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1,372 results for “engagement”

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zenodo52/100

[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data

Explanation/Overview: Corresponding raw data for the analyses described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised. The raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it. Purpose: The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks. Relatedness: The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'. Content: The dataset contains three files: <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> Grouping: The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.

opencc-by-4.0Nov 2022View 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

augMENTOR: Simulated Student Learning Profiles and their Engagement Metrics in TryHackMe Platform_V1

The dataset provides simulated insights into student engagement and performance within the THM platform. It outlines mathematical representations of student learning profiles, detailing behaviors ranging from high achievers to inconsistent performers. Additionally, the dataset includes key performance indicators, offering metrics like room completion, points earned, and time spent to gauge student progress and interaction within the platform's modules.Here are definitions of the learning profiles, along with mathematical representations of their behaviors:<ul><li>High Achiever: These are students who consistently perform well across all modules. Their performance can be described as a normal distribution centered at a high mean value. Their performance P in a given module can be modelled as: P = N(90, 5) where N is the normal distribution function, 90 is the mean, and 5 is the standard deviation.</li><li>Average Performer: These are students who typically perform at the average level across all modules. Their performance can be described as a normal distribution centered at a medium mean value: P = N(70, 10), where 70 is the mean, and 10 is the standard deviation.</li><li>Late Bloomer: These are students whose performance improves as they progress through the modules. Their performance can be modelled as: P = N(50 + i*10, 10), where i is the module index and shows an increasing trend.</li><li>Specialized Talent: These are students who have average performance in most modules but excel in a particular module (e.g., module5). Their performance can be described as: P = N(90, 5) if the module is module 5, else P = N(70, 10).</li><li>Inconsistent Performer: These are students whose performance varies significantly across modules. Their performance can be described as a normal distribution with a high standard deviation: P = N(70, 30), where 70 is the mean, and 30 is the high standard deviation, reflecting inconsistency.</li></ul>Note that the actual performances are bounded between 0 and 100 using the function max(0, min(100, performance)) to ensure valid percentages.In these formulas, the np.random.normal function is used to simulate the variability in student performance around the mean values. The first argument to this function is the mean, and the second argument is the standard deviation, reflecting the level of variability around the mean. The function returns a number drawn from the normal distribution described by these parameters. Note that the proposed method is experimental and has not been validated.  List of Key Performance Indicators (KPIs) for Student Engagement and Progress within the Platform:<ul><li>Room Name: This represents the unique identifier or name of a specific room (or module). Think of each room as a separate module or lesson within an educational platform. For example, Room1, Room2, etc.</li><li>Total rooms completed: Indicates the cumulative number of rooms that a student has fully completed. Completion is typically determined by meeting certain criteria, like answering all questions or achieving a certain score.</li><li>Rooms registered in: Represents the number of rooms a student has registered or enrolled in. This could be different from the total number of rooms they've completed.</li><li>Ratio of Questions completed per room: This gives an insight into a student's progress in a particular room. For instance, a ratio of 7/10 suggests the student has completed 7 out of 10 available questions in that room.</li><li>Room Completed (yes no): Indicates whether a student has fully completed a specific room or not. This could be determined by the percentage of material covered, questions answered, or a certain score achieved.</li><li>Room Last deploy (count of days): Refers to the number of days since the last update or deployment was made to that room. It can give an idea about the effort of the student.</li><li>Points in room used for the leaderboard (range 0-560): Each room assigns points based on student performance, and these points contribute to leaderboards. The range suggests that a student can earn anywhere from 0 to 560 points in a particular room.</li><li>Last answered question in a room (27th Jan 2023): This indicates the date when a student last answered a question in a specific room. It can provide insights into a student's recent activity and engagement.</li><li>Total points in all rooms (range 0-560): The cumulative score a student has achieved across all rooms.</li><li>Path Percentage completed (range 0-100): Indicates the percentage of the overall learning path that the student has completed. A path could consist of multiple modules or rooms.</li><li>Module Percentage completed (range 0-100): Represents how much of a specific module (which could have multiple lessons or topics) a student has completed.</li><li>Room Percentage completed (range 0-100): Shows the percentage of a specific room that has been completed by a student.</li><li>Time Spent on the platform (seconds): This provides an aggregate of the total time a student has spent on the entire educational platform.</li><li>Time spent on each room (seconds): Represents the amount of time a student has dedicated to a specific room. This can give insights into which rooms or modules are the most time-consuming or engaging for students.</li></ul>

opencc-by-4.0Nov 2023View details →

zenodo48/100

Database on the History of Participation and Engagement

The dataset presents a timeline and database of sustainability-related, innovative, and inclusive participation and engagement practices in Europe since the early days of digitalisation.  The data reported here was assembled from Democratic Innovations collected from publicly accessible sources (Partecipedia, OECD's database of Representative Deliberate Processes and Institutions, Knowledge Network on Climate Assemblies (KNOCA), G1000, and the International Observatory on Participatory Democracy), with a specific focus on their contribution to social and environmental sustainability. By mapping the dynamics of implementation of Democratic Innovations across Europe, the dataset provides valuable insights into the potential for these innovations to foster more inclusive and resilient societies in line with social and environmental development goals. Each of the cases collected here was categorised on specific criteria, like geographical distribution, scales of governance, policy areas, citizen involvement, and digitalization. INCITE-DEM is funded by the European Union (INCITE-DEM, GA nº 101094258). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or of the European Research Executive Agency (REA). Neither the European Union nor the granting authority (REA) can be held responsible for them

opencc-by-nc-4.0Mar 2024View details →

zenodo48/100

[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects

Explanation/Overview: Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. The usernames have been anonymised. Purpose: The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it does not represent raw data, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R. Relatedness: The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'. Content: In this Zenodo entry, several files can be found. The structure is as follows (<code>files</code> and folders and descriptions). <ul> <li><code>corresponding_calculations.html</code> <ul> <li>Quarto-notebook to view in browser</li> </ul> </li> <li><code>corresponding_calculations.qmd</code> <ul> <li>Quarto-notebook to view in RStudio</li> </ul> </li> <li>assets <ul> <li>data <ul> <li>annotations <ul> <li><code>annotations.csv</code> <ul> <li>List of annotations made per day for each of the analysed projects</li> </ul> </li> </ul> </li> <li>comments <ul> <li><code>comments.csv </code> <ul> <li>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</li> </ul> </li> </ul> </li> <li>rolechanges <ul> <li><code>478_rolechanges.csv</code> <ul> <li>List of roles per user to determine number of role changes </li> </ul> </li> <li><code>1104_rolechanges.csv</code> <ul> <li>...</li> </ul> </li> <li><code>...</code></li> </ul> </li> <li>totalnetworkdata <ul> <li>Edges  <ul> <li><code>478_edges.csv</code> <ul> <li>Network data (edge set) for the given projects (without time slices)</li> </ul> </li> <li><code>1104_edges.csv</code> <ul> <li>...</li> </ul> </li> <li><code>...</code></li> </ul> </li> <li>Nodes  <ul> <li><code>478_nodes.csv</code>  <ul> <li>Network data (node set) for the given projects (without time slices)</li> </ul> </li> <li><code>1104_nodes.csv</code>  <ul> <li>...</li> </ul> </li> <li><code>...</code></li> </ul> </li> </ul> </li> <li>trajectories <ul> <li>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</li> <li>478  <ul> <li>Edges  <ul> <li> <code>edges_4782016_q1.csv</code> </li> <li> <code>edges_4782016_q2.csv</code> </li> <li> <code>edges_4782016_q3.csv</code> </li> <li> <code>edges_4782016_q4.csv</code> </li> <li> <code>...</code> </li> </ul> </li> <li>Nodes  <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <code>nodes_4782016_q4.csv</code> </li> <li> <code>nodes_4782016_q3.csv</code> </li> <li> <code>nodes_4782016_q2.csv</code> </li> <li> <code>...</code> </li> </ul> </li> </ul> </li> <li> 1104  <ul> <li> Edges  <ul> <li> <code>...</code> </li> </ul> </li> <li> Nodes  <ul> <li> <code>...</code> </li> </ul> </li> </ul> </li> <li> ... </li> </ul> </li> </ul> </li> <li>scripts <ul> <li><code>datavizfuncs.R</code> <ul> <li>script for the data visualisation functions, automatically executed from within <code>corresponding_calculations.qmd</code></li> </ul> </li> <li><code>import.R</code> <ul> <li>script for the import of data, automatically executed from within <code>corresponding_calculations.qmd</code></li> </ul> </li> </ul> </li> </ul> </li> <li>corresponding_calculations_files <ul> <li>files for the html/qmd view in the browser/RStudio</li> </ul> </li> </ul> Grouping: The data is grouped according to given criteria (e.g., <code>project_title </code>or  <code>time</code>). Accordingly, the respective files can be found in the data structure

opencc-by-4.0Nov 2022View details →

zenodo44/100

Video Analysis Scale of Engagement (VASE): Initial and Final Protocols

These files contain the initial and final protocols that were used to develop the Video Analysis Scale for Engagement (VASE). The abstract of our article in Wellcome Open Research, 5:230: https://wellcomeopenresearch.org/articles/5-230/v2: Background: The current study sought to develop a valid, reliable and unobtrusive tablet computer-based observational measure to assess engagement of people with advanced dementia. The Video Analysis Scale of Engagement (VASE) was designed to enable the rating of moment-by-moment changes in engagement during an activity, which would be useful for both future research and current residential care. Methods: An initial version of the VASE was tested. Face validity and content validity were assessed to validate an operational definition of engagement and develop an acceptable protocol for the scale. Thirty-seven non-professional and professional volunteers were recruited to view and rate level of engagement in music activities using the VASE. Results: An inter-class coefficient (ICC) test gave a high level of rating agreement across professionals and non-professionals.  However, the ICC results of within-professionals were mixed. Linear mixed modelling suggested that the types of interventions (active or passive music listening), the particular intervention session being rated, time period of video and the age of raters could affect the ratings. Conclusions: Results suggested that raters used the VASE in a dynamic fashion and that the measure was able to distinguish between interventions. Further investigation and adjustments are warranted for this to be considered a valid and reliable scale in the measurement of engagement of people with advanced dementia in a residential care setting.

openmit-licenseAug 2020View details →

zenodo44/100

Parental Engagement and Relationships (PEAR) in Early Childhood (EC). Impact study: Parents' responses

Dataset with parents' quantitative responses collected within the impact study of the project Parental Engagement and Relationships (PEAR) in Early Childhood (EC). The file (.sav) can be opened using IBM SPSS Software. The file is named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925.

opencc-by-4.0Aug 2023View details →

zenodo44/100

How does moving Public Engagement with Research Online Change Audience Diversity? Comparing Inclusion Indicators for 2019 & 2020 European Researchers' Night events

Taking place annually in more than 400 cities, European Researchers’ Night is a pan- European synchronized event that aims to bring researchers closer to the public. In this paper audience profiles are compared from events in 2019 and 2020. In 2019, face-to-face events reached an estimated 1.6 million attendees, while in 2020, events shifted online due to the COVID-19 pandemic and reached an estimated 2.3 million attendees. Focusing on social inclusion metrics, survey data is analyzed across two national contexts (Ireland and Malta) in 2019 (n=656) and 2020 (n=506). The results from this exploratory, descriptive study shed light on how moving public engagement with research online shifted audience profiles. Based on prior research about the digital divide in access and use of online media, hypotheses were proposed that online European Researchers’ Night events would attract audiences with higher educational attainment levels and greater self-reported, subjective economic well-being. While changes were observed from 2019 to 2020, results for each hypothesis show a mixed picture. The first hypothesis was upheld for the highest education levels but failed for the lowest levels suggesting that the pivot to online events simultaneously attracted participants with no formal education and those with postgraduate qualifications, while attracting less of those with undergraduate or lower levels of education. The second hypothesis was not upheld, with online European Researchers’ Night events attracting audiences with slightly higher levels of economic well-being compared to face-to-face events. The findings of this study indicate that European Researchers’ Night events present a clear opportunity to measure the effects of the digital divide in relation to public engagement with research across Europe.

opencc-by-4.0Jan 2022View details →

zenodo44/100

Effects of the type of lunch meal and teaching-learning methods on CRI student engagement in afternoon classes.

The post-lunch slump is a natural dip in energy caused not only by our circadian cycles but also as a result of digestive processes in our body after consuming lunch meals. It being a widely known concept, there are ample studies that focus on its effects on academic performance of students. While this dip in energy does affect student engagement to a large extent, there are also studies on how the teaching-learning methods are a factor affecting academic performance. However, there are no relevant studies that aim to look at both these factors in tandem, i.e. the effect of lunch along with the teaching-learning methods in the afternoon classrooms as factors influencing academic performance and student engagement. This paper aims to study this area of topic hypothesising that having a more balanced lunch along with engaging in more interactive classes would positively correlate with student engagement in afternoon classes. The study was conducted with bachelors and masters students at the CRI, University of Paris through surveys that were sent to students at the end of their class day.  Due to lack of adequate data, the study was not able to produce significant correlations. However smaller nuances of the relationship between lunch, teaching-learning methods and student engagement were found. While engagement in classrooms did seem to be maximum when it was interactive and only 50% of students' lunch was formed of carbohydrates, no significant correlations were found to confirm the hypotheses.

opencc-by-4.0Jan 2022View details →

zenodo44/100

Leaflet Engage IoT Societal Engagements with the Internet of Things

Leaflet of the project Engage IoT, funded by the Fundação para a Ciência e a Tecnologia (EXPL/SOC-SOC/1375/2021), in the shape of a fortune teller. Website of the project: https://engageiot.wordpress.com/

opencc-by-4.0Jul 2024View details →

zenodo44/100

Driving social and economic development in the MENA region: the role of international organizations - Stakeholders' engagement meeting report

On 15 November 2018, the Center for Public Policy and Democracy Studies (PODEM) hosted a stakeholders’ meeting in Istanbul with twenty-nine participants, including academics, experts, and representatives from civil society and international humanitarian and development organizations from Europe and the MENA region, as part of the Middle East North Africa Regional Architecture Project (MENARA). Through two panels of expert presentations and facilitated discussion, the participants addressed present challenges, ongoing transformations and future opportunities facing societies across the region, as well as the role of international organizations in addressing these challenges and opportunities. This report will summarize the discussion during that meeting in two broad parts, focusing on the region’s challenges and positive developments respectively and is further divided into sections by issue subject. Two sets of policy recommendations are provided at the end of the report.

opencc-by-4.0Jan 2019View details →

zenodo44/100

[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects

Corresponding dataset for the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised. The structure of the dataset is as follows: Annotations  List of annotations made per day for each of the analysed projects. <code>annotations.csv </code> Comments  Total list of comments with several data fields (i.e., comment id, text, reply_user_id) <code>comments.csv </code> Rolechanges  List of roles per user to determine number of role changes  <code>478_rolechanges.csv</code> <code>1104_rolechanges.csv</code> <code>...</code> Totalnetworkdata  Network data (edge and node sets) for the given projects (without time slices). Edges  <ul> <li> <code>478_edges.csv</code> </li> <li> <code>1104_edges.csv</code> </li> </ul> Nodes  <ul> <li> <code>478_nodes.csv</code>  </li> <li> <code>1104_nodes.csv</code>  </li> </ul> Trajectories  Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021) 478  <ul> <li>Edges  <ul> <li> <code>edges_4782016_q1.csv</code> </li> <li> <code>edges_4782016_q2.csv</code> </li> <li> <code>edges_4782016_q3.csv</code> </li> <li> <code>edges_4782016_q4.csv</code> </li> </ul> </li> <li> ... </li> <li>Nodes  <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <code>nodes_4782016_q4.csv</code> </li> <li> <code>nodes_4782016_q3.csv</code> </li> <li> <code>nodes_4782016_q2.csv</code> </li> <li> <code>...</code> </li> </ul> </li> </ul>   1104  <ul> <li> Edges  <ul> <li> <code>...</code> </li> </ul> </li> <li> Nodes  <ul> <li> <code>...</code> </li> </ul> </li> <li> <code>...</code> </li> </ul>

opencc-by-4.0Nov 2022View details →

zenodo44/100

Creatures Engagement Events (data)

This dataset includes information on the engagement activities realized and curated around the CreaTures project outcomes and productions, including co-creative play/workshops, panel discussions, public seminars, and other interactive actions

opencc-by-4.0Dec 2022View details →

zenodo44/100

A study on biomedical researchers' perspectives on public engagement in Southeast Asia

Survey data from biomedical researchers in Southeast Asia about their perceptions of public engagement. The survey used open and closed questions.

opencc-by-4.0Feb 2023View details →

zenodo44/100

A study on biomedical researchers' perspectives on public engagement in Southeast Asia

Qualitative data on researcher's perceptions of public and community engagement in South and Southeast Asia

opencc-by-4.0Apr 2023View details →

zenodo44/100

Experimental Protocol on Creative Engagement and Meaning Creation in Interactive Experiences for Cultural Heritage. Tabular Data

The following document contains the material produced for the testing of the "caring prototype" MyTISSE, an interactive user experience on Matisse's painting Bathers by a river (Art Institute of Chicago). Focusing on the change of the pigments made by the artist during the gestation of this canvas, the UX exploits creative engagement to catalyze meaning creation processes. This pubblication contains the data collected through the form. All the participants agreed to the reuse and sharing of these data for academic purposes, filling in an ad hoc privacy form.

opencc-by-4.0Jul 2023View details →

zenodo44/100

Datasets from the paper "Measuring the relationship between the use of typical Manosphere discourse and the engagement of a user with the pick-up artist community"

Datasets from the paper "Measuring the relationship between the use of typical Manosphere discourse and the engagement of a user with the pick-up artist community", presented at the 24th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL). The dataset consists in 2 files: <ul> <li>tweets.csv: This file contains the ids of the tweets and the user that published the tweet.</li> <li>df_users_seeds.csv: This file contains a table were the first field is the user id; the other columns indicate the seed number and if the user follows that seed (1) or not (0)</li> </ul>

opencc-by-4.0Sep 2023View details →

zenodo40/100

Hackathons as a Pedagogical Strategy to Engage Students to Learn and to Adopt Software Engineering Practices

Teaching Software Engineering is not a trivial duty since several pedagogical strategies can be used and sometimes the impact of these on students is uncertain. Hackathons are similar to marathons, however used to produce solutions to solve a specific problem in a short period of time and based on intense collaboration. Educational hackathons aim to promote learning in such an environment. The Undergraduate computing programs of PUCRS decided to use a hackathon as a pedagogical strategy aiming to motivate the students to practice the adoption of software development practices and to work in groups as a means to practice the development of social skills. Therefore, we conducted a case study to investigate: 1) The motivations to students to attend or not attend an educational hackathon, 2) The students perceptions about this hackathon, 3) The Software Engineering practices adopted by students. In this study, we identified factors that may affect students motivation to participate (e.g., improve the teamwork skills), some students expectations about the hackathon (e.g., work in teams), and the practices adopted by the students (e.g., pair programming). Some of our findings include that students enjoy participating in an informal educational environment (e.g., hackathons) to improve their technical skills and to build network with some colleagues. This study can provide insights to teachers that wants to organize some activity than traditional teaching and the students perspective about this kind of strategy.

opencc-by-4.0Oct 2020View details →

zenodo40/100

Risk Communication and Community Engagement: A Ghanaian Perspective

Risk communication and community engagement is essential component of health emergency preparedness and response action. Dataset encompasses responses from all the sixteen (16) administrative regions of Ghana. Responses to risky behaviors, perceived probability of covid-19 infection, sources of information, perceived severity etc. are well captured by the dataset.

opencc-by-4.0Jan 2021View details →

zenodo40/100

Engaging the Business and Tourism Industry in Visualizing Sea Level Rise Impacts to Transportation Infrastructure in Waikiki, Hawaii

Transportation planners in coastal communities plan for future hazards and risks of sea-level rise (SLR), and often, they communicate risk in public meetings via PowerPoint presentations with charts as well as two-dimensional (2D) maps that visualize information using Geographic Information Systems (GIS) technologies. The project investigates the use of immersive technology to communicate SLR risk, including the development of an immersive three-dimensional (3D) model of the Waikiki neighborhood of Honolulu, Hawaii. According to the project’s original methodology, participants would have experienced the model using virtual reality (VR). However, due to the COVID-19 pandemic, the team pivoted to creating and implementing an internet-based survey instrument with embedded 2D charts and video of the animated 3D model. The flooding projections were derived from National Oceanic and Atmospheric Administration (NOAA) data. NOAA supplies the SLR Viewer, a screening-level tool that uses the best-available national projections to map areas vulnerable to current and future flood risks.

opencc-by-4.0Jan 2021View details →

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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