Find research datasets worth reusing

opencc-by-4.0Jul 2020View details →

SSH data set used for Rossby Wave Analysis, extraction from ORCA12.L46-MJM189 DRAKKAR simulation

This data set corresponds to the Sea Surface Heigh (SSH) silmulated by the NEMO ocean circulation model, under the ORCA12.L46-MJM189 configuration, developped in the frame of the DRAKKAR project.  This particular data set is an extraction from the native numerical grid, covering the area between 38N and 40N in the North Altantic ocean, for the period 1970 to 2015. The data are concatenated in a single file with 5-days average of SSH. The corresponding metrics for this sub domain are also present in this netcdf file.  This subset was used in Watelet et al. (2020) submitted paper, dealing with Rossby waves analysis.

opencc-by-4.0Sep 2020View details →

Example data set for 2D adaptive slice-specific z-shimming

Input data and corresponding results for the scripts provided on github (https://github.com/neuroimaging-mug/R2s-mapping) for adpative slice-specific z-shimming in presence of macroscopic field variations. Please unzip all file in the repository path of ‘R2s-mapping’.

opencc-by-4.0Jun 2020View details →

The BrukerAPI test data set (Bruker ParaVision v6.0.1)

This data set contains data from 45 experiments performed using the Bruker ParaVision v6.0.1 software. The purpose of this data set is to test the unified application programming interface to the Bruker data format - the BrukerAPI. See the software <a href="https://github.com/isi-nmr/brukerapi-python">repository</a> for more information. The data was measured using the following pulse sequences: <ul> <li>FLASH.ppg</li> <li>FLASHAngio.ppg</li> <li>IgFLASH.ppg</li> <li>MGE.ppg</li> <li>MSME.ppg</li> <li>RARE.ppg</li> <li>FAIR_RARE.ppg</li> <li>RAREVTR.ppg</li> <li>RAREst.ppg</li> <li>MDEFT.ppg</li> <li>FISP.ppg</li> <li>FLOWMAP.ppg</li> <li>DtiStandard.ppg</li> <li>EPI.ppg</li> <li>FAIR_EPI.ppg</li> <li>CASL_EPI.ppg</li> <li>DtiEpi.ppg</li> <li>T1_EPI.ppg</li> <li>T2_EPI.ppg</li> <li>T2S_EPI.ppg</li> <li>SPIRAL.ppg</li> <li>DtiSpiral.ppg</li> <li>UTE.ppg</li> <li>UTE3D.ppg</li> <li>ZTE.ppg</li> <li>CSI.ppg</li> <li>FieldMap.ppg</li> <li>SINGLEPULSE.ppg</li> <li>NSPECT.ppg</li> <li>EPSI.ppg</li> <li>PRESS.ppg</li> <li>STEAM.ppg</li> <li>ISIS.ppg</li> <li>CPMG.ppg</li> <li>RfProfile.ppg</li> </ul> The data was measured by the NMR group at the Institute of Scientific Instruments of the CAS, v. v. i. Change Log: <ul> <li>Version 1.0.0 <ul> <li>Initial release</li> </ul> </li> <li>Version 1.1.0 <ul> <li>Compressed NumPy arrays were added to each data set to enable data reading tests</li> </ul> </li> </ul>

opencc-by-4.0Feb 2020View details →

The CH-IRP data set: fortnightly data of δ2H and δ18O in streamflow and precipitation in Switzerland

The data set contains δ2H and δ18O from fortnightly grab samples in streamflow and corresponding monthly precipitation derived  from interpolation for 23 Swiss hydrological catchments. δ2H and δ18O in streamflow are provided as one ASCII file for each station. Additionally to these time series each of the files contains the Deuterium excess, the streamflow conditions preceding the sampling as well as the z-scores indicating if a sample might be a statistical outlier, assuming the data are normally distributed. All files contain further information for each sample whether double measurement was performed in the lab comments indicating for instance special sampling conditions or storage-related issues that could alter the isotopic composition due to fractionation. For each data file for streamflow data there is a corresponding ASCII file for catchment precipitation. These contain the interpolated δ2H and δ18O in precipitation for the catchment as well as the source data that were used to derive the interpolated values. Associated data that can be useful for applications are provided. This is mean areal precipitation and temperature (ASCII files) as well as the topographic catchment boundaries (shape files).

opencc-by-4.0Oct 2020View details →

Data set associated to the publication "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology"

Data set of the scientific publication entitled "An active source seismo-acoustic experiment using tethered balloons to validate instrument concepts and modelling tools for atmospheric seismology": Seismological sensors Microphones Barometers Accelerometers Detailed test report.

opencc-by-4.0Oct 2020View details →

Data set for paper Predicting time to graduation at a large enrollmentAmerican university

A longitudinal data set of student grades, demographics, course participation, etc. for attending a large American university.

opencc-by-4.0Nov 2020View details →

Supplementary material (buffet data set): Egeler, G.-A., von Rickenbach, F., & Baur, P. (2020). Menüwahl in der Hochschulmensa: Design & Durchführung Feldexperiment (NOVANIMAL Kurzbericht). ZHAW. https://doi.org/10.21256/zhaw-1408

During the fieldexperiment in the NOVANIMAL Project several informations about the offer of the buffet were collected. The report of the fieldexperiment see: <a href="https://zenodo.org/deposit/4115429">Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405</a>

opencc-by-nc-sa-4.0Nov 2020View details →

AIT Log Data Set V1.1

AIT Log Data Sets This repository contains synthetic log data suitable for evaluation of intrusion detection systems. The logs were collected from four independent testbeds that were built at the Austrian Institute of Technology (AIT) following the approach by Landauer et al. (2020) [1]. Please refer to the paper for more detailed information on automatic testbed generation and cite it if the data is used for academic publications. In brief, each testbed simulates user accesses to a webserver that runs Horde Webmail and OkayCMS. The duration of the simulation is six days. On the fifth day (2020-03-04) two attacks are launched against each web server. The archive AIT-LDS-v1_0.zip contains the directories "data" and "labels". The data directory is structured as follows. Each directory mail.<name>.com contains the logs of one web server. Each directory user-<ID> contains the logs of one user host machine, where one or more users are simulated. Each file log<UID>.log in the user-<ID> directories contains the activity logs of one particular user. Setup details of the web servers: <ul> <li>OS: Debian Stretch 9.11.6</li> <li>Services: <ul> <li>Apache2</li> <li>PHP7</li> <li>Exim 4.89</li> <li>Horde 5.2.22</li> <li>OkayCMS 2.3.4</li> <li>Suricata</li> <li>ClamAV</li> <li>MariaDB</li> </ul> </li> </ul> Setup details of user machines: <ul> <li>OS: Ubuntu Bionic</li> <li>Services: <ul> <li>Chromium</li> <li>Firefox</li> </ul> </li> </ul> User host machines are assigned to web servers in the following way: <ul> <li>mail.cup.com is accessed by users from host machines user-{0, 1, 2, 6}</li> <li>mail.spiral.com is accessed by users from host machines user-{3, 5, 8}</li> <li>mail.insect.com is accessed by users from host machines user-{4, 9}</li> <li>mail.onion.com is accessed by users from host machines user-{7, 10}</li> </ul> The following attacks are launched against the web servers (different starting times for each web server, please check the labels for exact attack times): <ul> <li>Attack 1: multi-step attack with sequential execution of the following attacks: <ul> <li>nmap scan</li> <li>nikto scan</li> <li>smtp-user-enum tool for account enumeration</li> <li>hydra brute force login</li> <li>webshell upload through Horde exploit (CVE-2019-9858)</li> <li>privilege escalation through Exim exploit (CVE-2019-10149)</li> </ul> </li> <li>Attack 2: webshell injection through malicious cookie (CVE-2019-16885)</li> </ul> Attacks are launched from the following user host machines. In each of the corresponding directories user-<ID>, logs of the attack execution are found in the file attackLog.txt: <ul> <li>user-6 attacks mail.cup.com</li> <li>user-5 attacks mail.spiral.com</li> <li>user-4 attacks mail.insect.com</li> <li>user-7 attacks mail.onion.com</li> </ul> The log data collected from the web servers includes <ul> <li> Apache access and error logs</li> <li> syscall logs collected with the Linux audit daemon</li> <li> suricata logs</li> <li> exim logs</li> <li> auth logs</li> <li> daemon logs</li> <li> mail logs</li> <li> syslogs</li> <li> user logs</li> </ul>   Note that due to their large size, the audit/audit.log files of each server were compressed in a .zip-archive. In case that these logs are needed for analysis, they must first be unzipped.   Labels are organized in the same directory structure as logs. Each file contains two labels for each log line separated by a comma, the first one based on the occurrence time, the second one based on similarity and ordering. Note that this does not guarantee correct labeling for all lines and that no manual corrections were conducted. Version history and related data sets: <ul> <li><a href="https://doi.org/10.5281/zenodo.3723083">AIT-LDS-v1.0</a>: Four datasets, logs from single host, fine-granular audit logs, mail/CMS. <ul> <li><a href="https://doi.org/10.5281/zenodo.4264796">AIT-LDS-v1.1</a>: Removed carriage return of line endings in audit.log files.</li> </ul> </li> <li><a href="http://doi.org/10.5281/zenodo.5789064">AIT-LDS-v2.0</a>: Eight datasets, logs from all hosts, system logs and network traffic, mail/CMS/cloud/web.</li> </ul> Acknowledgements: Partially funded by the FFG projects INDICAETING (868306) and DECEPT (873980), and the EU project GUARD (833456). If you use the dataset, please cite the following publication: [1] M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner and A. Rauber, <a href="https://ieeexplore.ieee.org/document/9262078">"Have it Your Way: Generating Customized Log Datasets With a Model-Driven Simulation Testbed,"</a> in IEEE Transactions on Reliability, vol. 70, no. 1, pp. 402-415, March 2021, doi: 10.1109/TR.2020.3031317. [<a href="https://www.skopik.at/ait/2020_trel.pdf">PDF</a>]

opencc-by-4.0Nov 2020View details →

CellO Data Sets

Overview A permanent archive of the datasets used in the CellO manuscript (<a href="https://www.biorxiv.org/content/10.1101/634097v2">https://www.biorxiv.org/content/10.1101/634097v2</a>).   Expression data <ul> <li>Quantified gene expression for all bulk RNA-seq samples used in this study are available as an HDF5 file (in log transcripts per million): bulk_log_tpm.h5</li> <li>Each bulk RNA-seq experiment accession is mapped to a set of cell type labels from the Cell Ontology: bulk_labels.json</li> <li>The single-cell data used in this study are also available as an HDF5 file (in log transcripts per million): single_cell_log_tpm.h5</li> <li>Each single-cell experiment accession is mapped to a set of cell type labels from the Cell Ontology: single_cell_labels.json</li> </ul> Dataset partitions We partitioned the bulk RNA-seq data into several subsets that were used for various purposes in the study: <ul> <li>The list of bulk RNA-seq samples used for training the classifier for evaluation on the bulk validation-set (i.e. the pre-taining set): pre_training_bulk_experiments.json</li> <li>The list of bulk RNA-seq samples in the validation-set: validation_bulk_experiments.json</li> <li>The list of single-cell experiments in the test set used for evaluting CellO. These are all samples with cell type terms that also appear in the bulk RNA-seq data (i.e. the training data): test_single_cell_experiments.json</li> </ul> Technical variable annotations We annotated 27,097 RNA-seq samples in the Sequence Read Archive (SRA) with technical variables in order to derive a set of primary, healthy, untreated samples (i.e. the datasets above). <ul> <li>Our annotations were based on a custom label-hierarchy of technical variables: tags.json</li> <li>The mapping from each SRA experiment accession to its set of technical variable labels: experiment_tags.json</li> </ul> Trained model coefficients After training the binary classifiers for each cell type, the model coefficients can be used to investigate up and downregulated genes in each cell type. Below, we post the model coefficients for the one-versus-rest trained binary classifiers (used in the Isotonic Regression and True Path Rule algorithms) as well as the coefficients for the classifiers in the Cascaded Logistic Regression algorithm. Each model was trained on the full set of bulk RNA-seq samples used in the study. Each algorithm's cell type model coefficients are available in a tab-separated-value file: <ul> <li>One-versus-rest classifier coefficients: one_vs_rest_coefficients.tsv.gz</li> <li>Cascaded logistic regression coefficients: cascaded_logistic_regression_coefficients.tsv.gz</li> </ul>

data set regarding environmental impact of building facades

The file includes a set of reference data and sources used for the determination of the environmental impact (LCA and EPD) for selected materials used for building facades, with a special emphasis on bio-based solutions.

Data set for the article "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD"

Data set to accompany the article  "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD" written by B.A. Wilson (Swansea University) and P.D. Ledger (Keele University)

Data set 2 anonymized collection of data on BRAD research participants

Database on research participants in the BRAD project. The Personal Data have been removed in order to make the identification of the research participants impossible. For Polish migrants in the UK, the database contains the information about the application to European Union Settlement Scheme.

opencc-by-4.0Dec 2019View details →

Data set 1 discourse analysis BRAD research project

Discourse analysis data set with excerpts of press articles generated in the coding (coded with keywords ‘Brexit’ and ‘deportations’). This data set connects to the WP3 of the BRAD research project.

Data set 3 photographic documentation BRAD research project

Photographic documentation collected during the BRAD research project. For the personal data protection reasons, the published pictures do not represent recognizable people. The pictures present the places where part of the fieldwork was done (London, Croydon  in the UK, Poznań in Poland). A separate folder contains images related to EUSS application.

Fishtrap Creek thermal dynamics - scripts and data sets

This repository contains data sets and scripts used in the analysis reported in an article titled "Predicting latent and sensible heat fluxes in stream temperature models -- current challenges and potential solutions"

Data Set for: Bloch point-mediated skyrmion annihilation in three dimensions

This DOI contains both the experimental data and the simulation scripts to reproduce the results of Bloch point-mediated skyrmion annihilation in three dimensions by M. T. Birch, D. Cortés-Ortuño, N. D. Khanh, S. Seki, A. &Scaron;tefančič, G. Balakrishnan, Y. Tokura and P. D. Hatton. Eprint available at <a href="https://arxiv.org/abs/2012.14813">https://arxiv.org/abs/2012.14813</a> Updates to these data files can be found at the corresponding Github repository: <a href="https://github.com/davidcortesortuno/paper-2021_bloch_point_mediated_skyrmion_annihilation_in_three_dimensions">https://github.com/davidcortesortuno/paper-2021_bloch_point_mediated_skyrmion_annihilation_in_three_dimensions</a> To cite this data set, you can use the following Bibtex entry: <pre><code>@Misc{Birch2021, author = {M. T. Birch and D. Cort\'es-Ortu\~no}, title = {{Data set for: Bloch point-mediated skyrmion annihilation in three dimensions}}, howpublished = {Zenodo \url{doi:10.5281/zenodo.4384569}. Github: \url{https://github.com/davidcortesortuno/https://github.com/davidcortesortuno/paper-2021_bloch_point_mediated_skyrmion_annihilation_in_three_dimensions}}, year = {2021}, doi = {10.5281/zenodo.4384569}, url = {https://doi.org/10.5281/zenodo.4384569}, }</code></pre>

Data set of the article: Using Machine Learning for Web Page Classification in Search Engine Optimization

Data of investigation published in the article: "Using Machine Learning for Web Page Classification in Search Engine Optimization" Abstract of the article: This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research.

Automatic plankton image classification - can capsules and filters help coping with data set shift?

This data set is related to the article 'Automatic plankton image classification - can capsules and filters help coping with data set shift?' published in 'Limnology and Oceanography: Methods' by Plonus et al. (2021). The images belong to the trainings set used to train the models in the aforementioned paper (training_) and three different additional data sets which were used to evaluate the performance of the trained models in application mode (fs446_; fs466_; fs534_). The Python-Script 'separate_files.py' can be used to move all the images in different folders for each data set and class respectively.

Peri Lake Experimental Catchment Data Set

This is the Peri Lake Experimental Catchment data set (PLEC) developed by the Hydrology Laboratory - LabHidro group at the Federal University of Santa Catarina, Florianópolis - Brazil. PLEC data set provides: (i) meteorological data for the Peri Lake catchment (solar radiation, relative humidity, air temperature, wind velocity and wind direction data); (ii) rainfall data (rain gauges located at the Peri Lake park headquarters (HQ) and Retiro meteorological station); (iii) experimental interception data (23 and 24 throughfall gauges and 18 and 20 stemflow gauges in the HQ and Retiro plots, respectively); (iv) overland flow data measured in Retiro headwater catchment (23 Overland Flow Detectors (OFD)); (v) streamflow and flow velocity data measured in 31 cross sections of the Peri Lake watershed; (vi) groundwater levels mesured in 10 wells installed close to the flow lines in Retiro headwater catchment; (vii) soil characteristics estimated in the Retiro headwater catchment (hydraulic conductivity and infiltration rate); (viii) geographic information system (GIS) data for the Peri lakecatchment (digital elevation model (DEM) of Peri Lake Watershed, delimitation of the Peri Lake watershed, delimitation of Peri Lake, stream network of Peri Lake Watershed, information of streamflow and flow velocity, land cover of Peri Lake Watershed, location of Water Level Gauge of Ribeirao Grande Watershed, delimitation of the Ribeirao Grande watershed, location of Interception gauge, delimitation of the Retiro Headwater, drainage network lines, intermittent flow lines, location of Hydraulic Conductivity  test of soil, locations of the overland flow detectors, locations of the wells to monitor the groudwater level). More details on each data can be found in the readme.txt files available within each subdirectory of this data set.  For more information on the available data and collaborations, please communicate with the lead author pedro.chaffe@ufsc.br (www.labhidro.ufsc.br).

Data set: "A 21st Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss"

This data set includes the materials required to reproduce the figures and tables presented in the study: "A 21st Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss". The data consist of: <ul> <li>Maps of annual historical and projected surface mass balance (SMB) of the Greenland ice sheet (GrIS) from RACMO2.3p2 at 1 km spatial resolution in NetCDF format.</li> </ul> <ol> <li>smb_rec.1950-2014.BN_RACMO2.3p2-CESM2-Historical.1km.YY.nc: annual cumulative GrIS SMB (kg m-2 or mm w.e. per year) from RACMO2.3p2 forced by CESM2 for the historical period 1950-2014, further statistically downscaled to 1 km spatial resolution.</li> <li>smb_rec.2015-2099.BN_RACMO2.3p2-CESM2-SSP5-85.1km.YY.nc: annual cumulative GrIS SMB (kg m-2 or mm w.e. per year) from RACMO2.3p2 forced by CESM2 under a high-end warming scenario SSP5-8.5 for the period 2015-2099, further statistically downscaled to 1 km spatial resolution.</li> <li>Icemask_Topography_lon_lat_average_1km_GrIS.nc: mask file including an ice mask (Promicemask) separating Greenland's peripheral glaciers and ice caps (values 1 and 2) from the main ice sheet (value 3), surface topography derived from the GIMP DEM, and longitude/latitude coordinates on the 1 km grid. </li> </ol> NB: the NetCDF files above use a Polar Stereographic North (EPSG:3413) projection with a horizontal resolution of 1 km x 1 km. The reference point is located at 45ºW longitude and 70ºN latitude. <ul> <li>Time series of historical and projected annual GrIS-integrated SMB (Gigatons or Gt per year) and annual mean GrIS temperature (TGrIS; K) from RACMO2.3p2 at 1 km spatial resolution in ASCII format.</li> </ul> <ol> <li>SMB_TGrIS_RACMO2.3p2-CESM2_Historical_1950-2014.dat: time series of annual GrIS-integrated SMB (Gt yr-1) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 simulation for the historical period 1950-2014.</li> <li>SMB_TGrIS_RACMO2.3p2-CESM2_SSP5-8.5_2015-2099.dat: time series of annual GrIS-integrated SMB (Gt yr-1) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 projection under a high-end warming scenario SSP5-8.5 (2015-2099).</li> </ol> <ul> <li>Time series of historical and projected reconstruction of annual GrIS-integrated SMB (Gt yr-1) and mean TGrIS (K) from 12 CESM2 historical members and 10 CESM2 projections under various warming scenarios, namely SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.</li> </ul> <ol> <li>Reconstructed_SMB_CESM2_Historical_1950-2014.dat: time series of reconstructed annual GrIS-integrated SMB (Gt yr-1) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014.  </li> <li>Reconstructed_SMB_CESM2_Projections_2015-2099.dat:  time series of reconstructed annual GrIS-integrated SMB (Gt yr-1) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> <li>TGrIS_CESM2_Historical_1950-2014.dat: time series of annual mean TGrIS (K) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014.</li> <li>TGrIS_CESM2_Projections_2015-2099.dat: time series of annual mean TGrIS (K) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> </ol> NB: the file Crossref_sim_names.txt cross-references the simulation abbreviations in the above .dat files to official simulation names from the National Center for Atmospheric Research (NCAR). The daily downscaled SMB data set from the CESM2-forced RACMO2.3p2 historical simulation and SSP5-8.5 projection are freely available from the authors upon request and without conditions (contact: b.p.y.noel@uu.nl). Besides SMB, the data set includes daily total precipitation (snow and rain), snowfall, total melt (snow and ice), meltwater runoff, retention and refreezing, total sublimation (surface and drifting snow), snow drift erosion, as well as 2 m air temperature at 1 km horizontal resolution.  Abstract: "Under anticipated future warming, the Greenland ice sheet (GrIS) will pass a threshold when meltwater runoff exceeds the accumulation of snow, resulting in a negative surface mass balance (SMB < 0) and sustained mass loss. In spite of several recent warm summers with high melt rates, SMB < 0 has not been reached since at least the year 1958. Here we dynamically and statistically downscale the outputs of an Earth system model to 1 km resolution to infer that a Greenland near-surface atmospheric warming of 4.5 ± 0.3 °C—relative to pre-industrial—is required for GrIS SMB to become persistently negative. Climate models from CMIP5 and CMIP6 translate this regional temperature change to a global warming threshold of 2.7 ± 0.2 °C. Under a high-end warming scenario, this threshold may be reached around 2055, while for a strong mitigation scenario it will likely not be passed."