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Open Education in European Libraries of Higher Education 2023 Dataset

This is the dataset that appends the 2023 edition of the SPARC Europe Open Education Survey amongst Higher Education institutions in Europe, in consultation with the European Network of Open Education Librarians (ENOEL). The report is for policymakers and practitioners who support or intend to support OE and OER in higher education institutions and academic libraries.

opencc-by-4.0Mar 2024View details →

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Student discourse in small-group collaborative contexts in real-world higher education

This dataset contains anonymised student discourse in small-group collaborative contexts in real-world higher education settings. There are 28 sessions, composed of 10799 utterances. The columns consist of 13 columns: <ul> <li>session: the session number</li> <li>start: the starting time of the utterance</li> <li>end: the ending time of the utterance</li> <li>speaker: anonymised speaker name</li> <li>content: the content of the utterance</li> <li>ep: the episode number of the utterance. An episode refers to a single topic of discussion with multiple utterances.</li> <li>C: a binary value indicating the presence or absence of a cognitive challenge, where 0 means no challenge, and 1 means there is a cognitive challenge.</li> <li>E: a binary value indicating the presence or absence of an emotional/motivational challenge, where 0 means no challenge, and 1 means there is an emotional/motivational challenge.</li> <li>M: a binary value indicating the presence or absence of a metacognitive challenge, where 0 means no challenge, and 1 means there is a metacognitive challenge.</li> <li>T: a binary value indicating the presence or absence of a technical/other challenge, where 0 means no challenge, and 1 means there is a technical/other challenge.</li> <li>TA: a binary value indicating the presence or absence of the regulatory process "task analysis," where 0 means no regulation and 1 means task analysis is present.</li> <li>MC: a binary value indicating the presence or absence of the regulatory process "monitoring/control," where 0 means no regulation and 1 means monitoring/control is present.</li> <li>RA: a binary value indicating the presence or absence of the regulatory process "reflection/adaptation," where 0 means no regulation and 1 means reflection/adaptation is present.</li> </ul> This annotated dataset was used in the following paper to model challenge moments. For more details on how the dataset was generated, please refer to the paper. <div> <div>Suraworachet, W., Seon, J., & Cukurova, M. (2024). Predicting challenge moments from students’ discourse: A comparison of GPT-4 to two traditional natural language processing approaches. Proceedings of the 14th Learning Analytics and Knowledge Conference, 473–485. <a href="https://doi.org/10.1145/3636555.3636905">https://doi.org/10.1145/3636555.3636905</a></div> <div> </div> </div>

opencc-by-4.0Nov 2024View details →

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European Education Provision on Public Procuement of Innovation: find your study course

The growth of procurement and attracting future leaders can be enhanced through the visibility of procurement education. With the goal to map procurement education, PROCEDIN surveyed universities across Europe. The project partners compiled data on <a href="https://procedin.eu/database-of-european-education-provision/">European universities that provide master’s or bachelor’s level education in procurement</a>, sustainability, and entrepreneurship. In the present database, there are 114 different universities from 27 different countries, which together offer 1679 courses. In this dataset you can find all the opportunities for training in POI in European universities and beyond.

opencc-by-4.0Sep 2024View details →

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Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort

# Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort" - Rev #1 This is the dataset for the paper titled "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort". In case of questions, feel free to contact the authors, *anonymised*, ORCID: https://orcid.org/*anonymised*, current affiliation and email: *anonymised* ## Survey 2019 ## The raw survey data for the initial 2019 survey is available in the file *survey2019_anon.csv*. Note that the data is anonymised as free-text comments have been removed. Explanations on the variables and their levels are given in the files *variables_survey2019.csv* and *values_survey2019.csv*. The questionnaire for the 2019 survey is contained in *survey2019_instrument.pdf*. ## Survey 2020 ## The raw survey data for the 2020 survey is available in the file *rdata_anon_survey2020.csv*. Additional scripts are supplied to reproduce the exploratory factor analysis. The main entry is the file *EFA.R*, which imports the data. The file contains some comments on the process. The questionnaire for the 2020 survey is contained in *survey2020_instrument.pdf*. ## Interviews ## The interview guide used for the five interviews is available in the file *interview_instrument.pdf*.

opencc-by-4.0May 2021View details →

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Indices of the supply of Chinese Higher Education

The higher education indices for 31 Chinese provinces proposed by Borsi, Valerio Mendoza, and Comim (2021).

opencc-by-4.0Nov 2021View details →

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Educational transformation and network learning dataset – qualitative data from an international collaborative EU-project

We are releasing our dataset of workshop outcomes acquired from the annual consortium conferences organized by the international “NextFood” consortium. The purpose of this project is to develop new ways of educating the future sustainability leaders of the agrifood sector, making sure that the professionals (farmers, advisers, businesses, students) have the right set of skills and competences needed to tackle the sustainability challenges we face ahead. Data gathering started from May 2018 yielding considerable amount of data on achievements, challenges and action plans related to educational transformation. This dataset will be updated by the time of project finalization. This work was funded by the European Union, through the Horizon 2020 project “NextFood”, Grant agreement No. 771738.

opencc-by-4.0Dec 2021View details →

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Data Evolution of Blended Learning and its Prospects in Management Education

Data Scopus - Article "Evolution of Blended Learning and its Prospects in Management Education"

opencc-by-4.0Jan 2022View details →

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Brazilian BIM certifications: continuing education

Survey of specializations, lato sensu graduate programs, which address BIM by Brazilian federative unit. So far, the study has been carried out for Pernambuco and Panará. The specialization courses were characterized by the fields: DATE OF SURVEY: year of data collection in the e-MEC; MEC IES CODE: Institution Code in the e-MEC; COURSE CODE: Course code in the e-MEC https://emec.mec.gov.br/; COURSE NAME: Title of the specialization; COURSE CLASSIFICATION: indicates the BIM focus adopted in the PROJECT, MANAGEMENT, WORK, OPERATION or INDUSTRIALIZATION. INSTITUTION (IES): name of the institution offering the specialization; Acronym: acronym for the institution that offers the specialization; SITE: URL of disclosure and with specialization data; MODALITY: in person or at a distance, as informed in the e-MEC; UF: federative unit of the institution offering the specialization; VACANCIES: number of vacancies offered per class; HOURS: total class hours to obtain the title of specialist; TARGET AUDIENCE: ENG for engineer; ARQ for architect; URB for urban planner; CONSTR for constructors; DES for designer; ILUM for lighting specialist; TITULATION: degree obtained with the specialization; BIM-ORIENTED SUBJECTS: number of specific disciplines for the development of BIM skills: TOTAL NUMBER OF SUBJECTS; total number of subjects in the specialization; %BIM: relationship between BIM-oriented courses and the total number of courses; TCC: if the specialization requires the development of a Course Completion Work, it can be the values YES, NO, NOT DECLARED; INTEGRATING SUBJECT: whether the specialization offers a specific discipline of collaborative design or content integration. It can be filled in as UNIDENTIFIED or the name of the discipline with this focus; ADMINISTRATIVE CATEGORY: administrative category of HEI, if it is Public or Private for profit START DATE: start date of offering the specialization.

opencc-by-4.0Feb 2022View details →

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SCoRe-LFC: Platform data on crowd collaboration in higher education

SCoRe (short for Student Crowd Research) was a joint research project between the Universities of Bremen (UB), Hamburg (UHH) and Kiel (CAU), the Macromedia University of Applied Sciences (HMM) and the Ghostthinker GmbH (GT). The overall aim of the project was to develop a digital learning and research environment as well as didactic scenarios that foster collaborative processes of research-based learning in large groups of students (crowd). The main subject area was research for sustainable development. Towards this end, the project consortium drew on the partners’ expertise on advanced video-technologies (HHM), virtual collaboration in interdisciplinary and largescale groups (CAU), research-based learning (UHH) and education and research for sustainable development (UB). To achieve its goals, the project adopted a design-based research approach. The Project started in Oct. 2018 and was funded for 3.5 years by the Federal Ministry of Education and Research (BMBF) in a funding scheme on digital higher education. The work in the department of media-pedagogy and educational computer sciences at Kiel University was focused on the sub-project &bdquo;SCoRe - learning and researching in the crowd“. The sub-project was aimed at the development, implementation and evaluation of pedagogical and organizational measures for the seeding, coordination and orchestration of collaborative research and learning processes in crowd scenarios. Particular emphasis was placed on crowd-specific characteristics of productive knowledge work in large and interdisciplinary groups. This dataset contains interaction data as well as textual content data. As ongoing development of the software platform led to a continuous integration of new features into the platform itself as well as changes to the data collection functions, making this an evolving dataset. Some inconsistencies exist due to software bugs. <a href="https://scorelfc.github.io/gestaltungsbericht3/img/datastructure.png">Platform data structure diagram</a>  Further Readings to gain an understanding of the platform and its interaction posbilities (in german): <a href="https://scorelfc.github.io/gestaltungsbericht2">Design Report Prototype 2</a> <a href="https://scorelfc.github.io/gestaltungsbericht3">Design Report Prototype 3</a>   Contained Files <table> <tbody> <tr> <td> Filename </td> <td> Description </td> </tr> <tr> <td> annotations.csv </td> <td> Annotations (comment and/or drawings on the video) of video files </td> </tr> <tr> <td> content.csv </td> <td> Content of <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u03/">sections</a> </td> </tr> <tr> <td> events.csv </td> <td> All events triggered by user interaction </td> </tr> <tr> <td> media.csv </td> <td> Uploaded <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u03/">images and videos</a> </td> </tr> <tr> <td> messages.csv </td> <td> <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u08/">Chat messages</a> </td> </tr> <tr> <td> sequences.csv </td> <td> <a href="https://scorelfc.github.io/gestaltungsbericht3/umsetzungen/u21/">Sequences</a> of video files </td> </tr> </tbody> </table>   Columns (not all are present in each file. 0, “null” or “none” might mean not applicable) <table> <tbody> <tr> <td> Column name </td> <td> Description </td> <td> Format </td> </tr> <tr> <td> Index (empty column name)  </td> <td> unique identifier of the corresponding event in the original dataset </td> <td> UUID (int on rare occasions) </td> </tr> <tr> <td> Actor-Name </td> <td> Unique identifier of an actor – “MA” identifies project staff  </td> <td> string </td> </tr> <tr> <td> Annotation-ID </td> <td> Unique identifier of an annotation </td> <td> int </td> </tr> <tr> <td> Annotation-Text </td> <td> Label of an annotation </td> <td> string </td> </tr> <tr> <td> Version-ID </td> <td> Unique identifier of a version of an auditable object (e.g. a section) </td> <td> int </td> </tr> <tr> <td> Version-Changelog </td> <td> Changelog message on saving a new version of a section </td> <td> string </td> </tr> <tr> <td> Case-ID </td> <td> Unique identifier of a case (if applicable, coded by research team) </td> <td> String  </td> </tr> <tr> <td> Media-Caption </td> <td> Title of a media file (image, video) </td> <td> string </td> </tr> <tr> <td> Media-ID </td> <td> Unique identifier of a media file (image, video) </td> <td> int </td> </tr> <tr> <td> Media-Timestamp </td> <td> Timestamp in a video </td> <td> int </td> </tr> <tr> <td> Message-ID </td> <td> Unique identifier of a chat message </td> <td> int </td> </tr> <tr> <td> Message-Text </td> <td> Content of a chat-message </td> <td> string </td> </tr> <tr> <td> Object-Type </td> <td> Type of an object an action refers to </td> <td> string </td> </tr> <tr> <td> Project-ID </td> <td> Unique identifier of a project </td> <td> int </td> </tr> <tr> <td> Research-Task-Type </td> <td> Type of research task (if applicable, coded by research team, see table below) </td> <td> string </td> </tr> <tr> <td> Section-Content </td> <td> Content of a section (in a specific version)  </td> <td> string </td> </tr> <tr> <td> Section-Outline-Level </td> <td> Outline level of a section (in a specific version) </td> <td> int </td> </tr> <tr> <td> Section-ID </td> <td> Unique identifier of a section </td> <td> int </td> </tr> <tr> <td> Section-Index </td> <td> Position of a section in the project (in a specific version) </td> <td> int </td> </tr> <tr> <td> Section-Status </td> <td> Status of a section </td> <td> int </td> </tr> <tr> <td> Section-Title </td> <td> Title of a section (in a specific version) </td> <td> string </td> </tr> <tr> <td> Sequence-Description </td> <td> Description of a video sequence </td> <td> string </td> </tr> <tr> <td> Sequence-Duration </td> <td> Length of a video sequence </td> <td> int </td> </tr> <tr> <td> Sequence-ID </td> <td> Unique identifier of a sequence </td> <td> int </td> </tr> <tr> <td> Sequence-Timestamp </td> <td> Timestamp of the start of a sequence in a video </td> <td> int </td> </tr> <tr> <td> timestamp </td> <td> timestamp of an event </td> <td> datetime </td> </tr> <tr> <td> Verb </td> <td> Action type of an event (see table below) </td> <td> string </td> </tr> </tbody> </table>   Verbs <table> <tbody> <tr> <td> Value </td> <td> Description </td> </tr> <tr> <td> canceled editing of </td> <td> Actor canceled editing of a section </td> </tr> <tr> <td> clicked </td> <td> Actor clicked a link </td> </tr> <tr> <td> collapsed </td> <td> Actor collapsed a section (hides its content form being viewed) </td> </tr> <tr> <td> compared versions of </td> <td> Actor compared two versions of a section </td> </tr> <tr> <td> created </td> <td> Actor created a new section, video sequence, video annotation, video playback command, project or news </td> </tr> <tr> <td> deleted </td> <td> Actor deleted a section, video sequence, video annotation, video playback command, project or news </td> </tr> <tr> <td> ended </td> <td> Actor played a video hitting its end </td> </tr> <tr> <td> expanded </td> <td> Actor expanded a collapsed section </td> </tr> <tr> <td> inserted </td> <td> Actor inserted a video comment (on occasions instead of created) </td> </tr> <tr> <td> left </td> <td> Actor left a context (e.g. a project, a chat window) by e.g. closing it using platform functions, changing a browser tab, etc. </td> </tr> <tr> <td> mentioned </td> <td> Actor mentioned another actor in a chat message </td> </tr> <tr> <td> opened </td> <td> Actor opened a context (e.g. a project, a chat window) by e.g. accessing it using platform functions or changing a browser tab </td> </tr> <tr> <td> paused </td> <td> Actor paused a video </td> </tr> <tr> <td> played </td> <td> Actor played a video </td> </tr> <tr> <td> read </td> <td> Actor read an activity message or news </td> </tr> <tr> <td> read all messages and activities of </td> <td> Actor used switch to mark all chat and activity messages read </td> </tr> <tr> <td> restored </td> <td> Actor restored a deleted section </td> </tr> <tr> <td> reverted </td> <td> Actor restored a deleted section </td> </tr> <tr> <td> reverted version of </td> <td> Actor reverted a section to an earlier version </td> </tr> <tr> <td> seeked </td> <td> Actor seeked on a video timeline </td> </tr> <tr> <td> sent </td> <td> Actor sent a chat message </td> </tr> <tr> <td> started editing of </td> <td> Actor started editing of a section </td> </tr> <tr> <td> switched </td> <td> Actor switched chat focus between project and section chat </td> </tr> <tr> <td> typed </td> <td> Actor typed into the chat </td> </tr> <tr> <td> updated </td> <td> Actor updated an existing section (changing content, heading, heading-depth or status), video sequence, video annotation, video playback command, project or news </td> </tr> <tr> <td> uploaded </td> <td> Actor uploaded an image or video </td> </tr> <tr> <td> viewed </td> <td> Actor viewed an entity (had it on screen for 5 seconds), e.g. a section or video comment </td> </tr> <tr> <td> viewed history of </td> <td> Actor viewed history of a section </td> </tr> </tbody> </table>   Project-ID <table> <tbody> <tr> <td> Project-ID </td> <td> Case-IDs </td> <td> Title </td> <td>   Type </td> <td> Period </td> </tr> <tr> <td> 2 </td> <td> a1-a* </td> <td> Urbane Grünflächen </td> <td> Research project </td> <td> 1.11.20-31.3.21 </td> </tr> <tr> <td> 4 </td> <td> b1-b* </td> <td> Nachhaltiger Verkehr </td> <td> Research project </td> <td> 1.11.20-31.3.21 </td> </tr> <tr> <td> 166 </td> <td> c1-c* </td> <td> UGF - Urbane Grünflächen </td> <td> Research project </td> <td> 1.4.21-30.09.21 </td> </tr> <tr> <td> 168 </td> <td>   </td> <td> LGS - Foyer </td> <td> Onboarding of students in LGS Projects </td> <td> 1.4.21-30.09.21 </td> </tr> <tr> <td> 188 </td> <td>   </td> <td> LGS - Reflexionsraum </td> <td> Reflection project for students in LGS Projects </td> <td> 1.4.21-30.09.21 </td> </tr> <tr> <td> 207 </td> <td>   </td> <td> LGS - Nachhaltiger Konsum </td> <td> Research project </td> <td> 1.4.21-30.09.21 </td> </tr> <tr> <td> 210 </td> <td>   </td> <td> LGS - Bildungsangebote für nachhaltige Entwicklung </td> <td> Research project </td> <td> 1.4.21-30.09.21 </td> </tr> <tr> <td> 264 </td> <td>   </td> <td> LGS - Fahrradmobilität in Städten </td> <td> Research project </td> <td> 1.4.21-30.09.21 </td> </tr> <tr> <td> 271 </td> <td>   </td> <td> Fahrradmobilität in Städten </td> <td> Research project </td> <td> 1.10.21-30.11.21 </td> </tr> <tr> <td> 269 </td> <td> e1-e* </td> <td> Kaufentscheidung vs. Nachhaltigkeit </td> <td> Research project </td> <td> 1.10.21-30.11.21 </td> </tr> <tr> <td> 262 </td> <td> d1-d* </td> <td> Urbane Grünflächen </td> <td> Research project </td> <td> 1.10.21-30.11.21 </td> </tr> <tr> <td> 199 </td> <td>   </td> <td> Basiskurs </td> <td> Basic course for onboarding of students on the platform </td> <td> 1.10.21-30.11.21 </td> </tr> <tr> <td> 5 </td> <td>   </td> <td> Glossar </td> <td> Glossar of definitions </td> <td> persistent </td> </tr> <tr> <td> 6 </td> <td>   </td> <td> Erste Schritte </td> <td> How to start using score-docs platform </td> <td> persistent </td> </tr> <tr> <td> 8 </td> <td>   </td> <td> Testbereich </td> <td> Area for testing score-docs functionalities </td> <td> persistent </td> </tr> <tr> <td> 7 </td> <td>   </td> <td> Hilfestellungen </td> <td> Helpful links  </td> <td> persistent   </td> </tr> </tbody> </table> Other Project-IDs refer to personal assessment documents of individual students which are not included in content.csv Research-Task-Type <table> <tbody> <tr> <td> Value </td> <td> Description </td> </tr> <tr> <td> Erheben </td> <td> Data collection </td> </tr> <tr> <td> Analysieren </td> <td> Case-specific data analysis and sensemaking </td> </tr> <tr> <td> Synthetisieren </td> <td> Cross-case data analysis and sensemaking </td> </tr> <tr> <td> Sonstiges </td> <td> Other, e.g. communication between participants </td> </tr> </tbody> </table>

opencc-by-4.0Feb 2022View details →

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THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE "HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM" AND ITS IMPACTS

Dataset name: asppl_dataset_v2.csv  Version: 2.0  Dataset period: 06/07/2018 - 01/14/2022 Dataset Characteristics: Multivalued  Number of Instances: 8118 Number of Attributes: 9 Missing Values: Yes Area(s): Health and education  Sources:  <ul> <li> Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);  </li> <li> Brazilian Occupational Classification (CBO) (Brasil, 2022b); </li> <li> National Registry of Health Establishments (CNES) (Brasil, 2022c);  </li> <li> Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).  </li> </ul> Description: The data contained in the asppl_dataset_v2.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021). Table 1: Description of AVASUS dataset features.  <table> <tbody> <tr> <td> Attributes  </td> <td> Description  </td> <td> datatype  </td> <td> Value </td> </tr> <tr> <td> gender  </td> <td> Gender of the course participant.  </td> <td> Categorical.  </td> <td> Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed) </td> </tr> <tr> <td> course_progress </td> <td> Percentage of completion of the course.  </td> <td> Numerical.  </td> <td> Range from 0 to 100. </td> </tr> <tr> <td> course_evaluation </td> <td> A score given to the course by the participant.  </td> <td> Numerical.  </td> <td> 0, 1, 2, 3, 4, 5 or NaN. </td> </tr> <tr> <td> evaluation_commentary </td> <td> Comment made by the participant about the course. </td> <td> Categorical.  </td> <td> Free text or NaN. </td> </tr> <tr> <td> region </td> <td> Brazilian region in which the participant resides. </td> <td> Categorical.  </td> <td> Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South).  </td> </tr> <tr> <td> CNES </td> <td> The CNES code refers to the health establishment where the participant works. </td> <td> Numerical.  </td> <td> CNES Code or NaN. </td> </tr> <tr> <td> health_care_level </td> <td> Identification of the health care network level for which the course participant works. </td> <td> Categorical. </td> <td> “ATENCAO PRIMARIA”, “MEDIA COMPLEXIDADE”,  “ALTA COMPLEXIDADE”,  and their possible combinations. (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")  </td> </tr> <tr> <td> year_enrollment </td> <td> Year in which the course participant registered. </td> <td> Numerical. </td> <td> Year (YYYY). </td> </tr> <tr> <td> CBO </td> <td> Participant occupation. </td> <td> Categorical.  </td> <td> Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”) </td> </tr> </tbody> </table>   Dataset name: prison_syphilis_and_population_brazil.csv Dataset period: 2017 - 2020 Dataset Characteristics: Multivalued  Number of Instances: 6 Number of Attributes: 13 Missing Values: No Source:  <ul> <li> National Penitentiary Department (DEPEN) (Brasil, 2022d);  </li> </ul> Description: The data contained in the prison_syphilis_and_population_brazil.csv dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. Table 2: Description of DEPEN dataset Features.  <table align="center"> <tbody> <tr> <td> Attributes  </td> <td> Description  </td> <td> datatype  </td> <td> Value </td> </tr> <tr> <td> Region </td> <td> Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil. </td> <td> Categorical.  </td> <td> Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South. </td> </tr> <tr> <td> syphilis_2017 </td> <td> Number of syphilis cases in the prison system in 2017. </td> <td> Numerical.  </td> <td> Number of syphilis cases. </td> </tr> <tr> <td> syphilis_rate_2017 </td> <td> Normalized rate of syphilis cases in 2017. </td> <td> Numerical.  </td> <td> Syphilis case rate. </td> </tr> <tr> <td> syphilis_2018 </td> <td> Number of syphilis cases in the prison system in 2018. </td> <td> Numerical.  </td> <td> Number of syphilis cases. </td> </tr> <tr> <td> syphilis_rate_2018 </td> <td> Normalized rate of syphilis cases in 2018. </td> <td> Numerical.  </td> <td> Syphilis case rate. </td> </tr> <tr> <td> syphilis_2019 </td> <td> Number of syphilis cases in the prison system in 2019. </td> <td> Numerical.  </td> <td> Number of syphilis cases. </td> </tr> <tr> <td> syphilis_rate_2019 </td> <td> Normalized rate of syphilis cases in 2019. </td> <td> Numerical.  </td> <td> Syphilis case rate. </td> </tr> <tr> <td> syphilis_2020 </td> <td> Number of syphilis cases in the prison system in 2020. </td> <td> Numerical.  </td> <td> Number of syphilis cases. </td> </tr> <tr> <td> syphilis_rate_2020 </td> <td> Normalized rate of syphilis cases in 2020. </td> <td> Numerical.  </td> <td> Syphilis case rate. </td> </tr> <tr> <td> pop_2017 </td> <td> Prison population in 2017. </td> <td> Numerical.  </td> <td> Population number. </td> </tr> <tr> <td> pop_2018 </td> <td> Prison population in 2018. </td> <td> Numerical.  </td> <td> Population number. </td> </tr> <tr> <td> pop_2019 </td> <td> Prison population in 2019. </td> <td> Numerical.  </td> <td> Population number. </td> </tr> <tr> <td> pop_2020 </td> <td> Prison population in 2020. </td> <td> Numerical.  </td> <td> Population number. </td> </tr> </tbody> </table>   Dataset name: students_cumulative_sum.csv Dataset period: 2018 - 2020 Dataset Characteristics: Multivalued  Number of Instances: 6 Number of Attributes: 7 Missing Values: No Source:  <ul> <li> Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a); </li> <li> Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).  </li> </ul> Description: The data contained in the students_cumulative_sum.csv dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate. Table 3: Description of Students dataset Features.  <table align="center"> <tbody> <tr> <td> Attributes  </td> <td> Description  </td> <td> datatype  </td> <td> Value </td> </tr> <tr> <td> Region </td> <td> Brazilian region of the course participant. In addition, the sum of the regions, which refers to Brazil. </td> <td> Categorical.  </td> <td> Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.  </td> </tr> <tr> <td> 2018 </td> <td> Number of students enrolled in the course in 2018. </td> <td> Numerical.  </td> <td> Number of students. </td> </tr> <tr> <td> rate_2018 </td> <td> Standardized rate of students in the course in 2018. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2019 </td> <td> Sum of students enrolled in the course in 2019. </td> <td> Numerical.  </td> <td> Number of students. </td> </tr> <tr> <td> rate_2019 </td> <td> Standardized rate of students in the course in 2019. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2020 </td> <td> Sum of students enrolled in the course in 2020. </td> <td> Numerical.  </td> <td> Number of students. </td> </tr> <tr> <td> rate_2020 </td> <td> Standardized rate of students in the course in 2020. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> </tbody> </table>   Dataset name: syphilis_tests_brazil.csv Dataset period: 2017 - 2020 Dataset Characteristics: Multivalued  Number of Instances: 6 Number of Attributes: 9 Missing Values: No Source:  <ul> <li> Brazilian Ministry of Health, through the Outpatient Information System of the Brazilian Health System (SIA/SUS) (Brasil, 2022f); </li> <li> Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).  </li> </ul> Description: The data contained in the syphilis_tests_brazil.csv dataset (see Table 4) originate mainly from the Outpatient Information System of the Brazilian Health System (SIA/SUS). This dataset provides data on the number of tests for syphilis detection by region and year. In addition, it brings a rate that represents the normalized data to compare the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate. Table 4: Description of Syphilis Testes dataset Features.  <table align="center"> <tbody> <tr> <td> Attributes  </td> <td> Description  </td> <td> datatype  </td> <td> Value </td> </tr> <tr> <td> Region </td> <td> Brazilian region where tests for syphilis were performed. In addition, the sum of the regions, which refers to Brazil. </td> <td> Categorical.  </td> <td> Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South. </td> </tr> <tr> <td> 2017 </td> <td> The number of tests for syphilis performed in 2017. </td> <td> Numerical.  </td> <td> The number of tests. </td> </tr> <tr> <td> rate_2017 </td> <td> Syphilis testing rate in 2017. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2018 </td> <td> The number of tests for syphilis performed in 2018. </td> <td> Numerical.  </td> <td> The number of tests. </td> </tr> <tr> <td> rate_2018 </td> <td> Syphilis testing rate in 2018. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2019 </td> <td> The number of tests for syphilis performed in 2019. </td> <td> Numerical.  </td> <td> The number of tests. </td> </tr> <tr> <td> rate_2019 </td> <td> Syphilis testing rate in 2019. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2020 </td> <td> The number of tests for syphilis performed in 2020. </td> <td> Numerical.  </td> <td> The number of tests. </td> </tr> <tr> <td> rate_2020 </td> <td> Syphilis testing rate in 2020. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> </tbody> </table>   REFERENCES Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: <a href="https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114">https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114</a> . Brasil (2022b). Cbo - classificação brasileira de ocupações. Available from: <a href="http://www.mtecbo.gov.br/cbosite/pages/home.jsf">http://www.mtecbo.gov.br/cbosite/pages/home.jsf</a> . Brasil (2022c). Cnes - cadastro nacional de estabelecimentos de saúde. Available from: <a href="http://cnes.datasus.gov.br/">http://cnes.datasus.gov.br/</a> . Brasil (2022d). Departamento penitenciário nacional. levantamento nacional de informações penitenciárias. Available from: <a href="https://www.gov.br/depen/pt-br/servicos/sisdepen">https://www.gov.br/depen/pt-br/servicos/sisdepen</a> . Brasil (2022e). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados">https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados</a> . Brasil (2022f). Ministério da saúde - sistema de informações ambulatoriais do sus (sia/sus). Available from: <a href="https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/">https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/</a> . Valentim, J., Oliveira, E. d. S. G., Valentim, R. A. d. M., Dias-Trindade, S., Dias, A. d. P., Cunha-Oliveira, A., et al. (2021). Data report: “health care of persons deprived of liberty” course from brazil’s unified health system virtual learning environment. Frontiers in Medicine 8. doi:10.3389/fmed.2021.742071.   ARTICLE: THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE “HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM” AND ITS IMPACTS    AUTHORS: Janaína L. R. S. Valentim1,2, Sara Dias-Trindade2,3, Eloiza da S. G. Oliveira1,4, José A. M. Moreira2,5, Felipe Fernandes1, Manoel Honório Romão1, Philippi S. G. de Morais1, Alexandre R. Caitano1, Aline P. Dias1, Carlos A. P. Oliveira1,4,6, Karilany D. Coutinho1, Ricardo B. Ceccim7, Ricardo A. M. Valentim1   1Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil  2Univ Coimbra, Centre for Interdisciplinary Studies, Coimbra, Portugal 3Univ Coimbra, Centre for Interdisciplinary Studies, Faculty of Arts and Humanities, Coimbra, Portugal 4Multidisciplinary Institute for Human Development with Technologies, State University of Rio de Janeiro (UERJ), Rio de Janeiro, RJ, Brazil 5Open University (Universidade Aberta), Department of Education and Distance Learning (DEED), Lisbon, Portugal 6International Council for Open and Distance Education, Oslo, Norway 7Postgraduate Program in Education, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil

opencc-by-4.0Apr 2022View details →

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Perspectives on Medical Education Journal Data and Supplemental Files (2012 - 2019)

This is the supplemental data, figures, and tables for Joining the meta-research movement: A bibliometric case study of Perspectives on Medical Education.  For Figures 2-4 from the manuscript, to open the network maps, use both the network and map file for each figure in VoS viewer - https://www.vosviewer.com/

opencc-by-4.0Apr 2022View details →

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Massive Health Education through Technological Mediation: analyzes and impacts on the syphilis epidemic in Brazil

Repository  Dataset name: avasus_syphilis_trail_dataset.csv  Version: 1.0  Dataset period: 05/12/2016 - 01/14/2022  Dataset Characteristics: Multivalued  Number of Instances: 177732  Number of Attributes: 16  Missing Values: Yes  Area(s): Health and education  Sources:  <ul> <li> Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);  </li> <li> Brazilian Occupational Classification (CBO) (Brasil, 2022b); </li> <li> National Registry of Health Establishments (CNES) (Brasil, 2022c). </li> </ul> Description: The data contained in the avasus_syphilis_trail_dataset.csv dataset (see Table 1) originate from AVASUS users who have taken a course on the “Syphilis and other STI” learning path. This dataset provides elemental data to analyze the impact and reach of the trails and the profile of their participants. Table 1: Description of Dataset Features.  <table> <tbody> <tr> <td> Attributes  </td> <td> Description  </td> <td> datatype  </td> <td> Value </td> <td> Source </td> </tr> <tr> <td> user_id </td> <td> Unique identifier of the user (anonymously). </td> <td> Numerical.  </td> <td> Randomly generated integer. </td> <td> AVASUS. </td> </tr> <tr> <td> user_gender </td> <td> Gender of the user.  </td> <td> Categorical.  </td> <td> Feminino / Masculino / Não Informado. (In English: Female, Male or Uninformed) </td> <td> AVASUS. </td> </tr> <tr> <td> user_occupation </td> <td> User occupation </td> <td> Categorical.  </td> <td> Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”) </td> <td> CNES. </td> </tr> <tr> <td> user_cnes </td> <td> The CNES code refers to the health establishment where the user works. </td> <td> Numerical. </td> <td> CNES Code or NaN. </td> <td> CNES. </td> </tr> <tr> <td> user_level_attention </td> <td> Identification of the health care network level for which the user works. </td> <td> Categorical. </td> <td> “ATENCAO PRIMARIA”,   “MEDIA COMPLEXIDADE”,    “ALTA COMPLEXIDADE”,    and their possible combinations.   (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")   Or NaN. </td> <td> CNES. </td> </tr> <tr> <td> user_region </td> <td> Brazilian region in which the user resides. </td> <td> Categorical.  </td> <td> Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South). Other options: “Exterior” or “Não Informado” (In English: Outside or Not informed). </td> <td> AVASUS. </td> </tr> <tr> <td> course_id </td> <td> Unique identifier of the course performed by the avasus user. </td> <td> Numerical.  </td> <td> Code list according to AVASUS. </td> <td> AVASUS. </td> </tr> <tr> <td> course_name </td> <td> Name of the course taken by the avasus user. </td> <td> Categorical. </td> <td> Course name according to AVASUS. </td> <td> AVASUS. </td> </tr> <tr> <td> course_workload </td> <td> Course timetable. </td> <td> Numerical. </td> <td> Range from 0 to 120. </td> <td> AVASUS. </td> </tr> <tr> <td> course_creation_date </td> <td> Course creation date. </td> <td> Date. </td> <td> “YYYY-MM-DD” or NaN. </td> <td> AVASUS. </td> </tr> <tr> <td> enrollment_id </td> <td> Unique identification of the enrollment carried out by the student in some course of the trail. </td> <td> Numerical.  </td> <td> Randomly generated single integer. </td> <td> AVASUS. </td> </tr> <tr> <td> enrollment_creation </td> <td> Date the student registered. </td> <td> Date. </td> <td> “YYYY-MM-DD” or NaN. </td> <td> AVASUS. </td> </tr> <tr> <td> enrollment_completion_date </td> <td> Date the student completed the course. </td> <td> Date. </td> <td> “YYYY-MM-DD” or NaN. </td> <td> AVASUS. </td> </tr> <tr> <td> enrollment_current_progress </td> <td> Student progress regarding course completion. </td> <td> Numerical. </td> <td> Range from 0 to 100.  </td> <td> AVASUS. </td> </tr> <tr> <td> enrollment_text_evaluation </td> <td> Comment made by the student about the course. </td> <td> Categorial.  </td> <td> Free text or NaN. </td> <td> AVASUS. </td> </tr> <tr> <td> enrollment_course_evaluation </td> <td> A score given to the course by the participant. </td> <td> Numerical. </td> <td> 0, 1, 2, 3, 4, 5 or NaN. </td> <td> AVASUS. </td> </tr> </tbody> </table>   References  [1] Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114 .  [2] Brasil (2022b). Classificação brasileira de ocupações - CBO. Available from: http://www.mtecbo.gov.br/cbosite/pages/home.jsf .  [3] Brasil (2022c). Cadastro nacional de estabelecimentos de saúde - CNES. Available from: http://cnes.datasus.gov.br/ . Article:  Massive Health Education with Technological Mediation: analyzes and impacts on the syphilis epidemic in Brazil

opencc-by-4.0May 2022View details →

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How do native and non-native speakers recognize emotions in the instructor's voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners [dataset]

Dataset for the journal article How do native and non-native speakers recognize emotions in the instructor’s voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners.

opencc-by-4.0Jun 2022View details →

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History in mathematics education - 6th grade

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). The experiment was carried out in October 2021.

opencc-by-4.0Jul 2022View details →

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Dataset: Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses

This dataset contains anonymised raw data and examples of accommodations made for neurodiverse students in four undergraduate courses in Computer Science and Software Engineering programmes. The dataset is published as a part of a book chapter in which we report the accommodations. Overall guidelines we followed, including their sources, are contained in guidelines.md. The raw data for the two surveys is contained in the two Excel files survey1.xlsx and survey2.xlsx. Free-text answers have been aggregated by neurodiverse and neurotypical students and anonymised, and are available in the files survey1_freetext_neurodiverse.txt, survey1_freetext_neurotypical.txt, survey2_freetext_neurodiverse.txt, and survey2_freetext_neurotypical.txt. The remaining files are examples of the adapted lecture slides and assignment texts. Here, files starting with WEBcourse are from a mandatory undergraduate course on web development, while files starting with SEcourse are from a mandatory undergraduate course giving an overview of Software Engineering.

opencc-by-4.0Oct 2022View details →

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Participant survey for the article: More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education

The artcile More than Formulas - Integrity, Communication, Computing and Reproducibility in Statistics Education concerns the introduction of a new course format in the Master Program in Biostatistics at the University of Zurich. This data set contains the results fo a survey among the participants in this new course. Sepcifically it contains the answers of 22 participants to the following questions: 1) Did you use the following concepts or tools since you took STA472?  Good practice for... ... spreadsheets ... file and folder organization ... version control ... dynamic reporting ... LaTeX ... presentation slide design  ... oral presentations ... designing graphs ... designing tables ... structure for manuscript ... logic of a paragraph ... writing style ... writing R functions ... using unit tests ... setting up simulations ... code styling ... writing vectorized code ... writing parallelized code ... containerizing code Answers are in the scale: never since, rarely, sometimes, often, frequently, I do not know 2) If you used the above concepts at least rarely, did the training of STA472 help you? Good paractice for... ... spreadsheets ... file and folder organization ... version control ... dynamic reporting ... LaTeX ... presentation slide design  ... oral presentations ... designing graphs ... designing tables ... structure for manuscript ... logic of a paragraph ... writing style ... writing R functions ... using unit tests ... setting up simulations ... code styling ... writing vectorized code ... writing parallelized code ... containerizing code Answers are in the scale: Not really   Somewhat  Definitively   I do not know I do not use this concept

opencc-by-4.0Jul 2024View details →

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Integrated pedagogical methods effectiveness in Physics' preliminary undergraduate education within the context of large size lectures.

Three files relating the first round of analysis testing active methods for large size lectures. The Presentation including the research design, methods, main results in synthesis is available here: https://www.researchgate.net/project/Getting-started-with-Physics-preliminary-undergraduated-strategies/update/5a44cac6b53d2f0bba475104 2- Dataset on Students' Learning Outcomes. Dataset adopted in the first experimental round. The dataset includes data used for the first type of analysis (learning outcomes) carried out for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". The data includes the results of the initial, baseline Test, the final Test, and two other variables that could be used to analyse covariance: Sex and Type of Group (Large/Small). 3- Dataset on Students' Opinion. Dataset adopted in the first experimental round. The dataset includes data used for the second type of analysis (students' opinion) carried out for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". The data includes the results of a final questionnaire gathering the students opinion on the four types of pedagogical factors affecting their experience within a large size lecture: MOOCs, Active Learning, Self-Assessment tools, Tutors’ guidance. 4- Codes and analysis adopted in the first experimental round. The Document includes two analysis carried on for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". These are: Test (measuring students' knowledge on the subject taught) and Students' Opinion/satisfaction on the several pedagogical methods adopted along the experimental intervention.

opencc-by-4.0Nov 2018View details →

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Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data

This is a release of the Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data. ERuDIte contains over 11,000 training resources on data science including courses (MOOCs), video tutorials, conference talks, and other materials. The metadata of these resources is described uniformly using schema.org. In addition, we use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology (DSEO), which we developed to further describe the contents of the training resources. Resource relevance and tags are curated by experts to ensure high quality. Finally, we map the references to people and organizations in the learning resource metadata to entities in DBpedia, DBLP, and ORCID, thus embedding our collection in the web of linked data. Our collection is continually growing. We hope that ERuDIte will provide a framework to foster open linked educational resources on the web.  Distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/)

openother-openMay 2018View details →

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Unpacking the concept of "educators' data literacy in Higher Education" - Systematic Review of the literature and Keyword Map

As algorithmic decision-making and data collection become pervasive within higher education, how can educators make sense of the systems that shape life and learning in the 21st century? Through a systematic review of the literature, the paper investigates the gaps in the literature, which prevent the formulation of potential pathways and principles on which educators’ data literacy can - and should - be developed and fostered. The analysis of 137 papers through the methods of classification under relevant categories, and key words mapping, showed that there is little attention on HE teachers, and most approaches to educators’ data literacy address management and technical abilities for data processing, with less concern on critical, ethical and personal approaches to datafication in education. The present dataset shows the full list of articles analysed. The dataset, and ods file, is composed by the following sheets: <ol> <li>Codebook</li> <li>List of articles extracted from SCOPUS</li> <li>List of articles extracted from WOS</li> <li>List of articles extracted from ERIC</li> <li>List of articles extracted from DOAJ</li> <li>Interrater Agreement</li> <li>PRISMA workflow</li> <li>Analysis - First Level (classification of 137 articles selected)</li> <li>Analysis - Second Level (List of articles relating faculty development)</li> <li>Supplementary tables (counting articles in relation to the categories of analysis).</li> </ol> As for the Keywords' Map, a second file .csv displays the text  over which basis was performed the keyword maps analysis. A .txt file shows notes relating the analysis procedures using the software VOS-Viewer <a href="http://www.vosviewer.com/">http://www.vosviewer.com/</a>

opencc-by-4.0Jun 2019View details →

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Investigating secondary students' stance on IoT driven educational activities - Dataset

This data set supports the research and the results that are presented in Glaroudis, D., Iossifides, A., Spyropoulou, N., Zaharakis, I. D., “Investigating Secondary Students' Stance on IoT Driven Educational Activities”. In Kameas A, and Stathis K. (Eds) Ambient Intelligence, LNCS 11249, 2018, pp. 188-203. Springer Nature Switzerland AG. DOI: https://doi.org/10.1007/978-3-030-03062-9_15.

opencc-by-4.0Jun 2019View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record