USING A MIXED-METHODS RESEARCH APPROACH VIA AN ADAPTED DATA ASSET FRAMEWORK (DAF) METHODOLOGY Exploring Scientists’ Research Data Management Practices and Perspectives Plato L. Smith II, FSU CCI– School of Information, Florida’s iSchool University of Maryland’s iSchool Lecture February 20, 2014
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Exploring Scientists’ Research Data Management Practices and Perspectives
This presentation was presented to the University of Maryland ISchool via Skype as part of the campus interview for an assistant professor position. The presentation include some results, conclusions, and recommendations for funding stemming from my dissertation research.
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USING A MIXED-METHODS RESEARCH APPROACH VIA AN ADAPTED DATA ASSET FRAMEWORK (DAF) METHODOLOGY
Exploring Scientists’ Research Data Management Practices and Perspectives
Plato L. Smith II, FSU CCI– School of Information, Florida’s iSchoolUniversity of Maryland’s iSchool LectureFebruary 20, 2014
Plato L. Smith II
1. Background & Significance
2. Research Purpose
3. Research Questions
4. Research Opportunity
5. Research Design & Methodology
6. Target Population & Purposive Sampling
7. Findings
8. Implications
9. Conclusions
Table of Contents
2/20/2014
Plato L. Smith II 2/20/2014
A. Data management and curation (DMC) is a research data management (RDM) concept that includes (1) data management planning, (2) data curation, (3) digital curation, and (4) digital preservation key concepts. These concepts focus on the lifecycle management of data.
B. These key RDM concepts have sometimes been expressed as competing models and frameworks in literature and in practice thus leaving theory in an under-developed state.
C. This project seeks to combine two data curation models into a DMC Framework while adapting a conceptual framework to explore research data management within and across multiple scientific disciplines.
Background & Significance
Plato L. Smith II 2/20/2014
Background & Significance – Data Management & Curation (DMC) Framework
• Metadata• Archived data• Level 2 Curation
• Trusted repository
• Technical & strategic storage actions
• Level 3 Curation
• Data creation• Representation over lifecycle
• Level 1 Curation
• DMP (i.e. NSF)• RDM Policies• DCC Curation Lifecycle Model
Data Management
Planning
Data Curation
Digital Curation
Digital Preservation
Plato L. Smith II 2/20/2014
The purpose of this research project is to investigate scientists’ current DMC practices
across multiple disciplines and explore opportunities for improving data management
activities where applicable.
Research Purpose
Plato L. Smith II 2/20/2014
① What types of data do scientists create?
② How do scientists manage, store, and preserve research data?
③ What are some of the types of theories, practices, or methods disciplines use in research data management?
④ How can multiple disciplines perspectives on data management and curation (DMC) practices within and across disciplinary domains contribute to building underdeveloped DMC theory?
Research Questions
Plato L. Smith II 2/20/2014
What does this research want to discover?Investigate how scientists manage, store, & preserve research
data
Why are the research questions are important?Address funding agencies data management requirements
Educate, articulate, and promote scientists’ need for improved DMC
How is this research going to answer the research questions?Discover, map, and correlate data management synergies across
disciplines
Introduce/share data management concepts & models across disciplines
The DCC Curation Lifecycle Model list some major stages in data management that encompasses the four key concepts of data management and curation (DMC).
Findings – DAF Interviews (Q7)
The DCC Curation Lifecycle Model (DCC, 2007/2014)
Plato L. Smith II 2/20/2014
1. Level 1 Curation – traditional academic information flow
2. Level 2 Curation – information flow with data archiving
3. Level 3 curation – information flow with data curation (Lord & Macdonald, 2003)
Findings – DAF Interviews (Q8)
Level 3 Curaton – information flow with data curation – (Lord & Macdonald, 2003, p. 45 )
Plato L. Smith II 2/20/2014
“The framework reflects the basic philosophical presuppositions or metatheoretical assumptions underlying scientific inquiry” Solem, 1993, p. 595).
Findings – DAF Interviews (Q9)
Burrell & Morgan (1979); Morgan & Smircich (1980); Morgan (1983); Solem (1993, p. 595); Smith II, (2013)
Plato L. Smith II 2/20/2014
① “Mostly geological based: all measurements are of a physical reality.” – P1
② “Most of the data in my domain are spatio-temporally organized.” – P2
③ “It analyzes reality by making observations…” – P3
④ “In meteorology, we seek patterns in a chaotic system. Through organization and classification, patterns emerge that subsequently support understanding of underlying physical relationships…” – P4
⑤ “Physics is the study of reality so a supposition that there is an objective reality is the core of the discipline.” – P5
⑥ “We use experimental methods to reveal the true reality.” – P6
Findings – DAF Interviews (How does your discipline look at and understand reality?)
Plato L. Smith II 2/20/2014
① “I don’t participate in this.” – P1
② “My discipline uses numerical models, field sampling and controlled experiments to learn about reality.” – P2
③ “I guess from the sensors it employs.” – P3
④ “Primarily through observation and modeling. Meteorology is based on the physical observation of our world. Through observation patterns emerge that support the development of conceptual models for atmospheric systems…” – P4
⑤ “Experiments and observations.” – P5
⑥ “Via carefully controlled experiment.” – P6
Findings – DAF Interviews (How does your discipline learn about reality?)
Plato L. Smith II 2/20/2014D
isco
very Integration
Application
Teaching Research
Practical
Societal
Implications –Boyer’s Model of Scholarship (Nibert, 2008)
Plato L. Smith II 2/20/2014
• Facilitate the use & interpretation of research data management practices across disciplines
• Broaden literature review contribution & application
• Collaborate on core or RDM special topics course design & delivery
INTEGRATION
• Improve data description, representation, & publication
• Allow new research based on accessible & discoverable data