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48 results for “measurement precision”
[Dataset] One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors, located in Graz, Austria
<p><strong>Highlights:</strong></p> <ul> <li>High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.</li> <li>The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.</li> <li>Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.</li> <li>In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.</li> <li>Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.</li> <li>The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).</li> <li>Additional information is provided in a "Data in Brief" journal article: <a href="https://doi.org/10.1016/j.dib.2023.109224">https://doi.org/10.1016/j.dib.2023.109224</a></li> </ul> <p> </p> <p><strong>Collector array description: </strong>The data is from a flat plate collector array with a total gross collector area of 516 m<sup>2</sup> (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.</p> <p> </p> <p><strong>Data files:</strong></p> <ul> <li><strong>FHW_ArcS__main__2017.csv</strong> – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix __calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix __std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).</li> <li><strong>FHW_ArcS__main__2017.parquet</strong> – Same as FHW_ArcS__main__2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.</li> <li><strong>FHW_ArcS__parameters.json</strong> – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.</li> <li><strong>FHW_ArcS__raw__2017.csv </strong>– Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS__main__2017.csv, which includes all calculated data channels, is a superset of this file.</li> </ul> <p> </p> <p><strong>Scripts: </strong></p> <ul> <li><strong>make_data.py</strong> – This Python script exposes the calculation process of the calculated data channels (suffix __calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.</li> <li><strong>make_plots.py</strong> – This Python script, together with utils_plots.py, generates several figures based on the main dataset.</li> </ul> <p> </p> <p><strong>Data collection and preparation</strong>: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria</p> <p> </p> <p><strong>Data owner</strong>: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria</p> <p> </p> <p><strong>Additional information</strong> is provided in a journal article in "Data in Brief", titled <a href="https://doi.org/10.1016/j.dib.2023.109224">"One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria"</a>.</p> <p> </p> <p><strong>Note: </strong>A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: <a href="https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017">https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017</a></p> <p> </p>
Precise Lifetime Measurement of the Cesium 5²D₅⸝₂ State
<p>This repository contains data and software related to an experiment in which we determine the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state using atoms in a vapor cell. More information is available in the following paper:</p> <ul> <li>arXiv:1912.10089</li> </ul> <p>We provide the data and Python scripts for data evaluation in six folders. We zipped these folders with Windows 10 Enterprise, Version 1903. In the following, we describe how to use data and scripts to get the lifetime results published in our paper.</p> <p> </p> <p><strong>Raw Time-Tags</strong></p> <p>Here, we provide the raw measurement data. We perform several experiment cycles. An excitation laser is switched on at the beginning of each cycle. In the middle of the cycle, it is switched off. We use two single-photon counting modules (SPCM): one detects fluorescence photons emitted by the atoms, the other reference light from the excitation laser beam. We record the arrival times of those photons with respect to the beginning of the cycle. These time delays can be used to create a histogram and to determine the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state.</p> <p>For each measurement, we provide two data files which are encoded in ‘UTF-8’:</p> <ul> <li>‘figx_xxx_reference_time_tags.dat’</li> <li>‘figx_xxx_fluorescence_time_tags.dat’</li> </ul> <p>where ‘figx_xxx’ is a unique tag indicating the figure and point to which this data corresponds in our paper. The ‘figx_xxx_fluorescence_raw_data.dat’ and ‘figx_xxx_reference_raw_data.dat’ files contain the raw time delays in picoseconds of the fluorescence and the reference photons, respectively.</p> <p>We provide raw time delays in the following folders:</p> <ul> <li>‘fig3_time_tags’: The data used in figure 3.</li> <li>‘fig4_time_tags’: The data used in figure 4. This folder has six subfolders, named ‘point_x’, where x indicates to which point of figure 4 the data belongs. The data of the subfolders ‘point_x_y’ was used for points x and y of figure 4 (the time-tags of the fluorescence photons were split into two sub-datasets with equal size).</li> <li>‘fig5_time_tags’: The data underlying figure 5. This folder has subfolders from ‘23C’ to ‘116C’ where the name indicates the temperature in units of °C of the vapor cell during the measurement. Note that the various measurements have different cycle lengths because reabsorption makes the decay of the fluorescence signal longer. For the lifetime value at a temperature of 23 °C, we used the lifetime which we found in figure 4. For some measurements, the time-tags of the reference SPCM are missing because only one SPCM was available for these measurements.</li> </ul> <p> </p> <p><strong>Histograms</strong></p> <p>Since the files of the raw measurement data are large, we also provide histograms of the time tags. For all datasets discussed above, we generated a histogram with a bin length of 5 ns. We save these histograms with the same file name as the files with the raw time tags but with the ending ‘_histo’ instead of ‘_time_tags’, e.g., ‘fig3_fluorescence_histo.dat’ and ‘fig3_reference_histo.dat’.</p> <p>We always provide two file formats:</p> <ul> <li>a data file (.dat), containing rows with the start time of a bin in microseconds, and the number of SPCM counts due to the fluorescence signal until the start of the next bin, separated by a comma. These files are encoded in ‘UTF-8’.</li> <li>a NumPy compressed array format file (.npz), which includes two arrays: The first array is called ‘time’ and contains the starting times of the bins. The second array is called ‘counts’ and includes the corresponding measured number of fluorescence photons per bin. It is possible to load the arrays into a Python script with numpy.load (tested with NumPy version 1.18.1).</li> </ul> <p> </p> <p><strong>Additional Information on the Measurements</strong></p> <p>We provide a JavaScript Object Notation file (.json) for each measurement. These files provide the following information about every measurement: temperature of the cell, number of detected photons, photons per cycle, and the total measurement duration. They are named ‘figx_xxx_info.json’, where ‘figx_xxx’ is the same indicator as discussed in section ‘Raw Time-Tags’.</p> <p> </p> <p><strong>Scripts</strong></p> <p>This folder contains two sample scripts to illustrate how our data can be processed with Python. The first Python script (generate_histograms.py) generates a histogram of the photon arrival times. The second Python script performs a fit in order to determine the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state. We wrote these scripts with Python 3.6.5. To avoid errors, one should download all zipped folders and extract them to the same folder.</p> <ul> <li>The script ‘generate_histograms.py’ processes the fluorescence photon detection events stored in the folder ‘fig3_time_tags’. The file ‘fig3 _fluorescence_time_tags.dat’ is read into the script, and a histogram is generated. To run the script, the following Python libraries are required: NumPy (version 1.18.1), os (version 0.1.4), and json (version 2.0.9).</li> <li>The script ‘fit_data.py’ loads the file ‘fig3_fluorescence_histogram.npz’ from the folder ‘histograms\ fig3_time_tags’ in NumPy arrays. We perform a least-square fit on the histogram of the fluorescence decay. From the fit, we get the lifetime of the cesium 5<sup>2</sup>D<sub>5/2</sub> state. Optionally, it is possible to print a fit report and to plot the fit with its residuals. The following Python libraries are required to run the script: NumPy (version 1.18.1), os (version 0.1.4), json (version 2.0.9), pyplot from matplotlib (version 3.1.1), and Parameters, ExponentialModel, and ConstantModel from LmFit (version 1.0.0).</li> </ul> <p> </p> <p><strong>Figures</strong></p> <p>In the folder ‘figures’, we provide the values of the points which we used to generate figure 4 and figure 5. For both figures, we made a JavaScript Object Notation file (.json) where the data of every point is stored in a dictionary. This data contains the fit result of the lifetime and the temperature of the measurement. Additionally, it contains the corresponding errors and the units of every value.</p>
Datasets for "Evaluating the performance of a Picarro G2207-i analyser for high-precision atmospheric O2 measurements"
<p>These data files contain the data used in the manuscript "Evaluating the performance of a picarro G2207-i analyser for high-precision atmospheric O2 measurements" submitted to Atmospheric Measurement Techniques. </p> <p>WAO_G2207i_O2_calibrated_AM_all : Calibrated O2 measurements from the G2207-i during the no-drying, partial-drying, and full-drying periods at WAO, for both the water-corrected and non-water corrected outputs</p> <p>CRAM_lab_run1_noRT : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, without reference tank correction</p> <p>CRAM_lab_run1_wRT : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied</p> <p>CRAM_lab_run2_noRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, without reference tank correction</p> <p>CRAM_lab_run2_wRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied</p>
Measuring platelet function: new strategies for precision medicine to prevent thrombosis - Prof Jon Gibbins (University of Reading)
<p>This video is the third talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Measuring platelet function: new strategies for precision medicine to prevent thrombosis - Prof Jon Gibbins (University of Reading).</p> <p>Bio: Jon Gibbins is Professor of Cell Biology within the School of Biological Sciences at the University and is Director of the Institute for Cardiovascular and Metabolic Research. He is a graduate of the University, obtaining a degree in Pathobiology with Chemistry in 1991 and a PhD in Molecular Endocrinology in 1995. Following a period of postdoctoral research at the Oxford University, he returned to Reading in 1998 as a lecturer. Jon has established an internationally leading research group that studies blood clotting, with a particular focus on the development of more effective clinical strategies for the prevention and treatment of heart attacks and strokes, and thrombosis associated with infection. Jon values greatly working in an active, engaging and successful school, in which all aspects of biology are represented, and he champions cross-disciplinary working to approach today’s most challenging and pressing questions in new ways. He believes strongly in widening participation and improving levels of equity, diversity and inclusion across our institution.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/8vJZ_WO-dvk</p>
Datasets for "Precision and accuracy of single-molecule FRET measurements – a multi-laboratory benchmark study"
<p>Supplementary material (raw data) for Fig. 2 in "<strong>Precision and accuracy of single-molecule FRET measurements – a multi-laboratory benchmark study</strong>" to be published with Nature Methods</p> <p>The confocal data is given in ht3 and hdf5 format.</p> <p>For the TIRF data the original TIFF-stacks are uploaded including the calibration files.</p>
Controlling long ion strings for quantum simulation and precision measurements
<p>Experimental data for the publication "Controlling long ion strings for quantum simulation and precision measurements" published in Physical Review A, 105, 052426 (2022)</p>
Extended data for Manuscript: Precision medicine implementation and research-practice partnerships: implications of measurement scale differential item functioning (DIF)
<p>This is the extended data for the manuscript submitted to F1000 Research titled:</p> <p>Precision medicine implementation and research-practice partnerships: implications of measurement scale differential item functioning (DIF).</p> <p>File 1 is the redacted survey responses from a card sort exercise carried out to find out how the study participants sorted and ranked various measures of factors thought to influence precision medicine implementation at health systems level.</p> <p>File 2 consists of the study package as described in the article</p>
Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow</em>”</strong> in Biomedical Optics Express (doi.org/10.1364/BOE.505847<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 5-10 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails.</p> <p>For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate.</p> <p>The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>Diffusion_03032023.oct</p> </td> <td> <p>Diffusion measurement file.</p> </td> <td> <p>Na=4096, Nb=1100, 5.5 kHz</p> </td> </tr> <tr> <td> <p>Diffusion_07032023.oct</p> </td> <td> <p>Diffusion calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=10, 36 kHz</p> </td> </tr> <tr> <td> <p>Waist_07032023.oct</p> </td> <td> <p>Beam waist calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=40, 36 kHz</p> </td> </tr> <tr> <td> <p>Q=2_07032023.oct</p> </td> <td> <p>Flow measurement file for a discharge rate of 2 ml/min.</p> </td> <td> <p>Na=4096, Nb=1000, 36 kHz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis, simulation and processing routines.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_diffusion.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_flow.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from flowing and diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Diffusion_parallel.py</p> </td> <td> <p>This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Flow_parallel.py</p> </td> <td> <p>This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Measuring multi-asperity wear with nanoscle precision
<p>Replication package of the paper "Measuring multi-asperity wear with nanoscale precision".</p> <p>Includes: Figures, raw data and scripts to obtain the figures.</p>
Dataset for "Characterization of a modified printed optical particle spectrometer for high-frequency and high-precision laboratory and field measurements."
<p><strong>The folder includes scripts and data to create figures in the manuscript titled "Characterization of a modified printed optical particle spectrometer for high-frequency and high-precision laboratory and field measurements."</strong></p>
Data and Code from: Using cameras for precise measurement of two-dimensional plant features: CASS
<p>Computer vision explanation: The code (https://github.com/amy-tabb/CASS, referred to as CASS) takes an image of an object on top of an aruco calibration pattern, calibrates the camera using the detected aruco information as well as EXIF tag information, and undistorts and computes the homography from the current location of the aruco calibration pattern in the image to its location in physical space. Then the image is warped to match the coordinate system of the aruco coordinate system, scaled by a user-selected parameter.</p> <p>This dataset provides examples of properly-formatted input images and accompanying text files, as well as a successful run where the option of writing intermediate results has been selected. Details about how to format the directories is found in the README of https://github.com/amy-tabb/CASS.</p> <ul> <li><code>iphone6</code>. is a directory of input files using the camera of a iPhone 6 cellular phone.</li> <li><code>iphone6_results</code>. is the directory of results created from running CASS on <code>iphone6</code>.</li> <li><code>CanonEOS60D</code>. is a directory of input files using a DSLR camera from Canon, model name EOS 60D.</li> <li><code>CanonEOS60D_results.</code> is the directory of results created from running CASS on <code>CanonEOS60D</code>.</li> </ul> <p>See the <code>Write directory format</code> section of CASS's README for details of all of the files; briefly for this example, <code>warped_ORIGINALFILENAME.jpg</code> is the original image, transformed such that 10 pixels corresponds to 1 millimeter on the two dimensional plane of the calibration pattern.</p> <p>https://github.com/amy-tabb/CASS provides code in C++ for processing on this dataset, as well as as Docker image.</p> <p> </p>
FIGURE 6. Both MFL and C2T in Repeatability and precision in proboscis length measurements for long proboscid flies
FIGURE 6. Both MFL and C2T measurements of Prosoeca spp. are plotted against prementum length to visualize their consistency. Lack of precision of C2T measurements (open circles) is evident from their scatter between the prementum length (1:1 line) and the MFL (closed circles). In some cases, the VRM displacement was not measured so there is no corresponding C2T measurement.
FIGURE 5 in Repeatability and precision in proboscis length measurements for long proboscid flies
FIGURE 5. Paired measurements of MFL (closed circles) and C2T (open circles) plotted against the prementum length (1:1 line) for Philoliche spp. Paired measurements are joined by vertical lines. Note that in some cases, the C2T measurement for a specimen is greater than its MFL, or less than the prementum length. Negative VRM displacement values result when the prementum is fully retracted into the head.
FIGURE 4 in Repeatability and precision in proboscis length measurements for long proboscid flies
FIGURE 4. Extent of potential proboscis extension by species/species group (labrum length mm). Bold bar indicates median, upper and lower box bounds indicate 25th and 75th percentiles (=middle 50% of data). Error bars indicate the smaller of either 1) 1.5 x interquartile range (~ 2 standard deviations), or 2) maximum/minimum value; outliners are points beyond these error bars.
FIGURE 3 in Repeatability and precision in proboscis length measurements for long proboscid flies
FIGURE 3. Morphology of the ventral rostral membrane. A and B: Close up of proximal section of the proboscis in Prosoeca ganglbaueri showing the partially extended VRM (green), labrum (LBR, part of syntrophium, blue), and prementum (orange). Note the finger-like projection of the distal end of the prementum. Because this varies in size and is often not visible, measurement of the prementum is taken from the first point the prementum is complete ventrally. C. Lateral view of disarticulated proboscis in Prosoeca ganglbaueri. D. VRM displacement of the prementum can be difficult to see except upon close examination. Here the stretched VRM has dried inconspicuously onto the syntrophium.
FIGURE 1. A in Repeatability and precision in proboscis length measurements for long proboscid flies
FIGURE 1. A. Three specimens of Philoliche gulosa showing the proboscis in various states of preservation: maximum ventral rostral membrane (VRM) displacement, moderate VRM displacement, full retraction of the prementum into the head (from top to bottom); B. Extension of the VRM changes the appearance of the proboscis length of two specimens with similar proboscis lengths (PL= prementum length, MFL=Maximum Functional length, C2T= Clypeus to tip length); C. Close up of proboscis belonging to top-most specimen in A and B above (SIM 1807B), showing the disarticulation of the syntrophium from the VRM and prementum (LBR=labrum, part of syntrophium).
FIGURE 4 in An essay on precision in morphometric measurements in anurans: inter-individual, intra-individual and temporal comparisons
FIGURE 4. Projection of morphometric measurements of the three study species on the first two canonical variables from a multiple discriminant function analysis, according to the temporal comparison obtained by the five people. A: living animals; B: freshly preserved specimens; C: specimens after five months of preservation.
FIGURE 3 in An essay on precision in morphometric measurements in anurans: inter-individual, intra-individual and temporal comparisons
FIGURE 3. Projection of inter-individual morphometric measurements of Dendropsophus microcephalus on the first two canonical variables from a multiple discriminant function analysis. A: living animals; B: freshly preserved specimens; C: specimens after five months of preservation.
FIGURE 2 in An essay on precision in morphometric measurements in anurans: inter-individual, intra-individual and temporal comparisons
FIGURE 2. Projection of inter-individual morphometric measurements of Scinax ruber on the first two canonical variables from a multiple discriminant function analysis. A: living animals; B: freshly preserved specimens; C: specimens after five months of preservation.
FIGURE 1 in An essay on precision in morphometric measurements in anurans: inter-individual, intra-individual and temporal comparisons
FIGURE 1. Projection of inter-individual morphometric measurements of Hypsiboas crepitans on the first two canonical variables from a multiple discriminant function analysis. A: living animals; B: freshly preserved specimens; C: specimens after five months of preservation.
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.