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Measuring maturity
Alternative #1
2019-04-03 www.rd-alliance.org - @resdatall 19
Five options for R1.1 [metadata/data]
Level 0: no licence
Level 1: non standard licence in a human-readable format allowing access
Level 2: standard licence in a human-readable format allowing access
Level 3: standard open licence in a human-readable format allowing reuse
Level 4: standard open licence in a machine-readable format allowing reuse
Level 5: standard open licence in a machine-readable format with clear
criteria allowing reuse
Each option is defining a maturity level
Method step 9
Level 0
Level 1
Level 2
Level 3
Level 4
FAIRNess compliance for R1.1
Level 5](https://image.slidesharecdn.com/forefairismb-190723132329/85/Fore-FAIR-ISMB-2019-9-320.jpg)
Uploaded byIan Fore
Fore FAIR ISMB 2019
This document summarizes a presentation on using FAIR principles to build a Cancer Research Data Commons. It discusses three main themes: experiences implementing FAIR, capturing the diversity of cancer science data, and allowing more people to comply with FAIR. It also describes plans to build a Cancer Data Aggregator that would provide unified access to multiple cancer data repositories via an API. Proposals to build this aggregator are due by August 15, 2019.
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Fore FAIR ISMB 2019
- 1. FAIR as aBasis for the Cancer Research Data Commons Ian Fore, D.Phil. NCI Center for Biomedical Informatics and Information Technology @ianmfore Demystifying FAIR Science: Examples, Tools and Use Cases ISMB/ECCB 2019 July 23 2019
- 2. Three themes • Experienceof FAIR • Capturing diversity of science • Allow more people to be compliant • Understanding collaboration
- 3.
- 4. Under the hypecurve “there are emerging indications that the original meanings of findable, accessible, interoperable, and reusable sometimes may be stretched” “the proposed implementation of these principles, with the goal of an Inter- net of FAIR Data and Services, is beginning to raise concern and confusion” Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud Mons et al (2017) Information Services & Use, vol. 37, pp. 49-56
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- 6. Desirable characteristics ofFAIR Assessment • Transparency • What are the criteria • Who is doing the assessment • Qualifications for doing so • Not just a score • Non judgmental • Reflect qualities of a given resource • Not strict • Important to leave scope for the novelty of science
- 8. CC BY-SA 4.0 Casestatement of the WG 2019-04-03 www.rd-alliance.org - @resdatall 3 Challenge Ambiguity and wide range of interpretations of FAIRness Lack of a common set of core assessment criteria and a minimum set of shared guidelines Approach Bring together stakeholders Build on existing approaches and expertise Intended results RDA Recommendation of core assessment criteria Generic and expandable self-assessment model Self-assessment toolset FAIR data checklist
- 9. 9 CC BY-SA 4.0 Measuringmaturity Alternative #1 2019-04-03 www.rd-alliance.org - @resdatall 19 Five options for R1.1 [metadata/data] Level 0: no licence Level 1: non standard licence in a human-readable format allowing access Level 2: standard licence in a human-readable format allowing access Level 3: standard open licence in a human-readable format allowing reuse Level 4: standard open licence in a machine-readable format allowing reuse Level 5: standard open licence in a machine-readable format with clear criteria allowing reuse Each option is defining a maturity level Method step 9 Level 0 Level 1 Level 2 Level 3 Level 4 FAIRNess compliance for R1.1 Level 5
- 11. 11 Data Commons Framework ClinicalProteomics ImagingGenomics Immuno- oncology Cancer Models Biomarkers NCI Cancer Research Data Commons SBG CGC Broad FireCloud ISB CGC Elastic Compute Query Visualization Clinical Proteomics Tumor Analysis Consortium* Tool Deployment The Cancer Imaging Archive* TCIA Web Interface APIs Data Submission Authentication & AuthorizationAuthentication & Authorization Data Models & Dictionaries Computational Workspaces Data Contributors and Consumers Tool Repositories Metadata Validation & Tools Analysis As Is Genomics GDC and Cloud Resources are available now; Framework, As Is Genomics, PDC, IDC are in development; all else is notional.
- 12. NCBI Sequence DataDelivery Pilot (SDDP) • Sequence data will be stored in commercial cloud • Data submission by existing DbGaP mechanisms • Familiar to investigators • Minimal change to existing DbGaP sequence submission • VCF and phenotype observations in DbGaP • Relieves pressure on SRA by allowing NCI to provide directly for storage of data from its programs
- 13. Data “As Is?” •Not as harmonized and restructured as e.g. Genomic Data Commons • Goal: • Usable by those not engaged in the creation or production of the dataset • FAIR as a general principle • Ensure the context of samples is captured
- 14. Standards essential but… •Science inherently non-standard • Even when standard – large diversity of biomedical data • Need to understand data standards from other domains • Domains constantly in flux • Data models reflect unique study designs • Harmonization has proven expensive
- 15. Shift what weask for Not just “These are the columns you must provide” “Fit your data into our model” But also (not strict) What are the variables that define your study? What is the model of your data? Standardized or not.
- 16. Standardizing the descriptionof the non- standard <variable id="phv00357184.v1"> <name>SEX</name> <description>Sex of participant</description> <type>string</type> <value>Female</value> <value>Male</value> </variable> <variable id="phv00169062.v7"> <name>SEX</name> <description>Sex</description> <type>integer, encoded value</type> <comment>Sex The Donor's Identification of sex based upon self-report, family/next of kin, or medical record abstraction. </comment> <value code="1">Male</value> <value code="2">Female</value> </variable> One dbGaP study - GECCO Another dbGaP study - GTEx
- 17. What is neededfor standard description of variables? <variable id="phv00217659.v3"> <name>SMUBRTRM</name> <description>Uberon Term, anatomical location as described by the Uber Anatomy Ontology (UBERON)</description> <type>string</type> <comment>Generated at LDACC Term as specified by the Uber Anatomy Ontology (UBERON), http://bioportal.bioontology.org/ontologies/UBERON. </comment> </variable> <variable id="phv00169242.v7"> <name>SMTSISCH</name> <description>Total Ischemic time for a sample</description> <type>integer</type> <unit>Minutes</unit> <comment>Sample Ischemic Time Interval between actual death, presumed death, or cross clamp application and final tissue stabilization. </comment> </variable> From GTEX Sample Attributes
- 18. What is thestandard to describe data Things, Attributes, Relationships • Some existing candidates • Metadata repositories • ISO11179 style (caDSR, CDISC, etc.) • RDA Data Type Registries • GA4GH – Discovery Work Stream – Search/SchemaBlocks • BDBag • PFB – Portable Format for Biomedical Data • JSON-LD
- 19. Data Sharing: It’s notthe technology, it’s the attitude we take Greg Simon, Director – Cancer Moonshot Task Force
- 22. NIH Institutes Representedin BD2K Radical Collaboration Training • Center for Scientific Review • National Center for Advancing Translational Sciences • National Cancer Institute • National Human Genome Research Institute • National Heart, Lung, and Blood Institute • National Institute on Aging • National Institute of Allergy and Infectious Diseases • National Institute of Biomedical Imaging and Bioengineering • National Institute of Child Health and Human Development • National Institute on Drug Abuse • National Institute of General Medical Sciences • National Institute of Mental Health • National Institute on Minority Health and Health Disparities • NIH Office of the Director Thanks to: Phil Bourne and Warren Kibbe
- 23. we wrestle withthe question of whether natural selection inherently favors selfish behavior. Is the process of evolutionary competion cruel, or does it sometimes pay to be nice? https://www.wnycstudios.org/story/104082-prisoners-dilemma
- 24. FIRO-B All Human beingsshare some basic similarities All people want to feel: Significant Competent Likable All people have some fear of being: Ignored Humiliated Rejected All people have behavior preferences about: Inclusion Control Openness By creating environments that invite people to feel significant, competent and likable, you reduce the level of fear and create environments that are more conducive to honesty, collaboration, accountability and fun! It is in these environments where people bring their best to the workplace and productivity soars in an atmosphere of trust. By being accountable for your own internal environment, you take responsibility for your own psychological patterns and mental models and do not inappropriately associate your own deeper self-concept with the substantive issues you are working with. Celeste Blackman, Green Zone Culture Group
- 25. Operational phase mattersmore than the outset • FAIR has served to bring the community together • Success depends on the follow through • There is no silver bullet • Fred Brooks
- 26. 26 Submit Proposal toBuild the Cancer Data Aggregator • Accessible via NCI Cloud Resources and other applications. • API layer will allow users to query across: • NCI Cancer Research Data Commons (CRDC) • NCI Data Coordinating Centers (e.g. HTAN DCC) • Additional Repositories (e.g. KidsFirst DRC) Proposals due by August 15, 2019: https://go.usa.gov/xyKe3
Editor's Notes
- #5 FAIR Findable, Accessible, Interoperable, Reusable
- #12 Building upon this foundation, our vision for a Cancer Research Data Commons: Is a virtual, expandable infrastructure that will support collaboration among researchers, clinicians, patients, computational scientists, and tool developers. It will house multiple cloud-based Commons Nodes for multiple data types initially including: Genomic, Imaging, and Proteomics data. In the future, we envision additional nodes that support other data types. ANIMATE: The genomics data node is built upon the foundation of the GDC, which provides a means of data submission, user interfaces, and visualization tools. ANIMATE: “As is genomics” is similar, in principle, to the Sequence Read Archive within dbGaP. ANIMATE: The Cloud Resources provide search, compute, and analytical resources, as well as a way for researchers to use their own data and tools. ANIMATE: The Data Commons Framework provides secure access, an approach to metadata validation, user workspaces, shared data models and dictionaries to facilitate interoperability, and a tool repository to allow users to use and share new algorithms, tools, pipelines, and visualizations. ANIMATE: The Proteomics Data node will incorporate data from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) and other sources. ANIMATE: The Imaging Data node will work with the Cancer Imaging Archive (TCIA) and other sources of imaging data. In terms of status, GDC and Cloud Resources are operational; Framework, As Is Genomics, PDC, and IDC are in development; and more is planned for the future.
- #16 There should be a different obligation - to require the investigator to share what they know rather than a fixed set of attributes. This should be more motivating to the investigator as it unlocks the creativity inherent in how they designed their study. “Metadata” is a seen as a burden Can we lessen the burden by making sure we capture what scientists are doing anyway?
- #18 Two examples here 1. A variable that uses an ontology. How could the term be more explicity referenced? 2. A numeric variable where the type (integer) and unit are specified.