Exploring the Data Management and Curation (DMC) Practices of Scientists in Research Labs within a Research University

Beginning January 18, 2011, proposals submitted to The National Science Foundation (NSF) must include a supplementary Data Management Plan (DMP) of no more than two pages. The NSF DMP requirement has significantly redefined the role of scientists, researchers, and practitioners in the United States of America (USA) by presenting the opportunity to engage in effective data management planning and practices for current and future use. In order for data to be useful to research, science, scholarship, and education, data must be identified, described, shared, discovered, extended, stored, managed, and consulted over its lifecycle (Bush, 1945; Lord & Macdonald, 2003; Hunter, 2005; JISC, 2006; UIUC GLIS, 2006/2010; NSF, 2011). Within the scope of this research study data management planning is defined as the planning of policies for the management of data types, formats, metadata, standards, integrity, privacy, protection, confidentiality, security, intellectual property rights, dissemination, reuse/re-distribution, derivatives, archives, preservation, and access (NSF, 2011). The management of data includes analog [physical], digitized [made electronic] & born digital [no physical surrogate] data. NSF's data management plan requirements have incentivized the development of a multitude of programs, projects, and initiatives aimed at promoting and providing data management planning knowledge, skills, and abilities for NSF data management plan requirements compliancy. Without the specification, clarification, & definition of key concepts; assessment of current data management practices, experiences, & methods; interrelationships of key concepts; and utilization of multiple methodological approaches, data management will be problematic, fragmented, and ineffective. The accomplishment of effective data management is contingent on funders, stakeholders, and users' investment and support in Infrastructure, Cultural Change, Economic Sustainability, Data Management Guidelines, and Ethics and Internet Protocol (Blatecky, 2012, p. 5) across organizations, institutions, & domains. One of the goals of the researcher "is to select a theory or combine [multiple theoretical perspectives] so they resonate with the guiding research questions, data-collection methods, analysis procedures, and presentation of findings" (Bodner & Orgill, 2007, p. 115) within a conceptual framework that "places its assumptions in view for practitioners" (Crotty, 1998). The introduction of the Conceptual Framework for Analyzing Methodological Suppositions (Burrell & Morgan, 1979: Morgan & Smircich, 1980: Morgan, 1983, Solem, 1993) to gather competing approaches and paradigmatic assumptions for multiple paradigm integration and crossing via interplay (Schultz & Hatch, 1996) is an attempt by the researcher to build theory from multiple paradigms through Metatriangulation (Lewis & Grimes, 1999), a theory-building approach. Within this study, the Data Asset Framework (DAF) is framed as a sequential mixed methods explanatory research design (Creswell and Plano Clark, 2011) and applies social science research to facilitate scientific inquiry. The purpose of this study is to investigate the data management and curation practices of scientists at several research laboratories at the Florida State University and select scientists associated with the National Science Foundation (NSF) EarthCube project. The goal of this research is not to provide extensive literature review to prove the need for effective data management practices but to provide empirical evidence to support current data management and curation practices. Within the scope of this dissertation, data management and curation practices will be generally defined as the effective aggregation, organization, representation, dissemination, and preservation of data. Data refers to analog and digital objects, databases, data sets, and research data. For purposes of discussions in this study, data is both singular and plural. Data management and curation practices include four key concepts: (1) data management planning, (2) data curation, (3) digital curation, and (4) digital preservation. Literature review suggests that these key concepts when applied with relevant standards, best practices, and guidelines can assist scientists in ensuring the integrity, accessibility, and stewardship of research data throughout its lifecycle. The combination of the conceptual framework for analyzing methodological suppositions (Burrell & Morgan, 1979; Morgan & Smircich, 1980; Morgan, 1983; Solem, 1993), Metatriangulation (Lewis & Grimes, 1999), and the Data Asset Framework (DAF) (JISC, 2009) contributes to the development of an interdisciplinary conceptual framework model concept capable of addressing the data management and curation issues common across disciplines. For the purpose of this dissertation "research data are being understood as both primary input into research and first order results of that research " (ESRC, 2010, p. 2).