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1,146 results for “collaboration;”
Supplementary material 1 from: Just A, Gourvil J, Millet J, Boullet V, Milon T, Mandon I, Dutrève B (2015) SIFlore, a dataset of geographical distribution of vascular plants covering five centuries of knowledge in France: Results of a collaborative project coordinated by the Federation of the National Botanical Conservatories. PhytoKeys 56: 47-60. https://doi.org/10.3897/phytokeys.56.5723
Numerical appendix: Explanation note: A shapefile representing the dataset completeness (based on the Jackknife 1, a non-parametric estimator) on a grid of 10 km by 10 km cells. The number of records in each cell was used as an estimator of the sampling effort. The ratio between the observed and estimated richness of species measures the completeness of the inventory in each surveyed cell (Vallet et al. 2012).
Asymmetric Collaborative Bar Stabilization Tethered to Two Heterogeneous Aerial Vehicles
<p>We consider a system composed of a bar tethered to two unmanned aerial vehicles (UAVs), where the cables behave as rigid links under tensile forces, and with the control objective of stabilizing the bar's pose around a desired pose. Each UAV is equipped with a PID control law, and we verify that the bar's motion is decomposable into three decoupled motions, namely a longitudinal, a lateral and a vertical. We then provide relations between the UAVs' gains, which, if satisfied, allows us to decompose each of those motions into two cascaded motions; the latter relations between the UAVs' gains are found so as to counteract the system asymmetries, such as the different cable lengths and the different UAVs' weights. Finally, we provide conditions, based on the system's physical parameters, that describe good and bad types of asymmetries. We present experiments that demonstrate the stabilization of the bar's pose.</p>
University-Industry Collaboration: Bibliometric Analysis of Current and Future Trends
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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>
Fair Graph Augmentation for Graph Collaborative Filtering
<p>Dataset for the paper submission `Fair Graph Augmentation for Graph Collaborative Filtering`. The included datasets are Foursquare New York City (FNYC), Foursquare Tokyo (FKTY), MovieLens 1M (ML1M), Last.FM 1M (LF1M), Rent The Runway (RENT)</p>
Quantitative Assessment of G7's Collaboration in Sustainable Development Goals
<div> </div> <p><strong>Authors</strong>: Kai Liu <sup>[1]</sup>, Ali Raisolsadat (<a href="mailto:arraisolsadat@uwaterloo.ca">arraisolsadat@uwaterloo.ca</a>) <sup>[2]</sup>, Xander Wang (<a href="mailto:xxwang@upei.ca">xxwang@upei.ca</a>) <sup>[3,4]</sup>, and Quan Van Dau (<a href="mailto:vdau@upei.ca">vdau@upei.ca</a>) <sup>[3,4]</sup></p> <p><strong>Institutions</strong>:</p> <ol> <li>School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada C1A 4P3</li> <li>Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1</li> <li>Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St. Peter's Bay, Prince Edward Island, Canada C0A 2A0</li> <li>School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada C1A 4P3</li> </ol> <p><strong>Corresponding Author</strong>: Dr. Xander Wang<br><strong>Contact Information</strong>: <a href="mailto:xxwang@upei.ca">xxwang@upei.ca</a></p> <div> <h2>Repository Contents</h2> </div> <p>This repository contains the code and data for the project titled "Quantitative Assessment of G7's Collaboration in Sustainable Development Goals".</p> <div> <h2>Information about the Folders</h2> </div> <ul> <li><strong><code>sdg_raw_data</code></strong>: Contains the raw Sustainable Development Goals indicator data from the "Our World in Data" database.</li> <li><strong><code>sdg_grouped_raw_data</code></strong>: Contains the raw SDG indicator data, but grouped for each goal (1-15).</li> <li><strong><code>results_datasets</code></strong>: Contains the main results for Domestic Changes, Foreign Changes, and Synergy data in <code>.CSV</code> and <code>.RData</code> formats.</li> <li><strong><code>main_manuscript_figures</code></strong>: Contains the 5 main figures used in the manuscript text.</li> <li><strong><code>s1_s12_supplementary_figures</code></strong>: Contains the 12 figures from the supplementary material of the manuscript.</li> <li><strong><code>partial_true_direction_un.csv</code></strong>: Contains the indicator directions from Table 1 of the manuscript.</li> <li><strong><code>SDG_Data.xlsx</code></strong>: An Excel file which contains all the data used in the manuscript results in multiple sheets, including SDG raw data and results datasets.</li> </ul> <div> <h2>Prerequisites</h2> </div> <ul> <li><strong>R</strong>: Please ensure that you have installed the latest version of the R software for your device. You can download it from <a href="https://cran.r-project.org/" rel="nofollow">CRAN</a>.</li> <li><strong>RStudio</strong>: It is recommended to use RStudio for running the R scripts. You can download it from <a href="https://rstudio.com/products/rstudio/download/" rel="nofollow">RStudio's official website</a>.</li> </ul> <div> <h2>How to Run</h2> </div> <ol> <li><strong>Download <a href="../api/records/11659806/draft/files/Synergy-2024-1.0.0.zip/content" target="_blank" rel="noopener noreferrer">Synergy-2024-1.0.0.zip</a> to your local computer and unzip it</strong></li> <li> <p><strong>Set the directory to the unzipped folder</strong></p> </li> <li><strong>Set the R working directory to the unzipped folder</strong></li> <li> <p><strong>Run Main Code</strong>:</p> <ul> <li>Open and run the <code>gross_synergy_markdown.Rmd</code> file. This is the main code for our manuscript.</li> <li>The resulting datasets will be saved in the <code>results_datasets</code> folder.</li> </ul> </li> <li> <p><strong>Generate Figure 1</strong>:</p> <ul> <li>Open and run <code>figure_1.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> folder.</li> </ul> </li> <li> <p><strong>Generate Figure 2</strong>:</p> <ul> <li>Open and run <code>figure_2.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> and <code>s1_s12_supplementary_figures</code> folders, respectively.</li> </ul> </li> <li> <p><strong>Generate Figure 3</strong>:</p> <ul> <li>Open and run <code>figure_3.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> folder.</li> </ul> </li> <li> <p><strong>Generate Figure 4</strong>:</p> <ul> <li>Open and run <code>figure_4.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> and <code>s1_s12_supplementary_figures</code> folders, respectively.</li> </ul> </li> <li> <p><strong>Generate Figure 5</strong>:</p> <ul> <li>Open and run <code>figure_5.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> folder.</li> </ul> </li> </ol>
Resources for BMF Collaborative Project 64: Predictors of premium for bio-based clothes and discount for second-hand clothes
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Hypothetical Git workflows for simple scientific collaboration
<p>These diagrams illustrate three common workflow scenarios of a relatively simple empirical study with a maximum of three authors using Git/Github versioning tools. Their design was informed by Ram's (2013) body of work. The first scenario is the same as the three-author hypothesis proposed by Ram (2013).</p> <p>In the other two hypothetical scenarios, a single author's workflow is examined. In the first, he coordinates and manages changes with the remote repository from the start of the study, and in the second, he only does this when it comes time to publish the research's materials, data, and history of changes.</p> <p>Six .JPG files, in English (_EN) and Brazilian Portuguese (_PT), depict these three potential scenarios. The repository also contains a .PPTX file that can be edited if the reader wants to utilize any of the figures, either as a basis for additional diagrams or to change the figure's colors.</p>
ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis - Data Set
<p>Data set of the Paper "ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://abunai.dev</p>
An Analysis of Collaboration on React.js
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FIGURE 13 in Laurence A. Mound 90 years: A collaborative life that laid the foundation of modern thrips studies
FIGURE 13. Alice Wells and Laurence Mound walking hand by hand in Tidbinbilla National Park, Canberra, Australia, 2011 (Photo: Adriano Cavalleri).
FIGURES 5–7 in Laurence A. Mound 90 years: A collaborative life that laid the foundation of modern thrips studies
FIGURES 5–7. (5) Xaniothrips xantes, described by Laurence Mound as one of his most exciting discoveries. Laurence Mound in various parts of the world 6–7: (6) In Linz, Austria, with Hermann Priesner, 1966; (7) In Ibadan, Nigeria, during his employment for the Federal Department of Agricultural Research, 1960.
FIGURES 8–12 in Laurence A. Mound 90 years: A collaborative life that laid the foundation of modern thrips studies
FIGURES 8–12. Laurence Mound in various parts of the world. (8) In Piracicaba, Brazil with Roberto Zucchi and Renata Monteiro, 1996; (9) In Beltsville, USA with Sueo Nakahara and Cheryle O'Donnell, 2015; (10) In Kunming, China with Xia Wang, Zhaohong Wang, Hongrui Zhang, Alice Wells, Lihong Dang, Shimeng Zhang and Luke Watson (from left to right) during the XIth International Symposium on Thysanoptera and Tospoviruses, 2019; (11) Online on YouTube with Elison Lima advising new thrips students during the I Brazilian Symposium of Thysanoptera, 2020; (12) In ANIC, Canberra, Australia, being interviewed by Desley Tree, 2023.
Retrieved studies for Impacts of the Adoption of Hybrid Work on Collaboration in Information Technology Teams
<p>Retrieved studies for Impacts of the Adoption of Hybrid Work on Collaboration in Information Technology Teams</p>
Dantas_de_Paula_et_al_2024_Fungal_Collaboration_Gradient_Results_Figures_PlotScript
<p>Simulation results, R plotting script and Figures of LPJ-GUESS-NTD regarding the Fungal Collaboration Gradient study by Dantas de Paula et al.</p>
Source code for CPBS Report 23UNM03 - Enhancing Collaboration through Web-based Visualization and Analysis of Traffic Crash Data
<p>Python source code for the crash mapping web application.</p>
How Far the Humanistic User Experience in Workspace Collaboration Can Foster Self-Competence and Teamwork: Indonesian Organizational View
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Data from: A general-purpose spatial survey design for collaborative science and monitoring of global environmental change: the global grid
Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation.
The hidden influence of communities in collaborative funding of clinical science
<p>Every year the National Institutes of Health allocates $10.7 billion (one-third of its funds) for clinical science research while the pharmaceutical companies spend $52.9 billion (90% of its annual budget). However, we know little about funder collaborations and the impact of collaboratively funded projects. As an initial effort towards this, we examine the cofunding network, where a funder represents a node and an edge signifies collaboration. Our core data include all papers that cite and receive citations by the Cochrane Database of Systemic Reviews, a prominent clinical review journal. We find that 65% of clinical papers have multiple funders and discover communities of funders that are formed by national boundaries and funding objectives. To quantify success in funding, we use a g-index metric that indicates efficiency of funders in supporting clinically relevant research. After controlling for authorship, we find that funders generally achieve higher success when collaborating than when solofunding. We also find that as a funder, seeking multiple, direct connections with various disconnected funders may be more beneficial than being part of a densely interconnected network of co-funders. The results of this paper indicate that collaborations can potentially accelerate innovation, not only among authors but also funders.</p>
The eWaterCycle platform for Open and FAIR Hydrological collaboration Video Abstract
<p>In this video we introduce The eWaterCycle platform for Open and FAIR Hydrological collaboration. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research fully compatible with the principles of Open Science as well as FAIR science.</p> <p>This is a video abstract with the publication "The eWaterCycle platform for Open and FAIR Hydrological collaboration." in the journal Geoscientific Model Development (GMD) (under review, link will be provided when available).</p> <p>For more information on the eWaterCycle platform see:</p> <ul> <li>the eWaterCycle package on Zenodo: https://doi.org/10.5281/zenodo.5119389</li> <li>the 5 use cases presented in the paper and video, on Zenodo: https://doi.org/10.5281/zenodo.5543899</li> <li>the eWaterCycle website at https://www.ewatercycle.org/</li> </ul>
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.