New paper [1] on using Semantic Web technologies to publish existing data according to the FAIR data principles [2]. 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 […]
New paper [1] on using Semantic Web technologies to publish existing data according to the FAIR data principles [2].
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]
![[doi]](http://www.macs.hw.ac.uk/~ajg33/wp-content/plugins/papercite/img/external.png)
Mark D. Wilkinson, Ruben Verborgh, Luiz Olavo {Bonino da Silva Santos}, Tim Clark, Morris A. Swertz, Fleur D. L. Kelpin, Alasdair J. G. Gray, Erik A. Schultes, Erik M. van Mulligen, Paolo Ciccarese, Arnold Kuzniar, Anand Gavai, Mark Thompson, Rajaram Kaliyaperumal, Jerven T. Bolleman, and Michel Dumontier. Interoperability and FAIRness through a novel combination of Web technologies.
PeerJ Computer Science, 3:e110, apr 2017.
[Bibtex]
@article{Wilkinson2017-FAIRness,
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 atthe 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.},
author = {Wilkinson, Mark D. and Verborgh, Ruben and {Bonino da Silva Santos}, Luiz Olavo 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},
doi = {10.7717/peerj-cs.110},
issn = {2376-5992},
journal = {PeerJ Computer Science},
month = {apr},
pages = {e110},
publisher = {PeerJ Inc.},
title = {{Interoperability and FAIRness through a novel combination of Web technologies}},
url = {https://peerj.com/articles/cs-110},
volume = {3},
year = {2017}
}
[2]
Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino {da Silva Santos}, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, Alasdair J. G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A. C. {‘t Hoen}, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, and Barend Mons. The FAIR Guiding Principles for scientific data management and stewardship.
Scientific Data, 3:160018, 2016.
[Bibtex]
@article{Wilkinson2016,
abstract = {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.},
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 Bouwman, Jildau and Brookes, Anthony J and Clark, Tim and Crosas, Merc{\`{e}} and Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo, Chris T and Finkers, Richard and Gonzalez-Beltran, Alejandra and Gray, Alasdair J.G. and Groth, Paul and Goble, Carole and Grethe, Jeffrey S and Heringa, Jaap and {'t Hoen}, Peter A.C and Hooft, Rob and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott J and Martone, Maryann E and Mons, Albert and Packer, Abel L and Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz, Morris A and Thompson, Mark and van der Lei, Johan and van Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and Mons, Barend},
doi = {10.1038/sdata.2016.18},
issn = {2052-4463},
journal = {Scientific Data},
month = mar,
pages = {160018},
publisher = {Macmillan Publishers Limited},
title = {{The FAIR Guiding Principles for scientific data management and stewardship}},
url = {http://www.nature.com/articles/sdata201618},
volume = {3},
year = {2016}
}
