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.
1,956
datasets available to search
ShareScore release 0.9.0
Dataset results
1,956 results for “test data”
Data on Direct-to-Consumer Genetic Testing and DNA testing companies
<p>This table was compiled between 2011 and 2018. It is a list of companies offering direct-to-consumer DNA tests over the internet. It is primarily concerned with direct-to-consumer genetic testing companies operating currently and also includes those, which are no longer operating. The table briefly summarizes the services offered by each company and gives the company’s location.</p> <p>I am continuing to update this list, so it is a work in progress, it does include some companies that advertise their services to physicians and some companies may have altered their offerings. Further updates will be provided later in 2018. I have added a shorter table at the end of relevant other companies. I hope to make a searchable database of a revised version of the document available through my website in the future.</p> <p>This work is being released for informational and educational purposes and should not be used for commercial purposes.</p> <p>Please refer to my website for further updates <a href="http://www.andelkamphillips.com">http://www.andelkamphillips.com</a></p>
Underlying data for "Microscale 3D Liver Bioreactor for In Vitro Hepatotoxicity Testing under Perfusion Conditions"
<p>Underlying data for the paper "Microscale 3D Liver Bioreactor for In Vitro Hepatotoxicity Testing under Perfusion Conditions" published in the journal <em>Bioengineering</em>.</p>
Execution and data log: on the effectiveness of random and adaptive random test case prioritization
<p>Execution and data logs automatically generated by the test driver during experimentation. These results are similar (but not identical) to the results summarized in Table 1 of the following paper:</p> <p>Z. Q. Zhou, A. Sinaga, and W. Susilo, "On the fault-detection capabilities of adaptive random test case prioritization: case studies with large test suites," in Proceedings of the 45th Annual Hawaii International Conference on System Sciences (HICSS-45). IEEE, 2012, pp. 5584-5593.<br> https://doi.org/10.1109/HICSS.2012.454</p> <p>Compared with the above paper, the current dataset was collected from a different but similar set of experiments and included faulty versions 13, 23, and 26 of the Replace program---although these three versions were excluded from the experiments reported in the above paper due to their instability.</p> <p>If you find these data useful, please cite the above paper.</p>
Data Set Used in Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS
<p>This document contains the data set used for the study Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS that is currently in submission.</p>
Repository of WESC2017 paper data - Benefits of subcomponent testing over full- scale blade testing elaborated on a trailing-edge bond line design
<p>Title:<br> <a href="https://doi.org/10.5194/wes-3-1-2018">Benefits of subcomponent over full-scale blade testing elaborated on a trailing-edge bond line design validation</a></p> <p>Authors:<br> <a href="mailto:malo.rosemeier@iwes.fraunhofer.de">Malo Rosemeier</a>, Gregor Basters, and Alexandros Antoniou</p> <p>Affiliation:<br> Division Structural Components, Fraunhofer IWES, Fraunhofer Institute for Wind Energy Systems, Am Seedeich 45, 27572 Bremerhaven, Germany</p> <p>The following data used for the study is provided:</p> <ul> <li> data_blade.zip contains input data for fusedwind-dev (https://github.com/FUSED-Wind/fusedwind-dev) of the DTU10MW blade.</li> <li>data_becas.zip contains input/ output data obtained for/ from BECAS with BECASWrapper (https://gitlab.windenergy.dtu.dk/HAWTOpt2/BECASWrapper).</li> <li>data_rfoil.zip contains polars obtained from Rfoil.</li> <li>data_feproc.zip contains the ANSYS APDL models obtained with FEPROCWrapper (https://gitlab.iwes.fraunhofer.de/git/bdt/FEPROCWrapper).</li> <li>*.sqlite data bases contain all results data which can be accessed with the `post.py` script.</li> <li>result_plots.zip contain the results generated by the `post.py` script.</li> </ul> <p>For further details see the manuscript.</p>
Widen crosswell slug test data
<p>Cross well slug test data gathers by Ralf Brauchler at the Widen test site in northern Switzerland.</p> <p>The program WidenReduction.f may be used to read the data.</p>
Final Report on Data Management - Raw data of DC-TRNG for D2.4 statistical testing
<p>Collected Raw & Post-processed data from DC-TRNG for both AIS-31 and NIST800-90B tests suites for Final Report on Data Management</p> <p>The purpose of the final report on data management is to provide an update of the analysis of the main elements of the data management policy used by the applications with regards to all the datasets that were generated by the project. Most important aspects regarding data management, like metadata generation, data preservation, and responsibilities, were updated compared to the initial report D5.2 (Data Management Plan) according to the outcome of the project.</p>
Widen Field Test Pressure Data - P02 Experiment
<p>Hydraulic tomography data from the crosswell pressure test P02.</p> <p>The data are referenced in the paper:</p> <p>Vasco, D. W., J. Doetsch, and R. Brauchler (2018). An extended trajectory-mechanics approach for calculating the path of a pressure transient: Hydraulic tomographic imaging, Water Resources Research, (submitted)</p> <p>and in</p> <p>Lochbuhler, T., J. Doetsch, R. Brauchler, and N. Linde (2013). Structure-coupled joint inversion of geophysical and hydrological data, Geophysics, 78, ID1-ID14, doi: 10.1190/GEO2012-0460.1</p> <p> </p>
Feature count data for Love et al. 2019 analysis for "Using equivalence class counts for fast and accurate testing of differential transcript usage" paper
<p>Feature count data for Love et al. 2019 analysis used in the "Using equivalence class counts for fast and accurate testing of differential transcript usage" paper. For reproducing the analyses and figures using the <a href="https://github.com/Oshlack/ec-dtu-paper/">ec-dtu-paper</a> code.</p> <p>Contains:</p> <ul> <li>Equivalence class count matrix for all 24 samples (using counts from Salmon)</li> <li>Salmon quantification results for all 24 samples</li> <li>Exon counts for all 24 samples using DEXSeq-count</li> </ul>
ViSAPy-generated test data from Lee JH., et al. Advances in Neural Information Processing Systems 30 (NIPS 2017), pp4002--4012
<p>This dataset corresponds to the simulated test data for spike-sorting algorithms in Figure 3 of:</p> <p>Lee, Jin Hyung and Carlson, David E and Shokri Razaghi, Hooshmand and Yao, Weichi and Goetz, Georges A and Hagen, Espen and Batty, Eleanor and Chichilnisky, E.J. and Einevoll, Gaute T. and Paninski, Liam. YASS: Yet Another Spike Sorter. Advances in Neural Information Processing Systems 30 (NIPS 2017). Editors I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett, year 2017, pp4002-4012.<br> publisher: Curran Associates, Inc. URL http://papers.nips.cc/paper/6989-yass-yet-another-spike-sorter.pdf</p>
Test data set for macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins
<p>This a bundle of test data can be used to run the macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins.</p> <p>These data sets can be used to run the following macros that can be found on GitHub:</p> <ol> <li><a href="https://github.com/molcyto/MC-Ratio-96-wells">https://github.com/molcyto/MC-Ratio-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Ratio-Petri-dish">https://github.com/molcyto/MC-Ratio-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-Petri-dish">https://github.com/molcyto/MC-FLIM-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-Bleach-96-wells">https://github.com/molcyto/MC-Bleach-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Scatter5D">https://github.com/molcyto/MC-Scatter5D</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-96-wells">https://github.com/molcyto/MC-FLIM-96-wells</a></li> </ol> <p>Funding:<br> This work was supported by the NWO CW-Echo grant 711.011.018 (M.A.H. and T.W.J.G.), grant 12149 (T.W.J.G.) from the Foundation for Technological Sciences (STW) from the Netherlands</p> <p> </p>
Adequate vs. Inadequate Test Suite Reduction Approaches. Raw Data
<p>Context: Regression testing is an important activity that allows ensuring the correct behavior of a system after a change. As the system grows, the time and resources to perform regression testing increase. Test Suite Reduction (TSR) approaches aim to speed up regression testing by removing obsolete or redundant test cases. These approaches can be classified as adequate or inadequate. Adequate TSR approaches reduce test suites and completely preserve test requirements (e.g., covered statements) of the original test suites. Inadequate TSR approaches do not preserve test requirements. The percentage of satisfed test requirements indicates the inadequacy level.</p> <p>Objective: We compare some state-of-the art adequate and inadequate TSR approaches with respect to the size of the reduced test suites and their fault-detection capability. Specifcally, we aim to increase our body of knowledge on TSR approaches by performing the following comparisons: (i) well-known adequate TSR approaches; (ii) their inadequate variants; and (iii) several variants of a novel Clustering-Based (CB) approach for (adequate and inadequate) TSR.</p> <p>Method: We conducted an experiment to compare adequate and inadequate TSR approaches and this comparison is founded on a public dataset containing information on real faults.</p> <p>Results: The most important findings from our experiment can be summarized as follows: (i) there is not an inadequate TSR approach that performs better than others; (ii) some variants of the CB approach, and a few well-known inadequate approaches, outperform the adequate ones in terms of the reductions in test suite size with a negligible, or no, eect on fault-detection capability; and (iii) the CB approach is less sensitive than the other inadequate approaches, that is, variations in the inadequacy level have a small effect on the reduction in test suite size and on the loss in fault-detection capability.</p> <p>Conclusions: These findings imply that inadequate TSR approaches and especially the CB approach might be appealing because they lead to a greater reduction in test suite size (with respect to the adequate ones) at the expense of a small loss in fault-detection capability.</p>
Extended Data for Publication "Testing the Spectroscopic Extraction of Suppression of Convective Blueshift"
<p>Efforts to detect low-mass exoplanets using stellar radial velocities (RVs) are currently limited by magnetic photospheric activity. Suppression of convective blueshift is the dominant magnetic contribution to RV variability in low-activity Sun-like stars. Due to convective plasma motions, the magnitude of RV contributions from the suppression of convective blueshift is roughly correlated with the depth of formation of photospheric spectral lines used to compute the RV time series. Meunier et al. (2017), used this relation to demonstrate a method for spectroscopic extraction of the suppression of convective blueshift in order to isolate RV contributions, including planetary RVs, that contribute equally to the timeseries for each spectral line. In this publication, we extract disk-integrated solar RVs from observations over a 2.5 year time span made with the solar telescope integrated with the HARPS-N spectrograph at the Telescopio Nazionale Galileo (La Palma, Canary Islands, Spain). We apply the methods outlined by Meunier et al. (2017) - as part of this analysis, we fit Gaussian line profiles to 765 iron lines measured over 457 exposures.</p> <p>Here, we provide the complete line list (Table 1; Table1_LineList.csv) used in our analysis, and the resulting RVs time series in their entirety (Table 3; Table3_TimeSeries.csv). Wavelengths are given in Angstroms, and RVs in m/s.</p> <p>We also include 4 CSV files with the line fit parameters of each line profile: Each row corresponds to a single exposure time (corresponding to the JDs in Table 3) and each column corresponds to a specific spectral line (with wavelength specified in Table 1).</p> <p>We fit each spectral line to a Gaussian of the form:</p> <p><span class="math-tex">\(f(\lambda) = p_1 - p_2 \exp \left[- {1 \over 2} \left({{\lambda - p_3} \over p_4}\right)^2 \right]\)</span></p> <p>p<sub>1</sub> is the continuum level in arbitrary units (LineProfiles_Continuum.csv)<br> p<sub>2</sub> is the line strength in arbitrary units (LineProfiles_Amplitude.csv)<br> p<sub>3</sub> is the line shift in Angstroms (LineProfiles_Shift.csv)<br> p<sub>4</sub> is the line width in Angstroms (LineProfiles_Width.csv)</p>
The Politecnico di Torino rolling bearing test rig: description of the open-access data for vibration monitoring and diagnostics
<p>Accelerometric measurements from the rolling bearing test rig of the Dynamic and Identification Research Group (DIRG), Department of Mechanical and Aerospace Engineering, Politecnico di Torino.</p> <p>Goals:</p> <p> • Vibration Monitoring, Bearing Diagnostics, Damage detection, Damage localization, Damage classification, Damage assessment.</p> <p>Features:</p> <p> • high-speed spindle driving a hollow shaft supported by a couple of identical roller bearings B1 and B3. B1 is the bearing under analysis and features various damages.</p> <p> • two damage types (indentations on a roller and on the inner ring) and severities (0, 150, 250, 450 µm).</p> <p> • a central, larger roller bearing (B2) is loaded through a sledge generating a controlled radial force measured by a load cell.</p> <p> • lubrication is obtained by oil injection into the hollow shaft.</p> <p> • a K-type thermocouple is used to monitor the temperature (manually recorded).</p> <p> • two triaxial accelerometers are mounted on the supports of bearings B1 and B2.</p> <p>Dataset:</p> <p> • stationary acquisitions at different speed & load combinations (speed: 0, 100, 200, 300, 400, 500 Hz; load: 0, 1000, 1400, 1800 N).</p> <p> • endurance acquisitions of the bearing featuring the 450µm roller indentation. Monitoring of the damage evolution for about 230 hours under the same speed and load condition.</p> <p> </p> <p>The extended description of the dataset can be found in the attached pdf "Description and analysis of open access data" or in:</p> <p>A.P. Daga, A. Fasana, S. Marchesiello, L. Garibaldi, The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data, Mechanical Systems and Signal Processing 120 (2019) 252–273. doi:10.1016/j.ymssp.2018.10.010.</p>
Test dataset for Signature 500 data processing
<p>Subset of dataset from a deployment of Nortek Signature500 on a ocean mooring (M1-1) in the Northern Barents Sea during 2018-2019. These files are intended for testing post-processing software. </p> <p>The files have been converted from the native .ad2cp format to .mat using Nortek <a href="https://www.nortekgroup.com/software" rel="nofollow">SignatureDeployment</a> software.</p> <p>Data in these files were collected during this period:</p> <pre>18 May 2019 12:15 --> 15 Jul 2019 08:00 (57.8 days)</pre> <p> </p>
Data: Testing the mating system model of parasite complex life cycle evolution reveals demographically driven mixed mating
<p>Abstract: Many parasite species use multiple host species to complete development; however, empirical tests of models that seek to understand factors impacting evolutionary changes or maintenance of host number in parasite life cycles are scarce. Specifically, Brown et al.’s (2001) mating system model, which posits multi-host life cycles are an adaptation to prevent inbreeding in hermaphroditic parasites and thus, preclude inbreeding depression, remains untested. The model assumes loss of a host results in parasite inbreeding and predicts host loss can only evolve if there is no parasite inbreeding depression. <a name="_Hlk169780726"></a>We provide the first empirical tests of this model using a novel approach we developed for assessing inbreeding depression from field-collected, parasite samples. The method compares genetically-based, selfing-rate estimates to a demographic-based selfing rate, which was derived from the closed mating system experienced by endoparasites. Results from the hermaphroditic trematode <em>Alloglossidium renale</em>, which has a derived 2-host life cycle, supported both the assumption and prediction of the mating system model as this highly inbred species had no indication of inbreeding depression. Additionally, comparisons of genetic and demographic selfing rates revealed <a name="_Hlk169781073"></a>a mixed mating system that could be explained completely by the parasite’s demography, i.e., its infection intensities.</p>
Data Set for the Development and Testing of the MC23 Nonclassical-Energy Functional
<p>This dataset contains files used to train and test the Multi-Configuration 23 (MC23) functional and to compare the results to other methods. It includes files to carry out electronic structure calculations. These include molecular geometries in xyz format, <em>OpenMolcas</em> input files for CASSCF calculations, converged CASSCF natural orbitals, <em>OpenMolcas</em> basis set files, and <em>Gaussian 16</em> formatted checkpoint files for KS-DFT calculations. It also includes data used for data processing such as stoichiometries, absolute energies, and reference energies.</p> <p>Each file in this dataset is a .tar.xz archive. One can extract them by the following command:</p> <pre>tar -xJf name_of_archive.tar.xz</pre> <p>Below is a description of the content of each archive.</p> <p><strong>gaussian_16_fchk.tar.xz</strong> contains <em>Gaussian 16</em> formatted checkpoint files for all KS-DFT calculations used in this work. The files in the archive are named as <em>functional</em>/<em>database</em>/<em>system</em>.fchk</p> <p><strong>openmolcas_basis_set.tar.xz</strong> contains <em>OpenMolcas</em> basis set files used for multireference calculations. To reproduce the results in this work, the basis set files should be placed in the “basis_library” directory in the <em>OpenMolcas</em> installation location.</p> <p><strong>openmolcas_wave_function.tar.xz</strong> contains files needed by <em>OpenMolcas</em> to reproduce the CASSCF wave function used in this work. The files in the archive are named <em>database</em>/<em>system</em>.*.</p> <ul> <li>The file <em>system</em>.xyz contains the Cartesian coordinates. Note that for Data Set 2, the coordinates are in the input files <em>system</em>.inp.</li> <li>The file <em>system</em>.inp contains the <em>OpenMolca</em>s input file to perform CASSCF calculations.</li> <li>The files <em>system</em>.RasOrb, <em>system</em>.rasscf.h5, and <em>system</em>.rasscf.molden contain the converged CASSCF natural orbitals.</li> </ul> <p><strong>gaussian_16_stoichiometry_energy.tar.xz</strong> and <strong>openmolcas_stoichiometry_energy.tar.xz</strong> contain files used for data processing.</p> <ul> <li>Files with names like <em>database</em>.ref contain information used to calculate the final energies and errors. They are tab-delimited files. Each row represents an energy difference (e.g. atomization energy, barrier height, etc.). The first column contains the name of the energy difference (note: spaces may be present in this column). This is followed by the file names of each electronic structure calculation and the stoichiometries used to calculate the energy difference from the absolute energies. Each name or stoichiometry occupies one column. The second from the last column contains the reference value in kcal/mol. The reference values contain spin–orbit coupling. The last column contains the factor by which the final energy should be divided. This factor usually equals 1, but it can be greater than 1 for databases calculating atomization energies per bond or per atom.</li> <li>Files with names like <em>method</em>.elist contain the absolute energies of each electronic structure calculation. They are tab-delimited files. Each row represents an electronic structure calculation, and each row always contains two columns. The first column is the file name of the calculation in the format <em>database</em>/<em>system</em>. The second column is the absolute energy in atomic units extracted from the output file of electronic structure programs.</li> <li>The file named SOC.dat contains the spin–orbit coupling term in kcal/mol to be added to each electronic structure calculation prior to calculating energy differences. It has the same format as files with names like <em>method</em>.elist.</li> </ul> <p>The database names in the directory names use a slightly different convention than the ones in the article describing MC23. A prefix DS2_ or DS3_ is used to indicate the data set to which a database belongs, and the number of data points is removed from the database name. For example, the MR-MGN-BE8 database from Data Set 2 has a file name DS2_MR-MGN-BE.</p>
Data of soil infiltration tests and soil samples, Los Arenales MAR Systems, Santiuste and La Laguna del Señor infiltration basins
<p><span>Infiltration test and soil sample data utilised in the article "a nature-based solution to enhance aquifer recharge: combining trees and infiltration basins"</span></p>
Data and Software associated with the paper "``A New Likelihood-based Test for Natural Selection''"
<p>Data and Software associated with the paper ``A New Likelihood-based Test for Natural Selection''</p>
Original data sets of a DEER/PELDOR ring test of four doubly spin-labelled mutants of the protein YopO
<p>The dataset is discussed in manuscript "Benchmark test and guidelines for DEER/PELDOR experiments on nitroxide-labeled biomolecules" by Olav Schiemann, Caspar A. Heubach, Dinar Abdullin, Katrin Ackermann, Mykhailo Azarkh, Elena Bagryanskaya, Malte Drescher, Burkhard Endeward, Jack H. Freed, Laura Galazzo, Daniella Goldfarb, Tobias Hett, Laura Esteban Hofer, Luis Fábregas Ibáñez, Eric J. Hustedt, Svetlana Kucher, Ilya Kuprov, Janet E. Lovett, Andreas Meyer, Sharon Ruthstein, Sunil Saxena, Stefan Stoll, Christiane Timmel, Marilena Di Valentin, Hassane S. Mchaourab, Thomas F. Prisner, Bela E. Bode, Enrica Bordignon, Marina Bennati, Gunnar Jeschke. It was generated in a ring test by seven laboratories. The authors names of individual data are not assigned on purpose, rather data sets are referred to by laboratory identifiers A, B, C, D, E, F, G. The Supplementary Information for the above mentioned manuscript, which contains a link to this dataset, describes how the samples were prepared and how the measurements were performed.</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.