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.
1,630
datasets available to search
ShareScore release 0.9.0
Dataset results
1,630 results for “stepping”
Iceland as stepping stone for intercontinental spread of highly pathogenic avian influenza H5N1 virus between Europe and North America: data set on phylogeographic analysis
<p>Highly pathogenic avian influenza viruses (HPAIV) subtype H5 clade 2.3.4.4b have widely spread within the northern hemisphere since 2020 and threaten wild bird populations as well as poultry production. For the very first time, HPAIV were detected in wild birds and, subsequently, in poultry holdings in Iceland.</p> <p>Here, we present phylogeographic evidence that Iceland has been used as a stepping stone for HPAIV translocation from Northern Europe to North America in 2021 and describe two independent incursions of HPAI H5N1 clade 2.3.4.4b viruses of two different genotypes to Iceland in 2021 and 2022.</p>
Dataset of "Single-step Purification and Characterization of Pseudomonas aeruginosa Azurin"
<p>Azurin is a small periplasmic blue copper protein. We have created a novel approach for expressing and purifying of azurin in E. coli with high yields and optimal metalation ratio. The azurin sequence was N-terminally fused with a GST tag and protein was purified by single-step affinity chromatography on a GST-trap column. The N-terminal tag was cleaved off by HRV 3C protease and sufficient metalation was endured by incubation with copper sulphate. UV-VIS absorption, mass spectroscopy, and circular dichroism analysis all validated the effective production of azurin, appropriate protein folding and the development of an active site with an associated cofactor. MD simulations verified that incorporation of the N-terminal GPLGS segment does not affect the wild-type azurin structure.</p>
Evidence of dual Shapiro steps in a Josephson junction array
<p>QCodes type databases containing raw data associated with the paper "Evidence of dual Shapiro steps in a Josephson junctions array" by N. Crescini, S. Cailleaux et al. acquired in the Institut Neel, CNRS, Grenoble, France between January 2022 and June 2022.</p> <p>There are two databases: one contains the characterization of the sample without microwave pump and the other one contains the study of the sample under microwave irradiation.</p> <p>Two Jupyter notebooks (python 3.8.11) are provided to analyze the datasets contained in the databases and reproduce the results of the article.</p> <p>For any additional information please contact: nicolo.crescini@neel.cnrs.fr or samuel.cailleaux@neel.cnrs.fr</p> <p> </p>
Dataset of Scanning Tunneling Microscopy (STM) images of model surfaces for elementary steps in catalytic reactions
<p>STM images presented in the dataset were recorded by the STRAS research group using a Omicron Variable Temperature STM (VT-STM) microscope, in the TASC laboratory of the CNR-IOM in Trieste.</p> <p>This work has been done within the NFFA-DI project funded by the European Union – NextGenerationEU - Missione 4, “Istruzione e Ricerca” – Componente 2, “Dalla ricerca all'impresa” – Linea di investimento 3.1,“Fondo per la realizzazione di un sistema integrato di infrastrutture di ricerca e innovazione” – Azione 3.1.1, “Creazione di nuove IR o potenziamento di quelle esistenti che concorrono agli obiettivi di Eccellenza Scientifica di Horizon Europe e costituzione di reti”.</p>
Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.
<p><span>This repository contains the supplementary data used in the publication Roche et al., 2024 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences), including (1) the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia, modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016), and (2) the pictures series used to build isochrone contour maps.</span></p> <p><span><span>1.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The seismicity catalogues from Cahuilla, Yellowstone and West Bohemia are modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue and filtering and modifications can be found in the associated publication.</span></p> <p><span> Dataset list:</span></p> <ul> <li><span>Cahuilla Catalogues (modified from Ross et al., 2019): </span></li> <ul> <li><span>Original data: File name: VR_sup_0021_Cah_All</span></li> <li><span>Filtered data: File name: VR_sup_0022_Cah_Filter</span></li> </ul> <li><span>Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0023_Boh_08_All</span></li> <li><span>Filtered data: File name: VR_sup_0024_Boh_08_Filter</span></li> </ul> <li><span>Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0025_Boh_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0026_Boh_14_Filter</span></li> </ul> <li><span>Yellowstone Catalogs (modified from Shelly et al., 2013): </span></li> <ul> <li><span>Original data: File name: VR_sup_0027_Yell_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0028_Yell_14_Filter</span></li> </ul> </ul> <p><span>The files are text files tab-delimited, with the following headers:</span></p> <ul> <li><span>Index: 1 by default</span></li> <li><span>Easting(m): hypocenter Easting in meters </span></li> <li><span>Northing(m): hypocenter Northing in meters </span></li> <li><span>Depth(m): hypocenter depth in meters </span></li> <li><span>Mw: magnitude</span></li> <li><span>Relative Time(s): date of the origin time in the format </span></li> </ul> <p><span><span>2.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The pictures series are images of seismicity at a regular time interval for each studied step.</span></p> <p><span>Dataset list:</span></p> <ul> <li><span>Step C1: File name: VR-sup-0012-Pictures_C1.</span></li> <li><span>Step C2: File name: VR-sup-0013-Pictures_C2.</span></li> <li><span>Step C3: File name: VR-sup-0014-Pictures_C3.</span></li> <li><span>Step C4: File name: VR-sup-0015-Pictures_C4.</span></li> <li><span>Step Y1: File name: VR-sup-0016-Pictures _Y1.</span></li> <li><span>Step B1I: File name: VR-sup-0017-Pictures _B1I.</span></li> <li><span>Step B1II: File name: VR-sup-0018-Pictures _B1II.</span></li> <li><span>Step B2: File name: VR-sup-0019-Pictures _B2.</span></li> <li><span>Step B3: File name: VR-sup-0020-Pictures _B3.</span></li> </ul> <p><span>Each file contains a series of pictures in JPEG format. For each picture, events in the overlying and underlying segments are indicated in blue and red. The full circles represent the events occurring during the last interval. The empty circles represent the events occurring in the previous intervals.</span></p> <p><span>If you find these data useful in your research, please cite Roche et al. (2024), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).</span></p>
VSR Databases used in article "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition"
<p>This dataset contains required volcano-seismic waveform DBs (<em>dec.95M.16c</em> and <em>dec.09U.4c</em>) used in the article:</p> <p>"<em>Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition</em>",</p> <p>published in the Seismological Research Letters (<a href="https://doi.org/10.1785/0220180334">https://doi.org/10.1785/0220180334</a>). The authors want to thank everyone at the Instituto Andaluz of Geofísica (<a href="http://iagpds.ugr.es">http://iagpds.ugr.es</a>), precisely to Prof. Jesús Ibáñez and Dr. Javier Almendros, IPs of several research projects which </p> <p>have made possible the monitoring of Deception Island since early 1990s.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
Intermediate processing steps of quality-checking of Antarctic Circumnavigation Expedition (ACE) cruise track data.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE), undertaken in the austral summer of 2016/2017 recorded the cruise track using two independent geo-location instruments: one using GLobal NAvigation Satellite Systems (GLONASS; hereafter referred to as GLONASS) and another primarily using the Global Positioning System (GPS; hereafter referred to as the Trimble GPS). Daily log files were recorded in real-time from both instruments during the expedition and added to MySQL database tables. Following the expedition, quality-checking work has been undertaken to provide a one-second resolution set of positions for the cruise track. This dataset presents the intermediate files that were produced during the quality-checking, therefore it could be used to check the processing steps that have been undertaken, but should not be used as a final source of the cruise track data. Both the original raw data files and final quality-checked cruise track can be found in related datasets.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_INSTRUMENT_YYYY-MM-DD.csv – daily files for each instrument that were output from the database, data file, comma-separated values</li> <li>ace_INSTRUMENT_concatenated_YYYY-MM.csv – input files concatenated by month and instrument, data file, comma-separated values</li> <li>flagging_data_ace_INSTRUMENT_YYYY-MM-DD.csv – daily output files for each instrument with flagged data points, data file, comma-separated values</li> <li>track_data_combined_overall_flags_YYYY-MM.csv – instrument data combined with overall data flag for each month, data file, comma-separated values</li> <li>track_data_prioritised_YYYY-MM.csv – prioritised data files with overall data flag for each month, data file, comma-separated values</li> <li>ace_INSTRUMENT_manual_position_errors.csv – files containing the manually-observed errors, metadata, comma-separated values</li> <li>in_port.csv - dates on when the ship was stationary in port, metadata, comma-separated values</li> <li>README.txt – metadata, text file</li> <li>data_file_header.txt – metadata, text file</li> </ul> <p><strong>Dataset license</strong></p> <p>This dataset containing intermediate processing files of the ACE cruise track is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Application of two-step clustering algorithm to QuaLiKiz-v2.6.2 turbulent transport simulation data
<p>QuaLiKiz simulation data in support of the two-step clustering algorithm, developed by Bart J. J. Kremers.</p> <p>The NETCDF file, generated via NETCDF4, contains the raw QuaLiKiz output for the 3-dimensional (2-input, 1-output) toy case used to develop the algorithm. Within the NETCDF file, the coordinates represent the code inputs and various vector indices and the data variables represent the code outputs.</p> <p>There are also 4 HDF5 files, containing the results from the two-step clustering reduction algorithm, where the data is saved under 2 keys: "/input" and "/flattened". The file names indicate the reduction algorithm settings used to produce the results within.</p> <p>The algorithm is available open-source at <a href="https://gitlab.com/BartKremers/two-step-clustering">https://gitlab.com/BartKremers/two-step-clustering</a>.</p>
First Steps towards a Risk of Bias Corpus of Randomized Controlled Trials
<p><strong>Abstract</strong></p> <p>Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to conducting systematic reviews. Manual RoB assessment for hundreds of RCTs is a cognitively demanding, lengthy process and is prone to subjective judgment. Supervised machine learning (ML) can help to accelerate this process but requires a hand-labelled corpus. There are currently no RoB annotation guidelines for randomized clinical trials or annotated corpora. In this pilot project, we test the practicality of directly using the revised Cochrane RoB 2.0 guidelines for developing an RoB annotated corpus using a novel multi-level annotation scheme. We report inter-annotator agreement among four annotators who used Cochrane RoB 2.0 guidelines. The agreement ranges between 0% for some bias classes and 76% for others. Finally, we discuss the shortcomings of this direct translation of annotation guidelines and scheme and suggest approaches to improve them to obtain an RoB annotated corpus suitable for ML.</p> <p> </p> <p><strong>Methods</strong></p> <p>The upload contains two zip files and a .json file.</p> <ul> <li>plain.html.zip</li> </ul> <p>Original corpus (n = 10) in .html format. The corpus was generated using the methodology described in the paper. Each .html file could be opened in any default text editor in any operating system or browser. A .html contains full text divided into several annotatable text parts. </p> <p> </p> <ul> <li>ann.json.zip</li> </ul> <p>The .zip contains RoB annotations conducted by the authors (R.H., M.S., K.G., R.C.). The annotation files are in .json format. Each .json is divided into two JSON objects and three JSON arrays. </p> <ol> <li>annotatable (object): Parts from the full-text document corresponding to the text parts from the plain .html files. </li> <li>metas (object): full-text document label</li> <li>entities (array): contains labelled entities. Each entity is linked to which part of the full-text it is linked to.</li> <li>relations (array)</li> <li>sources (array)</li> </ol> <p> </p> <ul> <li>annotations-legend.json</li> </ul> <p>This .json file contains entity and entity labels encoded to text legends. For example, entity class label "1_2_Yes_Good" is encoded as "e_113".</p> <p> </p> <p><strong>Resources</strong></p> <p>The code to parse annotations can be found on <a href="http:// https://github.com/anjani-dhrangadhariya">GitHub</a>.</p> <p> </p> <p><strong>Funding</strong></p> <p>HES-SO Valais-Wallis, Sierre, Switzerland</p>
Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions - data
<p>Data set pertaining to the manuscript "Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions", accepted for publication in Nature Chemistry.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data') if applicable.<br> 2. As-measured data ('raw').</p> <p>Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme.</p> <p><br> The following files are provided:</p> <p>Photoemission data pertaining to ETMD measurements:<br> alcl3-K-etmd.h5 (ETMD after Al K-shell photoionization)<br> alcl3-L23-etmd.h5 (ETMD after Al L-shell photoionization)</p> <p>Calculated energies of the ETMD final states after 1s ionization. The energies were calculated at the CAS-CI/cc-pVDZ level. The states were shifted so that the lowest-energy state corresponds to the LC-ωPBE/aug-cc-pVTZ and aug-cc-pCVTZ value obtained in a polarizable continuum:<br> Dataset_ETMD_after_1s_ionization.csv<br> Dataset_ETMD_after_2p_ionization.csv</p> <p>Geometrical coordinates of the clusters that were used for energy calculation:<br> clusters.dat<br> clusters_small.dat</p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p> <p> </p> <p>Version history:</p> <p>v3 - Al L2,3 data: Orientation of the analyser hemisphere corrected. Direction of the linear polarization vector added. All other data unchanged.<br> v2 - cluster coordinates added, all other data unchanged.<br> v1 - initial upload.</p>
Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला, Madhya Pradesh). Bhairava ghat, remains of steps.
<p>Mogha resevoir (मोघा जलाशय, near देवरी, उदयपुरा तहसील, रायसेन ज़िला, Madhya Pradesh). Bhairava ghat, remains of steps.</p>
A framework for step-wise explaining how to solve constraint satisfaction problems
<p>We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.</p>
Single-Step Generation of 1D FeCo Nanostructures
<p>This DOI address contains data regarding the manuscript entitled "Single-Step Generation of 1D FeCo Nanostructures", published in IOP Nano Express (2024) (Paper DOI: 10.1088/2632-959X/ad3e1c).</p> <p>Authors: Mehran Sedrpooshan,a,b Pau Ternero,a,c Claudiu Bulbucan,d Adam M. Burke,a,c Maria E. Messing,a,b,c Rasmus</p> <p>Westerström,a,b</p> <p>a NanoLund, Lund University, Box 118, 221 00 Lund, Sweden</p> <p>b Synchrotron Radiation Research, Lund University, Box 118, 221 00 Lund, Sweden</p> <p>c Solid State Physics, Lund University, Box 118, 221 00 Lund, Sweden</p> <p>d MAX IV Laboratory, Lund University, Lund, SE-22100, Sweden</p> <p> </p> <p>- This DOI contains separate figures from the manuscript</p> <p>- Data of the hysteresis loops</p> <p>- Data of the XAS and XMCD plots</p>
Automatic time step adjustment for shortening the runtime of the simulation of marine ecosystem models
<p><strong>Abstract:</strong></p> <p>In investigating the global carbon cycle, shortening the runtime of the simulation of marine ecosystem models is an important issue. More specifically, steady annual cycles mostly are used to assess and validate the models against<br> observational data and to identify relevant biogeochemical processes. Offline simulations based on the transport matrix method already reduce the high computational effort significantly. Furthermore, they facilitate the application<br> of larger time steps in a simple way. In this paper, we present two different methods that automatically adjust the time step during the simulation of a steady state using transport matrices. The algorithms use either an adaptive<br> step size control or decreasing time steps. Their aim is to apply always the time step as large as possible but without any manual selection. We applied the methods for a variety of ecosystem models of different complexity, using Latin<br> hypercube samples of size 100 for the model parameters of each model. We showed that both methods computed an approximation of the steady annual cycle that was of the same accuracy as solutions obtained with a fixed time step. Both algorithms lowered the runtime of the steady annual cycle computation significantly. The performance gain depended on the complexity of the models. Moreover, the adaptive method has a certain overhead that might lead to higher computational cost in special cases.</p> <p><strong>Content:</strong></p> <ul> <li>Tracer concentrations of a reference solution for all parameter vectors and biogeochemical models</li> <li>SQLite database including the results using the decreasing time steps algorithm</li> <li>Tracer concentrations of the results using the decreasing time steps algorithm</li> <li>SQLite database including the results using the step size control algorithm</li> <li>Tracer concentrations of the results using the step size control algorithm</li> </ul>
How do native and non-native speakers recognize emotions in the instructor's voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners [dataset]
<p>Dataset for the journal article <em>How do native and non-native speakers recognize emotions in the instructor’s voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners.</em></p>
Can a knee sleeve influence ground reaction forces and knee joint power during a step-down hop in participants following ACL reconstruction? Discrete and time-continuous datasets
<p>Using a cross-over design, we estimated GRF and knee kinematics and kinetics during a step-down hop for 30 participants (age 26.1 [SD 6.7] years, 14 women) following ACL reconstruction (median 16 months post-surgery) with and without wearing a knee sleeve. In a subsequent randomised clinical trial, participants in the ‘Sleeve Group’ (n=9) then wore the sleeve for 6 weeks at least 1 hour daily, while a ‘Control Group’ (n=9) did not wear the sleeve. Statistical parametric mapping (SPM) was used to compare (1) GRF trajectories in the three planes as well as knee joint power between three conditions at baseline (uninjured side, unsleeved injured and sleeved injured side); (2) within-participant changes for GRF and knee joint power trajectories from baseline to follow-up between groups. We also compared discrete peak GRFs and power, rate of (vertical) force development, and mean knee joint power in the first 5% of stance phase. Time-continuous and discrete data are included in this dataset.</p>
Bio-oil production from biogenic wastes, the hydrothermal conversion step - Data
<p>Food wastes are an abundant resource that can be effectively valorised by hydrothermal liquefaction to produce bio-fuels. The objective of the European project Waste2Road is to demonstrate the complete value chain from waste collection to engine tests. The principle of hydrothermal liquefaction is well known but there are still many factors that make the science very empirical. Most experiments in the literature are performed on batch reactors. Comparison of results from batch reactors with experiments with continuous reactors are rare in the literature.</p> <p>This dataset presents fully documented experiments, performed in this project, on food wastes, with different compositions, conditions and solvents. The data set is extended with data from the literature. This data set also includes bio-oil and aqueous phase analysis by gas chromatography coupled with mass spectrometry.</p>
I'm forking Ladybird and stepping down as SerenityOS BDFL
<p>UPDATE JUNE 04, 2024</p> <p>In 2018, I created the SerenityOS project after completing a drug rehab program. I needed something to soak up my free time while learning to live a normal life, and it turned out that building a new operating system was a task of just the right proportions.</p> <p>After six months of working on it by myself, I posted it online, and invited others to participate.</p> <p>Since then, SerenityOS has grown into a large OSS community with over one thousand contributors all over the world. We've built a friendly culture of setting differences aside and focusing on our shared love for programming. Countless people have poured their heart and soul into it, and I like to think it's inspired some to attempt more challenging things in life.</p>
Markaṇḍa (Gadchiroli District, Maharashtra). Waṅgaṅgā river and bathing steps at Markaṇḍeśvara temple.
<p>Markaṇḍa (Gadchiroli District, Maharashtra). Waṅgaṅgā river and bathing steps at Markaṇḍeśvara temple, looking south, as documented 02/2018</p>
Input files for "Faster Simulations with a 5 fs Time Step for Lipids in the CHARMM Force Field"
<p>The performance of all-atom molecular dynamics simulations is limited by an integration time step of 2 fs, which is needed to resolve the fastest degrees of freedom in the system, namely, the vibration of bonds and angles involving hydrogen atoms. The virtual interaction sites (VIS) method replaces hydrogen atoms by massless virtual interaction sites to eliminate these degrees of freedom while keeping intact nonbonded interactions and the explicit treatment of hydrogen atoms. We have modified the existing VIS algorithm for most lipids in the popular CHARMM36 force field by increasing the hydrogen atom masses at regular intervals in the lipid acyl chains and obtained lipid properties and pore formation free energies in very good agreement with those calculated in simulations without VIS. Our modified VIS scheme enables a 5 fs time step resulting in a significant performance gain for all-atom simulations of membranes. The method has the potential to make longer time and length scales accessible in all-atom simulations of membrane–protein complexes.</p> <p>The file set contains individual lipid topologies for virtual interaction sites for standard CHARMM lipids, as well as a README file with instructions on how to implement the VIS algorithm for membranes or membrane-protein complexes</p> <p>Please Cite: <a href="//pubs.acs.org/doi/10.1021/acs.jctc.8b00267">10.1021/acs.jctc.8b00267</a></p> <p> </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.