Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
867
datasets available to search
ShareScore release 0.9.0
Dataset results
867 results for “repositories”
Tracking magma spine extrusion from space: Implications for conduit and topography complexity at Shiveluch volcano, Kamchatka - Photogrammetric data repository
<p>This is a dataset relevant for a paper on lava spine extrusion at Shieveluch volcano, Kamchatka. Data was used to show that the spine elongates along a previously identified fracture line and bends to a preferred northerly direction. By repeated morphology analysis and feature tracking, we constrain a spine diameter of ~300 m, extruding at a velocity of 1.7 m/day and discharge rate of 0.3-0.7 m³/s. Results are relevant for understanding the growth and collapse hazards of spines and provide unique insights into the hidden magma-conduit architecture.</p> <p>The data consists of three parts. First, we provide the filtered and corrected three dimensional point clouds generated from Pleiades tristereo data. These 3D point clouds were co-aligned and now allow analysing subtle changes. Point clouds are provided in .las format. Second, we provide the filtered and corrected digital elevation models generated from the point cloud data, these DEMs are provided in geotiff format. The name of the files indicates the dates of their acquisition. Third and lastly, we provide an orthomap stack used to estimate displacements by tracking offsets.</p> <p> </p>
Smartphone imaging repository: a novel method for creating a CT image bank
<p>In a submitted manuscript, test an image capture method using smartphone camera video-derived images of brain computed tomography (CT) scans of traumatic intracranial hemorrhage. The deidentified videos are emailed or uploaded from the emergency department for central adjudication.</p> <p>We measured the time in seconds it took to capture and send the files. The primary outcomes were hematoma volume measured by ABC/2, Marshall Scale, midline shift measurement, image quality by contrast-to-noise ratio (CNR) and time to capture. A radiologist and an imaging scientist applied ABC/2 method, calculated the Marshall scale and midline shift on the data acquired on different smartphones and the PACS in a randomized order. We calculate the intraclass correlation coefficient (ICC). We measured image quality by calculating contrast-to-noise ratio (CNR). We report summary statistics on time to capture in the smartphone group without a comparator.</p>
Elmer/Ice repository for 3D Greenland Ice-Sheet initial states
<p>initial states of the Greenland Ice-Sheet produced with the <a href="http://elmerice.elmerfem.org/">Elmer/Ice model</a>.</p> <p>This initial states are obtained using a control inverse method that optimise the basal friction field to minimise the mismatch between model and observed velocities.</p> <p> </p> <p>Results used in N. Maier, F. Gimbert and F. Gillet-Chaulet, Threshold response to surface melt drives large-scale bed weakening in Greenland, submitted to Nature</p>
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−𝑡ℎ term contained in the j−𝑡ℎ user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
Experimental Repository for "Certified CNF Translations for Pseudo-Boolean Solving"
<p>Experimental repository</p> <p> </p> <p>Directory structure:</p> <ul> <li>`instances`: The instances used for the experiments sorted by their category.</li> <li>`output_data`: Raw output data from the experiments.</li> <li>`plots`: Plots generated from our experiments; also contain the plots used in the paper.</li> <li>`results`: Collected data on running time and proof size for the encodings.</li> <li>`source_code`: The source code of our `kissat_fork` to output VeriPB proofs, the proof checker `VeriPB` and our certifying PB-to-CNF translation library `VeritasPBLib` as used for our experiments.</li> </ul>
Data repository - Land use change and carbon emissions of a transformation to timber cities
<p>Data and model source code for the publication:</p> <p>Land use change and carbon emissions of a transformation to timber cities<br> (Nature Communications, 2022)<br> DOI: 10.1038/s41467-022-32244-w</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Galina Churkina1, Christopher P.O. Reyer1, Felicitas Beier1,2, Benjamin Leon Bodirsky1, Hans Joachim Schellnhuber1, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> May 2022</p> <p>See README.txt for further details.</p>
Data and code repository for Science Advances submission: Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy
<p>Data and codes related to the findings reported in the manuscript, "Uncovering the biological basis of control energy: structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy", are deposited. Please refer to the notes located within each folder for further descriptions.</p>
Analysis of scholarly repositories' availability. Data and notebooks.
<p>These datasets and companion Jupyter notebooks supplement the publication "Knock knock! Who's there?'' A study on scholarly repositories' availability" accepted at TPDL 2022, Padova, Italy.</p>
The Open Khipu Repository
<p><strong>Release v2.0.0 changes:</strong></p> <p>Implements the khipu renaming procedure described in Brezine, Clindaniel, Ghezzi, Hyland and Medrano (Under Review) "A New Naming Convention for Andean Khipus" (see replication code here: https://doi.org/10.5281/zenodo.6908292).</p> <p>Note that as a part of this release, we dropped several duplicate khipu recordings and implemented a more neutral "KH"-based naming scheme in a new <code>okr_num</code> field in the <code>khipu_main</code> table (see replication code linked above for more detail).</p> <p><strong>Description of Repository</strong></p> <p>This open-source digital repository stores the most up-to-date data and metadata on extant Inka-style khipus from archaeological sites in the Andes, as well as museums around the world. Inka khipus were unique pre-Columbian, Andean recording devices that used three-dimensional signs -- primarily knots, cords, and colors -- as symbols functionally akin to those of early writing systems in other cultures. Spanish chronicles, as well as contemporary khipu studies indicate that the Inka used khipus to record everything from accounting records to historical narratives. The khipu recording system remains undeciphered, however. The purpose of this repository is to enable computational khipu research and Inka khipu decipherment efforts.</p> <p>Currently, data is stored in a serverless SQLite relational database (khipu.db) that contains all known khipu data recorded and published by Inka khipu scholars. A list of known contributors is available <a href="https://github.com/khipulab/open-khipu-repository/blob/master/contributors">here</a> in the repository. If you have recorded khipu data in the database, but are not listed, please contact us (<a href="mailto:okr-team@googlegroups.com">okr-team@googlegroups.com</a>).</p> <p>The data in this repository will continue to be updated as more is learned about khipus and additional khipus become available for study. All new releases and changes to the repository are overseen by the Open Khipu Repository Advisory Board (<a href="mailto:okr-team@googlegroups.com">okr-team@googlegroups.com</a>), currently consisting of Carrie Brezine, Mackinley FitzPatrick, Iván Ghezzi, Sabine Hyland, Manuel Medrano, and Jeffrey Splitstoser. The repository is administered by the Open Khipu Research Laboratory, under the direction of Mackinley FitzPatrick (<a href="mailto:mackfitzpatrick@fas.harvard.edu">mackfitzpatrick@fas.harvard.edu</a>).</p> <p>A part of the OKR Advisory Board's mission is to positively influence the field of khipu studies in ways that are inclusive and respectful of all interested scholars. We recognize that the data in the OKR were not collected in a vacuum. The existence of the OKR is a result of power structures within the global discipline of Anthropology and of academia more generally. The data compiled here reflect funding disparities between scholars at different levels and between institutions in different countries. They reflect geographic privilege and economic privilege, for instance in scholars' disparate access to travel. The OKR is itself one artifact of colonialist Archaeology and Anthropology as practiced by North American and European scholars. It is also a product of unfortunate linguistic distancing -- we are well aware that native speakers of Andean languages have so far had little involvement in collecting the data in the OKR. We hope to change this as the field of khipu studies continues to evolve.</p> <p>In the wake of recent allegations of widespread sexual harassment within the field of khipu studies, Andean studies, Anthropology, and academia generally, the OKR board wishes to make it clear that we do not tolerate sexual harassment by members of the board or by those with whom we collaborate. We deplore the role that sexual exploitation and abuse have played in the compilation of this database in the past. We unequivocally condemn all forms of gender-based harassment and abuse. We do not tolerate discrimination based on sexual identity, gender, nationality, or ethnic identity. The OKR recognizes the patriarchal colonialist legacy of Anthropology and related disciplines, and we will actively work against the perpetuation of colonialist norms in Andean Studies.</p> <p>Our goal is to make the field of khipu studies open, inclusive, and safe for all scholars. To achieve that:</p> <ul> <li>We will not collaborate with scholars against whom there are credible allegations of sexual harassment.</li> <li>We will not collaborate with scholars against whom there are credible allegations of bullying or other identity-based discrimination or harassment.</li> <li>We recognize that khipu are not solely Peruvian. We will use language that does not exclude other Andean countries that consider khipu a part of their material heritage.</li> <li>We will include Andean scholars on the board whenever possible.</li> <li>We are in the process of implementing a more neutral khipu identification system that does not privilege the initials of scholars in each khipu name.</li> <li>We will actively work towards making the OKR available to all scholars with interest, recognizing that true accessibility will take time.</li> <li>We will commit to making OKR documentation publicly accessible and available in multiple languages, beginning with English and Spanish.</li> </ul>
Data repository for "3D coseismic surface displacements from historical aerial photographs of the 1987 Edgecumbe earthquake, New Zealand"
<p>This data repository includes supplementary files used in the accompanying manuscript: </p> <p>Delano, J. E, Howell, A., Stahl, T. A., Clark, K. (<em>submitted 2022</em>). 3D coseismic surface displacements from historical aerial photographs of the 1987 Edgecumbe earthquake, New Zealand. Journal of Geophysical Research: Solid Earth.</p> <p>Contents:</p> <ol> <li>Supplementary Text S1, containing additional methods and discussion</li> <li>Supplementary Figures S1-S9</li> <li>Supplementary Tables S1-S6 </li> <li>Raster files (TIFF) of SfM results and differenced DSM</li> <li>Raster files of orthophoto mosaics (pre- and post-earthquake)</li> <li>Shapefiles containing fault trace mapping and displacement locations</li> </ol> <p>See README for individual file descriptions.</p>
Fig. 6. A in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 6. A, left dorsolateral view of the skull of DN2019-T4-001, Balaenoptera omurai; B, dorsal view of the vertex of the skull of DN2019-T4-001, Balaenoptera omurai; C, left lateral view of the skull of DN2019-T8-001, Dugong dugon.
Fig. 5. A in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 5. A selection of odontocete skulls, highlighting most of the species identified during the survey. All photographs of additional specimens can be viewed in the Supplemental Information. A, dorsal view of the skull of CI2019-T1-002, Neophocaena phocaenoides; B, ventral view of the skull of CI2019-T1-002, Neophocaena phocaenoides; C, dorsal view of the skull of DN2019-T1-005, Tursiops aduncus; D, ventral view of the skull of DN2019-T1-005, Tursiops aduncus; E, dorsal view of the skull of HA2019-T4-006, Sousa chinensis; F, ventral view of the skull of HA2019-T4-006, Sousa chinensis; G, dorsal view of the skull of HA2019-T4-001, Stenella attenuata; H, ventral view of the skull of HA2019-T4-001, Stenella attenuata; I, dorsal view of the skull of DN2019-T2-003, Lagenodelphis hosei; J, ventral view of the skull of DN2019-T2-003, Lagenodelphis hosei; K, dorsal view of the skull of HA2019-T4-002, Pseudorca crassidens; L, ventral view of the skull of HA2019-T4-002, Pseudorca crassidens; M, dorsal view of the skull of CI2019-T1-001, Feresa attenuata; N, ventral view of the skull of CI2019-T1-001, Feresa attenuata; O, dorsal view of the skull of DN2019-T1-022, Globicephala macrorhynchus; P, ventral view of the skull of DN2019-T1-022, Globicephala macrorhynchus; Q, dorsal view of the skull of DN2019-T1-007, Delphinus delphis (long-beaked form); R, ventral view of the skull of DN2019-T1-007, Delphinus delphis (long-beaked form); S, dorsal view of the skull of DN2019-T4-008, Grampus griseus; T, ventral view of the skull of DN2019-T4-008, Grampus griseus.
Fig. 4. A in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 4. A, collection of urns with marine mammal skulls from DN2019-T8, Đà Nẵng; B, central altar of HA2019-T4, Hội An; C, central altar of DN2019-T5 (Đà Nẵng) with glass casket of bones in the background; D, large tomb at the temple CI2019-T1 on the Cham Islands.
Fig. 3 in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 3. Six examples of central buildings of traditional whale temple complexes. A, HA2019-T2, Hội An; B, DN2019-T6, Đà Nẵng; C, DN2019-T7, Đà Nẵng; D, DN2019-T1, Đà Nẵng; E, HA2019-T1, Hội An; F, DN2019-T1, Đà Nẵng.
Fig. 2. A in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 2. A, the central altar of DN2019-T4, Đà Nẵng; B, temple near Hội An, HA2019-T4 in the shape of a Vietnamese fishing boat.
Fig. 1 in Whale temples are unique repositories for understanding marine mammal diversity in Central Vietnam
Fig. 1. The locations of whale temples visited during this study in central Vietnam marked with red triangles.
Data format figures-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>The original data set included noisy, missing and inconsistent data. Data<br> preprocessing improved the quality of the data and facilitated e±cient data<br> mining tasks.<br> Before the experiment, we prepared data suitable to next operation as<br> following steps:<br> ² Delete or replace missing values;<br> ² Delete redundant properties (columns);<br> ² Data Transformation;<br> ² Data Discretization;<br> ² Export data to a required .ar® or .csv format ¯le [11].<br> The original and modi¯ed formats of data set are shown in Figure 1 and<br> Figure 2.<br> Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Figure 3. Data visualization-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
MultiDEFusion trial repository
<h1>Instructions for downloading a trial repository for the MultiDEFusion library</h1> <p>The following repository has been created as a trial dataset for the MultiDEFusion library.</p> <p>The dataset can be downloaded from GitHub or Zenodo platform.</p> <h2>Cloning the repository from GitHub</h2> <ol> <li>Open a terminal or command prompt.</li> <li>Use the git clone command to clone the repository to your device:<br><code>git clone https://github.com/damiantondas/multidefusion_trial.git</code></li> <li>The repository will be downloaded to the current directory. You can now navigate to the repository directory using the <code>cd</code> command: <code>cd multidefusion_trial</code></li> </ol> <h2>Cloning the repository from Zenodo</h2> <ol> <li>Download the <strong>multidefusion_trial.zip</strong> folder.</li> <li>Unzip the folder.</li> </ol> <h2>Running the integration procedure</h2> <ol> <li>To run the integration procedure in the Python environment, the initial parameters are required to be defined by the user. In the following, you can find an example script to run fusion for <code>ALL</code> stations in <code>multidefusion_trial</code> folder using <code>forward-backward</code> method with <code>0.03</code> mm/day2 noise level:</li> </ol> <p><code>from multidefusion.fusion import run_fusion</code></p> <p><code>integration = run_fusion(stations="ALL", path="/path/to/multidefusion_trial/", method="forward-backward", noise=0.03)</code></p> <p> 2. More examples can be found in the <a href="https://damiantondas.github.io/multidefusion/usage/">library documentation</a>.</p>
A Curated Solidity Smart Contracts Repository of Metrics and Vulnerabilities
<p>SmarthER provides the dataset related to the full-paper accepted to PROMISE 2024 (<a href="https://promiseconf.github.io/2024/index.html" rel="nofollow">https://promiseconf.github.io/2024/index.html</a>) <strong>"A Curated Solidity Smart Contracts Repository of Metrics and Vulnerability"</strong>.</p> <p>Authored by: Giacomo Ibba, Sabrina Aufiero, Rumyana Neykova, Silvia Bartolucci, Roberto Tonelli, Marco Ortu, Giuseppe Destefanis</p> <p>This repository aims to collect a significant sample of smart contracts with associated vulnerability reports, and traditional software metrics extracted from each smart contract. The repository contains:</p> <ul> <li>Smart contracts source code.</li> <li>The vulnerability report was built with Slither for each contract.</li> <li>Traditional software metrics extracted from each contract.</li> </ul>
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.