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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

Food and Agriculture Biomass Input–Output (FABIO) database

<p>This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry.&nbsp;</p> <p>The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers <strong>191 countries</strong> + RoW, <strong>118 processes</strong> and <strong>125 commodities</strong> (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on <a href="https://github.com/fineprint-global/fabio">GitHub</a>. For more information please refer to <a href="https://fabio.fineprint.global">https://fabio.fineprint.global</a>.</p> <p>The database consists of the following main components, in compressed .rds format:</p> <ul> <li>Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.</li> <li>Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.</li> <li>X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.</li> <li>L: the Leontief inverse, computed as (I &ndash; A)<sup>-1</sup>, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).</li> <li>E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).</li> <li>mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).</li> <li>mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).</li> </ul> <p>A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS</p> <p>Except of <em>X.rds</em>, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes <em>value </em>and <em>mass </em>only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see <a href="http://doi.org/10.1021/acs.est.9b03554">Bruckner et al. 2019</a>). Product, process and country classifications can be found in the file <em>fabio_classifications.xlsx</em>.</p> <p>Footprint results are not contained in the database but can be calculated, e.g. by using this script: <a href="https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R">https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R</a></p> <p>&nbsp;</p> <p><strong><em>How to cite: </em></strong></p> <p>To cite FABIO work please refer to this paper:</p> <p>Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., B&ouml;rner, J. 2019. FABIO &ndash; The Construction of the Food and Agriculture Input&ndash;Output Model. <em>Environmental Science &amp; Technology</em> 53(19), 11302&ndash;11312. DOI: <a href="https://doi.org/10.1021/acs.est.9b03554">10.1021/acs.est.9b03554</a></p> <p>&nbsp;</p> <p><em><strong>License:</strong></em></p> <p>This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at <a href="mailto:martin.bruckner@wu.ac.at">martin.bruckner@wu.ac.at</a>.</p> <p>&nbsp;</p> <p><strong><em>Known issues:</em></strong></p> <p>The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.</p>

opencc-by-nc-sa-4.0Sep 2020View details →
zenodo48/100

ParaTAXIS X-ray Scattering Input & Output

<p>This dataset describes the&nbsp;science case of the SIMEX platform tool chain for EUCALL WP4 Milestone M4.3.</p> <p>The file <em>opt_thick_1.6x1.6x3_micron_10fs_around_laser_max.h5</em> &nbsp;contains the ParaTAXIS density input data in openPMD format for the optically thick&nbsp;case (milestone 4.2.2.10). The data was obtained in a&nbsp;2D PICLS simulation (milestone 4.2.2.7) which modeled&nbsp;the temporal evolution of a silicon grating irradiated by a <span class="math-tex">\(\tau_\mathrm{FWHM} = 83\,\mathrm{fs},\ \lambda = 800\,\mathrm{nm}\)</span> laser pulse of normalized amplitude <span class="math-tex">\(a_0 = 0.25\)</span>. Here&nbsp;959 slices which correspond to subsequent PIC time steps of length <span class="math-tex">\(\Delta t_\mathrm{PIC} = 1.042 \cdot 10^{-17}\,\mathrm{s}\)</span> were stacked in propagation direction of the XFEL probe pulse thus taking time evolution of the target during X-ray pulse propagation into account.&nbsp; ParaTAXIS reads the density into&nbsp;a simulation volume of 1024 x 512 x 512 cells. The cell sizes of the PIC and the ParaTAXIS simulations are&nbsp;equally&nbsp;<span class="math-tex">\(3.125\,\mathrm{nm}\)</span> in every spatial direction. Density data is given in units of critical densities with respect to the <span class="math-tex">\(800\,\mathrm{nm}\)</span> laser. One critical density corresponds to&nbsp;<span class="math-tex">\(n_\mathrm{c} = 1.7422 \cdot 10^{27}\,\mathrm{m}^{-3}\)</span>. The time window&nbsp;chosen is situated from&nbsp;<span class="math-tex">\(5\,\mathrm{fs}\)</span>&nbsp;before until&nbsp;<span class="math-tex">\(5\,\mathrm{fs}\)</span>&nbsp;after the optical laser main pulse maximum hits the foil indicating a delay of <span class="math-tex">\(\Delta t = 0\)</span>. The optical laser incidence is in z-direction (ParaTAXIS coordinates) under 0&deg;.</p> <p>The total illuminated area for both the optically thick and thin cases was&nbsp;<span class="math-tex">\(1.6 \times 1.6\, \mathrm{\mu m}\)</span>. We assume&nbsp;a target thickness of&nbsp;<span class="math-tex">\(3\,\mathrm{\mu m}\)</span>. The detector distance was&nbsp;<span class="math-tex">\(d = 1.4\,\mathrm{m}\)</span>&nbsp;and the detector pixel size was&nbsp;<span class="math-tex">\(a_\mathrm{D} = 13.5\,\mathrm{\mu m}\)</span>. For Thomson scattering most photons are scattered in forward direction. We therefore assumed a maximum polar scattering angle of&nbsp;<span class="math-tex">\(0.01\,\mathrm{rad}\)</span> in order to increase statistics on the detector.</p> <p>Via 16 simulations we obtained the detector outputs in the optically thick case&nbsp;which can be found in&nbsp;opt_thick_run_&lt;run-number&gt;_detector_&lt;number-of-simulated-photons&gt;_photons.h5 in openPMD format.</p> <p>The file&nbsp;<em>opt_thin_integrated_1.6x1.6x3_micron_10fs_around_laser_max</em>&nbsp;contains the total electron density data for the optically thin&nbsp;case (milestone 4.2.2.9) integrated over 959 slices in the propagation direction of the probe laser beam. The&nbsp;density is only non-zero in the 6th cell of the simulation volume thus enforcing single-scattering in the ParaTAXIS simulation as can be assumed for an optically thin medium. This data was read by a&nbsp;ParaTAXIS into a simulation volume of 12 x 512 x 512 cells.</p> <p>We launched 10 parallel simulations, each arriving at detector images for <span class="math-tex">\(10^{12}\)</span>&nbsp;simulated photons. The detector output of these simulations can be found in&nbsp;<em>opt_thin_detector_1e12_photons_run&lt;run-number&gt;.h5</em>&nbsp;also in openPMD format.</p> <p>&nbsp;</p>

opencc-by-4.0Sep 2017View details →
zenodo44/100

Artificial Intelligence for EU Decision-Making: Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy

<p>The uploaded dataset was used for the statistical analysis of the pre-print &quot;Artificial Intelligence for EU Decision-Making: Effects on Citizens&rsquo; Perceptions of Input, Throughput and Output Legitimacy&quot; (Permanent identifier: <a href="https://arxiv.org/abs/2003.11320">arXiv:2003.11320</a>)</p> <p>A lack of political legitimacy undermines the ability of the European Union (EU) to resolve major crises and threatens the stability of the system as a whole. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. In particular, artificial intelligence (AI) systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, informing the policy process, and evaluating policy effectiveness. This paper investigates how citizens&rsquo; perceptions of EU input, throughput, and output legitimacy are influenced by three distinct decision-making arrangements: (1) independent human decision-making (HDM); (2) independent algorithmic decision-making (ADM) by AI-based systems; and (3) hybrid decision-making by EU politicians and AI-based systems together. The results of a pre-registered online experiment (n = 572) suggest that existing EU decision-making arrangements are still perceived as the most democratic (input legitimacy). However, regarding the decision-making process itself (throughput legitimacy) and its policy outcomes (output legitimacy), no difference was observed between the status quo and hybrid decision-making involving both ADM and democratically elected EU institutions. Where ADM systems are the sole decision-maker, respondents tend to perceive these as illegitimate. The paper discusses the implications of these findings for (a) EU legitimacy and (b) data-driven policy-making.</p>

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

Global physical input-output tables for iron and steel (2008-2017).

<p><strong>Dataset:</strong> Global physical input-output tables for iron and steel</p> <p><strong>Years:</strong> 2008-2017</p> <p><strong>Base classification:</strong> 32 regions, 39 processes and 30 flows</p> <p><strong>Associated journal article: </strong>The PIOLab - Building global physical input-output tables in a virtual laboratory (forthcoming, Journal for Industrial Ecology)</p> <p><strong>Associated GitHub repository</strong>: www.github.com/fineprint-global/PIOLab</p> <p><strong>Contact:</strong> hanspeter.wieland@wu.ac.at</p> <p>The folder <em>RawData</em> contains the unprocessed results of the reconciliation run in the PIOLab. These tables (in the Tvy format) form the basis for the R scripts that are available from the GitHub repository mentioned above. Please note the instructions on GitHub for further information and how i.e. where the content of <em>RawData</em> needs to be stored in your local repository.</p> <p>The folder <em>gPSUT</em> contains the processed physical supply-use tables, including final use matrices and boundary input and output blocks. The variable names are described in detail in the method section of the journal article.</p> <p>The folder <em>gPIOT</em> contains the process-by-process IO model, which was used for the calculation of the footprint indicators in the Journal article. Please read the information on the footprint calculus in the journal article.</p> <p>The folder<em> Diagnostics </em>contains, for all years of the time series, results from the analyses of the constraint realization. The journal article presents only the diagnostic test for the year 2008.</p>

opencc-by-4.0Dec 2020View 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

Data supporting: Improved Tangential Interpolation-based Multi-input Multi-output Modal Analysis of a Full Aircraft

Open the record for dataset details and reuse information.

opengpl-3.0-or-laterAug 2024View details →
zenodo44/100

RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"

<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in:</p> <ul> <li> <p>Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1.</p> </li> </ul> <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><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate.&nbsp; The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location.&nbsp; As a result, all values of &quot;measured&quot; discharge errors (i.e. Bias, STDE, and RMSE) are incorrect.&nbsp; This issue did not affect the values of &quot;estimated&quot; errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented.&nbsp; The figures published in the manuscript can all be recreated using the files in which &quot;BUG_DO_NOT_USE&quot; was appended to the name.&nbsp; Correct figures can also be created using corresponding file names that were not so appended.&nbsp;</p> <p>Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper.&nbsp; The slope of the daily discharge STDE trend initially reported as <span class="math-tex">\(\alpha = 0.3876\)</span> in Figure 5c changes to <span class="math-tex">\(\alpha = 0.4507\)</span> after correction.&nbsp; The resulting value of the ideal inflation factor hence changes from <span class="math-tex">\(I = {1 \over 0.3876} \approx 2.58\)</span> to <span class="math-tex">\(I = {1 \over 0.4507} \approx 2.22\)</span>. This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to <span class="math-tex">\(I = 2.58\)</span> than to <span class="math-tex">\(I = 1\)</span> or <span class="math-tex">\(I = 5\)</span>, <em>i.e.</em> the three values that were evaluated.</p> <p>Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: &quot;timeseries_rap_exp01.csv&quot;, &quot;timeseries_rap_exp18.csv&quot;, and &quot;stats_rap_exp18.csv&quot;. Faulty versions of the same files have &quot;BUG_NETCDF4&quot; appended to their names. Correct files have been recreated with file names that were not so appended.&nbsp;</p>

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

Example input files and output data for 1D hydrodynamic simulations of shock compressed iron

<p>Example input files and output data for 1D hydrodynamic simulations of shock compressed iron. Input files consists of 3 examples from the SIMEX github wiki page for a 50 micron CH ablator with 5 micro Fe foil (laser pulse is a 6 ns flat top pulse, 1064 nm with 0.3 TW/cm<sup>2</sup>). Output data are from Esther hydrocode in .txt format and the SIMEX opmd.h5 format.</p>

opencc-by-4.0Aug 2017View details →
zenodo44/100

Turkiye, Regional Domestic Input Output Tables, 2022, 26 Region, 62 X 62 Matris

<p>Regional data is crucial for conducting inter-regional economic analyses, understanding productivity, wages, and minimum wage dynamics, assessing the impact of unforeseen events like natural disasters, comparing sectors, and evaluating development levels. Supply-use tables, employment, and demographic statistics are the most commonly used data sources for these analyses.</p> <p>To address these needs and for my own economic analyses, I have created regional supply-use tables at the province level. Additionally, I have generated domestic input-output tables using the NUTS 2 regional classification for conducting regional and inter-regional analyses in Turkiye.</p> <h2>Scope of the Research</h2> <p>This research presents domestic input-output tables for 62 industries in 26 NUTS 2 regions of Turkiye for the year 2022.</p> <h2>Methodology</h2> <p>Regional accounts are published at the A10 level for every province in Turkey. In the Input-Output tables, which consist of 62 sectors, the input-output distribution has been conducted on a sector basis to derive the total at the A10 level. The estimations at the provincial level are automatically derived from regional accounts data.</p> <p>The NUTS 2 level regional input-output data have been published and disseminated with an A62 matrix. Additionally, province-level input-output data have been published and disseminated with an A62 matrix through the application. After summations the regional input output tables have been estimated.</p> <h2><strong>Application</strong></h2> <p>The link to the relevant application is attached:</p> <h3><a href="https://turkiye.streamlit.app/" target="_blank" rel="noopener ugc nofollow">https://turkiye.streamlit.app/</a></h3> <p>&nbsp;</p>

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

Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements

<p>The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled &ldquo;Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.&rdquo; The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017).</p> <p>&nbsp;</p> <p>Software versions used:</p> <ul> <li>Image Processing Workbench v2.2.0 (Marks et al., 2017)</li> <li>Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019)</li> </ul> <p>&nbsp;</p> <p><strong>NOTE:</strong> Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file.</p> <p>&nbsp;</p> <p><strong>topo.nc</strong> &ndash; Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are:</p> <ul> <li>dem &ndash; digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017)</li> <li>mask &ndash; basin mask for the Boise River Basin</li> <li>veg_height &ndash; vegetation height in meters from the National Land Cover Database (Homer et al., 2015)</li> <li>veg_type &ndash; vegetation type from the National Land Cover Database</li> <li>veg_tau &ndash; vegetation fractional transmissivity derived from the vegetation type</li> <li>veg_k &ndash; vegetation emissivity derived from the vegetation type</li> </ul> <p>&nbsp;</p> <p><strong>maxus.nc</strong> &ndash; maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002)</p> <p>&nbsp;</p> <p><strong>Station data:</strong></p> <ul> <li>Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF.</li> <li>metadata.csv &ndash; metadata for 40 stations</li> <li>air_temp.csv &ndash; 38 stations</li> <li>cloud_factor.csv &ndash; 7 stations</li> <li>precip.csv &ndash; 21 stations</li> <li>vapor_pressure.csv &ndash; 19 stations</li> <li>wind_direction.csv &ndash; 14 stations</li> <li>wind_speed.csv &ndash; 14 stations</li> </ul> <p>&nbsp;</p> <p><strong>smrf_config.ini</strong> &ndash; Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.</p>

opencc-by-4.0Feb 2019View details →
zenodo44/100

Test Input and Output Files for Cloud Resolving Radar Simulator (CR-SIM) Version 4.0

<h2>Overview</h2> <p>The dataset includes input and output files for testing the Cloud-Resolving Radar Simulator (Oue et al. 2020) version 4.0.&nbsp;</p> <p>The following files are included:</p> <ul> <li>crsimtest1_inp_MP10.tar.gz includes input files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_inp_MP50.tar.gz includes input files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_inp_MP40.tar.gz includes input files for Test-3 with the microphysical option MP40</li> <li>crsimtest1_out_ref_MP10.tar.gz includes example output files for Test-1 with the microphysical option MP10</li> <li>crsimtest2_out_ref _MP50.tar.gz includes example output files for Test-2 with the microphysical option MP50</li> <li>crsimtest3_out_ref _MP40.tar.gz includes example output files for Test-3 with the microphysical option MP40</li> </ul> <p>Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v4.0).</p>

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

Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)

<p>This repository concerns the supplementary material data of the research article entitled "<em>Personalised in silico model for radiation-induced pulmonary fibrosis</em>" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.</p>

opengpl-3.0-or-laterSep 2024View details →
zenodo44/100

Labor flows and input-output connections in Hungary

<p>Aggregated data on labor flows and input-output connections in Hungary (2015-2017).</p> <p>Related preprint: https://arxiv.org/abs/2405.07071</p> <p>Interactive visualization site: https://vis.csh.ac.at/colocation-suppliers/</p> <p>The related research by S&aacute;ndor Juh&aacute;sz was supported by the European Union&rsquo;s Marie Sklodowska-Curie Postdoctoral Fellowship Program (SUPPED, grant number 101062606).</p> <p>The data preparation was done with the help of the Databank of HUN-REN Centre for Economic and Regional Studies. The shared datasets are based on the value-added tax return data files of the Hungarian Central Statistical Office. The calculations and conclusions drawn from them are the sole intellectual property of the authors.</p> <p>&nbsp;</p>

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

Integration of RISE innovations in the fields of OELF, RLA and SHM: input and output datasets (Version 1.0)

<p>Repository of input and output data files associated with Deliverable 6.1 of the <a href="http://rise-eu.org/home/">RISE project</a>.</p> <p>Full deliverable available <a href="http://static.seismo.ethz.ch/rise/deliverables/Deliverable_6.1.pdf">here</a>.</p>

opencc-by-sa-4.0Mar 2023View details →
zenodo44/100

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)

<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2&nbsp;(Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)</p>

opencc-by-4.0May 2023View details →
zenodo44/100

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)

<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2&nbsp;(Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)</p>

opencc-by-4.0May 2023View details →
zenodo44/100

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)

<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2&nbsp;(Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)</p>

opencc-by-4.0May 2023View details →
zenodo44/100

Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"

<p><strong>File 1:</strong> raw_data_BOULDERING.zip</p> <p><strong>Size:</strong> 8.8 GB</p> <p><strong>Summary: </strong>It contains all of the rasters (planetary images) and labeled boulders (raw data):</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches within a raster.</p> </li> </ul> <p>There are multiple locations/images per planetary body.</p> <p><strong>Structure:</strong></p> <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │ &nbsp; ├── shp/ │ &nbsp; │ ├── &lt;image_name&gt;-ROM.shp │ &nbsp; │ ├── &lt;image_name&gt;-boulder-mapping.shp │ &nbsp; │ └── &lt;image_name&gt;-global-tiles.shp │ &nbsp; └── raster/ │ &nbsp; &nbsp; └── &lt;image_name&gt;.tif ├── mars/ │ └── image_name/ │ &nbsp; ├── shp/ │ &nbsp; │ ├── &lt;image_name&gt;-ROM.shp │ &nbsp; │ ├── &lt;image_name&gt;-boulder-mapping.shp │ &nbsp; │ └── &lt;image_name&gt;-global-tiles.shp │ &nbsp; └── raster/ │ &nbsp; &nbsp; └── &lt;image_name&gt;.tif └── moon/ &nbsp; └── image_name/ &nbsp; &nbsp; ├── shp/ &nbsp; &nbsp; │ ├── &lt;image_name&gt;-ROM.shp &nbsp; &nbsp; │ ├── &lt;image_name&gt;-boulder-mapping.shp &nbsp; &nbsp; │ └── &lt;image_name&gt;-global-tiles.shp &nbsp; &nbsp; └── raster/ &nbsp; &nbsp; &nbsp; └── &lt;image_name&gt;.tif</pre> <p>&nbsp;</p> <p><strong>File 2:</strong> best_model.zip</p> <p><strong>Size:</strong> 624.7 MB</p> <p><strong>Summary:</strong></p> <p>This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics):</p> <ul> <li> <p>augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information.</p> </li> </ul> <ul> <li> <p>Base-RCNN-FPN.yaml (base model setup file).</p> </li> <li> <p>config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file).</p> </li> <li> <p>Mars-MoonEarth-v050...yaml (model setup file).</p> </li> <li> <p>log.txt (log during training of the algorithm).</p> </li> <li> <p>model_0055999.pth (model weights at second last saving step)</p> </li> <li> <p>model_0063999.pth (model weights at last saving step)</p> </li> </ul> <p>We advice the use of model weights model_0055999.pth (to avoid slight overfitting).</p> <p><strong>File 3:</strong> Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images)</p> <p><strong>Size:</strong> 252.8 MB</p> <p><strong>Summary:</strong></p> <p>This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files.</p> <ul> <li> <p>The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use &quot;Apr2023-Mars-Moon-Earth-mask-5px.json&quot;.</p> </li> <li> <p>The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset.</p> </li> <li> <p>The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles.</p> </li> <li> <p>The shapefile folder is actually empty (it should not be there!).</p> </li> </ul> <p><strong>Structure:</strong></p> <pre>. └── preprocessed_inputs/ &nbsp; ├── json &nbsp; ├── pkl &nbsp; ├── preprocessing/ &nbsp; │ &nbsp; ├── train/ &nbsp; │ &nbsp; │ &nbsp; ├── images &nbsp; │ &nbsp; │ &nbsp; └── labels &nbsp; │ &nbsp; ├── validation/ &nbsp; │ &nbsp; │ &nbsp; ├── images &nbsp; │ &nbsp; │ &nbsp; └── labels &nbsp; │ &nbsp; └── test/ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── images &nbsp; │ &nbsp; &nbsp; &nbsp; └── labels &nbsp; └── shp</pre> <p>&nbsp;</p>

opencc-by-4.0Jul 2023View details →
zenodo40/100

Data used in "Fast metabolite identification with Input Output Kernel Regression"

<p>This repository contains the data used in [1] to evaluate the performance for metabolite identification from tandem mass spectra. These data have been extracted and processed in [2]. We used a subset of 4138 MS/MS spectra extracted from the GNPS public spectral library (https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp) for training and evaluation. For searching, we used molecular structures from PubChem as candidate sets.</p> <p>Please mention and cite GNPS when using these data.</p> <p>The implementation of the method proposed in [1] is available on: https://version.aalto.fi/gitlab/kepaco/Fast-metabolite-identification-with-IOKR</p> <p><strong>Files description:</strong></p> <ul> <li><em>spectra.txt</em>: informations about the MS/MS spectra (GNPS identifier, compound name and INCHI identifier)</li> <li><em>data_GNPS.mat</em>: contains the molecular fingerprints, molecular formula and InCHI corresponding to the MS/MS spectra</li> <li><em>cv_ind.txt</em>: indices of the cross-validation folds</li> <li><em>ind_eval.txt</em>: indices of the examples used for evaluation</li> <li><em>candidates</em>: fingerprints and INCHI for the different candidate sets</li> <li><em>input_kernels</em>: contains 24 input kernel matrices</li> </ul> <p><strong>References:</strong></p> <p>[1] Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression. In the proceedings of ISMB 2016, Bioinformatics 32(12): i28-i36, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw246</p> <p>[2] Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS, 112(41), 12580-12585, 2015. doi:10.1073/pnas.1509788112</p> <p> </p> <p> </p>

opencc-by-4.0Jun 2017View details →

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