Manage Your Data: Information Management Strategies for DH Practitioners

Digital humanities (DH) research benefits from the integration of social and scientific research methods and tools. Multidisciplinary methodologies regularly facilitate the production and use of increasingly large datasets and complex technical infrastructures. The growing potential for multidisciplinary research and innovation has brought more attention to a broader interest in traditional humanities and humanities data. (ECAR;2017). The problem: DH research relies heavily on data collected and analysed for increasingly diverse reasons. While multidisciplinary methods and practices offer immense opportunities for innovation, there is, simultaneously, a higher likelihood that researchers will struggle to communicate strategies for curating and preserving their data. What can be done? Research funding agencies are acknowledging the value and importance of research data management strategies, but have done little to reconcile the multi or interdisciplinary nature of DH research. Acknowledging and tackling the challenge of data management and digital curation is not, thankfully, an unknown issue. For at least as long as DH has existed as an area of research and work, information professionals (e.g. librarians, archivists and research data managers) have investigated and developed strategies for data management. The Academic Libraries and Research Data Services outlines that information professionals can aid DH researchers in the areas of data discovery, mining, analysis, metadata creation, selection, curation, and research partnerships. These partnerships can result in the creation of research data services departments and DH labs in academic libraries and facilitate data management services to researchers who need support with the creation and curation of their research data. However, despite emerging best practices in the information professions, researchers are not always aware of them. To address this potential roadblock, our chapter presents methods for negotiating interests, needs and understandings of research data management