cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2txvtm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Glossogobius giuris",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034552-251025141854904/datasets/export for details.
DOI: 10.15468/dl.rcksuw
title: Occurrence Download

description: A dataset containing 221 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.7188 23.24913, -108.5688 23.24913, -108.5688 23.39913, -108.7188 23.39913, -108.7188 23.24913))"
 ]
}

 The dataset includes 221 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034553-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rm72wk
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "StateProvince is one of (四川, 四川省)",
 "TaxonKey is Sonchus asper"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034554-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s9n2c9
title: Occurrence Download

description: A dataset containing 20047 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.34_1",
 "Geometry POLYGON((-79.61602 35.09274,-79.63108 35.10662,-79.64584 35.11872,-79.65351 35.12935,-79.67181 35.1261,-79.68657 35.12256,-79.70074 35.11488,-79.69868 35.10632,-79.68805 35.09599,-79.67624 35.09629,-79.68451 35.08389,-79.69779 35.07858,-79.70163 35.06293,-79.69986 35.04817,-79.69395 35.02898,-79.67742 35.03489,-79.6603 35.03695,-79.6665 35.02426,-79.66503 35.01393,-79.64554 35.00566,-79.62547 34.98677,-79.61396 34.98766,-79.58473 34.99504,-79.57735 34.99947,-79.56584 35.01334,-79.57322 35.02397,-79.58633 35.02456,-79.60316 35.01777,-79.61674 35.02426,-79.58633 35.03754,-79.58692 35.04788,-79.57659 35.04817,-79.57187 35.03194,-79.55888 35.03371,-79.55386 35.03813,-79.56449 35.05348,-79.56242 35.06204,-79.56331 35.07238,-79.5701 35.07946,-79.61602 35.09274))",
 "OccurrenceStatus is Present",
 "Year 2015-2025"
 ]
}

 The dataset includes 20047 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034555-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e2xwq9
title: Occurrence Download

description: A dataset containing 4650 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Siparuna)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2025",
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Country is one of (Colombia)",
 {
 "not" : {
 "Issue" : [
 "is one of (Zero coordinate, Country coordinate mismatch, Coordinate invalid, Coordinate out of range, Geodetic datum invalid, Coordinate reprojection suspicious, Coordinate reprojection failed, Coordinate precision invalid, Presumed negated longitude, Presumed negated latitude, Presumed swapped coordinate, Taxon match higher rank, Taxon match none, The scientific name differs on the record from that found in the checklist, The backbone match found using the taxon IDs differs from what would be found if using the scientific names, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, Recorded date invalid, Recorded date mismatch, Recorded date unlikely, Basis of record invalid, Occurrence status unparsable)"
 ]
 }
 }
 ]
}

 The dataset includes 4650 records from 218 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034556-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17382614
title: Migraine Dataset Derived from Claims Database for Drug Repositioning Using Genetic Evidence

description: This dataset contains de-identified patient-level records derived from the JMDC Claims Database, focusing on migraine diagnoses, comorbidities, and drug prescriptions. It was curated for the study “Integrated analysis for drug repositioning targeting migraine using genetic evidence and claims database” (doi: 10.3389/fdata.2025.1677167).

In this study, drug candidates were identified using published GWAS results and perturbagen profiles obtained from the LINCS L1000 dataset. These candidates were subsequently evaluated using routinely collected claims data from the JMDC Claims Database.

The dataset is intended for statistical analysis and is structured in a long format, where each row corresponds to an individual patient at a specific time point. Variables include demographic information, comorbidities, migraine-related drug exposures, other relevant medications, and temporal indicators.

The full raw data cannot be publicly shared due to data privacy, but may be made available upon reasonable request to the corresponding author (Shoichiro Inokuchi; shoichiro.inokuchi.nhy@jmdc.co.jp).
DOI: 10.5281/zenodo.17382613
title: Migraine Dataset Derived from Claims Database for Drug Repositioning Using Genetic Evidence

description: This dataset contains de-identified patient-level records derived from the JMDC Claims Database, focusing on migraine diagnoses, comorbidities, and drug prescriptions. It was curated for the study “Integrated analysis for drug repositioning targeting migraine using genetic evidence and claims database” (doi: 10.3389/fdata.2025.1677167).

In this study, drug candidates were identified using published GWAS results and perturbagen profiles obtained from the LINCS L1000 dataset. These candidates were subsequently evaluated using routinely collected claims data from the JMDC Claims Database.

The dataset is intended for statistical analysis and is structured in a long format, where each row corresponds to an individual patient at a specific time point. Variables include demographic information, comorbidities, migraine-related drug exposures, other relevant medications, and temporal indicators.

The full raw data cannot be publicly shared due to data privacy, but may be made available upon reasonable request to the corresponding author (Shoichiro Inokuchi; shoichiro.inokuchi.nhy@jmdc.co.jp).
DOI: 10.15468/dl.7npesx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Glossolepis incisus",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034557-251025141854904/datasets/export for details.
DOI: 10.15468/dl.uetykj
title: Occurrence Download

description: A dataset containing 842 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.5688 23.24913, -108.4188 23.24913, -108.4188 23.39913, -108.5688 23.39913, -108.5688 23.24913))"
 ]
}

 The dataset includes 842 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034558-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17613831
title: SMILES Biohack 2025 Munich
DOI: 10.34740/kaggle/dsv/13737797
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.wcuk7p
title: Occurrence Download

description: A dataset containing 223 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Iran (Islamic Republic of)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Calopteryx splendens"
 ]
}

 The dataset includes 223 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034559-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17613385
title: Underwater images collected by a Kite surf in Le-Morne-With-Aerial, Mauritius - 2018-04-07

description: This dataset was collected by a Kite surf in Le-Morne-With-Aerial, Mauritius - 2018-04-07. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.15468/dl.hwhjcv
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pterygoplichthys gibbiceps",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034560-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b25c9h
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.4188 23.24913, -108.2688 23.24913, -108.2688 23.39913, -108.4188 23.39913, -108.4188 23.24913))"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034561-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17595874
title: Public procurement cartels: A large-sample testing of screens using machine learning – Dataset

description: This release provides a comprehensive public procurement dataset for replication and prediction of cartel-detection models in the "Public procurement cartels: A large-sample testing of screens using machine learning" publication. The dataset combines around 3 million contracts from seven European countries—Bulgaria, France, Hungary, Latvia, Portugal, Spain, and Sweden—covering the period from 2004 to 2021.The data were collected from official government publication portals and open data repositories, primarily opentender.eu, and harmonized into a consistent format to enable cross-country comparisons despite differing original data structures.

Each record includes detailed bid-level information, beginning with anonymized buyer and supplier identifiers and CPV-based product classifications. The dataset also records the total number of bids submitted for each contract and the corresponding tender publication and award dates. In addition, it reports bid prices and estimated contract values, along with binary indicators that capture key bidding characteristics such as whether the bid won, whether it involved subcontracting, and whether the supplier participated as part of a consortium. A detailed summary of availble variables is provided in the README.

A critical feature of the dataset is the integration of confirmed cartel case information, sourced from competition authorities’ court rulings and official reports. Cartel cases are linked to procurement contracts through company names and cartel activity periods. While exact identification of rigged contracts remains challenging, contracts awarded to cartel-involved firms during their documented collusion periods are labeled accordingly to facilitate analysis of cartel behavior.

The final dataset comprises 73 confirmed cartel cases and over 15,000 contracts awarded to cartel members. Additionaly, we also include the unlabelled contract level data to be used for prediction. It includes multiple cartel screen capturing pricing irregularities and bidding patterns consistent with collusion, aggregated at both market and company-year levels. These indicators support machine learning models that distinguish between collusive and competitive procurement activity.

This dataset is a valuable resource for researchers, policymakers, and competition authorities focused on detecting anti-competitive practices in public procurement. Its standardized structure and continuous data coverage allow for ongoing application in cartel screening, market monitoring, and supporting competition authorities.

For a comprehensive data description, please refer to the README or more details can be found in this publication (https://www.govtransparency.eu/wp-content/uploads/2023/04/Fazekas-et-al_PP-cartel-detection_GTI-WP_2023.pdf)

The work was generously supported by the Swedish Competition Authority (grant nr. KKV 72/2018-Ab-1) and the World Bank (Bulgaria - Country Economic Memorandum. 2023).
DOI: 10.5281/zenodo.17595875
title: Public procurement cartels: A large-sample testing of screens using machine learning – Dataset

description: This release provides a comprehensive public procurement dataset for replication and prediction of cartel-detection models in the "Public procurement cartels: A large-sample testing of screens using machine learning" publication. The dataset combines around 3 million contracts from seven European countries—Bulgaria, France, Hungary, Latvia, Portugal, Spain, and Sweden—covering the period from 2004 to 2021.The data were collected from official government publication portals and open data repositories, primarily opentender.eu, and harmonized into a consistent format to enable cross-country comparisons despite differing original data structures.

Each record includes detailed bid-level information, beginning with anonymized buyer and supplier identifiers and CPV-based product classifications. The dataset also records the total number of bids submitted for each contract and the corresponding tender publication and award dates. In addition, it reports bid prices and estimated contract values, along with binary indicators that capture key bidding characteristics such as whether the bid won, whether it involved subcontracting, and whether the supplier participated as part of a consortium. A detailed summary of availble variables is provided in the README.

A critical feature of the dataset is the integration of confirmed cartel case information, sourced from competition authorities’ court rulings and official reports. Cartel cases are linked to procurement contracts through company names and cartel activity periods. While exact identification of rigged contracts remains challenging, contracts awarded to cartel-involved firms during their documented collusion periods are labeled accordingly to facilitate analysis of cartel behavior.

The final dataset comprises 73 confirmed cartel cases and over 15,000 contracts awarded to cartel members. Additionaly, we also include the unlabelled contract level data to be used for prediction. It includes multiple cartel screen capturing pricing irregularities and bidding patterns consistent with collusion, aggregated at both market and company-year levels. These indicators support machine learning models that distinguish between collusive and competitive procurement activity.

This dataset is a valuable resource for researchers, policymakers, and competition authorities focused on detecting anti-competitive practices in public procurement. Its standardized structure and continuous data coverage allow for ongoing application in cartel screening, market monitoring, and supporting competition authorities.

For a comprehensive data description, please refer to the README or more details can be found in this publication (https://www.govtransparency.eu/wp-content/uploads/2023/04/Fazekas-et-al_PP-cartel-detection_GTI-WP_2023.pdf)

The work was generously supported by the Swedish Competition Authority (grant nr. KKV 72/2018-Ab-1) and the World Bank (Bulgaria - Country Economic Memorandum. 2023).
DOI: 10.5281/zenodo.17613820
title: EPT_scientometrics_Brazil_1980_2025
title: Scientometric dataset on Ephemeroptera, Plecoptera and Trichoptera (EPT) research across Brazilian biomes (1980–2025)

description: This dataset contains the complete bibliometric database used in the study “Scientometric insights into gaps in aquatic insect research across Neotropical biomes: implications for conservation policy”. The dataset compiles information extracted from the Web of Science (WoS) and Scopus databases following a standardized and reproducible search protocol. After duplicate removal and application of predefined inclusion and exclusion criteria, a total of 395 records were retained for analysis.

The final dataset includes detailed metadata for each publication, encompassing bibliographic, geographic, institutional, and thematic variables relevant to scientometric and biodiversity research. Variables include authorship information, affiliations, study location, biomes, publication metrics, thematic categories, citation data, funding sources, and research approaches. The data support analyses on spatial distribution of studies, research effort, knowledge gaps, thematic structure, collaboration networks, and temporal trends in EPT research across Brazil.

A complete data dictionary (DATA DICTIONARY — EPT_scientometrics_Brazil_1980_2025 Dataset.xlsx) accompanies the dataset, providing standardized definitions for all 56 variables, including data type, description, scope, and controlled vocabulary. A README file is also included, documenting data sources, filtering procedures, variable definitions, and methodological considerations necessary for full reproducibility.

The dataset is provided as an Excel file (EPT_scientometrics_Brazil_1980_2025.xlsx) in its cleaned and structured form, suitable for scientometric analyses, ecological synthesis, network analyses, and biodiversity research. These data support transparency, reproducibility, and reuse in studies addressing freshwater biodiversity, aquatic entomology, conservation planning, and environmental policy.
DOI: 10.5281/zenodo.17613821
title: EPT_scientometrics_Brazil_1980_2025
title: Scientometric dataset on Ephemeroptera, Plecoptera and Trichoptera (EPT) research across Brazilian biomes (1980–2025)

description: This dataset contains the complete bibliometric database used in the study “Scientometric insights into gaps in aquatic insect research across Neotropical biomes: implications for conservation policy”. The dataset compiles information extracted from the Web of Science (WoS) and Scopus databases following a standardized and reproducible search protocol. After duplicate removal and application of predefined inclusion and exclusion criteria, a total of 395 records were retained for analysis.

The final dataset includes detailed metadata for each publication, encompassing bibliographic, geographic, institutional, and thematic variables relevant to scientometric and biodiversity research. Variables include authorship information, affiliations, study location, biomes, publication metrics, thematic categories, citation data, funding sources, and research approaches. The data support analyses on spatial distribution of studies, research effort, knowledge gaps, thematic structure, collaboration networks, and temporal trends in EPT research across Brazil.

A complete data dictionary (DATA DICTIONARY — EPT_scientometrics_Brazil_1980_2025 Dataset.xlsx) accompanies the dataset, providing standardized definitions for all 56 variables, including data type, description, scope, and controlled vocabulary. A README file is also included, documenting data sources, filtering procedures, variable definitions, and methodological considerations necessary for full reproducibility.

The dataset is provided as an Excel file (EPT_scientometrics_Brazil_1980_2025.xlsx) in its cleaned and structured form, suitable for scientometric analyses, ecological synthesis, network analyses, and biodiversity research. These data support transparency, reproducibility, and reuse in studies addressing freshwater biodiversity, aquatic entomology, conservation planning, and environmental policy.
DOI: 10.15468/dl.anabps
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gnathonemus petersii",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034562-251025141854904/datasets/export for details.
DOI: 10.15468/dl.dnwkjc
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-108.2688 23.24913, -108.1188 23.24913, -108.1188 23.39913, -108.2688 23.39913, -108.2688 23.24913))"
 ]
}

 The dataset includes 16 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034563-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gs7umt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gnathopogon caerulescens",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034565-251025141854904/datasets/export for details.