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483 results for “SEMANTICS”
RESTBERTa: A Transformer-based Question Answering Approach for Semantic Search in Web API Documentation
<p>This repository contains the datasets and evaluation results of our study.<br> For a detailed overview regarding the provided materials, please refer to README.md.</p>
Small spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation
<p>A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches.<br>The dataset includes a trained U-net network for detection of small random spots of 2 colors on a black background.</p><p>The network can be opened by loading "2D_SmallSpots_3cl_Unet.mibCfg" file by</p><ul><li><i>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </i></li><li>Drag and drop of the config file into DeepMIB window</li></ul><p>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a></p>
Large Spots DeepMIB project, synthetic dataset for testing 2.5D semantic segmentation
<p>A complete DeepMIB project with a synthetic dataset generated for quick tests of 2.5D semantic segmentation approaches.<br>The dataset includes trained</p><ul><li>2.5D DeepLabV3-Resnet18 depth2color (Spots_25D_DLv3RN18_Z2C_xy200z5)</li><li>2.5D U-net depth2color (Spots_25D_Unet_Z2C_xy200z5)</li></ul><p>networkd for detection of large white 3D spots on a black background. The spots that are present on a single slice only are considered as background.</p><p>The network can be opened by loading the config files "*.mibCfg" by</p><ul><li><i>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </i></li><li>Drag and drop of the config file into DeepMIB window</li></ul><p>Microscopy Image Browser: <a href="https://mib.helsinki.fi/">https://mib.helsinki.fi</a></p>
Pano3D: Matterport3D Semantic & Layout Low Resolution
<p>Spherical cameras capture scenes in a holistic manner and have been used for room layout estimation. Recently, with the availability of appropriate datasets, there has also been progress in depth estimation from a single omnidirectional image. While these two tasks are complementary, few works have been able to explore them in parallel to advance indoor geometric perception, and those that have done so either relied on synthetic data, or used small scale datasets, as few options are available that include both layout annotations and dense depth maps in real scenes. This is partly due to the necessity of manual annotations for room layouts. In this work, we move beyond this limitation and generate a 360° geometric vision (360V) dataset that includes multiple modalities, multi-view stereo data and automatically generated weak layout cues. We also explore an explicit coupling between the two tasks to integrate them into a single-shot trained model. We rely on depth-based layout reconstruction and layout-based depth attention, demonstrating increased performance across both tasks. By using single 360° cameras to scan rooms, the opportunity for facile and quick building-scale 3D scanning arises. The project page is available at <a href="https://vcl3d.github.io/ExplicitLayoutDepth/">https://vcl3d.github.io/ExplicitLayoutDepth/</a>.</p>
ARENA_Hierarchical Organization of Distributed Semantic Knowledge in the Human Language System_Language Study Pt. 1: stimulus set
<p>P4_WP1_01 Language Study Pt. 1: stimulus set</p> <p> </p> <p>Folder structure: </p> <p>Raw_input<br>The raw_input contains the text for every chapter of the book to be used in the experiment (Moonwalk mit Einstein: Wie aus einem vergeßlichen Mann ein Gedächtnis-Champion wurde, by Joshua Foer, translated by Ulla Rahn-Huber. Published by Riemann Verlag (28 Mar. 2011)). <br>Additionally, in the folder there is the pretrained vector model for German words (model comes from https://fasttext.cc/docs/en/crawl-vectors.html), ratings for concreteness and word frequency (all references are included in the scripts). Moreover, there is a translated version of the Things labels for intersecting the single words with the THINGS dataset (Hebart MN, Dickter AH, Kidder A, Kwok WY, Corriveau A, et al. (2019) THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE 14(10): e0223792. https://doi.org/10.1371/journal.pone.0223792)</p> <p>Scripts<br>In this folder, there are 6 scripts to sample single words from the raw text. The scripts are numbered according to the intended order of use. <br>- 1_from_text_to_df.py: from raw text only nouns and verbs are extracted with their relative word frequency, concreteness, lemma form, and number of characters. </p> <p>- 2_cluster_words.py: cluster analysis of word vectors to sample the semantic space as broadly as possible. Loosely based on Pereira, F., Lou, B., Pritchett, B. et al. Toward a universal decoder of linguistic meaning from brain activation. Nat Commun 9, 963 (2018). https://doi.org/10.1038/s41467-018-03068-4. </p> <p>- 3_compute_orthographic_density.py: add information for OND20 for both word forms and lemma forms. </p> <p>- 4_syntactic_valency.py: this applies only to verbs. It counts the number of arguments necessary for a verb to saturate its syntactic valency (e.g., subject + object). </p> <p>- 5_sampling_nouns.py: it samples nouns by preserving the distributions of all variables. The number of characters for every word is kept under 10. Additionally, the extreme quantiles of concreteness are matched by all other variables to ensure that more concrete and more abstract words in the set are still match along the other ratings. </p> <p>- 6_sampling_verbs.py: same as above but for verbs. </p> <p>Stimuli <br>In this folder, the pool of words before sampling is included. Note that some words have been manually excluded for several reasons: e.g., parsed wrongly in their lemma form; offensive words; words coming from other languages. </p> <p> </p>
GEOSatDB: global civil earth observation satellite semantic database
<p>The new version at <a href="https://doi.org/10.57760/sciencedb.11805">https://doi.org/10.57760/sciencedb.11805</a></p> <p>GEOSatDB is a semantic representation of Earth observation satellites and sensors that can be used to easily discover available Earth observation resources for specific research objectives.</p> <p><strong>Relevant Papers</strong></p> <p>Ming Lin, Meng Jin, Juanzi Li & Yuqi Bai (2024) GEOSatDB: global civil earth observation satellite semantic database, Big Earth Data, DOI: <a href="https://doi.org/10.1080/20964471.2024.2331992">10.1080/20964471.2024.2331992</a></p> <p><strong>Background</strong></p> <p>The widespread availability of coordinated and publicly accessible Earth observation (EO) data empowers decision-makers worldwide to comprehend global challenges and develop more effective policies. Space-based satellite remote sensing, which serves as the primary tool for EO, provides essential information about the Earth and its environment by measuring various geophysical variables. This contributes significantly to our understanding of the fundamental Earth system and the impact of human activities.</p> <p>Over the past few decades, many countries and organizations have markedly improved their regional and global EO capabilities by deploying a variety of advanced remote sensing satellites. The rapid growth of EO satellites and advances in on-board sensors have significantly enhanced remote sensing data quality by expanding spectral bands and increasing spatio-temporal resolutions. However, users face challenges in accessing available EO resources, which are often maintained independently by various nations, organizations, or companies. As a result, a substantial portion of archived EO satellite resources remains underutilized. Enhancing the discoverability of EO satellites and sensors can effectively utilize the vast amount of EO resources that continue to accumulate at a rapid pace, thereby better supporting data for global change research.</p> <p><strong>Methodology</strong></p> <p>This study introduces GEOSatDB, a comprehensive semantic database specifically tailored for civil Earth observation satellites. The foundation of the database is an ontology model conforming to standards set by the International Organization for Standardization (ISO) and the World Wide Web Consortium (W3C). This conformity enables data integration and promotes the reuse of accumulated knowledge. Our approach advocates a novel method for integrating Earth observation satellite information from diverse sources. It notably incorporates a structured prompt strategy utilizing a large language model to derive detailed sensor information from vast volumes of unstructured text.</p> <p><strong>Dataset Information</strong></p> <p>The downloadable files in RDF Turtle format are located in the data directory and contain a total of 130,134 statements:</p> <p>- GEOSatDB_ontology.ttl: Ontology modeling of concepts, relations, and properties.</p> <p>- satellite.ttl: 2,365 Earth observation satellites and their associated entities.</p> <p>- sensor.ttl: 1,021 Earth observation sensors and their associated entities.</p> <p>- sensor2satellite.ttl: relations between Earth observation satellites and sensors.</p> <p>In addition, a user-friendly portal is under development to facilitate easy access to GEOSatDB. The portal currently offers preliminary SPARQL query functionality, enabling the execution of SPARQL query examples.</p> <p>GEOSatDB undergoes quarterly updates, involving the addition of new satellites and sensors, revisions based on expert feedback, and the implementation of additional enhancements.</p>
SEMANTIC-SIGMATIC ETYMOLOGY OF GERMAN MILITARY PHRASEOLOGICAL UNITS
Open the record for dataset details and reuse information.
Understanding New Semantic Memory Learnings Across the Lifespan
ClinicalTrials.gov study NCT06442670. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Rehabilitation of Post-stroke Aphasia by Targeting Phonological, and Lexico-semantic Deficits With Speech Output Tasks
ClinicalTrials.gov study NCT06451731. IPD Sharing: YES. Countries: 0. Publications: 0.
Ramakrishnan: Semantics on the Web
It is becoming increasingly clear that the next generation of web search and advertising will rely on a deeper understanding of user intent and task modeling, and a correspondingly richer interpretation of content on the web. How we get there, in particular, how we understand web content in richer terms than bags of words and links, is a wide open and fascinating question. I will discuss some of the options here, and look closely at the role that information extraction can play. **Speaker Bio** Raghu Ramakrishnan is Chief Scientist for Audience and Cloud Computing at Yahoo!, and is a Research Fellow, heading the Community Systems area in Yahoo! Research. He was Professor of Computer Sciences at the University of Wisconsin-Madison, and was founder and CTO of QUIQ, a company that pioneered question-answering communities, powering Ask Jeeves' AnswerPoint as well as customer-support for companies such as Compaq. His research has influenced query optimization in commercial database systems, and the design of window functions in SQL:1999. His paper on the Birch clustering algorithm received the SIGMOD 10-Year Test-of-Time award, and he has written the widely-used text "Database Management Systems" (with Johannes Gehrke). He is Chair of ACM SIGMOD, on the Board of Directors of ACM SIGKDD and the Board of Trustees of the VLDB Endowment, and has served as editor-in-chief of the Journal of Data Mining and Knowledge Discovery, associate editor of ACM Transactions on Database Systems, and the Database area editor of the Journal of Logic Programming. Ramakrishnan is a Fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), and has received several awards, including a Distinguished Alumnus Award from IIT Madras, a Packard Foundation Fellowship in Science and Engineering, an NSF Presidential Young Investigator Award, and an ACM SIGMOD Contributions Award.
A proposal From Data Requirements to Semantic Data in the Context of MDA
<p>This work proposes a process for the identification of the requirements associated with the data, along with and a set of transformations in Model Driven Architecture (MDA) context, in order to obtain a semantically annotated dataset, as a result of the unification and alignment of the data in the context of its initial domain. Our proposal identifies four phases (from CIM to code), in which is describe the artifacts and transformations required to progress to the next phase: a target domain model is first obtained from the data requirements, after which the ontological schema and the ontology is generated from the previous model. A domain-specific language (DSL), also proposed in this work, is then used to obtain the semantic data model (the DSL code), which gener-ates the final semantic dataset. We have validated the proposal by studying two cases: one with data from the public transport domain and the other with data concerning those affected by the COVID-19 pandemic.</p>
Evaluation Data for "Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation"
<p><strong>Contents</strong><br> <br> The four CSV files are the data used for the evaluation in:</p> <ul> <li>Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035.</li> <li>DOI: <a href="http://doi.org/10.1007/978-3-030-45439-5_15">10.1007/978-3-030-45439-5_15</a></li> <li>Code: <a href="https://github.com/IllDepence/ecir2020">github.com/IllDepence/ecir2020</a></li> </ul> <p>The evaluation was conducted in a citation re-prediction setting.</p> <p><strong>CSV Format</strong></p> <ul> <li>7 columns divided by \u241E <ol> <li>cited document ID <ul> <li>for *_nomarker.csv: citation marker position ambiguous</li> <li> for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context</li> </ul> </li> <li>adjacent cited document IDs <ul> <li>only given in citrec_unarxive_*.csv</li> <li>divided by \u241F</li> <li>order matches 'CIT' markers in citation context</li> </ul> </li> <li>citing document ID</li> <li>citation context</li> <li>MAG field of study IDs <ul> <li>divided by \u241F</li> </ul> </li> <li>predicate:argument tuples generated based on PredPatt <ul> <li>JSON</li> </ul> </li> <li>noun phrases <ul> <li>for *_nomarker.csv: divided by \u241F</li> <li>for *_withmarker.csv: <ul> <li>divided by \u241D into</li> <li>noun phrases</li> <li>noun phrase directly preceding citation marker</li> </ul> </li> </ul> </li> </ol> </li> </ul> <p><strong>Data Sources</strong></p> <ol> <li>citrec_unarxive_cs_withmarker.csv <ul> <li>data set <ul> <li>unarXive</li> <li>Paper DOI: <a href="http://doi.org/10.1007/s11192-020-03382-z">10.1007/s11192-020-03382-z</a></li> <li>Data DOI: <a href="http://doi.org/10.5281/zenodo.2553522">10.5281/zenodo.2553522</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science</li> <li>cited doc is cited at least 5 times</li> </ul> </li> </ul> </li> <li>citrec_mag_cs_en.csv <ul> <li>data set <ul> <li>Microsoft Academic Graph (MAG)</li> <li>Paper DOI: <a href="http://doi.org/10.1145/2740908.2742839">10.1145/2740908.2742839</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science and in English</li> <li>citing doc abstract in MAG given</li> <li>cited doc is cited at least 50 times</li> </ul> </li> </ul> </li> <li>citrec_refseer.csv <ul> <li>data set <ul> <li>RefSeer</li> <li>Paper URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">ojs.aaai.org/index.php/AAAI/article/view/9528</a></li> <li>Data URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">psu.app.box.com/v/refseer</a></li> </ul> </li> <li>filter <ul> <li>for citing and cited docs title, venue, venuetype, abstract, and year not NULL</li> </ul> </li> </ul> </li> <li>citrec_acl-arc_withmarker.csv <ul> <li>data set <ul> <li>ACL ARC</li> <li>Paper URL: <a href="http://aclanthology.org/L08-1005">aclanthology.org/L08-1005</a></li> <li>Data URL: <a href="http://acl-arc.comp.nus.edu.sg/">acl-arc.comp.nus.edu.sg/</a></li> </ul> </li> <li>filter <ul> <li>cited doc has a DBLP ID</li> </ul> </li> </ul> </li> </ol> <p> </p> <p><strong>Paper Citation</strong></p> <pre>@inproceedings{Saier2020ECIR, author = {Tarek Saier and Michael F{\"{a}}rber}, title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval}, pages = {220--233}, year = {2020}, month = apr, doi = {10.1007/978-3-030-45439-5_15}, } </pre>
A semantic segmentation dataset consisting of soil-root images with multiple growth stages.
<p> A semantic segmentation dataset consisting of soil-root images with multiple growth stages.</p> <blockquote> <p><strong>Last Update: 2022.08.10</strong></p> </blockquote>
ForestSemantics:A Dataset for Forest Semantic Learning of Forest from Close-Range Sensing
<h1>ForestSemantic:A Dataset for Forest Semantic Learning of Forest from Close-Range Sensing</h1> <p><strong>ForestSemantic</strong> is a new open point cloud dataset ForestSemantic for forest semantic studies at both individual tree- and plot-levels. The dataset supports both instance and semantic segmentation, such as tree detection and segmentation and classification between ground, trunk, branches, and foliage components at both tree- and plot- levels. Also, the instance of each first-order branch is provided,</p> <p>For each plot, three files are provided, i.e., "Plotx.las", "plotx_Tree_Reference.xlsx" and "plotx_Branch_Reference.txt", where x means the x-th plot.<br>1) "Plotx.las" is the data file, which records the position, tree-ID, classification and First-order branch ID and other attributes of each point. The tree-ID is stored in the field of "Point Source ID", classification is stored in the field of "Classification" and First-order branch ID is stored in the field of "GPS Time".<br>2) "plotx_Tree_Reference.xlsx" provides the reference structure traits of each tree in the plot. The reference of each tree takes up one row, and each column in turn is tree-ID, position_x, position_y, tree height (m), DBH (m), First-order branch (m), Crown Projection area (m2), Crown Surface area (m2), Crown Volume (m3).<br>3) "plotx_Branch_Reference.txt" provides the reference length of each First-order branch in the plot. Each individual First-order branch takes up one row, and each column in turn is tree-ID, First-order Branch ID, Start_x, Start_y, Start_z, End_x, End_y, End_z, Length.</p> <p><strong>More details please refer to "Read me.pdf".</strong></p>
CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2
<p><strong>Description</strong></p> <p>CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs). Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates; we manually choose the images to guarantee that each IP inside an ROI matches one of the following cloud cover groups:</p> <p>- clear (0%)</p> <p>- low-cloudy (1% - 25%) </p> <p>- almost clear (25% - 45%)</p> <p>- mid-cloudy (45% - 65%)</p> <p>- cloudy (65% >)</p> <p>An IP is the core unit in CloudSEN12. Each IP contains data from Sentinel-2 optical levels 1C and 2A, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from eight cutting-edge cloud detection algorithms. Besides, in order to support standard, weakly, and self-/semi-supervised learning procedures, cloudSEN12 includes three distinct forms of hand-crafted labelling data: high-quality, scribble, and no annotation. Consequently, each ROI is randomly assigned to a different annotation group:</p> <ul> <li> <p>2000 ROIs with pixel-level annotation, where the average annotation time is 150 minutes (high-quality group).</p> </li> <li> <p>2000 ROIs with scribble level annotation, where the annotation time is 15 minutes (scribble group).</p> </li> <li> <p>5880 ROIs with annotation only in the cloud-free (0\%) image (no annotation group).</p> </li> </ul> <p>For high-quality labels, we use the Intelligence foR Image Segmentation\cite{iris2019} (IRIS) active learning technology, a system that combines human photo-interpretation and machine learning. For scribble, ground truth pixels were drawn using IRIS but without ML support. Finally, the no annotation dataset is generated automatically, with manual annotation only in the clear image patch. The dataset is already available here: <strong><a href="https://shorturl.at/cgjtz">https://shorturl.at/cgjtz</a></strong>. Check out our website <strong><a href="https://cloudsen12.github.io/">https://cloudsen12.github.io/</a></strong> for examples of how to download the dataset via STAC.</p>
DATASET RELATED TO ARTICLE "THE SEMANTICS OF NATURAL OBJECTS AND TOOLS IN THE BRAIN: A COMBINED BEHAVIORAL AND MEG STUDY"
<p>The dataset contains behavioral, time-frequency and virtual channel data</p>
TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 4, January 2021 - August 2022)
<p><strong><a href="https://data.gesis.org/tweetscov19/">TweetsCOV19</a></strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of <a href="https://data.gesis.org/tweetskb">TweetsKB</a> and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*.</p> <p>We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:</p> <ol> <li>Tweet Id: Long.</li> <li>Username: String. Encrypted for privacy issues*.</li> <li>Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).</li> <li>#Followers: Integer.</li> <li>#Friends: Integer.</li> <li>#Retweets: Integer.</li> <li>#Favorites: Integer.</li> <li>Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from <a href="https://github.com/yahoo/FEL">FEL</a> library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".</li> <li>Sentiment: String. <a href="http://sentistrength.wlv.ac.uk/">SentiStrength</a> produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").</li> <li>Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".</li> <li>Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".</li> <li>URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"</li> </ol> <p>To extract the dataset from <a href="https://data.gesis.org/tweetskb">TweetsKB</a>, we compiled a seed list of 268 COVID-19-related <a href="https://data.gesis.org/tweetscov19/keywords_v1.1.txt">keywords</a>.</p> <p><em>* For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets.</em></p>
Interview Fragenkatalog: Unlocking AI-based Knowledge Management Potential for SMEs: Exploring Semantic Search Adoption
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Cultural influences on word meanings revealed through large-scale semantic alignment: cultural measures
<p>Data files for Thompson, Roberts & Lupyan (in prep): Cultural influences on word meanings revealed through large-scale semantic alignment. This repository includes data and scripts to create the cultural similarity measure and analyse the relationships between cultural similarity and semantic alignment. This is a release of the GitHub repository here: https://github.com/seannyD/ImputeEACulturalDifferences</p>
Rehabilitation of post-stroke aphasia by a single protocol targeting phonological, lexical, and semantic deficits with speech output tasks
<p>This study assessed the effectiveness of a novel rehabilitation protocol (PHOLEXSEM), focused on PHonological, SEmantic, and LExical deficits, aiming at improving lexical retrieval, and, generally, spoken output. The study is published here: https://doi.org/10.23736/S1973-9087.24.08576-9</p> <p><span>These data cannot be made publicly available because they include sensitive information, but can be made available to interested researchers upon reasonable request. Please, forward your request to Prof. Nadia Bolognini (n.bolognini@auxologico.it).</span></p>
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.