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15,459 results for “Factors”
Canopy Sky Factors in CRUI Land Use Project at Harvard Forest 1998
Hemispherical photos were taken at 35 interior grid corners in each land use site’s 30 m x 50 m permanent plot on uniformly overcast days during summer 1998. A Canon A-1 35 mm camera with a 7.5 mm lens was mounted on a tripod, positioned so the film plane was 0.5 m above ground, oriented N-S with a compass, and leveled. Ektachrome 100 slide film was used. Two exposures were made for each location. The slides were processed and digitized in jpeg format by Kodak, Inc. at a resolution of 1536 x 1024 dpi. The jpeg images were screened for nearground obstructions. Fifteen of the 35 photo locations per site were used in subsequent analysis. Hemiview software (v. 2.0, Delta-T Devices Ltd., Cambridge, UK) was used to process the images and calculate indirect, direct, and global (= indirect + direct) sky factors for each sample location, plus the proportion of global sky factor represented by direct beam radiation. Several images spanning a wide range of canopy density were used to develop protocols for setting optimal thresholds in converting from color to black and white images. Each B/W image was then analyzed at three threshold levels, the optimum level plus 5 threshold units (~ 2.5%) above and below the optimum. The results were averaged across the three threshold levels to provide one set of sky factor data per sample location. Above-canopy incident radiation was calculated daily for the June-September growing season based on (1) latitude and longitude, (2) average atmospheric transmission, and (3) monthly averages for fractions of the day in diffuse radiation due to cloud cover for the Harvard Forest area. Data for 2 and 3 above were provided by researchers at the State University of New York at Albany. Below-canopy radiation was estimated daily across half-hour intervals as the sum of diffuse and direct beam components. Sky factors were then calculated from the above- and below-canopy radiation values and averaged both monthly and across the growing season.
Correction Factors for Dissolved Organic Carbon Extracted from Soil in New England 2012-2013
Oxidizable dissolved organic carbon (DOC) is regularly measured in environmental samples using a colorimetric method with Mn(III)-pyrophosphate as the oxidizing agent. It is simpler to use and has a much higher throughput than the commonly used dichromate oxidation and combustion methods. Here, we demonstrate that the method often leads to an underestimation or overestimation of the concentration of common organic compounds in solutions. To our knowledge, no published study has taken this fact into account when analyzing DOC data. Hence, we compared Mn(III)-pyrophosphate-based results with measurements performed with a total organic carbon combustion analyzer for samples of organic and mineral soil horizons of two temperate deciduous forests (Harvard Forest, Hubbard Brook), of organic soil horizon of a primary growth hemlock stand (Harvard Forest), and of a peatland (Caribou Bog) located in New England, USA. The Mn(III)-pyrophosphate method consistently underestimated DOC concentration in soil extracts. We present correction factors for the different types of soil studied. By employing correction factors, we find the method can be an inexpensive, accurate, and high throughput tool to measure DOC in environmental samples.
Which multiband factor should you choose for your resting-state fMRI study? The Emory Multiband Dataset
Open the record for dataset details and reuse information.
Row data for the experiment: "Clinical, psychosocial and demographic factors affect decisions in SLE people".
<p>These datasets correspond to the article titled: “Clinical, psychosocial and demographic factors affect decisions in SLE people”, which can be found at <a href="https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf">https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf</a></p> <p>Analysis scripts, and an explanation of variables, can be found at: <a href="https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE">https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE</a></p> <p>Abstract</p> <p><span>Neurological and psychiatric manifestations affect most lupus individuals and include depression, anxiety, mood disorders, and cognitive dysfunction. Although there is evidence supporting suboptimal decision-making in lupus and its association with glucocorticoids consumption, it is not clear what variables impact such decisions. The aim of this study is to explore how social, clinical, psychological, and demographic factors impact social and temporal decision-making in people with lupus. Through a within-subjects experimental-design, our participants responded to social, clinical, psychological, and demographic electronic questionnaires. Then, they participated in two behavioral economics experiments: the third-party dictator game, and the delay discounting task. Our results show that hostility, and age are essential predictors of social decisions, whereas obsessive-compulsiveness and anxiety better predict temporal decisions. These variables behave as expected, but anxiety shows unexpected results: most anxious people act patiently and prefer delayed but bigger rewards. Finally, clinical factors are critical decision predictors for social and temporal decisions. When people are in remission, they tend to impose higher punishment on those who violate the social norm, and they also tend to prefer immediate rewards. When taking glucocorticoids, they also prefer immediate rewards, and as the dosage of glucocorticoids intake increases, they tend to impose higher punishment on norm violators. Clinicians, researchers, and practitioners must consider the side effects of glucocorticoids on decision-making.</span></p>
Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey
<p>These are pseudo-anonymised data from the ComBaCaL survey and belong to the manuscript "Prevalence and determinants of cardiovascular risk factors in Lesotho: a population-based survey" which can be found at <a href="https://doi.org/10.1093/inthealth/ihad058">https://doi.org/10.1093/inthealth/ihad058</a>. </p> <p>The data dictionary explains the critical data available in the dataset. Between November 2021 and August 2022 , 6061 participants over 18 years old were visited in their households in two districts of Lesotho. </p>
Lead Risk Factors for West and North Philadelphia: 2007-2020
Provides all data used for the creation of the maps and spearman correlation analysis for a lead risk assessment of North and West Philadelphia. This includes data on: lead-in-soil, land recycled sites, demolitions, housing code violations, age of housing, smelters, and elevated blood lead levels of children. The data was used for a spatial analysis of historic and current lead sources in West and North Philadelphia to determine which lead sources act as primary lead-risk factors, identify future soil sampling sites and high-risk neighborhoods in Philadelphia.
Monthly fluorescence parallel factor analysis (PARAFAC) components for Shark River Slough, Taylor Slough, and Florida Bay, Everglades National Park (FCE LTER), Florida, USA, April 2011 - ongoing
Dissolved organic matter plays an important role in biogeochemical processes in aquatic environments such as elemental cycling, microbial loop energetics, and the transport of materials across landscapes. Since most of N (> 90%) and P (around 90%) is in the organic form in the oligotrophic subtropical Florida Coastal Everglades (FCE), study of the source and dynamics of dissolved organic matter (DOM) in the ecosystem is crucial for the better understanding of the biogeochemical cycling of nutrients. FCE are composed of estuaries with distinct regions with different biogeochemical processes. Freshwater marsh primarily receives terrestrial input and local autochthonous vegetation production. Mangrove ecotone, nevertheless, is affected by the tidal contributions from Florida Bay and local mangrove production. Florida Bay (FB) is a wedge-shaped shallow oligotrophic estuary which lays south of the Everglades, the bottom of which is covered with a dense biomass of seagrass. The sources of both freshwater and nutrients in FCE are difficult to quantify, owing to the non-point source nature of runoff from the Everglades and the dendritic cross channels in the mangroves. Furthermore, the combination of multiple DOM sources (freshwater marsh vegetation, mangroves, phytoplankton, seagrass, etc.), and the potential seasonal variability of their relative contribution, along with the history of (photo)chemical and microbial diagenetic processing, and complex advective circulation, makes the study of DOM dynamics in FCE particularly difficult using standard schemes of estuarine ecology. Quantitative information of DOM is very useful to investigate the biogeochemical cycling of DOM to a certain degree, however, qualitative information is necessary to better understand the source and dynamics of DOM. Since fluorescence spectroscopic techniques are very sensitive, quick and simple, they have been applied to investigate the fate of DOM in estuaries. Here, we have quantified a series of
SBC LTER: Ocean: HFR-derived surface flow metrics, surface water retention times, and related factors in the Santa Barbara Channel (2012-2019)
This data package include three files: 1. daily maps of High-Frequency Radar (HFR) measured surface currents, indices of mesoscale eddy locations, and local retention times on a 2km grid; 2. monthly time series of wind stress, alongshore pressure gradient, surface current EOF principal components, vorticity, eddy area, eddy presence, and spatially averaged retention times from January 2012 to December 2019; 3. A MATLAB script for plotting the maps and timeseries. These data were processed in order to investigate the drivers of surface water retention in the Santa Barbara Channel, CA, details of which are available in the study: Brokaw, R.J., D.A. Siegel, and L. Washburn. Physical Drivers of Surface Water Retention in the Santa Barbara Channel. [In preparation for Journal of Geophysical Research: Oceans.]
Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains capacity factors timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000--2016. It has been generated using <a href="https://www.renewables.ninja">Renewables.ninja</a> and is based on MERRA-2 reanalysis data. For each of the ~2700 onshore location, it contains one time series for onshore wind turbines and five time series for PV installations with different orientations and tilts. PV time series exist for (1) installations on open fields, (2) installations on all possible rooftops, (3) south-facing and flat rooftops, (4) east- and west-facing rooftops, (5) north-facing rooftops. For each of the ~2800 offshore location there is one timeseries for offshore wind turbines.</p> <p>Two GeoTIFF files contain spatial information of onshore and offshore locations. For each of the three technologies -- onshore wind, offshore wind, and PV -- there is one NetCDF file determining the temporal dimension and containing the data. The GeoTIFF and NetCDF files are linked through unique IDs for all locations.</p> <p>This data serves as input data to euro-calliope, a model of the European electricity system.</p> <p>The following parameters have been used to generate the timeseries:</p> <pre><code>resolution-grid: 50 # [km^2] corresponding to MERRA resolution pv-performance-ratio: 0.9 hub-height: onshore: 105 # m, median hub height of V90/2000 in Europe between 2010 and 2018 offshore: 87 # m, median hub height of SWT-3.6-107 in Europe between 2010 and 2018 turbine: onshore: "vestas v90 2000" # most built between 2010 and 2018 in Europe offshore: "siemens swt 3.6 107" # most built between 2010 and 2018 in Europe</code></pre> <p>CHANGELOG:</p> <p>Version 3 (2022-05-18)</p> <p>* Update spatial scope to include Iceland and its offshore EEZ.<br> * Update temporal scope to include 2017 and 2018.</p> <p>Effect of increasing spatial scope is a slight change in the spatial position of the data points.</p> <p>Version 2 (2020-06-18)</p> <p>* Add time series for rooftop PV with different orientations.</p>
Dataset of "Elucidation of factors shaping reactivity of 5'-deoxyadenosyl – a prominent organic radical in biology"
<p>This study investigates the factors modulating the reactivity of 5'-deoxyadenosyl (5'dAdo•) radical, a potent hydrogen atom abstractor, present in the active sites of radical SAM enzymes, but otherwise undergoing a rapid self-decay in aqueous solution. Here, we compare hydrogen atom abstraction (HAA) reactions between native substrates of radical SAM enzymes and 5'dAdo• in aqueous solution and in two enzymatic microenvironments and reveal that HAA efficiency of 5'dAdo• depends on (i) formation of 5'dAdo• in a pre-ordered complex with a substrate, which attenuates the unfavorable effect of substrate:5'dAdo• complex formation, (ii) hindering the conformational change associated with self-decay by performing the reaction in a tight cavity. The enzymatic cavity, however, does not have a strong effect on the HAA activity of 5'dAdo•. We performed an analysis of HAA performed by 5'dAdo• based on the three-component thermodynamic model incorporating the diagonal effect of the free energy of reaction, and the off-diagonal effect of asynchronicity and frustration. The study is based on the straightforward relationship between the off-diagonal thermodynamic effects and the electronic-structure descriptor – the redistribution of charge between the reactants during the reaction. It allows to access HAA-competent redox and acidobasic properties of 5'dAdo• that are otherwise unavailable due to its instability upon one-electron reduction and protonation. The results show that all reactions feature a favourable thermodynamic driving force and tunneling, the latter of which lowers systematically barriers by ~2 kcal mol-1. In addition, most of the reaction experience a favourable off-diagonal thermodynamic contribution. In HAA reactions, 5'dAdo• acts as a weak oxidant as well as a base, also 5'dAdo•-promoted HAA reactions proceed with quite low degree of asynchronicity of proton and electron transfer. Finally, the study elucidates the crucial and dual role of asynchronicity. It directly lowers the barrier as a part of the off-diagonal thermodynamic contribution, but also indirectly increases the non-thermodynamic part of the barrier by controlling the adiabatic coupling between proton and electron transfer. The latter signals that the reaction proceeds as a hydrogen atom transfer rather than a proton-coupled electron transfer.</p>
Astrophysical S-factors for H-burning stars
<p> This dataset contains the latest recommendations of astrophysical S-factors for nuclear fusion reactions occurring in hydrogen-burning stars, included in the <strong><em>Solar Fusion III </em></strong>decadal review article (submitted for publication, e-print available at <a href="https://arxiv.org/abs/2405.06470" target="_blank" rel="noopener">arXiv:2405.06470</a>).</p> <p> The data includes S-factors and their derivatives at zero energy (where available). That is, <em>S(0)</em>,<em> S´(0)</em>, <em>S´´(0)</em>, in units of MeV·b, b, and b/MeV, respectively. Fractional uncertainties are also provided (marked as <em>fr_err</em>). Unavailable data are marked as <em>NA</em>.</p> <p> This data was used to compute the <a href="https://zenodo.org/records/10822316">Standard Solar Models B23 / SF-III</a>. </p> <p> For further information and references consult the <strong><em>Solar Fusion III </em></strong> article linked above.</p>
Murine norovirus virulence factor 1 (VF1) protein contributes to viral fitness during persistent infection [Primary data]
<p>Primary data underlying journal article titled "<strong>Murine norovirus virulence factor 1 (VF1) protein contributes to viral fitness during persistent infection</strong>"</p>
Global consensus map of human transcription factor footprints
<p>Vierstra, J. <em>et al.</em> <strong>Global reference mapping of human transcription factor footprints.</strong> <em>Nature</em><strong> </strong>583, 729–736 (2020). <a href="https://doi.org/10.1038/s41586-020-2528-x">https://doi.org/10.1038/s41586-020-2528-x</a></p> <p>Preprint @ bioRxiv: <a href="https://doi.org/10.1101/2020.01.31.927798">https://doi.org/10.1101/2020.01.31.927798</a></p> <p><strong>Contact:</strong> Jeff Vierstra (<a href="mailto:jvierstra@altius.org?subject=Consensus%20DNase%20I%20footprints">jvierstra@altius.org</a>)</p> <p>Genomic DNase I footprinting enables quantitative, nucleotide-resolution delineation of sites of transcription factor occupancy within native chromatin. We combined sampling of >67 billion uniquely mapping DNase I cleavages from >240 human cell types and states to index, with unprecedented accuracy and resolution, human genomic footprints and thereby the sequence elements that encode transcription factor recognition sites.</p> <p>Please see <a href="http://vierstra.org/resources/dgf">http://vierstra.org/resources/dgf </a>for additional information and a complete set of raw DNase I data for individual datasets. Additionally, raw data can also be accessed via the ENCODE data portal (<a href="http://encodeproject.org">http://encodeproject.org</a>) using the dataset accessions found in Supplementary Table 1.</p> <p>Code for footprint analysis and tutorials on how to access and manipulate digital genomic footprint data can be found at <a href="https://footprint-tools.readthedocs.io/en/latest/">https://footprint-tools.readthedocs.io/en/latest/</a>.</p> <p>All files herein correspond to human genome build version GRCh38 (UCSC hg38).</p> <p><strong>Dataset contents:</strong></p> <ul> <li><strong>Biosample metadata</strong> – Supplementary_Table_1.xlsx</li> <li><strong>Motif clustering metadata </strong>– Supplementary_Table_2.xlsx</li> <li><strong>ChIP-seq validation metadata </strong>–<strong> </strong>Supplementary_Table_3.xlsx</li> <li><strong>Consensus footprint coordinates and assigned motif archetypes</strong><br> TSV file (BED-format) with consensus footprint (posterior probability>0.99) coordinates and overlaps with matches to motif model clusters. The legend file contains column definitions in detail. <ul> <li>consensus_footprints_and_motifs_hg38.bed.gz</li> <li>consensus_footprints_and_motifs_legend.txt</li> </ul> </li> <li><strong>Motif archetype matches overlapping consensus footprints</strong><br> TSV file (BED-format) containing the coordinates for clustered motif model matches that overlap consensus footprints <ul> <li>collapsed_motifs_overlaping_consensus_footprints.bed.gz</li> <li>collapsed_motifs_overlaping_consensus_footprints_legend.txt</li> </ul> </li> <li><strong>Footprint occupancy matrix of consensus footprints</strong><br> Rows are same order as the consensus footprint file and columns are same order as in the metadata files. <ul> <li>consensus_index_matrix_full_hg38.txt.gz (Values are –log(1-posterior))</li> <li>consensus_index_matrix_binary_hg38.txt.gz (binary occupancy matrix, where footprints with posterior footprint probability >0.99 are considered occupied)</li> </ul> </li> <li><strong>Single nucleotide variants tested for allelic imbalance </strong><br> The legend file contains column definitions in detail. <ul> <li>genotypes.vcf.gz - Genotyping and allelic read depth for each biosample (see header for more information)</li> <li>tested_snvs_padj.bed.gz - SNVs tested for imbalance (TSV, BED-format)</li> <li>tested_snvs_padj_legend.txt</li> </ul> </li> </ul>
JCR Journals, sorted by Impact Factor 2011 with the JCR edition indication
Description of the spreadsheet: “Journals in JCR sorted by IF’11” lists the journals from Thomson Reuters JCR website; it’s sorted by edition (science and social science) and Impact Factor 2011 descending (but not difunded). Fields: Abbreviated Journal title, ISSN, JCR ed. Methodology: 1. We copy and paste from the web pages the list in a unique spreadsheet. 2. We agregate the JCR edition: SCI=1 and SSCI=2. 3. We sort by Edition and Impact Factor and delete this column values. 4. We upload the excel file to data banks
Dataset for: A systematic literature review on user factors to support the sense of presence
<p>This dataset was created for a publication of Wiepke, Axel and Heinemann, Birte called "A systematic literature review on user factors to support the sense of presence". In this paper we used the PRISMA-method to collect Papers via Google Scholar on the third of April 2023 with the search term:<br>(framework OR model OR frameworks OR models OR processes OR ontologies) AND ((“personality traits” OR “personality variables” OR “personality factors”) AND “spatial presence”) AND (“virtual reality”) AND (learn OR edu\*)</p> <p>The results were pictured in "agreed" findings, where more than 50% of found studies supported a category of results and in "controversial", where there were significant findings, but less than 50% of the studies reported significance.</p> <p>This dataset contains:</p> <ul> <li>raw data for our literature review in .bib</li> <li>our main findings with categories in .csv</li> <li>a short Jupyter notebook script for one graphic in ipynb</li> <li>other graphics as .png</li> </ul>
Colebrook-White friction factor data
<p>The Colebrook-White equation is a fundamental formula in fluid mechanics, widely used to determine the friction factor in turbulent flow through pipes. It is crucial for calculating head losses in pipelines and for the design and optimization of hydraulic systems. In this dataset, the friction factor (f) is obtained via numerical solution using the Newton-Raphson method, with a truncation accuracy of 1e-6.</p> <p>The first and second columns of the provided dataset pertain to the Reynolds number (Re) and relative roughness (ε/D) variables, respectively. The data were generated across specified ranges [4500-2250000] with constant step of 4500 for Re and [0.00005, 0.0005, 0.005, 0.05, and 0.5] for ε/D. The third column presents of the f values calculated using the Colebrook-White equation, which is given as:</p> <div> </div> <div>1/(\sqrt(f)) = -2*log((\epsilon/D)/3.7 + 2.51/(Re*\sqrt(f)))</div> <div> </div> <div> </div> <div>This dataset has multiple applications: it facilitates evaluating empirical formulas approximating the Colebrook-White equation and provides a valuable foundation for developing machine learning models to accurately predict the friction factor. Beyond aiding hydraulic system design, this dataset is a useful resource for researchers and engineers aiming to optimize pipeline flow, model turbulent pipe flow behavior, and advance computational methods in fluid dynamics.</div>
Inter-Chemical Correlation results for the study: HHEARx2017-1863 (Investigating Overweight/Obesity and Poor Diet as Susceptibility Factors for Secondhand Smoke in Childhood Asthma)
Title: Investigating Overweight/Obesity and Poor Diet as Susceptibility Factors for Secondhand Smoke in Childhood Asthma <br>Species: Homo sapiens <br>Number of samples: 960 <br>Number of named analytes: 37 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=29 <br>
Inter-Chemical Correlation results for the study: HHEARx2017-1967 (Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial)
Title: Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial <br>Species: Homo sapiens <br>Number of samples: 1085 <br>Number of named analytes: 8 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=36 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1432 (Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children)
Title: Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children <br>Species: Homo sapiens <br>Number of samples: 1256 <br>Number of named analytes: 51 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=5 <br>
Source code and simulation results for nanoantennas supporting an enhanced Purcell factor due to interfering resonances
<p><strong>Summary</strong></p> <p>Data and source code relate to the article "<a href="https://doi.org/10.1103/PhysRevResearch.4.023189">Enhanced Purcell factor for nanoantennas supporting interfering resonances</a>" [1], whose subject are the effects of coupled resonances and quasibound states in the continuum on the Purcell factor in dielectric resonant nanoantennas. The provided scripts reproduce the analysis of interfering resonances in a nanodisk coupled to an enclosed emitter and can be easily adapted for further investigations. </p> <p><strong>Structure</strong></p> <p>The cases refer to different aspect ratios of the nanodisk with (a and b) and without (c and d) substrate. The scans reproduce the data used to find the aspect ratios (a and c) supporting the maximal Purcell enhancement. </p> <p><a href="https://doi.org/10.1016/j.softx.2021.100763">RPExpand</a> [2] is used for Riesz projection expansions, which quantify the interactions of the resonances.</p> <p>The directories <strong>resonance</strong> and <strong>scattering </strong>contain input files for the commercial software JCMsuite, which rigorously solves Maxwell's equations with the finite-element method (FEM). In order to switch to a custom setup, you must adapt these input files. If you want to recalculate all results, make sure that you remove the directories containing resultbags. These are stored in the directory <strong>results</strong>, e.g., results/case_a/resultbags.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (tested with version: 4.6.3)</li> <li>Matlab (tested with version: R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p>[1] Rémi Colom, Felix Binkowski, Fridtjof Betz, Yuri Kivshar, Sven Burger, Enhanced Purcell factor for nanoantennas supporting interfering resonances, Physical Review Research <strong>4</strong>, 023189 (2022), https://doi.org/10.1103/PhysRevResearch.4.023189</p> <p>[2] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</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.