Research data management (RDM) is a term that describes the organization, storage, preservation, and sharing of data collected and used in a research project. It involves the daily management of research data throughout the life of a research project, such as using consistent file naming conventions. Examples of data management plans (DMPs) can be found in universities, such as the University of Minnesota. These plans can be concise or detailed, depending on the type of data used (secondary or primary).
Research data can include video, sound, or text data, as long as it is used for systematic analysis. For example, a collection of video interviews used to collect and identify facial gestures and expressions in a study of emotional responses to stimuli would be considered research data. Making this data accessible to everyone in the group, even those who are not on the team but who are in the same discipline, can open up enormous opportunities to advance their own research. The Stata logging function can record all activities and store them in the relevant designated folders.
Qualitative data is subjective and exists only in relation to the observer (McLeod, 201). The Stanford Digital Repository (SDR) provides digital preservation, hosting and access services that allow researchers to preserve, manage and share research data in a secure environment for citation, access and long-term reuse. In addition, DRUM provides long-term preservation of digital data files for at least 10 years using services such as migration (limited format types), secure backups, bit-level checksums, and maintains persistent DOIs for data sets. The DMPTool includes data management plan templates along with a wealth of information and assistance to guide you through the process of creating a ready-to-use DMP for your specific research project and funding agency.
Some demographics may not be shareable on an individual level and would therefore only be provided in aggregate form. In accordance with DRUM policies, unidentified data will be accompanied by appropriate documentation, metadata and code to facilitate reuse and provide the potential for interoperability with similar data sets. It is important to remember that you can generate data at any point in your research but if you don't document it properly it will become useless. For example, observation data should be recorded immediately to avoid data loss while reference data is not as time-sensitive.
Research data management describes a way to organize and store the data that a research project has accumulated in the most efficient way possible. The willingness of researchers to manage and share their data has been evolving under increasing pressure from government mandates of the National Institutes of Health and the data exchange policies of major publishers that now require researchers to share their data and the processes they took to collect the data if they want to continue receiving funding or have their articles published. Librarians have begun to provide a range of services in this area and are now teaching data management to researchers, working with individual researchers to improve their data management practices, create thematic data management guides, and help support agencies' data requirements funding and publishers. Some operating systems also support embedding metadata in this way, such as Microsoft Document Properties.