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zenodo52/100

LIAS light – A Database for Rapid Identification of Lichens – Subset Switzerland p. pte.

This subset of the LIAS light database focuses on lichens found in Switzerland, providing comprehensive data for ecological research and taxon identification purposes. Not all taxa and only categorical (but 2 numerical) characters (descriptors) of the recorded taxa are covered. Updates and additional data will be published subsequently.

opencc-by-4.0Aug 2024View details →

zenodo52/100

Dataset for "Study of Rapid Capacity Fade in Prismatic Li-ion Cells with Flexible Packaging"

Prismatic lithium-ion batteries (LIBs) are considered promising electric energy sources in electromobility applications due to their cell to pack density. However, their sensitivity to external and internal influences, and reduced durability lead to inflation risk and potential explosions throughout their lifecycle. These critical processes are strongly influenced by the inner construction of the cell, especially concerning the coating and mechanical fixation. This study subjects a commercially available prismatic LIB cell to comprehensive, correlative analysis employing various imaging techniques. The inner structure of the entire cell is visualized non-destructively by X-ray computed tomography (CT), enabling the identification of critical design flaws prior to electrochemical cycling. Electrochemical cycling simulates the battery lifecycle, and the cell is subsequently disassembled in the fully charged state. The usage of the inert-gas transfer system allowed the preparation of Broad Ion Beam (BIB) electrodes cross-sections in a fully native state and for the first time to observe the tearing of graphite particles due to over-lithiation. Established region labeling system allowed to use CT and scanning electron microscopy (SEM) correlatively to identify critical regions. After 100 cycles, a 40% capacity loss was observed and event diagram describing deagradation mechanisms, related both to the cell design and to the processes occurring at high load, was created.

opencc-by-4.0Aug 2024View details →

zenodo48/100

Input Runoff Data for RAPID Model Pre-Processor (RRR) from ECMWF ERA-Interim/Land

This database can be used as the input runoff files in the RAPID model [David et al., 2011] pre-processor (RRR). The runoff files were acquired/derived from the ECMWF ERA-Interim/Land [Balsamo et al., 2015] outputs, available from ECMWF Data Server. The ERA-Interim/Land outputs are available in daily temporal resolution. The database contains the following files;         ECMWF_Interim_Land_yyyy.tar.gz           (Note: yyyy = 2000 to 2009)   Note: These runoff data were used by Sikder et al. [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.   Other necessary links associated with this database: RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a> RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a> ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a>   References: Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a> David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a> Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>

opencc-by-4.0Jan 2020View details →

zenodo48/100

RAPID Model Input Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins

This database contains Inputs and intermediate files of the RAPID model pre-processor (RRR), and also outputs from the RRR (i.e., Inputs for RAPID); which were used by Sikder et al. [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. If you use this RAPID Model Input Files for Mekong-Indus-Ganges-Brahmaputra-Megna (MIGBM) River Basins in your work, please cite: Sikder et al., [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>. The database contains; <ul> <li>Global River basin and Network Shapefiles: HydroSHEDS.tar.gz</li> <li>Extracted Basin Shapefile:                            MIGBM_basin.tar.gz</li> <li>Extracted River Network Shapefiles:             MIGBM_res_ntwk.tar.gz    (Note: res = fine or coarse)</li> <li>Catchment Files:             rapid_catchment_as_riv_res.csv                     (Note: res = fine or coarse)</li> <li>Connectivity Files:           rapid_connect_res_MIGBM.csv                       (Note: res = fine or coarse)</li> <li>Coordinate Files:             coords_res_MIGBM.csv                                   (Note: res = fine or coarse)</li> <li>Base Parameter Files:     pfac_res_MIGBM_1km_hour.csv                     (Note: p = k or x; res = fine or coarse)</li> <li>Sort Files:                         sort_res_MIGBM_topo.csv                              (Note: res = fine or coarse)</li> <li>Sorted Basin Files:           riv_bas_id_res_MIGBM_topo.csv                    (Note: res = fine or coarse)</li> <li>Coupling Files:                 rapid_coupling.tar.gz</li> <li>Parameter Files:              rapid_param.tar.gz</li> <li>Volume Files:                   m3_riv_res_MIGBM_20000101_20091231_prj_LSMsr_tr_utc.nc          (Note: res = fine or coarse; prj = GLDAS or GLDAS.2.0 or GLDAS.2.1 or ECMWF; LSM = CLM, MOS, NOAH, VIC, ERAint; sr = 10 or 025; tr = 3H or D)</li> </ul>   Other necessary links associated with this database: RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a> RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a> GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a> ECMWF outputs: <a href="https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land">https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land</a>   References: Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, <a href="https://doi.org/10.5194/hess-19-389-2015">https://doi.org/10.5194/hess-19-389-2015</a> David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a> Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a> Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>

opencc-by-4.0Jan 2020View details →

zenodo48/100

RAPID input and output files corresponding to "River Network Routing on the NHDPlus Dataset"

Corresponding peer-reviewed publication This dataset corresponds to all the RAPID input and output files that were used in the study reported in: <ul> <li>David, Cédric H., David R. Maidment, Guo-Yue Niu, Zong-Liang Yang, Florence Habets and Victor Eijkhout (2011), River Network Routing on the NHDPlus Dataset, Journal of Hydrometeorology, 12(5), 913-934. DOI: 10.1175/2011JHM1345.1. </li> </ul>   When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.    Time format The times reported in this description all follow the ISO 8601 format.  For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1st 2000 (2000-01-01), Central Standard Time (-06:00).  Additionally, when time ranges with inner time steps are reported, the first time corresponds to the beginning of the first time step, and the second time corresponds to the end of the last time step.  For example, the 3-hourly time range from 2000-01-01T03:00+00:00 to 2000-01-01T09:00+00:00 contains two 3-hourly time steps.  The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1st 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1st 2000, Universal Time.   Data sources The following sources were used to produce files in this dataset: <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 1, obtained from http://www.horizon-systems.com/nhdplus. </li> <li>The National Water Information System (NWIS), obtained from http://waterdata.usgs.gov/nwis.   </li> <li>Outputs from a simulation using the community Noah land surface model with multiparameterization options (Noah-MP, Niu et al. 2011, http://www.jsg.utexas.edu/noah-mp).  The simulation was run by Guo-Yue Niu, and produced 3-hourly time steps from 2004-01-01T00:00+00:00 to 2008-01-01T00:00+00:00.  Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul>   Software The following software were used to produce files in this dataset: <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.0.0.  Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).</li> <li>ESRI ArcGIS (http://www.arcgis.com). </li> <li>Microsoft Excel (https://products.office.com/en-us/excel). </li> <li>CUAHSI HydroGET (http://his.cuahsi.org/hydroget.html). </li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). </li> </ul>   Study domain The files in this dataset correspond to two study domains: <ul> <li>The combination of the San Antonio and Guadalupe River Basins, TX.  RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 5,175).  The temporal range corresponding to this domain is from 2004-01-01T00:00-06:00 to 2007-12-31 T00:00-06:00.</li> <li>The Upper Mississippi River Basin.  RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 182,240).  The temporal range corresponding to this domain spans 100 fictitious days.</li> </ul>   Description of files for the San Antonio and Guadalupe River Basins All files below were prepared by Cédric H. David, using the data sources and software mentioned above.  <ul> <li>rapid_connect_San_Guad.csv.  This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs).  For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches.  A value of zero is used in place of NoData.  The river reaches are sorted in increasing value of COMID.  The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE.  This file was prepared using ArcGIS and Excel.</li> <li>m3_riv_San_Guad_2004_2007_cst.nc.  This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2007/12/31T18:00-06:00.  The values were computed by superimposing a 900-m gridded map of NHDPlus catchments to the outputs of Noah-MP.  This file was prepared using ArcGIS and a Fortran program.</li> <li>kfac_San_Guad_1km_hour.csv.  This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using a wave celerity of 1 km/h.  This file was prepared using a Fortran program.</li> <li>kfac_San_Guad_celerity.csv.  This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using the wave celerity numbers of Table 2 in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>k_San_Guad_2004_1.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (17) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>k_San_Guad_2004_2.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (18) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>k_San_Guad_2004_3.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (19) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>k_San_Guad_2004_4.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (21) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>x_San_Guad_2004_1.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Equation (17) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>x_San_Guad_2004_2.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Equation (18) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>x_San_Guad_2004_3.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Equation (19) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>x_San_Guad_2004_4.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Equation (21) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li>basin_id_San_Guad_hydroseq.csv.  This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the San Antonio and Guadalupe River Basins.  The river reaches are sorted from upstream to downstream.  The values were computed using the following NHDPlus fields: COMID and HYDROSEQ.  This file was prepared using Excel.</li> <li>Qout_San_Guad_1460days_p1_dtR=900s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>Qout_San_Guad_1460days_p2_dtR=900s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>Qout_San_Guad_1460days_p3_dtR=900s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>Qout_San_Guad_1460days_p4_dtR=900s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>QoutR_San_Guad_182days_p1_dtR=900s.nc. This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>QoutR_San_Guad_182days_p2_dtR=900s.nc. This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>QoutR_San_Guad_182days_p3_dtR=900s.nc. This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>QoutR_San_Guad_182days_p4_dtR=900s.nc. This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv.  The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li>gage_id_San_Guad_2004_2007_full.csv.  This CSV file contains the list of COMIDs of rivers containing USGS gauges and with full daily data record.   The river reaches are sorted in increasing value of COMID.  The time range used for determining a full record is daily from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00.  The values were computed using the following NHDPlus field: COMID.  This file was prepared using ArcGIS, HydroGET, and Excel.</li> <li>Qobs_San_Guad_2004_2007_full.csv.  This CSV file contains daily averaged measured stream flow (in cubic meters per second).  The river reaches have the same COMIDs and are sorted similarly to gage_id_San_Guad_2004_2007_full.csv.  The time range for the daily values is from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00.  The values were computed using the following NHDPlus field: COMID, and the observations from NWIS.   This file was prepared using ArcGIS, HydroGET, and Excel.</li> </ul>   Description of files for the Upper Mississippi River Basin All files below were prepared by Cédric H. David, using the data sources and software mentioned above.  <ul> <li>rapid_connect_Reg07.csv.  This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs).  For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches.  A value of zero is used in place of NoData.  The river reaches are sorted in increasing value of COMID.  The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE.  This file was prepared using ArcGIS and Excel. </li> <li>m3_riv_Reg07_100days_dummy.nc.  This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_Reg07.csv.  The time range for this file is for 100 fictitious days.  The values were computed using a unique value of 1 cubic meter for all river reaches and all time steps.  This file was prepared using a Fortran program.</li> <li>kfac_Reg07_2.5ms.csv.  This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_Reg07.csv.  The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (22) in David et al. (2011).  This file was prepared using a Fortran program.    </li> <li>xfac_Reg07_0.3.csv.  This CSV file contains a first guess of Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_Reg07.csv.  The values were computed based on Equation (22) in David et al. (2011).  This file was prepared using a Fortran program.    </li> <li>basin_id_Reg07_hydroseq.csv.  This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the Upper Mississippi River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the following NHDPlus fields: COMID and HYDROSEQ.  This file was prepared using Excel.</li> <li>Qout_Reg07_100days_pfac_dtR900s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same COMIDs and are sorted similarly to basin_id_Reg07_hydroseq.csv.  The time range for this file spans 100 fictitous days. The values were computed using the Muskingum method with parameters of Equation (22) in David et al. (2011).  This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> </ul>   Known bugs and limitations in this dataset or the associated manuscript. The confluence of the Missouri River and the Upper Mississippi River upstream of Saint Louis, MO was overlooked.  The contribution from the Missouri River is therefore not accounted for in the network connectivity corresponding to the Upper Mississippi River Basin.  This has no effect on the conclusions of David et al. (2011) since the Upper Mississippi River Basin was studied with synthetic data and solely to evaluate parallel performance of RAPID.   Funding This work was partially supported by the U.S. National Aeronautics and Space Administration under the Interdisciplinary Science Project NNX07AL79G; by the U.S. National Science Foundation under project EAR-0413265: CUAHSI Hydrologic Information Systems; by Ecole des Mines de Paris, France; and by the American Geophysical Union under a Horton (Hydrology) Research Grant.

opencc-by-4.0Sep 2011View details →

zenodo48/100

Lagrangian Decomposition of the Meridional Heat Transport at 26.5N - Water Parcel Crossings of the RAPID 26.5N Array

This dataset contains the initial and final positions and properties of Lagrangian trajectories evaluated using 5-day mean velocity and tracer fields output from the ORCA0083-N06 ocean sea-ice model hindcast (1958-2015). Numerical water parcels are initialised to sample the full-depth southward transport across the RAPID 26.5N array every month during 2004-2015. Water parcels are advected backwards-in-time using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool which enables users to specify a custom domain using a mask netCDF file.Particles are initialised on the first-available day of each month (based on the centre of the model 5-day mean field windows) between 2004 and 2015 (inclusive) before being advected backwards-in-time within the North Atlantic Ocean until any one of four termination conditions are met: (1) water parcels reach the RAPID 26.5N array, (2) water parcels reach the OSNAP (West or East) arrays in the subpolar North Atlantic, (3) water parcels reach either the English Channel or Gibraltar Strait, or (4) particles reach the maximum advection time of 25-years. The 25-year maximum advection time ensures that we adequately resolve the subtropical gyre circulation north to the RAPID 26.5N array. The pathway transporting dense North Atlantic Deep Water from the OSNAP arrays to RAPID at 26.5N is not fully resolved in this Lagrangian experiment since these water parcels transit on multi-decadal timescales.The number of water parcels initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 5.0 mSv (mSv == 10-3 Sv), enabling the calculation of robust Lagrangian statistics. In reality, the average. water parcel has an associated volume transport of 3.3 mSv which is conserved throughout its circulation.Water parcel locations (converted to geographical coordinates) and properties (conservative temperature, absolute salinity, potential density [TEOS-10]) are output on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. Here, we provide the initial and final locations and properties of all water parcels initialised from RAPID 26.5N.All Lagrangian experiments were completed using the JASMIN High-Performance Computing facility (<a href="https://jasmin.ac.uk">https://jasmin.ac.uk</a>).For a complete description of the ORCA0083-N06 hindcast configuration see: Moat et al. (2016).For a complete description of TRACMASS v7.1 see: <a href="https://www.tracmass.org">https://www.tracmass.org</a>

opencc-by-4.0Nov 2023View details →

zenodo48/100

Data for "Rapid seaward expansion of seaport footprints worldwide"

[updated November 2023]This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta & Lazarus, 2023; <a href="https://doi.org/10.1038/s43247-023-01110-y">https://doi.org/10.1038/s43247-023-01110-y</a>).This repository includes three .csv files, one .xlsx file, and a .ipynb file:'Sengupta_Lazarus_REC_1990_2020_v05.csv' – annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>). Dataset includes Year, Seaport, Country, Region, and Reclaimed area (km^2) [raw measurement]. 'Sengupta_Lazarus_REC_TEU_2011_2020_v03.csv' – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and TEU (millions), collated from archived Lloyd's List reports.'Sengupta_Lazarus_REC_TEU_totals_v04.csv' – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 65 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (<a href="https://lloydslist.maritimeintelligence.informa.com/-/media/lloyds-list/images/top-100-ports-2021/top-100-ports-2021-digital-edition.pdf">Lloyd's List, 2021</a>).'Sengupta_Lazarus_2023_ports_excluded_v2.xlsx' – contains list of 35 ports excluded from thus analysis because they are either not on an open coastline (e.g. estuarine, riverine) or expanded less than 1 km^2 seaward between 1990–2020.'RECLAIM_port_trajectories_v11.ipynb' – Jupyter notebook for data wrangling and plotting figures presented in Sengupta & Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.)The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (<a href="https://doi.org/10.1029/2022EF002927">2023</a>), and the GEE code is available here: <a href="https://github.com/dhritirajsen/Mapping_Coastal_land_reclamation">https://github.com/dhritirajsen/Seaport_reclamation</a>These data and code are also available here: <a href="https://github.com/edlazarus/Seaports">https://github.com/edlazarus/Seaports</a>

opencc-by-4.0Feb 2023View details →

zenodo48/100

Associated data from: An end-to-end workflow to study newly synthesized mRNA following rapid protein depletion in Saccharomyces cerevisiae

This dataset includes two custom BED files described in "An end-to-end workflow to study newly synthesized mRNA following rapid protein depletion in Saccharomyces cerevisiae" (Ridenour and Donczew, submitted), which were used to define counting windows for processing SLAM-seq data in SLAM-DUNK (version 0.4.3) [1]. The BED files contain all annotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome or the Schizosaccharomyces pombe genome and were created using BEDOPS (version 2.4.3) [2]. All ORFs were then extended 250 bp beyond their stop position to capture 3′ untranslated regions (UTRs) using SAMtools (version 1.14) [3] and BEDTools (version 2.30.0) [4]. The reference genome annotations for S. cerevisiae strain S288C (version R64-3-1, RefSeq Assembly GCF_000146045.2) and S. pombe strain 972h- (version ASM294v2, RefSeq Assembly GCF_000002945.1) were retrieved from the NCBI Datasets repository. The S. cerevisiae chromosome names were modified to reflect standard nomenclature (https://www.yeastgenome.org/).

opencc-by-4.0Feb 2024View details →

zenodo48/100

Rapid spectroscopy-based screening techniques for spices data

The dataset from the analysis of spices with a rapid spectroscopy-based screening technique, Fourier transform-Raman (FT-Raman) spectroscopy. Measurements are taken for the authentication of spice (i.e. turmeric) using FT-Raman spectroscopy as part of WP3 (Task 3.1): Implementation of innovations in food authenticity. The dataset is generated to develop a method for the rapid detection of lead chromate in turmeric powder. Measurements (FT-Raman spectra) are averaged per sample and only the final average spectral data is provided in the Excel sheets. The data is useful for anyone working with spectral data and its use for the authentication of spices. Data underlying the publication: Real or fake yellow in the vibrant colour craze: Rapid detection of lead chromate in turmeric.

opencc-by-4.0Dec 2021View details →

zenodo48/100

SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages

Supplementary information relating to the manuscript titled "SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages" that has been published on the preprint server arXiv. <ul> <li>PersistentInfectionScore.nb: Mathematica code used to process the data and generate Figure 2</li> <li>PersistentInfectionScore.pdf: pdf version of the above file</li> <li>Supplementary_tables_Harari_et_al_2022.xlsx: raw data from (<a href="https://www.nature.com/articles/s41591-022-01882-4#Sec19">Harari et al. 2022</a>) that was used to generate mutation distributions</li> </ul>

opencc-by-4.0Jul 2024View details →

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Data from Neutral genetic structuring of pathogen populations during rapid adaptation

Datasets and temporary dataframes relating to the article "Neutral genetic structuring of pathogen populations during rapid adaptation". These datasets and temporary dataframes are necessary to run the scripts from the public GitLab repository: <a href="https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation">https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation</a>. Please refer to this public GitLab repository for the latest version of the codes and to perform all analyses presented in the article. Original datasets from the demogenetic model: <ul> <li>Output_RandomDesign.txt</li> <li>Output_RegularDesign_With_host_alternation.txt</li> <li>Output_RegularDesign_Without_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_With_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_Without_host_alternation.txt</li> </ul> All remaining files correspond to temporary dataframes generated by the scripts in the GitLab repository, provided here for reproducibility of the results and to save time at certain time-consuming scripts.

opencc-by-4.0Jul 2024View details →

zenodo48/100

Remote Rapid Visual Screening (RRVS) Buildings Survey Data - DESTRESS - France

The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Alsace, France, within the framework of the DESTRESS project. The survey has been carried out between May and June 2017 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: February 2011) and footprints from OpenStreetMap. Surveyor: Konstantinos G. Megalooikonomou (GFZ-Potsdam) The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org).  The following attributes are defined (not all are observable in the RRVS survey): code,description lon, longitude in fraction of degrees lat, latitude in fraction of degrees object_id, unique id of the building surveyed  MAT_TYPE,Material Type MAT_TECH,Material Technology MAT_PROP,Material Property LLRS,Type of Lateral Load-Resisting System LLRS_DUCT,System Ductility HEIGHT,Height YR_BUILT,Date of Construction or Retrofit OCCUPY,Building Occupancy Class - General OCCUPY_DT,Building Occupancy Class - Detail POSITION,Building Position within a Block PLAN_SHAPE,Shape of the Building Plan STR_IRREG,Regular or Irregular STR_IRREG_DT,Plan Irregularity or Vertical Irregularity STR_IRREG_TYPE,Type of Irregularity NONSTRCEXW,Exterior walls ROOF_SHAPE,Roof Shape ROOFCOVMAT,Roof Covering ROOFSYSMAT,Roof System Material ROOFSYSTYP,Roof System Type ROOF_CONN,Roof Connections FLOOR_MAT,Floor Material FLOOR_TYPE,Floor System Type FLOOR_CONN,Floor Connections

opencc-by-4.0Mar 2018View details →

zenodo48/100

Data from: Padfield et al. (2016) Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters.

This repository provides the data from the TPC and logistic growth curves from the paper: Padfield, D., Yvon‐Durocher, G., Buckling, A., Jennings, S., & Yvon‐Durocher, G. (2016). Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecology letters, 19(2), 133-142. metadata.pdf gives a more detailed explanation of the data.

opencc-by-4.0Jun 2018View details →

zenodo48/100

Rapid structure determination of microcrystalline molecular compounds using electron diffraction (nanoArgovia Project A3EDPI)

The are the data linked to the publication "Rapid structure determination of microcrystalline molecular compounds using electron diffraction", <a href="https://doi.org/10.1002/anie.201811318">10.1002/anie.201811318</a>. Electron Diffraction data collected with an EIGER X 1M detector (DECTRIS Ltd.). Each tar file contains the raw files in HDF5 format, together with the XDS.INP file used for data integration. Images of the respective crystals have '_img_' in their file names. The log files for recording the stage alpha angle are included with the same name and suffix .txt. See publication for details. NB: The meta-data in the HDF5 files have no meaning, please refer to the respective XDS.INP file for respective information. The crystallographic data (CIF-files) have been uploaded to the ICSD (High--throughput Structural Chemistry with Electron Diffraction) and CSD (https://www.ccdc.cam.ac.uk/) respectively: Paracetamol from Grippostad CCDC 1856579 electron structure of MBBF4 CCDC 1856580 ZSM-5 x227 CSD 1856581 ZSM-5 x331 CSD 1856582 ZSM-5 x79  CSD 1856583 ZSM-5 x811 CSD 1856584

opencc-by-4.0Aug 2018View details →

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NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion"

Liquid-state and solid-state NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion". The data enclosed are NMR data generated by the software Topspin by Burker Biospin. The experiments are dDNP runs that come in two parts: a solid-state and a liquid-state part. <ul> <li>Experiments from 1 to 9 are reference experiments used to quantify polarization in other experiments</li> <li>Experiments 11-19, 21-29, 31-39, ... 61-69 correspond to 6 dDNP runs performed a different samples from the same batch. The numbers correspond between solid and liquid-state datasets</li> </ul> The codes used to analyze the data are available at in a next upload. Refer to the main text of the paper and its supplementary material at 10.26434/chemrxiv-2023-6gd0l for more information.

opencc-by-4.0Aug 2023View details →

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Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"

We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.

opencc-by-4.0Sep 2023View details →

zenodo48/100

Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain)

<h2>Dataset name</h2> Small_Scale_Fishery_Data_2023_v2                   <h2>Title</h2> Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain).                           <h2>Description         </h2>                This dataset was created for the Fish2Sustainability research project, which aims to evaluate how small-scale fisheries (SSF) contribute to Sustainable Development Goals (SDGs). The dataset includes 60 case studies across eight countries and was developed using a rapid appraisal framework. The framework includes a four-step process:                1.     Identifying specific SDG targets influenced by SSF;                2.     Extracting relevant variables from UN indicators;                3.     Gathering expert input via a questionnaire to score these variables;                4.     Creating composite indicators to measure SSF performance against SDGs.                The dataset contains raw data from step 3, case study details, variable scores, and comments from data collectors (contributing authors). The dataset is valuable for researchers interested in small-scale fisheries and socio-ecological systems. By incorporating expert judgments from individuals with expertise in SSF, particularly in data-poor contexts, the dataset offers a wealth of knowledge for conducting comparative analyses across different contexts.               <h2>Authors               </h2> Léopold, M.1, Bitoun, R.E.2, Beckensteiner, J.3, Chuenpagdee, R.4, Fondo, E.N.5, Akintola, S.L.6, Bach, P.7, Frangoudes, K.8, Gaibor, N.9, Gutierrez-Cala, L.10, Massey, Y.7, Randrianandrasana, R.11, Razanakoto, T.11, Saavedra-Díaz, L.M.10, Schreiber Arias, M.12,13, Salas, S.14, Devillers, R.2,4          <h3>Affiliations          </h3> 1             ENTROPIE (IRD, University of La Reunion, CNRS, University of New Caledonia, Ifremer), c/o IUEM, Plouzané, France 2             Espace-Dev (IRD, Univ. Montpellier, Univ. Guyane, Univ. La Réunion, Univ. Antilles, Univ. Nouvelle Calédonie), Montpellier, France 3             AMURE (Ifremer, UBO, CNRS), Plouzané, France 4             Department of Geography, Memorial University of Newfoundland, St. John’s, NL, Canada 5             Kenya Marine and Fisheries Research Institute, Mombasa, Kenya 6             Department of Fisheries, Faculty of Science, Lagos State University, Nigeria 7             MARBEC, University of Montpellier, CNRS, Ifremer, IRD, Sète, France 8             Université de Bretagne Occidentale: Brest, France 9             Instituto Público de Investigación de Acuicultura y Pesca (IPIAP), Universidad del Pacifico (UPAC), Guayaquil, Ecuador 10          Grupo de Investigación en Sistemas Socioecológicos para el Bienestar Humano (GISSBH), Programa de Biología, Universidad del Magdalena, Colombia 11          Centre d’Etudes et de Recherches Economiques pour le Développement (CERED), Université d’Antananarivo, Madagascar 12          EqualSea Lab, Universidad Santiago de Compostela, A Coruña, Spain 13          School of Global Studies, University of Gothenburg, Gothenburg, Sweden 14          Centro de Investigación y de Estudios Avanzados (CINVESTAV), IPN, Unidad Mérida, Mexico               <h2>Method               </h2> Case studies were selected in eight countries by national SSF experts, based on specific criteria and research priorities. Case studies were not selected to represent the full diversity of SSF globally or even nationally. Instead, they were chosen to capture a range of fisheries that could showcase different contributions to SDGs. SSF were defined based on various characteristics, such as resources harvested, gear used, and location of the fishery.                <h3>Geographical Coverage </h3> 60 small-scale fisheries located in seven countries are documented in the data: <ul> <li>Colombia (4 case studies) – Pacifico: La Guajira, San Andrés y Providencia; Caribe: Chocó, Cauca, Valle del Cauca, Nariño.</li> <li>Ecuador (3) – Region: Esmeraldas, Manabi, Guayas, El Oro.</li> <li>France (2) – Region: Bretagne, Occitanie.</li> <li>Kenya (22) – County: Kilifi, Kwale, Lamu, Mombasa, Tana River.</li> <li>Madagascar (20) – Region: Analanjirofo, Anosy, Atsimo Andrefana, Boeny, Diana, Menabe, Vatovavy Fitovinany.</li> <li>Mexico (2) – State: Baja California Sur, Campeche, Yucatan.</li> <li>Nigeria (6) – State: Bayelsa, Cross River, Lagos, Ondo, Ogun. </li> <li>Spain (1) – State: Galicia.              </li> </ul> <h3>Data Collection </h3> Data collection took place from November 30, 2022, to July 3, 2023, spanning approximately seven months. The data presented serve as a snapshot of the conditions within a specific small-scale fishery during the assessment period. To consider the evolution of trends such as exports, economic growth, and income, we considered any relevant variables over the past decade. Data collection approaches varied depending on the context, and data collectors received training to ensure survey consistency. We used primary data sources such as interviews, observations, and measurements whenever possible. In cases where resources were limited, we preferred secondary sources such as existing datasets and literature. Our methods were standardized, but data collectors could adjust them based on their resources. We primarily used direct observation, focus groups, and interviews to collect data. Scoring in interviews and focus groups was done directly or through group analysis by interviewers. Disagreements were resolved through additional interviews or group discussions, with secondary data used if needed. Please refer to the methods in : Bitoun et al., (2024). A methodological framework for capturing marine small-scale fisheries’ contributions to the sustainable development goals. Sustainability Science, 19(4), 1119–1137. https://doi.org/10.1007/s11625-024-01470-0.             <h3>Ethics    </h3> Participants had the option to join of their own accord, were fully briefed on the research goals, and were given the opportunity to review interview guidelines before proceeding. Depending on the circumstances, interviews could last 45 minutes to 4.5 hours. Participants were guaranteed confidentiality and anonymity in the handling and reporting of their data.               <h3>Suggested citation</h3> Léopold, M., Bitoun, R., & Devillers, R. (2023). Qualitative Data on 61 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16077739 <h2>Data Files</h2> The dataset includes the following: <ul> <li>The raw dataset (.xls format).</li> <li>A data dictionary describing and defining each dataset column (.xls format).</li> </ul>

opencc-by-nc-4.0Sep 2023View details →

edi48/100

Rapid root to leaf uptake of inorganic and amino acid nitrogen in three dryland plant species.

Our aim was to quantify inorganic and organic nitrogen (N) uptake and compare short-term nutrient acquisition patterns among three dryland plant species: Bouteloua eriopoda, Achnatherum hymenoides, and Gutierrezia sarothrae collected from a mixed grassland community in the Northern Chihuahuan Desert to better understand how asynchronous resource availability may influence biotic interactions and nutrient retention in these ecosystems. We collected living plants from two locations within the Sevilleta National Wildlife Refuge and transplanted them into pots maintained in the greenhouse with supplemental light and water for two months. We then conducted a greenhouse experiment using these species to compare nutrient uptake of 15N-labeled ammonium (NH4+), nitrate (NO3-), and glutamate (an amino acid) over 12 to 48 hours. Our study examined three main questions: (1) How rapidly do these dryland plants take up available soil N?, (2) Does leaf uptake differ among inorganic and amino acid N forms?, and (3) Do plant species differ in the speed or form of short-term N uptake?. In the greenhouse, we applied one of three isotopic 15N tracers directly to plant roots and quantified N uptake and recovery in leaves after 12, 24, and 48 hours. We found that plants took up inorganic and amino acid N to leaves as rapidly as 12 h following application, and N uptake more than doubled between 24 and 48 h. Inorganic N uptake was 3-4x higher than organic N uptake in all three species, and plants took up ammonium and nitrate at 2-3x faster rates than glutamate. On average, B. eriopoda had higher inorganic N recovery and uptake speeds, while G. sarothrae had the highest organic N uptake over time. A. hymenoides root to leaf uptake was ~50% lower than the other two species after 48 h. Plants showed similar patterns of short-term foliar uptake and recovery indicating a lack of niche partitioning by N form among the three dryland species measured. Our results suggest that soil inorganic N, par

openCC0Aug 2024View details →

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Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.0

This database can be used as the input runoff files in the RAPID model [David et al., 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.0 [Rodell et al., 2004] LSM outputs, available at; <a href="http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi">http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi</a> The GLDAS-v.2.0 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files;         GLDAS.2.0_NOAHres_3H_yyyy.tar.gz           (Note: res = 10 or 025; yyyy = 2000 to 2009)   Note: These runoff data were used by Sikder et al. [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins.   Other necessary links associated with this database: RAPID model: <a href="https://github.com/c-h-david/rapid">https://github.com/c-h-david/rapid</a> RAPID model pre-processor (rrr): <a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a> GLDAS outputs: <a href="https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS">https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS</a>   References: David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, <a href="https://doi.org/10.1175/2011JHM1345.1">https://doi.org/10.1175/2011JHM1345.1</a> Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, <a href="https://doi.org/10.1175/BAMS-85-3-381">https://doi.org/10.1175/BAMS-85-3-381</a> Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, <a href="https://doi.org/10.3389/fenvs.2019.00171">https://doi.org/10.3389/fenvs.2019.00171</a>

opencc-by-4.0Jan 2020View details →

zenodo44/100

Data from: "Rapid molecular evolution of Spiroplasma symbionts of Drosophila"

This repository contains data and information to reproduce the findings reported in the paper. File descriptions: <ul> <li>OTU_sequences.fasta – all Spiroplasma sequences that contained an <a href="https://pfam.xfam.org/family/OTU">OTU domain</a> as predicted by <a href="https://www.ebi.ac.uk/Tools/pfa/pfamscan/">PfamScan</a></li> <li>OTU_alignments.fasta – alignment of OTU domains performed using <a href="https://mafft.cbrc.jp/alignment/software/">Mafft</a></li> <li>RIP_sequences.fasta – all Spiroplasma sequences that contained an <a href="https://pfam.xfam.org/family/RIP">RIP domain</a> as predicted by <a href="https://www.ebi.ac.uk/Tools/pfa/pfamscan/">PfamScan</a></li> <li>RIP_alignments.fasta – alignment of RIP domains performed using the <a href="http://hmmer.org/">HMMER package</a></li> <li>Spiroplasma_supermatrix.fasta – Fasta alignment of concatenated single copy Spiroplasma loci conserved across the investigated strains. Loci that showed signs of recombination were not included</li> <li>Spiroplasma_partitions.txt – Lists the loci that make up the Spiroplasma supermatrix</li> <li>Spiroplasma_partitioning.scheme.txt – Partitioning scheme employed in our Maximum Likelihood analysis of the supermatrix. This was the best fitting partitioning scheme as determined with <a href="http://www.iqtree.org/">IQ-TREE</a></li> <li>Protocol_1.pdf – Chloroform–Ethanol protocol used for extracting Spiroplasma DNA for sHy-Tx</li> </ul>

opencc-by-4.0Jun 2020View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record