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.
5,371
datasets available to search
ShareScore release 0.9.0
Dataset results
5,371 results for “Flow”
Flow behaviour of magnetic steel powder
<p>These are the data used in the article "Flow behaviour of magnetic steel powder". This dataset includes:</p> <ul> <li>Optical micrographs used to determine the mean size and circularity in each size class after sieving <ul> <li>Scale bars are included for two images and may be used to determine the scale of all micrographs included - all images were taken using the same microscope, camera and software. All images are as-recorded.</li> </ul> </li> <li>X-ray diffractograms used to determine the fraction of martensite present in each sample <ul> <li>Calibration data for the diffraction instrument are included</li> </ul> </li> <li>Vibrating sample magnetometry data, used to determine the saturation and remanent magnetisation.</li> <li>Shear cell metrics derived automatically form the shear cell tests</li> <li>Hall flow times, both with and without drying</li> <li>Angle of repose dat</li> <li>Videos of all angle of repose tests</li> </ul> <p>Other data can be provided on request.</p>
Measurement and prediction of bottom boundary layer hydrodynamics under modulated oscillatory flows
<p>Experimental and numerical model data pertaining to the manuscript "Measurement and prediction of bottom boundary layer<br> hydrodynamics under modulated oscillatory flows" accepted for publication in Coastal Engineering (<a href="https://doi.org/10.1016/j.coastaleng.2021.103954">https://doi.org/10.1016/j.coastaleng.2021.103954</a>)</p> <p>Please read the README.txt file for more information.</p>
Dataset and Code for Manuscript "Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification"
<p>Dataset of measurements for cell cycle analysis with description:</p> <ul> <li>ReadMe file with explanations on the data set and analysis</li> <li>exemplary Matlab script file for analysis</li> <li>binary data files conatining the pulse shapes in all channels</li> <li>FCS data files containing common flow cytometry parameters in each channel</li> </ul> <p>Data on unsorted HEK cells, HEK cells sorted for cell cycle phases, and unsorted Jurkat cell are included.</p>
Flow-topography interactions in the western tropical Atlantic boundary off Northeast Brazil.
<p>Figures and other media files of the paper Flow-topography interactions in the western tropical Atlantic boundary 2off Northeast Brazil.</p>
A dataset for comparing filtering methods used to wave and non-wave flow at the surface of the Agulhas region
<p>This dataset comprises sea surface height (SSH) and velocity data at the ocean surface in two small regions near the Agulhas retroflection. The unfiltered SSH and a horizontal velocity field are provided, along with the same fields after various kinds of filtering, as described in the accompanying manuscript, <em>Using Lagrangian filtering to remove waves from the ocean surface velocity field</em><em> (</em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a>)<em>. </em>The code repository for this work is <a href="https://github.com/cspencerjones/separating-balanced">https://github.com/cspencerjones/separating-balanced</a> . </p> <p>Two time-resolutions are provided: two weeks of hourly data and 70 days of daily data.</p> <p>Seventy_daysA.nc contains daily data for region A and Seventy_daysB.nc contains daily data for region B, including unfiltered, lagrangian filtered and omega-filtered velocity and sea-surface height. </p> <p>two_weeksA.nc contains hourly data for region A and two_weeksB.nc contains hourly data for region B, including unfiltered and lagrangian filtered velocity and sea-surface height. </p> <p>Note that region A has been moved in version 2 of this dataset. </p> <p>See the manuscript and code repository for more information. </p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
Dataset for "Infiltration and resuspension of dilute particle suspensions in micro cavity flow "
<p>This dataset contains the numerical simulation data describing the migration behaviour of particles. Detailed study on the infiltration and resuspension of particle in a cavity flow is presented in a journal paper entitled "Infiltration and resuspension of dilute particle suspensions in micro cavity flow ". We provide all the essential data in a single excel file with the description below:</p> <p> </p> <p>Dataset 1- Particle trajectory for different releasing positions. The parameters are <em>Re</em>=56.9, <em>h</em>=32, <em>l</em>=80. The particle densities are <em>ρ<sub>p</sub></em>=1.3 in plot (Fig. 7a) and <em>ρ<sub>p</sub></em>=2.0 in plot (Fig. 7b). (Data of Fig. 7)</p> <p>Dataset 2 - Particle trajectory for different periodic domain lengths. The parameters are <em>ρ<sub>p</sub></em>=1.3, <em>Re</em>=56.9, <em>h</em>=32 and <em>l</em>=80. (Data of Fig. 8)</p> <p>Dataset 3 - The time evolution of (Fig. 9a) the vertical position <em>z</em>, (Fig. 9b) the horizontal velocity <em>u<sub>x</sub></em>, and (Fig. 9c) the vertical velocity <em>u<sub>z</sub></em> of a single particle with the same Renolds number <em>Re</em>=56.9 and cavity size of <em>h</em>=32 and <em>l</em>=80, but different particle densities. (Data of Fig. 9)</p> <p>Dataset 4 - The trajectories of a single particle over the cavity with: (Fig. 10a) different particle densities but the same Renolds number <em>Re</em>=85.3 and cavity size of <em>h</em>=16 and <em>l</em>=80; (Fig. 10b) different Reynolds numbers but the same particle density <em>ρ<sub>p</sub></em>=1.3 and cavity size of <em>h</em>=32 and <em>l</em>=80; (Fig. 10c) different cavity sizes but the same Renolds number<em> Re</em>=56.9 and particle density <em>ρ<sub>p</sub></em>=1.3. The dashed lines represent the boundary of the cavity. (Data of Fig. 10)</p> <p>Dataset 5 - Typical particle trajectories in the cavity. The dashed lines represent the boundary of the trap. (Data of Fig. 12)</p> <p>Dataset 6 - The variation of trap efficiency with particle density for (Fig. 13a) different cavity sizes with the same <em>Re</em>=56.9, and (Fig. 13b) different Reynolds numbers with the same cavity size <em>h</em>=32, <em>l</em>=80, respectively. (Data of Fig. 13)</p> <p>Dataset 7 - The critical entering distance as a function of the scaling parameter <em>T<sub>h</sub></em>. The fitting line is performed with an exponential function with the same form as Eq. (19). (Data of Fig.14)</p> <p>Dataset 8 - The variation of the trap efficiency with the trap number. (Data of Fig.15)</p> <p> </p>
Microsatellite genotype data and leaf morphological data of the publication "Bidirectional gene flow between Fagus sylvatica L. and F. orientalis Lipsky despite strong genetic divergence"
<p>These data sets were used for analyses in the publication "Bidirectional gene flow between <em>Fagus sylvatica</em> L. and<em> F. orientalis</em> Lipsky despite strong genetic divergence" accepted in Forest Ecology and Management <a href="https://www.sciencedirect.com/journal/forest-ecology-and-management/vol/537/suppl/C">Volume 537</a>, 1 June 2023, 120947, <a href="https://doi.org/10.1016/j.foreco.2023.120947">https://doi.org/10.1016/j.foreco.2023.120947</a></p> <p>For details about the data, please read the corresponding ReadMe files.</p>
HyUSPRe Report & Data on 'New experimental data on reactions between H2 and well cement and effects on fluid flow and mechanical properties of well cement
<p>In this study, new experimental data is presented of the effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties of oil well (class G) cement, relevant for underground hydrogen storage operations. Changes in mechanical properties (Young’s modulus, Poisson’s ratio and ultimate strength) have been analyzed using unconfined compressive strength (UCS) tests and confined cyclic loading tests on class G cement samples that were unreacted (cured for 3 days at 80°C) and exposed to lime-saturated brine and N<sub>2</sub> or H<sub>2</sub> for 1 and 2 months. Changes in cement mineralogy were analyzed by XRD analysis of the unreacted and exposed samples. The mechanical properties of elastic modulus and Poisson’s ratio are within the expected range of an oil well cement. Differences in Young’s modulus, Poisson’s ratio and ultimate strength are limited between unreacted, N<sub>2</sub>-exposed and H<sub>2</sub>-exposed samples, when comparing UCS tests or confined cyclic loading tests. Repeated UCS tests seem to indicate that the variation in Young’s modulus and ultimate strength increases after N<sub>2</sub> and H<sub>2</sub> exposure, but this observation needs to be confirmed in additional tests. During cyclic axial loading of confined cement samples, irreversible (plastic) deformation (compaction) occurs that affect static Young’s modulus. Also, effects of exceeding yield and failure strength on Young’s modulus are observed. Dynamic Young’s moduli and Poisson’s ratios derived from acoustic velocity measurements during confined cyclic tests show limited variation, in particular if static and dynamic Young’s modulus are compared. The mineralogical changes as identified using XRD analysis suggest minor changes between unexposed and H<sub>2</sub>- and N<sub>2</sub>-exposed samples, although XRD patterns indicate some minerals that could not be identified. The main conclusion is that effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties and mineralogical changes of class G cement is limited compared to unreacted or N<sub>2</sub> exposed samples for the investigated conditions. There is no indication that changes in mechanical properties of cement are such that cement integrity of wells used for underground hydrogen storage will be significantly affected. It should be emphasized that this conclusion is based on experiments on one type of cement (class G) and a limited set of conditions. In particular, additional tests to assess the reproducibility of current results and tests on samples that were exposed longer to H<sub>2</sub> and N<sub>2</sub> are of interest. Detailed effects of changing properties for the durability and integrity of wells can be derived by performing a parameter sensitivity analysis with well integrity modelling for the range in mechanical properties measured in this study.</p>
Network traffic datasets created by Single Flow Time Series Analysis
<p><strong>Network traffic datasets created by Single Flow Time Series Analysis</strong></p> <p>Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:<br> </p> <blockquote> <p>J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," <em>2023 19th International Conference on Network and Service Management (CNSM)</em>, Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.</p> </blockquote> <p>This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: <em>feature_description.pdf</em></p> <p> </p> <p>In the following table is a description of each dataset file:</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Detection problem</strong></td> <td><strong>Citation of original raw dataset</strong></td> </tr> <tr> <td>botnet_binary.csv </td> <td>Binary detection of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>botnet_multiclass.csv </td> <td>Multi-class classification of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>cryptomining_design.csv</td> <td>Binary detection of cryptomining; the design part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>cryptomining_evaluation.csv </td> <td>Binary detection of cryptomining; the evaluation part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>dns_malware.csv </td> <td>Binary detection of malware DNS </td> <td>Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. </td> </tr> <tr> <td>doh_cic.csv </td> <td>Binary detection of DoH </td> <td> <p>Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 </p> </td> </tr> <tr> <td>doh_real_world.csv </td> <td>Binary detection of DoH </td> <td>Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 </td> </tr> <tr> <td>dos.csv </td> <td>Binary detection of DoS </td> <td>Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.</td> </tr> <tr> <td>edge_iiot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.</td> </tr> <tr> <td>edge_iiot_multiclass.csv</td> <td>Multi-class classification of IoT malware</td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.</td> </tr> <tr> <td>https_brute_force.csv</td> <td>Binary detection of HTTPS Brute Force</td> <td>Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020</td> </tr> <tr> <td>ids_cic_binary.csv</td> <td>Binary detection of intrusion in IDS</td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.</td> </tr> <tr> <td>ids_cic_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>ids_unsw_nb_15_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.</td> </tr> <tr> <td>ids_unsw_nb_15_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.</td> </tr> <tr> <td>iot_23.csv </td> <td>Binary detection of IoT malware </td> <td>Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23</td> </tr> <tr> <td>ton_iot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021</td> </tr> <tr> <td>ton_iot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021</td> </tr> <tr> <td>tor_binary.csv </td> <td>Binary detection of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>tor_multiclass.csv </td> <td>Multi-class classification of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>vpn_iscx_binary.csv </td> <td>Binary detection of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_iscx_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_vnat_binary.csv </td> <td>Binary detection of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022</td> </tr> <tr> <td>vpn_vnat_multiclass.csv</td> <td>Multi-class classification of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022</td> </tr> </tbody> </table> <p> </p>
Data and scripts for reproducing "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow", currently under review.</p>
Rayleigh-Bénard flow in closed cubical cell
<p>The repository contains simulation snapshots from a long-term trajectory of turbulent Rayleigh-Bénard flows in a cubical closed cell at two Rayleigh numbers.</p>
Coupling Microkinetics with Continuum Transport Models to Understand Electrochemical CO2 Reduction in Flow Reactors
<p>Data supporting the manuscript published in PRX Energy titled "Coupling Microkinetics with Continuum Transport Models to Understand Electrochemical CO2 Reduction in Flow Reactors". Jupyter notebook and included data for recreating the figures in the paper and for additional analysis.</p>
MCMC simulations from the posterior distributions of European migration flows
<p>This folder contains three RData files each containing MCMC simulations from the posterior distributions of a Bayesian hierarchal model used to estimate European migration flows, developed as part of the project Quantifying Migration Scenarios for Better Policy (QuantMig, <a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.quantmig.eu%2F&data=05%7C01%7CP.W.Smith%40soton.ac.uk%7Cc166e7bff7f840a7983b08db94af1244%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C638267252014422806%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=yMDYBngYz%2BnzvXmNj6MqseYwso0ICNbREE8TmqQ%2BRJ0%3D&reserved=0">www.quantmig.eu</a>); see Aristotelous, Smith and Bijak (2022) for details. The first set of estimates are disaggregated by origin, destination and time, a breakdown which we denote as ODT. The second and third sets of estimates are again disaggregated by origin, destination and time, but they are additionally disaggregated by other factors. The second set is further disaggregated by age and sex and the third by just birth region. We respectively denote these breakdowns as ODAST and ODBT. Also, included in this folder is an R script to calculate summaries of these posterior distributions (means, and lower and upper quartiles) and output them to csv files, which are also in the folder. For further details, see deliverable_6_4_v1_2.pdf also in the folder.</p>
Simulated datasets for detector and particle flow reconstruction: CLIC detector
<p><strong>Data description</strong></p> <p>Datasets generated using Key4HEP and the CLIC detector model suitable for particle flow reconstruction studies.</p> <p>The datasets contain generator particles, reconstructed tracks and calorimeter hits, reconstructed Pandora PF particles and their respective links in the <a href="https://doi.org/10.5281/zenodo.7221473">EDM4HEP</a> format.</p> <p>The following processes have been simulated with Pythia 8:</p> <ul> <li>p8_ee_tt_ecm380: ee -> ttbar, center of mass energy at 380 GeV</li> <li>p8_ee_qq_ecm380: ee -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>p8_ee_ZH_Htautau: ee -> ZH -> Higgs decaying to tau leptons, center of mass energy at 380 GeV</li> <li>p8_ee_WW_fullhad: ee -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>p8_ee_tt_ecm380_PU10: ee -> ttbar with on average 10 Poisson-distributed events from ee->gg overlayed, center of mass energy at 380 GeV</li> </ul> <p>The following single particle gun samples have been generated with ddsim:</p> <ul> <li>e+/e-: single electron with energy between 1 and 100 GeV</li> <li>mu+/mu-: single muon with energy between 1 and 100 GeV</li> <li>kaon0L: single K0L with energy between 1 and 100 GeV</li> <li>neutron: single neutron with energy between 1 and 100 GeV</li> <li>pi+/pi-: single charged pion with energy between 1 and 100 GeV</li> <li>pi0: single neutral pion with energy between 1 and 100 GeV</li> <li>gamma: single photon with energy between 1 and 100 GeV</li> </ul> <p>The detector simulation has been done with Geant4, the reconstruction with Marlin interfaced via Key4HEP which includes PF reconstruction with Pandora, all using publicly available models and code.</p> <p> </p> <p><strong>Contents</strong></p> <p>This record includes the following files:</p> <ul> <li>*_10files.tar: small archives of 10 files for each data sample, suitable for testing</li> <li>dataset_full.txt: the full list of files, hosted at the Julich HPC courtesy of the Raise CoE project, ~2.5TB total</li> <li>*.cmd: the Pythia8 cards</li> <li>pythia.py: the pythia steering code for Key4HEP</li> <li>run_sim.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380, p8_ee_qq_ecm380, p8_ee_ZH_Htautau, p8_ee_WW_fullhad datasets</li> <li>run_sim_pu.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380_PU10 dataset</li> <li>run_sim_gun.sh: the steering script for generating the single-particle gun samples</li> <li>run_sim_gun_np.sh: the steering script for generating multi-particle gun samples (extensive datasets have not yet been generated)</li> <li>check_files.py: the main driver script that configures the full statistics and creates submission scripts for all the simulations</li> <li>PandoraSettings.zip: the settings used for Pandora PF reconstruction</li> <li>main19.cc: the Pythia8+HepMC driver code for generating the events with PU overlay</li> <li>clicRec_e4h_input.py: the steering configuration of the reconstruction modules in Key4HEP</li> <li>clic_steer.py: the steering configuration of the Geant4 simulation modules in Key4HEP</li> <li>clic-visualize.ipynb: an example notebook demonstrating how the dataset can be loaded and events visualized in Python</li> <li>visualization.mp4: an example visualization of the hits and generator particles of a single ttbar event from the dataset</li> </ul> <p> </p> <p><strong>Dataset semantics</strong></p> <p>Each file consists of event records. Each event contains structured branches of the relevant physics data. The branches relevant to particle flow reconstruction include:</p> <ul> <li>MCParticles: the ground truth generator particles</li> <li>ECALBarrel, ECALEndcap, ECALOther, HCALBarrel, HCALEndcap, HCALOther, MUON: reconstructed hits in the various calorimeter subsystems</li> <li>SiTracks_Refitted: the reconstructed tracks</li> <li>PandoraClusters: the calorimeter hits, clustered by Pandora to calorimeter clusters</li> <li>MergedRecoParticles: the reconstructed particles from the Pandora particle flow algorithm</li> <li>CalohitMCTruthLink: the links between MC particles and reconstructed calorimeter hits</li> <li>SiTracksMCTruthLink: the links between MC particles and reconstructed tracks</li> </ul> <p>The full details of the EDM4HEP format are available <a href="https://doi.org/10.5281/zenodo.7221473">here</a>.</p> <p> </p> <p><strong>Dataset characteristics</strong></p> <p>The full dataset in dataset_full.txt consists of 43 tar files of up to 100GB each. The tar files contain in total 58068 files, 2.5TB in the ROOT EDM4HEP format.</p> <p>The subset in *_10files.tar for consists of 150 files, 26GB in the ROOT EDM4HEP format.</p> <p> </p> <p><strong>How can you use these data?</strong></p> <p>The ROOT files can be directly loaded with the <a href="https://zenodo.org/record/8239801">uproot</a> Python library.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>These are simulated samples suitable for conceptual machine learning R&D and software performance studies. They have not been calibrated with respect to real data, and should not be used to derive physics projections about the detectors.</p> <p>Neither CLIC nor CERN endorse any works, scientific or otherwise, produced using these data. All releases will have a unique DOI that you are requested to cite in any applications or publications.</p>
Dataset for: Improving the accuracy of δ18O and δ17O values of O2 measured by continuous-flow isotope-ratio mass spectrometry with a multi-point isotope-ratio calibration
<p>Dataset containing raw and treated isotope-ratio mass spectrometry and laser absorption spectroscopy data, accompanying the manuscript "Improving the accuracy of δ18O and δ17O values of O2 measured by continuous-flow isotope-ratio mass spectrometry with a multi-point isotope-ratio calibration". This dataset also contains O2 concentration and partial pressure data used for figures in the associated manuscript.</p>
High Temporal Resolution Records of Hansbreen Ice Flow Velocity for Years 2006-2019
<p>This repository contains the datasets of the positions of 16 mass balance stakes, horizontal velocity (m/yr) and accuracy of velocity (m/yr) for Hansbreen, a tidewater glacier in southern Svalbard. Data were derived from GNSS measurements conducted in the period 2006-2019. Stake positions are given in UTM zone 33X, and elevation in geoidal height (EGM96). Additionally, we provide files with annual, summer and winter velocities (m/yr) with a standard deviation of velocity, estimated for the hydrological year. The file „Hansbreen_preprocessing_code_stakes.zip” contains the code used for the velocity estimation.</p>
Network traffic datasets with novel extended IP flow called NetTiSA flow
<p><strong>Network traffic datasets with novel extended IP flow called NetTiSA flow</strong></p> <p>Datasets were created for the paper: NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification -- Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka -- which is published in The International Journal of Computer and Telecommunications Networking <a href="https://doi.org/10.1016/j.comnet.2023.110147" rel="nofollow">https://doi.org/10.1016/j.comnet.2023.110147</a><br><br>Please cite the usage of our datasets as:</p> <blockquote> <p>Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka, "NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification", Computer Networks, Volume 240, 2024, 110147, ISSN 1389-1286<br><br></p> <pre><code>@article{KOUMAR2024110147, title = {NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification}, journal = {Computer Networks}, volume = {240}, pages = {110147}, year = {2024}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2023.110147}, url = {https://www.sciencedirect.com/science/article/pii/S1389128623005923}, author = {Josef Koumar and Karel Hynek and Jaroslav Pešek and Tomáš Čejka} } </code></pre> </blockquote> <p>This Zenodo repository contains 23 datasets created from 15 well-known published datasets, which are cited in the table below. Each dataset contains the NetTiSA flow feature vector.<br><br> </p> <p><strong>NetTiSA flow feature vector</strong></p> <p><br>The novel extended IP flow called NetTiSA (Network Time Series Analysed) flow contains a universal bandwidth-constrained feature vector consisting of 20 features. We divide the NetTiSA flow classification features into three groups by computation. The first group of features is based on classical bidirectional flow information---a number of transferred bytes, and packets. The second group contains statistical and time-based features calculated using the time-series analysis of the packet sequences. The third type of features can be computed from the previous groups (i.e., on the flow collector) and improve the classification performance without any impact on the telemetry bandwidth.</p> <p> </p> <p><strong>Flow features</strong></p> <p>The flow features are:</p> <ul> <li><strong><em>Packets</em></strong> is the number of packets in the direction from the source to the destination IP address.</li> <li><em><strong>Packets in reverse order</strong></em> is the number of packets in the direction from the destination to the source IP address.</li> <li><strong><em>Bytes</em> </strong>is the size of the payload in bytes transferred in the direction from the source to the destination IP address.</li> <li><strong><em>Bytes in reverse order</em></strong> is the size of the payload in bytes transferred in the direction from the destination to the source IP address.</li> </ul> <p> </p> <p><strong>Statistical and Time-based features</strong></p> <p>The features that are exported in the extended part of the flow. All of them can be computed (exactly or in approximative) by stream-wise computation, which is necessary for keeping memory requirements low. The second type of feature set contains the following features:</p> <ul> <li><strong><em>Mean</em></strong> represents mean of the payload lengths of packets</li> <li><strong><em>Min</em></strong> is the minimal value from payload lengths of all packets in a flow</li> <li><strong><em>Max</em></strong> is the maximum value from payload lengths of all packets in a flow</li> <li><strong><em>Standard deviation</em></strong> is a measure of the variation of payload lengths from the mean payload length</li> <li><strong><em>Root mean square</em></strong> is the measure of the magnitude of payload lengths of packets</li> <li><strong><em>Average dispersion</em></strong> is the average absolute difference between each payload length of the packet and the mean value</li> <li><strong><em>Kurtosis</em></strong> is the measure describing the extent to which the tails of a distribution differ from the tails of a normal distribution</li> <li><em><strong>Mean of relative times</strong></em> is the mean of the relative times which is a sequence defined as <span>\(st = \{t_1 - t_1, t_2 - t_1, ..., t_n - t_1\} \)</span></li> <li><em><strong>Mean of time differences</strong></em> is the mean of the time differences which is a sequence defined as <span>\(dt = \{ t_j - t_i | j = i + 1, i \in \{1, 2, \dots, n - 1\} \}.\)</span></li> <li><em><strong>Min from time differences</strong></em> is the minimal value from all time differences, i.e., min space between packets.</li> <li><em><strong>Max from time differences</strong></em> is the maximum value from all time differences, i.e., max space between packets.</li> <li><em><strong>Time distribution</strong></em> describes the deviation of time differences between individual packets within the time series. The feature is computed by the following equation:<br><span>\(tdist = \frac{ \frac{1}{n-1} \sum_{i=1}^{n-1} \left| \mu_{\{dt_{n-1}\}} - dt_i \right| }{ \frac{1}{2} \left(max\left(\{dt_{n-1}\}\right) - min\left(\{dt_{n-1}\}\right) \right) }\)</span></li> <li><em><strong>Switching ratio</strong></em> represents a value change ratio (switching) between payload lengths. The switching ratio is computed by equation:<br><span>\(sr = \frac{s_n}{\frac{1}{2} (n - 1)}\)</span></li> </ul> <p> where <span>\(s_n\)</span> is number of switches.</p> <p> </p> <p><strong>Features computed at the collector</strong><br>The third set contains features that are computed from the previous two groups prior to classification. Therefore, they do not influence the network telemetry size and their computation does not put additional load to resource-constrained flow monitoring probes. The NetTiSA flow combined with this feature set is called the Enhanced NetTiSA flow and contains the following features:</p> <ul> <li><em><strong>Max minus min</strong></em> is the difference between minimum and maximum payload lengths</li> <li><em><strong>Percent deviation</strong></em> is the dispersion of the average absolute difference to the mean value</li> <li><em><strong>Variance</strong></em> is the spread measure of the data from its mean</li> <li><em><strong>Burstiness</strong></em> is the degree of peakedness in the central part of the distribution</li> <li><em><strong>Coefficient of variation</strong></em> is a dimensionless quantity that compares the dispersion of a time series to its mean value and is often used to compare the variability of different time series that have different units of measurement</li> <li><em><strong>Directions</strong></em> describe a percentage ratio of packet direction computed as <span>\(\frac{d_1}{ d_1 + d_0}\)</span>, where <span>\(d_1\)</span> is a number of packets in a direction from source to destination IP address and <span>\(d_0\)</span> the opposite direction. Both <span>\(d_1\)</span> and <span>\(d_0\)</span> are inside the classical bidirectional flow.</li> <li><em><strong>Duration</strong></em> is the duration of the flow</li> </ul> <p> </p> <p>The NetTiSA flow is implemented into IP flow exporter <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a>.</p> <p> </p> <p><strong>Description of dataset files</strong></p> <p>In the following table is a description of each dataset file:</p> <table> <tbody> <tr> <td> <p><strong>File name</strong></p> </td> <td> <p><strong>Detection problem</strong></p> </td> <td> <p><strong>Citation of the original raw dataset</strong></p> </td> </tr> <tr> <td>botnet_binary.csv </td> <td>Binary detection of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>botnet_multiclass.csv </td> <td>Multi-class classification of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>cryptomining_design.csv </td> <td>Binary detection of cryptomining; the design part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>cryptomining_evaluation.csv </td> <td>Binary detection of cryptomining; the evaluation part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>dns_malware.csv </td> <td>Binary detection of malware DNS </td> <td>Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. </td> </tr> <tr> <td>doh_cic.csv </td> <td>Binary detection of DoH </td> <td>Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 </td> </tr> <tr> <td>doh_real_world.csv </td> <td>Binary detection of DoH </td> <td>Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 </td> </tr> <tr> <td>dos.csv </td> <td>Binary detection of DoS </td> <td>Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019. </td> </tr> <tr> <td>edge_iiot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>edge_iiot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>https_brute_force.csv </td> <td>Binary detection of HTTPS Brute Force </td> <td>Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020 </td> </tr> <tr> <td>ids_cic_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>ids_cic_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>unsw_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>unsw_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>iot_23.csv </td> <td>Binary detection of IoT malware </td> <td>Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23 </td> </tr> <tr> <td>ton_iot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>ton_iot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>tor_binary.csv </td> <td>Binary detection of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>tor_multiclass.csv </td> <td>Multi-class classification of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>vpn_iscx_binary.csv </td> <td>Binary detection of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_iscx_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_vnat_binary.csv </td> <td>Binary detection of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> <tr> <td>vpn_vnat_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p>
Data describing the life cycle and material flows of neodymium contained in products
<p>Assumptions used to calculate flows of neodymium (Nd) in Europe. For various products (consumer products and industrial goods), the dataset describes the following properties:</p> <ul> <li>lifespan,</li> <li>product weight,</li> <li>neodymium content,</li> <li>end-of-life (EoL) fate,</li> <li>component weight,</li> <li>market share of Nd-containing components</li> </ul> <p>The classification of products is based on UNU Keys.</p>
Woody biomass flows in the EU Member States for the years 2009-2017
<p>This dataset, available in the file JRC_Forestry_Sankey_2009_2017_vers2022.csv, contains the amounts of woody biomass flowing across the different sectors of the forest-based bioeconomy, for all the Member States of the European Union, from 2009 to 2017. These amounts, here expressed in thousand cubic meters solid wood equivalent, represent the arrows of a Sankey diagram per each Member State and per each year.<br>The interactive graphical visualisation of this dataset is available at: https://knowledge4policy.ec.europa.eu/visualisation/interactive-sankey-diagrams-woody-biomass-flows-eu-member-states_en.<br>The codes of the arrows (flows) and the codes of the starting and end nodes, together with the type of biomass that flows from one node to the other, are reported in detail in the file: arrow_codes.csv.<br>All the definitions are available in the file: Definitions.txt.</p> <p>© European Union, 1995-2023</p>
Flow discharge impact competition for food and shelter between two overlapping species of crayfish
Competition between aquatic organisms is heavily influenced by abiotic factors in the environment, specifically flow regime in aquatic systems. Flow regime has been shown to significantly affect the way in which a species uses the environmental resources and alterations in flow can exasperate competitive advantages by congenerics. However, little work has concentrated on the competitive outcome between native and invasive organisms as a function of flow regime. Here, we sought to uncover how competition between two crayfish species (the native virile crayfish [Faxonius virilis] and invasive rusty crayfish [Faxonius rusticus]) was affected by varying flow rates. To do this, we size-matched crayfish and quantified three separate behaviors (food use, shelter use, and fights) of crayfish in four different discharge levels (no discharge—0 cm3/s; low discharge—116 cm3/s; intermediate discharge—345 cm3 /s; high discharge—450 cm3/s). We found that the number of foraging bouts was significantly (p < 0.001) influenced by discharge levels for both species, where rusty crayfish foraged more often in no and discharge. The number of times crayfish sheltered also varied between species and was significantly (p < 0.001) altered by discharge level, where rusty crayfish sheltered more often in no discharge and virile crayfish sheltered more in low and intermediate discharge levels. Similarly, the number of fights that crayfish engaged in also significantly (p < 0.001) depended upon species and discharge levels. Rusty crayfish engaged in more agonistic activities in no and high discharge levels while virile crayfish participated most often in low and intermediate discharge levels
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.