cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18724306
title: congolapia_crassa_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.18724307
title: congolapia_crassa_decontaminated_FSCR.fasta.gz
DOI: 10.3886/e246123
title: Abubakar Tafawa Balewa University (ATBU) 2026/2027 IJMB Form, Form Is On sales Now! 09024168245 To apply call the admin office on (+2349024168245) for more information on how to make your proper registration and before closing

description: Abubakar Tafawa Balewa University (ATBU) 2026/2027 IJMB Form, Form Is On sales Now! 09024168245 To apply call the admin office on (+2349024168245) for more information on how to make your proper registration and before closing<br>
DOI: 10.15468/dl.hd9t2s
title: Occurrence Download

description: A dataset containing 782 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cynoscion acoupa",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Occurrence evidence, Machine Observation, Observation, Specimen)"
 ]
}

 The dataset includes 782 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033933-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.shazsy
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Rhopaloblaste"
 ]
}

 The dataset includes 154 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033934-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bc2d8w
title: Occurrence Download

description: A dataset containing 73482 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhinolophus hipposideros"
 ]
}

 The dataset includes 73482 records from 225 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033901-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18724317
title: coptodon_tholloni_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.18724318
title: coptodon_tholloni_decontaminated_FSCR.fasta.gz
DOI: 10.6084/m9.figshare.31384075
title: data

description: This repository provides the dataset used for the analyses in the accompanying manuscript submitted to <i>Sports</i>.<b>Files</b><code>data.xlsx</code> (sheet name: <code>data</code>): longitudinal decathlon competition records.Rows: 716 (athlete-season / athlete-grade records)Columns: 26 variables<b>Study population and time span</b>Japanese university decathlon athletes (anonymized).Calendar years covered: 2001–2024 (rows with a valid <code>year</code>).Unique athletes: 179 (<code>ID</code> = 1–179).<b>Variables (codebook)</b><code>ID</code>: anonymized athlete identifier (integer; consistent across years).<code>year</code>: calendar year of the competition record (integer).<br><i>Note:</i> Some rows have missing <code>year</code> and no performance data (see “Missing data” below).<code>grade</code>: academic year in university (1–4).<code>region</code>: region code (1–8) for the qualifying/competition region (categorical code).Event marks (units):<code>HundM</code> (100 m, seconds), <code>LJ</code> (long jump, meters), <code>SP</code> (shot put, meters), <code>HJ</code> (high jump, meters),<code>FourM</code> (400 m, seconds), <code>Hurdle</code> (110 m hurdles, seconds),<code>DT</code> (discus throw, meters), <code>PV</code> (pole vault, meters), <code>JT</code> (javelin throw, meters),<code>FifthM_sec</code> (1500 m, seconds).Event points and total points:<code>P_*</code> columns are event points computed from the standard combined-events scoring tables (World Athletics/IAAF).<code>P_total</code> is the total decathlon score (sum of event points).qualifying_standards :If the region has qualifying standards, we assign a value of 1; if not, we assign a value of 0.<b>Missing data and special codes</b><br><code>NA</code> indicates that an event record was not available for that athlete–year entry.<br>A value of <code>0</code> in event-mark variables indicates that the athlete participated in the event but obtained <b>no valid mark/time</b> (e.g., DNF, foul, or no height). Corresponding event points are coded as <code>0</code> where applicable.<br>Users should treat <code>0</code> as a <b>special outcome code</b> (no valid mark/time) rather than a measured performance value.
DOI: 10.6084/m9.figshare.31384075.v2
title: data

description: This repository provides the dataset used for the analyses in the accompanying manuscript submitted to <i>Sports</i>.<b>Files</b><code>data.xlsx</code> (sheet name: <code>data</code>): longitudinal decathlon competition records.Rows: 716 (athlete-season / athlete-grade records)Columns: 26 variables<b>Study population and time span</b>Japanese university decathlon athletes (anonymized).Calendar years covered: 2001–2024 (rows with a valid <code>year</code>).Unique athletes: 179 (<code>ID</code> = 1–179).<b>Variables (codebook)</b><code>ID</code>: anonymized athlete identifier (integer; consistent across years).<code>year</code>: calendar year of the competition record (integer).<br><i>Note:</i> Some rows have missing <code>year</code> and no performance data (see “Missing data” below).<code>grade</code>: academic year in university (1–4).<code>region</code>: region code (1–8) for the qualifying/competition region (categorical code).Event marks (units):<code>HundM</code> (100 m, seconds), <code>LJ</code> (long jump, meters), <code>SP</code> (shot put, meters), <code>HJ</code> (high jump, meters),<code>FourM</code> (400 m, seconds), <code>Hurdle</code> (110 m hurdles, seconds),<code>DT</code> (discus throw, meters), <code>PV</code> (pole vault, meters), <code>JT</code> (javelin throw, meters),<code>FifthM_sec</code> (1500 m, seconds).Event points and total points:<code>P_*</code> columns are event points computed from the standard combined-events scoring tables (World Athletics/IAAF).<code>P_total</code> is the total decathlon score (sum of event points).qualifying_standards :If the region has qualifying standards, we assign a value of 1; if not, we assign a value of 0.<b>Missing data and special codes</b><br><code>NA</code> indicates that an event record was not available for that athlete–year entry.<br>A value of <code>0</code> in event-mark variables indicates that the athlete participated in the event but obtained <b>no valid mark/time</b> (e.g., DNF, foul, or no height). Corresponding event points are coded as <code>0</code> where applicable.<br>Users should treat <code>0</code> as a <b>special outcome code</b> (no valid mark/time) rather than a measured performance value.
DOI: 10.6084/m9.figshare.31384075.v1
title: data

description: This repository provides the dataset used for the analyses in the accompanying manuscript submitted to <i>Sports</i>.<b>Files</b><code>data.xlsx</code> (sheet name: <code>data</code>): longitudinal decathlon competition records.Rows: 716 (athlete-season / athlete-grade records)Columns: 25 variables<b>Study population and time span</b>Japanese university decathlon athletes (anonymized).Calendar years covered: 2001–2024 (rows with a valid <code>year</code>).Unique athletes: 179 (<code>ID</code> = 1–179).<b>Variables (codebook)</b><code>ID</code>: anonymized athlete identifier (integer; consistent across years).<code>year</code>: calendar year of the competition record (integer).<br><i>Note:</i> Some rows have missing <code>year</code> and no performance data (see “Missing data” below).<code>grade</code>: academic year in university (1–4).<code>region</code>: region code (1–8) for the qualifying/competition region (categorical code).Event marks (units):<code>HundM</code> (100 m, seconds), <code>LJ</code> (long jump, meters), <code>SP</code> (shot put, meters), <code>HJ</code> (high jump, meters),<code>FourM</code> (400 m, seconds), <code>Hurdle</code> (110 m hurdles, seconds),<code>DT</code> (discus throw, meters), <code>PV</code> (pole vault, meters), <code>JT</code> (javelin throw, meters),<code>FifthM_sec</code> (1500 m, seconds).Event points and total points:<code>P_*</code> columns are event points computed from the standard combined-events scoring tables (World Athletics/IAAF).<code>P_total</code> is the total decathlon score (sum of event points).<b>Missing data and special codes</b><br><code>NA</code> indicates that an event record was not available for that athlete–year entry.<br>A value of <code>0</code> in event-mark variables indicates that the athlete participated in the event but obtained <b>no valid mark/time</b> (e.g., DNF, foul, or no height). Corresponding event points are coded as <code>0</code> where applicable.<br>Users should treat <code>0</code> as a <b>special outcome code</b> (no valid mark/time) rather than a measured performance value.
DOI: 10.15468/dl.53z37c
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Remiz macronyx ssaposhnikowi"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033935-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wdurv4
title: Occurrence Download

description: A dataset containing 306 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhinolophus macrotis"
 ]
}

 The dataset includes 306 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033936-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18724305
title: A Standardized Cross-Linguistic Dataset of Traditional Chinese Medicine Formula Names

description: This dataset provides a standardized cross-linguistic table of Traditional Chinese Medicine (TCM) formula names, covering Chinese, Japanese (Kampo), Korean (Han medicine), and English usages.The dataset was compiled based on official formularies and commonly used clinical references in Taiwan, with careful distinction between established translations, descriptive English renderings, and cases where no stable English equivalent exists.Japanese formula names are presented primarily in kanji with Hepburn romanization for cross-linguistic reference, while Korean names are given in Hangul with Revised Romanization. English names are included only when supported by consistent usage in international literature; otherwise, the absence of a standardized English term is explicitly indicated.This dataset is intended to support comparative research, terminology standardization, translation studies, and international communication in East Asian traditional medicine.
DOI: 10.5281/zenodo.18115481
title: A Standardized Cross-Linguistic Dataset of Traditional Chinese Medicine Formula Names

description: This dataset provides a standardized cross-linguistic table of Traditional Chinese Medicine (TCM) formula names, covering Chinese, Japanese (Kampo), Korean (Han medicine), and English usages.The dataset was compiled based on official formularies and commonly used clinical references in Taiwan, with careful distinction between established translations, descriptive English renderings, and cases where no stable English equivalent exists.Japanese formula names are presented primarily in kanji with Hepburn romanization for cross-linguistic reference, while Korean names are given in Hangul with Revised Romanization. English names are included only when supported by consistent usage in international literature; otherwise, the absence of a standardized English term is explicitly indicated.This dataset is intended to support comparative research, terminology standardization, translation studies, and international communication in East Asian traditional medicine.
DOI: 10.15468/dl.zkq734
title: Occurrence Download

description: A dataset containing 40898 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lampetra fluviatilis"
 ]
}

 The dataset includes 40898 records from 228 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033937-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17119849
title: Impact monitoring raw data reported by case studies in MERLIN (EU Horizon 2020) - Climate regulation

description: This dataset contains csv files with raw data of the impact monitoring within the EU Horizon 2020 project MERLIN (grant agreement No. 101036337) reported by case studies till end of May 2025 for theClimate regulation criterion.
DOI: 10.5281/zenodo.17119850
title: Impact monitoring raw data reported by case studies in MERLIN (EU Horizon 2020) - Climate regulation

description: This dataset contains csv files with raw data of the impact monitoring within the EU Horizon 2020 project MERLIN (grant agreement No. 101036337) reported by case studies till end of May 2025 for theClimate regulation criterion.
DOI: 10.15156/bio/sh1389270.10fu
title: SH1389270.10FU

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: 10.6084/m9.figshare.30773390
title: EmoRoad: A Multimodal Dataset of Psychological, Physiological, and Behavioural Responses in Diverse Driving Scenarios (Controlled Access)

description: <b>Description</b>This Figshare record describes the EmoRoad dataset: <i>“EmoRoad: A Multimodal Dataset of Psychological, Physiological, and Behavioural Responses in Diverse Driving Scenarios”</i>. The dataset contains human‑subject multimodal data collected in driving scenarios, including identifiable facial video recordings, EEG signals, and other physiological and behavioural measures. Because these data cannot be fully anonymised, access to the raw files is provided under a controlled‑access model and governed by a Data Usage Agreement (DUA).<b>Access procedure</b>Download the “Data Usage Agreement-EmoRoad” file (Data Usage Agreement) from our figshare item. To simplify your download, the link ( https://figshare.com/s/8ee495bc93d2b02253c3 ) is attached here.Read the DUA carefully and, if you agree to the terms, fill in and sign the form<br>(electronic signature is acceptable, if permitted by your institution).Send the signed DUA to the following contact email address:<br><b>stephen.j.wang@polyu.edu.hk</b>After verifying your information and the signed DUA, we will provide a download link to the restricted dataset files via email.<b>Notes</b>The DUA prohibits any attempt to re‑identify participants, prohibits redistribution of the raw data to third parties, and restricts use to non‑commercial research and academic purposes under appropriate secure data‑handling practices.All bona fide researchers who provide verifiable identity and institutional affiliation, and who agree to the DUA terms, are eligible to request access.The DUA itself is publicly available from this Figshare record and can be downloaded without requesting access to the data.When using this dataset in publications or presentations, please cite the dataset DOI for this record and follow the citation and acknowledgement instructions specified in the DUA.
DOI: 10.6084/m9.figshare.30773390.v1
title: EmoRoad: A Multimodal Dataset of Psychological, Physiological, and Behavioural Responses in Diverse Driving Scenarios (Controlled Access)

description: <b>Description</b>This Figshare record describes the EmoRoad dataset: <i>“EmoRoad: A Multimodal Dataset of Psychological, Physiological, and Behavioural Responses in Diverse Driving Scenarios”</i>. The dataset contains human‑subject multimodal data collected in driving scenarios, including identifiable facial video recordings, EEG signals, and other physiological and behavioural measures. Because these data cannot be fully anonymised, access to the raw files is provided under a controlled‑access model and governed by a Data Usage Agreement (DUA).<b>Access procedure</b>Download the “Data Usage Agreement-EmoRoad” file (Data Usage Agreement) from our figshare item. To simplify your download, the link ( https://figshare.com/s/8ee495bc93d2b02253c3 ) is attached here.Read the DUA carefully and, if you agree to the terms, fill in and sign the form<br>(electronic signature is acceptable, if permitted by your institution).Send the signed DUA to the following contact email address:<br><b>stephen.j.wang@polyu.edu.hk</b>After verifying your information and the signed DUA, we will provide a download link to the restricted dataset files via email.<b>Notes</b>The DUA prohibits any attempt to re‑identify participants, prohibits redistribution of the raw data to third parties, and restricts use to non‑commercial research and academic purposes under appropriate secure data‑handling practices.All bona fide researchers who provide verifiable identity and institutional affiliation, and who agree to the DUA terms, are eligible to request access.The DUA itself is publicly available from this Figshare record and can be downloaded without requesting access to the data.When using this dataset in publications or presentations, please cite the dataset DOI for this record and follow the citation and acknowledgement instructions specified in the DUA.
DOI: 10.15156/bio/sh1389272.10fu
title: SH1389272.10FU

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/sh1389279.10fu
title: SH1389279.10FU

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/sh1389283.10fu
title: SH1389283.10FU

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/sh1389284.10fu
title: SH1389284.10FU

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.