Your Research data is your craft

All PGRs at the University of Birmingham are required to have a data management plan in place as part of their first Annual Progress Review. In this post, Angeliki Andrikopoulou, Research Data Librarian in Scholarly Communication Services in Library Services, explains why they are important and where you can get support.

Research data management (RDM) is a crucial element of the research lifecycle as it consists of activities related to creating, collecting and reusing research data during a research project. Data governance skills and knowledge are required from all researchers, either creating new data or reusing data.

Angeliki set up for her RDM drop-in, in the Researcher Suite on the first floor of the Main Library.

In this digital era, research is data-centric. Research data is a significant output that has become increasingly important and equally valuable over the last years equal to journal articles, books, and other research outputs. Consequently, data management planning is the first and most appropriate step to undertake at the beginning of the project to ensure data quality.

A helpful way to visualise the data management plan is as a path you must step on throughout your research project. A data management plan will allow you to plan and make decisions about all the critical research data lifecycle stages beforehand and save you valuable time and effort. For this reason, writing and updating this important document should be a meticulously performed act.

Furthermore, data management planning will guide you in making your data open, if appropriate, and FAIR. To make your data FAIR, you must follow four principles: Findability, Accessibility, Interoperability and Reusability. FAIRness ensures that your research outputs are valid, reusable and reproducible, increasing the impact of your research, the quality and the resilience of your data. Data management planning and FAIR are unique tools that will guide you in organising, publishing and possibly sharing data in appropriate formats and manner that others can understand. Your data is your craft, the result of your hard work and should be considered a valuable asset and looked after accordingly. Adopting those tools will ensure that you and others and society in general can benefit from your research outputs.

Library Services provide various support dedicated to your research data journey. Recently, a new facility was added to this support. Scholarly Communications Services have launched a weekly RDM Drop-in session. It takes place in person every Friday 1-2 pm in the Researcher Suite on the Main Library’s First Floor, and no booking is required. We anticipate that this relatively new drop-in session will help to increase researchers’ awareness and improve their skills related to RDM. Thus, if you have queries or wish to discuss your research’s particularities concerning research data, grab your coffee or tea and join a research data librarian every Friday after lunch -no pre-booking is required. Dates can be found on the library website, or you can contact us if you require further information.

Planning to manage your data

We’ve talked a bit about Research Data Management (RDM) on this blog before, with a post from our Research Repository Advisor in Library Services and another from a current PGR.   However, now that there is a requirement for all PGRs who started their research programmes in or after September 2017 to produce a Data Management Plan (DMP) in advance of their first annual review, it seems a good time to revisit this topic, with a focus on DMPs.

A DMP is a living document that outlines how data are to be handled during and after a research project.  A good DMP will protect you against data loss and ensure you have well-documented data to assist with writing up and possible future data sharing.

Big data, small data, no data

There are many aspects to a successful PhD project and challenges to master on your way to graduation. You most certainly are aware that you should acquire e.g. writing and referencing skills, but how much time have you spent thinking about the research data management activities you might need to undertake as part of your research?  None yet? Time to get started with our introduction to research data management!

Spotlight on the RDF: “Appropriate practice”

In this series of “Spotlight on…” posts, we’ll be delving into the detail of the descriptors in Vitae‘s Researcher Development Framework (RDF).  Each one of the sixty-three descriptors is a characteristic of an excellent researcher, and we’ll be looking at how UoB PGRs can develop these characteristics.

The RDF  descriptor “appropriate practice” is one which is easier to define through its opposite:  academic malpractice is any activity – intentional or not – that is likely to undermine the integrity essential to scholarship and research.  Examples of academic malpractice include plagiarism and falsification/fabrication of results.