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5,141 results for “Educators”
TOPSIS method application on sustainability indicators of Higher Education Institutions of Brazilian municipalities
<p>TOPSIS method application on sustainability indicators of Higher Education Institutions of Brazilian municipalities</p>
Metrics Literacies: On the State of the Art of Multimedia Scholarly Metrics Education (Video presentation)
<p>Scholarly metrics, such as h-index or impact factor, are widely applied in academic tenure and funding decisions but often inappropriately. The quantification of research impact has created a pressure to publish that harms all scholarly disciplines by creating adverse effects, such as plagiarism, gratuitous self-citation, and honorary authorship. The Metrics Literacies project led by Prof. Stefanie Haustein aims to reduce the misuse of metrics and adverse effects by improving the understanding and use of scholarly metrics in academia through the development, testing, and dissemination of multimedia resources. This poster talk shows the current state of the art on the project.</p>
Times Higher Education Latin America Ranking's data, 2016-2019
<p>Top fifty latin american universities's data from Times Higher Education Latin America Ranking, 2016-2019</p>
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>
Weather data from Environmental Education Center at Lancer Park from 2021-06-05 to 2021-07-19
<p>General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>LP_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>Metadata File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
Weather data from Environmental Education Center at Lancer Park from 2021-07-19 to 2021-08-17
<p>General Metadata for Lancer Park Environmental Education Center Atmospheric Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>LP_weather_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>Metadata File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p>File Modified</p> <p>Description</p> <p>These data are from a weather station installed at the Longwood University Environmental Education Center at Lancer Park (37.308189, -78.402768) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes</code></pre> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)</code></pre>
Database: Frameworks 21 century skills for development future skills in education 4.0: Systematic Literature Review
<p> Los datos revelan (a) la literatura de los frameworks destacan estudios de casos y estrategias de enseñanza aprendizaje vinculadas con las competencias del siglo XXI; (b) se ubican áreas de oportunidad para evidenciar publicaciones que aborden competencias de carácter y de metalearning; (c) los componentes de educación 4.0 que han sido abordados en los frameworks son: research strategies to apply knowledge and reflection strategies to encourage self system thinking; (d) las dimensiones de aprendizaje donde se han ubicado los frameworks son Skills and Knowledge y, (e ) destaca ausencia de frameworks dirigidos hacia profesores y a escuelas</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>
Survey Comics and Education. ICOn MICs CA19119 Validation 2021
<p>Survey "Comics and education" of the WG5 of the COST ICOnMICs action. Presentation at Teaching with comics Congress, Valencia. Presentation of the validated survey. Results (anonymous) of the validation by the members of the working group 5.</p> <p>More information on: <a href="https://iconmics.hypotheses.org/2140">https://iconmics.hypotheses.org/2140</a></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>
Romanian Civil State Higher Education Institutions Index
<p>OpenAlex data scrape for all the Romanian Civil State Higher Education Institutions, as per https://www.edu.ro/institutii%20inv_superior%20de%20stat%20civile (November 19, 2022 archived version: https://web.archive.org/web/20221119104847/https%3A%2F%2Fwww.edu.ro%2Finstitutii%2520inv_superior%2520de%2520stat%2520civile)</p>
Romanian Accredited Private Higher Education Institutions Index
<p>OpenAlex data scrape for all the Romanian Accredited Private Higher Education Institutions, as per https://www.edu.ro/institutii%20de%20inv_superior%20particulare%20acreditate (November 19, 2022 archived version: https://web.archive.org/web/20221119194024/https%3A%2F%2Fwww.edu.ro%2Finstitutii%2520de%2520inv_superior%2520particulare%2520acreditate)</p>
Romanian Higher Education Institutions with a Military Profile Index
<p>OpenAlex data scrape for all the Romanian Higher Education Institutions with a Military Profile, as per https://www.edu.ro/institutii%20inv_superior%20de%20stat%20militare (November 19, 2022 archived version: https://web.archive.org/web/20221119194032/https%3A%2F%2Fwww.edu.ro%2Finstitutii%2520inv_superior%2520de%2520stat%2520militare)</p>
Romanian Higher Education Institutions Index
<p>Aggregated index of all the Romanian Higher Education Institutions:</p> <ul> <li> <p>Romanian Civil State Higher Education Institutions Index (https://zenodo.org/record/7337498)</p> </li> <li> <p>Romanian Higher Education Institutions with a Military Profile Index (https://zenodo.org/record/7339338)</p> </li> <li> <p>Romanian Accredited Private Higher Education Institutions Index (https://zenodo.org/record/7339344)</p> </li> </ul>
Informal Adult Education in the time of COVID
<p>The COVID-19 pandemic caused a global shutdown where different sectors in different countries were closed down in waves to mitigate the spread of the disease. During this time, work and education moved to a virtual format. Adults with access to technology could continue to participate in educational opportunities. Those without access to technology fell farther behind in the digital divide. </p> <p>A scoping literature review was undertaken to answer the research question: How did adult learners engage in informal learning during the COVID-19 pandemic.</p> <p>This dataset is the extraction table for the scoping review. </p>
Demographic information of individuals participating in educational research questionnaire
<p>Pre- and post-intervention participant demographic information of individuals participating in educational research questionnaire.</p>
E-Learning Readiness In Higher Education Institutions In Nigeria during the COVID-19 Pandemic
<p>Data set for the paper " E-Learning Readiness In Higher Education Institutions In Nigeria during the COVID-19 Pandemic"</p>
Supplementary materials of the active learning methodology for online engineering education
<p>These are the supplementary materials for the active learning methodology for engineering education integrating online and mobile learning. The methodology is focused on teaching electronics, physical computing, basic robotics, and programming. The purpose of the methodology was to provide active learning, experimentation, and reflection in online classes to the students increasing their motivation and self-efficacy.</p> <p>The file contains the following elements:</p> <ol> <li>Rubric employed to evaluate the student-created videos and blogs.</li> <li>The survey's questions of the different courses that employ online and mobile learning modalities.</li> <li>A set of URLs with examples of the blogs and videos constructed by the students.</li> </ol>
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>
98 selected publications analysed in-depth in the context of a systematic literature review on Computational Thinking in compulsory education
<p>This is the list of the <strong>98 publications from between 2016 and 2021 </strong>analysed in-depth through a review matrix in the context of the <strong>"</strong><a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347">Reviewing Computational Thinking in Compulsory Education: State of Play and</a> <a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347"> Practices from the Field</a><strong>"</strong> <strong>research study.</strong> The <a href="https://computhink2study.eu/">study</a> was designed, funded, and followed by the European Commission’s Joint Research Centre (JRC) to investigate <strong>how Computational Thinking (CT) is currently positioned within compulsory school education in Europe’s various Member States, as well as outside the EU</strong>. The study was carried out from April to December 2021 by the Institute for Educational Technology of the Italian National Research Council (CNR-ITD), together with European Schoolnet (EUN) and Vilnius University (VU).</p> <p>Bocconi, S., Chioccariello, A., Kampylis, P., Dagienė, V., Wastiau, P., Engelhardt, K., Earp, J., Horvath, M.A., Jasutė, E., Malagoli, C., Masiulionytė-Dagienė, V., & Stupurienė, G. (2022). <em>Reviewing Computational Thinking in Compulsory Education</em>. Publications Office of the European Union. <a href="https://doi.org/10.2760/126955">https://doi.org/10.2760/126955</a></p> <p> </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.