The Final Updates. Philippe Rocca-Serra Alejandra Gonzalez-Beltran, Susanna-Assunta Sansone, Oxford e-research Centre, University of Oxford, UK

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1 The Final Updates Supported by the NIH grant 1U24 AI to UCSD PI, Co-Investigators at: Philippe Rocca-Serra Alejandra Gonzalez-Beltran, Susanna-Assunta Sansone, Oxford e-research Centre, University of Oxford, UK biocaddie DATS, San Diego, August 7 th, 2017

2 What is? Like the JATS (Journal Article Tag Suite) is used by PubMed to index literature, a DATS (DatA Tag Suite) is needed for a scalable way to index data sources in the DataMed prototype

3 Where do I find the documentation? Like the JATS (Journal Article Tag Suite) is used by PubMed to index literature, a DATS (DatA Tag Suite) is needed for a scalable way to index data sources in the DataMed prototype

4 biocaddie team s work on DATS Phase 1 Phase 2 Phase 3 Design and development Evaluation & iterative refinement Continued evaluation & consolidation SOP and metadata strawman Metadata specification V1.0 with JSON schema <DATS> name DATS v1.1 DATS v2.0 (with access metadata, WG7) DATS v2.1 (schema.org JSON-LD) DATS v2.2 Mar 15 Jun 15 Aug 15 Dec 15 May 16 Jun 16 Sep 16 Mar 17 Use cases workshop May 17 WG3 formed; telecons start; dissemination via WG7 formed; telecons start 1 st DATS workshop WG12 formed; telecons start 2 nd DATS workshop primarily metadata modelers primarily implementers Our community engagement: input, feedback and links

5 What is supposed to do and be? Enabling discoverability: find and access datasets Focusing on surfacing key metadata descriptors, such as information and relations between authors, datasets, publication, funding sources, nature of biological signal and perturbation etc. Not the perfect model to represent the experimental details the level of details and metadata needed to ensure interoperability and reusability are left to the indexed databases Better than just having keywords we have aimed to have maximum coverage of use cases with minimal number of data elements and relations

6 The development process in a nutshell Metadata elements identified by combining the two complementary approaches USE CASES: top-down approach SCHEMAS: bottom-up approach (v1.0, v1.1, v2.0, v2.1, v2.2)

7 model for scalable indexing Convergence of elements extracted from competency questions and existing (generic and biomedical) data models (incl. DataCite, DCAT, schema.org, HCLS dataset, RIF- CS, ISA-Tab, SRAxml etc.) core entities extended entities Adoption of elements extracted from and from

8 Standing on the shoulders of giants BioProject BioSample MiNIML PRIDE-ml MAGE-tab GA4GH metadata schema SRA xml ISA bottom-up approach CDISC SDM / element of BRIDGE model (full list in the DATS specification) DataCite RIF-CS W3C HCLS dataset descriptions (mapping of many models including DCAT, PROV, VOID, Dublin Core) Project Open Metadata (used by HealthData.gov is being added in this new iteration) schema.org

9 Key features of The descriptors for each metadata element (Entity), include Property (describing the Entity), Definition (of each Entity and Property), Value(s) (allowed for each Property) We have defined a set of core and extended entities Core elements are generic and applicable to any type of datasets, like the JATS can describe any type of publication. Extended elements includes an additional elements, some of which are specific for life, environmental and biomedical science domains this set can be further extended as needed Entities are not mandatory, in both core and extended set An entity is used only when applicable to the dataset to be described In that case, only few of its properties are defined as mandatory

10 General design of the Dataset, a core entity catering for any unit of information archived experimental datasets, which do not change after deposition to the repository => examples available for dbgap, GEO, ClinicalTrials.org datasets in reference knowledge bases, describing dynamic concepts, such as genes, whose definition morphs over time => examples available for UniProt Dataset entity is also linked to other digital research objects Software and Data Standard, which are also part of the NIH Commons, but the focus on other discovery indexes and therefore are not described in detail in this model

11 core and extended elements

12 Core elements provide the basic info What is the dataset about? Material, Data How was the dataset produced? Which information does it hold? Dataset / Data Type with its Dimension, Method/Technology, Instrument Where can a dataset be found? Dataset, Distribution, Access objects (links to License, Formats) When was the datasets produced, released etc.? Dates to specify the nature of an event {create, modify, start, end...} and its timestamp Who did the work, funded the research, hosts the resources etc.? Person, Organization and their roles, Grant

13 Of the 20 core elements none is mandatory, only few of their properties are

14 Serializations and use of schema.org DATS model in JSON schema, serialized as: JSON* format, and JSON-LD** with vocabulary from schema.org serializations in other formats can also be done, as / if needed Benefits for DataMed and databases index by DataMed Increased visibility (by both popular search engines), accessibility (via common query interfaces) and possibly improved ranking Extending schema.org Submitted to their tracker missing DATS core elements Coordinating via the bioschemas.org initiative (ELIXIR is also part of) the extension of schema.org for life science * JavaScript Object Notation ** JavaScript Object Notation for Linked Data

15 DATS influence on schema.org evolution search/docs/data-types/ datasets

16 Relating and data standards

17 Relating and data standards

18 DATS implementations Bethesda Meeting Experience

19 ImmPort - Immunology Database and Analysis Portal Data warehouse holding metadata about clinical and mechanistic results Data model was compared with DATS specification ImmPort data model is study centric while DATS is dataset-centric (even though it also models studies that yield datasets): ImmPort dataset was defined as the data produced by a single study A subset of the ImmPort relational model was extracted programmatically, transformed to the DATS instance specification using templates, and then validated against the DATS schema

20 ICPSR - Inter-university Consortium for Political and Social Research Disciplinary data repository, focused on social and behavioral science research All ICPSR collection-level metadata rely on the Data Documentation Initiative (DDI) metadata standard. The study-level metadata served as the starting point for mapping to DATS.

21 OMOP CDM - Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) means to transform heterogeneous formats of observational health data into a common form enabling integrative systematic analyses shares many features with the CDISC SDTM model; its genericity means it is also suited to accommodate electronic health records (EHRs)

22 Lessons learned Identification of a Dataset: Identifying what is a dataset for a particular source is crucial for setting up an indexing pipeline to DataMed Use of DATS Dimensions (a high-level representation of quantitative or qualitative properties of an entity) E.g. in OMOP CDM there was a need to split a single entity into its procedural (mapped to DATS.DataAcquisition) and its variable information (mapped to DATS.Dimension) Documentation The available DATS documentation was useful Support infrastructure For the future, include more examples and validation infrastructure

23 Documenting the use of DATS Dimensions Dimension: meant to be used to report what data points are about in a dataset, their nature, their units Dimension must be typed Application: List all measurement types performed in a clinical trial Purpose: allow indexing on measurement types / variables / dimensions biocaddie DATS Dimension was discussed with Google schema.org team, who added variablemeasured property in schema.org to cover the notion

24 Using DATS Dimensions Data Dictionnaries/CDE Dimension AQ5.json

25 Questions 25