fan2019.pdf

Zhenjia Fan*
Context-Based Roles and Competencies of Data
Curators in Supporting Research Data Lifecycle
Management: Multi-Case Study in China
https://doi.org/10.1515/libri-2018-0065
Received May 31, 2018; accepted August 06, 2018
Abstract: Focusing on the main research question of what
the critical roles and competencies of data curation are in
supporting research data life cycle management, this
paper adopts a multi-case study method, with data gov-
ernance frameworks, to analyze stakeholders and data
curators, and their competencies, based on different con-
texts from cases from enterprises and academic libraries
in mainland China. Via the context and business analysis
on different cases, critical roles such as data supervisor,
data steward, and data custodian in guaranteeing data
quality and efficiency of data reuse are put forward.
Based on the general factor framework summarized via
existing literature, suggestions for empowering data cura-
tors’ competencies are raised according to the cases. The
findings of this paper are as follows: besides digital
archiving and preservation, more emphasis should be
placed on data governance in the field of data curation.
Data curators are closely related but not equivalent to
stakeholders of data governance. The different roles of
data curators would play their own part in the process of
data curation and can be specified as data supervisor,
data steward, and data custodian according to given
contexts. The roles, competencies, and empowerment
strategies presented in this paper might have both theo-
retical and practical significance for the fields of both
data curation and data governance.
Keywords: data curation, data governance, data life cycle,
data curator, research data management
Introduction
As the data deluge (Poole 2016) progresses, nowadays,
issues about big data are easily becoming hot topics;
however, what we should never neglect is that data
curation appeared much earlier than big data and has
been experienced in librarianship and information
resource management even in the era of “small data”.
It is vital for information professions to recognize data
curators, and their competencies are required in given
contexts regardless of being a “small” or “big” data
era. In January 2018, the General Office of the State
Council of the People’s Republic of China promulgated
the “Measures on Scientific Data Management” (State
Council Document No. 17 2018), which clearly points to
the corresponding duties of government departments,
research institutes, universities, and scientific data
centers concerning the process of scientific data man-
agement. Furthermore, the process of scientific data
collection, submitting, saving, sharing, use, and secur-
ity has also been stated in this legal document. It is a
fact that some R&D institutes, universities, and enter-
prises in China have undertaken extensive practices in
the field of data management before such a document is
issued. As one of the key activities in innovative enter-
prises, research institutes, and universities, data cura-
tion has improved the efficiency and quality of R&D
data management in the era of data-intensive research.
Different agents such as university libraries, enterprises,
data centers, and other institutions based on specific
business situations have accumulated many valuable
cases which might have significance for data curation
in both theory and practice.
The majority of studies related to data curation
concentrate on the field of Library and Information
Science, especially focusing on the topic of librarianship.
However, data curation is a business issue that reaches
beyond librarianship. Data curation can be considered as
one of the critical components in R&D management and
it is reflected in different duties such as research man-
agement, information management, data management,
librarianship, and archives (Noonan and Chute 2014).
Although facing a similar practical issue in curation,
data curators will appear as holding different roles with
respect to the context: data librarian in a library; chief
data officer and data manager in an enterprise; data
*Corresponding author: Zhenjia Fan, Department of Information
Resources Management, Business School, Nankai University,
94 Weijin Road, Nankai District, Tianjin, China,
E-mail: fanzhenjia@nankai.edu.cn
LIBRI 2019; 69(2): 127–137
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steward and data scientist in a research institute etc.
There is no doubt that data curator, data steward, and
data custodian are sometimes used interchangeably in
both literature and practices, while it is also difficult to
make a clear distinction among them in the Chinese
context. However, there are different word habits to
describe data curator in different practical fields. For
example, in many Chinese enterprises, data steward
refers to data curators who mainly focus on data assets,
while data custodian usually refers to data curators who
offer technical supporting in the process of data cura-
tion. The competencies required also vary in different
contexts; for example, between the field of digital huma-
nities on the one hand and engineering on the other.
One basic consensus of the purpose of data curation
is to guarantee the quality of research data, improve the
efficiency of data reuse, and serve the research and
development process. Therefore, rather than being simply
a technical job, a holistic approach is needed, involving
governance of multi-stakeholders. When facing multi-sta-
keholders in data curation, a realistic problem becomes
how to identify common roles in the process of research
data management and seek for common discourses
among the diverse identifications. It is essential to sum-
marize the roles and competencies from different con-
texts of real data curation practices.
Data literacy is a much debated topic related to roles
and competencies in data curation, and the literacy fra-
meworks of librarians are always taken into considera-
tion first (e. g. Carlson and Johnston 2016; Koltay 2015,
2017). Moreover, data governance (Otto 2011; Soares
2014), with its strong theoretical capacity to incorporate
human, technological, and organizational factors into
one framework to understand the problem, is always
used as the perspective to analyze the stakeholders and
propose effective suggestions. Tools, such as databases
and cloud platforms (Soares 2014; Witt 2008), have also
been paid increasingly more attention concerning solu-
tions for data curation in recent years. Of course, some
are soft compared to the technological factors, such as
regulations and rules (Pryor 2009; Smith 2014) for the
process of data curation, which have also attracted sev-
eral researchers in order to guarantee a system for the
implementation of data curation.
We must acknowledge that research achievements in
data curation have provided us with too many essential
theoretical aids. However, it is also obvious that some
basic questions remain when searching for the perfect
answer. For example, what is the objective of data cura-
tion: data set as results, or data stream as a business
process? If the answer is the former, another question
concerning how to distinguish data curation and digital
archiving will be raised; if we prefer the latter as the
better answer, how do we solve the data governance
issue while it is difficult to consider data curation as an
independent process in a real business context? This
brings practical challenges in data curation for data col-
lection and quality control involving intellectual prop-
erty, business secrets, and state securities etc. It is
necessary to organize and curate data from the data life
cycle under the perspective of data governance.
In addition, it is too simplified to consider data cura-
tion as one independent process in one given organiza-
tion although it is, indeed, beneficial to find problems
during each step because data curation is often involved
with other business issues in practice. This makes it
difficult to answer whether data curation is effective
when no specific context is taken into consideration
and is why it is essential to focus on the business context
when researching data curation.
Based on the background to this problem, this paper
summarizes the critical roles and competencies from a
multi-case study approach. This essay describes the sta-
tus quo of data curation in agents including enterprises
and libraries in China and discusses the roles of data
curation in such contexts. The cases in this paper cover
significant enterprises including Neusoft, one of the lar-
gest IT corporations in China, and several academic and
university libraries in mainland China.
Literature Review
The process of data curation involves different stake-
holders who take corresponding roles. As discussed in
the introduction, it is essential to review topics such as
data governance, data curation, data literacy and more.
Data Governance and Stakeholders
As governance is mainly related to the interests of busi-
ness sectors, it is understandable that data governance is
seldom addressed by the LIS (Library and Information
Science) literature with few exceptions such as Krier
and Strasser (2014). Similar to corporate governance,
data governance enables better decision-making and pro-
tects the needs of stakeholders. Data governance can be
considered as a set of activities which include planning,
supervision, and enforcement that govern the process
128 Zhenjia Fan: Context-Based Roles and Competencies
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and methodologies carried out to guarantee and improve
the quality of data. Otto (2011) points out that a data asset
is the object of data governance and data management,
with the common purpose to maximize the value of the
data. Data curation, as a data management practice, aims
to improve the level of data reuse and needs to follow
certain governance frameworks and guidance strategies.
Sarsfield (2009, 38) considers data governance as “guar-
antees that data can be trusted and that people can be
made accountable for any adverse event that happens
because of poor quality”.
In the LIS field of China, Gu (2016) emphasizes data
management from the perspective of data life cycle,
which consists of data acquisition, data sharing, data
reuse, and data appreciation. Huang and Lai (2016)
explored the library as the core stakeholder and sum-
marized library, data center, research institutions, gov-
ernment and public sectors, policymakers, funders and
data publishers as the main categories of data govern-
ance subjects.
Data Curation and Roles
The concept of “curation” originated from the field of
museum science and has since been applied in the field
of data management. Since “data curation”, firstly used by
Gray and Szalay (2002), is a popular buzzword (Abrams
2014), it soon led to the recognition of, and attention paid
to, LIS disciplines. Data management objects, including
observation, calculation, experiment, derived, and other
means to obtain data, can be expressed as text, number,
image, video, audio, software, algorithms, reports, models,
and other forms. The Digital Curation Center (DCC) defines
data curation as all activities that maintain, preserve, and
add value during the life cycle of digital data. In addition
to the “preservation” of the data, the emphasis is on value-
added data and reuse. In this process, the collaboration
between data researchers, publishers, managers, and
users is highlighted (Heidorn et al. 2007).
With the continuous advancement in the field of data
management, agents who are engaged in data manage-
ment are also attracting the attention of academia. Swan
and Brown (2008) presented data creators, data scien-
tists, data managers, and data librarians as four profes-
sional roles in data management. Furthermore, the four
roles have relationships with each other and, notably,
data librarians are similar to data curators (Tammaro,
Ross, and Casarosa 2014). After resolving the roles in
the context of enterprise research data management,
Fan (2017) points that data curators can be further sub-
divided into specific roles such as data supervisor, data
steward, and data custodian.
Lesk (2013) believes that data curators would be a
high-demand profession in the era of big data while
Walters (2011) discusses the new roles of libraries and
librarians, and further emphasizes the collaborative stra-
tegies of data management and long-term preservation.
Academic librarians are often integrated into the research
process, initially in the framework of research data ser-
vices (RDSs) (Tenopir et al. 2016). Henderson (2017) indi-
cates the roles of librarians in data management through
five aspects: collection (data documentation); storage
(data storage, archiving, and preservation); organization
(metadata creation and controlled vocabularies, file nam-
ing); retrieval (data sharing and reusing, data access);
and presentation/dissemination (data privacy, rights,
and publishing). In order to fulfil the requirements of
data curation, data librarians should understand meta-
data, information literacy, scholarly communication,
open access, and both collection and repository manage-
ment (Corrall, Kennan, and Afzal 2013).
Data Literacy and Competencies
Similar to the concept of information literacy, data lit-
eracy is becoming a critical concept in LIS, which is
related to data management throughout the life cycle of
data management. Data literacy can be defined as a
specific skill set and knowledge base, which empowers
individuals to transform data into information and
actionable knowledge by enabling them to access, inter-
pret, critically assess, manage, and ethically use data
(Koltay 2015). According to Carlson and Johnston (2016),
data literacy can be summarized as when data literacy is
embedded in the R&D data flow and related to data life
cycle; R&D management and data utilization are the
main perspectives in data literacy; practical skills such
as data analysis, description, and tools would be the
main focus in data literacy.
Data literacy is closely related to research data ser-
vices that include research data management. In the
context of data governance, skills such as dealing with
licensing terms and agreements, as well as knowledge
about copyright, are already possessed by librarians as
the components of data literacy (Krier and Strasser 2014).
Prado and Marzal (2013) identify a number of abilities as
data literacy while some clearly show their origin from
ALA and ACRL:
Zhenjia Fan: Context-Based Roles and Competencies 129
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– determining when data is needed;
– accessing data sources appropriate to the information
needed;
– recognizing source data value, types, and formats;
– critically assessing data and its sources;
– knowing how to select and synthesize data and combine
it with other information sources and prior knowledge;
– using data ethically;
– applying results to learning, decision making, or pro-
blem solving.
Similar to the list above, a pilot data literacy program,
offered at Purdue University, was built around the follow-
ing skills (Carlson and Bracke 2013):
– planning;
– life cycle models;
– discovery and acquisition;
– description and metadata;
– security and storage;
– copyright and licensing;
– sharing;
– management and documentation;
– visualizations;
– repositories;
– preservation;
– publication and curation.
The ability to identify the context in which data are
produced and reused has also been emphasized.
Through analysis of the relevant connotation, data
literacy has the following characteristics: it is closely con-
nected with and embedded in the research business work-
flow; it is both for the data service supplier and data user; it
emphasizes the practical skills of analyzing, displaying,
and using data management tools. Taking the factors dis-
cussed above, it can be summarized as the general factor
framework, as in Figure 1. The factors can be divided into
structural and agency; factors outside the data curators’
control are structural factors, and agency factors are mainly
related to data literacy. Both structural and agency factors
will affect data curation, and data curation can lead to
achievements. Furthermore, achievements can loop back
to the context of the data curation. So, data curation can
be regarded as a bridge among different stakeholders.
Figure 1: General factors framework of data curators.
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Research Design
Multi-perspectives on data curation vary, and we should
take this variability into consideration to couple it with
different contexts. In view of the existing literature, this
study explores the analysis approach of “context-business-
role-competency” to summarize the roles from real facts.
The core research questions of this study are as follows:
– How does the context affect the process of data
curation?
– Who are the stakeholders and what are the key roles
in data curation?
– What are the competencies required for data curators
in different contexts?
Based on the above questions, this study adopts a multi-
case study method to summarize roles and corresponding
competencies.
Case study can be used to describe and analyze
the business issue in depth. In view of the lack of effec-
tive localization of data curation in China, this study
attempts to summarize the theoretical framework from
the practice of data curation in different contexts. The
Neusoft Corporation (hereinafter referred to as “Neusoft”)
and several academic libraries in China were selected
as the main case objectives. Participatory observation,
in-depth interview, and document investigation have
been adopted for data collection. The semi-structured
interview outline was summarized according to former
research literature and data collection was conducted
from December 2016 to December 2017. This study was
conducted based on a data collection approach from
multi-time, multi-channel, and multi-source in order to
construct the complete evidence chain. In addition,
open interviews and participatory observations were
used in fieldwork to facilitate a comprehensive under-
standing of the cases. The above aspects can guarantee
the validity of the qualitative data. After the cases were
collected, critical points of the research life cycle were
coded according to files and interview records, following
which the key roles and relationships were analyzed.
Finally, the general competencies framework was dis-
cussed based on the above tasks.
Case Studies and Discussion
As mentioned above, LIS has paid much attention to data
curation research; however, practices of data curation are
never limited to librarianship but also include many
other fields related to research data management. We
firstly introduce and analyze one case beyond the library
via the perspective of data governance, and then compare
the roles with cases from libraries to summarize the
competencies required in common.
Case Beyond Library
Founded in 1991, as one of the largest IT companies in
China with more than 16,000 employees at the end of
2017, Neusoft has established a two-level R&D system
including both enterprise and departmental levels, and
serves society with industry solutions, intelligent inter-
connection products, platform products, and cloud and
data services. Research data have been generated during
the R&D process, and increasingly more value has been
added via the reuse of data. Therefore, data curation is of
significance in R&D management.
Context and Business
Department Responsible for Research Data
Management
Based on open innovation strategy, Neusoft traditionally
pays significant attention to R&D and process manage-
ment. If we adopt the data governance theory to analyze
the stakeholders of the research data management of
Neusoft, Figure 2 shows the stakeholders’ structure. In
2008 Neusoft Research (NSR) was established based on
the Department of Research of Neusoft, and has taken the
responsibilities of R&D management of the whole cor-
poration. There are more than ten R&D managers respon-
sible for special areas in Neusoft.
Types of Data
According to the management files, types of data mana-
ged by NSR include scientific data, metadata, and related
management data.
Tools for Data Curation
NSR adopts SEAS (Super Electronic Archive System), a
platform developed by Neusoft, to collect and curate
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data. It sets different administration authorities to cor-
responding roles in R&D management. Within their
respective authorities, R&D managers can complete
operations such as record creation, data upload, data
update, data deletion, data retrieval, and data down-
load. The information security manager of NSR is
responsible for the daily operation and maintenance of
the platform and data monitoring.
Life Cycle Management
As data collector, NSR completes the task of data collec-
tion according to the R&D project life cycle; that is, from
the start to implementation and to the end of research
projects. We should note that some research data might
be kept by the creators as they are always considered as
private assets, especially in the context of some core
business. Therefore, it is often an issue requiring the
coordination of higher level stakeholders (e. g. super
vice presidents) during the process.
Policy and Institution
The system of data curation in Neusoft can be traced back
to the R&D archives management system. Policies cover,
amongst others, the topics of innovation platform man-
agement, research achievements, intellectual property,
and research cooperation. Files of policy and institution
indicate the detailed business processes, roles, responsi-
bilities, relevant documents and templates, and examples
of application etc.
Roles and Competencies
Roles
Based on this case, the data governance framework needs
to establish a clear organizational structure and division
of responsibilities in the context of enterprise data cura-
tion. According to the actual roles in the data curation,
the three main roles are data supervisor, data custodian,
and data steward. Other roles also include data creators
and data users, which can be cross-converted. For the
basis of the relevant roles see Figure 3.
It is necessary to distinguish the responsibilities
between data business process and technical manage-
ment process in data curation. As shown in Figure 3,
the data steward and data custodian are always corre-
sponding roles for the two functional processes. As
Rosenbaum (2010) indicates, the data steward focuses
mainly on taking care of data assets for others accord-
ing to policies and practices as determined through
data governance, while the data custodian is always
responsible for the technical support of the data cura-
tion process. Generally speaking, the data steward
deals with content and the data custodian deals with
technical support; however, they both share the name
data curator.
According to codes analysis of regulation files for
data curation in case 1, the core competencies of data
curators can be concluded as follows:
– Data planning: be familiar with data types,
volumes, formats, metadata, standard, reservation,
and access etc.;
– Data creation and collection: be familiar with data
sources, tools, skills, and evaluation criteria;
Figure 2: Stakeholders of research data curation in Neusoft.
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– Data processing: be familiar with the description,
cleaning, and cataloguing for data;
– Data analysis: be familiar with metadata and visual
tools to service the decision-making;
– Data preservation: be familiar with long-term preser-
vation, data security, and problem solving;
– Data sharing and reuse: be familiar with data sharing
platform, policy, and regulations;
– Common competency: including communication skills,
background knowledge, collaboration abilities etc.
Case from Academic Libraries
As an ideal center of data curation in colleges and uni-
versities, the academic library has the opportunity to be
the key actor in data services for research. Data curation,
for librarianship, represents the indescribable temptation
in data-intensive era, and it also shows that few know
exactly where it will go.
Founded on October 23, 2014, the China Academic
Library Research Data Management Implementation
Group (CALRDMIG) consists of nine libraries including
Peking University Library, Tsinghua University Library,
Fudan University Library, Zhejiang University Library,
Wuhan University Library, Beijing Institute of Technology
Library, Shanghai Jiaotong University Library, Tongji
University Library, and Shanghai International Studies
University Library, aiming to promote the development of
research data management between different academic
libraries in mainland China. The CALRDMIG sets several
working tasks:
– environmental scanning;
– research data management framework;
– research data management measures and policies;
– formulation and implementation of related standards;
– platform and tools for research data management;
– training courses of research data management;
– planning for research data management for academic
libraries and best practice cases collection.
In China, the majority of academic libraries are under the
direct supervision of the CPC committee and university
offices which mean that academic library running funds
and business content are directed by university adminis-
trative departments acting as the service agent for col-
leges and institutes at the same level as the library itself.
At the same time, the academic library is supervised
by a virtual agent, the Academic Libraries Consulting
Committee of the State Education Department which sim-
ply offers business direction but holds no funds. This
means the data curation business issued by an academic
library is always under the basic governance framework,
as shown in Figure 4.
Interviews were undertaken in the nine academic
libraries and some typical cases have also been col-
lected. Peking University Library, as an example, pro-
vides research data management services, including
data mining, classification, and archiving etc., to help
understand the data value and promote data utilization
in the process of the whole life cycle. At the same time,
it can effectively meet the new demands of researchers
for funding and data verification, and assist the effi-
ciency of research work. All the structured, semi-struc-
tured, and unstructured data are included in the scope
of data curation. Moving to another academic library,
Fudan University Library has set up the Department of
Data Management and Technology which is responsible
for frontier research, project planning, and construction
of the data library, as well as data management-related
projects of the library, colleges, and institution. It
Figure 3: Roles in data curation.
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provides technical and platform support in both system
development and data analysis; academic research,
technical services, external cooperation, and data lit-
eracy training to researchers. The other seven libraries
also offer services related to research data curation.
There is no exact definition of data curation although
there is no denying that it covers the whole life cycle of
data management. The general process of research data
curation can be generally described as shown in Figure 5.
From the interview and survey data, it can be
observed that few libraries have implemented indepen-
dent data curation, that is, data curation is always
embedded in other library business such as the insti-
tute’s repository (IR). Data curation as one of the most
important tendencies in academic libraries has obtained
increasingly more consensus. However, practice experi-
ence in such a field is far different. At present, if lack of
clear top-level design for data management might be one
of the common reasons for the status quo of data cura-
tion in academic libraries, then competency framework
would be another. Since it is embedded in a context
different from that of enterprise, roles and competencies
of data curators in academic libraries would be dis-
cussed in the corresponding context.
Roles and Competencies of Data
Curators in Academic Libraries
From the case description above, it is not difficult to find
that the context of academic libraries is obviously differ-
ent from that of enterprise. In enterprise, it appears more
freedom is given as the whole enterprise would share a
common interest in pursuit of increased benefits. Data
curation can be considered as one of the components of
corporate governance. Different roles such as data super-
visors, stewards, and custodians can communicate with
data creators and users effectively. When it comes to the
context of academic libraries, a different perspective
appears. Both the CPC committee and president of the
university always consider the library as a supplemental
department to colleges and institutes, even as simply a
document repository. Moreover, few researchers consider
the library to be a reliable cooperator. Therefore, much of
the effort, including data curation, by librarians, is not
recognized, which is a disappointing fact in many aca-
demic libraries in China.
It is essential to borrow some experience beyond librar-
ianship, with perhaps data governance from enterprise
Figure 4: Basic governance framework of a typical library for data curation.
Figure 5: Life cycle of research data curation.
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one example. As governance is a concept mainly focused on
risk management, legal, and policy issues, data governance
can be regarded as issues about attribution and citation,
archiving and preservation, discovery and provenance,
data schema and ontologies, and infrastructure (Smith
2014). Under the perspective of data governance, as Pryor
(2012) indicates, the process of approaching, preserving,
and adding value to data of stakeholders can be analyzed
in data life cycle.
Though data curation in academic libraries involves
several stakeholders beyond libraries, academic libraries
still play a critical role. Poole (2016) indicates that digital/
data curation needs to be embedded in institutional and
scholarly infrastructure: archives, centers, libraries, and
institutional repositories (IRs) are key components of
such an infrastructure. IRs, always associated with and
implemented in academic libraries in China, are taking
their data management and knowledge service roles in
the R & D supporting process. As Walters (2014) indi-
cates, data exchange and storage is a key function of
IRs, who can manage not only scholarship and data,
but also software, tools, and code (Cragin et al. 2010;
Walters 2014). To some extent, data curation has been
implemented on the basis of IRs, and data curators are
always the librarians issued about IRs.
Technical skills are essential to data curation; however,
technologies can never solve all the problems in data cura-
tion. Both LIS and non-LIS domain skills (Vivarelli, Cassella,
and Valacchi 2013) and so-called soft skills, namely project
management, negotiation, team-building, and collaborative
problem solving (Oliver and Harvey 2016; Swan and Brown
2008), would be required. According to the discussion
above, common competencies of data curators in academic
libraries can be concluded as follows:
– General competencies: institution management, general
information technology;
– Specific competencies: data related technology, aca-
demic research;
– Competencies of LIS: resource construction, training,
and lifelong learning, knowledge acquisition and
consultation service, knowledge organization.
As the most used name for the role of data curation in
academic libraries, data librarian is “the professional all-
rounder like a Swiss army knife” (Osswald 2013). Beyond
being an information professional, the data librarian
should also be an effective communicator.
If we take the “ownership” or “collections” paradigm
into consideration in data curation, which has an assump-
tion that the academic library should and could collect all
data and therefore be able to adequately satisfy research
needs, it meets too many obstacles in real practices. For
example, the construction of an institutional repository is
the basis of data management; however, one of the biggest
obstacles is that researchers do not cooperate or are
unwilling to provide the data. Data are more valuable
than published papers and monographs, to some extent,
and researchers will not submit them easily.
Few researchers are good at creating sharable meta-
data and curating their data efficiently; however, it is a
pity that even fewer are aware of seeking help from
libraries and librarians. Besides, the stereotype of lamen-
ted competencies of librarians means they will not be
considered as true collaborators by researchers (Wright
et al. 2014). One case is from NSF Data Management
Planning (Hswe and Holt 2011), where researchers failing
to consult librarians would reinforce this bad impression.
Competencies about collaboration with other depart-
ments and being embedded in research team service
would therefore also be considered in the context of the
academic library.
Competencies for data librarians include:
– Communication competencies: skills of effective com-
munication, need analysis, IT and project manage-
ment training, and cooperation with researchers;
– IT competencies: master database design tools, web
development tools and content management systems,
server management, programming software and dis-
tributed collaboration tools;
– Interdisciplinary competencies: background of, and
beyond, LIS; working experience in reference ser-
vice, collection development, and information orga-
nization etc.
Taking into consideration the data life cycle and data
governance framework, the general framework of data
curators in the context of the academic library can be
concluded as shown in Figure 6.
Conclusion and Implications
In the context of research data curation both from and
beyond academic libraries, data supervisors and data
stewards often come from a background of data crea-
tors and data users. The roles of data creators, data
managers, and data users are often relatively easily
distinguished, but data stewards and data custodians
typically behave as research managers, and it is neces-
sary to further subdivide these two roles when defining
the post.
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Combined with the status of data curation in universities,
certain problems faced by practitioners concerning data
literacy are highlighted as lack of policy, unclear responsi-
bilities, mismatch in quality and demand of staff members’
data literacy, lack of professional education etc. Aiming at
the problems mentioned above, this study suggests corre-
sponding countermeasures and suggestions. Firstly, clarify
the job responsibilities and professional quality, and accel-
erate standards setting and policy construction in data cura-
tion in academic libraries. Secondly, establish data literacy
training systems as well as strengthen data consciousness
and data ethics education. Thirdly, promote innovation on
data curation and realize the mutual promotion of theory
and practice. Finally, cooperate with professional depart-
ments in university to establish relevant courses and culti-
vate professionals concerning data curation.
Based on a real business situation, this study com-
bines the stakeholders involved in the data governance
framework to resolve the role of scientific research data
management and complete the role recognition of data
curation. The main conclusions are:
– stakeholders involved in the data governance frame-
work are not fully related to the subject of data curation;
– the framework of data curation needs to establish
a clear organizational structure and division of
responsibilities, and clarify the synergistic relation-
ship between different stakeholders;
– data curation as a specific business data manage-
ment, core roles, including data supervisors, data
stewards and data custodians, should be subdivided
in post setting.
Data governance is a service to enable the transparency of
data related processes and effective use; it can be used
also in the field of data curation in the academic library. It
is important for the library profession to take this chal-
lenge seriously and acquire the competencies required to
provide effective data curation. Competencies framework
should be developed according to the contexts in which
data curation is implemented.
Because of the inherent limitations of the case study,
this study explores the roles of the subject of data man-
agement in the framework of data governance, and needs
to be tested for more case studies. In addition, based on
the data management framework, different data manage-
ment roles should have a data literacy framework, as well
as data continuity, master data maturity, data manage-
ment performance measurement, and other aspects of
how to achieve different management role coordination
research issues, pending more in-depth study.
Acknowledgements: This study is supported by the
Fundamental Research Funds for the Central Universities
(Project: Innovation-Driven Enterprise Data Governance,
No. 63172077) in Nankai University. We offer a sincere
thanks to participants in the long-term field study and
manuscript reviewers for their useful suggestions.
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