cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13318018
title: USDA NRCS PLANTS Database: USDA PLANTS images DwCA

description: The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories. It includes names, plant symbols, checklists, distributional data, species abstracts, characteristics, images, crop information, automated tools, onward Web links, and references. This information primarily promotes land conservation in the United States and its territories, but academic, educational, and general use is encouraged. PLANTS reduces government spending by minimizing duplication and making information exchange possible across agencies and disciplines.

Data published on EOL by the PLANTS database include attribute data, images and descriptive text.
description: The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories. It includes names, plant symbols, checklists, distributional data, species abstracts, characteristics, images, crop information, automated tools, onward Web links, and references. This information primarily promotes land conservation in the United States and its territories, but academic, educational, and general use is encouraged. PLANTS reduces government spending by minimizing duplication and making information exchange possible across agencies and disciplines.

Data published on EOL by the PLANTS database include attribute data, images and descriptive text.

This is primarily an image resource.


####--- __EOL DwCA resource last updated: Jun 11, 2024 12:57 PM__ ---####
This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13743941
title: USDA NRCS PLANTS Database: USDA PLANTS images DwCA

description: The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories. It includes names, plant symbols, checklists, distributional data, species abstracts, characteristics, images, crop information, automated tools, onward Web links, and references. This information primarily promotes land conservation in the United States and its territories, but academic, educational, and general use is encouraged. PLANTS reduces government spending by minimizing duplication and making information exchange possible across agencies and disciplines. Data published on EOL by the PLANTS database include attribute data, images and descriptive text.

This is primarily an image resource.
Captured data during API bulk updates: {"United States Department of Agriculture":{"ror":"01na82s61","isni":"0000 0004 0478 6311"}}
DOI: 10.5281/zenodo.13886436
title: USDA NRCS PLANTS Database: USDA PLANTS images DwCA

description: The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories. It includes names, plant symbols, checklists, distributional data, species abstracts, characteristics, images, crop information, automated tools, onward Web links, and references. This information primarily promotes land conservation in the United States and its territories, but academic, educational, and general use is encouraged. PLANTS reduces government spending by minimizing duplication and making information exchange possible across agencies and disciplines. Data published on EOL by the PLANTS database include attribute data, images and descriptive text.

This is primarily an image resource.

 

Captured data during API bulk updates: {"United States Department of Agriculture":{"ror":"01na82s61","isni":"0000 0004 0478 6311"}}
DOI: 10.5281/zenodo.15577659
title: USDA NRCS PLANTS Database: USDA PLANTS images DwCA

description: The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories. It includes names, plant symbols, checklists, distributional data, species abstracts, characteristics, images, crop information, automated tools, onward Web links, and references. This information primarily promotes land conservation in the United States and its territories, but academic, educational, and general use is encouraged. PLANTS reduces government spending by minimizing duplication and making information exchange possible across agencies and disciplines. Data published on EOL by the PLANTS database include attribute data, images and descriptive text.

This is primarily an image resource.

 

Captured data during API bulk updates: {"United States Department of Agriculture":{"ror":"01na82s61","isni":"0000 0004 0478 6311"}}
DOI: 10.5281/zenodo.15582450
title: USDA NRCS PLANTS Database: USDA PLANTS images DwCA

description: The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories. It includes names, plant symbols, checklists, distributional data, species abstracts, characteristics, images, crop information, automated tools, onward Web links, and references. This information primarily promotes land conservation in the United States and its territories, but academic, educational, and general use is encouraged. PLANTS reduces government spending by minimizing duplication and making information exchange possible across agencies and disciplines. Data published on EOL by the PLANTS database include attribute data, images and descriptive text.

This is primarily an image resource.

 

Captured data during API bulk updates: {"United States Department of Agriculture":{"ror":"01na82s61","isni":"0000 0004 0478 6311"}}
DOI: 10.15468/dl.x6n7dd
title: Occurrence Download

description: A dataset containing 8936 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.2_1",
 "TaxonKey is Spilopelia chinensis (Scopoli, 1786)",
 "Year 2020-2025"
 ]
}

 The dataset includes 8936 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017253-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15108531
title: Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods.

description: This repository is for the paper Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods. by Aina Zhang. It is associated with the manuscript number AAS59841.
DOI: 10.5281/zenodo.15545272
title: Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods.

description: This repository is for the paper Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods. by Aina Zhang. It is associated with the manuscript number AAS59841R2.
DOI: 10.15468/dl.xpeue8
title: Occurrence Download

description: A dataset containing 60 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 GBIF_WITHIN('POLYGON((19.85277 34.74421,56.14723 34.74421,56.14723 49.25579,19.85277 49.25579,19.85277 34.74421))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.taxonkey = 4164146 OR occurrence.acceptedtaxonkey = 4164146 OR occurrence.kingdomkey = 4164146 OR occurrence.phylumkey = 4164146 OR occurrence.classkey = 4164146 OR occurrence.orderkey = 4164146 OR occurrence.familykey = 4164146 OR occurrence.genuskey = 4164146 OR occurrence.specieskey = 4164146)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 60 records were derived from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017246-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17617/3.p0phtc
title: Automated atmospheric profiling with the Robotic Lift (RoLi) at the Amazon Tall Tower Observatory

description: An automatic robotic Lift (RoLi) was used to measure vertical profiles (8.3 - 318.3 m) of basic meteorological parameters and atmospheric aerosol characteristics at the 325 m tall ATTO Tower, located at the Amazon Tall Tower Observatory in Brazil (ATTO, -59.005626 E, -2.145898 N). 
This dataset includes exemplary measurements collected on January 8, 2023, and between April 24 and April 28, 2024.
The technical information about RoLi can be found at 'Automated atmospheric profiling with the Robotic Lift (RoLi) at the Amazon Tall Tower Observatory', Brill et al., 2025 (https://doi.org/10.5194/egusphere-2025-295).
description: File format: Files are ASCII files containing tab-separated tables, cf "https://doi.org/10.17617/3.10" for details.
DOI: 10.15468/dl.kyyfyp
title: Occurrence Download

description: A dataset containing 1297 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Atractylodes lancea (Thunb.) DC."
 ]
}

 The dataset includes 1297 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017256-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5mnjzq
title: Occurrence Download

description: A dataset containing 24695 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Stephania Lour."
 ]
}

 The dataset includes 24695 records from 262 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017257-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17043/tarfala-2
title: Data from Tarfala Research Station, Kebnekaise mountains, northern Sweden
DOI: 10.6084/m9.figshare.29222060
title: Supplementary Material for: A risk score prognostic model based on four ubiquitination and immune-related genes in sepsis for outcome prediction and treatment guidance

description: Introduction: Ubiquitination and immune regulation play an important role in sepsis. The purpose of this study is to explore the potential value of ubiquitination and immune related genes in sepsis and develop a risk score prognostic model based on sepsis ubiquitination and immune related genes for accurate outcome prediction and better treatment guidance.
Methods: Differential expression and univariate Cox analyses were used to identify ubiquitination and immune-related DEGs related to prognosis, and then a risk model was constructed using LASSO regression analysis. Subsequently, Kaplan-Meier analysis, time-dependent ROC curve analysis, immune cell infiltration analysis, functional enrichment analysis, drug prediction and molecular docking were performed.
Results: A total of 4 ubiquitination and immune-related DEGs related to the prognosis of sepsis were identified. A risk score model was constructed based on these 4 genes. The proportion of death samples in the high risk score group was higher and the OS was worse. The risk score was an independent prognostic factor for sepsis. The time-dependent ROC curve indicated that the risk score model had good predictive ability. The results of ssGSEA and GSEA showed that most immune cell infiltration levels decreased and immune and inflammation related pathways showed inhibitory states in the high risk group. In addition, 7 protein-drug docking results were obtained. The binding energy of LCK and JNJ-26483327 was the lowest.
Conclusion: The 4 ubiquitination and immune-related model genes may play an important role in sepsis by regulating immune cell infiltration and immune and inflammatory related pathways. The model constructed based on these 4 genes has good predictive value, which may help clinical doctors better evaluate the prognosis of sepsis patients and develop personalized treatment plans.
DOI: 10.6084/m9.figshare.29222060.v1
title: Supplementary Material for: A risk score prognostic model based on four ubiquitination and immune-related genes in sepsis for outcome prediction and treatment guidance

description: Introduction: Ubiquitination and immune regulation play an important role in sepsis. The purpose of this study is to explore the potential value of ubiquitination and immune related genes in sepsis and develop a risk score prognostic model based on sepsis ubiquitination and immune related genes for accurate outcome prediction and better treatment guidance.
Methods: Differential expression and univariate Cox analyses were used to identify ubiquitination and immune-related DEGs related to prognosis, and then a risk model was constructed using LASSO regression analysis. Subsequently, Kaplan-Meier analysis, time-dependent ROC curve analysis, immune cell infiltration analysis, functional enrichment analysis, drug prediction and molecular docking were performed.
Results: A total of 4 ubiquitination and immune-related DEGs related to the prognosis of sepsis were identified. A risk score model was constructed based on these 4 genes. The proportion of death samples in the high risk score group was higher and the OS was worse. The risk score was an independent prognostic factor for sepsis. The time-dependent ROC curve indicated that the risk score model had good predictive ability. The results of ssGSEA and GSEA showed that most immune cell infiltration levels decreased and immune and inflammation related pathways showed inhibitory states in the high risk group. In addition, 7 protein-drug docking results were obtained. The binding energy of LCK and JNJ-26483327 was the lowest.
Conclusion: The 4 ubiquitination and immune-related model genes may play an important role in sepsis by regulating immune cell infiltration and immune and inflammatory related pathways. The model constructed based on these 4 genes has good predictive value, which may help clinical doctors better evaluate the prognosis of sepsis patients and develop personalized treatment plans.
DOI: 10.15468/dl.t89aw7
title: Occurrence Download

description: A dataset containing 65827 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Mycobacterium Lehmann & Neumann, 1896"
 ]
}

 The dataset includes 65827 records from 508 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017247-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rvusvt
title: Occurrence Download

description: A dataset containing 6545 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.2_1",
 "TaxonKey is Turdus mandarinus Bonaparte, 1850",
 "Year 2020-2025"
 ]
}

 The dataset includes 6545 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017260-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xhj9nn
title: Occurrence Download

description: A dataset containing 75 records in GBIF matching the query:
SELECT "year", GBIF_EEARGCode( 1000, decimalLatitude, decimalLongitude, COALESCE(coordinateUncertaintyInMeters, 1000) ) AS eeaCellCode, classKey, class, genusKey, genus, COUNT(*) AS occurrences, MIN(COALESCE(coordinateUncertaintyInMeters, 1000)) AS minCoordinateUncertaintyInMeters, MIN(GBIF_TemporalUncertainty(eventDate, null)) AS minTemporalUncertainty FROM occurrence WHERE occurrenceStatus = 'PRESENT' AND genusKey IN (2284443, 2202786, 2161911, 2161467, 7652364, 2176168, 3092125, 2771094, 8782549, 2457378, 2453882, 2457825, 2454645, 2465626, 2466496, 2459675, 2444141, 2444221, 2447418, 2441658, 2442664, 3266899, 2473221, 2445496, 5221394, 2443339, 4553469) AND countrycode = 'BE' AND "year" &gt;= 1900 AND hasCoordinate = TRUE AND genusKey IS NOT NULL AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID') AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH') AND (LOWER(identificationVerificationStatus) NOT IN ( 'unverified', 'unvalidated', 'not validated', 'under validation', 'not able to validate', 'control could not be conclusive due to insufficient knowledge', 'uncertain', 'unconfirmed', 'unconfirmed - not reviewed', 'validation requested' ) OR identificationVerificationStatus IS NULL) AND COALESCE(coordinateUncertaintyInMeters, 1000) &lt;= 10000 GROUP BY "year", eeaCellCode, classKey, class, genusKey, genus ORDER BY "year" DESC, eeaCellCode ASC, genusKey ASC;

 The dataset's 75 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017255-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17909/q9xf-tx22
title: NGC4388 HST IR
DOI: 10.15468/dl.paahbh
title: Occurrence Download

description: A dataset containing 36448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Streptomyces Waksman & Henrici, 1943"
 ]
}

 The dataset includes 36448 records from 425 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017262-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s2bahf
title: Occurrence Download

description: A dataset containing 2471136 records in GBIF matching the query:
SELECT "year", GBIF_EEARGCode( 1000, decimalLatitude, decimalLongitude, COALESCE(coordinateUncertaintyInMeters, 1000) ) AS eeaCellCode, classKey, class, COUNT(*) AS occurrences, MIN(COALESCE(coordinateUncertaintyInMeters, 1000)) AS minCoordinateUncertaintyInMeters, MIN(GBIF_TemporalUncertainty(eventDate, null)) AS minTemporalUncertainty FROM occurrence WHERE occurrenceStatus = 'PRESENT' AND countrycode = 'BE' AND "year" &gt;= 1900 AND hasCoordinate = TRUE AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID') AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH') AND (LOWER(identificationVerificationStatus) NOT IN ( 'unverified', 'unvalidated', 'not validated', 'under validation', 'not able to validate', 'control could not be conclusive due to insufficient knowledge', 'uncertain', 'unconfirmed', 'unconfirmed - not reviewed', 'validation requested' ) OR identificationVerificationStatus IS NULL)AND classKey IN (126, 131, 137, 139, 148, 151, 179, 183, 186, 194, 196, 199, 203, 205, 206, 212, 216, 220, 225, 229, 232, 244, 245, 252, 255, 256, 270, 281, 282, 307, 327, 328, 354, 356, 359, 360, 361, 365, 367, 7073593, 7228684, 7742773, 7774442, 7947184, 8179345, 9273948, 10689325, 11133537, 11418114, 11493978, 11545536, 11592253)AND COALESCE(coordinateUncertaintyInMeters, 1000) &lt;= 10000GROUP BY "year", eeaCellCode, classKey, class ORDER BY "year" DESC, eeaCellCode ASC, classKey ASC;

 The dataset's 2471136 records were derived from 675 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017251-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12011725
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.31223635
title: Alumni vs. Non-Alumni Faculty Hiring and Research Productivity in Chinese 985 Business Schools

description: This dataset contains faculty-level panel data from 985 business schools in China, used to examine whether alumni versus non-alumni hiring status affects research productivity. The sample covers 1,063 faculty members observed over six years post-employment (tenure year 1–6), yielding faculty-year observations. Faculty were employed between 2006 and 2017. Publication and citation data were matched from Web of Science (SSCI) and CNKI (CSSCI). School-level variables include prestige scores from the MOE fourth-round subject assessment, departmental diversity (Shannon index and inverse HHI), grant funding, and faculty composition measures. Regional variables include the Fan Gang Marketization Index, GDP per capita, and R&D intensity. The codebook (codebook.xlsx) documents all 51 variables, their definitions, types, and value ranges.
DOI: 10.6084/m9.figshare.31223635.v1
title: Alumni vs. Non-Alumni Faculty Hiring and Research Productivity in Chinese 985 Business Schools

description: This dataset contains faculty-level panel data from 985 business schools in China, used to examine whether alumni versus non-alumni hiring status affects research productivity. The sample covers 1,063 faculty members observed over six years post-employment (tenure year 1–6), yielding faculty-year observations. Faculty were employed between 2006 and 2017. Publication and citation data were matched from Web of Science (SSCI) and CNKI (CSSCI). School-level variables include prestige scores from the MOE fourth-round subject assessment, departmental diversity (Shannon index and inverse HHI), grant funding, and faculty composition measures. Regional variables include the Fan Gang Marketization Index, GDP per capita, and R&D intensity. The codebook (codebook.xlsx) documents all 51 variables, their definitions, types, and value ranges.
DOI: 10.15468/dl.r8wkv7
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rafflesia cantleyi",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004671-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18426870
title: Morphogen-dependent anemia: BMP10 couples vascular morphogenesis with erythroid differentiation and iron metabolism