ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset
CGROM Open Edition v2.0: Cyber Governance and Risk Operating Model<p dir="ltr">CGROM is an open operating model that connects day to day security work to risk decisions made and overseen by leadership in regulated organizations. Version 2.0 expands the Open Edition from six to ten governance mechanisms, adding policy architecture and exception management, control evidence and audit readiness, incident governance and materiality determination, and artificial inte
ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue
More Than Code: Development of Technical and Behavioral Competencies Through a Real R&D&I Project Involving University and Industry - Complementary Students' Thematic Analyses<p dir="ltr">Thematic analysis of open-ended questionnaire responses from 19 students in a university–industry RD&I software project. Each of the nine files covers one question and includes the anonymized responses, the assigned codes and themes, and a codebook with the percentage of students per theme. All files are available in Portuguese (original) and English.</p>
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset
More Than Code: Development of Technical and Behavioral Competencies Through a Real R&D&I Project Involving University and Industry - Complementary - Blank Student Questionnaire<p dir="ltr">This document contains the blank survey form (uncompleted questionnaire) administered to computing students in the study <i>"More Than Code: Development of Technical and Behavioral Competencies Through a Real R&D&I Project Involving University and Industry"</i>. The instrument evaluates students' self-perception regarding their development of technical and socioemotional competencies
ZivaHub + Deakin Research Online + DMU Figshare2026 · dataset
More Than Code: Development of Technical and Behavioral Competencies Through a Real R&D&I Project Involving University and Industry - Complementary - Blank Faculty Questionnaire<p dir="ltr">This document contains the blank survey form (uncompleted questionnaire) administered to faculty members in the study "More Than Code: Development of Technical and Behavioral Competencies Through a Real R&D&I Project Involving University and Industry". The instrument evaluates professors' perceptions regarding students' technical and socioemotional competency development, professional
ZivaHub + Deakin Research Online + DMU Figshare + HKU DataHub + Swinburne Figshare + DaYta Ya Rona + SUNScholarData + figshare + Loughborough Research Repository + GRANTS Data + UP Research Data Repository2026 · Astronomical catalogue
More than code: developing hard and soft skills through a real RD&I project involving university and industry - Complementary Faculty Thematic Analyses<p dir="ltr">Thematic analysis of open-ended questionnaire responses from 4 faculty members in a university–industry RD&I software project. Each of the four files covers one question and includes the anonymized responses, the assigned codes and themes, and a codebook with the percentage of faculty per theme. All files are available in Portuguese (original) and English.</p>
HKU DataHub + figshare + Loughborough Research Repository + UP Research Data Repository2026 · Astronomical catalogue
Dataset and Python Code for Reproducing the Analytical Results of Web Browser Ranking Among Nigerian Undergraduates Using Fuzzy Analytic Hierarchy Process (FAHP)<p dir="ltr">This repository contains the dataset and Python source code used in the analysis presented in the associated research study. The materials are provided to support transparency, reproducibility, and independent verification of the reported findings. The dataset contains the data analysed in the study, while the Python scripts contain the procedures used for data processing, statistical
ZivaHub + HKU DataHub + figshare + Loughborough Research Repository + UP Research Data Repository2026 · Astronomical catalogue
More than code: developing hard and soft skills through a real RD&I project involving university and industry - Complementary Student Assessment Material<p dir="ltr">Anonymized responses from a questionnaire administered in September 2026 to 19 students and former participants of a software development R&D&I project conducted through a university-industry partnership. The questionnaire assesses how participation in the project contributed to the students' professional development. It gathers profile data, 20 items regarding technical competencies,
ZivaHub + HKU DataHub + figshare + Loughborough Research Repository + UP Research Data Repository2026 · Astronomical catalogue
More than code: developing hard and soft skills through a real RD&I project involving university and industry - Complementary Faculty Assessment Material<p dir="ltr">Anonymized questionnaire responses from 19 students and 4 faculty members in a university–industry Research, Development and Innovation (RD&I) software project, collected in September 2026. The student data include profile information, 1-5 Likert self-assessments of technical competencies, soft skills and perceptions, and open-ended responses. Summary files give the response distribut
figshare2026 · Astronomical catalogue
An Open Multi-Campus Dataset of Academic Service Requests Before and After Administrative Process Redesign<p dir="ltr">This record contains the anonymized and aggregated dataset associated with the manuscript “An Open Multi-Campus Dataset of Academic Service Requests Before and After Administrative Process Redesign”.</p><p dir="ltr">The dataset comprises 432 aggregated rows representing 97,809 academic service requests across three pseudonymized campuses, 25 request types, 13 workflow states, and two
figshare2026 · Astronomical catalogue
Automation Bias and Legal Reasoning in Law Students: Experimental Dataset on Generative AI Errors<p dir="ltr"><b>This dataset contains experimental data collected from 360 law students to examine automation bias and legal reasoning when using generative artificial intelligence systems that may provide erroneous legal information.</b></p><p dir="ltr">The study includes <b>2,880 student-case observations</b> generated under controlled experimental conditions. Participants were assigned to diffe