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10,016 results for “Observation”
Data for: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3
<p>We present all of the data across our SNR and abundance study for the molecules O2 and O3 for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-20, and the abundance values range in log space in steps of 0.5 and/or 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input O2 and O3 values (abundance case), the retrieved O2 and O3 values (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for O2 and O3. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input O2': str, {'Input O3': str}})</p>
Synthetic JWST MIRI-MRS Observations of Mid-IR Noble Gas Emission from T Cha
<p>Synthetic detctor images for the continuum + line emission from [Ne II], [Ne III], [Ar II], [Ar III] for T Cha made with MIRISim (Klaassen et al. 2021) for the overall best fitting model with r_in = 0.1 rG (both with and without a cavity). Also provided are backgrounds for the source observation (applicable to both with and without a cavity) and models for a synthetic standard star and its background. </p> <p>The .fits files are the underlying data cubes for the [Ne II] and [Ar II] lines which were provided as inputs to the simulator. </p> <p>For a full description, see Sellek et al. (2024a).</p>
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Mapping the global distribution of C4 vegetation using observations and optimality theory
<p>This dataset includes annual C4 vegetation distribution and its uncertainty from 2001 to 2019. We also provide the distribution of C4 natural grasses and C4 crops during the same period, as well as the code and interim dataset to generate the main figures. Please refer to manuscript for more details:</p> <p>Luo, X., Zhou, H., Satriawan, T.W., Tian, J., Zhao, R., Keenan, T.F., Griffith, D. M., Sitch, S. Smith, N.G. & Still, C.J. (2024). Mapping the global distribution of C4 vegetation using observations and optimality theory. <em>Nature Communications.</em> https://doi.org/10.1038/s41467-024-45606-3.</p> <p><strong>Update (Nov 2023): </strong>we have updated the observational constraint from a linear model to a non-linear model - logistic curve, to better depict how C4 photosynthetic advantage translates into C4 grass coverage changes (C4_distribution_NUS_v2.2.nc).</p> <p><strong>Update (August 2023): </strong>we corrected the issue caused by a bias in the remote sensing grassland base map, and released the version 2 of the C4 vmap (C4_distribution_NUS_v2.nc).</p> <p><strong>Update (June 2023): </strong>we noticed there is a critical issue in the version 1 of our C4 map, due to the quality of remote sensing grassland base map used. We are now working on providing a new version (V2) in the next few months (Jun 2023).</p>
The state of the lower Volta Delta Beaches in Ghana from field observations.
<p>The continuous collection of data is important, particularly for developing models for local applications. It is also important to collect in situ data in this observation scarce region to improve our understanding of coastal evolution. The data presented here were planned and collected along a 90 km coastline. The experiment holds significant scientific value as it marks the first comprehensive data-gathering effort along the Volta Delta within the Bight of Benin, West Africa. Several projects, decision-makers, and scientists will make use of this data to calibrate their models. The study was executed in 2023, and it provides a comprehensive description of the morphodynamical characteristics of the study area and identifies the factors influencing the changes. The data include nearshore and riverine bathymetry, topography, sediment grain size distribution, and waves in the study area. The research experiment provides a foundation for future extensive and long-term investigations along the LVD and promotes localized investigations. </p>
High Speed Rail Seismic Observation in Baoding, Hebei Province of China
<p>H5 files includes all train events collected in the observation. Raw data in sac format is too big (600GB) to upload.</p> <p>To access all continuous data, please contact shiyxg@mail.iggcas.ac.cn/wenjc@pku.edu.cn/njy@pku.edu.cn</p> <p> </p> <table> <tbody> <tr> <td>File names</td> <td>Format</td> <td>date</td> </tr> <tr> <td>BSPK095*</td> <td>-100s-100s, dt=0.01</td> <td>0424-0504</td> </tr> <tr> <td>BSPKU87*</td> <td>-100s~100s, dt=0.01</td> <td>0510-0518</td> </tr> <tr> <td> <p>hsr_coor_stacked_201804*</p> </td> <td>stacked traces in different frequency bands</td> <td>0424-0504</td> </tr> <tr> <td> <p>hsr_coor_stacked_201805*</p> </td> <td>stacked traces in different frequency bands</td> <td>0510-0518</td> </tr> <tr> <td> <div>coor_all_201804_YNPK_CZ_158.npy</div> </td> <td> <p>ambient noise results at night, between 158 stations</p> <p>(S158_1804.txt)</p> </td> <td>0424-0504</td> </tr> <tr> <td> <div>coor_all_201805_YNPK_CZ_143.npy</div> </td> <td> <p>ambient noise results at night, between 143 stations</p> <p>(S143_18045txt)</p> </td> <td>0510-0518</td> </tr> <tr> <td> <div>1804_YNPK_CZ_f0.2_20_all_night.h5</div> </td> <td> <p>Continuous data at 13 nights of 4 stations for stability comparsion</p> </td> <td>0424-0504</td> </tr> <tr> <td> <div>H1.h5</div> </td> <td> <p>Correlation results of array in Baoding, 2023 March.</p> </td> <td>2023/0311-0328</td> </tr> </tbody> </table>
108 Basins containing a minimum of 50 years of daily discharge and precipitation observations
<p>Contained within this dataset are the (1) list of USGS basins used in the Livneh et al. 2024 (in-review) analysis of the intermittency-flooding relationship, (2) shapefile of the basins, and (3) the script used to generate the list of basins from the GAGES II dataset. If using these basins or the script to generate a list of basins that meet a certain set of criteria, please cite this dataset accordingly. </p>
Maps of the simulations and observations (TURBAN-D17)
<h3>Basic information</h3> <p>This dataset contains two folders; in <em>output-measurements</em> are daily averaged <strong>TURDATA</strong> (see <a href="../records/10655033">https://zenodo.org/records/10655033</a>) results for air quality measurements. In folder <em>output-simulations</em> contains simulation results for the so-called Legerova domain, an area in the city of Prague, Czech Republic, centered around the traffic-heavy streets Legerova and Sokolská. All times are in UTC (local time in winter, CET, is UTC +01:00, summer time, CEST, is UTC +02:00). In total 6 episodes in 2022 and 2023 was selected:</p> <ol> <li>s1 2022-07-17 00:00:00 - 2022-07-20 00:00:00</li> <li>s2: 2022-08-02 00:00:00 - 2022-08-05 00:00:00</li> <li>s3: 2022-09-22 00:00:00 - 2022-09-25 00:00:00</li> <li>s4: 2022-12-08 00:00:00 - 2022-12-11 00:00:00</li> <li>s5: 2023-01-27 00:00:00 - 2023-01-30 00:00:00</li> <li>s6: 2023-02-13 00:00:00 - 2023-02-16 00:00:00</li> </ol> <p>For all episode’s boundary conditions from regional model ICON (see <em>icon</em> in folder nomenclature) were used. However, for a very stable condition during s6 two additional regional models were used (WRF and ALADIN; see <em>wrf</em> or <em>alad</em>) that provided boundary conditions closer to the observed meteorology. For more detailed description of the experiments see the <strong>TURBAN</strong> project website at <a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>.</p> <h3>General organisation</h3> <p>Each selected epizode (s1-s6) has three folders; post-processed results from the PALM model as averaged ASCII (output-ascii) or GeoTiff (output-gis) files that can be viewed in many GIS applications and maps in the PNG format (output-png). Each variable was averaged from original 10min values to 1 and 24-hour averages.</p> <h3>Modelled variables</h3> <p>Each subfolder includes 3 subfolders with variables. Variable <em>bio_pet</em> is the Physiological Equivalent Temperature (PET), <em>bio_utci</em> is the Universal Thermal Climate Index (UTCI) and <em>kc_PM10</em> is the concentration of PM10 at 3m above ground. Episodes s4 and s6 includes 3 more subfolders: <em>kc_NO</em> is the concentration of NO, <em>kc_NO2</em> is the concentration of NO2 and <em>kc_PM25</em> is the concentration of PM2.5 at 3m above ground.</p> <h3>File nomenclature</h3> <p>Each file (PRJ, TIF, ASC or PNG) has the same nomenclature. An example (bio_utci_abs-01h_20190724_1200-1300.png) could be parsed as: variable name (bio_utci), processed output (abs-01h), date (20190724) and averaged period (1200-1300). So, the result is a map with hourly averaged UTCI for 24 Jul 2019 between 12:00 and 13:00 UTC.</p> <h3>Important note</h3> <p>During the processing phase a few potentially important problems were identified and need to be analysed in detail. One of them are extremely overestimated concentrations due to stable conditions from boundary condition inputs. In certain situations, it can happen that the overall best regional meteorological model can provide inappropriate input conditions for some episode. This needs to be checked in detail before any following interpretation. The issue will be addressed in an upcoming manuscript that will be available through the project website.</p> <h3>Acknowledgements</h3> <p>The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Institute of Computer Science of the Czech Academy of Sciences (ICS), supported by the long-term strategic development financing of the ICS (RVO:67985807) and partially on the IT4I HPC infrastructure supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254). The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic.</p>
Quality controlled observations of hourly incoming shortwave radiation data at the surface for solar resource mapping in Norway (2016-2020).
<p>Observed hourly incoming shortwave radiation data at the surface of Norway for the years 2016-2020 along with quality control flags, visualization plots and a descriptive report. The data has been collected, visually inspected and quality controlled within the SunPoint project (SUn in Norway - POtential and INTegration of the solar energy resource, Norwegian Research Council project 320750). The main data source is frost.met.no but some gaps were filled with data directly obtained by the station holders.</p> <p>There are three NetDCF files for 47 stations selected after quality control:</p> <ul> <li>rsds_1hr_selection_v5_2016-2020.nc: Raw data</li> <li>rsds_flagged_1hr_selection_v5_2016-2020.nc: Raw data with flags</li> <li>rsds_cleaned_1hr_selection_v5_2016-2020.nc: Filtered data (i.e. all flagged data has been removed)</li> </ul> <p>and one NetCDF file for all available stations (106 stations)</p> <ul> <li>rsds_1hr_frost_and_more_2016-2020.nc</li> </ul> <p>Version 3.5 of the McClear clear-sky model is used for flagging which reduces the bias to ground measurements compared to earlier versions. </p> <p>The visualization and automated quality control routines are available in the Scripts.zip file (python).</p>
PDS 111: 1.3mm continuum and 12CO emission - ALMA observations
<p>The self-calibrated ALMA observations at 1.3mm wavelength of the PDS 111 system, published in Derkink et al. (2024). These observations were part of the ALMA Program ID 201.1.01705.S. Please check the README.txt for more details about each file. </p>
Research data supporting "Observation of a Topological Edge State Stabilized by Dissipation"
<div> <p>This repository contains the data presented in the manuscript titled "Observation of a Topological Edge State Stabilized by Dissipation" by H. Wetter et al., Phys. Rev. Lett. 131, 083801 (2023). The files contain the final data sets relevant to reproduce all plots shown in the paper. Data types are CSV, TIF, SVG, TXT, PNG. No licensed software is required for opening and reading the files.</p> </div>
Tabular Data for "A Comprehensive Catalog of UVIT Observations I: Catalog Description and First Release of Source Catalog (UVIT DR1)"
<p>The first comprehensive catalog of UVIT includes the sources from the observations between 2016 and 2017. </p> <p>The catalog is formatted according to the machine-readable format used by the AAS Journals and CDS/VizieR. Specific information on the structure of MRT files can be found at:</p> <p><span> </span>AAS: <a href="https://journals.aas.org/mrt-overview/"><span>https://journals.aas.org/mrt-overview/</span></a></p> <p><span> </span>CDS: <a href="http://cds.u-strasbg.fr/doc/catstd.htx"><span>http://cds.u-strasbg.fr/doc/catstd.htx</span></a></p> <p><span> </span>These files can be read in python using the astropy package<span> </span>or with the most recent version of TOPCAT (> Version 4.8)</p>
Adjusted ERA5, COREv2 and JRA-55 products constrained by ocean observations
<p>This dataset contains ERA5, JRA-55 and COREv2 air-sea flux fields that have been adjusted to match ocean heat and salt content change in EN4 and IAP ocean observations. It also contains estimes of meridional heat and freshwater transports, globally and in the Atlantic and Indo-Pacific, based on these adjusted air-sea surface flux fields. Please consult the README for more information on the dataset. <br><br>The net heat flux and net freshwater flux into the ocean have been adjusted using the "Optimal Transformation Method" (OTM), a watermass-based inverse method that uses physics-based constraints to close the observed ocean heat and salt budgets. The formulation of OTM and a model validation is provided at Zika & Sohail (2024). The process of producing these adjusted air-sea fluxes is described in Sohail & Zika (2025).</p>
Data and code associated with "The Observed Availability of Data and Code in Earth Science and Artificial Intelligence"
<p>Data and code associated with "The Observed Availability of Data and Code in Earth Science <br>and Artificial Intelligence" by Erin A. Jones, Brandon McClung, Hadi Fawad, and Amy McGovern.</p> <p>Instructions: To reproduce figures, download all associated Python and CSV files and place<br> in a single directory.<br> Run BAMS_plot.py as you would run Python code on your system.</p> <p>Code:<br>BAMS_plot.py: Python code for categorizing data availability statements based on given data<br> documented below and creating figures 1-3. </p> <p> Code was originally developed for Python 3.11.7 and run in the Spyder <br> (version 5.4.3) IDE.<br> <br> Libraries utilized:<br> numpy (version 1.26.4) <br> pandas (version 2.1.4)<br> matplotlib (version 3.8.0)<br> <br> For additional documentation, please see code file.</p> <p>Data:<br>ASDC_AIES.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence for the Earth Systems (AIES)<br>ASDC_AI_in_Geo.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence in Geosciences (AI in Geo.)<br>ASDC_AIJ.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence (AIJ)<br>ASDC_MWR.csv: CSV file containing relevant availability statement data for Monthly <br> Weather Review (MWR)<br><br></p> <p><br>Data documentation:<br>All CSV files contain the same format of information for each journal. The CSV files above are <br>needed for the BAMS_plot.py code attached.</p> <p>Records were analyzed based on the criteria below.</p> <p> Records:<br> 1) Title of paper<br> The title of the examined journal article.<br> 2) Article DOI (or URL)<br> A link to the examined journal article. For AIES, AI in Geo., MWR, the DOI is <br> generally given. For AIJ, the URL is given.<br> 3) Journal name<br> The name of the journal where the examined article is published. Either a full<br> journal name (e.g., Monthly Weather Review), or the acronym used in the <br> associated paper (e.g., AIES) is used.<br> 4) Year of publication<br> The year the article was posted online/in print.<br> 5) Is there an ASDC?<br> If the article contains an availability statement in any form, "yes" is <br> recorded. Otherwise, "no" is recorded.<br> 6) Justification for non-open data?<br> If an availability statement contains some justification for why data is not <br> openly available, the justification is summarized and recorded as one of the <br> following options: 1) Dataset too large, 2) Licensing/Proprietary, 3) Can be <br> obtained from other entities, 4) Sensitive information, 5) Available at later <br> date. If the statement indicates any data is not openly available and no <br> justification is provided, or if no statement is provided is provided "None" <br> is recorded. If the statement indicates openly available data or no data <br> produced, "N/A" is recorded.<br> 7) All data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access data associated with the article are given and there is no <br> indication that any data is not openly available; "n" is recorded if no means <br> to access data are given or there is some indication that some or all data is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 8) At least some data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access data associated with the article are given; "n" is <br> recorded if no means to access data are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 9) All code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access code associated with the article are given and there is no <br> indication that any code is not openly available; "n" is recorded if no means <br> to access code are given or there is some indication that some or all code is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 10) At least some code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access code associated with the article are given; "n" is <br> recorded if no means to access code are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 11) All data available upon request<br> If there is an availability statement indicating data is produced and no data <br> is openly available, "y" is recorded if any data is available upon request to <br> the authors of the examined journal article (not a request to any other <br> entity); "n" is recorded if no data is available upon request to the authors <br> of the examined journal article. If there is no availability statement, any <br> data is openly available, or no data is produced, the record is left blank.<br> 12) At least some data available upon request<br> If there is an availability statement indicating data is produced and not all <br> data is openly available, "y" is recorded if all data is available upon <br> request to the authors of the examined journal article (not a request to any <br> other entity); "n" is recorded if not all data is available upon request to <br> the authors of the examined journal article. If there is no availability <br> statement, all data is openly available, or no data is produced, the record<br> is left blank.<br> 13) no data produced<br> If there is an availability statement that indicates that no data was<br> produced for the examined journal article, "y" is recorded. Otherwise, the<br> record is left blank.<br> 14) links work<br> If the availability statement contains one or more links to a data or code <br> repository, "y" is recorded if all links work; "n" is recorded if one or more <br> links do not work. If there is no availability statement or the statement <br> does not contain any links to a data or code repository, the record is left <br> blank. </p>
CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000
<p><strong>CitiesGOER</strong> is a database that provides environmental data for 52,602 cities and 48 environmental variables, including 38 bioclimatic variables, 8 soil variables and 2 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database that is available from <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparations of these layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. CitiesGOER was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site.</p> <p>The identities and coordinates of cities were sourced from a data set with information for cities with a population size larger than 1000 that was created by <a href="https://public.opendatasoft.com/explore/?sort=modified">Opendatasoft</a> and made available from <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name</a>. The data was downloaded on 22-JULY-2023 and afterwards filtered for cities with a population of 5000 or above. Cities where information on the country was missing were removed. The coordinates of cities were used to extract the environmental data via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.6-47) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>.</p> <p>Version 2023.08 provided median values from 23 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 18 GCMs for SSP 3-7.0, both for the 2050s (2041-2060). Similar methods were used to calculate these median values as in the case studies for the TreeGOER manuscript (calculations were partially done via the <a href="https://rdrr.io/cran/BiodiversityR/man/ensemble.envirem.html">BiodiversityR::ensemble.envirem.run</a> function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes <a href="https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html">future grid layers available from WorldClim 2.1</a>).</p> <p>Version 2023.09 used similar methods as for previous versions to provide median values from 13 GCMs for the 2090s (2081-2100) for SSP 5-8.5.</p> <p>The locations of the 52,602 cities are mapped in one of the series available from the <strong>TreeGOER Global Zones</strong> atlas that can be obtained from <a href="https://doi.org/10.5281/zenodo.8252756">https://doi.org/10.5281/zenodo.8252756</a>.</p> <p>Version 2024.10 includes a new data set that documents the location of the city locations in <strong>Holdridge Life Zones</strong>. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) climates inferred from global raster layers that are <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.41ns1rnff">available for download from DRYAD</a> and were created for the following article: Elsen et al. 2022. <strong>Accelerated shifts in terrestrial life zones under rapid climate change.</strong> <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a>. Version 2024.10 further includes Holdridge Life Zones for the climates that were available from the previous versions, calculating biotemperatures and life zones with similar methods as used by Holdridge (<a href="https://www.jstor.org/stable/1675393?seq=1">1947</a>; <a href="https://app.ingemmet.gob.pe/biblioteca/pdf/Amb-56.pdf">1967</a>) and Elsen et al. (<a href="https://doi.org/10.1111/gcb.15962">2022</a>) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: <a href="https://zenodo.org/records/14020914">https://zenodo.org/records/14020914</a>.</p> <p>Version 2024.11 includes a new data set that documents the location of the city locations in <strong>Köppen-Geiger climate zones</strong>. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from 30 arc-second raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a> </p> <p>Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database. One of these zones are the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">Whittaker biome types</a>, available as a polygon from the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">plotbiomes</a> package (see also <a href="https://www.davidzeleny.net/wiki/lib/exe/fetch.php/vegecol:materials:ricklefs_bioms_chapter_5.pdf">here</a>). Whittaker biome types were extracted with similar R scripts as described by <a href="https://rpubs.com/Roeland-KINDT/1275232">Kindt 2025</a> (these were also used to calculate environmental ranges of TreeGOER species, as archived <a href="https://zenodo.org/records/14908944">here</a>).</p> <p>Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. <a href="https://gmd.copernicus.org/articles/17/275/2024/">2024</a>). Also included was the elevation, obtained from the same WorldClim 2.1 raster layer used to prepare TreeGOER.</p> <p> </p> <p>As an alternative to CitiesGOER, the <strong>ClimateForecasts</strong> database (<a href="https://zenodo.org/records/10776414">https://zenodo.org/records/10776414</a>) documents the environmental conditions at the locations of 15,504 weather stations. ClimateForecasts was integrated in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees"><strong>GlobalUsefulNativeTrees</strong> database</a> (see <a href="https://doi.org/10.1038/s41598-023-39552-1">Kindt et al. 2023</a>).</p> <p> </p> <p>When using CitiesGOER in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. <a href="https://doi.org/10.5194/soil-7-217-2021">https://doi.org/10.5194/soil-7-217-2021</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> </ul> <p>When using information from the Holdridge Life Zones, also cite:</p> <ul> <li>Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a></li> </ul> <p>When using information from Köppen-Geiger climate zones, also cite:</p> <ul> <li>Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a></li> </ul> <p>When using information on the Whittaker biome types, also cite:</p> <ul> <li>Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.</li> <li>Whittaker, R. H. (1970). Communities and ecosystems.</li> <li>Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7145245">https://doi.org/10.5281/zenodo.7145245</a></li> </ul> <p>When using information on the steady state water table depth, also cite:</p> <ul> <li>Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. <a href="https://gmd.copernicus.org/articles/17/275/2024/">https://gmd.copernicus.org/articles/17/275/2024/</a></li> </ul> <p> </p> <p>The development of <strong>CitiesGOER</strong> was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative</strong> through the Royal Norwegian Embassy in Ethiopia to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, and by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project. Development of version 2024.10 was further supported by the <strong>Green Climate Fund</strong> through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em> project, by the <strong>Bezos Earth Fund</strong> to the <em>Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data Quality Products for GW Searches
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p>This release contains two data-quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data. Gating removes short-duration artifacts from the data by smoothly rolling the affected data to zero. The <a href="https://doi.org/10.1088/2632-2153/abab5f">iDQ glitch likelihood</a> uses machine learning to predict the probability that a non-Gaussian transient is present using information from auxiliary channels.</p> <p><br> <strong>Gating files used in analyses of O3 LIGO data</strong></p> <p>As a pre-processing step, the <a href="https://pycbc.org/">PyCBC</a> search pipeline uses an inverted-Tukey window to mitigate the effect of loud, non-Gaussian features in the data. This is further described in <a href="https://dx.doi.org/10.1088/0264-9381/33/21/215004">Usman <em>et al.</em> 2016</a>.</p> <p>A subset of these times are the times listed in the txt files</p> <ul> <li>H1-O3_GATES_1238166018-31197600.txt</li> <li>L1-O3_GATES_1238166018-31197600.txt</li> </ul> <p>These times in these files were chosen based on auxiliary monitors of overflows in the digital-to-analog converters used to control the positions of the test masses. The gated times (i.e. the time period where the data is zeroed) are time segments where these monitors recorded an overflow were. The central time and suggested half-width of zero time were chosen to fully cover these time seconds. The final gating parameter, the suggested taper time was chosen to be 0.5 to balance the cost of impacting more data with the window function versus introducing additional artifacts into the data.</p> <p>The syntax of the files themselves is</p> <p>{central time} {suggested half-width of zero time} {suggested taper time}</p> <p>with each row containing the parameters of a single gate.</p> <p>The included notebook provides an example of how to read in and apply one of the suggested gates.</p> <p><br> <strong>Renormalized iDQ timeseries</strong></p> <p>The renormalized iDQ timeseries data-quality product is used within the GstLAL search pipeline to generate results for GWTC-3. This data product was found to be statistically helpful in improving data quality within the <a href="https://lscsoft.docs.ligo.org/gstlal/">GstLAL</a> search pipeline. This is further described in <a href="https://arxiv.org/abs/2010.15282">Godwin <em>et al</em>. 2020</a>.</p> <p>This file contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in analyzed auxiliary channels monitoring the behavior of the detectors and their environment.</p> <ul> <li>H1L1-IDQ_TIMESERIES-1256655642-12905976.h5</li> </ul> <p>The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in <a href="http://doi.org/10.1088/2632-2153/abab5f">Godwin <em>et al</em>. 2020</a>, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset.</p> <p> </p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5636795 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data quality, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>. </p>
Odour observations from Odourcollect app (D-NOSES)
<p>This is the anonymized dump (28 November 2021) of the OdourCollect tool from the D-NOSES project ( Grant Agreement No 789315. ). There are 10332 Odour Observations from volunteers, including type and subtype of odour.</p> <p>The OdourCollect tool is provide as web-page: <a href="https://odourcollect.eu/">https://odourcollect.eu/ </a>and as <a href="https://play.google.com/store/apps/details?id=es.nobone.manchesterwebapp&hl=en_US">android</a> and <a href="https://apps.apple.com/mx/app/odourcollect/id1457119732">iphone</a> apps.</p> <p>Find out more about the project in the main webpage: <a href="http://dnoses.eu">http://dnoses.eu</a></p>
Southern South Island, New Zealand, seismic observations and velocity model for Tectonics 2021TC007006
<p>This archive has data and results from the Tectonics paper, “The influence of basement terranes on tectonic deformation: joint earthquake travel-time and ambient noise tomography of the southern South Island, New Zealand” (Eberhart-Phillips et al., 2022; doi:10.1029/2021TC007006). That work incorporated earthquake observations from the 2014-2015 Otago temporary broadband network. Group velocity observations are in ‘groupvel_obs_otaf_T5_T9.vgsw’. We used earthquakes observed on the COSA temporary network. The Otago-COSA travel-times and hypocenters are in ‘tt_archive.tar’. The Otago earthquakes were evaluated within Seiscomp. Those observation files, which include polarity, are in ‘polarity_bulletin.tar’. We did not use the polarities in the Tectonics paper, but we archive them here for completeness. The waveform data will be available from IRIS.</p> <p>The southern South Island 3-D velocity model is provided in the table ‘vlotf30xyzltlnDWSSF.mod’, with DWS (derivative weight sum) describing the distribution of data, and SF (spread function) describing the resolution averaging. Later on, this will be merged into the New Zealand wide velocity model version 2.3, which will be placed on Zenodo.</p>
Observation based gridded annual runoff estimates over Victoria, Australia
<p>The dataset provides observation-based interpolated gridded annual runoff estimates over Victoria, Australia during 1982 - 2012. The methodology extended the R package <em>rtop</em> to allow <em>top-kriging</em> with external drift by employing spatial variability of gridded rainfall estimates. This dataset can be useful to estimate runoff at ungauged or poorly gauged catchments in Victoria. The full paper with the methodology is available at https://mssanz.org.au/modsim2021/papers/K11/weligamage.pdf</p> <p> </p>
Poker Flat Incoherent Scatter Radar (PFISR) Observations of E-region Neutral Winds
<p>Updated: 12-15-2021</p> <p><strong>RULES OF THE ROAD:</strong></p> <p>You are welcome to use the data 'as is', however, please inform me via email if you plan to use the dataset. There are a number of small issues with the dataset that are best discussed. We are interested in publications that use the data and derived values that are presented within the dataset. <strong>If you plan to publish these results, please circulate a draft by me (SRK) and we would appreciate an offer of co-authorship or at minimum an acknowledgement. You should include the NSF funding numbers NSF AGS - 1853408</strong></p> <p> </p> <p>As a general warning, the data from PFISR are quite noisy and you may need to perform significant averaging to produce usable results. Again, please contact me and we can discuss this in more detail.</p> <p>Version v0.6.4.2021.07.12 - This was the final processed version at the time that the final report was submitted to the NSF.</p> <p> </p> <p><strong>--------------- Previous from before ------------------</strong></p> <p>This file contains Poker Flat Incoherent Scatter Radar (PFISR) E-region Neutral Winds Data. These data correspond to monthly data files that include the E-region neutral winds and other parameters for the from March 2013-June 2019.</p> <p><strong>Publications of the Joule Heating Results:</strong></p> <p>https://doi.org/10.1029/2021JA029371</p> <p>https://doi.org/10.1029/2021JA029719</p> <p> </p> <p><strong>Publication of Neutral Wind Results:</strong></p> <p>Hopefully we will have something in 2021. </p> <p> </p> <p><strong>RAW ISR Data:</strong> These data were processed from the following files found in: https://data.amisr.com/database/tmp/Kaeppler/winds/ and https://data.amisr.com/database/tmp/Kaeppler/missing_IPY.tar.gz Please note that the error on the line of sight velocities may have been overestimated in these data and we scaled them by a eVLOS/sqrt(10). Interested persons should contact Ashton Reimer or Roger Varney at SRI International for more information about these data, please see amisr.com</p> <p>Truthfully, the ISR data should eventually be reprocessed and then the winds algorithm run over it again. This is a step for future work.</p> <p> </p> <p><strong>Processing Code is available upon request via email.</strong></p> <p> </p> <p><strong>File Documentation:</strong></p> <p> </p> <p><strong>Please see the change log:</strong></p> <p>Purpose: This is the overarching program and functions which process the<br> E region neutral winds from the fitted AC and LP data from PFISR.<br> This is a conversion fo process_eregwinds_srk.py which was originally written by<br> Nicolls into a more formal python class structure.</p> <p>2017-10-05 - v0.2</p> <p>The ProcessEregionNeutralWinds.py file has been validated against process_eregwinds_srk.py<br> using 20161121.001_ac_3min-fitcal.h5, 20170301.013_ac_3min-fitcal.h5, 20170302.001_ac_3min-fitcal.h5.<br> The program to run these is ComparePrograms.py. At this point these program match.<br> I am going to start diverging the code base, first subtly in the Joule Heating<br> since I found that Mike just looped over Nbeams, which isn't quite right, you need to loop<br> over the beams that were selected.</p> <p>Changes from this point forward will produce different results.</p> <p>2017-10-10 - v0.3.2017.10.10</p> <p>Version v0.3, I made some IO changes but I may start processing some data with this version.</p> <p>Version v0.4 - lots of small edits made to the IO and the plotting software. It all seems to work<br> I have also included the SNR and Ne into the monthly plots and other information.<br> Made processing smoother.</p> <p>03 13 2018 - added solar local time converion</p> <p>v0.4.1 - 09 08 2018 added some ability to extract out the raw electron and SNR densities for each altitude bin<br> v0.4.2 - 10 15 2018 added in obtaining the F-region flows - want to check against the electric field.<br> v0.4.3 - 10 29 2018 added in some more altitude into the Joule Heating so I can make better figures<br> v0.4.4 - 11 20 2018 made some pretty major changes to IO to include consistent calculation of<br> Pedersen conductivity from FastConductivity.py. Made some changes to the Joule heating calculation and checked<br> formulas. It is worth checking again.</p> <p>v0.4.5 - 11 20 2018: added in Hall and Pedersen conductivities from fitted electron density data.<br> v0.4.6 - 12 03 2018: Tried to fix some of the double counting and time problems in testMakeMonthlyh5</p> <p>05 22 2019: added some statements to bypass the geophysical parameters. Also need in config file now.<br> Additionally wrote in IOEregionwinds a try except statement</p> <p>07 29 2019: Running the code for the 06 data reprocessed by Ashton</p> <p>v0.5.0 - 10-15-2019: put in some filtering on the LOS velocity discharging bad Chi square and bad error codes on the fit.</p> <p>v0.5.1 - 10-23-2019: changed chi square to 0.01 for lower boundary</p> <p>v0.5.3 - 12-02-2019: Added in that now passing in the Chi2 and Fitcode filtering by Config file<br> Bigger change that I am scaling the AC dVlos by some sort of factor while Ashton figures this out.<br> We decided that a conversative scaling would be to reduce the dVLOS by 1/sqrt(10).<br> The chi square produced in the data Ashton sent me typically was around 0.01, so the uncertaintiies on the LOS velocities<br> may be over estimated. So we are just changing this as a temporary fix while Ashton fixes the uncertainty estimation.</p> <p>v0.5.5 02 01 2020 - Added in calculation of Coriolis, Centrifugal, and Lorentz forcing<br> v0.5.5 02 10 2020 - Added a correction to qvert so that way I can calculate the lorentz term.<br> Found an error where qvert = 0 in the if statement goes to false.</p> <p>v0.5.5 02 15 2020 - Put in nuInscaler into the main program, scaling ALL kappas by the scaler number</p> <p>v0.5.6 02 28 2020 -- Added some more vlos diagostics and the calculation of the scale height. Added Altitude offset</p> <p>v0.5.6.2020.03.12_nuin_fracoff - testing putting in the Brekke formula for ion neutral collision frequency and took out frac</p> <p>v0.5.7.2020.04.10 - Put in Ashton's revised ion neutral collision frequency formulas into IO.<br> Also wrote a testscript and at least for the file I used was only different by 2.5%.<br> Revised where the mag data is being pulled from since the URL is deprecated<br> Added in Kappa which is now being interpolate - plan to see where kappa =1 is located for the paper.<br> commented out nuin scaler just so I am not chasing my tail</p> <p> test v0.5.7.2020.04.13_org commented back in original ion neutral collision frequency method<br> possible mistake that not summing up properly.</p> <p> test v0.5.7.2020.04.13_newnuin_orgsum_noTr800 - new formula for nuin except took off Tr>800.<br> I expect this should be almost the same as before since the formulas are basically the same.<br> did the original sum using frac[0] and frac[1] want to see if I am underestimating</p> <p> v0.5.7.2020.04.13_newnuin_orgsum_yesTr800 - same as above except now including Tr>800.</p> <p> 'v0.5.7.2020.04.13_newnuin_newsum_noTr800' - using the new sum now and new col freq</p> <p> v0.5.7.2020.04.13_updatedorg - updated original uses original method but including the NO term</p> <p>v0.6.0.2020.04.15 -- Now think I have the new ion neutral collision frequency working and validated.<br> Found a mistake in how I was calculating the ion neutral collision frequency that<br> the fraction weight I was using only included the O+ and O2+ terms and not NO+<br> Turns out I was basically weighting by about 0.5, so I was effectively reducing the<br> ion neutral collision frequency by about a factor of 0.5 or less...<br> From this point forward need to start using any results from > v0.6<br> This revision has changed previous results signi</p> <p>v0.6.0.2020.04.21 -- updated to now include the temperature correction for the O2+</p> <p>v0.6.1.2020.04.23 -- made a number of changes to the geomagnetic files and reprocessed from CDAweb.<br> Wrote new code to be able to process the files from CDAweb in the new format.<br> Also changed the geomagnetic data files</p> <p>v0.6.1.2020.06.07 -- changed the generation of Monthly files to hopefully be in order now<br> Added in missingIPY files given to me by ashton, maybe improve data covarege<br> Some work going to need to be done to make sure that all of the 10, 15, and 20 minute data are there.</p> <p>v0.6.1.2020.06.15_Weijia -- Updated the data for Weijia's study in particular since we are missing a lot of IPY data for 02-04 2013 and 2014.</p> <p>'v0.6.2.2020.07.01' -- Updated the data with new IPY27 mode for 2013 and 2014 Ashton processed. Also now put in mechanical Joule heating term.<br> Put in the conductance and conductivity now too.</p> <p>v0.6.2.2020.07.30 -- Made some changes to IO since Weijia noticed the mechanical heating terms were missing from the monthly files.</p> <p>v0.6.3.2020.10.19 -- Tried to elimated all extra instance of nuinscaler, and also output that variable. Added in variables<br> To get the Ti, Tn, ion neutral collision frequency along the vertical beam for diagnostic purposes<br> included dVest for F-region plasma drifts for Rafael</p> <p>v0.6.4.2020.11.20 -- Extracted some more parameters including F107 and the Hall and Pedersen Drags</p> <p>v0.6.4.2021.07.21 -- Final Run of data for NSF project</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.