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1,146 results for “collaboration;”
Data from: International authorship and collaboration across bioRxiv preprints
<p>Data and supplementary tables for <a href="https://doi.org/10.7554/eLife.58496">"International authorship and collaboration across bioRxiv preprints,"</a> a paper first posted to <a href="https://doi.org/10.1101/2020.04.25.060756">bioRxiv</a> and now published in <em>eLife</em>.</p> <ul> <li><strong>"reproduce.md"</strong> includes all R code used to generate figures and perform analyses described in the paper.</li> <li><strong>"biorxiv_countries.postgres.backup"</strong> is a database snapshot that can be loaded into a PostgreSQL database to access all data collected and used in the study.</li> <li><strong>"schema.pdf"</strong> describes each field in each table of the database.</li> <li><strong>"manual_edits.sql"</strong> describes all corrections made to the automated inference of the country-level affiliations inferred for all authors.</li> <li><strong>"affiliation_corrections.csv"</strong> lists every unique affiliation string that was re-categorized after institutional corrections. The consequences of the corrections described in "manual_edits.sql."</li> <li><strong>"institution_corrections_summary.csv"</strong> summarizes "affiliation_corrections.csv" by listing each "before" and "after" correction one time. It is important to note that each before/after pair does not necessarily indicate that <em>every</em> affiliation string from the "before" institution was reassigned to the "after" institution, just that at least one affiliation string was switched from one to the other. <ul> <li>Note that the final two "corrections" files describe steps taken to correct the institution-level associations between authors and countries. The final set of corrections assigned authors to countries <strong>using heuristics that did not take institution-level accuracy into account</strong>.</li> </ul> </li> </ul> <p><strong>Version history:</strong></p> <ul> <li><strong>1.0.0: </strong>New files uploaded reflecting substantial corrections to the data, mostly linked to classification of authors and preprints previously without a country classification. (26 Jun 2020)</li> <li><strong>0.2.1:</strong> Added "schema.pdf" file, previously only in the manuscript.</li> <li><strong>0.2.0:</strong> Added new files "affiliation_corrections.csv" and "institution_corrections_summary.csv"</li> <li><strong>0.1.1: </strong>Database snapshot added.</li> <li><strong>0.1.0: </strong>First version with supplementary tables added.</li> </ul>
Sustainability challenges and collaboration in the global clothing industry
<p>This dataset contains a social network of collaborative relationships among organizations working to address sustainability challenges in the global clothing industry. The dataset consists of a square matrix containing 1,455 undirected ties among 455 organizations, and a list of organizational attributes. Attribute information includes: 1) the type of organization – keystone actor, clothing company, retailer, manufacturer, trade association, multi-stakeholder platform, NGO, scientific research organization, government institution, and other; and 2) the sustainability challenges addressed by each organization – hazardous chemicals, waste reduction, energy consumption, air emissions, land use, recycling, wastewater, water conservation, and socioeconomic wellbeing. The data were gathered from corporate sustainability reports and related information found on the organizations’ websites.</p>
2x6 - a collaborative project in generative literature and translation
<p>5<sup>th</sup> Project Presentation</p>
Distant Gatherings: A Text-Case for Digital Manuscript Collaborations
<p>The restrictions imposed by COVID19 have challenged all who work in academia, and particularly those who work with material sources like medieval manuscripts, to reconsider normal practices of research exchange and how they might transfer to the online environment. Rather than slavishly replicating previous in-person formats which are invariably less effective when staged virtually, we have the chance instead to think creatively about how to capitalize on the digital medium, to investigate why people already willingly dedicate so much of their time to computer-based forms of communication. By identifying and co-opting what makes some kinds of virtual exchange successful, we can inspire new forms of academic exchange that re-invent rather than mimic older forms of teaching, research, and learning.</p> <p> </p>
Wikidata: Persistent identifiers as the basis for multilingual and human-machine collaboration
<p>This repository hosts a video recording of a dry-run of the PIDapalooza 2021 <a href="https://www.pidapalooza.org/schedule">https://www.pidapalooza.org/schedule</a> session</p> <p><strong>"Wikidata: Persistent identifiers as the basis for multilingual and human-machine collaboration"</strong><strong> </strong></p> <p>taking place Thursday January 28, 2021 00:00 - 00:30 UTC on Stage 1, as per <a href="https://sched.co/gD2n">https://sched.co/gD2n</a> .</p> <p>The video is also available on YouTube via <a href="https://youtu.be/g5VCr--Q1Ig">https://youtu.be/g5VCr--Q1Ig</a> .</p> <p>See <a href="https://etherpad.wikimedia.org/p/zenodo.4253308">https://etherpad.wikimedia.org/p/zenodo.4253308</a> for notes and <a href="https://github.com/Daniel-Mietchen/events/blob/master/PIDapalooza-2021.md">https://github.com/Daniel-Mietchen/events/blob/master/PIDapalooza-2021.md</a> for background on the session.</p> <p>Resources demoed in the recording:</p> <ul> <li>Wikidata: <a href="https://www.wikidata.org/wiki/Q420330">https://www.wikidata.org/wiki/Q420330</a> - persistent identifier</li> <li>Scholia: <a href="https://scholia.toolforge.org/event/Q47486859">https://scholia.toolforge.org/event/Q47486859</a> - PIDapalooza 2018 </li> <li>Ordia: <a href="https://ordia.toolforge.org/">https://ordia.toolforge.org/</a> <ul> <li><a href="https://ordia.toolforge.org/text-to-languages">https://ordia.toolforge.org/text-to-languages</a> </li> <li><a href="https://ordia.toolforge.org/text-to-lexemes">https://ordia.toolforge.org/text-to-lexemes</a> <ul> <li><a href="https://www.wikidata.org/wiki/Lexeme:L407266">https://www.wikidata.org/wiki/Lexeme:L407266</a> - biocapacity</li> </ul> </li> </ul> </li> </ul> <ul> <li>Lingua Libre: <a href="https://lingualibre.org/">https://lingualibre.org/</a> <ul> <li><a href="https://w.wiki/wBL">https://w.wiki/wBL</a> - audio recording example</li> </ul> </li> <li>Sample content: <ul> <li><a href="https://doi.org/10.3389/fcosc.2020.615419">https://doi.org/10.3389/fcosc.2020.615419</a> - Underestimating the Challenges of Avoiding a Ghastly Future</li> <li><a href="https://en.wikipedia.org/wiki/Persistent_identifier">https://en.wikipedia.org/wiki/Persistent_identifier</a> </li> </ul> </li> <li>Testing: <a href="https://www.wikidata.org/wiki/Q13406268">https://www.wikidata.org/wiki/Q13406268</a> - Wikidata Sandbox 2</li> </ul> <p>Also relevant:</p> <ul> <li><a href="https://w.wiki/hmb">https://w.wiki/hmb</a> - Items with Disease Ontology ID and MeSH Descriptor ID and optional descriptions in multiple languages</li> <li><a href="https://en.wikiquote.org/wiki/Identity">https://en.wikiquote.org/wiki/Identity</a> - quotes around "identity"</li> </ul> <ul> </ul>
Data from: Genomic analysis and prediction within a US public collaborative winter wheat regional testing nursery
The development of inexpensive, whole-genome profiling enables a transition to allele-based breeding using genomic prediction models. These models consider alleles shared between lines to predict phenotypes and select new lines based on estimated breeding values. This approach can leverage highly-unbalanced datasets common to breeding programs. The Southern Regional Performance Nursery (SRPN) is a public nursery established by the USDA-ARS in 1931 to characterize performance and quality of near-release wheat varieties from breeding programs in the US Central Plains. New entries are submitted annually and can be reentered only once. The trial is grown at more than 30 locations each year and lines are evaluated for grain yield, disease resistance, and agronomic traits. Overall genetic gain is measured across years by including common check cultivars for comparison. We have generated whole-genome profiles via genotyping-by-sequencing for 939 SPRN entries dating back to 1992. We measured the diversity within the nursery and have explored its potential use as a GS training population. GS prediction models across years (average r= 0.33) outperformed year-to-year phenotypic correlation for yield (r=0.27) for a majority of the years evaluated, suggesting that genomic selection has the potential to outperform low heritability selection on yield in these highly variable environments. We also examined the predictability of programs using both program-specific and whole-set training populations. Generally, the predictability of a program was similar with both approaches. These results suggest that wheat breeding programs can collaboratively leverage the immense datasets that are generated from regional testing networks.
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>
Data from: Vegetation response to control of invasive Tamarix in southwestern US rivers: a collaborative study including 416 sites
Most studies assessing vegetation response following control of invasive Tamarix trees along southwestern U.S. rivers have been small in scale (e.g., river reach), or at a regional scale but with poor spatial-temporal replication, and most have not included testing the effects of a now widely-used biological control. We monitored plant composition following Tamarix control along hydrologic, soil and climatic gradients in 244 treated and 172 reference sites across six U.S. States. This represents the largest comprehensive assessment to date on the vegetation response to the four most common Tamarix control treatments. Biocontrol by a defoliating beetle (treatment #1) reduced the abundance of Tamarix less than active removal by mechanically using hand and chain-saws (#2), heavy machinery (#3) or burning (#4). Tamarix abundance also decreased with lower temperatures, higher precipitation, and follow-up treatments for Tamarix resprouting. Native cover generally increased over time in active Tamarix removal sites, however, the increases observed were small and was not consistently increased by active revegetation. Overall, native cover was correlated to permanent stream flow, lower grazing pressure, lower soil salinity and temperatures, and higher precipitation. Species diversity also increased where Tamarix was removed. However, Tamarix treatments, especially those generating the highest disturbance (burning and heavy machinery), also often promoted secondary invasions of exotic forbs. The abundance of hydrophytic species was much lower in treated than in reference sites, suggesting that management of southwestern U.S. rivers has focused too much on weed control, overlooking restoration of fluvial processes that provide habitat for hydrophytic and floodplain vegetation. These results can help inform future management of Tamarix-infested rivers to restore hydrogeomorphic processes, increase native biodiversity and reduce abundance of noxious species.
The Software Sustainability Institute's Collaborations Workshop 2015 (CW15) attendees computational tools word-cloud
<p>Word cloud representing the computational tools used by those attending the Software Sustainability Institute's Collaborations Workshop 2015 (CW15).</p> <p>For more information see www.software.ac.uk/cw15</p> <p>Please note there is an ERROR in the diagram for some reason wordle.net did not pickup 'R' in the dataset - http://dx.doi.org/10.5281/zenodo.19828 - i.e. the usage of R in research software in the people who attended CW15 is not represented in this diagram.</p>
A Collaborative Approach to Teaching Software Architecture (SIGCSE 2017 Appendix)
<p>Zip file containing the appendix of our paper "A Collaborative Approach to Teaching Software Architecture" at SIGCSE 2017.</p> <p>* Link to the blog post that we shared the very first version of our course: https://avandeursen.com/2013/12/30/teaching-software-architecture-with-github/</p> <p>* Examples of the 2016 rubrics: rubrics-2016.pdf</p> <p>* Full anwers to the 2016 edition: responses.xlsx</p> <p>* Full questionnaire: survey-p1.pdf to survey-p9.pdf</p> <p>* Editions of our book: https://delftswa.gitbooks.io/desosa2016/content/ (2016), https://delftswa.github.io/ (2015).</p>
Collaborative Reference Database as of 2017-02-17
<p><strong>Abstract</strong> (our paper)</p> <p><strong>Code</strong></p> <p>crd-reference-v2.pl:<br> mew.</p> <p><strong>Data</strong></p> <p>crd-reference-v2.txt.gz:<br> mew.</p> <p>crd-reference-v2-urls.txt.gz:<br> mew.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Sho Sato, Mitsuo Yoshida. Current Status of Reference Services in Japanese Libraries Analyzed with the Collaborative Reference Database. <em>Current Awareness (in Japanese)</em>. no.332, pp.xx-xx, 2017.<br> http://current.ndl.go.jp/ca_list</p> <p><strong>Note</strong></p> <p>The raw data is available in the following page.<br> http://crd.ndl.go.jp/jp/help/general/api.html</p>
Experimental Validation video for paper "The Critical Role of Effective Communication in Human-Robot Collaborative Assembly"
<p>Experimental Validation video for paper "The Critical Role of Effective Communication in Human-Robot Collaborative Assembly".</p><p>The video shows a collaborative manipulator executing a collaborative assembly job with the user using a natural vocal communication architecture. The experiment compares the proposed framework with the state-of-art interaction and highlight the differences.</p>
Models for Building Relationships and Refining Approaches for Collaborating with Indigenous Communities: Reporting on Five Years of Online Work with the Nuer.
<p>Models for Building Relationships and Refining Approaches for Collaborating with Indigenous Communities: Reporting on Five Years of Online Work with the Nuer.</p> <p>Presentation given by Tatiana Reid at the Language Documentation and Archiving conference on the 5. October 2022 at the Berlin-Brandenburg Academy of Sciences and Humanities.</p> <p>I report on my experience of working with the Nuer-speaking community. Nuer is a little studied West Nilotic language of South Sudan and Ethiopia. Over the past five years I have carried description, documentation and language development exclusively online through social media such as Messenger and WhatsApp, and more recently, via Zoom.</p> <p>I will highlight various aspects of this mode of work, showing what is possible to achieve working remotely and the practicalities of doing so. These include, but are not limited to: utilising google documents when working on recorded narratives with reference speakers; and collecting data using a hybrid mode where the researcher (online) and a community member (in-person) conduct interviews with speakers producing high quality recordings. I will also draw on my experience of outsourced documentation – where audio and video recordings are made by local organisations in East Africa. I will touch on the experience (together with the colleagues at the University of Surrey) of working towards literacy development. This work involved creating databases that the community members populated with data collected via our ‘Nuer Lexicon’ Facebook group; and running an online Nuer literacy development workshops attended by over 30 community members from South Sudan, Ethiopia and from around the world.</p> <p>I claim that the prerequisite for conducting remote work with a language community is the collaborative approach between the researchers and the speakers of the language, which places greater emphasis on the 1) ability of the community members to to carry out data collection and processing and 2) continuity. My experience shows that ELDP/ELAR’s advice for field linguists to strengthen capacity building within language communities is gaining even more relevance in the predominantly virtually connected world.</p>
Set of 4 597 baits designed in collaboration with RapidGenomics (Gainesville, Florida, USA) to capture the identified low‐ to single‐copy nuclear genes (LSCN).
<p>This dataset presents a set of 4,597 baits designed in collaboration with RapidGenomics (Gainesville, Florida, USA) to capture the identified low- to single-copy nuclear genes (LSCN) in our study. These baits were instrumental in our research on the evolutionary relationships of the Neotropical magnolias based on plastome and nuclear phylogenomics. The data provided are crucial for understanding the methodology and results of our study.</p>
Pl@ntNet-CrowdSWE: Pl@ntNet collaborative learning with South-Western-Europe dataset
<p>This repository contains the files for the Pl@ntNet South Western Europe (SWE) crowdsourced dataset.<br>It contains all species identification and user votes for observations made between 2017 and 2023 in the SWE flora.</p> <p>In total, more than 6 699 593 plant observations are labeld by 823 251 users between january 2017 and october 2023. In addition, 98 experts were selected to obtain ground truth values for 26 811 observations.</p> <p>The structure of the dataset is described below, and a `readme.md` file is available in the record.</p> <h2>In short directory structure</h2> <pre><code>Pl@ntNet SWE dataset ├── answers │ ├── answers.json │ └── ground_truth.txt ├── converters │ ├── tasks.json │ └── classes.json └── aggregation ├── authors.txt ├── ai_classes.json ├── ai_answers.json ├── ai_scores.json └── k-southwestern-europe.json</code></pre> <h2>Crowdsourced data</h2> <p>In the <code>answers</code> folder are located the crowdsourced answers and the associated ground truths.<br>The crowdsourced answers are stored in the <code>answers.json</code> file. It gathers more than 6 million tasks with answers from 823 251 users. It is formatted as a json entry with levels representing the observation ID, the users, and their associated vote for the species label.</p> <pre><code>{ obsID: {userID: vote, userID2: vote,...}, ... }</code></pre> <p>A list of 98 experts was created to gather a partial ground truth in the <code>ground_truth.txt</code> file.<br>Each row represents an observation and the associated class label is the current considered ground truth.<br>This file lets us compute several performance metrics such as the accuracy of the label aggregation.</p> <h2>Converters</h2> <p>In the <code>converters</code> folder, you can find the converters to obtain the Pl@ntNet official observation numbers (the last part of the URL <code>https://identify.plantnet.org/fr/k-world-flora/observations/<id></code>) from the obsID used in <code>answers.json</code>. This is stored in the <code>tasks.json</code> file.<br>A similar dictionary converts the species proposed by users to a single label in {0, 1, 2, ...}.<br>This mapping is stored in <code>classes.json</code>.</p> <p>As plant species can also have synonyms, we release the two files used to clean the user answers. The <code>species.json</code> file contains a list with all the accepted species determinations from the World Checklist of Vascular Plants.<br>Then, we focused on the SWE flora and replaced synonyms with the underlying species using the <code>k-southwestern-europe.json</code> checklist by Plants Of the World Online (POWO) by Kew’s Royal Botanical Garden. This checklist is written as follows:</p> <pre><code>[ { "species": species name, "synonyms": [ synonym1, synonym2, ... ] }, ... ]</code></pre> <h2>Files to run the Pl@ntNet label aggregation strategy</h2> <p><br>To run the Pl@ntNet label aggregation strategy available in the <a href="https://peerannot.github.io/" target="_blank" rel="noopener">peerannot library</a>, several other pieces of information are needed and located in the <code>aggregation</code> folder.</p> <p>- First, we need to know for each task which user was the author (if they proposed an initial species determination).<br>This information is stored in the <code>authors.txt</code> dataset, where each row is the obsID and the value is the userID of the author. If the author did not propose any species, this identification is set to -1.</p> <p>- Then, to run the label aggregation strategies taking into account the AI vote, we extend the `classes.json` file with the AI-predicted classes into the <code>ai_classes.json</code> file. Each species is associated with a number, including newly introduced species by the AI.<br>- Then, we need the AI predictions. The AI answers are stored in the <code>ai_answers.json</code> file where each key is the obsID and each value represents the class predicted by the AI. Synonyms were also removed using the <code>k-southwestern-europe.json</code> file.<br>- Finally, for strategies taking into account the prediction score, we release the <code>ai_scores.json</code> file, where each key is the obsID and each value is the probability given for the predicted class.</p>
"WLRI-HRC" - A Dataset of Infrared Images for Human-Robot Collaboration in Manufacturing Environment
<p>This repository contains all needed data sets for the contribution in Journal of Sensors and Sensor Systems "Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset WLRI-HRC and evaluation of convolutional neural networks". You may use this data for scientific, non-commercial purposes, provided that you give credit to the owners when publishing any work based on this data.</p> <p><strong>DOI: 10.5194/jsss-14-37-2025</strong></p> <p> </p> <p><strong>or as BibTex:</strong></p> <div> <div>@article{sume_enhancing_2025,</div> <div> title = {Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset {WLRI}-{HRC} and evaluation of convolutional neural networks},</div> <div> volume = {14},</div> <div> issn = {2194-8771},</div> <div> shorttitle = {Enhancing human–robot collaboration with thermal images and deep neural networks},</div> <div> url = {https://jsss.copernicus.org/articles/14/37/2025/},</div> <div> doi = {10.5194/jsss-14-37-2025},</div> <div> abstract = {This contribution introduces the use of convolutional neural networks to detect humans and collaborative robots (cobots) in human–robot collaboration (HRC) workspaces based on their thermal radiation fingerprint. The unique data acquisition includes an infrared camera, two cobots, and up to two persons walking and interacting with the cobots in real industrial settings. The dataset also includes different thermal distortions from other heat sources. In contrast to data from the public environment, this data collection addresses the challenges of indoor manufacturing, such as heat distortions from the environment, and allows for it to be applicable in indoor manufacturing. The Work-Life Robotics Institute HRC (WLRI-HRC) dataset contains 6485 images with over 20 000 instances to detect. In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). The results indicate promising results with the best mean average precision at an intersection over union (IoU) of 50 (mAP50) value achieved by YOLOv9s (99.4 \%), the best mAP50-95 value achieved by YOLOv9s and YOLOv8m (90.2 \%), and the fastest prediction time of 2.2 ms achieved by the YOLOv10n model. Further differences in detection precision and time between the one-stage and multi-stage methods are discussed. Finally, this paper examines the possibility of the Clever Hans phenomenon to verify the validity of the training data and the models’ prediction capabilities.},</div> <div> language = {English},</div> <div> number = {1},</div> <div> journal = {Journal of Sensors and Sensor Systems},</div> <div> author = {Süme, Sinan and Ponomarjova, Katrin-Misel and Wendt, Thomas M. and Rupitsch, Stefan J.},</div> <div> month = feb,</div> <div> year = {2025},</div> <div> note = {Publisher: Copernicus GmbH},</div> <div> pages = {37--46},</div> <div>}</div> </div>
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>
On the determinants of low-carbon innovations: Roles of technological diversification and international collaborations
<p>This data describes the role of technological diversification and international collaborations, and how they affect the intensity of low-carbon innovations. The dataset is made up of a global panel dataset consisting of patents and macroeconomic data for 90 countries, covering a period of 32 years. It also contains indices for technological diversifications and collaborations.</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 6
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. The green highlight indicates an SCR concurred with the teacher report: "When students told me that after increasing time in the "improving phase" they could not continue editing their improved answer.".</p>
Estimating Orchestration Load in Collaborative Learning Situations Using EDA - Activity 2
<p>Skin conductivity of the teacher while orchestrating a Pyramid activity. During this activity, the teacher reported: "I noticed that the scenario for the task that I shared with student was not the one I planned (I have several ... and was confused with the one I picked) so I had to read the scenario as well .. while students where completing the Pyramid activity. In any case I know all scenarios very well and was quick for me to remember it.".</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.