{"data":{"id":"10.4121/f7e9b088-cf91-464d-a6a9-772fde5d8bff","type":"dois","attributes":{"doi":"10.4121/f7e9b088-cf91-464d-a6a9-772fde5d8bff","prefix":"10.4121","suffix":"f7e9b088-cf91-464d-a6a9-772fde5d8bff","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Grens, Nikki","nameType":"Personal","givenName":"Nikki","familyName":"Grens","nameIdentifiers":[{"nameIdentifier":"0009-0004-7775-6495","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Analysis Code Supporting \"RDM 101 Impact Evaluation: A Study on Training Effectiveness and Research Practice Changes\""}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2025,"subjects":[{"subject":"Learner and Learning","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"9301"},{"subject":"Education and Training Systems","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"9305"},{"subject":"Other Education and Training","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"9399"},{"subject":"Teaching and Instruction","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"9302"},{"subject":"Education and Training","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"93"},{"subject":"research data management"},{"subject":"FAIR principles"},{"subject":"training"},{"subject":"Open Science"},{"subject":"survey"},{"subject":"interview"},{"subject":"data stewardship"},{"subject":"impact evaluation"},{"subject":"feedback"}],"contributors":[{"name":"TU Delft Library, Research Data and Software","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2025-12-12","dateType":"Issued"}],"language":"en","types":{"ris":"COMP","bibtex":"misc","citeproc":"article","schemaOrg":"SoftwareSourceCode","resourceType":"Software","resourceTypeGeneral":"Software"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.5281/zenodo.17737360","relatedIdentifierType":"DOI"},{"relationType":"References","relatedIdentifier":"https://doi.org/10.5281/zenodo.10732095","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/f7e9b088-cf91-464d-a6a9-772fde5d8bff.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["R files, Markdown (MD) files, R project (Rproj)."],"version":"1","rightsList":[{"rights":"MIT License","rightsUri":"https://opensource.org/licenses/MIT","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"mit","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This repository contains the R scripts used to preprocess, analyse, and visualise the data underlying the \"RDM 101 Impact Evaluation: A Study on Training Effectiveness and Research Practice Changes\" project. The scripts support the mixed-methods analysis presented in the study report (DOI provided below) and reproduce all quantitative and qualitative processing steps that do not involve confidential raw data. The code includes:Data preprocessing workflows for pre- and post -training surveys and GS feedback formsCalculation of descriptive statistics and pre/post-training comparisonsFunctions used to generate category frequenciesVisualisation scripts used for figures in the reportThe scripts are written in R (4.2.2) and make use of packages such as readxl, dplyr, ggplot2, stringr, tidyr, stringdist, tidyverse, tm, wordcloud2, writexl, cluster, patchwork, viridis.A complete list of package dependencies is provided in the README files for the two analyses. No confidential and potentially identifiable personal information is embedded in the code. The datasets from Graduate School feedback forms and surveys are not included in this repository due to privacy restrictions. Instead, placeholder file paths and template structures are provided to enable users to understand the workflow and adapt it to their datasets if appropriate.This code repository is intended to support transparency and reproducibility of the analytical workflow and to complement the published report and accompanying data.\u003cstrong\u003eAcknoledgments\u003c/strong\u003eWe would like to thank Elviss Dvinskis and Selin Kubilay for their help with the analysis and contributions to it.\u003cstrong\u003eContact Information\u003c/strong\u003eFor questions, issues, or contributions, contact TU Delft Library Research Data and Software Training Team 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