GEOINFORMATICS DOES THE DATA LIFECYCLE

Geoinformatics, like other data intensive sciences, requires data that are discoverable and usable by a variety of users for a multitude of purposes. Therefore, the data must be collected, documented, organized, managed, and curated with sharing the data in mind. The practices of data managers, framed along the sequential steps of the data lifecycle, provide some insight into how geoinformatics data are preserved and made available, but also the skills required to perform the tasks. This study’s purpose was to explore the job practices of geoinformatics data managers as they relate to the sequential steps of the data lifecycle. Although there are many data lifecycle models, this study used the UK Digital Curation Centre (DCC) Curation Lifecycle model to frame the job analyses because of the clear delineation of sequential actions, acknowledgement of conceptualization as a step outside of curation activities, and its extensive use in digital curation.

This study adopted a qualitative, semi-structured interview approach derived from the (Developing a Curriculum) DACUM approach. A DACUM creates a list of knowledge, skills, and abilities, operationalized job descriptions, and eventually learning outcomes for use in education and training for jobs. Recruitment was conducted at a national conference on science data with the incentive of helping create a core of expertise for geoinformatics data management.

Results indicate the most mentioned skills do not relate directly to sequential actions in the data lifecycle, but instead to communication and project management activities beyond the data. Data management requires communication with scientists working in many different places, modes, and in many meetings. Communication skills, then appear throughout the lifecycle, and the “collaborative nature of data management work” entails efficient and effective communication with producers and users of data. Job analyses indicate data managers require domain knowledge of science and management skills beyond the data to do their jobs. Still, several actions related to the data lifecycle, such as data discovery, do require an understanding of the data, technology, and information infrastructures that could inform curricula and training in other areas of geoinformatics.