Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
34
datasets available to search
ShareScore release 0.9.0
Dataset results
34 results for “Hands-On!”
Dataset: Reinforcing Cybersecurity Hands-on Training With Adaptive Learning
<p>This repository contains supplementary materials for the following conference paper:<br> <br> Pavel Seda, Jan Vykopal, Valdemar Švábenský, Pavel Čeleda.<em><br> Reinforcing Cybersecurity Hands-on Training With Adaptive Learning. </em><br> In Proceedings of the 51st IEEE Frontiers in Education Conference (FIE 2021).<br> <a href="https://doi.org/10.1109/FIE49875.2021.9637252">https://doi.org/10.1109/FIE49875.2021.9637252</a><br> <br> Preprint available at: <a href="https://arxiv.org/abs/2201.01574">https://arxiv.org/abs/2201.01574</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> <p>Some of the linked repositories have their separate citation entry; please use that one as well, if possible.</p> <pre><code>@inproceedings{Seda2021reinforcing, author = {Seda, Pavel and Vykopal, Jan and \v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel}, title = {{Reinforcing Cybersecurity Hands-on Training With Adaptive Learning}}, booktitle = {Proceedings of the 51st IEEE Frontiers in Education Conference}, series = {FIE '21}, location = {Lincoln, NE, USA}, publisher = {IEEE}, address = {New York, NY, USA}, month = {10}, year = {2021}, pages = {1--9}, numpages = {9}, isbn = {978-1-6654-3851-3}, url = {https://doi.org/10.1109/FIE49875.2021.9637252}, doi = {10.1109/FIE49875.2021.9637252}, }</code></pre> <p> </p>
HandCT: hands-on computational dataset for X-Ray Computed Tomography
<p>HandCT is a computational dataset to train machine-learning models for X-Ray Computed Tomography (CT). It consists of a meshed hand model, of which pose and anatomical properties are computed at run-time from a script. As such, it is an accurate modeling of anatomical phantoms of only 1.35 mB, and reproducibility is ensured using random seeds. It allows the user to have full control over the imaging chain, from projection to reconstruction, and over the X-Ray interaction with the different parts of the model by a simple variable editing. This open-source solution relies on the freeware Blender for the modelling and Python for the computations. The first deals with modelling, rigging and deformations, whilst the later ensures transformations such as scaling, translation, or else forward projection. This dataset can be used to train and evaluate regularisation procedures for low-energy, dual-energy and scarce-view CT.</p>
Dataset: Shell Commands Used by Participants of Hands-on Cybersecurity Training
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Seda, Pavel Čeleda.<br> <em>Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training.</em><br> In Elsevier Data in Brief. 2021.<br> <a href="https://doi.org/10.1016/j.dib.2021.107398">https://doi.org/10.1016/j.dib.2021.107398</a></p> <ul> </ul> <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{Svabensky2021dataset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Seda, Pavel and \v{C}eleda, Pavel}, title = {{Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training}}, journal = {Data in Brief}, publisher = {Elsevier}, volume = {38}, year = {2021}, issn = {2352-3409}, url = {https://doi.org/10.1016/j.dib.2021.107398}, doi = {10.1016/j.dib.2021.107398}, }</code></pre> <p>The data were collected using a logging toolset referenced <a href="https://zenodo.org/record/5126693">here</a>.</p> <p><strong>Attached content</strong></p> <ol> <li><strong>Dataset (data.zip).</strong> The collected data are attached here on Zenodo. A copy is also available in <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/datasets/commands">this repository</a>.</li> <li><strong>Analytical tools (toolset.zip).</strong> To analyze the data, you can instantiate the toolset or <a href="https://gitlab.ics.muni.cz/muni-kypo/tools/commands-elk">this project for ELK</a>.</li> </ol> <p><strong>Version history</strong></p> <ul> <li>Version 1 (<a href="https://zenodo.org/record/5137355">https://zenodo.org/record/5137355</a>) contains 13446 log records from 175 trainees. These data are precisely those that are described in the associated journal paper. Version 1 provides a snapshot of the state when the article was published.</li> <li>Version 2 (<a href="https://zenodo.org/record/5517479">https://zenodo.org/record/5517479</a>) contains 13446 log records from 175 trainees. The data are unchanged from Version 1, but the analytical toolset includes a minor fix.</li> <li>Version 3 (<a href="https://zenodo.org/record/6670113">https://zenodo.org/record/6670113</a>) contains 21762 log records from 275 trainees. It is a superset of Version 2, with newly collected data added to the dataset.</li> <li>The current Version 4 (<a href="https://zenodo.org/record/8136017">https://zenodo.org/record/8136017</a>) contains 21459 log records from 275 trainees. Compared to Version 3, we cleaned 303 invalid/duplicate command records.</li> </ul>
Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Radek Ošlejšek, Martin Macák, Karolína Dočkalová Burská.<br><em>Hands-on cybersecurity training behavior data for process mining.</em><br>In Elsevier Data in Brief. 2023.<br>Available as open-access article on <a href="https://doi.org/10.1016/j.dib.2023.109956">https://doi.org/10.1016/j.dib.2023.109956</a></p> <p><strong>Contents</strong></p> <p>Datasets store event logs of trainees participating in hands-on cybersecurity exercises organized in the <a href="https://www.kypo.cz">KYPO Cyber Range</a>. The data includes training scenarios (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.</p> <ol> <li><strong>Data1:</strong> A dataset of 52 trainees participating in the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise adapted an insider attack scenario. No time restrictions were posed on playtime. The data file is structured as follows: <ul> <li>training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> game adapted to an insider attack scenario.</li> <li>training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.</li> <li>command_histories: Recorded commands executed on network hosts.</li> <li>process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.</li> <li>process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.</li> </ul> </li> <li><strong>Data2:</strong> A dataset of 48 trainees participating in the original <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/locust-3302">Locust 3302</a> exercise. Three supervised training sessions were restricted to two hours of playtime. The structure follows the structure of Data1.</li> <li><strong>Tool:</strong> A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.</li> </ol> <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 work.</p> <pre><code>@article{Oslejsek2023dataset, author = {Radek O\v{s}lej\v{s}ek and Martin Mac\'{a}k and Karol\'{i}na {Do\v{c}kalov\'{a} Bursk\'{a}}}, title = {Hands-on cybersecurity training behavior data for process mining}, journal = {{Data in Brief}}, publisher = {Elsevier}, issn = {2352-3409}, year = {2023}, volume = {52}, doi = {10.1016/j.dib.2023.109956}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923009873} }</code></pre>
Hands-On! Video demonstration - Paper folding components
<p>This video demonstrates all possible actions for the <strong>Paper folding task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong>Hands-On!</strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong>Hands-On!</strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a></p>
Hands-On! Video demonstration - Writing components
<p><span>This video demonstrates all possible actions for the <strong>Writing task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong><span>Hands-On!</span></strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong><span>Hands-On!</span></strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a> </span></p>
Hands-On! Video demonstration - Paper cutting components
<p><span>This video demonstrates all possible actions for the <strong>Paper cutting task</strong>, providing real-time examples to help you accurately identify and assess fine motor performance. Use this video alongside the written descriptions and images from the <strong><span>Hands-On!</span></strong> observation tool for a comprehensive understanding of this fine motor task. For more information on the use of this video, please refer to <strong><span>Hands-On!</span></strong> via: <a href="https://doi.org/10.5281/zenodo.14185207">https://doi.org/10.5281/zenodo.14185207</a> </span></p>
Results of the ROSCon24 Workshop Survey 'Hands-on with ROS2 Deliberation Technologies'
<p><span>Results of the ROSCon24 Deliberation Workshop Survey:</span></p> <p><span>The members of the ROS deliberation community group (ros-wg-delib) hosted a 'Hands-on with ROS2 Deliberation Technologies' workshop at ROSCon24 in Odense. The workshop aimed to showcase the diversity of deliberation technologies, including behaviour trees, finite-state machines, task planning, and verification and validation systems. Participants had the opportunity to tackle predefined tasks using deliberation tools such as BehaviorTree.CPP, FlexBE, ros_bt_py, SkiROS2, and the AS2FM tool by CONVINCE. Guided by the tools' creators, they explored how different technologies can be applied to solve various robotic challenges.</span></p> <p><span>A survey was conducted at the end of the workshop to gather feedback from participants about the tools they explored. The survey results highlight the strengths and limitations of each tool, offering insights into areas for improvement, and are particularly valuable for the newly introduced tools developed by the CONVINCE consortium.</span></p>
BUCSS22 hands-on data
<p>This is the dataset used in all hands-on sessions of the 4th edition of BUCSS.<br> <br> This Bochum Urban Climate Summer School took place from 26 to 29 September 2022 at <a href="https://www.ruhr-uni-bochum.de/en">Ruhr-University Bochum (Germany)</a>, and aimed at 1) providing a general introduction to different facets of urban climatology with a special focus on urban climate informatics, 2) providing structured information and skill-building capabilities related to urban climate monitoring, remote sensing and modelling, and 3) strengthening an active pool of young scientists to tackle the major urban sustainability challenges of future generations.<br> <br> The programme consisted out of state-of-the-art lectures and hands-on tutorials on remote sensing in urban areas, crowdsourcing and urban climate modelling. All of these teaching resources are available here: <a href="https://github.com/RUBclim/BUCSS22">https://github.com/RUBclim/BUCSS22</a></p>
Dataset: Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training
<p>This repository contains supplementary materials for the following conference paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Daniel Tovarňák, Pavel Čeleda.<br> <em>Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training.</em><br> In Proceedings of the 51st IEEE Frontiers in Education Conference (FIE 2021).<br> <a href="https://doi.org/10.1109/FIE49875.2021.9637052">https://doi.org/10.1109/FIE49875.2021.9637052</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2112.11118">https://arxiv.org/abs/2112.11118</a></p> <ol> </ol> <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> <p>Some of the linked repositories have their separate citation entry; please use that one as well, if possible.</p> <pre><code>@inproceedings{Svabensky2021toolset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Tovar\v{n}\'{a}k, Daniel and \v{C}eleda, Pavel}, title = {{Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training}}, booktitle = {Proceedings of the 51st IEEE Frontiers in Education Conference}, series = {FIE '21}, location = {Lincoln, NE, USA}, publisher = {IEEE}, address = {New York, NY, USA}, month = {10}, year = {2021}, pages = {1--9}, numpages = {9}, isbn = {978-1-6654-3851-3}, url = {https://doi.org/10.1109/FIE49875.2021.9637052}, doi = {10.1109/FIE49875.2021.9637052}, }</code></pre> <p><strong>Structure of the repository</strong></p> <p>We share four types of content described below. Each of the four types of materials includes:</p> <ul> <li>a link to an up-to-date GitLab repository, which may contain possible future revisions and error corrections, and</li> <li>a ZIP archive here on Zenodo that serves as a snapshot of the state when the article was published.</li> </ul> <p><strong>Attached content</strong></p> <ol> <li><strong>Logging toolset.</strong> It is implemented in the form of <a href="https://www.ansible.com/">Ansible</a> roles and consists of three separate projects: for <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-bash">Bash logging</a>, <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-msf">Metasploit logging</a>, and <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-forward">log forwarding within the sandbox</a>.</li> <li><strong>Sample training.</strong> To quickly test the toolset, instantiate the exemplary cybersecurity game called <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/junior-hacker">Junior hacker training</a>. It already deploys the logging; no further setup is needed.</li> <li><strong>Dataset.</strong> The data collected with the toolset are available at <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/datasets/commands">this repository</a>, which is being gradually updated with new data. Attached is a subset of this repository that includes the data analyzed in the paper.</li> <li><strong>Analytical tools.</strong> To analyze the data, you can either use the attached Python scripts, or instantiate <a href="https://gitlab.ics.muni.cz/muni-kypo/tools/commands-elk">this project for ELK</a>.</li> </ol>
Epistemic Insight Initiative Data set: Findings from a hands-on science intervention to build school students' agency and epistemic insight into the power and limitations of science.
<p>Data set that reports on the Findings from a hands-on science intervention to build school students’ agency and epistemic insight into the power and limitations of science.</p> <p>We report on an empirical study that sought to address three questions:</p> <ul> <li>A study to extend the cognitive science of insight into the field of children’s developing understanding of the power and limitations of science</li> <li>A study to examine the effectiveness of the Epistemic Insight (EI) strategy which is designed to give each child hands-on, minds-on science</li> <li>A study to examine the effectiveness of the EI strategy to sustain science activity in schools during the challenging conditions of the pandemic</li> </ul> <p>1919 children and 63 teachers from 24 schools in England took part in the project. This includes both primary and secondary schools. The data reported here are taken from ten primary schools representing 1045 children aged 8-12 in years 4 to 7.</p> <p> </p>
HANDS-ON: a Personalized, Brief and Intensive Exposure-based Intervention for Youth With Persistent Anxiety or OCD
ClinicalTrials.gov study NCT06152913. IPD Sharing: YES. Countries: 1. Publications: 3.
Data from: A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams
Open the record for dataset details and reuse information.
Fermilab LHC Physics Center Machine Learning Hands-On Advanced Tutorial Session Datasets
<p>The dataset jet_raw.tar.gz contains a sample of 2011 CMS Open Simulation in numpy arrays. The columns correspond to</p> <pre><code>['run', 'lumi', 'event', 'met', 'sumet', 'rho', 'pthat', 'mcweight', 'njet_ak7', 'jet_pt_ak7', 'jet_eta_ak7', 'jet_phi_ak7', 'jet_E_ak7', 'jet_msd_ak7', 'jet_area_ak7', 'jet_jes_ak7', 'jet_tau21_ak7', 'jet_isW_ak7', 'jet_ncand_ak7', 'ak7pfcand_ijet']</code></pre> <p>Each row is a separate anti-k<sub>T</sub> R=0.7 (AK7) jet. The code to produce the numpy arrays is located at https://doi.org/10.5281/zenodo.3901871</p> <p>The dataset jet_images.h5 contains preprocessed 2D jet images.</p> <p>The datasets ntuple_4mu_bkg.root, ntuple_4mu_gg.root, and ntuple_4mu_VV.root contain simulated LHC events with 4 muons for the background process, gluon fusion Higgs boson production, and vector boson fusion Higgs boson production.</p> <p> </p>
Refactoring Code Smells in Open Source Projects: A Hands-on Approach to Teaching Software Maintenance
<p>Code smells are suboptimal code structures that can undermine software quality and maintainability. On the one hand, software engineers commonly apply refactoring techniques to address these deficiencies and improve internal quality attributes. On the other hand, when performed manually and without discipline, refactoring can lead to code degradation. Despite its importance, refactoring and code smells are rarely explored in depth in undergraduate computing courses, which can be reflected in industry practices. To address this gap, this paper presents a hands-on approach to teaching code smell refactoring through contributions to Open Source Software (OSS) projects, an environment where developers with diverse skill levels collaborate, and maintaining code quality is particularly challenging. Code smells accumulate over time in such scenarios, hindering software evolution and collaboration. Our study in two undergraduate Software Quality and Software Maintenance courses expands on previous findings by incorporating an in-depth analysis of students’ learning experiences. The results indicate that: (i) students rec- ognized improvements in code quality after refactoring; (ii) they identified strong connections between refactoring, testing, and debugging; (iii) their confidence decreased when refactoring required changes across multiple files; (iv) code complexity posed a significant challenge to refactoring; (v) students’ choices of refactoring techniques were influenced by project structure and personal preferences, often combining multiple techniques to address a single smell; (vi) in some cases, refactoring introduced new code smells; (vii) the longest refactoring efforts were also the most likely to reintroduce code smells; (viii) contributing to OSS projects improved students’ programming skills and fostered a sense of professional growth; (ix) students faced challenges in understanding OSS contribution processes, particularly regarding issue resolution, adherence to contribution guidelines, and responding to maintainer feedback; (x) automated checks and review workflows varied across projects, affecting students’ ability to submit successful contributions; and (xi) despite these challenges, engagement with OSS enabled students to gain practical experience in collaborative software development. Our findings offer valuable insights for software engineering educators seeking to integrate refactoring practices into coursework while leveraging OSS contributions as an educational tool.</p>
Hands-on: How to add a service demand in MUSE
<p>Files available in this repository:</p> <p>1) Default example of MUSE</p> <p>2) Final input data</p> <p>3) Results spreadsheet</p>
Hands-on 2: How to add a service demand in MUSE
<p>Files available:</p> <ol> <li>default.zip - the default, unedited example</li> <li>default_final.zip - the final, edited example</li> <li>capacity_results.xlsx - the visualised results</li> </ol>
FLIM Data and analysis of the hands-on sessions "CF44-1 Label-free metabolic FLIM with 2 photon excitation"
<p>This FLIM dataset on HeLa cells and human astrocytes was acquired during the hands-on sessions <em>CF44-1 Label-free metabolic FLIM with 2 photon excitation</em> during the <a href="https://www.bioimaging.bmc.med.uni-muenchen.de/gerbiflim2024/flimprogram/index.html">German BioImaging </a><a href="https://www.bioimaging.bmc.med.uni-muenchen.de/gerbiflim2024/flimprogram/index.html">workshop on FLIM in Munich</a>.</p> <p>The FLIM data are analyzed with the open source software FLUTE available on GitHub: <a href="https://github.com/LaboratoryOpticsBiosciences/FLUTE"><strong><em>https://github.com/LaboratoryOpticsBiosciences/FLUTE</em></strong></a></p> <p>and published on Biological imaging Journal: <a href="https://www.cambridge.org/core/journals/biological-imaging/article/flute-a-python-gui-for-interactive-phasor-analysis-of-flim-data/862F290EC14187741BDA6B58E9868FA2"><strong><em>Gottlieb, D., Asadipour, B., Kostina, P., Ung, T., & Stringari, C. (2023). FLUTE: A Python GUI for interactive phasor analysis of FLIM data. Biological Imaging, 1-22. doi:10.1017/S2633903X23000211</em></strong></a></p>
Dataset for "Open Educational Resources for distributed hands-on teaching in molecular biology"
<p>This is the raw data for an article that describes novel OER consisting of low cost and locally produced public domain biological reagents, open source hardware and free software collaborative notebooks to teach LAMP DNA amplification, RT-PCR RNA detection, enzyme kinetics and fluorescence imaging. </p>
UnFAIR dataset for hands-on FAIR data sharing course
<p>UnFAIR dataset for hands-on FAIR data sharing course</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.