Reproducible and citable data and models: an introduction.

FAIRDOM
Sep. 23, 2015
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
Reproducible and citable data and models: an introduction.
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Reproducible and citable data and models: an introduction.

Editor's Notes

  1. 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)