Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
75
datasets available to search
ShareScore release 0.9.0
Dataset results
75 results for “Emission Measurements”
Local to regional methane emissions from the Upper Silesia Coal Basin (USCB) quantified using UAV-based atmospheric measurements
<p>Raw data for Andersen et al., 2021 (Local to regional methane emissions from the Upper Silesia Coal Basin (USCB) quantified using UAV-based atmospheric measurements)</p>
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>
Measuring the Variability of Hydroxyl Emissions in Infrared Sky Spectra using SPIRou: Dataset
<p>Abstract</p> <p>Subtracting the changing sky contribution from the near-infrared (NIR) spectra of faint astronomical<br>objects is challenging and crucial to a wide range of science cases such as estimating the velocity<br>dispersions of dwarf galaxies, studying the gas dynamics in faint galaxies, and accurate redshifts, and<br>any spectroscopic studies of faint targets. Since the sky background varies with time and location, NIR<br>spectral observations, especially those employing fiber spectrometers and targeting extended sources,<br>require frequent sky-only observations for calibration. However, sky subtraction can be optimized<br>with sufficient a priori knowledge of the sky’s variability. In this work, we explore how to optimize sky<br>subtraction by analyzing 1075 high-resolution NIR spectra from the CFHT’s SPIRou on Maunakea,<br>and we estimate the variability of 481 hydroxyl (OH) lines. These spectra were collected during two<br>sets of three nights dedicated to obtaining sky observations every five and a half minutes. During<br>the first set, we observed how the Moon affects the NIR, which has not been accurately measured at<br>these wavelengths. We suggest that if one uses a principal component analysis reconstruction of the<br>sky spectrum and attempts to observe targets at Y JHK mags fainter than ∼15 and attempts a sky<br>subtraction better than 1%, then the Moon contribution must be accounted for at Moon separation<br>distances of at least 10◦. We also identified 126 spectral doublet, or OH lines that split into at least two<br>components, at SPIRou’s resolution. In addition, we used Lomb-Scargle Periodograms and Gaussian<br>process regression to estimate most OH lines vary on similar timescales, which provides a valuable<br>input for IR spectroscopic survey strategies. The data and code developed for this study are publicly<br>available here.</p> <p>Dataset description</p> <p>In total, we collected 1075 sky observations, which spanned from July 28th, 2018 to January 10th, 2022, or approximately 3.5 years. These observations included two sets of three days dedicated to sky measurement where each day, a sky spectrum was observed approximately every 5.5 minutes for 12 hours. These days occurred on December 14th, 15th, and 16th of 2019 and January 22nd, 23rd, and 25th 2020. These 1D extracted and flat fielded sky spectra have a wavelength range of 0.965 − 2.500μm containing 285,377 wavelength bins resampled on a uniform wavelength grid with a step of 1 km/s/pixel. Since the pixels were constant in velocity, the change in wavelength increased from 3x10^−6 − 8x10^−6 μm per pixel. All observations were affected by a steep black body curve starting at 2.1μm, which was caused by thermal emission.</p> <p>We also present a table of doublets identified during this study with the transition, the measured singlet line from Rousselot 2000 (mu0), the doublet lines (mu1 and mu2), and if the line was identified as a doublet (Y/N).</p>
Measurement report: Unexpected high volatile organic compounds emission from vehicles on the Tibetan Plateau Dataset
<p>This dataset includes various emission profiles and related data, specifically:</p> <ol> <li> <p><strong>Source Profile Data at Different Altitudes</strong>.</p> </li> <li> <p><strong>Emission Factor Data</strong>.</p> </li> <li> <p><strong>Emission Ratio Data</strong>.</p> </li> <li><strong>Source Profile Data from PMF Source Apportionment</strong>: Data obtained through Positive Matrix Factorization (PMF), revealing the composition of emission sources.</li> <li> <p><strong>Average Profiles of Gasoline Vapors</strong>: Derived from sealed housing evaporative determination (SHED) tests, with references 1-7.</p> </li> <li> <p><strong>Average Profiles of Gasoline Vehicle Exhaust</strong>: Based on dynamometer tests, with references 2, 8-13.</p> </li> <li> <p><strong>Average Profiles of Vehicular Emissions</strong>: Collected from low-altitude tunnel measurements, reflecting emissions in real-world driving scenarios, with references 14-24.</p> </li> </ol>
Emission library populated with emission rates and effectiveness rates from risk mitigation measures.
<p>This document contains an emission library populated with emission rates from at least 10-15 industrial activities and effectiveness rates from at least 30 risk mitigation measures (RMMs)”, which includes a literature review of emission factors from diverse sources and a review of effectiveness rates for different risk mitigation measures (RMMs), as well as a description of the methodology applied during the reviews.</p> <p>This document is part of the report of Action B5 of NANOHEALTH project (LIFE20 ENV/ES/000187).</p>
Measurement-Based Spatially Explicit Methane Emission Inventory (EI-ME)
<p>Accurate and comprehensive assessment of methane emissions, a powerful climate warming pollutant, is a key first step in reducing these emissions, while supporting the ability to track progress toward such reductions over time. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, adn their reliance on generic emission factors produces underestimations when compared with measurement-based inventories.</p> <p>In this work, we compile and analyze previous peer-reviewed measurement-based data on facility-level methane emissions in the US oil and gas sector and use these data to develop statistically robust emissions models from which we estimate total methane emissions for the population of major US oil and gas facilities.</p> <p>This dataset (EI_ME_v1.0.gpkg) aggregates the results of this measurement-based methane emission inventory (EI-ME), which is focused on oil and gas methane emissions in the US onshore production regions. The emissions estimates are spatially resolved at 0.1 x 0.1 degree spatial scales.</p> <p>The data layers in the GeoPackage are:</p> <ul> <li><em>EI-ME_gridded_ch4_emissions:</em> estimated methane emissions, spatially resolved at 0.1x0.1 degree spatial grids</li> <li><em>EI-ME_US_oil_gas_basins:</em> major US oil and gas basin boundaries, based on <a href="https://www.eia.gov/maps/maps.php">EIA</a> basin boundary definitions.</li> <li><em>EI-ME_facility_ch4_measurements_data: </em>A compilation of previous peer-reviewed facility-level measurement-based data for oil and gas methane emissions in the US.</li> </ul> <p>We also provide a netcdf version ("EI_ME_2021_inventory_CONUS_point1_degrees_v1.nc") which includes estimated mean oil and gas methane emissions over the contiguous US (excludes Alaska) aggregated over a slightly offset spatial grid compared to the full domain in the above .gpkg.</p> <p>Complete details of the emissions model development and dataset creation can be found in the following manuscript:</p> <ul> <li><strong>How to cite: </strong>Omara, M., Himmelberger, A., MacKay, K., Williams, J. P., Benmergui, J., Sargent, M., Wofsy, S. C., and Gautam, R.: Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021), Earth Syst. Sci. Data, 16, 3973–3991, https://doi.org/10.5194/essd-16-3973-2024, 2024.</li> </ul> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>UPDATE (10/10/2025):</p> <p>The spatially explicit measurement-based oil and gas methane emissions inventory (EI-ME) is developed by MethaneSAT, a wholly owned subsidiary of Environmental Defense Fund, to support comprehensive oil and gas methane assessment, methane source attribution, and mitigation. The inventory combines ground-based measurement-based data with statistically robust methane emissions modeling to provide representative estimates of total methane emissions for key facility categories in the contiguous US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. It is spatially resolved at 0.1x.0.1 degree spatial scales.</p> <p> Version 1 of the EI-ME inventory for the contiguous United States was published in 2024 and provided an estimate of the 2021 oil and gas methane emissions and uncertainties that are spatially resolved at 0.1x0.1 degree spatial scales.</p> <p> Here, we provide an update to the EI-ME inventory for the years 2023 and 2024. In this update, we follow the same methodology and use the same input emissions datasets as described in detail in Omara et al. (2024), https://doi.org/10.5194/essd-16-3973-2024. We incorporate the latest available oil and gas activity data for the years 2023 and 2024 based on data from Enverus Prism (<a href="http://www.eneverus.com/">www.eneverus.com</a>), supplemented with additional information from the Oil and Gas Infrastructure Mapping database (OGIM v2.7, <a href="https://doi.org/10.5281/zenodo.15103476">https://doi.org/10.5281/zenodo.15103476</a>) and global annual gas flaring data from VIIRS (Visible Infraed Imagin Radiometer Suite), available from the Earth Observation Group (<a href="https://eogdata.mines.edu/products/vnf/global_gas_flare.html">https://eogdata.mines.edu/products/vnf/global_gas_flare.html</a>).</p> <p>---</p> <p>Contact at Environmental Defense Fund: Mark Omara (momara@edf.org), Anthony Himmelberger (ahimmelberger@methanesat.org)</p> <p>---</p>
Combined fluorescence fluctuation and spectrofluorometric measure-ments reveal a red-shifted, near-IR emissive photo-isomerized form of Cyanine 5
<p><strong>This folder contains all raw data underlying the results presented in a manuscript, accepted for publication in International Journal of Molecular Sciences, and entitled:</strong></p> <p> </p> <p><strong>Combined fluorescence fluctuation and spectrofluorometric measurements reveal a red-shifted, near-IR emissive photo-isomerized form of Cyanine 5</strong></p> <p> </p> <p><strong>Authored by:</strong></p> <p>Elin Sandberg <sup>1</sup>, Joachim Piguet <sup>1</sup>, Haichun Liu <sup>1</sup> and Jerker Widengren <sup>1,</sup>*</p> <p> </p> <p><sup>1</sup> Experimental Biomolecular Physics, Department of Applied Physics, Royal Institute of Technology (KTH), Stockholm, Sweden</p> <p><sup>* </sup>To whom correspondence should be addressed. Email: jwideng@kth.se. Tel: +46-8-7907813</p> <p> </p> <p><strong>The data files are grouped into the different techniques used to generate them, and refer to the figures/tables in the manuscript where the extracted results are presented. </strong></p> <p> </p> <p><strong>ABSTRACT</strong></p> <p>Cyanine fluorophores are extensively used in fluorescence spectroscopy and imaging. Upon continuous exciation, especially at excitation conditions used in single-molecule and super-resolution experiments, photo-isomerized states of cyanines easily reach population probabilies of around 50%. Still, effects of photo-isomerization are largely ignored in such experiments. Here, we studied the photo-isomerization of the pentamethine Cyanine 5 (Cy5) by two similar, yet complementary means to follow fluorophore blinking dynamics: fluorescence correlation spectroscopy (FCS) and transient state (TRAST) excitation-modulation spectroscopy. Additionally, we combined TRAST and spectrofluorimetry (spectral-TRAST), whereby emission spectra of Cy5 were recorded upon different rectangular pulse-train excitations. We also developed a framework for analyzing transitions between multiple emissive states in FCS and TRAST experiments, how the brightness of the different states is weighted, and what initial conditions that apply. Our FCS, TRAST and spectral-TRAST experiments showed significant differences in dark state relaxation amplitudes for different spectral detection ranges, which we attribute to an additional, red-shifted emissive photo-isomerized state of Cy5, not previously considered in FCS and single-molecule experiments. The photo-isomerization kinetics of this state indicate that it is formed under moderate excitation conditions, and its population and emission may thus deserve also more general consideration in fluorescence imaging and spectroscopy experiments.</p>
Urban flask measurements of CO2ff and CO to identify emission sources at different site types in Auckland, New Zealand
<p>As part of the CarbonWatch-NZ research programme, air samples were collected at 28 sites around Auckland, New Zealand to determine the atmospheric ratio (R<sub>CO</sub>) of excess (local enhancement over background) carbon monoxide to fossil CO<sub>2</sub> (CO<sub>2</sub>ff). Sites were categorised into seven types (background, forest, industrial, suburban, urban, downwind, and motorway) to observe R<sub>CO</sub> around Auckland. Flasks from motorway sites observed R<sub>CO</sub> of 14 ± 1 ppb/ppm and were used to evaluate traffic R<sub>CO</sub>. The similarity between suburban (14 ± 1 ppb/ppm) and traffic R<sub>CO</sub> suggests that traffic dominates suburban CO<sub>2</sub>ff emissions during daytime hours, the period of flask collection. The lower urban R<sub>CO</sub> (11 ± 1 ppb/ppm) suggests that urban CO<sub>2</sub>ff emissions are comprised of more than just traffic, with contributions from residential, commercial, and industrial sources, all with a lower R<sub>CO</sub> than traffic. Finally, the downwind sites were believed to best represent R<sub>CO</sub> for Auckland City overall (11 ± 1 ppb/ppm). We demonstrate that the initial discrepancy between the downwind R<sub>CO</sub> and Auckland's estimated daytime inventory R<sub>CO</sub> (15 ppb/ppm) can be attributed to an overestimation in inventory traffic CO emissions. After revision based on our observed motorway R<sub>CO</sub>, the revised inventory R<sub>CO</sub> (12 ppb/ppm) is consistent with our observations. </p>
Dataset for Manuscript: Comparing Urban Anthropogenic NMVOC Measurements with Representation in Emission Inventories - A Global Perspective
<p>Urban observations of individual NMVOCs and the calculated or reported emission ratios used for comparison to emission inventories.</p>
Synthetic and measured emission spectra for testing and validation of MEC-BP
<p>The data contain synthetic and measured (spark discharge) emission spectra in order to test and validate the results of the so called multi-element combinatory Boltzmann plot method. This is an OES-based approach to deduce the number concentration ratio of two elements present in a spark discharge plasma employed for binary NP generation in the gas phase. It is aimed to provide a tool for investigating the evolution of the concentration ratio corresponding to the ablated electrode materials in spark-based NP generators under real operational conditions. The method is based on the construction of a Boltzmann plot for the spectral line intensity ratios at every combination. The produced plots (the so-called multi-element combinatory Boltzmann plots, MEC-BPs) are directly related to the LTE plasma temperature and the number concentration ratio of the neutral atoms. The total concentration ratio – including ions – is calculated from a simple plasma model, without requiring further measurements.</p> <p>The python project in which the method is implemented can be found here: https://pypi.org/project/spark-mec-bp/0.1.0/</p>
A Phase 1 Positron Emission Tomography Study to Measure Cholesterol 24S-Hydroxylase Target Occupancy of TAK-935
ClinicalTrials.gov study NCT02497235. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Novel Positron Emission Tomography (PET) Approach to Measuring Myocardial Metabolism
ClinicalTrials.gov study NCT02563834. IPD Sharing: NO. Countries: 1. Publications: 1.
Direct measurements of ozone response to emissions perturbations in California
Open the record for dataset details and reuse information.
Urban flask measurements of CO2ff and CO to identify emission sources at different site types in Auckland, New Zealand
Open the record for dataset details and reuse information.
Colorado Ongoing Basin Emissions Study (COBE) anonymized final data set of emissions measurements
Open the record for dataset details and reuse information.
Quantifying methane emissions from Laurentian Great Lakes estuaries using in situ measurements, remote sensing and machine learning
Open the record for dataset details and reuse information.
Laboratory mesocosm data measuring the impact of bioturbation frequency on greenhouse gas emissions from reservoir sediments
Inland aquatic systems are major global contributors to the atmospheric carbon budget through greenhouse gas (GHG) emissions, although the amount and form of carbon released varies widely across and within systems. Bioturbation of aquatic sediments can impact biogeochemical conditions and physically release sediment-bound bubbles containing GHGs, but variation in the frequency of such disturbance may modify the rate and composition of resulting GHG emissions. We hypothesized that an intermediate bioturbation frequency would result in the greatest methane (CH4) releases due to mechanical release of trapped bubbles, while frequent disturbance would result in greater diffusive carbon dioxide (CO2) releases relative to CH4, due to increased aeration of the sediment. We tested this bioturbation frequency hypothesis using laboratory mesocosms containing homogenized reservoir sediment. We used mechanical disturbance to simulate bioturbation at 3, 7, 14, or 21-day intervals; a control treatment was undisturbed for the duration of the experiment. We measured GHG emission (ebullition and diffusion) rates. An intermediate frequency of disturbance (7 days) produced the highest total GHG emission rate, while the most frequent disturbance interval (3 days) and least frequent interval (0 days) reduced overall GHG emissions relative to weekly disturbance by 24% and 15%, respectively. These patterns were primarily driven by differences in CH4 ebullition. Contrary to our hypothesis, there was no relationship between disturbance frequency and diffusive CO2 emissions. For all disturbance treatments, the majority of ebullition occurred during disturbance events, suggesting mechanical release of entrapped bubbles is an important emission mechanism. The frequency of disturbance has variable effects on GHG emissions and may explain conflicting results in prior studies of bioturbation. Our study provides insight into bioturbation as a driver of within-system variation in GHG emissions and h
Dataset for AMT paper "Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling"
<p>Single excel file containing 30 minute averaged measurements and model outputs. Includes miniDOAS concentration measurements, sonic anemometer (Gill WindMaster) wind & turbulence, environmental measurements (relative humidity, rainfall, temperature, net radiation flux), deposition velocity resistance components (measured Ra and Rb and modelled Rc), and bLS-R model outputs (dispersion coefficients, also known as C/E ratios). These data provide the model inputs required to run the bLS-R dispersion model, (alternatively the WindTrax dispersion model can be used). The emissions from the field during both measuring periods can be determined from the measured horizontal concentration gradient and the simulated dispersion coefficients (Emissions = (Cdownwind-Cupwind)/Dispersion coefficient).</p>
DatabaseNH3 : EOM ammonia emission factor measured with INRAE Caract'Air device (under controlled conditions)
<p>This dataset includes ammonia volatilization measurements led by ECOSYS INRAE with Caract’Air device. Ammonia measurements, based on the principle of a mass balance in dynamic chambers are performed under thoroughly controlled and replicative conditions. Caract’Air was designed to be as close as possible to field conditions (in situ soil cores) while optimizing ambient conditions (temperature, air humidity and soil water content) and exchange conditions (flow rate, head volume, air circulation conditions) (Génermont et al., 2021; Décuq et al., 2023). A variety of EOMs are represented ranging from historic livestock effluents and manure to emerging biowastes produced by human urban and agro-industrial activities, all these biowastes having undergone a variety of treatments: raw, separated, composted, anaerobically stored slurries, farm yard manure, sewage sludges, municipal and domestic wastes; various digestats from mechanization; urine based fertilizers; etc. The types and origins of the EOM are reported. The physico-chemical properties of the EOM as well as soils on which the EOM were applied are detailed, leading to 20 and 30 parameters accompanied by information on analytical methods. Measurement conditions are also described including the application dose, the experimental set duration, the ambient conditions, and also the reproductibily conditions, etc. Finally, ammonia volatilization data are compiled, in terms of total cumulative loss (kg N/ha) or volatilization rates (% N and % N-NH4 applied).</p>
Ammonia emissions from urea fertilization – multi-annual micrometeorological measurements across Germany [Dataset]
<p>To evaluate the accuracy of the current emission factor (EF) for conventional urea in Germany, ammonia (NH₃) emissions were measured using the integrated horizontal flux (IHF) method with ALPHA passive samplers following urea applications in 2021–2023. Measurements were conducted across six agroecological regions under winter wheat cropping, with nitrogen application rates of 145–230 kg N ha⁻¹, resulting in 51 campaigns.</p> <p>Emission rates (NH₃Ninput) ranged from 1.1% to 20.7% (median 4.8%, mean 8.5%), significantly lower than the 2019 IPCC (14.2%) and 2023 EMEP (16.1% for pH < 7) estimates. NH₃Ninput was influenced by soil texture and rainfall but not nitrogen application rates, with higher emissions observed on sandy soils.</p> <p>These findings suggest current EFs overestimate NH₃ emissions from urea applied to winter wheat in Germany. While emissions may vary due to inter-annual and environmental factors, a national EF for Germany could improve NH₃ emission estimates and the evaluation of mitigation strategies.</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.