cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18430552
title: Supplementary data and software for Communications Earth and Environment paper by Joshi, Gallovič, Sgobba (2026)

description: Supplementary data and software for paper by Joshi, Gallovič, Sgobba (2026), Dynamic rupture complexity explains observed azimuthal variability in earthquake source radiation, Communications Earth and Environment:



fd3d_tsn_pt.zip: Code for earthquake dynamic rupture simulation

Event_data.zip: Data of all the 49 events used in the study

plot_event.py: Python script to reproduce the plots for all the events


The code fd3d_tsn_pt is under continuous development. For updates, see the GitHub repository specified in the paper.
DOI: 10.5281/zenodo.18430551
title: Supplementary data and software for Communications Earth and Environment paper by Joshi, Gallovič, Sgobba (2026)

description: Supplementary data and software for paper by Joshi, Gallovič, Sgobba (2026), Dynamic rupture complexity explains observed azimuthal variability in earthquake source radiation, Communications Earth and Environment:



fd3d_tsn_pt.zip: Code for earthquake dynamic rupture simulation

Event_data.zip: Data of all the 49 events used in the study

plot_event.py: Python script to reproduce the plots for all the events


The code fd3d_tsn_pt is under continuous development. For updates, see the GitHub repository specified in the paper.
DOI: 10.34740/kaggle/dsv/14719345
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.25921/bzmv-4w15
title: Water temperature and depth data from Loggerhead Sea turtle (Caretta caretta) dive behavior before, during, and after Hurricane Irene in the Northwest Atlantic Ocean in August and September 2011 (NCEI Accession 0310451)

description: In this study, we analyzed the movements and dive behavior of juvenile and adult-sized loggerhead sea turtles (n = 18) that were foraging in the Mid-Atlantic Bight (MAB) as Hurricane Irene moved through the region during August and September 2011. The satellite tags deployed on these turtles transmitted location data, dive behavior, sea surface temperature (SST), and temperature-depth profiles before, during and after the hurricane. A dictionary of terms describing the variables collected during the project is also included.
DOI: 10.25921/11gg-bz22
title: Integrated Marine Observing System (IMOS) sea surface and atmospheric fCO2 measurements in the South Pacific Ocean, Southern Ocean, Tasman Sea onboard R/V L'Astrolabe in 2025 (NCEI Accession 0311455)

description: This dataset includes surface underway, chemical, meteorological and physical data collected from R/V L'Astrolabe in the South Pacific Ocean, Southern Ocean, Tasman Sea in 2025. These data include partial pressure (or fugacity) of carbon dioxide (pCO2) in water and air, sea surface temperature, sea surface salinity, barometric pressure, wind direction and wind speed. The instruments used to collect these data include barometric pressure sensor and carbon dioxide (CO2) gas analyzer.
DOI: 10.5281/zenodo.17849157
title: WorldCereal crop calendars

description: This dataset contains the global crop calendar training and validation data used in the manuscript “Enhancing WorldCereal Crop Calendars with Land Surface Phenology and Machine Learning” (Moletto-Lobos et al., 2025). It includes harmonized reference crop calendar points derived from GEOGLAM Crop Monitor, USDA-FAS, JRC ASAP, and FAO sources, together with phenology-based samples retrieved from MODIS Aqua NDVI Land Surface Phenology (LSP) time series and ERA5-Land climate variables.

The dataset is organized into two main seasonal groups that follow the nomenclature used in the manuscript:





S1 = Winter crops (equivalent to winter cereals in the paper)

Includes reference and LSP-derived Start of Season (SOS) and End of Season (EOS) dates, including explicitly modeled dormancy periods.




S2 = Summer crops (equivalent to summer cropping systems in the paper)

Includes SOS and EOS samples for rainfed and monsoon-driven summer crops, corresponding to the “summer season” definition of the study.
DOI: 10.5281/zenodo.17866271
title: WorldCereal crop calendars

description: This dataset contains the global crop calendar training and validation data used in the manuscript “Enhancing WorldCereal Crop Calendars with Land Surface Phenology and Machine Learning” (Moletto-Lobos et al., 2025). It includes harmonized reference crop calendar points derived from GEOGLAM Crop Monitor, USDA-FAS, JRC ASAP, and FAO sources, together with phenology-based samples retrieved from MODIS Aqua NDVI Land Surface Phenology (LSP) time series and ERA5-Land climate variables.

The dataset is organized into two main seasonal groups that follow the nomenclature used in the manuscript:





S1 = Winter crops (equivalent to winter cereals in the paper)

Includes reference and LSP-derived Start of Season (SOS) and End of Season (EOS) dates, including explicitly modeled dormancy periods.




S2 = Summer crops (equivalent to summer cropping systems in the paper)

Includes SOS and EOS samples for rainfed and monsoon-driven summer crops, corresponding to the “summer season” definition of the study.
DOI: 10.5281/zenodo.18468289
title: Data for Kinetic NMR screening: rapidly quantifying fast ligand dissociation in fragment mixtures using ¹⁹F relaxation dispersion

description: Raw experimental NMR data
DOI: 10.5281/zenodo.18468290
title: Data for Kinetic NMR screening: rapidly quantifying fast ligand dissociation in fragment mixtures using ¹⁹F relaxation dispersion

description: Raw experimental NMR data
DOI: 10.25921/hbbh-0q04
title: NOAA Earth System Research Laboratory Surface Ozone Data, Version 2 from 1973-09-13 to 2024-12-31 (NCEI Accession 0312023)

description: This data set contains continuous UV photometric data of surface level ozone collected at 6m above ground level. Data records consist of UTC time, date, and processed ozone mixing ratio (parts per billion by volume). Data is collected from global locations and is provided in 1 hour averages. The data were produced and are available from the NOAA Earth System Research Laboratory (ESRL) Global Monitoring Laboratory, and the data are archived by the NOAA National Centers for Environmental Information (NCEI). Version 2 is a re-processing of the version 1 dataset with corrections for calibration and quality assurance errors. The re-processing was done based on historical calibration and quality control documentation.
DOI: 10.15468/dl.bf9e5g
title: Occurrence Download

description: A dataset containing 4909 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-7.29864 40.31545,-8.02727 38.19162,-7.36293 37.82013,-5.84138 38.14108,-4.64128 39.26158,-5.26276 39.98785,-7.29864 40.31545))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Alopochen aegyptiaca",
 "Year 2023-2026"
 ]
}

 The dataset includes 4909 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009498-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mzbpww
title: Occurrence Download

description: A dataset containing 17505 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Veronica arvensis, Veronica arvensis)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 17505 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009499-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2hx28x
title: Occurrence Download

description: A dataset containing 743 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ceratocarpus arenarius",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 743 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009500-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7wzr7v
title: Occurrence Download

description: A dataset containing 1122872 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Crataegus monogyna",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1122872 records from 3980 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009452-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gj6zgp
title: Occurrence Download

description: A dataset containing 12889 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Spizaetus tyrannus (Wied-Neuwied, 1820), Chlorochrysa nitidissima P.L.Sclater, 1874, Tyrannus melancholicus Vieillot, 1819, Cercomacroides parkeri (G.R.Graves, 1997), Ardea cocoi Linnaeus, 1766, Crypturellus duidae J.T.Zimmer, 1938, Ramphastos ambiguus Swainson, 1823, Ensifera ensifera (Boissonneau, 1840), Sturnella magna (Linnaeus, 1758), Hydropsalis maculicaudus (Lawrence, 1862))",
 "HasCoordinate is true",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Continent is one of (North America, South America)",
 {
 "not" : {
 "Issue" : [
 "is one of (Zero coordinate, 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, The scientific name ID was not used in backbone matching, The taxon concept ID was not used in backbone matching, Recorded date invalid, Recorded date mismatch, Recorded date unlikely, Basis of record invalid, Occurrence status unparsable, Individual count invalid, Individual count conflicts with occurrence status)"
 ]
 }
 }
 ]
}

 The dataset includes 12889 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014168-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wabzqu
title: Occurrence Download

description: A dataset containing 28447 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Canada",
 "Country is Canada"
 ]
 },
 "TaxonKey is one of (Oncorhynchus mykiss (Walbaum, 1792))"
 ]
}

 The dataset includes 28447 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014164-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1208408.10fu
title: SH1208408.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1208412.10fu
title: SH1208412.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1208413.10fu
title: SH1208413.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1208414.10fu
title: SH1208414.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1208415.10fu
title: SH1208415.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive169788.20250331.9.3
title: Arcanobacterium buesumense Alssahen et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169788.20251217.10
title: Arcanobacterium buesumense Alssahen et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/6nls9fxm
title: LA and SD

description: The dataset used in this paper for climate modeling with neural diffusion equations.
DOI: 10.13145/bacdive169789.20250331.9.3
title: Quadrisphaera setariae Kim et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169789.20251217.10
title: Quadrisphaera setariae Kim et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/kc7bfmxb
title: Glial-Neuronal Interactions

description: The dataset used in the paper describes the dynamics of metabolic resource transport across the network of glial cells, which can stabilize learning dynamics in neuronal networks.
DOI: 10.13145/bacdive169790.20240510.9
title: Glycomyces salinus Li et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169790.20251217.10
title: Glycomyces salinus Li et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169791.20250331.9.3
title: Occultella gossypii Ge et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/oisentkx
title: HNSCC-3DCT-RT Dataset

description: The HNSCC-3DCT-RT dataset is a real CT image of the human brain used for testing the proposed method.
DOI: 10.13145/bacdive169791.20251217.10
title: Occultella gossypii Ge et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169792.20250331.9.3
title: Ruania halotolerans Xu et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169792.20251217.10
title: Ruania halotolerans Xu et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/vridr2uf
title: Automatic semantic role labeling on non-revised syntactic trees

description: Semantic Role Labeling (SRL) is a Natural Language Processing task that enables the detection of events described in sentences and the participants of these events.
DOI: 10.13145/bacdive169793.20250331.9.3
title: Ruania suaedae Xu et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169793.20251217.10
title: Ruania suaedae Xu et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169794.20240510.9
title: Cellulomonas aurantiaca Kim et al. 2020

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169794.20251217.10
title: Cellulomonas aurantiaca Kim et al. 2020

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169795.20250331.9.3
title: Cellulomonas palmilytica Siriatcharanon et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169795.20251217.10
title: Cellulomonas palmilytica Siriatcharanon et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.57702/boxybka9
title: ToTTo

description: Controlled table-to-text generation seeks to generate natural language descriptions for content and entailed conclusions in tables.
DOI: 10.13145/bacdive169796.20250331.9.3
title: Cellulomonas dongxiuzhuiae Zhang et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive169796.20251217.10
title: Cellulomonas dongxiuzhuiae Zhang et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.5281/zenodo.18172959
title: Hidden Complexity in Reservoir Flow Regulation Revealed by Spectral Analysis

description: This dataset contains spectral regulation signatures and identified operational modes for 205 large reservoirs across the contiguous United States (CONUS). Derived from daily inflow and outflow observations, the data includes: (1) identified operational modes (Modes 1-4) reflecting distinct filtering philosophies; (2) regulation intensity (I); and (3) log10 power ratios across four temporal scales: synoptic (3-5 days), weekly (6-8 days), seasonal (300-420 days), and inter-annual (2-5 years). These metrics provide a novel frequency-domain perspective on how reservoirs modify streamflow variability.
DOI: 10.57702/x3ysbqeo
title: MedMNIST

description: The MedMNIST dataset is a large standardized biomedical image collection, including 12 number of 2D datasets and 6 number of 3D datasets.
DOI: 10.13145/bacdive169797.20250331.9.3
title: Cellulomonas fengjieae Zhang et al. 2022

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.