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Despite this, it is essential in RDM to discover trends and themes using text mining, which is scarce. The purpose of this study is to employ text mining to discover insights from job advertisements associated with RDM profiles, which collected 810 advertisements. We found RDM-related patterns using latent Dirichlet allocation (LDA) and identified three key contexts. The first is \u2018research services in libraries\u2019, with the topics of research services, research information, research universities, collection processes and library services. The second context is \u2018research data\u2019, which includes RDM, business data, university data, research data, health research, science research, social science research, data centres, data services, statistical software, digital scholarship and digital preservation. The third context is \u2018workplace environment\u2019, and the topics are leadership, work development and scientific position. Job title normalisation reveals names such as \u2018data librarian\u2019, \u2018librarian\u2019, \u2018director\u2019, \u2018data curator\u2019, \u2018data manager\u2019, \u2018research data librarian\u2019, \u2018data specialist\u2019 and \u2018data officer\u2019 are frequently employed. Focusing on titles with a single or double occurrence is new and interesting for developing nations. Reputable institutions such as Harvard, Stanford and the Massachusetts Institute of Technology, as well as countries such as the United States, the United Kingdom, Canada and Germany, are the major participants in RDM practises and services. This discovery will assist higher education institutions, RDM stakeholders, which aid in the formulation of curriculum, and job seekers to familiarise themselves with the themes.<\/jats:p>","DOI":"10.1177\/01655515231193845","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T09:01:29Z","timestamp":1694768489000},"page":"484-500","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Discovering research data management trends from job advertisements using a text-mining approach"],"prefix":"10.1177","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1952-5934","authenticated-orcid":false,"given":"Naseema","family":"Sheriff","sequence":"first","affiliation":[{"name":"Department of Library and Information Science, Pondicherry University, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R","family":"Sevukan","sequence":"additional","affiliation":[{"name":"Department of Library and Information Science, Pondicherry University, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/0340035216672238"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/10572317.2020.1793448"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/elsc.202200005"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.18"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1162\/dint_a_00028"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1162\/dint_a_00043"},{"key":"e_1_3_3_8_2","article-title":"From raw data to FAIR data: the FAIRification workflow for health research","volume":"59","author":"Sinaci AA","year":"2020","unstructured":"Sinaci AA, N\u00fa\u00f1ez-Benjumea FJ, Gencturk M, et al. 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