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708 results for “Global dataset”
Validation of a new global irrigation scheme in the ORCHIDEE land surface model - Dataset
<p>Datasets used in the paper 'Validation of a new global irrigation scheme in the ORCHIDEE land surface model', submitted to GMD</p>
WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Train part
<p>The training parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons.</p> <p>The validation and testing parts can be found on: <a href="https://doi.org/10.5281/zenodo.8035349">https://doi.org/10.5281/zenodo.8035349</a></p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1]J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373-389, Aug. 2020,<a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>.</p> <p>[2]J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>.</p> <p>[3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, <a href="http://10.1109/JSTARS.2022.3211857">http://doi.org/10.1109/JSTARS.2022.3211857</a>.</p>
Dataset for "Responses of Soil Greenhouse Gas Fluxes to Land Management in Forests and Grasslands : A Global Meta-analysis"
<p>This dataset archives the original data for the study "Responses of Soil Greenhouse Gases Fluxes to Land Management in Forests and Grasslands: A Global Meta-analysis".</p>
Disentangling coastal groundwater level dynamics in a global dataset - data
<p>Data to reproduce the research study "Disentangling coastal groundwater level dynamics in a global dataset" by Nolte et al. containing:</p> <ol> <li>cluster_indices.csv: well identifier; cluster from k-means; country code (ISO 3166-1 alpha-2); 45 index values</li> <li>cluster_attributes.csv: well identifier; cluster from k-means; country code (ISO 3166-1 alpha-2); 28 attribute values</li> </ol> <p>These data enable cluster analysis and random forest modeling. However, please note that the repository does not include raw groundwater level time series data.</p> <p>The code to reproduce the cluster analysis and random forest modeling as conducted in the study can be made available upon request from the first author.</p>
SPEI-GD: The first global multi-scale daily SPEI dataset for 1983-2020
<p>The global daily SPEI dataset (SEPI-GD) at 0.25° spatial resolution form 1982 to 2021. This depository includes the five files of the daily SPEI data with five time scales (5, 30, 90, 180, and 360 days). The calculation based on ERA5's precipitation and Singer's potential evapotranspiration. All data are geographic latitude-longitude projection and NetCDF format. See paper for detailed explanation: Liu X, Yu S, Yang Z, et al. The first global multi-timescale daily SPEI dataset from 1982 to 2021[J]. Scientific Data, 2024, 11(1): 223.</p>
GloUTCI-M: A Global Monthly 1 km Universal Thermal Climate Index Dataset from 2000 to 2022
<p>The GloUTCI-M comprises global monthly UTCI data at a spatial resolution of 1km, spanning from March 2000 to October 2022. The dataset is expressed in degrees Celsius (°C) and is stored as an integer type (Int16). To utilize it appropriately, one must divide the values by 100.</p>
Dataset for Global Reference Seismological Data Sets: Multimode Surface Wave Dispersion
<ul> <li><strong>How fast do surface waves travel globally after any earthquake?</strong></li> <li><strong>Do we get the same information from various measurement techniques?</strong></li> <li><strong>Which features in the Earth are robust and can be resolved by a reference model?</strong></li> </ul> <p>Reference data with uncertainties are useful for improving existing measurement techniques, validating models of interior structure, calculating teleseismic data corrections in local or multiscale investigations and developing a 3-D reference Earth model. This study was done in collaboration with 18 scientists from 16 institutions in 7 countries who actively participated in the <a href="http://rem3d.org">REM3D</a> project. The project assimilated, archived, reconciled and modeled big (>200 million measurements) and diverse <a href="https://globalseismology.princeton.edu/data/surface-waves">surface-wave datasets</a> for global subsurface structure.</p> <p>The reference data set summarizes measurements of dispersion of fundamental-mode surface waves and up to six overtone branches from 44,871 earthquakes recorded on 12,222 globally distributed seismographic stations. Dispersion curves are specified at a set of reference periods between 25 and 250 s to determine propagation-phase anomalies with respect to a reference Earth model. Empirically determined observational uncertainties (1 sigma) for each wave type, branch number and period can be found in Table 3. </p> <p><strong>Summary:</strong></p> <p><strong>[I]</strong> <strong>Reconciled large and diverse catalogues</strong> of Love-wave (49.65 million) and Rayleigh-wave dispersion (177.66 million) from eight groups worldwide.<br> <strong>[II]</strong> Retrieved missing station and earthquake <strong>metadata</strong> in several legacy compilations and codified <strong>scalable formats</strong> to facilitate reproducibility, easy storage and fast I/O on HPC systems.<br> <strong>[III]</strong> <strong>Systematic discrepancies </strong>between raw phase anomalies can be attributed to discrepant theoretical approximations, reference Earth models and processing schemes.<br> <strong>[IV]</strong> <strong>Phase-velocity variations</strong> yielded by the inversion of the summary data set are <strong>highly correlated</strong> (R ≥ 0.8) with those from the quality-controlled contributing data sets, especially for long-wavelength variations (up to degree ∼25) in fundamental-mode dispersion (50–100 s).<br> <strong>[IV]</strong> <strong>Only 2ζ azimuthal variations</strong> in phase velocity of <strong>fundamental-mode Rayleigh waves</strong> are <strong>required</strong>; maps of 2ζ azimuthal variations are highly consistent between catalogues ( R = 0.6–0.8).</p> <p><strong>Feedback/Questions?</strong> Please contact Raj Moulik (<a href="https://rajmoulik.com">rajmoulik.com</a>) at <a href="mailto:moulik@caa.columbia.edu?subject=Query%20from%20Zenodo">moulik@caa.columbia.edu</a> </p> <p><strong>Reference:</strong></p> <p><em>Please cite the following work if you use this data or software.</em></p> <ul> <li>Moulik, P. <em>et al., </em>(2022) Global reference seismological data sets: multimode surface wave dispersion. <em>Geophys J Int</em> <strong>228</strong>, 1808–1849, doi: <a href="https://doi.org/10.1093/gji/ggab418">10.1093/gji/ggab418</a>. <em><a href="https://rajmoulik.com/Publications/Moulik_Reference_Surface_Waves_GJI2022.pdf">pdf</a></em></li> </ul> <p><em>You can also cite the dataset and software from this Zenodo page (Optional).</em></p> <ul> <li> <p>Moulik, P. (2022) Dataset for Global Reference Seismological Data Sets: Multimode Surface Wave Dispersion. In Geophys. J. Int. (v1.0, Vol. 228, pp. 1808–1849). Zenodo. doi: <a href="https://doi.org/10.5281/zenodo.8371228">10.5281/zenodo.8371228</a></p> </li> </ul> <p><strong>HDF5 Container Format</strong></p> <ul> <li><strong>Reference Love waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Love.data.h5">Download All Periods and Branches as Summary.SW.Love.data.h5</a>)</li> <li><strong>Reference Rayleigh waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Rayl.data.h5">Download All Periods and Branches as Summary.SW.Rayl.data.h5</a>)</li> </ul> <p>Summary (reference) data between pairs of 2562 evenly-spaced knot points with an average spacing of 4.33◦. These files store the data in the RSDF HDF5 container format. These can be read using standard HDF5 modules (e.g. h5py) or using <a href="http://avni.globalseismology.org/">AVNI</a>. For example, to read the reference data for fundamental mode R1 waves at 100s into a Pandas Dataframe containing data (df['data']) and a dictionary with the metadata (df['metadata']), and thereafter write contents to an ASCII text file, enter the following in Python:</p> <ul> <li><em>from avni.data.SW import readSWhdf5,writeSWascii</em></li> <li><em>df=readSWhdf5(query='0/100.0/R1/REM3D',hdffile='Summary.SW.Rayl.data.h5',datatype='summary')</em></li> <li><em>writeSWascii(df,'test.txt')</em></li> </ul> <p><strong>ASCII (text) Format</strong></p> <p>These files contain the same reference data as the HDF5 files above but in gzipped ASCII files. The files are named according to the overtone branch, wave type and period as <em>Summary.$overtone.$wave.$period.REM3D.gz</em> Table A1 from the paper describes the various columns in the surface-wave RSDF ASCII format files.</p> <ul> <li><strong>Love waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Love.data.zip">Download All Periods and Branches as Summary.SW.Love.data.zip</a>) <ul> <li>Fundamental Modes <ul> <li>Minor Arc Arrivals (L1) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.25s.REM3D.gz">25s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.27s.REM3D.gz">27s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.30s.REM3D.gz">30s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.32s.REM3D.gz">32s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.35s.REM3D.gz">35s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.250s.REM3D.gz">250s</a></li> <li>Major Arc Arrivals (L2) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.250s.REM3D.gz">250s</a></li> <li>Higher Obit Arrivals - L3 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.250s.REM3D.gz">250s</a>; L4 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.250s.REM3D.gz">250s</a>; L5 at at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.250s.REM3D.gz">250s</a>.</li> </ul> </li> <li>I<sup>st</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.175s.REM3D.gz">175s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.200s.REM3D.gz">200s</a></li> <li>II<sup>nd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.125s.REM3D.gz">125s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.150s.REM3D.gz">150s</a></li> <li>III<sup>rd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.60s.REM3D.gz">60s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.75s.REM3D.gz">75s</a></li> <li>IV<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.50s.REM3D.gz">50s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.60s.REM3D.gz">60s</a></li> <li>V<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.50s.REM3D.gz">50s</a></li> </ul> </li> <li><strong>Rayleigh waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Rayl.data.zip">Download All Periods and Branches as Summary.SW.Rayl.data.zip</a>) <ul> <li>Fundamental Modes <ul> <li>Minor Arc Arrivals (R1) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.25s.REM3D.gz">25s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.27s.REM3D.gz">27s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.30s.REM3D.gz">30s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.32s.REM3D.gz">32s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.35s.REM3D.gz">35s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.250s.REM3D.gz">250s</a></li> <li>Major Arc Arrivals (R2) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.250s.REM3D.gz">250s</a></li> <li>Higher Obit Arrivals - R3 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.250s.REM3D.gz">250s</a>; R4 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.250s.REM3D.gz">250s</a>; R5 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.250s.REM3D.gz">250s</a>.</li> </ul> </li> <li>I<sup>st</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.175s.REM3D.gz">175s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.200s.REM3D.gz">200s</a></li> <li>II<sup>nd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.125s.REM3D.gz">125s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.150s.REM3D.gz">150s</a></li> <li>III<sup>rd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.60s.REM3D.gz">60s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.75s.REM3D.gz">75s</a></li> <li>IV<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.50s.REM3D.gz">50s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.60s.REM3D.gz">60s</a></li> <li>V<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.50s.REM3D.gz">50s</a></li> <li>VI<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.50s.REM3D.gz">50s</a></li> </ul> </li> </ul> <p><strong>Other Data Products:</strong></p> <ul> </ul> <ul> <li><strong>ReferenceSW_Moulik2022_Figures(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/ReferenceSW_Moulik2022_Figures.zip">.zip</a> or <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/ReferenceSW_Moulik2022_Figures.pdf">.pdf</a>)</strong> - contains all figures from the paper in .png format</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Scatter_Plots.zip"><strong>Scatter_Plots.zip</strong></a> - contains scatter plots similar to Figure 5 in the paper, which compares measurements between two sets of techniques. The files with the suffix *raw.png are comparisons for original raw datasets, while those with the suffix *.clean.png are comparisons after the entire workflow is completed to create the clean datasets (e.g. Figure 13, bottom row).</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Half_cycle.zip"><strong>Half_cycle.zip</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cycle_skips.zip"><strong>Cycle_skips.zip</strong></a> - contains list of source-station paths where discrepancies were found between pairs of techniques. Half (±0.9–1.1 · π ) or full-cycle discrepancies (±0.9–1.1 · 2π ) identified in Section 4.5 are used during outlier analysis (Section 5.3) to create the clean summary dataset. Half- and full-cycle discrepancies identified in these files indicate potential polarity reversals and cycle skips respectively. Note that all of these discrepancies have not been checked for specific causes manually. </li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/vflip-table.REM3D"><strong>vflip-table.REM3D</strong></a> - an ASCII file containing station names and start/end times where polarity reversal issues have been confirmed through manual analysis. This is in contrast to the automated half-cycle discrepancies identified in <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Half_cycle.zip"><strong>Half_cycle.zip</strong></a> above.</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/M1442"><strong>M1442</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/B2562"><strong>B2562</strong></a> - Files containing the knot locations of evenly-spaced points on the surface. B2562 has an average knot spacing of 4.33◦ and is used as the underlying grid for the homogenization process to get summary data (Section 5.1). In order to obtain 2-D variations in local phase slowness or velocity, we use 1442 splines with an average knot spacing of 5.77◦ (Section 6.1)</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Love.data.h5"><strong>Cleanhomo.SW.Love.data.h5</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.h5"><strong>Cleanhomo.SW.Rayl.data.h5</strong></a> - Clean homogenized data for each research group obtained at the end of our workflow (Figure 2). The ASCII files containing the same data are provided in <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Love.data.zip"><strong>Cleanhomo.SW.Love.data.zip</strong></a> and <strong><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.zip">Cleanhomo.SW.Rayl.data.zip</a>. </strong>The summary dataset listed earlier represents the reconciled measurements, and should be preferred over those from individual groups in most applications.</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Inversion_Example.zip"><strong>Inversion_Example.zip</strong></a> - Contains an example of a 2D slowness map inversion with 2ζ azimuthal variations using the reference summary dataset at 100s for fundamental-mode minor-arc Rayleigh waves (R1). Also provided are plots for anistropic variation (<em>Anisotropy_Plots</em>), spline coeffients of 1442 evenly-spaced spherical splines (<em>Spline_Coefficients</em>), and corresponding values at every 1X1 degree pixel in extended pixel format (<em>Maps_epix</em>). The aim of this study is to provide dispersion measurements of surface-wave arrivals, not to provide detailed 2D phase velocity/slowness models. </li> </ul> <p><strong>Note about Data Format</strong></p> <p>The underlying philosophy and format of data files are discussed in the <a href="https://globalseismology.princeton.edu/rsdf">reference seismic data format (RSDF) project</a>. Table A1 from the GJI paper describes the various columns in the surface-wave RSDF format files above.</p>
Dataset, models and code for "Automating global landslide detection with heterogeneous ensemble deep-learning classification"
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Drivers of global variation in land ownership - dataset
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Data from: A global dataset for economic losses of extreme hydrological events during 1960-2014
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A global dataset on paired leaf Na and root Na contents
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Data from: A daily global mesoscale ocean eddy dataset from satellite altimetry
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Datasets from: The distribution of covert natural enemies of a globally invasive crop pest, the fall armyworm, in Africa; enemy-release and spillover events
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Data from: An updated global dataset for diet preferences in terrestrial mammals: testing the validity of extrapolation
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Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
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LEN-DB - Local earthquakes detection: a benchmark dataset of 3-component seismograms built on a global scale
<p>In this study ( <a href="http://www.sciencedirect.com/science/article/pii/S2666544120300010">The paper</a> ) we present a large dataset of 1,249,411 3-component seismograms, recorded along the vertical, north, and east components of 1487 broad-band or very broad-band receivers distributed worldwide, including 631,105 3-component seismograms generated by 304,878 local earthquakes and labeled as earthquakes (EQ), and 618,306 ones labeled as noise (AN). The choice of collecting only local earthquake-data is motivated by the fact that small-magnitude events, which generate relatively small amplitudes and are easily attenuated, are often problematic to detect but provide valuable information about earthquake processes. The labeled data are split into HDF5-Groups: <em>EQ</em> and <em>AN</em>. Each of these groups contains as many HDF5-Datasets as the number of 3-component seismograms; these are labeled in accordance to the format <em>net_sta_starttime</em>, where <em>net</em>, <em>sta</em>, and <em>starttime</em> represent the seismic network, station, and start time of the seismograms. Each HDF5-Dataset (i.e. each triplet of seismograms) has an attribute, which allows accessing the respective metadata. In addition, the HDF5-Group <em>Stations</em> allows accessing stations’ metadata through as many HDF5-Datasets (which are labeled in accordance to the format <em>net_sta)</em> as the number of receivers employed for collecting the waveforms.</p> <p>This global dataset is intended to be used for carrying out a multitude of seismological and signal processing tasks on single-station recordings, and its size particularly suits machine learning (ML) applications.. Application of ML to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings with high accuracy (93.2%), even if applied in regions not investigated by the training set. We make the dataset publicly available as a unique file in HDF5 data format, intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in seismology and signal processing.</p>
Dataset of annual and monthly nitrous oxide flux from global forests
<p> The database of N<sub>2</sub>O fluxes was constructed from published literature that was searched for using the keywords “nitrous oxide flux” and “forests” in the database of ISI Web of Science. The site-level N<sub>2</sub>O flux data were compiled from articles published until 2018. The sites consist of natural or semi-natural forests, and samplings from economic forests were rejected to avoid the influence of human activity, and laboratory studies were not included. In addition, the chamber method was only selected in this database to avoid the uncertainties caused by different measurement techniques. Ultimately, a total of 191 records of annual N<sub>2</sub>O fluxes from 99 published literatures were collected to form our database. Moreover, 112 sites in the database have monthly N<sub>2</sub>O flux data, totaling 2,053 records.</p> <p> The species in our database were classified into different biotic forest groups (leaf traits (i.e., broad and coniferous, LT) and leaf habits (evergreen and deciduous, LH)), according to the information which selected from corresponding articles (e.g. dominant species). Geographic, climatic, vegetation, including latitude, longitude, soil type, vegetation type, climate variables (i.e., mean annual temperature and mean annual precipitation), and edaphic factors (e.g., soil dissolved organic carbon (DOC), ammonium concentration (NH<sub>4</sub><sup>+</sup>), nitrate concentration (NO<sub>3</sub><sup>-</sup>), water filled pore space (WFPS) and soil temperature), were also collected from corresponding articles. For each site, we calculated the means of annual N<sub>2</sub>O fluxes during the observation period, and the monthly values of N<sub>2</sub>O fluxes were calculated based on the average of two or three daily fluxes obtained from corresponding articles. The daily N<sub>2</sub>O flux dataset was extracted from the published figures and tables using GetData Graph Digitizer version 2.24.</p> <p> More information please contact Kerou Zhang (zhangkerou1991@nwafu.edu.cn).</p>
The vertical distribution of soil microbial biomass carbon: A global dataset
<p>Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data are collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data were also collectd including climate, ecosystem types, and edaphic factors. More specifically, we develop this dataset by compiling data from 59 papers published in the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data included in this dataset meet two creteria: 1) there are at least three soil layers along a soil profile, and 2) soil MBC is measured using the fumigation extraction method. The data were obtained from tables and texts from literature directly, and the data in figures were extracted using GetData Graph digitizer software version 2.25. When climate and soil properties are not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org).</p> <p>The units of all the variables are converted to the standard international units or commonly used ones and the values are converted correspondingly. For example, the value of soil organic matter (SOM) is converted to SOC using the equation (SOC = SOM × 0.58). Soil depth is calculated as the arithmetic mean value of the upper and lower boundaries for a given soil layer.</p> <p>This dataset can be used in predicting global SOC change along soil profiles using the multi-layer soil C models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.</p>
Global 0.1° rootzone soil moisture dataset (1980-1989)
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Global precipitation dataset (GMCMOP)
<p>Global precipitation dataset (GMCMOP, 0.1°, 1-hourly,25-40N-75-105E)</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.