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604 results for “Teams”
Raw Data for the article: Post-crisis debriefing: A tool for improving quality in the medical emergency team system
<p><strong>Objectives: </strong>To examine clinicians' perception of quality of technical and non-technical response to emergencies and application of post crisis debriefing.</p> <p><strong>Design: </strong>Descriptive, anonymous, self-reporting survey on the needs and perception of a post-crisis debriefing implementation.</p> <p><strong>Setting: </strong>Multi-specialist medical institute in Italy focused on solid organ transplantation and organ failure support.</p> <p><strong>Main outcomes: </strong>Perception of application of guidelines and evaluation of debriefing implementation during in-hospital emergencies.</p> <p><strong>Results: </strong>Response rate to the survey was 25% (148 health care workers). Of all respondents, 86% were employed >10 years, 75% were involved in ≤5 emergencies over the previous year. Resuscitation guidelines were considered fully applied by 55%; 64% of respondents considered the teaching programme as sufficient. Of all participants, 97% were aware of the importance of teamwork dynamics, 79% were aware of the importance of the personal performance, and 52% considered emergencies as valid opportunities for professional growth. Leadership was considered important by 45% of respondents; debriefing implemented by 41%, and considered a potentially useful tool by 85%.</p> <p><strong>Conclusion: </strong>Post-crisis debriefing is a way to diffuse self-reflective and life-long learning culture; it is perceived as a powerful tool for improving quality of the rapid response system by the vast majority of those surveyed.</p> <p><strong>Keywords: </strong>Communication; Crisis resource management; Emergency; ICU; Intensive care medicine; Leadership; Patient safety; Teamwork.</p>
nba-teams-standings
<p>These datasets contain the team standings from the NBA conferences (from the season when these datasets have been published).</p>
GEOROC data as SQLite database derived from GEOROC Compilations provided by DIGIS Team
<p>Cite the sources of this dataset as:</p> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Archaean Cratons", https://doi.org/10.25625/1KRR1P, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Complex Volcanic Settings", https://doi.org/10.25625/1VOFM5, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Continental Flood Basalts", https://doi.org/10.25625/WSTPOX, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Convergent Margins", https://doi.org/10.25625/PVFZCE, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Intraplate Volcanic Rocks", https://doi.org/10.25625/RZZ9VM, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Ocean Basin Flood Basalts", https://doi.org/10.25625/AVLFC2, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Ocean Island Groups", https://doi.org/10.25625/WFJZKY, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Oceanic Plateaus", https://doi.org/10.25625/JRZIJF, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Rift Volcanics", https://doi.org/10.25625/KAIVCT, GRO.data, V3</p> </blockquote> <blockquote> <p>DIGIS Team, 2022, "GEOROC Compilation: Seamounts", https://doi.org/10.25625/JUQK7N, GRO.data, V3</p> </blockquote>
A Theory of Scrum Team Effectiveness: Dataset and Supplementary Materials
<p>This bundle contains supplementary materials for an upcoming academic publication <em>A Theory of Scrum Team Effectiveness</em>, by Christiaan Verwijs and Daniel Russo. Included in the bundle are the dataset, SPSS syntaxes, and model definitions (AMOS). This replication package is made available by C. Verwijs under a "Creative Commons Attribution Non-Commercial Share-Alike 4.0 International"-license (CC-BY-NC-SA 4.0).</p> <p><strong>About the dataset</strong></p> <p>The dataset (SPSS) contains anonymized response data from 4.940 respondents from 1.978 Scrum Teams that participated from the <a href="https://scrumteamsurvey.org">https://scrumteamsurvey.org</a>. Data was gathered between June 3, 2020, and October 13, 2021. We cleaned the individual response data from careless responses and removed all data that could potentially identify teams, individuals, or their parent organizations. Because we wanted to analyze our measures at the team level, we calculated a team level mean for each item in the survey. Such aggregation is only justified when at least 10% of the variance exists at the team level (Hair, 2019), which was the case (ICC = 51%). Because the percentage of missing data was modest, and to prevent list-wise deletion of cases and lose information, we performed EM maximum likelihood imputation in SPSS.</p> <p>The dataset contains question labels and answer option definitions. To conform to the privacy statement of scrumteamsurvey.org, the bundle does not include individual response data from before the team-level aggregation.</p> <p><strong>About the model definitions</strong></p> <p>The bundle includes definitions for Structural Equation Models (SEM) for AMOS. We added the four iterations of the measurement model, four models used to perform a test for common method bias, the final path model, and the model used for mediation testing. Mediation testing was performed with the procedure outlined by Podsakoff (2003). Mediation testing was performed with the <a href="http://statwiki.gaskination.com/index.php?title=CFA">"Indirect Effects" plugin</a> for AMOS by James Gaskin.</p> <p><strong>About the SPSS syntaxes</strong></p> <p>The bundle includes the syntaxes we used to prepare the dataset from the raw import, as well as the syntax we used to generate descriptives. This is mostly there for other researchers to verify our procedure.</p>
Decentralized Motion Planning with Collision Avoidance for a Team of UAVs under High Level Goals
<p>The video illustrates simulation and experimental results of a team of unmanned aerial vehicles executing Linear Temporal Logic (LTL) tasks. More specifically, given a certain LTL task over predefined regions of interest, each agent derives a high-level plan that satisfies the given task. Then, it executes the plan using a continuous controller that is based on decentralized navigation functions, which also guarantee inter-agent collision avoidance. The video shows one simulation and two experimental scenarios.</p>
Model generated open-closed field topology maps produced by ISSI team "Magnetic Open Flux And Solar Wind Structuring Of Interplanetary Space"
<p>Open and closed magnetic field topologies of the solar corona generated using four PFSS based coronal models (EUHFORIA, WSA, MULTI-VP, PSI-PFSS) and one full MHD coronal model (PSSI-MHD) using two different types of HMI-ADAPT magnetic field maps (with and without Active Regions (AR) added retrospectively). The magnetograms used are accessible here <a href="https://doi.org/10.5281/zenodo.10211762" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10211762</a> (Henney, Carl. ‘ADAPT Global Solar Magnetic Maps - 2010 Sep 18-20 (w/ & W/o Farside Active Region Input)’. Zenodo, 28 November 2023).</p> <p>These model outputs were generated by the ISSI team "Magnetic Open Flux And Solar Wind Structuring Of Interplanetary Space" and were used for the paper accessible on Arxiv via this link: https://arxiv.org/abs/2311.04024</p>
Data for "Statistical Adjustment for Tactical Choices When Evaluating Team's Offensive Output Across Five Major European Club Soccer Leagues"
<p>Data files for a paper titled "Statistical Adjustment for Tactical Choices When Evaluating Team's Offensive Output Across Five Major European Club Soccer Leagues"</p>
Supplementary Material for "Investigating Software Development Teams Members' Perceptions of Data Privacy in the Use of Large Language Models (LLMs)"
<h3>ABSTRACT<strong>: </strong></h3> <p><strong>Context</strong>: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties. <br><strong>Goal</strong>: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners. <br><strong>Method</strong>: We conducted a survey with 78 ICT practitioners from five regions of the country. <br><strong>Results</strong>: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use. <br><strong>Conclusions</strong>: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.</p>
EIT-team/Stroke_EIT_Dataset: Publication release
<p>The version used to accompany publication of the data. This has minor DOI and file structure updates from previous version</p>
Training, executive, attention and motor skills (TEAMS) training versus standard treatment for preschool children with attention deficit hyperactivity disorder: a randomised clinical trial
<p>This is the dataset used in the trial entitled Training, executive, attention and motor skills (TEAMS) training versus standard treatment for preschool children with attention deficit hyperactivity disorder, published in BMC Research Notes.</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>
MLR - Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams
<p> Supplementary artifact for Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams</p>
Do Team Trust and Leader Support Matter at Work and Beyond?
<p>Team effectiveness is examined using the Arc of Purposeful Leadership model to investigate mediational effects of employees’ departmental impact, organizational commitment, and leader support on work-family synergy and emotional exhaustion. Structural equation modelling and Hayes’ PROCESS macro were used to analyze the responses of 319 participants. Findings support the Input-Process-Output (IPO) model of team effectiveness. Relationships between team purpose/support and outcome variables (work-family synergy and emotional exhaustion) were mediated by employees’ departmental impact, organizational commitment, and leader support. Findings reveal the complexities of team processes in relation to outcomes, including work-family synergy and emotional exhaustion, that extend beyond the workplace.</p> <p>Data were collected at University of Redlands in 2020 during the COVID-19 pandemic.</p>
Sleep Architecture in Response to a Late Evening Competition in Team-Sport Athletes
<p>Purpose: The aim of this study was to assess the effect of a late evening competition on sleep characteristics and perceived recovery in team-sport players using a validated self-applicable and portable polysomnographic device. Methods: Sixteen teamsport players (age: 25.4 [1.4] y; body mass index: 23.6 [0.5] kg/m2) completed the study. Objective and subjective sleep data were collected for 4 consecutive nights: 2 nights before (PRE2 and PRE1) and the 2 nights after an eveningmatch (POST1), performed between 6:00 PM and 9:00 PM. Total quality of recovery values were also collected in the morning. Results: A significantly delayed bedtime (P < .0001; η2p = .68, large) was observed in the first night after the competition (2 h 29 min [1 h 15 min]) compared both to PRE2 (+88 min; P < .0001), PRE1 (+98 min; P < .0001), and POST1 (+100 min; P < .0001), and similar results were observed for wake-up time (P = .033; η2p = .39, large): Players woke up significantly later in evening match (9 h 20 min [1 h 55 min]) compared with PRE2 (+85 min; P = .050) and POST1 (+85 min; P = .049). Conversely, total sleep time; sleep efficiency; sleep onset latency; wake after sleep onset; cortical arousals; N1, N2, N3, and REM (rapid eye movement) percentages; total quality of recovery values; and scores of subjective sleep quality did not vary among the 4 study nights. Conclusions: Team-sport players had delayed bedtime and wake up time following an evening competition; however, sleep quality, duration, and subjective scores of recovery were not affected by the evening match. The delayed wake-up time seems to protect athletes’ sleep efficiency/duration against the evening-match-induced delayed bedtime.</p>
Transition from transperitoneal to retroperitoneal approach in laparoscopic living donor nephrectomy: Team-based and individual learning curve: A cross-sectional study
<p><strong><span>Background</span></strong><span>: </span><span>Laparoscopic living donor nephrectomy (LLDN) is a standard practice for kidney donor transplantation due to fewer complications associated with the treatment. Retroperitoneal approach to LLDN is thought to be more advantageous in preventing injuries to various abdominal organs. However, the transition process from transperitoneal to retroperitoneal LLDN is important to ensure the best outcome for the patients</span><span>. The aim of this study was to investigate the learning curve for the retroperitoneal approach in LLDN among urologists in an individual and team-based approach</span></p> <p><span><strong>Methods</strong>: </span><span>A retrospective analysis</span> <span>of retroperitoneal LLDN was performed on procedures performed by a kidney donor team consisting of four (4) urologists from January 2019 to January 2022 at Cipto Mangunkusumo National General Hospital, Indonesia. The data were taken from pre-existing medical records. The learning curve for the operation time, warm ischemic time, and estimated blood loss were analyzed using cumulative sum (CUSUM) analysis. Phase 1 represents the initial learning curve, the phase 2 plateau represents the period of proficiency, and phase 3 represents the mastery period.</span></p> <p><span><strong>Results</strong>: </span><span>A total of 127 retroperitoneal LLDN procedures were done during the study period by four operators with various experience. The average procedure needed to achieve proficiency was 16.5 procedures. Meanwhile, the average procedure needed to achieve mastery was 28 procedures. A shorter learning curve was achieved by operators with more experience in other laparoscopic procedures.</span></p> <p><strong><span>Conclusions</span></strong><span>: </span><span>The learning curve for transition from transperitoneal to retroperitoneal approach to LLDN is relatively short and feasible for both individuals and teams of urologists. Therefore, the transition to a retroperitoneal approach is a feasible option to reduce the complication rate of LLDN.</span></p>
[Replication package] Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning
<p>Anonymized replication package of submission #1917: "Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning".</p> <p>See README.md for further instructions.</p>
ISMRM 2022/23 Reproducibility Team Challenge: Replication results for MReplIcators team
<p>This repository contains the <em><strong>MR</strong></em>epl<em><strong>I</strong></em>cators team’s replication attempt results from the 2022-23 International Society of Magnetic Resonance in Medicine (ISMRM) Challenge ‘Repeat It With Me: Reproducibility Team Challenge’ (<a href="https://challenge.ismrm.org/">ISMRM Challenge – ISMRM's Challenge Forums</a>).</p> <p>The challenge aims to “raise awareness of the importance of reproducible science through fostering collaboration between labs by reproducing ISMRM abstracts”.</p> <p>The Reproducer sub-team was formed by Ebony R. Gunwhy (ERG) and Jemima Pilgrim-Morris (JPM) from POLARIS (<a href="https://www.sheffield.ac.uk/polaris">POLARIS (Pulmonary, Lung and Respiratory Imaging Sheffield) | POLARIS | The University of Sheffield</a>) at The University of Sheffield and the team worked on reproducing abstract #1700 "Sensitivity Analysis of the Bloch Equations" (ISMRM 2022) by Author sub-team Nick Scholand and Martin Uecker from The Institute of Medical Engineering at Graz University of Technology, Austria and Institute for Diagnostic and Interventional Radiology University Medical Center, Göttingen (1).</p> <p>In this archive, a zipped results folder contains the independent results and notes from each member of the replicator sub-team (in ‘results/JPM’ and ‘results/ERG’, respectively). A more detailed overview of the abstract and replication (motivation, approach, challenges, and outcomes) can also be found in ‘Abstract replication details.pdf’. A shorter summary is provided here:</p> <p><em><strong>A successful replication ‘attempt’ was defined as…</strong></em></p> <ul> <li> <p>Installation of provided software</p> </li> <li> <p>Creation of simulations with the BART (2) simulation interface</p> </li> </ul> <p><em><strong>A fully successful replication was defined as…</strong></em></p> <ul> <li> <p>Successful recreation of the figures provided in the abstract</p> </li> <li> <p>Examining goodness-of-fit by matching the provided ground truth dataset to some tolerances (normalised root mean square error; NRMSE)</p> </li> <li> <p>Using quantitative measures of reproducibility (e.g., coefficient of variation) between original data computed by author sub-team and data computed by replicator sub-team</p> </li> <li> <p>Application of technique to further sequences and tissue/sequence parameters</p> </li> </ul> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p><em><strong>Contact between Replicator and Author sub-teams</strong></em></p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p>An initial meeting was conducted to discuss how we should organise our participation as a team in this challenge. Some starting information was exchanged, in addition to the original abstract:</p> <p><strong>(A)</strong> Publicly published information about the studies:</p> <p>(i) arXiv preprint of more detailed manuscript (2)</p> <p>(ii) GitHub repository associated with the arXiv preprint, containing steps for reproducing the work relating to Figure 2 of the abstract (<a href="https://github.com/mrirecon/bloch-moba/tree/main/02_sens_analysis">https://github.com/mrirecon/bloch-moba/tree/main/02_sens_analysis</a>)</p> <p>(iii) GitHub repositories with tutorials on using the BART reconstruction toolbox (<a href="https://github.com/mrirecon/bart-workshop">https://github.com/mrirecon/bart-workshop</a>)</p> <p><strong>(B)</strong> The author sub-team also created an additional GitHub repository made publicly available, containing interactive Google Colab and Binder notebooks introducing BART and its simulation framework, and providing a more step-by-step guide to allow for an exact replication of the work in the abstract (<a href="https://github.com/mrirecon/ismrm-2022-sensitivity-analysis-bloch-eq">https://github.com/mrirecon/ismrm-2022-sensitivity-analysis-bloch-eq</a>). The author sub-team also uploaded the ground truth dataset (their own simulation results, located within ‘02_irbssfp/ref/’ and ‘03_unprep_irbssfp/ref/) from the abstract to this repository.</p> <p>In the first instance, the Replicator sub-team attempted a replication using only the publicly available information outlined in (A). Each member of the Replicator sub-team created their own GitHub forks of the repository listed in (A)(ii) to provide a starting point from which to document their replication from. These can be found at <a href="https://github.com/JemimaPM/bloch-moba-jpm">https://github.com/JemimaPM/bloch-moba-jp</a>m (JPM) and <a href="https://github.com/EbonyGunwhy/bloch-moba-erg/tree/MReplIcators">https://github.com/EbonyGunwhy/bloch-moba-erg/tree/MReplIcators</a> (ERG) and contain a commit history of all replication steps taken.</p> <p>The additional repository described in (B) was only used to download the ground truth dataset for quantitatively assessing the replication results. This limitation was imposed to allow the Replicator sub-team autonomy in reproducing the work and to aid in gaining a more thorough understanding of the methodology involved. It was agreed that if required, the replicator sub-team would take help from the extra repository described in (B) and provide feedback to the authors afterwards summarising what degree of information was useful for replicating the work. Further interaction was limited to creating issues on GitHub and to quick queries via email, as needed.</p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p><em><strong>Expected outcomes</strong></em></p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p>The original study was based on numerical experiments. Therefore, the results were expected to be directly reproducible, however it was expected that possible differences may occur due to numerical noise. The major reason for this is the single floating-point arithmetic used in the provided BART software. Its presence can be tested by reproducing the study results on various machines multiple times. Numerical noise is expected to change between different hardware. Therefore, both members of the Replicator sub-team attempted to independently replicate the results on their own hardware. The used hardware in this challenge is specified below.</p> <p><em>Replicators:</em></p> <p>ERG: Intel(R) Core(TM) i5-1145G7 CPU @ 2.60GHz (4 cores, 8 logical processors)</p> <p>JPM: Intel(R) Core(TM) i7-4790 CPU @ 3.60GHz (4 cores, 8 logical processors)</p> <p><em>Authors:</em></p> <p>Intel(R) Core(TM) i7-8565U CPU @ 1.80GHz (4 cores, 8 logical processors)</p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p><em><strong>Replication results</strong></em></p> <p><em><strong>----------------------------------------------------------------------------</strong></em></p> <p>Prior to starting the replication attempt, we agreed on the NRMSE tolerances below which we would define the replication as successful. These were NRMSE = 0.005 for the DQ derivative and NRMSE = 0.0001 for the SA derivative.</p> <p>Due to time constraints, only a direct replication was performed. This was deemed successful as both replicators were able to reproduce the results within these tolerances. As expected, differences occurred between hardware, with 0% NMRSE achieved in the replication attempt of JPM. In addition, the reproduced figures were qualitatively compared to the abstract figures and these were found to agree.</p> <p>Further notes and results from the Replicator sub-team attempts can be found within the ‘results’ folder for each respective replictor.</p> <p> </p> <p><strong><em>===========================================</em></strong></p> <p><em><strong>Carbon footprint</strong></em></p> <p>This algorithm runs in 0.08 mins on 4 CPUs and on:</p> <ul> <li> <p>Intel i5-10400F: draws 572.04 Wh. Based in the United Kingdom, this has a carbon footprint of 132.21 g CO2e, which is equivalent to 0.14 tree-months (<a href="http://calculator.green-algorithms.org//?runTime_hour=12&runTime_min=0&appVersion=v2.2&locationContinent=Europe&locationCountry=United%20Kingdom&locationRegion=GB&coreType=CPU&numberCPUs=4&CPUmodel=Core%20i5-10400F&memory=12&platformType=personalComputer&PSFradio=Yes&PSF=1">Green Algorithms i5-10400F (green-algorithms.org)</a>)</p> </li> <li> <p>Intel i7-4930K: draws 1.10 kWh. Based in the United Kingdom, this has a carbon footprint of 253.13 g CO2e, which is equivalent to 0.28 tree-months (<a href="http://calculator.green-algorithms.org//?runTime_hour=12&runTime_min=0&appVersion=v2.2&locationContinent=Europe&locationCountry=United%20Kingdom&locationRegion=GB&coreType=CPU&numberCPUs=4&CPUmodel=Core%20i7-4930K&memory=12&platformType=personalComputer&PSFradio=Yes&PSF=1">Green Algorithms i7-4930K (green-algorithms.org)</a>)</p> </li> </ul> <p>(calculated using green-algorithms.org v2.2 [1]. Note: at time of writing, the Green Algorithms calculator did not include our specific CPUs, therefore here we have calculated with similar processors. A request has been made on the Green Algorithms GitHub repository for our processors to be added in future).</p> <p><em><strong>===========================================</strong></em></p> <p><em><strong>References</strong></em></p> <p><em>1. Scholand N, & Uecker M. Sensitivity Analysis of the Bloch Equations [abstract]. In: Proceedings of the 31st Annual Meeting of ISMRM, London, 2022. Abstract nr 1700.</em></p> <p><em>2. Uecker M, Virtue P , Ong F, Murphy MJ, Alley MT, Vasanawala SS, Lustig M. Software Toolbox and Programming Library for Compressed Sensing and Parallel Imaging [abstract]. ISMRM Workshop on Data Sampling and Image Reconstruction, Sedona, 2013.</em></p> <p><em>3. Scholand N, Wang X, Roeloffs V, Rosenzweig S, Uecker M. Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations. arXiv:2209.08027 [Preprint]. 2022. doi: <a href="https://doi.org/10.48550/arXiv.2209.08027">10.48550/arXiv.2209.08027</a></em></p> <p><em>4. Lannelongue L, Grealey J, Inouye M. Green Algorithms: Quantifying the Carbon Footprint of Computation. Adv. Sci. 2021;8(12):2100707. doi: <a href="https://doi.org/10.1002/advs.202100707">10.1002/advs.202100707</a></em></p> <p> </p> <p> </p>
Statistics for different european teams
<p>This dataset was generated through web scraping with the intention to provide data in order to analyze the teams performance.</p>
Beetle Team Tribolium Data Archive
<p>The Beetle Team Tribolium Data Archive is a collection of 10 data sets created and/or analyzed over a 20-year period by a multidisciplinary group of collaborators, the "Beetle Team" (Jim Cushing, R. F. Costantino, Brian Dennis, Robert A. Desharnais, Shandelle M. Henson, Aaron A. King, and Jeffrey Edmunds). The data are from population laboratory experiments using the flour beetle <em>Tribolium castaneum</em>. Their research program was focused on providing biological evidence of complex nonlinear population dynamics.</p>
Data from: Co-creation in fully remote software teams
<p>The dataset consists of the interview protocols and codebook used in the design and data analysis steps of our study entitled "Co-Creation in Fully Remote Software Teams".</p> <p>The interview-based study consisted of a first round of interviews with 25 software professionals with a subsequent follow-up interview with 5 of these original participants. The interview transcripts were coded inductively resulting in the final code book shared here.</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.