for multiple time points and file includes soil chemistry data and soil PCA axis scores that were used for data analysis. Soil samples were only collected for first time point after fire; thus the soil variables for other timepoints are listed as NA.  \u003cstrong\u003eAPSoil_otu_Bacteria_16S_rarefied data.csv\u003c/strong\u003e Bacterial OTU table after rarefication adjustments. The first column is a sample ID key that can be connected to plot treatment information found in APSoil_Bacterial-Metadata-withsoilPCA_final.csv \u003cstrong\u003eAPSoil_Fungal-Metadata-withsoilPCA_final.csv\u003c/strong\u003e Fungal richness, diversity and abundance response variables for grassland plots sampled in prescribed and wildfire study sites that were either exposed to fire or not. Data are reported for multiple time points and file includes soil chemistry data and soil PCA axis scores that were used for data analysis. Soil samples were only collected for first time point after fire; thus the soil variables for other timepoints are listed as NA. \u003cstrong\u003eAPSoil_otu_Fungal_ITS_rarefied data.csv\u003c/strong\u003e Fungal OTU table after rarefication adjustments. The first column is a sample ID key that can be connected to plot treatment information found in APSoil_Fungal-Metadata-withsoilPCA_final.csv \u003cstrong\u003eAPSoil_AMF data.csv\u003c/strong\u003e Compiled data file that has summary spring plant richness and diversity metrics and richness, diversity metrics for arbuscular fungal mycorrhizae","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of the Interior","awardNumber":"L19AC00280","funderIdentifier":"https://ror.org/03v0pmy70","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1VQ36","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":340,"downloadCount":105,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-02-01T08:19:06Z","registered":"2023-02-01T08:19:07Z","published":null,"updated":"2026-01-28T13:02:02Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1n682","type":"dois","attributes":{"doi":"10.6086/d1n682","identifiers":[],"creators":[{"name":"Rankin, Erin","nameType":"Personal","givenName":"Erin","familyName":"Rankin","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7741-113X","nameIdentifierScheme":"ORCID"}]},{"name":"Sidhu, C. Sheena","nameType":"Personal","givenName":"C. Sheena","familyName":"Sidhu","affiliation":["Stanford University"],"nameIdentifiers":[]}],"titles":[{"title":"Dataset: Sibara filifolia pollination ecology"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"FOS: Natural sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Natural sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"pollination ecology"},{"subject":"seed set"},{"subject":"fruit set"},{"subject":"self-pollination"},{"subject":"Cross-pollination"}],"contributors":[],"dates":[{"date":"2022-11-29T00:18:03Z","dateType":"Submitted"},{"date":"2022-12-05T00:00:00Z","dateType":"Issued"},{"date":"2022-12-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.3398/064.082.0401","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["190313 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This dataset comprises the field and greenhouse pollination studies into\n the rare plant, Sibara filifolia. It describes how plant reproductive\n fitness measures differed across different pollination treatments.","descriptionType":"Abstract"},{"description":"Plants were grown from seeds from four source populations. These\n plants were then exposed to various pollination treatments, and we noted\n the number of pods (fruits) and seeds resulting from each treatment. We\n took a subset of these plants and did a second round of crosses, again\n noting the number or pods and seeds resulting from each pollination\n treatment.","descriptionType":"Methods"},{"description":"Data are saved as an xlsx and README is a text file.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of the Navy","funderIdentifier":"https://ror.org/03ar0mv07","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1N682","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":89,"downloadCount":4,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-12-05T19:02:26Z","registered":"2022-12-05T19:02:27Z","published":null,"updated":"2026-01-28T12:36:51Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d11d5d","type":"dois","attributes":{"doi":"10.6086/d11d5d","identifiers":[],"creators":[{"name":"Andrews, Holly","nameType":"Personal","givenName":"Holly","familyName":"Andrews","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5173-0826","nameIdentifierScheme":"ORCID"}]},{"name":"Krichels, Alexander","nameType":"Personal","givenName":"Alexander","familyName":"Krichels","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Homyak, Peter","nameType":"Personal","givenName":"Peter","familyName":"Homyak","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Piper, Stephanie","nameType":"Personal","givenName":"Stephanie","familyName":"Piper","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Aronson, Emma","nameType":"Personal","givenName":"Emma","familyName":"Aronson","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Botthoff, Jon","nameType":"Personal","givenName":"Jon","familyName":"Botthoff","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Greene, Aral","nameType":"Personal","givenName":"Aral","familyName":"Greene","affiliation":["University of California, Riverside"],"nameIdentifiers":[]},{"name":"Jenerette, G. Darrel","nameType":"Personal","givenName":"G. Darrel","familyName":"Jenerette","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2387-7537","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Wetting‐induced soil CO2 emission pulses are driven by interactions among soil temperature, carbon, and nitrogen limitation in the Colorado Desert"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Earth and related environmental sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[{"name":"University of California Natural Reserve System","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2023-07-07T15:49:38Z","dateType":"Created"},{"date":"2023-07-07T15:50:46Z","dateType":"Submitted"},{"date":"2023-07-12T00:00:00Z","dateType":"Issued"},{"date":"2023-07-12T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/gcb.16669","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2418476 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Warming-induced changes in precipitation regimes, coupled with\n anthropogenically-enhanced nitrogen (N) deposition, are likely to increase\n the prevalence, duration, and magnitude of soil respiration pulses\n following soil wetting via interactions among temperature and C and N\n availability. Quantifying the importance of these interactive controls on\n soil respiration is a key challenge as pulses can be large terrestrial\n sources of atmospheric CO2 over comparatively short timescales. Using an\n automated sensor system, we measured soil CO2 flux dynamics in the\n Colorado Desert—a system characterized by pronounced transitions from\n dry-to-wet soil conditions—through a multi-year series of experimental\n wetting campaigns. Experimental manipulations included combinations of C\n and N additions across a range of ambient temperatures and across five\n sites varying in atmospheric N deposition. We found soil CO2 pulses\n following wetting were highly predictable from peak instantaneous CO2 flux\n measurements. CO2 pulses consistently increased with temperature, and\n temperature at time of wetting positively correlated to CO2 pulse\n magnitude. Experimentally adding N along the N deposition gradient\n generated contrasting pulse responses: adding N increased CO2 pulses in\n low N deposition sites, whereas adding N decreased CO2 pulses in high N\n deposition sites. At the lowest N deposition site, simultaneous additions\n of C and N during wetting led to the highest observed soil CO2 fluxes\n reported globally at 299.5 µmol CO2 m-2 s-1. Our results suggest that\n soils have the capacity to emit high amounts of CO2 within small\n timeframes following infrequent wetting, and pulse sizes reflect a\n non-linear combination of soil resource and temperature interactions.\n Importantly, the largest soil CO2 emissions occurred when multiple\n resources were flushed simultaneously in historically resource-limited\n desert soils, pointing to regions experiencing simultaneous effects of\n desertification and urbanization as key players in future global C\n balance.","descriptionType":"Abstract"},{"description":"Our eight field experiments, which varied in experimental manipu-\n lations of temperature and C and N substrates, all used the same\n  measurement system to capture soil temperature, moisture, and\n CO\u003csub\u003e2\u003c/sub\u003e fluxes before and after soil wetting. Prior to\n each field campaign,  pairs of polyvinyl chloride (PVC) soil collars\n measuring 20-cm in diam- eter were installed to 5-cm depth and adjacent to\n each other under  L. tridentata canopies or in interspaces between shrubs.\n One collar  per pair was used for trace gas measurements and the other was\n used  for soil temperature, moisture, and ancillary soil measurements.\n This  secondary collar was necessary because temperature and moisture\n probes contained wiring attachments that would interfere with the\n chamber's ability to seal around the soil collar; however, we assumed\n that collar pairs would experience similar climate and edaphic conditions\n given their proximity to each other. Both collars in each pair received\n identical wetting treatments and collar pairs were situated at least 2 m\n apart from other pairs. Beginning at 15 min following wetting, soil\n temperature, moisture, and fluxes of CO\u003csub\u003e2\u003c/sub\u003e  were\n  measured  at  30-min  intervals  over  24-45 h. Soils were also measured\n for up to 24 h prior to wetting as an assessment of dry conditions. For\n these instantaneous measurements, we used a robotic chamber array and\n sampling procedure previously described in Andrews et al. (2022 AGEE). In\n this system, eight automated long-term chambers (LI- 8100-104; LI-COR\n Biosciences) with  soil  temperature  (LI-8150-203 thermistor  probe;\n LI-COR Biosciences) and moisture (LI-GS1 probe; LI-COR Biosciences) probe\n attachments were installed on soil PVC collars; probes were inserted to 5\n cm below the soil surface and provided integrated measurements of 3–5  cm\n soil depth. Each chamber collected measurements on a 30-min interval. Air\n collected from an actively measuring chamber was passed through a\n multiplexer (LI-8150; LI-COR Biosciences) and delivered to a gas analyzer\n suite, including a CO\u003csub\u003e2\u003c/sub\u003e infrared gas analyzer system\n (LI-8100A; LI-COR Biosciences), in a closed loop. Each chamber measurement\n sequence included a 30-s pre-measurement purge, a 2.5-min active\n measurement period of trace gas concentrations and soil climate status,\n and a 30-s post-measurement purge. All eight chambers were sampled at\n 30-min intervals over a 24- to 45-h measurement period.\n Raw CO\u003csub\u003e2\u003c/sub\u003e concentration and soil probe\n measurements were batch processed into fluxes and associated soil\n temperature and moisture data using algorithms adapted from previous work\n using this chamber array (Andrews et al., 2022 AGEE; Krichels et al.,\n 2022). Instantaneous fluxes of CO\u003csub\u003e2\u003c/sub\u003e were calculated\n as the regression coefficient of linear increase in gas concentration data\n during the 2.5-min active chamber measurement period, accounting for soil\n collar dimensions and atmospheric parameters following the Ideal Gas Law\n (Davidson et al., 2000). Instantaneous fluxes of\n CO\u003csub\u003e2\u003c/sub\u003e were compiled and integrated with instantaneous\n soil temperature and moisture measurements using a publicly-available R\n script (Andrews \u0026amp; Krichels,  2021). Additional post-processing\n filtering steps were conducted when data failed to cross a threshold of\n data quality and control due to chamber or analyzer malfunctions. Our\n final eight-campaign dataset consisted of 22,553\n CO\u003csub\u003e2\u003c/sub\u003e fluxes and corresponding soil temperature\n and/or moisture measurements. From continuous measurements of each\n chamber, we constructed 24-h time series following wetting and extracted\n the magnitude and timing of maximum instantaneous flux. We also calculated\n 24-h cumulative CO\u003csub\u003e2\u003c/sub\u003e fluxes using linear trapezoidal\n integration of chamber observations which occurred at 30-min\n intervals.","descriptionType":"Methods"},{"description":"Statistical analyses were conducted  in JMP 14 (2021) and data\n management and visualization were performed in R 4.1.3 (2021) and RStudio\n (RStudio Team, 2020).","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"Division of Environmental Biology","awardNumber":"1656062","funderIdentifier":"https://ror.org/03g87he71","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","awardTitle":"\n        Collaborative Research: How can we assess nitrogen saturation in xeric\n        ecosystems? Accounting for water, time, and nitrogen availability\n      ","funderName":"Division of Environmental Biology","awardNumber":"1916622","funderIdentifier":"https://ror.org/03g87he71","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DEB 1656062","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D11D5D","contentUrl":null,"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":111,"downloadCount":9,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-07-13T06:59:22Z","registered":"2023-07-13T06:59:23Z","published":null,"updated":"2026-01-28T12:10:19Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d18x0v","type":"dois","attributes":{"doi":"10.6086/d18x0v","identifiers":[],"creators":[{"name":"Wu, Guoyuan","nameType":"Personal","givenName":"Guoyuan","familyName":"Wu","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6707-6366","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Driving data from multi-human-in-the-loop simulation experiments"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"FOS: Engineering and technology","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Engineering and technology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Multi-human-in-the-loop"},{"subject":"Multimodal"},{"subject":"cooperative ramp merging"},{"subject":"Game theory","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"name":"CE-CERT*","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2022-09-12T15:25:07Z","dateType":"Submitted"},{"date":"2022-10-10T00:00:00Z","dateType":"Issued"},{"date":"2022-10-10T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["36314529 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Freeway ramp merging involves conflict of vehicle movements that may lead\n to traffic bottlenecks or accidents. Thanks to advances in connected and\n automated vehicle (CAV) technology, a number of efficient ramp merging\n strategies have been developed. However, most of the existing CAV-based\n ramp merging strategies assume that all the vehicles are CAVs or do not\n differentiate vehicle type (i.e., passenger cars vs. heavy-duty trucks).\n In this study, we propose a decentralized cooperative ramp merging\n application for connected vehicles (both connected trucks and connected\n cars) in a mixed-traffic environment. In addition, we develop a\n multi-human-in-the-loop (MHuiL) simulation platform that integrates SUMO\n traffic simulator with two game engine-based driving simulators, allowing\n us to investigate the interactions between two human drivers under various\n traffic scenarios. The case study shows that the decentralized cooperative\n ramp merging application, which provides speed guidance to the connected\n vehicles involved in ramp merging, helps increase the time headways of the\n involved vehicles and smooths their speed profiles. With the speed\n guidance, the median minimum time headway for the yielding car on the\n mainline increases by 57%. Also, its speed variation decreases by 17%\n while the speed variation of the merging truck from the on-ramp decreases\n by 19%. These results demonstrate the potential for the proposed\n application to improve the safety and efficiency of ramp merging for\n heavy-duty trucks, which will be particularly useful at on-ramps with\n relatively short merging lanes. The experiments conducted also validate\n the effectiveness of the developed MHuiL platform for human factor\n research.","descriptionType":"Abstract"},{"description":"The dataset was collected from the custom-built\n multi-human-in-the-loop simulation platform by the research team. This\n platform consists of a microscopic traffic simulator, SUMO, and two game\n engine-based driving simulators (one for trucks and the other for\n passenger cars) using Unity. The data has been processed with Python code\n to capture detailed driving information and surrounding vehicles (with\n respect to the two driving simulators) information.","descriptionType":"Methods"},{"description":"The trajectories of each driver and the surrounding vehicles are\n saved in a txt file using JSON format, which should be easily accessible\n by most programs/software.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of Transportation","funderIdentifier":"https://ror.org/02xfw2e90","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6086/D18X0V","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":137,"downloadCount":9,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-10-10T20:09:55Z","registered":"2022-10-10T20:09:56Z","published":null,"updated":"2026-01-28T11:35:22Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6086/d1p40n","type":"dois","attributes":{"doi":"10.6086/d1p40n","identifiers":[],"creators":[{"name":"Yi, Li","nameType":"Personal","givenName":"Li","familyName":"Yi","affiliation":["Ocean University of China"],"nameIdentifiers":[]},{"name":"Li, King-Fai","nameType":"Personal","givenName":"King-Fai","familyName":"Li","affiliation":["University of California, Riverside"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0150-2910","nameIdentifierScheme":"ORCID"}]},{"name":"Chen, Xianyao","nameType":"Personal","givenName":"Xianyao","familyName":"Chen","affiliation":["Ocean University of China"],"nameIdentifiers":[]},{"name":"Tung, Ka-Kit","nameType":"Personal","givenName":"Ka-Kit","familyName":"Tung","affiliation":["University of Washington"],"nameIdentifiers":[]}],"titles":[{"title":"Summer marine fog distribution in the Chukchi–Beaufort Seas"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"FOS: Earth and related environmental sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Xuelong"},{"subject":"CALIPSO"},{"subject":"in-situ visibility"},{"subject":"spaceborne lidar"},{"subject":"Pacific Arctic"},{"subject":"Beaufort Gyre"}],"contributors":[],"dates":[{"date":"2022-08-11T17:50:05Z","dateType":"Submitted"},{"date":"2022-08-12T00:00:00Z","dateType":"Issued"},{"date":"2022-08-12T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2021ea002049","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["16883530 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"We study the spatial and temporal variability of summer marine fog in the\n Chukchi–Beaufort region (175°E–150°W, 70°N–86°N) using the in-situ\n visibility measurements aboard the Chinese research fleet Xuelong (I and\n II) and a fog product we derive using the Vertical Feature Mask product of\n the spaceborne CALIPSO observations. The Xuelong in-situ observations show\n that the fog frequency in the Chukchi–Beaufort region has a maximum of\n ~18% in the early morning and is less than 10% in the rest of the day. The\n latitudinal distribution of the Xuelong-based in-situ fog frequency\n further shows high fog occurrences at 74°N and 79°N, which are related to\n the local high fog occurrences near 72°N–74°N and 76°N–80°N in the central\n Chukchi–Beaufort region, as revealed by the longitude-latitude pattern of\n the CALIPSO-based spaceborne fog frequency distribution. The CALIPSO-based\n fog frequency is also shown to be lower along the continental coastlines\n than in the Chukchi–Beaufort region. This longitude-latitude distribution\n may be explained by a reduced fog formation due to the Pacific warm\n current flowing into the Arctic region through the Bering Strait in the\n summer as well as an enhanced fog formation in the Chukchi–Beaufort region\n when the southward flow of the Beaufort Gyre interacts with the Pacific\n warm current.","descriptionType":"Abstract"},{"description":"The data was originally obtained freely from https://www.chinare.org.cn/en/metadata/c381eed1-0fe0-4efd-a4ab-4f099cf2c6ad.  The dataset archived on Dryad is a subset of measurements used in the published article (doi:10.1029/2021EA002049).","descriptionType":"Methods"},{"description":"The dataset archived on Dryad are tabulated ASCII files.  The\n dataset can be read using an ASCII text editor.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"Ministry of Science and Technology of the People's Republic of China","awardNumber":"2019YFA0607000","funderIdentifier":"https://ror.org/027s68j25","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Natural Science Foundation of China","awardNumber":"41975024","funderIdentifier":"https://ror.org/01h0zpd94","funderIdentifierType":"ROR"},{"funderName":"Belmont Forum and the U.S. National Science Foundation*","awardNumber":"NSF1536175"},{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNX14AR40G","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"funderName":"Belmont Forum and the Natural Science Foundation of China*","awardNumber":"41561144001"},{"funderName":"Basic Science foundation of Ocean University of China*","awardNumber":"202071001"}],"url":"https://datadryad.org/dataset/doi:10.6086/D1P40N","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":135,"downloadCount":8,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-08-12T15:33:16Z","registered":"2022-08-12T15:33:17Z","published":null,"updated":"2026-01-28T11:10:27Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}}],"meta":{"total":298,"totalPages":12,"page":1},"links":{"self":"https://api.datacite.org/dois?prefix=10.6086","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026prefix=10.6086"}}