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Includes corrected REMA DSM stripes at the central ice shelf region of Pine Island Ice Shelf, the double differential vertical displacement results from 2014 to 2023 which cazlculated from the offset tracking results output from GAMMA software. Other dataset for other analysis are also included in the ZIP file. Each MATLAB codes in MATLAB_function.zip includes the discriptions that guide the user how to used it and how to find the dataset that used for processing. Some sample files are provided in data_and_results.zip that can let user test the code easily. \n\n "},{"descriptionType":"Other","description":"Ephemeral grounding on the Pine Island Ice Shelf ,West Antarctica, from 2014 to 2023Description of the data and file structure\n\nThe MATLAB codes and related datasets are used for generating the figures for the paper \"Ephemeral grounding on the Pine Island Ice Shelf, West Antarctica, from 2014 to 2023\".\n\nFiles and variables\n\nFile: Main_function.zip\n\n**Description:****Include MATLAB scripts and functions. Each script include discriptions that guide the user how to used it and how to find the dataset that used for processing.\n\nMATLAB Main Scripts: Include the whole steps to process the data, output figures, and output videos.\n\n\n\nScript_1_DROT_calc.m\n\nScript_2_Range_displacement_process_flow.m\n\nScript_3_Extraction_results.m\n\nScript_4_Plot_Figures_and_output_avi.m\n\n\nMATLAB functions: six Files that includes MATLAB functions that support the main script:\n\n\n\n1_DROT_code: Include MATLAB functions related to convert range offset results output from GAMMA to differential vertical displacement.\n\n2_Range_displacement_code: Include MATLAB functions related to range displacement processing, includes remove outliers and filter.\n\n3_Extract_data_from_2D_result: Include MATLAB functions that used for extract profiles from 2D data.\n\n4_Movies_and_Series_Figures: Include MATLAB functions related to generate Movie S1.\n\n5_Figure_plotting_code: Include MATLAB functions related to Figures in the paper and support information.\n\n6_REMA_DEM_error_estimation: Include MATLAB functions related to error estimation for corrected REMA DEMs.\n\n7_Dr_timeseries_REMA_ICESat_2_Ice_thickness_changes: Preprocess code for plotting Figure 7 and 9.\n\n\nFile: data_and_result.zip\n\nDescription:**Include directories that store the results output from MATLAB. user only neeed to modify the path in MATLAB script to their own path.\n\n\n\n1_origin : Sample data (\"PS-20180323-20180329\", \"PS-20180329-20180404\", \"PS-20180404-20180410\") output from GAMMA software in Geotiff format that can be used to calculate DROT and velocity. Includes displacment, theta, phi, and ccp.?\n\n2_maskccpN: Remove outliers by ccp \u003c 0.05 and change displacement to velocity (m/day).\n\n3_rockpoint: Extract velocities at non-moving region\n\n4_constant_detrend: removed orbit error\n\n5_compare: store the DEM results before and after correction.\n\n6_Block_result: times series results that extract from 2D data.\n\n7_MATLAB_output_png_result: Times series Figures\n\n8_DEM: corrected and uncorrected REMA DEM strips, related dataset used for calculated basal melting\n\n9_DROT: Differential Range Offset Tracking results\n\n\n\nICESat_2: ICESat 2 Tracks 0965 and 1094 corrected data\n\n \n\n\n\n\nMODIS_images: Imgaes used for plotting Figure 8.\n\n \n\n\n\n\nSample_dr_and_inc: Data used for plotting Figure 6a.\n\n\nshp: grounding line, rock region, ice front, and other shape files.\n\n\nFile: PIG_front_1947_2023.zip\n\nDescription:**Includes Ice front positions shape files from 1947 to 2022, which used for plotting figure.1 in the paper.\n\nFile: Sample_double_diffrential_vertical_displacement_PIG.zip\n\nDescription:**Includes 16 double diffrential vertical displacement results for plotting Figure 3 in the manuscript in Geotiff format.\n\nData was derived from the following sources:\n\nThose links can be found in MATLAB scripts or in the paper \"**Open Research\" **section."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"Crossref Funder ID","funderName":"National Natural Science Foundation of China","funderIdentifier":"10.13039/501100001809","awardNumber":"41941010"},{"funderIdentifierType":"Crossref Funder ID","funderName":"National Natural Science Foundation of China","funderIdentifier":"10.13039/501100001809","awardNumber":"42171133"}],"url":"https://zenodo.org/doi/10.5281/zenodo.13357909","contentUrl":null,"metadataVersion":13,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":7,"versionOfCount":0,"created":"2024-08-22T05:41:16Z","registered":"2024-08-22T05:41:16Z","published":null,"updated":"2026-09-29T11:04:08Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.15468/4wc9r4","type":"dois","attributes":{"doi":"10.15468/4wc9r4","identifiers":[{"identifier":"edb656a0-71ad-418d-afb1-c5584283ba47","identifierType":"UUID"}],"creators":[{"name":"Miljødirektoratet Miljødirektoratet","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Arealrepresentativ naturovervåkning (ANO)"}],"publisher":"Norwegian Environment Agency","container":{},"publicationYear":2026,"subjects":[{"subject":"Samplingevent","subjectScheme":"GBIF Dataset Type Vocabulary: http://rs.gbif.org/vocabulary/gbif/dataset_type_2015-07-10.xml"}],"contributors":[{"name":"Endre Grüner Ofstad","contributorType":"RelatedPerson","affiliation":[],"nameIdentifiers":[]},{"affiliation":["Miljødirektoratet"],"name":"Miljødirektoratet Miljødirektoratet","contributorType":"RelatedPerson","nameIdentifiers":[]}],"dates":[{"date":"2024-12-16","dateType":"Created"},{"date":"2026-09-29","dateType":"Updated"}],"language":"nb","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"SAMPLING_EVENT"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Arealrepresentativ naturovervåking (ANO) er et nasjonalt overvåkingsprogram som samler inn data om viktige indikatorer for å vurdere økologisk tilstand i hovedøkosystemene. Det vil i tillegg gi en nøytral og helhetlig beskrivelse av status og utvikling av naturtyper i fastlands-Norge etter systemet\nNatur i Norge (NiN). Overvåkingen vil bidra til å styrke datagrunnlaget for miljøforvaltningen og miljøpolitiske beslutningsprosesser\n\nhttps://kartkatalog.miljodirektoratet.no/Dataset/Details/2054"}],"geoLocations":[{"geoLocationBox":{"northBoundLatitude":71.09586,"southBoundLatitude":58.10195,"eastBoundLongitude":30.963594,"westBoundLongitude":4.892521},"geoLocationPlace":"Mainland 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Siddique","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6228-5908"}]}],"titles":[{"title":"Prevention and Screening Innovation Project Towards Elimination of Cervical Cancer"},{"titleType":"Subtitle","title":"Implementation Research Project to study feasibility of WHO strategy for elimination of cervical cancer"},{"titleType":"AlternativeTitle","title":"PRESCRIP-TEC"}],"publisher":"DataverseNL","container":{},"publicationYear":2024,"subjects":[{"subject":"Medicine, Health and Life Sciences"},{"schemeUri":"https://www.ncbi.nlm.nih.gov/mesh/68030361","subject":"human papilloma virus infection","subjectScheme":"MeSH"},{"schemeUri":"https://www.ncbi.nlm.nih.gov/mesh/68002583","subject":"Uterine cervical neoplasm","subjectScheme":"MeSH"},{"schemeUri":"https://www.ncbi.nlm.nih.gov/mesh/68002578","subject":"Uterine cervical dysplasia","subjectScheme":"MeSH"},{"schemeUri":"https://www.ncbi.nlm.nih.gov/mesh/68005833","subject":"Genital Neoplasms, Female","subjectScheme":"MeSH"}],"contributors":[{"nameType":"Organizational","affiliation":["University Medical Center Groningen"],"name":"Global Health Unit, Department of Health Sciences","contributorType":"Producer","nameIdentifiers":[]},{"nameType":"Organizational","affiliation":["University Medical Center Groningen, the Netherlands"],"name":"Department of Health Sciences","contributorType":"Distributor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["rug.nl"],"name":"Groningen Digital Competence Centre","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Organizational","name":"University Medical Center Groningen","contributorType":"ProjectLeader","affiliation":[],"nameIdentifiers":[]},{"nameType":"Organizational","name":"Uganda Rural Development and Training, NGO, 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Commons Attribution Non Commercial Share Alike 4.0 International","lang":"en","rightsIdentifier":"cc-by-nc-sa-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The Prevention and Screening Innovation Project towards Elimination of Cervical Cancer (PRESCRIP-TEC) research project contributes to the evidence-base for the WHO strategy to eliminate cervical cancer as a public health problem. The project implements an innovative approach in cervical cancer screening, including direct treatment and follow-up, for women in resource-poor or hard-to-reach settings, by improving availability, accessibility, acceptability and quality of services. PRESCRIP-TEC focuses on implementation research into secondary prevention of cervical cancer in different settings in four countries over three continents: Bangladesh and India in Asia, Uganda in Africa, and Slovakia in Eastern Europe. \u0026lt;br\u0026gt;\u0026lt;br\u0026gt;\n\nThe project builds on interventions with promising or proven effectiveness including cost-effectiveness:\u0026lt;br\u0026gt;\n- hrHPV based screening is cost-effective when adequate coverage is reached. \u0026lt;br\u0026gt;\n- Self-swab for hrHPV leads to higher uptake of screening compared to sampling by clinicians. \u0026lt;br\u0026gt;\n- Visual inspection with acetic acid (VIA) is an approved screening method by the WHO and is part of the national cervical cancer prevention programme in Uganda, Bangladesh, India; in India AI to support VIA screening was shown to be effective in detecting VIA positive lesions.\u0026lt;br\u0026gt;\nThe following research questions will be answered per target country: \u0026lt;br\u0026gt;\n1. Which are the client-related factors for accessibility and acceptability and adherence to the enhanced screening protocol?\u0026lt;br\u0026gt;\n2. Which are the health system-related factors for availability and quality of the enhanced screening protocol?\u0026lt;br\u0026gt;\n3. What is the percentage of increase in coverage and uptake as result of implementation of the enhanced screening protocol?\u0026lt;br\u0026gt;\n4. What is the percentage of screened women who adhere to single-visit treatment and proposed follow-up of screening?\u0026lt;br\u0026gt;\n5. Does the artificial intelligence decision support system (AI-DSS) offer a solution for reducing intraobserver and inter-observer variation by HCWs and does it allow task shifting to non-specialist health staff?\u0026lt;br\u0026gt;\n6. At which level of unit cost per screening the new protocol (including self-test for hrHPV and AI-DSS) is affordable for the included countries?"},{"descriptionType":"Other","description":"Additional information related to article \"Investigating feasibility...\" is available from \nhttps://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-13488-z"}],"geoLocations":[{"geoLocationPlace":"Uganda, India, Bangladesh, Slovak Republic"}],"fundingReferences":[{"funderName":"European Union","awardNumber":"https://cordis.europa.eu/project/id/964270"},{"funderName":"Department of Bio Technical Research, Ministry of Science and Technology, 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functions"}],"publisher":"figshare","container":{},"publicationYear":2024,"subjects":[{"classificationCode":"310402","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subject":"Biogeography and phylogeography","subjectScheme":"ANZSRC Fields of Research"}],"contributors":[],"dates":[{"date":"2024-11-22","dateType":"Created"},{"date":"2026-09-29","dateType":"Updated"},{"date":"2022-06-08","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"Dataset"},"relatedIdentifiers":[{"relationType":"IsPreviousVersionOf","relatedIdentifier":"10.6084/m9.figshare.20004335","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["153799 Bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative 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interactions)."}],"geoLocations":[],"fundingReferences":[],"url":"https://figshare.com/articles/dataset/Water_availability_creates_global_thresholds_in_multidimensional_soil_biodiversity_and_functions/20004335/6","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":114,"downloadCount":55,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2024-11-22T11:55:42Z","registered":"2024-11-22T11:55:43Z","published":null,"updated":"2026-09-29T09:51:24Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}}}},{"id":"10.5281/zenodo.14513471","type":"dois","attributes":{"doi":"10.5281/zenodo.14513471","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Finnish Environment Institute"],"givenName":"Kimmo","familyName":"Nurmio","name":"Nurmio, 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been developed in GSFI (grant agreement number: 101112903 - 2022-FI-GEOS-GSFI) and GSFIBU (grant agreement number: 01195769 / SMP-ESS-2024-GEOS-IBA, 2024-FI-GEOS-GSFIBU) projects funded by Eurostat.\n\nTest small area dataset covers whole Finland and is intended for testing purposes. Please notice that the test dataset may contain flaws and errors. The readme-file (ReadME_testdata_En.txt) describes the dataset and method in more detail.\n\nDuring the development process in two projects: 1. A stakeholder survey was conducted on the need and content of small areas. 2. decision to propose creating an area classification system where small areas are based on smaller base areas that act as building blocks for small area aggregation. 3. Different approaches and datasets were tested for forming these base areas and aggregating them into small areas. 4. A pilot version of the actual small areas was created based on these base areas. 5. Pilot version was further developed both in terms of the base areas and small area aggregation methodology and criteria to create a nationwide test dataset.\n\nThe proposed small area division model establish a coherent and scalable small area division model that defines stable base units as building blocks and enables the flexible aggregation of small areas across the entire country. The small area division model follows a hierarchical structure in which municipalities are divided into fine-grained base areas derived from physical infrastructure, such as buildings, transport networks, land use, and natural boundaries. These base areas are then aggregated into statistically meaningful small areas using a network-based algorithm that incorporates population thresholds, spatial coherence, and data protection requirements.\n\nThe test version is a proposal, not yet the final dataset. 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Please cite this paper if you have used this dataset: https://doi.org/10.1016/j.media.2025.103716."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Wellcome Trust","funderIdentifier":"https://ror.org/029chgv08","schemeUri":"https://ror.org/","awardTitle":"Wellcome Trust Centre for Surgical and Interventional Sciences","awardNumber":"203145/Z/16/Z"},{"funderIdentifierType":"ROR","funderName":"Engineering and Physical Sciences Research Council","funderIdentifier":"https://ror.org/0439y7842","schemeUri":"https://ror.org/","awardTitle":"Context Aware Augmented Reality for Endonasal Endoscopic Surgery","awardNumber":"EP/W00805X/1"},{"funderIdentifierType":"ROR","funderName":"Engineering and Physical Sciences Research Council","funderIdentifier":"https://ror.org/0439y7842","schemeUri":"https://ror.org/","awardTitle":"AID-PitSurg: AI-enabled Decision support in Pituitary Surgery to reduce complications","awardNumber":"EP/Y01958X/1"},{"funderIdentifierType":"ROR","funderName":"National Institute for Health Research","funderIdentifier":"https://ror.org/0187kwz08","schemeUri":"https://ror.org/","awardTitle":"NIHR University College London Hospitals Biomedical Research Centre","awardNumber":"uclbrc-2012-1"}],"url":"https://rdr.ucl.ac.uk/articles/dataset/PitVis_Challenge_Endoscopic_Pituitary_Surgery_videos/26531686","contentUrl":null,"metadataVersion":4,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":1,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2024-08-10T12:15:15Z","registered":"2024-08-20T08:38:07Z","published":null,"updated":"2026-09-29T09:04:39Z"},"relationships":{"client":{"data":{"id":"bl.ucld","type":"clients"}}}},{"id":"10.5519/vfaeq4e5","type":"dois","attributes":{"doi":"10.5519/vfaeq4e5","identifiers":[],"creators":[{"nameType":"Personal","givenName":"Andrew","familyName":"Torres","name":"Torres, Andrew","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9879-0722"}],"affiliation":[]},{"nameType":"Personal","givenName":"Owen","familyName":"Wangensteen","name":"Wangensteen, Owen","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5593-348X"}],"affiliation":[]},{"nameType":"Personal","givenName":"Willem","familyName":"Renema","name":"Renema, Willem","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1627-5995"}],"affiliation":[]},{"nameType":"Personal","givenName":"Christopher","familyName":"Meyer","name":"Meyer, Christopher","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2501-7952"}],"affiliation":[]},{"nameType":"Personal","givenName":"Ian Kendrich","familyName":"Fontanilla","name":"Fontanilla, Ian Kendrich","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4544-4281"}],"affiliation":[]},{"nameType":"Personal","givenName":"Jonathan","familyName":"Todd","name":"Todd, Jonathan","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0433-1611"}],"affiliation":[]}],"titles":[{"title":"Global species hotspots and COI barcoding cold spots of marine Gastropoda"}],"publisher":"Natural History Museum","container":{},"publicationYear":2024,"subjects":[{"subject":"bold"},{"subject":"coi barcoding"},{"subject":"gastropoda"},{"subject":"gbif"},{"subject":"global biodiversity"},{"subject":"marine"},{"subject":"worms"}],"contributors":[],"dates":[{"date":"2024-04-03 13:37:11.745375","dateType":"Created"},{"date":"2026-09-29 08:30:45.312027","dateType":"Updated"},{"date":"2024-04-05 00:00:00","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"Research"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["671177 kb"],"formats":["ZIP"],"version":null,"rightsList":[{"rightsUri":"http://www.opendefinition.org/licenses/cc-by","rightsIdentifier":"cc-by"}],"descriptions":[{"descriptionType":"Abstract","description":"This compressed repository contains the processed datasets of species occurrence records (from GBIF) that were taxon-matched (WoRMS/LifeWatch) and analysed in the manuscript \"Global species hotspots and COI barcoding cold spots of marine Gastropoda,\" as well as the R script used for analysis and visualisation. The datasets contain species occurrence metadata of both dated/undated and geotagged/ungeotagged records, taxonomic information of the accepted extant marine gastropod species, and the metadata of COI barcodes (deposited in BOLD) and of available complete mitochondrial genomes (NCBI). \r\n\r\nThe raw species occurrence dataset compiling all recorded, present (i.e., non-absent) occurrences of gastropods (N = 11,085,172) can be downloaded at the GBIF portal (https://doi.org/10.15468/dl.qgsn2b)."}],"geoLocations":[],"fundingReferences":[],"url":"https://data.nhm.ac.uk/dataset/d84dad11-9275-437f-aa6e-62465d130f46","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":"2024-04-05T12:32:08Z","registered":"2024-04-05T12:32:08Z","published":null,"updated":"2026-09-29T08:30:46Z"},"relationships":{"client":{"data":{"id":"bl.nhm","type":"clients"}}}},{"id":"10.15468/5cvxc3","type":"dois","attributes":{"doi":"10.15468/5cvxc3","identifiers":[{"identifier":"f9510502-780b-40e6-ad80-f09c5c6e638c","identifierType":"UUID"}],"creators":[{"affiliation":["Siberian Federal University"],"name":"Nikolay Stepanov","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0692-8796"}]},{"affiliation":["Lomonosov Moscow State University"],"name":"Alexey Seregin","nameIdentifiers":[]}],"titles":[{"title":"KRSU Herbarium: collections of vascular plants"}],"publisher":"Siberian Federal University","container":{},"publicationYear":2026,"subjects":[{"subject":"Occurrence","subjectScheme":"GBIF Dataset Type Vocabulary: http://rs.gbif.org/vocabulary/gbif/dataset_type.xml"},{"subject":"Specimen","subjectScheme":"GBIF Dataset Subtype Vocabulary: http://rs.gbif.org/vocabulary/gbif/dataset_subtype.xml"}],"contributors":[{"affiliation":["Lomonosov Moscow State University"],"name":"Alexey Seregin","contributorType":"DataCurator","nameIdentifiers":[]},{"affiliation":["Lomonosov Moscow State University"],"name":"Alexey Seregin","contributorType":"Editor","nameIdentifiers":[]},{"affiliation":["Lomonosov Moscow State University"],"name":"Alexey Seregin","contributorType":"RelatedPerson","nameIdentifiers":[]}],"dates":[{"date":"2024-12-18","dateType":"Created"},{"date":"2026-09-29","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"OCCURRENCE"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Vascular plants of KRSU Herbarium (Siberian Federal University, Krasnoyarsk, Russia). These data were published in the Moscow Digital Herbarium in 2024 and now available in GBIF.The Herbarium of the Siberian Federal University (KRSU) is located in Krasnoyarsk. The herbarium was founded in 1975, which coincides with the formation of the Department of Ecology of Krasnoyarsk State University. The basis of the processed inserted collections are student collections, mainly from summer field practice at the biological station, starting in 1968. After 1975, the collections began to be formalized accordingly, specimens began to arrive from different regions of the former USSR, where students and teachers worked in expeditions. Since 1984, the herbarium has been purposefully replenished with collections of N.V. Stepanov from various regions of Southern Siberia. In 1995, after the formation of the Department of Forest Biogeocenology, the herbarium was transferred to it and moved to a separate facility; staff units appeared. In 2007, the herbarium became an independent division within the Institute of Fundamental Biology and Biotechnology of the Siberian Federal University. The herbarium collections comprise over 30,000 specimens of vascular plants, 15,000–20,000 specimens of bryophytes and lichens; more than 100 type specimens. Currently, the KRSU Herbarium comprise materials reflecting the diversity of plants in the Krasnoyarsk Krai and other regions of Russia and the world. The Electronic Herbarium of the Siberian Federal University KRSU (http://krsu.sfu-kras.ru/) was created in 2013 and has not been updated since. In December 2024, these data were transferred to the Moscow Digital Herbarium (1,268 specimens) and published in GBIF."}],"geoLocations":[{"geoLocationBox":{"northBoundLatitude":90.0,"southBoundLatitude":-90.0,"eastBoundLongitude":180.0,"westBoundLongitude":-180.0},"geoLocationPlace":"World flora with emphasis on Krasnoyarsk Krai, Russia"}],"fundingReferences":[],"url":"https://www.gbif.org/dataset/f9510502-780b-40e6-ad80-f09c5c6e638c","contentUrl":null,"metadataVersion":69,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2024-12-18T09:35:50Z","registered":"2024-12-18T09:35:50Z","published":null,"updated":"2026-09-29T08:01:40Z"},"relationships":{"client":{"data":{"id":"gbif.gbif","type":"clients"}}}},{"id":"10.15468/5yb2az","type":"dois","attributes":{"doi":"10.15468/5yb2az","identifiers":[{"identifier":"60ec9e8a-c220-41c9-8968-096287c1f20f","identifierType":"UUID"}],"creators":[{"affiliation":["Universidade do Estado do Rio de Janeiro"],"name":"Sebastião Neto","nameIdentifiers":[]}],"titles":[{"title":"HRJ - Herbário da Universidade do Estado do Rio de Janeiro. Jabot - Banco de Dados da Flora Brasileira"}],"publisher":"Universidade do Estado do Rio de Janeiro","container":{},"publicationYear":2026,"subjects":[{"subject":"Occurrence","subjectScheme":"GBIF Dataset Type Vocabulary: http://rs.gbif.org/vocabulary/gbif/dataset_type.xml"},{"subject":"Specimen","subjectScheme":"GBIF Dataset Subtype Vocabulary: http://rs.gbif.org/vocabulary/gbif/dataset_subtype.xml"},{"subject":"Occurrence","subjectScheme":"GBIF Dataset Type Vocabulary: http://rs.gbif.org/vocabulary/gbif/dataset_type_2015-07-10.xml"}],"contributors":[{"affiliation":["Universidade do Estado do Rio de Janeiro"],"name":"Sebastião Neto","contributorType":"DataCurator","nameIdentifiers":[]},{"affiliation":["Universidade do Estado do Rio de Janeiro"],"name":"Sebastião Neto","contributorType":"RelatedPerson","nameIdentifiers":[]},{"affiliation":["Jardim Botânico do Rio de Janeiro"],"name":"Luís Alexandre Silva","contributorType":"RelatedPerson","nameIdentifiers":[]}],"dates":[{"date":"2024-11-07","dateType":"Created"},{"date":"2026-09-29","dateType":"Updated"}],"language":"pt","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"OCCURRENCE"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"O Herbário HRJ, criado em 1983, está vinculado ao Instituto de Biologia Roberto Alcantara Gomes (IBRAG) da Universidade do Estado do Rio de Janeiro (UERJ) e possui um acervo botânico constituído por cerca de 15.000 amostras de plantas distribuídas em coleções de Algas, Briófitas, Samambaias e Licófitas, Gimnospermas e Angiospermas. O acervo encontra-se 100% informatizado e os dados das amostras foram inseridos no banco de dados JABOT versão 2.0, cujas imagens das exsicatas ainda não foram digitalizadas. São cerca de 15.000 amostras dos distintos grupos de plantas e oriundas do Brasil e de outros países, das quais 11.170 estão determinadas em espécie, 1305 em gênero e o restante somente em família. Os estados com maiores representatividade no HRJ são Rio de Janeiro (11.434 amostras) e Minas Gerais (575). Briófitas, com 5257 amostras, é o grupo mais representativo, seguido de Algas (4422), Angiospermas (3230), Samambaias e Licófitas (128) e Gimnospermas (10). As famílias mais representadas são: Lejeuneaceae, 764 amostras (5.70%); Dictyotaceae, 672 (5.01%); Rhodomelaceae, 621 (4.63%); Hypnaceae, 582 (4.34%) e Sematophyllaceae, 574 (4.28%)."}],"geoLocations":[{"geoLocationBox":{"northBoundLatitude":-23.067,"southBoundLatitude":-23.249,"eastBoundLongitude":-44.063,"westBoundLongitude":-44.398},"geoLocationPlace":"Ilha Grande, município de Angra dos Reis, estado do Rio de Janeiro, Bioma Mata Atlântica"}],"fundingReferences":[],"url":"https://www.gbif.org/dataset/60ec9e8a-c220-41c9-8968-096287c1f20f","contentUrl":null,"metadataVersion":34,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2024-11-07T08:09:47Z","registered":"2024-11-07T08:09:47Z","published":null,"updated":"2026-09-29T07:45:40Z"},"relationships":{"client":{"data":{"id":"gbif.gbif","type":"clients"}}}}],"meta":{"total":13921294,"totalPages":400,"page":1},"links":{"self":"https://api.datacite.org/dois?registered=2024\u0026resource-type-id=dataset","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026registered=2024\u0026resource-type-id=dataset"}}