Research Data Management
What is Research Data Management (RDM)?
Research Data Management (RDM) is part of a lifecycle that aims to facilitate effective and efficient research. It typically starts from data planning and proposal writing and continues through to dissemination and archiving.
This word cloud highlights some RDM work areas. Some may seem obvious and easy to tackle while others might be hard to get your head around. In any case, a good RDM workflow requires careful and thoughtful planning, and a methodical approach will definitely pay off in the long term and help you avoid last minute panic attacks.
Planning forces you to think through your objectives and avoid future headaches, particularly if you are not a naturally methodical person. Planning is also important because funders, both local and international, now increasingly require Data Management Plans (DMPs) to be submitted as part of grant applications.
Think back the number of times you lost data simply due to negligence and lack of planning, only to regret it afterwards. Planning not only safeguards your data, it also allows you to monitor and adapt to changes as well as facilitate sharing and long term preservation, helping to ensure data longevity.
A Data Management Plans (DMP) is not difficult to write. It is simply a document that describes the people behind the data, what data will be collected, and how they will be handled during and after the project. Here are the components of a typical DMP:
You can also download the HKBU DMP template to make a start. The two free DMP tools below can be helpful too, particularly if you are applying for overseas funding as they provide various funder templates to choose from.
As you start the process of creating and collecting data, organizing and documenting your work will become increasingly demanding. The following is a few areas to be aware of. Then, you can get more tips on managing research data here.
Use standardized terminology in your field to help avoid confusion. An example include the ICPSR Glossary of Social Science Terms. This Guide to developing taxonomies for effective data management provides an overview of the taxonomy concept.
File Naming Convention
Be consistent and descriptive with Data Best Practices: Name Files to ensure data discovery later on. Set up a clear Directory Structure that includes information like project title, date and unique identifiers. Versioning file names to end with YYYYMMDD or YYMMDD will help sort them into chronological order.
Data cleaning is almost always an extremely painful process and should be avoided at all costs. Exercise quality control from the start to ensure that you have Tidy Data - remember, prevention is always easier than cure!
Just as with any other scholarly resource, data also require citations to acknowledge the original author/producer and to help other researchers find them. A dataset citation includes the same components as other citations. DataCite recommends the following examples:
Creator (Publication Year). Title. Publisher. Identifier
Digital Curation Centre's (DDC) How to Cite Datasets and Link to Publications guide is very helpful, particularly for those working on data-led research.