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4,404 results for “digits”
Dataset from "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers" (Sverdljuk et al. 2022)
<p>Contains URNs (identifiers) for the newspapers used in the corpus study "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers".</p> <p>For each subcorpus there is an Excel file containing references to the objects used, together with basic metadata.</p> <p>The corpus definitions can be used in various webapps of the DH-LAB at the National Library of Norway, e.g.:</p> <p><a href="https://beta.nb.no/dhlab/concordances/">https://beta.nb.no/dhlab/concordances/</a></p> <p><a href="https://beta.nb.no/dhlab/collocations/">https://beta.nb.no/dhlab/collocations/</a></p> <p>See more at <a href="https://www.nb.no/dh-lab/">https://www.nb.no/dh-lab/</a></p>
Figura 27: Confluências entre marketing digital e marketing científico no contexto ASEO
<p>A Figura representa as confluências entre o <em>Marketing</em> Digital e <em>Marketing</em> Científico Digital no contexto ASEO, onde elementos de <em>marketing</em> digital convergem aos elementos de <em>marketing</em> científico, por caminhos que se entrelaçam em estratégias e ações específicas, perfazendo-se em caminhos possíveis no processo construtivo à visibilidade da produção científica.</p> <p> </p>
An open, collaborative, and scholarly digital edition of Anastās Mārī al-Karmalī's monthly journal "Lughat al-ʿArab" (Baghdad, 1911--14)
<p>This repository has seen a lot of edits since the last release:</p> <p>changes</p> <ul> <li>moved to central TEI boilerplate</li> <li>switched interface to Arabic</li> </ul> <p>edits</p> <ul> <li>added mastheads to vol.s 1 and 2</li> <li>some validation of automated structural mark-up</li> <li>manual validation of mark-up against the facsimile: vol. 1</li> <li>removed faulty automated mark-up of dates and periodical titles and re-added them through much improved process</li> <li>mark-up of named entities</li> <li>linked entity names to authority files</li> </ul>
An open, collaborative, and scholarly digital edition of ʿAbd al-Qādir al-Iskandarānī's monthly journal "al-Ḥaqāʾiq" (Damascus, 1910--12)
<p>The last release, v0.9, wasn't caught by Zenodo for some unknown reason. Nothing has been changed since. The following release message has been copied from v0.9:</p> <p>As the mark-up of al-Ḥaqāʾiq has been complete for some time now and we are waiting for our scans to be processed at Halle for some three years now, we have decided to release a practically complete version. There have been a couple of maintenance edits over the last years to unify the set-up of editions across the entire project. Thus, the folder with TEI files was renamed tei/. Entity linked as also considerably improved, particularly for periodical titles.</p>
Cellpose model for Digital Phase Contrast images
<p><strong>Name: </strong>Cellpose model for Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose model, trained via transfer learning from ‘cyto’ model.</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast) and Manual annotations (<em>10.5281/zenodo.5996883</em>)</p> <p><strong>Training Procedure: </strong>Model was trained using a Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> </p> <p>python -m cellpose --train --dir <em>TRAINING/DATASET/PATH/</em>train --test_dir <em>TRAINING/DATASET/PATH/</em>test --pretrained_model cyto --chan 0 --chan2 0</p> <p>The model file (MODEL NAME) in this repository is the result of this training.</p> <p><strong>Prediction Procedure: </strong>Using this model, a label image can be obtained from new unseen images in a given folder with</p> <p>python -m cellpose --dir <em>NEW/DATASET/PATH</em> --pretrained_model <em>FULL_MODEL_PATH</em> --chan 0 --chan2 0 --save_tif --no_npy</p>
Digital Phase Contrast on Primary Dermal Human Fibroblasts cells
<p><strong>Name</strong>: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells </p> <p><strong>Data type</strong>: Paired microscopy images (Digital Phase Contrast, <em>square rooted</em>) and corresponding labels/masks used for cellpose training (the corresponding Brightfield images are also present), organized as recommended by <a href="https://cellpose.readthedocs.io/en/latest/train.html">cellpose documentation</a>.</p> <p><strong>Microscopy data type</strong>: Light microscopy (Digital Phase Contrast and Brighfield )</p> <p><strong>Manual annotations</strong>: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects (Dust, fused cells) were left unannotated, so that the cellpose model (10.5281/zenodo.6023317) may mimic the same user bias during prediction. This was particularly necessary due to the accumulation of floating debris in the center of the well.</p> <p><strong>Microscope</strong>: Perkin Elmer Operetta microscope with a 10x 0.35 NA objective</p> <p><strong>Cell type</strong>: Primary Dermal Human Fibroblasts cells</p> <p><strong>File format</strong>: .tif (16-bit for DPC and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p>NOTE : This dataset was used to train cellpose model ( 10.5281/zenodo.6023317 )</p> <p> </p>
Digital Twins - from industrial management to healthcare practice
<p>A guest seminar offered by SCImPULSE Foundation CTO Taghi Aliyev for the University of Parma (IT) master "ARTE" https://www.masterarte-unipr.it/</p>
A survey of access to the digital collections of 195 UK GLAMs across internal and external platforms - Appendix 1 for A Culture of Copyright: A scoping study on open access to digital cultural heritage collections in the UK
<p>Created for the<a href="https://doi.org/10.5281/zenodo.6242611"> 'A Culture of Copyright: A scoping study on open access to digital cultural heritage collections in the UK' </a>report, this sample replicates and expands the Open galleries, libraries, archives and museums (GLAMs) Survey data extraction and methodology to include a range of GLAMs across the UK and new data points. The initial sample of 350 organisations included Independent Research Organisations (IROs) and Research Centre Institutes (RCIs), GLAMs associated with <a href="https://www.nationalcollection.org.uk/">Towards a National Collection</a> Foundation and Discovery projects, UK GLAMs in the Open GLAM Survey, and other UK GLAMs and related organisations. An initial review was performed to identify and remove organisations outside the scope of inquiry (<em>e.g.</em>, no permanent collections). The final sample included 195 organisations.</p> <p>From the final sample, 24 are IROs (all RCIs were removed). Another 32 are Universities (including GLAMs within universities). This brings the total number of organisations eligible for AHRC funding to 56 (or 28.6%). The remaining 140 include public and private GLAMs at national, regional and local levels (<em>e.g., </em>councils, historic buildings) and research initiatives or data aggregators (<em>e.g.,</em> Portable Antiquities Scheme, Culture Grid, Archaeology Data Service). Organisations are distributed across the UK as follows: Channel Islands (1 total); England (154 total); Isle of Man (1 total); Northern Ireland (5 total); Scotland (28 total); Wales (6 total).</p> <p>A survey of the copyright and open access policies of 63 GLAMs from the UK GLAM Sample is available on Zenodo at: <a href="https://doi.org/10.5281/zenodo.6242559">https://doi.org/10.5281/zenodo.6242559</a></p>
Dzongkha Handwritten Digit Dataset
<p>Dzongkha, the national language of Bhutan, has limited resources available for Natural Language Processing (NLP) tasks because the language is relatively understudied. However, there is no publicly available benchmark dataset for handwritten character identification in the Dzongkha digit script. The dataset contains 1000 images of handwritten Dzongkha digits that are captured using Google Jamboard in JPG format. The image data is assembled from a total of 100 indigenous and non-indigenous people of Bhutan irrespective of age, gender, educational background, etc. In the designed dataset, there are 10 different classes of Dzongkha digits which range from 0 to 9. The labels of these classes are: 0 (༠), 1 (༡), 2 (༢), 3 (༣), 4 (༤), 5 (༥), 6 (༦), 7 (༧), 8 (༨), 9 (༩).</p>
Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images
<p><strong>Name: </strong>Cellpose models for Brightfield and Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional <strong>Training Dataset . Includes</strong> corresponding csv files with 'Quality Control' metrics(§) (model.zip).</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>)</p> <p><strong>Training Procedure: </strong>The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> . Training was done using a <a href="https://datascience.ch/renku/">Renku </a>environment (<a href="https://github.com/BIOP/renku-templates/tree/main/VNC-Napari-Fiji-Omero-CUDA11.4-cellpose-omnipose">renku template</a>).</p> <p> </p> <p><strong>Command Line Execution for the different trained models</strong></p> <p><strong>nuclei_from_bf: </strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_bf</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_dpc:</strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_dpc</strong>:</p> <pre><code>cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><em><strong>NOTE </strong></em>(§): We provide a notebook for Quality Control, which is an adaptation of the <a href="https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb">"Cellpose (2D and 3D)" notebook from ZeroCostDL4Mic</a> .</p> <p><em><strong>NOTE</strong></em>: This dataset used a training dataset from the Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>) generated from the “HeLa “Kyoto” cells under the scope” dataset Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6139958">https://doi.org/10.5281/zenodo.6139958</a>) in order to automatically generate the label images.</p> <p><strong><em>NOTE</em></strong>:<strong> </strong>Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.</p> <p> </p>
Understanding Digits in Identifier Names: An Exploratory Study
<p>This is the dataset that accompanies the study: "<strong>Understanding Digits in Identifier Names: An Exploratory Study</strong>." This study has been accepted for publication at: The 1<sup>st</sup> International Workshop on Natural Language-based Software Engineering (NLBSE 2022).</p> <p> </p> <p><strong><em>Following is the abstract of the study:</em></strong></p> <p>Before any software maintenance can occur, developers must read the identifier names found in the code to be maintained. Thus, high-quality identifier names are essential for productive program comprehension and maintenance activities. With developers free to construct identifier names to their liking, it can be difficult to automatically reason about the quality and semantics behind an identifier name. Studying the structure of identifier names can help alleviate this problem. Existing research focuses on studying words within identifiers, but there are other symbols that appear in identifier names-- such as digits. This paper explores the presence and purpose of digits in identifier names through an empirical study of 800 open-source Java systems. We study how digits contribute to the semantics of identifier names and how identifier names that contain digits evolve over time through renaming. We envision our findings improving the efficiency of name appraisal and recommendation tools and techniques.</p> <p> </p>
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
Dataset from Remote analysis of Sputum Smears for Mycobacterium Tuberculosis Quantification using Digital Crowdsourcing
<p>Worldwide, TB is one of the top 10 causes of death and the leading cause from a single infectious agent. Although the development and roll out of Xpert MTB/RIF has recently become a major breakthrough in the field of TB diagnosis, smear microscopy remains the most widely used method for TB diagnosis, especially in low- and middle-income countries.</p> <p>This is a minimal dataset to reproduce our research that tests the feasibility of a crowdsourced approach to tuberculosis image analysis. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count acid-fast bacilli in digitized images of sputum smears by playing an online game. Following this approach 1790 people identified the acid-fast bacilli present in 60 digitized images, the best overall performance was obtained with a specific number of combined analysis from different players and the performance was evaluated with the F1 score, sensitivity and positive predictive value, reaching values of 0.933, 0.968 and 0.91, respectively.</p> <p>The dataset includes 24 digitized images of sputum smears and the corresponding gameplays clicks. </p>
Digital Elevation Model from Pléiades stereo images - Upper Tuolumne basin, California, 2017-08-13
<p><br> Digital Elevation Model (DEM) derived from a triplet of stereo images of the Pléiades satellite.<br> The images were acquired on 13th August 2017 and cover a part of the upper Tuolumne basin (California).<br> The DEM was calculated at a 3 m resolution with the Ames Stereo Pipeline software (Beyer et al., 2018).<br> Details about the processing of the images and the accuracy of the DEM are given in Deschamps-Berger et al. (2020).</p> <p> </p> <p>References<br> Beyer, R. A., Alexandrov, O., and McMichael, S. (2018). The Ames Stereo Pipeline: NASA’s open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537–548. https://doi.org/10.1029/2018EA00040</p> <p>Deschamps-Berger, C., Gascoin, S., Berthier,<br> E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M. (2020).<br> Snow depth mapping from stereo satellite imagery in mountainous terrain :<br> evaluation using airborne lidar data. <em>The Cryosphere</em>, (February):1–28. https://doi.org/10.5194/tc-14-2925-2020</p>
African Red Slip Ware Digital (ARS3D) - Comparison (Archaeology)
<p>Characteristic of the North African bowls, plates, and jugs are their pictorial decorations applied mainly by appliqués and stamps. As mass-produced image carriers and everyday objects, the ARS spread throughout the empire.</p> <p>The range of motifs includes mythological scenes as well as scenes from the Old and New Testament, circus, arena and hunting scenes as well as fish and plant motifs. The appliqués-decorated pottery thus provides insights into Late Antique imagination and its changes, as well as into the economic history of the period between the 3rd and 5th centuries AD in North Africa.</p> <p>Previous documentation methods were not able to capture the objects and their decoration in an adequate way. The digital recording of the RGZM's collections by 3D scans allows to compare potentially identical appliqués and to assign them to their negative forms and the corresponding stamps.</p> <p>Whereas vessel curvature previously falsified the assignment of appliqués and models, 3D analysis and visualisation tools now allow a comparison . Metadata created for each object increases the effectiveness and accuracy of determining image context and content. Issues related to the production of the ARS and the process flows within the workshops can be investigated through the analysis of the 3D data.</p> <p>The developed ontology uses CIDOC CRM and various extensions. The data mainly consists of objects (E24), on which e.g. applications (features, E25) can be found. These objects can be described semantically, e.g. by the shape or time epoch. Features are described with observations, e.g. a human type man standing and wearing a beard. These observations lead to interpretations whose arguments can be observations.</p>
Video - The PTANOIS POSIN project, digital publication
<p>Video presentation of the PTANOIS POSIN project website: description of the project; presentation and description of the digital product</p>
Challenges of the New Accreditation in Digital Competences for Citizens (ACTIC): Update, Validation and Results
<p>The aim of this study is to explain the process used to update and validate the Catalan Accreditation of Information and Communications Technology Skills (ACTIC), conducted by the Open University of Catalonia and the Blanquerna Foundation of Ramon Llull University, at the initiative of the Catalan Department for Digital Policy and Public Administration, Government of Catalonia.</p> <p>Using a mixed method, the study is divided into 3 phases and presents the definitions of the 5 competence areas, 19 competences, 3 achievement levels and a total of 233 indicators. The definitions were validated by a group of experts. As a result of the study, three key dimensions have emerged that improve digital awareness among citizens while empowering them and providing them with a way of having their skills accredited and acknowledged in the workplace: (i) identity, centred around participatory, social, ethical and civic issues; (ii) skills learning among citizens; and (iii) technological development and digital autonomy of citizens.</p>
LadiesDebating-KG: A Knowlege Graph for representing the "Edinburgh Ladies' Debating Society Digital Collection" (1865 - 1880)
<p>This Knowlege Graph represents the information of the "Edinburgh Ladies’ Debating Society<strong>"</strong> (years: 1865 - 1880) collection in RDF (ttl format). This collection consists of the complete runs of two Edinburgh journals, <strong>‘The Attempt’ (10 volumes, 1865-74)</strong> and its successor ‘<strong>The Ladies’ Edinburgh Magazine’ (6 volumes, 1875-80)</strong>. These publications were produced by a leading Edinburgh women’s club, known during the period as the Edinburgh Essay Society or the Ladies’ Edinburgh Essay Society, but subsequently as the Ladies’ Edinburgh Debating Society. The Society existed from 1865 to 1935. The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/edinburgh-ladies-debating-society/">link</a>. As other NLS data collections, they are originally provided using two XMLs schemas: METS for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO for encoding the OCR text of a page.</p> <p>In this work, we have extracted the information from METS and ALTO XMLS using <a href="https://github.com/francesNLP/defoe">defoe</a> tool and developed a <a href="https://github.com/francesNLP/defoe/blob/master/defoe/nls/queries/write_metadata_pages_yml.py">new information extraction defoe query</a> , and created a new Knowlege Graph called LadiesDebating-KG. The LadiesDebating-KG uses the <a href="https://francesnlp.github.io/NLS-ontology/doc/index-en.html">NLS Ontology </a>to represent the information extracted. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation.</p> <p>The LadiesDebating-KG contains 38,279 RDF triples. It has information from 2 series and 16 volumes: <strong>'The attempt' </strong>serie has 10 volumes and <strong>'The Ladies' </strong>serie<strong> </strong>has 6 volumes . Each serie has an Editor, mmsid, Shelf-Locator, publication year, etc. A Volume has several Pages, with text in them. The data model of the LadiesDebating-KG can be found <a href="https://francesnlp.github.io/NLS-ontology/doc/dataModel.png">here</a>.</p> <pre> </pre>
Documentation and digital files in support of "Aftershock regions of Aleutian–Alaska megathrust earthquakes, 1938-2021" by Carl Tape and Anthony Lomax: Part A
<p>These files support a paper entitled "Aftershock regions of Aleutian–Alaska megathrust earthquakes, 1938–2021," by Carl Tape and Anthony Lomax, published in Journal of Geophysical Research Solid Earth. This collection contains Part A. A separate collection contains Parts, B, C, and D. This research was supported by the U.S. Geological Survey (USGS), Department of the Interior, under USGS award number G19AP00050.</p>
Aftershock regions of Aleutian–Alaska megathrust earthquakes: digital files
<p>These files support a published manuscript entitled "Aftershock regions of Aleutian–Alaska megathrust earthquakes, 1938-2021," by Carl Tape and Anthony Lomax. A subset of these files is also available within the online supplement of the published manuscript. This research was supported by the U.S. Geological Survey (USGS), Department of the Interior, under USGS award number G19AP00050.</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.