cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4h934n
title: Occurrence Download

description: A dataset containing 122 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alpheus pachychirus Stimpson, 1860"
 ]
}

 The dataset includes 122 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053540-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.96zsf6
title: Occurrence Download

description: A dataset containing 24490 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year is greater than or equal to 1970",
 "TaxonKey is Philaenus spumarius (Linnaeus, 1758)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 24490 records from 338 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0435731-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1050101.10fu
title: SH1050101.10FU

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/sh1050116.10fu
title: SH1050116.10FU

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/sh1050125.10fu
title: SH1050125.10FU

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/sh1050149.10fu
title: SH1050149.10FU

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: 10.15468/dl.ez33ne
title: Occurrence Download

description: A dataset containing 123603 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Gallinula chloropus (Linnaeus, 1758)",
 "Year 2024-2025"
 ]
}

 The dataset includes 123603 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000660-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nq7psx
title: Occurrence Download

description: A dataset containing 33634 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erigeron philadelphicus L."
 ]
}

 The dataset includes 33634 records from 379 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000676-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.22786244
title: Data and analysis to accompany the article "Submovements in manual tracking: People with Parkinson’s disease produce more submovements than age-matched controls" by Noy et al., 2025

description: This is the data from the paper "Submovements in manual tracking: People with Parkinson's disease produce more submovements than age-matched controls" by Noy et al., 2025, published in the Journal of NeuroEngineering and RehabilitationThe folder "results" contains the raw data collected from the tablet (the .csv files) where column 1 is the x (left-right) data analyzed in the paper. Column 6 contains the time.The folder "stimuli" contains the stimuli (which were shown at 60 Hz), 1 line per frame. Each line describes the x-y position of the ellipseThe .mat files contain the processed data:tablet_pixels.mat contains the resampled data and stimuliregistrationTablet.mat contains the registration (to match the data to the stimuli, used for the error calculations)ampfreq.mat contains the error measures, by subject / trialTo run the matlab files:first run startup.mThen, the matlab file makeMirrorGameGraphs.m generates the graphs from the paperThe matlab file allsubjects.m loads the raw experimental dataIf you have any questions about the data or analysis, please contact Prof. Jason Friedman, Tel Aviv University: jason@tauex.tau.ac.il
DOI: 10.5281/zenodo.14928645
title: The effect of self-depletion on memory integration: the role of positive emotions
DOI: 10.5281/zenodo.14928646
title: The effect of self-depletion on memory integration: the role of positive emotions
DOI: 10.15468/dl.rjuxga
title: Occurrence Download

description: A dataset containing 307567 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Columba palumbus Linnaeus, 1758",
 "Year 2024-2025"
 ]
}

 The dataset includes 307567 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000659-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cbmgt2
title: Occurrence Download

description: A dataset containing 119 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Ursus thibetanus G"
 ]
}

 The dataset includes 119 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000678-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/cqh1-8908
title: National biodiversity data for ecosystem accounting in Australia: 1990 to 2022

description: This collection comprises biodiversity data for Australia for use in national ecosystem accounts. The data provided are spatial data and summary tables of predicted status of different biodiversity features across Australia over time. Data are provided separately for two types of biodiversity features: (1) nationally listed threatened species; (2) all native species in broad taxonomic groups (vascular plants, birds, reptiles, fungi). The status of all biodiversity features has been estimated annually, for each year from 1990 to 2022.

The data are generated using a habitat-based biodiversity assessment approach, where expected spatial patterns of biodiversity in reference condition are combined with a time series of spatial patterns of in ecosystem condition. For all biodiversity features, annual patterns of expected (or potential) status were derived by combining remotely sensed data with information on inferred reference patterns in biodiversity (pre-European), incorporating field observation data.

These data align with other data prepared for Australia under the National Ecosystem Accounting Project, including for ecosystem extent, ecosystem condition and ecosystem services.

The biodiversity data comprise spatial raster layers and summary tables of predicted status of different biodiversity features across Australia over time. Also provided are supplementary data tables, maps and charts.
DOI: 10.25919/nzrp-6702
title: National data for Australia's ecosystem services: fisheries biomass provisioning services (2000-01 to 2020-21)

description: This collection comprises data for fisheries biomass provisioning services for the purposes of national ecosystem services accounts. Only Commonwealth-managed fisheries are considered in the first release of the accounts. The Fisheries Biomass Provisioning Services input data comprise information on both physical and monetary values associated with the ecosystem service by Integrated Marine and Coastal Regionalisation of Australia (IMCRA) region (Integrated Marine and Coastal Regionalisation of Australia Provincial Bioregions, IUCN Global Ecosystem Typology ecosystem functional group and a combination of both. Also provided are supplementary data tables.
DOI: 10.15468/dl.x95fys
title: Occurrence Download

description: A dataset containing 351 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Capra hircus Linnaeus, 1758"
 ]
}

 The dataset includes 351 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000682-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yfcp5j
title: Occurrence Download

description: A dataset containing 13637 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hibiscus tiliaceus L."
 ]
}

 The dataset includes 13637 records from 329 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000681-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uzhrpf
title: Occurrence Download

description: A dataset containing 87580 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Continent is one of (Asia, Europe, Africa)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Sus scrofa Linnaeus, 1758",
 "Year 1981-2010"
 ]
}

 The dataset includes 87580 records from 464 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000680-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25502/nqeb-q165/d
title: IPM of cassava whiteflies and whitefly-transmitted viruses through farmer participatory trials using insecticidal cutting
DOI: 10.25919/7jj7-8826
title: National data for Australia's ecosystem services: irrigated agricultural water supply (250 m resolution): 2000-01 to 2022-23

description: This collection provides agricultural water supply data created for the purposes of national ecosystem services accounts.
The agricultural water supply data provide information on final and intermediate ecosystem services across Australia. This comprises spatial data (GeoTIFF 250m grid) of annual (financial year, 2000-01 to 2022-23) irrigated agricultural water use and irrigated agricultural land; data tables of irrigated agricultural water use by State/Territory; data tables of the amount of water supplied by different ecosystem types, and maps and charts.
DOI: 10.15468/dl.nwvnru
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Podocarpus bracteatus Blume"
 ]
}

 The dataset includes 42 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000685-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9wk7t5
title: Occurrence Download

description: A dataset containing 14265 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is MEX.3.5_2",
 "TaxonKey is Passeriformes",
 "Year 1994-2023"
 ]
}

 The dataset includes 14265 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000684-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d8dvzr
title: Occurrence Download

description: A dataset containing 621 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Sus scrofa Linnaeus, 1758"
 ]
}

 The dataset includes 621 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000683-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mpvrc6
title: Occurrence Download

description: A dataset containing 107825 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erigeron sumatrensis Retz."
 ]
}

 The dataset includes 107825 records from 1078 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000686-250225214225278/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11155235
title: Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-03-13

description: This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-03-13. 

            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!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1
DOI: 10.5281/zenodo.11128472
title: Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-03-13

description: This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-03-13. 

            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!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1