Datasets:

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ftopal

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huggingface-datasets

like 0

[ "region:us" ]

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[ "# Dataset Card for \"SGD_Events\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Events\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_Music\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Music\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_Movies\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Movies\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_Flights\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Flights\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_RideSharing\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_RideSharing\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_RentalCars\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_RentalCars\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_Buses\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Buses\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_Hotels\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Hotels\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_Services\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Services\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_Homes\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Homes\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"SGD_Banks\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Banks\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"SGD_Calendar\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"SGD_Calendar\"\n\nMore Information needed" ]

[ "license:unknown", "region:us" ]

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[ "TAGS\n#license-unknown #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"lolita-dress-ENG\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lolita-dress-ENG\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"stereoset_zero\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"stereoset_zero\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"dalio-reward-model-hackathon-dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dalio-reward-model-hackathon-dataset\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"rick-and-morty-manual-captions\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"rick-and-morty-manual-captions\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"dalio-conversations-hackathon-dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dalio-conversations-hackathon-dataset\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "TAGS\n#region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "TAGS\n#region-us \n", "# Dataset Card for [Dataset Name]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]

[ "task_categories:text-classification", "language:en", "region:us" ]

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "task_categories:text-classification", "language:en", "region:us" ]

[]

[ "en" ]

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "task_categories:text-generation", "task_ids:language-modeling", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:ch", "license:cc", "wikipedia", "region:us" ]

[]

[ "ch" ]

[ "# Dataset Card for Leipzig Corpora Swiss German", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL", "### Dataset Summary\n\nSwiss German Wikipedia corpus based on material from 2021. \nThe corpus gsw_wikipedia_2021 is a Swiss German Wikipedia corpus based on material from 2021. It contains 232,933 sentences and 3,824,547 tokens.", "### Languages\n\nSwiss-German", "## Dataset Structure", "### Data Instances\n\nSingle sentences.", "### Data Fields\n\n'sentence': Text as string.", "### Data Splits", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nURL", "## Additional Information", "### Licensing Information\n\nCreative-Commons-Lizenz CC BY-NC\n\n\n\nLeipzig Corpora Collection: Swiss German Wikipedia corpus based on material from 2021. Leipzig Corpora Collection. Dataset. URL?corpusId=gsw_wikipedia_2021" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #language-Chamorro #license-cc #wikipedia #region-us \n", "# Dataset Card for Leipzig Corpora Swiss German", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL", "### Dataset Summary\n\nSwiss German Wikipedia corpus based on material from 2021. \nThe corpus gsw_wikipedia_2021 is a Swiss German Wikipedia corpus based on material from 2021. It contains 232,933 sentences and 3,824,547 tokens.", "### Languages\n\nSwiss-German", "## Dataset Structure", "### Data Instances\n\nSingle sentences.", "### Data Fields\n\n'sentence': Text as string.", "### Data Splits", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nURL", "## Additional Information", "### Licensing Information\n\nCreative-Commons-Lizenz CC BY-NC\n\n\n\nLeipzig Corpora Collection: Swiss German Wikipedia corpus based on material from 2021. Leipzig Corpora Collection. Dataset. URL?corpusId=gsw_wikipedia_2021" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"yair_gal_small_resized\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"yair_gal_small_resized\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"nlplegal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"nlplegal\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"gal_yair_8300_256x256\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gal_yair_8300_256x256\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"tokenedlegal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"tokenedlegal\"\n\nMore Information needed" ]

[ "language:en", "license:creativeml-openrail-m", "stable-diffusion", "text-to-image", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#language-English #license-creativeml-openrail-m #stable-diffusion #text-to-image #region-us \n" ]

[ "annotations_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "digimon", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Digimon BLIP captions\n\nThis project was inspired by the labelled Pokemon dataset.\n\nThe captions were generated using the BLIP Model found in the LAVIS Library for Language-Vision Intelligence. \n\nLike the Pokemon equivalent, each row in the dataset contains the 'image' and 'text' keys. 'Image' is a varying size pixel jpeg, and 'text' is the corresponding text caption.\n\nIf you use this dataset, please cite it as:" ]

[ "TAGS\n#annotations_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-nc-4.0 #digimon #region-us \n", "# Dataset Card for Digimon BLIP captions\n\nThis project was inspired by the labelled Pokemon dataset.\n\nThe captions were generated using the BLIP Model found in the LAVIS Library for Language-Vision Intelligence. \n\nLike the Pokemon equivalent, each row in the dataset contains the 'image' and 'text' keys. 'Image' is a varying size pixel jpeg, and 'text' is the corresponding text caption.\n\nIf you use this dataset, please cite it as:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"lolita-dress-ENG256\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"lolita-dress-ENG256\"\n\nMore Information needed" ]

[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_categories:image-classification", "task_ids:speaker-identification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "license:cc-by-4.0", "region:us" ]

[]

[]

[ "# Dataset Card for VoxCeleb", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description", "### Dataset Summary\n\nVoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube.\n\nNOTE: Although this dataset can be automatically downloaded, you must manually request credentials to access it from the creators' website.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances\n\nEach datapoint has a path to the audio/video clip along with metadata about the speaker.", "### Data Fields\n\nEach row includes the following fields:\n- 'file': The path to the audio/video clip\n- 'file_format': The file format in which the clip is stored (e.g. 'wav', 'aac', 'mp4')\n- 'dataset_id': The ID of the dataset this clip is from ('vox1', 'vox2')\n- 'speaker_id': The ID of the speaker in this clip\n- 'speaker_gender': The gender of the speaker ('m'/'f')\n- 'speaker_name' (VoxCeleb1 only): The full name of the speaker in the clip\n- 'speaker_nationality' (VoxCeleb1 only): The speaker's country of origin\n- 'video_id': The ID of the video from which this clip was taken\n- 'clip_index': The index of the clip for this specific video\n- 'audio' (Audio dataset only): The audio signal data", "### Data Splits\n\nThe dataset has a predefined dev set and test set. The dev set has been renamed to a \"train\" split.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nThe dataset includes recordings of clips (mostly of celebrities and public figures) from public YouTube videos. The names of speakers in VoxCeleb1 are provided.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\n\n\n\n\nThe VoxCeleb authors request that anyone who uses VoxCeleb1 or VoxCeleb2 includes the following three citations:", "### Contributions\n\nThanks to @101arrowz for adding this dataset." ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-audio-classification #task_categories-image-classification #task_ids-speaker-identification #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #license-cc-by-4.0 #region-us \n", "# Dataset Card for VoxCeleb", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description", "### Dataset Summary\n\nVoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube.\n\nNOTE: Although this dataset can be automatically downloaded, you must manually request credentials to access it from the creators' website.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances\n\nEach datapoint has a path to the audio/video clip along with metadata about the speaker.", "### Data Fields\n\nEach row includes the following fields:\n- 'file': The path to the audio/video clip\n- 'file_format': The file format in which the clip is stored (e.g. 'wav', 'aac', 'mp4')\n- 'dataset_id': The ID of the dataset this clip is from ('vox1', 'vox2')\n- 'speaker_id': The ID of the speaker in this clip\n- 'speaker_gender': The gender of the speaker ('m'/'f')\n- 'speaker_name' (VoxCeleb1 only): The full name of the speaker in the clip\n- 'speaker_nationality' (VoxCeleb1 only): The speaker's country of origin\n- 'video_id': The ID of the video from which this clip was taken\n- 'clip_index': The index of the clip for this specific video\n- 'audio' (Audio dataset only): The audio signal data", "### Data Splits\n\nThe dataset has a predefined dev set and test set. The dev set has been renamed to a \"train\" split.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nThe dataset includes recordings of clips (mostly of celebrities and public figures) from public YouTube videos. The names of speakers in VoxCeleb1 are provided.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\n\n\n\n\nThe VoxCeleb authors request that anyone who uses VoxCeleb1 or VoxCeleb2 includes the following three citations:", "### Contributions\n\nThanks to @101arrowz for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"FGVC_Aircraft_train\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"FGVC_Aircraft_train\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "task_ids:multi-class-classification", "multilinguality:monolingual", "language:ko", "license:cc-by-nc-nd-4.0", "region:us" ]

[]

[ "ko" ]

[ "# Dataset Card for KLAID", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Other Known Limitations\n- Additional Information\n - Other Inquiries\n - Licensing Information\n - Contributions", "## Dataset Description\n- Homepage: URL\n- Leaderboard: URL\n- Point of Contact: klaid@URL", "### Dataset Summary\nKorean Legal Artificial Intelligence Datasets(KLAID) is a dataset for the development of Korean legal artificial intelligence technology. This time we offer 1 task, which is legal judgment prediction(LJP).", "### Supported Tasks and Leaderboards\nLegal Judgment Prediction(LJP)", "### Languages\n'korean'", "### How to use", "## Dataset Structure", "### Data Instances", "#### ljp\nAn example of 'train' looks as follows.\n\n Other References\nYou can refer to each label's 'laws service content' here.\n'Laws service content' is the statute(source) corresponding to each label.", "### Data Fields", "#### ljp\n+ \"fact\": a 'string' feature\n+ \"laws_service\": a 'string' feature\n+ \"laws_service_id\": a classification label, with 177 legal judgment values", "### Data Splits", "#### ljp\n+ train: 161,192", "## Dataset Creation", "### Curation Rationale\nThe legal domain is arguably one of the most expertise fields that require expert knowledge to comprehend. Natural language processing requires many aspects, and we focus on the dataset requirements. As a gold standard is necessary for the testing and the training of a neural model, we hope that our dataset release will help the advances in natural language processing in the legal domain, especially for those for the Korean legal system.", "### Source Data\nThese are datasets based on Korean legal case data.", "### Personal and Sensitive Information\nDue to the nature of legal case data, personal and sensitive information may be included. Therefore, in order to prevent problems that may occur with personal and sensitive information, we proceeded to de-realize the legal case.", "## Considerations for Using the Data", "### Other Known Limitations\nWe plan to upload more data and update them as some of the court records may be revised from now on, based on the ever-evolving legal system.", "## Additional Information", "### Other Inquiries\nklaid@URL", "### Licensing Information\nCopyright 2022-present Law&Company Co. Ltd.\n\nLicensed under the CC-BY-NC-ND-4.0", "### Contributions" ]

[ "TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #multilinguality-monolingual #language-Korean #license-cc-by-nc-nd-4.0 #region-us \n", "# Dataset Card for KLAID", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Other Known Limitations\n- Additional Information\n - Other Inquiries\n - Licensing Information\n - Contributions", "## Dataset Description\n- Homepage: URL\n- Leaderboard: URL\n- Point of Contact: klaid@URL", "### Dataset Summary\nKorean Legal Artificial Intelligence Datasets(KLAID) is a dataset for the development of Korean legal artificial intelligence technology. This time we offer 1 task, which is legal judgment prediction(LJP).", "### Supported Tasks and Leaderboards\nLegal Judgment Prediction(LJP)", "### Languages\n'korean'", "### How to use", "## Dataset Structure", "### Data Instances", "#### ljp\nAn example of 'train' looks as follows.\n\n Other References\nYou can refer to each label's 'laws service content' here.\n'Laws service content' is the statute(source) corresponding to each label.", "### Data Fields", "#### ljp\n+ \"fact\": a 'string' feature\n+ \"laws_service\": a 'string' feature\n+ \"laws_service_id\": a classification label, with 177 legal judgment values", "### Data Splits", "#### ljp\n+ train: 161,192", "## Dataset Creation", "### Curation Rationale\nThe legal domain is arguably one of the most expertise fields that require expert knowledge to comprehend. Natural language processing requires many aspects, and we focus on the dataset requirements. As a gold standard is necessary for the testing and the training of a neural model, we hope that our dataset release will help the advances in natural language processing in the legal domain, especially for those for the Korean legal system.", "### Source Data\nThese are datasets based on Korean legal case data.", "### Personal and Sensitive Information\nDue to the nature of legal case data, personal and sensitive information may be included. Therefore, in order to prevent problems that may occur with personal and sensitive information, we proceeded to de-realize the legal case.", "## Considerations for Using the Data", "### Other Known Limitations\nWe plan to upload more data and update them as some of the court records may be revised from now on, based on the ever-evolving legal system.", "## Additional Information", "### Other Inquiries\nklaid@URL", "### Licensing Information\nCopyright 2022-present Law&Company Co. Ltd.\n\nLicensed under the CC-BY-NC-ND-4.0", "### Contributions" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dataset-v-1.5\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset-v-1.5\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"amazon-shoe-reviews\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"amazon-shoe-reviews\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"msp_train_hubert\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"msp_train_hubert\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"msp_val_hubert\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"msp_val_hubert\"\n\nMore Information needed" ]

[ "task_categories:text-generation", "task_categories:text-classification", "task_ids:language-modeling", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:ch", "license:cc-by-nc-4.0", "dialect", "region:us" ]

[]

[ "ch" ]

[ "# Dataset Card for ArchiMod Corpus", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL", "### Dataset Summary\n\nThe ArchiMob corpus represents German linguistic varieties spoken within the territory of Switzerland. This corpus is the first electronic resource containing long samples of transcribed text in Swiss German, intended for studying the spatial distribution of morphosyntactic features and for natural language processing.", "### Languages\n\nSwiss-German", "## Dataset Structure", "### Data Instances\n\n''\n{\n 'sentence': Sentence in Swiss-German,\n 'label': Dialect as category\n}\n''", "### Data Fields\n\n'sentence': Text as string.\n'label': Label as string.", "### Data Splits", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nURL", "## Additional Information", "### Licensing Information\n\nCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\n\n\n\nScherrer, Y., T. Samardžić, E. Glaser (2019). \"Digitising Swiss German -- How to process and study a polycentric spoken language\". Language Resources and Evaluation. (First online) \n\nScherrer, Y., T. Samardžić, E. Glaser (2019). \"ArchiMob: Ein multidialektales Korpus schweizerdeutscher Spontansprache\". Linguistik Online, 98(5), 425-454. URL" ]

[ "TAGS\n#task_categories-text-generation #task_categories-text-classification #task_ids-language-modeling #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-Chamorro #license-cc-by-nc-4.0 #dialect #region-us \n", "# Dataset Card for ArchiMod Corpus", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL", "### Dataset Summary\n\nThe ArchiMob corpus represents German linguistic varieties spoken within the territory of Switzerland. This corpus is the first electronic resource containing long samples of transcribed text in Swiss German, intended for studying the spatial distribution of morphosyntactic features and for natural language processing.", "### Languages\n\nSwiss-German", "## Dataset Structure", "### Data Instances\n\n''\n{\n 'sentence': Sentence in Swiss-German,\n 'label': Dialect as category\n}\n''", "### Data Fields\n\n'sentence': Text as string.\n'label': Label as string.", "### Data Splits", "## Dataset Creation", "### Source Data", "#### Initial Data Collection and Normalization\n\nURL", "## Additional Information", "### Licensing Information\n\nCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\n\n\n\nScherrer, Y., T. Samardžić, E. Glaser (2019). \"Digitising Swiss German -- How to process and study a polycentric spoken language\". Language Resources and Evaluation. (First online) \n\nScherrer, Y., T. Samardžić, E. Glaser (2019). \"ArchiMob: Ein multidialektales Korpus schweizerdeutscher Spontansprache\". Linguistik Online, 98(5), 425-454. URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"multi-label-classification-test-small\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"multi-label-classification-test-small\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"dataset-v-1.4\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset-v-1.4\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"legaltokenized512\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"legaltokenized512\"\n\nMore Information needed" ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for AnEM", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,COREF,RE\n\n\nAnEM corpus is a domain- and species-independent resource manually annotated for anatomical\nentity mentions using a fine-grained classification system. The corpus consists of 500 documents\n(over 90,000 words) selected randomly from citation abstracts and full-text papers with\nthe aim of making the corpus representative of the entire available biomedical scientific\nliterature. The corpus annotation covers mentions of both healthy and pathological anatomical\nentities and contains over 3,000 annotated mentions." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-sa-3.0 #region-us \n", "# Dataset Card for AnEM", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,COREF,RE\n\n\nAnEM corpus is a domain- and species-independent resource manually annotated for anatomical\nentity mentions using a fine-grained classification system. The corpus consists of 500 documents\n(over 90,000 words) selected randomly from citation abstracts and full-text papers with\nthe aim of making the corpus representative of the entire available biomedical scientific\nliterature. The corpus annotation covers mentions of both healthy and pathological anatomical\nentities and contains over 3,000 annotated mentions." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for AnatEM", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 documents (approx. 250,000 words) manually annotated to identify over 13,000 mentions of anatomical entities. Each annotation is assigned one of 12 granularity-based types such as Cellular component, Tissue and Organ, defined with reference to the Common Anatomy Reference Ontology." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-sa-3.0 #region-us \n", "# Dataset Card for AnatEM", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 documents (approx. 250,000 words) manually annotated to identify over 13,000 mentions of anatomical entities. Each annotation is assigned one of 12 granularity-based types such as Cellular component, Tissue and Organ, defined with reference to the Common Anatomy Reference Ontology." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for AskAPatient", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\n\nThe AskAPatient dataset contains medical concepts written on social media mapped to how they are formally written in medical ontologies (SNOMED-CT and AMT)." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for AskAPatient", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\n\nThe AskAPatient dataset contains medical concepts written on social media mapped to how they are formally written in medical ontologies (SNOMED-CT and AMT)." ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "license:creativeml-openrail-m", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-creativeml-openrail-m #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"pile-tokenized-2b\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pile-tokenized-2b\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"c4-code-tokenized-2b\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"c4-code-tokenized-2b\"\n\nMore Information needed" ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BC5CDR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED,RE\n\n\nThe BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated text corpus of human annotations of all chemicals, diseases and their interactions in 1,500 PubMed articles." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BC5CDR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED,RE\n\n\nThe BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated text corpus of human annotations of all chemicals, diseases and their interactions in 1,500 PubMed articles." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BC7-LitCovid", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: TXTCLASS\n\n\nThe training and development datasets contain the publicly-available text of over 30 thousand COVID-19-related articles and their metadata (e.g., title, abstract, journal). Articles in both datasets have been manually reviewed and articles annotated by in-house models." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for BC7-LitCovid", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: TXTCLASS\n\n\nThe training and development datasets contain the publicly-available text of over 30 thousand COVID-19-related articles and their metadata (e.g., title, abstract, journal). Articles in both datasets have been manually reviewed and articles annotated by in-house models." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Bio-SimVerb", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: STS\n\n\n\nThis repository contains the evaluation datasets for the paper Bio-SimVerb and Bio-SimLex: Wide-coverage Evaluation Sets of Word Similarity in Biomedicine by Billy Chiu, Sampo Pyysalo and Anna Korhonen." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for Bio-SimVerb", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: STS\n\n\n\nThis repository contains the evaluation datasets for the paper Bio-SimVerb and Bio-SimLex: Wide-coverage Evaluation Sets of Word Similarity in Biomedicine by Billy Chiu, Sampo Pyysalo and Anna Korhonen." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Bio-SimLex", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: STS\n\n\nBio-SimLex enables intrinsic evaluation of word representations. This evaluation can serve as a predictor of performance on various downstream tasks in the biomedical domain. The results on Bio-SimLex using standard word representation models highlight the importance of developing dedicated evaluation resources for NLP in biomedicine for particular word classes (e.g. verbs)." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for Bio-SimLex", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: STS\n\n\nBio-SimLex enables intrinsic evaluation of word representations. This evaluation can serve as a predictor of performance on various downstream tasks in the biomedical domain. The results on Bio-SimLex using standard word representation models highlight the importance of developing dedicated evaluation resources for NLP in biomedicine for particular word classes (e.g. verbs)." ]

[ "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for MESINESP 2021", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\nThe main aim of MESINESP2 is to promote the development of practically relevant semantic indexing tools for biomedical content in non-English language. We have generated a manually annotated corpus, where domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts. All the documents were labeled with DeCS descriptors, which is a structured controlled vocabulary created by BIREME to index scientific publications on BvSalud, the largest database of scientific documents in Spanish, which hosts records from the databases LILACS, MEDLINE, IBECS, among others.\n\nMESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS to different types of biomedical documents. To that purpose, we have divided the task into three subtracks depending on the document type. Then, for each one we generated an annotated corpus which was provided to participating teams:\n\n- [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all Spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish.\n- [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC API.\n- [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in Spanish extracted from Google Patents which have the IPC code “A61P” and “A61K31”. In addition, we also provide a set of complementary data such as: the DeCS terminology file, a silver standard with the participants' predictions to the task background set and the entities of medications, diseases, symptoms and medical procedures extracted from the BSC NERs documents." ]

[ "TAGS\n#multilinguality-monolingual #language-Spanish #license-cc-by-4.0 #region-us \n", "# Dataset Card for MESINESP 2021", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\nThe main aim of MESINESP2 is to promote the development of practically relevant semantic indexing tools for biomedical content in non-English language. We have generated a manually annotated corpus, where domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts. All the documents were labeled with DeCS descriptors, which is a structured controlled vocabulary created by BIREME to index scientific publications on BvSalud, the largest database of scientific documents in Spanish, which hosts records from the databases LILACS, MEDLINE, IBECS, among others.\n\nMESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS to different types of biomedical documents. To that purpose, we have divided the task into three subtracks depending on the document type. Then, for each one we generated an annotated corpus which was provided to participating teams:\n\n- [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all Spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish.\n- [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC API.\n- [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in Spanish extracted from Google Patents which have the IPC code “A61P” and “A61K31”. In addition, we also provide a set of complementary data such as: the DeCS terminology file, a silver standard with the participants' predictions to the task background set and the entities of medications, diseases, symptoms and medical procedures extracted from the BSC NERs documents." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioASQ Task C 2017", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: TXTCLASS\n\n\nThe training data set for this task contains annotated biomedical articles\npublished in PubMed and corresponding full text from PMC. By annotated is meant\nthat GrantIDs and corresponding Grant Agencies have been identified in the full\ntext of articles" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BioASQ Task C 2017", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: False\n- Tasks: TXTCLASS\n\n\nThe training data set for this task contains annotated biomedical articles\npublished in PubMed and corresponding full text from PMC. By annotated is meant\nthat GrantIDs and corresponding Grant Agencies have been identified in the full\ntext of articles" ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioInfer", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE,NER\n\n\nA corpus targeted at protein, gene, and RNA relationships which serves as a\nresource for the development of information extraction systems and their\ncomponents such as parsers and domain analyzers. Currently, the corpus contains\n1100 sentences from abstracts of biomedical research articles annotated for\nrelationships, named entities, as well as syntactic dependencies." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-2.0 #region-us \n", "# Dataset Card for BioInfer", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE,NER\n\n\nA corpus targeted at protein, gene, and RNA relationships which serves as a\nresource for the development of information extraction systems and their\ncomponents such as parsers and domain analyzers. Currently, the corpus contains\n1100 sentences from abstracts of biomedical research articles annotated for\nrelationships, named entities, as well as syntactic dependencies." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BiologyHowWhyCorpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: QA\n\n\nThis dataset consists of 185 \"how\" and 193 \"why\" biology questions authored by a domain expert, with one or more gold \nanswer passages identified in an undergraduate textbook. The expert was not constrained in any way during the \nannotation process, so gold answers might be smaller than a paragraph or span multiple paragraphs. This dataset was \nused for the question-answering system described in the paper “Discourse Complements Lexical Semantics for Non-factoid \nAnswer Reranking” (ACL 2014)." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for BiologyHowWhyCorpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: QA\n\n\nThis dataset consists of 185 \"how\" and 193 \"why\" biology questions authored by a domain expert, with one or more gold \nanswer passages identified in an undergraduate textbook. The expert was not constrained in any way during the \nannotation process, so gold answers might be smaller than a paragraph or span multiple paragraphs. This dataset was \nused for the question-answering system described in the paper “Discourse Complements Lexical Semantics for Non-factoid \nAnswer Reranking” (ACL 2014)." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BIOMRC", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: QA\n\n\nWe introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the\nprevious BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the\nnew dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating\nthat the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is\nalso higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new\nBERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or\nsurpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different\nsizes, also releasing our code, and providing a leaderboard." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for BIOMRC", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: QA\n\n\nWe introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the\nprevious BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the\nnew dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating\nthat the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is\nalso higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new\nBERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or\nsurpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different\nsizes, also releasing our code, and providing a leaderboard." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2009", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,EE,COREF\n\n\nThe BioNLP Shared Task 2009 was organized by GENIA Project and its corpora were curated based\non the annotations of the publicly available GENIA Event corpus and an unreleased (blind) section\nof the GENIA Event corpus annotations, used for evaluation." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BioNLP 2009", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,EE,COREF\n\n\nThe BioNLP Shared Task 2009 was organized by GENIA Project and its corpora were curated based\non the annotations of the publicly available GENIA Event corpus and an unreleased (blind) section\nof the GENIA Event corpus annotations, used for evaluation." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2011 EPI", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,COREF\n\n\nThe dataset of the Epigenetics and Post-translational Modifications (EPI) task\nof BioNLP Shared Task 2011." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BioNLP 2011 EPI", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,COREF\n\n\nThe dataset of the Epigenetics and Post-translational Modifications (EPI) task\nof BioNLP Shared Task 2011." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2011 GE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,COREF\n\n\nThe BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical\ndocuments, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE.\nThe GENIA task aims at extracting events occurring upon genes or gene products, which are typed as \"Protein\"\nwithout differentiating genes from gene products. Other types of physical entities, e.g. cells, cell components,\nare not differentiated from each other, and their type is given as \"Entity\"." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-3.0 #region-us \n", "# Dataset Card for BioNLP 2011 GE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,COREF\n\n\nThe BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical\ndocuments, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE.\nThe GENIA task aims at extracting events occurring upon genes or gene products, which are typed as \"Protein\"\nwithout differentiating genes from gene products. Other types of physical entities, e.g. cells, cell components,\nare not differentiated from each other, and their type is given as \"Entity\"." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2011 ID", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,COREF,NER\n\n\nThe dataset of the Infectious Diseases (ID) task of\nBioNLP Shared Task 2011." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BioNLP 2011 ID", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,COREF,NER\n\n\nThe dataset of the Infectious Diseases (ID) task of\nBioNLP Shared Task 2011." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2011 REL", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE,COREF\n\n\nThe Entity Relations (REL) task is a supporting task of the BioNLP Shared Task 2011.\nThe task concerns the extraction of two types of part-of relations between a\ngene/protein and an associated entity." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BioNLP 2011 REL", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE,COREF\n\n\nThe Entity Relations (REL) task is a supporting task of the BioNLP Shared Task 2011.\nThe task concerns the extraction of two types of part-of relations between a\ngene/protein and an associated entity." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2013 CG", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,COREF\n\n\nthe Cancer Genetics (CG) is a event extraction task and a main task of the BioNLP Shared Task (ST) 2013.\nThe CG task is an information extraction task targeting the recognition of events in text,\nrepresented as structured n-ary associations of given physical entities. In addition to\naddressing the cancer domain, the CG task is differentiated from previous event extraction\ntasks in the BioNLP ST series in addressing a wide range of pathological processes and multiple\nlevels of biological organization, ranging from the molecular through the cellular and organ\nlevels up to whole organisms. Final test set submissions were accepted from six teams" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BioNLP 2013 CG", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,COREF\n\n\nthe Cancer Genetics (CG) is a event extraction task and a main task of the BioNLP Shared Task (ST) 2013.\nThe CG task is an information extraction task targeting the recognition of events in text,\nrepresented as structured n-ary associations of given physical entities. In addition to\naddressing the cancer domain, the CG task is differentiated from previous event extraction\ntasks in the BioNLP ST series in addressing a wide range of pathological processes and multiple\nlevels of biological organization, ranging from the molecular through the cellular and organ\nlevels up to whole organisms. Final test set submissions were accepted from six teams" ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2013 GE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,RE,COREF\n\n\nThe BioNLP-ST GE task has been promoting development of fine-grained\ninformation extraction (IE) from biomedical\ndocuments, since 2009. Particularly, it has focused on the domain of\nNFkB as a model domain of Biomedical IE" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BioNLP 2013 GE", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,RE,COREF\n\n\nThe BioNLP-ST GE task has been promoting development of fine-grained\ninformation extraction (IE) from biomedical\ndocuments, since 2009. Particularly, it has focused on the domain of\nNFkB as a model domain of Biomedical IE" ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2013 GRO", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,RE\n\n\nGRO Task: Populating the Gene Regulation Ontology with events and\nrelations. A data set from the bio NLP shared tasks competition from 2013" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BioNLP 2013 GRO", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,RE\n\n\nGRO Task: Populating the Gene Regulation Ontology with events and\nrelations. A data set from the bio NLP shared tasks competition from 2013" ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2013 PC", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,COREF\n\n\nthe Pathway Curation (PC) task is a main event extraction task of the BioNLP shared task (ST) 2013.\nThe PC task concerns the automatic extraction of biomolecular reactions from text.\nThe task setting, representation and semantics are defined with respect to pathway\nmodel standards and ontologies (SBML, BioPAX, SBO) and documents selected by relevance\nto specific model reactions. Two BioNLP ST 2013 participants successfully completed\nthe PC task. The highest achieved F-score, 52.8%, indicates that event extraction is\na promising approach to supporting pathway curation efforts." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for BioNLP 2013 PC", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: EE,NER,COREF\n\n\nthe Pathway Curation (PC) task is a main event extraction task of the BioNLP shared task (ST) 2013.\nThe PC task concerns the automatic extraction of biomolecular reactions from text.\nThe task setting, representation and semantics are defined with respect to pathway\nmodel standards and ontologies (SBML, BioPAX, SBO) and documents selected by relevance\nto specific model reactions. Two BioNLP ST 2013 participants successfully completed\nthe PC task. The highest achieved F-score, 52.8%, indicates that event extraction is\na promising approach to supporting pathway curation efforts." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioNLP 2019 BB", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED,RE\n\n\nThe task focuses on the extraction of the locations and phenotypes of\nmicroorganisms from PubMed abstracts and full-text excerpts, and the\ncharacterization of these entities with respect to reference knowledge\nsources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by\nthe importance of the knowledge on biodiversity for fundamental research\nand applications in microbiology." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for BioNLP 2019 BB", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED,RE\n\n\nThe task focuses on the extraction of the locations and phenotypes of\nmicroorganisms from PubMed abstracts and full-text excerpts, and the\ncharacterization of these entities with respect to reference knowledge\nsources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by\nthe importance of the knowledge on biodiversity for fundamental research\nand applications in microbiology." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "arxiv:2204.04263", "region:us" ]

[ "2204.04263" ]

[ "en" ]

[ "# Dataset Card for BioRED", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nRelation Extraction corpus with multiple entity types (e.g., gene/protein,\ndisease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical),\non a set of 600 PubMed articles" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #arxiv-2204.04263 #region-us \n", "# Dataset Card for BioRED", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nRelation Extraction corpus with multiple entity types (e.g., gene/protein,\ndisease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical),\non a set of 600 PubMed articles" ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioRelEx", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED,RE,COREF\n\n\nBioRelEx is a biological relation extraction dataset. Version 1.0 contains 2010\nannotated sentences that describe binding interactions between various\nbiological entities (proteins, chemicals, etc.). 1405 sentences are for\ntraining, another 201 sentences are for validation. They are publicly available\nat URL Another 404 sentences are for\ntesting which are kept private for at this Codalab competition\nURL All sentences contain words\n\"bind\", \"bound\" or \"binding\". For every sentence we provide: 1) Complete\nannotations of all biological entities that appear in the sentence 2) Entity\ntypes (32 types) and grounding information for most of the proteins and families\n(links to uniprot, interpro and other databases) 3) Coreference between entities\nin the same sentence (e.g. abbreviations and synonyms) 4) Binding interactions\nbetween the annotated entities 5) Binding interaction types: positive, negative\n(A does not bind B) and neutral (A may bind to B)" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for BioRelEx", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED,RE,COREF\n\n\nBioRelEx is a biological relation extraction dataset. Version 1.0 contains 2010\nannotated sentences that describe binding interactions between various\nbiological entities (proteins, chemicals, etc.). 1405 sentences are for\ntraining, another 201 sentences are for validation. They are publicly available\nat URL Another 404 sentences are for\ntesting which are kept private for at this Codalab competition\nURL All sentences contain words\n\"bind\", \"bound\" or \"binding\". For every sentence we provide: 1) Complete\nannotations of all biological entities that appear in the sentence 2) Entity\ntypes (32 types) and grounding information for most of the proteins and families\n(links to uniprot, interpro and other databases) 3) Coreference between entities\nin the same sentence (e.g. abbreviations and synonyms) 4) Binding interactions\nbetween the annotated entities 5) Binding interaction types: positive, negative\n(A does not bind B) and neutral (A may bind to B)" ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for BioScope", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe BioScope corpus consists of medical and biological texts annotated for\nnegation, speculation and their linguistic scope. This was done to allow a\ncomparison between the development of systems for negation/hedge detection and\nscope resolution. The BioScope corpus was annotated by two independent linguists\nfollowing the guidelines written by our linguist expert before the annotation of\nthe corpus was initiated." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-2.0 #region-us \n", "# Dataset Card for BioScope", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe BioScope corpus consists of medical and biological texts annotated for\nnegation, speculation and their linguistic scope. This was done to allow a\ncomparison between the development of systems for negation/hedge detection and\nscope resolution. The BioScope corpus was annotated by two independent linguists\nfollowing the guidelines written by our linguist expert before the annotation of\nthe corpus was initiated." ]

[ "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for CANTEMIST", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,NED,TXTCLASS\n\n\nCollection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).\n\nThe original dataset is distributed in Brat format, and was randomly sampled into 3 subsets. The training, development and test sets contain 501, 500 and 300 documents each, respectively.\n\nThis dataset was designed for the CANcer TExt Mining Shared Task, sponsored by Plan-TL. The task is divided in 3 subtasks: CANTEMIST-NER, CANTEMIST_NORM and CANTEMIST-CODING.\n\nCANTEMIST-NER track: requires finding automatically tumor morphology mentions. All tumor morphology mentions are defined by their corresponding character offsets in UTF-8 plain text medical documents. \n\nCANTEMIST-NORM track: clinical concept normalization or named entity normalization task that requires to return all tumor morphology entity mentions together with their corresponding eCIE-O-3.1 codes i.e. finding and normalizing tumor morphology mentions.\n\nCANTEMIST-CODING track: requires returning for each of document a ranked list of its corresponding ICD-O-3 codes. This it is essentially a sort of indexing or multi-label classification task or oncology clinical coding. \n\nFor further information, please visit URL or send an email to encargo-pln-life@URL" ]

[ "TAGS\n#multilinguality-monolingual #language-Spanish #license-cc-by-4.0 #region-us \n", "# Dataset Card for CANTEMIST", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,NED,TXTCLASS\n\n\nCollection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).\n\nThe original dataset is distributed in Brat format, and was randomly sampled into 3 subsets. The training, development and test sets contain 501, 500 and 300 documents each, respectively.\n\nThis dataset was designed for the CANcer TExt Mining Shared Task, sponsored by Plan-TL. The task is divided in 3 subtasks: CANTEMIST-NER, CANTEMIST_NORM and CANTEMIST-CODING.\n\nCANTEMIST-NER track: requires finding automatically tumor morphology mentions. All tumor morphology mentions are defined by their corresponding character offsets in UTF-8 plain text medical documents. \n\nCANTEMIST-NORM track: clinical concept normalization or named entity normalization task that requires to return all tumor morphology entity mentions together with their corresponding eCIE-O-3.1 codes i.e. finding and normalizing tumor morphology mentions.\n\nCANTEMIST-CODING track: requires returning for each of document a ranked list of its corresponding ICD-O-3 codes. This it is essentially a sort of indexing or multi-label classification task or oncology clinical coding. \n\nFor further information, please visit URL or send an email to encargo-pln-life@URL" ]

[ "multilinguality:monolingual", "language:fr", "license:other", "region:us" ]

[]

[ "fr" ]

[ "# Dataset Card for CAS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TXTCLASS\n\n\nWe manually annotated two corpora from the biomedical field. The ESSAI corpus contains clinical trial protocols in French. They were mainly obtained from the National Cancer Institute The typical protocol consists of two parts: the summary of the trial, which indicates the purpose of the trial and the methods applied; and a detailed description of the trial with the inclusion and exclusion criteria. The CAS corpus contains clinical cases published in scientific literature and training material. They are published in different journals from French-speaking countries (France, Belgium, Switzerland, Canada, African countries, tropical countries) and are related to various medical specialties (cardiology, urology, oncology, obstetrics, pulmonology, gastro-enterology). The purpose of clinical cases is to describe clinical situations of patients. Hence, their content is close to the content of clinical narratives (description of diagnoses, treatments or procedures, evolution, family history, expected audience, etc.). In clinical cases, the negation is frequently used for describing the patient signs, symptoms, and diagnosis. Speculation is present as well but less frequently.\n\nThis version only contain the annotated CAS corpus" ]

[ "TAGS\n#multilinguality-monolingual #language-French #license-other #region-us \n", "# Dataset Card for CAS", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TXTCLASS\n\n\nWe manually annotated two corpora from the biomedical field. The ESSAI corpus contains clinical trial protocols in French. They were mainly obtained from the National Cancer Institute The typical protocol consists of two parts: the summary of the trial, which indicates the purpose of the trial and the methods applied; and a detailed description of the trial with the inclusion and exclusion criteria. The CAS corpus contains clinical cases published in scientific literature and training material. They are published in different journals from French-speaking countries (France, Belgium, Switzerland, Canada, African countries, tropical countries) and are related to various medical specialties (cardiology, urology, oncology, obstetrics, pulmonology, gastro-enterology). The purpose of clinical cases is to describe clinical situations of patients. Hence, their content is close to the content of clinical narratives (description of diagnoses, treatments or procedures, evolution, family history, expected audience, etc.). In clinical cases, the negation is frequently used for describing the patient signs, symptoms, and diagnosis. Speculation is present as well but less frequently.\n\nThis version only contain the annotated CAS corpus" ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for CellFinder", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe CellFinder project aims to create a stem cell data repository by linking information from existing public databases and by performing text mining on the research literature. The first version of the corpus is composed of 10 full text documents containing more than 2,100 sentences, 65,000 tokens and 5,200 annotations for entities. The corpus has been annotated with six types of entities (anatomical parts, cell components, cell lines, cell types, genes/protein and species) with an overall inter-annotator agreement around 80%.\n\nSee: URL" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-sa-3.0 #region-us \n", "# Dataset Card for CellFinder", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe CellFinder project aims to create a stem cell data repository by linking information from existing public databases and by performing text mining on the research literature. The first version of the corpus is composed of 10 full text documents containing more than 2,100 sentences, 65,000 tokens and 5,200 annotations for entities. The corpus has been annotated with six types of entities (anatomical parts, cell components, cell lines, cell types, genes/protein and species) with an overall inter-annotator agreement around 80%.\n\nSee: URL" ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for CHEBI Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nThe ChEBI corpus contains 199 annotated abstracts and 100 annotated full papers.\nAll documents in the corpus have been annotated for named entities and relations\nbetween these. In total, our corpus provides over 15000 named entity annotations\nand over 6,000 relations between entities." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for CHEBI Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nThe ChEBI corpus contains 199 annotated abstracts and 100 annotated full papers.\nAll documents in the corpus have been annotated for named entities and relations\nbetween these. In total, our corpus provides over 15000 named entity annotations\nand over 6,000 relations between entities." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for CHEMDNER", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,TXTCLASS\n\n\nWe present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that\ncontain a total of 84,355 chemical entity mentions labeled manually by expert\nchemistry literature curators, following annotation guidelines specifically\ndefined for this task. The abstracts of the CHEMDNER corpus were selected to be\nrepresentative for all major chemical disciplines. Each of the chemical entity\nmentions was manually labeled according to its structure-associated chemical\nentity mention (SACEM) class: abbreviation, family, formula, identifier,\nmultiple, systematic and trivial." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for CHEMDNER", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,TXTCLASS\n\n\nWe present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that\ncontain a total of 84,355 chemical entity mentions labeled manually by expert\nchemistry literature curators, following annotation guidelines specifically\ndefined for this task. The abstracts of the CHEMDNER corpus were selected to be\nrepresentative for all major chemical disciplines. Each of the chemical entity\nmentions was manually labeled according to its structure-associated chemical\nentity mention (SACEM) class: abbreviation, family, formula, identifier,\nmultiple, systematic and trivial." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for ChemProt", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE,NER\n\n\nThe BioCreative VI Chemical-Protein interaction dataset identifies entities of\nchemicals and proteins and their likely relation to one other. Compounds are\ngenerally agonists (activators) or antagonists (inhibitors) of proteins." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for ChemProt", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE,NER\n\n\nThe BioCreative VI Chemical-Protein interaction dataset identifies entities of\nchemicals and proteins and their likely relation to one other. Compounds are\ngenerally agonists (activators) or antagonists (inhibitors) of proteins." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for CHIA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,RE\n\n\nA large annotated corpus of patient eligibility criteria extracted from 1,000\ninterventional, Phase IV clinical trials registered in URL. This\ndataset includes 12,409 annotated eligibility criteria, represented by 41,487\ndistinctive entities of 15 entity types and 25,017 relationships of 12\nrelationship types." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-4.0 #region-us \n", "# Dataset Card for CHIA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,RE\n\n\nA large annotated corpus of patient eligibility criteria extracted from 1,000\ninterventional, Phase IV clinical trials registered in URL. This\ndataset includes 12,409 annotated eligibility criteria, represented by 41,487\ndistinctive entities of 15 entity types and 25,017 relationships of 12\nrelationship types." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Citation GIA Test Collection", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe Citation GIA Test Collection was recently created for gene indexing at the\nNLM and includes 151 PubMed abstracts with both mention-level and document-level\nannotations. They are selected because both have a focus on human genes." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for Citation GIA Test Collection", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nThe Citation GIA Test Collection was recently created for gene indexing at the\nNLM and includes 151 PubMed abstracts with both mention-level and document-level\nannotations. They are selected because both have a focus on human genes." ]

[ "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for CodiEsp", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS,NER,NED\n\n\nSynthetic corpus of 1,000 manually selected clinical case studies in Spanish\nthat was designed for the Clinical Case Coding in Spanish Shared Task, as part\nof the CLEF 2020 conference.\n\nThe goal of the task was to automatically assign ICD10 codes (CIE-10, in\nSpanish) to clinical case documents, being evaluated against manually generated\nICD10 codifications. The CodiEsp corpus was selected manually by practicing\nphysicians and clinical documentalists and annotated by clinical coding\nprofessionals meeting strict quality criteria. They reached an inter-annotator\nagreement of 88.6% for diagnosis coding, 88.9% for procedure coding and 80.5%\nfor the textual reference annotation.\n\nThe final collection of 1,000 clinical cases that make up the corpus had a total\nof 16,504 sentences and 396,988 words. All documents are in Spanish language and\nCIE10 is the coding terminology (the Spanish version of ICD10-CM and ICD10-PCS).\nThe CodiEsp corpus has been randomly sampled into three subsets. The train set\ncontains 500 clinical cases, while the development and test sets have 250\nclinical cases each. In addition to these, a collection of 176,294 abstracts\nfrom Lilacs and Ibecs with the corresponding ICD10 codes (ICD10-CM and\nICD10-PCS) was provided by the task organizers. Every abstract has at least one\nassociated code, with an average of 2.5 ICD10 codes per abstract.\n\nThe CodiEsp track was divided into three sub-tracks (2 main and 1 exploratory):\n\n- CodiEsp-D: The Diagnosis Coding sub-task, which requires automatic ICD10-CM\n [CIE10-Diagnóstico] code assignment.\n- CodiEsp-P: The Procedure Coding sub-task, which requires automatic ICD10-PCS\n [CIE10-Procedimiento] code assignment.\n- CodiEsp-X: The Explainable AI exploratory sub-task, which requires to submit\n the reference to the predicted codes (both ICD10-CM and ICD10-PCS). The goal \n of this novel task was not only to predict the correct codes but also to \n present the reference in the text that supports the code predictions.\n\nFor further information, please visit URL or send an\nemail to encargo-pln-life@URL" ]

[ "TAGS\n#multilinguality-monolingual #language-Spanish #license-cc-by-4.0 #region-us \n", "# Dataset Card for CodiEsp", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS,NER,NED\n\n\nSynthetic corpus of 1,000 manually selected clinical case studies in Spanish\nthat was designed for the Clinical Case Coding in Spanish Shared Task, as part\nof the CLEF 2020 conference.\n\nThe goal of the task was to automatically assign ICD10 codes (CIE-10, in\nSpanish) to clinical case documents, being evaluated against manually generated\nICD10 codifications. The CodiEsp corpus was selected manually by practicing\nphysicians and clinical documentalists and annotated by clinical coding\nprofessionals meeting strict quality criteria. They reached an inter-annotator\nagreement of 88.6% for diagnosis coding, 88.9% for procedure coding and 80.5%\nfor the textual reference annotation.\n\nThe final collection of 1,000 clinical cases that make up the corpus had a total\nof 16,504 sentences and 396,988 words. All documents are in Spanish language and\nCIE10 is the coding terminology (the Spanish version of ICD10-CM and ICD10-PCS).\nThe CodiEsp corpus has been randomly sampled into three subsets. The train set\ncontains 500 clinical cases, while the development and test sets have 250\nclinical cases each. In addition to these, a collection of 176,294 abstracts\nfrom Lilacs and Ibecs with the corresponding ICD10 codes (ICD10-CM and\nICD10-PCS) was provided by the task organizers. Every abstract has at least one\nassociated code, with an average of 2.5 ICD10 codes per abstract.\n\nThe CodiEsp track was divided into three sub-tracks (2 main and 1 exploratory):\n\n- CodiEsp-D: The Diagnosis Coding sub-task, which requires automatic ICD10-CM\n [CIE10-Diagnóstico] code assignment.\n- CodiEsp-P: The Procedure Coding sub-task, which requires automatic ICD10-PCS\n [CIE10-Procedimiento] code assignment.\n- CodiEsp-X: The Explainable AI exploratory sub-task, which requires to submit\n the reference to the predicted codes (both ICD10-CM and ICD10-PCS). The goal \n of this novel task was not only to predict the correct codes but also to \n present the reference in the text that supports the code predictions.\n\nFor further information, please visit URL or send an\nemail to encargo-pln-life@URL" ]

[ "multilinguality:monolingual", "language:es", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for CT-EBM-SP", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER", "### Ctebmsp Abstracts\n\nThe \"abstracts\" subset of the Clinical Trials for Evidence-Based Medicine in Spanish\n(CT-EBM-SP) corpus contains 500 abstracts of clinical trial studies in Spanish,\npublished in journals with a Creative Commons license. Most were downloaded from\nthe SciELO repository and free abstracts in PubMed.\n\nAbstracts were retrieved with the query:\nClinical Trial[ptyp] AND “loattrfree full text”[sb] AND “spanish”[la].\n\n(Information collected from 10.1186/s12911-021-01395-z)", "### Ctebmsp Eudract\n\nThe \"abstracts\" subset of the Clinical Trials for Evidence-Based Medicine in Spanish\n(CT-EBM-SP) corpus contains 500 abstracts of clinical trial studies in Spanish,\npublished in journals with a Creative Commons license. Most were downloaded from\nthe SciELO repository and free abstracts in PubMed.\n\nAbstracts were retrieved with the query:\nClinical Trial[ptyp] AND “loattrfree full text”[sb] AND “spanish”[la].\n\n(Information collected from 10.1186/s12911-021-01395-z)" ]

[ "TAGS\n#multilinguality-monolingual #language-Spanish #license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for CT-EBM-SP", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER", "### Ctebmsp Abstracts\n\nThe \"abstracts\" subset of the Clinical Trials for Evidence-Based Medicine in Spanish\n(CT-EBM-SP) corpus contains 500 abstracts of clinical trial studies in Spanish,\npublished in journals with a Creative Commons license. Most were downloaded from\nthe SciELO repository and free abstracts in PubMed.\n\nAbstracts were retrieved with the query:\nClinical Trial[ptyp] AND “loattrfree full text”[sb] AND “spanish”[la].\n\n(Information collected from 10.1186/s12911-021-01395-z)", "### Ctebmsp Eudract\n\nThe \"abstracts\" subset of the Clinical Trials for Evidence-Based Medicine in Spanish\n(CT-EBM-SP) corpus contains 500 abstracts of clinical trial studies in Spanish,\npublished in journals with a Creative Commons license. Most were downloaded from\nthe SciELO repository and free abstracts in PubMed.\n\nAbstracts were retrieved with the query:\nClinical Trial[ptyp] AND “loattrfree full text”[sb] AND “spanish”[la].\n\n(Information collected from 10.1186/s12911-021-01395-z)" ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for DDI Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nThe DDI corpus has been manually annotated with drugs and pharmacokinetics and pharmacodynamics interactions. It contains 1025 documents from two different sources: DrugBank database and MedLine." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for DDI Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nThe DDI corpus has been manually annotated with drugs and pharmacokinetics and pharmacodynamics interactions. It contains 1025 documents from two different sources: DrugBank database and MedLine." ]

[ "multilinguality:monolingual", "language:es", "license:cc-by-4.0", "region:us" ]

[]

[ "es" ]

[ "# Dataset Card for DisTEMIST", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,NED\n\n\nThe DisTEMIST corpus is a collection of 1000 clinical cases with disease annotations linked with Snomed-CT concepts.\nAll documents are released in the context of the BioASQ DisTEMIST track for CLEF 2022." ]

[ "TAGS\n#multilinguality-monolingual #language-Spanish #license-cc-by-4.0 #region-us \n", "# Dataset Card for DisTEMIST", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: NER,NED\n\n\nThe DisTEMIST corpus is a collection of 1000 clinical cases with disease annotations linked with Snomed-CT concepts.\nAll documents are released in the context of the BioASQ DisTEMIST track for CLEF 2022." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for EBM NLP", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThis corpus release contains 4,993 abstracts annotated with (P)articipants,\n(I)nterventions, and (O)utcomes. Training labels are sourced from AMT workers and\naggregated to reduce noise. Test labels are collected from medical professionals." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for EBM NLP", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThis corpus release contains 4,993 abstracts annotated with (P)articipants,\n(I)nterventions, and (O)utcomes. Training labels are sourced from AMT workers and\naggregated to reduce noise. Test labels are collected from medical professionals." ]

[ "multilinguality:monolingual", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for EHR-Rel", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nEHR-Rel is a novel open-source1 biomedical concept relatedness dataset consisting of 3630 concept pairs, six times more\nthan the largest existing dataset. Instead of manually selecting and pairing concepts as done in previous work,\nthe dataset is sampled from EHRs to ensure concepts are relevant for the EHR concept retrieval task.\nA detailed analysis of the concepts in the dataset reveals a far larger coverage compared to existing datasets." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-apache-2.0 #region-us \n", "# Dataset Card for EHR-Rel", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: STS\n\n\nEHR-Rel is a novel open-source1 biomedical concept relatedness dataset consisting of 3630 concept pairs, six times more\nthan the largest existing dataset. Instead of manually selecting and pairing concepts as done in previous work,\nthe dataset is sampled from EHRs to ensure concepts are relevant for the EHR concept retrieval task.\nA detailed analysis of the concepts in the dataset reveals a far larger coverage compared to existing datasets." ]

[ "multilinguality:monolingual", "language:fr", "license:other", "region:us" ]

[]

[ "fr" ]

[ "# Dataset Card for ESSAI", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TXTCLASS\n\n\nWe manually annotated two corpora from the biomedical field. The ESSAI corpus contains clinical trial protocols in French. They were mainly obtained from the National Cancer Institute The typical protocol consists of two parts: the summary of the trial, which indicates the purpose of the trial and the methods applied; and a detailed description of the trial with the inclusion and exclusion criteria. The CAS corpus contains clinical cases published in scientific literature and training material. They are published in different journals from French-speaking countries (France, Belgium, Switzerland, Canada, African countries, tropical countries) and are related to various medical specialties (cardiology, urology, oncology, obstetrics, pulmonology, gastro-enterology). The purpose of clinical cases is to describe clinical situations of patients. Hence, their content is close to the content of clinical narratives (description of diagnoses, treatments or procedures, evolution, family history, expected audience, etc.). In clinical cases, the negation is frequently used for describing the patient signs, symptoms, and diagnosis. Speculation is present as well but less frequently.\n\nThis version only contain the annotated ESSAI corpus" ]

[ "TAGS\n#multilinguality-monolingual #language-French #license-other #region-us \n", "# Dataset Card for ESSAI", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: False\n- Tasks: TXTCLASS\n\n\nWe manually annotated two corpora from the biomedical field. The ESSAI corpus contains clinical trial protocols in French. They were mainly obtained from the National Cancer Institute The typical protocol consists of two parts: the summary of the trial, which indicates the purpose of the trial and the methods applied; and a detailed description of the trial with the inclusion and exclusion criteria. The CAS corpus contains clinical cases published in scientific literature and training material. They are published in different journals from French-speaking countries (France, Belgium, Switzerland, Canada, African countries, tropical countries) and are related to various medical specialties (cardiology, urology, oncology, obstetrics, pulmonology, gastro-enterology). The purpose of clinical cases is to describe clinical situations of patients. Hence, their content is close to the content of clinical narratives (description of diagnoses, treatments or procedures, evolution, family history, expected audience, etc.). In clinical cases, the negation is frequently used for describing the patient signs, symptoms, and diagnosis. Speculation is present as well but less frequently.\n\nThis version only contain the annotated ESSAI corpus" ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for EU-ADR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nCorpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug-disorder, drug-target, and target-disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for EU-ADR", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,RE\n\n\nCorpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug-disorder, drug-target, and target-disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts." ]

[ "multilinguality:monolingual", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Evidence Inference 2.0", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: QA\n\n\nThe dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple\ntreatments. Each of these articles will have multiple questions, or 'prompts' associated with them.\nThese prompts will ask about the relationship between an intervention and comparator with respect to an outcome,\nas reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared\nto placebo on the duration of headaches. For the sake of this task, we assume that a particular article\nwill report that the intervention of interest either significantly increased, significantly decreased\nor had significant effect on the outcome, relative to the comparator." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-mit #region-us \n", "# Dataset Card for Evidence Inference 2.0", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: QA\n\n\nThe dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple\ntreatments. Each of these articles will have multiple questions, or 'prompts' associated with them.\nThese prompts will ask about the relationship between an intervention and comparator with respect to an outcome,\nas reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared\nto placebo on the duration of headaches. For the sake of this task, we assume that a particular article\nwill report that the intervention of interest either significantly increased, significantly decreased\nor had significant effect on the outcome, relative to the comparator." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for GENETAG", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nNamed entity recognition (NER) is an important first step for text mining the biomedical literature.\nEvaluating the performance of biomedical NER systems is impossible without a standardized test corpus.\nThe annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity\nof gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE®\nsentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition.." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for GENETAG", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nNamed entity recognition (NER) is an important first step for text mining the biomedical literature.\nEvaluating the performance of biomedical NER systems is impossible without a standardized test corpus.\nThe annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity\nof gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE®\nsentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition.." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for PTM Events", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,COREF,EE\n\n\nPost-translational-modifications (PTM), amino acid modifications of proteins after translation, are one of the posterior processes of protein biosynthesis for many proteins, and they are critical for determining protein function such as its activity state, localization, turnover and interactions with other biomolecules. While there have been many studies of information extraction targeting individual PTM types, there was until recently little effort to address extraction of multiple PTM types at once in a unified framework." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for PTM Events", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,COREF,EE\n\n\nPost-translational-modifications (PTM), amino acid modifications of proteins after translation, are one of the posterior processes of protein biosynthesis for many proteins, and they are critical for determining protein function such as its activity state, localization, turnover and interactions with other biomolecules. While there have been many studies of information extraction targeting individual PTM types, there was until recently little effort to address extraction of multiple PTM types at once in a unified framework." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for GENIA Relation Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE\n\n\nThe extraction of various relations stated to hold between biomolecular entities is one of the most frequently\naddressed information extraction tasks in domain studies. Typical relation extraction targets involve protein-protein\ninteractions or gene regulatory relations. However, in the GENIA corpus, such associations involving change in the\nstate or properties of biomolecules are captured in the event annotation.\n\nThe GENIA corpus relation annotation aims to complement the event annotation of the corpus by capturing (primarily)\nstatic relations, relations such as part-of that hold between entities without (necessarily) involving change." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for GENIA Relation Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE\n\n\nThe extraction of various relations stated to hold between biomolecular entities is one of the most frequently\naddressed information extraction tasks in domain studies. Typical relation extraction targets involve protein-protein\ninteractions or gene regulatory relations. However, in the GENIA corpus, such associations involving change in the\nstate or properties of biomolecules are captured in the event annotation.\n\nThe GENIA corpus relation annotation aims to complement the event annotation of the corpus by capturing (primarily)\nstatic relations, relations such as part-of that hold between entities without (necessarily) involving change." ]

[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for GENIA Term Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe identification of linguistic expressions referring to entities of interest in molecular biology such as proteins,\ngenes and cells is a fundamental task in biomolecular text mining. The GENIA technical term annotation covers the\nidentification of physical biological entities as well as other important terms. The corpus annotation covers the full\n1,999 abstracts of the primary GENIA corpus." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for GENIA Term Corpus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER\n\n\nThe identification of linguistic expressions referring to entities of interest in molecular biology such as proteins,\ngenes and cells is a fundamental task in biomolecular text mining. The GENIA technical term annotation covers the\nidentification of physical biological entities as well as other important terms. The corpus annotation covers the full\n1,999 abstracts of the primary GENIA corpus." ]

[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for GEOKhoj v1", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\nGEOKhoj v1 is a annotated corpus of control/perturbation labels for 30,000 samples\nfrom Microarray, Transcriptomics and Single cell experiments which are available on\nthe GEO (Gene Expression Omnibus) database" ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-cc-by-nc-4.0 #region-us \n", "# Dataset Card for GEOKhoj v1", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: False\n- Public: True\n- Tasks: TXTCLASS\n\n\nGEOKhoj v1 is a annotated corpus of control/perturbation labels for 30,000 samples\nfrom Microarray, Transcriptomics and Single cell experiments which are available on\nthe GEO (Gene Expression Omnibus) database" ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for GNormPlus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nWe re-annotated two existing gene corpora. The BioCreative II GN corpus is a widely used data set for benchmarking GN\ntools and includes document-level annotations for a total of 543 articles (281 in its training set; and 262 in test).\nThe Citation GIA Test Collection was recently created for gene indexing at the NLM and includes 151 PubMed abstracts\nwith both mention-level and document-level annotations. They are selected because both have a focus on human genes.\nFor both corpora, we added annotations of gene families and protein domains. For the BioCreative GN corpus, we also\nadded mention-level gene annotations. As a result, in our new corpus, there are a total of 694 PubMed articles.\nPubTator was used as our annotation tool along with BioC formats." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for GNormPlus", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED\n\n\nWe re-annotated two existing gene corpora. The BioCreative II GN corpus is a widely used data set for benchmarking GN\ntools and includes document-level annotations for a total of 543 articles (281 in its training set; and 262 in test).\nThe Citation GIA Test Collection was recently created for gene indexing at the NLM and includes 151 PubMed abstracts\nwith both mention-level and document-level annotations. They are selected because both have a focus on human genes.\nFor both corpora, we added annotations of gene families and protein domains. For the BioCreative GN corpus, we also\nadded mention-level gene annotations. As a result, in our new corpus, there are a total of 694 PubMed articles.\nPubTator was used as our annotation tool along with BioC formats." ]

[ "multilinguality:monolingual", "language:en", "license:gpl-3.0", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Hallmarks of Cancer", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: TXTCLASS\n\n\nThe Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication\nabstracts manually annotated by experts according to a taxonomy. The taxonomy\nconsists of 37 classes in a hierarchy. Zero or more class labels are assigned\nto each sentence in the corpus. The labels are found under the \"labels\"\ndirectory, while the tokenized text can be found under \"text\" directory.\nThe filenames are the corresponding PubMed IDs (PMID)." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-gpl-3.0 #region-us \n", "# Dataset Card for Hallmarks of Cancer", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: TXTCLASS\n\n\nThe Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication\nabstracts manually annotated by experts according to a taxonomy. The taxonomy\nconsists of 37 classes in a hierarchy. Zero or more class labels are assigned\nto each sentence in the corpus. The labels are found under the \"labels\"\ndirectory, while the tokenized text can be found under \"text\" directory.\nThe filenames are the corresponding PubMed IDs (PMID)." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for HPRD50", "## Dataset Description\n\n- Homepage: \n- Pubmed: True\n- Public: True\n- Tasks: RE,NER\n\n\nHPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers\nreferenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,\nsplitting each abstract into sentences, and in each sentence there may be entities and\ninteractions between those entities. In this particular dataset, entities are all\nproteins and interactions are thus protein-protein interactions.\n\nMoreover, all entities are normalized to the HPRD database. These normalized terms are\nstored in each entity's 'type' attribute in the source XML. This means the dataset can\ndetermine e.g. that \"Janus kinase 2\" and \"Jak2\" are referencing the same normalized\nentity.\n\nBecause the dataset contains entities and relations, it is suitable for Named Entity\nRecognition and Relation Extraction." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for HPRD50", "## Dataset Description\n\n- Homepage: \n- Pubmed: True\n- Public: True\n- Tasks: RE,NER\n\n\nHPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers\nreferenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,\nsplitting each abstract into sentences, and in each sentence there may be entities and\ninteractions between those entities. In this particular dataset, entities are all\nproteins and interactions are thus protein-protein interactions.\n\nMoreover, all entities are normalized to the HPRD database. These normalized terms are\nstored in each entity's 'type' attribute in the source XML. This means the dataset can\ndetermine e.g. that \"Janus kinase 2\" and \"Jak2\" are referencing the same normalized\nentity.\n\nBecause the dataset contains entities and relations, it is suitable for Named Entity\nRecognition and Relation Extraction." ]

[ "multilinguality:monolingual", "language:en", "license:unknown", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for IEPA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE\n\n\nThe IEPA benchmark PPI corpus is designed for relation extraction. It was created from 303 PubMed abstracts, each of which contains a specific pair of co-occurring chemicals." ]

[ "TAGS\n#multilinguality-monolingual #language-English #license-unknown #region-us \n", "# Dataset Card for IEPA", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: RE\n\n\nThe IEPA benchmark PPI corpus is designed for relation extraction. It was created from 303 PubMed abstracts, each of which contains a specific pair of co-occurring chemicals." ]