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1,956 results for “test data”
BAM reference data: results of ASTM E139 -11 creep tests on a reference material of Nimonic 75 nickel-base alloy
<p>Results of creep tests on a certified reference material at T = 600°C and a tensile creep load of 160 MPa are provided. The raw data are available in ASCII format (*.lis files). <br> The file "Inhalt_Content_V1.1.pdf" contains further information about the files provided.<br> The evaluated results include the times to reach 2% and 4% creep strain, respectively, and the creep rate after 400 h.</p> <p>The tests were carried out in an accredited test laboratory. The calibrations of all measurands and test and measuring equipment are documented. The calibrations meet the requirements of the test procedure and are metrologically traceable.</p>
Consensual videos of potentially re-identifiable individuals recorded at the Autonomous Driving Test Area Baden-Württemberg (raw images with location and IMU data).
<p>For the purpose of research on data intermediaries and data anonymisation, it is necessary to test these processes with realistic video data containing personal data. For this purpose, the <a href="http://treumoda.de">Treumoda</a> project, funded by the German Federal Ministry of Education and Research (BMBF), has created a dataset of different traffic scenes containing identifiable persons.</p> <p>This video data was collected at the <a href="https://taf-bw.de/">Autonomous Driving Test Area Baden-Württemberg</a>. On the one hand, it should be possible to recognise people in traffic, including their line of sight. On the other hand, it should be usable for the demonstration and evaluation of anonymisation techniques.</p> <p><strong>The legal basis for the publication of this data set the consent given by the participants as documented in the file Consent.pdf (all purposes) in accordance with Art. 6 1 (a) and Art. 9 2 (a) GDPR. Any further processing is subject to the GDPR.</strong></p> <p>We make this dataset available for non-commercial purposes such as teaching, research and scientific communication. Please note that this licence is limited by the provisions of the GDPR. Anyone downloading this data will become an independent controller of the data. This data has been collected with the consent of the identifiable individuals depicted.</p> <p>Any consensual use must take into account the purposes mentioned in the uploaded consent forms and in the privacy terms and conditions provided to the participants (see Consent.pdf). All participants consented to all three purposes, and no consent was withdrawn at the time of publication. KIT is unable to provide you with contact details for any of the participants, as we have removed all links to personal data other than that contained in the published images.</p>
sager package test data
<p>This repository contains the data files used in the <a href="https://uclouvain-cbio.github.io/sager/index.html">sager</a> package. The <a href="https://uclouvain-cbio.github.io/sager/reference/sagerData.html">sagerData()</a> manual page describes the functions that download, cache the files and returns them to the user, where the data were originally <a href="https://www.ebi.ac.uk/pride/archive/projects/PXD016766">retrieved from</a> and how they were processed. </p> <p><strong>ChangeLog:</strong></p> <ul> <li>version 2: subset data files updates and added config file</li> <li>version 3: provide 3 separate subsetted mzML files, and update quant and id files (generated from re-running sage on the mzML subsets).</li> <li>version 4: udpate subset files, and remove the prefix from three subsetted mzML files.</li> </ul>
VoroIF-GNN training, validation, and testing data
<p>Data used to train, validate, and test the VoroIF-GNN method described in the paper "VoroIF-GNN: Voronoi tessellation-derived protein-protein interface assessment using a graph neural network".</p>
Data from: How important are functional and developmental constraints on phenotypic evolution? An empirical test with the stomatal anatomy of flowering plants
<p>Quantifying the relative contribution of functional and developmental constraints on phenotypic variation is a longstanding goal of macroevolution, but it is often difficult to distinguish different types of constraints. Alternatively, selection can limit phenotypic (co)variation if some trait combinations are generally maladaptive. The anatomy of leaves with stomata on both surfaces (amphistomatous) presents a unique opportunity to test the importance of functional and developmental constraints on phenotypyic evolution. The key insight is that stomata on each leaf surface encounter the same functional and developmental constraints, but potentially different selective pressures because of leaf asymmetry in light capture, gas exchange, and other features. Independent evolution of stomatal traits on each surface implies that functional and developmental constraints alone likely do not explain trait covariance. Packing limits on how many stomata can fit into a finite epidermis and cell-size-mediated developmental integration are hypothesized to constrain variation in stomatal anatomy. The simple geometry of the planar leaf surface and knowledge of stomatal development makes it possible to derive equations for phenotypic (co)variance caused by these constraints and compare them with data. We analyzed evolutionary covariance between stomatal density and length in amphistomatous leaves from 236 phylogenetically independent contrasts using a robust Bayesian model. Stomatal anatomy on each surface diverges partially independently, meaning that packing limits and developmental integration are not sufficient to explain phenotypic (co)variation. Hence, (co)variation in ecologically important traits like stomata arises in part because there is a limited range of evolutionary optima. We show how it is possible to evaluate the contribution of different constraints by deriving expected patterns of (co)variance and testing them using similar but separate tissues, organs, or sexes.</p>
Neural net training and test data set
<p>In this zip file you will find several folders as well as a readme file explaining the dataset. The neural net file can be directly used in cellpose to segment <strong>widefield</strong> <strong>20x objective</strong> imaging data of neutrophils. The neural net could be used for other types of data, but might perform below expectations.</p>
U-T training data and test data for Sigsbee2A m odel
<p>Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: SigsbeeA model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.</p>
U-T training and test data for Saltblock model
<p>Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: Saltblock model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.</p>
Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study'
<p>Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study' consisting of (1) the original and complete dataset ('Data_Brain-IT-Reliability-of-HRV-during-Exergaming_for-publication'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Test data and analysis script for manuscript: Uncovering the complex relationship between balding, testosterone and skin cancers in men
<p>Test data for the manuscript entitled: "<strong>Uncovering the complex relationship between balding, testosterone and skin cancers in men"</strong><br> <br> Includes: <br> --Readme.txt<br> --folder: example<br> --folder: script</p>
Data and Code for the paper "GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies"
<p>This package contains data and code to replicate the findings presented in our paper titled "<em>GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies</em>".</p> <p><strong>Abstract</strong></p> <p>Graphical User Interface (GUI) testing plays a pivotal role in ensuring the quality and functionality of mobile apps. In this context, Exploratory Testing (ET), a distinctive methodology in which individual testers pursue a creative, and experience-based approach to test design, is often used as an alternative or in addition to traditional scripted testing. Managing the exploratory testing process is a challenging task, that can easily result either in wasteful spending or in inadequate software quality, due to the relative unpredictability of exploratory testing activities, which depend on the skills and abilities of individual testers. A number of works have investigated the<br> diversity of testers’ performance when using ET strategies, often in a crowdtesting setting. These works, however, investigated ET effectiveness in detecting bugs, and not in scenarios in which the goal is to generate a re-executable test suite, as well. Moreover, less work has been conducted on evaluating the impact of adopting different exploratory testing strategies. As a first step towards filling this gap in the literature, in this work we conduct an empirical evaluation involving four open-source Android apps and twenty masters students, that we believe can be representative of practitioners partaking in exploratory testing activities. The students were asked to generate test suites for the apps using a Capture and Replay tool and different exploratory testing strategies. We then compare the effectiveness, in terms of aggregate code coverage, that different-sized groups of students using different exploratory testing strategies may achieve. Results provide deeper insights into code coverage dynamics to project managers interested in using exploratory approaches to test simple Android apps, on which they can make more informed decisions.</p> <p> </p> <p><strong>Contents and Instructions</strong></p> <p>This package contains:</p> <ul> <li><strong>apps-under-test.zip</strong> A zip archive containing the source code of the four Android applications we considered in our study, namely MunchLife, TippyTipper, Trolly, and SimplyDo.</li> <li><strong>apps-under-test-instrumented.zip</strong> A zip archive containing the instrumented source code of the four Android applications we used to compute branch coverage.</li> <li><strong>students-test-suites.zip</strong> A zip archive containing the test suites developed by the students using Uninformed Exploratory Testing (referred to as "Black Box" in the subdirectories) and Informed Exploratory Testing (referred to as "White Box" in the subdirectories). This also includes coverage reports.</li> <li><strong>compute-coverage-unions.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate LOC coverage of all possible subsets of students. The scripts have been tested on MS Windows. To compute the LOC coverage achieved by any possible subsets of testers using IET and UET strategies, run the <em>analysisAndReport.py</em> script. To compute the LOC coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>analysisAndReport_UET_IET_combinations_emma.py</em> script.</li> <li><strong>branch-coverage-computation.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate branch coverage of all considered subsets of students. The scripts have been tested on MS Windows. To compute the branch coverage achieved by any possible subsets of testers using UET and I+UET strategies, run the <em>branch_coverage_analysis.py</em> script. To compute the code coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>mixed_branch_coverage_analysis.py</em> script.</li> <li><strong>data-analysis-scripts.zip</strong> A zip archive containing R scripts to merge and manipulate coverage data, to carry out statistical analysis and draw plots. All data concerning RQ1 and RQ2 is available as a ready-to-use R data frame in the <em>./data/all_coverage_data.rds</em> file. All data concerning RQ3 is available in the <em>./data/all_mixed_coverage_data.rds </em>file.</li> </ul>
Replication Data for "How Do Different Types of Testing Goals Affect Test Case Design?"
<p># Replication Data for "How Do Different Types of Testing Goals Affect Test Case Design?"</p> <p>## Overview</p> <p>Background: Test cases are designed in service of one or more goals, e.g., assessing functional correctness or performance. We lack a clear understanding of how specific goal types influence test design.</p> <p>Aims: We explore the relationship between types of testing goals and test design, including identification and importance of goal types, quantitative relations between goal types and test cases, and personal, organizational, methodological, and technological factors that may influence this relationship.</p> <p>Method: We have conducted both qualitative and quantitative analysis of interviews and a survey with software developers in various domains and of varying experience.</p> <p>Results: We identify nine goal types, and focus on correctness, reliability, and quality. We observe that test design for correctness forms a "default" design process that is modified when pursuing other goals. For the examined goal types, test cases tend to be simple, with many tests targeting a single goal and each test focusing on 1-2 goals at a time. Testers often start by using past tests as templates. Testing practices, tools, and system types of interest vary between goal types. Test design can be influenced by organization, process, and team makeup.</p> <p>Conclusions: This study provides a foundation for future research on test case design and testing goals.</p> <p>The paper can be found at http://greg4cr.github.io/pdf/23goals.pdf </p> <p>## Data Contained in This Package</p> <p>- thematic_coding.pdf</p> <p>This is the theme map created from the interview data. We extracted important statements from the interviews (codes) and clustered them into themes and sub-themes.</p> <p>- survey_responses.pdf</p> <p>This file contains all survey responses.</p> <p>Both interview and survey data has been anonymized to protect the privacy of the participants.</p>
Data to test IDP for agriculture in developing countries
<p>The data has variables capturing net outward foreign direct investment per capita, gross domestic product per capita and trade openness for agriculture in developing countries. The other variables are the official exchange rate, gross secondary school enrolment in per cent and inflation measured as per cent of CPI growth. These are for the total economy. </p> <p>Net outward foreign direct investment per capita (NOFDIPC) was constructed as outward foreign direct investment less inward foreign direct investment for agriculture, forestry and fishing. The sum is divided by the population of both sexes. The foreign direct investment and population data were obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/FDI; https://www.fao.org/faostat/en/#data/OA). The gross domestic product per capita (GDPPC) was computed as agricultural value added divided by the population. Agricultural value added was also obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/MK). Trade openness (AGTO) was computed as agricultural exports plus imports divided by agricultural value added. The exports and imports were obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/TCL). Others; official exchange rate (EXRATE), gross secondary school enrolment in per cent (HC) and inflation measured as per cent of CPI growth (INFLA) were drawn from the world development indicators database of the World Bank (https://databank.worldbank.org/source/world-development-indicators#).</p>
Test SpeX data for SpeXtool and pyspextool
<p>This zip directory contains SpeX data for two modes and two different instrument configurations, that can be used for testing either SpeXtool or pyspextool:</p> <ul> <li>spex-prism: prism data from original SpeX instrument from 2003 May 21 program 2003A013 (PI Burgasser)</li> <li>spex-SXD: SXD data from original SpeX instrument 2003 Jul 7 program 2003A098 (PI Rayner)</li> <li>uspex-prism: prism data from upgraded SpeX instrument from 2022 Oct 19 program 2022B046 (PI Theissen)</li> <li>spex-SXD: SXD data from upgraded SpeX instrument from 2015 June 3 program 2015A078 (PI Rayner)</li> </ul> <p>These folders contain both raw data files ("data"), processed data files ("cals", "proc") and quality assurance files ("qa") based on a beta version of pyspextool.</p> <p>The most recent version of pyspextool can be downloaded at https://github.com/pyspextool/pyspextool </p>
Experimental data: Single- and double-wythe brick masonry walls subjected to four-point bending tests under different support conditions: Simply supported, rigid, non-rigid
<p>This dataset contains the results of laboratory quasi-static monotonic four-point bending tests conducted at RISE Research Institutes of Sweden on eleven natural-scale unreinforced brick masonry walls. The walls were spanning vertically between two reinforced concrete slabs and were tested under three different support conditions defined according to the American manual UFC 3-340-02: simply supported, rigid, non-rigid. The influence of these support conditions on the out-of-plane behavior of the walls was studied on elements with varying thickness – single and double wythe – and subjected to different levels of axial compression (or overload). The walls were tested inside of a bi-axial test setup that allowed not only the lateral, out-of-plane force but also the axial, arching action to be measured throughout the tests. Optical full-field displacement measurements were also acquired by two systems of cameras making use of the 2D and 3D Digital Image Correlation (DIC) technique.</p> <p>The data generated from these tests are made here available to support further investigations on masonry structures subjected to extreme lateral, out-of-plane actions. The dataset includes 3 compressed folders, ordered from 01 to 03, along with an auxiliary document describing the content and organization of the dataset. </p> <p>The data presented here are described in the following research article:</p> <blockquote> <p><a href="https://www.sciencedirect.com/science/article/pii/S0950061823022602?via%3Dihub">Godio M, Flansbjer M, Williams Portal N (2023). Single- and double-wythe brick masonry walls subjected to four-point bending tests under different support conditions: simply supported, rigid, non-rigid, Construction and Building Materials</a></p> </blockquote> <p>To cite this dataset, please refer to the article.</p> <p>The Authors</p>
Test data for RAW high dynamic range merging
<p>* the data contains a set of .CR2 raw images taken with a Canon 5D mark II<br> * the exposures where done on a tripod in manual mode, the EXIF information is correct<br> * only the exposure times were varied:</p> <p>IMG_7220.CR2<br> Exposure Time : 1<br> IMG_7221.CR2<br> Exposure Time : 1/5<br> IMG_7222.CR2<br> Exposure Time : 1/20<br> IMG_7223.CR2<br> Exposure Time : 1/80<br> IMG_7224.CR2<br> Exposure Time : 1/320<br> IMG_7225.CR2<br> Exposure Time : 1/1250</p> <p>* Author: Ivo Ihrke, ~2011, Universität des Saarlandes / MPI Informatik</p> <p> </p>
Data and supplementary material for the paper "Development of an IntelliCage based Cognitive Bias Test for Mice."
<p>All raw data, R scripts for analysis and the supplementary material related to the paper "Development of an IntelliCage based Cognitive Bias Test for Mice." are available to the scientific public here. A preprint version of the paper will be published on bioRxiv. </p> <p>Version 2 contains R scripts, version 1 does not.</p> <p>Version 3: For better clarity, the data were saved as .ods files. Each .ods file contains the data for one developmental step. The R scripts are still available as .txt files. In addition, an ARRIVE checklist was added.</p> <p>Version 4: xlsx instead of ods fiels</p> <p>Version 5: Extended supplemet PDF file<br> Added GroupTwo_Entries and GroupThree_Entries txt files</p> <p>A pre-print version can be found at bioRxiv: https://www.biorxiv.org/content/10.1101/2022.10.19.512853v1 </p>
Test data for 3D with focal stacking
<p>* the data contains a set of .tiff images of a butterfly wing 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>
Paper data for DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles
<p>This repo contains the study and appendix data for "DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles". DOI 10.1109/TSE.2023.3301443.</p>
Test data set for CRIMAC-RAW-To-Svf-TSf
<p>Test data set for https://github.com/CRIMAC-WP4-Machine-learning/CRIMAC-Raw-To-Svf-TSf, accompanying code to the paper "Quantitative processing of broadband data as implemented in a scientific splitbeam echosounder" submitted to Ecology and Evolution (Wiley). This is the Simrad EK80 raw files collected by the Norwegian Institute of Marine Research and used in the paper, mainly through the subset json files in https://github.com/CRIMAC-WP4-Machine-learning/CRIMAC-Raw-To-Svf-TSf/tree/main/Data. To reproduce echogram figures the files Zenodo-hosted files IMR-D20210507-T074652-Svf.raw and IMR-D20211215-T143432-TSf.raw must be downloaded. This research is a part of the CRIMAC - Centre for research-based innovation in marine acoustic abundance estimation and backscatter classification funded by the Research Council of Norway (grant no. 309512).</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.