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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

Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing (code, data and scripts to reproduce paper results)

<p>This repository contains the data, code, and scripts required to reproduce the results of the paper &quot;Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing&quot; by Daniele De Sensi, Salvatore Di Girolamo and Torsten Hoefler, presented at the 2019 International Conference for High Performance Computing, Networking, Storage, and Analysis.&nbsp;</p> <p>This repository does not contains the code of the library used to automatically tune the routing algorithm, which can be found at http://doi.org/10.5281/zenodo.3372785</p>

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

Dataset defining representative route network for GLOWOPT market segments

<p>For calculating the GLOWOPT representative route network, a forecast model chain was used. The model was calibrated with 2019 flight movement data (unimpeded by COVID-19) and provided forecasted aircraft movements from the year 2019 (~2020) to 2050 in 5 years intervals.</p> <p>Two formats of datasets are generated with the results of the forecast model chain, a csv file format and 4-dimensional array supported with MATLAB (.mat).</p> <p><strong>CSV Datasets</strong></p> <p>For each forecasted year a csv file is generated with the information on the origin-destination (OD) airports IATA codes, region, latitude and longitude of OD pair, representative aircraft type along with the aircraft category , the average load factor and finally, the distance between the OD pair. The airports worldwide are sub-dived into nine regions namely Africa, Asia, Caribbean, Central America, Europe, Middle East, North America, Oceania and South America. There are total of seven datasets, one for each forecasted year i.e. for years 2019 (~2020), 2025, 2030, 2035, 2040, 2045 and 2050.</p> <p><strong>Description of the data labels:</strong></p> <p><strong>Origin-</strong> Origin airport IATA code</p> <p><strong>Origin_Region-</strong> Region of the Origin Airport</p> <p><strong>Origin_Latitude-</strong> Latitude of the Origin Airport</p> <p><strong>Origin_Longitude-</strong> Longitude of the Origin Airport</p> <p><strong>Destination-</strong> Destination airport IATA code</p> <p><strong>Destination_Region-</strong> Region of the Destination Airport</p> <p><strong>Destination_Latitude-</strong> Latitude of the Destination Airport</p> <p><strong>Destination_Longitude-</strong> Longitude of the Destination Airport</p> <p><strong>AcType- </strong>Representative aircraft type</p> <p><strong>Load_Factor- </strong>Average load factor per flight</p> <p><strong>Yearly_Frequency-</strong> Total aircraft movements per annum</p> <p><strong>RefACType-</strong> Aircraft Category based on number of seats (Category 6 represents aircraft with seats 252-301 and category 7 represents aircraft with seats greater than 302.)</p> <p><strong>Distance-</strong> Great circle distance between Origin and Destination in Km.</p> <p>&nbsp;</p> <p><strong>MATLAB Datasets</strong></p> <p>The dataset generated with MATLAB is a 4-dimensional array with the extension *.mat. The first dimension is the region of the origin airport and subsequently the second dimensions contains the region of the destination airport. The third and fourth dimension are the aircraft category based on seat numbers and the categorized great circle distances. The information received therein is a 1X1 cell with the IATA codes of the OD pairs, frequency and great circle distance in Km.</p> <p>The 4D array is categorised such that the user can select the route segment specific to a region or a combination of regions. The range categorisation in combination with an aircraft category additionally offers the user the possibility to select routes depending on their great circle distances. The ranges are categorised to represent very short range (0-2000 km), short range (2000-6000 km), medium range (6000-10000 km) and long range (10000 &ndash; 15000 km).</p> <p><strong>Indexing based on the categorisation of the 4D array dataset</strong> - Refer to file &#39;Indexing_MAT_Dataset.PNG&#39;</p> <p>For example:</p> <p>To derive the OD pairs and yearly frequency of aircraft movements for routes which originate from Europe and are destined to Asia, operated with category 6 aircraft type and are separated by distances between 10,000 to 15,000 km:</p> <p><strong>In MATLAB (Indexing based on file </strong> &#39;Indexing_MAT_Dataset.PNG&#39; <strong>): </strong></p> <p><strong>Route_Network (5,2,1,4), </strong></p> <p>Description on Index:</p> <p>5 &ndash; Europe: Origin Region&nbsp;</p> <p>2 &ndash; Asia: Destination Region</p> <p>1&ndash; Category 6: Aircraft Type</p> <p>4 &ndash; 10000-15000 km: Range</p>

opencc-by-4.0Jul 2021View details →
zenodo44/100

Traffic network routing index for the Czech Republic

<p><strong>Graph representation of road network of the Czech Republic</strong></p> <p>This dataset contains graph of the entire Czech road network. The data are derived from the Open Street Map project and stored in a HDF5 file. The file contains graph topology and metadata for edges and vertices.&nbsp;</p> <p>Spatial index is also included for the purpose of point snapping and other spatial queries. The spatial index is stored in a SQLite file with SpatiaLite extension.</p> <p>Creation of this dataset has been supported by the <a href="http://antarex-project.eu">Antarex project</a>.</p> <p><strong>Routing Index in HDF5</strong></p> <p><strong>File:&nbsp;</strong><a href="/api/files/f1fb6db8-a2e2-425b-b15e-8e401eb22d44/CZE-1528295206-proc-20180724134457.hdf?versionId=3294ae0b-c3b2-4155-ad36-311d2bb06ed9">CZE-1528295206-proc-20180724134457.hdf</a></p> <p>The index is divided into parts according to geographical boundaries defined by country borders. In this case it contains only single part - CZE. This information along with the creation time is stored as an attribute in the root group of the file.</p> <p>The graph topology is stored in the following way. Nodes have assigned a row index in the edges dataset which points to an outbound edge plus a number of the subsequent edges which are also output to this node. The edge metadata are stored in the EdgeData dataset to avoid redundancy. NodeMap dataset provides a convenient way to query nodes based on their uniqe identifiers.</p> <p><strong>File structure:</strong></p> <pre><code class="language-javascript">HDF5 "CZE-1528295206-proc-20180724134457.hdf" { GROUP "/" { GROUP "Index" { ATTRIBUTE "CreationTime" { DATATYPE H5T_STD_I64LE DATASPACE SCALAR DATA { (0): 1535451896 } } ATTRIBUTE "PartsCount" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 1 } } ATTRIBUTE "PartsInfo" { DATATYPE H5T_COMPOUND { H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } "id"; H5T_STD_I64LE "nodeCount"; H5T_STD_I64LE "edgeCount"; } DATASPACE SIMPLE { ( 1 ) / ( 1 ) } DATA { (0): { "CZE\000", 904085, 2223222 } } } GROUP "CZE" { ATTRIBUTE "PartInfo" { DATATYPE H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SCALAR DATA { (0): "CZE\000" } } DATASET "Edges" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "edgeId"; H5T_STD_I32LE "nodeIndex"; H5T_STD_I32LE "computed_speed"; H5T_STD_I32LE "length"; H5T_STD_I32LE "edgeDataIndex"; } DATASPACE SIMPLE { ( 2223222, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } DATASET "Nodes" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "id"; H5T_STD_I32LE "latitudeInt"; H5T_STD_I32LE "longtitudeInt"; H5T_STD_U8LE "edgeOutCount"; H5T_STD_I32LE "edgeOutIndex"; H5T_STD_U8LE "edgeInCount"; } DATASPACE SIMPLE { ( 904085, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } } DATASET "EdgeData" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "id"; H5T_STD_U8LE "speed"; H5T_STD_U8LE "funcClass"; H5T_STD_U8LE "lanes"; H5T_STD_U8LE "vehicleAccess"; H5T_STD_U16LE "specificInfo"; H5T_STD_U16LE "maxWeight"; H5T_STD_U16LE "maxHeight"; H5T_STD_U8LE "maxAxleLoad"; H5T_STD_U8LE "maxWidth"; H5T_STD_U8LE "maxLength"; H5T_STD_I8LE "incline"; } DATASPACE SIMPLE { ( 2838, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } DATASET "NodeMap" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "nodeId"; H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } "partId"; H5T_STD_I32LE "nodeIndex"; } DATASPACE SIMPLE { ( 904085, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } } } }</code></pre> <p><strong>Spatial index</strong></p> <p><strong>File:&nbsp;</strong><a href="https://zenodo.org/api/files/f1fb6db8-a2e2-425b-b15e-8e401eb22d44/CZE-1528295206-proc-20180829135251.sqlite?versionId=56bb1e60-ff7c-407f-8041-3c628ef78db0">CZE-1528295206-proc-20180829135251.sqlite</a><br> &nbsp;</p> <p>SQLite database contains two primary tables. Table&nbsp;<em>nodes</em>&nbsp;and table&nbsp;<em>segments</em>, that&nbsp;resluts&nbsp;of selection and projection from the tables of the same name in primary db. Both tables are suitable for&nbsp;<em>searching nearest lines</em>&nbsp;task and&nbsp;<em>searching closest node of nearest line</em>&nbsp;task.&nbsp;</p> <pre><code class="language-sql">CREATE TABLE nodes  (      gid INTEGER,      part_gid INTEGER,      node_type INTEGER,      geom POINT  );  CREATE TABLE segments  (      gid INTEGER,      node_gid_from INTEGER,       node_gid_to INTEGER,      frc TEXT,      transition_time DOUBLE,      computed_speed REAL,      geom_length DOUBLE,      geom LINESTRING  ); </code></pre> <p>There are three more tables. Table&nbsp;<em>segments_rt</em>&nbsp;is a copy of table&nbsp;<em>segments</em>, that exclude loops in segments, hence it supports network routing task in&nbsp;SpatiaLite. Next table is&nbsp;<em>rt_network</em>&nbsp;(static graph generated from table&nbsp;<em>segments_rt</em>&nbsp;suitable for routing). The last one is table&nbsp;<em>virtual_rt_network</em>, that is an interface for routing query.&nbsp;&nbsp;</p> <p>&nbsp;</p>

openodc-byDec 2018View details →
zenodo40/100

Local Optima Network Analysis of Multi-attribute Vehicle Routing Problem

<p>Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), there are other restrictions that model diverse real-life system attributes. Among the most common attributes studied in the literature are the vehicle capacity and the maximum route length constraints. The impact of these restrictions on the overall structure of the problem and on the performance of local search algorithms used to solve it is not well known. This paper aims to explain how constraints impact different variants of VRP by altering the structure of the underlying search space. We focus on the analysis of Local Optima Networks (LON) for multiple Traveling Salesman Problem (m-TSP), and VRP with capacity (CVRP), distance (DVRP), and both (DCVRP) constraints. We present results that indicate that metrics obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the constraints of the problem.&nbsp;<br> The dataset contains the data extracted from the local optima network&nbsp;for a set of variants belonging to the family of vehicle routing problems.</p>

opencc-by-4.0May 2022View details →
dryad40/100

Data from: Multiplexed subspaces route neural activity across brain-wide networks

Open the record for dataset details and reuse information.

publicMar 2025View details →
zenodo36/100

Simulation results of routing algorithms for multilayer networks

<p>Most current routing protocols are based on path computation algorithms in graphs (e.g., Dijkstra, Bellman-Ford, etc.). These algorithms have been studied for a long time and are very well understood, both in a centralized and distributed context, as long as they are applied to a network having a single communication protocol. The problem becomes more complex in the multi-protocol case, where there is a possibility of encapsulation of some network protocols into others, therefore inducing nested tunnels. The classic algorithms cited above no longer work in this case because they cannot manage the protocol encapsulations and the corresponding protocol stacks. In this work, we propose a highly parallelizable algorithm that takes into account protocol encapsulations as well as protocol conversions in order to compute shortest paths in a multi-protocol network. To achieve this computation efficiently, we study the transitive closure between subpaths (i.e., the concatenation of two subpaths to obtain a longer one) in the case where each subpath induces a protocol stack, and thus tunnels. Leveraging on Software-Defined Networks with a controller having a highly parallel architecture enables us to compute the routing tables of all nodes in a very efficient way. Experimentation results on both random and realistic topologies show that our algorithm outperforms the previous solutions proposed in the literature.</p>

opencc-by-4.0Oct 2023View details →
zenodo36/100

Reliable Many-to-Many Routing in Wireless Sensor Networks Using Ant Colony Optimisation

<p>Results files for testing of ACO protocol for many to many routing in wireless sensor networks.&nbsp;</p>

opencc-by-4.0Mar 2019View details →
zenodo36/100

MUSE: Multimodal Separators for Efficient Route Planning in Transportation Networks

<p>This dataset was used in the experimental evaluation of the MUSE route planning algorithm. It encompasses the Ile-de-France multimodal network. The dataset contains:</p> <p>1. The raw osm file of the Ile-de-France region from OpenStreetMap.</p> <p>2. The raw GTFS data for the public transit network.</p> <p>3. The multimodal graph based on 5 partitions: 100, 200, 300, 400, and 500 cells.</p> <p>4. The Nondeterministic Finite Automata (NFA) used during the preprocessing and query stages of MUSE.</p> <p>5. The graph overlay evaluated during&nbsp;the preprocessing stage of MUSE.</p> <p>To test&nbsp;MUSE and review the details of this dataset,&nbsp;please visit https://github.com/aminefalek/muse</p>

opencc-by-4.0Aug 2021View details →
zenodo32/100

Reproducibility Package for SyRep: Efficient Synthesis and Repair of Fast Re-Route Forwarding Tables for Resilient Networks

<p>This is a reproducibility package for the paper SyRep: Efficient Synthesis and Repaird of Fast Re-Route Forwarding Tables for Resilient Networks that is going to appear at DSN'24 conference.</p>

opencc-by-4.0Apr 2024View details →
zenodo32/100

CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks - Dataset

<p>This contains the raw and processed data for the paper "CoolWalks: Assessing the potential of shaded routing for active mobility in urban street networks" by H. Wolf, M. Szell and A. R. Vier&oslash;.</p>

openMay 2024View details →
zenodo32/100

Deploying Near Optimal Delay Constrained Paths with Segment-Routing in Massive Scale Networks

<p>Dataset consisting of 5 large topologies used for the evaluation of Best2cop - multi-area version.&nbsp;<br> <br> The topologies themselves are written as a list of edges following the format :&nbsp;<br> <strong>source target delay cost area</strong>&nbsp;<br> &quot;cost&quot; being the IGP cost of the link, and &quot;area&quot;, the IGP area to which the edge belongs.</p> <p>Each topology has a descriptive file showcasing some of the network&#39;s properties (density, diameter...) as well as an HTML file allowing to see the topology on an actual world map. Note that while we consider the road infrastructure, the links are represented in an abstract fashion for readability purposes.</p> <p>&nbsp;</p>

opencc-by-4.0Sep 2021View details →
zenodo32/100

Metnet Auxiliary Route Network Illustrations

<p><strong>Metnet Auxiliary Route Network Illustrations</strong></p> <ul> <li> <p>This repository contains interactive illustrations for the auxiliary networks at 25&ordm;C and 37&ordm;C defined in Gopalakrishnan Meena and Lane et al. PNAS Nexus 2023.</p> </li> </ul> <p><strong>Interactive Site:</strong></p> <ul> <li> <p>The interactive visualization can be <a href="https://web.eecs.utk.edu/~mlane42/">found here.</a></p> </li> </ul> <p><strong>Authors:</strong></p> <p>Matthew J. Lane and Muralikrishnan Gopalakrishnan Meena&nbsp;</p> <p><strong>Reference</strong>:</p> <p>Gopalakrishnan Meena and Lane et al., &quot;A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs&quot;, PNAS Nexus, 2023 <a href="https://doi.org/10.1093/pnasnexus/pgad322">https://doi.org/10.1093/pnasnexus/pgad322</a></p>

opencc-by-4.0Oct 2023View details →
zenodo24/100

Data for manuscript: Sediment Routing and Floodplain Exchange (SeRFE): A spatially explicit model of sediment balance and connectivity through river networks

<p>Data used to calibrate and run the SeRFE model in the applications presented in the manuscript &quot;Sediment Routing and Floodplain Exchange (SeRFE): A spatially explicit model of sediment balance and connectivity through river networks.&quot;</p>

opencc-byApr 2020View details →
zenodo24/100

Solving the multi-commodity flow problem using an evolutionary routing algorithm in a computer network environment

<p>The continued increase in Internet traffic requires that routing algorithms make the best use of all available network resources. Most of the current deployed networks are not doing so due to their use of single path routing algorithms. In this work we propose the use of a multipath capable routing algorithm using Evolutionary Algorithms (EAs) that takes into account all the traffic going over the network and the link capacities by leveraging the information available at the Software Defined Networks (SDN) controller. The use of such information ensures that no link is used beyond its capacity, eliminating network congestion. We use EAs as true multi-objective solvers to provide a set of valid routing solutions from a single run of the algorithm. Modifications to the Multipath TCP (MPTCP) protocol are proposed to overcome the multipath problems associated with TCP.</p>

opencc-by-4.0Jun 2022View 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

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