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Harvard Forest Summer Research Program in Ecology Student Surveys 2016-2021
Program Context Since its inception in 1985, when a single undergraduate worked on a study of old-growth forests, the Harvard Forest Summer Research Program in Ecology (HF-SRPE) has developed into a thriving and well-coordinated program that is central to the educational and research mission of the biological field station. With core support since 1993 from a succession of NSF REU Site awards and NSF REU supplements, and with additional funding from Harvard University, HF-SRPE has grown to support 20-30 undergraduate students annually. Students are mentored by principle investigators and senior scientists in conducting research in ecology, soil science, paleoecology, wildlife biology, conservation biology, and atmospheric sciences. The research conducted by our HF-SRPE students contributes substantially to long-term scientific investigations supported by NSF’s Long-Term Ecological Research (LTER) and National Ecological Observatory Network (NEON), NASA Earth System Science Pathfinder (ESSP) – Earth Ventures (EV) programs, The Smithsonian Institution’s ForestGEO network of plots, and Department of Energy’s (DOE) National Institute for Global Environmental Change (NIGEC). The overarching objectives of the program are to: enhance the ability of students to undertake high-quality interdisciplinary research; build teams of researchers in which students bring different strengths to the table, collaborate on cutting-edge projects, and find their own intellectual “voice”; encourage students to link fundamental and applied issues in their research; and cultivate the next generation of ecological scientists and educators that reflects the diversity of backgrounds and experiences of students in the United States. Problem Statement Starting in 2005 the National Science Foundation (NSF), as one of the largest funders of undergraduate research programs, began emphasizing the use of project evaluations to both qualitatively and quantitatively measure the success of REU programs (R
Supplementary Materials: A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education
<p>Example data sets, syntax files and macros for the tutorials in: Balloo, K., & Winstone, N. E. (2021). A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education.<em> Frontline Learning Research</em>. <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">https://doi.org/10.14786/flr.v9i2</a><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">.675</a> </p> <p><strong>The data for all examples are fictional, and have only been designed to simulate the possible behaviour of institutional data for the purposes of demonstrating the analytical approaches in the primer. No inferences or conclusions should be drawn from the findings of these examples, because the results are not real. </strong></p> <p>We anticipate that readers can use the example data sets as templates and substitute in their own data.</p>
augMENTOR: Simulated Student Learning Profiles and their Engagement Metrics in TryHackMe Platform_V1
<p>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.</p><p>Here are definitions of the learning profiles, along with mathematical representations of their behaviors:</p><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><p>Note that the actual performances are bounded between 0 and 100 using the function max(0, min(100, performance)) to ensure valid percentages.</p><p>In these formulas, the <i>np.random.normal</i> 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. </p><p> </p><p>List of Key Performance Indicators (KPIs) for Student Engagement and Progress within the Platform:</p><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>
Online survey among students and teachers in the Swiss farm management course
<p>This dataset contains survey data including the codebook for an online survey conducted in German and French in Switzerland in spring 2021. With this survey, we aimed to find out what students learn and what teachers teach in this course about digital technologies in agriculture. </p>
Data for the article "Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic"
<p>Dataset from a study of attitudes and perceptions of medicine and nursing students in Peru during the COVID-19 pandemic. Survey was applied from 2020-07-24 to 2021-04-16.</p> <p>This dataset is described in the article: </p> <p>Castagnetto, J.M., Hancco-Monrroy, D.E., Caballero-Apaza, L.M. <em>et al.</em> Professionalism, emotional wellbeing, and dropout intention in health professions students during the pandemic. <em>Sci Data</em> <strong>12</strong>, 1259 (2025). <a href="https://doi.org/10.1038/s41597-025-05508-5">https://doi.org/10.1038/s41597-025-05508-5</a> (<a href="https://www.nature.com/articles/s41597-025-05508-5">https://www.nature.com/articles/s41597-025-05508-5</a>)</p>
COVID-19 German Student Well-being Study (C19 GSWS)
<p><strong>COVID-19 German Student Well-being Study (C19 GSWS)</strong></p> <p>Following the COVID-19 International Student Well-being Study (C19 ISWS; survey phase: May 13<sup>th</sup>, 2020 to May 29<sup>th</sup>, 2020 in 27 European countries coordinated by the University of Antwerp), well-being of university students during the COVID-19 pandemic continued to be the focus of the collaborative COVID-19 German Student Well-being Study (C19 GSWS) which was conducted at five universities in Germany.</p> <p> </p> <p>The five German universities taking part in the study were the Charité – Universitätsmedizin Berlin (PI: Prof. Christiane Stock), the University of Bremen (PI: Dr. Heide Busse), Heinrich-Heine-University Duesseldorf (PI: Prof. Claudia Pischke), University of Siegen (PI: Prof. Claus Wendt) and Martin-Luther University Halle-Wittenberg (PI: Prof. Rafael Mikolajczyk).</p> <p> </p> <p>The following research questions were addressed:</p> <p> </p> <p>- How did university students' (physical and socioeconomic) living conditions and academic workload change during the COVID-19 pandemic?</p> <p>- How were living and study conditions associated with mental health outcomes among university students during the COVID-19 pandemic?</p> <p>- How were living conditions and academic workload associated with health behaviours (e.g., substance use) among university students during the pandemic?</p> <p>- Which attitudes towards COVID-19 vaccination and determinants of vaccination behavior were prevalent r among university students?</p> <p> </p> <p>To answer the research questions, an online survey among university students was conducted at all participating universities from October 27<sup>th</sup>, 2021 to November 14<sup>th</sup>, 2021. The resulting data allow for a description of living conditions, as well as well-being, during the ongoing COVID-19 pandemic in German university student populations.</p> <p> </p> <p>Information about C19 ISWS on Zenodo:</p> <p>https://zenodo.org/communities/c19-isws/?page=1&size=20</p>
Members of the Chinese Students' Alliance in the United States (1912)
<p>The dataset is a list of members of the Chinese Students Alliance in the United States for the academic year 1911-1912. It is sourced from <em>The Directory of Chinese Students in the United States, 1911-1912</em>, compiled by the Chinese Students Alliance in 1912. The directory has been digitized by Google and is available in full view on <a href="https://babel.hathitrust.org/cgi/pt?id=nnc2.ark:/13960/t9574zp5q&seq=5">HathiTrust</a> and <a href="https://archive.org/details/ldpd_11381020_000">Internet Archive</a>.</p> <p>The attached table contains information on the students' names (in Chinese, English, and pinyin transliteration), gender, address in the United States, university in the United States (when available), and the alliance section (Eastern, Midwest, Western) to which they belonged (when available). Additionally, the table provides the geographical coordinates of the cities.</p> <p>The data was extracted using <a href="https://claude.ai/">Claude (AI</a>) and curated using Excel and R. The complete code for extracting and curating the data is available on <a href="https://github.com/carmand03/csa-directories">GitHub</a>. Additionally, the GitHub repository contains various statistics and visualizations, such as the distribution of students by city.</p> <p>The dataset contains 882 students (unique individuals), 802 men and 80 women. </p> <p>Distribution by sections (p.119): </p> <table> <tbody> <tr> <td><strong>Sections</strong></td> <td><strong>Members</strong></td> <td><strong>Non-Members</strong></td> <td><strong>Total</strong></td> </tr> <tr> <td>Eastern</td> <td>201</td> <td>127</td> <td>328</td> </tr> <tr> <td>Midwest</td> <td>121</td> <td>123</td> <td>244</td> </tr> <tr> <td>Western</td> <td>42</td> <td>66</td> <td>108</td> </tr> <tr> <td>Unreturned (Missing)</td> <td>101</td> <td>96</td> <td>197</td> </tr> <tr> <td>Total</td> <td>465</td> <td>412</td> <td>877</td> </tr> </tbody> </table>
Values, beliefs, norms, and circular citizenship behaviours in a student sample
<p>Data set of 229 students from an online survey, run between March 7th 2024 and April 2nd 2024.</p> <p><strong>Gender</strong>: 43 men (18.8%), 181 women (79.0%), 3 non-binary persons (1.2%), 2 participants (0.9%) preferred not to indicate their gender.</p> <p><strong>Age</strong>: The age ranged from 17 to 29 years (M = 19.7, SD = 1.83).</p> <p><strong>Variables</strong>: Biospheric, altruistic, hedonic, and egoistic values, problem awareness, ascription of causal responsibility, self-efficacy, outcome efficacy, personal norms, circular citizenship behaviours</p> <p><strong>Data analysis </strong>by creating scales for the variables and running regressions/GLM.</p>
Student activity data
<p>This dataset includes anonymized records of student activities in a peer review activity performed through Synergy platform. This data comes from an educational technology course delivered in an European university. There were 30 participants from different departments (Computer science, Math, Physics, Preschool and Elementary Education) In this course, the main course assignment was a learning design project in which students worked in groups. The activity lasted 3 weeks and for each project 2-3 reviewers were assigned.</p>
Dataset and R script for the analysis in the article "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", Journal of Cleaner Production
<p>We hereby publish the dataset (with metadata) and the R script (R Core team, 2018) used for implementing the analysis presented in the paper "Food waste between environmental education, peers, and family influence. Insights from primary school students in Northern Italy", <em>Journal of Cleaner Production </em>(Piras et al., 2023). The dataset is provided in csv format with semicolons as separators and "NA" for missing data. The dataset includes all the variables used in at least one of the models presented in the paper, either in the main text or in the Supplementary Material. Other variables gathered by means of the questionnaires included as Supplementary Material of the paper have been removed. The dataset includes inputted values for missing data on independent variables. These were inputted using two approaches: last observation carried forward (LOCF) - preferred when possible - and last observation carried backward (LOCB). The metadata are presented as a PDF file.</p>
Missouri reservoir water quality data from the Statewide Lake Assessment Program (SLAP), the Lakes of Missouri Volunteer Program (LMVP), and the Reservoir Observer Student Scientists (ROSS) program
This dataset of limnological water quality data continues from Jones et al., 2024, starting in 2017 until 2021. It is from 195 reservoirs, the majority of which are in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: areal pigment absorption coefficient, alkalinity, alpha (light utilization efficiency P-E parameter), ammonium (NH4), ammonium-debt, anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, seston d13C, seston d15N, dissolved turbidity, dissolved organic carbon, Ek (light saturation P-E parameter), FVFM (maximum quantum yield of PSII for photochemistry), gross primary production, microcystin, nitrate & nitrite (NO3), nitrate-debt, particulate nitrogen, particulate phosphorus, phosphorus-debt, pheophytin, particulate carbon, particulate inorganic matter, particulate organic matter, phycocyanin (PHYCO), saxitoxin, Secchi disk depth, silica, soluble reactive phosphorus, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. Most of the data come from the Statewide Lake Assessment Project (SLAP) and the Lakes of Missouri Volunteer Program (LMVP) funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete dep
Albero study: a longitudinal database of the social network and personal networks of a cohort of students at the end of high school
<p><strong>ABSTRACT</strong></p> <p>The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.</p> <p><strong>INTRODUCTION</strong></p> <p>Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.</p> <p>The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).</p> <p>Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).</p> <p>These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.</p> <p><strong>PARTICIPANTS</strong></p> <p>The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).</p> <p><strong>DATE STRUCTURE AND ARCHIVES FORMAT</strong></p> <p>The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.</p> <p><strong>Social network</strong></p> <p>The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.</p> <p>The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.</p> <p>In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.</p> <p><strong>Personal networks</strong></p> <p>Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).</p> <p>Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.</p> <p><strong>Sense of community and metropolitan displacements</strong></p> <p>The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:</p> <p> • Socio-economic data.</p> <p> • Data on habitual residence.</p> <p> • Information on intercity journeys.</p> <p> • Identity and sense of community.</p> <p> • Personal network indicators.</p> <p> • Social network indicators.</p> <p><strong>DATA ACCESS</strong></p> <p>Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.</p> <p>The visual representation of the personal networks of the respondents in both waves is available in the following album of the <em>Graphic Gallery of Personal Networks</em> on Flickr: <<a href="https://www.flickr.com/photos/25906481@N07/albums/72157667029974755">https://www.flickr.com/photos/25906481@N07/albums/72157667029974755</a>>.</p> <p>In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:</p> <ul> <li>Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. <em>Universitas Psychologica, 17</em>(1), 86-98. <a href="https://doi.org/10.11144/Javeriana.upsy17-1.eerp">https://doi.org/10.11144/Javeriana.upsy17-1.eerp</a> </li> </ul> <p>The English version of this article can be downloaded from: <a href="https://tinyurl.com/yy9s2byl">https://tinyurl.com/yy9s2byl</a></p> <p><strong>CONCLUSION</strong></p> <p>The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.</p> <p>The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.</p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals, groups, organizations and social settings” (2006 -2009) of the European Science Foundation (ESF). The data was presented for the first time on June 30, 2009, at the European Research Collaborative Project Meeting on Dynamic Analysis of Networks and Behaviors, held at the Nuffield College of the University of Oxford.</p> <p><strong>REFERENCES</strong></p> <p><strong>Brandes, U., & Wagner, D. (2004). </strong>Visone - Analysis and Visualization of Social Networks. In M. Jünger, & P. Mutzel (Eds.), <em>Graph Drawing Software</em> (pp. 321-340). New York: Springer-Verlag. </p> <p><strong>Maya-Jariego, I. (2018).</strong> Why name generators with a fixed number of alters may be a pragmatic option for personal network analysis. <em>American Journal of Community Psychology, 62</em>(1-2), 233-238. DOI 10.1002/ajcp.12271</p> <p><strong>Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018).</strong> Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. <em>Universitas Psychologica, 17</em>(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp</p> <p><strong>Maya-Jariego, I., Lubbers, M. J. & Molina, J. L. (2019).</strong> A friendship network in decay: The dynamics of social relationships of a secondary school cohort over the transition to university. <em>Remitido</em>.</p> <p><strong>Seidman, E., & French, S. E. (2004).</strong> Developmental trajectories and ecological transitions: A two-step procedure to aid in the choice of prevention and promotion interventions. <em>Development and Psychopathology, 16</em>(4), 1141-1159. https://doi.org/10.1017/s0954579404040179</p>
Data from the "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation"
<p>Data associated with:</p> <p>Dumas, D., Doherty, M., Organisciak, P. (2020) "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation". PLOS ONE.</p> <p>Description of work associated with this data:</p> <blockquote> <p>As a profession, acting is marked by a high-level of economic and social riskiness concomitantly with the possibility for artistic satisfaction and/or public admiration. Current understanding of the psychological attributes that distinguish professional actors is incomplete. Here, we compare samples of professional actors (n = 104), undergraduate student actors (n = 100), and non-acting adults (n = 92) on 26 psychological dimensions and use machine-learning methods to classify participants based on these attributes. Nearly all of the attributes measured here displayed significant univariate mean differences across the three groups, with the strongest effect sizes being on Creative Activities, Openness, and Extraversion. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) classification model was capable of identifying actors (either professional or student) from non-actors with a 92% accuracy and was able to sort professional from student actors with a 96% accuracy when age was included in the model, and a 68% accuracy with only psychological attributes included. In these LASSO models, actors in general were distinguished by high levels of Openness, Assertiveness, and Elaboration, but professional actors were specifically marked by high levels of Originality, Volatility, and Literary Activities.</p> </blockquote>
Albero study: a longitudinal database of the social network and personal networks of a cohort of students at the end of high school
<p>The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.</p> <p><strong>INTRODUCTION</strong></p> <p>Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context.</p>
Dataset: Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions
<p># Dataset for Paper "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions" - Rev 1#</p> <p>This is the dataset for the paper titled "Ethical Issues in Empirical Studies using Student Subjects: Re-visiting Practices and Perceptions". All mapping study data has the prefix *MAP*, while all survey data the prefix *SUR*. It has been updated for a major revision at Springer Empirical Software Engineering (Rev 1).</p> <p>In case of questions, feel free to contact the author, Grischa Liebel, ORCID: https://orcid.org/0000-0002-3884-815X, current affiliation and email: Reykjavik University, Iceland, grischal@ru.is</p> <p>## Systematic Mapping Study ##<br> The mapping study data is mainly contained in the *MAPmappingStudy.xlsx* file. Different tabs are used for the two phases: exclusion by title and abstract (tab *title_abs*), and exclusion by fulltext (tab *fulltextScreening*). The final set of papers is obtained by filtering the *fulltextScreening* tab by included papers (Column V).</p> <p>The tab *fulltextScreening* contains a number of columns named \*range (e.g., *noStudentsRange*). These columns contain the unified/categorised values for the verbatim values listed in the column with the same name without *range*. For instance, *noStudentsRange* contains the range of students in the primary study, while *noStudents* contains the actual number obtained from the studies.</p> <p>The file *MAPvenues.txt* contains the included venues in the mapping study.</p> <p>Finally, the raw search results are provided as BIB/RIS files with the prefix *MAPRAW*.</p> <p>## Survey ##<br> The survey folder contains the survey pages (named *surveyPageN.pdf*), as well as the raw data in *surveyDataAnon.xlsx*. Note that free-text answers have been aggregated, anonymised, and sorted alphabetically in individual tabs. Similarly, countries that only occur once have been changed to Do Not Disclose answers, and all answers have been sorted randomly. All questions are listed by their acronym. The corresponding questions, as well as possible answers, are described in the *QuestionKey* tab.</p>
Discrepancies between parents and teachers of students with autism spectrum disorder (ASD) in executive functioning according to the BRIEF
<p>This database corresponds to the results of the paper:</p><p>González Sala, F., Pastor-Cerezuela, G., Sanz-Cervera, P., & Tárraga-Mínguez, R. (2023). Discrepancies between parents and teachers of students with autism spectrum disorder (ASD) in executive functioning according to the BRIEF. <i>Anales de Psicología / Annals of Psychology, 39</i>(1), 81–87. https://doi.org/10.6018/analesps.481531</p>
Student discourse in small-group collaborative contexts in real-world higher education
<p>This dataset contains anonymised student discourse in small-group collaborative contexts in real-world higher education settings.</p> <p>There are 28 sessions, composed of 10799 utterances.</p> <p>The columns consist of 13 columns:</p> <ul> <li>session: the session number</li> <li>start: the starting time of the utterance</li> <li>end: the ending time of the utterance</li> <li>speaker: anonymised speaker name</li> <li>content: the content of the utterance</li> <li>ep: the episode number of the utterance. An episode refers to a single topic of discussion with multiple utterances.</li> <li>C: a binary value indicating the presence or absence of a cognitive challenge, where 0 means no challenge, and 1 means there is a cognitive challenge.</li> <li>E: a binary value indicating the presence or absence of an emotional/motivational challenge, where 0 means no challenge, and 1 means there is an emotional/motivational challenge.</li> <li>M: a binary value indicating the presence or absence of a metacognitive challenge, where 0 means no challenge, and 1 means there is a metacognitive challenge.</li> <li>T: a binary value indicating the presence or absence of a technical/other challenge, where 0 means no challenge, and 1 means there is a technical/other challenge.</li> <li>TA: a binary value indicating the presence or absence of the regulatory process "task analysis," where 0 means no regulation and 1 means task analysis is present.</li> <li>MC: a binary value indicating the presence or absence of the regulatory process "monitoring/control," where 0 means no regulation and 1 means monitoring/control is present.</li> <li>RA: a binary value indicating the presence or absence of the regulatory process "reflection/adaptation," where 0 means no regulation and 1 means reflection/adaptation is present.</li> </ul> <p>This annotated dataset was used in the following paper to model challenge moments.</p> <p>For more details on how the dataset was generated, please refer to the paper.</p> <div> <div>Suraworachet, W., Seon, J., & Cukurova, M. (2024). Predicting challenge moments from students’ discourse: A comparison of GPT-4 to two traditional natural language processing approaches. <em>Proceedings of the 14th Learning Analytics and Knowledge Conference</em>, 473–485. <a href="https://doi.org/10.1145/3636555.3636905">https://doi.org/10.1145/3636555.3636905</a></div> <div> </div> </div>
Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort
<p># 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</p> <p>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".</p> <p>In case of questions, feel free to contact the authors, *anonymised*, ORCID: https://orcid.org/*anonymised*, current affiliation and email: *anonymised*</p> <p>## Survey 2019 ##<br> 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*.<br> The questionnaire for the 2019 survey is contained in *survey2019_instrument.pdf*.</p> <p>## Survey 2020 ##<br> 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.<br> The questionnaire for the 2020 survey is contained in *survey2020_instrument.pdf*.</p> <p>## Interviews ##<br> The interview guide used for the five interviews is available in the file *interview_instrument.pdf*.</p>
From the Horse's Mouth: The Words We Use to Teach Diverse Student Groups Across Three Continents
<p>Word frequency pairs for courses A, B, C from: </p> <p>Brett A. Becker, Daniel Gallagher, Paul Denny, James Prather, Colleen Gostomski, Kelli Norris, and Garrett Powell. 2022. From the Horse’s Mouth: The Words We Use to Teach Diverse Student Groups Across Three Continents. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2022), March 3–5, 2022, Providence, RI, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3478431.3499392</p> <p><strong>When referring to this dataset, please cite the above article. That contains the DOI of this dataset. Please do not cite this dataset directly without citing the article.</strong></p>
Students' perceived obstacles with Forced Online Distance Learning during the CoVID-19 outbreak and their preferences to continue with the introduced teaching methods after the reopening of the University of Maribor [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics,</li> <li>and SPSS dataset.</li> </ul>
ScienceDex guides
Understand access before you commit
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.