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1,477 results for “earth”
Data for "Two-phase mixture of iron-nickel-silicon alloys in the Earth's inner core"
<p>This file is the dataset used in the article "Two-phase mixture of iron-nickel-silicon alloys in the Earth's inner core", <em>Commun. Earth Environ.</em> <strong>2</strong>, 225 (2021). https://doi.org/10.1038/s43247-021-00298-1</p>
A Spherical Harmonic model of Earth's lithospheric magnetic field up to degree 1050
<p>This model describes the Earth's vector lithospheric (or crustal ) magnetic field to about 40-km spatial resolution. It is provided in the form of Spherical Harmonic (SH) Gauss coefficients using the norm commonly used in geomagnetism (the Schmidt normalization). The file is in (zipped) ascii with 4 columns: the SH degree, the SH order, the Gnm and the Hnm parameters. The model was obtain after selecting and processing magnetic field measurements from the German CHAMP and ESA Swarm satellites that were merged with worldwide near-surface scalar anomaly data compiled over decades. A description of the scientific procedure and the model assessments are described in a paper under publication at Geophysical Research Letters. Please consider citing the GRL paper: Thébault E., Hulot G., Langlais B., and Vigneron P., A Spherical Harmonic model of Earth’s lithospheric magnetic field up to degree 1050, Geophys. Res. Lett., 2021</p>
Mirror of data from NOAA U.S. Climate Reference Network for Research Computing in Earth Science
<p>This is a mirror of data from the NOAA U.S. Climate Reference Network (https://www.ncei.noaa.gov/products/land-based-station/us-climate-reference-network).</p> <p>It was created because outbound FTP access is not allowed from some cloud-based JupyterHub setups.</p>
Hydrothermohaline streamfunctions computed in EC-Earth
<p>NetCDF files containing hydrothermohaline streamfunctions computed from the ESM EC-Earth. The streamfunctions have been computed for the last 10 years of a historical run (1996-2005) and the last 10 years of a RCP 8.5 scenario run (2090-2100).</p>
Thermosphere Hough Mode Extensions (HMEs) for Solar Tides in Earth's Atmosphere
<p>The scientific basis for the atmospheric solar tide HME files contained here is described in Forbes et al. (2022). The directory "HME2022-Hough-UV functions-Tides" contains solutions to Laplace's Tidal Equation for the eastward(westward) propagating diurnal tides with zonal wavenumbers s = -1, -2, -3(+1, +2); the eastward(westward) propagating semidiurnal tides with zonal wavenumbers s = -1, -2, -3(+1, +2, +3, +4, +6); the zonally-symmetric (s = 0) diurnal and semidiurnal tides; and the westward-propagating terdiurnal tide with s = +3. In a widely-used abbreviated notation, these are, respectively: DE1, DE2, DE3, DW1, DW2; SE1, SE2, SE3, SW1, SW2, SW3, SW4, SW6; D0, S0; and TW3. These tides correspond to those measured in Earth's mesosphere and thermosphere. The directory "HME2022-output files-Tides" provides the amplitudes and phases (UT hour of maximum at 0 longitude) of eastward, southward, and vertical winds (m/s), temperatures (K), density perturbations relative to mean, and geopotential height (m) as a function of height (z, every ~4 km) and latitude (deg, every 3 deg), corresponding to several (2 to 7) HMEs for each of the aforementioned tides. Generally the number of HMEs corresponding to each tide is determined by the vertical wavelength Lz of each HME; HMEs with Lz < 30 km are excluded due to their inability to effectively penetrate into the thermosphere above 100 km altitude. The directory "HME_Legacy output files-Tides" includes a prior version of these HMEs that were used in several papers in the literature, but not publicly distributed. The data for each HME extend from pole to pole and from 0 to 400 km altitude. Each directory contains a README file containing further information on the data files.</p>
Parameters of dielectric properties and emissivity estimated for Earth arid areas.
<p>Dataset gives the parameters estimated from passive microwave observations of the dielectric properties and emissivities of arid areas at 10 to 89 GHz.</p>
Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output
<p>We produce a dataset that uses pattern scaling, a common method of emulating climate models. Our dataset is built on the Pangeo CMIP6 archive, which has the advantage that we don't need to actually download the climate model output. Here we demonstrate the utility of our dataset, called Pangeo-Enabled ESM Pattern Scaling (PEEPS). The dataset, which is encapsulated in a Jupyter notebook (and replicated in a Python file), is flexible and can be extended to multiple scenarios and multiple variables, as long as they are in the Pangeo-accessible archive.</p>
Dataset for "Review article: Earth's ice imbalance"
<p>##################################</p> <p>This archive contains source data to reproduce Figure 4 from Slater et al. 2021, "Review article: Earth’s ice imbalance"</p> <p><a href="https://tc.copernicus.org/articles/15/233/2021/">https://tc.copernicus.org/articles/15/233/2021/</a></p> <p>Centre for Polar Observation and Modelling, School of Earth and Environment, University of Leeds<br> Corresponding author: t.slater1@leeds.ac.uk</p> <p>##################################</p> <p>Time series of annual cumulative ice mass change are provided for each individual component between 1994 and 2017 in the following data files:</p> <p>--------------------------------------------------------------<br> AIS_cumul_1994_2017_annual.csv<br> --------------------------------------------------------------<br> Antarctic Ice Sheet</p> <p>--------------------------------------------------------------<br> Glacier_cumul_1994_2017_annual.csv<br> --------------------------------------------------------------</p> <p>Mountain glaciers</p> <p>--------------------------------------------------------------<br> GrIS_cumul_1994_2017_annual.csv<br> --------------------------------------------------------------</p> <p>Greenland Ice Sheet</p> <p>--------------------------------------------------------------<br> iceshelves_calving_cumul_1994_2017_annual.csv<br> --------------------------------------------------------------</p> <p>Antarctic ice shelf calving</p> <p>--------------------------------------------------------------<br> iceshelves_thinning_cumul_1994_2017_annual.csv<br> --------------------------------------------------------------</p> <p>Antarctic ice shelf thinning</p> <p>--------------------------------------------------------------<br> NH_seaice_cumul_1994_2017_annual.csv<br> --------------------------------------------------------------</p> <p>Sea ice in the Northern Hemisphere</p> <p>--------------------------------------------------------------<br> SH_seaice_cumul_1994_2017_annual.csv<br> --------------------------------------------------------------</p> <p>Sea ice in the Southern Hemisphere</p> <p>--------------------------------------------------------------</p> <p>Acknowledgements: </p> <p>We thank Axel Schweiger and Jinlun Zhang for their help with PIOMAS/GIOMAS data and Susheel Adusumilli for providing ice shelf thickness change data. We thank the editor, Xavier Fettweis, and two anonymous reviewers for their comments, which greatly improved the manuscript.</p>
Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows
<p>Title: <strong>Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows</strong> Author: Pamela A. Green (<a href="mailto:pg@pamelaagreen.com">pg@pamelaagreen.com</a>), Advanced Science Research Center, CUNY, New York, NY USA <a href="https://orcid.org/0009-0006-7803-8182">https://orcid.org/0009-0006-7803-8182</a></p> <p>The python Jupyter Notebook <strong>SafeJustEarthSysBnd_EstressCUNY-Griffith2022-23.ipynb</strong> and accompanying data sets represent spatial modelling for development of the safe and just surface water target for Working Group 3 of the Earth Commission for the Earth Commission Long Report and the "Safe and Just Earth Systems Boundaries" publication. The surface water target includes spatial modelling of the extent of global-scale hydrological alteration of environmental flows.</p> <p>All input datasets required to run the model are located under the <strong>ModelInput</strong> folder with raster data in zipped format to minimize space requirements. The code extracts the zipped files and then deletes the uncompressed files upon completion. All model outputs are located under the <strong>ModelOutput</strong> folder.</p> <p>Please reference the <strong>README.xlsx</strong> file for a full listing of the model input and output data files.</p>
Dataset for: The redlegged earth mite draft genome provides new insights into pesticide resistance evolution and demography in its invasive Australian range
<p>Data and analyses for Thia et al. "The redlegged earth mite draft genome provides new insights into pesticide resistance evolution and demography in its invasive Australian range" submitted to <em>Journal of Evolutionary Biology</em>.</p> <p>This repository comprises data and scripts used to replicate the analyses in this paper.</p> <p>The goals of this study were to: (1) assemble a draft reference genome for <em>Halotydeus destructor</em>; (2) perform a comparative analysis of acetylcholinesterase genes among different agricultural arthropod pests; (3) characterise the population genetic patterns among Australian <em>H. destructor</em> populations; and (4) perform demographic modelling to understand the evolutionary relationships between eastern and western populations of <em>H. destructor</em> in Australia.</p>
Scanned data in support of "Paleomagnetic Study of Antarctic Deep-Sea Cores: Paleomagnetic study of sediments in a revolutionary method of dating events in Earth's History"
<p>Data files contain paleomagnetic data in an archival format, scanned into PDF documents. Data files contain 11 columns and include:</p> <p>Column 1) Sample identifier with cruise, core, and sample depth</p> <p>Column 2) Demagnetization field or temperature</p> <p>Column 3) Sample Magnetization</p> <p>Column 8) Declination</p> <p>Column 9) Inclination</p> <p> </p> <p>PDFs include paleomagnetic data for:</p> <p>V16-132</p> <p>V16-133</p> <p>V16-134</p> <p>V18-72</p> <p>V16-57</p> <p>V16-60</p> <p>V16-66</p>
Dataset for: Ace and ace-like genes of invasive redlegged earth mite: Copy number variation, target-site mutations, and their associations with organophosphate insensitivity
<p class="MsoNormal">This repository contains the scripts and data required to replicate the analyses in Thia et al.'s, "Evolution of an acetylcholinesterase<em> </em>gene complex and its contribution toward organophosphate insensitivity in an invasive mite pest", submitted to <em>Pest Management Science</em>.</p> <p class="MsoNormal">In this work, Thia et al. use a combination of experimental selection and pool-seq genomic analyses to understand the genetic mechanisms underpinning organophosphate insensitivity in the redlegged earth mite, <em>Halotydeus destructor</em>. There is a special emphasis on disentangling the roles of copy number variation and target-site mutations in the acetylcholinesterase genes, <em>ace,</em> and radiated <em>ace</em>-like genes<span>.</span></p> <p class="MsoNormal">There are three major analyses: (1) an F<sub>ST</sub> genome scan to identify outlier loci between alive (insensitive) and dead (sensitive) mites; (2) an analysis of <em>ace </em>copy number variation between alive and dead mites; and (3) an analysis of candidate target-site mutations in the <em>ace</em> gene.</p>
Files associated with Christopher Holder and Anand Gnanadesikan, How well do Earth System Models capture apparent relationships between phytoplankton biomass and environmental variables? [Version 1]
<p><strong>1. process_cmip_rf.m</strong> is a matlab script that reads a single file, generates a random forest using the parameters in the associated paper and computes permutation importance and sensitivities. Note- in order to get process_cmip_rf.m to work as written you must have the Statistics and Machine Learning toolbox installed on Matlab and download the table_modis.asc file below. </p> <p>Files 2-16 are tabular filew containing all datapoints used in Random Forest analysis for the NCAR CESM2 model. Columns are</p> <p> 1. Index of point, enabling a mapping back to the model grid if the resolution is known.</p> <p> 2. Longitude</p> <p> 3. Latitude</p> <p> 4. Month</p> <p> 5. Iron in mol/m<sup>3</sup>.</p> <p> 6. Mixed layer in m.</p> <p> 7. Ammonia in mol/m<sup>3</sup></p> <p> 8. Nitrate in mol/m<sup>3</sup>.</p> <p> 9. Phytoplankton carbon in mol/m<sup>3</sup>.</p> <p> 10. Phosphate in mol/m<sup>3</sup>.</p> <p> 11. Shortwave radiation (net solar radiation at ocean surface in W/m<sup>2</sup>).</p> <p> 12. Silicate in mol/m<sup>3</sup>.</p> <p> 13. Salinity in PSU</p> <p> 14. Temperature in C.</p> <p> 15. Upwelling velocity in m/s.</p> <p>If variable is not included in the dataset, the column will be filled with zeros.</p> <p><strong>2.table_cesm2.asc:</strong> Data created from Danabasoglu, G., 2019, NCAR CESM model output prepared for CMIP6 CMIP esm-pi-control <a href="http://doi.org/10.22033/ESGF/CMIP6.7579">http://doi.org/10.22033/ESGF/CMIP6.7579</a>. Grid is 360x180x12</p> <p><strong>3.table_cems2_fv2.asc:</strong> Data created from Danabasoglu, G., 2019, NCAR CESM-FV2 model output prepared for CMIP6 CMIP pi-control <a href="http://doi.org/10.22033/ESGF/CMIP6.11301">http://doi.org/10.22033/ESGF/CMIP6.11301</a>. Grid is 360x180x12</p> <p><strong>4. table_cesm2_waccm.asc: </strong>Data created from Danabasoglu, G., 2019, NCAR CESM2-WACCM model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.10094">http://doi.org/10.22033/ESGF/CMIP6.10094</a>. Grid is 360x180x12</p> <p><strong>5. table_cesm2_waccm_fv2.asc</strong>: Data created from Danabasoglu, G., 2019, NCAR CESM-WACCM-FV2 model output prepared for CMIP CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.11302">http://doi.org/10.22033/ESGF/CMIP6.11302</a>. Grid is 360x180x12</p> <p><strong>6. table_gfdl_cm4.asc</strong>: Data created from Guo, Huan; John, Jasmin G; Blanton, Chris et al,2018, NOAA-GFDL GFDL-CM4 model output piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8666">http://doi.org/10.22033/ESGF/CMIP6.8666</a>. Grid is 360x180x12</p> <p><strong>7.table_gfdl_esm4.asc</strong> Data created from Krasting, John P.; John, Jasmin G; Blanton, Chris et al., 2018, NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8669">http://doi.org/10.22033/ESGF/CMIP6.8669</a>. Grid 360x180x12</p> <p><strong>8. table_ipsl_cm5a2_inca.asc:</strong> Data created from Boucher, Olivier; Denvil, Sébastien; Levavasseur, Guillaume et al.: 2021, IPSL IPSL-CM5A2-INCA model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.13683">http://doi.org/10.22033/ESGF/CMIP6.13683</a>. Grid is 182x149x12</p> <p><strong>9.</strong> <strong>table_ipsl_cm6a_lr.asc:</strong> Data created from Boucher, Olivier; Denvil, Sébastien; Levavasseur, Guillaume et al., 2018:, IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.5251">http://doi.org/10.22033/ESGF/CMIP6.5251</a>. Grid is 362x332x12.</p> <p><strong>10</strong>. <strong>table_mpi_esm1-2-ham.asc:</strong> Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe et al., 2019: HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.5037">http://doi.org/10.22033/ESGF/CMIP6.5037</a>. Grid is 256x220x12.</p> <p><strong>11</strong>. <strong>table_mpi_esm1-2-hr.asc:</strong> Data created from Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann et al., 2019: MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.6674">http://doi.org/10.22033/ESGF/CMIP6.6674</a>. Grid is 802x404x12.</p> <p><strong>12</strong>. <strong>table_mpi_esm1-2-lr.asc:</strong> Data created from Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann et al. 2019:MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP piControl</p> <p> <a href="http://doi.org/10.22033/ESGF/CMIP6.6675">http://doi.org/10.22033/ESGF/CMIP6.6675</a>. Grid is 256x220x12.</p> <p><strong>13. </strong><strong>table_noresm2-lm.asc: </strong>Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo et al.,2019 NCC NorESM2-LM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8217">http://doi.org/10.22033/ESGF/CMIP6.8217</a>. Grid is 360x385x12</p> <p><strong>14.</strong><strong> table_noresm2-mm.asc</strong>: Data created from Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind et al.,2019 <strong>:</strong> NCC NorESM2-MM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8221">http://doi.org/10.22033/ESGF/CMIP6.8221</a>. Grid is 360x385x12.</p> <p>15-16. <strong>table_kostadinov.asc, </strong><strong>table_modis.asc</strong> Data is a merger of observational products and model output Observational climatologies for temperature, salinity, mixed layer depth, silicate, phosphate, and nitrate were downloaded from the World Ocean Atlas (WOA) 2018 (Garcia et al., 2019; Locarnini et al., 2019; Zweng et al., 2019). MODIS-POC was downloaded from oceancolor.nasa.gov. Kostadinov POC is taken from <a href="https://doi.pangaea.de/10.1594/PANGAEA.859005">https://doi.org/10.1594/PANGAEA.859005</a> Grid is 360x180x12.</p>
The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections
<p>These files are associated with the article "The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections". </p> <p>1. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/soil_hyper_albedo_RF_int.nc">soil_hyper_albedo_RF_int.nc</a> - hyperspectral soil albedo </p> <p>2. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/lai_hyper_albedo_RF_int.nc">lai_hyper_albedo_RF_int.nc</a> - hyperspectral surface albedo</p> <p>3. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_bl ...</a> - diagnostic results of the atmospheric model CAM between broadband and hyperspectral simulations. </p> <p>4. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_dif ...</a> - diagnostic results of the land model CLM between broadband and hyperspectral simulations. </p>
Data for: Higher water loss on Earth-like exoplanets in eccentric orbits
<p>The climate of a terrestrial exoplanet is controlled by the type of host star, the orbital configuration and the characteristics of the atmosphere and the surface. Many rocky exoplanets have higher eccentricities than those in the Solar System, and about 18% of planets with masses < 10 M⊕ have 𝑒 > 0.1. Underexplored are the implications of such high eccentricities on the atmosphere, climate, and potential habitability on such planets. We use WACCM6, a state-of-the-art fully-coupled Earth-system model, to simulate the climates of two Earth-like planets; one in a circular orbit (𝑒 = 0), and one in an eccentric orbit (𝑒 = 0.4). We quantify the effects of eccentricity on the atmospheric water abundance and loss given the importance of liquid water for habitability. The asymmetric temperature response in the eccentric orbit results in a water vapour mixing ratio in the stratosphere (> 20 ppmv) that is approximately five times greater than that for circular orbit (∼ 4 ppmv). This leads to a ∼ 3 time increase in the atmospheric hydrogen loss rate and a corresponding ∼ 3 times decrease in the ocean loss timescale. Thus, highly-eccentric Earth-like exoplanets can still retain their oceans over the lifetime of the system. Using the Planetary Spectrum Generator, we simulate the idealised transmission spectra for both cases. We find that the water absorption features are stronger at all wavelengths for the 𝑒 = 0.4 spectrum than for the circular case. Hence, highly-eccentric Earth-like exoplanets may be prime targets for future transmission spectroscopy observations to confirm, or otherwise, the presence of atmospheric water vapour.</p>
Data and scripts to generate all figures for Communications Earth and Environment paper COMMSENV-21-0361D
<p>In this data set, it includes all the data and NCL scripts which are necessary to generate all figures used in the manuscript COMMSENV-21-0361D.E</p>
Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Earth Syst. Dynam.</i>, 2023). For details see the README.md file.</p>
Benthic d18O records Earth's energy imbalance
<p>Data include previously published data that were used to produce reconstructions of global energy anomalies and Earth's energy imbalance, as well as the aforementioned reconstructions. Previously published data include 1) a global reconstruction of benthic d18O for the last 150,000 years, 2) ice core noble gas - based reconstructions of mean ocean temperature and the atmospheric noble gas ratios used for these reconstructions for the last 25,000 years, and 3) two eustatic sea level reconstructions from coral and tidal indicators combined with glacio-isostatic adjustment models for the last 25,000 years. From these previously published data we derive 1) the estimated contributions of temperature (or the equilibrium isotope effect) and ice volume (or d18O of seawater) to the total benthic d18O signal, 2) reconstructions of global energy change, and 3) reconstructions of Earth's energy imbalance.</p>
Earth's transit spectrum from Macdonald and Cowan (2019)
<p>Earth's transit spectrum from Macdonald and Cowan (2019), constructed from Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) solar occultation data (Hughes et al. 2014). The Atmospheric Chemistry Experiment (ACE), also known as SCISAT, is a Canadian-led mission mainly supported by the Canadian Space Agency. The occultation data used are available at http://www.ace.uwaterloo.ca/atlas.php. </p> <p> </p>
Supplementary material for Dynamics of rare earth elements and associated major and trace elements during Douglas-fir (Pseudotsuga menziesii) and European beech (Fagus sylvatica L.) litter degradation
<p>Database and figures related to the results that are presented in the manusciript Montemagno et al. that will be submitted as research paper in the journal Copernicus/Biogeosciences.</p> <p>Title of the submission: Dynamics of Rare Earth Elements and associated major cations during Douglas-fir (Pseudotsuga menziesii) and European beech (Fagus sylvatica L.) litter degradation.</p> <p>Authorship: </p> <p>Alessandro Montemagno<sup>1,3</sup>, Christophe Hissler<sup>1</sup>, Johanna Ziebel<sup>2</sup>, Victor Bense<sup>3</sup>, Laurent Pfister<sup>1</sup>, Adriaan J. Teuling<sup>3</sup></p> <p><sup>1</sup>CATchment and ecohydrology research group (CAT/ENVISION/ERIN), Luxembourg Institute of Science and Technology, Belvaux, 4408, Luxembourg</p> <p><sup>2</sup>Biotechnologies and Environmental Analytics Platform (BEAP/ERIN), Luxembourg Institute of Science and Technology, Belvaux, 4408, Luxembourg</p> <p><sup>3</sup>Department of Environmental Sciences, subdivision Hydrology and Quantitative Water Management (HWQM), Wageningen University and Research, Droevendaalsesteeg 4, Wageningen, 6708 PB, The Netherlands</p> <p><em>Correspondence: </em>Alessandro Montemagno (<a href="mailto:alessandro.montemagno@list.lu">alessandro.montemagno@list.lu</a>), Christophe Hissler (<a href="mailto:christophe.hissler@list.lu">christophe.hissler@list.lu</a>)</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.