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1,956 results for “test data”
NHS England COVID-19 Exposure Notification App and Test Availability Data
<p>This dataset was scraped from the API serving the NHS COVID-19 App for England and Wales, and the NHS COVID-19 test availability service.</p> <p>It contains the following files:</p> <p><strong>exposure_keys.csv</strong><br> Metadata associated with the published exposure keys for the Bluetooth Google/Apple Exposure Notification (GAEN) system. The actual broadcast keys were not collected, only the metadata attached to them. The columns in the table match those in the <a href="https://developers.google.com/android/exposure-notifications/exposure-key-file-format">exposure key export format</a>, with the exception of the "export_date" field which is the "end_timestamp" of the key export in which that key was first seen. This file is believed to be complete between 2020-09-13 and 2023-04-29 when the NHS COVID-19 app was retired.</p> <p><strong>exposure_configuration.csv</strong><br> This table contains the NHS COVID-19 App's exposure configuration JSON file fetched from the API, with a new record inserted whenever this changed. History of this file is also available in the <a href="https://github.com/ukhsa-collaboration/covid19-app-system-public">app's git repository</a>, and entries in this file from before 2021-07-11 were imported from there. The timestamps for entries dated since that point will match the time that the configuration was published to the API, which may not be the case for the git repository as this was normally updated after a delay.</p> <p><strong>risky_venues.csv</strong><br> "Risky venue" notification data for the COVID-19 App. This was an NHS App-specific feature, not part of the GAEN specification, which allowed users to "check in" to a venue and receive a notification if they were present at the same time as someone who subsequently tested positive for COVID-19. This file is believed to be complete between 2020-09-24 and 2022-02-22 (when it appears the "risky venue" feature was retired), with a known data collection gap between 2021-08-03 and 2021-08-06.</p> <p><strong>walk_in_pcr_availability.csv</strong><br> Walk-in PCR test availability for the entire UK, used by the NHS PCR test booking service. This contains JSON objects provided by the API, which are broken down by region. A new row was inserted whenever this JSON object changed. This file is believed to be complete between 2021-12-27 and 2022-03-30 (after which it appears the online test booking service was retired).</p> <p><strong>home_test_availability.csv</strong><br> Home test (PCR or Lateral Flow Device) availability, for the public and for "key workers" who had priority ordering PCR tests. A new row was inserted whenever the availability changed. This file is believed to be complete between 2021-12-27 and 2022-08-22 when the data ended.</p> <p> </p> <p>The final version of the source code used to fetch this data is <a href="https://doi.org/10.5281/zenodo.7883754">available here</a>.</p>
DA-TCA test data sets (KIAPS-GIST)
<p><strong>About the data sets:</strong></p> <p>The test datasets from SMOS/ASCAT will be utilized to validate the efficacy of SMAP data assimilation in numerical weather forecasting. This project is being conducted in collaboration with the Korea Institute of Atmospheric Prediction Systems (KIAPS) and the Gwangju Institute of Science and Technology (GIST) in South Korea.</p> <p><strong>About the project:</strong></p> <p>Soil moisture assimilation from satellites can significantly improve weather prediction accuracy by providing valuable information about the moisture content in the soil. When integrated into numerical weather forecasting models, satellite-derived soil moisture data enhances the representation of land surface processes and interactions between the atmosphere and the Earth's surface.</p> <p>Here's how soil moisture assimilation benefits weather prediction:</p> <ol> <li> <p>Initialization of Models: Accurate initial conditions are crucial for reliable weather forecasting. Satellite-derived soil moisture data serves as an important source of information for initializing models. By incorporating this data, forecast models can start with more realistic representations of the state of the land surface, enabling a better starting point for predictions.</p> </li> <li> <p>Land-Atmosphere Interaction: Soil moisture plays a significant role in land-atmosphere interactions. It influences the partitioning of energy, the transfer of moisture, and the formation of clouds and precipitation. Assimilating satellite-derived soil moisture data allows forecast models to better capture these interactions and improve the representation of feedback mechanisms between the land surface and the atmosphere.</p> </li> <li> <p>Precipitation Forecasting: Soil moisture assimilation also enhances precipitation forecasting. The availability of accurate soil moisture data helps in understanding soil moisture-atmosphere feedback processes, which impact the formation, intensity, and movement of precipitation systems. By incorporating this information into forecasting models, more accurate precipitation predictions can be made.</p> </li> <li> <p>Drought and Flood Monitoring: Satellite-derived soil moisture data aids in monitoring and predicting droughts and floods. Real-time updates of soil moisture conditions can be integrated into forecasting models, allowing for timely and accurate assessments of soil moisture deficits or surpluses. This information is crucial for managing water resources, agriculture, and mitigating the impacts of extreme weather events.</p> </li> </ol> <p><strong>Related code in Github</strong>:</p> <p>https://github.com/Hyunglok-Kim/HydroAI/blob/main/Ex_in_TCA.ipynb</p>
Characterisation of post-scrap material from coupons including melting test data
<p>Remelting tests data including characterisation and melting process parameters of post-scrap material from LBW and FSW coupons.</p> <p>Research pertaining to Task 4.1 (WP4), Deliverable D13 (D4.2)</p>
Companion data for RAT 3.0: Global Database, Test data, Parameter files and Routing Script
<p><a href="https://depts.washington.edu/saswe/rat/">Reservoir Assessment Tool version 3.0</a> is a scalable and user-friendly software platform to mobilize the global water management community. RAT uses satellite remote sensing data to monitor water surface area and water level changes in artificial reservoirs. It uses this information, along with topographical information (either derived from satellite data, or in-situ topo maps) to estimate the Storage Change (∆S) in the reservoirs. Additionally, RAT models the Inflow (I) and the Evaporation (E) of each reservoir. Finally, RAT uses the modeled I, and E, and estimated ∆S, to estimate the Outflow (O) from reservoirs. The datasets and files provided here are used by RAT 3.0 as default inputs to make it easy to set up and execute RAT for first-time users.</p> <p><strong>global_data.zip</strong> - It includes <a href="https://rat-satellitedams.readthedocs.io/en/latest/RAT_Data/GlobalDatabase/">Global Database</a> encompassing global elevation data, global reservoir and dam data, major river basins in the world and the river networks, flow direction file, and geoid model. It is used by RAT 3.0 as default input for easy execution for first-time users.</p> <p><strong>global_vic_params.zip</strong> - It contains <a href="https://zenodo.org/record/3475602">global VIC soil and domain parameters</a> for executing the hydrological model within RAT 3.0. It is considered a part of the Global Database but is packaged separately.</p> <p><strong>params.zip</strong> - It consists of all the default parameter files used by RAT 3.0 to execute the hydrological model within it and to execute RAT itself. </p> <p><strong>routing.zip</strong> - It consists of the Fortran code for <a href="https://vic.readthedocs.io/en/vic.4.2.d/Documentation/Routing/RunRouting/">the Routing model</a> for easy installation for users. </p> <p><strong>test_data.zip</strong> - It consists of data used by RAT 3.0 to test whether it has been installed and initialized properly in a user's system.</p>
Test data for 3D with focal stacking
<p>* the data contains a set of .tiff images of a grasshopper eye taken with a Canon</p> <p>* shutter speed: 1/5, ISO: 200</p> <p>* objective Met 20/0.5</p> <p>* speed within stack: 10 um/s, step size: 5 um</p> <p> </p> <p>* Authors: Stefanie Homberger, John Meshreki, Ivo Ihrke, 2023, Universität Siegen / Chair of Computational Sensorics / Communications Engineering</p> <p> </p>
Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data
<p>Dataset with Zambia data belonging to the publication: Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data</p>
Bibliographic data for the systematic review on tilting table tests of masonry assemblies
<p>This database contains all the bibliographic information about the records found after applying the Search Strategy used for the Systematic Review on Tilting Table Tests of Masonry Assemblies. The search was conducted in Scopus, Web of Science, IEEE Explore, Engineering Village, and Wiley Online Library databases. It was performed on 13/09/2023. The bibliographic data of the records found in the different databases is presented in .ris, .bib, and .csv format.</p>
Chlorophyll data from experiments testing for nitrogen, phosphorus, and thiamine limitation of phytoplankton in 39 Ohio lakes of varying trophic status during the growing seasons (April – October) of 2008–2009
Although nitrogen and phosphorus deficiency of algal blooms have been the focus of substantial attention, organic nutrients can limit algal growth in aquatic systems. Growing evidence indicates thiamine (vitamin B1) can influence the community of primary producers in marine systems, but comparatively little is known about the effect of thiamine on freshwater algal productivity. We conducted 106 nutrient deficiency experiments with water from 39 Ohio lakes of varying trophic status during the growing seasons (April – October) of 2008–2009. Specifically, we tested the response of phytoplankton biomass (as chlorophyll a, chl-a) relative to controls to added nitrogen (N), phosphorus (P), thiamine (Th), or combinations of N+P and N+P+Th in integrated surface water collected from the inflow and outflow of each lake. The data presented here show the average chl-a of two replicate samples of each treatment (control, N, P, Th, N+P, N+P+Th), ratio of treatment/control response, and growth response ratio as ln(treatment chl-a/control chl-a). Each entry also includes lake surface water pH at time of collection and the initial chl-a concentration at time of experiment start.
Biogeochemical rate data and sediment properties of samples used for a controlled flow through experiment testing the effect of nitrate on organic matter decomposition, PIE LTER, Plum Island Sound estuary, Massachusetts.
In this dataset, we used a controlled flow-through reactor (FTR) experiment to test the role of nitrate as an electron acceptor, and its effect on organic matter decomposition and the associated microbial community in salt marsh sediments. Organic matter decomposition significantly increased in response to nitrate, even at sediment depths typically considered resistant to decomposition. The use of isotope tracers suggests this pattern was largely driven by stimulated denitrification. Nitrate addition also significantly altered the microbial community and decreased alpha diversity, selecting for taxa belonging to groups known to reduce nitrate and oxidize more complex forms of organic matter. Fourier Transform-Infrared Spectroscopy further supported these results, suggesting that nitrate facilitated decomposition of complex organic matter compounds into more bioavailable forms. Taken together, these results suggest the existence of organic matter pools that only become accessible with nitrate and would otherwise remain stabilized in the sediment. The existence of such pools could have important implications for carbon storage, since greater decomposition rates as N loading increases may result in less overall burial of organic-rich sediment. Given the extent of nitrogen loading along our coastlines, it is imperative that we better understand the resilience of salt marsh systems to nutrient enrichment, especially if we hope to rely on salt marshes, and other blue carbon systems, for long-term carbon storage.
Supporting data for "In situ Quantitative Tensile Tests on Antigorite in a Transmission Electron Microscope"
<p>Abstract: The determination of the mechanical properties of serpentinites is essential towards the understanding of the mechanics of faulting and subduction. Here, we present the first in situ tensile tests on antigorite in a transmission electron microscope. A push-to-pull deformation device is used to perform quantitative tensile tests, during which force and displacement are measured, while the microstructure is imaged with the microscope. The experiments have been performed at room temperature on beams prepared by focused ion beam. The specimens are not single crystals despite their small sizes. Orientation mapping indicated that some grains were well-oriented for plastic slip. However, no dislocation activity has been observed even though engineering tensile stress went up to 700 MPa. We show also that antigorite does not exhibit an pure elastic-brittle behaviour since, despite the presence of defects, the specimens underwent plastic deformation and did not fail within the elastic regime. Instead, we observe that strain localizes at grain boundaries. All observations concur to show that under our experimental conditions, grain boundary sliding is the dominant deformation mechanism. This study sheds a new light on the mechanical properties of antigorite and calls for further studies on the structure and properties of grain boundaries in antigorite and more generally in phyllosilicates.</p>
Test data for running snakePipes : scRNA-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run scRNA-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for mouse (<strong>mm10</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
ERT data collected at the Corona volcano (Lanzarote, Canary Islands) during the European Space Agency (ESA) testing campaign PANGAEA-X 2017
<p>This dataset contains the ERT (Electrical Resistivity Tomography) data collected between 22 and 23 November 2017 at the Corona volcano (Lanzarote, Canary Islands, Fig. 1) for the detection of lava tubes and the stratigraphic investigation of planetary volcanic analogues. This geophysical survey was carried out within the European Space Agency (ESA) testing campaign PANGAEA-X 2017 (Bessone et al., 2018), aimed at integrating astronaut training-data collection, documentation, analogue field geology procedures with remote sensing and in situ geophysical methods. </p> <p>Two ERT profiles were acquired in NE-SW and NNE-SSW orientations (Fig. 1). These were located roughly orthogonal to the Corona lava tube system and as far as possible on top of the main lava tube axes. The longer profile, profile D, is 470 m in length and was obtained using 48 electrodes spaced 10 m apart. The profile orientation is from SW to NE (electrode 1 to 48). The profile was acquired to detect lava tubes in test site D (sub-area south) where the exact location of a lava tube was known thanks to a LiDAR TLS (Terrestrial Laser Scan) subsurface survey (Santagata et al., 2018). A shorter profile, profile E, is 235 m long and was obtained using 48 electrodes 5 m apart. The profile orientation is from SSW to NNE (electrode 1 to 48). This profile was acquired in test site E (sub-area north) to provide a more detailed investigation of the potential existence of inaccessible sections of the tube whose location could be indicated by the evidence of closely-spaced aligned collapse structures.</p> <p>Each profile was collected using measure sequences compounded by 276 Wenner-Schlumberger array quadrupoles which ensure high vertical resolution and signal amplitude and 328 dipole-dipole array quadrupoles which provide enhanced lateral resolution. A fully automatic multi-electrode resistivity meter SYSCAL Jr Switch-48 by IRIS Instruments (400 V max output voltage, 1200 mA max output current, 100 W max output power, <a href="http://www.iris-instruments.com/syscal-juniorsw.html">http://www.iris-instruments.com/syscal-juniorsw.html</a>), was used for data collection.</p> <p>At most of the measurement points, it was necessary to drill the basalt using a hand drilling machine in order to place the tips of the electrodes into the ground at a depth of approximately 40 cm. The electrodes also needed to kept moist to reduce contact resistance between the electrode and the ground. A large amount of water (up to 2 liters per point) was needed for profile D, situated in an area above the lava tubes with very porous dry soil cover.</p> <p>The dataset is presented as a spreadsheet format which has the "space" as separator and the ".txt" extension. The structure of such a file is the following one:</p> <p>#, El array, Spa1/4, Rho, Dev, M, Sp, Vp, In, Time, Spa5/12, M1/20</p> <p>- #: Data point number</p> <p>- El array: Electrode array</p> <p>- Spa. 1/4: four spacing parameters (corresponding to the electrode array – in m)</p> <p>- Rho: resistivity value (in Ohm.m)</p> <p>- Dev: standard deviation (quality factor, in %)</p> <p>- M: global chargeability value (induced polarization parameter (in mV/V – "=0" if only-resistivity data))</p> <p>- Sp: spontaneous polarization (measured just before the injection, in mV)</p> <p>- Vp: measured primary voltage (in mV)</p> <p>- In: injected current intensity (in mA)</p> <p>- Time: injection time (pulse duration, in s)</p> <p>- Spa. 5/8: other spacing parameters (in m)</p> <p>- Spa. 9/12: electrode elevation (in m)</p> <p>- M1/M20: partial chargeability values (induced polarization window (in mV/V – "=0" if only-resistivity data))</p> <p> </p> <p>Acknowledgements</p> <p>The authors are grateful to ESA and all PANGAEA-X 2017 staff, particularly Loredana Bessone, Matthias Maurer, Herve Stevenin and Igor Drozdovskiy for their participation in data collection during some of the experiments and to the MilesBeyond Team, particularly Francesco Maria Sauro for his logistical support. Regional and local remote sensing data were obtained by the Spanish Instituto Geográfico Nacional (https://www.ign.es) and Gobierno de Canarias (https://www.grafcan.es, <a href="https://opendata.sitcan.es/">https://opendata.sitcan.es</a>).</p> <p> </p> <p>References</p> <p>Bessone, L., et al., 2018, Testing technologies and operational concepts for field geology exploration of the Moon and beyond: the ESA PANGAEA-X campaign, Geophysical Research Abstract, #EGU2018-4013.</p> <p>Santagata, T., Sauro, F., Massironi, M., Pozzobon, R., Del Vecchio, U., Lazzaroni, M., Damiano, N., Tonello, M., Tomasi, I., Martínez-Frìas, J. and Mateo Medero, E., 2018. Subsurface laser scanning and photogrammetry in the Corona Lava Tube System, Lanzarote, Spain, EGU General Assembly 2018, pp. EGU2018-5290.</p>
NEMO ORCA05 FOCI Test Data
<p>This is nine years of monthly NEMO data that is very similar to data written by the Ocean component of FOCI from GEOMAR.</p> <p>Feel free to use it for development and testing but be aware that these data are in no way suitable for scientific use.</p> <p>Data prepared by Jan Harlaß and Willi Rath (2020)</p>
NEMO ORCA05 Test Data
<p>This is two years of 5daily NEMO data that is very similar to model hindcasts produced at GEOMAR.</p> <p>Feel free to use it for development and testing but be aware that these data are in no way suitable for scientific use.</p> <p>Data prepared by Jan Harlaß and Willi Rath (2020)</p> <ul> <li>NEMO version: release-3.6.x (revision 6721)</li> <li>Components: OPA_SRC LIM_SRC_2</li> <li>Grid: orca, zps (z-coordinate with partial steps)</li> <li>Resolution: 05</li> <li>Horizontal Gridpoints: 722 x 511</li> <li>Vertical Levels: 46</li> </ul>
ParaVision Mouse, Rat, and Lemur Testing Data for SAMRI
<p>This is a testing data set encompassing mouse, rat, and lemur MRI data, acquired with diverse protocols, and stored in the Bruker ParaVision format. It serves as a demo and testing substrate for the SAMRI software package.</p>
Data from: Exploring rainforest diversification using demographic model testing in the African foam-nest treefrog (Chiromantis rufescens)
Aim: Species with wide distributions spanning the African Guinean and Congolian rainforests are often composed of genetically distinct populations or cryptic species with geographic distributions that mirror the locations of the remaining forest habitats. We used phylogeographic inference and demographic model testing to evaluate diversification models in a widespread rainforest species, the African Foam-nest Treefrog (Chiromantis rufescens). Location: Guinean and Congolian rainforests, West and Central Africa. Taxon: Chiromantis rufescens. Methods: We collected mitochondrial DNA (mtDNA) and single nucleotide polymorphism (SNP) data for 130 samples of Chiromantis rufescens. After estimating population structure and inferring species trees using coalescent methods, we tested demographic models to evaluate alternative population divergence histories that varied with respect to gene flow, population size change, and periods of isolation and secondary contact. Species distribution models were used to identify regions of climatic stability that could have served as forest refugia since the Last Interglacial. Results: Population structure within Chiromantis rufescens resembles the major biogeographic regions of the Guinean and Congolian forests. Coalescent-based phylogenetic analyses provide strong support for an early divergence between the western Upper Guinean forest and the remaining populations. Demographic inferences support diversification models with gene flow and population size changes even in cases where contemporary populations are currently allopatric, which provides support for forest refugia and barrier models. Species distribution models suggest that forest refugia were available for each of the populations throughout the Pleistocene. Main conclusions: Considering historical demography is essential for understanding population diversification, especially in complex landscapes such as those found in the Guineo-Congolian forest. Population demographic inferences help connect patterns of genetic variation to diversification model predictions. The diversification history of Chiromantis rufescens was shaped by a variety of processes, including vicariance from river barriers, forest fragmentation, and adaptive evolution along environmental gradients.
The BrukerAPI test data set (Bruker ParaVision v6.0.1)
<p>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 <strong>BrukerAPI</strong>. See the software <a href="https://github.com/isi-nmr/brukerapi-python">repository</a> for more information.</p> <p>The data was measured using the following pulse sequences:</p> <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> <p>The data was measured by the NMR group at the Institute of Scientific Instruments of the CAS, v. v. i.</p> <p>Change Log:</p> <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> <p> </p>
Netflow data without sampling for test (D2)
<p>NetFlow traffic generated using <strong>DOROTHEA</strong> (<strong>DO</strong>cker-based f<strong>R</strong>amework f<strong>O</strong>r ga<strong>TH</strong>ering n<strong>E</strong>tflow tr<strong>A</strong>ffic)</p> <p>NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p>NetFlow flows have been captured without sampling at the packet level. A sampling means that 1 out of every X packets is selected to be flow while the rest of the packets are not valued.</p> <p>The version of NetFlow used to build the datasets is 5.</p> <p>In the construction of the datasets, different percentages of flows considered attacks and flows considered normal traffic have been used.</p> <p>These datasets have been used to test machine learning models.</p>
Quasi-static cyclic tests on masonry spandrels - Experimental data
<p>This data set is the experimental data underlying the publication:</p> <p>Beyer K, Dazio A (2012) Quasi-static cyclic tests on masonry spandrels, Earthquake Spectra 28(3): 907-929. http://dx.doi.org/10.1193/1.4000063</p> <p>Abstract of publication:</p> <p>This paper presents the results of an experimental campaign on masonry spandrels. Within this campaign, four masonry spandrels were subjected to quasi-static cyclic loading. Two different spandrel configurations were tested. The first configuration comprised a masonry spandrel with a timber lintel, and the second configuration, a masonry spandrel on a shallow masonry arch. For each configuration, two specimens were tested. The first was tested with a constant axial load in the spandrel, while for the second specimen, the axial load in the spandrel depended on the axial elongation of the spandrel. This paper summarizes the properties of the four test units, the test setup, and the most important results from the experiments, documenting the failure mechanisms that developed and the force-deformation hysteresis of the spandrel elements. The paper also presents a mechanical model for estimating the peak strength of masonry spandrels.</p>
Data and material for the manuscript "Mutation testing and self/peer assessment: analyzing their effect on students in a software testing course"
<p><strong>This repository is composed of two different parts: </strong></p> <ul> <li><a href="https://zenodo.org/record/4464300/files/Assessment%20data%20and%20Mutation%20Scores.xlsx?download=1">Assessment data and Mutation Scores</a> file contains the student-generated data used in the experience.</li> <li><a href="https://zenodo.org/record/4464300/files/experience-material.zip?download=1">Experience-material</a> file contains the files to be able to reproduce the experience.</li> </ul> <p> </p> <p><strong>The </strong><strong> <a href="https://zenodo.org/record/4464300/files/experience-material.zip?download=1">Experience-material</a> file for the lab is used in two sessions:</strong></p> <p>Session 1: Development and assessment of test suites</p> <p>In this session, the student has to develop a test suite for a program under test. At the end of the session, the test suite will be evaluated against a set of assessment criteria regarding the quality of the developed test suite.</p> <p>Files for this session:</p> <ul> <li>VVS-Lab6-S1 pdf file , with the description of this session.</li> <li>Material-S1 zip file, with the files required to complete this session.</li> </ul> <p>Session 2: Evaluation applying mutation testing with MuCPP</p> <p>In this session, the test cases designed in the first part of this lab will be evaluated based on the mutation adequacy criterion. This will be done by using the <a href="https://ucase.uca.es/mucpp/">MuCPP mutation tool</a>.</p> <p>Files for this session:</p> <ul> <li>VVS-Lab6-S2 pdf file, with the description of this session.</li> <li>Material-S2 zip file, with the files required to complete this session.</li> </ul> <p><em>The source code files family.[cpp|hpp] have been adapted from a listing in [1]. Note that, while considered to be fault free in this lab, these source files are used in other sessions where students are expected to detect some defects in them.</em></p> <p>[1] S. Wiener and L. J. Pinson, The C++ Workbook. USA: Addison-Wesley Longman Publishing Co., Inc., 1990.</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.