Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,140
datasets available to search
ShareScore release 0.7.1
Dataset results
9,140 results for “networking”
Heterotrophic Respiration from Nine Nutrient Network Grasslands in North America
This experiment tracks the response of soil heterotrophic respiration (i.e., soil organic matter decomposition), enzyme expression, and microbial biomass in nine US grasslands participating in the globally-distributed Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year). These samples in the present dataset were collected at the same time as soil and plant samples for https://doi.org/10.6073/pasta/7f984c2ed9e63754577ee711e6d74a6e.
Structural brain network of gifted children
Open the record for dataset details and reuse information.
Fine-scale structure of the 2016-2017 Central Italy Seismic Sequence from data recorded at the Italian National Network
<p><strong>Data Set </strong></p> <p>Catalog of 33,983 earthquakes located during the 2016-2017 Central Italy seismic sequence. The velocity model used is the 1D gradient P- and S-wave velocity models (after Carannante et al., 2013). We used the highest quality P- and S-wave arrival times manually picked by analysts of the National Institute of Geophysics and Volcanology (INGV) seismic monitoring room, having an uncertainty lower than 0.6 s. </p> <p>Events were located by means of a 2-step procedure: the INGV routine absolute locations computation for all events with ML ≥ 1.5 that occurred in the study area between August 2016 and January 2018, using the method described in Chiaraluce et al. (2017); the determination of relative locations by applying the HypoDD code (Waldhauser, 2001) to the catalog picks and phase delay times measured from waveform cross correlation.</p> <p>The time domain cross-correlation method (Schaff et al., 2004; Schaff and Waldhauser, 2005) was applied to seismograms of all pairs of events separated by 3 km or less and recorded at common stations. Seismograms were filtered in the 1-15 Hz frequency range using a 4 pole, zero phase band‐pass Butterworth filter. The correlations measurements were performed on 0.7 s long window for P-waves and 1 s windows for S-waves. Only measurements with correlation coefficients greater than 0.7 were kept, resulting in a total of ~4.4 million P and ~1.1 million S wave delay times. </p> <p>We sub-divided the entire dataset in 18 rectangular boxes, containing a maximum of 6000 earthquakes, orthogonal to and centered on the mean strike of the seismic sequence. The overlap between neighboring boxes is 50% with respect to the NW-SE extension. HypoDD is run separately on each box. Resulting relative locations from all boxes were combined into a single catalog, computing the weighted mean of double hypocenters in the overlapping regions (Waldhauser and Schaff, 2008).</p> <p>The final double-difference catalog includes 33,982 events occurring between 24<sup>th</sup> of August 2016 and 18<sup>th</sup> of January 2018.</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-ingv: ingv eventid, useful to link to the QuakeML phase file through the INGV fdsnws/event webservice (<a href="https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2Fwebservices.ingv.it%2Fswagger-ui%2Fdist%2F%3Furl%3Dhttps%3A%2F%2Fingv.github.io%2Fopenapi%2Ffdsnws%2Fevent%2F0.0.1%2Fevent.yaml">http://webservices.ingv.it/swagger-ui/dist/?url=https://ingv.github.io/openapi/fdsnws/event/0.0.1/event.yaml</a>) and to the reported magnitude;</li> <li>Latitude(°) expressed in decimal degrees;</li> <li>Longitude(°) expressed in decimal degrees;</li> <li>Depth(km) hypocentral depth expressed in kilometers;</li> <li>Year of origin time in the format yyyy;</li> <li>Month of origin time in the format mm;</li> <li>Day of origin time in the format dd; </li> <li>Hour of origin time in the format hh;</li> <li>Minute of origin time in the format min;</li> <li>Second of origin time in the format ??.?????? s;</li> <li>Magnitude: the value available at the phases downloading time (see Id-ingv fdsnws/event)</li> </ul> <p> </p> <p> </p> <p> </p> <p><br> </p>
Leaf Vein Network CNN Results
<p>Results for leaf vein networks extracted using the LeafVeinCNN software package. The original image data set is available from Blonder et al. (2019) <a href="https://doi.org/10.1002/ecy.2844">https://doi.org/10.1002/ecy.2844</a>. The LeafVeinCNN software used in the analysis is available at <a href="https://doi.org/10.5281/zenodo.4007731"> </a><a href="https://doi.org/10.5281/zenodo.4007730">https://doi.org/10.5281/zenodo.4007730</a></p> <ul> <li>The Results_xxx.zip files contain all the Excel results spreadsheets separated by the code for each field site.</li> <li>results.xls provides a summary of all the network metrics for each file that was analysable</li> <li>Results_figures.pdf provides a summary image of the processing steps and results for each leaf segment</li> <li>Network_images.pdf contains a colour-coded image of each network superimposed on the leaf segment</li> <li>HLD_plots shows the binary tree following Hierarchical Network Decomposition</li> <li>PR_results.zip contains the Excel spreadsheets for evaluation of different enhancement methods for each leaf segment.</li> <li>PR_summary.xls provides a summary of the performance of each enhancement method.</li> <li>PR_F1_images.pdf and PR_FBeta2_images.pdf show the pixel classification for each enhancement and segmentation method compared to the manual ground-truth using two different optimum criteria (F1 and FBeta2).</li> <li>PR_fullwidth_plots show the full Precision-Recall plots for the full-width binary image compared to the manual ground-truth using the FBeta2 metric.</li> <li>PR_skeleton_plots show the full Precision-Recall plots for the skeletonised binary image compared to the manual ground-truth using the FBeta2 metric.</li> <li>PR_threshold_plots.pdf show how a set of network metrics vary with the segmentation threshold for each enhancement method.</li> </ul>
Fedora and Debian software package dependency networks along with description text associated with nodes
<p>Fedora (version 28) and Debian (version 9.5) software package dependency networks along with description text associated with nodes. Also includes learned vectors by using PCTADW-* as in "Kexuan Sun, Shudan Zhong, and Hong Xu. 2020. Learning Embeddings of Directed Networks with Text-Associated Nodes---with Application in Software Package Dependency Networks. 2020 BigGraphs Workshop at IEEE BigData 2020."</p>
LoRaWAN Dense Indoor Sensor Network (DISN) Transmission Meta Data
<p>We present a large data of indoor Long Range Wide Area Network (LoRaWAN) network metadata to study Dense Indoor Sensor Networks (DISN). We collected 14 million transmissions from 390 sensors between date February 2020 and date September 2020. The transmissions have been received by 3 gateways across 8 floors and distances up to 64 m. The prototype will run in the background throughout the project and the data set will be regularly updated.</p> <p> </p>
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, Cédric H., David R. Maidment, Guo-Yue Niu, Zong-Liang Yang, Florence Habets and Victor Eijkhout (2011), River Network Routing on the NHDPlus Dataset, Journal of Hydrometeorology, 12(5), 913-934. DOI: 10.1175/2011JHM1345.1. </li> </ul> <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 National Hydrography Dataset Plus (NHDPlus) Version 1, obtained from http://www.horizon-systems.com/nhdplus. </li> <li>The National Water Information System (NWIS), obtained from http://waterdata.usgs.gov/nwis. </li> <li>Outputs from a simulation using the community Noah land surface model with multiparameterization options (Noah-MP, Niu et al. 2011, http://www.jsg.utexas.edu/noah-mp). The simulation was run by Guo-Yue Niu, and produced 3-hourly time steps from 2004-01-01T00:00+00:00 to 2008-01-01T00:00+00:00. Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul> <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 1.0.0. Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).</li> <li>ESRI ArcGIS (http://www.arcgis.com). </li> <li>Microsoft Excel (https://products.office.com/en-us/excel). </li> <li>CUAHSI HydroGET (http://his.cuahsi.org/hydroget.html). </li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). </li> </ul> <p> </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. RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 5,175). The temporal range corresponding to this domain is from 2004-01-01T00:00-06:00 to 2007-12-31 T00:00-06:00.</li> <li>The Upper Mississippi River Basin. RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 182,240). The temporal range corresponding to this domain spans 100 fictitious days.</li> </ul> <p> </p> <p><strong>Description of files for the San Antonio and Guadalupe River Basins</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_San_Guad.csv.</em> This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs). For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of COMID. The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE. This file was prepared using ArcGIS and Excel.</li> <li><em>m3_riv_San_Guad_2004_2007_cst.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 COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007/12/31T18:00-06:00. The values were computed by superimposing a 900-m gridded map of NHDPlus catchments to the outputs of Noah-MP. This file was prepared using ArcGIS and a Fortran program.</li> <li><em>kfac_San_Guad_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 COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using a wave celerity of 1 km/h. This file was prepared using a Fortran program.</li> <li><em>kfac_San_Guad_celerity.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 COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using the wave celerity numbers of Table 2 in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_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 NHDPlus fields: COMID, LENGTHKM, and using Equation (17) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_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 NHDPlus fields: COMID, LENGTHKM, and using Equation (18) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_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 NHDPlus fields: COMID, LENGTHKM, and using Equation (19) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_San_Guad_2004_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 NHDPlus fields: COMID, LENGTHKM, and using Equation (21) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_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 Equation (17) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_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 Equation (18) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_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 Equation (19) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_San_Guad_2004_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 Equation (21) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>basin_id_San_Guad_hydroseq.csv. </em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the San Antonio and Guadalupe River Basins. The river reaches are sorted from upstream to downstream. The values were computed using the following NHDPlus fields: COMID and HYDROSEQ. This file was prepared using Excel.</li> <li><em>Qout_San_Guad_1460days_p1_dtR=900s.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 COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p2_dtR=900s.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 COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p3_dtR=900s.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 COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_San_Guad_1460days_p4_dtR=900s.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 COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><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. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><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. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><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. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (19) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><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. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_San_Guad_hydroseq.csv</em>. The time range for this file is from 2004-01-01T00:00-06:00 to 2004-07-01-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (21) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> <li><em>gage_id_San_Guad_2004_2007_full.csv. </em>This CSV file contains the list of COMIDs of rivers containing USGS gauges and with full daily data record. The river reaches are sorted in increasing value of COMID. The time range used for determining a full record is daily from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00. The values were computed using the following NHDPlus field: COMID. This file was prepared using ArcGIS, HydroGET, and Excel.</li> <li><em>Qobs_San_Guad_2004_2007_full.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same COMIDs and are sorted similarly to <em>gage_id_San_Guad_2004_2007_full.csv</em>. The time range for the daily values is from 2004-01-01T00:00-06:00 to 2008-01-01T00:00-06:00. The values were computed using the following NHDPlus field: COMID, and the observations from NWIS. This file was prepared using ArcGIS, HydroGET, and Excel.</li> </ul> <p> </p> <p><strong>Description of files for the Upper Mississippi River Basin</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_Reg07.csv. </em>This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs). For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of COMID. The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE. This file was prepared using ArcGIS and Excel. </li> <li><em>m3_riv_Reg07_100days_dummy.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 COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>. The time range for this file is for 100 fictitious days. The values were computed using a unique value of 1 cubic meter for all river reaches and all time steps. This file was prepared using a Fortran program.</li> <li><em>kfac_Reg07_2.5ms.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 COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (22) in David et al. (2011). This file was prepared using a Fortran program. </li> <li><em>xfac_Reg07_0.3.csv. </em>This CSV file contains a first guess of Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_Reg07.csv</em>. The values were computed based on Equation (22) in David et al. (2011). This file was prepared using a Fortran program. </li> <li><em>basin_id_Reg07_hydroseq.csv. </em>This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the Upper Mississippi River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the following NHDPlus fields: COMID and HYDROSEQ. This file was prepared using Excel.</li> <li><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. The river reaches have the same COMIDs and are sorted similarly to <em>basin_id_Reg07_hydroseq.csv</em>. The time range for this file spans 100 fictitous days. The values were computed using the Muskingum method with parameters of Equation (22) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.</li> </ul> <p> </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. The contribution from the Missouri River is therefore not accounted for in the network connectivity corresponding to the Upper Mississippi River Basin. This has no effect on the conclusions of David et al. (2011) since the Upper Mississippi River Basin was studied with synthetic data and solely to evaluate parallel performance of RAPID.</p> <p> </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>
Pythia Generated Jet Images with Alternative Rotation Scheme for Location Aware Generative Adversarial Network Training
<p>Dataset containing 300k jet images that can be used to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics, such as the one in [arXiv:1701.05927].</p> <p><strong>Format</strong>:</p> <p>HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (300000, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., <em>Jet-Images -- Deep Learning Edition </em>[arXiv:1511.05190]</li> <li>scikit-image==0.10.0 implementation of cubic spline rotation with fewer low energy artifacts than scikit-image>=0.12.0</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Dataset supporting the paper: Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks
<p>Dataset supporting the paper:</p> <p>Matthew England, Hassan Errami, Dima Grigoriev, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber. Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks. In Proceedings of CASC ’17, Beijing, China, September 18-22 2017, 15 pages. Springer, 2017.</p> <p>The files whose name starts with "SamplePoints" are text files containing the data that produced the plots in the paper.</p> <p>The files whose name starts with "Sys" show the Maple computations used to produce the data. The mw files are to be run with the Maple Computer Algebra System (https://www.maplesoft.com/products/maple/). Pdf printouts of these have also been included for those who do not have access to Maple.</p> <p> </p>
ADS-C Air Traffic Data Collected by the OpenSky Network
<p>ADS-C data collected by the OpenSky Network since 7th July 2023. </p> <p>Data underlying (Version 1.1)</p> <h1>A First Look at Exploiting the Automatic Dependent Surveillance-Contract Protocol for Open Aviation Research</h1> <p>https://journals.open.tudelft.nl/joas/article/view/7229</p>
Data for "PTP Over Wide Area Networks With Offset Measurement Outlier Filtering"
<p>Dataset used in the manuscript "PTP Over Wide Area Networks With Offset Measurement Outlier Filtering". This dataset contains synchronization accuracy measurements over long distance links using both NTP and PTP, as well as synthetically generated PTP replays used for offline testing.</p> <p>A detailed description of the contents is found in the <code>README.md</code> file at the root of the dataset.</p>
SCLabels: Labelled rectified RGB images from the Spanish CoastSnap network
<h1>Training dataset</h1> <p><span>The SCLabels dataset is intended to be used in the exploring and development of Artificial Intelligence (AI) applications aimed at the automation of the shoreline extraction process from rectified images. SCLabels includes rectified RGB images from the Spanish CoastSnap network and their corresponding masks, together with a metadata file and a README file. RGB images encompass variable geographic locations, fields of view, beach types and degrees of occupation, tidal regimes, meteoceanic and lightning conditions, and a variety of environmental characteristics. Masks account for dense pixel labels including 5 categories: i) No data; ii) Not classified; iii) Landwards; iv) Seawards; and v) Shoreline. In the metadata file, images are linked to their corresponding masks, and information about the geographic location of each image, capture characteristics and image source, shoreline position and other auxiliary data are provided. The README file enhances the explainability and comprehension of the dataset, elaborating on the context and contents, and providing detailed explanations of the metadata, potential limitations, technical aspects of the image processing and annotation stages, usage recommendations, and related works. </span></p> <h1>Technical details</h1> <p>The SCLabels dataset version 1.0.0 is packaged in a compressed file (SCLabels_v1.0.0.zip). A total of 1717 RGB images are shared in JPG format, corresponding masks in PNG format, a metadata file in JSON format, and the README file in PDF format.</p> <h2>Data preprocessing</h2> <p><span>To generate the SCLabels masks, rectified RGB images and their corresponding shorelines were used. RGB images were cropped to the minimum and maximum alongshore pixel coordinates of the shoreline (vertical axis) plus 10 additional pixels above and below to preserve contextual information. A grayscale image was then derived from each cropped RGB image for subsequent pixel labelling. First, a binary mask was derived, marking "NoData'' for black and white padded pixels resulting from the registration and rectification steps. Subsequently, the shoreline was densified, ensuring at least one pixel per row was assigned the "Shoreline" label. Next, "Landwards" and "Seawards" labels were assigned to the right and left of the shoreline. Pixels left unlabelled were categorised as "NotClassified". Finally, masks’ values were reclassified to align with the predefined labels, and the grayscale masks were exported. For additional information, please consult the README file. </span></p> <h2>Data splitting</h2> <p><span>Data splitting requirements may vary depending on the chosen AI approach (e.g., splitting by entire images, image patches, or image rows). Researchers should use a consistent data splitting method and document the approach and splits used in publications. This transparency enables reproducible results and facilitates comparisons between studies.</span></p> <h2>Classes, labels and annotations</h2> <p><span>The SCLabels dataset includes one mask per rectified RGB image, sharing the same width and height. These masks are in greyscale and PNG format, and consist of five different labels:</span></p> <table> <tbody> <tr> <td><strong> Mask value</strong></td> <td><strong> Label</strong></td> <td><strong> Description</strong></td> </tr> <tr> <td>0</td> <td>NoData</td> <td>High probability of being black or white padded pixels, used to pad non-rectangular images within the image registration and rectification processes</td> </tr> <tr> <td>25</td> <td>NotClassified</td> <td>Not labeled pixels</td> </tr> <tr> <td>75</td> <td>Landwards</td> <td>All pixels that are towards the landside with respect to the shoreline (row-wise), excluding “NoData” ones</td> </tr> <tr> <td>150</td> <td>Seawards</td> <td>All pixels that are towards the seaside with respect to the shoreline (row-wise), excluding “NoData” ones</td> </tr> <tr> <td>255</td> <td>Shoreline</td> <td>Pixels intersected by the mapped shoreline densified to cover one pixel per row, at least</td> </tr> </tbody> </table> <h2>Parameters</h2> <p><span>RGB values or any transformation in the colour space can be used as parameters.</span><span> </span></p> <h2>Data sources</h2> <p><span>In the CoastSnap initiative, citizens capture images (oblique smartphone photos) from fixed CoastSnap stations and share them with the scientific managers. Images are subjected to a quality control process, spatially registered to a designated target image, and rectified (georeferencing). The shoreline is subsequently digitised from each rectified image.</span><span> </span></p> <h2>Data quality</h2> <p><span>All images included have been supervised by CSs’ scientific managers. However, citizen scientists take images by smartphones (different camera quality) at irregular intervals across various sites with varying weather and illumination conditions. Users of SCLabels dataset must be aware of this variance. </span></p> <h2>Image resolution</h2> <p><span>The resolution of the images depends on the CoastSnap station and the length of the shoreline, ranging from 241x188 pixels to 801x796 pixels.</span></p> <h2>Spatial coverage</h2> <p><span>The SCLabels dataset version 1.0.0 contains data from five Spanish CoastSnap stations, including sandy beaches in the northwest (</span><span>agrelo</span><span>), the Cíes Islands (</span><span>cies</span><span>), the south (</span><span>cadiz</span><span>), and the Balearic Islands (</span><span>samarador </span><span>and </span><span>arenaldentem</span><span>).</span></p> <table> <tbody> <tr> <td><strong> CoastSnap station</strong></td> <td><strong> Longitude</strong></td> <td><strong> Latitude</strong></td> </tr> <tr> <td><em>agrelo</em></td> <td>-8.772</td> <td>42.331</td> </tr> <tr> <td><em>cies</em></td> <td>-8.900</td> <td>42.226</td> </tr> <tr> <td><em>cadiz</em></td> <td>-6.288</td> <td>36.522</td> </tr> <tr> <td><em>samarador</em></td> <td>3.185</td> <td>39.350</td> </tr> <tr> <td><em>arenaldentem</em></td> <td>2.974</td> <td>39.353</td> </tr> </tbody> </table> <h2>Contact information</h2> <p><span>For further technical inquiries or additional information about the annotated dataset, please contact jsoriano@socib.es.</span></p>
ALL-READY Questionnare on potential drivers and barriers to the adoption of innovation management, open science, and Intellectual Property Rights (IPR) among the members of the Pilot Network
<p><strong>Background & Summary</strong>: </p><p>The ALL-READY project unites a diverse consortium of Research Infrastructures (RI) and Living Labs, instrumental in developing new methodologies and technologies in agroecology. The project focuses on effective management of innovation, adherence to open science principles, and strategic application of Intellectual Property Rights (IPR). Task 6.4 of the project, which concentrates on Innovation and IPR Management, seeks to understand the dynamics influencing the adoption of these practices among its members. Recognizing the need for end-to-end data management, the project emphasizes standardized data collection and management while adhering to FAIR principles.</p><p><strong>Methods</strong>: </p><p>The questionnaire was developed by LifeWatch ERIC to capture data reflecting current practices and perceptions in agroecology. It included 26 questions divided into four sections, focusing on existing practices, potential drivers, and barriers in innovation management, open science, and IPR. The survey was disseminated via an online platform to the ALLREADY Pilot Network, ensuring a representative sample from diverse organizations. The data collection process was closely monitored, and the responses were analyzed using a mixed-methods approach to extract meaningful insights.</p><p><strong>Data Records of the ALLREADY Project Questionnaire</strong>: </p><p>The dataset, collected through an online survey platform, underwent a meticulous process of data preparation, download, formatting, and anonymization. It consists of one text file containing metadata (Readme.txt) and a single CSV file encompassing all questionnaire responses. The dataset provides a comprehensive view of innovation management, open science adoption, and IPR handling within the agroecology sector, particularly among the network of RIs and Living Labs involved in the project.</p><p><strong>Technical Validation of the ALLREADY Project Questionnaire</strong>: </p><p>Several critical steps were taken to ensure the accuracy, reliability, and overall quality of the data collected. This included development and testing of the questionnaire, rigorous monitoring of the data collection process, and thorough checks for data quality and completeness. The representativeness of the sample was analyzed specifically with respect to the Pilot Network rather than the broader population involved in agroecology. Strategies were employed to counter survey fatigue and maintain respondent engagement.</p><p><strong>Usage Notes for the ALLREADY Project Questionnaire</strong>: </p><p>The dataset's proper usage is vital for ensuring the validity and reproducibility of research. Researchers are advised to consider the nature of the data, the representativeness of the dataset, and its generalizability. The dataset allows for comprehensive analysis and integration of different sections, and analysts have the flexibility to handle open and write-in responses according to their research needs. Additional information to facilitate analysis is provided in a separate documentation file.</p><p> </p>
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p><p>This dataset provides supplementary data to "A reactive neural network framework for water-loaded acidic zeolites". It contains trained Neural Network Potentials (NNP and ΔNNP model), scripts, and all energy and force data used in this work at the (Δ)NNP, ReaxFF, and DFT (SCAN+D3(BJ) and ωB97X-D3(BJ)) level. Energy and forces are stored as ASE trajectory files (traj), readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOH.db) file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>.</p><ol><li>"aimd_simulations.zip" - VASP INCAR file, XDATCAR and traj file for 10 ps AIMD run (Supplementary Figure 6) and NNP level (re-)calculated energies/forces ("aimd_nnp_recalc.traj")</li><li>"biased_dynamics.zip" - VASP/Plumed input and output files for DFT (SCAN+D3(BJ)) and NNP level biased dynamics including traj files (Supplementary Figure 12)</li><li>"database_input.zip" - structure (cif) files of the initial structures used for database generation (Supplementary Table 1)</li><li>"delta_nnp.zip" - (pytorch) ΔNNP model (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>) together with example scripts </li><li>"error_stats.zip" - traj files of all generalization tests (Figure 1 and Supplementary Figure 4) storing energies/forces at the SCAN+D3(BJ), ReaxFF, and NNP level as well as traj files with ΔNNP and ωB97X-D3(BJ) energies/forces for a subset taken from biased dynamics runs (Supplementary Figure 11)</li><li>"md_simulations.zip" - NNP level MD trajectories of all generalization test (Figure 1 and Supplementary Figure 4) runs including an example script for an MD run</li><li>"neb_calculations.zip" - traj files and example scripts for NEB calculations at the (Δ)NNP along with the corresponding DFT energy/force data (SCAN+D3(BJ) and ωB97X-D3(BJ))</li><li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li><li>"silica_database.zip" - output files of the single-point (SP) and optimization test runs (Supplementary Figure 1) of pure silica structures together with an example structure optimization script </li><li>"SiAlOH.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li></ol>
Dataset - Terminology of e-Oral Health: Consensus Report of the IADR's e-Oral Health Network Terminology Task Force.
<p>README<br>====================<br>This repository contains the data and documentation for a research project. It includes the dataset,<br>which is provided in CSV format and the original PDF with the survey answers.</p> <p>Research Information<br>====================<br>Terminology of e-Oral Health: Consensus Report of the IADR’s e-Oral Health Network Terminology<br>Task Force. Authors reported multiple definitions of e-oral health and related terms, and used several definitions<br>interchangeably, like mhealth, teledentistry, teleoral medicine and telehealth. The International<br>Association of Dental Research e-Oral Health Network (e-OHN) aimed to establish a consensus on<br>terminology related to digital technologies used in oral healthcare.</p> <p>This dataset contains data from a survey about digital oral health. The survey asked participants to provide their definition of various terms related to digital oral health, as well as their agreement with the provided definitions. The dataset also includes three figures that the participants were asked to review.</p> <p>The purpose of this dataset is to collect data on the public's understanding of digital oral health terms and to identify areas where there may be confusion or misinterpretation. The data from this dataset could be used to develop educational materials or to improve the way that digital oral health information is communicated to the public.</p> <p>Additional notes<br>====================<br>The data is not currently cleaned or preprocessed.</p> <p>Dataset<br>====================<br>The dataset file, named "dataset.csv," is in this repository. It contains the raw anonymized data<br>collected from the participants in a structured format. Each row represents a respondent, and the<br>columns correspond to different variables.</p> <p>Codebook<br>====================<br>The codebook file, named "codebook.pdf," is also included in this repository. It provides a<br>comprehensive description of the variables present in the dataset. The codebook outlines each<br>variable's meaning, type, and possible values, allowing users to understand and analyze the data<br>effectively.</p> <p>Metadata<br>====================<br>No metadata is provided</p> <p>Files<br>====================<br>01_readme.txt this readme file<br>02_codebook.pdf The codebook of the dataset<br>03_dataset.csv The dataset in csv format<br>04_e-OHN Delphi (2023-02-03).pdf The output from the survey</p> <p>Usage<br>====================<br>To work with the dataset, you can download the "dataset.csv" file and import it into your preferred<br>software or programming language for analysis. The codebook provides valuable information about<br>the variables, allowing you to understand the data structure and make informed decisions during your<br>analysis.<br>Please note that while every effort has been made to ensure the accuracy and quality of the data, it is<br>important to review the codebook and understand the context of the research before concluding the<br>dataset.</p> <p>License<br>====================<br>The data and documentation in this repository are provided under the CC BY-SA.<br>This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. If you remix, adapt, or build upon the material, you must license the modified material under identical terms. CC BY-SA includes the following elements:</p> <p> BY: credit must be given to the creator.<br> SA: Adaptations must be shared under the same terms.<br> <br>Please refer to the license file for further details on how the data can be used and shared.</p> <p>Contact Information<br>====================<br>For any questions, clarifications, or inquiries related to the dataset or research project, please contact<br>Assoc Prof Dr Sergio Uribe, sergio.uribe@rsu.lv</p>
Data for: Temperature-controlled Molecular Bonding Hysteresis: Interphase Dynamics of a Nanoparticle-modified Polymer Network
<p>The data is supplementary to the publication "Temperature-controlled Molecular Bonding Hysteresis: Interphase Dynamics of a Nanoparticle-modified Polymer Network", DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.4c00406">10.1021/acs.jpclett.4c00406</a></p> <p>Key words: Thermal volume expansion, Interphase dynamics, Temperature-modulated optical refractometry, Nanoparticles, Optical Remanence, Hysteresis, Refractive index</p> <p>The data sets contain measured and processed data on the interphase dynamics of a nanoparticle modified epoxy resin collected via Temperature-modulated optical refractometry (TMOR).</p> <p>Material details:</p> <ul> <li>Cycloaliphatic epoxy resin + Anhydride curing agent + 1-methylimidazole</li> <li>Core-shell rubber nanoparticles, 100 nm, dispersed in a cycloaliphatic epoxy carrier resin</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> </ul>
Charting nanocluster structures via convolutional neural networks
<p>The repository contains a notebook for the training of the autoencoder for the RDFs for structural classification. The notebook describes the procedure going from RDFs calculation to clustering of the reduced space. In the folder are contained Au147 structures, together with the associated pretrained AE, the 3D chart and the different clustering performed varying mean shift bandwidth.</p> <p>Files:</p> <p>- ChartAu147.ipynb: notebook</p> <p>- Configurations: directory with the dataset divided according to the CNA classification of the structures, xyz format with no headers, every 147 lines is a single structure</p> <p>- Libraries: directory with functions imported in the notebook</p> <p>- Precomputed: directory with the precomputed outputs</p> <p> - rdfs.npy: preocmputed RDFs of the data stored in configurations, npy format to load with NumPy</p> <p> - labels.npy: CNA labels of the RDFs, npy format to load with NumPy</p> <p> - model_au147.pth: pretrained model for au147</p> <p> - scaler_au147.pkl: minmax scaler of the RDFs</p> <p> - chart_3d.dat: 3d space generated via the encoder on the au147 dataset</p> <p> - ae_reconstructions.npy: reconstructions of the rdfs of the model (model_au147.pth)</p> <p> - MSscanbw: pretrained mean shift clustering with different bandwidths, the file "clus_vs_bw.dat" reports the number of clusters associated to each bandwidth</p>
Access Network of Henri III of France's apartment according to his 1585 court ordinances
<p>This dataset collects the stipulations of access for courtiers in the royal apartment as described in Henri III of France's 1585 court ordinance (Paris: Archives Nationales, KK 544, fol. 55r-141r).</p> <p>The dataset was created with the aim to establish the degree of accessibility of both the (individual) spaces in the royal apartmant as well as the king himself and analyse in which ways king Henri III of France managed to balance his need for privacy with the courtiers' expectations of access. The results are to be / were published in <em>Current Research in Digital History</em>. </p>
Code and Data for "Anticoncentration and state design of random tensor networks"
<p>We investigate quantum random tensor network states where the bond dimensions scale polynomially with the system size, N. Specifically, we examine the delocalization properties of random Matrix Product States (RMPS) in the computational basis by deriving an exact analytical expression for the Inverse Participation Ratio (IPR) of any degree, applicable to both open and closed boundary conditions. For bond dimensions χ∼γN, we determine the leading order of the associated overlaps probability distribution and demonstrate its convergence to the Porter-Thomas distribution, characteristic of Haar-random states, as γ increases. Additionally, we provide numerical evidence for the frame potential, measuring the 2-distance from the Haar ensemble, which confirms the convergence of random MPS to Haar-like behavior for χ≫\sqrt{N}. We extend this analysis to two-dimensional systems using random Projected Entangled Pair States (PEPS), where we similarly observe the convergence of IPRs to their Haar values for χ≫\sqrt{N}. These findings demonstrate that random tensor networks with bond dimensions scaling polynomially in the system size are fully Haar-anticoncentrated and approximate unitary designs, regardless of the spatial dimension.</p>
Network Digital Twin-Generated Dataset for Machine Learning-based Detection of Benign and Malicious Heavy Hitter Flows
<h3>Overview</h3> <p>This record provides a dataset created as part of the study presented in the following publication and is made <strong>publicly available for research purposes</strong>. The associated article provides a comprehensive description of the dataset, its structure, and the methodology used in its creation. If you use this dataset, please <strong>cite the following article </strong>published in the journal <strong>IEEE Communications Magazine</strong>:</p> <blockquote> <p><strong>A. Karamchandani, J. Nunez, L. de-la-Cal, Y. Moreno, A. Mozo, and A. Pastor, “On the Applicability of Network Digital Twins in Generating Synthetic Data for Heavy Hitter Discrimination,” IEEE Communications Magazine, pp. 2–8, 2025, DOI: 10.1109/MCOM.003.2400648.</strong></p> </blockquote> <p>More specifically, the record contains several synthetic datasets generated to differentiate between benign and malicious heavy hitter flows within a realistic virtualized network environment. Heavy Hitter flows, which include high-volume data transfers, can significantly impact network performance, leading to congestion and degraded quality of service. Distinguishing legitimate heavy hitter activity from malicious Distributed Denial-of-Service traffic is critical for network management and security, yet existing datasets lack the granularity needed for training machine learning models to effectively make this distinction.</p> <p>To address this, a Network Digital Twin (NDT) approach was utilized to emulate realistic network conditions and traffic patterns, enabling automated generation of labeled data for both benign and malicious HH flows alongside regular traffic.</p> <h3>Feature Set:</h3> <p>The feature set includes the following flow statistics commonly used in the literature on network traffic classification:</p> <ul> <li>The protocol used for the connection, identifying whether it is TCP, UDP, ICMP, or OSPF.</li> <li>The time (relative to the connection start) of the most recent packet sent from source to destination at the time of each snapshot.</li> <li>The time (relative to the connection start) of the most recent packet sent from destination to source at the time of each snapshot.</li> <li>The cumulative count of data packets sent from source to destination at the time of each snapshot.</li> <li>The cumulative count of data packets sent from destination to source at the time of each snapshot.</li> <li>The cumulative bytes sent from source to destination at the time of each snapshot.</li> <li>The cumulative bytes sent from destination to source at the time of each snapshot.</li> <li>The time difference between the first packet sent from source to destination and the first packet sent from destination to source.</li> </ul> <h3>Dataset Variations:</h3> <p>To accommodate diverse research needs and scenarios, the dataset is provided in the following variations:</p> <ol> <li> <p><strong><code>All at Once</code></strong>:</p> <ol> <li>Contains a synthetic dataset where all traffic types, including benign, normal, and malicious DDoS heavy hitter (HH) flows, are combined into a single dataset.</li> <li>This version represents a holistic view of the traffic environment, simulating real-world scenarios where all traffic occurs simultaneously.</li> </ol> </li> <li> <p><strong><code>Balanced Traffic Generation</code></strong>:</p> <ol> <li>Represents a balanced traffic dataset with an equal proportion of benign, normal, and malicious DDoS traffic.</li> <li>Designed for scenarios where a balanced dataset is needed for fair training and evaluation of machine learning models.</li> </ol> </li> <li> <p><strong><code>DDoS at Intervals</code></strong>:</p> <ol> <li>Contains traffic data where malicious DDoS HH traffic occurs at specific time intervals, mimicking real-world attack patterns.</li> <li>Useful for studying the impact and detection of intermittent malicious activities.</li> </ol> </li> <li> <p><strong><code>Only Benign HH Traffic</code></strong>:</p> <ol> <li>Includes only benign HH traffic flows.</li> <li>Suitable for training and evaluating models to identify and differentiate benign heavy hitter traffic patterns.</li> </ol> </li> <li> <p><strong><code>Only DDoS Traffic</code></strong>:</p> <ol> <li>Contains only malicious DDoS HH traffic.</li> <li>Helps in isolating and analyzing attack characteristics for targeted threat detection.</li> </ol> </li> <li> <p><strong><code>Only Normal Traffic</code></strong>:</p> <ol> <li>Comprises only regular, non-HH traffic flows.</li> <li>Useful for understanding baseline network behavior in the absence of heavy hitters.</li> </ol> </li> <li> <p><strong><code>Unbalanced Traffic Generation</code></strong>:</p> <ol> <li>Features an unbalanced dataset with varying proportions of benign, normal, and malicious traffic.</li> <li>Simulates real-world scenarios where certain types of traffic dominate, providing insights into model performance in unbalanced conditions.</li> </ol> </li> </ol> <p>For each variation, the output of the different packet aggregators is provided separated in its respective folder.</p> <p>Each variation was generated using the NDT approach to demonstrate its flexibility and ensure the reproducibility of our study's experiments, while also contributing to future research on network traffic patterns and the detection and classification of heavy hitter traffic flows. The dataset is designed to support research in network security, machine learning model development, and applications of digital twin technology.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.