Slides by Dr. Mansur Darlington, University of Bath for the Orbital project, University of Lincoln. Part II.

1. Managing Engineering Research Data
at the University of Lincoln
Part 2 of 2
Mansur Darlington
20 January 2012
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2. THE NATURE OF ENGINEERING RESEARCH DATA
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3. ERIM
Engineering Research Information
Management
JISC MRD Programme Phase 1
http://www.ukoln.ac.uk/projects/erim/
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4. ERIM Project Overview
• Primarily associated with the engineering
research domain.
• To better understand the research data that
are collected, generated and used in
engineering research activities.
• To better understand the context in which the
data are collected, generated and used.
• To inform the way that the data can be
managed so that they are more easily used
or re-used.
• To increase their value to the community.
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5. The Aims
• Achieving an understanding of the diversity
and character of research data.
• Devising a means by which research data
can be classified in respect of research data
management.
• Developing models of the research data life-
cycle which characterize the information flow
in the research process and identify critical
points in the management process.
• Providing exemplars of best-practice
research data management strategies.
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6. The Objectives
• Identify opportunities for and the benefits of
research data re-use and re-purposing.
• Identify the contextual, technical, legal and
social barriers to the re-use and repurposing
of research data.
• Establish whether and what data might be
used in a raw form, what data would require
reprocessing and how this might be achieved.
• Understand what contextual information is
required for research data to be understood
for the purpose of re-use.
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7. Theoretical Elements to the Research
1. Understanding & Defining the ‘Space’
2. Terminology
https://wiki.bath.ac.uk/display/ERIMterminology/ERIM+Terminology+V4
3. Identifying the Objects in the space
4. Understanding the Relationships between
Objects
5. Modelling the Relationships
6. Understanding the Outcomes
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8. Understanding & Defining the ‘Space’
for Current Research
known
Making data available and fit for the current
CURRENT-ACTIVITY MANAGEMENT SPACE
PURPOSING:
Management for A by A
purpose
FUTURE-ACTIVITY MANAGEMENT SPACE
Management for X by Y,
where Y can be X Management for X by Y
for
supporting data RE-USE: RE-PURPOSING:
Raw Future
Research Managing data such that it will be available Making data available and Research
Data for a future unknown purpose fit for a future known purpose
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9. ERIM Project Terminology for
Research Data Mangement
https://wiki.bath.ac.uk/display/ERIMterminolo
gy/ERIM+Terminology+V4
(source definitions for terms found in slides 10-14)
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10. 2. The Terminology I
Preparation Activities:
• Data Purposing Making research data available and fit for the
current research activity.
• Data Re-purposing Making existing research data available
and fit for a future known research activity
• Supporting Data Re-use: Managing existing research data
such that it will be available for a future unknown research activity
Use Activities
• Data Use Using research data for the current research
purpose/activity to infer new knowledge about the research subject.
• Data Re-use Using research data for a research purpose other
than that for which it was intended.
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11. 2. The Terminology II
What do we Mean by DATA?
• Data. Reinterpretable representations of information
in a formalized manner suitable for communication,
interpretation or processing.
• Information. Any type of knowledge that can be
exchanged. In an exchange, it is represented by
data.
***********************
• Data Object. Either a physical object or a digital
object containing data.
• Data Record. A data object created, received and
maintained as evidence of an activity.
• Data Case. The set of data records associated with
some discrete research activity (project, task,
experiment, etc.).
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12. 3. Identifying the Objects
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13. 4. Understanding the Relationships
Data Preparation in
Data Development Purposing Supporting Re-use Re-
purposing
Association
Aggregation
Annotation
Augmentation
Collection
Collation
Generation
Derivation
Refinement
Migration
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14. 6. Understanding the Outcomes
Management and Development Side-effects
• Information Loss
• Information Gain
• Function Loss
• State Loss
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15. 5. Modelling the Objects’ Relationships
RESEARCH TIME LINE
Data level
Gather RDR1 Refine RDR1' Refine RDR1''
Associate
Derive RDR4
Gather RDR2 Derive RDR3
CDR1 Aggregate RDR6
RDR5
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16. Capturing the Context through RAID-modelling
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17. Research Data Scoping Survey
• 12 Research Cases
• 46 Data Assets
• 12 questions for each
• Researchers from:
Bath, Lancaster, Leeds,
Salford, Strathclyde,
Heriot-Watt
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18. Scoping Survey Targets
1. Airframe Stress Data Reuse
2. Snow Mobile Design Activity Observation
3. Aerospace Cost Forecasting
4. Large-Scale Metrology Shared Resources
5. Form-fill-feed Packaging Modelling
6. CNC Machine Measurement
7. Cryogenic Machining
8. Information Management Tool
9. Knowledge Enhanced Notes
10. Service Design Research
11. Design Activity & Knowledge Capture Research
12. Understanding the Learning Organization
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19. Selecting 5 Case Studies using Binary Classification
Research Generated Data vs Pre-existing Data
Homogeneous Media vs Heterogeneous Media
1. Costing
Descriptive vs Prescriptive
2. Company
case studies
Real vs Simulated 3. Programming
4. Interview analysis 5. Metrology
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20. ERIM Research Summary
• Designed and carried out scoping survey.
• Theory development and revision
• Designed and carried out audit on 5 cases
• RAID Modelled the case research activities.
• Complete analysis and characterization of
audit cases
• Identified barriers to data re-use and
strategies to mitigate.
• Established critical points in Information Flow.
• Developed research data management plan
process ‘cascade’.
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21. Key ERIM Research Findings I
• Great diversity of data type and quality.
• Complex and chaotic nature of data
development.
• Outputs not linked to data.
• Supporting documents not situated with the data
files.
• Little use of metadata to support future use.
• Immature understanding of benefits of sharing
and thus need for management.
• Limited understanding of the barriers to or
opportunities for information sharing and re-use.
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22. Key ERIM Research Findings II
Poor framework for:
• pre-project considerations of data management.
• data management during the research.
• during-project data management for post-project re-
use.
Poor knowledge of context in which data were
generated:
• engineering research data are very diverse.
• large number of diverse research data records.
• relations between data records complex.
Knowing the context is vital for understanding data.
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23. What Needs Managing?
• Research Data:
– Data Data Objects Information.
• Their life cycle processes:
– Collection
– Generation
– Development
– Organization
– Disposal, etc.
• The process of data management itself.
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24. ERDM PLANNING & EXECUTION
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25. What is the Purpose of RDM Planning?
• Reduction in duplicated work.
• Inspiration for new/continuation research &
funding.
• Greater transparency of research.
• Improved basis for validation.
• Obviating the need for re-collection and
generation.
• Providing basis for reliable data citation.
• Increasing scholarly output.
Relies upon RE-USING DATA
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26. Amenability Criteria & ‘Re-usefulness’
‘To manage research data such that they are
highly amenable to re-use.’
‘What is the nature of these data that makes them more
or less amenable to re-use?’
• Findability
• Readability
• Comprehensibility
• Interpretability
• Admissibility
• Desirability
Data ‘RE-USEFULNESS’
(some data will remain forever re-useless)
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27. DATA MANAGEMENT PLANNING TASKS
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28. DMP Task Dimensions and Topics
• Access, Data Sharing and Re-use.
• Data Types, Format, Standards and Capture
Methods.
• Ethical and Privacy Concerns.
• Resourcing.
• Short-term Storage and Data Management.
• Deposit and Long-term preservation.
• DMP Adherence and Review.
DCC DMP Checklist
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29. Data Management is not just about
STORAGE!
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30. Guidance Required to Support Tasks
• Data management planning.
• Data management execution.
• Bid submission.
• Project planning.
• During-project management whilst doing the
research.
• Collaboration with colleagues, industry and
others.
• Supporting data use, re-use, re-purposing.
• Preparation for long-term preservation.
• End-of-life concerns.
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31. The Two Stages of RDM
‘Managing research data such that they are
highly amenable to re-use.’
• Good DM planning provides the
potential to increase data re-usefulness.
• The execution of good DMPs promotes
data re-usefulness.
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32. Key Building Blocks for Practical Data Management
1. DMP Guidance, Documentation &
Procedures.
2. Storage and security
3. Data Organization
4. Data Documentation
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33. 1. DMP Guidance, Documentation & Procedures
Principles for Engineering Research Thematic Analysis of Data Management
Data Management. Plan Tools and Exemplars.
(erim6rep101028mjd) (erim6rep100701ab)
Engineering Research Data Management
1 Plan Requirement Specification Being a specification for 2
(erim6rep100901ab)
REDm-MED
The Draft IdMRC Projects Data
Model DMP for
Being an implementation of 1 2 Management Plan
Mech. Eng. Depts.
(erim6rep101015mjd)
RAIDmap Use Cases RAID Associative Tool Specification Prototype
(erim6rep101125mjd) (erim6rep101109mjd) RAIDmap Tool
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34. Security: Document/Data Access Levels
Level 1 – In the public domain with an unrestricted
readership and can distributed at will.
Level 2 – Viewable by any individual who has
password access to the main project web site.
Level 3 – Viewable by any individual who has
password access to the main project file store and is
a project-affiliated member of a university research
team.
Level 4 – For sensitive documents. Must carry a
distribution list identifying for whom it is intended and
be disseminated through a nominated secure
passwork-protected portal. Distribution controlled by
PI and Collaborator liaison officer.
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35. 3. Data organization
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39. Work package Task Author initials
number number
Project
name
Revision
number
kim12rep05pjw01.doc
Document Document File type
type rank number
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40. 3 Data Organization: 3 steps to document happiness
1. Fill in the document properties (use
metadata!).
2. Make them visible by browsing or by search
– in particular rehabitate the record TITLE.
3. Use a file NAME coding convention that
captures human-readable context
information.
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41. 4. Data Documentation
• RAID-mapping: provision of context.
• Project Document Records:
– Existence Location of key Project Documents
• Project Plan
• Project data management plan
• Project document manifest
– Location of Data Records
– Some description of relations between data
records
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