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195 results for “Educational Data”

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zenodo52/100

Code and data set for data analysis published as manuscript "Bacttle: a microbiology educational board game for lay public and schools"

Code that processed raw data and plots the figures of the manuscript "Bacttle: a microbiology educational board game for lay public and schools" Below is a table with the original survey questions. The ID corresponds to the column displayed on the data set. When letters are followed by a number (1 or 2), it means that the question was answered before playing the game (1) and after playing the game (2). <table> <tbody> <tr> <td> ID1 </td> <td> Question text </td> <td> Possible answers2 </td> </tr> <tr> <td> A </td> <td> How old are you? </td> <td>   </td> </tr> <tr> <td> B </td> <td> Do you know what a bacterium is? </td> <td> y/n </td> </tr> <tr> <td> C </td> <td> Do you know what a bacterial capsule is? </td> <td> y/n </td> </tr> <tr> <td> D </td> <td> Do bacteria have tools to harm each other? </td> <td> y/n/idk </td> </tr> <tr> <td> E </td> <td> Do bacteria reproduce at the same pace? </td> <td> y/n/idk </td> </tr> <tr> <td> F </td> <td> What is sporulation? </td> <td> A resistant state that some bacteria can achieve under unfavorable conditions. </td> </tr> <tr> <td> The release of toxins by bacteria. </td> </tr> <tr> <td> idk </td> </tr> <tr> <td> G </td> <td> What are flagella used for? </td> <td> Sticking to surfaces. </td> </tr> <tr> <td> Motility in liquid environments. </td> </tr> <tr> <td> idk </td> </tr> <tr> <td> H </td> <td> What does it mean to be lithotrophic? </td> <td> A bacterium can get energy from minerals. </td> </tr> <tr> <td> A bacterium can get energy from the sunlight. </td> </tr> <tr> <td> idk </td> </tr> <tr> <td> I </td> <td> Can bacteria be infected by viruses? </td> <td> y/n/idk </td> </tr> <tr> <td> J </td> <td> Are all bacteria harmful for humans? </td> <td> y/n/idk </td> </tr> <tr> <td> K </td> <td> How many bacteria are in a coffee spoon of yoghurt? </td> <td> Millions </td> </tr> <tr> <td> Hundreds </td> </tr> <tr> <td> idk </td> </tr> <tr> <td> L </td> <td> How easy did you find the gameplay? </td> <td> VE/E/A/D/VD </td> </tr> <tr> <td> M </td> <td> Did you find the card content easy to understand? </td> <td> VE/E/A/D/VD </td> </tr> <tr> <td> N </td> <td> Did you like the setup of the game? </td> <td> y/n/idk </td> </tr> <tr> <td> O </td> <td> Would you like to play this game again? </td> <td> y/n/idk </td> </tr> <tr> <td> P </td> <td> What can we improve? </td> <td>   </td> </tr> </tbody> </table> 1) Question A categorizes the player’s age; B and C assess the initial level of knowledge in microbiology (none -both questions are answered negatively-, basic -player knows what a bacterium is but not a bacterial capsule-, or advanced -both answers are positive-); questions D-I score knowledge acquisition; J and K are control questions; L-O evaluate the appreciation of the game; and P is an optional free text-entry answer for additional feedback.  2) y= yes, n=no, idk=I don’t know, VE=very easy, E=easy, A=adequate, D=difficult, VD=very difficult.

opencc-by-4.0Jul 2024View details →

zenodo48/100

Adaptive PE-HRI: Data for research on Social Educational Robots driven by a Productive Engagement Framework

This dataset corresponds to our work on developing autonomous social educational robots (namely Harry and Hermione) driven by a productive engagement framework in open ended collaborative learning environments. The data is collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink where each team interacts with the activity for around 1 hour consisting of a 30 minute collaborative play.  In this data set, team level multi-modal behavioral data is collected from 52 teams of two (104 children) where the children are aged between 9 and 12. The definitions are given below:  <ul> <li>condition: This column indicates which condition do the teams belong in. 0 and 1 for teams interacting with Harry and Hermione, respectively.</li> <li>Error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task. </li> <li>Learning Gain: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.</li> <li>Usefulness Score: The score quantifies the team's subjective evaluation of a robot intervention in terms of it's usefulness as perceived by each team member individually. The score can assume values of 1, 0, 0.5 if both found the suggestion useful, not useful, or if they differed in their evaluation, respectively</li> <li>PE Score: It is a quantification of the Productive Engagement state of the team, computed on the basis of quantifiable observable behaviors found conducive to learning in training phase</li> <li>Right_Suggestions: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot was giving us the right suggestions". It is an average of the team member's individual answers. </li> <li>Right_Time: This metric captures the team's subjective evaluation of the robot's competence on a five-points likert scale to the statement "I think the robot gave us suggestions at the right time". It is an average of the team member's individual answers.</li> <li>Exploration: This variable represents how many interventions of Exploration type were received by a particular team normalized with respect to the entire data set. </li> <li>Reflection: This variable represents how many interventions of Reflection type were received by a particular team normalized with respect to the entire data set. </li> <li>Communication: This variable represents how many interventions of Communication type were received by a particular team normalized with respect to the entire data set. </li> <li>LG_status: This column indicates if a team belongs to a high learning or low learning group based on a mean split on the entire data set. </li> </ul> This dataset corresponds to the publication "Social robots as skilled ignorant peers for supporting learning": <a href="https://doi.org/10.3389/frobt.2024.1385780">https://doi.org/10.3389/frobt.2024.1385780</a>

opencc-by-4.0Oct 2023View details →

zenodo48/100

Survey questions and raw data for the study in the paper "Educational Technology for Tutors – What are Useful Tools and Information?"

The data include the questions data set, the answers dataset and the codebook for the questions conducted with soscisurvey (https://www.soscisurvey.de/de/index). The survey itself can be imported in soscisurvey (via the XML data) and reused. The answers are unedited.

opencc-by-4.0Jun 2024View details →

zenodo44/100

Education Data in the Biographical Dictionary of Republican China

This dataset contains records of education of the 589 historical figures in the Biographical Dictionary of Republican China.

opencc-by-4.0Dec 2020View details →

zenodo44/100

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 →

zenodo44/100

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 →

zenodo44/100

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 →

zenodo44/100

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 →

zenodo44/100

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 →

zenodo44/100

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 →

zenodo44/100

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 →

zenodo44/100

Doctoral Students' Educational Needs in Research Data Management: Quantitative Data of Perceived Importance and Current Competencies

These data sets include numerically coded answers to Likert-like scale questions concerning the importance and perceived current research data management competencies of doctoral students. Interviewees were 35 doctoral students and faculty members. Interview forms are attached. The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684

opencc-by-4.0Feb 2020View details →

zenodo44/100

Data and code for "Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education"

Code and data for the paper "Carbon neutrality should not be the end goal:  Lessons for institutional climate action from U.S. higher education" File descriptions: 'HEI_analysis_OneEarth.Rmd' is the code with improved annotation and colorblind-friendly figures. All other data files are provided as excel and csv for convenience. 'working_master_data' contains data from the Second Nature reporting platform on emissions by category for each institution analyzed in the paper (measured in metric tons). All adjustments necessary to fill in the data gaps in this file are documented at the beginning of 'HEI_analysis'. 'offsets' contains data on the type(s) of offsets purchased by each school in their carbon neutral year (measured in metric tons). This data was assembled from a variety of sources which are documented at the beginning of 'HEI_analysis'. 'carbon_neutral_years' contains yearly counts of higher education neutrality goals that were reported to Second Nature as of November 2020.

opencc-by-4.0Jan 2021View details →

zenodo44/100

Data Extraction table summarizing studies in the scoping review on co-creation of patient education materials

Data extraction table for scoping review on best practices for co-creating patient-facing educational materials

opencc-by-4.0Aug 2023View details →

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KEE01 Prairie phenology data from Konza Environmental Education Program (KEEP) since 2001

This data represents the "phenology" or timing of observable biological processes, e.g. first day of blooming, first sighting of a migratory species, etc... as reported by volunteers with the Konza Environmental Education Program. The reports do not follow a specific experimental design and are based on the individual's knowledge and skills. Data is also available on the KEEP website (https://keep.konza.k-state.edu/prairieecology/index.html).

openCC0Mar 2025View details →

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Data Files and Documentation - National Coordination of Data Steward Education in Denmark

This site contains the Data Files and Documentation Files from the National Coordination of Data Steward Education in Denmark Project (2019-2020). The main deliverables from the project are: The Main Report: <a href="https://doi.org/10.5282/zenodo.3609516">National Coordination of Data Steward Education in Denmark</a>  (Main Report) and Wildgaard, Lorna <a href="https://doi.org/10.5281/zenodo.3628375">Reframing Data Stewardship educations in Denmark and abroad</a>  (Wildgaard)   List of available Data and Documentation Files below: Wildgaard and Main Report (section 2 Review of Data Steward Education) 1. DS_education_1_review_thematic_analysis.nvp (NVIVO-file)  2. DS_education_2_review_coding_scheme (Excel)  (Created by Wildgaard, Lorna) Main Report (section 3 LinkedIn analysis) No data available due to GDPR Main Report (section 4 Job vacancies analysis) 3. DS_education_3_vacancies_and_method.zip (zip-file)  4. DS_education_4_vacancies_top10_word_frequencies_geographical_location (Excel) (Created by Vlachos, Evgenios) The zip-file contains a text file describing the method used in the analysis (Method) and 119 pdf files with Data Steward vacancies. Main Report (section 5 Questionnaire) 5. DS_education_5_questionnaire_questions 6. DS_education_6_questionnaire_survey_report  (Created by Vlachos, Evgenios & Knudsen, Christian B.) Main Report (section 6 Interviews) 7. DS_education_7_interviews_summaries_interview_1-4   (Created by Hüser, Falco)

opencc-by-4.0Jan 2020View details →

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Data for D7.1 FAIR in European Higher Education

As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The FAIRsFAIR project runs from March 2019-February 2022. FAIRsFAIR Work Package 7 “FAIR Data Science and Professionalisation” aims to develop resources and build communities that support the uptake of RDM and FAIR practice within higher education curricula. The data published here stems from a both a web-based questionnaire with 90 responses conducted within FAIRsFAIR WP7 between 19 September and 15 November 2019. The questionnaire covered several dimensions of research data management at HEIs relevant for the implementation of FAIRsFAIR WP7, as well as WP3 “FAIR Data Policy Practice” and WP6 “FAIR Competence Centre”. These dimensions included: <ul> <li>Institutional research data management policies </li> <li>Support services for research data management</li> <li>Competence development of students and graduates</li> <li>Universities and EOSC</li> <li>FAIRsFAIR support for universities</li> </ul> The data resulting from the survey has been used as the basis for <a href="http://doi.org/10.5281/zenodo.3629683">D7.1 FAIR in European Higher Education</a>. The following files are available: <ul> <li>Codebook including original questionnaire</li> <li>Dataset</li> </ul>

opencc-by-4.0Jun 2020View details →

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Data Visualization - Individual Project - G20 Countries - Military, Health Care and Educational Spendings

This Project is part of the course work for Data visualization DATS 6401. In this project, I have created webpage to show data analysis on G20 countries - military, health care and educational spending from 2011 - 2017.  Google Visualization API is used for all visualization graphs in the webpage.

opencc-by-4.0Oct 2020View details →

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South African higher education data 1 - Data resources

GIS-based map visualisation of the data resources providing open data on South African higher education data. Generated as part of research conducted for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.

opencc-by-sa-4.0May 2014View details →

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Relevance of national policy in the provision of open data on South African higher education sector

Spreadsheet providing breakdown of the national Acts, Bills and Standards relevant to sharing open data on the governance of higher education in South Africa. Data generated as a component of the situational analysis conducted for the 'Use of open data in the governance of South African higher education' research project, in the IDRC/WWWF 'Exploring Emerging Impacts of Open Data in the South' initiative.

opencc-by-sa-4.0May 2014View details →

ScienceDex guides

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

Compare curated 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.

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