Curating Research Data - Cyber security perspective from a nascent Brain Machine Interface Laboratory

Abstract

Researchers across disciplines are in dissonance between data curation and open access versus data security. To accelerate new discoveries, preserving scientific artifacts such as research protocols and raw data is a rational expectation. Brain-machine interface research is particularly sensitive to data curation as data sets are large, archived in disparate formats, and bereft of useful metadata associated with mode of capture, number of channels, or even activities performed by participants during capture. Moreover, curating such data in a secure fashion is little discussed and requires expertise not shared across all fields. Time and labor costs for research data curation can be exorbitant without the addition concern of security. Such factors amplify further when a laboratory is young and resident at a small school with limited resources. Thus, this paper describes the development of a secure research data curation model within a nascent brain-machine interface laboratory at a small, private four-year institution in the mid-Atlantic region of the United States. We utilized a case study design to analyze existing data curation and cybersecurity literature for best practices. Using five unique search strings, we identified eight thematic best practices across three cyber security dimensions (integrity, availability, and access control). Recommendations and future work are discussed in response to the execution of the case research and the findings.