Abstract：Maturing research data services and the transformation of academic libraries Purpose A major development in academic libraries in the last decade has been recognition of the need to support research data managementRDMThe purpose of this paper is to capture how library research data servicesRDShave developed and to assess the impact of this on the nature of academic librariesDesignmethodologyapproach Questionnaire responses from libraries in AustraliaCanadaGermanyIrelandthe NetherlandsNew Zealandthe UK and USA from 2018 are compared to a previous data set from 2014Findings The evidence supports a picture of the spread of RDSespecially advisory onesHoweverfuture ambitions do not seem to have seen much evolutionThere is limited evidence of organisational change and skills shortages remainMost service development can be explained as the extension of traditional library services to research dataYet there remains the potential for transformational impactswhen combined with the demands implied by other new services such as around text and data miningbibliometrics and artificial intelligenceA revised maturity model is presented that summarises typical stages of development of servicesstructures and skillsResearch limitationsimplications The research models show how RDS are developingIt also reflects on the extent to which RDM represents a transformation of the role of academic librariesPractical implications Practitioners working in the RDM arena can benchmark their current practices and future plans against wider patternsOriginalityvalue The study offers a clear picture of the evolution of research data services internationally and proposes a maturity model to capture typical stages of developmentIt contributes to the wider discussion of how the nature of academic libraries are changingLibrary Automation Consulting Service Retrieval and Indexing The development of research data services mentioned in the articleespecially the needs of new services related to text and data miningmeasurement and artificial intelligenceData exploration involves useful information and models from large data setswhich is related to the purpose and method of studying data management