Toward Semantic Representation of Science in Electronic Laboratory Notebooks (ELNs)

An electronic laboratory Notebook (ELN) can be characterized as a system that allows scientists to capture the data and resources used in performing scientific experiments. This allows users to easily organize and find their data however, little information about the scientific process is recorded.

In this paper we highlight the current status of progress toward semantic representation of science in ELNs.

1. Toward Semantic Representation of Science in Electronic Laboratory Notebooks (ELNs) Stuart J. Chalk Department of Chemistry, University of North Florida [email protected] CINF Paper 50 – 251 st ACS Meeting Spring 2016 # ACSCINFDataSummit

2. Utopia: A Global Research Network What is an Electronic Notebook? The Semantics of Semantics What Needs to be Semantically Represented? Current lay of the land ELN Item Manifest P-PLAN Ontology VIVO-ISF Ontology Chemical Analysis Metadata Platform HCLS Community Profiles Electronic Notebook Ontology A generic scientific data model Experimental information for LD ( ExptLD ) Take Home Conclusion Outline

3. “Big Data” and the “Semantic Web” are the current buzz words du jour but what do they mean for chemistry? Lots of heterogeneous data and metadata with even more “semantic” data to represent it Look at what we want rather that what we have … We went chemical data that is: Easy to share, find, and compare Freely available but with provenance Globally sourced and without IP restrictions on reuse Utopia: A Global Research Network

4. An electronic way to record data… ...equivalent to a laboratory notebook But ELN’s should not be thought of so lowly... An ELN must:* Keep track of research data Reference resources used in research and … … capture the story of research What is an Electronic Laboratory Notebook? * Insight from Tony Williams

5. The interface should mirror a laboratory notebook Behind the scenes though it should use state of the art software, data formats, data/metadata practices, and web technologies to manage data generation, workflows, remote data access, authentication etc … As a result it needs to speak the same language as other data sources and store data in a format that others can read and reuse Foundational building block of a Global Research Network What should an ELN be?

6. Semantics is the study of meaning -> We need to give meaning to what is created in an ELN Described in computers using the Resource Description Framework (RDF) which: Makes statements about objects … … their relationships to other objects... ...using subject-predicate-object “triples” RDF allows knowledge representation Meaning is represented by using one or more ontologies The Semantics of Semantics

7. RDF in JSON-LD { "@context": { "name": "http:// schema.org /name", " isAlive ": "http:// example.org / isAlive ", "age": "http:// example.org /age", "height": "http:// schema.org /height", "@base": "http:// www.unf.edu /chemistry/ stuart_chalk.aspx " }, "@id": "", "name": "Stuart Chalk", " isAlive ": true, "age": 49, "height": 188.0 } http:// json-ld.org /playground/

8. <http :// www.unf.edu /chemistry/ stuart_chalk.aspx > < http:// example.org /age> " 49"^^<http://www.w3.org/2001/ XMLSchema#integer > . < http:// www.unf.edu /chemistry/ stuart_chalk.aspx > < http:// example.org / isAlive > " true"^^<http://www.w3.org/2001/ XMLSchema#boolean > . <http:// www.unf.edu /chemistry/ stuart_chalk.aspx > < http:// schema.org /height> " 188"^^<http://www.w3.org/2001/ XMLSchema#integer > . < http:// www.unf.edu /chemistry/ stuart_chalk.aspx > <http :// schema.org / name> " Stuart Chalk" . RDF in JSON-LD

9. Everything! What areas? Data, Results and Resources Models, Tools for Data Workup (Equations, Tests, Stats) General Workflows (Protocols and Procedures) The Research Story (What, Why, How) User discussion and annotation ELN usage timeline The Science (Area, Hypotheses, Theories) The People (Expertise , Provenance, Integrity, Eminence) What Needs to be Semantically Represented?

10. The P-PLAN Ontology http://purl.org/net/p- plan Workflows Implement in Kepler , Taverna , Knime ?

11. People: The VIVO-ISF Ontology https:// wiki.duraspace.org /download/attachments/51052811/PeopleOrgsRolesGrants.2014-03-14. png

12. The Chemical Analysis Metadata Platform (ChAMP) http://champ-project.org / Identification of metadata related to chemical analysis and definition of an ontology to describe terms Examples in both XML and JSON-LD with associate XML Schema and JSON-LD context Journal Article Standard Method of Analysis Reference Material The Science: ChAMP (an example)

13. ChAMP

14. ChAMP

15. The Healthcare and Life Science (HCLS) Community Profile is a Note from the Semantic Web HCLS Interest Group Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. This document describes a consensus among participating stakeholders in the Health Care and the Life Sciences domain on the description of datasets using the Resource Description Framework (RDF). This specification meets key functional requirements, reuses existing vocabularies to the extent that it is possible, and addresses elements of data description, versioning, provenance, discovery, exchange, query, and retrieval . Data Descriptions: HCLS Community Profile http://www.w3.org/TR/hcls-dataset /

16. Describes three levels for description of datasets Summary Level Type declaration ( rdf:type = dctypes:Dataset ) Title ( dct:title = rdf:langString ) Description ( dct:description = rdf:langString ) Publisher ( dct:publisher = IRI) Version Level Type declaration ( rdf:type = dctypes:Dataset ) Title ( dct:title = rdf:langString ) Description ( dct:description = rdf:langString ) Creator ( dct:creator = IRI) Publisher ( dct:publisher = IRI) Version identifier ( pav:version = xsd:string ) Version linking ( dct:isVersionOf = IRI ) Distribution Level Type declaration ( rdf:type = void:Dataset OR dcat:Distribution ) Title ( dct:title = rdf:langString ) Description ( dct:description = rdf:langString ) Creator ( dct:creator = IRI) Publisher ( dct:publisher = IRI) License ( rdf:type = IRI) Data Descriptions: HCLS Community Profile http://www.w3.org/TR/hcls-dataset/# datasetdescriptionlevels

17. Electronic Notebook Ontology (ENO)

18. ENO

19. ENO

20. Use a Generic Scientific Data Model Captures data and metadata about datasets and links to related data JSON-LD is ideal file format Data and Resources

21. A specification (written in XML) that describes different data types of information recorded during the scientific process ( http://exptml.sourceforge.net Experiment Markup Language (ExptML) Sample Solution Space Specimen Substance Task Template Timeline User Vendor Annotation Api Calculation Chemical Citation Communication Customer Data Dataset Definition Element Equipment Event Experiment Group Project Protocol Quote Report Result

22. Experimental Linked Data (ExptLD) Define data packets that capture the metadata of Resources Data Integrate with other ExptLD packets to create a SciData document Or convert to RDF and store in a triplestore

23. A lot exists to semantically represent the scientific process that can be leveraged as part of an ELN system A data standard needs to be agreed upon Agreeing on implementation standards will take time because of size of user community Integration and coverage of ontologies will be necessary to fully implement a system that underpins a Global Research Network Domain specific knowledge representation needed in many areas Take Home

24. [email protected] Phone: [phone] Skype: stuartchalk LinkedIn/Slidehare : https://www.linkedin.com/in/ stuchalk ORCID: http://orcid.org/0000 -0002-0703- 7776 ResearcherID : http://www.researcherid.com/rid/D-8577- 2013 Questions?