Reproducible and citable data and models: an introduction.
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Sep. 23, 2015•0 likes•4,209 views
Reproducible and citable data and models: an introduction.
Sep. 23, 2015•0 likes•4,209 views
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Prepared and presented by Carole Goble (University of Manchester), Wolfgang Mueller (HITS), Dagmar Waltermath (University of Rostock), at the Reproducible and Citable Data and Models Workshop, Warnemünde, Germany. September 14th - 16th 2015.
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Reproducible and citable data and models: an introduction.
- 2. Introduction: What do we mean by citable data and reproducible models? Carole Goble, Wolfgang Müller, Dagmar Waltemath FAIRDOM Consortium The University of Manchester, UK carole.goble@manchester.ac.uk EraSysAPP Workshop Data Citation and Model Reproducibility, Rostock, 14-16 Sept 2015
- 3. “An article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment, [the complete data] and the complete set of instructions which generated the figures.” David Donoho, “Wavelab and Reproducible Research,” 1995
- 4. Reproducible Codes and Available Data
- 5. Why Unreproducible Research Happens • Tainted resources • Black boxes • Poor Reporting • Unavailable resources / results: data, software • Bad maths • Sins of omission • Poor training, sloppiness https://www.sciencenews.org/article/12-reasons-research-goes-wrong (adapted) Ioannidis, Why Most Published Research Findings Are False, August 2005 Joppa, et al,TroublingTrends inScientificSoftwareUse SCIENCE 340 May 2013 Scientific method • Impact factor mania • Pressure to publish • Broken peer review • Research never reported • Disorganisation • Time pressures • Prep & curate costs Social environment
- 6. FAIR Reproducibility Find, Access, Interoperate, Reuse
- 7. FAIR Publishing Catalogues Data specific Public Archives Commons & Cross Resource Infra Portable Packaging STANDARDS
- 8. Standards
- 9. Packaging, Porting, Reusing Bergman et al COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project, BMC Bioinformatics 2014, 15:369
- 10. Citable Data • Persistent Identifiers • Resolution • Citation attribution • Credit lists • Citation infrastructure • Snapshots and versioning • Link Data with Publications
- 11. submit article and move on… publish article Research Environment Publication Environment Peer Review • Find, Access, Interoperate, Reuse – Catalogues, Public Archives, Licensing, Guidelines, Standards – Packaging models and Data • Data / Software Publishing – Link Data / Software and Literature • Credit and citation
- 12. submit article and move on… publish article Research Environment Publication Environment Peer Review Citing data in research articles: principles, implementation, challenges – and the benefits of changing our ways Johanna McEntyre (Mon) Publishing data and code openly Tom Ingraham (Mon) The FAIRDOM Commons for Systems Biology Carole Goble (Mon) The OpenAIRE infrastructure and RDA Data Publishing Working Group: results and vision Paolo Manghi (Wed) Data
- 13. submit article and move on… publish article Research Environment Publication Environment Peer Review Standards for reproducibility of model-based results Dagmar Waltemath (Tues) Reproducible model construction, validation and simulation Jacky Snoep (Tues) Capturing the context – one small(ish) step for modellers, one giant leap for mankind Mihai Glont (Tues) Archiving modeling results Finn Bacall, Stuart Owen, Martin Scharm (Weds) Models
- 14. submit article and move on… publish article Research Environment Publication Environment Peer Review Hands On
Editor's Notes
- Lots of research is incomparable. EXECUTION REPORTING Gathered: scattered across different repositories/catalogues Availability of dependencies: Know and have all necessary elements available, accessible, maybe open Change management: Data? Services? Methods? Prevent, Detect, Repair. Execution and Making Environments: Skills/Infrastructure to run it: Portability and the Execution Platform (which can be people…), authoring and reading Description: Explicit: How, Why, What, Where, Who, When, Comprehensive: Just Enough, Comprehensible: Independent understanding Purpose for doing it: reason and reward sensitivity Reporting and Preserving SOPs for methods Current work on research reproducibility has focused on the creation of tools for packaging research artefacts such as data and software so that the analyses can be run by others the creation of domain-specific guidelines and checklists for the reporting of research. How Open software (inspection) reproduce Closed software (execution, but not inspection) – VM! replication FAIR Model Description left to right Portability up and down FAIRport* Reproducibility Find, Access, Interoperate, Reuse, PortPreservation - Lots of copies keeps stuff safe Stability dimension Add two more dimensions to our classification of themes A virtual machine (VM) is a software implementation of a machine (i.e. a computer) that executes programs like a physical machine. Virtual machines are separated into two major classifications, based on their use and degree of correspondence to any real machine: System Overlap of course Static vs dynamic. GRANULARITY This model for audit and target of your systems overcoming data type silos public integrative data sets transparency matters cloud Recomputation.org Reproducibility by Execution Run It Reproducibility by Inspection Read It Availability – coverage Gathered: scattered across resources, across the paper and supplementary materials Availability of dependencies: Know and have all necessary elements Change management: Data? Services? Methods? Prevent, Detect, Repair. Execution and Making Environments: Skills/Infrastructure to run it: Portability and the Execution Platform (which can be people…), Skills/Infrastructure for authoring and reading Description: Explicit: How, Why, What, Where, Who, When, Comprehensive: Just Enough, Comprehensible: Independent understanding Documentation vs Bits (VMs) reproducibility Learn/understand (reproduce and validate, reproduce using different codes) vs Run (reuse, validate, repeat, reproduce under different configs/settings)