engineering research information management the erim project
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Managing Data in the Innovative Design & Manufacturing Research Centre at the University of Bath. Engineering Research Information Management The ERIM Project. Alex Ball, Steve Culley, Mansur Darlington , Michael Day, Tom Howard, Liz Lyon & Chris McMahon. - PowerPoint PPT PresentationTRANSCRIPT
Engineering Research Information Management The ERIM Project
Alex Ball, Steve Culley, Mansur Darlington, Michael Day, Tom Howard, Liz Lyon & Chris McMahon
Managing Datain the
Innovative Design & Manufacturing Research Centreat the University of Bath
JISC MRD Workshop 29th March 20112
Engineering Research Information Management
• 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, re-used and ‘re-purposed’.
• To increase their value to the research community.
JISC MRD Workshop 29th March 20113
ERIM Research Tasks
• Characterize Engineering Research Data• Characterize how it is used and managed
now. • Propose better data management methods
and approaches to deal with it for:Data use, re-use and re-purposing
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CHALLENGES for Engineering Research Information
Management
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IdMRC Research Themes
research programme divided into four complementary themes:
IdMRCTHEME Constraint-Based Design & OptimisationIdMRCTHEME Constraint-Based Design & Optimisation
IdMRCTHEME Advanced Machining Processes & SystemsIdMRCTHEME Advanced Machining Processes & Systems
IdMRCTHEME Metrology & Assembly Systems & Technologies IdMRCTHEME Metrology & Assembly Systems & Technologies
IdMRCTHEME Design Information & KnowledgeIdMRCTHEME Design Information & Knowledge
10 year research lifespan
16 Academics
65 Researchers
60 Research Students
700 Journal & Conference Papers
JISC MRD Workshop 29th March 20116
Engineering Research Project Diversity
Aerospace CostForecasting
Large-Scale Metrology Shared
Resources
Form-fill-feedPackagingModelling
CNC Machine Measurement
Snow MobileDesign
Observation
CryogenicMachining
Information Management Tool
Knowledge-Enhanced Notes
ServiceDesign Research
Design Activity& Knowledge
Capture
Understandingthe LearningOrganization
Airframe StressData Reuse
ERIM
JISC MRD Workshop 29th March 20117
Advanced Machining
Processes & Systems
CRYogenic MAchiNing (CRYMAN)
Machining through cryogenic cooling of soft materials
• Understand how to freeze soft plastics (polymers) so they are hard.
• Understand the machining requirements of freeze-hardened polymers.
ILLUSTRATIVE USE
CAD/CAM for a Customised
Sole
FreezeBillet
Material
CryogenicCooled Fixture
PersonalisedSoles
MachineSoles
JISC MRD Workshop 29th March 20118
Advanced Machining
Processes & Systems
CRYogenic MAchiNing (CRYMAN)
Research Data Store:• 3 Folder Levels• 27 folders, 498 files in L1• 79 folders, 2632 files in L2• 19 folder, 144 files in L3• 126 Folders • 3288 Files • 49 File types; of which 22 unknown• Physical entities
Highly Diverse:
content
representations
file formats
complex relations
JISC MRD Workshop 29th March 20119
Current Data Management Policy
• Compliance: Make sure its managed according to UK Law and research funder requirements.
• Security: All research data relating to live projects must be managed securely.
• Responsible management: Research data management and archiving is the responsibility of the principal investigator (PI) for each project and must be considered from the outset of new projects.
• Archiving: All digital primary research data should be deposited in the University’s research data archive as soon as practical once projects are completed
• Retention: Suitable retention periods must be established and implemented for all archived data.
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Solutions for Engineering Research Information
Management
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Key Research Findings
1. Poor framework for: – data management during the research.– during-project data management for post-project re-use.
2. 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.
3. Knowing the context is vital for understanding data.
JISC MRD Workshop 29th March 201112
Multi-level ERDMP Best-Practice Guidance
Principles for Engineering Research Data Management.
(erim6rep101028mjd)
Engineering Research Data Management Plan Requirement Specification
(erim6rep100901ab)
The Draft IdMRC Projects Data Management Plan
(erim6rep101015mjd)
RAID Associative Tool Requirements Specification
(erim6rep101111mjd11)
1
2
Being a specification for 2
Being an implementation of 1
1. Improving the DMP
Framework
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Engineering Research Data Management Plan Requirement Specification
1. PROJECT RECORD MANIFEST• Describe the method by which Data Records and the relationships between
them will be/were recorded in a project record manifest. Present this manifest (once it exists), or otherwise indicate how it may be accessed.
2. DATA GENERATION AND MANIPULATION• Give a detailed account of how the data will be/were generated and
manipulated, including the methods, technology, conventions, coding schemes, etc. that will be/were used.
3. DATA ORGANIZATION• Describe how the data will be/have been organized.
4. DATA STRUCTURES AND FORMATS• Specify the information, tools or resources that would be needed to manipulate
or render the Data Records, along with any special instructions. Provide an explanation of why a particular format has been selected for use.
5. DATA SEMANTICS • Provide any additional information that would be needed to understand the
Data Records, once rendered. Provide justification for the conventions used.
JISC MRD Workshop 29th March 201114
Multi-level ERDMP Best-Practice Guidance
Principles for Engineering Research Data Management.
(erim6rep101028mjd)
Engineering Research Data Management Plan Requirement Specification
(erim6rep100901ab)
The Draft IdMRC Projects Data Management Plan
(erim6rep101015mjd)
RAID Associative Tool Requirements Specification
(erim6rep101111mjd11)
1
2
Being a specification for 2
Being an implementation of 1
2. & 3.Recording
the Context
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Desirable Features in an Associative Tool
• Automatic association of data records.• Allow manual associations and annotations of data
assets.• Produce a graphical representation of the
associations of selected data records.• Log date and time stamp of association.• Enable the re-user to find the source (copy side) of
associated data.• Enable user to see the target (paste side) of
associated data.• Log modifications and manipulations to data (track
changes).• Automatically associate the data within the records.
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Capturing the Context (RAID)
CRYMAN PROJECT
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And Finally: ERIM Implications for the future
• Change in Culture.• Culture change reflected in funding and
resources made available:– Provision of institutional and departmental
infrastructure.– Appropriate methods and good-practice
guidelines.– Appropriate and non-invasive tools– Personnel and training
The CRYMAN Project
Data Management for the IdMRC
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The 12 Principles of ERDM
1. ERDM should be informed by the more general Principles of Engineering Information Management.
2. ERDM should be consistent with the approach outlined in the DCC Charter and Statement of Principles.
3. RDM should reflect the need for and provide guidance to support research data reproducibility.
4. The notions of re-usability and re-purposing should be supported by the use of generic or standard data generation and manipulation tools.
5. All DMPs should contains aspects which explicitly set out to help 'supporting data re-use' and re-purposing.
6. The data objects associated with a research activity should be considered to be and managed as a mutually explanatory set.
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The Twelve Principles of ERDM
7. The data objects associated with a research activity should be associated explicitly by the use of contextual information.
8. The specification of the contextualizing methods should be made available, to aid their interpretation.
9. Any agreement on the confidentiality or otherwise of research data should be as permissive as can be negotiated.
10. Due consideration should be given in research project planning to the role of data management and its cost.
11. Requirements specified in data management plans for the activities of 'supporting re-use' and 're-purposing' should hinder use activities as little as possible.
12. Data management tools should be simple, engaging and easy to access.
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Engineering Research Data Management Plan Requirement Specification
INFRASTRUCTURE AND IMPLEMENTATION ISSUES
• Relating the DMP to other documentation
• Relating other documentation to the DMP
• Understanding the DMP • Rôles and responsibilities
• Review of the DMP• Revision of the DMP
• Budget• Storage, back-up and
security• Receiving repository
DMP CONTENTS
• Data re-use
• Relating new data to existing data
• Future use of the data• Project record manifest• Data generation and
manipulation• Data organization• Data structures and formats• Data semantics • Data quality
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Data Management 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
Data 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.
Activities
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Engineering Research Data Record Types
ASSOCIATIVE DATA RECORD
EXPERIMENTAL APPARATUS
DATA RECORD
RESEARCH OBJECT DATA RECORD
Data Asset
Digital Object(aka digital data
asset)
Physical Object(aka physical data
asset)
Research Object
Description of Object
Pre-existingResearch Generated
First Generation Nth Generation
Pre-existingResearch Generated Pre-existing
Research Generated
Contextualizing Object
Intentional Contextualizing
Data
RESEARCH DATA RECORD
CONTEXT DATA RECORD
Unintentional Contextualizing
Data
Experimental Apparatus Data
Intentional Contextualizing
Data
2.
5.
1.
4. 3.
6.