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Dataset results
17 results for “Text generation”
Text Generation using N-gram and GPT (metrics: ROUGE, BLEU and BERTScore)
<p>This publication presents a set of spreadsheets listing the user stories generated using N-gram and GPT models with metrics ROUGE, BLEU and BERTScore calculated. Each spreadsheet refers to one corpus of user stories processed.</p>
Text-fig. 2. Map showing the distribution of localities included in the treatment of Brown (1962), based on the coordinates presented in Appendix Table 1. Localities are distributed across New Mexico (NM), Colorado (CO), Wyoming (WY), Montana (MT), South Dakota (SD) and North Dakota (ND). Also plotted are cited Canadian localities: Joffre Bridge, in Alberta (AB), and Ravenscrag, in Saskatchewan (SK). Map generated with Map-It (2014) website. in Revisions To Roland Brown'S North American Paleocene Flora
Text-fig. 2. Map showing the distribution of localities included in the treatment of Brown (1962), based on the coordinates presented in Appendix Table 1. Localities are distributed across New Mexico (NM), Colorado (CO), Wyoming (WY), Montana (MT), South Dakota (SD) and North Dakota (ND). Also plotted are cited Canadian localities: Joffre Bridge, in Alberta (AB), and Ravenscrag, in Saskatchewan (SK). Map generated with Map-It (2014) website.
Biomedical Data-to-Text Generation via Fine-Tuning Transformers
<p>Biomedical Dataset (”BioLeaflets”) for the paper "Biomedical Data2Text Generation via fine-tuning transformers" (INLG'21)</p>
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text
<p>This is the repository for ISWC 2023 Resource Track submission for <code>Text2KGBench: Benchmark for Ontology-Driven Knowledge Graph Generation from Text</code>. Text2KGBench is a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology. Given an input ontology and a set of sentences, the task is to extract facts from the text while complying with the given ontology (concepts, relations, domain/range constraints) and being faithful to the input sentences.</p> <p>It contains two datasets (i) Wikidata-TekGen with 10 ontologies and 13,474 sentences and (ii) DBpedia-WebNLG with 19 ontologies and 4,860 sentences.</p> <p><strong>An example</strong></p> <p>An example test sentence:</p> <pre><code>Test Sentence: {"id": "ont_music_test_n", "sent": "\"The Loco-Motion\" is a 1962 pop song written by American songwriters Gerry Goffin and Carole King."} </code></pre> <p>An example of ontology:</p> <p>Ontology: <a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ontologies/owl/ont_2_music.ttl">Music Ontology</a></p> <p>Expected Output:</p> <pre><code>{ "id": "ont_k_music_test_n", "sent": "\"The Loco-Motion\" is a 1962 pop song written by American songwriters Gerry Goffin and Carole King.", "triples": [ { "sub": "The Loco-Motion", "rel": "publication date", "obj": "01 January 1962" },{ "sub": "The Loco-Motion", "rel": "lyrics by", "obj": "Gerry Goffin" },{ "sub": "The Loco-Motion", "rel": "lyrics by", "obj": "Carole King" },] } </code></pre> <p>The data is released under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY 4.0) License.</p> <p>The structure of the repo is as the following.</p> <ul> <li>Text2KGBench <ul> <li>src: the source code used for generation and evaluation, and baseline <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/benchmark"><code>benchmark</code></a> the code used to generate the benchmark</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/evaluation"><code>evaluation</code></a> evaluation scripts for calculating the results</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/src/evaluation">baseline</a> code for generating the baselines including prompts, sentence similarities, and LLM client.</li> </ul> </li> <li>data: the benchmark datasets and baseline data. There are two datasets: wikidata_tekgen and dbpedia_webnlg. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen">wikidata_tekgen</a> Wikidata-TekGen Dataset <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ontologies">ontologies</a> 10 ontologies used by this dataset</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/train">train</a> training data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/test">test</a> test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/manually_verified_sentences">manually_verified_sentences</a> ids of a subset of test cases manually validated</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences">unseen_sentences</a> new sentences that are added by the authors which are not part of Wikipedia <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences/test">test unseen</a> test unseen test sentences</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/unseen_sentences/ground_truth">ground_truth</a> ground truth for unseen test sentences.</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/ground_truth">ground_truth</a> ground truth for the test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines">baselines</a> data related to running the baselines. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/test_train_sent_similarity">test_train_sent_similarity</a> for each test case, 5 most similar train sentences generated using SBERT T5-XXL model.</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/prompts">prompts</a> prompts corresponding to each test file <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/prompts/unseen">unseen prompts</a> unseen prompts for the unseen test cases</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B">Alpaca-LoRA-13B</a> data related to the Alpaca-LoRA model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen">unseen results</a> results for the unseen test cases <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Alpaca-LoRA-13B/unseen/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B">Vicuna-13B</a> data related to the Vicuna-13B model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/wikidata_tekgen/baselines/Vicuna-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg">dbpedia_webnlg</a> DBpedia Dataset <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/ontologies">ontologies</a> 19 ontologies used by this dataset</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/train">train</a> training data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/test">test</a> test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/ground_truth">ground_truth</a> ground truth for the test data</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines">baselines</a> data related to running the baselines. <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/test_train_sent_similarity">test_train_sent_similarity</a> for each test case, 5 most similar train sentences generated using SBERT T5-XXL model.</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/prompts">prompts</a> prompts corresponding to each test file</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B">Alpaca-LoRA-13B</a> data related to the Alpaca-LoRA model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Alpaca-LoRA-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B">Vicuna-13B</a> data related to the Vicuna-13B model <ul> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B/llm_responses">llm_responses</a> raw LLM responses and extracted triples</li> <li><a href="https://github.com/cenguix/Text2KGBench/blob/main/data/dbpedia_webnlg/baselines/Vicuna-13B/eval_metrics">eval_metrics</a> ontology-level and aggregated evaluation results</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>This benchmark contains data derived from the TekGen corpus (part of the KELM corpus) [1] released under CC BY-SA 2.0 license and WebNLG 3.0 corpus [2] released under CC BY-NC-SA 4.0 license.</p> <p>[1] Oshin Agarwal, Heming Ge, Siamak Shakeri, and Rami Al-Rfou. 2021. Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3554–3565, Online. Association for Computational Linguistics.</p> <p>[2] Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017. Creating Training Corpora for NLG Micro-Planners. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 179–188, Vancouver, Canada. Association for Computational Linguistics.</p>
User Generated Content (EOL v2): User Added Text, full v2
with user IDs mapped to v3 IDs; static resource file. derived from <p></p>https://opendata.eol.org/dataset/user-generated-content/resource/6886fc1d-83f9-45a6-87c4-b6b7726f30b8 For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
User Generated Content (EOL v2): User Added Text, curated
For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
User Generated Content (EOL v2): user added text
<p></p>https://eol-jira.bibalex.org/browse/DATA-1730 Data as of Oct 25, 2018 For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
Swedish Diachronic Corpus - User-generated Data - Blog text
<p>The Swedish Diachronic Corpus (https://www2.lingfil.uu.se/person/pettersson/svediakorp/) is a project funded by Swe-Clarin (<a href="https://sweclarin.se/eng">https://sweclarin.se/eng</a>). The purpose of the project is to provide a corpus of texts covering the time period from Old Swedish to present day, with a wide variety of text types and freely available for download and search. The texts are provided in a plain text format and in a uniform CoNLL format, with placeholders for linguistic annotation. </p> <p>The dataset provided here is the blog section of the User-generated Data in the Swedish Diachronic Corpus. For other datasets within the corpus, see further the corpus website: https://www2.lingfil.uu.se/person/pettersson/svediakorp/. </p> <p>The project members are Eva Pettersson (Uppsala University) and Lars Borin (University of Gothenburg). For questions or comments, or if you are aware of any corpus resource that could be included in the Swedish Diachronic Corpus, don't hesitate to contact us!<br> <br> Eva Pettersson Department of Linguistics and Philology, Uppsala University eva.pettersson@lingfil.uu.se<br> Lars Borin Department of Swedish, University of Gothenburg lars.borin@svenska.gu.se</p>
AltGen: 1.3M Plausible Alternatives From Neural Text Generators
<h2>AltGen: 1.3M Plausible Alternatives From Neural Text Generators</h2><p>The AltGen dataset contains 1.3 million English texts generated by neural language generators conditioned on contexts from three corpora of acceptability judgements and two corpora of reading times. </p><p>For each corpus, each text generator, and each sampling algorithm,100 generations are sampled—for a total of 1,257,300 generations. Details about the language generators and the corpora are presented in a paper published at EMNLP 2023 (in particular, Section 4). Please cite this paper if you use any version of the dataset in your work:</p><blockquote><p>Mario Giulianelli, Sarenne Wallbridge, and Raquel Fernández. 2023. <strong>Information Value: Measuring Utterance Predictability as Distance from Plausible Alternatives</strong>. In <i>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</i>. Association for Computational Linguistics.</p></blockquote><p>The files are in jsonl format and include a <i>context_id</i> field, which allows retrieving the relevant entry from the original corpus, and the <i>alternatives</i> field, which contains the language model generations. Please note that the alternatives are not post-processed (see code and footnote 2 in the paper for further details). Filenames are built as follows: <i>DecodingAlgorithm</i>_<i>DecodingParameter</i>-n<i>NumAlternatives</i>-maxlen_<i>MaxGenerationLength</i>-sep_<i>Separator.</i>jsonl.</p>
Text generated by OPUS-MT and T5 models with single-bit errors in the parameters
<h2>Description</h2> <p>The dataset contains text generated using T5 and OPUS-MT model with and with single-bit errors in the parameters of the LLM. The T5 LLM used the <a href="https://huggingface.co/datasets/cnn_dailymail/viewer/3.0.0/test">CNN Daily Mail</a> dataset for summarization and OPUS-MT used the <a href="https://aclanthology.org/2017.iwslt-1.1/">IWSLT2017</a> dataset for Chinese-to-English translation.</p> <p> </p> <p>Folders:</p> <ul> <li>t5_fp32: T5 model with a quantified version of FP32</li> <li>t5_fp16: T5 model with a quantified version of FP16</li> <li>opus_fp32: OPUS-MT model with a quantified version of FP32</li> <li>opus_fp16: OPUS-MT model with a quantified version of FP16</li> </ul> <p>Files:</p> <ul> <li><strong>{cnn/iwslt2017}_input_text.txt</strong>: Input text, that is, text to summarize (cnn and T5) or Chinese text to translate (iwslt2017 and OPUS-MT). For each dataset in total there are <em>number_input_texts.</em></li> <li><strong>{cnn/iwslt2017}_output_reference.txt:</strong> Example of result expected for CNN (T5) and IWSLT2017 (OPUS-MT). For each dataset in total there are <em>number_input_texts.</em></li> <li><strong>{cnn/iwslt2017}_output_predict_fault_free:</strong> Example of predictions without single-bit errors. For each dataset in total there are <em>number_input_texts.</em></li> <li><strong>{cnn/iwslt2017}_output_predict_single_fi_bit_100times:</strong> Example of predictions with 100 different single-bit error. In each dataset in total there are <em>100*number input texts</em>.</li> </ul> <h2>Paper</h2> <ul> <li>Paper: <a href="https://doi.org/10.48550/arXiv.2403.16393">Concurrent Linguistic Error Detection (CLED) for Large Language Models</a></li> <li>Cite:</li> </ul> <p><code>@misc{zhu2024concurrent,</code><br><code> title={Concurrent Linguistic Error Detection (CLED) for Large Language Models}, </code><br><code> author={Jinhua Zhu and Javier Conde and Zhen Gao and Pedro Reviriego and Shanshan Liu and Fabrizio Lombardi},</code><br><code> year={2024},</code><br><code> eprint={2403.16393},</code><br><code> archivePrefix={arXiv},</code><br><code> primaryClass={cs.AI}</code><br><code>}</code></p> <p> </p>
Limitations of Human Identification of Automatically Generated Text: Dataset
<p>This repository contains the data for the paper:</p> <p><span>Nadège Alavoine, Maximin Coavoux, Emmanuelle Esperança-Rodier, Romane Gallienne, Carlos-Emiliano González-Gallardo, Jérôme Goulian, Jose G. Moreno, Aurélie Névéol, Didier Schwab, Vincent Segonne, and Johanna Simoens. 2024. <a href="https://aclanthology.org/2024.lrec-main.919">Limitations of Human Identification of Automatically Generated Text</a>. In <em>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</em>, pages 10511–10516, Torino, Italia. ELRA and ICCL.</span></p>
Deep Neural Models for Medical Concept Normalization in User-Generated Texts
<p>PsyTar folds used for experiments in the paper "Deep Neural Models for Medical Concept Normalization in User-Generated Texts" to be published at ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop. </p> <p>All other datasets used in the paper can be found in the following places:</p> <p>Cadec random: https://zenodo.org/record/55013#.XPE1MC1eN24<br> Cadec custom: https://yadi.sk/d/GZoWm1wBxzyW_w</p> <p>SMM4H dataset: in the paper "Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H) - 2017 shared task"<br> <br> Bibtex:</p> <p>@inproceedings{miftahutdinov2019,<br> title = "Deep Neural Models for Medical Concept Normalization in User-Generated Texts",<br> author = "Miftahutdinov, Zulfat and Tutubalina, Elena",<br> booktitle = "Proceedings of {ACL} 2019, Student Research Workshop",<br> month = jul,<br> year = "2019",<br> address = "Florence, Italy",<br> publisher = "Association for Computational Linguistics",<br> }</p>
SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Black-Box Machine-Generated Text Detection
<p>Large language models (LLMs) are becoming mainstream and easily accessible, ushering in an explosion of machine-generated content over various channels, such as news, social media, question-answering forums, educational, and even academic contexts. Recent LLMs, such as ChatGPT and GPT-4, generate remarkably fluent responses to a wide variety of user queries. The articulate nature of such generated texts makes LLMs attractive for replacing human labor in many scenarios. However, this has also resulted in concerns regarding their potential misuse, such as spreading misinformation and causing disruptions in the education system. Since humans perform only slightly better than chance when classifying machine-generated vs. human-written text, there is a need to develop automatic systems to identify machine-generated text with the goal of mitigating its potential misuse.</p> <p>We offer three subtasks over two paradigms of text generation: (1) <strong>full text</strong> when a considered text is entirely written by a human or generated by a machine; and (2) <strong>mixed text</strong> when a machine-generated text is refined by a human or a human-written text paraphrased by a machine.</p>
Investigating Gender Bias in Large Language Models Through Text Generation
<p>Presented at the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024). Published in the ACL Anthology.</p>
Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models
<h2>[Update] Looking for a larger unsafe image dataset? We publish a new dataset named UnsafeBench on Hugging Face. Take a look at <a href="https://huggingface.co/datasets/yiting/UnsafeBench">here</a>!</h2> <p>This dataset used in the paper <a href="https://arxiv.org/pdf/2305.13873.pdf">https://arxiv.org/pdf/2305.13873.pdf</a> contains four prompt sets and one image set.</p> <p>The four prompt sets were used to query Text-to-Image models and generate images for safety assessment. These sets include three harmful prompt sets and one harmless prompt set. The harmful prompts originate from different sources and contain various unsafe concepts, such as sexually explicit, violent, disturbing, hateful, and political content.</p> <p><strong>Prompt Sets</strong>:</p> <ul> <li>4chan Prompts: Harmful</li> <li>Lexica Prompts: Harmful</li> <li>Template Prompts: Harmful</li> <li>COCO Prompts: Harmless</li> </ul> <p><strong>Image Dataset</strong>:</p> <p>This dataset consists of 800 images, which were randomly selected from all the generated images from Text-to-Image models.</p> <ul> <li>Safe: 580 images</li> <li>Sexually Explicit: 48 images</li> <li>Violent: 45 images</li> <li>Disturbing: 68 images</li> <li>Hateful: 35 images</li> <li>Political: 50 images</li> </ul>
generated_texts
<p>The generated text from the finetuned models used for evaluation</p>
Pseudo-Label Generation for Multi-Label Text Classification
With the advent and expansion of social networking, the amount of generated text data has seen a sharp increase. In order to handle such a huge volume of text data, new and improved text mining techniques are a necessity. One of the characteristics of text data that makes text mining difficult, is multi-labelity. In order to build a robust and effective text classification method which is an integral part of text mining research, we must consider this property more closely. This kind of property is not unique to text data as it can be found in non-text (e.g., numeric) data as well. However, in text data, it is most prevalent. This property also puts the text classification problem in the domain of multi-label classification (MLC), where each instance is associated with a subset of class-labels instead of a single class, as in conventional classification. In this paper, we explore how the generation of pseudo labels (i.e., combinations of existing class labels) can help us in performing better text classification and under what kind of circumstances. During the classification, the high and sparse dimensionality of text data has also been considered. Although, here we are proposing and evaluating a text classification technique, our main focus is on the handling of the multi-labelity of text data while utilizing the correlation among multiple labels existing in the data set. Our text classification technique is called pseudo-LSC (pseudo-Label Based Subspace Clustering). It is a subspace clustering algorithm that considers the high and sparse dimensionality as well as the correlation among different class labels during the classification process to provide better performance than existing approaches. Results on three real world multi-label data sets provide us insight into how the multi-labelity is handled in our classification process and shows the effectiveness of our approach.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.