cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.yz4pp6
title: Occurrence Download

description: A dataset containing 12354 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Issue is one of (Zero coordinate, Coordinate out of range, Coordinate invalid, Coordinate rounded, Geodetic datum invalid, Geodetic datum assumed WGS84, Coordinate reprojected, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate accuracy invalid, Coordinate precision invalid, Coordinate uncertainty meters invalid, Coordinate precision/uncertainty mismatch, Footprint SRS invalid, Footprint WKT mismatch, Footprint WKT invalid, Country coordinate mismatch, Country mismatch, Country invalid, Country derived from coordinates, Continent coordinate mismatch, Continent country mismatch, Continent invalid, Continent derived from country, Continent derived from coordinates, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Recorded date mismatch, Recorded date invalid, Recorded date unlikely, Taxon match fuzzy, Taxon match higher rank, Taxon match aggregate, The scientific name ID was not used in backbone matching, The taxon concept ID was not used in backbone matching, The taxon ID was not used in backbone matching, The scientific name ID was not found in the checklist, The taxon concept ID was not found in the checklist, The taxon ID was not found in the checklist, The scientific name differs on the record from that found in the checklist, Taxon match none, The backbone match found using the taxon IDs differs from what would be found if using the scientific names , Depth not metric, Depth unlikely, Depth min max swapped, Depth non numeric, Elevation unlikely, Elevation min max swapped, Elevation not metric, Elevation non numeric, Modified date invalid, Modified date unlikely, Identified date unlikely, Identified date invalid, Basis of record invalid, Type status invalid, Multimedia date invalid, Multimedia URI invalid, References URI invalid, Error during interpretation, Individual count invalid, Individual count conflicts with occurrence status, Occurrence status unparsable, Occurrence status inferred from individual count, ccurrence status inferred from basis of record, Georeferenced date unlikely, Georeferenced date invalid, Ambiguous institution, Ambiguous collection, Institution match none, Collection match none, Institution match fuzzy, Collection match fuzzy, Institution collection mismatch, Possibly on loan, Different owner institution)",
 "OccurrenceStatus is Present",
 "TaxonKey is Alchornea triplinervia (Spreng.) Müll.Arg."
 ]
}

 The dataset includes 12354 records from 264 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114936-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hkwqm9
title: Occurrence Download

description: A dataset containing 89443 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Living Specimen, Specimen)",
 "Continent is South America",
 "Country is Colombia",
 "DatasetKey is one of (Línea base general de aves y mamíferos a través de cámaras trampa para el valle medio del Magdalena - VMM, Monitoreo pasivo de fauna silvestre con cámaras trampa en el Magdalena Medio, Santander y Cesar, Colombia - Proyecto FIBRAS, Línea base general de mamíferos para el valle medio del Magdalena - VMM, Muestreos de fauna silvestre existente en la franja de protección del embalse y áreas de reserva protectora de la Central Hidroeléctrica Sogamoso, Mamíferos asociados a un bosque húmedo tropical de Barrancabermeja, Santander, Fauna y flora asociada al Estudio de Impacto ambiental para el proyecto Piloto de Investigación Integral - Kalé, iNaturalist Research-grade Observations, Fauna y flora asociada al proyecto piloto de investigación integral - PPII Platero, departamento de Santander, Monitoreo de Felinos Silvestres asociados a Sistemas Agropecuarios en las Cuencas del Caño Zarzal y Caño La Vizcaína, Barrancabermeja, Santander, Línea base general derivada de sonidos unidireccionales (ultrasonido) para el valle medio del Magdalena - VMM)",
 "Geometry POLYGON((-74.19942 7.50205,-74.27543 6.19382,-71.76681 6.39531,-72.6537 8.05439,-74.19942 7.50205))",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (CR, EN, VU, NT, LC, DD, NE)",
 "License is one of (CC0 1.0, CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (2a7e3080-28a9-11dd-97cd-b8a03c50a862, d5ef14a1-5177-4547-9ce2-46d84a4214eb, 04ce62dd-30ec-4d98-8b30-b09cafc3ac38, e4a87e1f-4ff8-4d2e-bcd1-0929678abab9, 28eb1a3f-1c15-4a95-931a-4af90ecb574d, 21bad799-ed1e-46d5-a050-1412369c3f5e)",
 "TaxonKey is Mammalia",
 "Year 1993-2024"
 ]
}

 The dataset includes 89443 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114933-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2abyh8
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Argentina",
 "TaxonKey is Micropsis australis Cabrera"
 ]
}

 The dataset includes 19 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114937-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34622/datarepositorium/o63qrm
title: Motivo VRM003-016

description: Motivo (VRM003-016) figurativo de tipo arma de um escudo de tipo "caetra" utilizado pelo grande guerreiro. Foi gravado com incisões simples e encontra-se no direito centro do suporte. Este motivo encontra-se em bom estado de conservação.
DOI: 10.5281/zenodo.7707567
title: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

description: <strong>MEDDOPLACE</strong> stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish. This repository includes the <strong>Training Set</strong> of the corpus in three different formats (brat, .json, .tsv). For more information, please check the attached README file. MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace. <strong>Related Links:</strong> - MEDDOPLACE website: https://temu.bsc.es/meddoplace - Annotation Guidelines: https://doi.org/10.5281/zenodo.7775234 <strong>License</strong> This work is licensed under a Creative Commons Attribution 4.0 International License. <strong>Contact</strong> If you have any questions or suggestions, please contact us at: - Salvador Lima-López (&lt;salvador [dot] limalopez [at] gmail [dot] com&gt;)<br> - Martin Krallinger (&lt;krallinger [dot] martin [at] gmail [dot] com&gt;)
DOI: 10.5281/zenodo.7845223
title: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

description: <strong>MEDDOPLACE</strong> stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish. This repository includes the <strong>Training Set</strong> of the corpus in three different formats (brat, .json, .tsv), as well as relevant <strong>gazetteers</strong>. For more information, please check the attached README file. ** Update April 19th 2023: A new version has been uploaded, with the following changes: The format for subtask 3 has been changed, with the four columns for each class type being replaced with a single column; A few inconsistencies in the SNOMED normalization have been fixed; A gazetteer has been added for the SNOMED normalization MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace. <strong>Related Links:</strong> - MEDDOPLACE website: https://temu.bsc.es/meddoplace - Annotation Guidelines: https://doi.org/10.5281/zenodo.7775234 <strong>License</strong> This work is licensed under a Creative Commons Attribution 4.0 International License. <strong>Contact</strong> If you have any questions or suggestions, please contact us at: - Salvador Lima-López (&lt;salvador [dot] limalopez [at] gmail [dot] com&gt;)<br> - Martin Krallinger (&lt;krallinger [dot] martin [at] gmail [dot] com&gt;)
DOI: 10.5281/zenodo.7707566
title: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

description: MEDDOPLACE stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish.

This repository includes the corpus' train and test sets in multiple formats, as well as the SNOMED gazetteer, cross-mapping between SNOMED and MeSH and the multilingual silver standard in 8 languages (Catalan, English, French, Italian, Dutch, Portuguese, Romanian and Swedish). For more information, please check the attached README file.

MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace.

 

Please cite if you use this resource:

Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and Martin Krallinger. NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. In Procesamiento del Lenguaje Natural, 67. 2021.

@article{meddoplace,
    title={MEDDOPLACE Shared Task overview: recognition, normalization and classification of locations and patient movement in clinical texts},
    author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Brivá-Iglesias, Vicent and Gasco-Sanchez, Luis and Krallinger, Martin},
journal = {Procesamiento del Lenguaje Natural},
volume = {71},
year={2023},
issn = {1135-5948},DOI = {10.26342/2023-71-23},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561/3961},
pages = {301--311}
}

Related Links:

- MEDDOPLACE website: https://temu.bsc.es/meddoplace

- MEDDOPLACE overview paper: http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561

- Annotation Guidelines (Spanish): https://doi.org/10.5281/zenodo.7775234

- Annotation Guidelines (English): https://doi.org/10.5281/zenodo.7928145

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Contact

If you have any questions or suggestions, please contact us at:

- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)
DOI: 10.5281/zenodo.7795484
title: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

description: <strong>MEDDOPLACE</strong> stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish. This repository includes the <strong>Training Set</strong> of the corpus in three different formats (brat, .json, .tsv). For more information, please check the attached README file. ** Update April 3rd 2023: We've uploaded a new version with separate files for each of the subtracks along with the complete corpus files. Additionally, an error in the previous version's TSV file has been fixed (LUGAR-NATAL attributes weren't correctly added) MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace. <strong>Related Links:</strong> - MEDDOPLACE website: https://temu.bsc.es/meddoplace - Annotation Guidelines: https://doi.org/10.5281/zenodo.7775234 <strong>License</strong> This work is licensed under a Creative Commons Attribution 4.0 International License. <strong>Contact</strong> If you have any questions or suggestions, please contact us at: - Salvador Lima-López (&lt;salvador [dot] limalopez [at] gmail [dot] com&gt;)<br> - Martin Krallinger (&lt;krallinger [dot] martin [at] gmail [dot] com&gt;)
DOI: 10.5281/zenodo.7797872
title: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

description: <strong>MEDDOPLACE</strong> stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish. This repository includes the <strong>Training Set</strong> of the corpus in three different formats (brat, .json, .tsv). For more information, please check the attached README file. ** Update April 3rd 2023: We've uploaded a new version with separate files for each of the subtracks along with the complete corpus files. Additionally, an error in the previous version's TSV file has been fixed (LUGAR-NATAL attributes weren't correctly added) ** Update April 4th 2023: A new version has been uploaded that fixes some small consistency issues detected during the post-processing phase MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace. <strong>Related Links:</strong> - MEDDOPLACE website: https://temu.bsc.es/meddoplace - Annotation Guidelines: https://doi.org/10.5281/zenodo.7775234 <strong>License</strong> This work is licensed under a Creative Commons Attribution 4.0 International License. <strong>Contact</strong> If you have any questions or suggestions, please contact us at: - Salvador Lima-López (&lt;salvador [dot] limalopez [at] gmail [dot] com&gt;)<br> - Martin Krallinger (&lt;krallinger [dot] martin [at] gmail [dot] com&gt;)
DOI: 10.5281/zenodo.7799201
title: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

description: <strong>MEDDOPLACE</strong> stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish. This repository includes the <strong>Training Set</strong> of the corpus in three different formats (brat, .json, .tsv). For more information, please check the attached README file. ** Update April 3rd 2023: We've uploaded a new version with separate files for each of the subtracks along with the complete corpus files. Additionally, an error in the previous version's TSV file has been fixed (LUGAR-NATAL attributes weren't correctly added) ** Update April 4th 2023: A new version has been uploaded that fixes some small consistency issues detected during the post-processing phase MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace. <strong>Related Links:</strong> - MEDDOPLACE website: https://temu.bsc.es/meddoplace - Annotation Guidelines: https://doi.org/10.5281/zenodo.7775234 <strong>License</strong> This work is licensed under a Creative Commons Attribution 4.0 International License. <strong>Contact</strong> If you have any questions or suggestions, please contact us at: - Salvador Lima-López (&lt;salvador [dot] limalopez [at] gmail [dot] com&gt;)<br> - Martin Krallinger (&lt;krallinger [dot] martin [at] gmail [dot] com&gt;)
DOI: 10.5281/zenodo.7895700
title: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

description: <strong>MEDDOPLACE</strong> stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish. This repository includes the <strong>Training Set</strong> of the corpus in three different formats (brat, .json, .tsv), as well as relevant <strong>gazetteers </strong>and the <strong>test set text files</strong>. For more information, please check the attached README file. ** Update May 4th 2023: The test set's text files are now available. MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace. <strong>Related Links:</strong> - MEDDOPLACE website: https://temu.bsc.es/meddoplace - Annotation Guidelines (Spanish): https://doi.org/10.5281/zenodo.7775234 - Annotation Guidelines (English): https://doi.org/10.5281/zenodo.7928145 <strong>License</strong> This work is licensed under a Creative Commons Attribution 4.0 International License. <strong>Contact</strong> If you have any questions or suggestions, please contact us at: - Salvador Lima-López (&lt;salvador [dot] limalopez [at] gmail [dot] com&gt;)<br> - Martin Krallinger (&lt;krallinger [dot] martin [at] gmail [dot] com&gt;)
DOI: 10.5281/zenodo.8017179
title: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

description: <strong>MEDDOPLACE</strong> stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish. This repository includes the <strong>Training Set</strong> of the corpus in three different formats (brat, .json, .tsv), as well as relevant <strong>gazetteers </strong>and the <strong>test set files</strong>. For more information, please check the attached README file. ** Update June 8th 2023: The test set data for sub-tasks 2 and 3 (i.e. gold standard entity annotations) are now available! MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace. <strong>Related Links:</strong> - MEDDOPLACE website: https://temu.bsc.es/meddoplace - Annotation Guidelines (Spanish): https://doi.org/10.5281/zenodo.7775234 - Annotation Guidelines (English): https://doi.org/10.5281/zenodo.7928145 <strong>License</strong> This work is licensed under a Creative Commons Attribution 4.0 International License. <strong>Contact</strong> If you have any questions or suggestions, please contact us at: - Salvador Lima-López (&lt;salvador [dot] limalopez [at] gmail [dot] com&gt;)<br> - Martin Krallinger (&lt;krallinger [dot] martin [at] gmail [dot] com&gt;)
DOI: 10.5281/zenodo.8403498
title: MEDDOPLACE Corpus: Gold Standard annotations for Medical Documents Place-related Content Extraction

description: MEDDOPLACE stands for MEDical DOcument PLAce-related Content Extraction. It is a shared task and set of resources focused on the detection, normalization (entity linking/toponym resolution) and classification of different kinds of places, as well as related types of information such as clinical departments, nationalities or patient movements, in medical documents in Spanish.

This repository includes the corpus' train and test sets in multiple formats, as well as the SNOMED gazetteer, cross-mapping between SNOMED and MeSH and the multilingual silver standard in 8 languages (Catalan, English, French, Italian, Dutch, Portuguese, Romanian and Swedish). For more information, please check the attached README file.

MEDDOPLACE was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of IberLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/meddoplace.

 

Please cite if you use this resource:

Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and Martin Krallinger. NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. In Procesamiento del Lenguaje Natural, 67. 2021.

@article{meddoplace,
    title={MEDDOPLACE Shared Task overview: recognition, normalization and classification of locations and patient movement in clinical texts},
    author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Brivá-Iglesias, Vicent and Gasco-Sanchez, Luis and Krallinger, Martin},
journal = {Procesamiento del Lenguaje Natural},
volume = {71},
year={2023},
issn = {1135-5948},DOI = {10.26342/2023-71-23},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561/3961},
pages = {301--311}
}

Related Links:

- MEDDOPLACE website: https://temu.bsc.es/meddoplace

- MEDDOPLACE overview paper: http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6561

- Annotation Guidelines (Spanish): https://doi.org/10.5281/zenodo.7775234

- Annotation Guidelines (English): https://doi.org/10.5281/zenodo.7928145

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Contact

If you have any questions or suggestions, please contact us at:

- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)
DOI: 10.15468/dl.5d7e4n
title: Occurrence Download

description: A dataset containing 977 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Culex modestus Ficalbi, 1947"
 ]
}

 The dataset includes 977 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0114938-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.