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Dataset for algorithmic thinking skills assessment: Results from the virtual CAT large-scale study in Swiss compulsory education
<p><strong>Overview</strong><br>This dataset was collected during a main study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education.<br>As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.</p> <p><strong>Study Context, Location and Participants</strong><br>To comprehensively investigate algorithmic competencies within compulsory education, exploring their variations and determining the factors influencing them, in Spring 2023 we conducted an experimental study with the virtual CAT's.<br>The sample comprises 129 students (65 girls and 64 boys), selected from nine classes across five public schools in Ticino and Solothurn cantons.</p> <p><strong>Data Collection</strong><br>During the data collection process, session and participant details were manually recorded by the administrator. <br>Each session has been assigned a unique identifier, and specific details, such as the date, canton, school name and type, and the students’ HarmoS grade (HG) level, have been recorded. <br>Student information are limited to sex and date of birth, with birth dates used to calculate ages, a significant factor in our demographic analysis. <br>To protect student privacy, unique identifiers have been assigned to each participant, keeping the data anonymous and secure. <br>The assessment tool automatically tracked all user interaction within the platform.<br>All data collected have been pseudonymised, aligning with prevailing open science practices in Switzerland (SNSF, 2021). <br>Data collection was integrated into a validation module of the app. </p> <p><strong>Data Features</strong><br>The dataset comprises the following files:</p> <ul> <li>STUDENTS_SESSIONS.csv</li> <li>RESULTS.csv</li> <li>LOGS.csv</li> <li>CANTONS.csv</li> <li>ALGORITHMS.csv</li> </ul> <p>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.</p> <p><strong>Usage & Ethics</strong><br>In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment. <br>Initial authorisations were secured from school administrators, teachers, and parents. <br>Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders.</p> <p><strong>REFERENCES</strong></p> <p><strong>[1]</strong> A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a></p> <p><strong>[2]</strong> Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a></p> <p><strong>[3]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a></p> <p><strong>[4]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a></p> <p> </p>
Dataset for algorithmic thinking skills assessment: Results from the virtual CAT pilot study in Swiss compulsory education
<p><strong>Overview</strong><br>This dataset was collected during a pilot study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education.<br>As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.</p><p><strong>Study Context, Location and Participants</strong><br>To demonstrate the virtual CAT's effectiveness, we conducted a pilot study in March 2023.<br>The study was conducted in Switzerland, specifically within the Ticino canton.<br>The sample consisted of 31 students (21 girls and 10 boys) from a preschool class (ages 4-6) and two low secondary classes (1st grade, ages 11-12). </p><p><strong>Data Collection</strong><br>Data collection was integrated into a validation module of the app. <br>Sessions required manual input for details like date, canton, and school information. <br>Students' details, anonymised for privacy, encompassed their gender and date of birth. <br>Each interaction within the platform was meticulously logged, capturing operations like task confirmations, command updates, mode changes, and more.</p><p><strong>Data Features</strong><br>The dataset comprises the following files:</p><ul><li>ALGORITHMS.csv</li><li>CANTONS.csv</li><li>DF.csv</li><li>LOGS.csv</li><li>RESULTS.csv</li><li>SCHOOLS.csv</li><li>SESSIONS.csv</li><li>STUDENTS_SESSIONS.csv</li></ul><p>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.</p><p><strong>Usage & Ethics</strong><br>In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment. <br>Initial authorisations were secured from school administrators, teachers, and parents. <br>Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders.</p><p> </p><p><strong>REFERENCES</strong></p><p><strong>[1]</strong> A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a></p><p><strong>[2]</strong> Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a></p><p><strong>[3]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a></p><p><strong>[4]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a></p>
Open Research Skills Workshops - Open access publishing Workshop
<p><strong>This is the first workshop on Open Access Publishing in a series of workshops about Open Research Skills.</strong></p><p>This workshop covers:</p><p>Introduction to open access publishing</p><ul><li>Types of open access publishing</li><li>Examples of open access publishing journals and platforms</li><li>Benefits of open access publishing</li><li>Types of outputs that can be published</li></ul><p>Demonstration </p><ul><li>Demonstrating open publishing </li><li>Showing how a reproducible article is published and all the different outputs that are linked to it and how to do this</li></ul><p>Exercise</p><ul><li>Discuss and explore open publishing giving examples of different articles that show how open publishing works. We will pick those that show data and code deposited in repositories and also that use of protocol.io for publishing open methods</li></ul><p><strong>List of training workshops in Open Research Skills:</strong></p><ul><li><strong>24th February 2023 - Open access publishing</strong></li><li>24th March 2023 - Using repositories</li><li>21st April 2023 - GitHub basics</li><li>28th April 2023 - GitHub collaborative workflows</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>
Datasets from study: "Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability"
<p>This repository contains the datasets needed to reproduce the figures from manuscript: Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability.</p> <p>In this study, we examine the potential of land surface temperature and vegetation data, which are not routinely assimilated in NWP models, for enhancing temperature forecast skill. We build surrogate models for NWP using Long Short-Term Memory.</p>
Language-enhanced cognitive skills model
<p>This is a model of cognitive skills required in the workplace which enhance previous models by including a more detailed measurement of linguistic skills. Linguistic skills are defined as the set of abilities, competencies and knowledge which principally involve the use of linguistic code. More specifically, the linguistic items used are reading and writing competencies, ability to speak, listen or communicate, as well as knowledge of second languages (as a whole). These variables were factorialised together with a list of competencies from previous models of cognitive skills. Principal component analysis (PCA) with equamax rotation was applied to reduce the dimensionality of all items to a few interpretable dimensions according to the correlations between them. The result is nine factors with similar variances among at least three express linguistic-related skills: The first factor expresses the demand for scientific and engineering knowledge. The second refers to a collection of competencies which could be called verbal-reasoning. These include deductive and inductive reasoning skills or those of identifying and solving complex problems. Some linguistic competencies relating to the level of oral and written comprehension and expression are also relevant in this factor. The third factor expresses numerical or quantitative competencies. The fourth expresses the demand for communicative competencies, composed of variables related to efficient communication goals such as clarity of speech, active listening or speaking. The fifth factor expresses creative abilities. The sixth, competencies and knowledge linked to electronics and computers. The seventh expresses managerial competencies. The eighth expresses nurturing competencies and the ninth factor basically expresses knowledge of foreign languages.</p>
Behavioral and fMRI Data: Nurturing the reading brain: Home literacy practices are associated with children's neural response to printed words through vocabulary skills
<p>This is the behavioral and fMRI dataset described in "Nurturing the reading brain: Home literacy practices are associated with children’s neural response to printed words through vocabulary skills". </p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu">GDPR</a>), we cannot provide raw MRI data. Therefore, the fMRI data consists of individual pre-processed volumes, normalized into the MNI template (see paper for details about the preprocessing pipeline). Anonymized behavioral data and first level analyses are also provided for each participant (SPM.mat file as well as beta, con, spmT, RPV and ResMS files). Note that the dataset also include runs and GLM results for a third task (Dots) that was not analyzed in the paper. Finally, the <a href="https://www.psychopy.org">PsychoPy</a> implementation of the tasks is also provided. If you have any questions, please send an email to jerome.prado [at] univ-lyon1.fr. </p> <p><strong>IMPORTANT:</strong></p> <p>In accordance with EU privacy regulations, we ask that you sign and return a Data Use Agreement (DUA) before downloading the data. You can download the DUA <a href="https://zenodo.org/record/4965716/files/DUA.pdf?download=1">here</a>. Please, sign it and send it to jerome.prado [at] univ-lyon1.fr.</p>
SEEtheSkills resources database from the Interregional research on the status of energy skills
<p>This dataset includes a list of resources identified during the interregional research on the status of energy skills, done in the frame of SEEtheSkills project. The comprehensive overview of the information created in the area of Energy Efficiency (EE) and Renewable Energy Systems (RES), goes both wide, by trying to identify as many different examples as possible, and deep, by digging into the examples themselves. The key areas the research focused on: skills defined in national roadmaps; skills developed as part of previous BUS projects; developed training schemes; the number of trained workers and professionals; companies that design and produce EE materials; status of Recognition of Previous Learning (RPL); status of demand for energy skills; level of awareness of energy skills; available certification schemes; legal obligations promoting the use of energy skills and their timelines, predictions for future development of energy skills. The survey covers mainly the five countries participating in the project Slovenia, Spain, Netherlands, Slovakia and North Macedonia, but also beyond their geographical coverage.</p>
What Skills do IT Companies Look for in New Developers?
<p>This dataset contains the package for replicating the study "What Skills do IT Companies Look for in New Developers?".</p> <p>* high-level-hard-skills.csv: the list of hard skills with their categories;</p> <p>* jobs_complete.csv: This is the main file of this study. It basically contains the information about the jobs analyzed in this study;</p> <p>* script.html: Report which presents the results obtained in this study;</p> <p>* soft-skills-tagged.zip: Zip file with the soft skills annotated in this study;</p>
Twitter Poll: Is #OpenScience an essential rsrch skill Grad Schools should train in prep for #REF2020
<p>The Twitter Poll "Is #OpenScience an essential rsrch skill Grad Schools should train in prep for #REF2020" was run online in support of Horizon 2020 Project HEIRRI (Higher Education Insititutions & Responsible Research & Innovation) 1st Conference, 18 March 2016.</p> <p>The poll attracted 123 voters, 12,343 impressions and 517 engagements (4,2% conversion).</p> <p><em><strong>Event website:</strong></em><br /> HEIRRI 1st Conference http://heirri.eu/1st-heirri-conference/</p> <p><em><strong>CODE for EMBEDDING TWITTER POLL: </strong></em></p> <p><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">Is <a href="https://twitter.com/hashtag/OpenScience?src=hash">#OpenScience</a> an essential rsrch skill Grad Schools should train in prep for <a href="https://twitter.com/hashtag/REF2020?src=hash">#REF2020</a> ? <a href="https://twitter.com/hashtag/OpenSci4Doc?src=hash">#OpenSci4Doc</a> <a href="https://twitter.com/HEIRRI_">@HEIRRI_</a></p>&mdash; Foster Open Science (@fosterscience) <a href="https://twitter.com/fosterscience/status/709650182800068608">March 15, 2016</a></blockquote><br /> <script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script></p>
Open Research Skills Workshops - GitHub basics
<p>This is the third workshop on GitHub basics<strong> </strong>in a series of workshop about Open Research Skills.</p><p>This workshop covers:</p><p><strong>-</strong> Introduction to Github and its uses</p><p>- Demonstration on using GitHub <strong> </strong></p><p>- Basic repo set up and editing</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><strong>21st April 2023 - GitHub basics</strong></li><li>28th April 2023 - GitHub collaborative workflows</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>This video is a basic course in Github. Github is a tool that is used for research project management and history tracking of your work during projects. It can be used to store and collaborate during projects with data, code and documentation. It covers the basic web interface of Github and how to make repositories, add files and folders. It will also include some examples of uses of Github.</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>
Raw data for D1.1: Inventory of skills and competencies
<p>Raw data for the manuscript entitled: <strong>European Agrifood and Forestry Education for a Sustainable Future - Gap Analysis from an Informatics Approach</strong></p> <p><strong>Abstract</strong></p> <p><strong>Purpose: </strong>To evaluate how well European agrifood and forestry Masters program websites use vocabulary associated with the NextFood Project ‘categories of skills’.</p> <p><strong>Methodology: </strong>Web-scraping Python scripts were used to collect texts from European Masters programs websites, which were then analysed using statistical tools including Partial Least Squares Regression and contextual relation analysis. A total of fourteen countries, twenty-seven universities, 1303 European Masters programs, 3305 web-pages and almost two million words were studied using this approach.</p> <p><strong>Findings: </strong>While agrifood and forestry Masters programs used vocabulary from the NextFood Project ‘categories of skills’ in most cases equal to or more often than non-agrifood and forestry Masters programs, we found evidence for the relative underuse of words associated with networking skills, with least use among agriculture-related Masters programs. </p> <p><strong>Practical Implications: </strong>The informatic approach provides evidence that European agrifood and forestry Masters programs are for the most part following the educational paths for meeting future challenges as outlined by the NextFood Project, with the possible exception of networking skills.</p> <p><strong>Theoretical Implications: </strong>This text-based, informatic approach complements the more targeted approaches taken by the NextFood Project in studying the skilling-pathways, which involved focus-group interviews, surveys of stakeholders, interviews of individuals with expert-knowledge and literature reviews.</p> <p><strong>Originality: </strong>A text-based, web-scraping informatic approach has thus far been limited in the study of agrifood and forestry higher education, especially relative to recent advances made in the social sciences.</p>
Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 5
<p>Dataset containing four .xlsx and .csv files for the exercises in Episode 5 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
Inputlog Copy Task Corpus: Exploring and defining typing skills
<p><strong>Context</strong></p> <p>One of the components that is included in the keystroke logging program Inputlog (<a href="https://www.inputlog.net">https://www.inputlog.net</a>) is the Copy Task component. It consists of a multi-layered set of tasks that measure a person's typing skill:</p> <table> <tbody> <tr> <td>Tapping task</td> <td>press the ‘d’ and ‘k’ key alternatively during 15 s</td> </tr> <tr> <td>Sentence</td> <td>copy a sentence during 30 s</td> </tr> <tr> <td>Word combination 1</td> <td>copy a combination of three words seven times</td> </tr> <tr> <td>Word combination 2</td> <td>copy a combination of three words seven times</td> </tr> <tr> <td>Word combination 3</td> <td>copy a combination of three words seven times</td> </tr> <tr> <td>Word combination 4</td> <td>copy a combination of three words seven times</td> </tr> <tr> <td>Consonant groups</td> <td>copy four blocks of six consonants once</td> </tr> </tbody> </table> <p>The task is currently made available in twelve languages. </p> <p>For more information: <a href="https://doi.org/10.5334/jors.234 ">https://doi.org/10.5334/jors.234 </a></p> <p> </p> <p><strong>Interactive Dashboard</strong><br> Visit the webpage with an interactive dashboard to explore, filter, and download the +5K copy task corpus.</p> <p><em><strong>website</strong></em>: <a href="https://www.inputlog.net/copy-task/">https://www.inputlog.net/copy-task/</a><br> <em><strong>dashboard</strong></em>: <a href="https://inputlog-analysis.uantwerpen.be/expert">https://inputlog-analysis.uantwerpen.be/expert</a></p> <p> </p> <p><strong>Corpus</strong></p> <p>We are happy to make a multilingual corpus available (open access) that currently consists of more than 5000 copy tasks. </p> <ul> <li>The + 5K corpus is carefully cleaned and fully anonymized.</li> <li>The Shiny interface allows users to filter the corpus based on about 10 variables.</li> <li>The selection can be downloaded in different formats and levels of aggregation (from raw idfx to synthesized analysis).</li> <li>The selection can be explored using different interactive graph visualizations.</li> <li>Researchers can upload their own corpus (or single copy task file) and compare it to the (selected) corpus.</li> <li>An extra webpage is designed for laypersons wanting to take a copy task to test their typing skills. They get dashboard feedback in a user-friendly and attractive way and can compare their performance with (age-related) participants in the corpus. (Specially designed to further expand the corpus).</li> </ul> <p><strong>Facts and Figures</strong><br> Some facts and figures about the corpus' composition:</p> <p>Languages:</p> <ul> <li>Dutch 3130 files</li> <li>English 1163 files</li> <li>German 281 files</li> <li>French 201 files</li> <li>Other 378 file</li> </ul> <p><strong>Gender</strong></p> <ul> <li>Female: 3495 files</li> <li>Male: 1276 files</li> <li>X or missing 382 files</li> </ul> <p><strong>Age</strong></p> <ul> <li>15- 439 files</li> <li>16-20 1591 files</li> <li>21-25 2427 files</li> <li>26-35 478 files</li> <li>36-45 126 files</li> <li>46+ 230 files</li> </ul> <p>A subset of the total corpus has been uploaded here. The subset contains a dataset of about 500 tests (English | 21-25-year-olds).</p> <p> </p>
Data archive and code for "Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison"
<p>This upload contains data and code related to the paper "Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison" by M. Bushuk, S. Ali, D. Bailey, Q. Bao, L. Batte, U. S. Bhatt, E. Blanchard-Wrigglesworth, E. Blockley, G. Cawley, J. Chi, F. Counillon, P. Goulet Coulombe, R. Cullather, F. X. Diebold, A. Dirkson, E. Exarchou, M. Gobel, W. Gregory, V. Guemas, L. Hamilton, B. He, S. Horvath, M. Ionita, J. E. Kay, E. Kim, N. Kimura, D. Kondrashov, Z. M. Labe, W. Lee, Y. J. Lee, C. Li, X. Li, Y. Lin, Y. Liu, W. Maslowski, F. Massonnet, W. N. Meier, W. J. Merryfield, H. Myint, J. C. Acosta Navarro, A. Petty, F. Qiao, D. Schroder, A. Schweiger, Q. Shu, M. Sigmond, M. Steele, J. Stroeve, N. Sun, S. Tietsche, M. Tsamados, K. Wang, J. Wang, W. Wang, Y. Wang, Y. Wang, J. Williams, Q. Yang, X. Yuan, J. Zhang, and Y. Zhang, published in the Bulletin of the American Meteorological Society, DOI: https://doi.org/10.1175/BAMS-D-23-0163.1.</p> <p>See README.txt for a description of the datasets and code.</p>
Skills of science inquiry in citizen science - Datasets
<p>The dataset provides results of a prediction task aimed at predicting the presence of science inquiry skills in CS project descriptions. Only 2939 English project descriptions from the database were used for prediction and the results indicated 438 projects (around 15%) consist of one or more skills of science inquiry that we were interested in. In total 20 different types of science inquiry skills were considered for this study. </p> <p>See further detail in D2.2 section 7.4.</p> <p><strong>Content and grouping: </strong></p> <ul> <li> <p>The dataset contains the following details: Platform ID (from which platform the CS project descriptions were retrieved), Project Title (Name of the CS project), How many times each science inquiry skill (keywords) was mentioned in each project description.</p> </li> </ul>
Datasets for "Placebo effects of transcranial direct current stimulation on motor skill acquisition"
<p>The following two .csv files contain the participant level data for the primary analyses conducted within the research study:</p> <p>"Placebo effects of transcranial direct current stimulation on motor skill acquisition"</p> <p>Data are formatted in long format for ease of analysis</p> <p>Dataset used in first analysis - Estimation of TDCS effect and Placebo effect including a NO TDCS control group</p> <p>ALLGROUPS.csv</p> <p>subid = Participant specific identifier<br> Age = Participant age in years<br> Sex = Participant sex (M/F)<br> RASex = Sex of research assistant that conducted the study for the participant<br> TrialNum = Trial number for the reaching task<br> Performance = Total trial time of the trial in seconds<br> AssignGrp = Group participant was assigned: Active = Active TDCS, Sham = Sham TDCS, Ctrl = No TDCS</p> <p>Dataset used in second analysis - Estimation of expectancy effects on Performance among TDCS groups ONLY</p> <p>TDCSGroupsONLY.csv</p> <p>subid = Participant specific identifier<br> Age = Participant age in years<br> Sex = Participant sex (M/F)<br> RASex = Sex of research assistant that conducted the study for the participant<br> TrialNum = Trial number for the reaching task<br> Performance = Total trial time of the trial in seconds<br> AssignGrp = Group participant was assigned: Active = Active TDCS, Sham = Sham TDCS, Ctrl = No TDCS<br> PostExp = Expectancy score post practice<br> PreExp = Expectancy score pre practice<br> Suggestibility = Suggestibility score<br> Prior Know = Prior knowledge of TDCS (Yes/No)<br> Prior Study = Participation in a study using TDCS (Yes/No)</p>
IT Skills Survey in three albanian Social Work Faculties
<p>This IT Skills survey is part of the T@SK project and was distributed among students, administrative staff and teachers from the Social Work Faculties of Albanian universities:</p> <ul> <li>Universiteti i Tiranes</li> <li>Universiteti i Shkodres 'Luigj Gurakuqi'</li> <li>Universitei Aleksandër Xhuvan</li> </ul> <p>The fieldcamp was perfomerd in 2019</p>
Dataset: Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Pavel Čeleda, Jan Vykopal, Silvia Brišáková.<br> <em>Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges.</em><br> In Elsevier Computers & Security. 2020.<br> <a href="https://doi.org/10.1016/j.cose.2020.102154">https://doi.org/10.1016/j.cose.2020.102154</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2101.01421">https://arxiv.org/abs/2101.01421</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2020cybersecurity, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel and Vykopal, Jan and Bri\v{s}\'{a}kov\'{a}, Silvia}, title = {{Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges}}, journal = {Computers \& Security}, publisher = {Elsevier}, volume = {102}, year = {2020}, issn = {0167-4048}, url = {https://www.sciencedirect.com/science/article/pii/S0167404820304272}, doi = {10.1016/j.cose.2020.102154}, }</code></pre> <p><strong>Attached content</strong></p> <p>See the README.md file inside the attached ZIP file for more details.</p>
TalentCLEF 2025 corpus: Skill and Job Title Intelligence for Human Capital Management
<blockquote> <p><strong>🚨 Current Status: Submission of Working Notes</strong></p> </blockquote> <p><strong>If you use any data from this repository, please cite our scientific paper instead of the Zenodo repo: </strong></p> <pre><code>@inproceedings{gasco2025overview, title={{Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management}}, author={Gasco, Luis and Fabregat, Hermenegildo and Garc\'{\i}a-Sardi{\~n}a, Laura and Estrella, Paula and Deniz, Daniel and Rodrigo, \'{A}lvaro and Zbib, Rabih}, booktitle={{International Conference of the Cross-Language Evaluation Forum for European Languages}}, year={2025}, publisher={Springer} }</code></pre> <p> </p> <h2><strong><a href="https://talentclef.github.io/talentclef/" target="_blank" rel="noopener">TalentCLEF2025</a> corpus - </strong>Task B Test set release</h2> <h3><strong>Introduction:</strong></h3> <p>The first edition of TalentCLEF aims to develop and evaluate models designed to facilitate three essential tasks:</p> <ol> <li>Finding/ranking candidates for job positions based on their experience and professional skills.</li> <li>Implementing upskilling and reskilling strategies that promote the coninuous development of workers</li> <li>Detecting emerging skills and skills gaps of importance in organizations.</li> </ol> <p>With that aim, the task is divided into two tasks: </p> <ul> <li><strong>Task A - Multilingual Job Title Matching</strong>. This task involves developing systems to identify and rank the job titles most similar to a given one by generating a ranked list of similar titles from a specified knowledge base for each job title in a provided test set.</li> <li><strong>Task B - Job Title-Based Skill Prediction. </strong>Task B requires developing systems that can retrieve relevant skills associated with a specified job title.</li> </ul> <div> <p>This data repository contains the data for these two tasks. The data is being released progressively according to the<a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/schedule/"> task schedule</a>.</p> <p>The task evaluation takes place on Codabench (<a href="https://www.codabench.org/competitions/5842/">Task A</a> and <a href="https://www.codabench.org/competitions/7059/">Task B</a>). Participants must register for the competition through <a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a> to be part of the evaluation campaign.</p> <p> </p> </div> <h3><strong>File structure: </strong></h3> <div> <div> <div> <div> <blockquote> <p>For a detailed description of the data structure, you can refer to the <a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/description_corpus/">TalentCLEF2025 data description page,</a> where it is thoroughly explained.</p> </blockquote> </div> </div> </div> </div> <p>The files is organized into two <code>*.zip</code> files, <code>TaskA.zip</code> and <code>TaskB.zip</code>, each containing training, validation and test folders to support different stages of model development. So far, only the training set for both tasks has been released, but in future releases, as the tasks progress, additional data will be added to the different subfolders for each task.</p> <p><strong>TaskA</strong> includes language-specific subfolders within the training and validation directories, covering English, Spanish, German, and Chinese job title data. The training folders for TaskA contain language-specific .tsv files for each respective language. Validation folders include three essential files—queries, corpus_elements, and q_rels—for evaluating model relevance to search queries. TaskA’s test folder has queries and corpus_elements files for testing retrieval.<br><br></p> <pre><code>TaskA/ │ ├── training/ │ ├── english/ │ │ └── taskA_training_en.tsv │ ├── spanish/ │ │ └── taskA_training_es.tsv │ └── german/ │ └── taskA_training_de.tsv │ ├── validation/ │ ├── english/ │ │ ├── queries │ │ ├── corpus_elements │ │ └── qrels │ ├── spanish/ │ ├── german/ │ └── chinese/ │ └── test/ ├── english/ │ ├── queries │ └── corpus_elements ├── spanish/ ├── german/ └── chinese/ </code></pre> <p><strong>TaskB</strong> follows a similar structure but without language-specific subfolders, providing general .tsv files for training, validation, and testing. This consistent file organization enables efficient data access and structured updates as new data versions are published.</p> <pre><code>TaskB/ │ ├── training/ │ ├── job2skill.tsv │ ├── jobid2terms.json │ └── skillid2terms.json<br>│ ├── validation/ │ ├── queries │ ├── corpus_elements │ └── qrels │ └── test/ ├── queries └── corpus_elements </code></pre> <p><strong>Tutorials:</strong></p> <table> <tbody> <tr> <td><strong>Notebook</strong></td> <td>Link</td> </tr> <tr> <td>Data Download and Load using Python </td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_data_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Development set Baseline generation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskA_DevSet_Baseline.ipynb">Link to Colab</a></td> </tr> <tr> <td>Task B - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskB_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> </tbody> </table> <p><strong>Resources: </strong></p> <ul> <li><a href="https://talentclef.github.io/talentclef/docs/" target="_blank" rel="noopener">Web</a></li> <li><a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a></li> <li><a href="https://www.codabench.org/competitions/5842/">Codabench Task A</a></li> <li><a href="https://www.codabench.org/competitions/7059/">Codabench Task B</a></li> <li><a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/additional_resources/">Additional Resources</a></li> </ul>
ScienceDex guides
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OpenNeuro
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