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1,146 results for “Collaborative”
A Dataset of Science-Policy Collaborations from the International Geneva Ecosystem
<p><strong>This dataset contains data on the characteristics of science-policy collaborations (SPCs) as well as on the perceived value of the Geneva Science-Policy Interface as an intermediary actor facilitating collaborative processes. </strong></p> <p>----</p> <p>In 2020, the Geneva Science-Policy Interface deployed a call for projects explicitly targeting SPCs. The purpose of the call for projects is to motivate policy actors and scientists to join their efforts and propose collaboration projects, select the most promising projects, and provide them with financial and strategic support. Projects are eligible only if both academia and policy actors are represented, and if policy actors are part of the International Geneva ecosystem.</p> <p>The call for projects is a structured and standardised process that allows for the collection of identical data across a sample of SPCs, thus enabling comparative analyses. Data is collected through crowdsourcing, coupling challenge-based data collection and survey data. The Geneva Science-Policy Interface uses the call for projects to collect data on characteristics of SPCs as well as on the perceived value of the GSPI as an intermediary actor facilitating collaborative processes. </p>
Scrubbed data on Wikipedians in Residence in Libraries based on the Mapping GLAM-Wiki collaborations
<p><strong>Source</strong>: </p> <p><a href="https://docs.google.com/spreadsheets/d/1UVN-T19g5tE7cONFCkiBkquBJiecoU-w4Rb6F-qR6II/edit#gid=791098161">GLAM-Wiki Activities Mapping - Community Review and Feedback Sheet</a></p> <p><strong>Source's context: </strong></p> <p>Gill, Satdeep. ‘Mapping GLAM-Wiki Collaborations’. <em>This Month in GLAM</em>, March 2020. <a href="https://outreach.wikimedia.org/wiki/GLAM/Newsletter/March_2020/Contents/WMF_GLAM_report">https://outreach.wikimedia.org/wiki/GLAM/Newsletter/March_2020/Contents/WMF_GLAM_report</a>.</p> <p> </p> <p>Data was scrubbed using <a href="https://openrefine.org/download.html">OpenRefine 3.4.1</a></p> <p>The original spreadsheet had only partial information in many fields and it is a work in progress (for more see the "source's context" link above).</p> <p>I have only manually double checked those rows in which the “Primary partner institution” contains the stem “libr*” or “bibli*. The following eight rows where modified and “Library” was added in the “Type of institution” column: Municipal Library, Patiala, BRAU Library of the University of Naples Federico II, Library and Archives Canada, Eötvös Loránd University Library and Archives, National Health Library and Knowledge Service, National Doctors Training and Planning, Daniel Cosío Villegas Library, Cantonal and University Library, Nationaal Archief | Koninklijke Bibliotheek; and “Library association” was added to the Online Computer Library Center (OCLC) entry. All changes can be seen in the <a href="https://zenodo.org/api/files/ae475409-a2a8-4e51-98e2-68b3090d0fd0/WiRs-in-libraries_MGW_scrubbing-changes.json">WiRs-in-libraries_MGW_scrubbing-changes.json</a> file in this release.</p>
Dataset - Survey results - Applying Model-based Requirements Engineering in Three Large European Collaborative Projects
<p>This dataset and its associated report contain the results of an online survey on using a model-based requirements engineering approach in three European projects. </p>
An open, collaborative, and scholarly digital edition of Anṭūn al-Jumayyil's monthly journal "al-Zuhūr" (Cairo, 1910--1913)
<p>This release has been necessitated by the need for documenting changes and improvements that took place over the last two years:</p> <ol> <li>New sets of facsimiles have been added using IIIF</li> <li>The TEI Boilerplate has been updated to the latest version</li> <li>The mark-up of entities and their links to our authority files have been much improved</li> </ol>
Global scientific research commons under the Nagoya Protocol: Towards a collaborative economy model for the sharing of basic research assets
<p>This paper aims to get a better understanding of the motivational and transaction cost features of<br /> building global scientific research commons, with a view to contributing to the debate on the design of<br /> appropriate policy measures under the recently adopted Nagoya Protocol. For this purpose, the paper<br /> analyses the results of a world-wide survey of managers and users of microbial culture collections, which<br /> focused on the role of social and internalized motivations, organizational networks and external<br /> incentives in promoting the public availability of upstream research assets. Overall, the study confirms<br /> the hypotheses of the social production model of information and shareable goods, but it also shows the<br /> need to complete this model. For the sharing of materials, the underlying collaborative economy in<br /> excess capacity plays a key role in addition to the social production, while for data, competitive pressures<br /> amongst scientists tend to play a bigger role.</p>
Open Research Skills Workshops - GitHub collaborative workflows
<p>This is the fourth workshop on GitHub collaborative workflows in a series of workshop about Open Research Skills.</p><p>This workshop covers:</p><p>- Introduction to version control</p><p>- How to fork a repository</p><p>- Forking exercises</p><p>- How to work in a team and create and merge branches</p><p>- Branching exercises</p><p><strong>List of training workshops in Open Research Skills:</strong></p><ul><li>24th February 2023 - Open access publishing</li><li>24th March 2023 - Using repositories</li><li>21st April 2023 - GitHub basics</li><li><strong>28th April 2023 - GitHub collaborative workflows</strong></li><li>26th May 2023 - Standard vocabularies and ontologies</li><li>30th June 2023 - FAIR data</li></ul><p><strong>Project overview:</strong></p><p>Our project aims to upskill participants in open research skills to increase the quality and reusability of phytolith research and related disciplines such as archaeology, palaeosciences and plant sciences. We will run six hands-on training workshops on open access publishing and research outputs, using repositories, ontologies and standard vocabularies, implementation of FAIR Guidelines for phytolith research, and two workshops on Github basic and advanced skills. The materials from all workshops will be archived as self-study courses on our website (<a href="https://open-phytoliths.netlify.app/">https://open-phytoliths.netlify.app/</a>). We will also provide translation during workshops and training materials into multiple languages. </p><p>Github is a collaborative, project management tool used to run reproducible research projects with version control. In these videos, you will learn how to use version control, how to branch and fork a repository, how to pull a request and how to collaborate as part of a team on GitHub.</p>
Video 2 - Open Science to enable collaboration.
<p><span>Interview with Maria Bellantone, PhD in materials science; Bregt Saenen, Senior Policy Officer for Open Science at Science Europe; Pilar Rica Castro, Senior project officer for Open Access, Spanish Foundation for Science & Technology; and Iryna Kuchma, Open Access Programme Manager for EIFL on the importance of policies supporting Open Science infrastructures as a tool for implementing and promoting Open Science.</span></p> <p><span>Open Science fosters inter- and transdisciplinarity. This requires open data infrastructure and interoperability. Open Science policies can be a tool for changing how research is performed and assessed. The Spanish Foundation for Science and Technology funds such infrastructures at a local level. This provides digital infrastructures and expertise to make it possible to share interoperable data. This interoperability also makes it possible for infrastructures to collaborate. Funders have a responsibility to ensure that the research they fund makes an impact, and Open Science infrastructure increases the impact potential of research.</span></p>
Human-AI Collaboration: A tool to enable AI model generation with human-in-the-loop
<p>Human-AI collaboration enables domain experts to contribute their expertise with the goal of enhancing the knowledge learned by the AI models from the patterns in the data. This enables the integration of domain-specific knowledge to enrich the data for further improvement of the models through retraining. The human-AI collaboration is composed of multiple sub-components and interfaces that enables communication with external systems such as data sources, model repositories, machine configurations and decision support systems.</p> <p>Human-AI Collaboration component is developed using Python programming language. The frontend is developed using Streamlit1. The backend is developed using python and the API is implemented using FastAPI2. The choice of the programming language was made because of its wide usage and vast user base. The frameworks Streamlit and FastAPI are chosen because of the rich features for functionality and documentation as well as suitability for data analysis tasks. The applications are packaged as docker images for deployment. The application runs as a web application served by nginx for reverseproxying and users can access it via client applications such as web browsers or REST clients like Postman.</p>
Student discourse in small-group collaborative contexts in real-world higher education
<p>This dataset contains anonymised student discourse in small-group collaborative contexts in real-world higher education settings.</p> <p>There are 28 sessions, composed of 10799 utterances.</p> <p>The columns consist of 13 columns:</p> <ul> <li>session: the session number</li> <li>start: the starting time of the utterance</li> <li>end: the ending time of the utterance</li> <li>speaker: anonymised speaker name</li> <li>content: the content of the utterance</li> <li>ep: the episode number of the utterance. An episode refers to a single topic of discussion with multiple utterances.</li> <li>C: a binary value indicating the presence or absence of a cognitive challenge, where 0 means no challenge, and 1 means there is a cognitive challenge.</li> <li>E: a binary value indicating the presence or absence of an emotional/motivational challenge, where 0 means no challenge, and 1 means there is an emotional/motivational challenge.</li> <li>M: a binary value indicating the presence or absence of a metacognitive challenge, where 0 means no challenge, and 1 means there is a metacognitive challenge.</li> <li>T: a binary value indicating the presence or absence of a technical/other challenge, where 0 means no challenge, and 1 means there is a technical/other challenge.</li> <li>TA: a binary value indicating the presence or absence of the regulatory process "task analysis," where 0 means no regulation and 1 means task analysis is present.</li> <li>MC: a binary value indicating the presence or absence of the regulatory process "monitoring/control," where 0 means no regulation and 1 means monitoring/control is present.</li> <li>RA: a binary value indicating the presence or absence of the regulatory process "reflection/adaptation," where 0 means no regulation and 1 means reflection/adaptation is present.</li> </ul> <p>This annotated dataset was used in the following paper to model challenge moments.</p> <p>For more details on how the dataset was generated, please refer to the paper.</p> <div> <div>Suraworachet, W., Seon, J., & Cukurova, M. (2024). Predicting challenge moments from students’ discourse: A comparison of GPT-4 to two traditional natural language processing approaches. <em>Proceedings of the 14th Learning Analytics and Knowledge Conference</em>, 473–485. <a href="https://doi.org/10.1145/3636555.3636905">https://doi.org/10.1145/3636555.3636905</a></div> <div> </div> </div>
Collaborative UAV-based Orthomosaic of Isimila, Tanzania
<p>An UAV-based orthomosaic of the Middle-Pleistocene archaeological site of Isimila, Tanzania. This version is for the expressed purpose of fostering collaboration of research at the site. GPS coordinates of excavation trenches, surface finds, and other points of interest submitted by any researchers working at the site will be plotted on this regularly updated map. <br> <br> Instructions for submission and contact are available here:<br> <br> https://docs.google.com/document/d/12O3TN7-NuqaszEsiHJ7YUBSztQb1hPfOmaFDy8Zw8BU/edit?usp=sharing</p>
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>
SCoRe-LFC: Platform data on crowd collaboration in higher education
<p>SCoRe (short for Student Crowd Research) was a joint research project between the Universities of Bremen (UB), Hamburg (UHH) and Kiel (CAU), the Macromedia University of Applied Sciences (HMM) and the Ghostthinker GmbH (GT). The overall aim of the project was to develop a digital learning and research environment as well as didactic scenarios that foster collaborative processes of research-based learning in large groups of students (crowd). The main subject area was research for sustainable development. Towards this end, the project consortium drew on the partners’ expertise on advanced video-technologies (HHM), virtual collaboration in interdisciplinary and largescale groups (CAU), research-based learning (UHH) and education and research for sustainable development (UB). To achieve its goals, the project adopted a design-based research approach. The Project started in Oct. 2018 and was funded for 3.5 years by the Federal Ministry of Education and Research (BMBF) in a funding scheme on digital higher education.</p> <p>The work in the department of media-pedagogy and educational computer sciences at Kiel University was focused on the sub-project „SCoRe - learning and researching in the crowd“. The sub-project was aimed at the development, implementation and evaluation of pedagogical and organizational measures for the seeding, coordination and orchestration of collaborative research and learning processes in crowd scenarios. Particular emphasis was placed on crowd-specific characteristics of productive knowledge work in large and interdisciplinary groups.</p> <p>This dataset contains interaction data as well as textual content data. As ongoing development of the software platform led to a continuous integration of new features into the platform itself as well as changes to the data collection functions, making this an evolving dataset. Some inconsistencies exist due to software bugs.</p> <p><strong><a href="https://scorelfc.github.io/gestaltungsbericht3/img/datastructure.png">Platform data structure diagram</a> </strong></p> <p>Further Readings to gain an understanding of the platform and its interaction posbilities (in german):</p> <p><a href="https://scorelfc.github.io/gestaltungsbericht2">Design Report Prototype 2</a></p> <p><a href="https://scorelfc.github.io/gestaltungsbericht3">Design Report Prototype 3</a></p> <p> </p> <p><strong>Contained Files</strong></p> <table> <tbody> <tr> <td> <p><strong>Filename</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>annotations.csv</p> </td> <td> <p>Annotations (comment and/or drawings on the video) of video files</p> </td> </tr> <tr> <td> <p>content.csv</p> </td> <td> <p>Content of <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u03/">sections</a></p> </td> </tr> <tr> <td> <p>events.csv</p> </td> <td> <p>All events triggered by user interaction</p> </td> </tr> <tr> <td> <p>media.csv</p> </td> <td> <p>Uploaded <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u03/">images and videos</a></p> </td> </tr> <tr> <td> <p>messages.csv</p> </td> <td> <p><a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u08/">Chat messages</a></p> </td> </tr> <tr> <td> <p>sequences.csv</p> </td> <td> <p><a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u21/">Sequences</a> of video files</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Columns</strong></p> <p>(not all are present in each file. 0, “null” or “none” might mean not applicable)</p> <table> <tbody> <tr> <td> <p><strong>Column name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Format</strong></p> </td> </tr> <tr> <td> <p>Index (empty column name) </p> </td> <td> <p>unique identifier of the corresponding event in the original dataset</p> </td> <td> <p>UUID (int on rare occasions)</p> </td> </tr> <tr> <td> <p>Actor-Name</p> </td> <td> <p>Unique identifier of an actor – “MA” identifies project staff </p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Annotation-ID</p> </td> <td> <p>Unique identifier of an annotation</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Annotation-Text</p> </td> <td> <p>Label of an annotation</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Version-ID</p> </td> <td> <p>Unique identifier of a version of an auditable object (e.g. a section)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Version-Changelog</p> </td> <td> <p>Changelog message on saving a new version of a section</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Case-ID</p> </td> <td> <p>Unique identifier of a case (if applicable, coded by research team)</p> </td> <td> <p>String </p> </td> </tr> <tr> <td> <p>Media-Caption</p> </td> <td> <p>Title of a media file (image, video)</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Media-ID</p> </td> <td> <p>Unique identifier of a media file (image, video)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Media-Timestamp</p> </td> <td> <p>Timestamp in a video</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Message-ID</p> </td> <td> <p>Unique identifier of a chat message</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Message-Text</p> </td> <td> <p>Content of a chat-message</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Object-Type</p> </td> <td> <p>Type of an object an action refers to</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Project-ID</p> </td> <td> <p>Unique identifier of a project</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Research-Task-Type</p> </td> <td> <p>Type of research task (if applicable, coded by research team, see table below)</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Section-Content</p> </td> <td> <p>Content of a section (in a specific version) </p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Section-Outline-Level</p> </td> <td> <p>Outline level of a section (in a specific version)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-ID</p> </td> <td> <p>Unique identifier of a section</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-Index</p> </td> <td> <p>Position of a section in the project (in a specific version)</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-Status</p> </td> <td> <p>Status of a section</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Section-Title</p> </td> <td> <p>Title of a section (in a specific version)</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Sequence-Description</p> </td> <td> <p>Description of a video sequence</p> </td> <td> <p>string</p> </td> </tr> <tr> <td> <p>Sequence-Duration</p> </td> <td> <p>Length of a video sequence</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Sequence-ID</p> </td> <td> <p>Unique identifier of a sequence</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>Sequence-Timestamp</p> </td> <td> <p>Timestamp of the start of a sequence in a video</p> </td> <td> <p>int</p> </td> </tr> <tr> <td> <p>timestamp</p> </td> <td> <p>timestamp of an event</p> </td> <td> <p>datetime</p> </td> </tr> <tr> <td> <p>Verb</p> </td> <td> <p>Action type of an event (see table below)</p> </td> <td> <p>string</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Verbs</strong></p> <table> <tbody> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>canceled editing of</p> </td> <td> <p>Actor canceled editing of a section</p> </td> </tr> <tr> <td> <p>clicked</p> </td> <td> <p>Actor clicked a link</p> </td> </tr> <tr> <td> <p>collapsed</p> </td> <td> <p>Actor collapsed a section (hides its content form being viewed)</p> </td> </tr> <tr> <td> <p>compared versions of</p> </td> <td> <p>Actor compared two versions of a section</p> </td> </tr> <tr> <td> <p>created</p> </td> <td> <p>Actor created a new section, video sequence, video annotation, video playback command, project or news</p> </td> </tr> <tr> <td> <p>deleted</p> </td> <td> <p>Actor deleted a section, video sequence, video annotation, video playback command, project or news</p> </td> </tr> <tr> <td> <p>ended</p> </td> <td> <p>Actor played a video hitting its end</p> </td> </tr> <tr> <td> <p>expanded</p> </td> <td> <p>Actor expanded a collapsed section</p> </td> </tr> <tr> <td> <p>inserted</p> </td> <td> <p>Actor inserted a video comment (on occasions instead of created)</p> </td> </tr> <tr> <td> <p>left</p> </td> <td> <p>Actor left a context (e.g. a project, a chat window) by e.g. closing it using platform functions, changing a browser tab, etc.</p> </td> </tr> <tr> <td> <p>mentioned</p> </td> <td> <p>Actor mentioned another actor in a chat message</p> </td> </tr> <tr> <td> <p>opened</p> </td> <td> <p>Actor opened a context (e.g. a project, a chat window) by e.g. accessing it using platform functions or changing a browser tab</p> </td> </tr> <tr> <td> <p>paused</p> </td> <td> <p>Actor paused a video</p> </td> </tr> <tr> <td> <p>played</p> </td> <td> <p>Actor played a video</p> </td> </tr> <tr> <td> <p>read</p> </td> <td> <p>Actor read an activity message or news</p> </td> </tr> <tr> <td> <p>read all messages and activities of</p> </td> <td> <p>Actor used switch to mark all chat and activity messages read</p> </td> </tr> <tr> <td> <p>restored</p> </td> <td> <p>Actor restored a deleted section</p> </td> </tr> <tr> <td> <p>reverted</p> </td> <td> <p>Actor restored a deleted section</p> </td> </tr> <tr> <td> <p>reverted version of</p> </td> <td> <p>Actor reverted a section to an earlier version</p> </td> </tr> <tr> <td> <p>seeked</p> </td> <td> <p>Actor seeked on a video timeline</p> </td> </tr> <tr> <td> <p>sent</p> </td> <td> <p>Actor sent a chat message</p> </td> </tr> <tr> <td> <p>started editing of</p> </td> <td> <p>Actor started editing of a section</p> </td> </tr> <tr> <td> <p>switched</p> </td> <td> <p>Actor switched chat focus between project and section chat</p> </td> </tr> <tr> <td> <p>typed</p> </td> <td> <p>Actor typed into the chat</p> </td> </tr> <tr> <td> <p>updated</p> </td> <td> <p>Actor updated an existing section (changing content, heading, heading-depth or status), video sequence, video annotation, video playback command, project or news</p> </td> </tr> <tr> <td> <p>uploaded</p> </td> <td> <p>Actor uploaded an image or video</p> </td> </tr> <tr> <td> <p>viewed</p> </td> <td> <p>Actor viewed an entity (had it on screen for 5 seconds), e.g. a section or video comment</p> </td> </tr> <tr> <td> <p>viewed history of</p> </td> <td> <p>Actor viewed history of a section</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Project-ID</strong></p> <table> <tbody> <tr> <td> <p><strong>Project-ID</strong></p> </td> <td> <p><strong>Case-IDs</strong></p> </td> <td> <p><strong>Title</strong></p> </td> <td> <p> </p> <p><strong>Type</strong></p> </td> <td> <p><strong>Period</strong></p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>a1-a*</p> </td> <td> <p>Urbane Grünflächen</p> </td> <td> <p>Research project</p> </td> <td> <p>1.11.20-31.3.21</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>b1-b*</p> </td> <td> <p>Nachhaltiger Verkehr</p> </td> <td> <p>Research project</p> </td> <td> <p>1.11.20-31.3.21</p> </td> </tr> <tr> <td> <p>166</p> </td> <td> <p>c1-c*</p> </td> <td> <p>UGF - Urbane Grünflächen</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>168</p> </td> <td> <p> </p> </td> <td> <p>LGS - Foyer</p> </td> <td> <p>Onboarding of students in LGS Projects</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>188</p> </td> <td> <p> </p> </td> <td> <p>LGS - Reflexionsraum</p> </td> <td> <p>Reflection project for students in LGS Projects</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>207</p> </td> <td> <p> </p> </td> <td> <p>LGS - Nachhaltiger Konsum</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>210</p> </td> <td> <p> </p> </td> <td> <p>LGS - Bildungsangebote für nachhaltige Entwicklung</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>264</p> </td> <td> <p> </p> </td> <td> <p>LGS - Fahrradmobilität in Städten</p> </td> <td> <p>Research project</p> </td> <td> <p>1.4.21-30.09.21</p> </td> </tr> <tr> <td> <p>271</p> </td> <td> <p> </p> </td> <td> <p>Fahrradmobilität in Städten</p> </td> <td> <p>Research project</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>269</p> </td> <td> <p>e1-e*</p> </td> <td> <p>Kaufentscheidung vs. Nachhaltigkeit</p> </td> <td> <p>Research project</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>262</p> </td> <td> <p>d1-d*</p> </td> <td> <p>Urbane Grünflächen</p> </td> <td> <p>Research project</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>199</p> </td> <td> <p> </p> </td> <td> <p>Basiskurs</p> </td> <td> <p>Basic course for onboarding of students on the platform</p> </td> <td> <p>1.10.21-30.11.21</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p> </p> </td> <td> <p>Glossar</p> </td> <td> <p>Glossar of definitions</p> </td> <td> <p>persistent</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p> </p> </td> <td> <p>Erste Schritte</p> </td> <td> <p>How to start using score-docs platform</p> </td> <td> <p>persistent</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p> </p> </td> <td> <p>Testbereich</p> </td> <td> <p>Area for testing score-docs functionalities</p> </td> <td> <p>persistent</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p> </p> </td> <td> <p>Hilfestellungen</p> </td> <td> <p>Helpful links </p> </td> <td> <p>persistent</p> <p> </p> </td> </tr> </tbody> </table> <p>Other Project-IDs refer to personal assessment documents of individual students which are not included in content.csv</p> <p><strong>Research-Task-Type</strong></p> <table> <tbody> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Erheben</p> </td> <td> <p>Data collection</p> </td> </tr> <tr> <td> <p>Analysieren</p> </td> <td> <p>Case-specific data analysis and sensemaking</p> </td> </tr> <tr> <td> <p>Synthetisieren</p> </td> <td> <p>Cross-case data analysis and sensemaking</p> </td> </tr> <tr> <td> <p>Sonstiges</p> </td> <td> <p>Other, e.g. communication between participants</p> </td> </tr> </tbody> </table>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
High Payload Collaborative Robot - Joint States/Motor Current/TCP Force-Torque
<p>This dataset contains bag files, with data related to robot joint position, motor currents, robot tcp pose, robot tcp Force torque values etc. that were used for the design and development of a redundant collision detection for collisions with the robotic tool. There are also data with measurements from an external F/T sensor for the validation of the approach.</p>
Dataset for "AGU Publications updates authorship policy to foster better equity and transparency in global research collaboration"
<p>Dataset supporting "AGU Publications updates authorship policy to foster better equity and transparency in global research collaboration." </p> <p>This file provides summary data for new submissions for "Global Biogeochemical Cycles" (GBC) and "Journal of Geophysical Research: Biogeosciences" (JGR: Biogeo) from 2012 through 2023 including International Collaboration Status (whether more than one country was represented on the author list), Research4Life Author Status (whether any author was from a country on the Research4Life eligibility list [https://www.research4life.org/access/eligibility/]), and Research4Life Abstract Status (whether the submission abstract referenced a country on the Research4Life eligibility list). </p> <p>Summary data for all AGU journals combined are provided for years 2012 and 2023, including whether more than one country was represented by the author list and whether any author was from a country on the Research4Life eligibility list. </p> <p>Summary data are presented in compliance with AGU's Privacy Policy, https://www.agu.org/Privacy-Policy</p>
IAS-Lab Collaborative Draping HAR dataset
<h2>Description</h2> <p>This dataset contains movement data for several subjects performing actions related to human-robot collaboration in an industrial carbon fiber draping process, such as draping and collaborative transport of carbon fiber plies. The collected dataset has been used to train and evaluate skeleton-based Human Action Recognition (HAR) models developed to provide a simple and intuitive way for the human operator to interact with the robot, such as signaling start and stop of the process or requesting robot’s assistance with specific tasks (e.g., inspection of specific parts).</p> <h2><br>Actions of interest</h2> <p>The dataset includes gestures designed to provide a simple and intuitive way for the operator to interact with the robot (e.g., “OK/NEXT” and “POINT” actions), short duration actions related to the beginning and end of collaborative transport operations (e.g., “PICK” and “PLACE” actions), and long duration actions related to the draping process (e.g., “TRANSPORT” and “DRAPING” actions) or general movements of the operator (e.g., “REST” and “WALK”). <br>The dataset also includes an additional unknown class (“UNKWN”) which represents various random movements that the operator might make during the collaborative process but that do not correspond to any of the actions of interest.</p> <table> <tbody> <tr> <td><strong>Action ID</strong></td> <td><strong>Action Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>A001</td> <td>OK/NEXT</td> <td>Raise one arm to signal the robot to continue the draping process</td> </tr> <tr> <td>A002</td> <td>POINT</td> <td>Point at a desired location with a straight right arm to trigger inspection</td> </tr> <tr> <td>A003</td> <td>PICK</td> <td>Raise the ply to trigger the collaborative transport</td> </tr> <tr> <td>A004</td> <td>PLACE</td> <td>Place the ply on the mold</td> </tr> <tr> <td>A005</td> <td>TRANSPORT</td> <td>Collaborative transportation</td> </tr> <tr> <td>A006</td> <td>DRAPE</td> <td>Manual draping of a ply</td> </tr> <tr> <td>A007</td> <td>REST</td> <td>Resting position, mainly waiting for the robot to complete its task</td> </tr> <tr> <td>A008</td> <td>WALK</td> <td>Walking across the workcell</td> </tr> <tr> <td>A009</td> <td>UNKWN</td> <td>Operator movements not related to the draping process</td> </tr> </tbody> </table> <h2> </h2> <h2>Dataset</h2> <p>Data has been collected from 7 participants, 2 female and 5 male, average age 27 (SD=3.0). Each participant performed 6 repetitions of each of the 8 actions considered for a collaborative draping process, and 18 repetitions of random movements for the unknow class. This results in a collected dataset containing 462 trimmed samples, where each sample is a sequence of 3D skeletons lasting approximately 3 seconds, containing only one action being performed. For all samples, 3D skeletons were obtained by means of the camera network installed in the laboratory, providing: (i) 3D skeletons from each camera in the network and (ii) 3D skeletons obtained by fusing the detections from each camera with a tracking algorithm; the output of the tracking algorithm provides a 3D skeleton representation robust to occlusions. All the 3D skeletons acquired are composed of 15 joints, with 3D coordinates expressed with respect to the camera network reference frame.</p> <p>Skeleton data for all sequences are provided in the `skeleton_data` folder. The dataset consists of a folder for each action of interest, with a subfolder for each participant and an individual text file for each action repetition performed by the participant. The naming convention for these files follows a pattern of the type “AxxxPyyyRzzzCwww.skeleton”, where "Axxx" represents the action id, "Pyyy" represents the id assigned to the participant, "Rzzz" represents the repeat number, and "Cwww" represents the id of the camera from which the 3D skeleton is derived; a network of 4 cameras was used to acquire the data, so “C001” denotes the first camera, “C002” the second, and so on, while “C000” represents the 3D skeletons obtained by merging all views.<br>Each of these files is provided as a “.skeleton” text files, similar to popular human action recognition dataset (e.g., NTU RGB+D and NTU RGB+D 120 action recognition datasets). In particular, each file includes a sequence of 3D skeletons with 25 joints following the OpenPose convention for joint numbering, but only the first 15 joints contain valid values since face keypoints were not considered during the acquisitions.</p> <p>Example python code to read/write and visualize the skeleton data is also provided in the `scripts` folder:<br>```python<br>python3 plot_sequence.py --data_dir ../skeleton_data<br>```</p> <p> </p> <h2>References</h2> <ol> <li>Allegro, D., Terreran, M., & Ghidoni, S. (2023). METRIC—Multi-Eye to Robot Indoor Calibration Dataset. Information, 14(6), 314. https://doi.org/10.3390/info14060314</li> <li>Terreran, M., Barcellona, L., & Ghidoni, S. (2023). A general skeleton-based action and gesture recognition framework for human–robot collaboration. Robotics and Autonomous Systems, 170, 104523. https://doi.org/10.1016/j.robot.2023.104523</li> <li>Terreran, M., Lazzaretto, M., & Ghidoni, S. (2022, June). Skeleton-based action and gesture recognition for human-robot collaboration. In International Conference on Intelligent Autonomous Systems (pp. 29-45). Cham: Springer Nature Switzerland.</li> <li>Carraro, M., Munaro, M., Burke, J., & Menegatti, E. (2019). Real-time marker-less multi-person 3D pose estimation in RGB-depth camera networks. In Intelligent Autonomous Systems 15: Proceedings of the 15th International Conference IAS-15 (pp. 534-545). Springer International Publishing.</li> </ol>
Data from: Collaborative Research: Influence of phosphorus deficiency on enigmatic biological methane production in oxic freshwater lakes
<p>Data from: Collaborative Research: Influence of phosphorus deficiency on enigmatic biological methane production in oxic freshwater lakes</p> <p>NSF Projects 1951002 (PI: Matthew J. Church), 1950963 (PI: John E. Dore)</p>
COHERENT Collaboration data release from the first observation of coherent elastic neutrino-nucleus scattering
<p>Release of COHERENT Collaboration data associated with the first observation of coherent elastic neutrino-nucleus scattering (CEvNS), as published in Science (DOI: <a href="http://dx.doi.org/10.1126/science.aao0990">10.1126/science.aao0990</a>) and also available as arXiv:1708.01294[nucl-ex].</p> <p>This data set should enable researchers to extend the study of CEvNS as desired. Future COHERENT Collaboration results will have similar data releases.</p> <p>Example code can be accessed at https://code.ornl.gov/COHERENT/codeExamples_dataRelease_april2018.<br> The full data-release package, including data, code examples, and a descriptive accompanying document can be found at http://coherent.ornl.gov/data.</p>
Research institutions clustering based on the intensity of academic collaboration
<p>The clustering of research institutions has been conducted using the Louvain modularity algorithm. The Louvain modularity is a state-of-the-art method of identifying communities (clusters) in large networks. Modularity is a value between -1 and 1 that measures the density of edges inside communities to edges outside communities. Optimizing this value results in the best possible grouping of the nodes of a given network.</p> <p>In our exercise, the Louvain methods were applied to identify clusters of institutions within ACM and SSRN networks. In the network, nodes are constituted of institutions, and edges are represented by the intensity of research collaboration measured by number of papers co-authored by authors affiliated with the institutions.</p> <p>As an example, including the paper: <em>Fast unfolding of communities in large networks</em>, written by V. D. Blondel (Universite Catholique de Louvain), J-L. Guillaume (Universite Pierre et Marie Curie), R. Lambiotte (Imperial College London) and Etienne Lefebvre (Universite Catholique de Louvain) would impact the number of edges in our analysis in the following way:</p> <p>“Universite catholique de Louvain” ⇔ “Imperial College London” =+1</p> <p>“Universite catholique de Louvain” ⇔ “Universite Pierre et Marie Curie” =+1</p> <p>“Imperial College London” ⇔ “Universite Pierre et Marie Curie” =+1</p> <p>In our largest network we analyse 5362 institution nodes with 147 482 edges. The number of identified clusters highly depends on the resolution parameter. Resolution is a parameter for the Louvain community detection algorithm that affects the size of the recovered clusters. Smaller resolutions recover smaller, and therefore a larger number of clusters, and conversely, larger values recover clusters containing more data points. In all clusterizations, we have used a default resolution (1.0) tuned in the popular Gephi software for network analysis. Resolutions equal to one result in a moderate number of clusters, characterised by satisfactory statistical distribution. </p> <p><strong>Source:</strong></p> <p>- Association for Computing Machinery (ACM)</p> <p>Characteristics of the ACM Data Set following geographical classification</p> <p>Number of institutions: 5477</p> <p>Number of papers: 674684</p> <p>Number of countries: 122</p> <p>Years: 2011-2018</p> <p>As ACM contains publications across various areas of computer science, a more in-depth analysis requires the classification of papers into fields of interests. During the analysis, we looked at 3 wide areas:</p> <ul> <li> <p>Artificial intelligence and machine learning</p> </li> <li> <p>Technology (hardware, emerging technologies, infrastructure)</p> </li> <li> <p>Social issues</p> </li> </ul> <p>The 3 categories were set following expert analysis of the 1000 most frequent keywords in the dataset. If a term from the following list appeared among the paper’s keywords, the paper was assigned to that group, allowing a paper to assign to more than one group.</p> <p><strong>Files:</strong></p> <p>mod_ai.csv (based on keywords related to artificial intelligence)</p> <p>mod_tech.csv (based on keywords related to technologies)</p> <p>mod_soc.csv (based on keywords related to social issues)</p> <p>mod_all.csv (based on all papers)</p> <p> </p>
Datasets for Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs
<p><strong>Non-Parametric Class Completeness Estimators for Collaborative Knowledge Graphs</strong></p> <p>This are intermediary datasets used for the calculation of the Class Completeness Estimators on Wikidata. For more information see: https://github.com/eXascaleInfolab/cardinal/</p> <p><strong>edits_wikidatawiki-20181001-pages.csv</strong></p> <p>This is an extract from <em>wikidatawiki-20181001-pages-meta-history</em> (All pages with complete page edit history (.bz2)) found at <a href="https://dumps.wikimedia.org/wikidatawiki/">https://dumps.wikimedia.org/wikidatawiki/</a>.</p> <p>The extract was created by the following SQL query:</p> <pre> SELECT page_title, rev_comment, rev_user_text, rev_timestamp FROM revisions WHERE rev_comment LIKE '%[[Property:%]]%[[Q%' ORDER BY rev_id INTO OUTFILE 'edits_wikidatawiki-20181001-pages.csv'; </pre> <p> </p> <p><strong>wikidata-20180813-all.json.bz2.universe.noattr.gt.bz2</strong></p> <p>This is a graph-tool representation of the WikiData graph. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/1_create_inmemory_graph.py">https://github.com/eXascaleInfolab/cardinal/blob/master/1_create_inmemory_graph.py</a>.</p> <p><strong>observations_wikidatawiki-20181001-pages.pickle</strong></p> <p>Extracted observations. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/2_extract_observations.py">https://github.com/eXascaleInfolab/cardinal/blob/master/2_extract_observations.py</a>.</p> <p> </p> <p><strong>estimates_wikidatawiki-20181001-pages.pickle</strong></p> <p>Extracted estimates. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/3_calculate_estimates.py">https://github.com/eXascaleInfolab/cardinal/blob/master/3_calculate_estimates.py</a></p> <p> </p> <p><strong>results_wikidatawiki-20181001-pages.pickle </strong></p> <p>Results. Output of <a href="https://github.com/eXascaleInfolab/cardinal/blob/master/4_draw_graphs.py">https://github.com/eXascaleInfolab/cardinal/blob/master/4_draw_graphs.py</a></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.