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Dataset results
12 results for “Team Science”
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>
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>
Data and Code for the manuscript PhD students in life sciences can benefit from team cohesion
<p>This folder contains data and code to reproduce regression results of the manuscript "PhD students in life sciences can benefit from team cohesion".</p>
The impact of learning modality on team-based learning (TBL) outcomes in anatomical sciences education
<p>Team-based learning (TBL) is an instructional methodology that has been increasingly used in anatomy and physiology education in recent years. The appropriateness of TBL methods for students with diverse preferred sensory learning modalities has not been adequately examined. This study aimed to show the influence of students preferred sensory modality for learning on TBL and traditional academic outcome measures (tests, assignments, etc.). 157 American undergraduate Communication Sciences and Disorders students taking anatomy and physiology courses took the VARK, a questionnaire quantifying their preferred learning modality. These students traditional and TBL measures of academic success were compared by preferred learning modality. A one-way MANOVA failed to find any difference in TBL or other academic outcomes by preferred learning modality (<em>p</em> = .252). The results of this study support the effectiveness of TBL methods for undergraduate students regardless of what sensory mode they prefer to receive information</p>
Co-authoring graphs of research teams in a laboratory in computer science
<p>Our aim is to study inter-organisational collaborations initiated by researchers in their research activity. We considered the co-authoring graph involving at least researchers from LORIA (<a href="https://www.loria.fr/fr/">https://www.loria.fr/fr/</a>), a French laboratory in computer science.</p> <p>The dataset is collected from the open French archive HAL (<a href="https://data.archives-ouvertes.fr/">https://data.archives-ouvertes.fr/</a>).</p> <p>Each file encodes (in <a href="http://www.graphviz.org/about/">DOT</a>) the co-authoring graph of a team of LORIA. A node represents a researcher, two nodes are linked only if the corresponding researchers published together over the three considered years 2017, 2018 and 2019. An affiliation attribute is attached to each considered node.</p> <p>The name of researchers and the teams as well as the HAL:id are anonymised. Only affiliations remain the same.</p>
Open Science Website Review (Team 2)
<p>A document detailing Team 2's analysis of the various websites promoting the concept of open science for the "Open Science" Course and using Zenodo to generate a DOI for the aforementioned document</p>
Open Science Team 7 - Survey Raw Data
<p>Raw Data files (.csv and .xlxs) from Team 7's survey. All responses have been anonymized</p>
Analytics of collaborations and performance of citizen science teams during the GEAR cycle 2 of the Crowd4SDG project.
<p>This data frame is part of the deliverable 4.2 of the Crowd4SDG project.</p> <p>It contains the measured analytics of citizen collaborations using new metrics/descriptors developed during the first year of the Crowd4SDG project. </p> <pre>The goal of the Crowd4SDG project is to research the extent to which Citizen Science (CS) can provide an essential source of non-traditional data for tracking progress towards the SDGs, as well as the ability of CS to generate social innovations that enable such progress. In the Crowd4SDG project, the Work Package 4 aims to develop and monitor new metrics and develop statistical models of team engagement and collaboration that contribute to the many-faceted outcomes of the citizen science projects developed within the Crowd4SDG consortium over the 3-years course of the project. Here, we share a dataframe of features collected during the GEAR cycle 2 pertaining to team composition, activity, performance and interaction dynamics. In particular, we leveraged the CoSo platform for collecting self-reported data on collaborations and task allocation structure of participating teams, as well as Slack data for measuring communication networks. The related findings are presented in the deliverable 4.4 of the Crowd4SDG project and serve as a basis for i) exhibiting the potential of using digital traces to derive measures related to team process, ii) highlighting perspectives for monitoring metrics in the next GEAR cycle.</pre>
Team Science to Promote Physical Activity
ClinicalTrials.gov study NCT03906942. IPD Sharing: NO. Countries: 1. Publications: 10.
First Dataset - Open Sciences 21/22 - Team 4
<p>First Dataset of the discipline Open Sciences 21/22. Uploaded by the team 4.</p>
Team Science (The Liver Health Study)
ClinicalTrials.gov study NCT06924281. IPD Sharing: NO. Countries: 1. Publications: 0.
Pace Validation Science Team: Particles and Biological Rates
The project is focused on data collection for the NASA PACE validation efforts. Specifically, our core targeted observables are phytoplankton carbon biomass (Cphyto), total particulate organic carbon (POC) concentration, net primary production (NPP), and chlorophyll fluorescence quantum yields (Ïf) as well and inherent optical properties and radiometry.
ScienceDex guides
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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.