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106 results for “Educational dataset”
Dataset for the exploration of data practices in Higher Education
<p>The present dataset contains the results of a survey distributed to all the professoriate of two universities, between January and March 2020.</p> <p>The several variables present in the dataset relate to the professional areas of knowledge and activity; the ongoing data practices in relation to six scales (as published in Raffaghelli. (2019) and theoretically explored in Raffaghelli (2019b); professional learning ecologies relating data practices in teaching and research.</p> <p>The script 1 covers some analysis relating specific scales.</p> <p>Other scripts might be added /welcomed as far as successive analysis are done over the shared data.</p> <p>========</p> <p>Raffaghelli, J. (2019) Delphi Study to validate the survey exploring academic awareness and engagement with data-driven practices. Zenodo. https://doi.org/10.5281/zenodo.3581290</p> <p>Raffaghelli, J. E. (2019b). Developing a framework for educators’ data literacy in the European context: Proposal, Implications and debate. In <em>International Conference on Education and New Learning Technologies EDULEARN</em> (pp. 10520–10530). Palma de Mallorca: IATED. https://doi.org/10.21125/edulearn.2019.2655</p>
Dataset with survey answers about enginering studies opinion in each kind of high school Spanish studies (Compulsory Secondary Education, Vocational Education and Upper Secondary Education)
<p>The first line includes each question and the rest of the tuples include one answer per each filled survey. Depending on the type of high school studies, you find one different survey, because the questions are adapted to each particular high school education.</p> <p> </p>
DATASET SPARC Europe Open Education in European Libraries of Higher Education Survey 2022
<p>This is the anonymised dataset for the 2022 edition of the SPARC Europe Open Education Survey amongst Higher Education libraries in Europe.</p>
REFERENCES DATASET: A SYSTEMATIC REVIEW OF THE EDUCATIONAL USE OF MOBILE PHONES IN TIMES OF COVID-19
<p>The article "A systematic review of the educational use of mobile phones in times of COVID-19" aims to review what research has delved into the educational use of mobile phones during the COVID-19 pandemic. To do this, 38 papers indexed in the Journal Citation Reports database between 2020 and 2021 were analyzed. These works were categorized into the following categories: the mobile phone as part of educational innovation, improvement of results and academic performance, positive attitude towards mobile phone use in education, and risks and/or barriers to mobile phone use. The conclusions show that most teaching innovation experiences focus more on the device than on the student. Beyond its innovative nature, the mobile phone became a tool to allow access and continuity of training during the pandemic, especially in post-compulsory and higher education.</p> <p>This data set is composed of the table with the references used for the review.</p> <p> </p>
Data from: Transforming medical education in Liberia through an international community of inquiry (2017 dataset)
<p>A critical component of building capacity in Liberia's physician workforce involves strengthening the country's only medical school, A.M. Dogliotti College of Medicine. Beginning in 2015, senior health sector stakeholders in Liberia invited faculty and staff from U.S. academic institutions and non-governmental organizations to join a partnership focused on improving undergraduate medical education in Liberia. Over the subsequent six years, the members of this partnership came together through an iterative, mutual-learning process and created what William Torbert et al describe as a "community of inquiry," in which practitioners and researchers pair action and inquiry toward evidence-informed practice and organizational transformation. This community of inquiry developed around a few key institutional and interpersonal relationships but expanded over time. Incorporating faculty, practitioners, and students from Liberia and the U.S., the community of inquiry consistently focused on following the vision, goals, and priorities of leadership in Liberia, irrespective of funding source or institutional affiliation. The work of the community of inquiry has incorporated multiple mixed methods assessments, stakeholder discussions, strategic planning, and collaborative self-reflection, resulting in transformation of M.D. education in Liberia. We suggest that the community of inquiry approach reported here can serve as a model for others seeking to form sustainable, international global health partnerships focused on organizational transformation.</p>
Data from: Transforming medical education in Liberia through an international community of inquiry (2016 dataset)
<p class="MsoNormal">A critical component of building capacity in Liberia's physician workforce involves strengthening the country's only medical school, A.M. Dogliotti College of Medicine. Beginning in 2015, senior health sector stakeholders in Liberia invited faculty and staff from U.S. academic institutions and non-governmental organizations to join a partnership focused on improving undergraduate medical education in Liberia. Over the subsequent six years, the members of this partnership came together through an iterative, mutual-learning process and created what William Torbert et al describe as a "community of inquiry," in which practitioners and researchers pair action and inquiry toward evidence-informed practice and organizational transformation. This community of inquiry developed around a few key institutional and interpersonal relationships but expanded over time. Incorporating faculty, practitioners, and students from Liberia and the U.S., the community of inquiry consistently focused on following the vision, goals, and priorities of leadership in Liberia, irrespective of funding source or institutional affiliation. The work of the community of inquiry has incorporated multiple mixed methods assessments, stakeholder discussions, strategic planning, and collaborative self-reflection, resulting in transformation of M.D. education in Liberia. We suggest that the community of inquiry approach reported here can serve as a model for others seeking to form sustainable, international global health partnerships focused on organizational transformation.</p>
Data from: Transforming medical education in Liberia through an international community of inquiry (2018 dataset)
<p>A critical component of building capacity in Liberia's physician workforce involves strengthening the country's only medical school, A.M. Dogliotti College of Medicine. Beginning in 2015, senior health sector stakeholders in Liberia invited faculty and staff from U.S. academic institutions and non-governmental organizations to join a partnership focused on improving undergraduate medical education in Liberia. Over the subsequent six years, the members of this partnership came together through an iterative, mutual-learning process and created what William Torbert et al describe as a "community of inquiry," in which practitioners and researchers pair action and inquiry toward evidence-informed practice and organizational transformation. This community of inquiry developed around a few key institutional and interpersonal relationships but expanded over time. Incorporating faculty, practitioners, and students from Liberia and the U.S., the community of inquiry consistently focused on following the vision, goals, and priorities of leadership in Liberia, irrespective of funding source or institutional affiliation. The work of the community of inquiry has incorporated multiple mixed methods assessments, stakeholder discussions, strategic planning, and collaborative self-reflection, resulting in transformation of M.D. education in Liberia. We suggest that the community of inquiry approach reported here can serve as a model for others seeking to form sustainable, international global health partnerships focused on organizational transformation.</p>
Data from: Transforming medical education in Liberia through an international community of inquiry (2016 dataset)
Open the record for dataset details and reuse information.
Data from: Transforming medical education in Liberia through an international community of inquiry (2017 dataset)
Open the record for dataset details and reuse information.
Data from: Transforming medical education in Liberia through an international community of inquiry (2018 dataset)
Open the record for dataset details and reuse information.
Dataset for intercontinental assessment of the educational environmental project "IO-00118-17".
<p>Rams, S., Porcel de Peralta, M. & Odetti, H. (2020). <em>Dataset for intercontinental assessment of the educational environmental project "IO-00118-17".</em> DOI: 10.5281/zenodo.4295551</p>
SDG EDUCATION DATASET
<p>Dataset created in order to analyse the existence of documents around the Sustainable Development Goals in the scientific literature in Education produced since 2015</p>
Teacher Characteristics, Knowledge and Use of Evidence-Based Practices in Autism Education in Ireland_Dataset_V1
<p>Dataset to accompany the paper "<strong>Teacher Characteristics, Knowledge and Use of Evidence-Based Practices in Autism Education in Ireland". </strong>The purpose of this paper was to identify differences in teachers knowledge and use of evidence-based practices across different teacher characteristics. </p>
datasets bmc medical education
<p>Datasets of a research developed at Federal University of Viçosa, Brazil; it contains data from a survey on health-related quality of life, developed among medical students, at Federal University of Viçosa.</p> <p> </p>
Dataset Scooping review digital platforms and data management in Education
<p>Dataset used for a scoping review about digital platforms and data management in education</p>
Matematicast educational game Dataset for Multiple-try and KCR intervention
<p>Matematicast educational game Dataset for Multiple-try and KCR intervention</p>
Summer dataset of an operating education modular building
<p>We propose a dataset concerning a modular education building (NR3) located on CESI Engineering School of Nanterre (Approximate GPS coordinates: 48.9033,2.1925). NR3 building is composed of 16 shipping containers offering an available surface of ≈220m² on two levels. It integrates 4 classrooms named Turing (38 m²), Tesla (51 m²), Nobel (38 m²) and Lumière (38 m²); a IT room named Bell (8.7 m²); an office named Nobel2 (8.7m²); a technical room named Local_tech and a hall distributed on the 2 levels. NR3 building is equiped with approximately 200 sensors and actuators allowing the measurement of the physical behavior of the building and the control of HVAC systems.</p> <p>The dataset is composed of 14 csv tables including physical indoor and outdoor parameters (temperature, humidity, radiation…), total energetic consumption of the building, state and operating data of all systems (heat pump, CMV, shutters, windows…). Data were recorded during summer 2023, i.e. between the 21 of june and the 22 of september. A redme.xlsx file and a map of the building helps to understand each table.</p> <p> </p>
Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users' Readings
<p># Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings </p> <p>## Background</p> <p>This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil. </p> <p>In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil.</p> <p>The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked. </p> <p>For more information about how these measures were collected, please refer to this thesis at: https://doi.org/10.11606/T.55.2020.tde-31082020-093935</p> <p>Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column. </p> <p>## Description:<br>Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value).</p> <p><br>Deviations (integer): number of face deviations of the participant during the question answering identified by the system.</p> <p>EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system.</p> <p>NumberWord (integer): number of words comprised in the sentence the participant gave.</p> <p>SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers.</p> <p>Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds.</p> <p>True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions. </p> <p>Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201</p> <p><br>## Creators <br>Daniel Tozadore: dtozadore@gmail.com<br>Roseli Romero: rafrance@icmc.usp.br</p> <p><br>## License: <br>[Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)</p>
Times Higher Education Ranking Dataset
<p>Dataset containing university rankings. See source: <a href="https://www.timeshighereducation.com/world-university-rankings">https://www.timeshighereducation.com/world-university-rankings</a></p> <p>Originally used in:</p> <p>Anahideh, Hadis, and Nasrin Mohabbati-Kalejahi. "Local explanations of global rankings: insights for competitive rankings." IEEE Access 10 (2022): 30676-30693. <a href="https://ieeexplore.ieee.org/abstract/document/9733934" rel="nofollow">https://ieeexplore.ieee.org/abstract/document/9733934</a></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>
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.