{"data":[{"id":"10.7291/d13w28","type":"dois","attributes":{"doi":"10.7291/d13w28","identifiers":[],"creators":[{"name":"Beganskas, Sarah","nameType":"Personal","givenName":"Sarah","familyName":"Beganskas","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5146-9736","nameIdentifierScheme":"ORCID"}]},{"name":"Fisher, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Fisher","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2102-8320","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Monitoring data from a managed aquifer recharge system that collects stormwater runoff in central coastal California. Precipitation, runoff, infiltration, sediment, survey"}],"publisher":"Dryad","container":{},"publicationYear":2016,"subjects":[],"contributors":[],"dates":[{"date":"2016-11-19T00:27:39Z","dateType":"Issued"},{"date":"2016-11-19T00:27:39Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.jenvman.2017.05.058","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2341260 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Groundwater is increasingly important for satisfying California’s growing\n fresh water demand. Strategies like managed aquifer recharge (MAR) can\n improve groundwater supplies, mitigating the negative consequences of\n persistent groundwater overdraft. Distributed stormwater collection (DSC)\n MAR projects collect and infiltrate excess hillslope runoff before it\n reaches a stream, focusing on 40–400 ha drainage areas (100–1000 ac). We\n present results from six years of DSC–MAR operation—including high\n resolution analyses of precipitation, runoff generation, infiltration, and\n sediment transport—and discuss their implications for regional resource\n management. This project generated significant water supply benefit over\n six years, including an extended regional drought, collecting and\n infiltrating 5.3 × 105 m3 (426 ac-ft). Runoff generation was highly\n sensitive to sub-daily storm frequency, duration, and intensity, and a\n single intense storm often accounted for a large fraction of annual\n runoff. Observed infiltration rates varied widely in space and time. The\n basin-average infiltration rate during storms was 1–3 m/d, with\n point-specific rates up to 8 m/d. Despite efforts to limit sediment load,\n 8.2 × 105 kg of fine-grained sediment accumulated in the infiltration\n basin over three years, likely reducing soil infiltration capacity.\n Periodic removal of accumulated material, better source control, and/or\n improved sediment detention could mitigate this effect in the future.\n Regional soil analyses can maximize DSC–MAR benefits by identifying\n high-infiltration-capacity features and characterizing upland sediment\n sources. A regional network of DSC–MAR projects could increase groundwater\n supplies, while contributing to improved groundwater quality, flood\n mitigation, and stakeholder engagement.","descriptionType":"Abstract"},{"description":"This dataset contains six years of monitoring data from a managed\n aquifer recharge system that collects stormwater runoff. Runoff from a\n drainage area of 173 acres is diverted into a 4.3-acre infiltration basin.\n There is a small sediment detention basin that the runoff passes through\n before reaching the infiltration basin. This dataset includes:\n - Raw precipitation records from the field site and daily\n precipitation records - Calculations of daily runoff\n collected (measured as flow rate through a culvert feeding the\n infiltration basin) - Mass balance calculations to\n estimate the average daily infiltration rate - Field\n measurements of sediment accumulation throughout the basin\n - Survey of the field site (requires the program Surfer to\n open) - Grain size data from hundreds of sediment\n samples collected from throughout the system -\n Estimations of daily vertical infiltration rate at three points in the\n infiltration basin These data are part of a study that\n was published in \u003cem\u003eJournal of Environmental\n Management\u003c/em\u003e.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"funderName":"University of California Water Security and Sustainability Research Initiative","awardNumber":"#449214-RB-69085"},{"funderName":"California Institute for Water Resources","awardNumber":"#SA7750"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"funderName":"Charles and Jennifer Lawson Hydrology Awards"},{"funderName":"John Mason Clarke 1877 Fellowship, Amherst College"}],"url":"https://datadryad.org/dataset/doi:10.7291/D13W28","contentUrl":null,"metadataVersion":23,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":247,"downloadCount":18,"referenceCount":1,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2016-11-19T00:27:47Z","registered":"2016-11-19T00:27:48Z","published":null,"updated":"2026-04-10T12:41:45Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1kd69","type":"dois","attributes":{"doi":"10.7291/d1kd69","identifiers":[],"creators":[{"name":"Gilbert, Gregory","nameType":"Personal","givenName":"Gregory","familyName":"Gilbert","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5195-9903","nameIdentifierScheme":"ORCID"}]},{"name":"Cummings, Justin","nameType":"Personal","givenName":"Justin","familyName":"Cummings","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Parker, Ingrid","nameType":"Personal","givenName":"Ingrid","familyName":"Parker","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4847-1827","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Saccharum spontaneum biomass and associated soil and foliar nutrient data"}],"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":"Saccharum spontaneum"},{"subject":"soil nutrients"},{"subject":"plant nutrients"},{"subject":"Restoration ecology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"fertilizer addition"},{"subject":"invasive plants"}],"contributors":[],"dates":[{"date":"2023-04-15T17:32:51Z","dateType":"Submitted"},{"date":"2023-04-19T00:00:00Z","dateType":"Issued"},{"date":"2023-04-19T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.3390/ijpb14020036","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["58353 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":"Invasive C4 grasses that colonize tropical landscapes abandoned from use\n for intensive grazing and agriculture can inhibit natural regeneration of\n secondary forest. In Panama, dense stands of Saccharum spontaneum require\n active forest restoration to re-establish successional processes. In this\n region, restoration strategies typically involve clearing grass cover and\n applying fertilizer prior to planting tree seedlings. However, if\n fertilizers alleviate nutrient limitation in the grasses and enhance their\n competition with tree seedlings, it can add to costs for manual\n maintenance of the sites free of Saccharum. Here we evaluated how S.\n spontaneum responds to nitrogen and phosphorus addition in the field to\n determine whether S. spontaneum is nutrient limited in this system. S.\n spontaneum was both nitrogen and phosphorus as revealed through increased\n foliar nutrient concentrations. S. spontaneum biomass was significantly\n greater in both nitrogen and phosphorus addition plots after both the\n first growth period (early rainy season) and second growth period (late\n rainy season), with release from co-limitation of N and P, and the overall\n impact of N, greater during the second growth period. Nutrient limitation\n in S. spontaneum and seasonal shifts in resource allocation suggest\n caution when fertilizing areas under restoration that were previously\n dominated by exotic grasses.","descriptionType":"Abstract"},{"description":"This experiment consisted of\n twelve blocks, each with four nutrient treatments:  no nutrient addition\n (control; C), N addition (N), P addition (P), and N+P combination (N+P).\n Plots were 5 x 5 m\u003csup\u003e2\u003c/sup\u003e, with a 2-m buffer between\n plots within a block, and a 3-m buffer between blocks. Dry fertilizer was\n added by hand after clearing \u003cem\u003eS. spontaneum \u003c/em\u003ein July.\n Nitrogen was added as urea\n ((NH\u003csub\u003e2\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e2\u003c/sub\u003e), and phosphorus was added as triple super phosphate (Ca(H\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e•H\u003csub\u003e2\u003c/sub\u003eO). Nutrient application corresponded to 125 kg\u003csup\u003e \u003c/sup\u003eha\u003csup\u003e-1\u003c/sup\u003e N and 50 kg\u003csup\u003e \u003c/sup\u003eha\u003csup\u003e-1\u003c/sup\u003e P, as recommended for this region.  Nutrients were applied after clearing the site in July and again in October, after the first biomass harvest. Soil cores were taken at the end of the study (December) from each plot to compare final soil nutrient availability across treatments. We assessed \u003cem\u003eS. spontaneum\u003c/em\u003e performance, in terms of density and above-ground biomass, in September and December 2011. Additionally, in December we collected soil and leaf nutrient data to compare nutrient treatments. We randomly collected and homogenized 10 soil cores at 10-cm depth for each plot; a 20-g subsample was used to extract soil nitrogen and another for phosphorus. We followed the KCl and Mehlich standardized protocols to extract available nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), and phosphate  (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e-2\u003c/sup\u003e). Soil samples were placed directly into solution in the field and processed in the lab within 24 h of being collected. For plant tissue nutrient analysis, we collected the third mature leaf from the base of 15 randomly selected individuals in each plot. Leaves were dried for 3 days at 60\u003csup\u003eo\u003c/sup\u003eC, and samples were processed at the University of California Santa Cruz. Five leaves were selected from each plot and leaf N and P were extracted following the Kjeldahl acid digestion protocol using a Lachat BD 46 block digester (Lachat Instruments, Milwaukee, WI USA).","descriptionType":"Methods"},{"description":"Data are in a .csv file that can be opened with any spreadsheet\n or R. ","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1KD69","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":121,"downloadCount":5,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-04-20T03:09:27Z","registered":"2023-04-20T03:09:28Z","published":null,"updated":"2026-04-02T20:26:15Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d18q23","type":"dois","attributes":{"doi":"10.7291/d18q23","identifiers":[],"creators":[{"name":"Metcalfe, Jessica","nameType":"Personal","givenName":"Jessica","familyName":"Metcalfe","affiliation":["Lakehead University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9450-512X","nameIdentifierScheme":"ORCID"}]},{"name":"Ives, John","nameType":"Personal","givenName":"John","familyName":"Ives","affiliation":["University of Alberta"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7494-6363","nameIdentifierScheme":"ORCID"}]},{"name":"Shirazi, Sabrina","nameType":"Personal","givenName":"Sabrina","familyName":"Shirazi","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8300-5779","nameIdentifierScheme":"ORCID"}]},{"name":"Gilmore, Kevin","nameType":"Personal","givenName":"Kevin","familyName":"Gilmore","affiliation":["HDR, Inc."],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9751-8062","nameIdentifierScheme":"ORCID"}]},{"name":"Hallson, Jennifer","nameType":"Personal","givenName":"Jennifer","familyName":"Hallson","affiliation":["University of Alberta"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8476-3405","nameIdentifierScheme":"ORCID"}]},{"name":"Brock, Fiona","nameType":"Personal","givenName":"Fiona","familyName":"Brock","affiliation":["Cranfield University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0728-6091","nameIdentifierScheme":"ORCID"}]},{"name":"Clark, Bonnie","nameType":"Personal","givenName":"Bonnie","familyName":"Clark","affiliation":["University of Denver"],"nameIdentifiers":[]},{"name":"Shapiro, Beth","nameType":"Personal","givenName":"Beth","familyName":"Shapiro","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]}],"titles":[{"title":"Isotopic evidence for long-distance connections of the AD thirteenth century Promontory caves occupants"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[],"contributors":[],"dates":[{"date":"2020-12-14T16:35:08Z","dateType":"Submitted"},{"date":"2020-12-17T00:00:00Z","dateType":"Issued"},{"date":"2020-12-17T00:00:00Z","dateType":"Available"},{"date":"2021-07-23T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1017/aaq.2020.116","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["53388059 bytes"],"formats":[],"version":"7","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 Promontory caves (Utah) and Franktown Cave (Colorado) contain\n high-fidelity records of short-term occupations by groups with material\n culture connections to the Subarctic/Northern Plains. This research uses\n Promontory and Franktown bison dung, hair, hide, and bone collagen to\n establish local baseline carbon isotopic variability and identify leather\n from a distant source. The ankle wrap of one Promontory Cave 1 moccasin\n had a δ13C value that indicates a substantial\n C4 component to the animal’s diet, unlike the C3 diets\n inferred from 171 other Promontory and northern Utah bison samples. We\n draw on a unique combination of multi-tissue isotopic analysis, carbon\n isoscapes, ancient DNA (species and sex identification), tissue turnover\n rates, archaeological contexts, and bison ecology to show that the\n high δ13C value was not likely a result of local plant\n consumption, bison mobility, or trade. Rather, the bison hide was likely\n acquired via long-distance travel to/from an area of abundant\n C4 grasses far to the south or east. Expansive landscape\n knowledge gained through long-distance associations would have allowed\n Promontory caves inhabitants to make well-informed decisions about\n directions and routes of movement for a territorial shift, which seems to\n have occurred in the late thirteenth century.","descriptionType":"Abstract"},{"description":"We analyzed the FS-305 moccasin ankle wrap specimen at the UCSC\n Paleogenomics ancient DNA laboratory (PGL) to (1) obtain a taxonomic\n identification, and (2) determine the sex of the animal. Working in the\n sterile laboratory facilities at the PGL, we washed the sample in\n ultra-pure water to remove soil from the exterior and then extracted DNA\n from 0.15g of tissue following the Dabney et al. (2013) tissue extraction\n protocol. We prepared two shotgun sequencing libraries following the Meyer\n and Kircher (2010) method. We labelled the libraries using dual indices\n with truseq sequencing adapters and, after transferring the PCRs to the\n modern DNA facility, amplified them with Kappa Hifi for 25 cycles. We\n pooled and sequenced the libraries across several Illumina Miseq 2x75bp\n runs. We used Seqprep2 and prinseq (v. 0.20.4) to remove adapters and\n merge reads, and aligned the resulting data to the nucleotide BLAST\n database using MEGAN (v. 6.18.0) to identify the taxonomic origin of the\n tissue. In addition and to confirm the species ID, we used BWA (v.\n 0.7.12-r1039) to align each read to bison (Bison bison; NCBI\n GCA_000754665), cattle (Bos taurus; NCBI Btau_4.6.1), bighorn sheep (Ovis\n canadensis; NCBI CP011912.1), and two-toed sloth (Choloepus hoffmanni;\n NCBI5KN174222.1) genomes.","descriptionType":"Methods"},{"description":"We have uploaded alignments (.bam files) of these shotgun data to\n bison (Bison bison; NCBI GCA_000754665), cattle (Bos taurus; NCBI\n Btau_4.6.1), bighorn sheep (Ovis canadensis; NCBI CP011912.1), and\n two-toed sloth (Choloepus hoffmanni; NCBI5KN174222.1) genomes. We have not\n supplied raw shotgun data because there is a chance that ancient human DNA\n could be examined from these Moccasin samples and we do not have\n permission to study this DNA.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D18Q23","contentUrl":null,"metadataVersion":22,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":318,"downloadCount":7,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-12-17T18:52:38Z","registered":"2020-12-17T18:52:40Z","published":null,"updated":"2026-04-02T19:43:37Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1rh5h","type":"dois","attributes":{"doi":"10.7291/d1rh5h","identifiers":[],"creators":[{"name":"Friedlaender, Ari","nameType":"Personal","givenName":"Ari","familyName":"Friedlaender","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2822-233X","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Antarctic minke whale acoustic data"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[],"contributors":[],"dates":[{"date":"2022-04-14T19:07:05Z","dateType":"Submitted"},{"date":"2022-06-20T00:00:00Z","dateType":"Issued"},{"date":"2022-06-20T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1098/rsos.211557","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["805307 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":"Acoustic signaling is the predominant form of communication among\n cetaceans. Understanding the behavioral state of calling individuals can\n provide insights into the specific function of sound production; in turn,\n this information can aid the evaluation of passive monitoring data sets to\n estimate species presence, density, and behavior. Antarctic minke whales\n are the most numerous baleen whale species in the Southern Ocean. However,\n our knowledge of their vocal behavior is limited. Utilizing the first\n animal-borne audio-video documentation of underwater behavior in this\n species, we characterize Antarctic minke whale sound production and\n evaluate the association between acoustic behavior, foraging behavior,\n diel patterns, and the presence of conspecifics. In addition to the\n previously described downsweep call, we find evidence of three novel calls\n not previously described in their vocal repertoire. Overall, these signals\n displayed peak frequencies between 200 and 280 Hz and ranged from 0.2 to\n 0.9 s on average (90% duration). Additionally, each of the four call types\n were associated with measured behavioral and environmental parameters. Our\n results represent a significant advancement in understanding of the life\n history of this species and improve our capacity to acoustically monitor\n minke whales in a rapidly changing Antarctic region.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1RH5H","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":242,"downloadCount":54,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-06-20T14:27:35Z","registered":"2022-06-20T14:27:36Z","published":null,"updated":"2026-03-30T18:08:18Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1bq2q","type":"dois","attributes":{"doi":"10.7291/d1bq2q","identifiers":[],"creators":[{"name":"Lam, Jonathan","nameType":"Personal","givenName":"Jonathan","familyName":"Lam","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Manduchi, Roberto","nameType":"Personal","givenName":"Roberto","familyName":"Manduchi","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0007-9999-6360","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"S-BLE: A participatory BLE sensory data set recorded from real-world bus travel events"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"FOS: Computer and information sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Be-In-Be-Out systems"},{"subject":"BLE beacons"},{"subject":"Public transit"}],"contributors":[],"dates":[{"date":"2023-08-01T05:06:18Z","dateType":"Created"},{"date":"2023-08-01T05:07:33Z","dateType":"Submitted"},{"date":"2023-08-08T00:00:00Z","dateType":"Issued"},{"date":"2023-08-08T00:00:00Z","dateType":"Available"},{"date":"2026-01-20T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"https://escholarship.org/uc/item/03n467fn","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["112427655 bytes"],"formats":[],"version":"10","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":"S-BLE is a data set created for supporting the design of robust and\n reliable Be-In-Be-Out systems in public transit. S-BLE was recorded by the\n smartphones of 28 participants during their daily transit routines in a\n university campus setting. 20 shuttle bus vehicles in the campus fleet\n were equipped with two Bluetooth low energy (BLE) beacons each. RSSI data\n from these beacons was recorded during regular rides, along with odometry\n information (from GPS) and data from the smartphone’s inertial sensors.","descriptionType":"Abstract"},{"description":"# S-BLE: A participatory BLE sensory data set recorded from real-world bus\n travel events ## Description of the data and file structure All data is\n contained in tagged_sble_data.csv. Each row is a data point, collected at\n a specific timestamp. Note that if 2 (or more) beacons were within range\n at the same time, the content of a row is repeated for each beacon, except\n for three fields: minor; rssi; and rssi_accuracy. Each row contains:\n (note: the order of these fields in the csv file may be different than in\n this list - please look at file header) * timestamp (in Unix time) *\n username (a unique nickname assigned to each participant) [The following\n measurements are from iOS CoreLocation. See\n [https://developer.apple.com/documentation/corelocation](https://developer.apple.com/documentation/corelocation)] * latitude, longitude (in degrees) * speed (magnitude of velocity, in m/s) * speed accuracy (speedAcc - accuracy of speed) * altitude (alt - measured in meters above sea level) * vertical accuracy (vertical_acc - measured in meters) * horizontal accuracy (horzontal_acc, measured in meters) * course (direction of travel, in degrees relative to North) * course accuracy (courseAcc, in degrees) * heading (azimuth (orientation) of the user’s device, relative to true or magnetic north) * major (major ID of BLE beacon; identifies the bus vehicle) * minor (minor ID of BLE beacon; 1 for front beacon, 2 for rear beacon) * rssi (received signal strength from BLE beacon, in dBm. It is -100 if rssi is below -90 dBm) * accuracy (rssi_accuracy - the accuracy of the proximity value, measured in meters from the beacon) [The following measurements are from iOS CoreMotion. See [https://developer.apple.com/documentation/coremotion](https://developer.apple.com/documentation/coremotion)] * attitude_pitch,attitude_roll,attitude_yaw (Euler angles of phone's attitude with respect to a fixed reference frame with Z axis pointing down, in rads) * rotation_rate_x,rotation_rate_y,rotation_rate_z (angular rate around three phone axes, in rads/s) * gravity_accel_x,gravity_accel_y,gravity_accel_z (components of the gravity vector in three phone axes, in units of g) * user_accel_x,user_accel_y,user_accel_z (components of the phone's acceleration in three phone axes, in units of g) * magnetic_field_x,magnetic_field_y,magnetic_field_z: these values are always set to 0 * trip_idx (a unique identifier for a \"trip\", defined as a complete session including data recorded while the system is in the \"Detecting beacons\" state and in the following \"Traveling in bus\" state. A trip_idx of -1 indicates that the trip was not complete) * is_trip (0 while in the \"Detecting beacons\" state, 1 while in \"Traveling in bus\" state) Please read the accompanying article for clarification: [https://escholarship.org/uc/item/03n467fn](https://escholarship.org/uc/item/03n467fn)","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"2125279","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1BQ2Q","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":206,"downloadCount":6,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-08-08T15:28:53Z","registered":"2023-08-08T15:28:54Z","published":null,"updated":"2026-03-26T19:04:15Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1q386","type":"dois","attributes":{"doi":"10.7291/d1q386","identifiers":[],"creators":[{"name":"Daniels, Miles","nameType":"Personal","givenName":"Miles","familyName":"Daniels","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7126-9168","nameIdentifierScheme":"ORCID"}]},{"name":"Michel, Cyril","nameType":"Personal","givenName":"Cyril","familyName":"Michel","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1198-3837","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Sacramento River RAFT water temperature model simulations based on hypothetical reservoir perturbations in the historical record"}],"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, Santa Cruz","nameType":"Personal","givenName":"Santa Cruz","familyName":"University of California","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2023-04-16T02:20:27Z","dateType":"Submitted"},{"date":"2023-04-23T00:00:00Z","dateType":"Issued"},{"date":"2023-04-23T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2023wr035077","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["102313293 bytes"],"formats":[],"version":"7","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 used a process-based water temperature model (RAFT, Pike et al., 2013)\n to estimate the ability of reservoir discharge to mediate river\n temperature heating processes impacting downstream locations (i.e.,\n discharge-mediated temperature management) on California's Sacramento\n River. This was done by modeling water temperatures over the historical\n record (1990-2020) of model forcings and only perturbing reservoir\n discharge levels on a daily time step, ranging from 3000 to 15000 cubic\n feet per second as simulated at the Sacramento River at Wilkins Slough\n USGS gauging station\n (https://waterdata.usgs.gov/monitoring-location/11390500). Pike, A., E.\n Danner, D. Boughton, F. Melton, R. Nemani, B. Rajagopalan, and S. Lindley.\n 2013. Forecasting river temperatures in real time using a stochastic\n dynamics approach. Water Resources Research 49:5168-5182.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D1Q386","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":208,"downloadCount":82,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-04-24T01:58:23Z","registered":"2023-04-24T01:58:24Z","published":null,"updated":"2026-03-18T16:45:08Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d18x0d","type":"dois","attributes":{"doi":"10.7291/d18x0d","identifiers":[],"creators":[{"name":"Nimmo, Francis","nameType":"Personal","givenName":"Francis","familyName":"Nimmo","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3573-5915","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Code to track coupled orbital-rotational evolution of a planet and satellite"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Physical sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Earth and Planetary Science"},{"subject":"Orbital evolution"},{"subject":"Tidal dissipation"}],"contributors":[],"dates":[{"date":"2023-08-21T23:46:07Z","dateType":"Created"},{"date":"2023-08-11T04:04:48Z","dateType":"Submitted"},{"date":"2023-09-05T00:00:00Z","dateType":"Issued"},{"date":"2023-09-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1126/sciadv.adi9201","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["87684 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 large Kuiper Belt Object Eris is tidally locked to its small companion\n Dysnomia. Recently obtained bounds on the mass of Dysnomia demonstrate\n that Eris must be unexpectedly dissipative in order for it to have despun\n over the age of the solar system. Here we show that Eris must have\n differentiated into an ice shell and rocky core in order to explain the\n dissipation. We further demonstrate that Eris's ice shell must be\n convecting to be sufficiently dissipative, which distinguishes it from\n Pluto's conductive shell. The difference is likely due to Eris's\n apparent depletion in volatiles compared with Pluto, perhaps as the result\n of a more energetic impact.","descriptionType":"Abstract"},{"description":"This is a code which can be used to reproduce Figs 1 and 2 in the\n main text. Figure 1 plots the evolution of the spin and\n orbital periods of the primary and secondary as a function of distance. It\n also plots the log of Q/k\u003csub\u003e2\u003c/sub\u003e for the primary. Figure\n 2 plots Q/k\u003csub\u003e2\u003c/sub\u003e against the tidal forcing period. The\n data required to produce these plots are output as a textfile by the code.\n The caption of Figure 1 reads: \"Evolution of Dysnomia’s orbital\n period and Eris’s spin period. We use the methodology of ref. 21 and\n assume Eris’s \u003cem\u003eQ\u003c/em\u003e varies linearly with forcing\n frequency. The initial separation is\n 7\u003cem\u003eR\u003csub\u003ep\u003c/sub\u003e\u003c/em\u003e and Dysnomia is assumed\n synchronous throughout, with a mass ratio of 0.084. Crosses are at\n intervals of 200 Myr. The primary\n \u003cem\u003ek\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e is 0.12 and the\n \u003cem\u003eQ\u003c/em\u003e is 760 for a forcing period of 165 h. Other\n parameter values are given in Table 1.\"","descriptionType":"Methods"},{"description":"It runs on Fortran 77 and produces output (a text file) which can\n be graphed with commercially-available packages.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D18X0D","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":95,"downloadCount":8,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-09-05T23:54:21Z","registered":"2023-09-05T23:54:21Z","published":null,"updated":"2026-03-18T15:18:00Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d16h3s","type":"dois","attributes":{"doi":"10.7291/d16h3s","identifiers":[],"creators":[{"name":"Conrad, Jack","nameType":"Personal","givenName":"Jack","familyName":"Conrad","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7572-093X","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Pluto and Charon limb profile topography"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[{"subject":"Pluto"},{"subject":"Charon"},{"subject":"Geography","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"icy bodies"}],"contributors":[],"dates":[{"date":"2020-07-31T18:11:04Z","dateType":"Submitted"},{"date":"2020-09-11T00:00:00Z","dateType":"Issued"},{"date":"2020-09-11T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2020je006641","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2575089 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":"We derived updated and expanded topography datasets for Pluto and Charon.\n This is done through finding the body edge (i.e. the limb) in images, and\n with those body edge locations we can apply simple geographical techniques\n to produce limb profiles. The process involves some human intervention,\n but is primarily automation based. Our limb profile topography is useful\n for geologic and geophysical studies of Pluto and Charon. Additionally, we\n provide processed data of how we used the limb profile data in our study.\n The first set of additional data is the result of the processing required\n to determine the Fourier transformation that breaks down the data into its\n wavelength based components. This is done by interpolating the raw data\n onto a evenly spaced grid, then detrending the data by setting the\n end-points to equal elevations. Our second addition has the base 10\n logarithm of our variance spectra results for Pluto and Charon, which is\n the primary result of the scientific portion of the study. We found that\n Charon has a statistically significant break in slope, while Pluto does\n not. This is likely a signal of their crust's structures.","descriptionType":"Abstract"},{"description":"We achieve this by determining the edge of the planetary body\n from images (“limb picks”). Using map projections and the known observing\n geometry, we can transform the pixel locations of the limb picks into a\n list of elevations at specific latitude and longitude locations. While our\n methodology mostly matches that of Nimmo et al. (2017), we will reiterate\n some aspects of their methods to detail updates and changes.\n We began our process with a survey of \u003ci\u003eNew Horizons\n \u003c/i\u003eimages from the Long-Range Reconnaissance Imager (LORRI; Cheng\n et al., 2008) and the Ralph instrument’s Multispectral Visible Imaging\n Camera (MVIC; Reuter et al., 2008) sub-instrument. Data from both\n instruments have been used extensively to study Pluto, Charon, and most\n recently Arrokoth (Spencer et al., 2020). Starting with the Planetary Data\n System, we surveyed the images to create a list of images containing body\n edges starting when Pluto was ~100 pixels in diameter. In addition, we\n removed redundant images from the list. This list contained both day-side\n and night-side images from both LORRI and MVIC as the starting point for\n further analysis. While we initially performed limb\n picks and fits for both LORRI and MVIC images, we found large-amplitude,\n long-wavelength undulations for MVIC limb profiles. This occurs in MVIC\n images due to the line-scan exposure method combined with spacecraft\n motion (Weaver et al., 2009). Due to the relatively small number of MVIC\n images, we instead focus on the LORRI images in this analysis.\n For limb picks of day-side (i.e. front-lit) images, we use Method\n A as presented in Nimmo et al. (2017). This method scans each row and\n column in the image, using a threshold approach similar to the method\n described in Dermott and Thomas (1988). We generally use the same\n brightness threshold (50%) and calculate the average brightness over the\n same distance range (0.5\u003ci\u003ed\u003c/i\u003e to 0.9\u003ci\u003ed\u003c/i\u003e,\n where \u003ci\u003ed\u003c/i\u003e is the on-body profile length from center to\n body edge) as used in Nimmo et al. (2017). Once the\n list of limb pixel locations is determined, we visually verify the\n algorithmically chosen limb picks and manually remove picks that are\n obviously not on the limb. These false limb points are often the result of\n either albedo variations or the terminator. Large albedo variations are\n only an issue with Pluto images that contain Cthulhu Regio (CR), a region\n south-west of Sputnik Planitia (SP). Limb picks of images that contain\n areas of CR that border regions of high albedo (e.g. SP) tend to place the\n limb location in CR inward of the actual limb. This happens because the\n average disk brightness is incorporated into the algorithm, and if the\n brightest area of Pluto is near the middle while the darkest is at the\n edge, the 50% cutoff will occur before the actual limb location. However,\n we note that the local albedo at the limb does not show a systematic\n correlation with topography (see section 3.1), suggesting that this effect\n is minor. The algorithm also does not discriminate between the limb and\n terminator of the body, and we only use locations where the edge of the\n lighted hemisphere is caused by the edge of the body (limb) rather than\n the edge of the illuminated hemisphere (terminator). While some terminator\n picks can be accidentally incorporated into a set of limb picks, we apply\n additional geometric checks in the fitting method described below to\n remove points not located on the illuminated limb. \n After determining the limb location in terms of pixels (x,y), we\n project these points onto a spherical body to convert to an equivalent\n latitude, longitude position (\u003ci\u003eφ, θ\u003c/i\u003e) on a spherical body\n given the image coordinates of the body’s center\n (x\u003csub\u003e0\u003c/sub\u003e, y\u003csub\u003e0\u003c/sub\u003e), the latitude and\n longitude of the sub-spacecraft location\n (\u003ci\u003eφ\u003csub\u003e0\u003c/sub\u003e, θ\u003csub\u003e0\u003c/sub\u003e\u003c/i\u003e),\n the body’s radius \u003ci\u003eR\u003c/i\u003e and the orientation of the rotation\n pole relative to the image (\u003ci\u003eϕ\u003c/i\u003e). The spacecraft\n parameters are calculated using the most consistent SPICE information\n based on the smithed kernels from Schenk et al. (2018a\u0026amp;b). We\n report these values for all used images in supplementary Tables 1 and 2 of\n the study, and the results of the pixel locations for\n \u003ci\u003ex\u003csub\u003e0\u003c/sub\u003e, y\u003csub\u003e0\u003c/sub\u003e\u003c/i\u003e\n and \u003ci\u003eR\u003c/i\u003e (in pixels) in supplementary Tables 3 and 4. We\n utilize a general vertical perspective (GVP) projection for determining\n the latitude, longitude positions (\u003ci\u003eφ, θ\u003c/i\u003e) of limb pick\n locations (Snyder, 1987). Near closest approach, we account for the effect\n that can lead to a shifting of the limb to an angle less than\n 90\u003csup\u003e0\u003c/sup\u003e away from the sub-spacecraft point. At\n distances \u003ci\u003ed\u003c/i\u003e far from the body center (\u003ci\u003ed\n \u0026gt;\u0026gt; R\u003c/i\u003e), the GVP projection reduces to the\n orthographic projection, which is commonly used in limb profile fitting\n studies (Nimmo et al., 2017). In our projections we assume that both Pluto\n and Charon are spherical, based on the results of Nimmo et al. (2017).\n This simplifies the map projection calculations and determination of the\n radii from the (x,y) pixel locations. Elevation is reported as relative to\n the mean radius of Pluto (1188.3 km) and Charon (606 km) also determined\n in Nimmo et al. (2017). After we obtain raw limb\n profile topography, the data are processed to remove a few different\n possible problems as described below. Generally, we use the raw data for\n most of our analysis, while the processed data is used with presentation\n of limb profiles and some aspects of our analysis. When we apply the\n line-scan method, the algorithm scans both vertically and horizontally.\n This can introduce noise when the scan brightness curves to pick the limb\n near the same coordinate are different in the horizontal and vertical, and\n usually occurs in areas of varying brightness around the limb. To remove\n the shorter wavelength noise, as well as prepare our limb profiles for\n further analysis, we interpolate the limb topography onto a grid with\n constant spacing at a slightly worse ground sampling distance (half the\n total number of original data points). We use a gaussian weighted\n interpolation technique (equation 1 in the study). We then detrend the\n profiles by removing the linear trend through the end-points of the\n profile. Topography generated from limb profiles can be\n used to analyze the long-wavelength properties of worlds. Limb topography\n variance spectra are a useful way of quantifying roughness as a function\n of wavelength (Araki et al., 2009; Shepard et al., 2001; Nimmo et al.,\n 2011; Ermakov et al., 2018). To obtain the average variance spectrum, we\n calculate the discrete Fourier transform for each limb profile and then\n find the mean variance in a sequence of bins (Press, 1992; see equation 2\n in the study).","descriptionType":"Methods"},{"description":"The data is contained within comma separated values files with\n the .llk extension, treat them as text files with loading them into your\n mapping software or programs. A read me file is\n contained within the zip file, but the information contained is repeated\n here: This data repository contains Limb\n profile topography data of Pluto and Charon from New Horizons images of\n the two worlds, this in addition to 2 derivative products. The first is\n the processed data from the initial processing we do in order to perform\n Fourier transformations on the data. Second is the binned Variance Spectra\n results. -- Limb profile topography\n files are in a comma separated values format with the file type llk.\n \"llk\" refers to latitude, longitude, radius\n (kilometers). Each line of values includes:\u003cbr\u003e\n latitude, longitude, radius (kilometers), x pixel, y pixel, radius\n (pixels) Latitude is between +/- 90 degrees.\u003cbr\u003e\n Longitude is sometimes above 360 degrees if the limb profile wraps around\n the \"prime\" meridian.\u003cbr\u003e Radius is from the body center.\n The Nimmo et al. (2017) mean values for Pluto and Charon are 1188.6 km and\n 606.0 km respectively.\u003cbr\u003e x pixel/y pixel are based on the source\n image. -- Processed limb profile\n topography files are in a comma separated values format with the file type\n dlle. \"dlle\" refers to distance, latitude, longitude, elevation\n (kilometers, with mean body radius removed). The limb profiles were\n processes by interpolating onto an even grid spacing and detrended by\n setting the end points to equal elevations. Each line\n of values includes:\u003cbr\u003e distance, latitude, longitude,\n elevation Latitude is between +/- 90 degrees.\u003cbr\u003e\n Longitude is sometimes above 360 degrees if the limb profile wraps around\n the \"prime\" meridian. --\n Variance spectra files are in a comma separated values format\n with the standard file type .csv. There are column\n headers with labels of the contained information.\u003cbr\u003e Each line\n contains the base 10 logarithm of the Wavenumber (km**-1), Variance\n (km**2), and Standard Deviation  ","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"funderName":"Planetary Data Archiving, Restoration, and Tools Program*","awardNumber":"80NSSC18K0549"}],"url":"https://datadryad.org/dataset/doi:10.7291/D16H3S","contentUrl":null,"metadataVersion":13,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":201,"downloadCount":16,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-09-11T20:02:33Z","registered":"2020-09-11T20:02:35Z","published":null,"updated":"2026-03-18T15:08:18Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1vq3r","type":"dois","attributes":{"doi":"10.7291/d1vq3r","identifiers":[],"creators":[{"name":"Fisher, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Fisher","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2102-8320","nameIdentifierScheme":"ORCID"}]},{"name":"Dickerson, Kristin","nameType":"Personal","givenName":"Kristin","familyName":"Dickerson","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Blackman, Donna","nameType":"Personal","givenName":"Donna","familyName":"Blackman","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Randolph-Flagg, Noah","nameType":"Personal","givenName":"Noah","familyName":"Randolph-Flagg","affiliation":["Blue Marble Space Institute of Science"],"nameIdentifiers":[]},{"name":"German, Christopher","nameType":"Personal","givenName":"Christopher","familyName":"German","affiliation":["Woods Hole Oceanographic Institution"],"nameIdentifiers":[]},{"name":"Sotin, Christophe","nameType":"Personal","givenName":"Christophe","familyName":"Sotin","affiliation":["Nantes Université"],"nameIdentifiers":[]}],"titles":[{"title":"FEHM source code modifications and executables for use with ocean-world gravity"}],"publisher":"Dryad","container":{},"publicationYear":2024,"subjects":[{"subject":"Ocean worlds"},{"subject":"Seafloor"},{"subject":"FEHM"},{"subject":"Numerical Simulation"},{"subject":"hydrothermal"},{"subject":"Europa"},{"subject":"Enceladus"},{"subject":"Planetary sciences","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Physical sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[{"name":"University of California, Santa Cruz","nameType":"Personal","givenName":"Santa Cruz","familyName":"University of California","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2023-11-08T20:35:25Z","dateType":"Created"},{"date":"2023-11-08T20:36:40Z","dateType":"Submitted"},{"date":"2023-11-27T00:00:00Z","dateType":"Issued"},{"date":"2023-11-27T00:00:00Z","dateType":"Available"},{"date":"2024-06-07T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.15839204","relatedIdentifierType":"DOI"},{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.11495438","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2023je008202","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["195948699 bytes"],"formats":[],"version":"9","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 repository for compiled codes, source code, and input files used\n in this paper: Fisher, A. T., K. D. Dickerson, D. K. Blackman, N.\n Randolph-Flagg, C. R. German, and C. Sotin (2024), Sustained hydrothermal\n circulation under ocean-world gravity, J. Geophys. Res. - Planets,\n submitted and in review. Plain language summary from paper: Ocean\n worlds are planetary bodies that have a liquid ocean, often under an icy\n shell or within the rocky interior. In Earth's solar system, several\n moons of Jupiter and Saturn are ocean worlds. Some ocean worlds are\n thought to have hydrothermal circulation, where water, rocks, and heat\n combine to drive fluids in and out of the seafloor. Hydrothermal\n circulation would impact the chemistry of the water and rock of ocean\n worlds and could help life to develop deep below the icy surface. This\n study shows results from computer simulations of hydrothermal circulation,\n based on a well-understood system on Earth, to measure the influence of\n lower gravity like that appropriate for ocean worlds smaller than Earth.\n The simulations with ocean world (lower) gravity result in fluid\n circulation much like that occurring on and below Earth's seafloor,\n but with several important differences. Lower gravity reduces buoyancy,\n meaning warmed fluids don't become as light when heated. Lower\n buoyancy tends to reduce flow rates in a hydrothermal system, and this\n raises the temperatures of the circulating fluid, which would allow more\n extensive chemical reactions. Lower flow means less heat transport, and\n this could help these flows last longer in an ocean world.","descriptionType":"Abstract"},{"description":"There is a README file with information on files posted, and a\n Supporting Information document that goes with the paper that discusses\n modifications to code in some detail. The main research paper also\n discussed how the code was used. ","descriptionType":"Methods"},{"description":"# FEHM source code modifications and executables for use with ocean-world\n gravity [https://doi.org/10.7291/D1VQ3R](https://doi.org/10.7291/D1VQ3R)\n FEHM archive for Fisher et al. (2024), \"Sustaining hydrothermal\n circulation with gravity relevant to ocean worlds,\" revised following\n review for Journal of Geophysical Research - Planets. NB: Above will be\n revised when paper is in print, and a DOI will be added. ## Description of\n the data and file structure Three compressed (zip) files are included in\n this archive: 10_FEHM-Linux_Diffg_Exe.zip 20_FEHM-SourceEdited.zip\n 30_FEHM-InputDirs.zip ## Code/Software #### 10\\_FEHM-Linux\\_Diffg\\_Exe.zip\n Contains executable FEHM programs with gravity for Earth, Europa, and\n Enceladus: xfehm_v3.4g9_81 gravity for Earth, 9.81 m/s^2 xfehm_v3.4g1_3\n gravity for Europa, 1.3 m/s^2 xfehm_v3.4g_114 gravity for Enceladus, 0.114\n m/s^2 These codes were compiled with Rocky Linux using the gfortran\n compiler (part of gcc family of compilers). Posted executables may work\n for your system, or you may need/wish to recompile in order to access\n libraries. We have also supplied shell scripts we used to call the\n executables, based on having a set of input files with common root names\n and different file extensions. The \"Files\" file tells FEHM which\n files to use for input and output. You may need to modify these scripts\n depending on your workflow, how you name your files, etc. Please see file\n examples in 30_FEHM-InputDirs.zip, and follow the guidance given at the\n FEHM site at LANL ([https://fehm.lanl.gov/](https://fehm.lanl.gov/). ####\n 20\\_FEHM-SourceEdited.zip Please follow these steps to use the modified\n subroutines: (1) Start with the original source code in\n 20_FEHM-SourceEdited --\u0026gt; src subdirectory Place the full directory set\n in a suitable workspace, e.g., ~/code/FEHM \\[Note - we originally\n recommended that folks download source code from Github, but there have\n been changes made recently to some of the FEHM subroutines. For this\n reason, we have uploaded a \"snapshot\" of the sourcecode files\n for which we have confirmed compilation works.] \\- Before doing anything\n else, we strongly encouarge recompiling the original code, as this will\n tell you if any changes are needed to your system settings, paths,\n gfortran/gcc installation, libraries, etc. \\- You must edit the Makefile\n to be consistent with your computer system in order for the Make command\n to be successful in generating an executable program. \\- If you are not\n able to compile from the src distribution, please solve that problem\n first, before proceeding. We have noted differences in systems and\n settings in terms of compilers be tolerant of deviations in standard\n Fortran syntax, so you should expect some minor debugging on first\n compilation. (2) Create a working subdirectory for compilation of modified\n code, e.g., ~/code/FEHM/src_g9_81, and copy over the contents of the FEHM\n source directory, which has already been confirmed to allow successful\n recompilation. (3) Copy to the working subdirectory the appropriate files\n from the directory in the zip file, e.g., src_g9_81. In this directory you\n will find these files: comai.f convctr.f hstz.f inctrl.f input.f Makefile\n plot_new.f porosi.f startup.f write_avs_node_s.f wrtout.f In brief - these\n files REPLACE the files with the same names included in the original FEHM\n distribution. (4) Edit the Makefile to name the executable as you wish, on\n line that begins: EXE = ... Note - you might need to make additional\n updates to the revised Makefile if you had to modify the original Makefile\n when you recompiled without the modified subroutines. (5) If you wish to\n use g = 9.81 m/s^2, no other changes are needed before compilation. If you\n wish, you can redefine gravity for another ocean world. There are two\n lines that need to be changed in inctrl.f - search for 9.81 (or AF***) to\n find them, adjust as needed. All other files in FEHM will use values as\n defined in inctrl.f (6) At the command prompt in the working directory,\n run these commands: make clean make \\- The first command will clear any\n residual object files or remnants from any earlier attempts to compile the\n code. This is important so that the new object files are used to create\n the executable. \\- The second command will create new objects and an\n executable. (7) To run a quick test to see if the executable works, enter\n at command prompt in the working directory: ./FEHM-Filename where\n \"FEHM-Filename\" is what you defined at the line that begins EXE\n = in the Makefile You should see that FEHM runs, but there will be an\n error because input files have not been specified. To set up a FEHM\n simulation, you will need to follow instructions provided at the FEHM\n website. LANL has documentation here:\n [https://fehm.lanl.gov/](https://fehm.lanl.gov/) This includes links to\n source code on GitHub and gridding software. #### 30\\_FEHM-InputDirs.zip\n Example directories are provided for running FEHM to duplicate results\n shown in Figure 3 of the main paper. This figure shows snapshots of\n results from four simulations, files for each of which are stored in\n separate subdirectories: A_p12fto_OCv10_g981 B_p12d1200efto_OCv10_g1_3\n C_p12d600efto_OCv10_g1_3r D_p10d600efto_OCv10_g_114 #####\n A\\_p12fto\\_OCv10\\_g981 Shallow aquifer, aquifer permeabilty = 1e-12 m^2,\n Earth gravity ##### B\\_p12d1200efto\\_OCv10\\_g1\\_3 Deep-thick aquifer,\n aquifer permeabilty = 1e-12 m^2, Europa gravity #####\n C\\_p12d600efto\\_OCv10\\_g1\\_3r Deep-thin aquifer, aquifer permeabilty =\n 1e-12 m^2, Europa gravity ##### D\\_p10d600efto\\_OCv10\\_g\\_114 Deep-thin\n aquifer, aquifer permeabilty = 1e-10 m^2, Enceladus gravity In addition to\n the input files in each directory, the user will need to copy over or add\n a static link for the fluid properties lookup table, nist120-1800.out, and\n update the name in the .files file to point to this table. The lookup\n table is provided in the parent folder for the four example folders listed\n above. Please see the FEHM User Manual for more detailed instructions for\n use of FEHM, available with the GitHub repository:\n [https://github.com/lanl/FEHM](https://github.com/lanl/FEHM) Code\n modifications included in this repository are covered by the FEHM license,\n as described in the associated README file.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"80NSSC19K1427","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"OCE-0939564","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1VQ3R","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":123,"downloadCount":11,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:47:06Z","registered":"2023-10-04T22:47:07Z","published":null,"updated":"2026-03-18T11:54:44Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1059r","type":"dois","attributes":{"doi":"10.7291/d1059r","identifiers":[],"creators":[{"name":"Guthman, Julie","nameType":"Personal","givenName":"Julie","familyName":"Guthman","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]}],"titles":[{"title":"Fumigant use on California strawberry fields, 2004-2013"}],"publisher":"Dryad","container":{},"publicationYear":2017,"subjects":[],"contributors":[],"dates":[{"date":"2017-05-05T21:33:39Z","dateType":"Issued"},{"date":"2017-05-05T21:33:39Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.3733/ca.2017a0017","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.jrurstud.2016.07.020","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["19985480 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This dataset contains an analysis of chemical fumigant usage for nine\n major strawberry producing counties in California from 2004 to 2013. Five\n counties are production counties; four are nursery counties. Raw data on\n all pesticide applications on a county basis was collected from\n California's Pesticide Use Reporting System and filtered by pesticide\n category (fumigant) and commodity (strawberry). These data are geo-coded\n by Township-Range-Section. The data set also contains pivot tables and\n charts that show trend data for pounds applied and acres treated over\n time.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D1059R","contentUrl":null,"metadataVersion":10,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":172,"downloadCount":9,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2017-05-05T21:34:03Z","registered":"2017-05-05T21:34:05Z","published":null,"updated":"2026-03-17T18:27:05Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1vh5f","type":"dois","attributes":{"doi":"10.7291/d1vh5f","identifiers":[],"creators":[{"name":"Thow, Caroline","nameType":"Personal","givenName":"Caroline","familyName":"Thow","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6630-0282","nameIdentifierScheme":"ORCID"}]},{"name":"Wells, Caitlin","nameType":"Personal","givenName":"Caitlin","familyName":"Wells","affiliation":["Colorado State University"],"nameIdentifiers":[]},{"name":"Eadie, John","nameType":"Personal","givenName":"John","familyName":"Eadie","affiliation":["University of California, Davis"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9573-2703","nameIdentifierScheme":"ORCID"}]},{"name":"Lyon, Bruce","nameType":"Personal","givenName":"Bruce","familyName":"Lyon","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8733-9944","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Simulated wood duck maternity analysis results from COLONY and CERVUS"}],"publisher":"Dryad","container":{},"publicationYear":2021,"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":"2021-07-08T08:15:11Z","dateType":"Submitted"},{"date":"2021-07-12T00:00:00Z","dateType":"Issued"},{"date":"2021-07-12T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/1755-0998.13466","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["46539536 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":"Modern genetic parentage methods reveal that alternative reproductive\n strategies are common in both males and females. Under ideal conditions,\n genetic methods accurately connect the parents to offspring produced by\n extra-pair matings or conspecific brood parasitism. However, some breeding\n systems and sampling scenarios present significant complications for\n accurate parentage assignment. We used simulated genetic pedigrees to\n assess the reliability of parentage assignment for a series of challenging\n sampling regimes that reflect realistic conditions for many\n brood-parasitic birds: absence of genetic samples from sires, absence of\n samples from brood parasites, and female kin-structured populations. Using\n 18 microsatellite markers and empirical allele frequencies from two\n populations of a conspecific brood parasite, the wood duck (Aix sponsa),\n we simulated brood parasitism and determined maternity using two widely\n used programs, CERVUS and COLONY. Errors in assignment were generally\n modest for most sampling scenarios but differed by program: CERVUS\n suffered from false assignment of parasitic offspring, whereas COLONY\n sometimes failed to assign offspring to their known mothers. Reducing the\n number of markers (9 loci rather than 18) caused the assignment error to\n slightly worsen with COLONY but balloon with CERVUS. One potential error\n with important biological implications was rare in all cases—few nesting\n females were incorrectly excluded as the mother of their own offspring, an\n error that could falsely indicate brood parasitism. We consider the\n implications of our findings for both a retrospective assessment of\n previous studies as well as suggestions for the best practices for future\n studies. ","descriptionType":"Abstract"},{"description":"Please refer to the publication for detailed methods on the data\n collection and processing.","descriptionType":"Methods"},{"description":"Please contact the corresponding author, Bruce Lyon\n (belyon@ucsc.edu) for any questions regarding the use of this\n dataset.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D1VH5F","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":167,"downloadCount":17,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-07-12T21:50:47Z","registered":"2021-07-12T21:50:48Z","published":null,"updated":"2026-03-17T15:51:06Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1t685","type":"dois","attributes":{"doi":"10.7291/d1t685","identifiers":[],"creators":[{"name":"Costa, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Costa","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0233-5782","nameIdentifierScheme":"ORCID"}]},{"name":"Beltran, Roxanne","nameType":"Personal","givenName":"Roxanne","familyName":"Beltran","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8520-1105","nameIdentifierScheme":"ORCID"}]},{"name":"Condit, Richard","nameType":"Personal","givenName":"Richard","familyName":"Condit","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Robinson, Patrick","nameType":"Personal","givenName":"Patrick","familyName":"Robinson","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Crocker, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Crocker","affiliation":["Sonoma State University"],"nameIdentifiers":[]},{"name":"Goetsch, Chandra","nameType":"Personal","givenName":"Chandra","familyName":"Goetsch","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]}],"titles":[{"title":"Foraging strategies and lifetime fitness in northern elephant seals"}],"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":"Elephant seal"},{"subject":"lifetime reproductive success"},{"subject":"survival"},{"subject":"Predation","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Starvation","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2022-07-29T18:05:05Z","dateType":"Submitted"},{"date":"2022-08-01T00:00:00Z","dateType":"Issued"},{"date":"2022-08-01T00:00:00Z","dateType":"Available"},{"date":"2023-01-25T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.7562018","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/ele.14193","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["124847 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":"We collected 25 years of demographic, foraging route, diving, and diet\n data from northern elephant seals (Mirounga angustirostris) to determine\n the influence of behavioral strategies and mass gain during 8-month\n foraging trips on reproduction, survival, and lifetime reproductive\n success.","descriptionType":"Abstract"},{"description":"Users interested in publishing results with these data should\n consult the principal investigators to best understand details of the\n methods.  Those PIs might request co-authorship on publications based\n largely on the data.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1T685","contentUrl":null,"metadataVersion":11,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":263,"downloadCount":57,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-08-01T18:53:04Z","registered":"2022-08-01T18:53:05Z","published":null,"updated":"2026-03-17T15:34:56Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1pq36","type":"dois","attributes":{"doi":"10.7291/d1pq36","identifiers":[],"creators":[{"name":"Killam, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Killam","affiliation":["University of Arizona"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7569-1828","nameIdentifierScheme":"ORCID"}]},{"name":"Clapham, Matthew","nameType":"Personal","givenName":"Matthew","familyName":"Clapham","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4867-7304","nameIdentifierScheme":"ORCID"}]},{"name":"Al-Najjar, Tariq","nameType":"Personal","givenName":"Tariq","familyName":"Al-Najjar","affiliation":["University of Jordan"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6462-0773","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Giant clam growth and isotope data"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[{"subject":"Paleobiology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"conservation paleobiology"},{"subject":"Sclerochronology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Bivalves","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"giant clams"},{"subject":"tridacna"},{"subject":"Red Sea","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Carbon and nitrogen stable isotopes"}],"contributors":[],"dates":[{"date":"2021-08-05T07:40:14Z","dateType":"Submitted"},{"date":"2021-08-10T00:00:00Z","dateType":"Issued"},{"date":"2021-08-10T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1098/rspb.2021.0991","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1159950 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 health of reef-building corals has declined due to climate change and\n pollution. However, less is known about whether giant clams, reef-dwelling\n bivalves with a photosymbiotic partnership similar to that found in\n reef-building corals, are also threatened by environmental degradation. To\n compare giant clam health against a prehistoric baseline, we collected\n fossil and modern Tridacna shells from the Gulf of Aqaba, Northern Red\n Sea. After calibrating daily/twice-daily growth lines from the outer shell\n layer, we determined that modern individuals of all three species\n (Tridacna maxima, T. squamosa and T. squamosina) grew faster than Holocene\n and Pleistocene specimens. Modern specimens also show median shell organic\n δ15N values 4.2‰ lower than fossil specimens, which we propose is most\n likely due to increased deposition of isotopically light nitrate aerosols\n in the modern era. Nitrate fertilization accelerates growth in cultured\n Tridacna, so nitrate aerosol deposition may contribute to faster growth in\n modern wild populations. Furthermore, colder winter temperatures and past\n summer monsoons may have depressed fossil giant clam growth. Giant clams\n can serve as sentinels of reef environmental change, both to determine\n their individual health and the health of the reefs they inhabit.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D1PQ36","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":173,"downloadCount":20,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-08-10T22:58:46Z","registered":"2021-08-10T22:58:48Z","published":null,"updated":"2026-03-17T12:58:31Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1cm20","type":"dois","attributes":{"doi":"10.7291/d1cm20","identifiers":[],"creators":[{"name":"Killam, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Killam","affiliation":["University of Arizona"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7569-1828","nameIdentifierScheme":"ORCID"}]},{"name":"Clapham, Matthew","nameType":"Personal","givenName":"Matthew","familyName":"Clapham","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4867-7304","nameIdentifierScheme":"ORCID"}]},{"name":"Al-Najjar, Tariq","nameType":"Personal","givenName":"Tariq","familyName":"Al-Najjar","affiliation":["University of Jordan"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6462-0773","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Giant clam growth and isotope data"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[{"subject":"Paleobiology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"conservation paleobiology"},{"subject":"Sclerochronology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Bivalves","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"giant clams"},{"subject":"tridacna"},{"subject":"Red Sea","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Carbon and nitrogen stable isotopes"}],"contributors":[],"dates":[{"date":"2021-08-05T07:40:14Z","dateType":"Submitted"},{"date":"2021-08-10T00:00:00Z","dateType":"Issued"},{"date":"2021-08-10T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["1159950 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 health of reef-building corals has declined due to climate change and\n pollution. However, less is known about whether giant clams, reef-dwelling\n bivalves with a photosymbiotic partnership similar to that found in\n reef-building corals, are also threatened by environmental degradation. To\n compare giant clam health against a prehistoric baseline, we collected\n fossil and modern Tridacna shells from the Gulf of Aqaba, Northern Red\n Sea. After calibrating daily/twice-daily growth lines from the outer shell\n layer, we determined that modern individuals of all three species\n (Tridacna maxima, T. squamosa and T. squamosina) grew faster than Holocene\n and Pleistocene specimens. Modern specimens also show median shell organic\n δ15N values 4.2‰ lower than fossil specimens, which we propose is most\n likely due to increased deposition of isotopically light nitrate aerosols\n in the modern era. Nitrate fertilization accelerates growth in cultured\n Tridacna, so nitrate aerosol deposition may contribute to faster growth in\n modern wild populations. Furthermore, colder winter temperatures and past\n summer monsoons may have depressed fossil giant clam growth. Giant clams\n can serve as sentinels of reef environmental change, both to determine\n their individual health and the health of the reefs they inhabit.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D1PQ36","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:09:21Z","registered":"2023-10-04T22:09:22Z","published":null,"updated":"2026-03-17T12:58:18Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1c10c","type":"dois","attributes":{"doi":"10.7291/d1c10c","identifiers":[],"creators":[{"name":"Beltran, Roxanne","nameType":"Personal","givenName":"Roxanne","familyName":"Beltran","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8520-1105","nameIdentifierScheme":"ORCID"}]},{"name":"Costa, Dan","nameType":"Personal","givenName":"Dan","familyName":"Costa","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0233-5782","nameIdentifierScheme":"ORCID"}]},{"name":"Condit, Rick","nameType":"Personal","givenName":"Rick","familyName":"Condit","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4191-1495","nameIdentifierScheme":"ORCID"}]},{"name":"Robinson, Patrick","nameType":"Personal","givenName":"Patrick","familyName":"Robinson","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3957-8347","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Data for: Molt timing and duration of northern elephant seals"}],"publisher":"Dryad","container":{},"publicationYear":2024,"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":"molt"},{"subject":"moult"}],"contributors":[],"dates":[{"date":"2023-10-15T21:20:44Z","dateType":"Created"},{"date":"2022-10-26T22:08:04Z","dateType":"Submitted"},{"date":"2022-11-06T00:00:00Z","dateType":"Issued"},{"date":"2022-11-06T00:00:00Z","dateType":"Available"},{"date":"2024-03-11T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.10728682","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.10728878","relatedIdentifierType":"DOI"},{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.10728877","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1098/rspb.2023.2335","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["153632 bytes"],"formats":[],"version":"10","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":"Life history timing influences many ecosystem processes including\n predator-prey interactions, information transfer, disease transmission,\n ecosystem services, and resource subsidies. Many animals and plants have\n species-typical annual cycles, but individuals vary in their exact timing\n of life history events. However, quantifying individual variation in the\n timing and duration of life history events requires longitudinal sampling\n and may necessitate advanced analytical techniques if observations are\n imperfect or asynchrony is prevalent. Our goal was to examine factors that\n influence variation in molt duration and timing across individual northern\n elephant seals (Mirounga angustirostris). We quantified the onset and\n progression of fur loss in 1,178 individual seals over seven years. We\n found a wide range of molt start dates (95% interval spanning 60.1 days\n within a single age-sex category) and rapid molt progression (6.0 days in\n adult females and 10.8 and 10.3 days in juvenile females and males,\n respectively) with high molt asynchrony (only 20% of individuals in the\n population molting at the same time). Individual variation in the timing\n of the molt within age-sex categories was much larger than between\n categories: 94% of the variation in haul-out duration, 74% of variation in\n molt start date, and 59% of variation in molt duration was among\n individuals within age-sex categories. Finally, we discovered that\n individuals arriving late for the molt spent less time on the beach than\n earlier arrivers, which allowed them to catch up if they fell behind in\n their annual cycles. These findings emphasize the importance of\n quantifying individual variation in critical life history events.","descriptionType":"Abstract"},{"description":"# Molt timing and duration of northern elephant seals --- Six .csv files\n contain processed data (each row is a year-seal combination), and one .r\n file contains the code to fit the models and reproduce the figures. ##\n Description of the Data and file structure *Headers in the\n \"MoltMSFinal.csv\" file are as follows:* X = unique\n year-identifier for each seal age = animal age, years N = number of\n observations for that year-seal best = molt date (50% progress), days\n since January 01 slope = molt duration (days) card = if a number is\n present, the female had a pup during the breeding season sex = male or\n female arrival = modeled arrival date, days since January 01 departure =\n modeled departure date, days since January 01 tenure = haul-out duration,\n days agesexcat = age-sex category *Headers in the\n \"MoltMSFinalModelOut.csv\" file are as follows:* graphx = date,\n days since January 01 pred1 = model fit (predicted molt progress, %)\n *Headers in the \"MoltMSFinalCircular.csv\" file are as follows:*\n Season = life history event (categorical) Q50 = median population-level\n timing (date) *Headers in the \"MoltMSFinalCircularSD.csv\" file\n are as follows:* Season = life history event (categorical) SD = standard\n deviation population-level timing (days) *Headers in the\n \"MoltMSFinalAnnot.csv\" file are as follows:* x = date location\n for annotation labels y = y location for annotation labels *Headers in the\n \"MoltMSFinalOut.csv\" file are as follows:* yday = yearday of\n observation (days since January 1) molt = percent of new fur on the body\n Please note that \"NA\" in cells mean that no data were observed.\n --- The \"eseal_molt_AppFigs 2024_02_13.pdf\" file contains\n supplemental tables and appendices.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D1C10C","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":211,"downloadCount":40,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-11-07T06:26:16Z","registered":"2022-11-07T06:26:17Z","published":null,"updated":"2026-03-16T18:47:13Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1038p","type":"dois","attributes":{"doi":"10.7291/d1038p","identifiers":[],"creators":[{"name":"Thornlow, Bryan","nameType":"Personal","givenName":"Bryan","familyName":"Thornlow","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6334-5186","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Online phylogenetics using parsimony produces slightly better trees and is dramatically more efficient for large SARS-CoV-2 phylogenies than de novo and maximum-likelihood approaches"}],"publisher":"Dryad","container":{},"publicationYear":2021,"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":[{"name":"University of California, Santa Cruz","nameType":"Personal","givenName":"Santa Cruz","familyName":"University of California","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2021-12-17T04:07:04Z","dateType":"Submitted"},{"date":"2021-12-31T00:00:00Z","dateType":"Issued"},{"date":"2021-12-31T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2021.12.02.471004","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.5787644","relatedIdentifierType":"DOI"},{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.5787646","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.7291/d13q2j","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/sysbio/syad031","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1411902973 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":"Phylogenetics has been foundational to SARS-CoV-2 research and public\n health policy, assisting in genomic surveillance, contact tracing, and\n assessing emergence and spread of new variants. However, phylogenetic\n analyses of SARS-CoV-2 have often relied on tools designed for de novo\n phylogenetic inference, in which all data are collected before any\n analysis is performed and the phylogeny is inferred once from scratch.\n SARS-CoV-2 datasets do not fit this mould. There are currently over 5\n million sequenced SARS-CoV-2 genomes in public databases, with tens of\n thousands of new genomes added every day. Continuous data collection,\n combined with the public health relevance of SARS-CoV-2, invites an\n \"online\" approach to phylogenetics, in which new samples are\n added to existing phylogenetic trees every day. The extremely dense\n sampling of SARS-CoV-2 genomes also invites a comparison between\n Likelihood and Parsimony approaches to phylogenetic inference. Maximum\n Likelihood (ML) methods are more accurate when there are multiple changes\n at a single site on a single branch, but this accuracy comes at a large\n computational cost, and the dense sampling of SARS-CoV-2 genomes means\n that these instances will be extremely rare. Therefore, it may be that\n approaches based on Maximum Parsimony (MP) are sufficiently accurate for\n reconstructing phylogenies of SARS-CoV-2, and their simplicity means that\n they can be applied to much larger datasets. Here, we evaluate the\n performance of de novo and online phylogenetic approaches, and ML and MP\n frameworks, for inferring large and dense SARS-CoV-2 phylogenies. Overall,\n we find that online phylogenetics produces similar phylogenetic trees to\n de novo analyses for SARS-CoV-2, and that MP optimizations produce more\n accurate SARS-CoV-2 phylogenies than do ML optimizations. Since MP is\n thousands of times faster than presently available implementations of ML\n and online phylogenetics is faster than de novo, we therefore propose\n that, in the context of comprehensive genomic epidemiology of SARS-CoV-2,\n MP online phylogenetics approaches should be favored.","descriptionType":"Abstract"},{"description":"All details for data collection and processing are described\n at https://github.com/bpt26/parsimony. In March 2021, we developed a\n phylogeny consisting of 364,427 SARS-CoV-2 whole genomes, pruned of long\n branches and sequences with multiple ambiguous nucleotides. We assessed\n several phylogenetic inference and optimization methods using this\n dataset, as described in our manuscript. Here we include all necessary\n starting materials for running our analyses.","descriptionType":"Methods"},{"description":"All details for this dataset can be found\n at https://github.com/bpt26/parsimony. The attached protobuf file is the\n outcome of the commands described in subrepository 1.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Human Genome Research Institute","awardNumber":"F31HG010584","funderIdentifier":"https://ror.org/00baak391","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1038P","contentUrl":null,"metadataVersion":11,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":98,"downloadCount":7,"referenceCount":1,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-12-31T09:20:31Z","registered":"2021-12-31T09:20:32Z","published":null,"updated":"2026-03-16T18:19:33Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d13q2j","type":"dois","attributes":{"doi":"10.7291/d13q2j","identifiers":[],"creators":[{"name":"Thornlow, Bryan","nameType":"Personal","givenName":"Bryan","familyName":"Thornlow","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6334-5186","nameIdentifierScheme":"ORCID"}]},{"name":"Ye, Cheng","nameType":"Personal","givenName":"Cheng","familyName":"Ye","affiliation":["University of California San Diego"],"nameIdentifiers":[]},{"name":"De Maio, Nicola","nameType":"Personal","givenName":"Nicola","familyName":"De Maio","affiliation":["European Bioinformatics Institute"],"nameIdentifiers":[]},{"name":"McBroome, Jakob","nameType":"Personal","givenName":"Jakob","familyName":"McBroome","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Hinrichs, Angie","nameType":"Personal","givenName":"Angie","familyName":"Hinrichs","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Lanfear, Robert","nameType":"Personal","givenName":"Robert","familyName":"Lanfear","affiliation":["Australian National University"],"nameIdentifiers":[]},{"name":"Turakhia, Yatish","nameType":"Personal","givenName":"Yatish","familyName":"Turakhia","affiliation":["University of California San Diego"],"nameIdentifiers":[]},{"name":"Corbett-Detig, Russell","nameType":"Personal","givenName":"Russell","familyName":"Corbett-Detig","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Kramer, Alexander","nameType":"Personal","givenName":"Alexander","familyName":"Kramer","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]}],"titles":[{"title":"Online phylogenetics with matOptimize for SARS-CoV-2"}],"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":"SARS-CoV-2"},{"subject":"parsimony trees"},{"subject":"Bioinformatics and computational genetics"},{"subject":"Phylogenetics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"parsimony"},{"subject":"maximum likelihood"},{"subject":"Optimization","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"name":"University of California, Santa Cruz","nameType":"Personal","givenName":"Santa Cruz","familyName":"University of California","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2022-06-10T04:18:04Z","dateType":"Submitted"},{"date":"2022-10-09T00:00:00Z","dateType":"Issued"},{"date":"2022-10-09T00:00:00Z","dateType":"Available"},{"date":"2023-03-01T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"https://github.com/bpt26/parsimony","relatedIdentifierType":"URL"},{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.7683327","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/sysbio/syad031","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2021.12.02.471004","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["437655300 bytes"],"formats":[],"version":"12","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":"Phylogenetics has been foundational to SARS-CoV-2 research and public\n health policy, assisting in genomic surveillance, contact tracing, and\n assessing emergence and spread of new variants. However, phylogenetic\n analyses of SARS-CoV-2 have often relied on tools designed for de novo\n phylogenetic inference, in which all data are collected before any\n analysis is performed and the phylogeny is inferred once from scratch.\n SARS-CoV-2 datasets do not fit this mould. There are currently over 14\n million sequenced SARS-CoV-2 genomes in online databases, with tens of\n thousands of new genomes added every day. Continuous data collection,\n combined with the public health relevance of SARS-CoV-2, invites an\n \"online\" approach to phylogenetics, in which new samples are\n added to existing phylogenetic trees every day. The extremely dense\n sampling of SARS-CoV-2 genomes also invites a comparison between\n likelihood and parsimony approaches to phylogenetic inference. Maximum\n likelihood (ML) and pseudo-ML methods may be more accurate when there are\n multiple changes at a single site on a single branch, but this accuracy\n comes at a large computational cost, and the dense sampling of SARS-CoV-2\n genomes means that these instances will be extremely rare because each\n internal branch is expected to be extremely short. Therefore, it may be\n that approaches based on maximum parsimony (MP) are sufficiently accurate\n for reconstructing phylogenies of SARS-CoV-2, and their simplicity means\n that they can be applied to much larger datasets. Here, we evaluate the\n performance of de novo and online phylogenetic approaches, as well as ML,\n pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2\n phylogenies. Overall, we find that online phylogenetics produces similar\n phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP\n optimization with UShER and matOptimize produces equivalent SARS-CoV-2\n phylogenies to some of the most popular ML and pseudo-ML inference tools.\n MP optimization with UShER and matOptimize is thousands of times faster\n than presently available implementations of ML and online phylogenetics is\n faster than de novo inference. Our results therefore suggest that\n parsimony-based methods like UShER and matOptimize represent an accurate\n and more practical alternative to established maximum likelihood\n implementations for large SARS-CoV-2 phylogenies.","descriptionType":"Abstract"},{"description":"Full details and scripts for the analyses are available at our\n Github repository: https://github.com/bpt26/parsimony\n \u003cstrong\u003eRepository 1: Make Starting\n Tree:\u003c/strong\u003e We first developed a \"global\n phylogeny\", from which all analyses in this study were performed. We\n began by downloading VCF and FASTA files corresponding to March 18, 2021\n from our own daily-updated database (McBroome et al. 2021). The VCF file\n contains pairwise alignments of each of the 434,063 samples to the\n SARS-CoV-2 reference genome. We then implemented filters, retaining only\n sequences containing at least 28,000 non-N nucleotides, and fewer than two\n non-[ACGTN-] characters. We used UShER to create a phylogeny from scratch\n using only the remaining 366,492 samples. To remove potentially erroneous\n sequences, we iteratively pruned this tree of highly divergent internal\n branches with branch parsimony scores greater than 30, then terminal\n branches with branch parsimony scores greater than 6, until convergence,\n resulting in a final global phylogeny containing 364,427 samples. The\n branch parsimony score indicates the total number of substitutions along a\n branch. Similar filters based on sequence divergence are used by existing\n SARS-CoV-2 phylogenetic inference methods. For full reproducibility, files\n used for creating the global phylogeny can be found in subrepository 1 on\n the project GitHub page (Thornlow et al. 2021b).\n \u003cstrong\u003eRepository 2: Optimize Starting\n Tree:\u003c/strong\u003e Following this, we tested several\n optimization strategies on this global phylogeny, hereafter the\n \"starting tree\". We used matOptimize, FastTree 2, and maximum\n parsimony (MP) IQ-TREE 2. MP IQ-TREE 2 uses parsimony as the optimality\n criterion in contrast to the maximum likelihood mode used in all other\n experiments, which was infeasible on a dataset of this size. In these\n optimization experiments, we used experimental versions of MP IQ-TREE 2\n that allow finer control of parsimony parameters (specific versions are\n listed in the supplemental Github repository). In one experiment, we used\n the starting tree and its corresponding alignment and ran five iterations\n of MP IQ-TREE 2, varying the SPR radius from 20 to 100 in increments of\n 20. Experiments on a small dataset indicated that there is little or no\n improvement in parsimony score beyond a radius of 100. Separately, we\n tested another strategy that applied two iterations of MP IQ-TREE 2 to the\n starting tree, the first iteration using an SPR radius of 20 and the\n second using a radius of 100. Finally, we tested a strategy of six\n iterations of pseudo-likelihood optimization with FastTree 2 followed by\n two iterations of parsimony optimization with matOptimize. The tree\n produced by this strategy, hereafter the \"ground truth\" tree,\n had the highest likelihood of all the strategies we tested. This tree\n (after_usher_optimized_fasttree_iter6.tree) and files for these\n optimization experiments can be found in subrepository 2. \n In the multifurcating ground truth tree of 364,427 samples, there\n are 265,289 unique (in FASTA sequence) samples. There are 447,643 nodes in\n the tree. For reference, a full binary tree with the same number of leaves\n has 728,853 nodes. 23,437 of the 29,903 sites in the alignment are\n polymorphic (they display at least two non-ambiguous nucleotides).\n Homoplasies are common in these data. In the starting tree, 19,090 sites\n display a mutation occurring on at least two different branches, and 4,976\n sites display a mutation occurring more than ten times in the\n tree. \u003cstrong\u003eRepository 3: Real Data\n Experiments:\u003c/strong\u003e To mimic\n pandemic-style phylogenetics, we separated a total of 233,326 samples from\n the starting tree of 364,427 samples into 50 batches of ~5,000 by sorting\n according to the date of sample collection. We then set up two frameworks\n for each of the three software packages (matOptimize (commit 66ca5ff,\n conda version 0.4.8), maximum-likelihood IQ-TREE 2 (multicore version\n 2.1.3 COVID-edition), and FastTree 2 (Double Precision version 2.1.10)).\n The online phylogenetics frameworks began by using UShER to infer a small\n tree de novo from the first batch of samples, followed by alternating\n steps of optimization using one of the three evaluated methods and\n placement of additional samples with UShER. In de novo phylogenetics, we\n supplied each software package with an alignment corresponding to all\n samples in that batch and its predecessors (or VCF for matOptimize)\n without a guide tree. For both cases, each tree is larger than its\n predecessor by ~5,000 samples, and each tree necessarily contains all\n samples in the immediately preceding tree. For FastTree 2, we used 2\n rounds of subtree-prune-regraft (SPR) moves (-spr 2), maximum SPR length\n of 1000 (-sprlength 1000), zero rounds of minimum evolution nearest\n neighbor interchanges (-nni 0), and the Generalised Time Reversible +\n Gamma (GTR+G) substitution model (-gtr -gamma). For IQ-TREE 2, we used a\n branch length minimum of 0.000000001 (-blmin 1e-9), zero rounds of\n stochastic tree search (-n 0), and the GTR+G substitution model (-m\n GTR+G). With these parameters, IQ-TREE 2 constructs a starting parsimony\n tree and then performs hill-climbing NNI steps to optimize likelihood,\n avoiding the significant time overhead of stochastic search. We ran all\n matOptimize analyses using an instance with 15 CPUs and 117.2 GB of RAM,\n and we ran all IQ-TREE 2 and FastTree 2 analyses on an instance with 31\n CPUs and 244.1 GB of RAM, but we limited each command to 15 threads for\n equivalence with matOptimize. Files for all simulated data experiments can\n be found in subrepository 3.  \u003cstrong\u003eRepository 4: Simulated Data\n Experiments:\u003c/strong\u003e To generate\n our simulated data, we used the SARS-CoV-2 reference genome (GISAID ID:\n EPI_ISL_402125; GenBank ID: MN908947.3) (Shu and McCauley 2017; Sayers et\n al. 2021) as the root sequence and used phastSim (De Maio et al. 2021b) to\n simulate according to the ground truth phylogeny described above.\n Intergenic regions were evolved using phastSim using the default neutral\n mutation rates estimated in ref. (De Maio et al. 2021a), with\n position-specific mean mutation rates sampled from a gamma distribution\n with alpha=beta=4, and with 1% of the genome having a 10-fold increase\n mutation rate for one specific mutation type (SARS-CoV-2 hypermutability\n model described in ref. (De Maio et al. 2021b)). Evolution of coding\n regions was simulated with the same neutral mutational distribution, with\n a mean nonsynonymous/synonymous rate ratio of omega=0.48 as estimated in\n (Turakhia et al. 2021a), with codon-specific omega values sampled from a\n gamma distribution with alpha=0.96 and beta=2. Rates for each intergenic\n and coding region were not normalized in order to have the same baseline\n neutral mutation rate distribution across the genome. \n We repeated our iterative experiments using de novo and online\n matOptimize, IQ-TREE 2 and FastTree 2 on this simulated alignment, using\n the same strategies as before. However, instead of computing parsimony and\n likelihood scores, we computed the Robinson-Foulds (RF) distance (Robinson\n and Foulds 1981) of each optimization to the ground truth tree, pruned to\n contain only the samples belonging to that batch. To calculate each RF\n distance, we used the -O (collapse tree) argument in matUtils extract\n (McBroome et al. 2021) and then used the dist.topo command in the ape\n package in R (Paradis and Schliep 2019), comparing the collapsed optimized\n tree and the pruned, collapsed ground truth tree at each iteration. We\n computed normalized RF distances as a proportion of the total possible RF\n distance, which is equivalent to two times the number of samples in the\n trees minus six (Steel and Penny 1993).       \n Eliminating the 24-hour runtime restriction, we also repeated the\n first three de novo iterative experiments on both real and simulated data\n to compare UShER+matOptimize, IQ-TREE 2 with stochastic search, and\n RAxML-NG. These iterations of ~4.5k, ~8.9k, and ~13.2k samples were\n allowed to run for up to 14 days. For runs that did not terminate within\n this time (the second and third iterations of RAxML-NG), we used the best\n tree inferred during the run for comparisons. We ran IQ-TREE 2 and\n RAxML-NG under the GTR+G model with the smallest minimum branch length\n parameter that did not cause numerical errors. To compare the trees\n inferred from real data, we computed log-likelihoods under the GTR+G model\n for all trees, fixing the model parameters to those estimated by IQ-TREE 2\n during tree inference. We also compared the log-likelihoods of the trees\n under the parameters estimated by RAxML-NG for the first iteration, but\n could not do so for the second and third iterations which did not\n terminate in under two weeks. We allowed optimization of branch lengths\n during likelihood calculation. For the UShER+matOptimize trees, before\n computing likelihoods, we converted the branch lengths into units of\n substitutions per site by dividing each branch length by the alignment\n length (29,903). To compare the trees inferred from simulated data, we\n computed the RF and quartet distances of each tree to the corresponding\n ground truth tree described above.","descriptionType":"Methods"},{"description":"This repository contains supplemental results, data, and scripts\n for Thornlow et al., 2022. It is split into four folders. See attached\n README.md for more detail. This submission contains the\n following datatypes:     .fa files which can be viewed\n with any text-editing software     .nwk files which can\n be visualized with FigTree or any other phylogenetics software\n     .tree/.treefile/.bestTree files which are functionally\n identical to .nwk files     .iqtree files which are\n text files describing each IQ-TREE command run     .vcf\n files which follow the format described here:\n https://en.wikipedia.org/wiki/Variant_Call_Format    \n .pb files which can be analyzed using matUtils\n (https://usher-wiki.readthedocs.io/en/latest/QuickStart.html#matutils).      .tar.xz files which can be extracted using the command \"tar xvf (filename)\"     Other files with extenstions .txt, .bestModel, .log, or others not specified here are standard text files.     Most files are compressed in either .xz format and can be decompressed using the command \"unxz (filename)\".","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Institute of General Medical Sciences","awardNumber":"R35GM128932","funderIdentifier":"https://ror.org/04q48ey07","funderIdentifierType":"ROR"},{"funderName":"\n        University of California Office of the President Emergency COVID-19\n        Research Seed Funding*\n      ","awardNumber":"R00RG2456"},{"schemeUri":"https://ror.org","funderName":"Australian Research Council","awardNumber":"DP200103151","funderIdentifier":"https://ror.org/05mmh0f86","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Human Genome Research Institute","awardNumber":"T32HG008345","funderIdentifier":"https://ror.org/00baak391","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Human Genome Research Institute","awardNumber":"F31HG010584","funderIdentifier":"https://ror.org/00baak391","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D13Q2J","contentUrl":null,"metadataVersion":14,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":220,"downloadCount":22,"referenceCount":0,"citationCount":4,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-10-10T01:38:48Z","registered":"2022-10-10T01:38:49Z","published":null,"updated":"2026-03-16T18:19:15Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d11969","type":"dois","attributes":{"doi":"10.7291/d11969","identifiers":[],"creators":[{"name":"Gyalay, Szilard","nameType":"Personal","givenName":"Szilard","familyName":"Gyalay","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7179-4608","nameIdentifierScheme":"ORCID"}]},{"name":"Nimmo, Francis","nameType":"Personal","givenName":"Francis","familyName":"Nimmo","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]}],"titles":[{"title":"Inferred tidal heating distribution and internal structure of Tethys and Enceladus"}],"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":[],"dates":[{"date":"2023-01-06T23:54:51Z","dateType":"Submitted"},{"date":"2023-01-06T00:00:00Z","dateType":"Issued"},{"date":"2023-01-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.7511016","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2022je007550","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["125397026 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":"In our submitted paper, \"Estimates for Tethys' Moment of\n Inertia, Heat Flux Distribution, and Interior Structure from its\n Long-Wavelength Topography\" (Gyalay \u0026amp; Nimmo, 2023), we sought\n to infer the heat flux distribution at the base of Tethys' ice shell.\n This heat flux distribution allows us to infer whether there is a fluid or\n rigid layer interior to the ice shell – the latter of which would suggest\n a global, subsurface ocean. We calculate the heat flux distribution from\n the long-wavelength (spherical harmonic degrees 2 and 4) topography but\n require some assumptions, such as how thick the ice layer is, if the upper\n portion is porous, and what the moment of inertia of Tethys is. Thus we\n fit for the tidal heating distribution for a wide suite of varying\n parameters to find which combination best represents the structure and\n thermal state of Tethys. We also test our procedure on Enceladus, a moon\n for which we already know the moment of inertia and interior structure\n (i.e. that it has a subsurface ocean). This dataset contains the code we\n used to model these icy satellites and generate our data, as well as the\n output data.","descriptionType":"Abstract"},{"description":"In our paper, we establish the mathematics behind how we use\n assumed parameters (upper ice shell porosity, total ice shell thickness,\n moment of inertia, basal temperature at the base of the ice shell) to\n infer the average basal heat flux and fit for spatial patterns of tidal\n heating (Beuthe, 2013, Icarus). Using this best-fit tidal heating for each\n set of parameters, we forward model the topography (also described in our\n paper) and calculate its spherical harmonic weights as well as compare\n them to the originally observed topography.","descriptionType":"Methods"},{"description":"The header of each data-output file should describe what each\n column refers to. MoI is the moment of inertia. From these data, one can\n compute density profiles for each model of Tethys (or Enceladus) and judge\n whether it is consistent with the inferred heating pattern weight. Values\n were not printed to file if the calculated average heat flux was NaN, if\n any of chi_A,B,C were not between 0 and 1, or if any of the spherical\n harmonic weights of forward-modeled topography were NaN. Further\n descriptions of parameters and their uses are described in the paper for\n which this dataset was produced. Further, we include files for Enceladus,\n and the code used to generate the data.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D11969","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":206,"downloadCount":61,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-01-07T01:49:31Z","registered":"2023-01-07T01:49:32Z","published":null,"updated":"2026-03-16T17:08:15Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1zm3n","type":"dois","attributes":{"doi":"10.7291/d1zm3n","identifiers":[],"creators":[{"name":"Gyalay, Szilard","nameType":"Personal","givenName":"Szilard","familyName":"Gyalay","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7179-4608","nameIdentifierScheme":"ORCID"}]},{"name":"Nimmo, Francis","nameType":"Personal","givenName":"Francis","familyName":"Nimmo","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Downey, Brynna","nameType":"Personal","givenName":"Brynna","familyName":"Downey","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]}],"titles":[{"title":"Inferring Mimas' spatial distribution of tidal heating from its long-wavelength topography"}],"publisher":"Dryad","container":{},"publicationYear":2024,"subjects":[{"subject":"FOS: Physical sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Mimas"},{"subject":"tidal heat"},{"subject":"tidal heating"},{"subject":"planetary"},{"subject":"satellite"},{"subject":"interior"},{"subject":"Geophysics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2024-04-19T10:43:13Z","dateType":"Created"},{"date":"2024-04-16T21:11:49Z","dateType":"Submitted"},{"date":"2024-04-19T00:00:00Z","dateType":"Issued"},{"date":"2024-04-19T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2022je007550","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.7291/d11969","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["10849381 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":"In new work (Gyalay et al., 2023), we infer the interior of Mimas from its\n global shape (long-wavelength topography). To do so, we have to make\n various assumptions on how the ice shell of Mimas operates. This includes\n the temperature at the base of the ice shell, the thickness of the ice\n shell, what mode of isostasy it operates under (equal-mass vs.\n equal-pressure and Airy vs. Pratt), whether tidal tidal heating is due to\n eccentricity vs. obliquity, and how porous the region of the ice shell\n with a temperature \u0026lt;140 K may be. Further, as it has not yet been\n measured for Mimas, we must make an assumption on its moment of inertia.\n We vary through these assumptions and calculate how well an inferred heat\n distribution matches with a tidal heating distribution, among other\n physical self-consistency checks. In the associated paper, we analyze the\n dataset we produced to make conclusions on Mimas' interior structure\n and orbital dynamic history.","descriptionType":"Abstract"},{"description":"This dataset was produced by a model using the methods described\n in Gyalay \u0026amp; Nimmo (2023a; \u003cem\u003eJGR: Planets\n 128\u003c/em\u003e(2), doi: 10.1029/2022JE007550). In that paper, we\n established the mathematics behind how we used assumed parameters (upper\n ice shell porosity, total ice shell thickness, moment of inertia, basal\n temperature at the base of the ice shell) to infer the average basal heat\n flux and fit for spatial patterns of tidal heating (Beuthe, 2013, Icarus).\n Using this best-fit tidal heating for each set of parameters, we forward\n model the topography (also described in that paper) and calculate its\n spherical harmonic weights as well as compare them to the originally\n observed topography. \u003cbr\u003e\u003cbr\u003eThe code used to generate this\n dataset and as well as the dataset associated with Gyalay \u0026amp; Nimmo\n (2023a) are included in that paper's associated repository (Gyalay\n \u0026amp; Nimmo, 2023b; Dryad, dataset, doi: doi.org/10.7291/D11969). This\n repository contains only the produced model output for Mimas.","descriptionType":"Methods"},{"description":"The header of each data-output file should describe what each\n column refers to. MoI is the moment of inertia. From these data, one can\n compute density profiles for each model of Tethys (or Enceladus) and judge\n whether it is consistent with the inferred heating pattern weight. Values\n were not printed to file if the calculated average heat flux was NaN, if\n any of chi_A,B,C were not between 0 and 1, or if any of the spherical\n harmonic weights of forward-modeled topography were NaN. Further\n descriptions of parameters and their uses are described in Gyalay\n \u0026amp; Nimmo (2023a) and Gyalay et al. (2023). We\n also include an input file for the solid body tidal heating code of\n Roberts \u0026amp; Nimmo (2008; \u003cem\u003eIcarus 194\u003c/em\u003e(2), doi:\n 10.1016/j.icarus.2007.11.010). Usage of the input file is included\n in the file README.md","descriptionType":"Other"},{"description":"# Inferring Mimas' spatial distribution of tidal heating from its\n long- ## wavelength topography In this repository there is a series of\n data files for outputs of Mimas modeled under different assumptions. The\n biggest indicators of well-fitting models are the r_sq, which is the\n coefficient of determination that shows how well the inferred heating\n pattern beneath the ice shell can be fit by spatial patterns of tidal\n heating, and the RMS, which is the root mean square difference between the\n observed topography of Mimas and the topography forward modeled from the\n best fit tidal heating pattern weights. In the associated paper, we\n conclude there was a past epoch of strong obliquity tides in a solid\n Mimas. Additionally, there is one last file that is input for the TIRADE\n solid-body tidal heating code of Roberts \u0026amp; Nimmo 2008. That code is\n not ours to provide, but we can at least provide the input. Further, the\n Roberts \u0026amp; Nimmo code needs to be updated to calculate the tidal\n dissipation and potential due to obliquity tides. The tidal dissipation is\n equation 42 of Beuthe 2013, while the tidal potential is equation 88 of\n Beuthe 2013. These updates must be made to to the file\n \"tidal_module.c\" at about lines 139 (the variable\n \"Ediss\") and 979 (the variable \"potential\"). The\n derivatives of Equation 88 must also be calculated to update variable\n \"dpot\". The input file is \"Mimas_AGU2022_conv\" and\n requires the creation of a directory \"mimas_agu2022_conv_dir\" in\n the same directory as the input file. Then the command\n \"./tidal_cond.x Mimas_AGU2022_conv\" will place all outputs\n within \"mimas_agu2022_conv_dir\", assuming all the TIRADE code is\n within the same directory as the input file. ## Description of the data\n and file structure The header of each file should describe what each\n column refers to. MoI is the moment of inertia. From these data, one can\n compute density profiles for each model of Tethys (or Enceladus) and judge\n whether it is consistent with the inferred heating pattern weight. Values\n were not printed to file if the calculated average heat flux was NaN, if\n any of chi_A,B,C were not between 0 and 1, or if any of the spherical\n harmonic weights of forward-modeled topography were NaN. Further\n descriptions of parameters and their uses are described in the paper for\n which this dataset was produced. Further, we include files for Enceladus.\n While the Tethys data include \"no_odysseus\" in the filename, the\n spherical harmonic coefficients utilized were derived from limb profiles\n that *do* include those that pass over Odysseus crater (Nimmo et al.,\n 2011\\) The headers contained appear like so: Assuming [isostasy-type]\n isostasy and [tide-type] tides upon Tethys, \\[True/False] weighted\n regression [states whether the multilinear regression was weighted] and\n [True/False] pressure isostasy [did we use equal-pressure or equal-mass\n isostasy], each given porosity, MoI [Moment of Inertia], basal temperature\n T_B, and total shell thickness d we calculate necessary basal heat flux\n (F_B). We then calculate the following values: chi_a, chi_b, chi_c:\n heating pattern weights r_sq: coefficient of determination rms: Square\n root of the weighted average of the square of modeled topography minus\n observed. Weighted by area of degree bin. CF20/CF22: Spherical harmonic\n coefficients of flux ratioed. normClm for l=2,4;m=0,2,4\u0026lt;=l: normalized\n spherical harmonic weights of topography from our best-fit interior.\n z_Clm: z-score of the normClm we calculate vs. those observed. This is\n (normClm-normClm_observed)/SD_normClm_observed where SD is the standard\n deviation","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1ZM3N","contentUrl":null,"metadataVersion":5,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":41,"downloadCount":3,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:19:47Z","registered":"2023-10-04T22:19:48Z","published":null,"updated":"2026-03-14T23:19:50Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1zt2b","type":"dois","attributes":{"doi":"10.7291/d1zt2b","identifiers":[],"creators":[{"name":"Kendall-Bar, Jessica","nameType":"Personal","givenName":"Jessica","familyName":"Kendall-Bar","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4758-1386","nameIdentifierScheme":"ORCID"}]},{"name":"Williams, Terrie","nameType":"Personal","givenName":"Terrie","familyName":"Williams","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8170-009X","nameIdentifierScheme":"ORCID"}]},{"name":"Mukherji, Ritika","nameType":"Personal","givenName":"Ritika","familyName":"Mukherji","affiliation":["University of Oxford"],"nameIdentifiers":[]},{"name":"Lozano, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Lozano","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Pitman, Julie","nameType":"Personal","givenName":"Julie","familyName":"Pitman","affiliation":["Sleep Health, Santa Cruz"],"nameIdentifiers":[]},{"name":"Holser, Rachel","nameType":"Personal","givenName":"Rachel","familyName":"Holser","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8668-3839","nameIdentifierScheme":"ORCID"}]},{"name":"Beltran, Roxanne","nameType":"Personal","givenName":"Roxanne","familyName":"Beltran","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Robinson, Patrick","nameType":"Personal","givenName":"Patrick","familyName":"Robinson","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Crocker, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Crocker","affiliation":["Sonoma State University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7940-8011","nameIdentifierScheme":"ORCID"}]},{"name":"Adachi, Taiki","nameType":"Personal","givenName":"Taiki","familyName":"Adachi","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8395-4245","nameIdentifierScheme":"ORCID"}]},{"name":"Lyamin, Oleg","nameType":"Personal","givenName":"Oleg","familyName":"Lyamin","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Vyssotski, Alexei","nameType":"Personal","givenName":"Alexei","familyName":"Vyssotski","affiliation":["University of Zurich"],"nameIdentifiers":[]},{"name":"Costa, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Costa","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0233-5782","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Data for: Brain activity of diving seals reveals short sleep cycles at depth"}],"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":"Electrophysiology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Neurophysiology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"biologging"},{"subject":"wildlife ecology"},{"subject":"Sleep","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Seals","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"true seals"},{"subject":"Marine mammals","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2024-03-21T18:29:35Z","dateType":"Created"},{"date":"2023-03-23T04:14:01Z","dateType":"Submitted"},{"date":"2023-04-03T00:00:00Z","dateType":"Issued"},{"date":"2023-04-03T00:00:00Z","dateType":"Available"},{"date":"2023-04-02T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.7702650","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1126/science.adf0566","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["101784645582 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":"Sleep is a crucial part of the daily activity patterns of mammals.\n However, in marine species that spend months or entire lifetimes at sea,\n the location, timing, and duration of sleep may be constrained. To\n understand how marine mammals satisfy their daily sleep requirements while\n at sea, we monitored electroencephalographic (EEG) activity in wild\n northern elephant seals (Mirounga angustirostris) diving in Monterey Bay,\n California, USA. In this study, we characterized the sleep\n patterns of northern elephant seals from land to sea. Periods of\n electrophysiological sleep (slow wave sleep and rapid-eye-movement sleep)\n were recorded in seals on land, floating in shallow water, on the ocean\n floor in shallow water and the continental shelf, and during open ocean\n drift dives. While there was considerable variation in sleep patterns\n across individuals, total sleep time was lowest while sleeping at sea\n (\u0026lt;2 h/day) and highest while sleeping on land (~10 h/day). We\n linked sleep patterns to accelerometry and the time-depth profiles of 334\n free-ranging seals (514,406 sleeping dives) to reveal a North Pacific\n sleepscape where seals averaged only 2 hours of sleep per day. This rivals\n the record for the least sleep among all mammals, currently held by the\n African elephant (~ 2 hours per day). This integrative study of sleep in\n wild northern elephant seals can help identify critical resting habitats\n and set the stage for comparative and translational studies of\n sleep. This repository contains raw electrophysiological data,\n processed hypnograms with identified sleep states, and integrated\n three-dimensional motion and electrophysiology data (“hypnotracks”). We\n also include data related to our sleep estimation algorithm for time-depth\n records of adult female northern elephant seals. All animal procedures\n were approved at the federal and institutional levels under National\n Marine Fisheries Permits 496, 836, 786–1463, 87-1743, 19108, 14636, and\n 23188, and by the Institutional Animal Care and Use Committee (IACUC) of\n the University of California Santa Cruz.","descriptionType":"Abstract"},{"description":"See the associated code repository and manuscript (links in the\n Related Works section) for additional information on the methods for data\n collection and processing.","descriptionType":"Methods"},{"description":"This repository contains data relating to the manuscript\n “\u003cem\u003eBrain activity of diving seals reveals short sleep cycles at\n depth\u003c/em\u003e.” Science (2023). Related code for this paper can be\n found in the versioned Zenodo repository.  ","descriptionType":"Other"},{"description":"# Data for: Brain activity of diving seals reveals short sleep cycles at\n depth **Suggested citation for this dataset:** Kendall-Bar, JM; Williams,\n TM; Mukherji, R; Lozano, DA; Pitman, JK; Holser, RR; Keates, T; Beltran,\n RS; Robinson, PW; Crocker, DE; Adachi, T; Lyamin, OI; Vyssotski, AL;\n Costa, DP (2023). Data for: Brain activity of diving seals reveals short\n sleep cycles at depth. **This repository contains the data and code for\n our paper:** Kendall-Bar, JM; Williams, TM; Mukherji, R; Lozano, DA;\n Pitman, JK; Holser, RR; Keates, T; Beltran, RS; Robinson, PW; Crocker, DE;\n Adachi, T; Lyamin, OI; Vyssotski, AL; Costa, DP (2023). Brain activity of\n diving seals reveals short sleep cycles at depth. *Science*. ## Data\n structure and files Missing values may be represented by NA, NaN, or -\n when data are not available. See Column Description dropdown menus for\n dataset-specific information. ## 00 Metadata and Summary Data files: ###\n Results Summary * Wide-format table with metadata for EEG animals and\n summarizing all sleep data by location and sleep stage. This table shows\n calculations for Total Sleep Time with and without putative REM sleep.\n Blank ('-' values) in this table designate that there are no\n data available for the given combination of SealID and recording location.\n * **00_00_Sleep-Results-Summary-Table.xlsx** ### EEG Metadata *\n **00_00_Sleep-Recording-Metadata.xlsx** - Wide-format table with EEG\n recording metadata. * Column descriptions: * Deployment - Chronological\n EEG deployment sequence (1-13) * TestNumber - Recording ID number * SealID\n - Unique identifier for each seal * Recording.ID - identifier combining\n the location (in the lab [CAPTIVE], in the wild [WILD], or translocated\n [XLOC]), age (in years [yr] or months [mo]), and age class (juvenile or\n weanling) of the seal * TOPPID - Unique ID to match to\n 00_Sleep-Results-Summary-Table.xlsx and TOPP database ('20'\n stands for *Mirounga angustirostris*, next two digits represent year, next\n three digits represent deployment number per year). * StartLogger_DateTime\n - start date \u0026amp; time (format: 'YYYY-MM-DD HH:MM:SS') for the\n recording * OnAnimal_DateTime - date \u0026amp; time logger was attached to the\n animal (as detected by ECG) * Duration_OnAnimal - Duration of recording in\n hours (after OnAnimal_DateTime) * ChannelConfiguration - Vector of Channel\n #s for Raw EDF files that correspond to the vector of channel names: LEOG\n REOG LEMG REMG LEEG1 REEG2 LEEG3 REEG4 ### Adult Female Metadata *\n **02_00_AdultFemaleData_Metadata.csv** - Metadata table for adult female\n deployments * Column descriptions. * TOPPID - Unique ID per deployment\n matching those in the TOPP database * Year - Year of start of deployment *\n Season - Season (Post-Breeding or Post-Molt) * TDR_QC - Binary to\n designate whether the time-depth record was of sufficient quality to run\n the sleep identification model. * Track_QC - Binary to designate whether\n the track was of sufficient quality (and length) to visualize spatial\n sleep results in summary figure ## 01 Processed data files: ### Hypnograms\n * Processed Sleep Scoring (lab, wild, \u0026amp; at sea) *\n **00_Hypnogram_30s_ALL_ANIMALS.csv** - Processed sleep scoring data for\n 30s epochs for all animals. * Column descriptions: * timebins - Time in R\n format for the beginning of the 30s epoch * SealID - unique identifier for\n each seal * Recording.ID - identifier combining the location (in the lab\n [CAPTIVE], in the wild [WILD], or translocated [XLOC]), age (in years [yr]\n or months [mo]), and age class (juvenile or weanling) of the seal * ID -\n in the lab [CAPTIVE], in the wild [WILD], or translocated [XLOC] *\n Sleep.Code - Specific sleep state designation: * Active Waking * Quiet\n Waking * Drowsiness - Intermittent slow waves * LV Slow Wave SLeep -\n Low-voltage slow wave sleep * HV Slow Wave Sleep - High-voltage slow wave\n sleep * Certain REM Sleep - Rapid-Eye-Movement (REM) * Sleep scored with\n high confidence (high degree of Heart Rate Variability [HRV]) * Putative\n REM Sleep - REM Sleep scored with low confidence (low HRV) * Unscorable -\n Data not scorable due to interference, motion artifacts, or signal quality\n * Simple.Sleep.Code - Simplified sleep state designation: * Active Waking\n * Quiet Waking * Drowsiness - Intermittent slow waves * SWS - Slow wave\n sleep (LV \u0026amp; HV combined) * REM - REM Sleep (certain and putative\n combined) * Unscorable - Data not scorable due to interference, motion\n artifacts, or signal quality * Resp.Code - Respiratory state designation:\n * Eupnea - between first breath and last breath transition to Eupnea -\n transition to tachycardia * Apnea - between last breath and first breath\n transition to Apnea - transition to bradycardia * Unscorable - not\n scorable due to noise obscuring HR detection * Water.Code - Location of\n animal * LAND - on land (in pen in the lab or on beach in the wild) *\n SHALLOW WATER - in water \u0026lt; 2m deep (in pool in the lab or in the lagoon\n at Ano Nuevo) * DEEP WATER - animal traversing the continental shelf (\u0026lt;\n 200 m / in water shallow enough that the animal can rest / travel along\n bottom) * OPEN OCEAN - animal in water deeper than 200 m / in water deep\n enough that the animal cannot rest / travel along bottom * Timesper_day -\n Time of day in seconds (out of 86400) * Day - Day of the recording ###\n Hypnotracks * Processed Sleep Scoring \u0026amp; Motion Data (3D tracks \u0026amp;\n Sleep State for seals at sea) * **01_Hypnotrack_1Hz_ALL_ANIMALS.csv** -\n Timeseries data at 1Hz showing sleep state and processed motion data. *\n Column descriptions. * Note: Sleep_Num, SimpleSleepNum, Water_Num, and\n Resp_Num redundantly code categorical/string data into numerical values\n for ease of analysis and plotting. * Missing values are represented by NaN\n (these appear at the beginning and end of recordings for statistics that\n rely on a data window [such as Heart Rate, Stroke Rate, and FFT Delta\n Spectral Power analysis]). ### Hypnotrack Excerpt * 1Hz Excerpt from a\n sleep dive at sea (used in data visualization) *\n **01_02_AnimationExcerpt_Hypnotrack_1Hz.csv** - Timeseries data at 1Hz\n with x y z positions and sleep data. * Column descriptions: * Seconds -\n Seconds elapsed for each recording * R_Time - Local time [PST] in R Format\n (YYYY-MM-DD HH:MM:SS) * SealID - unique identifier for each seal *\n Recording_ID - identifier combining the location (in the lab [CAPTIVE], in\n the wild [WILD], or translocated [XLOC]), age (in years [yr] or months\n [mo]), and age class (juvenile or weanling) of the seal * ID - in the lab\n [CAPTIVE], in the wild [WILD], or translocated [XLOC] * Sleep_Code -\n Specific sleep state designation: * Active Waking * Quiet Waking *\n Drowsiness - Intermittent slow waves * LV Slow Wave SLeep - Low-voltage\n slow wave sleep * HV Slow Wave Sleep - High-voltage slow wave sleep *\n Certain REM Sleep - Rapid-Eye-Movement (REM) Sleep scored with high\n confidence (high degree of Heart Rate * Variability [HRV]) * Putative REM\n Sleep - REM Sleep scored with low confidence (low HRV) * Unscorable - Data\n not scorable due to interference, motion artifacts, or signal quality *\n SimpleSleepCode - Simplified sleep state designation: * Active Waking *\n Quiet Waking * Drowsiness - Intermittent slow waves * SWS - Slow wave\n sleep (LV \u0026amp; HV combined) * REM - REM Sleep (certain and putative\n combined) * Unscorable - Data not scorable due to interference, motion\n artifacts, or signal quality * Resp_Code - Respiratory state designation:\n * Eupnea - between first breath and last breath transition to Eupnea -\n transition to tachycardia * Apnea - between last breath and first breath\n transition to Apnea - transition to bradycardia * Unscorable - not\n scorable due to noise obscuring HR detection * Water_Code - Location of\n animal * LAND - on land (in pen in the lab or on beach in the wild) *\n SHALLOW WATER - in water \u0026lt; 2m deep (in pool in the lab or in the lagoon\n at Ano Nuevo) * DEEP WATER - animal traversing the continental shelf (\u0026lt;\n 200 m / in water shallow enough that the animal can rest / travel along\n bottom) * OPEN OCEAN - animal in water deeper than 200 m / in water deep\n enough that the animal cannot rest / travel along bottom * DN - Matlab\n date number for local time * pitch - angle downward (-) or upward (+) in\n radians * roll - angle of roll to the right (+) or left (-) in radians *\n heading - angle of rotation to the left (counterclockwise: +) or right\n (clockwise: -) in radians * x - pseudotrack's x-position translation\n from the origin/start of deployment in meters * y - pseudotrack's\n y-position translation from the origin/start of deployment in meters * z -\n pseudotrack's z-position translation from the origin/start of\n deployment in meters * geoX - x position translation from the origin/start\n of deployment in meters using geo-referenced pseudotrack * geoY - y\n position translation from the origin/start of deployment in meters using\n geo-referenced pseudotrack * Depth - depth in meters (same as z except\n *(-1)) * speed - estimated speed in meters per second * Lat - Latitude in\n Decimal Degrees * Long - Longitude in Decimal Degrees * Stroke_Rate -\n automated peak detection result for stroke frequency in strokes per minute\n * Heart_Rate - automated peak detection result for heart rate in beats per\n minute * LEEGDelta - Delta power (0.5-4Hz) for Left Hemisphere\n electroencephalogram (EEG) * REEGDelta - Delta power (0.5-4Hz) for Right\n Hemisphere electroencephalogram (EEG) * HRVLFPower - Very low frequency\n (0-0.005 Hz) power for Heart Rate (quantification of HRV) ###\n Higher-resolution Rotation and Swimming Data * 10Hz Excerpt from a sleep\n dive at sea (rotation and heart rate data for visualization) *\n **01_03_AnimationExcerpt_RotationSwim_10Hz.csv** - Timeseries data at 10Hz\n showing rotation and swimming behavior. Stroke rate and glide controller\n data were processed using the methods demonstrated in Kendall-Bar et al.\n 2021. * Column descriptions: * Seconds - Seconds elapsed for each\n recording * ECG - Electrocardiogram (ECG) data in microvolts * pitch -\n angle downward (-) or upward (+) in degrees * roll - angle of roll to the\n right (+) or left (-) in degrees * heading - angle of rotation to the left\n (+) or right (-) in degrees * GyrZ - gyroscope data (angular acceleration)\n showing stroking behavior * Glide_Controller - glide controller for\n animation (1 for gliding 0 for stroking) and smoothed over a 5-second\n window * Depth - depth in meters * Heart_Rate - heart rate in beats per\n minute * Stroke_Rate - stroke rate in stroked per minute * Heart_Detected\n - binary (0 or 1- heartbeat detected) * Stroke_Detected - binary (0 or 1-\n stroke detected) ### EEG/ECG Excerpt for a Sleep Dive * Processed Sleep\n Scoring \u0026amp; Motion Data (3D tracks \u0026amp; Sleep State for seals at sea) *\n **01_04_AnimationExcerpt_ECGEEGs_50Hz.csv** - Timeseries data at 50Hz with\n ECG, LEEG, and REEG during a sleeping dive. * Column descriptions: *\n Missing values are represented by NaN (inserted for skipped data sectors\n to maintain time alignment). * Seconds - Time in R format for the\n beginning of the 30s epoch * ECG - Electrocardiogram (ECG) data (in\n microVolts) * LEEG - Electroencephalogram (EEG) data (in microVolts) *\n REEG - Electroencephalogram (EEG) data (in microVolts) ### Time-depth Data\n for Sleep Estimation * Processed Data for Sleep Estimation Script *\n **02_00_SLEEP_TOPPID_testNN_10_NewRaw.csv** - Timeseries data at 8 second\n intervals with sleep scoring information, geographic locations, and\n time-depth data. * Column descriptions: * Missing values are represented\n by NaN (these appear at the beginning and end of recordings for statistics\n that rely on a data window [such as Heart Rate, Stroke Rate, and FFT Delta\n Spectral Power analysis]). * Columns same as Hypnotrack Plus the required\n columns for this script: * time - Matlab date number for time *\n CorrectedDepth - Zero-offset corrected depth ### Sleep Estimation Script\n Ouput * **02_01_2011020_1015_Daily_Activity.csv /\n 02_01_2011034_2036_Daily_Activity.csv** - Wide format observations of each\n day for two specific deployments. Activity labels are in individual\n columns (compared with long format above). * Column descriptions: *\n Missing values are represented by NaN (these appear when stroke data were\n not available). * TOPPID - unique identifier for each instrument\n deployment * SEALID - unique identifier for each seal * unique_Days -\n Matlab date number for the day of the observation * Days_Elapsed - Number\n of days into the trip * PercentofTrip - The percent of the trip that has\n elapsed * Daily_recording - number of recording hours per day (should be\n 24 or close to it) * Daily_diving - number of diving hours per day (time\n spent below 2 m) * DailylongSI - number of hours in an extended surface\n interval (at surface for \u0026gt; 10 min) * Dailyfilteredlongdriftlong_SI -\n number of hours of estimated sleep (includes potential sleep while\n drifting, on the ocean floor, and in extended surface intervals) *\n Dailydivelongglide - number of hours spent in an extended glide (to be\n roughly compared to estimated sleep (Dailyfilteredlongdriftlong_SI)). *\n Lat - Latitude in decimal degrees * Long - Longitude in decimal degrees *\n Lon360 - Longitude in decimal degrees from 0 to 360 (no negative values)\n ### Compiled Sleep Estimate Data * Intermediate outputs with summarized\n data across seals * **02_01_SleepEstimates_ALL_SealsUsed.csv** Metadata\n for all seals included in daily sleep analysis. * Column descriptions: *\n TOPPID - Unique deployment ID matching TOPP Database * haveStrokes -\n Binary code designating the presence of stroke rate data * haveSleep -\n Binary code designating the presence of sleep data * haveLatLong - Binary\n code designating the presence of LatLong data SIslong Driftslong Flatslong\n * FilteredDriftslong SeasonCode** Plus the required columns for this\n script: * Dives - Number of total dives (for each seal) * SIs_long -\n Number of extended surface intervals (\u0026gt;10 min) * Flats_long - Number of\n estimated naps on the sea floor * FilteredDriftslong - Number of estimated\n naps Season_Code - Season designation (PB- post-breeding or PM- post molt)\n * **02_01_SleepEstimates_ALL_DailyActivity.csv** Data for each seal day. *\n Column descriptions: * Missing values are represented by NA when data were\n not available. * TOPPID - unique identifier for each instrument deployment\n * SEALID - unique identifier for each seal * unique_Days - Matlab date\n number for the day of the observation - Days_Elapsed * Number of days into\n the trip * PercentofTrip - The percent of the trip that has elapsed *\n Daily_recording - number of recording hours per day (should be 24 or close\n to it) * Daily_diving - number of diving hours per day (time spent below 2\n m) * DailylongSI - number of hours in an extended surface interval (at\n surface for \u0026gt; 10 min) * Dailyfilteredlongdriftlong_SI - number of hours\n of estimated sleep (includes potential sleep while drifting, on the ocean\n floor, and in extended surface intervals) * Dailydivelongglide - number of\n hours spent in an extended glide (to be roughly compared to estimated\n sleep (Dailyfilteredlongdriftlong_SI)). * Lat - Latitude in decimal\n degrees * Long - Longitude in decimal degrees - Lon360 - Longitude in\n decimal degrees from 0 to 360 (no negative values) ### Adult Female Sleep\n Estimate Data * **02_01_AdultFemaleData_DailySleepEstimates_long.csv** -\n Long format observations of hours per day performing different activities\n for Sleep Estimate analysis for all animals. Observations for the same\n seal (across multiple deployments) were later grouped and averaged for\n each trip percentile to compare individuals. Activity labels are in column\n DailyActivity_label and values are in column h_per_day. * Column\n descriptions: * Missing values are represented by NA when data were not\n available. * TOPPID - unique identifier for each instrument deployment *\n SEALID - unique identifier for each seal * Season_Code - PB [Post-breeding\n (short trip ~ 2 months)] or PM [Post-molt (long trip ~ 7 months)] *\n triprecord_days - total number of days for the deployment * deploysperseal\n - number of deployments for the seal * unique_Days - Matlab date number\n for the day of the observation * Days_Elapsed - Number of days into the\n trip * PercentofTrip - The percent of the trip that has elapsed *\n DailyActivity_label - Labels for the type of observation in each row: *\n daily_recording - number of recording hours per day (should be 24 or close\n to it) * daily_diving - number of diving hours per day (time spent below 2\n m) * dailylongSI - number of hours in an extended surface interval (at\n surface for \u0026gt; 10 min) * dailyfilteredlongdriftlong_SI - number of hours\n of estimated sleep (includes potential sleep while drifting, on the ocean\n floor, and in extended surface intervals) * dailylongglide - where\n available, number of hours of gliding as measured with a kami kami stroke\n rate logger * hperday - Hours per day (out of 24) for each of the activity\n labels listed above ### Adult Female Sleep Estimate Data for Geospatial\n Analysis *\n **02_02_AdultFemaleData_DailySleepEstimates_wide_goodtracks.csv** - Wide\n format observations of each day for deployments with tracks of sufficient\n quality to be included in geospatial sleep analysis. Activity labels are\n now in individual columns (compared with long format above). * Column\n descriptions: * Missing values are represented by NA when data were not\n available. * TOPPID - unique identifier for each instrument deployment *\n SEALID - unique identifier for each seal * Season_Code - PB [Post-breeding\n (short trip ~ 2 months)] or PM [Post-molt (long trip ~ 7 months)] *\n unique_Days - Matlab date number for the day of the observation *\n Days_Elapsed - Number of days into the trip * PercentofTrip - The percent\n of the trip that has elapsed * Daily_recording - number of recording hours\n per day (should be 24 or close to it) * Daily_diving - number of diving\n hours per day (time spent below 2 m) * DailylongSI - number of hours in an\n extended surface interval (at the surface for \u0026gt; 10 min) *\n Dailyfilteredlongdriftlong_SI - number of hours of estimated sleep\n (includes potential sleep while drifting, on the ocean floor, and in\n extended surface intervals) * Lat - Latitude in decimal degrees * Long -\n Longitude in decimal degrees * Lon360 - Longitude in decimal degrees from\n 0 to 360 (no negative values) ## Raw data files: ### EEG Raw Data Raw EEG,\n EMG, EOG, and motion sensor data for all deployments, labeled by\n TestNumber (see 00_Sleep-Recording-Metadata.xlsx for related metadata\n including start times and channel configuration details). ## Licenses Text\n and figures: CC-BY-4.0 Please provide attribution (Jessica Kendall-Bar et\n al., 2023) when using our figures. Code: MIT Please cite the Zenodo DOI\n provided above when using our code. Data: CC-0 attribution requested in\n reuse. Please cite the Dryad DOI provided above when using our code.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"funderName":"National Ocean Partnership Program*","awardNumber":"N00014-02-1-1012"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"N1656282","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://www.crossref.org/services/funder-registry/","funderName":"Strategic Environmental Research and Development Program","awardNumber":"RC20-C2-1284","funderIdentifier":"https://doi.org/10.13039/100013316","funderIdentifierType":"Crossref Funder ID"},{"schemeUri":"https://ror.org","funderName":"Office of Naval Research","awardNumber":"N00014-18-1-2822","funderIdentifier":"https://ror.org/00rk2pe57","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Office of Naval 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(IOGP)*\n      ","awardNumber":"JIP2207-23"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"funderName":"Steve \u0026 Rebecca Sooy Graduate Research Fellowship*"},{"schemeUri":"https://ror.org","funderName":"Achievement Rewards for College Scientists Foundation","funderIdentifier":"https://ror.org/054awkm93","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"University of California, Santa Cruz","funderIdentifier":"https://ror.org/03s65by71","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1ZT2B","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":553,"downloadCount":117,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-04-03T10:44:35Z","registered":"2023-04-03T10:44:35Z","published":null,"updated":"2026-03-14T23:09:46Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1gd50","type":"dois","attributes":{"doi":"10.7291/d1gd50","identifiers":[],"creators":[{"name":"Cristescu, Bogdan","nameType":"Personal","givenName":"Bogdan","familyName":"Cristescu","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2964-5040","nameIdentifierScheme":"ORCID"}]},{"name":"Elbroch, Mark","nameType":"Personal","givenName":"Mark","familyName":"Elbroch","affiliation":["Panthera Corporation"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0429-4179","nameIdentifierScheme":"ORCID"}]},{"name":"Forrester, Tavis","nameType":"Personal","givenName":"Tavis","familyName":"Forrester","affiliation":["Oregon Department of Fish and Wildlife"],"nameIdentifiers":[]},{"name":"Allen, Maximilian","nameType":"Personal","givenName":"Maximilian","familyName":"Allen","affiliation":["University of Illinois Urbana-Champaign"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8976-889X","nameIdentifierScheme":"ORCID"}]},{"name":"Spitz, Derek","nameType":"Personal","givenName":"Derek","familyName":"Spitz","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Wilmers, Christopher","nameType":"Personal","givenName":"Christopher","familyName":"Wilmers","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Wittmer, Heiko","nameType":"Personal","givenName":"Heiko","familyName":"Wittmer","affiliation":["Victoria University of Wellington"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8861-188X","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Standardizing protocols for determining the cause of mortality in wildlife studies"}],"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":"cause of death"},{"subject":"kill site"},{"subject":"mule deer"},{"subject":"Predation","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Survival analysis","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"temperate ecosystem"},{"subject":"ungulate neonate"}],"contributors":[],"dates":[{"date":"2022-05-31T08:55:06Z","dateType":"Submitted"},{"date":"2022-06-22T00:00:00Z","dateType":"Issued"},{"date":"2022-06-22T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/ece3.9034","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.6685080","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["237002 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":"Mortality site investigations of telemetered wildlife are important for\n cause-specific survival analyses and understanding underlying causes of\n observed population dynamics. Yet eroding ecoliteracy and a lack of\n quality control in data collection can lead researchers to make incorrect\n conclusions, which may negatively impact management decisions for wildlife\n populations. We reviewed a random sample of 50 peer-reviewed studies\n published between 2000 and 2019 on survival and cause-specific mortality\n of ungulates monitored with telemetry devices. This concise review\n revealed extensive variation in reporting of field procedures, with many\n studies omitting critical information for cause of mortality inference.\n Field protocols used to investigate mortality sites and ascertain the\n cause of mortality are often minimally described and frequently fail to\n address how investigators dealt with uncertainty. We outline a\n step-by-step procedure for mortality site investigations of telemetered\n ungulates, including evidence that should be documented in the field.\n Specifically, we highlight data that can be useful to differentiate\n predation from scavenging and more conclusively identify the predator\n species that killed the ungulate. We also outline how uncertainty in\n identifying the cause of mortality could be acknowledged and reported. We\n demonstrate the importance of rigorous protocols and prompt site\n investigations using data from our 5-year study on survival and\n cause-specific mortality of telemetered mule deer (Odocoileus hemionus) in\n northern California. Over the course of our study, we visited mortality\n sites of neonates (n = 91) and adults (n = 23) to ascertain the cause of\n mortality. Rapid site visitations significantly improved the successful\n identification of the cause of mortality and confidence levels for\n neonates. We discuss the need for rigorous and standardized protocols that\n include measures of confidence for mortality site investigations. We\n invite reviewers and journal editors to encourage authors to provide\n supportive information associated with the identification of causes of\n mortality, including uncertainty.","descriptionType":"Abstract"},{"description":"Three datasets on neonate and adult mule deer\n (\u003cem\u003eOdocoileus hemionus\u003c/em\u003e) mortality site investigations\n were generated through ecological fieldwork in northern California, USA\n (2015-2020). The datasets in Dryad are: Does.csv (for\n use with R); Fawns.csv (for use with R); Full_data.xlsx (which\n combines the 2 .csv files and includes additional information)\n Two R code files associated with the 2 .csv datasets above are\n available in Zenodo:  RScript_Does.R;\n RScript_Fawns.R The data were analyzed using RStudio\n v.1.1.447 and a variety of packages, including: broom, caret, ciTools,\n effects, lattice, modEvA, nnet, and tidyverse. The data are associated\n with the publication \"Standardizing protocols for determining the\n cause of mortality in wildlife studies\" in \u003cem\u003eEcology and\n Evolution\u003c/em\u003e.","descriptionType":"Methods"},{"description":"The datasets can be opened using Microsoft Excel and R.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"California Department of Fish and Wildlife","funderIdentifier":"https://ror.org/02v6w2r95","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1GD50","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":134,"downloadCount":10,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-06-22T13:35:15Z","registered":"2022-06-22T13:35:16Z","published":null,"updated":"2026-03-13T23:25:56Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d16m46","type":"dois","attributes":{"doi":"10.7291/d16m46","identifiers":[],"creators":[{"name":"Rüger, Nadja","nameType":"Personal","givenName":"Nadja","familyName":"Rüger","affiliation":["German Centre for Integrative Biodiversity Research"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2371-4172","nameIdentifierScheme":"ORCID"}]},{"name":"Hubbell, Stephen P.","nameType":"Personal","givenName":"Stephen P.","familyName":"Hubbell","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2797-3411","nameIdentifierScheme":"ORCID"}]},{"name":"Condit, Richard","nameType":"Personal","givenName":"Richard","familyName":"Condit","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4191-1495","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Demographic response to light environment of all species in the Barro Colorado plot: recruitment, growth, and mortality"}],"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":"2022-01-19T19:50:05Z","dateType":"Submitted"},{"date":"2022-02-03T00:00:00Z","dateType":"Issued"},{"date":"2022-02-03T00:00:00Z","dateType":"Available"},{"date":"2022-05-04T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/ele.12974","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1126/science.aaz4797","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/j.1365-2745.2009.01552.x","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.pone.0025330","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/j.1600-0706.2010.19021.x","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["797693 bytes"],"formats":[],"version":"11","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 measured growth, death, and recruitment of 250,000 individual trees of\n 300 species in the Barro Colorado 50-ha plot in Panama. To understand how\n light limits demography, we also carried out a detailed map of the light\n environment across the 50 hectares, providing an estimate of the light at\n the top of every tree in the plot (Rüger et al. 2009, 2011ab, 2012, 2018,\n 2020). These tables combine results from the three main studies on the\n demographic responses of tree species to light, covering recruitment,\n growth, and mortality. Each study was based on rigorous statistical\n estimates of demographic rate parameters as a function of available light\n across most species in the forest. The model parameters provided here\n allow demographic rates of every species to be estimated under any light\n condition, and at any diameter, for example, growth rate of 5-cm trees in\n 3% light, or death rate of 2-cm trees in 10% light. Most useful is a\n single table that provides estimated responses in a consistent way across\n all species covering recruitment, growth, and mortality. The response is\n defined as the ratio of species performance at high light to performance\n at low light, and thus describes how every species responds to increase in\n light caused by treefall gaps in the canopy. These response parameters\n offer comprehensive demographic traits on more than 90% of the species in\n the forest. The references provide context and describe the models in\n detail. References: Condit, R., 1998. Tropical Forest Census Plots:\n Methods and Results from Barro Colorado Island, Panama and a Comparison\n with Other Plots. Springer-Verlag, Berlin. Rüger, N., A. Huth, S.P.\n Hubbell, and R. Condit. 2009. Response of recruitment to light\n availability across a tropical lowland rainforest community. Journal of\n Ecology 97: 1360–1368. Rüger, N., U. Berger, S.P. Hubbell, G. Vieilledent,\n and R. Condit. 2011a. Growth strategies of tropical tree species:\n Disentangling light and size effects. PLoS ONE 6(9): e25330. Rüger, N., A.\n Huth, S.P. Hubbell, and R. Condit. 2011b. Determinants of mortality across\n a tropical lowland rainforest community. Oikos 120: 1047–1056. Rüger, N.,\n C. Wirth, S.J. Wright, and R. Condit. 2012. Functional traits explain\n light and size response of growth rates in tropical tree species. Ecology\n 93: 2626–2636. Rüger, N., L. S. Comita, R. Condit, D. Purves, B.\n Rosenbaum, M. D. Visser, S. J. Wright, and C. Wirth. 2018. Beyond the\n fast–slow continuum: demographic dimensions structuring a tropical tree\n community. Ecology Letters 21:1075–1084. Rüger, N., R. Condit, D. H. Dent,\n S. J. DeWalt, S. P. Hubbell, O. R. Lopez, C. Wirth, and C. E. Farrior.\n 2020. Demographic trade-offs predict tropical forest dynamics. Science\n 368:165–168.","descriptionType":"Abstract"},{"description":"See Condit (1998) and Rüger et al. (2009)","descriptionType":"Methods"},{"description":"Download README.pdf for descriptions of the tables and equations\n needed to calculate predicted demographic rates.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D16M46","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":250,"downloadCount":58,"referenceCount":0,"citationCount":7,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-02-04T00:28:41Z","registered":"2022-02-04T00:28:42Z","published":null,"updated":"2026-03-13T22:44:17Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1jt30","type":"dois","attributes":{"doi":"10.7291/d1jt30","identifiers":[],"creators":[{"name":"Nisi, Anna","nameType":"Personal","givenName":"Anna","familyName":"Nisi","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0286-3187","nameIdentifierScheme":"ORCID"}]},{"name":"Benson, John","nameType":"Personal","givenName":"John","familyName":"Benson","affiliation":["University of Nebraska–Lincoln"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3993-4340","nameIdentifierScheme":"ORCID"}]},{"name":"Wilmers, Christopher C.","nameType":"Personal","givenName":"Christopher C.","familyName":"Wilmers","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Puma responses to unreliable human cues suggest an ecological trap in a fragmented landscape"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"ecological trap"},{"subject":"habitat fragmentation"},{"subject":"habitat selection"},{"subject":"human-dominated landscape"},{"subject":"Puma concolor"},{"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":"2022-02-23T02:25:07Z","dateType":"Submitted"},{"date":"2022-03-21T00:00:00Z","dateType":"Issued"},{"date":"2022-03-21T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/oik.09051","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["333617964 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":"Animals’ fear of people is widespread across taxa and can mitigate the\n risk of human-induced mortality, facilitating coexistence in\n human-dominated landscapes. However, humans can be unpredictable predators\n and anthropogenic cues that animals perceive may not be reliable\n indicators of the risk of being killed. In these cases, animal fear\n responses may be ineffective and may even exacerbate the risk of\n anthropogenic mortality. Here, we explore these questions using a 10-year\n dataset of movement and mortality events for the puma (Puma concolor)\n population in the fragmented Santa Cruz Mountains of California, for whom\n the leading cause of death was retaliatory killings by people following\n livestock loss. We modeled retaliatory killing risk and puma habitat\n selection relative to residential housing density to evaluate whether puma\n avoidance of human cues reflected their risk of being killed. We\n documented a mismatch between human cues, fear responses, and actual risk.\n Rather than scaling directly with housing density, retaliatory killings\n occurred at intermediate levels of human development and at night. Pumas\n avoided these areas during the day but selected for these high-risk areas\n at night, resulting in a mismatch between cue and risk impacting 17% of\n the study area. These results are unlikely to be driven by puma hunting\n behavior: livestock constitute a very small proportion of puma diets, and\n we found no evidence for the alternative hypothesis that state-dependent\n foraging drove depredation of livestock and subsequent retaliatory\n killings. Our findings indicate that puma responses to human cues are not\n sufficient to enable human-carnivore coexistence in this area and suggest\n that reducing risk from humans in places with few perceptible human cues\n would facilitate carnivore conservation in human-dominated landscapes.\n Furthermore, a mismatch between human cues and responses by carnivores can\n lead to selection rather than avoidance of risky areas, which could result\n in an ecological trap.","descriptionType":"Abstract"},{"description":"We captured adult and subadult pumas from 2009-2019 and fit pumas\n with GPS collars set to record a GPS location every 4 hours. For animals\n that died during the study, we recorded date, location, and cause of\n death. We 1) modeled overall and cause-specific mortality rates using the\n Kaplan-Meier procedure and non-parametric cumulative incidence functions,\n 2) modeled habitat selection using step selection functions, 3) modeled\n spatial predictors of the distribution of sites where retaliatory killings\n occurred, and 4) compared time since last predicted deer kill and body\n weights between animals killed after depredating livestock and the overall\n puma population. Included in this data product are the time-to-event data\n (tte_cause.csv) for analysis 1, covariate information for 4-hour puma\n locations (ssf_data.csv) for analysis 2, covariate information for\n locations of retaliatory killings (rk_rsf_data.csv) and other sources of\n mortality (other_mort_rsf_data.csv) for analysis 3, body weight data\n (body_weight_data.csv) and kill rate data (times_since_last_kill.csv and\n kill_rates.csv) for analysis 4. Please see Nisi AC,\n Benson JF, Wilmers CC. 2022. Puma responses to unreliable human cues\n suggest an ecological trap in a fragmented landscape. Oikos:\n 10.1111/oik.09051 for a full description of data collection methods, and\n the README file for detailed descriptions of each dataset. ","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7291/D1JT30","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":143,"downloadCount":18,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-03-22T04:35:33Z","registered":"2022-03-22T04:35:35Z","published":null,"updated":"2026-03-13T21:54:23Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1dd40","type":"dois","attributes":{"doi":"10.7291/d1dd40","identifiers":[],"creators":[{"name":"Morales, David","nameType":"Personal","givenName":"David","familyName":"Morales","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Cole, R. J.","nameType":"Personal","givenName":"R. J.","familyName":"Cole","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9217-796X","nameIdentifierScheme":"ORCID"}]},{"name":"Holl, Karen D.","nameType":"Personal","givenName":"Karen D.","familyName":"Holl","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2893-6161","nameIdentifierScheme":"ORCID"}]},{"name":"Zahawi, R. A.","nameType":"Personal","givenName":"R. A.","familyName":"Zahawi","affiliation":["Organization for Tropical Studies"],"nameIdentifiers":[]}],"titles":[{"title":"Surrounding forest cover dataset 2005"}],"publisher":"Dryad","container":{},"publicationYear":2014,"subjects":[],"contributors":[],"dates":[{"date":"2021-04-29T21:45:04Z","dateType":"Submitted"},{"date":"2014-12-17T00:00:00Z","dateType":"Issued"},{"date":"2014-12-17T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/1365-2664.13684","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1890/09-0714.1","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1890/14-1399.1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["24483 bytes"],"formats":[],"version":"1","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":"Forest cover within 100 and 500 m radii from the center of each\n experimental plot was hand digitized from ortho-rectified 2005 aerial\n photographs and comprehensively ground checked. Forest cover spans a range\n from \u0026lt;1% to 66% within a 100-m radius surrounding the plots and\n from 9% to 89% in a 500-m radius.","descriptionType":"Abstract"},{"description":"Used in varous publications. Research plots described in Holl, K.\n D., J. L. Reid, R. J. Cole, F. Oviedo-Brenes, J. A. Rosales, and R. A.\n Zahawi. 2020. Applied nucleation facilitates tropical forest recovery:\n Lessons learned from a 15-year study. Journal of Applied Ecology\n 57:2316-2328.","descriptionType":"Methods"},{"description":"See readme file for more details.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1DD40","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":133,"downloadCount":4,"referenceCount":0,"citationCount":3,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-04-30T21:50:29Z","registered":"2021-04-30T21:50:30Z","published":null,"updated":"2026-03-13T17:03:59Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7291/d1338m","type":"dois","attributes":{"doi":"10.7291/d1338m","identifiers":[],"creators":[{"name":"Yu, Xinting","nameType":"Personal","givenName":"Xinting","familyName":"Yu","affiliation":["University of California Santa Cruz"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7479-1437","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Photochemical model output data associated with: How to identify exoplanet surfaces using atmospheric trace species in hydrogen-dominated atmospheres"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[{"subject":"FOS: Physical sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Photochemical model"}],"contributors":[],"dates":[{"date":"2021-04-27T01:16:02Z","dateType":"Submitted"},{"date":"2021-05-07T00:00:00Z","dateType":"Issued"},{"date":"2021-05-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.3847/1538-4357/abfdc7","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1618907 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":"Sub-Neptunes (Rp~1.25-4 REarth) remain the most commonly detected\n exoplanets to date. However, it remains difficult for observations to tell\n whether these intermediate-sized exoplanets have surfaces and where their\n surfaces are located. Here we propose that the abundances of trace species\n in the visible atmospheres of these sub-Neptunes can be used as proxies\n for determining the existence of surfaces and approximate surface\n conditions. As an example, we used a state-of-the-art photochemical model\n to simulate the atmospheric evolution of K2-18b and investigate its final\n steady-state composition with surfaces located at different pressures\n levels (Psurf). We find the surface location has a significant impact on\n the atmospheric abundances of trace species, making them deviate\n significantly from their thermochemical equilibrium and “no-surface”\n conditions. This result arises primarily because the pressure-temperature\n conditions at the surface determine whether photochemically-produced\n species can be recycled back to their favored thermochemical-equilibrium\n forms and transported back to the upper atmosphere. For an assumed H2-rich\n atmosphere for K2-18b, we identify seven chemical species that are most\n sensitive to the existence of surfaces: ammonia (NH3), methane (CH4),\n hydrogen cyanide (HCN), acetylene (C2H2), ethane (C2H6), carbon monoxide\n (CO), and carbon dioxide (CO2). The ratio between the observed and the\n no-surface abundances of these species, can help distinguish the existence\n of a shallow surface (Psurf \u0026lt; 10 bar), an intermediate surface (10\n bar \u0026lt; Psurf \u0026lt; 100 bar), and a deep surface (Psurf \u0026gt;\n 100 bar). This framework can be applied together with future observations\n to other sub-Neptunes of interest.","descriptionType":"Abstract"},{"description":"These datafiles are output results using a one-dimensional (1D)\n thermochemical and photochemical kinetics and transport model, Caltech/JPL\n KINETICS code for a model exoplanet (K2-18b) without surfaces and with\n surfaces (model runs with surfaces located at 1, 10, 100 bars).","descriptionType":"Methods"},{"description":"kin*.pun files: final concentrations of the different\n species\u003cbr\u003e atm*.inp files: equilibrium concentrations of the\n different species In file titles, \"100x\"\n means 100 times solar metallicity, \"deep\" is the model with no\n surface, \"xxbar\" has surface at xx bar, \"hot\" is the\n model with four times stellar flux, \"t0\" is the model with\n intrinsic temperature Tint=0 K and \"t70\" is the model with\n Tint=70 K, \"Kzzdiv3\" is the model with the nominal eddy\n diffusion (Kzz) profile divided by three and \"Kzztimes3\" is the\n model with the nominal eddy diffusion (Kzz) profile timed by\n three. The atm*.inp and kin*.pun files have entries in\n row format: altitude is in km (above 1 bar at altitude\n = 0)\u003cbr\u003e pressure is in mbar\u003cbr\u003e temperature is in\n kelvins\u003cbr\u003e densitiy is in cm^-3\u003cbr\u003e eddy diffusion\n coefficient (ignore in atm file) is in cm^2 s^-1\u003cbr\u003e all species\n concentrations are in cm^-3 (divide by the density entry near the top of\n the file to get volume mixing ratio)","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"Heising-Simons Foundation","awardNumber":"51 Pegasi B Postdoctoral Fellowship","funderIdentifier":"https://ror.org/01mp52y34","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7291/D1338M","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":159,"downloadCount":10,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-05-07T22:39:27Z","registered":"2021-05-07T22:39:28Z","published":null,"updated":"2026-03-11T23:37:00Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}}],"meta":{"total":237,"totalPages":10,"page":1},"links":{"self":"https://api.datacite.org/dois?prefix=10.7291","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026prefix=10.7291"}}