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40 results for “collaboration networks”
Educational transformation and network learning dataset – qualitative data from an international collaborative EU-project
<p>We are releasing our dataset of workshop outcomes acquired from the annual consortium conferences organized by the international “NextFood” consortium. The purpose of this project is to develop new ways of educating the future sustainability leaders of the agrifood sector, making sure that the professionals (farmers, advisers, businesses, students) have the right set of skills and competences needed to tackle the sustainability challenges we face ahead. Data gathering started from May 2018 yielding considerable amount of data on achievements, challenges and action plans related to educational transformation. This dataset will be updated by the time of project finalization. This work was funded by the European Union, through the Horizon 2020 project “NextFood”, Grant agreement No. 771738.</p>
Structural Gender Imbalances in Ballet Collaboration Networks
<p>Data contains node list of company artists and their artist type and gender. Edge list provides collaboration network of each company. </p> <p>Null model data provides metrics obtained from null models where assortativity preferences are removed by shuffling collaborations (edges) or artists' attributes (gender) in the collaboration network.</p> <p>For more details, please see documentation in <a href="/api/files/aa096f3e-bc2f-400e-94a9-6bd76ea4a324/Ballet_data_dict.rtf?versionId=7e58ba3a-caae-490b-9da6-9285bd7e4807">Ballet_data_dict.rtf</a></p> <p>For company abbreviations:</p> <p>ABT: American Ballet Theater; NYBC: New York City Ballet; NBC: National Ballet of Canada; ROH: The Royal Ballet of The Royal Opera House. </p>
Figure 2. Workflow in Collaborative Portals-Questions Regarding Alterity in Social Collaborative Networks
<p>Despite the divergence of each members individual interests and structure, the media as well<br> as the Internet become new forms of social binding, and we believe they do not exclude the<br> traditional ways of communication and interaction, but help people in their duties, in addition to the<br> traditional, formal ways. In Figure 2 [7]. there is a clear image about how each of the benefficiaries<br> interact with the educational portal.</p>
Figure 1. 2nd year section in the Educational Portal - Questions Regarding Alterity in Social Collaborative Networks
<p>Portlets proved to be the most intelligent way of presenting information on an educational<br> portal, due to the richness of the user interface tools. The portal may be found at http://cursfs.ub.ro<br> URL. In Figure 1 there is a screenshot of the 2nd year students section in educational portal, which is<br> very easy to use and helpful.</p>
A network of countries collaborating on learning analytics research
<p>A network of countries collaborating on learning analytics research. It includes original research articles published in Scopus Database up to 16 January 2018. The file is an un-directed Graphml network of 76 countries. It can be opened in Social Network Analysis applications such as Gephi, or Igraph R package.</p>
Global health science leverages established collaboration network to fight COVID-19
<p>Compressed file containing the data for the <a href="https://arxiv.org/abs/2102.00298">Global health science leverages established collaboration network to fight COVID-19</a> paper.</p> <p>For simplicity of use you can find pubmed__2019_cleaned.rar at https://zenodo.org/record/5011448#.Ykb2PDU68mA which contains the cleaned data used for the analysis. However we do not own the data and The following 6 data sources have been used.</p> <p><br> 1. Publication data</p> <p>source: PubMed API (download 26.06.2023).</p> <p>Output: country_pub_info.csv and edge_list.csv</p> <p><br> 2. Covid cases (download 26.06.2023)</p> <p>file: owid-covid-data.csv</p> <p>website: https://github.com/owid/covid-19-data/tree/master/public/data</p> <p>original source (for some variables): COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University</p> <p><br> 3. Covid policy restrictions (last download 21.06.2021)</p> <p>website: https://ourworldindata.org/policy-responses-covid</p> <p>data file: OxCGRT_latest.csv</p> <p>Oxford Covid-19 Government Response Tracker</p> <p><br> cit: Thomas Hale, Noam Angrist, Rafael Goldszmidt, Beatriz Kira, Anna Petherick, Toby Phillips, Samuel Webster, Emily Cameron-Blake, Laura Hallas, Saptarshi Majumdar, and Helen Tatlow. (2021). “A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).” Nature Human Behaviour. https://doi.org/10.1038/s41562-021-01079-8</p> <p> </p> <p>4. Economic wealth</p> <p>Penn World Table version 10.0</p> <p>cit: Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt</p> <p><br> 5. Economic/social development</p> <p>Human Development Index (HDI) from http://hdr.undp.org/en/content/download-data</p> <p> </p>
Institutional Collaboration in the US LTER Network based on bibliometric information (1981-2018)
This is a dataset of supplementary materials of an accepted article in Bioscience entitled "Collaboration across time and space in LTER Network". It is based on the bibliography data maintained by LTER Network Office (https://www.zotero.org/groups/2055673/lter_network/items). The analysis was carried out in 2019 with the updated data collecting from individual sites. The dataset contains the basic information that the analysis relies on, and the temporal and spatial patterns of institutional collaboration in the US LTER Network.
Open-source software collaboration network mining dataset
<p>The resulting dataset of the <a href="https://github.com/gotec/git2net">git2net </a>and <a href="https://github.com/wschuell/repo_tools">repo_tools </a>mining process for randomly selected large open-source repositories.</p>
globalbioticinteractions/AEC-DBCNet: Collaborative databasing of North American bee collections within a global informatics network project archive
<p>Data in this archive are from the <em>Collaborative databasing of North American bee collections within a global informatics network project</em>. Data was originally captured using Arthropod Easy Capture software developed at the American Museum of Natural History (AMNH), New York. Project lead investigators are John Ascher (Principal Investigator) and Jerome Rozen (Co-Principal Investigator) at the AMNH, and Douglas Yanega (Principal Investigator), University of California Riverside.</p> <p><strong>Please use this citation for this archive: </strong>John Ascher, Digital Bee Collections Network data archive from the C<em>ollaborative databasing of North American bee collections within a global informatics network project</em>. Version: 08 Mar 2016. https://doi.org/10.5281/zenodo.1436853</p> <p>This project was supported by the National Science Foundation grant <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956388">DBI 0956388</a> and <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956340">DBI 0956340</a></p> <p><strong>ABSTRACT</strong> Natural history collections contain millions of bee specimens documenting the geographic ranges, temporal occurrence patterns, and floral associations of the 20,000 described bee species. This project will digitize and consolidate specimen records from 10 bee collections across the United States. The investigators will make or verify species identifications, capture full label data, georeference and error-check localities, and upload this information to publicly accessible databases. Web-based tools will be used to capture data across collections efficiently, validate bee and plant names through automated comparison with taxonomic authority files, and synthesize data on species pages with images, digitized literature records, and other information about bees and their host plants. Data will be uploaded to the Global Biodiversity Information Facility and to Discover Life (www.discoverlife.org), a website that features customizable global maps for all global bee species and dynamic identification keys for North American species. To obtain information needed to conserve and manage pollinators, the investigators will work with ecologists to model geographic and temporal trends in bee populations in relation to environmental variables. Bees are the most important pollinators of the approximately 1/3 of crops that require animal pollination. Recent declines in honey bee populations highlight the need to understand better the roles of native bees in agricultural and natural systems. This project will help predict risks to bees and their pollination services from climate change, habitat loss, and other factors. The outreach program Bee Hunt (www.discoverlife.org/bee) will educate the public, including students in underserved communities, about bee diversity and the importance of pollination services. Using digital photography and rigorous research protocols, Bee Hunt will empower people at biological field stations, nature centers, parks, schools, and other sites to collect high-quality data to augment information from specimen records.</p>
Collaboration Spotting X - A Visual Network Exploration Tool
<p>Due to many technological advancements, the amount of connected data drastically increased in the last decade. The analysis of this data and the insights it generates show great potential for supporting decision making processes in various industries and aspects of our lives. Multiple visual analytics solutions have been proposed to gain further insights into such data and gain explainable results. However, the majority of existing solutions are either closed sourced, not available or no longer developed. To mitigate the issues above and based on findings from expert interviews conducted using an existing tool, this paper introduces Collaboration Spotting X, a new network-based interactive visual analytics and information retrieval tool prototype. This prototype enables users to explore connected network datasets such as social network data and bibliometric data using multiple visual cues and interactions. Furthermore, to gain an insight into how this prototype is perceived by users and identify further improvements, a preliminary study with a class of 37 computer science graduate students is described. The study findings show that the students perceive Collaboration Spotting X as a useful tool that helps them complete tasks through visualisation and interaction. Additionally, multiple aspects were identified that might have caused users to experience in addition to positive emotions also some negative emotions during usage. These aspects might have also contributed to a lower usability score. Finally, multiple improvement directions have been identified, which will be implemented in future developments.</p>
Open Access in the Global South: Perspectives from the Open and Collaborative Science in Development Network
<p>As with science in general, the discussion around open access has generally been driven by the institutions and perspectives of western or global North countries. However, approaches to open access are far more diverse and there are alternative approaches that have not gained visibility, especially in historically marginalized communities. This presentation will present some key lessons of the OCSDNet http://ocsdnet.org. The OCSDNet is a research network that engaged in participatory research and consultation with scientists, development practitioners, community members and activists from 26 countries in Latin America, Africa, the Middle East and Asia to understand the values at the core of open science in development. What we learned is that there is not one right way to do open science, and “openness” requires constant negotiation and reflection, and the process will always differ by context due to historical and socio-political factors. The set of seven values and principles at the core of the OCSDNet manifesto will be discussed for a more inclusive open science in development. More important, we will discuss the implications of these values in relation to the aspirations and features of COAR’s Next Generation Repository.</p>
Results of "Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits"
<p>This dataset contains the results obtained for the article "Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits", authored by Francesc Wilhelmi, Cristina Cano, Gergely Neu, Boris Bellalta, Anders Jonsson and Sergio Barrachina. The article has been sent to Elsevier Ad-hoc Networks.</p> <p>The content of this dataset has been obtained by means of the code allocated in the following GitHub repository: <a href="https://github.com/fwilhelmi/collaborative_sr_in_wns_via_selfish_mabs">https://github.com/fwilhelmi/collaborative_sr_in_wns_via_selfish_mabs</a></p> <p>Contact information: francisco.wilhelmi@upf.edu</p>
Dataset on an online collaborative learning situation in acomputer networks course
<p>Here you can find a dataset of a collaborative learning situation. Students were enrolled in two undergradute courses on computer networks where they were required to carry out a set of learning activities supported by Moodle and an online collaborative environment called CoTrackV2. The data collected includes logs of the writing process of shared documents, logs of the chat messages between the group members, and logs from Moodle with coarser-grained information about course-level interactions. This dataset has been generated with the aim of allowing researchers to study self-and socially-shared regulation in online environments.</p> <p>There will be 6 files:</p> <ul> <li>document_logs.csv</li> <li>chat_logs.csv</li> <li>moodle_logs.csv</li> <li>individual_submissions.csv</li> <li>learning_design.csv</li> <li>final_questionnaire.csv</li> </ul>
Collaborative High-Resolution Observation Datasets of an Eddy Using an Underwater Glider Network
<p>This matlab mat data provides data from 12 underwater gliders in the northern South China Sea in 2017.</p> <p>Please contact Haibo Tang at tanghb6@mail2.sysu.edu.cn for any questions. <br> <br>Wish you good luck!</p>
Simulation of collaboration networks in software development
<p>Dataset resultant from Master Thesis 'Simulation of collaboration networks in software development' authored by José Miguel Gomes.</p>
Collaborative Network to Take Responsibility for Oral Anticancer Therapy 2
ClinicalTrials.gov study NCT04142463. IPD Sharing: NO. Countries: 1. Publications: 1.
Community Resilience Learning Collaborative and Research Network
ClinicalTrials.gov study NCT03977844. IPD Sharing: NO. Countries: 1. Publications: 3.
The COllaborative Neonatal Network for the First CPAM Trial
ClinicalTrials.gov study NCT05701514. IPD Sharing: YES. Countries: 1. Publications: 3.
Collaborative Network to Take Responsibility for Oral Anticancer Therapy
ClinicalTrials.gov study NCT02861209. IPD Sharing: NO. Countries: 1. Publications: 1.
California Collaborative Network to Promote Data Driven Care and Improve Outcomes in Early Psychosis
ClinicalTrials.gov study NCT04007510. IPD Sharing: NO. Countries: 1. Publications: 1.
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.