Since the early 2010s, there is a renewed interest and push for Open Science in this digital age. The Open Science movement aims to make scientific research and data accessible to all, promoting transparency and accountability to research funding and ensuring reproducible research.

The diagram (Petr Knoth and Nancy Pontika, 2016) below on Open Science Taxonomy shows the breakdown of different areas and activities which contributes to the Open Science movement.

In recent years, many research funding agencies (NIH, Bill and Melinda Gates Foundation, Wellcome Trust, etc.) and government research and innovation frameworks (EU HORIZON 2020) have incorporated elements of Open Science (i.e. open access requirements for publications and data sharing) into their policies and plans.

We have featured the advantages and addressed some myths of Open Access, which is one of the main components of Open Science in previous blog posts. Now, let’s zoom into another burgeoning area of Open Science – research data management for Open Data.

What is research data management (RDM)?

“Research data management concerns the organisation of data, from its entry to the research cycle through to the dissemination and archiving of valuable results. It aims to ensure reliable verification of results, and permits new and innovative research built on existing information.” (from, Whyte, A., Tedds, J. (2011). ‘Making the Case for Research Data Management’. DCC Briefing Papers. Edinburgh: Digital Curation Centre. Available online)

RDM occurs in every stage of the research lifecycle, not just at the end where all the data files are simply zipped up in a folder for storage.  The image below depicts the RDM lifecycle in conjunction with the different stages of research.

The University of California, Santa Cruz, Data Management LibGuide, Research Data Management Lifecycle, diagram, viewed 18^th April 2017 <http://guides.library.ucsc.edu/datamanagement>

Here we list some of the RDM practices corresponding to the different stages of the research life cycle:

1. Start of the research project:
   • Search for existing research data that are relevant to your project to avoid duplication of effort
   • Write a data management plan to detail how the data will be collected, stored, preserved and how it would be shared (if applicable)

2. Conducting the research:
   • Standardise file naming convention and versioning
   • Ensure sensitive data (if any) are well-encrypted and securely stored

3. Publishing the research findings:
   • Proper citation of secondary data used
   • Provide a persistent link (DOI) to supplementary data

4. End of the research project:
   • Ensure that data files are stored in recommended file formats for long term preservation
   • Deposit research data in an appropriate data repository to allow discovery and reuse by other researchers

Why is research data management important?

You may wonder – how will these RDM practices benefit researchers and institutions? In this short documentary by the Digital Curation Centre (DCC), UK, the importance of good research data management is presented in anecdotal examples.

In essence, good research data management can give rise to multiple benefits such as

• • Research reproducibility
  • Defense against research fraud
  • Reduce wastage of research funds and duplication of research efforts
  • Promotion of reuse and new discoveries from existing research data

Research data management policy in NUS

Let’s focus on our local environment after having an idea of what’s happening on the global stage with regards to research data management. Are you aware that there is a research data management policy in NUS which all staff and students of NUS have to abide by?

Here are some of the important highlights of the policy:

• All records of research project generated by NUS staff and students are the property of NUS.
• One copy of primary data in original format, (e.g. in a laboratory notebook or in secure electronic form) should be kept securely within the Dept/RIC. Formats of retention should be adequately documented, secure and, as far as possible, immune to subsequent alteration.
• Minimum retention period of 10 years

Some departments might have developed more specific guidelines due to the nature of research in their particular fields. While the policy has stated that the data should be ‘adequately documented and secure’, what does it mean to be ‘adequate’ and are your current research data management practices adequate enough?

If you are wondering about the above questions too, fret not, we will cover some of the best practices in research data management in future blog posts to address these queries!

In the meantime, we would like to introduce you to some upcoming services from NUS Libraries relating to research data management…

Upcoming research data management services by NUS Libraries

NUS Libraries will be launching an institutional data repository as a complementary component to our current institutional repository, Scholarbank@NUS by the third quarter of 2017.

This service is introduced to help NUS researchers comply with the NUS research data management policy and also, to help them fulfil funders’ requirements for Open Data and/or to facilitate their support for Open Science movement.

We welcome all NUS researchers or a research administrators looking after the department’s/faculty’s research output to contact us for any of the following services:

• using the data repository for storage and/or sharing
• consultation and workshops on good research data management practices and writing data management plans

Further readings:

1. Nielsen, M. A. (2012). Reinventing discovery: The new era of networked science. Princeton, N.J: Princeton University Press.
2. Pryor, G. (2012). Managing research data. London: Facet Publishing.
3. Corti, L., Eynden, V. v. d., Bishop, L., & Woollard, M. (2014). Managing and sharing research data: A guide to good practice. Los Angeles: SAGE.

Chew Shu Wen
Research Data Librarian