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134 results for “global estimates”
Estimating global transpiration from TROPOMI SIF with angular normalization and separation for sunlit and shaded leaves
<p>All three types of SIF-driven T models integrate canopy conductance (gc) with the Penman-Monteith model, differing in how gc is derived: from a SIFobs driven semi-mechanistic equation, a SIFsunlit and SIFshaded driven semi-mechanistic equation, and a SIFsunlit and SIFshaded driven machine learning model. </p> <p>The difference between a simplified SIF-gc equation and a SIF-gc equation is the treatment of some parameters and is shown in <a href="https://doi.org/10.1016/j.rse.2024.114586" rel="noreferrer">https://doi.org/10.1016/j.rse.2024.114586</a>.</p> <p>In this dataset, the temporal resolution is 1 day, and the spatial resolution is 0.2 degree.</p> <p>BL: SIFobs driven semi-mechanistic model</p> <p>TL: SIFsunlit and SIFshaded driven semi-mechanistic model</p> <p>hybrid models: SIFsunlit and SIFshaded driven machine learning model.</p>
Synthetic data (Part 2) for HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the rendered images and the segmentation masks that we use to train our model on HO3Dv2 dataset. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the rendered images for HO3Dv2.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed SDF samples. Meanwhile, we also include rendered data for HO3Dv2 here. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 rendered images.</div> <div>├── <a href="../api/records/13228003/draft/files/train_ho3d.zip/content" target="_blank" rel="noopener noreferrer">train_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 training set.</div> <div>├── <a href="../api/records/13228003/draft/files/full_test_dexycb.zip/content" target="_blank" rel="noopener noreferrer">full_test_dexycb.zip</a> - Contains the processed SDF files for DexYCB full test set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a></p> <p>Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a></p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a> - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a> - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a> - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a> - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Global estimates of marine gross primary production based on machine‐learning upscaling of field observations
<p>4 variables (excluding dimension variables):</p> <p>double GPP_LD_MLD_RF[Lon,Lat,Month] <br> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly mixed-layer integration of gross primary production trained from the<br> dataset determined by the light-dark bottle incubation using Random Forest<br> algorithm<br> coordinates: [Longitude, Latitude Month]</p> <p>double GPP_LD_ZEU_RF[Lon,Lat,Month] </p> <p> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly euphotic-zone integration of gross primary production trained from<br> the dataset determined by the light-dark bottle incubation using Random<br> Forest algorithm<br> coordinates: [Longitude, Latitude Month]</p> <p>double GPP_Triple_MLD_RF[Lon,Lat,Month] <br> units: mmol mmol O2 m-2 d-1<br> fillvalue: NaN<br> long_name: Monthly mixed-layer integration of gross primary production trained from<br> the dataset determined by the triple isotopes of dissolved oxygen using<br> Random Forest algorithm<br> coordinates: [Longitude, Latitude Month]<br> <br> double GPP_Triple_ZEU_RF[Lon,Lat,Month] <br> units: mmol O2 m-2 d-1<br> fill value: NaN<br> long_name: Monthly euphotic-zone integration of gross primary production trained from<br> the dataset determined by the triple isotopes of dissolved oxygen using<br> Random Forest algorithm</p> <p>3 dimensions:</p> <p> Lon Size:181<br> units: degree_north<br> long_name: Longitude</p> <p> Lat Size:91<br> units: degree_east<br> long_name: Latitude</p> <p> Month Size:13<br> units: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Annuual_mean<br> long_name: Month</p> <p><br> Author: Yibin Huang & Nicolas Cassar<br> Correspond: nicolas.cassar@duke.edu<br> <br> Request_for_citation: If you use these data in publications or presentations, please cite: Huang,<br> Y., Nicholson, D., Huang, B., & Cassar, N. (2021). Global estimates of<br> marine gross primary production based on machine‐learning upscaling of<br> field observations. Global Biogeochemical Cycles, 35, e2020GB006718.<br> https://doi.org/10.1029/2020GB006718<br> <br> Creation date: Dec/6th/2021</p>
Incorporating plant access to groundwater in existing global, satellite-based evaporation estimates
<p>This repository contains data used in the paper "Incorporating plant access to groundwater in existing global, satellite-based evaporation estimates".</p> <p>This repository includes the following netcdf files: 1) daily evaporation based on GLEAM-Hydro [mm/d], 2) daily evaporation based on GLEAM v3 [mm/d], 3) annual-mean groundwater-sourced evaporation (E_GW) [mm/year], and 4) temporally averaged groundwater contribution fraction (f_GW) [-].</p>
Key dataset used in the paper of "Emergent constraints reveal lower estimates of global river flow"
<p>This dataset includes key data used in the emergent constraint approach for refined partitioning of global water cycle components. </p>
Global nitrous oxide fluxes estimated using atmospheric inversions
<p>Nitrous oxide emissions are presented from three independent atmospheric inversion frameworks. The frameworks are: 1) INVICAT: an inversion using the atmospheric transport model, TOMCAT and a 4D-Var optimisation method; 2) JAMSTEC: an inversion using the MIROC4-ACTM atmospheric transport model and a Bayesian analytical optimisation method; and 3) PYVAR: an inversion using the LMDZ5 atmospheric transport model and a 4D-var optimisation method. The emissions were optimised monthly and have been re-gridded from the model native resolution to 1.0 by 1.0 degrees. The files for TOMCAT and LMDZ5 (i.e. the inversion frameworks INVICAT and PYVAR, respectively) contain two flux variables: 1) the prior fluxes as estimated a priori, and 2) the posterior fluxes as estimated by the inversion. The file for the JAMSTEC inversion, contains five flux variables: 1) flux_apri_land: the prior fluxes over land, 2) flux_apri_ocean: the prior fluxes over ocean, 3) flux_apri_fossil: the prior estimate of emissions from combustion, 4) flux_apos_land: posterior fluxes over land estimated by the inversion, and 5) flux_apos_ocean: the posterior fluxes over ocean estimated by the inversion. Note that flux_apri_fossil was not optimised in the inversion but for the total posterior N<sub>2</sub>O emission, needs to be added to the flux_apos_ocean and flux_apos_land variables.</p>
Global canopy top height estimates from GEDI LIDAR waveforms for 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data from April-July 2020. The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data.</p> <p>See also the repository for the data from April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a>. This repository also contains the file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>with more information on how to parse and load the prediction orbit files.</p> <p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p> <p><strong>Citation: </strong></p> <p>Use of these data require citation of this dataset:</p> <p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2020 (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7737869</p> <p>Original research article:</p> <p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <em>Remote Sensing of Environment</em>, <em>268</em>, 112760.</p>
Global continental discharge estimates from ocean mass balance
<p>These files include a time series of global continental discharge estimated from ocean mass balance following Chandanpurkar et al., 2017. </p> <p>The ocean mass balance is obtained from these components:</p> <p>dM/dt (change in ocean mass): From altimetry and from GRACE/FO. When derived from altimetry, steric level change is subtracted from the GMSL using EN4.2.2 temperature and salinity data. </p> <p>E-P: Here, two methods are used:</p> <p>1. Directly, using estimates of ocean E and P, using OAFlux for E and GPCP and CMAP separately for P</p> <p>2. Indirectly, using atmospheric moisture balance using vertically integrated horizontal moisture flux divergence, and change in the total column water vapor. These are obtained using ERA5 and MERRA-2 reanalyses products.</p> <p>The eight discharge estimates are combinations of the above, and the exact combination is mentioned in the filename. </p>
Scripts and datas for "Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance"
<p>Input and output datasets used for a global reconstruction of the eddy kinetic energy (EKE) dissipation rate in relation to a submitted work :</p> <p><strong>R. Torres, R. Waldman, J. Mak and R. Séférian </strong>: <em>Global Estimation of the Eddy Kinetic Energy Dissipation from a Diagnostic Energy Balance</em>.</p> <p>Inputs datas include a merge of 2 datasets from the World Ocean Atlas 2018 (WOA18, Garcia et al., 2019) and cover the 1995-2017 (95B7) period. Folder structure for the surface altimetry L4 datasets from the EU-Copernicus Marine Services (2021) is kept empty in order to limit the archive size. Datas can be download <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_L4_MY_008_047/services">here</a>.</p> <p>Optional datasets include CMEMS MDT product (<em>CMEMS/SEALEVEL_GLO_PHY_MDT_008_063/P20Y</em>) downloaded <a href="https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_MDT_008_063/">here</a> and ocean masks (<em>misc/basins/doi_10.5281</em>) from Martinez-Moreno et al. (2021).</p> <p>In addition, simulation outputs from the NEMO-OMIP2 model runned with the GEOMETRIC parameterization are processed (mainly time-averaged) and stored in <em>CNRM/runs/omip2_LR.Geom_Emin0-alpha01_1cyc-trd/post</em>. These files are used in the uncertainties and errors quantification.</p> <p>Outputs and published results are stored in each individual product post-processing folder while final EKE dissipation computation are located in the <em>EKE_dissipation_rate</em> folder since it results from a combination of multiple products.</p> <p>IPython notebooks for computing and plotting global maps are also provided :</p> <ul> <li><em>1-post_process_climato.ipynb</em> : compute from the climatology (e.g. WOA18 datas) the EKE dissipation timescales (units in days) and the surface modes with rough topography (LaCasce and Groeskamp, 2020).</li> <li><em>2-post_process_altimetry.ipynb</em> : compute from altimetry (CMEMS) datasets the EKE at surface and eventually coarsen the grid from 0.25 to 1 degree in order to match the climatology grid.</li> <li><em>3-compute_global_eke_dissipation.ipynb</em> : combine both outputs from the two above scripts to compute the global EKE dissipation. The script also plots new maps.</li> <li><em>0-plot_global_maps.ipynb</em> : plot the global maps for climatology and altimetry products.</li> <li><em>0-plot_lbekedis_ogcm.ipynb</em> : plot and analyse EKE timescale errors from the NEMO-OMIP2 simulation outputs.</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins
<p>This is the readme file for the ET-WB dataset described in the ESSD paper "ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins" from Xiong et al. (2023)<br> ET-WB dataset-The monthly water balance data from May 2002-December 2021 for the 168 river basins and global land from 23 precipitation, 29 runoff, and 7 terrestrial water storage changes datasets.<br> The five dimensions (236*169*23*7*29) of the matrix represent the time, regions, precipitation, terrestrial water storage changes, and runoff datasets used respectively.<br> ET-WB is distributed in three kind of formats: Mat (ET-WB.mat), NetCDF (ET_WB.nc), and Shapefile (ET_WB.shp) (only for the ensemble median value). All the formats share the same definitions of dimensions (as below), except for the ArcGIS shapefile that is provided for individual regions (168 river basins and global land excluding Antarctic and Greenland).<br> File shapefile.rar is the geospatial database of the study area that can be opened in ArcGIS software. </p> <p>Please find more details in the Readme file.</p>
FIGURE 3 in Inferring global species richness from megatransect data and undetected species estimates
FIGURE 3 Latitudinal distribution of currently valid species of Pholcidae. Numbers of Pholci- dae species (x-axis) known from different latitudes (y-axis; N, north; S, south), with the land mass distribution shown in grey (from www.ecoclimax.com; excluding Antarctica). Pholcidae species richness is slightly shifted towards the north, possibly as a result of the unbalanced land masses and/or taxonomists' biases, but most diversity is in tropical regions.
FIGURE 2 in Inferring global species richness from megatransect data and undetected species estimates
FIGURE 2 Cumulative percentages of new species (y-axis) as a function of cumulative field days (x-axis) (left) and cumulative number of total (upper/blue line) and new (lower/red line) species (y-axis) as a function of culumative field days (x-axis) (right), for the three major tropical megatransects shown on the map. Each green dot represents a sampling locality. For raw data of all geographic regions, see supplementary tables S1–S5.
FIGURE 1 in Inferring global species richness from megatransect data and undetected species estimates
FIGURE 1 Cumulative curve of currently valid species of Pholcidae (y-axis) as a function of time (x-axis). The curve suggests that we are far from approaching a complete taxonomic knowledge of the family.
A data-driven supervised machine learning approach to estimating global ambient air pollution concentrations with associated prediction intervals
Open the record for dataset details and reuse information.
Collected data on early estimates of global fossil CO2 emissions
<p>This dataset collects together a number of early estimates made of global emissions of fossil CO2, starting with Arvid Högbom in 1894. Microsoft Excel files for original data sources where these are time series, including images of the tables from the original sources, in addition to one CSV file of CO2 emissions from all sources. Sources that only reported emissions for a short period are not included in the Excel collection, but are included in the CSV file. In addition the original United Nations energy data used by several sources is included as UN1956.xlsx.</p>
Data from: Bayesian estimation of the global biogeographical history of the Solanaceae
Aim: The tomato family Solanaceae is distributed on all major continents except Antarctica and has its centre of diversity in South America. Its worldwide distribution suggests multiple long-distance dispersals within and between the New and Old Worlds. Here, we apply maximum likelihood (ML) methods and newly developed biogeographical stochastic mapping (BSM) to infer the ancestral range of the family and to estimate the frequency of dispersal and vicariance events resulting in its present-day distribution. Location: Worldwide. Methods: Building on a recently inferred megaphylogeny of Solanaceae, we conducted ML model fitting of a range of biogeographical models with the program 'BioGeoBEARS'. We used the parameters from the best fitting model to estimate ancestral range probabilities and conduct stochastic mapping, from which we estimated the number and type of biogeographical events. Results: Our best model supported South America as the ancestral area for the Solanaceae and its major clades. The BSM analyses showed that dispersal events, particularly range expansions, are the principal mode by which members of the family have spread beyond South America. Main conclusions: For Solanaceae, South America is not only the family's current centre of diversity but also its ancestral range, and dispersal was the principal driver of range evolution. The most common dispersal patterns involved range expansions from South America into North and Central America, while dispersal in the reverse direction was less common. This directionality may be due to the early build-up of species richness in South America, resulting in large pool of potential migrants. These results demonstrate the utility of BSM not only for estimating ancestral ranges but also in inferring the frequency, direction and timing of biogeographical events in a statistically rigorous framework.
Dataset for paper "Global method for gender profile estimation from distribution of first names"
<p>Full dataset for paper "<i>Global method for gender profile estimation from distribution of first names</i>". See https://arxiv.org/abs/2305.07587</p>
Afterslip Model Database RC2022 (Afterslip Moment Scaling and Variability from a Global Compilation of Estimates)
<p>Churchill2022AfterslipDatabase.xlsx is a detailed database of aseismic afterslip models and corresponding mainshock information compiled by Robert Churchill (under the supervision of Maximilian Werner, Juliet Biggs and Åke Fagereng). The database contains afterslip models of mainshocks since 1979, with a publication cut-off at the end of 2018. The database is near complete, but not exhaustive. Descriptions of each column can be found as comments in the header field, as well as in the accompanying paper. Not all fields are not complete, some are also approximate or inferred values.</p> <p>This accompanies the paper:</p> <p>Churchill, R.M., Werner, M.J., Biggs, J. and Fagereng, Å., 2022. Afterslip Moment Scaling and Variability from a Global Compilation of Estimates. <em>Journal of Geophysical Research: Solid Earth</em>, p.e2021JB023897. <a href="https://doi.org/10.1029/2021JB023897">https://doi.org/10.1029/2021JB023897</a>.</p> <p>We hope the database serves as a useful resource to the afterslip community. Please reference our associated paper when using this database, as well as the database itself. References for individual afterslip papers can be found on the second sheet of the database, and references for additional data used in our study can be found in the third sheet. In the future, the database may be updated to include additional (missed) studies, however, this first version accompanies our study.</p> <p>*Headers for columns U and V are mislabelled Afterslip Upper Depth Limit (km) and Afterslip Lower Depth Limit (km), when these should be Coseismic Slip Upper Depth Limit (km) and Coseismic Slip Lower Depth Limit (km). The data in these columns is otherwise correct.</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.