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69 results for “perceptions students”
Dataset: Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions
<p># Dataset for Paper "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions" - Rev 1#</p> <p>This is the dataset for the paper titled "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions". All mapping study data has the prefix *MAP*, while all survey data the prefix *SUR*. It has been updated for a major revision at Springer Empirical Software Engineering (Rev 1).</p> <p>In case of questions, feel free to contact the author, Grischa Liebel, ORCID: https://orcid.org/0000-0002-3884-815X, current affiliation and email: Reykjavik University, Iceland, grischal@ru.is</p> <p>## Systematic Mapping Study ##<br> The mapping study data is mainly contained in the *MAPmappingStudy.xlsx* file. Different tabs are used for the two phases: exclusion by title and abstract (tab *title_abs*), and exclusion by fulltext (tab *fulltextScreening*). The final set of papers is obtained by filtering the *fulltextScreening* tab by included papers (Column V).</p> <p>The tab *fulltextScreening* contains a number of columns named \*range (e.g., *noStudentsRange*). These columns contain the unified/categorised values for the verbatim values listed in the column with the same name without *range*. For instance, *noStudentsRange* contains the range of students in the primary study, while *noStudents* contains the actual number obtained from the studies.</p> <p>The file *MAPvenues.txt* contains the included venues in the mapping study.</p> <p>Finally, the raw search results are provided as BIB/RIS files with the prefix *MAPRAW*.</p> <p>## Survey ##<br> The survey folder contains the survey pages (named *surveyPageN.pdf*), as well as the raw data in *surveyDataAnon.xlsx*. Note that free-text answers have been aggregated, anonymised, and sorted alphabetically in individual tabs. Similarly, countries that only occur once have been changed to Do Not Disclose answers, and all answers have been sorted randomly. All questions are listed by their acronym. The corresponding questions, as well as possible answers, are described in the *QuestionKey* tab.</p>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 1: Essays, Finland.
<p><strong>Version 1.1.</strong></p> <p><strong>Updated from </strong>https://zenodo.org/record/5517595</p> <p><strong>Changes: </strong>added .csv copy of the dataset. Clarified the README below, and added name of publishing journal. No other changes.</p> <p>Added a FEDORA project README below.</p> <p> </p> <p><strong>Description of dataset:</strong></p> <p>This matrix, presented in two formats (.xlsx and .csv), contains an English-language dataset (translated from original Finnish). The data relate to a research article <em>Students’ technological images of the future: implications for science and technology education, </em>accepted to be published in European Journal of Futures Research.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Here, excerpts from students' essays (the context of which is given in the article) are given. The excerpts are the ones that have been used in analysis for the article identified above. Further details will be available in the published article.</p> <p>385 such excerpts are given, originating in 57 essays in which upper-secondary students imagine the year 2035 or 2040 and the technological environment in which they would like to live at that time. The numbering was used to group codes for the analysis: type of technology (1), effect of technology (1E), and positive/negative framing (2A-C).</p> <p>The dataset is intended for providing transparency, but it may also be used for further research. Assistance may be available from the authors at reasonable request. Please note that the dataset presented here contains redundancies and a few additional codes that were not used in the analysis. The redundant quotations from the essays were not duplicated in the analysis, but were not removed from this spreadsheet export. Apologies for any inconvenience.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the quotations are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters, as these can easily be inferred.</p> <p>The related research article gives a fuller description of the dataset and analysis.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>--</p> <p> </p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2021</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Students_images_of_technological_futures_DATA_Zenodo_csv.csv</p> <p>Students_images_of_technological_futures_DATA_Zenodo_xlsx.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. https://zenodo.org/record/6397196</em></p>
Student's logs and perceptions of an automated assessment tool in a software engineering MOOC specialization
<p>Our dataset contains students' perceptions and usage of an automated assessment tool (MOOCauto) for obtaining formative feedback in software engineering assignments that are part of a MOOC specialization at Universidad Politécnica de Madrid (Spain), delivered by the MiriadaX platform. The dataset has previously been used in a study to evaluate students' perceptions of the tool and to analyze their usage patterns using Growth Mixture Models <a href="https://www.computer.org/csdl/magazine/so/5555/01/10196480/1P9AhkBLYXK">(López-Pernas et al., 2023)</a>. The code of each of the assignments is available on Github: <a href="https://github.com/ging-moocs">https://github.com/ging-moocs</a>.</p> <p>Our dataset contains two files:</p> <h2>MOOCauto usage logs</h2> <p>The first file is called<strong> moocauto_logs.csv </strong>and it contains 9,108 anonymized logs of students' use of the automated assessment tool in the MOOC specialization assignments. The columns of the dataset are as follows:</p> <ul> <li><strong>MOOCid</strong>: Unique numeric identifier for the MOOC (1-4)</li> <li><strong>MOOC: </strong>Name of the MOOC: Frontend Development, Backend Development, Git & Github, Fullstack Development</li> <li><strong>AssignmentName</strong>: Name of the assignment.</li> <li><strong>AssignmentId</strong>: Unique identifier for each assignment (1-17)</li> <li><strong>user: </strong>Unique identifier of the student (it varies per assignment)</li> <li><strong>timestamp: </strong>Time in which the assessment was performed</li> <li><strong>score</strong>: Score obtained (0-10)</li> </ul> <h2>Students' perceptions of MOOCauto</h2> <p>The second file is called <strong>moocauto_questionnaire.csv</strong> and it contains 213 students' responses to the questionnaire conducted at the end of each MOOC in order to evaluate their opinion of the tool and perception on usefulness, ease of use, and other aspects related to the Technology Acceptance Model (TAM). The questions were as follows:</p> <ul> <li><strong>What is your general opinion of MOOCauto?</strong> (1 Horrible - 5 Excellent)</li> <li><strong>Indicate your level of agreement with the following statements </strong>(1 Strongly disagree - 5 Strongly agree) <ul> <li>MOOCauto has been easy to install</li> <li>MOOCauto has been easy to use</li> <li>The feedback provided by MOOCauto was easy to understand</li> <li>The feedback provided by MOOCauto was useful</li> <li>The feedback provided by MOOCauto helped me improve my assignments</li> <li>The documentation Of MOOCauto was useful</li> <li>MOOCauto has increased my motivation to work on the assignments</li> <li>I prefer the feedback from MOOCauto than from peer assessment</li> <li>I would like to have a bot like MOOCauto in other MOOCs</li> </ul> </li> <li><strong>How useful do you perceive the following features of MOOCauto?</strong> (1 Useless - 5 Very useful) <ul> <li>It works locally on my computer</li> <li>It allows to run the test suite as many times as I want</li> <li>It provides instantaneous feedback every time the test suite is executed</li> <li>It has documentation that explains its use and available options</li> </ul> </li> </ul>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 3: Interviews, Finland.
<p>Finnish-language dataset. Related to a research article that is awaiting acceptance for publication: <em>Future, technology and agency: Students’ experiences from a course on futures thinking and quantum computing</em>.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Instead of students' interviews (the context of which is given in the article). In a nutshell, 21 upper-secondary school students were interviewed in 2018 regarding their experiences on taking an experimental science course that combined ideas from futures thinking and quantum computing. The present dataset contains all 245 transcribed passages from 21 student interviews that were initially marked as relevant to the research goals (i.e. how students saw their conceptions change over the course). Additionally, for each passage the final coding that was used in the analysis for the research paper is shown. The "number-letter codes" were used as shorthands; the full names of the codes correspond closely with the final, English-language codes in the paper.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the passages are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters. Please also note that the character > marks change of speaker. Identifying the interviewer and interviewee should be straighforward based on the context.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>--</p> <p> </p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, Elina Palmgren (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2021</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Future_technology_agency_DATA_CSV.csv</p> <p>Future_technology_agency_DATA_XLSX.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. https://zenodo.org/record/4734161</em></p>
Students' perceptions of an environmental education program.
<p>This database contains 185 records. It was developed using a questionnaire to evaluate students' perceptions of an environmental educommunication program. The variables measured are:</p> <p>1 Gender: 1 male, 2 female, 3 non-binary</p> <p>2 Age: 12 to 17</p> <p>3 Type of school. 1: private, 2: public, 3: state-funded private school</p> <p>4 -11: measured using the following scale: </p> <p> 5 totally agree</p> <p> 4 agree</p> <p> 3 neither agree nor disagree</p> <p> 2 disagree</p> <p> 1 strongly disagree</p>
Dataset: We Do Not Understand What It Says -- Studying Student Perceptions of Software Modelling
<p>The dataset contains two supporting documents for the paper title, "We Do Not Understand What It Says -- Studying Student Perceptions of Software Modelling". The first one is an excel sheet containing interview transcripts of 13 of the participants of this study (who agreed to publish their statements) and the second is an appendix file containing the interview guide (questionnaire used for interviews with students and instructors) used in the case study. </p> <p>The interview transcripts are supported by "in-vivo coding" used by both authors separately during analysis. </p>
Students' perceptions and attitudes towards science in PERFORM: Survey template
<p>This document contains the survey instrument developed to measure the impact of the PERFORM project RRI approach in students’ attitudes and pro-scientific behaviour and learning. It was a self-administered questionnaire combining close-ended with some open-ended qüestions. It was handled to students before and after the development of PERFORM participatory workshops to the participant students and a control group in order to:</p> <ul> <li>Obtain basic demographic data (those compatible with PERFORM ethical guidelines)</li> <li>Explore initial attitudes and perceptions towards science and STEM careers, with an emphasis on RRI-related dimensions (gender stereotypes, ethical issues, inclusiveness, engagement and critical/creative thinking) and potential changes after the implementation of participative performances (PERSEIAS in PERFORM jergon)</li> <li>Explore participants’ perceptions towards the PERSEIAS process, also as an input to inform the design of focus groups</li> </ul>
Data sets - The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities
<p>Data sets </p> <p>The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities. <br>In the period from February to October 2024, a survey of 112 computer science teachers in Kazakhstan (Pavlodar region) was conducted to determine attitudes to inclusive education, motivation to teach, and perception of the possible impact of computer science on students with mental disabilities.</p> <p>Questionnaire <br>https://docs.google.com/document/d/1LzukKSqW_mHMZXbMtN0ecmmU4cKJiwgf0laTWBHQSng/edit?usp=sharing</p> <p><strong>This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP14872400).</strong></p>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 2: Essays, Finland.
<p><strong>Version 1.0.</strong></p> <p><strong>Related to </strong>https://zenodo.org/record/4734161</p> <p><strong>Changes: README </strong>added to this description page.</p> <p> </p> <p>Description</p> <p>This matrix, presented in two formats (.xlsx and .csv), contains an English-language dataset (translated from original Finnish). The data relate to a research article <em>Agency and transformative potential of technology in students’ images of the future: Futures thinking as critical scientific literacy, </em>accepted to be published in Science & Education.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Here, excerpts from students' essays (the context of which is given in the article) are given. The excerpts are the ones that have been used in analysis for the article identified above. Further details will be available in the published article.</p> <p>A number of excerpts are given, originating in 57 essays in which upper-secondary students imagine the year 2035 or 2040 and the technological environment in which they would like to live at that time. This overlaps with another dataset (see link above); a numbering scheme was used to group codes for the analysis: type of technology (1), effect of technology (1E), and positive/negative framing (2A). The 1-codes are omitted. While these are unrelated to the article of this analysis, the 2B-2D codes correspond to the categories in the article. Due to some unfortunate redundancies, some excerpts are separated in this version. However, the data should provide transparency for the analysis.</p> <p>The dataset is intended for providing transparency, but it may also be used for further research, in which case some processing is needed. Assistance (clarification) may be available from the authors at reasonable request. Please note that the dataset presented here contains redundancies and may contain a few additional codes that were not used in the analysis. The redundant quotations from the essays were not duplicated in the analysis, but were not removed from this spreadsheet export. Apologies for any inconvenience.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the quotations are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters, as these can easily be inferred.</p> <p>The related research article gives a fuller description of the dataset and analysis.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>---</p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, Jari Lavonen (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2023</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Students_images_of_tech_futures_agency_DATA_Zenodo_csv.csv</p> <p>Students_images_of_tech_futures_agency_DATA_Zenodo_xlsx.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. </em><em>https://zenodo.org/record/6397196</em></p> <p> </p>
Corporate Social Responsibility & Students' Perceptions: Evidence from Indian Higher Education Institutions
<p>Table 1 - Methodology </p> <p>Table 2 - Affirmations on CSR under study</p> <p>Table 3 - Characterization of the Sample</p> <p>Table 4 - Factor Analysis Results</p> <p>Table 5 - Validation of the assumptions of MANOVA</p>
Corporate Social Responsibility & Students' Perceptions: Evidence from Indian Higher Education Institutions
<p>Raw Data Table</p> <p>Table 1 - Methodology </p> <p>Table 2 - Affirmations on CSR under study</p> <p>Table 3 - Characterization of the Sample</p> <p>Table 4 - Factor Analysis Results</p> <p>Table 5 - Validation of the assumptions of MANOVA</p>
Corporate Social Responsibility & Students' Perceptions: Evidence from Indian Higher Education Institutions
<p>Blank Instrument</p> <p>Coding File</p> <p>Data File</p> <p>Raw Data</p> <p>Table 1 - Methodology </p> <p>Table 2 - Affirmations on CSR under study</p> <p>Table 3 - Characterization of the Sample</p> <p>Table 4 - Factor Analysis Results</p> <p>Table 5 - Validation of the assumptions of MANOVA</p>
Corporate Social Responsibility & Students' Perceptions: Evidence from Indian Higher Education Institutions
<p>Raw Data</p> <p>Table 1 - Methodology </p> <p>Table 2 - Affirmations on CSR under study</p> <p>Table 3 - Characterization of the Sample</p> <p>Table 4 - Factor Analysis Results</p> <p>Table 5 - Validation of the assumptions of MANOVA</p>
The Effects of Education on Students’ Perception of Modeling in Software Engineering
<p>The attached file accompanies the paper titled "The Effects of Education on Students’ Perception of Modeling in Software Engineering". This file contains both the raw data and summary data from the survey conducted at the three institutions, NAU, BGU, and Concordia.</p>
Undergraduate Students' Perceptions and Experiences in Learning Statistics
<p>This dataset is an online survey participated voluntarily by undergraduate students in 2021 at the University of Hargeisa. The main variables included were sociodemographic variables, students' perceptions of statistics, challenges faced by students in learning statistics, engagement with statistics by students, and finally (but not least) students' performance in statistics.</p>
Students' Perception towards e-learning during COVID-19 Pandemic in Indonesia
<p>The study explores university students' perceptions of e-learning in the context of the ongoing COVID-19 epidemic. The study finds that students prefer e-learning because it enables them to connect with their lecturers and fellow students and engage with their study materials at their leisure, and with the freedom to choose their preferred location and time. One of the key reason students choose e-learning is the ease with which they may obtain study resources. The research methods used with a quantitative model, where the sample tested represented the student of 1137 respondents from 43 universities in Indonesia. The study's findings show the weakness of e-learning is that the majority of respondents responded in turn to interaction with lecturers (52,7%). The study also identified that the majority of respondents have a moderate mastery of technology, 1038 individuals (77.6%), while the remainder has poor knowledge of technology, as many as 52 people (3.9%). According to the study, e-learning technology enables quick access to information, which results in students developing a favorable attitude toward it based on its utility, self-efficacy, the convenience of use, and student behavior related to e-learning. The study verifies the utility of e-learning by demonstrating how it enables students to study from any geographical location, which is not achievable with face-to-face instruction.</p>
College students' perception of e-learning during COVID-19 pandemic in Indonesia: a cross -sectional study
<p>Covid-19 has prompted higher education institutions around the globe to relocate offline classes to online classes. Universities in Indonesia were no exception. Indonesia has already developed distance education systems, and there were many challenges to utilizing e-learning. Due to the pandemic, all colleges across Indonesia were compelled to use online platforms to resume their studies.</p> <p>This study indicates e-learning perceptions' importance in knowledge mastery, social competence, and media literacy abilities. The study assesses college students' attitudes toward e-learning during the ongoing COVID-19 and will be utilized as an evaluation tool by The Higher Education of Education and Culture of the Republic of Indonesia.</p> <p>The research methods used with a quantitative model, where the sample tested represented the student of 1137 <a href="#_msocom_1">[DJ1]</a> respondents from 43 universities in Indonesia.</p> <p>The study's findings show a commonly perceived weakness of e-learning was that the majority of respondents got a lack interaction with lecturers (57,6%). The study also identified that the majority of respondents have a moderate mastery of technology 1038 respondents (77.6%), while the remainder has poor and high knowledge of technology. One of the key reason students implement e-learning is the ease with which they may obtain study resources. </p> <p>According to the study, e-learning technology enables quick access to information, which results in students developing a favorable attitude toward it based on its utility, self-efficacy, the convenience of use, and student behavior related to e-learning. The study verifies the utility of e-learning by demonstrating how it enables students to study from any geographical location, which is not achievable with face-to-face instruction.</p>
The Relationship between Self-Perception of Smile Aesthetics and Personality Traits of University Students: A Cross-Sectional Study
<p>Los documentos contienen la base de datos que se obtuvo del estudio "The Relationship between Self-Perception of Smile Aesthetics and Personality Traits of University Students: A Cross-Sectional Study", siendo datos reales de estudiantes de una universidad nacional de Perú. Luego se encuentra el documento con los instrumentos, en formato como se presentó para la ejecución. </p> <p> </p>
Dataset on motivations and perceptions regarding teaching as a career among teacher education students in Japan
<p>This dataset was generated using the adapted Japanese version of the FIT-Choice scale, a framework originally proposed by Watt and Richardson. The adaptation and validation of the Japanese version were carried out by Saito, who utilized this dataset for the process. The data provide insights into the characteristics of motivations and perceptions regarding teaching as a career among teacher education students in Japan.</p>
Metagenomics education in a modular CURE format positively affects students' scientific discovery perception and data analytical skills
<p>The targeted metagenomics study performed by Pollock et al. <em>(Pollock 2018)</em> was part of the Global Coral Microbiome Project <em>(Vega Thurber Lab 2014)</em>. Since the original publication of Pollock et al. <em>(Pollock 2018) </em>was written for a highly specialized academic public, we provided Bsc. students with a short introduction of the paper and guided them through the metadata table stored in the <em>gcmp16S_map_r25.txt </em>file under the <em>GCMP Australia sequence data, OTU tables, and metadata</em> folder (<a href="https://doi.org/10.6084/m9.figshare.c.3855466.v2">https://doi.org/10.6084/m9.figshare.c.3855466.v2</a>).</p> <p>In order to process the fastq-files and obtain OTU tables, a custom-made pipeline on a Galaxy instance <em>(Galaxy Community)</em> was used. This pipeline contained data analytical tools obtained from the Naturalis Biodiversity Center GitHub environment (<a href="https://github.com/naturalis">https://github.com/naturalis</a>). In order to align with the newest Galaxy best practices, we updated the tools and links to the original as well as the updated GitHub and toolshed versions are included in the references list.</p> <p>The names of the original raw fastq-files contain many dots which can hamper file-type recognition during downstream analyses in Galaxy. Therefore, prior to distributing the fastq-files to the students, these names were renamed using the ManageZIP tool <em>(The BLFS Development Team 1999-2023)</em>. Dots were replaced with underscores, with an exception made for the last 2 file type extension dots. For example, the file name <em>E1.2.Tur.pelt.1.20140814.M_S71_L001_R1_001.fastq.gz</em> was changed into <em>E1_2_Tur_pelt_1_20140814_M_S71_L001_R1_001_1.fastq.gz</em>. Similarly, the filenames in the metadata table were adjusted accordingly. The total collection of renamed file names is available through this zenodo repository as wel as the accompanying metadata file.</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.