cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.xw64zm
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Dryophytes femoralis (Daudin, 1800))"
 ]
}

 The dataset includes 7 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007932-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2x7umm
title: Occurrence Download

description: A dataset containing 753 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Plethodon chattahoochee Highton, 1989)"
 ]
}

 The dataset includes 753 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007933-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fz49zv
title: Occurrence Download

description: A dataset containing 1406 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Stenachaenium Benth."
 ]
}

 The dataset includes 1406 records from 111 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007934-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15310529
title: BIOMAT-AnatNER Corpus: Train and Validation Sets

description: BIOMAT-AnatNER Train and Validation Sets

BIOMAT-AnatNER stands for BIOMATerials Anatomical Structure Named Entity Recognition. It is a corpus developed as part of the Horizon Europe BIOMATDB project to support the extraction and classification of anatomical structure mentions in scientific literature related to biomaterials. The corpus focuses specifically on the annotation of entities encompassing tissues, organs and body parts that appear in the context of biomaterials applications, such as mentions of tissues involved in implantation studies, or organs relevant to biocompatibility testing.

The corpus was created through a collaborative effort involving domain experts, who were tasked with the establishment of comprehensive and accurate annotation guidelines for the manual annotation of the final gold standard corpus. On this basis, PubMed abstracts were carefully selected based on MeSH (Medical Subject Headings) terms associated with relevant disciplines, such as regenerative medicine, orthopedics, dentistry and cardiology, to reflect the terminology commonly used in biomaterials research and manually annotated according to the rules predefinedin the annotation guidelines.

This repository contains the train (750 documents) and validation (100 documents) sets of the BIOMAT-AnatNER Corpus, which are made available under open access for public use. These sets have been released to support the development of Named Entity Recognition (NER) models for biomaterials-related concept extraction from scientific literature, particularly those mentions referring to anatomical structures.

The test set is not included in this repository, as it is reserved for a future shared task planned within the scope of the project. For this reason, access to the full corpus remains restricted, but will be made publicly available upon completion of the shared task.

Resources



Project Website

Biomaterials Marketplace

Biomaterials Database

BIOMAT-AnatNER full corpus

Annotation Guidelines
DOI: 10.5281/zenodo.15310530
title: BIOMAT-AnatNER Corpus: Train and Validation Sets

description: BIOMAT-AnatNER Train and Validation Sets

BIOMAT-AnatNER stands for BIOMATerials Anatomical Structure Named Entity Recognition. It is a corpus developed as part of the Horizon Europe BIOMATDB project to support the extraction and classification of anatomical structure mentions in scientific literature related to biomaterials. The corpus focuses specifically on the annotation of entities encompassing tissues, organs and body parts that appear in the context of biomaterials applications, such as mentions of tissues involved in implantation studies, or organs relevant to biocompatibility testing.

The corpus was created through a collaborative effort involving domain experts, who were tasked with the establishment of comprehensive and accurate annotation guidelines for the manual annotation of the final gold standard corpus. On this basis, PubMed abstracts were carefully selected based on MeSH (Medical Subject Headings) terms associated with relevant disciplines, such as regenerative medicine, orthopedics, dentistry and cardiology, to reflect the terminology commonly used in biomaterials research and manually annotated according to the rules predefinedin the annotation guidelines.

This repository contains the train (750 documents) and validation (100 documents) sets of the BIOMAT-AnatNER Corpus, which are made available under open access for public use. These sets have been released to support the development of Named Entity Recognition (NER) models for biomaterials-related concept extraction from scientific literature, particularly those mentions referring to anatomical structures.

The test set is not included in this repository, as it is reserved for a future shared task planned within the scope of the project. For this reason, access to the full corpus remains restricted, but will be made publicly available upon completion of the shared task.

Resources



Project Website

Biomaterials Marketplace

Biomaterials Database

BIOMAT-AnatNER full corpus

Annotation Guidelines
DOI: 10.5281/zenodo.15303995
title: Algerian Forest Fire Dataset based Meteorological data - Khenchla (2009–2023)

description: This dataset contains meteorological data and forest fire occurrence records for Khenchla city, Algeria, covering the period from 2009 to 2023. The meteorological data was sourced from historique-meteo.net, providing daily records of key environmental variables, including maximum temperature (MAX_TEMPERATURE_C), maximum wind speed (WINDSPEED_MAX_KMH), total daily precipitation (PRECIP_TOTAL_DAY_MM), and maximum humidity (HUMIDITY_MAX_PERCENT).

The fire presence data was obtained from the Forestry Authorities of Khenchla, and is represented in the CLASSE column, which indicates the presence of a fire for each day (with possible values indicating either fire or no fire).

The dataset includes the following columns:





Day: The day of the observation




Month: The month of the observation




Year: The year of the observation




MAX_TEMPERATURE_C: The maximum temperature for the day in Celsius




WINDSPEED_MAX_KMH: The maximum wind speed recorded for the day in kilometers per hour




PRECIP_TOTAL_DAY_MM: Total daily precipitation in millimeters




HUMIDITY_MAX_PERCENT: Maximum humidity recorded for the day as a percentage




CLASSE: A binary or categorical variable indicating whether a fire occurred (values such as 0 = no fire, 1 = fire occurred)



This dataset can be used to develop and test machine learning models for forest fire prediction based on meteorological conditions. It is well-suited for supervised learning tasks such as classification or regression, where the objective is to predict the likelihood of a forest fire occurring based on weather factors.

The dataset provides valuable insights for climate research, fire management, and disaster prediction, particularly for regions like Khenchla city, Algeria, which face challenges with forest fire prevention and management.
DOI: 10.5281/zenodo.15303996
title: Algerian Forest Fire Dataset based Meteorological data - Khenchla (2009–2023)

description: This dataset contains meteorological data and forest fire occurrence records for Khenchla city, Algeria, covering the period from 2009 to 2023. The meteorological data was sourced from historique-meteo.net, providing daily records of key environmental variables, including maximum temperature (MAX_TEMPERATURE_C), maximum wind speed (WINDSPEED_MAX_KMH), total daily precipitation (PRECIP_TOTAL_DAY_MM), and maximum humidity (HUMIDITY_MAX_PERCENT).

The fire presence data was obtained from the Forestry Authorities of Khenchla, and is represented in the CLASSE column, which indicates the presence of a fire for each day (with possible values indicating either fire or no fire).

The dataset includes the following columns:





Day: The day of the observation




Month: The month of the observation




Year: The year of the observation




MAX_TEMPERATURE_C: The maximum temperature for the day in Celsius




WINDSPEED_MAX_KMH: The maximum wind speed recorded for the day in kilometers per hour




PRECIP_TOTAL_DAY_MM: Total daily precipitation in millimeters




HUMIDITY_MAX_PERCENT: Maximum humidity recorded for the day as a percentage




CLASSE: A binary or categorical variable indicating whether a fire occurred (values such as 0 = no fire, 1 = fire occurred)



This dataset can be used to develop and test machine learning models for forest fire prediction based on meteorological conditions. It is well-suited for supervised learning tasks such as classification or regression, where the objective is to predict the likelihood of a forest fire occurring based on weather factors.

The dataset provides valuable insights for climate research, fire management, and disaster prediction, particularly for regions like Khenchla city, Algeria, which face challenges with forest fire prevention and management.
DOI: 10.5281/zenodo.15307807
title: Datasets related to the publication "Amino acids catalyse RNA formation under ambient alkaline conditions"

description: This deposition contains the data necessary to independently review and reproduce the MD simulations and QM calculations reported in the paper "Amino acids catalyse RNA formation under ambient alkaline conditions", by Rout et al, published in Nature Communications in 2025. Please address all scientific questions about the paper to the corresponding author, Prof. Dieter Braun. Technical questions about the deposited data may also be addressed to Dr. Miroslav Krepl (krepl@seznam.cz).
DOI: 10.5281/zenodo.15307808
title: Datasets related to the publication "Amino acids catalyse RNA formation under ambient alkaline conditions"

description: This deposition contains the data necessary to independently review and reproduce the MD simulations and QM calculations reported in the paper "Amino acids catalyse RNA formation under ambient alkaline conditions", by Rout et al, published in Nature Communications in 2025. Please address all scientific questions about the paper to the corresponding author, Prof. Dieter Braun. Technical questions about the deposited data may also be addressed to Dr. Miroslav Krepl (krepl@seznam.cz).
DOI: 10.5281/zenodo.15309611
title: Odd-even effects in lead-iodide-based Ruddlesden-Popper 2D perovskites
DOI: 10.5281/zenodo.15309612
title: Odd-even effects in lead-iodide-based Ruddlesden-Popper 2D perovskites
DOI: 10.15468/dl.fnghnj
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Brazil",
 "OccurrenceStatus is Present",
 "TaxonKey is Amanita muscaria (L.) Lam."
 ]
}

 The dataset includes 46 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007935-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e3kzbf
title: Occurrence Download

description: A dataset containing 115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Guatemala",
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Agalychnis Cope, 1864"
 ]
}

 The dataset includes 115 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007936-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34810/data1790
title: Replication Data for: Positive Psychology Intervention in Enhancing Optimism and Reducing Depression Among University Students

description: The purpose of this dataset is to share the positive psychology intervention of university students. The results obtained from this dataset, the descriptive analysis and the statistical analyses performed on this data were developed using SPSS and give rise to a scientific article (currently under review).
DOI: 10.48804/cykahi
title: MaSTer: Maliciously Secure Truncation for Replicated Secret Sharing without Pre-Processing
title: Master Truncation

description: The software extends the MP-SPDZ library for Multi-party computation protocols by an implementation of the MaSTer protocol (https://eprint.iacr.org/2024/1026). The extension includes full implementation of the MPC protocol integrated into the MP-SPDZ framework, together with benchmarking results. The repository is further extended by MIT-BIH (https://physionet.org/content/mitdb/1.0.0/) and PTB (https://www.physionet.org/content/ptbdb/1.0.0/) datasets, both available under the Open Data Commons Attribution License v1.0. The ECG heartbeat data is used for ML inference benchmarking, as specified in the paper.
DOI: 10.6084/m9.figshare.30343420
title: <b>Standard Language Proficiency as Social Capital: Impacts on Subjective Socioeconomic Status in China</b>

description: Drawing on data from the 2021 China General Social Survey, this study investigates the relationship between Mandarin proficiency and Chinese individuals’ subjective SES, including subjective class identity and perceptions of social mobility. The sample comprises 5,997 individuals aged 18-95 from urban and rural areas nationwide, with balanced sex distribution and diverse educational and occupational backgrounds. Mandarin proficiency is positively associated with static subjective class identity. This association is more pronounced among younger, less-educated, and rural groups. Additionally, proficiency alleviates the downward deviation, where subjective class identity falls below objective SES. This effect is stronger in coastal regions. However, it shows little association to perceptions of past or future mobility.
DOI: 10.6084/m9.figshare.30343420.v2
title: <b>Standard Language Proficiency as Social Capital: Impacts on Subjective Socioeconomic Status in China</b>

description: Drawing on data from the 2021 China General Social Survey, this study investigates the relationship between Mandarin proficiency and Chinese individuals’ subjective SES, including subjective class identity and perceptions of social mobility. The sample comprises 5,997 individuals aged 18-95 from urban and rural areas nationwide, with balanced sex distribution and diverse educational and occupational backgrounds. Mandarin proficiency is positively associated with static subjective class identity. This association is more pronounced among younger, less-educated, and rural groups. Additionally, proficiency alleviates the downward deviation, where subjective class identity falls below objective SES. This effect is stronger in coastal regions. However, it shows little association to perceptions of past or future mobility.
DOI: 10.6084/m9.figshare.30343420.v1
title: <b>标准语言能力作为社会资本：对中国主观社会经济</b><b>地位</b><b> in China</b>

description: 本研究利用2021年中国社会综合调查数据，调查了普通话水平与中国个人主观社会社会教育之间的，包括主观阶级认同和对社会流动性的看法。样本包括 5,997 名年龄在 18-95 岁之间的人-来自全国城乡性别分布均衡，教育和职业背景多样化。 普通话熟练程度与静态主观阶级认同。 这种关联 是 在年轻、受教育程度较低的农村群体中更为明显。此外，熟练程度减轻了向下偏差，即主观阶级认同低于客观社会社会。这种影响在沿海地区更为强烈。 然而，它与对过去或未来流动性的看法几乎没有关联。
DOI: 10.15468/dl.jtb3y9
title: Occurrence Download

description: A dataset containing 20957 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gastropoda)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-30 0,0 0,0 30,-30 30,-30 0))"
 ]
}

 The dataset includes 20957 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012635-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k3k7qp
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Polyplacophora)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((150 0,180 0,180 30,150 30,150 0))"
 ]
}

 The dataset includes 15 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012636-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bv3zc6
title: Occurrence Download

description: A dataset containing 470 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tritia gibbosula"
 ]
}

 The dataset includes 470 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009401-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1376967.10fu
title: SH1376967.10FU

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/sh1376968.10fu
title: SH1376968.10FU

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.8ekgex
title: Occurrence Download

description: A dataset containing 255 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"IND.29.29_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 255 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005690-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1376970.10fu
title: SH1376970.10FU

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/sh1376972.10fu
title: SH1376972.10FU

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.