This presentation was delivered by Tomasz Miksa (SBA Research) at the final PERICLES project conference 'Acting on Change: New Approaches and Future Practices in LTDP' (Wellcome Collection Conference Centre, London, 30 Nov -1 Dec 2016). Tomasz Miksa joined Simon Waddington (King's College London/PERICLES) and George Papastefanatos (Athena-Research and Innovation Center) in a workshop on Proactive Data Management, where they discussed approaches to automating long-term management including automated extraction and analysis of environments, and approaches to capturing and applying knowledge and metadata and creation of evidence to support verification and validation. Whilst full automation of preservation processes is still a distant goal, the workshop aimed at investigating how current techniques can be applied for automating decision support and enabling proactive solutions. http://pericles-project.eu/

1. Pro-active solutions for higher reproducibility
of scientific experiments
Tomasz Miksa
Vienna University of Technology
& SBA Research, Austria

2. Tomasz Miksa tmiksa@sba-research.org
Agenda
 Replicability
 VFramework
 Context Model
 Machine Actionable Data Management Plans
 Conclusions

3. Tomasz Miksa tmiksa@sba-research.org
Replicability
Re-executed
experiment
Original
experiment
• Can I reuse her workflow?
• Do the results match?
• Have I done it correctly?

4. Tomasz Miksa tmiksa@sba-research.org
Challenge

5. Tomasz Miksa tmiksa@sba-research.org
VFramework

6. Tomasz Miksa tmiksa@sba-research.org
Context Model
Workflow
definition
Validation
metrics
ProvenanceSoftware
dependenciesFile formats

7. Tomasz Miksa tmiksa@sba-research.org

8. Tomasz Miksa tmiksa@sba-research.org

9. Tomasz Miksa tmiksa@sba-research.org
ADDED
NOT USED

10. Tomasz Miksa tmiksa@sba-research.org
How does it make us pro-active?
 External service monitoring
- check availability and functional conformance
- deploy mock-ups
- notification mechanisms using context descriptions
 Context Model monitoring
- identify all workflows impacted by change, e.g. obsolescence
 Evaluation of impact of changes
- re-execute modified workflows and validate them

11. Tomasz Miksa tmiksa@sba-research.org
Machine-actionable
Data Management Plans
 DMPs
- describe research data
- required by funders
- completed using checklists
- hand written
- vague
 Machine-actionable DMPs
- future work
- enable pro-active data management

12. Tomasz Miksa tmiksa@sba-research.org
Actionable DMPs integrate domains and tools

13. Tomasz Miksa tmiksa@sba-research.org
Actionable DMPs integrate domains and tools

14. Tomasz Miksa tmiksa@sba-research.org
Conclusions
 We have developed concepts and tools for
- context description
- information capturing
- service monitoring
 We need to integrate them and
deploy monitoring and
risk mitigation scenarios