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10,554 results for “measurements”
Dataset for publication: "Measuring Losses of an Air-Core Shunt Reactor with an Advanced Loss Measuring System"
<p>Dataset for publication: "Measuring Losses of an Air-Core Shunt Reactor with an Advanced Loss Measuring System". Excel file contains the data for Figures 7, 8, 9 and 10.</p>
Time series of gas, particle, and environmental variables measured at the Mäkelänkatu urban street canyon site in May 2017
<p>Time series data of gas, particle, and environmental variables measured at the Mäkelänkatu urban street canyon site, in Helsinki, Finland, in May 2017.</p>
Comparison of Reference Setups for Calibrating Power Transformer Loss Measurement Systems
<p>Data set belonging to the IEEE Trans. Instr. Meas. paper with DOI: <a href="https://doi.org/10.1109/TIM.2018.2879171">10.1109/TIM.2018.2879171</a></p> <p>G. Rietveld, E. Mohns, E. Houtzager, H. Badura, and D. Hoogenboom,<br> <em>Comparison of Reference Setups for Calibrating Power Transformer Loss Measurement Systems</em></p> <p>This project has received funding from the European Metrology Programme for Innovation and Research co-financed by the Participating States and in part by the European Union’s Horizon 2020 Research and Innovation Programme.</p>
Figure 4 in Assessing a ReviTec Measure to Combat Soil Degradation by studying Acari and Collembola from Ngaoundéré, Adamawa, Cameroon
Figure 4. Temporal variation of Oribatida and Gamasina in control plots. Details as in Fig. 3.
Text-fig. 2 Cluster diagram of the considered acritarch assemblages. Paired group, Jaccard measure. in Overview Of The Stratigraphy And Initial Quantitative Biogeographical Results From The Devonian Of The Albergaria-A-Velha Unit (Ossa-Morena Zone, W Portugal)
Text-fig. 2 Cluster diagram of the considered acritarch assemblages. Paired group, Jaccard measure.
Figure 1 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 1. Drifter device [Lagrangian drifter laboratory, 2024]
Applicability of the inverse dispersion method to measure emissions from animal housings - data set & R scripts
<h2>Data availability</h2> <p>Provided are:<br>- raw data of the instruments<br>- R Scripts to reproduce the findings in the publication<br>- R outputs</p> <h2>Scripts</h2> <p>In total, there are 10 scripts provided, of which most of them are needed to reproduce the data in the publication.</p> <p>Below, a brief explanation of the content of the different scripts.</p> <ul> <li>01_Datatreatment_01_Weatherstation.r ## This script reads in the weather station data and makes it ready for further use.</li> <li>01_Datatreatment_02_Sonics.r ## This script reads in the 3D ultrasonic data and makes it ready for further use.</li> <li>01_Datatreatment_03_GasFinder.r ## This script reads in the GasFinder data and makes it ready for further use.</li> <li>01_Datatreatment_04_MFC_Pressuresensor.r ## This script reads in the mass flow controller (MFC) and pressure sensor data and makes it ready for further use.</li> <li>02_Calculation_01_bLS.r ## This script is made to run the bLSmodelR and tailored to the number cruncher of the University of Applied Sciences BFH. The code should also work on your computer but you have to adopt the number of cores.</li> <li>02_Calculation_02_Concentration.r ## This script treats the unprocessed concentration data. It removes false concentrations, applies an intercalibration, and makes the data ready for further use.</li> <li>02_Calculation_03_Emissions.r ## This script calculates emissions and makes it ready for further use.</li> <li>02_Calculation_04_contourXYZ_Plume.r ## This script calculates the plume contours in the XY and XZ plane. This script is not necessary to reproduce the findings of the publication.</li> <li>03_Apply_filter.r ## This script applies the quality filtering and makes the data ready for further use.</li> <li>04_Plots_Tables.r ## With this script one can recreate all the plots and values in the tables of the publication, the supplement, and the initial submission.</li> </ul> <p>Note, for the geometry, there is no script provided. The coordinates of the different sensors and the source are solely provided as R output.</p> <h3>Naming of instruments</h3> <p>The instruments in the publication have different names than in the scripts. In some scripts the final names are also provided but throughout the evaluation the original device names are used. Only in the script 04_Plots_Tables.r are the final names introduced. Below is an overview of what original name corresponds to the final name of the devices:</p> <h4><strong>GasFinder instruments called 'OP' in the publication</strong></h4> <ul> <li>OP-UW = GF26</li> <li>OP-2.0h = GF17</li> <li>OP-5.3h = GF18</li> <li>OP-6.8h = GF16</li> <li>OP-12h = GF25</li> </ul> <p><strong>3D ultrasonic anemometer instruments called 'UA' in the publication</strong></p> <ul> <li>UA-UW = SonicC</li> <li>UA-2.0h = SonicA</li> <li>UA-5.3h = Sonic2</li> <li>UA-6.8h = SonicB</li> </ul> <p><strong>Source</strong><br>In some of the scripts, the source might be called 'Schopf' which is a local term for 'shed'.</p> <h2>Note</h2> <p>This code was written by Marcel Bühler (minor code chunks were originally written by Christoph Häni) and is intended to reproduce the findings of the linked publication. Please feel free to use and modify it (e.g., use it to run different dispersion models), but attribution is appreciated.</p> <h2>Disclaimer</h2> <p>I do not guarantee that everything works. It might be that not all variables were changed to English for better understanding correctly. Unfortunately, it is not possible to provide all the catalogs of the bLS run, as the total size is several 100s of GB. In case you run the bLS model on your own, the result will have a minimal difference, as no bLS run produces the same result twice. This should, however, not alter the findings.</p> <h2>Contact</h2> <p>In case you have questions, please contact Marcel Bühler (mb@bce.au.dk). In case this does not work, Christoph Häni might also be able to help (christoph.haeni@bfh.ch).</p>
Dataset from two meteorological stations with water and soil temperature measurements in the Alqueva reservoir (Portugal)
<p>In the multidisciplinary <strong>AL</strong>entejo <strong>O</strong>bservation and <strong>P</strong>rediction systems<strong> </strong>project (ALT20-03-0145-FEDER-000004), which aims to strengthen research and innovation in the Alentejo region (southern Portugal), one of the main objectives was to study and model the meteorological conditions in the Alqueva reservoir, in particular their spatial variations within a few hundred meters.</p> <p>The shared hourly dataset, using Coordinated Universal Time (UTC), covers the period from 2018 to 2023 and includes measurements from two meteorological stations located in the Alqueva reservoir, the largest artificial lake in Europe. One station, Montante, is located on a floating platform with a water depth of approximately 70 meters (38.2235 N, 7.4595 W), to the west of the second station, CidAlmeida (38.21539 N, 7.45454 W), which is about 1 km away on land, very close to the water.</p> <p>According to the World Meteorological Organisation (WMO) standards, the data were sampled every second, and hourly data were calculated in post-processing. The dataset includes hourly accumulated precipitation (<em>mm</em>) and hourly average measurements of surface water temperature (at a depth of 0.25 m), soil temperature (at a depth of 0.15 m) and various meteorological parameters: wind speed (<em>m/s</em>) and direction (<em>degrees</em>), relative humidity (%), upward/downward solar radiation (<em>W/m</em><sup><em>2 </em></sup>) and air temperature (<em>°C </em>). All parameters are measured at both stations, except the hourly average water temperature at a depth of 0.25 m, which is only available at the Montante station, and the hourly accumulated precipitation, hourly average soil temperature and wind direction, which are only available at the CidAlmeida station.</p> <p>Hourly data were not subjected to rejection criteria based on the percentage of errors; instead, a column with this percentage is provided, allowing potential data users to apply their own rejection criteria. Daily extremes (daily maximums and minimums for air temperature, relative humidity, and daily maximum gust) are only provided for days where the percentage of errors does not exceed 25% of the 1440 minutes of each day. The daily error percentage for each of the measured parameters at the two stations for the period 2018-2023 is available.</p> <p>Finally, the repository also includes two codes (one for each weather station) written in Visual Basic, which enable data transmission and real-time statistical processing.</p> <p><strong>Fundings:</strong></p> <p>Gonçalo Rodrigues was supported by the Portuguese Foundation for Science and Technology, I.P (Grant 2020.05752.BD). The work is co-funded by national funds through FCT – Fundação para a Ciência e Tecnologia, I.P., in the framework of the ICT project (references UIDB/04683/2020 and UIDP/04683/2020) and by the ALOP project (ALT20-03-0145-FEDER-000004). </p>
Plantae datasets: Plantae measurements (n=12)
From [DATA-1689](<p></p>https://eol-jira.bibalex.org/browse/DATA-1689)<p></p>invasive in, flower color, dispersal vector, leaf area, leaf color, nitrogen fixation, plant height, plant propagation method, salt tolerance, soil pH, soil requirements, vegetative spread rate
Data release for the "First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K"
<p>### On-/Off-Axis Data Release<br>#### (Version 1.0.1, dated 2024/08/12)</p> <p>This tar archive contains the data release for ‘First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K’. It contains the cross-section data points and supporting information in ROOT and text format, which are detailed below:</p> <p>+ `onoffaxis_xsec_data.root`<br>This ROOT file contains the extracted cross section and the nominal MC prediction as TH1D histograms for both the flattened 1D array of bins and in the angle binning for the analysis. The ROOT file also contains both the covariance and inverted covariance matrix for the result stored as TH2D histograms. The angle bin numbering and the corresponding bin edges are detailed at the end of the README.</p> <p>+ `flux_analysis.root`<br>This ROOT file contains the nominal and post-fit flux histograms for ND280 and INGRID. Two different binnings are included: a fine binned histogram (220 bins) and a coarse binned histogram (20 bins). The coarse binned histogram corresponds to the flux parameters detailed in the paper (and bin edges listed in the appendix).</p> <p>+ `xsec_data_mc.csv`<br>The extracted cross-section data points and the nominal MC prediction for each bin is stored as a comma-separated value (CSV) file with header row.</p> <p>+ `cov_matrix.csv` and `inv_matrix.csv`<br>The covariance matrix and the inverted covariance matrix are both stored as CSV files with each row stored as a single line and columns separated by commas (there is no header row). Matrix element (0,0) corresponds to the first number in the file.</p> <p>+ `nd280_analysis_binning.csv` and `ingrid_analysis_binning.csv`<br>The analysis bin edges are included as CSV files. The columns are labeled with a header row and denote the linear bin index and the lower and upper bin edge for the angle and momentum bins. The units are in cos(angle) for the angle bins and in MeV/c for the momentum bins.</p> <p>+ `calc_chisq.cxx`<br>This is an example ROOT script to calculate the chi-square between the data and the nominal MC prediction using the ROOT file in the data release. To run, open ROOT and load the script (`.L calc_chisq.cxx`) and execute the function `calc_chisq("/path/to/file.root")`.</p> <p>+ `calc_chisq.py`<br>This is an example Python script to calculate the chi-square between the data and the nominal MC prediction using the text/CSV files in the data release. The code requires NumPy as an external dependency, but otherwise uses built-in modules. To run, execute using a Python3 interpreter and give the file paths to the data/MC text file and the inverse covariance text file as the first and second arguments respectively -- e.g. `python3 calc_chisq.py /path/to/xsec_data_mc.csv /path/to/inv_matrix.csv`</p> <p>+ ND280 angle bin numbering<br> - 0: `-1.0 < cos(#theta) < 0.20`<br> - 1: `0.20 < cos(#theta) < 0.60`<br> - 2: `0.60 < cos(#theta) < 0.70`<br> - 3: `0.70 < cos(#theta) < 0.80`<br> - 4: `0.80 < cos(#theta) < 0.85`<br> - 5: `0.85 < cos(#theta) < 0.90`<br> - 6: `0.90 < cos(#theta) < 0.94`<br> - 7: `0.94 < cos(#theta) < 0.98`<br> - 8: `0.98 < cos(#theta) < 1.00`</p> <p>+ INGRID angle bin numbering<br> - 0: `0.50 < cos(#theta) < 0.82`<br> - 1: `0.82 < cos(#theta) < 0.94`<br> - 2: `0.94 < cos(#theta) < 1.00`<br> <br>### Changelog</p> <p>#### v1.0.1<br>Fix transcription error in INGRID momentum binning. The lowest momentum bin edge is at 350 MeV/c, not 300 MeV/c.</p>
Surplus Stock Measurement and Management Tool
<p>Surplus Stock Measurement and Management Tool will enable food chain retailers to identify the food waste at source and plan their procurement and operational processes to eliminate food waste by measuring it. Company-based information will be kept in a product-based cumulative structure and analysed based on region, city, and Storage Keeping Unit (SKU) group. The first part of the tool aims to provide more reliable and validated food waste data at the retailer level. The main purpose of this development represents SKU-based exploratory data analysis by establishing a relational database structure within SKU, regional-based, season-based, etc. The second part of the tool, the Management System, will be coded in line with the Food Recovery Hierarchy. The Food Recovery Hierarchy principle will be coded into the system as a part of the decision-making process. As a result, the system will create lists of products informing the beneficiary if they are suitable for human consumption (e.g. resell, donation), animal feed, biogas production, or recycling.</p>
NMNH Primate measurements: NMNH Primate Measurements
Measurements derived from label data of specimens in the collection of the Division of Mammals, Department of Vertebrate Zoology, Smithsonian Institution, National Museum of Natural History. Data prepared & uploaded by Abigail Nishimura.<p></p>Measurements derived from label data of specimens in the collection of the Division of Mammals, Department of Vertebrate Zoology, Smithsonian Institution, National Museum of Natural History. Data prepared & uploaded by Abigail Nishimura.
Multi-year high time resolution measurements of fine PM at 13 sites of the French Operational Network (CARA program)
<p>These datasets correspond to long-term measurements of atmospheric aerosol components from Aerosol Chemical Speciation Monitor (ACSM) and multi-wavelength Aethalometer (AE33) instruments collected between 2015 and 2021 at 13 (sub)urban sites as part of the French CARA program. </p> <p>The datasets contain the mass concentrations of major chemical species within PM1, namely organic aerosols (OA), nitrate (NO3-), ammonium (NH4+), sulfate (SO42-), non-sea-salt chloride (Cl-), and equivalent black carbon (eBC). </p> <p>Rigorous quality control, technical validation, and environmental evaluation processes were applied, adhering to both the guidance from the French reference laboratory for air quality monitoring and the Aerosol, Clouds, and Trace gases Research Infrastructure (ACTRIS) standard operating procedures.</p> <p>These data are discussed in an article in submission, please cite it when using the data.</p>
Data files and computer code scripts for reproducing the results of a manuscript on the measurement and ranking of cotton drought tolerance capacity
<p>This upload contains the data files and computer code scripts for reproducing the main results, esp. figures, of the manuscript entitled<br>"Rapid measurement and statistical ranking of leaf drought tolerance capacity in cotton," by X. Dong, D. A. Mott, J. Garg, Q. Zhou, J. Sunoj V. S., and B. M. McKnight. The manuscriptt is currently under peer review.</p>
Production of Alternate Realizations of DESI Fiber Assignment for Unbiased Clustering Measurement in Data and Simulations
<p>A critical requirement of spectroscopic large scale structure analyses is correcting for selection of which galaxies to observe from an isotropic target list. This selection is often limited by the hardware used to perform the survey which will impose angular constraints of simultaneously observable targets, requiring multiple passes to observe all of them. In SDSS this manifested solely as the collision of physical fibers and plugs placed in plates. In DESI, there is the additional constraint of the robotic positioner which controls each fiber being limited to a finite patrol radius. A number of approximate methods have previously been proposed to correct the galaxy clustering statistics for these effects, but these generally fail on small scales. To accurately correct the clustering we need to upweight pairs of galaxies based on the inverse probability that those pairs would be observed (Bianchi & Percival 2017). This paper details an implementation of that method to correct the Dark Energy Spectroscopic Instrument (DESI) survey for incompleteness. To calculate the required probabilities, we need a set of alternate realizations of DESI where we vary the relative priority of otherwise identical targets. These realizations take the form of alternate Merged Target Ledgers (AMTL), the files that link DESI observations and targets. We present the method used to generate these alternate realizations and how they are tracked forward in time using the real observational record and hardware status, propagating the survey as though the alternate orderings had been adopted. We detail the first applications of this method to the DESI One-Percent Survey (SV3) and the DESI year 1 data. We include evaluations of the pipeline outputs, estimation of survey completeness from this and other methods, and validation of the method using mock galaxy catalogs. </p>
A Novel Integrated Reference-Counter Electrode for Electrochemical Measurements of HOMO and LUMO Levels in Small-Molecule Thin-Film Semiconductors for OLEDs
<p>This dataset <span>includes all raw data, processed data, and analysis scripts necessary to replicate the findings reported in the published paper.</span></p>
Dataset of "3D generative adversarial networks for turbulent flow estimation from wall measurements"
<p>Dataset of the article 'Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements' (https://doi.org/10.1017/jfm.2024.432). The codes processing data here are on https://github.com/erc-nextflow/3D-GAN.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 949085, NEXTFLOW). Views and opinions expressed are, however, those of the authors only, and do not necessarily reflect those of the European Union or the ERC. Neither the European Union nor the granting authority can be held responsible for them. A.C.M. acknowledges financial support from the Spanish Ministry of Universities under the Formación de Profesorado Universitario (FPU) programme 2020. R.V. acknowledges financial support from ERC (grant agreement no. 2021-CoG-101043998, DEEPCONTROL).</p>
Compressive strength measurements on binary and ternary blended cementitious paste specimens produced with seawater-mixing
<p>The following file consists of data from the published article Rathnarajan et al., (2024) Comprehensive evaluation of early-age hydration and compressive strength development in seawater-mixed binary and ternary cementitious systems published in Archives of Civil and Mechanical Engineering. </p> <p>The excel file contains of compressive strength data of cement paste specimens of size 2 cm. </p> <p>1. From column B to Column N: Mix proportion details including the type of binders, total binder content, and clinker substitution level with SCMs are provided</p> <p>2. From Column O to Column AM: Compressive strength data of the paste specimens were listed out for FW-mixed and SW-mixed cementitious systems for the testing ages, 2, 7, 14, 28 and 90 days. </p> <p>3. Also the % change in compressive strength is calculated by calculating the difference in strength between SW-mixed and FW-mixed specimen divided by strength of FW-mixed specimens</p> <p>SYMBOLS AND NOTATIONS</p> <p>PC - CEM I <br>PF - CEM I + Fly ash <br>PS - CEM I + Slag<br>PM - CEM I + Metakaolin<br>PL - CEM I + Limestone<br>FW - Fresh water mixed <br>SW - Seawater mixed<br>SCM - Supplementary cementitious materials like Fly ash, slag, metakaolin and Limestone<br>SD - Standard deviation<br>%change - Percentage change in compressive strength with respect to strength of FW-mixed paste cubes. </p>
Measured properties in soil samples and marine sediment collected in Galion Bay (Martinique, France) in order to trace erosion sources in insular tropical catchments
<p>This dataset was compiled in order to select the optimal suite of tracers and identify and quantify the main sources of sediment deposited in Galion Bay and associated chlordecone transfers since the 1960s. It includes measured properties for potential sources collected across the Galion catchment (Martinique, France) and along a sediment core sampled in Galion Bay (GAL17-04, N°IGSN TOAE0000000573). Associated with this dataset, metadata are integrated for sources and targets registered using International Geological Sample Numbers (IGSN).</p>
Code and measurement data - Capacity and internal resistance diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (Matlab code, measurement data, figure files) of the journal article: <a href="https://doi.org/10.1149/1945-7111/ad6938">Wolfgang G. Bessler, “Capacity and resistance diagnosis of batteries with voltage-controlled models,” J. Electrochem. Soc. 171, 080510 (2024), https://doi.org/10.1149/1945-7111/ad6938</a>.</strong><br><br></p> <p><strong>Abstract:</strong></p> <p>Capacity and internal resistance are key properties of batteries determining energy content and power capability. We present a novel algorithm for estimating the absolute values of capacity and internal resistance from voltage and current data. The algorithm is based on voltage-controlled models (VCM). Experimentally-measured voltage is used as input variable to an equivalent circuit model. The simulation gives current as output, which is compared to the experimentally-measured current. We show that capacity loss and resistance increase lead to characteristic fingerprints in the current output of the simulation. In order to exploit these fingerprints, a theory is developed for calculating capacity and resistance from the difference between simulated and measured current. The findings are cast into an algorithm for operando diagnosis of batteries operated with arbitrary load profiles. The algorithm is demonstrated using cycling data from lithium-ion pouch cells operated on full cycles, shallow cycles, and dynamic cycles typical for electric vehicles. Capacity and internal resistance of a “fresh” cell was estimated with high accuracy (mean absolute errors of 0.9 % and 1.8 %, respectively). For an “aged” cell, the algorithm required adaptation of the model’s open-circuit voltage curve in order to obtain high accuracies.</p> <p> </p> <p><strong>Copyright and IP information:</strong></p> <p>Copyright 2024 by Wolfgang G. Bessler. The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license (Creative Commons Attribution Non Commercial 4.0 International). Please note that the algorithms themselves are subject to intellectual property rights, including, but not necessarily limited to, German patent DE102022129314 and international patent WO2024/099513A1. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p><br><strong>Quick start:</strong></p> <p>Copy all files into one folder. Open and run capacityAndResistanceDiagnosisFigures8and10.m with Matlab. Observe the reproduction of Figure 8 of the paper.</p> <p><br><strong>Description of the files:</strong></p> <p>Matlab code (tested using versions R2019a and R2022b):</p> <ul> <li>capacityAndResistanceDiagnosisFigures8and10.m: this is the main Matlab script. It performs the capacity and resistance diagnosis on experimental data V(t) and I(t). The script reproduces Figures 8 ("fresh" cell) or 10 ("aged" cell) of the paper, depending on which lines you uncomment in upper part of the script.</li> <li>calculateDeltaR.m, calculatefC.m: functions that evalue deltaR and fC, which are two key outputs of the diagnosis algorithm. </li> <li>simulateVCMSimple.m, simulateVCMDynamic.m: functions that simulate the voltage-controlled equivalent circuit models (either "simple" of "dynamic"). Input is V(t), output are I(t) and SOC(t).</li> <li>interpolateCurve.m: performs linear interpolation of the OCV(SOC) curve. We use this because Matlab's interp1() function is awfully slow.</li> </ul> <p>Experimental data:</p> <ul> <li>Experimental_data_fresh_cell.csv: Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a "fresh" lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</li> <li>Experimental_data_aged_cell.csv: Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a "pre-aged" lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</li> <li>OCV_vs_SOC_curve_fresh_cell.csv: Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC) of the "fresh" lithium-ion cell. 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</li> <li>OCV_vs_SOC_curve_aged_cell.csv: Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC) of the "aged" lithium-ion cell. 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</li> </ul> <p>Figure files:</p> <ul> <li>Figures.zip: contains .emf (Windows format) and .fig (Matlab format) versions of Figures 2-10 of the paper.</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.