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2,366 results for “GRADE”
Dataset for the comparison of two Computational Thinking (CT) test for upper primary school (grades 3-4) : the Beginners' CT test (BCTt) and the competent CT test (cCTt)
<p>This dataset contains quantitative student data acquired during the administration of two validated Computational Thinking (CT) assessments for upper primary school (grades 3 and 4): the Beginners' CT test (BCTt) [1] and the comptent CT test (cCTt) [2]</p> <p>To compare the psychometric properties of both instruments a comparative analysis was conducted with data acquired in schools in Portugal from the same school districts. More specifically, we analyse the results of: </p> <p>- the BCTt test administered in March 2020 to 374 students in grades 3-4,</p> <p>- the cCTt test administered in April 2021 to 201 different students in grades 3-4.</p> <p>These students had no prior experience in Computational Thinking, as this was not part of the national curriculum at the times of administration. </p> <p> </p> <p>The detailed psychometric comparison is published in Frontiers in Psychology - Educational Psychology [3] and provides indications regarding the use of both instruments for grades 3-4. </p> <p> </p> <p>A README is included and provides additional information regarding :</p> <p>- the requirements for re-use. </p> <p>- the specific content of the 2 csv files</p> <p> </p> <p>The BCTt is available upon request to maria.zapata@urjc.es and the cCTt items are available in [2] with an editable version being available upon request to laila.elhamamsy@epfl.ch. </p> <p>In case of other inquiries, please contact: laila.elhamamsy@epfl.ch, maria.zapata@urjc.es or pedro.marcelino@treetree2.org</p> <p> </p> <p><strong>References</strong></p> <p>[1] M. Zapata-Cáceres, E. Martín-Barroso and M. Román-González, "Computational Thinking Test for Beginners: Design and Content Validation," <em>2020 IEEE Global Engineering Education Conference (EDUCON)</em>, 2020, pp. 1905-1914, doi: 10.1109/EDUCON45650.2020.9125368.</p> <p>[2] El-Hamamsy, L., Zapata-Cáceres, M., Barroso, E. M., Mondada, F., Zufferey, J. D., & Bruno, B. (2022). The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School. <em>Journal of Educational Computing Research</em>, <em>60</em>(7), 1818–1866. <a href="https://doi.org/10.1177/07356331221081753">https://doi.org/10.1177/07356331221081753</a></p> <p>[3] <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=LailaEl-Hamamsy&UID=781667">Laila El-Hamamsy</a>* , <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=Mar%C3%ADaZapata-C%C3%A1ceres&UID=2073859">María Zapata-Cáceres</a>, Pedro Marcelino, Jessica Dehler Zufferey, <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=BarbaraBruno&UID=893934">Barbara Bruno</a>, <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=EstefaniaMart%C3%ADn&UID=2086979">Estefanía Martín-Barroso</a> and <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=MarcosRom%C3%A1n-Gonz%C3%A1lez&UID=760761">Marcos Román-González</a> (2022). <a href="http://www.frontiersin.org/Journal/Abstract.aspx?d=0&name=Educational_Psychology&ART_DOI=10.3389/fpsyg.2022.1082659">Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: the Beginners' CT test (BCTt) and the competent CT test (cCTt)</a>. <em>Front. Psychol.</em> doi:10.3389/fpsyg.2022.1082659</p>
Dataset for the validation of a Computational Thinking test for upper primary school (grades 3-4)
<p>This dataset contains quantitative student data acquired during the administration of a new computational thinking assessment for upper primary school (grades 3 and 4). Over 1500 students (approximately half in grade 3 and half in grade 4) participated in the data collection which took place in January 2021 in the Canon Vaud in Switzerland. The data was used to validate the psychometric properties of the instrument in the referenced article. </p> <p> </p> <p>If you use any of the resources provided in this repository, please cite the following</p> <p>• The Zenodo repository, DOI: 10.5281/zenodo.5865573</p> <p>• The corresponding journal article</p> <p>• Licence : CC-BY-NC</p> <p> </p> <p>In case of inquiries, please contact laila.elhamamsy@epfl.ch</p>
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
Neonatal EEG Graded for Severity of Background Abnormalities
<p>The dataset consists of 169 multichannel EEG files of 1-hour in duration, recorded from 53 full-term newborns in the neonatal intensive care unit of the Cork University Maternity Hospital, Ireland. All 53 infants had received a diagnosis of hypoxic-ischaemic encephalopathy. The study to record the EEG was approved by the Cork Research Ethics Committee of the Cork Teaching Hospitals. Neonates were enrolled in the study after obtaining written and informed consent from a guardian or parent. The Cork Research Ethics Committee approved the publication of this fully-anonymised data set.</p> <p>Each 1-hour EEG was graded for severity of background abnormalities. Two experts in neonatal EEG graded each epoch independently. When grades differed between the experts, they jointly reviewed the EEG and agreed on a consensus grade. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Four grades were used: normal or mildly abnormal (grade 1), moderately abnormal (grade 2), severely abnormal (grade 3), and inactive (grade 4). The EEG data could be used to develop automated grading algorithms or to assist in training for the review of background neonatal EEG.</p> <p>See article O'Toole <em>et al</em>., Scientific Data, 2023 <a href="https://doi.org/10.1038/s41597-023-02002-8">DOI: 10.1038/s41597-023-02002-8</a> for a complete description of the dataset.</p> <p> </p> <p> </p>
TCGA Lower Grade Glioma (LGG) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about LGG. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
Synchrotron Diffraction During Stress Relaxation in CP Ti (grade 4)
<p>These data support the associated paper:</p> <p><strong>Cold Creep of Titanium: Analysis of stress relaxation using synchrotron diffraction and crystal plasticity simulations</strong><br> Yi Xiong, Phani Karamched, Chi-Toan Nguyen, David M Collins, Christopher M Magazzeni, Edmund Tarleton, Angus J Wilkinson<br> Acta Materialia (2020) vol. 199, 561-577<br> <a href="https://doi.org/10.1016/j.actamat.2020.08.010">https://doi.org/10.1016/j.actamat.2020.08.010</a></p> <p>The Authors' Accepted Manuscript version of the paper is available open access on arXiv:<br> <a href="https://arxiv.org/ftp/arxiv/papers/2003/2003.01682.pdf">https://arxiv.org/ftp/arxiv/papers/2003/2003.01682.pdf</a></p> <p> </p> <p>This dataset arises from an in situ stress relaxation experiment on commercially pure (grade 4) Ti undertaken at the Diamond Light Source, beamline ID12, as part of experiment EE17222. The sample was loaded to just beyond the yield point, and then held at constant strain for 5 minutes over which time the stress relaxed. The sample was then reloaded elastically and a further period of stress relaxation at fixed total strain undertaken. in total five periods of stress relaxation were imposed.</p> <p>Throughout the mechanical testing cycle powder diffraction patterns were recorded in the transmission geometry, at 1 second intervals using a 2d Pixium detector held 1097 mm from the sample. The beam energy was determined to be 79.79 keV. </p> <ul> <li>Diffraction Patterns are contained as 16 bit TIF files bundled into the Patterns_72995.zip file.</li> <li>Macroscopic mechanical test data are in the excel file MechTest_2995.xlsx.</li> <li>Small EBSD map in #.ctf format converted from Bruker #bcf file in CPg4.ctf file.</li> <li>Matlab + MTEX script to load and make simple plots from EBSD data file in CPg4Ti_EBSDmap.m file.</li> </ul> <p> </p> <p>Details of the MTEX orientation analysis package from Matlab can be found and freely downloaded at:<br> <a href="https://mtex-toolbox.github.io/">https://mtex-toolbox.github.io/</a></p> <p> </p> <p> </p>
Photonics4All Bookmark Grade (German)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How can light help improve students' grades?<br> <br> In a continent like Africa, night time comes early. For many families with no access to electricity, it means the end of access to light or the use of dangerous kerosene lamps. Thanks to photonics, cheap, portable, rechargeable lights can be provided to kids so they can continue their homework even when the sun has gone down. Families can recharge the lights during the day using in-built solar panels, and these lights can provide up to 4 hours of light and help students improve their grades at school.<br> All thanks to progress in Photonics.</p>
Photonics4All Bookmark Grade (Swedish)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How can light help improve students' grades?<br> <br> In a continent like Africa, night time comes early. For many families with no access to electricity, it means the end of access to light or the use of dangerous kerosene lamps. Thanks to photonics, cheap, portable, rechargeable lights can be provided to kids so they can continue their homework even when the sun has gone down. Families can recharge the lights during the day using in-built solar panels, and these lights can provide up to 4 hours of light and help students improve their grades at school.<br> All thanks to progress in Photonics.</p>
Photonics4All Bookmark Grade (French)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How can light help improve students' grades?<br> <br> In a continent like Africa, night time comes early. For many families with no access to electricity, it means the end of access to light or the use of dangerous kerosene lamps. Thanks to photonics, cheap, portable, rechargeable lights can be provided to kids so they can continue their homework even when the sun has gone down. Families can recharge the lights during the day using in-built solar panels, and these lights can provide up to 4 hours of light and help students improve their grades at school.<br> All thanks to progress in Photonics.</p>
Photonics4All Bookmark Grade (English)
<p>The purpose of the bookmarks for the project Photonics4All is to increase the public awareness of photonics and especially of the technological advances of photonics which have changed and improved everyday life (basic technology introduction).<br> <br> How can light help improve students' grades?<br> <br> In a continent like Africa, night time comes early. For many families with no access to electricity, it means the end of access to light or the use of dangerous kerosene lamps. Thanks to photonics, cheap, portable, rechargeable lights can be provided to kids so they can continue their homework even when the sun has gone down. Families can recharge the lights during the day using in-built solar panels, and these lights can provide up to 4 hours of light and help students improve their grades at school.<br> All thanks to progress in Photonics.</p>
Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
<p>This repository includes source data used to genrtate all tables and figures for published stduy "Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer". Besides, a small set of digital images for different class of cervical smear samples are included.</p>
Data from a cross-sectional study of fifth grade children in a sample of primary schools in Belgium that differ in amount of greenness at school and landscape level
<p>The data in this deposit were collected as part of the <code>B@SEBALL</code> project (Biodiversity at School Environments - Benefits for All). </p> <p>The project investigated how biodiversity in the school environment can positively affect children’s health and mental well-being. <code>B@SEBALL</code> also investigated the opportunities for reducing health inequalities among children via biodiversity at school environments.</p> <p>The data are organized according to the <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package standard</a>. All child-level and school-level data have been anonymized. Each data package is a collection of <code>csv</code> files and a <code>json</code> file. The <code>json</code> file holds descriptive information for all variables in all <code>csv</code> files. The <code>zip</code> file contains two frictionless data packages. The data packages contain information on 37 primary schools and 513 children. </p> <p>The data package, <code>data_package_an_zenodo_cleaned_data</code>, contains the original data in a tidied and cleaned format. It consists of 46 <code>csv</code> files. The files relate to the following contents:</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>landscape level variables</td> <td>wp1_landscape_level_data.csv</td> </tr> <tr> <td>metadata about participants</td> <td>wp2_participants_metadata.csv</td> </tr> <tr> <td>general school level data</td> <td>wp2_school_data.csv</td> </tr> <tr> <td>pollution data at school level</td> <td>wp3_ua_sirm_data.csv</td> </tr> <tr> <td>classroom data about air quality</td> <td>wp3_ucl_classroom_airquality.csv</td> </tr> <tr> <td>area of ecotopes in the school environment</td> <td>wp3_ucl_ecotope_categories.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_indicators.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_key.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenpatches.csv</td> </tr> <tr> <td>playground biodiversity indicators</td> <td>wp3_ucl_playground_biodiversity.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_child.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_line.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_linegroup.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_data.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_questions.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_data.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_questions.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_data.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_questions.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_data.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_key.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part1.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part2.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_key.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_data.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_key.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_data.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_key.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_data.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_key.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_data.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_key.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_data.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_key.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_data.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_key.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part1.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part2.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part3.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part4.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_key.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part1.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part2.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_key.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_data.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_key.csv</td> </tr> </tbody> </table> <p> </p> <p>The <code>data_package_an_zenodo_derived_data</code> data package, contains derived data that was calculated based on input from <code>data_package_an_zenodo_cleaned_data</code> at either child-level or at school-level.</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>derived data at child level</td> <td>wp1_child_level_key_variables.csv</td> </tr> <tr> <td>derived attention score based on d2-test data, aggregated to line-level</td> <td>wp1_d2_by_line_attention_score.csv</td> </tr> <tr> <td>derived data at school level</td> <td>wp1_school_level_key_variables.csv</td> </tr> </tbody> </table> <p>These data packages only store information for participants that gave consent for a particular part of the study and that gave consent for long-term storage of the data. There may therefore be slight differences between results published as part of the project consortium, which could make use of participant data that did not give consent for long-term data storage, and reproduction of these results based on the data in this data repository. We also note that the derived variables in the derived data package were calculated with these participants included and removal of participants for which we had no long-term storage consent was done after these calculations.</p> <p>As part of the project, microbiome data were also collected (both from cheek swabs on the children and from environmental samples), but this part of the data are not a part of this deposit and will be deposited in the European Nucleotide Archive (ENA).</p>
Extended dataset for the validation the competent Computational Thinking test in grades 3-6
<p>Extended dataset for the validation the competent Computational Thinking test in grades 3-6<br>=======================================================</p> <p>• If you publish material based on this dataset, please cite the following :</p> <p> • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, & Francesco Mondada (2023). Extended dataset for the validation of the competent Computational Thinking test in grades 3-6 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7983525 </p> <p> • The article on the validation of the computational thinking test for grades 3-6 : El-Hamamsy, L., Zapata-Cáceres, M., Martín-Barroso, E., Mondada, F., Zufferey, J. D., Bruno, B., & Román-González, M. (2025). The competent Computational Thinking test (cCTt): A valid, reliable and gender-fair test for longitudinal CT studies in grades 3–6. <em>Technology, Knowledge and Learning</em>, 1-55. https://doi.org/10.1007/s10758-024-09777-8 </p> <p>• License : This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0)</p> <p>• Creators : El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F.</p> <p>• Date May 30th 2023</p> <p>• Subject : Computational Thinking (CT), Assessment, Primary education, Psychometric validation</p> <p>• Dataset format : CSV. The dataset contains four files (one per grade, see detailed description below). Please note that the spreadsheets may contain missing values due to students not being present for a part of the data collection. To have access to the specific cCTt questions please refer to the original publication [1] and Zenodo repository [2] which provide the full set of questions and correct responses.</p> <p>• Dataset size < 500 kB</p> <p>• Data collection period : January and November 2021</p> <p>• Abbreviations :<br> - CT : Computational Thinking<br> - cCTt: competent CT test</p> <p>• Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543)</p> <p># References</p> <p>[1] El-Hamamsy, L., Zapata-Cáceres, M., Barroso, E. M., Mondada, F., Zufferey, J. D., & Bruno, B. (2022). The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School. Journal of Educational Computing Research, 60(7), 1818–1866. https://doi.org/10.1177/07356331221081753 </p> <p>[2] El-Hamamsy, L., Zapata-Cáceres, M., Marcelino, P., Dehler Zufferey, J., Bruno, B., Martín Barroso, E., & Román-González, M. (2022). Dataset for the comparison of two Computational Thinking (CT) test for upper primary school (grades 3-4) : the Beginners' CT test (BCTt) and the competent CT test (cCTt) (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5885034 </p> <p>[3] El-Hamamsy, L., Zapata-Cáceres, M., Martín-Barroso, E. <em>et al.</em> The Competent Computational Thinking Test (cCTt): A Valid, Reliable and Gender-Fair Test for Longitudinal CT Studies in Grades 3–6. <em>Tech Know Learn</em> (2025). https://doi.org/10.1007/s10758-024-09777-8</p> <p>[4] Brennan, K. and Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. page 25</p> <p>[5] El-Hamamsy, L., Zapata-Cáceres, M., Marcelino, P., Bruno, B., Dehler Zufferey, J., Martín-Barroso, E., & Román-González, M. (2022). Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: The Beginners’ CT test (BCTt) and the competent CT test (cCTt). Frontiers in Psychology, 13. https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1082659</p>
History in mathematics education - 6th grade
<p>Data set related to an experiment on the use of history of mathematics (ancient Chinese numeration) carried out with 108 sixth grade students. The experiment consists of three parts: ordinary mathematical exercises (items Mxx), an activity (in class, without data), and an evaluation (items Hxx).</p> <p>The experiment was carried out in October 2021.</p>
Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference grade performances - dataset
<p>This repository contains the data used for the analysis of the paper "Characterisation and calibration of PM sensors at high temporal resolution to reference grade performances" submitted to Heliyon and available as a pre-print:</p> <p>Bulot, Florentin M. J. and Ossont, Steven J. and Morris, Andrew and Basford, Philip J. and Easton, Natasha H. C. and Mitchell, Hazel L. and Foster, Gavin L. and Cox, Simon J. and Loxham, Matthew, Characterisation and Calibration of Low-Cost Pm Sensors at High Temporal Resolution to Reference-Grade Performance. Available at SSRN: <a href="https://ssrn.com/abstract=4360707">https://ssrn.com/abstract=4360707</a> or <a href="http://dx.doi.org/10.2139/ssrn.4360707">http://dx.doi.org/10.2139/ssrn.4360707</a></p> <p> </p> <p>The code used to conduct the data analysis is available at <a href="https://doi.org/10.5281/zenodo.7261417">https://doi.org/10.5281/zenodo.7261417</a></p> <p> </p> <p>.</p> <p> </p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p>Description of the files.</p> <p>202007_to_202107_nocs - contains the data from the low-cost sensors</p> <p>It contains the following headers:<br> - "sensor" - sensor id<br> - "site" - name of the air quality monitor hosting the sensor<br> - "median_PM1" - PM1 mass concentration (ug/m3)<br> - "median_PM10" - PM10 mass concentration (ug/m3)<br> - "median_PM25" - PM25 mass concentration (ug/m3)<br> - "median_PM4" - PM4 mass concentration (ug/m3) (only available for SPS30)<br> - "median_n05" - particle number concentration (SPS30) of particles between 0.3um and 0.5um<br> - "median_n1" - particle number concentration (SPS30) of particles between 0.3um and 1um<br> - "median_n10" - particle number concentration (SPS30) of particles between 0.3um and 10um<br> - "median_n25" - particle number concentration (SPS30) of particles between 0.3um and 2.5um<br> - "median_n4" - particle number concentration (SPS30) of particles between 0.3um and 4um<br> - "median_gr03um" - particle number concentration (PMS5003) of particles >0.3um<br> - "median_gr05um" - particle number concentration (PMS5003) of particles >0.5um<br> - "median_gr100um" - particle number concentration (PMS5003) of particles >10um<br> - "median_gr10um" - particle number concentration (PMS5003) of particles >1um<br> - "median_gr25um" - particle number concentration (PMS5003) of particles >2.5um<br> - "median_gr50um" - particle number concentration (PMS5003) of particles >5um<br> - "median_pm100_cf1" - PM10 mass concentration with cf1 calibration for PMS5003<br> - "median_pm10_cf1" - PM1 mass concentration with cf1 calibration for PMS5003<br> - "median_pm25_cf1" - PM25 mass concentration with cf1 calibration for PMS5003<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_pm_2min - contains the PM mass concentration data from the Fidas 200S.</p> <p>It contains the following headers:<br> - "PM2.5" - PM2.5 mass concentration (ug/m3) Fidas 200S<br> - "PM10" - PM10 mass concentration (ug/m3) Fidas 200S<br> - "PMtot" - PM total mass concentration (ug/m3) Fidas 200S<br> - "PM1" - PM1 mass concentration (ug/m3) Fidas 200S<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_weather_2min - contains the weather data from the Fidas 200S</p> <p>It contains the following headers:<br> - "rh" - relative humidity (%)<br> - "dew_point_temperature" - dew point temperature (Celsius)<br> - "air_pressure" - Air pressure (hPa)<br> - "temperature" - temperature (Celsius)<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"</p>
Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
<p>Original videos and reference bulk velocity and water depth data sets used to develop the study: <em>Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.</em></p> <p>The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.</p>
A subjective image quality assessment dataset of color graded inverse tone-mapped HDR images
<p>A subjective image quality assessment dataset that includes quality scores of HDR images generated by nine inverse tone mapping methods. The images in the dataset show a wide variety of artifacts commonly present in dynamic range expanded HDR images. Twelve image pairs comprised of an LDR image and its corresponding HDR version were used to conduct the subjective assessment study. These images contain scenes with a wide range of light conditions, representing challenging situations for dynamic range expansion methods.</p> <p>The image quality dataset includes subjective quality scores for 108 inverse HDR images obtained by the different dynamic range expansion methods, scaled in Just Objectionable Differences (JODs). In addition, it includes the raw data from pairwise comparisons obtained from subjective experimentation. The raw data is composed of 6480 trials collected from 15 human observers.</p> <p><strong>Files included</strong></p> <ul> <li>List of images used in our experiments (images.csv).</li> <li>LDR images used as input (ldr.zip).</li> <li>HDR images used as reference (hdr.zip).</li> <li>Inverse tone-mapped HDR images evaluated in our study (hdr_itmo.zip).</li> <li>Pairwise comparison results and JOD scores (subjective-scores.zip)</li> <li>The objective quality scores of the inverse tone-mapped HDR images, computed by each quality metric assessed (objective-scores.zip).</li> </ul> <p> </p>
Random forest regression for fuzzy grades
<p>The used dataset contained information about:</p> <ul> <li>students' gender (1 = male, 2 = female);</li> <li>schools' type (1 = scientific lyceums, 2 = other lyceums, 3 = technical schools, 4 = vocational schools);</li> <li>schools' macreregion (1 = Northwester Italy, 2 = Northeastern Italy, 3 = Central Italy, 4 = Southern Italy, 5 = Southern Italy and Isles);</li> <li>students' origin (1 = native Italian student, 2 = first-generation immigrant student, 3 = second-generation immigrant student);</li> <li>students' ESCS (continuous data);</li> <li>teacher-given grades in mathematics (from 1 to 10);</li> <li>students' achievements on the INVALSI mathematics test (continuous data);</li> <li>students' fuzzy grade, obtained as a combination of teacher-given grades and achievements in mathematics (from 1 to 10).</li> </ul>
A Comparison of Recreational and Survey-Grade Side-Scan Sonar Systems in Mapping Reservoir Fish Habitat in 3 Southwest Ohio Reservoirs
Littoral zone aquatic habitat is thought to play an important driver of aquatic organism population dynamics, but historically has been difficult to obtain at the whole waterbody scale because it is costly and time-consuming to collect with traditional aquatic habitat sampling methods. Here we used side-scan sonar to quantification of habitat features over large areas using two levels of equipment: recreational (consumer-grade) and professional (survey-grade). Our goal was to compare performance of the different side-scan sonars by analyzing their ability to map shoreline habitat features (wood, vegetation, and substrate) in three southwest Ohio reservoirs that contain the range of habitat features of interest to fisheries biologists. We used a low-cost Lowrance Active Imaging 3-in-1 system (≈$2,000 USD) recreational sonar and an EdgeTech 6205 system (≈$150,000 USD) survey-grade sonar to collect imagery along the shoreline of three reservoirs in Ohio. Using imagery from each system, We manually delineated patches of submerged woody debris, standing timber, aquatic vegetation, and benthic substrate in GIS. We also compared the size of uniquely identifiable submerged wood from paired imagery to understand potential biases between the systems.
BAGS: an automated barcode, audit & grade system for DNA barcode reference libraries
<p>Biodiversity studies greatly benefit from molecular tools, such as DNA metabarcoding, which provides an effective identification tool in biomonitoring and conservation programmes. The accuracy of species-level assignment, and consequent taxonomic coverage, relies on comprehensive DNA barcode reference libraries. The role of these libraries is to support species identification, but accidental errors in the generation of the barcodes may compromise their accuracy. Here we present an R-based application, BAGS (Barcode, Audit & Grade System; https://github.com/tadeu95/BAGS), that performs automated auditing and annotation of cytochrome c oxidase subunit I (COI) sequences libraries, for a given taxonomic group of animals, available in the Barcode of Life Data System (BOLD). This is followed by implementing a qualitative ranking system that assigns one of five grades (A to E) to each species in the reference library, according to the attributes of the data and congruency of species names with sequences clustered in Barcode Index Numbers (BINs). Our goal is to allow researchers to obtain the most useful and reliable data, highlighting and segregating records according to their congruency. Different tests were performed to perceive its usefulness and limitations. BAGS fulfils a significant gap in the current landscape of DNA barcoding research tools by quickly screening reference libraries to gauge the congruence status of data and facilitate the triage of ambiguous data for posterior review. Thereby, BAGS has the potential to become a valuable addition in forthcoming DNA metabarcoding studies, in the long term contributing to globally improve the quality and reliability of the public reference libraries.</p>
ScienceDex guides
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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.