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11,879 results for “participation”
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Total Cost and Machine Occupancy Deviation Optimization
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a total cost and machine occupancy deviation optimization scenario was formulated that aims to demonstrate how the proposed scheduler is able to balance tasks between machines while also reducing overall costs. For this scenario, it was considered an optimization weight of 0.5 for both the total costs and machine occupancy deviation objectives and the genetic algorithm was executed for 2 hours.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON input data for the total cost and machine occupancy deviation optimization</li> <li>Output_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON output data for the total cost and machine occupancy deviation optimization</li> <li>Output_Statistics_Total_Cost_Machine_Occupancy_Deviation_Optimization - Excel output total cost and machine occupancy deviation optimization statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Energy Cost Optimization with Energy Selling
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an energy cost optimization considering the presence of an energy buyer is proposed to validate the scheduler’s ability to maximize profits while also minimizing energy costs. The scenario considers an added sales value corresponding to 50% of the buying. For this scenario, the genetic algorithm was executed for 2 hours, with 1 and 0 for the optimization weights total cost and machine occupancy deviation, respectively.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Energy_Cost_Energy_Selling_Optimization - JSON input data for the energy cost optimization with energy selling</li> <li>Output_JSON_Energy_Cost_Energy_Selling_Optimization - JSON output data for the energy cost optimization with energy selling</li> <li>Output_Statistics_Energy_Cost_Energy_Selling_Optimization - Excel output energy cost optimization with energy selling statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Joint Optimization of Production and Maintenance
<p>Using the previous datasets at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>>, <<a href="https://zenodo.org/record/7055698">https://zenodo.org/record/7055698</a>>, <<a href="https://zenodo.org/record/7055580">https://zenodo.org/record/7055580</a>>, and <<a href="https://zenodo.org/record/7055573">https://zenodo.org/record/7055573</a>> a joint optimization of production and maintenance scenario was formulated which aims at combining all the features from the cited scenarios. For energy selling, it was considered an added sales value corresponding to 50% of the buying. Regarding maintenance activities, it was simulated an announcement of a maintenance activity for MAQ118 from Monday at 07:00 (i.e., period 1) to Monday at 17:00 (i.e., period 120), and another for MAQ120 which can be done at any time. These maintenance activities take 6 hours and 10 minutes to complete and have an associated labor cost of 3,22 EUR/hour in maintenance hours, and a double cost penalty (i.e., 6.44 EUR/hour) if done out of maintenance hours. The scenario was executed in 2 hours, with the corresponding optimization weights of 0.8 and 0.2 for the total cost and machine occupancy deviation, respectively.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Joint_Optimization_Production_Maintenance - JSON input data for the joint optimization of production and maintenance</li> <li>Output_JSON_Joint_Optimization_Production_Maintenance - JSON output data for the joint optimization of production and maintenance</li> <li>Output_Statistics_Joint_Optimization_Production_Maintenance - Excel output joint optimization of production and maintenance statistics</li> </ul>
Birdwatching, eBird and citizen science in India: qualitative interviews with participants, practitioners and ecologists
<h1>Abstract</h1> <p>This study consists of qualitative interviews about birdwatching, citizen science, and the use of the birdwatching data platform <em>eBird </em>in India. Interview partners are birdwatchers, citizen science practitioners, and ecologists who have used eBird data. Some of the main topics covered include: the nature of the birdwatching community and styles of birdwatching in India; the history of the adoption of eBird in India; the value of birdwatching and citizen science; challenges involved in conducting or participating in citizen science; opportunities and limitations of using data from eBird and citizen science; processes of data collection and quality control in eBird; and ecological research, conservation priorities, and environmental activism in India. This study is part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS).</p> <h1>Methods</h1> <p>The data in this study was collected using semi-structured qualitative interviews.</p> <p>Interview partners were recruited by snowball sampling through their engagement with eBird India and related organisations. There were 17 interview partners, interviewed either once or several times. 19 interviews were conducted in total.</p> <p>Interview guides/questionnaires were designed for each interviewee depending on their status as birdwatchers, citizen science coordinators, and eBird data users.</p> <p>Interviews were conducted between April 2022 and June 2023. The interviews took place online using Zoom videoconferencing software. Interviews lasted 35-70 minutes. When participants provided their written consent, interviews were audio-recorded and transcribed smart verbatim using otter.ai and manual proofreading. Sensitive information was removed before publishing transcripts.</p> <p>Transcripts were analysed using semi-grounded coding. Codes were organised into parent codes using an inductive approach based on emergent categories.</p> <h1>Description of the data and file structure</h1> <p>Documentation files include interview guides, the information sheet and consent form, ethics approval, and the data narrative. Documentation files are named according to the structure: authorname_filename_DOCUMENTATION.</p> <p>Data files consist of a summary of participants, 17 of the interview transcripts, and a code list. Interview transcript files are named according to the structure: authorname_interviewnumber_date.</p> <p>A full list of files is provided in the README file.</p> <h1>Notes</h1> <p>This study was conducted as part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS). More information can be found at <a href="https://opensciencestudies.eu/">https://opensciencestudies.eu</a></p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101001145).</p>
Survey data on behaviours and attitudes towards green food consumption of participants of the SmartFood Urban Living Lab in Warsaw, Poland
<p>In this dataset, we present raw data of a survey on behaviours and attitudes towards green food consumption, conducted between June 2023 and April 2024 among a group of 21 households from Warsaw, participating in a SmartFood Urban Living Lab (ULL). The dataset is complemented with results collected from two control groups. The SmartFood Urban Living Lab was an intervention aimed at providing residents of urban blocks of flats with a novel technology for growing their own food. The ULL served as an experimental ground for testing and refining innovations such as hydroponic cabins, rainwater management systems, solar energy systems, and insect farming units. Residents actively participated in the lab, providing valuable insights into the practical challenges and benefits of urban farming, which helped refine and adapt the technologies for broader application. After each month of the intervention, a survey was conducted to check participants' behaviours and attitudes towards green food consumption</p>
Participant survey for the article: More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education
<p>The artcile More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education concerns the introduction of a new course format in the Master Program in Biostatistics at the University of Zurich. This data set contains the results fo a survey among the participants in this new course.</p> <p>Sepcifically it contains the answers of 22 participants to the following questions:</p> <p>1) Did you use the following concepts or tools since you took STA472? <br>Good practice for...</p> <p>... spreadsheets<br>... file and folder organization<br>... version control<br>... dynamic reporting<br>... LaTeX<br>... presentation slide design <br>... oral presentations<br>... designing graphs<br>... designing tables<br>... structure for manuscript<br>... logic of a paragraph<br>... writing style<br>... writing R functions<br>... using unit tests<br>... setting up simulations<br>... code styling<br>... writing vectorized code<br>... writing parallelized code<br>... containerizing code</p> <p>Answers are in the scale: never since, rarely, sometimes, often, frequently, I do not know</p> <p>2) If you used the above concepts at least rarely, did the training of STA472 help you?</p> <p>Good paractice for...</p> <p>... spreadsheets<br>... file and folder organization<br>... version control<br>... dynamic reporting<br>... LaTeX<br>... presentation slide design <br>... oral presentations<br>... designing graphs<br>... designing tables<br>... structure for manuscript<br>... logic of a paragraph<br>... writing style<br>... writing R functions<br>... using unit tests<br>... setting up simulations<br>... code styling<br>... writing vectorized code<br>... writing parallelized code<br>... containerizing code</p> <p>Answers are in the scale: Not really Somewhat Definitively I do not know I do not use this concept</p>
Participant survey data from the citizen science project FLOW, 2021
<p>This dataset is linked to the following publication:</p> <p>von Gönner, J., Masson, T., Köhler, S., Fritsche, I., Bonn, A. (in press): Citizen science promotes knowledge, skills and collective action to monitor and protect freshwater streams. People and Nature.</p> <p> </p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p>Corresponding dataset for the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The structure of the dataset is as follows:</p> <p><strong>Annotations</strong> </p> <p><em>List of annotations made per day for each of the analysed projects.</em></p> <p><code>annotations.csv </code></p> <p><strong>Comments </strong></p> <p><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></p> <p><code>comments.csv </code></p> <p><strong>Rolechanges</strong> </p> <p><em>List of roles per user to determine number of role changes </em></p> <p><code>478_rolechanges.csv</code></p> <p><code>1104_rolechanges.csv</code></p> <p><code>...</code></p> <p><strong>Totalnetworkdata</strong> </p> <p><em>Network data (edge and node sets) for the given projects (without time slices).</em></p> <p>Edges </p> <ul> <li> <p><code>478_edges.csv</code></p> </li> <li> <p><code>1104_edges.csv</code></p> </li> </ul> <p>Nodes </p> <ul> <li> <p><code>478_nodes.csv</code> </p> </li> <li> <p><code>1104_nodes.csv</code> </p> </li> </ul> <p><strong>Trajectories</strong> </p> <p><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></p> <p>478 </p> <ul> <li>Edges <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> </ul> </li> <li> <p>...</p> </li> <li>Nodes <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> <p> </p> <p>1104 </p> <ul> <li> <p>Edges </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p>Nodes </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><code>...</code></p> </li> </ul> <p> </p>
Diversity Awareness in Software Engineering Participant Research
<p>This dataset contains the result of a classification of three ICSE venues namely, ICSE 2019, 2020, and 2021 technical tracks, as stated in the methodology of the paper “Diversity awareness in software engineering participant studies” by Dutta et al. (2023).</p>
Dataset: Shell Commands Used by Participants of Hands-on Cybersecurity Training
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Seda, Pavel Čeleda.<br> <em>Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training.</em><br> In Elsevier Data in Brief. 2021.<br> <a href="https://doi.org/10.1016/j.dib.2021.107398">https://doi.org/10.1016/j.dib.2021.107398</a></p> <ul> </ul> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2021dataset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Seda, Pavel and \v{C}eleda, Pavel}, title = {{Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training}}, journal = {Data in Brief}, publisher = {Elsevier}, volume = {38}, year = {2021}, issn = {2352-3409}, url = {https://doi.org/10.1016/j.dib.2021.107398}, doi = {10.1016/j.dib.2021.107398}, }</code></pre> <p>The data were collected using a logging toolset referenced <a href="https://zenodo.org/record/5126693">here</a>.</p> <p><strong>Attached content</strong></p> <ol> <li><strong>Dataset (data.zip).</strong> The collected data are attached here on Zenodo. A copy is also available in <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/datasets/commands">this repository</a>.</li> <li><strong>Analytical tools (toolset.zip).</strong> To analyze the data, you can instantiate the toolset or <a href="https://gitlab.ics.muni.cz/muni-kypo/tools/commands-elk">this project for ELK</a>.</li> </ol> <p><strong>Version history</strong></p> <ul> <li>Version 1 (<a href="https://zenodo.org/record/5137355">https://zenodo.org/record/5137355</a>) contains 13446 log records from 175 trainees. These data are precisely those that are described in the associated journal paper. Version 1 provides a snapshot of the state when the article was published.</li> <li>Version 2 (<a href="https://zenodo.org/record/5517479">https://zenodo.org/record/5517479</a>) contains 13446 log records from 175 trainees. The data are unchanged from Version 1, but the analytical toolset includes a minor fix.</li> <li>Version 3 (<a href="https://zenodo.org/record/6670113">https://zenodo.org/record/6670113</a>) contains 21762 log records from 275 trainees. It is a superset of Version 2, with newly collected data added to the dataset.</li> <li>The current Version 4 (<a href="https://zenodo.org/record/8136017">https://zenodo.org/record/8136017</a>) contains 21459 log records from 275 trainees. Compared to Version 3, we cleaned 303 invalid/duplicate command records.</li> </ul>
Participant Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Compilation of electronic meeting notes made by attendees at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes.</p> <p>Files are provided for Days 1-3 of the meeting. Day 4 inputs are included in Discussion notes under a separate doi.</p> <p>The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants 936723.02.01.09.14 and 936723.02.01.11.21</p>
PharmaCoNER Silver Standard: Participant predictions in PharmaCoNER shared task
<p><strong>Introduction</strong></p> <p>Predictions in the test and background set of PharmaCoNER participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per PharmaCoNER participant. Within each participant directory, there is one directory per run.</p> <p> </p> <p><strong>Corpus format description</strong></p> <p>For subtask 1 annotations are distributed in Brat format. See Brat webpage for more information <a href="https://brat.nlplab.org/standoff.html">https://brat.nlplab.org/standoff.html</a></p> <p>For subtask-2, codes are associated with each document are given in a TSV file with the following columns: </p> <pre>articleID code </pre> <p><br> <strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/pharmaconer/">Web</a></strong></li> <li><strong>Citation: </strong>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</li> <li><a href="https://doi.org/10.5281/zenodo.4270157"><strong>Gold Standard corpus</strong></a></li> <li><a href="https://doi.org/10.5281/zenodo.3763276"><strong>Annotation guidelines</strong></a></li> <li><a href="https://github.com/TeMU-BSC/PharmaCoNER-Tagger"><strong>PharmaCoNER tagger</strong></a></li> </ul> <p> </p> <p>All credit to PharmaCoNER participants. </p> <p>For further information, please visit <a href="https://temu.bsc.es/pharmaconer/">https://temu.bsc.es/pharmaconer/</a> or email us at encargo-pln-life@bsc.es</p> <p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)</p>
32 minutes of participants interactions at #i2k2020 virtual conference
<p>Anonymised dataset showing participants positions at 1s intervals for 32 minutes during the online conference #i2k2020.</p> <p>https://www.janelia.org/you-janelia/conferences/from-images-to-knowledge-with-imagej-friends</p> <p> </p> <p> </p>
Participant Faces from a Repeated Prisoner's Dilemma
<p>The authors (ES and TS) conducted a computerized laboratory experiment in an experimental economics laboratory using a modified Prisoner’s Dilemma known as <em>Friend </em>or <em>Foe </em>(e.g. see List, 2006. <em>The Review of Economics and Statistics</em>, <em>88</em>(3), 463-471). 96 young adults aged 18 to 25 years old (51 men, 45 women) gave permission to be video recorded at intervals throughout the experimental procedure under standardized videographic conditions and for their recordings and experiment data to be made available for later research. All individuals video recorded were students at Chapman University in Orange, CA. We provide a dataset described below based on behavioral game and survey data collected from participants over a computer network, media from video recordings, and media analysis measures.</p> <p><em><strong>Description of Experimental Task:</strong> </em>Participants were provided a printed copy of the instructions (Instructions.pdf) explaining the following experimental procedure. Participants were randomly paired with one other person, with whom they anonymously interacted over a computer network throughout the experimental session. Participants were told that at no time would their or other participants’ true identity or videorecorded images be revealed to participants in the experiment session. Participants were told to expect a minimum of two rounds of game interactions with possibility for more rounds. Each round required the participants to first privately state their (non-binding) intention for the camera, saying either “I intend to Split” or “I intend to Take All” (which we videorecorded, see <em>Media: Videos of Stated Intent</em>), then to choose an interaction strategy (either “Split” or “Take All”). Each round we provided participants the results of their pair’s game interaction on their computer screens. We videorecorded participants discovery, viewing, and reaction to the interaction results (see <em>Media: Videos of Viewed Results</em>). After the last round of interaction, we collected response data using an exit survey.</p> <p><strong><em>Game and Survey Data</em>:</strong> Data includes participants’ stated intent and game behavior for each round, emotional reactions (each on a scale of 1 to 5) self-reported after learning the results of each round (measured with a modified 20 item PANAS), and the following data collected during an exit survey: responses (each on a scale of 0 to 4) to six questions used for scoring the Revised Life Orientation Test (from Scheier et al., 1994. <em>Journal of Personality and Social Psychology, 67</em>, 1063-1078), age, gender, first language, and English language fluency.</p> <p><strong><em>Media:</em></strong> Videos of participants were taken using computer display mounted Logitech C920 1080P HD digital cameras, at computer terminals in individual cubicles. The cameras were aimed at participants who sat against a background of either a gray carpeted cubicle or a brown wall under standardized diffuse lighting conditions. For video recordings in this data set, participants were instructed to wear microphone-earphone headsets, to keep hair away from their face, and to sit upright and look at the computer screen they were facing. Camera-to-head distance was controlled by the cubicle space and chair position, and factory default camera settings were held constant.</p> <p><em>Video of Stated Intent.</em> 204 eight-second videos with audio were taken after an on-screen prompt instructed participants to make a spoken non-binding statement of intent. During this phase, we played grey noise over a public address system that masked ambient sound from beyond the individual cubicle within which the video recording equipment was situated. In combination with well insulated over-ear headphones worn by participants, the grey noise was helpful for preventing interference or leakage of other participants’ statements.</p> <p><em>Video of Viewed Results</em>. 204 eight-second videos with audio were taken that capture the moment of participants discovery, viewing, and reaction to the interaction results. The interaction results appear and remain visible to participants on their computer screens from around 2.5 seconds (no earlier than 2 seconds) into the 8 second videos.</p> <p><em>Thin Slice Video.</em> 204 videos (two to three seconds in length) without audio were trimmed from full <em>Videos of Stated Intent</em>. These thin slice videos capture the 2-3 second interval directly following the concluded stated intent.</p> <p><em>Photograph</em>. 204 photographs (640 x 480 pixel .jpg files, 24 Bit depth) including a participant’s full face were captured from video using the VLC media player (3.0.11) “snapshot” tool.</p> <p><strong><em>Media Analysis Measures:</em></strong></p> <p><em>Stated Intent</em>. The particular statement of intent (either “I intend to Split” or “I intend to Take All”) unambiguously recognized by the authors upon inspection of the sound and appearance of each <em>Video of Stated Intent’s</em> recorded statement, is noted in the attached Media.csv file.</p> <p><em>Skin Coloration in Photographs</em>. Using Adobe Photoshop 2020, skin patches (51 × 51 pixels) were selected from the left and right cheeks of each <em>photograph </em>face image. These cheek areas were selected so that they included only skin surfaces but never the edge of a face, ears, lips, or eyelashes. The light (L*),red (a*), and yellow(b*) values that are relevant to human color perception were then measured by centering a "color sampler tool" in Photoshop that averaged over a 51x51 pixel area the CIELab color space values. L*,a*,b* values for right and left cheeks in <em>Photographs</em> are noted in the attached Media.csv file.</p> <p><em>Facial Width to Height in Photographs</em>: To measure facial width to height ratio (fWHR) in photographs, we used tools in Photoshop 2020: first we rotated the photos as necessary using image rotation so that pupils were levelled horizontally, we record the degree of positive or negative rotation used, then we used the <em>frame tool </em>to measure the upperface “height” distance between the upper edge of upper lip and lower edge of brows and the “width” distance between left and right zygion (byzygomatic width) of face images. These methods are consistent with Carre and McCormick (2008, <em>Proceedings of the Royal Society B</em><em>‐</em><em> Biological Sciences, 275</em>, 2651–2656.) methods for measuring the height of upperface and byzygomatic width of face images. The facial width and height in <em>Photographs</em> are noted in the attached Media.csv file.</p> <p><strong>Data and files: </strong>We provide audio-video and image files of <em>Media </em>along with the open-access .csv files detailed below<em>. </em>The above data is organized by the following three data structures in .csv file format.</p> <p>Fields.csv<strong> </strong>is an open-access file includes <em>Game and Survey Data</em> organized by participants and rounds in the experiment. Fields_codebook.csv is a codebook explaining the column headers and participants’ data in the Fields.csv file.</p> <p>Media.csv is an open-access file that catalogues the names and types of <em>Media </em>files provided and includes <em>Media Analysis Measures</em>. Media_codebook.csv is a codebook explaining column headers and participants’ data in the Media.csv file.</p> <p>MappingFile.csv<strong> </strong>is a restricted file linking the participant data in the Fields.csv and Media.csv files. Please contact the authors for this access to this key at <a href="http://doi.org/10.5281/zenodo.4321814">http://doi.org/10.5281/zenodo.4321814</a> if you plan to work at the intersection of these data or replicate published work based on these data.</p> <p>Instructions.pdf is the instructions document provided to all participants in the experiment from which the above data was collected.</p>
Data set 2 anonymized collection of data on BRAD research participants
<p>Database on research participants in the BRAD project. The Personal Data have been removed in order to make the identification of the research participants impossible. For Polish migrants in the UK, the database contains the information about the application to European Union Settlement Scheme.</p>
Underlying data - Results from the Open Call: How Citizens can participate in solar energy research?
<p>Underlying data to the "Results from the Open Call: How Citizens can participate in solar energy research?" @</p> <pre>https://zenodo.org/record/3554901#.YAgimxaCE2w</pre> <p>Answers to the online survey in "Call for ideas_answers online_survey.xlsx"</p> <p>Notes from the World Cafe and other meetings from the secretaries: "notes_MMLs_GRECO_2019.pdf</p> <p> </p>
Research Artifact: How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem
<p><strong>Research Artifact: How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem</strong></p> <p><a href="https://github.com/yusufsn/EclipseForumData">https://github.com/yusufsn/EclipseForumData</a></p> <p>This is a research artefact for the paper: <strong>How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem</strong>. This artifact is a repository consisting of collected dataset including (i) 289,061 threads, (ii) 216,864 extracted links from threads, (iii) 2,170 contributors, and the results of our qualitative analysis, (i) 1,142 manually annotated type of discussion and (ii) 1,142 manually annotated sentiment analysis. This artefact aims to enable researchers to replicate our mixed-methods quantitative results of the paper and reuse the dataset for further software engineering research.</p> <p>Contents</p> <ul> <li>dataset: <ul> <li><code>289061_threads.csv.zip</code> - 289,061 collected main threads of all users without post from webmaster (.zip format)</li> <li><code>216864_links.csv.zip</code> - 216,864 extracted links from collected threads (.zip format)</li> <li><code>2170_contributors.csv</code> - 2,170 list of contributions (.csv format)</li> </ul> </li> <li>Results of manual analysis: <ul> <li><a href="https://docs.google.com/spreadsheets/d/e/2PACX-1vQoRpcbrV66OEB4vaCG9Njq65zW7XpLRITYG3BlUOoa_DmeOKcdQIgYJ8y2aSlmL3y9bCUhjpP3rYmT/pubhtml">Manual annotation of discussion type</a></li> <li><a href="https://docs.google.com/spreadsheets/d/e/2PACX-1vQfyvsP1Zq3b9p_BYwkkgYXuEePJB6sIroC47jOUMcR5P8t7DCJFNpOJD565SPgqi--L3AAIQWs2kC5/pubhtml">Manual annotation of sentiment analysis</a></li> </ul> </li> </ul> <p>Authors</p> <ul> <li><a href="https://yusufsn.github.io/">Yusuf Sulistyo Nugroho</a></li> <li><a href="https://syful-is.github.io/">Syful Islam</a></li> <li>Keitaro Nakasai</li> <li><a href="https://ifrazrehman.github.io/">Ifraz Rehman</a></li> <li><a href="https://hideakihata.github.io/">Hideaki Hata</a></li> <li><a href="https://raux.github.io/">Raula Gaikovina Kula</a></li> <li><a href="https://cs.uwaterloo.ca/~m2nagapp/">Meiyappan Nagappan</a></li> <li><a href="https://matsumotokenichi.github.io/">Kenichi Matsumoto</a></li> </ul>
Data from all control participants (ACI experiment)
<p>Each .mat file contains all behavioral data from one single participant, across 10.000 trials :<br> - n_signal corresponds to the presented speech signal (1:Alda, 2:Alga, 3:Arda, 4:Arga)<br> - correct_answer : 1 if the participant correctly identified the target (da or ga), 0 otherwise.<br> - SNR : Signal to noise ratio at which stimuli were presented<br> - date : date of the corresponding trial (year, month, day, h, m, s)<br> - stim_order : random order of presentation of noise sounds.</p>
Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Radek Ošlejšek, Martin Macák, Karolína Dočkalová Burská.<br><em>Hands-on cybersecurity training behavior data for process mining.</em><br>In Elsevier Data in Brief. 2023.<br>Available as open-access article on <a href="https://doi.org/10.1016/j.dib.2023.109956">https://doi.org/10.1016/j.dib.2023.109956</a></p> <p><strong>Contents</strong></p> <p>Datasets store event logs of trainees participating in hands-on cybersecurity exercises organized in the <a href="https://www.kypo.cz">KYPO Cyber Range</a>. The data includes training scenarios (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.</p> <ol> <li><strong>Data1:</strong> A dataset of 52 trainees participating in the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise adapted an insider attack scenario. No time restrictions were posed on playtime. The data file is structured as follows: <ul> <li>training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> game adapted to an insider attack scenario.</li> <li>training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.</li> <li>command_histories: Recorded commands executed on network hosts.</li> <li>process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.</li> <li>process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.</li> </ul> </li> <li><strong>Data2:</strong> A dataset of 48 trainees participating in the original <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise. Three supervised training sessions were restricted to two hours of playtime. The structure follows the structure of Data1.</li> <li><strong>Tool:</strong> A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.</li> </ol> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original work.</p> <pre><code>@article{Oslejsek2023dataset, author = {Radek O\v{s}lej\v{s}ek and Martin Mac\'{a}k and Karol\'{i}na {Do\v{c}kalov\'{a} Bursk\'{a}}}, title = {Hands-on cybersecurity training behavior data for process mining}, journal = {{Data in Brief}}, publisher = {Elsevier}, issn = {2352-3409}, year = {2023}, volume = {52}, doi = {10.1016/j.dib.2023.109956}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923009873} }</code></pre>
Supplementary Data for "Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"
<p>This dataset contains quality assessment results for 26 vocabularies. The assessment was conducted using the <a href="https://skos-play.sparna.fr/skos-testing-tool/">qSKOS vocabulary quality assessment tool</a>.</p> <p>The 26 assessed vocabularies were converted from their original formats into the Simple Knowledge Organization System (SKOS) data model using the approach described in our paper titled <a href="https://doi.org/10.1007/978-3-031-62362-2_9">"Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"</a>, presented at the <a href="https://doi.org/10.1007/978-3-031-62362-2">24th International Conference on Web Engineering (ICWE 2024)</a>.</p> <p>The dataset contains a quality assessment for the following vocabularies:</p> <ol> <li>A Taxonomy of Evaluation Towards Standards</li> <li>Cross-Device Taxonomy</li> <li>What Makes a Data-driven Business Model? A Consolidated Taxonomy</li> <li>DDI Aggregation Method</li> <li>DDI Mode of Collection</li> <li>Building a New Taxonomy for Data Discretization Techniques</li> <li>Demopaedia</li> <li>Data Science Glossary</li> <li>A Taxonomy of Evaluation Approaches in Software Engineering</li> <li>Evaluation Thesaurus</li> <li>The Glossary of Human Computer Interaction</li> <li>Human-Factors Taxonomy</li> <li>A Taxonomy to Structure and Analyze Human–Robot Interaction</li> <li>A Taxonomy of Interaction for Instructional Multimedia</li> <li>A Taxonomy of Interrogation Methods</li> <li>Design Vocabulary for Human–IoT Systems Communication</li> <li>Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors</li> <li>Thesaurus Mass Communication</li> <li>Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey</li> <li>A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction</li> <li>A Human-Centered Taxonomy of Interaction Modalities and Devices</li> <li>A Taxonomy of Spatial Interaction Patterns and Techniques</li> <li>A Taxonomy of Social Errors in Human-Robot Interaction</li> <li>Taxonomy of Digital Research Activities in the Humanities</li> <li>Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions </li> <li>Cross-Device Interaction</li> </ol>
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.