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1,956 results for “test data”
Test data sets: COLTest
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Test data sets: Amoebozoa Test
The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](<p></p>https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](<p></p>https://eol.org/docs/eol-dynamic-hierarchy)
Checkweigher Conformity Test Data
<p>Checkweighers (a type of industrial scale) were tested under different circumstances in laboratory conditions. Mass and speed were varied and, for each variation, 60 deviations of the measured mass from the nominal mass recorded. The checkweigher passes the test if the absolute mean error is at most the Maximum Permissible Mean Error (MPME) and the standard deviation is at most the Maximum Permissible Standard Deviation (MPSD). If any or both of these values are exceeded, the checkweigher fails the test. This dataset contains only passing checkweighers.</p> <p>File Format:</p> <p>The file contains one instance per row. The columns are tab-separated. The file extension (*.tsv) stands for "tab-separated values".</p> <p>Attributes:</p> <ul> <li>identifier: Data with the same identifier were collected on the same machine. String.</li> <li>mass: Nominal ("true") mass in g. Float.</li> <li>speed: Conveyor belt speed of the checkweigher in m/s. Not available for all instances. Float.</li> <li>mpme: Maximum Permissible Mean Error according to OIML R 51. Float.</li> <li>mpsd: Maximum Permissible Standard Deviation according to OIML R51. Float.</li> <li>deviation_0 - deviation_59: Deviations of the measured value from the nominal mass in g (60 repeated measurements). Float.</li> </ul> <p>Remarks:</p> <p>There is a typo in the OIML Bulletin publication using this dataset. It speaks of 95 measurements, but actually used and contained in this dataset are 94.</p>
Usability Testing Data for Web Application Prototype: Enhancing Efficiency and Transparency in Ghana's Rental Housing Market
<p><span>The dataset includes both quantitative and qualitative responses from participants who tested the web application prototype designed to enhance decision-making in Ghana's rental housing market. The testing focused on evaluating the user interface, ease of use, satisfaction levels, and the effectiveness of key functionalities.</span></p>
Gait Kinematics Data from 2 minute walk test assessment
<p>The dataset consists of gait parameters collected from a single male participant (age: 29 years, height: 1.72 m, mass: 78.3 kg) at four time points: baseline, post-immobilization (post-IM), post-resistance training (post-RT), and 14 weeks post-RT (post-14). The participant underwent a 14-day single-leg immobilization followed by an 8-week resistance training (RT) program. Gait data were recorded during the Two-Minute Walk Test (2MWT) under two conditions: comfortable (COM) and fast (MAX) walking speeds. The dataset includes kinematic data captured using ten synchronized Opal inertial sensors (APDM Inc.) placed at specific anatomical locations, sampled at 128 Hz. The recorded signals were processed using the Mobility Lab™ software.</p>
Simulation data for eddy current rail testing - simulation accuracy and evaluation uncertainty quantification
<p>This dataset serve to quantify the simulation error and the evaluation uncertainties in the context of eddy current rail testing. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection) with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. For an analysis see the article below.</p>
Twin Test 2: Wake interactions of a cluster of turbines and wake steering techniques. Wind tunnel data.
<p>The aerodynamic performance of two identical wind turbine models was characterized under various static and dynamic conditions in a synchronous configuration within the wind tunnel test section. Two experimental campaigns were performed at Technische Universität München (TUM) and at the National Technical University of Athens (NTUA) to investigate wake flow control techniques. This document contains the necessary information to understand the performed experiments and to access and use the available data. While both experimental set ups are detailed, only data from the TUM campaign are available at the time of writing, as the NTUA campaign results will form Phase II of an ongoing blind test campaign and cannot be published.</p>
DRALOD D1.3 Results of performance testing of the prototype of energy recovery system data set
<p>Data set for delivery D1.3 Results of performance testing of the prototype of energy recovery system </p>
Louhi EM test data
<p>This ZIP compressed distribution file contains the original data related to manuscript "Drone-based electromagnetic survey system for geophysical applications" by Markku Pirttijärvi, Ari Saartenoja & Pekka Korkeakangas submitted to Open Research Europe (https://open-research-europe.ec.europa.eu/) in June 2021. The data, presented as simple column formatted text files, are put into two folders, Lintumaansuo and Ervastinranta. Lintumaansuo contains data related to Figs. 11-14 of the manuscript. Ervastinranta contains data related to Figs. 15-17 of the manuscript. Extra data provide background maps and digital elevation models (copyright National Land Survey of Finland, 2020) for Lintumaansuo and Ervastinranta survey sites. The data in this distribution originated in Horizon 2020 project, NEXT - New Exploration Technologies (Grant Agreement No. 776804 — H2020-SC5-2017). For more detailed information about the data, please, refer to the Readme*.txt file included with the distribution.</p> <p> </p>
RE-Lab-Projects/TRY_DE_2015_2045: Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale
<p>Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale</p> <p><strong>Summary:</strong></p> <p>The data set contains the updated test reference years (TRY) of the German Weather Service (DWD). By subdividing into 15 TRY regions, each postcode area can be assigned a representative weather data set. It should be emphasized that in addition to a mean, current test reference year for a region, there is also a year with extreme summer and extreme winter weather. To take climate change into account, there is then a time series for the year 2045 for each test reference year based on the IPCC climate models. This means that a total of 90 weather data sets are available with a one-hour time resolution.</p> <p>In order to use the data in simulations with a temporal resolution of 1min or 15min, the data set was extended by linear interpolation. While this approach is justifiable for air pressure and temperature, for example, it does not depict high fluctuations in solar radiation. Therefore, based on the one-minute open data measurement data set of the Baseline Surface Radiation Network, with an algorithm by Hofmann et. al. the time series of global radiation are newly generated for all test reference years. Another algorithm by Hofmann et. al. was used to calculate the corresponding diffuse radiation times series.</p> <p><strong>Sources:</strong></p> <ul> <li>Raw data from DWD: <a href="https://kunden.dwd.de/obt/">https://kunden.dwd.de/obt/</a> -> <code>1_raw-data</code></li> <li>Synthetic 1min radiation data: <a href="http://pvmodelling.org/">http://pvmodelling.org/</a> -> <code>2_synthetic-radiation</code></li> </ul> <p><strong>How to use or recreate the final dataset:</strong></p> <ol> <li>clone/download this repository</li> <li>unzip the files from the data.zip file <ol> <li><a href="https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip">https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip</a></li> </ol> </li> <li>Use or recreate the final dataset <ol> <li>use: Final datasets are then located in -> <code>3_processed-data</code></li> <li>recreate: run the <code>process-data.py</code></li> </ol> </li> </ol> <p><strong>Test reference stations / regions</strong></p> <p>No. | lon | lat | station | region<br> 1 | 53.5591 | 8.5872 | Bremerhaven | Nordseeküste<br> 2 | 54.0878 | 12.1088 | Rostock | Ostseeküste<br> 3 | 53.5299 | 10.0078 | Hamburg | Nordwestdeutsches Tiefland<br> 4 | 52.3938 | 13.0651 | Potsdam | Nordostdeutsches Tiefland<br> 5 | 51.4562 | 7.0568 | Essen | Niederrheinisch-westfälische Bucht und Emsland<br> 6 | 550.6461 | 7.9426 | Bad Marienburg | Nördliche und westliche Mittelgebirge, Randgebiete<br> 7 | 51.3334 | 9.4725 | Kassel | Nördliche und westliche Mittelgebirge, zentrale Bereiche<br> 8 | 51.7239 | 10.6069 | Braunlage | Oberharz und Schwarzwald (mittlere Lagen)<br> 9 | 50.8233 | 12.9181 | Chemnitz | Thüringer Becken und Sächsisches Hügelland<br> 10 | 50.3226 | 11.9124 | Hof | Südöstliche Mittelgebirge bis 1000 m<br> 11 | 50.4312 | 12.9522 | Fichtelberg | Erzgebirge, Böhmer- und Schwarzwald oberhalb 1000 m<br> 12 | 49.4902 | 8.4637 | Mannheim | Oberrheingraben und unteres Neckartal<br> 13 | 48.2432 | 12.5286 | Mühldorf | Schwäbisch-fränkisches Stufenland und Alpenvorland<br> 14 | 48.6536 | 9.8666 | Stötten | Schwäbische Alb und Baar<br> 15 | 47.4945 | 11.1046 | Garmisch Partenkirchen | Alpenrand und -täler</p> <p><strong>Content</strong></p> <ul> <li><strong>files</strong>: 90 test reference years (TRY) <pre><code>15 test reference regions x 3 reference conditions (average year, extreme summer, extreme winter) x 2 reference projections (year 2015 and year 2045) </code></pre> </li> <li><strong>columns per file</strong>: <pre><code>datetime [yyyy-MM-dd hh:mm:ss+01:00/02:00] temperature [degC] pressure [hPa] wind direction [deg] wind speed [m/s] cloud coverage [1/8] humidity [%] direct irradiance [W/m^2] diffuse irradiance [W/m^2] synthetic global irradiance [W/m^2] synthetic diffuse irradiance [W/m^2] clear sky irradiance [W/m^2] </code></pre> </li> <li><strong>length</strong>: 1 year</li> <li><strong>time increment</strong>: 60s / 900s / 3600s</li> </ul> <p><strong>Important hints</strong>:</p> <ul> <li>all files in <code>3_processed-data</code> were calculated with the skript <code>process-data.py</code></li> <li><em>A value with, for example, a timestamp 12:00:00 represents the mean value from this timestamp until the following timestamp.</em></li> <li><em>datetime column is in CET / CEST</em></li> </ul>
Resarch data for common faults tested on a variable-speed propane-charged heat pump on heating mode
<p>Experimental data of common faults emulated on a 10 kW water-to-water variable-speed heat pump charged with propane. The faults emulated are evaporator fouling, compressor valve leakage, liquid line restriction and refrigerant overcharge. The faults are tested with 10 kW and 12 kW load demand.</p> <p>This data can be used to develop fault detection and diagnosis systems.</p>
Meteorological data from the experimental period of the submersion test of photovoltaic cables
<p>Meteorological data recorded by the onsite weather station (coordinates: 38°31'50.0"N 8°00'40.3"W) regarding the study of submersion of photovoltaic cables (with two different insulation materials) in freshwater and artificial seawater. The metereological data is logged with 1minute time resolution for the period from 16/10/2020 to 25/01/2021.</p> <p>The meteorological station is composed by:</p> <p>Kipp and Zonen Solys2 Sun tracker</p> <p>Kipp and Zonen CMP6 Pyranometer (horizontal global solar radiation, data units W/m2)</p> <p>RH and Air temperature sensor (air relative humidity, data units % and ambient air temperature, data units ºC)</p> <p>Rain Gauge (precipitation, data units mm)</p>
Testing data from SUPER PV demo site in Vilnius (Lithuania)
<p>Sets of the data, collected with SuperPV MLPE boxes from the Lithuanian demo site during the period 2021/08/15 - 2021/08/30. Contain information of the PV modules parameters, as follows:</p> <p>"1122334455667788" - Nr.1 / 90-degree angle (vertical);<br> "FFFFFFFFFFFFFFFF" - Nr.2 / 90-degree angle (vertical);</p> <p>"3333333333333333" - Nr.3 / 45-degree angle;<br> "4444444444444444" - Nr.4 / 45-degree angle;</p> <p>Data files column names explanation:</p> <p>Uoc - open circuit voltage<br> Isc - short circuit current<br> Uin - voltage at maximum power point<br> Iin - current at maximum power point</p> <p>Temp - temperature<br> P - power</p>
Data to reproduce the results: Statistical power of spatial earthquake forecast tests
<p>We provide data needed to reproduce the figures from the publication titled "Statistical power of spatial earthquake forecast tests".</p>
Diversity-Driven Unit Test Generation (Data Set)
<p>The goal of automated unit test generation tools is to create a set of test cases for the software under test that achieve the highest possible coverage for the selected test quality criteria. The most effective approaches for achieving this goal at the present time use meta-heuristic optimization algorithms to search for new test cases using fitness functions defined on existing sets of test<br> cases and the system under test. Regardless of how their search algorithms are controlled, however, all existing approaches focus on the analysis of exactly one implementation, the software under test, to drive their search processes, which is a limitation on the information they have available. In this paper we investigate whether the practical effectiveness of white box unit test generation tools can be increased by giving them access to multiple, diverse implementations of the functionality under test harvested from widely available Open Source software repositories. After presenting a basic implementation of such an approach, DivGen (Diversity-driven Generation), on top of the leading test generation tool for Java (EvoSuite), we assess the performance of DivGen compared to EvoSuite when applied in its traditional, mono-implementation oriented mode (MonoGen). The results show that while DivGen outperforms MonoGen in 33% of the sampled classes for mutation coverage (+16% higher on average), MonoGen outperforms<br> DivGen in 12.4% of the classes for branch coverage (+10% higher average).</p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
Research Data for System test results for the R290 systems
<p>This dataset provides the research data system test results of R-290 (propane) systems. In the TRI-HP project, a new heat pump system with R-290 was integrated into multiple renewable energy sources. There were two experimental campaigns. After analysing the results of the first experimental campaign, the prototype was further improved. The second experimental campaign was conducted with the improved prototype.</p> <p>The detailed information can be found in ZENODO:</p> <p><a href="https://zenodo.org/record/7324246">Refined heat pump design and results of final testing</a></p> <p><a href="https://zenodo.org/record/7285499">Critical review of heat pump prototype operation and required modications</a></p> <p><a href="https://zenodo.org/record/5937065">Experimental results of dual source heat exchanger</a></p> <p>The background of this dataset:</p> <p>During the first experimental campaign, the heat pump prototypes were tested in the laboratories of the project<br> partners in static conditions. These conditions are defined in standards like, for example, EN 14511 [1], providing<br> specific temperatures on the evaporator and condenser sides. These tests enable us to characterize the COP and<br> EER in the nominal conditions of the equipment. However, when such heat pumps are installed in a real building<br> in a specific climate, they will be subject to varying conditions, depending on the load, the weather along<br> the seasons, intermediate storage vessels etc. For this reason, it is important also to carry out</p> <p>dynamic tests.<br> The dynamic tests presented in this deliverable were performed in two laboratories, following the principles of the<br> hardware-in-the-loop where part of the systems is installed in the lab (e.g. the heat pump), and part of the systems<br> is simulated and emulated to provide to real installed systems the response of the virtual element (e.g. building,<br> ground source, weather, solar thermal and PV). The full methodology and setups are described in detail in the<br> deliverable D7.1 [2].<br> The solar-ice system was tested in SPF-OST facilities in Rapperswil, Switzerland. The applied methodology was<br> the Concise Cycle Test (CCT), where 7 individual days along the year for the given climate of Bern were selected.<br> The complete system includes solar thermal, ice storage and water storage. The system was assembled in<br> the laboratory, and the actual setup is described in deliverable D7.3 [3]. The electrical part, including<br> household electricity, PV and the battery, will be tested for the CO2 slurry system due to time constraints since this<br> part does not affect the thermal behaviour of the installed system. The full efficiency of the propane solar-ice<br> slurry system, including the electrical part, will be provided in D7.9, "Demonstrated energetic performance and cost<br> competitiveness of systems".<br> The dual-source system was tested in the IREC laboratory SEILAB in Tarragona, Spain. The methodology is slightly<br> different since the selected periods are not individual days but a series of four consecutive days in<br> the summer and winter seasons. The overall system includes storage tanks, a geothermal loop as well as<br> Photovoltaics (PV) and an electrical battery. The assembled system and the lab setup were described in <br> deliverable D7.2 [4].<br> The present deliverable compiles the results of these two series of dynamic experiments with the propane heat<br> pumps. The results of the solar ice system are first analyzed in section 2, including a summary of the laboratory<br> setup and the control strategies used. The same structure is followed for the dual source system in section 3.<br> 2 Deliverable D7.4</p>
Research data for Refined heat pump design and results of final testing
<p>In this dataset, the data of the second experimental test campaign of the CO2-ice heat pump is shared. The report, which analyzes the data and makes the necessary explanations, has already been shared as a "Refined heat pump design and results of final testing (Deliverable: D5.6)". The report has already been published on ZENODO.</p>
Experimental data of photovoltaic cable submersion tests
<p>Experimental data tables regarding the study of submersion of photovoltaic cables (with two different insulation materials) in freshwater and artificial seawater. Subjected to real life conditions, replicating when FPV systems are located in reservoirs or in the marine environment. Electrical insulation tests were carried out weekly to assess possible cable degradation, the physical-chemical characteristics of the water were also periodically monitored, complemented by analysis to detect traces of copper and microplastics in the water.</p>
ViF-GTAD: A new Automotive Data Set with Ground Truth for ADAS/AD Development, Testing and Validation
<p>A new dataset for automated driving, which is the subject matter of this paper, identifies and addresses a gap in existing similar perception data sets. While the most state-of-the-art perception data sets primarily focus on provision of various on-board sensor measurements along with the semantic information under various driving conditions, the provided information is often insufficient since the object list and position data provided include unknown and time-varying errors. The current paper and the associated data-set describes the first publicly available perception measurement data that include not only the on-board sensor information from camera, Lidar and radar with semantically classified objects, but also the high precision ground-truth position measurements enabled by the accurate RTK assisted GPS localization systems available on both the ego vehicle and the dynamic target objects. This paper provides insight on the capturing of the data, explicitly explaining the meta data structure and the content, as well as the potential application examples where it has been, and can potentially be, applied and implemented in relation to automated driving and environmental perception systems development, testing and validation.</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.