cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.s94c55
title: Occurrence Download

description: A dataset containing 2518 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Iran (Islamic Republic of)",
 "GadmGid is IRN.13_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 2518 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019273-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wy65r3
title: Occurrence Download

description: A dataset containing 180 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chionoecetes opilio (Fabricius, 1788)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 180 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019274-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/jmluox
title: Asaf Tsoar bats database

description: Asaf Tsoar bats database
DOI: 10.15468/dl.7mj8a9
title: Occurrence Download

description: A dataset containing 5926 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "Geometry POLYGON((110 45,120 45,120 55,110 55,110 45))",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 5926 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019276-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/10098558
title: pakistani_ambulances
DOI: 10.17632/mct3prb32k.1
title: Data and codes to produce a geocoded dataset of primary health care clinics in Brazil

description: The code and raw data files in this data repository produce a geocoded dataset of primary care medical clinics in Brazil. The final dataset is an unbalanced panel of clinics with monthly variation (from January 2018 to December 2023). The dataset contains 15,455,219 observations from 293,698 primary care clinics. Refer to the 'ReadMe' file for additional information.
DOI: 10.17632/8gnrwfmmpp.1
title: Underpricing Fraud Detection using Artificial Intelligence Technology to reduce Tax evasion in Tanzaniaa

description: The dataset includes both qualitative and quantitative data collected during research on the prevalence of tax evasion through product underpricing. This dataset is designed to be suitable for machine learning applications to detect underpricing fraud in real time.

The qualitative data can be analyzed using Atlas.ti software, while the quantitative data is compatible with SPSS. The qualitative data was gathered from traders in the Arusha region, with ethical clearance from the Nelson Mandela African Institute of Science and Technology and informed consent from participants. Google Forms was used to distribute questionnaires to those with internet access, while in-person visits were made to businesses for participants without smartphones or computers. No data was collected from individuals who declined to participate, adhering strictly to ethical guidelines. All collected data is anonymous, except for publicly available information.

Interviews were conducted with tax authority officials based at the headquarters in Dar es Salaam, including officers from the EFD department, ICT officers, and economists. These interviews provided insights into the extent of tax evasion in Tanzania, existing mechanisms to combat it, persistent loopholes, and opinions on proposed measures to address underpricing fraud.

Additionally, document analysis was performed on publicly available government reports to assess trends in tax collection. Media sources, including images, videos, and magazines, were also analyzed qualitatively to better understand the intensity of tax evasion, the challenges the government faces, and the efforts made to boost revenue.
DOI: 10.17632/skbvhkz5th.1
title: Dataset of Math Word Problems In Spanish and MathML

description: The dataset contains 150 Math Word Problems(MWP). Each problem consists of textual math problems that involve the application of first and second-degree mathematical equations for their resolution. To create this set, academic and educational sources containing first and second-degree math problems were selected, and some original problems were also included.

Each problem in the dataset is structured as follows:

"question": A textual description of the math problem in Spanish 
"mathml_equations": The corresponding equation for the problem, expressed in MathML format to facilitate processing and manipulation by machine learning models.
"Difficulty": The number of variables in the equation.
"Grade": The grade of the equation, with 1 indicating a linear equation and 2 indicating a quadratic equation.
"Index: A unique identifier for each problem in the dataset.
"Author": The creator or source of the problem.
"Ref": The source or citation for the problem, if applicable.
DOI: 10.17632/skbvhkz5th.2
title: Dataset of Math Word Problems In Spanish and MathML

description: The dataset contains 150 Math Word Problems(MWP). Each problem consists of textual math problems that involve the application of first and second-degree mathematical equations for their resolution. To create this set, academic and educational sources containing first and second-degree math problems were selected, and some original problems were also included.

Each problem in the dataset is structured as follows:

"question": A textual description of the math problem in Spanish 
"question_english": A textual description of the math problem in English
"mathml_equations": The corresponding equation for the problem, expressed in MathML format to facilitate processing and manipulation by machine learning models.
"Difficulty": The number of variables in the equation.
"Grade": The grade of the equation, with 1 indicating a linear equation and 2 indicating a quadratic equation.
"Index: A unique identifier for each problem in the dataset.
"Author": The creator or source of the problem.
"Ref": The source or citation for the problem, if applicable.
DOI: 10.15468/dl.jy688b
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phoca vitulina Linnaeus, 1758",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 44 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019279-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h38e57
title: Occurrence Download

description: A dataset containing 52200 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Rumex scutatus L."
 ]
}

 The dataset includes 52200 records from 512 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019277-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eb36f3
title: Occurrence Download

description: A dataset containing 6087 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Schistura McClelland, 1838"
 ]
}

 The dataset includes 6087 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019280-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9xwyaq
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Radulinus boleoides Gilbert, 1898",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 24 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019282-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.79e4dd
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Larix decidua Mill.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 112 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019283-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qsetnv
title: Occurrence Download

description: A dataset containing 325 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cleisthenes pinetorum Jordan & Starks, 1904",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 325 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019284-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.20350/digitalcsic/16706
title: Corpus for Complex Word Identification in Medical Spanish Texts (CWI-Med-Sp)

description: [Description of methods used for collection/generation of data] The corpus statistics and methods are explained in the following article: Federico Ortega-Riba, Leonardo Campillos-Llanos, Doaa Samy (2025) "Lexical Simplification in Spanish Texts For Patients: The Complex Word Identification Task". (Under review). [Methods for processing the data] Manual annotation of complex words (CW) according to the criteria defined in the guideline explained in the companion article.
description: The corpus is made up of 225 texts in Spanish annotated with complex words (CW). It contains three text types: consent forms (75 texts), clinical trial announcements (75 texts) and patient information documents (75 texts). This resource is aimed at training models, evaluating and performing experiments on complex word identification of Spanish medical texts.
description: The corpus is made up of 225 texts. It is aimed at training models, evaluating and performing experiments on complex word identification of Spanish medical texts. The corpus contains three text types: • Consent forms (75 texts) • Clinical trial announcements (75 texts) • Patient information leaflets (75 texts) - ANN: Contains BRAT annotated files (.ann) and corresponding text files (.txt): These are separated in three folders and subfolders (corresponding to each text type): • TRAIN: ▫ ci: 51 consent forms ('consentimientos informados') ▫ eudract: 51 clinical trial announcements from REEC and EudraCT ▫ info: 51 patient-oriented information leaflets • DEV: ▫ ci: 9 consent forms FALTA UNO ▫ eudract: 9 clinical trial announcements ▫ info: 9 patient-oriented information leaflets • TEST: ▫ ci: 15 consent forms ▫ eudract: 15 clinical trial announcements ▫ info: 15 patient-oriented information leaflets - JSON files for transformer models: These are separated in TRAIN, DEV and TEST. - CSV files with the processed data, corresponding to TRAIN, DEV and TEST data. These were used for the machine learning experiments. Each file has the following fields: • Token • Label: it encodes the class (CW, 'complex word') and if the token is the Beginning of the entity (B), if it is Inside (I) or Outside (O).
DOI: 10.15468/dl.ctzug4
title: Occurrence Download

description: A dataset containing 20514 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Sida acuta Burm.fil."
 ]
}

 The dataset includes 20514 records from 573 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019281-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1082699.10fu
title: SH1082699.10FU

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/sh1082700.10fu
title: SH1082700.10FU

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/sh1082701.10fu
title: SH1082701.10FU

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/sh1082704.10fu
title: SH1082704.10FU

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/sh1082705.10fu
title: SH1082705.10FU

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/sh1082706.10fu
title: SH1082706.10FU

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/sh1082711.10fu
title: SH1082711.10FU

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/sh1082713.10fu
title: SH1082713.10FU

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/sh1082717.10fu
title: SH1082717.10FU

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/sh1082718.10fu
title: SH1082718.10FU

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.