Interoperability and FAIRness through a novel combination of Web technologies

New paper (Wilkinson et al., 2017) on using Semantic Web technologies to publish existing data according to the FAIR data principles (Wilkinson et al., 2016).

Abstract: Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved at the level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.

1. Interoperability and FAIRness through a novel combination of Web technologies
   Wilkinson, Mark D. and Verborgh, Ruben and da Silva Santos, Luiz Olavo Bonino and Clark, Tim and Swertz, Morris A. and Kelpin, Fleur D. L. and Gray, Alasdair J. G. and Schultes, Erik A. and van Mulligen, Erik M. and Ciccarese, Paolo and Kuzniar, Arnold and Gavai, Anand and Thompson, Mark and Kaliyaperumal, Rajaram and Bolleman, Jerven T. and Dumontier, Michel
   PeerJ Computer Science, 3:e110, 2017
   

   Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved at the level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs.

   @article{Wilkinson:InteropFAIRWebTech:PeerJ2017,
     author = {Wilkinson, Mark D. and Verborgh, Ruben and da Silva Santos, Luiz Olavo Bonino and Clark, Tim and Swertz, Morris A. and Kelpin, Fleur D. L. and Gray, Alasdair J. G. and Schultes, Erik A. and van Mulligen, Erik M. and Ciccarese, Paolo and Kuzniar, Arnold and Gavai, Anand and Thompson, Mark and Kaliyaperumal, Rajaram and Bolleman, Jerven T. and Dumontier, Michel},
     title = {Interoperability and FAIRness through a novel combination of Web technologies},
     journal = {PeerJ Computer Science},
     volume = {3},
     pages = {e110},
     year = {2017},
     url = {https://doi.org/10.7717/peerj-cs.110},
     doi = {10.7717/peerj-cs.110}
   }
   
2. The FAIR Guiding Principles for scientific data management and stewardship
   Wilkinson, Mark D and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E and others
   Scientific data, 3(1):1–9, Nature Publishing Group, 2016
   

   There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

   @article{Wilkinson:FAIRPrinciples:SciData2016,
     title = {The FAIR Guiding Principles for scientific data management and stewardship},
     author = {Wilkinson, Mark D and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E and others},
     journal = {Scientific data},
     volume = {3},
     number = {1},
     pages = {1--9},
     year = {2016},
     publisher = {Nature Publishing Group},
     doi = {10.1038/sdata.2016.18}
   }
   

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I'm an Associate Professor in Computer Science at Heriot-Watt University. My research focuses on linking datasets. Read more

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