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1,092 results for “quantifiers”
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VII: Coarse and Fine Woody Debris Inventory 2022
This dataset contains characteristics of coarse woody debris and snags collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VIII: Seedling Inventory 2022
This dataset contains characteristics of post-fire seedlings and resprouts collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022
This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.
WPE01 Assessing the value added of NEON for using machine learning to quantify vegetation mosaics and woody plant encroachment at Konza Prairie
Woody encroachment, or invasion of woody plants, is rapidly shifting tallgrass prairie into shrub and evergreen dominated ecosystems, mainly due to exclusion of fire. Tracking the pace and extent of woody encroachment is difficult because shrubs and small trees are much smaller than the coarse resolution (>10m2) of common remote sensed images. However, the US government has been investing in finer resolution (<2m2) remote sensing through USDA NAIP and the National Ecological Observatory Network (NEON), both of which cost multi-million dollars each year and contain different remote sensed products. We compared two methods of classification (random forests and support vector machines) with these two freely available remotely sensed aerial images to determine if and how much NEON adds to classification accuracy and determine which method of machine learning was more accurate. All models have very high overall classification accuracy (>91%), with the NEON image a few percent more accurate than NAIP. The NEON image significantly relies on canopy height (LiDAR) to make classifications, but the importance of bands is more evenly distributed during NAIP classification. Lastly, accuracy for Eastern Red Cedar specifically is high with NEON (78-84%), compared to the relatively low classification accuracy using NAIP imagery (55-61%).
Effects of biotic (shrimp) and abiotic (discharge) factors on the depositional environment quantified in a montane stream in Puerto Rico. (Shrimp/Algae/Can J. Fish Aquat. Sci. (1994))
Effects of biotic (shrimp) and abiotic (discharge) factors on the depositional environment were quantified in a montane stream in Puerto Rico. Electricity was used experimentally to exclude large (approximately >1cm in length) biota without artificially increasing sedimentation as in cage enclosure/exclosure experiments in stream systems. Shrimp (>1cm in length) were excluded from substrata by semicircular fence hooked up to battery-powered fence chargers which emitted continuous pulses of electricity. Unelectrified control substrata had natural high densities of atyid shrimp. Significantly greater masses of total sediment, fine and large organic particles, and algal biovolume occurred in shrimp exclusion treatments relative to controls. Shrimp exclusion treatments experienced slow and steady accumulation of sediments under base flow conditions and a large stepwise increase in sediment following a storm. No measurable sediment accrued in the presence of natural densities of shrimp under base flow conditions. Shrimp rapidly removed sediments that accrued during the storm (440-620 g*m2 dry mass-1), decreasing sediment mass in control treatments to near prestorm levels (5-13 g*m2 dry mass-1) within 30 h. Atyid shrimp can significantly affect the accumulation of organic and inorganic materials on rock substrata in stream pools between high-discharge events. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Data for Quantifying Ecosystem Services Under Various Oyster Reef Designs
This data provides the basis for comparison of the services provided by reef designs varying in elevation and width located adjacent to an intertidal marsh within a coastal bay of Virginia, USA. We quantified wave attenuation to determine potential coastal protection of the adjacent marsh, and changes to sediment composition and infaunal communities before and after reef construction for three years. After construction we also quantified oyster size and population density to compare high and low elevation reef designs. Data consists of 4 tables: 1) Depths and wave heights, adjacent to the bay and marsh sides of the reef 2) Abundance of organisms 3) Biomass of organisms 4) Sediment organic matter There is also a .zip file containing the original Excel files from which the consolidated tables were extracted.
Quantifying hail and lightning risk factors using long-term observations around Australia - Hail and Lightning Datasets
<p>These data accompany a paper referenced as doi: 10.1029/2020JD033101. Two spreadsheets are provided as used to derive the hourly and monthly occurrence of hail or lightning events within the domains of the ten radars sites (Melbourne, Wollongong, Gympie, Grafton, Canberra, Marburg, Adelaide, Namoi, Perth, Hobart). Lightning events as defined in this paper (derived from post-processed lightning information) were provided for 2005-2018 and hail data from 1997-2018 (noting that the start date of hail is dependent on when the respective radar site was established). A value of 1 indicates a lightning/hail event occurred during that hour (in UTC +0). Information on processing methods and data are provided in the published paper for the doi listed above.</p>
Repository of data supporting the thesis "Poly-algorithmic Techniques in Real Quantifier Elimination"
<p>Dataset of various files (as a .zip) supporting the PhD thesis "Poly-algorithmic Techniques in Real Quantifier Elimination" by Zak Tonks, University of Bath. The PhD thesis is in the area of Quantifier Elimination over the Reals (QE) in Computer Algebra. The PhD thesis concerns implementation of algorithms in Quantifier Elimination, which largely culminates in the package QuantifierElimination for the Computer Algebra software Maple. Much of this repository is output of the benchmarking of this package against various other packes in Maple and otherwise. Otherwise there are some auxiliary tools and files to assist with working with QE in Maple, converting between various formats, and understanding case studies and the package QuantifierElimination via software demoes as Maple worksheets.</p> <p>An overview of the contents of this repository (as a .zip file, which contains subdirectories described in the README):</p> <ul> <li>The example databases contributed from the project, as files that can be read into Maple defining tables of examples, and associated functions to build or examine various examples,</li> <li>A pdf file providing the references for all examples from the example databases, and typesetting of the examples as associated QE problems,</li> <li>The benchmarking data produced from the benchmarking of the project as csv (comma separated value) files, and the Excel workbooks (xlsx files) processing said data into survival plots for the thesis,</li> <li>Copies of the survival plots themselves as .png files,</li> <li>The bash and Maple scripts used to generate the raw benchmarking data, that can be reused, including documentation how to do so in the associated README,</li> <li>Other auxiliary tools allowing for conversion of QE formulae between formats (such as that of SyNRAC, RegularChains, QuantifierElimination (amongst packages in Maple), and QEPCAD B.</li> <li>Maple worksheets and the associated exported pdf files used in software demos at conferences to demonstrate features of QuantifierElimination.</li> <li>Some pdf files demonstrating early case studies on Lazard curtains generated from an early development build of QuantifierElimination.</li> </ul> <p>Lastly, there is a README with more detail on the files of the repository further. To emulate the benchmarking of the thesis, an understanding of bash and potentially Maple is assumed, but the raw data from the project is provided here. Before QuantifierElimination's official release, the source code and/or Maple package as an .mla file is available for interested parties upon request to the author Zak Tonks (<a href="mailto:zak.p.tonks@bath.edu">zak.p.tonks@bath.edu</a>). Any other queries about this data or associated work should be directed to this email address. The author's PhD supervisor's email address is <a href="mailto:J.H.Davenport@bath.ac.uk">J.H.Davenport@bath.ac.uk</a>.</p>
Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer
<p>This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the <span>Laboratoire de Glaciologie (GLACIOL)</span> at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf). </p>
Quantifying wind-driven dispersal of zooplankton in a Mediterranean pond
<p>Dispersal is an essential component in the life history of organisms and has strong ecological implications. Although it has been assumed that small organisms have very high dispersal rates, quantitative data supporting this claim remains scarce. In the context of zooplankton, wind stands out as a primary vector facilitating passive dispersal. We quantified short-distance wind-driven dispersal of propagules across various zooplankton taxa in a Spanish Mediterranean coastal temporary pond. We have also studied dispersal patterns in relation to the wind regime (intensity, steadiness and gust direction), source pond status (volume of water) and demographic dynamics (the abundance of individuals in water column populations). Further, we related propagules size merurements (surface, L2 and volume, L3) with dispersal distances. Additionally, we have performed measurements of the dispersed propagules and investigated the relationship between their dimensions and the dispersive distance on a local scale. Here, we present the raw data gathered during research.</p>
Data and code for "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations"
<p>This record contains data, scripts, and models associated with the publication "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations".</p> <h3>Databases</h3> <p>The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access.</p> <h3>Models</h3> <p>The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.</p> <h3>Primitive structures</h3> <p>Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.</p> <h3>Tools for analyzing tilt angles</h3> <p><strong><code>standardize-cell-orientation.py</code></strong><br>Converts each frame in `movie.xyz` to the standardized cell setting and writes the resulting trajectory to `movie-standardized.xyz`.<br>This is useful when working with triclinic cells.</p> <p><code><strong>analyze-tilt-angles-for-orthorhombic-cells.py</strong></code><br>Analyzes the distribution of tilt angles for each frame in a trajectory.<br>The analysis assumes that the B-B bonds are oriented along the Cartesian coordinate system either without further rotation or after a "simple" rotation of the entire cell.</p> <p><code><strong>angle-analysis.ovito</strong></code><br>Ovito script that approximately implements the procedure used in <code>analyze-tilt-angles-for-orthorhombic-cells.py</code>.</p>
Dataset from paper "Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest"
<h3><strong><span>Dataset from the paper “Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest”</span></strong></h3> <p><span> </span><span>This repository contains:</span></p> <ul> <li><span>Dataset Description: “raster_labels.xlsx” (an Excel spreadsheet detailing raster pixel values and their respective fragmentation classes).</span></li> <li><span>Fragmentation Raster Files: “forest_fragmentation_mspa_2020.tif” and “forest_fragmentation_mspa_2020.tif” (GeoTIFF files of landscape forest fragmentation for 1985 and 2020).</span></li> </ul> <p><span> </span><span>If you need anything else, please contact the corresponding author, Igor Broggio (<a href="mailto:isbbroggio@gmail.com">isbbroggio@gmail.com</a>).</span></p> <p><span> </span></p> <p><span>If you use these data, please cite the paper: </span><span>[Citation]</span></p>
DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region
<p>This dataset has been prepared as part of the Interreg Alpine Space project Eco-AlpsWater (ASP569) - <em>Innovative Ecological Assessment and Water Management Strategy for the Protection of Ecosystem Services in Alpine Lakes and Rivers</em>, <a href="https://www.alpine-space.eu/projects/eco-alpswater/en/home">https://www.alpine-space.eu/projects/eco-alpswater/en/home</a></p> <p>Individual archives include 16S rRNA (cyanobacteria) and 18S rRNA (microalgae) FASTA sequences and associated blastn results obtained from the high throughput sequencing of plankton and biofilm bulk/eDNA samples collected in 2019 in 37 lakes and 22 rivers across the Alpine region. These are supporting files for the paper by Salmaso et al., 2022. DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region. Science of the Total Environment, in press.</p>
Public Dataset for "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"
<p>Dataset for the "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" paper. </p> <p>The full text of the paper can be found <a href="https://zenodo.org/record/4699959#.YngKatNBy3K">here</a>.</p> <p>The folder "Tweet_IDs" contains the complete list of the 152,514,929 tweet IDs (together with their timestamps) which we used for the analysis in the study: "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"</p> <p>by Nikos Salamanos, Michael J. Jensen, Costas Iordanou and Michael Sirivianos</p> <p>We have split the tweets into separate .zip files based on the date listed in their timestamps.</p> <p>The crawling took place from September 21 to November 7, 2016 (47 days; we did not collect data on 02/10/2016).</p> <p>Each "tweet_day_X.zip" file contains the file "tweet_day_X.csv", where X in [1,2,...,47]. For instance, the file "tweets_day_1.zip" contains the tweets of the 1st day: 09/21/2016.</p> <p>Please cite the paper in any published work that uses any of these resources. </p> <p>@misc{nikos_salamanos_2021_4699959,<br> author = {Nikos Salamanos and<br> Michael J. Jensen and<br> Costas Iordanou and<br> Michael Sirivianos},<br> title = {{Did State-sponsored Trolls Shape the 2016 US <br> Presidential Election Discourse? Quantifying<br> Influence on Twitter}},<br> month = apr,<br> year = 2021,<br> publisher = {Zenodo},<br> version = 3,<br> doi = {10.5281/zenodo.4699959},<br> url = {https://doi.org/10.5281/zenodo.4699959}<br> }</p>
Scripts for quantifying the effect of diamond nano-pillars on the fluorescence of NV centers
<p><strong>Summary</strong></p> <p>Scripts and data can be used to reproduce and build on the numerical results published under the title: "<a href="http://doi.org/10.3390/nano12091516">Optical and Spin Properties of NV Center Ensembles in Diamond Nano-Pillars</a>" by Kseniia Volkova, Julia Heupel, Sergei Trofimov, Fridtjof Betz, Rémi Colom, Rowan W. MacQueen, Sapida Akhundzada, Meike Reginka, Arno Ehresmann, Johann P. Reithmaier, Sven Burger, Cyril Popov, and Boris Naydenov (Nanomaterials 12(9), 1516, 2022).</p> <p><strong>Method</strong></p> <p>The dipole emitters are assumed to be distributed uniformly 30 nm below the top surface of the nano-pillars. They are first integrated with a trapezoidal rule along the azimuth (because of the periodicity this results in a geometrical convergence) and with a 15 point Gauss-Kronrod quadrature rule in radial direction.</p> <p>The main source of error results from the dipole positions being integrated only from 0 to R - min_dist, as it is challenging to model a dipole emitter located only few nanometers from the curved material interface. Further numerical parameters can be adjusted in the input files for JCMsuite. Both, a 3D setup and a 2D setup are provided. The letter exploits the rotational symmetry which results in a smaller memory footprint. Yet, as the distance of the dipole from the symmetry axis increases, many Fourier components are required which leads to long computation times.</p> <p>For further quantitative studies we propose the 3D setup that is the default in the script 'integration.m', which allows to integrate closer to the side walls without increasing the costs. Furthermore in the second data set, shipped together with the data published in the paper, the height has been kept constant. In the paper the height has been chosen according to the fabricated samples. The 90° angle has been assigned to the [111] samples and the correspondingt height of 1400 nm and the 35.3° angle was assigned to the [100] samples and a height of 2200 nm.</p> <p><strong>Structure</strong></p> <p>The directories <strong>scattering2D</strong>, <strong>scattering3D </strong>and <strong>scatteringFlat</strong> contain input files for JCMsuite. The script 'integration.m' can be used to produce new data. With 'plotresults.m' you can either plot the results produced with 'integration.m' or those which were published in the related paper. Please note that the provided example produced with the script 'integration.m' differs from the published data, which has been computed with slightly different parameters.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite 5.2.0</li> <li>Matlab R2019b</li> </ul> <p>In order to produce new data, 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>
Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile
<p>The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.</p>
Quantifying the basic reproduction number and the under-estimated fraction of mpox cases around the world at the onset of the outbreak: a mathematical modeling and machine learning- based study
<p><span>In 2022, there was a global resurgence of mpox, with different clinico-epidemiological features compared with previous</span><br><span>outbreaks. During this resurgence, sexual contact was hypothesized as the primary transmission route, with the community</span><br><span>of men having sex with men (MSM) being disproportionately affected. Because of the stigma associated with sexually</span><br><span>transmitted infections, especially those impacting MSM, the real burden of mpox could be masked.</span><br><span>We quantified the basic reproduction number (R</span><span>0</span><span>) and the under-estimated fraction of mpox cases in 16 countries, from the</span><br><span>onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-</span><br><span>and low-risk populations), two-route (sexual and non-sexual transmission) mathematical model. Machine learning (ML) was</span><br><span>leveraged to identify under-estimation determinants.</span><br><span>Estimated R</span><span>0</span><span> </span><span>ranged between 1·37 (Canada) and 3·68 (Germany). The under-estimation rates for the high- and low-risk</span><br><span>populations varied between 25-93% and 65-85%, respectively. The estimated total number of mpox cases, relative to the</span><br><span>reported cases, is highest in Colombia (3·60) and lowest in Canada (1·08). In the ML analysis, two clusters of countries could</span><br><span>be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and importance of religion.</span><br><span>Given the substantial mpox under-estimation, surveillance should be enhanced and campaigns against the stigmatization of</span><br><span>MSM should be organized. Countries have different social characteristics, potentially explaining the various degrees of under-</span><br><span>reporting in mpox cases, which should be considered by studies assessing the effectiveness of community-based</span><br><span>interventions.</span></p>
Comparatives, Quantifiers, Proportions
<p>The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.</p>
Supporting data for "Quantifying the Strength of a Salt Bridge by Neutron Scattering and Molecular Dynamics"
<p>Supporting data for the following published paper: Mason, Jungwirth, Duboué-Dijon, 2019, JPhysChemLett, 10, 3254-3259</p> <p>Contains both data from neutron scattering measurements and input simulation files necessary for reproduction of the work.</p>
Datasets for "Quantifying nanodiamonds biodistribution in whole cells with correlative iono-nanoscopy"
<p><strong>Readme:</strong></p> <p>These are datasets that were generated during experiments, simulations, and data processing.</p> <p><strong>[1]</strong> Data saved in .xlsx files are data points for plots and can be opened with Microsoft Excel.</p> <p><strong>[2]</strong> Data saved in .txt files are image files recorded in 1024 x 1024 pixel arrays. A custom software IonDAQ-lite (ref: Bettiol, A. A. et al. Nucl. Instrum. Methods Phys. Res. B 267, 2069-2072, 2009) was used to open and process these image files, however some commonly used image processing programs (e.g. Matlab) can also be used to open these files.</p> <p><strong>[3]</strong> The .lsm and .czi files are 2D or 3D confocal image files and can be viewed with ZEN 3.4 (ZEN lite, Carl Zeiss).</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.