THE VALUE OF RESEARCH DATA MANAGEMENT (RDM) IN OPEN ACCESS INITIATIVES IN INSTITUTIONS

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1.0 INTRODUCTION

Research data is vital for continued research development and discovery of new ideas. It is

important to manage research data in order to accrue value from it. In addition, open access

initiatives depend on organised research data for access and use by the user community. In

fact, open access to research data is made possible through Research Data Management

(RDM). This paper, therefore, will discuss RDM in the context of open access initiatives in

institutions. The types and perspectives of data will be explained, the role of libraries in RDM

will be discussed, challenges of open access initiatives will be outlined, and lastly, the role of

RDM in open access initiatives will be explored and recommendations drawn for further

action in implementing RDM in open access initiatives.

2.0 TYPES AND PERSPECTIVES OF DATA

In order to understand the concept of RDM, it is important to discuss the classifications or

categories of data a priori. Different perspectives have been highlighted as regards to data

classification. The first perspective looks at data classification from the prior category. The

second perspective looks at data from the functional or domain specific category. According

to Blue Ribbon Task Force on Sustainable Economics for a Digital Planet (2010, p. 56) states

that “data are the primary inputs into research, as well as the first order results of that

research”. This definition proposes that data may be categorised into two. First, data acquired

from another research project; and data produced within a given field of research as, for

example: (a) observational, (b) computational, and (c) experimental data, as distinguished in

a report produced by the National Science Foundation (National Science Board, 2005). In

addition to the three types of research data identified by the National Science Board,

Borgman has also identified the types of data as (d) data of records (Borgman, 2007). For

example, records of government, business, and curating research data of public and private

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life. The author notes that these four categories “tend to obscure the many kinds of data that

may be collected in any given scholarly endeavour” (Borgman, 2010, p. 3). One could also

suggest the existence of (e) data from works of art and literature and artefacts of cultural

heritage as studied by the humanities. It can be deduced, therefore, that the categories (d) and

(e) are data as inputs to the research project and not produced within a given research study.

However, the description does not imply that categories (d) and (e) are less relevant

compared to the rest. Finally, we can say that the natural sciences, in addition to the

humanities, depend on data derived from collections of preserved objects (or “documents”)

such as herbaria.

Furthermore, the above classification of research data, however, under-emphasize the

interaction that may occur between research and data, so that data is used as an input to

produce new research outputs that are in turn used as new inputs. The major question might

be; what is the implication of this classification on the whole concept of RDM? Do the

National Science Board (2005) and Borgman (2010) suggest that different kinds of policies,

systems and services are needed for each of these kinds of data? The National Science Board

(2005) has emphasized that the distinction between observational, computational, or

experimental data is crucial to choices made for proper archiving and preservation, in that

observational data are historical records that cannot be recollected, and therefore “are usually

archived indefinitely” thereby suggesting that observational data should automatically be

given priority over other kinds of data in future infrastructures. On the whole, the

classification may render itself irrelevant as hundreds of classes emerge across disciplines

hence difficult to manage research data. Therefore, a more domain specific classification is

needed that will look at the human activities involved in the creation of data. With this

methodology, the value of research data based on human activities involved in research can

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then be identified and accorded to the data. Some of the examples of human activities are

business activities, health activities, legal activities, cultural activities, research activities, and

data are produced in relation to each kind of activity. Data about populations are important

for government institutions, and are why many statistical agencies were initially created and

have since been maintained, for example, National Statistical Office of Malawi (NSO).

Therefore, data classification based on human activities is a better way of managing research

data and promoting access to it.

In libraries, for example, data are produced about users and their needs (e.g. students, course

information, etc.). Each human activity implies not just the production of some data, but also

the need for systems within which we might organize such data for future use. Each human

activity creates important experiences that provide useful information on how to conduct

RDM activities such as data creation, classification, storage, access and preservation. These

human activities determine the relevance of the data and ways of managing such data.

According to Jørn, Nielsen and Hjørland (2014) argue that research as a human activity

depends on data from other research projects as well as its own data. In information sciences,

bibliometric data are important to the study of bibliographic databases and their functions in

library and information centres. It is true that all types of data are useful and relevant but not

all data are equally relevant in relation to on-going research or to predictions/anticipations

about developments in research activities. Therefore, the relevance of any given set of data

depends on the aim of the research, research questions, theoretical framework and paradigm.

3.0 RESEARCH DATA MANAGEMENT (RDM)

Having discussed the types and perspectives of data, it is important to consider the

management aspect of research data. RDM has been defined differently by different authors.

According to Whyte and Tedds (2011, p. 1) define RDM as "the organisation of data, from its

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entry to the research cycle through to the dissemination and archiving of valuable results".

Furthermore, the authors state that RDM consist of a number of activities such as research

data creation, storage, security, preservation, retrieval, and sharing and reuse of data through

different platforms such as conferences and Institutional Digital Repositories (IDRs). In

addition, Childs, McLeod and Lomas (2014, p. 156) define RDM from a records management

perspective as "records management for research data". According to Henty (2008) argues

that data management means different things to different people, usually according to the part

they play in managing some part of the data life cycle. Furthermore, the author explains that

researchers are primarily involved in data creation and analysis, but the decisions they take in

deciding what formats to use to collect and store their data, what metadata they will use to

describe it, who owns it, who has access to it, what software they will use to analyse it, what

outputs there will be from the research, and countless other activities will have an impact

further along the line. Data management for the person who then takes on responsibility for

data stewardship (such as librarians and records managers) will involve another set of

activities, including organisation, preservation and the provision of access. In addition, Fox

(2013) argues that data management activities for an academic setting require multiple

disciplines such as Information Technology (IT) and Librarianship. Apart from data creators

and data stewards, other activities involved in data management involve systems

architectures, policies and procedures which have to be specified and communicated to

everyone engaged in the process. In any case, RDM activities are multidimensional and

require the participation of different players in the research project life cycle.

4.0 ROLE OF THE LIBRARY IN RDM

The role of the library has been a centre of discussion as far as RDM is concerned. Different

models have been developed as regards to the role of libraries in managing research data. Dr.

Christopher Greer, Program Director of the Office of Cyberinfrastructure, National Science

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Foundation, USA, has developed a pentagram that represents the role of the library in the

future of data curation within the larger field of e-Science (Mullins, 2006). Referring to it as

the I-Center, in order to break down perceptions or stereotypes of the role of a library, the

author places the I-Center in the middle of the pentagram with the other five points identified

as: domain science, computer science, library/information science, archival science, and

cyber infrastructure. The author's argument is that data curation activities are

multidimensional but cantered around the library. Therefore, all five players as described in

the pentagram below should collaborate to develop a model that will be practical and

workable to curate the massive datasets that are now being generated.

Source: The I-Center, Dr. Christopher Greer, Office of Cyberinfrastructure, National

Science Foundation (Greer, 2006)

Figure 1 The role of the library in data curation

According to Greer (2006) states that all major points in the pentagram have roles to play in

building an I-Centre or digital repository from masses of data produced everyday from e-

science. Cyberinfrastructure makes up the technical aspects of the system such as computer

I-Centre Computer

Science

Archival

Science

Library/Infor

Science

Cyber

Infrastructure

Domain

Science

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hardware, software, and network topologies. Library/information science is interested in

organisation, classification, metadata, copyright, and fair use of data. On the other hand,

Archival science involves long term preservation of data sets. Computer science is another

important player in RDM. Computer science is concerned with management and

implementation of technical aspects of RDM. Issues of hardware maintenance, repair,

migration, backup and recovery are handled by this group. Lastly, domain science is related

to the subjects of the various data sets being managed in the I-Centre. Moreover, domain

science considers all different subjects and their needs as regards to language, and

epistemology.

5.0 OPEN ACCESS OF RESEARCH DATA

The term open access has become the buzz word in the 21st century. Academic institutions

have constructed and managed digital repositories in order to enhance open access to digital

objects. Undeniably, different factors have contributed to the development of open access

agenda. Some of the factors include cost implications, institutional policy requirements,

regulatory requirements and funder requirements (Henty, 2008). Besides, digital repositories

have also accelerated the need to share and access research data across user communities.

Unfortunately, institutions of higher learning continuously face challenges of meeting the

cost of subscribed journal articles from commercial publishers and consequently the need for

open access information (Aliyu and Mohammed, 2013). Open access initiatives enable the

large community to access information while improving research activities. Research

institutions also expect researchers to deposit their research findings with the institutional

repository to encourage access and use. In addition, the regulatory frameworks of countries

also compels the research community to make research data openly accessible. For example,

countries such as United States of America (USA) and United Kingdom (UK) have Freedom

of Access to Information legislations. Any research endeavour related to public institutions is

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expected to be openly accessible as part of public information. Lastly, funded research

activities by institutions such as Welcome Trust and Medical Research Council of the UK

require research data to be openly accessible as open data.

Again, Childs, McLeod and Lomas (2014) have used the term 'open research data' to refer to

open access data. The authors define open research data as "information that is available for

anyone to use, for any purpose, at no cost" but under licence (Childs, McLeod and Lomas,

2014, p. 143). In the context of the authors, the licence refers to attribution and share-alike.

First, attribution entails that the people who use the open research data must credit whosoever

is publishing. Second, share-alike is a condition that people who mix open research data with

other data have to release the results as open research data as well. These conditions

attributed to open research data license promote the culture of sharing data as open access

data.

Equally important, Henty (2008) states that open access was related to the preservation of

text based materials such as research articles and book chapters. The author also argues that

the concept has changed tremendously and incorporates two major premises. First, providing

a platform for open access to publications in an institution and providing alternative way of

publishing with permission from publishers as open access while maintaining peer review

and scholarly integrity. In brief, open access agenda will continue to develop further as

research endeavours grows.

6.0 THE CHALLENGES FACING OPEN ACCESS INITIATIVES

Open access is an important concept in enhancing research and discovery of new ideas.

Nevertheless, while open access is generally agreed to be a good thing it has not been as

successful as anticipated with researchers slow to deposit in repositories. Despite efforts for

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open access data and many benefits accrued from using such a platform, many research

studies are closed from open access. According to Henty (2008) argues that institutions still

have “a system where gateways limit access to research results, and as a consequence only a

small fraction of the world's research libraries subscribe to some journals. As a result, the

concept only exists as a conceptual model and not a practical one. Therefore, there is need for

considerable effort by all players in the research project life cycle in ensuring that open

access initiatives in institutions are supported.

However, there are many reasons for this, not the least of which is that making publications

accessible on open access is not well integrated into the scholarly communication cycle. In

order for researchers to make their publications available on open access, they have to take

extraordinally steps to their usual publication processes. Even where deposit of publications

has been mandated, deposit rates, while improved, do not reach 100% compliance (Henty,

2008). Making data available on open access presents a similar, and possibly more complex,

challenge. At present there are no policies, attitudes, understanding, commitment or

mechanisms in place to allow data deposit to occur as a matter of course and it is here that

institutional policies and advocacy have a major role to play. Two other developments are

having an impact: learned journals, especially in the sciences, are starting to insist that the

data on which articles are based are made available together with the article and research

funders are starting to insist that all the outputs of research are made more accessible with

few or no restrictions at all.

Another challenge of open research data stems from the methodological orientation of certain

research studies. This is generally true with qualitative research. Qualitative research unlike

quantitative research poses major challenges for reuse of data sets. Qualitative data is

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generated through interviews, observations, diary entries and video recordings. Such data

provide a basis for understanding of the phenomena under study. Thus, the researcher spends

a considerable amount of time in the field collecting data. The environment, verbal and non-

verbal cues of research participants and field notes make important sets of data for analysis.

With such prolonged presence in the field, other researchers may find difficulties to use data

from qualitative research for three major reasons (Heaton, 2008). The author argues that there

are issues to consider when making qualitative data accessible for sharing and reuse. First, the

problem of data fit will occur where data collected for one purpose may not be used for

another especially when context of qualitative research is important in understanding the data

sets. Second, the problem of not being there. The author argues that it is difficult to

understand and also interpret data where researchers not involved in data collection exercise

use the data. Third, the problem of verification. The author also suggests that methodology of

verification should be different and adapted to qualitative research in order to properly use

qualitative data.

However, not all types of qualitative research studies pose similar challenges. Qualitative

studies with semi-structured interview guides may be less problematic in terms of data

interpretation. On the other hand, ethnographic research studies may be difficult to make

openly accessible research data because context is highly important in understanding the data.

The solution to this problem may involve undertaking secondary analysis of qualitative data

and exploring the philosophical and practical issues of using data sets from different

qualitative research studies.

Besides, another challenge facing open research data is research ethics. In the context of the

internet era where data can be shared online and used widely amongst researchers across

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national borders, ethical violations are bound to occur. It is difficult to identify the user of the

data, for what purpose and to what extent. It is also difficult to enforce ethical behaviour on

community of users distributed across networks. The major concern emanates from the health

discipline where patients' data is used for research purposes. To address this problem, several

techniques have been used in hiding the identity of participants through the use of codes to

represent the names of participants (for example, D to represent John). In addition, the use of

codes to protect the identity and confidentiality of participants has proven futile in recent

times considering the fact that identity can also be exposed through connection of several

data sets. Therefore, more sophisticated means of protecting the identity of research

participants is needed. The protection and confidentiality of patients’ data is equally

important in the context of regulatory frameworks across countries. Personal data is bound

for protection under Data Protection Act (DPA). According to Open Data Institute (2014)

states the need for open data licences such as Creative Commons Licences for non-

governemntal organisations in order to safeguard against any ethical violations during the

conduct of research.

Furthermore, Information Commissioner's Office (ICO) of the United Kingdom (UK)

developed anonymisation tool called ICO code of practice for anonymisation. The tool

categories various data sets according to their susceptibility to disclosure. The first one is

called data for publication where risk of identifying participants is low. The second category

is personal confidential data. The risk of identification from this category is high hence

disclosure of information requires consent or statute with a contract of agreement. The last

group is data for limited disclosure or limited access. Identification of individuals is also

high, therefore, access is limited to a closed community of users. Conversely, research data

should be anonymised to an extent where data can be usable. Extreme anonymisation can

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render data unusable and render it irrelevant. Therefore, a balance should be reached between

confidentiality and access of data for use.

Another major challenge facing open access initiatives is lack of funding and inadequate

human resource. Preparation of research data for open access is resource intensive.

Preparation for research data as open access starts at the early stages of the research project

life cycle. Several decisions have to be made regarding the format of the data such as data

format, ethics, copyright, access, preservation, and storage media. The task calls for human

resources and finances to accomplish good RDM. However, providing sufficient contextual

information to enable data reuse is also a significant challenge for quantitative research

projects (Faniel and Jacobsen, 2010). The author further suggests that project resources are

needed to enable open access, but observed that this has not been an accepted costed

component of research proposals in the past. In such scenarios, the question of who will fund

the activities remains unclear. For example, in universities with institutional repositories, the

management should take the responsibility of supporting such research activities with

adequate resources. Therefore, information professionals should lead in advocating for such

agenda because managing data is within the mandate of information professionals. In terms

of qualitative research, all contextual information and project documentation data such as

project description, participant context and characteristics, methodological approach, data

collection, processing and analysis, consent agreements, metadata, and techniques

preservation have to be made available as part of research data.

Lack of RDM skills is also a challenge in preparing data for open access. Librarians as data

stewards have long being involved in managing information through the library functions of

cataloguing, classification, indexing, and providing access to such information. It can also be

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argued that the role of librarians in research data management is similar to traditional library

management functions. According to Smith (2012) argues that it is evident that librarians are

playing an increasingly integral role in data management, both in the paper and electronic

environment.

Nevertheless, the need to support RDM calls for special skills in managing research data and

not necessarily information sources. For example, librarians should acquire research skills in

order to support researchers in the e-science era. According to Henty (2008) argues that

creating mechanisms required to support e-Research implies a different kind of service, one

where the library is engaged in the research process. This process calls for new skills, new

collaborations and new partnerships. Libraries, therefore, need to redefine their roles and

responsibilities and embrace and adapt to the rapidly changing technological and research

environment. The process will also require a change in the organisational structure of

libraries and professional titles, for example, from librarians or documentation officers to data

managers, research data scientists, data curators, and subject liaison librarians.

7.0 ROLE OF RDM IN OPEN ACCESS INITIATIVES

One of the solutions to challenges facing open access initiatives in institutions is adopting

RDM. RDM provides an opportunity for all players in the research cycle to make decisions as

a team regarding research data, research integrity, managing ethical dilemmas, ensure long

term preservation, and achieve open research data. According to Childs, McLeod, Lomas and

Cook (2014) argue that good RDM can enable open data and must begin from the outset of a

research project, from the proposal stage. The author further acknowledge that RDM is a

central component of the research process supporting informal proposal development,

research governance, ethical review, methodology and project management. Regardless of

the differences between research data management and open access, research data

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management has a capacity to accelerate open access and enhanced its visibility. In the same

vein, Cox and Pinfield (2014) argue that there is also a potential connection between RDM

and the open-access agenda that libraries have been so active in promoting, although the

argument for RDM is not simply or necessarily related to openness. In a nutshell, RDM will

continue to accelerate the open access agenda for decades.

There are several ways in which RDM plays an important role in open access agenda. First,

RDM manages huge volume of research data for access and use. Developments in IT have

created a conducive environment for research data sharing and reuse. However, the

environment has also created huge challenges for researchers which Smith calls "data deluge"

(Smith, 2012). According to the author, data deluge is the situation where the sheer volume of

data surpasses the capacity for institutions to manage and researchers to use it. The situation

has raised major concerns for information professionals and other players in the scholarly

communication cycle. Therefore, RDM should be implemented across all research

institutions to manage research data. There is need for data organisation, classification,

licensing, and preservation of data. In the same vein, RDM will also support open access

initiatives and make data accessible for sharing and reuse. During the course of RDM,

libraries will be able to advocate for open access of research data by influencing national and

institutional policies on research. Lewis (2010) outline a pyramid model of nine areas of

RDM activity for libraries. Through Lewis model, libraries can, therefore, take advantage of

the areas related RDM and manage research data for open access. The model is outlined

below.

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Source: Lewis, 2010

Figure 2 The research data management pyramid for libraries

At the top level of the pyramid is influencing national policy. Through professional

associations such as Malawi Library Association (MALA), libraries can advocate for RDM

implementation and support by the government in different institutions. On second level,

libraries should take a leading role in influencing institutional policy, developing local

curation capacity through capacity development and working with LIS schools to identify

required skills for data curation or data management. In the same vein, libraries should also

utilise the partnerships with other players such as researchers and academicians and influence

policy at a university or institutional level. On the third level, libraries should develop Library

and Information Science (LIS) workforce confidence with data, teaching undergraduate and

postgraduate students about RDM, and advice services and raising data awareness among

Influence

National

Data Policy

Lead local

data

Policy Develop

local data

curation

capacity

Identify

required

data skills

with LIS

Teach

data

literacy to

PG

Data into

UG RB

learning

Provide

researcher

data

advice

Develop

library

workforce data

confidence

Develop

researcher

confidence

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researchers. At this lower level, libraries can provide support to students and other library

staff in RDM training and also raising awareness of the importance of managing research

data.

Another important contribution of RDM in open access initiatives is ethical issues related to

research data. Research data is embodied in varying degrees of ethical dilemmas. In order to

use research data, researchers and other players in the research communication cycle should

identify and manage ethical issues appropriately. Research related to health and social

welfare attracts ethical issues of varying degrees. To illustrate, health data from patients is

associated with ethical issues that needs to be resolved to make research data useful. For

example, data about the names and health condition of patients should be treated with utmost

confidentiality and thus restricted from access. In addition, data about the social benefits of

vulnerable groups such as children may need to be protected from access in order to protect

the identity of the children.

In the presence of ethical issues, RDM enhances adherence to ethics while ensuring that

research data is open for reuse. In the same vein, Childs, McLeod, Lomas and Cook (2014)

states that researchers need to carry out RDM activities through the research project to

produce data that are capable of being safely made openly accessible and stored long-term.

One of the most important tools that support RDM activities in the context of ethics is Data

Management Plan (DMP). DMP consist of a series of items on a checklist that helps a

researcher to make important decisions on the management of data throughout the research

project. Some of items on the DMP checklist include documentation and metadata, ethical

and legal compliance, and storage and backup. Through the tool, the researcher can manage

ethical issues and make research data as open as possible with few or no restrictions.

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Furthermore, RDM ensures that research data is stored for a long period of time and remain

accessible to users. In order to accomplish this task, research data needs to be preserved with

appropriate metadata called preservation metadata. Preservation metadata gives data in form

of digital objects the form and structure for long-term storage and use. In data preservation,

the creation of a digital object is an important step towards access and long-term storage of

research data. Nevertheless, more effort is needed in preserving data for future use through

addition metadata. It is also important to note that data preservation is more profound in

digital data compared to physical data. The reason is because digital data is vulnerable to loss

due to technological changes and other factors compared to physical data. Open access to

research data means the removal of restrictions to data. Furthermore, Groenewald and

Breytenbach (2011) have argued that information [data] created in digital format and selected

for archiving needs to be preserved in the format of creation without any restrictions

embedded in the document. In essence, open access to research data also depends on

preservation metadata for continuous access to data. In summing up, RDM ensures that all

necessary preservation techniques have been added to data in the research project for long-

term storage and continuous access.

At the heart of every research project is data sharing either under restrictions or not. Research

data are meant to be shared with the community of users for several reasons. First,

accountability issues especially in funded research projects. Second, accrue value from the

research data. Research data stimulates further research interests among researchers and

contributes to discovery of new ideas. Last, sharing of research data avoids duplication of

efforts hence saving cost of conducting research. RDM promote the altitude of sharing

research data which is the core attribute of open access. For example, Data Curation Centre

(DCC) of the UK trough the DMP checklist encourages researchers to decide on data

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selection and preservation (DCC, 2013). Under this item, researchers are meant to provide

options on data sharing and reuse. This is important especially in publicly funded research

where validation of research data is important. Sharing of data ensures that research data is

placed under stern validation process by other researchers to produce quality data for reuse.

Data appraisal is also an important component of the RDM and Open Access. Advancements

in IT has accelerated research activities and sharing of data. This has resulted in the

generation of masses of data. Therefore, access to relevant data becomes a challenge in such

circumstances. RDM provides a solution for enhancing data organisation for easy access and

use through data appraisal. Meanwhile, different players have been proposed as the right

candidates for the exercise. Such candidates include researchers, IT managers, librarians and

records managers. But librarians have long been involved in appraisal activities though in a

different kind of information resources such as books through collection development (for

example, weeding), however, such skills should also be extended to researchers. Conversely,

Childs, McLeod, Lomas and Cook (2014) argue that researchers are in a better position to

make appraisal decisions because they have an in-depth understanding of the research process

and context. Therefore, researchers should be trained in data appraisal as part of RDM

training in order to effectively appraise data. In a nutshell, a DMP designed appropriately

with data appraisal guidelines, will help to produce better quality data and enable the

publication of open data.

8.0 CONCLUSION

In conclusion, advancements in IT have created both opportunities and challenges for the

research community. The major challenge created by the IT development is creation of

masses of data at an alarming rate. The rate surpasses the capacity for institutions to manage

such vita data. Coupled with the call for open access to data due cost implications of

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commercial scholarly journals in institutions, RDM has become a solution to managing data.

RDM ensures that research data is made accessible despite of numerous challenges facing the

research community such as data deluge, cost, inadequate human resource, stringent ethical

issues, lack of integration of research activities, and lack of training in RDM among different

players in research project. All things considered, there is need for collaboration among

researchers, librarians, records managers, IT managers, data curators and other players in

implementing RDM to make the research data openly accessible to users.

9.0 RECOMMENDATIONS

Based on the discussion in this paper, the following are the recommendations:

1. Institutions should invest in training research data managers such as researchers,

librarians and records managers in RDM in order to manage vast amount of research

data and make it openly accessible.

2. Deliberate policies and procedures should be implemented in institutions in order to

support open access initiatives through deposit of research data into the IDRs.

3. The Malawi Government through the National Assembly should fast track the

discussion on Freedom of Access to Information Bill. Such bill once passed into law

will compel public institutions to make openly accessible the public information

including research data.

4. Professional associations such as MALA should advocate for open access to

research data in institutions through meetings, media, and petitions to the Ministry of

Information and Civic Education and National Commission for Science and

Technology (NCST).

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10.0 REFERENCES

Aliyu, A. & Mohammed, Z. (2013). Deployment of Ahmadu Bello University Zaria, Nigeria

Institutional Digital Repository. The Information Manager, 13(1,2).

Blue Ribbon Task Force on Sustainable Economics for a Digital Planet (2013). Sustainable

economics for a digital planet. ensuring long-term access to digital information. Retrieved

March 9, 2015 from http://brtf.sdsc.edu/biblio/BRTF_Final_Report.pdf.

Borgman, C. (2010). Research data: who will share what with whom, when, and why.Paper

presented at the Fifth China-North America Library Conference, 8-12 September, Beijing,

China. Retrieved May 9, 2015 from http://works.bepress.com/cgi/viewcontent

.cgi?article¼1237 &context¼.

Borgman, C.L. (2007). Scholarship in the digital Age: information, infrastructure, and the

internet, MIT Press, Cambridge, MA.

Childs, S., McLeod, J., Lomas, E., & Cook, G. (2014). Opening research data: issues and

opportunities. Records Management Journal, 24(2), 142 - 162. Retrieved April 10, 2015

from http://dx.doi.org/10.1108/RMJ-01-2014-0005

Cox, A.M. & Pinfield, S. (2014). Research data management and libraries: current activities

and future priorities. Journal of Librarianship and Information Science, 46(4), 299-316.

Retrieved June 1, 2015 from DOI: 10.1177/0961000613492542.

DCC (2013). Checklist for a data management plan. v.4.0. Edinburgh: Digital Curation

Centre. Retrieved May 6, 2015 from http://www.dcc.ac.uk/resources/data-management-

plans.

Faniel, I.M. & Jacobsen, T.E. (2010). Reusing scientific data: how earthquake engineering

researchers assess the reusability of colleagues’ data. Computer Supported Cooperative

Work, 19 no. 3/4, 355-375. Retrieved May 22, 2015 from http://dx.doi.org /10.1007

/s10606- 010-9117-8.

Fox, R. (2013). The art and science of data curation, OCLC systems & services. International

digital library perspectives, 29(4), 195 - 199. Retrieved May 10, 2015

http://dx.doi.org/10.1108/OCLC-07-2013-0021.

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Greer, C. (2006). A Vision for the digital data universe. Purdue University, West Lafayette,

IN, USA, Presentation, December 19, 2006.

Groenewald, R., & Breytenbach, A. (2011). The use of metadata and preservation methods

for continuous access to digital data. The Electronic Library, 29(2), 236 - 248 Retrieved

May 22, 2015 from http://dx.doi.org/10.1108/02640471111125195.

Heaton, J. (2008). Secondary analysis of qualitative data: an overview. Historical Social

Research, 33(3), 33-45.

Henty, M. (2008). Dreaming of data: the library’s role in supporting e-research and data

management. In: Australian Library and Information Association biennial conference,

Alice Springs, Australia, 2–5 September 2008. Retrieved April 10, 2015 from

http://apsr.anu. edu.au/presentations/ henty_alia_08.pdf.

Information Commissioner’s Office (2012). Anonymisation: managing data protection risk

code of practice, Information Commissioner’s Office. Retrieved May 22, 2015 from

www.ico.org.uk/for_organisations/guidance_index/_/media/documents/library/Data_Prote

ction/Practical_ application/anonymisation-codev2.pdf.

Lewis, M. (2010). Libraries and the management of research data. In: McKnight S (ed.)

Envisioning Future Academic Library Services: Initiatives, Ideas and Challenges.

London: Facet, pp. 145–168. Retrieved May 26, 2015 from http://eprints.whiterose.ac.uk

/11171/ 1/LEWIS_Chapter_v10.pdf.

Mullins, J. L. (2006). Enabling international access to scientific data sets: creation of the

distributed data curation center (d2c2). Retrieved May 26, 2015 from http://docs.lib.

purdue.edu/lib_research/85/.

National Science Board (2005). Long-lived digital data collections: enabling research and

education in the 21st Century, National Science Foundation, US. Retrieved May 9, 2015

from www.nsf.gov/pubs/2005/nsb0540/.

Nielsen, H.J. & Hjørland, B. (2014). Curating research data: the potential roles of libraries

and information professionals", Journal of Documentation, 70(2), 221 - 240. (Retrieved

April 21, 2015 from http://dx.doi.org/10.1108/JD-03-2013-0034.

21

Open Data Institute (2014). Guides: what is open data?.Retrieved May 22, 2015 from

http://theodi.org/ guides/ what-open-data.

Smith, K. (2012). Managing research data. Library Management, 33(6/7), 437 - 439.

Retrieved June 3, 2015 from http://dx.doi.org/10.1108/01435121211266438.

Whyte, A & Tedds, J (2011). Making the case for research data management. DCC Briefing

Papers. Edinburgh: Digital Curation Centre. Retrieved May 21, 2015 from http://www.dcc

.ac.uk/ webfm_ send/487.