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528 results for “correspondence”

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zenodo52/100

Correspondence of the natural oscillation frequencies of perforated plates depending on the type of holes, plate material and thickness, type of fixing (CCCS or CSCS)

<p>The method involved the analysis of oscillations of base plates: solid non-perforated and with round holes, as well as perforated plates with holes of complex geometry in the form of a five-petal epicycloid.</p> <p>As a result of the modeling (Abaqus), the natural oscillations frequencies of the studied plates were obtained depending on the type of perforation, material, thickness and type of their fixing. The use of different materials (steel and aluminium) showed an insignificant influence on the natural oscillation frequency of the plates. It was found that the plate thickness has the greatest influence (31.85&ndash; 33.35%), the following are the hole parameters: partition width between holes; pitch between hole centers.</p> <p>Analysis of the results showed that the natural vibrations of plates with holes of complex geometry differ by up to 7% compared to plates with basic round holes.&nbsp;</p>

opencc-by-4.0Oct 2024View details →
edi52/100

AOP01 Correspondence between plant traits and NEON Airborne Observatory Platform (AOP) data at Konza Prairie (2017)

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, allowing high-resolution trait mapping. However, the reliability of these data depend on establishing rigorous links with in-situ field measurements. We tested the accuracy of NEON’s readily available AOP derived data products – Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The weakest relationships were between AOP Canopy Nitrogen and ground-based measures of Nitrogen, as well as the CHM and ground-based canopy height measurements. We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had an R2 of more than 0.25. For all vegetation traits, R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products are unreliable, at least at this grassland site, and should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although additional assessment of varying spatial scales of field and AOP data, as well as corrections and data pre-processing to improve data quality, are recommended. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area,

openCC0Jan 2024View details →
zenodo48/100

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

<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RAPID input and output files that were used in the study reported in:</p> <ul> <li>David, C&eacute;dric 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.&nbsp;</li> </ul> <p>&nbsp;</p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.&nbsp;</p> <p>&nbsp;</p> <p><strong>Time format</strong></p> <p>The times reported in this description all follow the ISO 8601 format.&nbsp; For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1<sup>st</sup> 2000 (2000-01-01), Central Standard Time (-06:00).&nbsp; 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.&nbsp; 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.&nbsp; The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1<sup>st</sup> 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1<sup>st</sup> 2000, Universal Time.</p> <p>&nbsp;</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 1, obtained from http://www.horizon-systems.com/nhdplus.&nbsp;</li> <li>The National Water Information System (NWIS), obtained from http://waterdata.usgs.gov/nwis.&nbsp; &nbsp;</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). &nbsp;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. &nbsp;Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul> <p>&nbsp;</p> <p><strong>Software</strong></p> <p>The following software were used to produce files in this dataset:</p> <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.0.0.&nbsp; 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).&nbsp;</li> <li>Microsoft Excel (https://products.office.com/en-us/excel).&nbsp;</li> <li>CUAHSI HydroGET (http://his.cuahsi.org/hydroget.html).&nbsp;</li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers).&nbsp;</li> </ul> <p>&nbsp;</p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to two study domains:</p> <ul> <li>The combination of the San Antonio and Guadalupe River Basins, TX.&nbsp; 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).&nbsp; 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.&nbsp; 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).&nbsp; The temporal range corresponding to this domain spans 100 fictitious days.</li> </ul> <p>&nbsp;</p> <p><strong>Description of files for the San Antonio and Guadalupe River Basins</strong></p> <p>All files below were prepared by C&eacute;dric H. David, using the data sources and software mentioned above.&nbsp;</p> <ul> <li><em>rapid_connect_San_Guad.csv.</em>&nbsp; This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs). &nbsp;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.&nbsp; A value of zero is used in place of NoData.&nbsp; The river reaches are sorted in increasing value of COMID.&nbsp; The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE.&nbsp; This file was prepared using ArcGIS and Excel.</li> <li><em>m3_riv_San_Guad_2004_2007_cst.nc.&nbsp; </em>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 <em>rapid_connect_San_Guad.csv</em>. &nbsp;The time range for this file is from 2004-01-01T00:00-06:00 to 2007/12/31T18:00-06:00. &nbsp;The values were computed by superimposing a 900-m gridded map of NHDPlus catchments to the outputs of Noah-MP.&nbsp; This file was prepared using ArcGIS and a Fortran program.</li> <li><em>kfac_San_Guad_1km_hour.csv.&nbsp; </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; 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.&nbsp; This file was prepared using a Fortran program.</li> <li><em>kfac_San_Guad_celerity.csv.&nbsp; </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; 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).&nbsp; This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_1.csv.&nbsp; </em>This CSV file contains Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (17) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_2.csv.&nbsp; </em>This CSV file contains Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (18) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_3.csv.&nbsp; </em>This CSV file contains Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (19) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_4.csv.&nbsp; </em>This CSV file contains Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (21) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_1.csv.&nbsp; </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on Equation (17) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_2.csv.&nbsp; </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on Equation (18) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_3.csv.&nbsp; </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on Equation (19) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_4.csv.&nbsp; </em>This CSV file contains Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.&nbsp; The values were computed based on Equation (21) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.</li> <li><em>basin_id_San_Guad_hydroseq.csv. &nbsp;</em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the San Antonio and Guadalupe River Basins.&nbsp; The river reaches are sorted from upstream to downstream. &nbsp;The values were computed using the following NHDPlus fields: COMID and HYDROSEQ.&nbsp; This file was prepared using Excel.</li> <li><em>Qout_San_Guad_1460days_p1_dtR=900s.nc.</em> &nbsp;This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; 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).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p2_dtR=900s.nc.&nbsp; </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; 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).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p3_dtR=900s.nc.&nbsp; </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; 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).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p4_dtR=900s.nc. &nbsp;</em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; 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).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p1_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; 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).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p2_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; 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).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p3_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; 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).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>QoutR_San_Guad_182days_p4_dtR=900s.nc. </em>This netCDF file contains the 15-min outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>.&nbsp; 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).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>gage_id_San_Guad_2004_2007_full.csv.&nbsp; </em>This CSV file contains the list of COMIDs of rivers containing USGS gauges and with full daily data record.&nbsp; &nbsp;The river reaches are sorted in increasing value of COMID.&nbsp; 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.&nbsp; The values were computed using the following NHDPlus field: COMID.&nbsp; This file was prepared using ArcGIS, HydroGET, and Excel.</li> <li><em>Qobs_San_Guad_2004_2007_full.csv.&nbsp; </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). &nbsp;The river reaches have the same COMIDs and are sorted similarly to <em>gage_id_San_Guad_2004_2007_full.csv</em>.&nbsp; The time range for the daily values is from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00.&nbsp; The values were computed using the following NHDPlus field: COMID, and the observations from NWIS.&nbsp;&nbsp; This file was prepared using ArcGIS, HydroGET, and Excel.</li> </ul> <p>&nbsp;</p> <p><strong>Description of files for the Upper Mississippi River Basin</strong></p> <p>All files below were prepared by C&eacute;dric H. David, using the data sources and software mentioned above.&nbsp;</p> <ul> <li><em>rapid_connect_Reg07.csv.&nbsp; </em>This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs).&nbsp; 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.&nbsp; A value of zero is used in place of NoData.&nbsp; The river reaches are sorted in increasing value of COMID.&nbsp; The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE.&nbsp; This file was prepared using ArcGIS and Excel.&nbsp;</li> <li><em>m3_riv_Reg07_100days_dummy.nc.&nbsp; </em>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 <em>rapid_connect_Reg07.csv</em>.&nbsp; The time range for this file is for 100 fictitious days.&nbsp; The values were computed using a unique value of 1 cubic meter for all river reaches and all time steps.&nbsp; This file was prepared using a Fortran program.</li> <li><em>kfac_Reg07_2.5ms.csv.&nbsp; </em>This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>.&nbsp; The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (22) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.&nbsp;&nbsp;&nbsp;&nbsp;</li> <li><em>xfac_Reg07_0.3.csv.&nbsp; </em>This CSV file contains a first guess of Muskingum x values (dimensionless) for all river reaches.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>.&nbsp; The values were computed based on Equation (22) in David et al. (2011).&nbsp; This file was prepared using a Fortran program.&nbsp;&nbsp;&nbsp;&nbsp;</li> <li><em>basin_id_Reg07_hydroseq.csv.&nbsp; </em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the Upper Mississippi River Basin.&nbsp; The river reaches are sorted from upstream to downstream.&nbsp; The values were computed using the following NHDPlus fields: COMID and HYDROSEQ.&nbsp; This file was prepared using Excel.</li> <li><em>Qout_Reg07_100days_pfac_dtR900s.nc.</em> This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.&nbsp; The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_Reg07_hydroseq.csv</em>.&nbsp; 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).&nbsp; This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> </ul> <p>&nbsp;</p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>The confluence of the Missouri River and the Upper Mississippi River upstream of Saint Louis, MO was overlooked.&nbsp; The contribution from the Missouri River is therefore not accounted for in the network connectivity corresponding to the Upper Mississippi River Basin.&nbsp; 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.</p> <p>&nbsp;</p> <p><strong>Funding</strong></p> <p>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.</p>

opencc-by-4.0Sep 2011View details →
zenodo48/100

A global dataset gathering 37 field experiments involving cereal-legume intercrops and their corresponding sole crops.

<p>The overall description of the dataset is reported in the <strong>data_report.pdf</strong> file. The methodology for data curation and tidying is published in Peer Community Journal (<a href="https://doi.org/10.24072/pcjournal.389">Mahmoud2024</a>).</p> <p>This dataset gathers the results of 37 field experiments, which involved cereal-legume intercrops and their corresponding sole crops. The field experiments were carried in 5 European countries (France, Denmark, Italy, Germany and England) from 2001 to 2017.&nbsp;The dataset includes:</p> <ul> <li>5 legume species , <em>i.e.</em> chickpea (<em>Cicer arietinum</em> L.), faba bean (<em>Vicia faba</em> L.), lentil (<em>Lens culinaris</em> Med.), lupin (<em>Lupinus albus</em> L.) and pea (<em>Pisum sativum</em> L.),</li> <li>3 cereal species, <em>i.e.</em> barley (<em>Hordeum vulgare</em> L.), durum wheat (<em>Triticum turgidum</em> L.) and soft wheat (<em>Triticum aestivum</em> L.),&nbsp;</li> <li>8 resulting intercrops, <em>i.e.</em> i) barley associated with faba bean, lupin or pea, ii) durum wheat associated with chickpea, faba bean or pea, and iii) soft wheat associated with lentil or pea.&nbsp;</li> </ul> <p>In total, the dataset contains 299 sole crop and 308 intercrop experimental units, one given experimental unit being defined as the unique combination of {site, year, crop management}, with the crop management including species and cultivar choice as well as agricultural interventions (sowing conditions, inputs).</p> <p>The global dataset includes four tables, all sharing a common identifier (experiment_id):</p> <ul> <li>data_trials.csv: the global features describing the experimental sites,</li> <li>data_management.csv: the agricultural management actions carried out on each of the experimental sites,</li> <li>data_traits.csv: measured plant and crop characteristics,</li> <li>data_climate.csv: climate for the experimental sites, retrieved from NASA POWER API.</li> </ul> <p>Additionally, a metadata file is provided (<strong>metadata.xlsx</strong>), describing the table to which the variables belong (variable_type, i.e. trials, management, traits or climate), their name (variable_name), their significance (description) and their unit (unit). Finally, a table including the original references related to experimental files gathered (<strong>references.xlsx</strong>) is also provided.</p> <p>Data providers and field experiments: Laurent Bedoussac, Eric Justes, Etienne-Pascal Jour- net, Christophe Naudin, Henrik Hauggaard-Nielsen, Erik Steen Jensen, Elise Pelzer, Gu&eacute;na&euml;lle Corre-Hellou, Bochra Kammoun, Loic Viguier, Romain Barillot, Antoine Cou&euml;del, Philippe Hinsinger</p> <p>Database and management: No&eacute;mie Gaudio, R&eacute;mi Mahmoud, Pierre Casadebaig</p>

opencc-by-4.0Jun 2023View details →
zenodo48/100

Live-cell STED dataset of mitochondria containing ground truth and corresponding low intensity noisy images

<p>The dataset was acquired as part of the manuscript "Denoising diffusion models for high-resolution microscopy image restoration". The dataset contains ground truth and low intensity STED images of mitochondria acquired in live U2-OS cells stably expressing TOM20 coupled to the dead mutant of HaloTag7 which was made fluorescent by using the exchangeable ligand Hy4 bound to the fluorophore SiR.&nbsp;</p>

opencc-by-4.0Nov 2024View details →
zenodo48/100

FASTA file containing to the MYB encoding gene Ant1 genomic sequences corresponding to wild and cultivated tomato accessions

<p>Fasta sequence correspond to the MYB encoding gene&nbsp;<em>An2-like</em>. The genomic&nbsp;sequences correspond to&nbsp;<em>Solanum&nbsp;galagpagnese</em> accession LA1141 (this study), <em>S.&nbsp;lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome, and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014).&nbsp;Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>).</p>

opencc-by-4.0Nov 2021View details →
zenodo48/100

FASTA file containing the MYB encoding gene An2-like genomic sequences corresponding to wild and cultivated tomato accessions

<p>FASTA sequence corresponds&nbsp;to the MYB encoding gene&nbsp;<em>An2-like</em>. The genomic&nbsp;sequences correspond to&nbsp;<em>Solanum&nbsp;galagpagnese</em> accession LA1141 (this study), <em>S.&nbsp;lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome (Hosmani et al., 2019),&nbsp;<em>S. lycopersicum </em>variety Indigo Rose (Yan et al., 2020), <em>S. lycopersicum</em> accession LA1996 [MN242011.1&nbsp;(Colanero et al., 2020)], <em>S. chilense&nbsp;</em>accession LA1930 [MN242012.1 (Colanero et al., 2020)], and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014).&nbsp;Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), &nbsp;Indigo Rose [MN433087 (Yan et al., 2020)], <em>S. lycopersicum </em>accession LA1996 [MN242011.1, EF433417.1 (Sapir et al., 2008; Colanero et al., 2020)], <em>S. chilense</em> accession LA1930 [MN242012.1 (Colanero et al., 2020)] were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>)&nbsp;and&nbsp;the National Center for Biotechnology Information (NCBI)(available at NCBI: <a href="https://www.ncbi.nlm.nih.gov">https://www.ncbi.nlm.nih.gov</a>).</p>

opencc-by-4.0Nov 2021View details →
zenodo48/100

FASTA file containing the MYB encoding genes at the Aft locus with genomic sequences corresponding to wild and cultivated tomato accessions

<p>FASTA sequences correspond to the MYB encoding genes&nbsp;<em>An2-like </em>and <em>Ant1</em>. The genomic&nbsp;sequences were combined correspond to&nbsp;<em>Solanum&nbsp;galagpagnese</em>&nbsp;accession LA1141 (this study),&nbsp;<em>S.&nbsp;lycopersicum</em>&nbsp;variety OH8245 (this study),&nbsp;<em>S. lycopersicum</em>&nbsp;variety Heinz 1706 reference genome (Hosmani et al., 2019),&nbsp;LA1996 [MN242011.1, EF433417.1(Sapir et al., 2008; Colanero et al., 2020)],&nbsp;and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014).&nbsp;Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018),&nbsp;<em>S. lycopersicum&nbsp;</em>accession LA1996 [MN242011.1, EF433417.1 (Sapir et al., 2008; Colanero et al., 2020)],&nbsp;<em>S. chilense</em>&nbsp;accession LA1930 [MN242012.1 (Colanero et al., 2020)] were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at&nbsp;<a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>)&nbsp;and&nbsp;the National Center for Biotechnology Information (NCBI) (available at NCBI:&nbsp;<a href="https://www.ncbi.nlm.nih.gov/">https://www.ncbi.nlm.nih.gov</a>).</p>

opencc-by-4.0Nov 2021View details →
zenodo48/100

Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"

<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php.  Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS.  Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>.  This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>

opencc-by-4.0Dec 2016View details →
zenodo48/100

Data in New $^{63}$Ga(p,$\gamma$)$^{64}$Ge and $^{64}$Ge(p,$\gamma$)$^{65}$As reaction rates corresponding to the temperature regime of thermonuclear X-ray bursts

<p>Abstract quoted from <a href="https://doi.org/10.1103/PhysRevC.110.065804" target="_blank" rel="noopener">Physical Review C 110 (2024) 065804</a> [<a href="https://arxiv.org/abs/2406.14624">arXiv:2406.14624</a>]&nbsp;</p> <p>We compute the $^{63}$Ga(p,$\gamma$)$^{64}$Ge and $^{64}$Ge(p,$\gamma$)$^{65}$As thermonuclear reaction rates using the latest experimental input supplemented with theoretical nuclear spectroscopic information. The experimental input consists of the latest proton thresholds of $^{64}$Ge and $^{65}$As, and the nuclear spectroscopic information of $^{65}$As, whereas the theoretical nuclear spectroscopic information for $^{64}$Ge and $^{65}$As are deduced from the full <em>pf</em>-shell space configuration-interaction shell-model calculations with the GXPF1A Hamiltonian. Both thermonuclear reaction rates are determined with known uncertainties at the energies that correspond to the Gamow windows of the temperature regime relevant to type I x-ray bursts, covering the typical temperature range of the thermonuclear runaway of the GS 1826$-$24 periodic bursts and SAX J1808.4$-$3658 photospheric radius expansion bursts.&nbsp;</p>

opencc-by-4.0Oct 2024View details →
zenodo48/100

Dataset of knee joint contact force peaks and corresponding subject characteristics from 4 open datasets

<p>This dataset contains data from overground walking trials of 166 subjects with several trials per subject (approximately 2900 trials total).</p> <p><strong>DATA ORIGINS &amp; LICENSE INFORMATION</strong></p> <p>The data comes from four existing open datasets collected by others:</p> <p>Schreiber &amp; Moissenet, A multimodal dataset of human gait at different walking speeds established on injury-free adult participants</p> <ul> <li>article: https://www.nature.com/articles/s41597-019-0124-4</li> <li>dataset: https://figshare.com/articles/dataset/A_multimodal_dataset_of_human_gait_at_different_walking_speeds/7734767</li> </ul> <p>Fukuchi et al., A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals</p> <ul> <li>article: https://peerj.com/articles/4640/</li> <li>dataset: https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711</li> </ul> <p>Horst et al., A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals</p> <ul> <li>article: https://www.nature.com/articles/s41598-019-38748-8</li> <li>dataset: https://data.mendeley.com/datasets/svx74xcrjr/3</li> </ul> <p>Camargo et al., A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions</p> <ul> <li>article: https://www.sciencedirect.com/science/article/pii/S0021929021001007</li> <li>dataset (3 links): https://data.mendeley.com/datasets/fcgm3chfff/1 https://data.mendeley.com/datasets/k9kvm5tn3f/1 https://data.mendeley.com/datasets/jj3r5f9pnf/1</li> </ul> <p>In this dataset, those datasets are referred to as the Schreiber, Fukuchi, Horst, and Camargo datasets, respectively.<br> The Schreiber, Fukuchi, Horst, and Camargo datasets are licensed under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).</p> <p>We have modified the datasets by analyzing the data with musculoskeletal simulations &amp; analysis software (OpenSim).<br> In this dataset, we publish modified data as well as some of the original data.</p> <p><br> <strong>STRUCTURE OF THE DATASET</strong><br> The dataset contains two kinds of text files: those starting with &quot;predictors_&quot; and those starting with &quot;response_&quot;.</p> <p>Predictors comprise 12 text files, each describing the input (predictor) variables we used to train artifical neural networks to predict knee joint loading peaks.<br> Responses similarly comprise 12 text files, each describing the response (outcome) variables that we trained and evaluated the network on.<br> The file names are of the form &quot;predictors_X&quot; for predictors and &quot;response_X&quot; for responses, where X describes which response (outcome) variable is predicted with them.<br> X can be:<br> - loading_response_both: the maximum of the first peak of stance for the sum of the loading of the medial and lateral compartments<br> - loading_response_lateral: the maximum of the first peak of stance for the loading of the lateral compartment<br> - loading_response_medial: the maximum of the first peak of stance for the loading of the medial compartment<br> - terminal_extension_both: the maximum of the second peak of stance for the sum of the loading of the medial and lateral compartments<br> - terminal_extension_lateral: the maximum of the second peak of stance for the loading of the lateral compartment<br> - terminal_extension_medial: the maximum of the second peak of stance for the loading of the medial compartment<br> - max_peak_both: the maximum of the entire stance phase for the sum of the loading of the medial and lateral compartments<br> - max_peak_lateral: the maximum of the entire stance phase for the loading of the lateral compartment<br> - max_peak_medial: the maximum of the entire stance phase for the loading of the medial compartment<br> - MFR_common: the medial force ratio for the entire stance phase<br> - MFR_LR: the medial force ratio for the first peak of stance<br> - MFR_TE: the medial force ratio for the second peak of stance</p> <p>The predictor text files are organized as comma-separated values. Each row corresponds to one walking trial. A single subject typically has several trials.<br> The column labels are DATASET_INDEX,SUBJECT_INDEX,KNEE_ADDUCTION,MASS,HEIGHT,BMI,WALKING_SPEED,HEEL_STRIKE_VELOCITY,AGE,GENDER.</p> <ul> <li>DATASET_INDEX describes which original dataset the trial is from, where {1=Schreiber, 2=Fukuchi, 3=Horst, 4=Camargo}</li> <li>SUBJECT_INDEX is the index of the subject in the original dataset. If you use this column, you will have to rewrite these to avoid duplicates (e.g., several datasets probably have subject &quot;3&quot;).</li> <li>KNEE_ADDUCTION is the knee adduction-abduction angle (positive for adduction, negative for abduction) of the subject in static pose, estimated from motion capture markers.</li> <li>MASS is the mass of the subject in kilograms</li> <li>HEIGHT is the height of the subject in millimeters</li> <li>BMI is the body mass index of the subject</li> <li>WALKING_SPEED is the mean walking speed of the subject during the trial</li> <li>HEEL_STRIKE_VELOCITY is the mean of the velocities of the subject&#39;s pelvis markers at the instant of heel strike</li> <li>AGE is the age of the subject in years</li> <li>GENDER is an integer/boolean where {1=male, 0=female}</li> </ul> <p>The response text files contain one floating-point value per row, describing the knee joint contact force peak for the trial in newtons (or the medial force ratio). Each row corresponds to one walking trial.<br> The rows in predictor and response text files match each other (e.g., row 7 describes the same trial in both predictors_max_peak_medial.txt and response_max_peak_medial.txt).</p> <p><br> See our journal article &quot;Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks&quot; (https://doi.org/10.1007/s10439-023-03278-y) for more information.</p> <p>Questions &amp; other contacts: jere.lavikainen@uef.fi</p>

opencc-by-4.0Oct 2022View details →
zenodo44/100

Dataset corresponding to scientific paper "Improved reperfusion following alternative surgical approach for experimental stroke in mice"

<p>Acquired raw experimental data using laser speckle contrast imaging (LSCI) following middle cerebral artery occlusion (MCAO)&nbsp;in mice. Data obtained from mice undergoing&nbsp;standard CCA ligation technique and mice undergoing CCA vessel repair technique<sup>1</sup>.</p> <p>The dataset is linked to paper &quot;Improved reperfusion following alternative surgical approach for experimental stroke in mice&quot;.&nbsp;</p> <p>The dataset consists of the following:</p> <ul> <li>Raw LSCI flux values, from ipsilateral and contralateral hemispheres, measured at baseline, 24hours post-MCAO and 48hours post-MCAO.&nbsp;</li> <li>Normalised data expressing ispilateral hemisphere as a % of the control contralateral hemisphere.</li> <li>Mean normalised values for each subject.&nbsp;&nbsp;</li> </ul> <p>&nbsp;</p> <p><strong>References</strong></p> <ol> <li>Trotman-Lucas,M., Kelly, M.E., Janus,&nbsp;J.,&nbsp;Fern, R., Gibson, C.L. (2017) &#39;An alternative surgical approach reduces variability following filament induction of experimental stroke in mice&#39;.&nbsp;<em>Disease Models &amp; Mechanisms,</em>&nbsp;10, 931-938.</li> </ol> <p>&nbsp;</p>

opencc-by-4.0Feb 2020View details →
zenodo44/100

ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"

<p>Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period&nbsp;on 10 April 2009.&nbsp;</p> <p>The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (&deg;), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (&deg;) and computed specific differential phase (&deg; km<sup>-1</sup>) data (Hubbert and Bringi 1995).&nbsp;</p> <p>&nbsp;</p> <p>ARMOR file naming conventions are as follows:&nbsp;</p> <p>&nbsp;</p> <p>RAW_NA_000_125_20090410171216.gz</p> <p>RAW: file format</p> <p>125: can scan type, where 125 indicates&nbsp;a full or sector volume plan position indicator&nbsp;</p> <p>20090410171216: date and time in the order of year, month, day, hour, minute, and second</p> <p>&nbsp;</p> <p>ARMOR_20090410171216_qc1.uf.gz</p> <p>ARMOR: radar name</p> <p>20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS)</p> <p>qc1: denotes ARMOR processed data</p> <p>uf: denotes the file format&nbsp;</p> <p>&nbsp;</p> <p>NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics.</p> <p>Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi<sup>2</sup>&nbsp;value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is&nbsp;(f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x).&nbsp;</p> <p>&nbsp;</p> <p>Hourly file naming conventions are as follows:</p> <p>&nbsp;</p> <p>LYLOUT_090410_160000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>090410: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>160000: time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p>&nbsp;</p> <p>Acknowledgments:&nbsp;</p> <p>Data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p>&nbsp;</p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., &amp; Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints.&nbsp;<em>IEEE Transactions on Geoscience and Remote Sensing</em>,&nbsp;<em>39</em>(9), 1906&ndash;1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements.&nbsp;<em>Journal of&nbsp;Atmospheric and Oceanic Technology</em>,&nbsp;<strong>12</strong>, 643&ndash;648.&nbsp;</p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., &hellip; Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses.&nbsp;<em>Journal of Atmospheric and Oceanic Technology</em>,&nbsp;<em>21</em>(4), 543&ndash;558. https://doi.org/10.1175/1520-0426(2004)021&lt;0543:NALMAL&gt;2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., &amp; Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico.&nbsp;<em>Geophysical Research Letters</em>,&nbsp;<em>26</em>(23), 3573&ndash;3576.</p>

opencc-by-4.0Apr 2020View details →
zenodo44/100

ARMOR and NALMA data corresponding to 2008 storms analyzed in "Examining conditions supporting the development of anomalous charge structures in supercell thunderstorms in the Southeastern United States"

<p>Total lightning and dual-polarization Doppler velocity data are available from the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) and the C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR), respectively, over selected periods on 6 February 2008 and 11 April 2008. NALMA data are provided in American Standard Code for Information Interchange (ASCII) format and ARMOR data are provided in Raw and quality-controlled Universal Format (UF), where quality control methods are described below.&nbsp;</p> <p>&nbsp;</p> <p>The NALMA data are provided in hourly files which include undecimated point location (source-level) data corresponding to the detection of very high frequency (VHF) radiation emitted during the breakdown of lightning (Rison et al., 1999; Thomas et al., 2001). Source locations were reported from active sensors configured in an 11-sensor array distributed throughout North Alabama and South Central Tennessee, the center of which is located at 34.72641, -86.64533 (Koshak et al. 2004).&nbsp;&nbsp;Data files include information on the time that each source was detected (UTC seconds of the day), the latitude, longitude, and altitude of each source&rsquo;s location (decimal degrees and m, respectively), the reduced chi<sup>2</sup>&nbsp;value associated with data processing (unitless), a station mask indicating which sensors contributed to the resolved location of each source (unitless).&nbsp;These data are provided in a line-by-line format of&nbsp;(f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x).&nbsp;The 2008 data files additionally include a header section that provides further information about each sensor in the network and its relative contribution to the dataset.&nbsp;</p> <p>&nbsp;</p> <p>The hourly fine naming conventions are as follows for the February 2008 data:</p> <p>LMA_NA_6.2_125_2008-02-06_10-00-00.dat.gz</p> <p>LMA_NA: LMA file designator corresponding to the NALMA</p> <p>2008-02-06: year (YYYY)-month (MM)-day (DD)</p> <p>10-00-00: UTC time, (HH)-minute (MM)-second (SS)</p> <p>&nbsp;</p> <p>And for the April 2008 data:</p> <p>LYLOUT_080411_180000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>080411: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>180000: UTC time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p>&nbsp;</p> <p>ARMOR data are provided as sets of 14 (14) sampling volumes corresponding to the 6 February 2008 (11 April 2008) periods between 1002 UTC and 1119 UTC (1844 UTC and 1952 UTC). Each RAW and processed UF file contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (&ordm;), and total power (dBZ) data. Horizontal reflectivity and differential reflectivity data were corrected for attenuation and differential attenuation, differential propagation phase (&ordm;) was estimated, and specific differential phase (&ordm; km<sup>-1</sup>) was calculated during post-processing (Hubbert and Bringi 1995, Bringi et al. 2001).</p> <p>&nbsp;</p> <p>Acknowledgments:&nbsp;</p> <p>NALMA data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p>&nbsp;</p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., &amp; Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints.&nbsp;<em>IEEE Transactions on Geoscience and Remote Sensing</em>,&nbsp;<em>39</em>(9), 1906&ndash;1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements.&nbsp;<em>Journal of&nbsp;Atmospheric and Oceanic Technology</em>,&nbsp;<strong>12</strong>, 643&ndash;648.&nbsp;</p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., &hellip; Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses.&nbsp;<em>Journal of Atmospheric and Oceanic Technology</em>,&nbsp;<em>21</em>(4), 543&ndash;558. https://doi.org/10.1175/1520-0426(2004)021&lt;0543:NALMAL&gt;2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., &amp; Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico.&nbsp;<em>Geophysical Research Letters</em>,&nbsp;<em>26</em>(23), 3573&ndash;3576.</p> <p>Thomas, R. J., Krehbiel, P. R., Hamlin, T., Harlin, J., &amp; Shown, D. (2001). Observations of VHF source powers radiated by lightning.&nbsp;<em>Geophysical Research Letters</em>,&nbsp;<em>28</em>(1), 143&ndash;146. https://doi.org/10.1029/2000GL011464</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2020View details →
zenodo44/100

watercourse_100mseg: the Flemish watercourses represented by 100-meter line segments and corresponding downstream endpoints

<p>The data source <code>watercourse_100mseg</code> is derived from the raw data source &#39;<a href="https://doi.org/10.5281/zenodo.4420904">watercourses</a>&#39;. It represents all officially known watercourses of the Flemish Region as line segments of <strong>100 m</strong> (or &lt; 100 m, for the most upstream segment of a watercourse). The data source can be used as a base layer of statistical <strong>population units</strong> (line segments) and corresponding anchor points, in the design of monitoring and research of watercourses.</p> <p>The data source is a GeoPackage with <strong>two spatial layers</strong>:</p> <ul> <li> <p><code>watercourse_100mseg_lines</code>: the line segments;</p> </li> <li> <p><code>watercourse_100mseg_points</code>: the corresponding downstream endpoints (&#39;downstream&#39; as defined in <code>watercourses</code>).</p> </li> </ul> <p>The coordinate reference system is &#39;Belge 72 / Belgian Lambert 72&#39; (EPSG-code <a href="https://epsg.io/31370">31370</a>). Both layers have the same number of rows, and they share the same <strong>attributes</strong>:</p> <ul> <li><code>rank</code>: a unique, incremental number for each segment/endpoint. It just reflects the downstream-to-upstream order of segments within each original line.</li> <li><code>vhag_code</code>: the VHAG code from the raw <code>watercourses</code> data source. It distinguishes the different watercourses, so it is common to all segments/points that belong to the same watercourse.</li> </ul> <p>This version was derived from version &#39;<code>watercourses_20200807</code>&#39; (<a href="https://doi.org/10.5281/zenodo.4420905">Zenodo DOI</a>) as follows:</p> <ol> <li> <p>each line (&#39;watercourse&#39;) of <code>watercourses</code> is split into segments of 100 m, where the remaining segment of &lt; 100 m (per original line) is situated most upstream. For this step, the direction of the lines has been reverted (in <code>watercourses</code> the direction is from upstream to downstream). A unique rank number is assigned to each segment, as well as the VHAG code from the corresponding line in <code>watercourses</code>.</p> </li> <li> <p>the downstream endpoint of each segment is located, and assigned the same attributes (<code>rank</code> and <code>vhag_code</code>).</p> </li> </ol> <p>See R and GRASS code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/6b1d8f7/src/generate_watercourse_100mseg">&#39;n2khab-preprocessing&#39; at commit 6b1d8f7</a> for the creation from the <code>watercourses</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p>

opencc-zeroJan 2021View details →
zenodo44/100

RAPID input and output files corresponding to "RAPID Applied to the SIM-France Model"

<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RAPID input and output files that were used in the study reported in:</p> <ul> <li>David, Cédric H., Florence Habets, David R. Maidment and Zong-Liang Yang (2011), RAPID applied to the SIM-France model, Hydrological Processes, 25(22), 3412-3425. DOI: 10.1002/hyp.8070. </li> </ul> <p> </p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p> </p> <p><strong>Time format</strong></p> <p>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 1<sup>st</sup> 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 1<sup>st</sup> 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1<sup>st</sup> 2000, Universal Time.</p> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The hydrographic network of SIM-France, as published in Habets, F., A. Boone, J. L. Champeaux, P. Etchevers, L. Franchistéguy, E. Leblois, E. Ledoux, P. Le Moigne, E. Martin, S. Morel, J. Noilhan, P. Quintana Seguí, F. Rousset-Regimbeau, and P. Viennot (2008), The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, Journal of Geophysical Research: Atmospheres, 113(D6), DOI: 10.1029/2007JD008548.</li> <li>The observed flows are from Banque HYDRO, Service Central d’Hydrométéorologie et d’Appui à la Prévision des Inondations. Available at http://www.hydro.eaufrance.fr/index.php.</li> <li>Outputs from a simulation using SIM-France (Habets et al. 2008).  The simulation was run by Florence Habets, and produced 3-hourly time steps from 1995-08-01T00:00+02:00 to 2005-07-31T21:02+00:00.  Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul> <p> </p> <p><strong>Software</strong></p> <p>The following software were used to produce files in this dataset:</p> <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.1.0.  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>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). </li> </ul> <p> </p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to one study domain:</p> <ul> <li>The river network of SIM-France is made of 24264 river reaches.  The temporal range corresponding to this domain is from 1995-08-01T00:00+02:00 to 2005-07-31 T21:00+02:00.</li> </ul> <p> </p> <p><strong>Description of files </strong></p> <p>All files below were prepared by Cédric H. David, using the data sources and software mentioned above. </p> <ul> <li><em>rapid_connect_France.csv.</em>  This CSV file contains the river network connectivity information and is based on the unique IDs of the SIM-France river reaches (the IDs).  For each river reach, this file specifies: the ID of the reach, the ID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the IDs of all upstream reaches.  A value of zero is used in place of NoData.  The river reaches are sorted in increasing value of ID.  The values were computed based on the SIM-France FICVID file.  This file was prepared using a Fortran program.</li> <li><em>m3_riv_France_1995_2005_ksat_201101_c_zvol_ext.nc.  </em>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 IDs and are sorted similarly to <em>rapid_connect_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005/07/31T21:00+02:00.  The values were computed using the outputs of SIM-France.  This file was prepared using a Fortran program.</li> <li><em>kfac_modcou_1km_hour.csv.</em>  This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same IDs and are sorted similarly to <em>rapid_connect_France.csv</em>.  The values were computed based on the following information: ID, size of the side of the grid cell, Equation (5) in David et al. (2011), and using a wave celerity of 1 km/h.  This file was prepared using a Fortran program.</li> <li><em>kfac_modcou_ttra_length.csv.  </em>This CSV file contains a second guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same IDs and are sorted similarly to <em>rapid_connect_France.csv</em>.  The values were computed based on the following information: ID, size of the side of the grid cell, travel time, and Equation (9) in David et al. (2011).</li> </ul> <ul> <li><em>k_modcou_0.csv.</em>  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 <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> </ul> <ul> <li><em>k_modcou_1.csv.</em>  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 <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>k_modcou_2.csv.  </em>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 <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>k_modcou_3.csv.  </em>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 <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>k_modcou_4.csv.  </em>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 <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>k_modcou_a.csv.  </em>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 <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>k_modcou_b.csv.  </em>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 <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>k_modcou_c.csv.  </em>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 <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>x_modcou_0.csv.</em>  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>x_modcou_1.csv.</em>  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>x_modcou_2.csv.</em>  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>x_modcou_3.csv.</em>  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>x_modcou_4.csv.</em>  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>x_modcou_a.csv.</em>  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>x_modcou_b.csv.</em>  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>x_modcou_c.csv.</em>  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.</li> <li><em>rivsurf_France.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the SIM-France domain.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_adour.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Adour River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_allier.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Allier River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_ardeche.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Ardeche River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_dordogne.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Dordogne River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_garonne.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_garonne_reste.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin, downstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_garonneariege.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne and Ariege River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_herault.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Herault River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_loir.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loir River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_loire.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_loire_amont_nevers.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, upstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_loire_reste.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, downstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_lot.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Lot River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_meuse.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Meuse River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_oise.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Oise River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_rhone.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_rhone_reste.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, downstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_rhone_suisse.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, upstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_saone.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Saone River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_seine.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_seine_amont.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, upstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_seine_reste.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, downstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_tarn.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Tarn River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>rivsurf_vienne.csv.  </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Vienne River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p1_dtR1800s.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p2_dtR1800s.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p3_dtR1800s.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p4_dtR1800s.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_pa_dtR1800s.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_pb_dtR1800s.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_pc_dtR1800s.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_366days_p0_dtR1800s.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s_pougny.nc.  </em>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 IDs and are sorted similarly to <em>rivsurf_France.csv</em>.  The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>gage_id_1995_1996_full.csv.  </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with full daily data record.   The river reaches are sorted in increasing value of ID.  The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID.  This file was prepared using a Fortran program, and Excel.</li> <li><em>gage_id_1995_1996_full_nash.csv.  </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with full daily data record and for which RAPID simulations led to a positive efficiency value.   The river reaches are sorted in increasing value of ID.  The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID.  This file was prepared using a Fortran program, and Excel.</li> <li><em>gage_id_1995_2005_70.csv.  </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with 70% daily data record.   The river reaches are sorted in increasing value of ID.  The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 2005-07-31T21:00+02:00.  The values were computed using the following field: ID.  This file was prepared using a Fortran program, and Excel.</li> <li><em>Qobs_1995_1996_full.csv.  </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second).  The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full.csv</em>.  The time range for the daily values is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID, and the observations from SCHAPI.   This file was prepared using a Fortran program and Excel.</li> <li><em>Qobs_1995_1996_full_nash.csv.  </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second).  The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full_nash.csv</em>.  The time range for the daily values is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID, and the observations from SCHAPI.   This file was prepared using a Fortran program and Excel.</li> <li><em>Qobs_1995_1996_full_nash_93.csv.  </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second).  The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full_nash.csv</em>.  The time range for the daily values is from 1995-11-01T00:00+02:00 to 2005-07-31T21:00+02:00.  The values were computed using the following field: ID, and the observations from SCHAPI.   This file was prepared using a Fortran program and Excel.</li> <li><em>Qobs_1995_2005_70.csv.  </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second).  The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_2005_70.csv</em>.  The time range for the daily values is from 1995-08-01T00:00+02:00 to 2005-07-31T21:00+02:00.  The values were computed using the following field: ID, and the observations from SCHAPI.   This file was prepared using a Fortran program and Excel.</li> <li><em>Qobsbarrec_1995_1996_full_nash.csv.  </em>This CSV file contains the reciprocal of the averaged measured stream flow (in cubic meters per second).  The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full_nash.csv</em>.  The time range for the computation of the average is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID, and the observations from SCHAPI.   This file was prepared using a Fortran program and Excel.</li> <li><em>forcingtot_id_1995_1996_full.csv.  </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the SIM-France domain.   The river reaches are sorted in increasing value of ID.  The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID.  This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_garonne_reste.csv.  </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Garonne River Basin, downstream.   The river reaches are sorted in increasing value of ID.  The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID.  This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_loire_reste.csv.  </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Loire River Basin, downstream.   The river reaches are sorted in increasing value of ID.  The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID.  This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_rhone_pougny.csv.  </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Rhone River Basin, downstream of Lake Geneva.   The river reaches are sorted in increasing value of ID.  The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID.  This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_rhone_reste.csv.  </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Rhone River Basin, downstream.   The river reaches are sorted in increasing value of ID.  The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID.  This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_seine_reste.csv.  </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Seine River Basin, downstream.   The river reaches are sorted in increasing value of ID.  The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00.  The values were computed using the following field: ID.  This file was prepared using a Fortran program, and Excel.</li> <li><em>Qfor_1995_1996_full.csv.  </em>This CSV file is identical to <em>Qobs_1995_1996_full.csv.</em></li> <li><em>Qfor_1995_1996_full_93.csv.  </em>This CSV file is identical to <em>Qobs_1995_1996_full_nash_93.csv.</em></li> <li><em>Qinit_93.csv.  </em>This CSV file contains the final state of RAPID after a simulation ending on 1995-11-31T00:00+02:00</li> </ul> <p> </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>A small bug in RAPID v1.1.0 was discovered and fixed on 2011-07-16 that had an impact on the optimization of parameters when using forcing data to replace upstream simulations.  This bug led to erroneous results for only two of the basins where upstream forcing was used: Garonne River Basin, downstream; and Rhone River Basin, downstream.  The bug had no influence on: Loire River Basin, downstream, and Seine River Basin, downstream; or on any of the other simulations.   This should not affect the conclusions of David et al. (2011) since only a few locations were impacted. </p> <p> </p> <p><strong>Funding</strong></p> <p>This work was partially supported by the French Mines Paristech, by the French Agence Nationale de la Recherche under the Vulnérabilité de la nappe du Rhin (VulNaR) project, by the French Programme Interdisciplinaire de Recherche sur l’Environnement de la Seine (PIREN-Seine) project, 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, and by the American Geophysical Union under a Horton (Hydrology) Research Grant.</p>

opencc-by-4.0Oct 2011View details →
zenodo44/100

Output files corresponding to "Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities"

<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to the output files that were produced for the study reported in:</p> <ul> <li>Sawyer, Audrey H., C&eacute;dric H. David, and James S. Famiglietti, (2016), Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities, Science, 353(6300), 705-707. DOI:10.1126/science.aag1058.&nbsp;</li> </ul> <p>&nbsp;</p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.&nbsp;</p> <p>&nbsp;</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php.&nbsp; Regions used are: Northeast (NE: 01), Mid-Atlantic (MA: 02), South-Atlantic North (SAN: 03N), South-Atlantic South (SAS: 03S), South-Atlantic West (SAW: 03W), Lower Mississippi (MS: 08), Texas (TX: 12), California (CA: 18), and Pacific Northwest (NW: 17).</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS.&nbsp; Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2010 Census dataset (CENSUS 2010), obtained from: http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p>&nbsp;</p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>NHDFlowline_CONUS_coastline.zip.&nbsp; </em>This zip file contains a shapefile with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in regions used.&nbsp;</li> <li><em>Catchment_CONUS_coastline.zip.&nbsp; </em>This zip file contains a shapefile with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in regions used.&nbsp;</li> <li><em>Catchment_CONUS_coastline_centroid.zip</em>.<em>&nbsp; </em>This zip file contains a shapefile with the centroids of the above catchments.&nbsp;</li> <li><em>SGD_Coastal_Vulnerabilities.csv</em>.&nbsp; This .csv file contains the following data (units are in parentheses): <ul> <li>COMID. Unique feature identifier in NHDPlusV2 (-).</li> <li>LENGTHkm. Length of coastline feature (km).</li> <li>REACHCODE. Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature (-).</li> <li>AREAsqkm. Area of coastal catchment feature (km<sup>2</sup>).</li> <li>REGION. NHDPlusV2 region: NE = Northeast, MA = Mid-Atlantic, SAN = South Atlantic North, SAS = South Atlantic South, SAW = South Atlantic West, TX = Texas, MS = Lower Mississippi, CA = California, PN = Pacific Northwest (-).</li> <li>RLENGTHkm. Total length of coastline accumulated by REACHCODE (km).</li> <li>RAREAsqkm. Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>BGRUNkgpsqm. Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>SGDsqmpy. Average annual fresh SGD rate for REACHCODE (m<sup>2</sup>/y).</li> <li>RCOUNT. Number of features by REACHCODE (-).</li> <li>PDENpsqkm. Population density for coastal catchment feature (km<sup>-2</sup>).</li> <li>SWIVULN.&nbsp; Vulnerability to saltwater intrusion: - 1 = vulnerable, 0 = not vulnerable (-).</li> <li>PCTDEV11.&nbsp; Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>CONTVULN. Vulnerability to&nbsp;offshore contamination associated with direct groundwater discharge: - 1 = vulnerable, 0 = not vulnerable (-).</li> </ul> </li> </ul> <p>&nbsp;</p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>No bugs have been unveiled since publication of this dataset or the associated manuscript.&nbsp; Vulnerability thresholds are subjective and could be adjusted for different applications, refer to published manuscript for approaches used here.</p> <p>&nbsp;</p> <p><strong>Funding</strong></p> <p>This work was supported by the Ohio State University School of Earth Sciences, and NSF grant EAR-1446724 (A.H.S); the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, and grants from the NASA SWOT and Sea Level Science Teams (C.H.D. and J.S.F.).</p>

opencc-by-4.0Aug 2016View details →
zenodo44/100

Raw data corresponding to the scientific paper: "A modular telerehabilitation architecture for upper limb robotic therapy" (Advances in Mechanical Engineering 2017, Vol. 9(1) 1-13)

<p>Acquired raw data necessary to implement the adaptive control strategy grounded on multimodal information.<br>  In addition, raw data for the computation of the communication parameters needed for the assessment of the implemented telerehabilitation architecture are provided.</p> <p>a) End-effector positions and velocities (x, y, vx, vy) in three conditions: healthy (Fig 9) and constraint simulated stroke behaviour (Fig 10) without robotic assistance and simulated stroke behavior with robotic assistance (Fig 11)</p> <p>b) Performance indicators and control parameters for all the recruited subjects in both conditions healthy behaviour and simulated stroke behaviour (Fig 12a and Fig 12b)</p> <p>c) Computational values for evaluating telerehabilitation performance (Table 1)</p> <p> </p> <p> </p>

opencc-by-4.0Dec 2016View details →
zenodo44/100

Quantification of ROIs corresponding to MQs from fluorescence in vivo imaging experiments

<p>We injected DIr labelled macrophages into mice carring immunolgical hot and cold KPC pancreatic tumors and quantified the recruitment to the tumor sites and lungs of the injected cells at different days after injection using fluorescence imaging. We hypotesized that macrophages would be recruited into tumor tissue and in prevalence into cold tumors.</p>

opencc-by-4.0Mar 2024View details →
zenodo44/100

Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Development of novel squid gladius biomaterials for cornea tissue engineering"

<p>This dataset corresponds to the quantitative data generated in the work entitled "Development of novel squid gladius biomaterials for cornea tissue engineering".</p> <p>Cornea tissue engineering is strictly dependent on the development of biomaterials fulfilling the strict biocompatibility, biomechanical and optical requirements of this organ. In this work, we have generated novel biomaterials from the squid gladius (SG) and their application in cornea tissue engineering was evaluated. Results revealed that the native SG (N-SG) was biocompatible in laboratory animals, although a local inflammatory reaction was driven by the material. Cellularized biomaterials (C-SG) demonstrated that the SG provides an adequate substrate for cell attachment and growth, and corneal epithelial cells cultured on this biomaterial were able to express crystallin alpha, a marker for this type of cells. Biomechanical analyses showed that N-SG biomaterials have higher Young modulus and lower traction deformation than control native corneas (CTR), and C-SG showed similar Young modulus than CTR. Analysis of the optical properties of these samples revealed that the diffuse transmittance of N-SG and C-SG were higher than CTR, with the diffuse reflectance showing the opposite behavior. These results confirm the putative usefulness of this abundant marine-derived biomaterial that can be obtained as a byproduct of the fishing industry.</p>

opencc-by-4.0Oct 2024View details →

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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