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152 results for “Undergraduates”
Small Group and Survey Datasets: "Fostering Metacognition and Feedback Loops in a Summer Undergraduate Research Program: A Pilot Study"
<p>These datasets accompany the paper "Fostering Metacognition and Feedback Loops in a Summer Undergraduate Research Program: A Pilot Study" by Chad Curtis PhD and Kaytlin Gomez.</p> <p>The first dataset consists of free responses from n=12 students researchers during a Small Group Metacognitive Practice (SGMP) intervention conducted during the 2023 INBRE Summer Undergraduate Research Fellowship (iSURF) at Nevada State University. The files include:</p> <ul> <li><strong>transcription.docx</strong>: Transcriptions of both individual (n=12) and small group (n=4) responses from the SGMP session.</li> <li><strong>codingR1.xlsx</strong>: Codings used for the thematic analysis, as coded by the primary investigator.</li> <li><strong>codingR2.xlsx: </strong>Codings used for the thematic analysis, as coded by the co-author.</li> <li><strong>KrippendorffAlpha.xlsx</strong>: Inter-coder reliability calculations using Krippendorff's alpha.</li> </ul> <p>The survey data was collected from n=11 participants at the end of the summer research program. The files include:</p> <ul> <li><strong>surveyData.csv</strong>: Anonymized responses to survey questions regarding the SGMP intervention.</li> </ul> <p>This study was approved by the Nevada State University Review Board (Protocol #2305-0346). All procedures in the study were conducted in accordance with approved protocols. Written informed consent was obtained from the subjects for their anonymized information to be published with the article.</p>
Dataset: Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses
<p>This dataset contains anonymised raw data and examples of accommodations made for neurodiverse students in four undergraduate courses in Computer Science and Software Engineering programmes. The dataset is published as a part of a book chapter in which we report the accommodations.</p> <p>Overall guidelines we followed, including their sources, are contained in <strong>guidelines.md.</strong></p> <p>The raw data for the two surveys is contained in the two Excel files <strong>survey1.xlsx</strong> and <strong>survey2.xlsx</strong>. Free-text answers have been aggregated by neurodiverse and neurotypical students and anonymised, and are available in the files<strong> survey1_freetext_neurodiverse.txt, survey1_freetext_neurotypical.txt, survey2_freetext_neurodiverse.txt, </strong>and<strong> survey2_freetext_neurotypical.txt.</strong></p> <p>The remaining files are examples of the adapted lecture slides and assignment texts. Here, files starting with WEBcourse are from a mandatory undergraduate course on web development, while files starting with SEcourse are from a mandatory undergraduate course giving an overview of Software Engineering.</p>
Integrated pedagogical methods effectiveness in Physics' preliminary undergraduate education within the context of large size lectures.
<p>Three files relating the first round of analysis testing active methods for large size lectures. The Presentation including the research design, methods, main results in synthesis is available here: https://www.researchgate.net/project/Getting-started-with-Physics-preliminary-undergraduated-strategies/update/5a44cac6b53d2f0bba475104</p> <p>2- Dataset on Students' Learning Outcomes. Dataset adopted in the first experimental round. The dataset includes data used for the first type of analysis (learning outcomes) carried out for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". The data includes the results of the initial, baseline Test, the final Test, and two other variables that could be used to analyse covariance: Sex and Type of Group (Large/Small).</p> <p>3- Dataset on Students' Opinion. Dataset adopted in the first experimental round. The dataset includes data used for the second type of analysis (students' opinion) carried out for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". The data includes the results of a final questionnaire gathering the students opinion on the four types of pedagogical factors affecting their experience within a large size lecture: MOOCs, Active Learning, Self-Assessment tools, Tutors’ guidance.</p> <p>4- Codes and analysis adopted in the first experimental round. The Document includes two analysis carried on for the ICEM2017 Conference presentation "Integrating MOOCs in Physics preliminary undergraduate education: beyond large size lectures". These are: Test (measuring students' knowledge on the subject taught) and Students' Opinion/satisfaction on the several pedagogical methods adopted along the experimental intervention.</p>
Geoscience undergraduate major course requirements
<p>This data compilation contains course requirements for undergraduate geoscience majors in Earth Science departments at 50 R1 universities in the United States. The authors of this compilation (Ken Ferrier, Eva Golos, and Marianne Haseloff at the University of Wisconsin-Madison) collected these data in March-April 2024 (n = 966 courses in total) from each department’s publicly accessible webpage. These data are organized in four tables, which contain data on the geoscience courses and credits required inside the home department and the cognate courses and credits required outside the home department (e.g., Math, Physics, Chemistry).</p>
Database for article: "Privacy Perceptions in Digital Games: A Study with Information Technology (IT) Undergraduates"
<p>This database is an addendum to the article "<strong>Privacy Perceptions in Digital Games: A Study with Information Technology (IT) Undergraduates</strong>" to provide information regarding the anonymously collected data.</p><p><strong>Abstract of the article</strong></p><p>This study explores the perceptions and practices of undergraduates in Information Technology (IT) regarding privacy issues in digital games. This topic becomes relevant in the current scenario where artificial intelligence (AI) is increasingly integrated into digital games, providing an enhanced experience for players. However, this integration poses security and privacy challenges, the understanding of which is crucial for both players and developers.<br>The primary objective of this research is to comprehend the participants' perceptions and understandings of privacy in digital games. We employed a qualitative and quantitative methodology to address our research inquiries. Through an online form of data collection, we obtained 61 responses. Among the obtained information, we observed that 40\% of the students are interested in pursuing a career in game development, and 49.18% would consider this possibility. Noteworthy among the identified issues is the necessity for companies to devise more effective means of communicating their privacy policies to players/users, adapting the language to their target audience. Participants reported attacks related to online multiplayer games and expressed concerns about the security of personal data.</p>
Presence and proportion of plants in biodiversity inventories conducted by undergraduate students enrolled in animal-related courses
<p>Dataset on biodiversity inventories conducted by 110 undergraduate students enrolled in animal-related courses using the iNaturalist platform.</p>
AI course names from undergraduate programs
<p><span>We collected 750 course names covered by 29 universities’ undergraduate AI edu</span><span>cation programs, providing a comprehensive dataset for analysis on the AI curriculum.</span></p>
Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)
<p>Supplementary data for "Undergraduate Gender Diversity and the Direction of Scientific Research" (PART 3)</p> <p>Contains publicly available data from:</p> <ul> <li>Higher Education Research Institute. 1966–2006. “Cooperative Institutional Research Program(CIRP) Data Archives of the Freshman Survey Trends from 1966 to 2006.”</li> <li>National Center for Science and Engineering Statistics. 1972–1990. “Higher Education Research and Development Survey (HERD).</li> <li>U.S. National Center for Education Statistics. 1993. 120 Years of American Education: A Statistical Portrait. Washington, DC:U.S. Department of Education.</li> <li>U.S. National Center for Education Statistics. 2005. “Digest of Education Statistics, 2005.”</li> <li>U.S. National Center for Education Statistics. 2019. “Digest of Education Statistics, 2019.”</li> </ul> <p>To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", unzip all folders in this repository and place all files and folders (except "PapersFieldsofStudy.txt.gz") in "data/raw". AEA Data and Code Repository openicpsr-204361 shows the expected file structure. </p> <p>Additionally, this repository contains "PapersFieldsofStudy.txt.gz" from Microsoft Academic Graph data (April 2018 Extract) used for "Undergraduate Gender Diversity and the Direction of Scientific Research." To replicate the results in "Undergraduate Gender Diversity and the Direction of Scientific Research", please decompress and place "PapersFieldsofStudy.txt.gz" into "data/raw/MAG/". The associated code repository can be found AEA Data and Code Repository openicpsr-204361.</p> <p>The attribution license of the Microsoft Academic Graph is <a href="https://opendatacommons.org/licenses/by/1-0/">ODC-BY</a></p> <p>Please cite the following paper in publications and reports using the Microsoft Academic Graph:</p> <p>Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839</p> <p> </p>
Longitudinal COVID-19 Antibody Testing in Indiana University Undergraduate Students
ClinicalTrials.gov study NCT04620798. IPD Sharing: YES. Countries: 1. Publications: 2.
Refining impact assessment in undergraduate STEM education: Differential item functioning analysis of field-based learning interventions
Open the record for dataset details and reuse information.
Social data from "Ecology, The Economy of Nature" (10th edition, 2025): Addressing diversity in undergraduate ecology textbooks"
This data package includes social data on the diversity and composition of people depicted in the text, photographs, and illustrations of the ecology textbook Ecology: The Economy of Nature (10th ed.) by Relyea (2025). The data were utilized in an article published in Frontiers in Ecology and the Environment, which explores the use of undergraduate textbooks as tools for building a diverse community of ecologists (Richards et al., 2025). Relyea, R. (2025). Ecology: The Economy of Nature (10th ed.). W.H. Freeman. ISBN 9781319524838 Richards JH, Charton KT, McFarlane SL, Widell AF, Haddad NM, Wyer MB, Damschen EI (2025) Rethinking the undergraduate textbook as a tool to build a diverse community of ecologists. Frontiers in Ecology and the Environment 23(3), e2819, https://doi.org/10.1002/fee.2819.
Assessing a Multidisciplinary Group of Undergraduate Students Applying the Challenge Based Learning Methodology to Learn Mobile Development
<p>That video presents the paper entitled "Assessing a Multidisciplinary Group of Undergraduate Students Applying the Challenge Based Learning Methodology to Learn Mobile Development" accepted in SBES 2020 conference (Education track).</p>
Prospective UK undergraduate attitudes towards Theology and Religious Studies
<p>The survey instrument was designed by Paul Ashby and Jeremy Kidwell at the University of Birmingham, with input from Amy Daughton, Jagbir Jhutti-Johal, Carissa Sharp, Rachael Shillitoe, and Karen Wenell. Survey data was collected via online survey by TSR Insight delivered to members of the online platform "The Student Room" which was open from 21st June and 4th July 2021. The resulting dataset is a random sample of 933 complete survey results from UK students (aged 16-18) in years 11, 12 and 13.</p>
Undergraduate Students' Perceptions and Experiences in Learning Statistics
<p>This dataset is an online survey participated voluntarily by undergraduate students in 2021 at the University of Hargeisa. The main variables included were sociodemographic variables, students' perceptions of statistics, challenges faced by students in learning statistics, engagement with statistics by students, and finally (but not least) students' performance in statistics.</p>
Data and Code: Detecting Blue Whale Calls in the Northeast Pacific Using Seismic Systems (Undergraduate Thesis)
<p><strong>SeismoData.ipynb</strong>: This Jupyter notebook is adapted from seismosocialdistancing.ipynb created by Thomas Lecocq, Fred Massin and Claudio Satriano. SeismoData.ipynb was used to retrieve seismic waveform data from the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC) (https://ds.iris.edu/ds/ nodes/dmc/) and convert files from miniSEED to SAC format. It was also used to preview waveform and spectrogram plots.</p> <p><strong>BlueWhaleDetectionResults_J53A_Dec132011.mat</strong>: This .mat file summarizes whale detection results from OBS J53A on December 13th 2011. The objective was to calibrate a detection algoritm created for Northwest Atlantic blue whale A calls by Plourde and Nedimovic (2022), so that it can target Northeast Pacific blue whale B calls using seismometers off Washington and California. Three tests were performed to find optimal parameters. <em>BlueWhaleDetections_J53A_Test1</em><strong> </strong>are the detection results of a control that uses Northwest Atlantic blue whale parameters (16.25-18Hz frequency and 68-78s period ranges). <em>BlueWhaleDetections_J53A_Test2</em> <strong> </strong>are the detection results using Northeast Pacific blue whale B call parameters (14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_Test2 </em>are the detection results using Northeast Pacific blue whale B call and C call parameters (10.5-12Hz and 14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_SCC</em> are the 95% probability detection results from the Wilcock and Hilmo (2021) blue whale catalogue created using spectrogram cross-correlation. </p> <p><strong>NEPBlueWhaleMATLABcodes.zip</strong>: Contains scripts to run the recurrence interval power ratio method created by Plourde and Nedimovic (2022), adjusted to detect Northeast Pacific blue whale B calls and plot waveforms/spectrograms. First run <em>DetectBlueWhales.m </em>to calculate the recurrence power ratio every 12 minutes, then run C<em>reateBlueWhaleDetectionList.m </em>to classify detections with high power ratios and likely blue whale call detections.</p> <p><strong>BlueWhaleDetectionResults_CapeMendocino_Dec15to292014.mat</strong>: This .mat file summarizes the blue whale detection results from 2 OBS (FS02D and FS07D) and 1 land seismometer (CM09A) in close proximity, using Test 2 parameters. Note if there are less than 3 BWD in a given day, these are likely false detections.</p>
Co-curricular Quantitative Data Analytics Training Program (CQDATP) for Undergraduate Natural Science Students
<p>While data analytics has increasingly become an essential skill for various professionals in natural science fields, college curricula often cannot be updated frequently to meet the emerging changes of the workforce’s needs. A drastic curricular change is particularly difficult at small colleges with limited availability of faculty. This study proposes a co-curricular training program that enables natural science students to master skills of applying quantitative data analytics on real datasets without requiring a significant curricular change. The training program was developed and implemented as a sequence of in-person one-hour training sessions at a small four-year college. The results show that the approach is effective in improving students’ data analytics skills. This program can be adapted by other small institutions to achieve similar goals.</p> <p> </p> <p>This dataset include the training materials and deidentified student performance and survey responses. </p>
Marriage Thoughts among Bangladeshi Undergraduate Students
<p><span>This research examines the perspectives of Bangladeshi undergraduate university students on getting married while studying. The survey was conducted with 131 participants from 27 public and private universities, medical colleges, and colleges affiliated with national university that offer honors programs. The research analyzed six main perspectives: the impact of marriage on academic results, mental health, financial situation, religious influence, responsibilities, and family's role in decision-making. The results indicated that most students believed that getting married during undergraduate studies has a positive impact on academic results, mental health, and financial situation. However, most students believed that family and religious influence plays a significant role in their decision to get married. The findings highlight the importance of considering the impact of marriage on undergraduate students' academic, financial, and mental wellbeing.</span></p>
Data from: Undergraduate research experiences broaden diversity in the scientific workforce
New data highlight the importance of undergraduate research experiences (UREs) for keeping underrepresented science students on the pathway to a scientific career. We used a large-scale, 10-year longitudinal, multi-institutional, propensity score matched research design to compare the academic performance and persistence in science of students that participated in URE(s) compared to similar students that had no research experience. Results showed that students who completed 10-or-more hours of co-curricular faculty mentored research per week across two or more academic semesters or summers were significantly more likely to graduate with a science-related bachelor's degree, be accepted into a science-related graduate training program, and be training for or working in the scientific workforce six years after graduation. Importantly, the findings show that just having an URE was not enough to influence persistence in science—it required a commitment of 10-or-more hours per week over two-or-more semesters of faculty mentored research.
Dataset for paper: Perception of Software Quality Teaching from Information Technology Undergraduate Final-Year Students
<p>Dataset mentioned in the paper "Perception of Software Quality Teaching from Information Technology Undergraduate Final-Year Students", submitted to XXVI Ibero-American Conference on Software Engineering (CIbSE 2023). There are two files available: one PDF file, where the survey presented as it was submitted to the participants of our study, in portuguese; and a .CSV one, with the answers of this survey.</p>
Dung Beetle at Maliau and SAFE project_Undergraduate thesis
<p>This is the undergraduate final year project intended to establish the diversity of dung beetle in three different land use types, primary forest, secondary forest, and oil palm plantation in Sabah Malaysia in January 2016. We used human dung for all the pitfall traps, and the traps were set in the field for 24 hours. The traps were spaced 100 metres from one another. The field works were conducted on 21st-22nd; 24th-25th; 26th-27th January 2016.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.