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604 results for “Teams”
TranscriboQuest Ancient Greek Team
<p>Dataset produced by the Ancient Greek team during the TranscriboQuest event in Lyon, September 11-13th 2024. See README for further informations.</p>
TranscriboQuest_Arabic_team
<p>▶ Data format: XML ALTO<br>▶ Number of transcribed lines: 153<br>▶ author/creator/curator of the dataset: Enki Baptiste and Ephrem Aboud Ishac <br>▶ Segmentation tools, HTR engine and interface: OpenITI model (https://github.com/OpenITI/acdc_results/blob/main/models/gen2-print-n7m5-union-ft_best.mlmodel); eScriptorium; Kraken<br>▶ Language of the corpus, Date: Arabic, end of the 16th century<br>▶ Type, support of documents, script: paper; mashriqi naskh <br>▶ Transcription method: diplomatic transcription respecting the tanwin, the shadda and the diacritic marks.<br>▶ Theme, collection, object of the dataset: theology; Maktabat al-Sālimī, Bidiyya, Oman, ms. AS 250 4v-5f (https://elibrary.mara.gov.om/en/omani-library/imam-nour-al-din-al-salmi-s-library/book/?id=324#book/7); St Mark Monastery, Jerusalem, SMMJ 00264 2v-5r</p>
Linked collectors and determiners for: TUR Amazon research team collections.
Natural history specimen data linked to collectors and determiners held within, "TUR Amazon research team collections". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/35e0b405-7fcf-4dd8-a5f9-e8b6a03b670e">https://bionomia.net/dataset/35e0b405-7fcf-4dd8-a5f9-e8b6a03b670e</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/35e0b405-7fcf-4dd8-a5f9-e8b6a03b670e">https://gbif.org/dataset/35e0b405-7fcf-4dd8-a5f9-e8b6a03b670e</a>. Formatted as a Frictionless Data package.
Ritratti del Team Stauffer
<p>Nelle immagini rappresentate sono protagonisti i membri del team dell'Accademia Stauffer. I soggetti Francesco Della Libera, Sonny Ciliberto, Paola Parati.</p> <p>Le immagini sono state realizzate dal fotografo Freelancer Michele Branca, di proprietà della Fondazione Stauffer</p>
Olfactory bulb snRNAseq dataset for Team Schlossmacher- ASAP
<p>C57BL/6 mice were nasally inoculated with either a control AAV (AAV-EF1a-TdTomato, B1-4) or an experimental AAV expressing humanized mutant alpha-synuclein (AAV-EF1a-SNCA(A53T), A1-4). The mice were 8 weeks of age at the time of inoculation and the virus was expressed for 4 weeks before the mice were sacrificed and the olfactory bulbs were collected for snRNAseq. Viral transduction of olfactory sensory neurons was confirmed in all 8 samples via cryosectioning of the contralateral olfactory bulb.</p>
Dissemination and Implementation of a Videoconference Antimicrobial Stewardship Team
ClinicalTrials.gov study NCT05319561. IPD Sharing: NO. Countries: 1. Publications: 1.
Treatment for Migraine and Mood (Team-M)
ClinicalTrials.gov study NCT04992494. IPD Sharing: YES. Countries: 1. Publications: 0.
Long-term Carbon and Nitrogen, and Phosphorus Dynamics of Leaf and Fine Root Litter project (LIDET-Long-term Intersite Decomposition Experiment Team) data for the ARC, Arctic LTER. 1990 to 2000.
This file is from the Long-term Carbon and Nitrogen, and Phosphorus Dynamics of Leaf and Fine Root Litter project (LIDET-Long-term Intersite Decomposition Experiment Team). This file contains only the Arctic LTER data. In particular the mass looses over the ten year study. Three types of fine roots (graminoid, hardwood, and conifer), six types of leaf litter (which ranged in lignin/nitrogen ratio from 5 to 75), and wooden dowels were used for litter incubations over a ten year period.
Human-Swarm Teaming with Proximal Interactions
<p>Autonomous swarms are capable of accomplishing simple tasks but when it comes to critical situations (e.g., disaster management) they cannot manage the whole mission. By establishing a human-swarm interaction we create a system that can outperform humans or autonomous swarms when working independently. We propose a human-swarm teaming with proximal interactions between human operators and robots in a multi-agent system. One of the major challenges in a human-swarm interaction is acquiring global information about the swarm's state and visualizing it to the human operators. Continuous swarm observation requires global communication between the human operator and the swarm that limits scalability and comes with a high communication cost. In our approach, we only allow one-to-one communications in local neighborhoods between agent-agent and operator-agent. By doing that, we decrease the cognitive complexity of human-swarm interaction to O(1). Agents collectively explore and map the environment by disseminating their observations and incorporating their neighboring agents' maps. Human operators then use these maps to estimate the swarm's state, manipulate the maps to control the swarm, and as a result, determine the level of autonomy of the swarm. We verify our approach with a disaster management scenario where a simulated aerial swarm is spread across a mission zone to explore an environment. Our proposed method for human-swarm teaming with proximal interactions successfully guides a swarm to explore a mission area in a dynamic environment and allows a single operator to control the swarm.</p>
A Case Study on the Communication of a Local Software Developer Team
<p>Raw data and scripts for the analysis of the paper "A Case Study on the Communication of a Local Software Developer Team"</p> <p>The files with the extension "list" contain the communication of the according channel. For example, "talks.list" contains all the recorded talks. The first two columns of a file contains the communication patners, the third column the date, the fourth the time of day. The sixth column contains the duration of a talk, the seventh column the rough topic, followed by the id of the event in column eight. Since often, additional developers entered the conversation, which we recorded as extra event, we have summarized the events to one conversation, denoted by the last column. That is, the last column contains the id of the conversation.</p> <p>The file analysis.Rmd contains the script that we used to analyze the data and create the plots. We used the library coronet (available at GitHub:https://github.com/ecklbarb/coronet/tree/read-data-from-company). The input data must have the folder structure as discribed in the Readme of the coronet project.</p> <p>The file analysis.Rmd must be copied in the folder of the coronet library.</p>
GR-SCBET-Y03Y07 Soccer bets and teams DB from coupons, Greece, Aug.2003 - Mar.2007
<p>========================================================</p> <p> Dataset: GR-SCBET-Y03Y07</p> <p> Soccer bets and teams DB from coupons<br /> Greece, Aug.2003 - Mar.2007<br /> <br /> Release Notes</p> <p> Copyright (c) 2016 by Harris V. Georgiou</p> <p>========================================================<br /> Release: Aug 3, 2016</p> <p> - Version: 1.1a<br /> - Format: .xls<br /> ========================================================</p> <p><br /> This file contains important information about the current version of the dataset package. Downloading and using this material hints that you accept the EULA/Terms-of-Use (please read carefully).</p> <p>We welcome your comments and suggestions.</p> <p>_______________________________________________<br /> WHAT'S IN THIS PACKAGE?</p> <p>- Overview<br /> - Available file formats<br /> - Files and Datasets<br /> - License Agreement</p> <p>_______________________________________________<br /> OVERVIEW</p> <p>Athletic betting and successful risk management is one of the most challenging tasks in Machine Learning and A.I. in general. With only limited information and very sparsely distributed data, an algorithm has to model the teams' competency and predict outcomes of games, even when two teams face each other for the first time.</p> <p>This package contains two main datasets, generated from soccer betting coupons in Greece throughout a 3.5 year period and including every game that was included for any championship and any team. The first dataset corresponds to 43.758 matches with complete odds, final result and goals. The second is team-based statistics from these matches for a total of 1.126 teams from various championships (European and other).</p> <p>These datasets can be used as benchmark training sets for odds-oriented or team-oriented predictive models and algorithms, relevant not only to athletic betting but also other contexts, lile in in financial risk management. In the case of team-oriented modeling, special ELO-like ranking algorithms can be developed and tested against the official team/country rankings, as in UEFA and FIFA, but tailored to specific teams or championships, in order to evaluate and fine-tune the models.</p> <p>Note: Team names are in Greek, but they can be used as-is for proper referencing to unique IDs, i.e., practically they can be ignored.</p> <p>_______________________________________________<br /> AVAILABLE FILE FORMATS</p> <p>The datasets are available in the following formats (included):</p> <p>*.xls : MS-Excel/LibreOffice worksheet data file (exportable)</p> <p> </p>
Multivocal Literature Review Protocol - Investigating the Barriers that Women face in Software Development Teams focusing on the context of Proprietary Software Ecosystems
Open the record for dataset details and reuse information.
The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Software Teams: Dataset and Supplementary Materials
<p>This bundle contains supplementary materials for an upcoming academic publication The Double-Edged Sword Of Diversity: How Diversity, Conflict, and Psychological Safety Impacts Software Teams, 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 1.118 respondents aggregated into 161 (Agile) software teams that participated from the <a href="https://scrumteamsurvey.org">https://scrumteamsurvey.org</a>. Data was gathered between September 2021, and January 2022. 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 = 35-45%). No data was missing on the team level.</p><p>The dataset contains question labels and answer option definitions. To conform to the privacy statement of [scrumteamsurvey.org](https://scrumteamsurvey.org), the bundle does not include 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 iterations of the measurement model, four models used to perform a test for common method bias, and the path model that includes interactions. Indirect effects were calculated 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>
Team East's pesäpallo shirt
This pesäpallo shirt was used in the annual all-star game between East and West Finland in 1982. Source: Objaverse 1.0 / Sketchfab
Team IUPUI - T-Shirt - Black
To celebrate IUPUI's 50th anniversary, we're 3D scanning some of the apparel the University has released throughout the years. This shirt was 3D scanned using a Creaform Go Scan 50. Source: Objaverse 1.0 / Sketchfab
Toward a Quality Model for Hybrid Intelligence Teams - Supplementary Material
<p>Supplementary material for paper:</p> <div><span>Dell’Anna D, Murukannaiah PK, Dudzik B, Grossi D, Jonker CM, Oertel C, et al.</span> <span>Toward a Qual</span><span>ity Model for Hybrid Intelligence Teams.</span> <span>In: Proceedings of the 23rd International Conference on </span><span>Autonomous Agents and MultiAgent Systems, AAMAS 2024<br><br></span> <div> </div> </div> <div> <div># GENERAL INFORMATION</div> <div> </div> <div> </div> <div>## Title</div> <div> </div> <div>Supplementary material for paper "Toward a Quality Model for Hybrid Intelligence Teams"</div> <div> </div> <div> </div> <div># FILE OVERVIEW</div> <div> </div> <div>```</div> <div>AAMAS2024_Toward_HI_Quality_Model_Supplementary_Material</div> <div>│ README.md</div> <div>│ Group Discussion - Handout - Essentials # The printed version of the questionnaires (Essentials) and team descriptions handed out to the groups for the group discussion</div> <div>│ Group Discussion - Handout - Enablers # The printed version of the questionnaires (Enablers, except Coaching) and team descriptions handed out to the groups for the group discussion</div> <div>│ Group Discussion - Handout - Processes # The printed version of the questionnaires (Coaching and Key Task Processes) and team descriptions handed out to the groups for the group discussion</div> <div>│ Individual Response - Online Survey # A PDF version of the online survey</div> <div>└───Individual Response - Results # Excel file with all the responses of the participants from the online survey. Excel sheets are organized as follows</div> <div>RAW DATA # all the raw data from the survey</div> <div>RAW DATA TRANSPOSED # raw data with rows and columns inverted</div> <div>ALL TEAMS SCORES # quantitative results about adequacy for each feature: team scores and importance</div> <div>ALL FEEDBACK PER FEATURE # qualitative results about adequacy for each feature: grouped feedback participants</div> <div>forlatex # results in latex-ready form</div> <div>forspider # results in a form that supports easy creation of spider plot</div> <div>EFFECTIVENESS # quantitative results about effectiveness measures</div> <div>FINAL COMMENTS # final qualitative feedback from participants</div> <div>essentials # RAW DATA TRANSPOSED (essentials only)</div> <div>enablers # RAW DATA TRANSPOSED (enablers only)</div> <div>processes # RAW DATA TRANSPOSED (processes only)</div> <div>*_num_scores # * in {essentials, enablers, processes}, likert-to-numerical scores</div> <div>*_num_scores_annotatio # same as *_num_scores, just with numerical values instead of formulas</div> <div>```</div> <div> </div> <div> </div> <div> </div> <div> </div> </div>
Cultivating Leadership and Team Management Skills for Business Success
<p>In today's dynamic and competitive business landscape, effective leadership and proficient team management are fundamental pillars driving organizational success (Rahman, R. (2023). Leadership encompasses not only setting the vision and goals of an organization but also inspiring and empowering employees to achieve them (Rahman, R. (2023). Similarly, adept team management is crucial for harnessing the collective potential of individuals toward common objectives (Rahman, R. (2023). This introduction sets the stage for exploring the significance of honing leadership and team management capabilities in driving business success, drawing insights from scholarly literature on the subject.</p> <p> </p> <p>In the face of rapid technological advancements, evolving consumer preferences, and global market dynamics, businesses must navigate increasingly complex challenges. Effective leadership serves as the guiding force that steers organizations through uncertainty and change, while adept team management ensures that resources are optimally utilized and diverse talents are leveraged for maximum impact (Rahman, R. (2023). Moreover, in an era where employee engagement and retention are critical for sustained growth, leadership that fosters a culture of trust, collaboration, and innovation is indispensable ( Donaldson, G. (2006).).</p>
Refactoring Dataset for SOEN 6491 Software Refactoring, Winter 2024 - Team Alpha
<p>The dataset collected by Team Alpha in SOEN 6491 Software Refactoring course, Winter 2024</p> <p><strong>Content Description</strong></p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>File type</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>vertx.db, spring-boot.db, mina-sshd.db, guava.db</td> <td>SQLite Database</td> <td> <p>Fully collected refactoring dataset:</p> <ul> <li>Refactoring data (via RefactoringMiner)</li> <li>Code smells (via Organic)</li> <li>Code duplications (via PMD)</li> <li>Code churn (via Git)</li> <li>Timeline and statistics of releases in relation to refactoring commits. Releases are collected via GitHub API.</li> </ul> </td> </tr> <tr> <td>flink.db, rocketmq.db, ant.db, dubbo.db, elasticsearch.db, neo4j.db, rxjava.db, zookeeper.db</td> <td>SQLite Database</td> <td> <p>Partially collected refactoring dataset:</p> <ul> <li>Refactoring data (via RefactoringMiner)</li> </ul> </td> </tr> </tbody> </table>
DReSA: Project team reflections
<p>This presentation provides thoughts and reflections from the Digital Research Skills Australaisa (DReSA) project team on DReSA. Team members highlight their perspectives on value propositions and benefits for their respective institutiosn/organisations and nationally, as well as individual reflections on collaboration and working together on the project so far.</p> <p><strong>You can watch the video on YouTube here: https://youtu.be/qqH92itI8SI </strong></p> <p> </p>
Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry
<p>Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry. </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.