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47,155 results for “evaluations”
RAGE - Data Second Evaluation RAGE Components
<p><strong>General description: </strong>Data from the second (preliminary summative) evaluation of RAGE game components (assets)</p> <p> </p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies, Usability testing</p> <p> </p> <p><strong>Name entitites</strong><br> Organizational information: Graz University of Technology<br> Geographical information: Europe<br> Time information: October-November 2017</p> <p><strong>Types</strong>: SPSS data file</p> <p><strong>RAGCS target group:</strong> target groups - supply side - industry participants</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: RAGE software components for applied games development<br> Evaluation variables: usability, usefulness, relevance, game engineering, benefits, quality of support material</p> <p><strong>Instruments:</strong> Usability Metric for User Experience - UMUX (Finstad, 2010); items adapted from perceived usefulness scale (Davis, 1989; Davis and Venkatesh, 2004); questionnaire items defined for the purpose of the evaluation<br> <br> <strong>Relationships</strong>: D8.3 First RAGE Evaluation Report<br> Related datasets: <a href="https://doi.org/10.5281/zenodo.1209200">10.5281/zenodo.1209200</a>, <a href="https://doi.org/10.5281/zenodo.2576901">10.5281/zenodo.2576901</a>.</p>
RAGE - Data Evaluation Ecosystem Portal
<p><strong>General description: </strong>Data from the first evaluation of the ecosystem portal</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies, Usability testing</p> <p><strong>Name entitites</strong><br> Organizational information: Graz University of Technology, Forschungsinstitut für Telekommunikation und Kooperation<br> Geographical information: Europe<br> Time information: May-June 2016</p> <p><strong>Types</strong>: SPSS data file</p> <p><strong>RAGCS target group:</strong> target groups - supply side - research institutions</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: RAGE Ecosystem portal<br> Evaluation variables: usability, user acceptance, system performance, user contribution, added value, tutorial quality</p> <p><strong>Instruments:</strong> UMUX - usability metric for user experience (Finstad, 2010); USE questionnaire (Usefulness, Satisfaction, Ease of use) subscale usefulness (Lund, 2001); questionnaire items adapted from Papadakis, Andreou & Chrissykopoulus (2002), Thielsch & Stegemöller, 2012); questionnaire items defined for the purpose of the evaluation<br> <br> <strong>Relationships</strong>: D8.3 - First RAGE Evaluation Report<br> Related datasets: <a href="https://doi.org/10.5281/zenodo.1209198">10.5281/zenodo.1209198, </a><a href="https://doi.org/10.5281/zenodo.2578728">10.5281/zenodo.2578728</a></p> <p> </p>
RAGE - Data First Evaluation RAGE Components
<p><strong>General description: </strong>Data from the first (formative) evaluation of RAGE game components (assets)</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies, Usability testing</p> <p><strong>Name entitites</strong><br> Organizational information: Graz University of Technology<br> Geographical information: Europe<br> Time information: 2016</p> <p><strong>Types</strong>: SPSS data file</p> <p><strong>RAGCS target group:</strong> target groups - supply side - industry participants</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: RAGE software components for applied games development<br> Evaluation variables: usability, usefulness, intention to use, relevance, game engineering, benefits, quality of support material</p> <p><strong>Instruments:</strong> Usability Metric for User Experience - UMUX (Finstad, 2010); items adapted from perceived usefulness scale (Davis, 1989; Davis and Venkatesh, 2004); questionnaire items defined for the purpose of the evaluation<br> <br> <strong>Relationships</strong>: D8.3 First RAGE Evaluation Report<br> Related datasets: <a href="https://doi.org/10.5281/zenodo.1209204">10.5281/zenodo.1209204</a>, <a href="https://doi.org/10.5281/zenodo.2576901">10.5281/zenodo.2576901</a></p>
RAGE - Data Evaluation Taxonomy Manager
<p><strong>General description: </strong>Data from the evaluation of the Taxonomy Manager in the RAGE Ecosystem</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies, Usability testing</p> <p><strong>Name entitites</strong><br> Organizational information: Graz University of Technology, Forschungsinstitut für Telekommunikation und Kooperation<br> Geographical information: Europe<br> Time information: 2017</p> <p><strong>Types</strong>: SPSS data file</p> <p><strong>RAGCS target group:</strong> target groups - supply side - research institutions, industry participants</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Taxonomy Manager in RAGE Ecosystem portal<br> Evaluation variables: usability, usefulness, tutorial quality, experience with taxonomies, version control, export/import quality</p> <p><strong>Instruments:</strong> UMUX - usability metric for user experience (Finstad, 2010); USE questionnaire - Usefulness, Satisfaction, Ease of use; subscale usefulness; questionnaire items adapted from Papadakis, Andreou & Chrissykopoulus (2002), Thielsch & Stegemöller (2012); questionnaire items defined for the purpose of the evaluation<br> <br> <strong>Relationships</strong>: D8.3 - First RAGE Evaluation Report<br> Related datasets: <a href="https://doi.org/10.5281/zenodo.1209192">10.5281/zenodo.1209192</a>, <a href="https://doi.org/10.5281/zenodo.2578728">10.5281/zenodo.2578728</a></p>
OWL Reasoner Collection for Evaluation
<p>Set of reasoners and auxiliary scripts for use with the evaluation benchmarking framework at <a href="https://github.com/sisinflab-swot/owl-reasoner-test-framework">https://github.com/sisinflab-swot/owl-reasoner-test-framework</a>. It includes:</p> <ul> <li> <p><a href="https://bitbucket.org/dtsarkov/factplusplus">Fact++</a> (version 1.6.5);</p> </li> <li> <p><a href="http://www.hermit-reasoner.com/">HermiT</a> (version 1.3.8);</p> </li> <li> <p><a href="http://derivo.de/en/products/konclude/">Konclude</a> (version 0.6.2-544);</p> </li> <li> <p><a href="http://trowl.org/">TrOWL</a> (version 1.5);</p> </li> <li> <p><a href="http://sisinflab.poliba.it/swottools/minime/">Mini-ME Java</a> (version 2.0);</p> </li> <li> <p><a href="http://sisinflab.poliba.it/swottools/minime-swift/">Mini-ME Swift</a> (version 1.0).</p> </li> </ul>
ALN Reasoner Evaluation Dataset
<p>Dataset for evaluation of Description Logics reasoners in the Attributive Language with unqualified Number restrictions (ALN). The dataset consists in two sections: a) for standard reasoning tasks, 1398 knowledge bases with expressivity up to ALN, extracted from the 2014 OWL Reasoner Evaluation Workshop competition dataset; b) for non-standard reasoning tasks, 4 knowledge bases, each with a different number of "request" and "resource" individuals.</p>
MATISE ("Mapping Tool for Innovation Systems Evaluation")
<p>This dataset contains the underlying data for the following publication: Nikas, A., Doukas, H., Lieu, J., Alvarez Tinoco, R., Charisopoulos, V., & van der Gaast, W. (2017). Managing stakeholder knowledge for the evaluation of innovation systems in the face of climate change. Journal of Knowledge Management, 21(5), 1013-1034, ISSN: 1367-3270<strong><em>. </em></strong>Full details of methods used to create the tool are provided within this publication<em>.</em></p>
Results from Performance Evaluation and Testing of Virtual Infrastructure Managers
<p>NFV leverages Cloud Computing principles to move the data-plane network functions from expensive, closed and proprietary hardware to so-called Virtual Network Functions (VNFs). We deal with the management of virtual computing resources (Unikernels) for the execution of VNFs. This functionality is performed by the Virtual Infrastructure Manager (VIM) in the NFV MANagement and Orchestration (MANO) reference architecture. In this data set we report the results of a performance evaluation we have realized of three open source VIMs, namely OpenStack, Nomad and OpenVIM; both considering stock and the tuned versions. The VIMs and the performance evaluation tools that we employ are provided openly and can be downloaded from our repositories (<a href="https://github.com/superfluidity/openvim4unikernels">https://github.com/superfluidity/openvim4unikernels</a> and <a href="https://github.com/netgroup/vim-tuning-and-eval-tools">https://github.com/netgroup/vim-tuning-and-eval-tools</a>).</p>
MakeCode and CODAL: Intuitive and Efficient Embedded Systems Programming for Education (Artifact Evaluation)
<p>This artifact allows others to reproduce the results seen in this paper for MakeCode and CODAL, using the BBC micro:bit. The artifact contains an offline build environment for CODAL and MakeCode, allowing evaluators to test and build programs locally. In addition, we also provide espruino and micropython virtual machines to further increase repeatability of our results. Evaluators should download the virtual machine containing all pre-requisite tools, and use an oscilloscope to observe wave forms (used for timing) generated by the micro:bit, and a serial terminal to observe results reported from the micro:bit over serial.</p> <p>Full documentation is available at: https://lancaster-university.github.io/lctes-artefact-evaluation/</p>
P4ML Evaluation Results, Apr2018
<p>Evaluation results for the P4ML system. There are four files:</p> <ul> <li>DSBox-reults-all: contains the results of the p4ml system</li> <li>sklearn-results.txt: contains the results of the auto-sklearn system</li> <li>join-results.csv: p4ml results and autosklearn results aggregated together</li> <li>processing.txt: script used to count which system was better in each case, according to the current performance metric</li> </ul> <p> </p>
Annotated dataset for sub-shot segmentation evaluation
<p>This dataset was created for evaluating the performance of the developed motion-driven approach for fine-grained temporal segmentation of user-generated videos, that is reported in [1].</p> <p>Given the fact that user-generated videos are most commonly captured without interruption using a single camera, thus being single-shot videos, their fine-grained temporal segmentation aims to identify visually coherent parts (called sub-shots) that correspond to individual actions taking place during the video recording, such as left camera panning, camera zoom-out or tracking of a moving object.</p> <p>Based on the above, the created dataset contains the ground-truth sub-shot segmentation for 33 single-shot videos. This ground-truth segmentation was created by human annotation of the sub-shot boundaries for each video, where each boundary indicates the end of a visually and temporally contiguous activity of the video recording device and the start of the next one (e.g. a downward camera tilting that is followed by a camera zoom-in). Overall, our dataset contains 674 sub-shot transitions.</p> <p>The set of videos can be divided in three parts:</p> <ol> <li><em>Own Videos</em>: 15 single-shot videos of total duration 6 minutes, which contain clearly defined fragments that correspond to several video recording activities;</li> <li><em>Amateur Videos</em>: 5 single-shot amateur videos of total duration 17 minutes, found on the YouTube platform;</li> <li><em>Movie Excerpts</em>: 13 single-shot parts of known movies of total duration 46 minutes which represent professional video content.</li> </ol> <p>The videos of the first part were recorded in the external spaces of our (CERTH-ITI) facilities using an iPhone 5 smart-phone. These videos and their ground-truth are freely provided through the "Provided files" section below.</p> <p>The videos of the second and third part can be found on the YouTube platform (links in the "Video collection" section below). For these videos we only provide the ground-truth data again through the "Provided files" section below.</p> <p>For each video of the dataset we provide:</p> <ul> <li>The video ID;</li> <li>The video filename for "Own videos" and the YouTube video name and URL for "Amateur videos" and "Movie Excerpts" (accessed on 12/11/2017);</li> <li>The video frame-rate that directly affects the ground-truth annotation; the videos of the second and third part should be downloaded at the corresponding frame-rates (default values when using the KeepVid web application - <a href="https://keepvid.com/">https://keepvid.com/</a>) to match the created annotations.</li> </ul> <p>The above information is included in the provided "video_collection_info.pdf" file.</p> <p>The needed files for using the created dataset are in the provided compressed "dataset.zip" file. After unpacking the compressed file a structure of directories will be generated. In this structure:</p> <ol> <li>The "videos/own_videos" directory contains: a) the video files of the first part of the dataset, and b) the "list.txt" file which lists the video id, the filename and the frame-rate for each video of the "Own videos" collection, in a tab-separated format.</li> <li>The "videos/amateur_videos" directory contains the "list.txt" file which lists the video id, the filename and the frame-rate for each video of the "Amateur videos" collection, in a tab-separated format.</li> <li>The "videos/movie_excerpts" directory contains the "list.txt" file which lists the video id, the filename and the frame-rate for each video of the "Movie excerpts" collection, in a tab-separated format.</li> <li>The "ground_truth" directory contains 33 txt files (one per video) where each file is named after the ID of the corresponding video and includes the annotation of the sub-shots in the video; each line of this file corresponds to the starting frame of a sub-shot of the video (using a zero-based index of frames).</li> <li>The "evaluation" directory contains a simple Matlab script, called "eval_segm.m", that was used for evaluating the results of the sub-shot segmentation analysis.</li> <li>A readme file that contains the documentation of the dataset (i.e. the information that is available in this webpage).</li> </ol> <p>CERTH-ITI holds the copyright of the “<em>Own videos</em>” part of the dataset. These videos can be freely used for academic/research purposes only and their public playback is not allowed. CERTH-ITI does not own the copyright of the “<em>Amateur Videos</em>” and “<em>Movie Excerpts</em>” parts of the dataset. The use of these videos must abide by the YouTube copyright policy (<a href="https://www.youtube.com/yt/about/copyright/">https://www.youtube.com/yt/about/copyright/</a>). Any users of these videos must accept full responsibility for the use of these parts of the dataset, including but not limited to the use of any copies of copyrighted videos that they may create from the dataset.</p> <p>If you use this dataset, please cite the following scientific work:</p> <p>[1] K. Apostolidis, E. Apostolidis, V. Mezaris, <em>"A motion-driven approach for fine-grained temporal segmentation of user-generated videos"</em>, Proc. 24th Int. Conf. on Multimedia Modeling (MMM2018), Bangkok, Thailand, Feb. 2018.</p> <p> </p> <p> </p>
Evaluation datasets for DCASE 2018 Bird Audio Detection
<p>Evaluation data audio for <a href="http://dcase.community/challenge2018/task-bird-audio-detection">the DCASE 2018 Bird Audio Detection task (Task 3)</a>.</p> <ol> <li> <p><strong>Crowdsourced dataset, UK ("warblrb10k")</strong> - a held-out set of 2,000 recordings from the same conditions as the Warblr development dataset.</p> </li> <li> <p><strong>Remote monitoring dataset, Chernobyl ("Chernobyl")</strong> - 6,620 audio clips collected from unattended remote monitoring equipment in the Chernobyl Exclusion Zone (CEZ). This data was collected as part of the <a href="https://wiki.ceh.ac.uk/display/NRT/NERC+RATE+TREE+Home">TREE</a> (Transfer-Exposure-Effects) research project into the long-term effects of the Chernobyl accident on local ecology. The audio covers a range of birds and includes weather, large mammal and insect noise sampled across various CEZ environments, including abandoned village, grassland and forest areas.</p> </li> <li> <p><strong>Remote monitoring night-flight calls, Poland ("PolandNFC")</strong> - 4,000 recordings from Hanna Pamuɫa's PhD project of monitoring autumn nocturnal bird migration. The recordings were collected every night, from September to November 2016 on the Baltic Sea coast, Poland, using Song Meter SM2 units with microphones mounted on 3–5 m poles. For this challenge, we use a subset derived from 15 nights with different weather conditions and background noise including wind, rain, sea noise, insect calls, human voice and deer calls.</p> </li> </ol>
RAW DATA_O4C_ALL pilots_Hackathon2_Evaluation of tookit use
<p>All O4C pilots' reflections on the use of the O4C toolkit in their individual hackathons. Questions inspired by Cherry and Latulipe's (2014) Creativity Support Index, combined with questions from the original O4C hackathon cycle 1 evaluation PowerPoint slide deck.</p>
Cloud management platform evaluation data generated by CMP²
<p>This repository contains exemplary results from using the CMP² (Comparing Cloud Management Platforms) testbed on CloudcheckR, ManageIQ, MistIO, Boto and Libcloud. The results give insight into the performance of multi-cloud middleware. All experiments were conducted as research in education linked to the Cloud Accounting and Billing research initiative at Service Prototyping Lab, Zurich University of Applied Sciences, Switzerland. Apart from the raw data in JSON format, generated graphs are also included.</p> <p> </p>
Data set: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain
<p>Data set of Wind Energy Science (WES) paper: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain</p>
Alignments of simulated data for PsiCLASS evaluation
<p>BAM alignment files of simulated RNA-seq data used to evaluate PsiCLASS and other transcript assemblers (archive contains 25 files).</p>
Open-Source Shared Memory implementation of the HPCG benchmark: analysis, improvements and evaluation on Cavium ThunderX2
<p>This archive contains the output files used to gerenate tables and plots in the paper "Open-Source Shared Memory implementation of the HPCG benchmark: analysis, improvements and evaluation on Cavium ThunderX2" submitted to the 9th IEEE International Workshop on "Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems" (PMBS18) held as part of ACM/IEEE Supercomputing 2018 (SC18), Dallas, TX, USA.</p>
An ultra-dense haploid genetic map for evaluating the highly fragmented genome assembly of Norway spruce (Picea abies)
<p>Data files for construction of the haploid genetic map for Norway spruce (<em>Picea abies</em>). Available at <a href="https://doi.org/10.1101/292151">https://doi.org/10.1101/292151</a></p>
Data set used in the Article "Evaluation of Monte Carlo tools for high-energy atmospheric physics II: relativistic runaway electron avalanches"
<p>Data used for the Relativistic Runaway Electron Avalanches (RREA) simulations of the Article : "Evaluation of Monte Carlo tools for high energy atmospheric physics II: relativistic runaway electron avalanches" by D. Sarria et al.</p> <p>Includes two set of results : The Probability of Generating RREAs, and the Characterizations of RREAs.</p> <p>Link to the article : <a href="https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html">https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html</a></p> <p>DOI of the article: 10.5194/gmd-2018-119</p>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </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.