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22,922 results for “collections as data”
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 9-16
<p>This upload contains samples 9 - 16 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 1-8
<p>This upload contains samples 1 - 8 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Data collection that describes the calibration of the temporal and displacement characteristics of an optical tweezers in buffered saline at room temperature.
<p><strong>Introduction</strong>. This data provides the calibration of an optical tweezers within the linear Hookian region. Once the laser was aligned, the power at the objective measured and a bead trapped the instrument was calibrated. <strong>Data Collection</strong>. Calibration was performed by moving a trapped bead by supplying a periodic square wave train input (amplitude: −800 to +800 nm, period: 80 ms) to an acousto-optical device, AOD and monitoring the trajectory of the bead in the 𝑋𝑌 plane with a quadrant photo diode, QPD for ~ one minute. Data was collected at 200 kHz from three (3) QPD channels: displacement in the 𝑋 (∆𝑋<sub>𝑀</sub>) (i) and 𝑌 directions (∆𝑌<sub>𝑀</sub>) (ii) and the sum of the fluorescent intensity (∑<sub>𝐿</sub>) (iii). Typically, each bead was stimulated with a train of square waves of constant amplitude in the 𝑋 direction. This stimulation was performed four (4) times at each of the different amplitudes i.e., −800, +800, −500, and +500 nm. The QPD signal in the dark, ∆𝑋<sub>𝐷</sub>, ∆𝑌<sub>𝐷</sub>, ∑<sub>𝐷</sub> was recorded and subtracted in real time, and the gain, 𝐺 of the QPD noted. The Stokes-Faxen coefficient, 𝛽 was calculated having recorded the height of the bead above the Petri dish, ℎ and the viscosity, 𝜂 of the saline solution. The detected fluorescent bead was excited either with a Xenon lamp or TLED transmitted light source. Some calibrations were performed with a TLED light that was borrowed from the manufacturer for testing, and the signal to noise of these measurements was significantly decreased.<strong> Data Transformation</strong>. For each of the two (2) channels the measured signal was normalized by the sum, and the ~750 waves were averaged for each pulse train stimulus. The reciprocal of the time constant (s<sup>-1</sup>) was determined from the exponential rise or decay of the back-ground subtracted averaged response. Spring constant (pN/nm) was determined from the product of this reciprocal time constant and the Stokes-Faxen coefficient (pNs/nm). The net displacement of the bead (nm) was calculated by determining the resultant vector of the 𝑋 and 𝑌 components of the QPD (V/V). For the four (4) stimuli determined for each bead the normalized displacement (V/V) was plotted as a function of net bead displacement and the slope (V/V/nm) calculated from best linear fit. For each calibration the mean spring constant, reciprocal time constant, and mean slope are provided. <strong>Data Format</strong>. The data was saved in LabView with the proprietary TDMS format (National Instruments, NI, Austin, TX). It was transformed to a text file and imported into MATLAB (The Mathworks, Natick, MA) and stored as struct and analyzed with a Python script. The collection contains raw and transformed results from 69 days for a total 153 beads. The data is provided with annotated descriptions in HDF5, a standard non-proprietary container storage format. Each file is about 1.1 GBs (HDF5).</p> <p> </p> <p>The package contains:</p> <p>1. Standard Container HDF5 with custom organization format of data with annotations.</p> <p>2. Python Script (calibrateopticaltweezers.py file) that describes how data is analyzed and written to HDF5 and .MAT formats. (1 file)<br> </p>
Raw multibeam bathymetry data collected around Southern Thule, part of the South Sandwich Island chain in the Southern Ocean on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around Southern Thule, part of the South Sandwich Island chain in the Southern Ocean in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>lineYYYYDDmonHHMMSS.ssv, data file, ASCII</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset 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>
Raw multibeam bathymetry data collected around Bouvetoya in the South Atlantic Ocean on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around Bouvetoya in the South Atlantic Ocean in the austral summer of 2016/2017.</p> <p>Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys.</p> <p>This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution.</p> <p><strong>Dataset contents</strong></p> <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This raw multibeam bathymetry dataset 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>
GNSS data collected with low-cost antennas
<p>The dataset contains GNSS data collected during ten days. To isolate the antenna-related errors from atmospheric propagation ones, an ultra-short baseline was used. A setup consisting of three types of low-cost antennas that support GPS, Galileo, GLONASS, and BeiDou systems measurements and gain<span> </span>> 25 dB was selected for the experiment. In addition, two surveying and geodetic grades Trimble antennas, provide benchmark performance results. The session duration at both mounting points and each measuring day was 16 hours, from 8:00:00 to 24:00:00 UTC. An interval of 5 s and an elevation mask of 0° were adopted.</p>
Raw Data and Codes for the Article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion"
<p>Dataset for the article "Strain-Affected Ferroelastic Domain Walls in RbMnFe Charge-Transfer Materials undergoing collective Jahn-Teller Distortion", containing:</p> <ul> <li>The data and the codes used to generate the figures</li> </ul>
Educational data collected from students - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The student questionnaire was designed with 20 questions. It was completed by 1,088 respondents and focuses on students' experiences related to online education. The collected data provides a broad perspective on various aspects of this, including access to technology, experiences with different platforms, perceptions of the advantages and disadvantages of this form of education, as well as direct feedback from students regarding their experiences. The full questionnaire can be accessed at: <a href="https://forms.gle/fhgzCUx1SDnxbCfZ6" target="_new" rel="noopener"><strong>https://forms.gle/fhgzCUx1SDnxbCfZ6</strong></a></p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Educational data collected from teachers - for the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The dataset comes from a questionnaire structured into 24 questions, which can be accessed at <a href="https://forms.gle/bUgYMfoNHh7r6ebs6" target="_new" rel="noopener">https://forms.gle/bUgYMfoNHh7r6ebs6</a>. This questionnaire was completed by 956 respondents and aims to analyze the online activities carried out during March - April 2020, being distributed to teachers.<br>Each question is designed to reveal different aspects of the experiences, skills, and perspectives of teaching staff regarding online teaching and learning.</p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
BRESOV common bean ILs collection , phenotypic data
<p><strong><span>Dataset_ILS_2018</span></strong></p> <p><span>Dataset including phenotypic data recorded on the introgression line population (IL), in the multi-locations field trials performed in Spain, Romania, Italy in 2018 </span></p> <p><strong><span>Dataset_ILS_2019</span></strong></p> <p><span>Dataset including phenotypic data recorded on the introgression line population (IL), in the multi-locations field trials performed in Spain, Romania, Italy in 2019 </span></p> <p><strong><span>Dataset_ILS_2020</span></strong></p> <p><span>Dataset including phenotypic data recorded on the introgression line population (IL), in the multi-locations field trials performed in Spain, Romania, Italy in 2020</span></p>
USGS National Elevation Dataset (NED) DEM reprojected into Collection 2 Landsat analysis ready data (ARD) tiles
<p>This dataset is used to test the Classifying the raw irregular time series (CRIT) codes and model for CONUS land cover classification with a deep learning model that can directly classify Landsat irrigular time series. See the code here https://github.com/hankui/CRIT</p>
Data collection of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset contains the definition and name of the data used in the study. It also contains rows of data for all flood parameters applied to the creation of flood vulnerability maps, namely rainfall data, landsat-8 files, DEM, DSMW and drainage survey data.</p>
Data and codes for the article "Quantum collective motion of macroscopic mechanical oscillators"
Open the record for dataset details and reuse information.
Cubic insulin data set collected from multiple lattices on i03 at Diamond Light Source
<p>Dataset collected as part of routine commissioning work, found to have more than one crystal present at the point where data were collected, allowing multiple lattices to be processed. </p> <p> </p> <p>While three lattices are present one is substantially weaker than the other two.</p> <p> </p> <p>Uploading to enable methods development and also to use for tutorials on how to use dials software.</p> <p> </p> <p>Processing data with the usual dials scripts (which will point to this deposition) result in statistics shown below. Tutorial to be uploaded to https://github.com/graeme-winter/dials_tutorials when available.</p> <p> </p> <p><code> -------------Summary of merging statistics-------------- </code></p> <p><code> Suggested Low High Overall</code><br><code>High resolution limit 1.51 4.10 1.51 1.48</code><br><code>Low resolution limit 54.89 54.93 1.54 54.89</code><br><code>Completeness 98.9 100.0 83.7 95.3</code><br><code>Multiplicity 54.7 78.1 4.6 53.5</code><br><code>I/sigma 18.9 89.2 0.3 18.4</code><br><code>Rmerge(I) 0.139 0.060 1.423 0.139</code><br><code>Rmerge(I+/-) 0.138 0.060 1.316 0.138</code><br><code>Rmeas(I) 0.140 0.061 1.590 0.140</code><br><code>Rmeas(I+/-) 0.140 0.060 1.609 0.140</code><br><code>Rpim(I) 0.016 0.007 0.683 0.016</code><br><code>Rpim(I+/-) 0.023 0.009 0.894 0.023</code><br><code>CC half 1.000 1.000 0.271 1.000</code><br><code>Anomalous completeness 97.8 100.0 67.8 92.3</code><br><code>Anomalous multiplicity 28.7 43.6 2.6 28.3</code><br><code>Anomalous correlation 0.038 0.276 -0.050 0.049</code><br><code>Anomalous slope 0.667 </code><br><code>dF/F 0.060 </code><br><code>dI/s(dI) 0.629 </code><br><code>Total observations 669615 52255 2342 670277</code><br><code>Total unique 12231 669 507 12529</code></p>
Planting time data based on automated data collection
<p>In this dataset, the planting time information is based on data collected by the Risutec Asta documentation system. The data consisted of nine planting sites in western Finland (the coordinates of the study area are 60°41'52"N–61°59'38"N and 21°36'24"E–23°48'49"E). Mechanized planting was carried out by one machine entrepreneur using a crawler excavator fitted with a Risutec PM-160 planting device during the planting seasons of 2019 and 2020. A total of 72,711 seedlings were planted at the study sites (40.6 ha). </p> <p> </p> <p>The collected Asta data were used to define the time consumption and productivity of the excavator-based planting machine, considering the production time consumed at the planting worksites (hours), the loading time of the seedling cassette (minutes), the planting time per seedling (seconds), and the operating hour productivity (seedlings G<sub>15</sub>-hour<sup>–1</sup>, including short [<15 min] delays). The timestamps of each mechanically planted seedlings were presented in chronological order, including the start and end times of planting work.</p> <p> </p> <p>The calculated planting time per seedling (s seedling<sup>–1</sup>) is presented for each planting observation (seedling). The planting time per seedling was calculated by subtracting the timestamp of the previously planted seedling from the timestamp of the planted seedling. In practice, all productivity and time consumption calculations in the study are based on the calculated planting times per seedling. Further details regarding the calculations and employed methodologies can be found in the article (Kemppainen et al. 2024).</p> <p> </p> <p>Description of all variables in the dataset:</p> <p> </p> <p>Site = Number of study site (1–9) </p> <p>Seedling = Order number of planted seedling</p> <p>Date = Date of planting observation (format: DD.MM.YYYY)</p> <p>Timestamp = Time of planting observation (format: HH.MM.SS)</p> <p>Planting time (s) = Calculated planting time per seedling (seconds)</p> <p> </p> <p>Note: In the study by Kemppainen et al. (2024), all planting times of less than 4 s were excluded from the final dataset. Following the correction of the data, a total of 71 903 seedlings included in the final dataset. In addition, the planting time of the first seedling planted at the worksite was defined as 9 s because there was no previous timestamp for the first seedling to allow an exact calculation of the planting time.</p> <p> </p> <p>References</p> <p>Kemppainen K., Kärhä K., Laitila J., Sairanen A., Kankaanhuhta V., Viiri H., Peltola H. (2024). Evaluation of the productivity and costs of excavator-based mechanized tree planting in Finland based on automated data collection. <a href="https://www.silvafennica.fi/">Silva Fennica</a> vol. <a href="https://www.silvafennica.fi/volume/58/1">58</a> no. <a href="https://www.silvafennica.fi/issue/3292">5</a> article id <a href="https://www.silvafennica.fi/article/24047">24047</a>. <a href="https://doi.org/10.14214/sf.24047">https://doi.org/10.14214/sf.24047</a></p>
Data from: Selection for collective aggressiveness favors social susceptibility in social spiders
Particularly socially influential individuals are present in many groups, but it is unclear whether their emergence is determined by their social influence versus the social susceptibility of others. The social spider Stegodyphus dumicola shows regional variation in apparent leader-follower dynamics. We use this variation to evaluate the relative contributions of leader social influence versus follower social susceptibility in driving this social order. Using chimeric colonies that combine potential leaders and followers, we discover that leader-follower dynamics emerge from the site-specific social susceptibility of followers. We further show that the presence of leaders increases colony survival in environments where leader-follower dynamics occur. Thus, leadership is driven by the "social susceptibility" of the population majority, rather than the social influence of key group members.
Data from: Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement
For group-living animals traveling through heterogeneous landscapes, collective movement can be influenced by both habitat structure and social interactions. Yet research in collective behavior has largely neglected habitat influences on movement. Here we integrate simultaneous, high-resolution, tracking of wild baboons within a troop with a 3-dimensional reconstruction of their habitat to identify key drivers of baboon movement. A previously unexplored social influence – baboons' preference for locations that other troop members have recently traversed – is the most important predictor of individual movement decisions. Habitat is shown to influence movement over multiple spatial scales, from long-range attraction and repulsion from the troop's sleeping site, to relatively local influences including road-following and a short-range avoidance of dense vegetation. Scaling to the collective level reveals a clear association between habitat features and the emergent structure of the group, highlighting the importance of habitat heterogeneity in shaping group coordination.
Comparing and Combining Existing and Emerging Data Collection and Modeling Strategies in Support of Signal Control Optimization and Management (Project M2)
<p>For decades, traffic signal management agencies have used signal timing optimization tools combined with fine-tuning of signal timing based on field observations in their updates of time-of-day signal timing plans. These traditional signal optimization methods and tools use very limited amount of data and depend on default values in the signal timing optimization/simulation tools to estimate network performance under different signal optimization strategies. In recent years, new data collection technologies are emerging including high resolution controller data, more advanced detection technologies such as video image detection that are based on vehicle tracking and possible integration with microwave detectors, automatic-vehicle based identification technologies, third party crowdsourcing data, connected vehicles, and connected automated vehicles data. The objective of the proposed study is to propose methods and algorithms to combine data collected from existing and emerging sources with enhanced models and optimization algorithms to optimize and manage signal operations. The results from applying the developed methods and algorithms will be compared with traditional signal timing and optimization methods currently used by transportation agencies. </p>
The bear cuscus presence data collection
<p>The table contains information about the number of points of the bear cuscus presence data collection in Bantimurung Bulusaraung National Park (BBNP) and Hasanuddin University Educational Forest (HUEF), South Sulawesi. The data collection consists of direct encounters, feed remains, claw marks, information from the local guides, calls, and BBNP's inventory data.</p>
Data for Ferretto et al, 2021 "Naturally-detached fragments of the endangered seagrass Posidonia australis collected by citizen scientists can be used to successfully restore fragmented meadows"
<p>Please find attached the data for the manuscript "Ferretto et al, 2021" and a brief description of each file.</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.