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Research team

Learn about the research on data management and reuse at the IdMRC, encompassing ERIM project overview, case studies, and key themes in design and manufacturing.

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Research team

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  1. Case Study 1: The Innovative design and Manufacturing Research Centre, IdMRCDCC Data Management Roadshow, Bath, 2-4 November 2010Chris McMahon, Department of Mechanical EngineeringUniversity of Bath Research team Alex Ball, Steve Culley, Mansur Darlington, Michael Day, Tom Howard, Liz Lyon & Chris McMahon

  2. Overview • The IdMRC • The ERIM Project • Some definitions • Reuse and repurposing • Scoping study • Five case studies – RAID • Barriers to sharing and effective data management

  3. IMRC Themes Design Manufacture THEME 2 Design Information & Knowledge THEME 1 Constraint-Based Design & Optimisation THEME 2 Advanced Machining Processes & Systems THEME 3 Verify Metrology Assembly Systems & Technologies THEME 4

  4. Constraint Modelling

  5. KIM Technologies - Summary flights Destination ID Start LBA CHG PHX AAL CHG LBA PHX AAL CHG LBA LBA CHG 1 2 3 4 5 6 planes ID Serial No 1 2 3 4 5 6 LBA123 CHG234 PHX124 AAL4667 CHG567 LBA654 Date This allows a team to identify all the decisions which were taken in relation to a particular time in the design process. As the team navigates to these various design instances, they are also able to review the status of the design at that point in time. This can be done by clicking on the relevant data object, allowing access to the latest specification, sketches and so on (Fig. B). In this • This allows a team to id • entity all the decisions which • ere taken in relation to a • particular time in the design • process. As the team Flight 100Monitoring systemdetected problem DefineCorrectionactions Root-CauseAnalysis RetrieveDesign Doc. ReviewServ.Doc. DesignDoc. Service Doc. ManufacturingFeatures TopicMaps LightweightRepresentation Semi-auto CAD records DesignRationale Multi-MediaMinutes

  6. 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 research community through better data management

  7. Institutional Context • Research carried out by a team from: • UKOLN (United Kingdom Office for Library and Information Networking) http://www.ukoln.ac.uk/ • DIAK Group, Innovative Design & Manufacturing Research Centre, University of Bath http://www.bath.ac.uk/idmrc/ • Working with data sets principally from: • IdMRC’s data assets (engineering disciplines) • KIM Grand Challenge Project http://www.kimproject.org

  8. The Research Challenges Currently little or no support for the re-use of data: methods, tools, practice guides Ignorance of: • Nature and diversity of engineering research data. • Rôles that data play in research. • Attributes of data and attributes important for data management. • Potential for data re-use. • Barriers to data re-use.

  9. Some definitions

  10. The ‘O’ Word! • Entities • Data itself • Information objects or ‘containers’ (Data Records) • Attributes • Processes and Activities: • Data micro-management/development activities • Data (macro-) management activities • Relationships • Horizontal • Vertical • Normative lexicon

  11. Things: Data Records Research Data Record: A record containing research data, i.e. data that is descriptive of the research object, or is the object itself. Context Data Record: A record containing data that supports the research activity but does not describe the research object nor is the research object itself.

  12. Activities 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 knownresearch 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.

  13. Management-centric view of Research Data PURPOSE: Gather, Collate, Associate, Annotate, Migrate, Refine, Augment, Derive, etc for the currentknown purpose for Current Research CURRENT ACTIVITY MANAGEMENT SPACE Management for A by A FUTURE ACTIVITY MANAGEMENT SPACE Management for X by Y, where Y can be X Management for X by Y ( for Future Research Supporting Data RE-USE: RE-PURPOSE: ( Raw Research Data Gather, Collate, Associate, Annotate, etc for a future unknown purpose Gather, Collate, Associate, Annotate, Augment, Migrate, Refine, etc. fit for a futureknown purpose

  14. Data (micro-)Management/Development Make explicit relationships Combine data from different sources Add information at anchor point

  15. Modelling & Analysing the Research Process Purposing: making research data available and fit for the current research purpose/activity. It is to do with data use and the research activity Research Time Line RDR1 RDR1' RDR1'' Refine Refine Gather Associate RDR4 Derive RDR2 RDR3 Derive Gather CDR1 RDR6 Aggregate RDR5

  16. Modelling & Analysing the Research Process Purposing: making research data available and fit for the current research purpose/activity. It is to do with data use and the research activity Research Time Line Data level RDR1' RDR1'' RDR1 Refine Refine Gather Associate RDR4 Derive RDR2 RDR3 Derive Gather CDR1 RDR6 Aggregate RDR5

  17. Modelling & Analysing the Research Process Purposing: making research data available and fit for the current research purpose/activity. It is to do with data use and the research activity Research Time Line RDR1' RDR1'' RDR1 Refine Refine Gather Associate RDR4 Derive RDR2 RDR3 Derive Gather Data Record level CDR1 RDR6 Aggregate RDR5

  18. Modelling & Analysing the Research Process Purposing: making research data available and fit for the current research purpose/activity. It is to do with data use and the research activity Research Time Line RDR1' RDR1'' RDR1 Refine Refine Associate RDR4 Derive RDR2 RDR3 Derive CDR1 RDR6 Aggregate Case level RDR5

  19. Research Activity ‘What characterizes engineering research data and how diverse are they?’

  20. 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

  21. Scoping Survey

  22. Case Study Selection 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 5. Metrology 4. Interview analysis

  23. Research Data Case Audits Carried out audit on 5 cases: • Map the research activity information flow using Research Activity Information Diagrams (RAID) • Establish purposes for the data • Understand the barriers to re-use • Understand the processing and interpretation requirements

  24. Research Data Development

  25. Example RAID for IdMRC Project

  26. RAID Snapshot

  27. Aerospace Cost Data

  28. Emerging Findings • 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 • Low investment in management support or activity • Immature understanding of benefits of sharing and thus need for management • Limited (but identifiable) desire to share data

  29. RC UK Research Data Management The RCUK says research organizations should have clear requirements for preservation of relevant primary data: • failing to keep clear and accurate records of research procedures and results obtained (including interim results) = mismanagement; • failing to hold records securely = mismanagement; • failing to make relevant primary data and research evidence accessible to others for reasonable periods after the completion of the research . . = mismanagement; • failing to deposit data permanently within a national collection, where this would have been possible = mismanagement.

  30. Barriers to be Overcome • Collaborator sensitivity • Motivating researchers to expend effort to future-proof their data • Providing support for ‘supporting re-use’ and ‘re-purposing’ • Providing the resources to allow ‘supporting re-use’ and ‘re-purposing’ • Incorporating RAID-type activity models in management records

  31. Conclusions • Research data management should be grounded in a solid theoretical understanding and agreed terminology • RDM should reflect the diversity of data types and usage, and should plan for both re-use and re-purposing • Interpretation of data require something like an RAID study and record • Even in such a case there are many barriers to be overcome.

  32. Thank you, any questions? c.a.mcmahon@bath.ac.uk

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