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1,146 results for “collaboration;”
Figure 1 in Building a global database of soil microbial biomass and function: a call for collaboration
Figure 1. Sampling locations and underrepresented environmental conditions in the dataset as of 19 November, 2019. For each pixel, we measured the percentage of environmental conditions (e.g. climate, soil characteristics, topographic information, vegetation indices) captured by the current dataset. Hot deserts, tropical rainforests and sub-Arctic regions are the least represented.
The Collaborative Skills Scale
<p>The Collaborative Skills Scale is an 18-item scale with three subscales: Participation, Perspective taking and Social regulation. These subscales are devided under nine subscales overall, in the original version every subskill had four items, 36 items in sum. Participation consists of 12 items for Action, Interaction and Task completion, Perspective taking is composed by 8 items for Adaptive responsiveness and Audience awareness, Social regulation has 16 items for Negotiation, Self evaluation, Transactive memory and Responsibility initiative in the data file. The scale applies 7-point Likert-type questions where 1 means Does not describe me at all, 7 means Completely desribes me.</p>
Experiences Applying Lean R&D in Industry-Academia Collaboration Projects
<p>Supplementary materials of the paper Experiences Applying Lean R&D in Industry-Academia Collaboration Projects</p>
Collaborative Program Comprehension based on Augmented Reality (Evaluation Results of Master's Thesis)
<p>The dataset contains feedback generated through a survey for an augmented reality approach in the ExplorViz project.</p> <p>The dataset includes the results for a pilot study with two probands and the results for a case study with 20 probands.</p>
Fig. 6 in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 6. Number of fishers and of syngnathids captured for ornamental purposes in Salvador, Bahia, Brazil, from January/ 1997 to June/2005.
Fig. 5 in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 5. Number of days of collection and number of fishers involved in the syngnathid fishery in Salvador, Bahia, Brazil, from January/1997 to June/2005.
Fig. 1 in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 1. Map showing the Baía de Todos os Santos, the main area of collection of live syngnathids in Salvador, Bahia State, NE Brazil.
Fig. 3. a in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 3. a) Specimen of Micrognathus sp., Baía de Todos os Santos, Bahia State, Brazil. b) Specimen of Cosmocampus albirostris, Baía de Todos os Santos, Bahia State, Brazil. Photos: Cláudio L. S. Sampaio.
Fig. 4 in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 4. Hooka-diver using hand-nets to collect marine ornamental fishes. Baía de Todos os Santos, Bahia State, Brazil. Photo: Leo Dutra.
The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document v1 Released Data Products
<p>This tarball includes software and data products associated with the DESC Science Requirements Document (SRD) v1. See the "Executive Summary and User Guide" in the enclosed PDF of the DESC SRD for instructions on how to use and cite those products. The DESC SRD is described on <a href="https://arxiv.org/abs/1809.01669">arXiv</a> as follows:</p> <p>The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. The Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment.</p>
Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts" - Umbrella Review
<p>Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts"</p> <p>Umbrella review - First review</p> <p>The objective of this umbrella review is to check that there are no reviews in the defined period from 2000 to 2024 that respond to the objective of this research</p> <p> </p>
Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts" - Systematic Literature Review
<p>Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts" </p> <p>Second review</p> <p> </p> <p>This is the dataset for the full systematic literature review</p>
ipaast - agrivation collaboration EMI survey Manor Farm Field 70
<p>These data were collected as part of the ipaast-Agrivation collaboration to develop survey workflows that produce data compatible with common applications across archaeological, agricultural, and environmental domains. The project is described in the report at: <a href="http://ipaast-czo.glasgow.ac.uk/index.php/ipaast-agrivation-ltd-collaboration/">https://ipaast-czo.glasgow.ac.uk/index.php/ipaast-agrivation-ltd-collaboration/</a>. The ipaast project is funded by the British Academy Award KF5210407.</p>
Proactive Conflict Detection for Collaborative Model-driven Software Engineering (Evaluation Data)
<p>Results of the evaluation for the paper "Proactive Conflict Detection for Collaborative Model-driven Software Engineering"</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>
Cambridge and collaborators butterfly wing collection batch 10
<p>Cambridge and collaborators butterfly wing collection batch 10</p> <p>Gabriela Montejo-Kovacevich; Camilo Salazar; Marianne Elias; Stephen Montgomery; Eva Wiltshire; Imogen Gavins; Ian Warren; Owen MacMillan; Chris Jiggins; </p> <p>EN:</p> <p>This upload contains photographs taken by Eva Wiltshire and Imogen Gavins in the Butterfly Genetics Group at the University of Cambridge from the 10th October 2017 until the 20th March 2019. </p> <p>ID range:</p> <p>CS00482-CS005166</p> <p>MAC001239-MAC004461</p> <p>CAM016520,</p> <p>CAM041807-CAM041912</p> <p>CAM020840</p> <p>CAM120424-CAM120740</p> <p>SHM000014-SHM000261</p> <p> </p> <p>Nomenclature</p> <ul> <li>CSXXXXXX : unit ID corresponding to individual samples</li> <li>_v _d: ventral or dorsal</li> </ul> <p>Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (click <a href="https://heliconius.ecdb.io/default.aspx">here for the database</a>, and <a href="http://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">here for FAQ</a>)</p> <p>Please contact Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk), Gabriela Montejo-Kovacevich (mgm49[at]cam.ac.uk) or Ian Warren (iaw22[at]cam.ac.uk) for requests.</p> <p> </p> <p>------------------------------------------------------</p> <p>ES:</p> <p>Este repositorio contiene fotografías tomadas por Eva Whiltshire y Imogen Gavins en el Butterfly Genetics Group de la Universidad de Cambridge desde el 10 de octubre de 2017 hasta el 10 de marzo de 2019.</p> <p>Rango de individuos:</p> <p>CS00482-CS005166</p> <p>MAC001239-MAC004461</p> <p>CAM016520,</p> <p>CAM041807-CAM041912</p> <p>CAM020840</p> <p>CAM120424-CAM120740</p> <p>SHM000014-SHM000261</p> <p> </p> <p>Nomenclatura</p> <ul> <li>CSXXXXXX: ID de unidad correspondiente a muestras individuales</li> <li>_v _d: ventral o dorsal</li> </ul> <p>Puede encontrar información sobre muestras individuales de Butterfly Genetics Group Collection en la base de datos pública Earthcape (haga clic <a href="https://heliconius.ecdb.io/default.aspx">aquí para la base de datos</a>, y <a href="http://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">aquí para preguntas frecuentes</a>)</p> <p>Por favor, póngase en contacto con Chris Jiggins (c.jiggins [arroba] zoo.cam.ac.uk), Gabriela Montejo-Kovacevich (mgm49 [arroba] cam.ac.uk) o Ian Warren (iaw22 [arroba] cam.ac.uk) con sus preguntas o peticiones.</p>
Collaborating to Heal Addiction and Mental Health in Primary Care
ClinicalTrials.gov study NCT04600414. IPD Sharing: YES. Countries: 1. Publications: 1.
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.
Coastal SEES Collaborative Research: Non-Market Value Meta-Data on Willingness to Pay for Coastal Marsh Habitat Change
These data represent a meta-dataset of observations on per household economic value - represented by per household willingness-to-pay (WTP) - for improvements in coastal marsh habitat, drawn from stated-preference studies in the research literature. The metadata allow estimation of benefit transfer functions via meta-regression modeling. Within these econometric functions, the dependent variable is a comparable estimate of economic value (e.g., WTP) drawn from extant primary valuation studies. Independent variables represent observable factors hypothesized to explain variation in this value measure across observations. These functions can be used to produce out-of-sample predictions of WTP for coastal marsh habitat improvements at sites for which no primary valuation studies have been conducted. They can also be used to understand the factors associated with systematic variations in marsh habitat values across different sites and studies. These data are described in Vedogbeton, H. and R.J. Johnston. 2020. Commodity Consistent Meta-Analysis of Wetland Values: An Illustration for Coastal Marsh Habitat. Environmental and Resource Economics 75(4), 835-865, and allow replication of the results presented therein. The metadata are extracted from primary studies that estimate total (use and nonuse) per household WTP for changes in the quantity or quality of coastal marsh wildlife habitats or their services, in US and Canada. These studies were identified via a systematic review of the literature. The metadata combine information provided by these primary non-market valuation studies with publicly available external data extracted from sources such as the US Census, US National Historical GIS (https://www.nhgis.org/), and US Fish and Wildlife Service National Wetlands Inventory (https://www.fws.gov/wetlands/Data/Mapper.html). Studies included in the metadata are restricted to those that estimate total per household WTP for coastal wetland habitat changes using generally acce
Collaborative Architecture, Urbanism, and Sustainability Web Archive (CAUSEWAY) collection derivatives
<p>Web archive derivatives of the <a href="https://archive-it.org/collections/4638">Collaborative Architecture, Urbanism, and Sustainability Web Archive (CAUSEWAY)</a> collection from the <a href="https://archive-it.org/home/IvyPlus">Ivy Plus Libraries Confederation</a>. The derivatives were created with the <a href="https://github.com/archivesunleashed/aut/">Archives Unleashed Toolkit</a> and <a href="https://cloud.archivesunleashed.org/">Archives Unleashed Cloud</a>.</p> <p>The <strong>ivy-4638-parquet.tar.gz</strong> derivatives are in the <a href="https://parquet.apache.org/">Apache Parquet format</a>, which is a <a href="http://en.wikipedia.org/wiki/Column-oriented_DBMS">columnar storage</a> format. These derivatives are generally small enough to work with on your local machine, and can be easily converted to Pandas DataFrames. See <a href="https://github.com/archivesunleashed/notebooks/blob/master/datathon-nyc/parquet_pandas_stonewall.ipynb">this</a> notebook for examples.</p> <p><strong>Domains</strong></p> <pre><code class="language-java">.webpages().groupBy(ExtractDomainDF($"url").alias("url")).count().sort($"count".desc)</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>domain</li> <li>count</li> </ul> <p><strong>Web Pages</strong></p> <pre><code class="language-java">.webpages().select($"crawl_date", $"url", $"mime_type_web_server", $"mime_type_tika", RemoveHTMLDF(RemoveHTTPHeaderDF(($"content"))).alias("content"))</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>url</li> <li>mime_type_web_server</li> <li>mime_type_tika</li> <li>content</li> </ul> <p><strong>Web Graph</strong></p> <pre><code class="language-java">.webgraph()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>crawl_date</li> <li>src</li> <li>dest</li> <li>anchor</li> </ul> <p><strong>Image Links</strong></p> <pre><code class="language-java">.imageLinks()</code></pre> <p>Produces a DataFrame with the following columns:</p> <ul> <li>src</li> <li>image_url</li> </ul> <p><a href="https://github.com/archivesunleashed/aut-docs/blob/master/current/binary-analysis.md#binary-analysis"><strong>Binary Analysis</strong></a></p> <ul> <li>Audio</li> <li>Images</li> <li>Presentation program files</li> <li>Spreadsheets</li> <li>Text files</li> <li>Word processor files<br> </li> </ul> <p>The <strong>ivy-4638-auk.tar.gz </strong>derivatives<strong> </strong>are the <a href="https://cloud.archivesunleashed.org/derivatives">standard set of web archive derivatives</a> produced by the Archives Unleashed Cloud.</p> <ul> <li><strong>Gephi </strong>file, which can be loaded into <a href="https://gephi.org/">Gephi</a>. It will have basic characteristics already computed and a basic layout.</li> <li><strong>Raw Network</strong> file, which can also be loaded into <a href="https://gephi.org/">Gephi</a>. You will have to use that network program to lay it out yourself.</li> <li><strong>Full text</strong> file. In it, each website within the web archive collection will have its full text presented on one line, along with information around when it was crawled, the name of the domain, and the full URL of the content.</li> <li><strong>Domains count</strong> file. A text file containing the frequency count of domains captured within your web archive.</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.