Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics

Qin, Jian; Crowston, Kevin; Kirkland, Arden

doi:10.7191/jeslib.2017.1113

Cited by 12 publications

(11 citation statements)

References 6 publications

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“…In trying to make sense of these patterns in a broader way, an obvious framework would be a maturity model. These are used in a wide range of fields to map evolutionary steps in growing formalisation, control and measurement of an area of activity (Qin et al , 2017). They allow organisations to benchmark performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…maturity models for digital preservation (Kenney and McGovern, 2003) and for digital asset management or for information governance as a whole (Proença et al , 2016). Maturity models developed specifically for RDM include the Stewardship Maturity Matrix (Peng et al , 2015) and the Capability Maturity Model for RDM (Qin et al , 2017; Crowston and Qin, 2011). These can be seen as evaluating maturity of projects or fields in terms of achieving the ends of better data management and reuse.…”

Section: Literature Reviewmentioning

confidence: 99%

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Maturing research data services and the transformation of academic libraries

et al. 2019

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Purpose A major development in academic libraries in the last decade has been recognition of the need to support research data management (RDM). The purpose of this paper is to capture how library research data services (RDS) have developed and to assess the impact of this on the nature of academic libraries. Design/methodology/approach Questionnaire responses from libraries in Australia, Canada, Germany, Ireland, the Netherlands, New Zealand, the UK and USA from 2018 are compared to a previous data set from 2014. Findings The evidence supports a picture of the spread of RDS, especially advisory ones. However, future ambitions do not seem to have seen much evolution. There is limited evidence of organisational change and skills shortages remain. Most service development can be explained as the extension of traditional library services to research data. Yet there remains the potential for transformational impacts, when combined with the demands implied by other new services such as around text and data mining, bibliometrics and artificial intelligence. A revised maturity model is presented that summarises typical stages of development of services, structures and skills. Research limitations/implications The research models show how RDS are developing. It also reflects on the extent to which RDM represents a transformation of the role of academic libraries. Practical implications Practitioners working in the RDM arena can benchmark their current practices and future plans against wider patterns. Originality/value The study offers a clear picture of the evolution of research data services internationally and proposes a maturity model to capture typical stages of development. It contributes to the wider discussion of how the nature of academic libraries are changing.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Maturing research data services and the transformation of academic libraries

et al. 2019

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“…One possible strategy could be classifying services into different levels in terms of importance and difficulties in implementation. Qin et al . (2014) proposed a capability maturity model (CMM) with five maturity levels for improving RDM.…”

Section: Methodsmentioning

confidence: 99%

Research data stewardship at the University of Hong Kong

Xiao

Yang

2021

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PurposeThe purpose of this paper is to look at the journey and experience of the University of Hong Kong (HKU) Research Data Management (RDM) practice to respond to the needs of researchers in an academic library.Design/methodology/approachThe research data services (RDS) practice is based on the FAIR data principle. And the authors designed the RDM Stewardship framework to implement the RDS step by step.FindingsThe HKU Libraries developed and implemented a set of RDS under a research data stewardship framework in response to the recent evolving research needs for RDM amongst the academic communities. The services cover policy and procedure settings for research data planning, research data infrastructure establishment, data curation services and provision of online resources and instructional guidelines.Originality/value This study provides an example of an approach to respond to the needs of the academic libraries about how to start the RDS including the data policy, data repository, data librarianship and data curation.

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“…Część tych prac dotyczyła udziału bibliotek w tych procesach. Wymienić tu można takie modele, jak Stewardship Maturity Matrix (Peng et al, 2014) oraz Capability Maturity Model dla RDM (Qin et al, 2017;Crowston & Qin, 2011). Modele te służą ocenie dojrzałości projektów lub ich części w celu osiągnięcia lepszego zarządzania danymi i ich wielokrotnego wykorzystania.…”

Section: Model Dojrzałości Usług Danych Badawczych (Rds)unclassified

Modele dojrzałości systemów informacyjnych na przykładzie bibliotek cyfrowych i serwisów danych badawczych

Nahotko

2022

ZIN

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Cel/Teza: Celem artykułu jest przedstawienie pojęcia dojrzałości obiektu/procesu/organizacji i jego przydatności dla oceny systemów informacyjnych. W tym celu tworzone są odpowiednie modele dojrzałości systemów informacyjnych. Koncepcja/Metody badań: W wyniku zastosowania analizy piśmiennictwa przedstawione zostały definicje dojrzałości systemu informacyjnego oraz modelu dojrzałości. Modele dojrzałości przedstawiono na przykładzie modeli przygotowanych dla wybranych rodzajów systemów informacyjnych: bibliotek cyfrowych oraz serwisów danych badawczych (RDS). Wyniki i wnioski: Modele dojrzałości systemów informacyjnych przydatne są do oceny systemów wszelkiego rodzaju, zarówno tradycyjnych, jak i komputerowych. Spełniają one trzy różne funkcje: diagnostyczną, porównawczą i preskryptywną. Oryginalność/Wartość poznawcza: W polskiej literaturze brak publikacji dotyczących modeli dojrzałości systemów informacyjnych i ich stosowania. Warto natomiast zwrócić uwagę na pokrewieństwo metody oceny usług informacyjnych LibQual+, stosowaną w polskich bibliotekach, z problematyką dojrzałości tych systemów informacyjnych.

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Pursuing Best Performance in Research Data Management by Using the Capability Maturity Model and Rubrics

Cited by 12 publications

References 6 publications

Maturing research data services and the transformation of academic libraries

Maturing research data services and the transformation of academic libraries

Research data stewardship at the University of Hong Kong

Modele dojrzałości systemów informacyjnych na przykładzie bibliotek cyfrowych i serwisów danych badawczych

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