cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13334983
title: Data

description: This dataset contains a list of 102 code smells detected from Copilot-generated Python code, along with Python code files generated by Copilot from the Repositories and Code label, respectively. This dataset also includes Copilot Chat’s responses to fixing the 102 detected code smells. A brief description of each document and folder in the dataset is provided below:

1. files folder

contains 311 Python code files generated by Copilot. In the 311 Python files, 171 are retrieved under the Repositories label, indicating Python code files entirely generated by Copilot, and 140 are retrieved under the Code label, indicating Python code snippets generated by Copilot.

2. results of RQ1.xlsx

contains a list of 102 code smells detected from Copilot-generated Python code.

3. results of RQ2.xlsx

contains Copilot Chat’s responses to fixing the detected 102 code smells instructed by three prompts of varying detail levels.
DOI: 10.5281/zenodo.13334967
title: Data

description: This dataset contains a list of 102 code smells detected from Copilot-generated Python code, along with Python code files generated by Copilot from the Repositories and Code label, respectively. This dataset also includes Copilot Chat’s responses to fixing the 102 detected code smells. A brief description of each document and folder in the dataset is provided below:

1. files folder

contains 311 Python code files generated by Copilot. In the 311 Python files, 171 are retrieved under the Repositories label, indicating Python code files entirely generated by Copilot, and 140 are retrieved under the Code label, indicating Python code snippets generated by Copilot.

2. results of RQ1.xlsx

contains a list of 102 code smells detected from Copilot-generated Python code.

3. results of RQ2.xlsx

contains Copilot Chat’s responses to fixing the detected 102 code smells instructed by three prompts of varying detail levels.
DOI: 10.5281/zenodo.11647452
title: Data

description: This dataset contains a list of 102 code smells detected from Copilot-generated Python code, along with Python code files generated by Copilot from the Repositories and Code label, respectively. This dataset also includes Copilot Chat’s responses to fixing the 102 detected code smells. A brief description of each document and folder in the dataset is provided below:

1. files folder

contains 311 Python code files generated by Copilot. In the 311 Python files, 171 are retrieved under the Repositories label, indicating Python code files entirely generated by Copilot, and 140 are retrieved under the Code label, indicating Python code snippets generated by Copilot.

2. results of RQ1.xlsx

contains a list of 102 code smells detected from Copilot-generated Python code.

3. results of RQ2.xlsx

contains Copilot Chat’s responses to fixing the detected 102 code smells instructed by three prompts of varying detail levels.
DOI: 10.5281/zenodo.11647451
title: Data

description: This dataset contains a list of 102 code smells detected from Copilot-generated Python code, along with Python code files generated by Copilot from the Repositories and Code label, respectively. This dataset also includes Copilot Chat’s responses to fixing the 102 detected code smells. A brief description of each document and folder in the dataset is provided below:

1. files folder

contains 311 Python code files generated by Copilot. In the 311 Python files, 171 are retrieved under the Repositories label, indicating Python code files entirely generated by Copilot, and 140 are retrieved under the Code label, indicating Python code snippets generated by Copilot.

2. results of RQ1.xlsx

contains a list of 102 code smells detected from Copilot-generated Python code.

3. results of RQ2.xlsx

contains Copilot Chat’s responses to fixing the detected 102 code smells instructed by three prompts of varying detail levels.
DOI: 10.5281/zenodo.13334993
title: Data

description: This dataset contains a list of 102 code smells detected from Copilot-generated Python code, along with Python code files generated by Copilot from the Repositories and Code label, respectively. This dataset also includes Copilot Chat’s responses to fixing the 102 detected code smells. A brief description of each document and folder in the dataset is provided below:

1. files folder

contains 311 Python code files generated by Copilot. In the 311 Python files, 171 are retrieved under the Repositories label, indicating Python code files entirely generated by Copilot, and 140 are retrieved under the Code label, indicating Python code snippets generated by Copilot.

2. results of RQ1.xlsx

contains a list of 102 code smells detected from Copilot-generated Python code.

3. results of RQ2.xlsx

contains Copilot Chat’s responses to fixing the detected 102 code smells instructed by three prompts of varying detail levels.
DOI: 10.15468/dl.yfp7j5
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alcis latifasciata Warren, 1893"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071683-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5ehmwg
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((148.573869 -34.504988, 148.573869 -34.509479, 148.576115 -34.509479, 148.576115 -34.507234, 148.580607 -34.507234, 148.580607 -34.504988, 148.573869 -34.504988)), ((148.576115 -34.509479, 148.576115 -34.513971, 148.580607 -34.513971, 148.580607 -34.509479, 148.576115 -34.509479)))"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071684-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.be585w
title: Occurrence Download

description: A dataset containing 163 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron ×pulchrum Sweet"
 ]
}

 The dataset includes 163 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071685-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qscqf3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.582853 -34.504988, 148.582853 -34.509479, 148.587344 -34.509479, 148.587344 -34.504988, 148.582853 -34.504988))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071686-240626123714530/datasets/export for details.
DOI: 10.15468/dl.tqfxxp
title: Occurrence Download

description: A dataset containing 14718 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron hirsutum L."
 ]
}

 The dataset includes 14718 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071687-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gjjz4y
title: Occurrence Download

description: A dataset containing 2917986 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "DatasetKey is EOD – eBird Observation Dataset",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2917986 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071670-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13332827
title: Data for: Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (Part 1/5)

description: This data set consists of MRI measurement data for liver scans acquired with a 3D stack-of-stars multi-echo FLASH sequence during free breathing as described in:

Zhengguo Tan, Christina Unterberg-Buchwald, Moritz Blumenthal, Nick Scholand, Philip Schaten, Christian Holme, Xiaoqing Wang, Dirk Raddatz, Martin Uecker. Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction. IEEE Transactions on Medical Imaging 42:1374-1387 (2023) 

The data stored in the format of the BART toolbox (DOI: 10.5281/zenodo.592960).
DOI: 10.5281/zenodo.13332828
title: Data for: Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (Part 1/5)

description: This data set consists of MRI measurement data for liver scans acquired with a 3D stack-of-stars multi-echo FLASH sequence during free breathing as described in:

Zhengguo Tan, Christina Unterberg-Buchwald, Moritz Blumenthal, Nick Scholand, Philip Schaten, Christian Holme, Xiaoqing Wang, Dirk Raddatz, Martin Uecker. Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction. IEEE Transactions on Medical Imaging 42:1374-1387 (2023) 

The data stored in the format of the BART toolbox (DOI: 10.5281/zenodo.592960).
DOI: 10.5281/zenodo.13332822
title: Data for: Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (Part 4/5)

description: This data set consists of MRI measurement data for liver scans acquired with a 3D stack-of-stars multi-echo FLASH sequence during free breathing as described in:

Zhengguo Tan, Christina Unterberg-Buchwald, Moritz Blumenthal, Nick Scholand, Philip Schaten, Christian Holme, Xiaoqing Wang, Dirk Raddatz, Martin Uecker. Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction. IEEE Transactions on Medical Imaging 42:1374-1387 (2023) 

The data stored in the format of the BART toolbox (DOI: 10.5281/zenodo.592960).
DOI: 10.5281/zenodo.13332823
title: Data for: Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (Part 4/5)

description: This data set consists of MRI measurement data for liver scans acquired with a 3D stack-of-stars multi-echo FLASH sequence during free breathing as described in:

Zhengguo Tan, Christina Unterberg-Buchwald, Moritz Blumenthal, Nick Scholand, Philip Schaten, Christian Holme, Xiaoqing Wang, Dirk Raddatz, Martin Uecker. Free-Breathing Liver Fat, R2* and B0 Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction. IEEE Transactions on Medical Imaging 42:1374-1387 (2023) 

The data stored in the format of the BART toolbox (DOI: 10.5281/zenodo.592960).
DOI: 10.15468/dl.ufdg6v
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.998326 -34.504988, 148.998326 -34.509479, 149.002817 -34.509479, 149.002817 -34.504988, 148.998326 -34.504988))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071688-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11518845
title: Easy ORCID

description: The first-party ORCID data dump uses a data structure that is overly complex for most use cases. This Zenodo record contains a derived version that is much more straightforwards, accessible, and smaller. So far, this includes employers, education, external identifiers, and publications linked to PubMed. It adds additional processing to ground employers and educational instutitions using the Research Organization Registry (ROR). It also does some minor string processing, such as standardization of education types (e.g., Bachelor of Science, Master of Science) and standardization of PubMed references.

Records

The records.jsonl.gz file is a JSON Lines file where each row represents a single ORCID record in a simple, well-defined schema (see schema.json). The records_hq.jsonl.gz file is a subset of the full records file that only contains records that have at least one ROR-grounded employer, at least one ROR-grounded education, or at least one publication indexed in PubMed. The point of this subset is to remove ORCID records that are generally not possible to match up to any external information.

Nomenclature Authority Cross-References

Websites, social links, and other identifiers are parsed and standardized to comply with the Bioregistry then shared using the Simple Standard for Sharing Ontological Mappings (SSSOM) in the sssom.tsv.gz file. This allows for getting Scopus, Web of Science, GitHub, Google Scholar, and other profiles for records that include them. This information is also available through the main records file.

Authorship Links

Authorships are extracted and standardized in the pubmeds.tsv.gz file, which contains an ORCID column and PubMed column that has been pre-sanitized to only contain local unique identifiers. This information is also available through the main records file.

Lexical Indexes

It includes two pre-built Gilda indexes for named entity recognition (NER) and named entity normalization (NEN). One contains all records, and the second is filtered to high-quality records. The following Python code snipped can be used for grounding:

from gilda import Grounder
url = "https://zenodo.org/records/11474470/files/gilda_hq.tsv.gz?download=1"
grounder = Grounder(url)
results = grounder.ground("Charles Tapley Hoyt")

Ontology Artifacts

The file orcid.ttl.gz is an OWL-ready RDF file that can be opened in Protégé or used with the Ontology Development Kit. It can also be converted into OWL XML, OWL Functional Notation, or other OWL formats using ROBOT. This artifact can serve as a replacement for the ones generated by https://github.com/cthoyt/orcidio, which was a smaller-scale way of turning ORCID records for contributors to OBO Foundry Ontologies into a small OWL file. Now, the export here contains all ORCID records with names.

Reproduction

It is automatically generated with code in https://github.com/cthoyt/orcid_downloader.
DOI: 10.5281/zenodo.11371268
title: Easy ORCID

description: The first-party ORCID data dump uses a data structure that is overly complex for most use cases. This Zenodo record contains a derived version that is much more straightforwards, accessible, and smaller. So far, this includes employers, education, external identifiers, and publications linked to PubMed. It adds additional processing to ground employers and educational instutitions using the Research Organization Registry (ROR). It also does some minor string processing, such as standardization of education types (e.g., Bachelor of Science, Master of Science).

It includes a pre-build Gilda index for named entity recognition (NER) and named entity normalization (NEN).

The records_hq.json.gz file is a subset of the full records file that only contains records that have at least one ROR-grounded employer, at least one ROR-grounded education, at least one standardized external identifier, or at least one publication indexed in PubMed. The point of this subset is to remove ORCID records that are generally not possible to match up to any external information.

It is automatically generated with code in https://github.com/cthoyt/orcid_downloader.
DOI: 10.5281/zenodo.11474470
title: Easy ORCID

description: The first-party ORCID data dump uses a data structure that is overly complex for most use cases. This Zenodo record contains a derived version that is much more straightforwards, accessible, and smaller. So far, this includes employers, education, external identifiers, and publications linked to PubMed. It adds additional processing to ground employers and educational instutitions using the Research Organization Registry (ROR). It also does some minor string processing, such as standardization of education types (e.g., Bachelor of Science, Master of Science) and standardization of PubMed references.

The records.jsonl.gz file is a JSON Lines file where each row represents a single ORCID record in a simple, well-defined schema (see schema.json). The records_hq.jsonl.gz file is a subset of the full records file that only contains records that have at least one ROR-grounded employer, at least one ROR-grounded education, or at least one publication indexed in PubMed. The point of this subset is to remove ORCID records that are generally not possible to match up to any external information.

Websites, social links, and other identifiers are parsed and standardized to comply with the Bioregistry then shared using the Simple Standard for Sharing Ontological Mappings (SSSOM) in the sssom.tsv.gz file. This allows for getting Scopus, Web of Science, GitHub, Google Scholar, and other profiles for records that include them. This information is also available through the main records file.

Authorships are extracted and standardized in the pubmeds.tsv.gz file, which contains an ORCID column and PubMed column that has been pre-sanitized to only contain local unique identifiers. This information is also available through the main records file.

It includes two pre-built Gilda indexes for named entity recognition (NER) and named entity normalization (NEN). One contains all records, and the second is filtered to high-quality records. The following Python code snipped can be used for grounding:

from gilda import Grounder
url = "https://zenodo.org/records/11474470/files/gilda_hq.tsv.gz?download=1"
grounder = Grounder(url)
results = grounder.ground("Charles Tapley Hoyt")

It is automatically generated with code in https://github.com/cthoyt/orcid_downloader.
DOI: 10.5281/zenodo.10137940
title: Easy ORCID

description: The first-party ORCID data dump uses a data structure that is overly complex for most use cases. This record contains a derived version that is much more straightforwards, accessible, and smaller. It also includes a pre-build Gilda index for named entity recognition (NER) and named entity normalization (NEN).

It is automatically generated with code in https://github.com/cthoyt/orcid_downloader.
DOI: 10.15468/dl.37beap
title: Occurrence Download

description: A dataset containing 22660 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ulva intestinalis L."
 ]
}

 The dataset includes 22660 records from 293 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071689-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bdy3kj
title: Occurrence Download

description: A dataset containing 1721 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Plectrophenax hyperboreus Ridgway, 1884"
 ]
}

 The dataset includes 1721 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058814-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k2unvq
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Bacteria from Penguin Guano, Antarctica"
 ]
}

 The dataset includes 70 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026857-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.whbj0x
title: Occurrence Download

description: A dataset containing 2946 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Specimen"
 ]
 },
 "Country is Mexico",
 "Year 1977-2018",
 {
 "or" : [
 "DatasetKey is Implementación del Programa de Monitoreo del Cocodrilo de Pantano (Crocodrylus moreletti) México-Belice-Guatemala en la Región de Coordinación MXRC3-Sur: sur de Veracruz, Tabasco, Chiapas y oeste de Campeche",
 "DatasetKey is Determinación del estado de las poblaciones silvestres del cocodrilo de pantano (Crocodylus moreletii) en México y evaluación de su estatus en la CITES",
 "DatasetKey is Implementación del programa de monitoreo del cocodrilo del pantano (Crocodylus moreletti) México-Belice-Guatemala: región de coordinación MXRC2-Golfo Centro, Veracruz centro y norte Oaxaca",
 "DatasetKey is Sistematización de las colecciones científicas del Instituto de Historia Natural y Ecología, (IHNE) Chiapas",
 "DatasetKey is Actualización de la base de datos de la colección nacional de anfibios y reptiles (CNAR)",
 "DatasetKey is Actualización y enriquecimiento de las bases de datos del proyecto de evaluación y análisis geográfico de la diversidad faunística de Chiapas"
 ]
 },
 "HasCoordinate is true",
 "TaxonKey is Crocodylus moreletii (Duméril & Bibron, 1851)"
 ]
}

 The dataset includes 2946 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011575-180412121330197/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/8605729
title: Data Analyst Job Postings
DOI: 10.15468/dl.v5gdi2
title: Occurrence Download

description: A dataset containing 170 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "Year 2000-2018",
 "HasCoordinate is true",
 "TaxonKey is Panthera onca (Linnaeus, 1758)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 170 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014364-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rda4ab
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Baccharis camporum DC.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 63 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011575-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ewcs3k
title: Occurrence Download

description: A dataset containing 152 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crithagra rothschildi (Ogilvie-Grant, 1902)"
 ]
}

 The dataset includes 152 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0118233-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rnwyz8
title: Occurrence Download

description: A dataset containing 573 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Empidonax traillii extimus A.R.Phillips, 1948"
 ]
}

 The dataset includes 573 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014364-190415153152247/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.