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604 results for “Teams”
Small mammal structure cover collected at Team Vole fences near Nome, Toolik Lake, and Utqaigvik, Alaska, summer 2019
Percent cover of tundra vole and brown lemming structures collected from within the Team Vole enclosure/exclosure fences near Nome, Toolik, Utqiagvik, AK 2019.
An Experiences Survey about Sprint Feedback for Teams
<p>This survey elicits thoughts and experiences from researchers and practitioners towards the relevance of sprint feedback for teams in agile software development. The survey covers the following aspects: demographics, team-driven factors, information usages for Sprint planning, and the overall experiences on lived team feedback, also Information demands by teams.</p> <p>Available under: <a href="http://survey.se.uni-hannover.de/index.php/57192?lang=en">http://survey.se.uni-hannover.de/index.php/57192?lang=en</a></p> <p> </p> <p> </p>
A 31-Year Bibliometric Review of Team Effectiveness Research: Evolution, Trends, and Future Directions (DATA).
<p>This dataset is derived from a comprehensive study of team effectiveness over three decades, utilizing bibliometric techniques to examine scholarly publications across multiple databases. The repository includes bibliometric data from 6,051 publications related to team effectiveness, featuring metadata such as authors, titles, publication years, citations, and keywords.</p> <h3>Repository Structure:</h3> <p>The data shared in this repository is organized as follows:</p> <ul> <li><strong>Bibliometrix database.xlsx:</strong> This excel file contains information on 6051 publications in the field of Team Effectiveness downloaded from the Scopus and Web of Science databases using specific search terms and inclusion criteria detailed in our associated paper (see our publication for more information on the methodology). The file contains the necessary headers for direct use in the Biblioshiny interface of the bibliometrix library for R.</li> <li><strong>List of stop words.txt:</strong> This file contains keywords, separated by commas, that we have decided to eliminate from our analyses due to their potential to introduce bias in the results.</li> <li><strong>List of synonyms.txt:</strong> This file contains groups of semantically synonymous keywords, separated by semicolons. Each line represents a group of synonyms, with the first keyword being the one that Bibliometrix will use to replace all other keywords in that line.</li> <li><strong>Appendix [A-H].pdf:</strong> List of appendices that complement the results of the study carried out.</li> </ul> <h3>Recommended Usage:</h3> <div> <div> <div> <div> <p>This dataset is ideal for researchers interested in conducting new analyses of research trends, co-authorship network analysis, and thematic evolution in the field of team effectiveness. For example, researchers can narrow the scope to focus exclusively on team effectiveness in educational contexts. Our publication provides all the necessary details to replicate or extend our methodology. For any queries related to the data, please contact <strong>Yeray Barrios-Fleitas</strong> at <em><a rel="noreferrer">y.d.c.barriosfleitas@utwente.nl</a></em>.</p> </div> </div> </div> </div> <div> </div> <h3>License and Citation:</h3> <p>The data are distributed under the CC BY license. Please cite any use of this dataset using the following format:<br>Barrios Fleitas, Y., Marcella A.M.G., Eysink, T.H., & Rensink , A. (2025). A 31-Year Bibliometric Review of Team Effectiveness Research: Evolution, Trends, and Future Directions. ZENODO, <a href="https://doi.org/10.5281/zenodo.12082529" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12082529</a></p>
GEDII Wearable Sensors Dataset of 10 Research Teams
<p>The dataset contains Bluetooth (proximity), Infrared (face-to-face), Speech (microphone) and Accelerometer (body activity) data of 10 research teams collected during 5 working days in each team. Altogether N=105 team members. Socio-demographic data as well as round-robin ratings regarding friendship and advice seeking is included. Data was collected using Sociometric badges by Humanyze (formerly Sociometric Solutions).</p> <p>The present dataset has been produced within the context of a EU funded H2020 research project called “Gender-Diversity-Impact: Improving Research and Innovation through Gender Diversity. (GEDII)”. The project has been running from 2015 to 2018 with the aim to develop new tools and methods for doing research on the impact of gender diversity in R&D teams. In order to address these questions, GEDII makes use of a variety of research methods, including a cross country survey, bibliometric & patent analysis and detailed case studies with R&D teams.</p> <p>The dataset is distributed as R package.</p>
iGEM: a model system for team science and innovation
<p>This dataset is extracted from the <strong>international Genetically Engineered Machine (iGEM) competition </strong>between years 2008 and 2018, and can be used as a model system for studying team science and innovation. It is described at length in <a href="https://arxiv.org/abs/2310.19858">this article</a>.</p> <p>The dataset encompass detailed records from the iGEM competition, capturing various aspects of team participation and achievements. Specifically, the <strong>Team Information</strong> dataset (<strong>teams_table.csv</strong>) provides insights into team characteristics and achievements, including medal status and region of origin. <strong>User Information</strong> (<strong>users_table.csv</strong>) offers a look into individual participants, detailing their roles in the team. <strong>Awards Information</strong> (<strong>awards_table.csv</strong>) and <strong>Medal Criteria</strong> (<strong>medals_criteria.csv</strong>) lay out the awards teams have garnered and the standards for medal attainment. The <strong>BioBricks Information</strong> (<strong>biobricks_table.csv</strong>) corresponds to the BioBrick sequences associated with each team, while <strong>Wiki Edits</strong> (<strong>wikis_table.csv</strong>) tracks the changes made by users on their team's (wiki) lab notebook. Finally, the <strong>Collaboration Network</strong> (<strong>collaboration_network.csv</strong>) corresponds to the weighted directed inter-team collaboration network collected using team mentions across team wikis.</p> <p>In addition to the structured dataframes above, we provide in <strong>team_wikis_full_text.zip</strong> the full texts of the wiki pages from the digital laboratory notebooks collaboratively edited by iGEM teams in the forms of wiki instances. There is a folder for each year from 2008-2018 and within which there are individual folders for each team. Each team folder has a file denoting the pagelist and two files for each page. One file is the html content, and the other the text content, extracted using the "KeepEverythingExtractor" option in the <em>boilerpipe.extract</em> library for processing and removing boilerplate content after webscraping.</p> <p> </p>
GEDII Survey on Research Teams Dataset
<p>This dataset contains the result of the GEDII cross-country survey carried out among EU based research teams during the year 2017. It contains research teams from Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Italy, Lithuania, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland and the UK. Overall, the Consortium recruited 159 research groups submitting a total of 1501 online questionnaires - out of which 1357 are complete.</p> <p>The dataset <strong>is unique</strong> in that it combines individual level responses from research group members regarding team climate, communication patterns, or leadership style among others with bibliometric performance data for each particular group retrieved from Web of Science (Clarivate Analytics).</p> <p>The data is distributed as R package "gediiccs", including extensive documentation.</p> <p>NOTE: The open access version of the "gediiccs" package contains the <strong>team level data only</strong>. This restriction is due to confidentiality concerns: given the large quantity of variables in relation to the relatively small research groups, it would be easy for group members to "reverse engineer" the identity of their own group as well as the responses from individual group members. Hence, only<strong> team level aggregated data</strong> has been released.</p>
Dataset and models of TMLR 2024 Paper "Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis"
<p>This is a part of dataset and models of the paper published in TMLR 2024 (Transactions on Machine Learning Research, <a href="https://jmlr.org/tmlr/" target="_blank" rel="noopener">https://jmlr.org/tmlr/</a>).</p> <p>Including training datasets, testing datasets, and models.</p> <p>The example program for using this file will be put on the author's github repo branch: <a href="https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr" target="_blank" rel="noopener">https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr</a></p> <p> </p>
Open Science Team 7 Data Set and Bibliography
<p>As a small exercise before delving into a group research project we generated a small data set based on the review of 7 websites ranging in subject matter. We provide the Data set and bibliography here. </p>
ePol: Impacts of Diversity on Software Teams Dataset
<p>Data set that has all the records of the activities carried out in the ePol Project, with their respective attributes. Additionally, it also includes code that does a cleanup according to some criteria. After the cleaning performed by the aforementioned code, the results are also available in the "dataset_epol_result.csv" file.</p> <p>For more information read: SOUZA, NATAN. MASSONI, TIAGO. SARMENTO, CAMILLA. <strong>Impactos da Diversidade em Equipes de Software</strong>, 2023.</p>
Rapid Review Dataset for Seeking Enlightenment: Incorporating Evidence-Based Practice Techniques in a Research Software Engineering Team
<p>A collection of evidence briefings produced through a rapid literature review protocol the Department of Software Engineering and Research at Sandia National Laboratories. These briefings are described in our research paper, "Seeking Enlightenment: Incorporating Evidence-Based Practice Techniques in a Research Software Engineering Team", which was accepted for publication at the 1st Annual Conference of the United States Research Software Engineer Association (US-RSE'23).</p> <p>Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2023-06549O.</p>
League of Legends Team Composition Data
<p>Data set for investigating team compositions in Legend of Legends. See the readme file for file and variable descriptions.</p>
SMA-TB Clinical trial research team's thoughts and opinions
<p>In this video SMA-TB Clinical trial research team members from Georgia and South Africa share their thoughts and opinions regarding the SMA-TB project and its impact both at scientific and personal level. </p> <p>SMA-TB team comprises of doctors, nurses, laboratory technicians and administrative. This video gives opportunity to look at and think of SMA-TB project from different perspectives. </p> <p>SMA-TB project has received funding from the European Union's Horizon 2020 research and innovation programme under grant ageement No 847762</p>
Replication package for "Teams and Individuals in Standard Auction Formats: Decision and Emotions"
<p>This is the replication package for the paper "Teams and Individuals in Standard Auction Formats: Decision and Emotions" by Maria Karmeliuk, Martin Kocher and Georg Schmidt, forthcoming in <em>Experimental Economics</em>.</p> <p>The package contains instructions and zTree code for running the laboratory experiment, as well as raw data from the experiment including the data from face recognition software FaceReader. It also contains Stata and R code for data cleaning, cleaned datasets and Stata code for replicating data analysis and statistics reported in the paper.</p>
EmoPairCompete - Physiological Signals Dataset for Emotion and Frustration Assessment under Team and Competitive Behaviours
<p>Please refer to the documentation at: https://github.com/DTUComputeStatisticsAndDataAnalysis/EmoPairCompete</p>
data remote team performance and wellbeing
<p><span>Data collected from 421 remote workers across diverse sectors underwent </span></p>
Dataset: Atlassian Corporation (TEAM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
RAGE pilot data from 1st evaluation of the Sports Team Manager game on soft skills for employability
<p><strong>General description: </strong>The dataset includes data from the first evaluation pilot which tested the Sports team manager game developed by PlayGen for the Okkam use case.</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human Computer Interaction (HCI) design and evaluation methods<br> PsycINFO Classification: 3620 Personnel Management & Selection & Training; 2228 Occupational & Employment Testing</p> <p><strong>Name entitites</strong><br> Organizational information: OKKAM, in collaboration with University of Trento<br> Geographical information: Italy<br> Time information: May-June 2017</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong> end users: other user groups</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Sports Team Manager game<br> Methodology/design: within subjects design<br> Evaluation variables: usability, user experience, learning</p> <p><strong>Instruments:</strong> 1. Questionnaire on Usability Game User Experience Satisfaction Scale (GUESS; Phan, Keebler, & Chaparro, 2016) – Usability subscale; 2. questionnaire on User Experience including 3 subscales: Enjoyment (GUESS -Enjoyment subscale); Usefulness (Intrinsic Motivation Questionnaire, IMI; Ryan, 1982) - Subscale Value/Usefulness; Flow (Flow Short Scale, FSS, Rheinberg et al., 2003; Vollmeyer & Rheinberg, 2006); 3. Pre-post questionnaire on learning; 4. Focus interview</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills: evaluating, analysing; affective skills: interpersonal skills<br> ESCO skills: social interaction (<a href="http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330">http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330)</a>; accept constructive criticism (<a href="http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30">http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30</a>); work in teams (<a href="http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0">http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0</a>); negotiate compromise <a href="http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74">(http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74)</a>; lead others (<a href="http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207">http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207</a>); motivate others <a href="http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507">(http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507</a>); support colleagues (<a href="http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224">http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224</a>); manage time <a href="http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8">(http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8</a>); make decisions (<a href="http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47">http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47</a>); develop strategies to solve problems (<a href="http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800">http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800</a>); evaluate information (<a href="http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9">http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9)</a><br> <br> <strong>Relationships</strong>: D8.3 First RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.2564742">10.5281/zenodo.2564742</a></p>
RAGE pilot data from 2nd evaluation of the Sports Team Manager game on soft skills for employability
<p><strong>General description: </strong>The dataset includes data from the second evaluation pilot which tested the Sports team manager game developed by PlayGen for the Okkam use case.</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human Computer Interaction (HCI) design and evaluation methods<br> PsycINFO Classification: 3620 Personnel Management & Selection & Training; 2228 Occupational & Employment Testing</p> <p><strong>Name entitites</strong><br> Organizational information: OKKAM, in collaboration with University of Trento<br> Geographical information: Italy<br> Time information: December 2017- November 2018</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong> end users: other user groups</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Sports Team Manager game<br> Methodology/design: within subjects design for learning<br> Evaluation variables: usability, user experience, learning</p> <p><strong>Instruments:</strong> 1. Questionnaire on Usability Game User Experience Satisfaction Scale (GUESS; Phan, Keebler, & Chaparro, 2016) – Usability subscale; 2. questionnaire on User Experience including 3 subscales: Enjoyment (GUESS -Enjoyment subscale); Usefulness (Intrinsic Motivation Questionnaire, IMI; Ryan, 1982) - Subscale Value/Usefulness; Flow (Flow Short Scale, FSS, Rheinberg et al., 2003; Vollmeyer & Rheinberg, 2006); 3. Pre-post questionnaire on learning; 4. Focus interview</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills: evaluating, analysing; affective skills: interpersonal skills<br> ESCO skills: social interaction: <a href="http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330">http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330</a>; accept constructive criticism: <a href="http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30">http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30</a>; work in teams: <a href="http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0">http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0</a>; negotiate compromise: <a href="http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74">http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74</a>; lead others: <a href="http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207">http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207</a>; motivate others: <a href="http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507">http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507</a>; support colleagues: <a href="http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224">http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224</a>; manage time: <a href="http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8">http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8</a>; make decisions: <a href="http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47">http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47</a>; develop strategies to solve problems: <a href="http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800">http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800</a>; evaluate information: <a href="http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9">http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9</a><br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.1209206">10.5281/zenodo.1209206</a></p>
Figure 1 in STEER - SUNDA STRAIT TSUNAMI (INDONESIA): PRELIMINARY VIRTUAL ASSESSMENT TEAM (P-VAT) REPORT
Figure 1. – Preserved specimens of Phenacoscorpius megalops from Malo Island, Vanuatu (MHMN-IC-2012-0833). A: 81.7 mm SL; B: 57.8 mm SL.
Figure 2 in STEER - SUNDA STRAIT TSUNAMI (INDONESIA): PRELIMINARY VIRTUAL ASSESSMENT TEAM (P-VAT) REPORT
Figure 2. – Distribution records of Phenacoscorpius megalops. Stars and circle indicate the previous locality records of the species and of the specimens report- ed in this study, respectively.
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.