An Identifier Scheme for the Digitising Scotland Project

The Digitising Scotland project is having the vital records of Scotland transcribed from images of the original handwritten civil registers . Linking the resulting dataset of 24 million vital records covering the lives of 18 million people is a major challenge requiring improved record linkage techniques. Discussions within the multidisciplinary, widely distributed Digitising Scotland project team have been hampered by the teams in each of the institutions using their own identification scheme. To enable fruitful discussions within the Digitising Scotland team, we required a mechanism for uniquely identifying each individual represented on the certificates. From the identifier it should be possible to determine the type of certificate and the role each person played. We have devised a protocol to generate for any individual on the certificate a unique identifier, without using a computer, by exploiting the National Records of Scotland’s registration districts. Importantly, the approach does not rely on the handwritten content of the certificates which reduces the risk of the content being misread resulting in an incorrect identifier. The resulting identifier scheme has improved the internal discussions within the project. This paper discusses the rationale behind the chosen identifier scheme, and presents the format of the different identifiers.

The work reported in the paper was supported by the British ESRC under grants ES/K00574X/1(Digitising Scotland) and ES/L007487/1 (Administrative Data Research Centre – Scotland).

My coauthors are:

  • Özgür Akgün, University of St Andrews
  • Ahamd Alsadeeqi, Heriot-Watt University
  • Peter Christen, Australian National University
  • Tom Dalton, University of St Andrews
  • Alan Dearle, University of St Andrews
  • Chris Dibben, University of Edinburgh
  • Eilidh Garret, University of Essex
  • Graham Kirby, University of St Andrews
  • Alice Reid, University of Cambridge
  • Lee Williamson, University of Edinburgh

The work reported in this talk is the result of the Digitising Scotland Raasay Retreat. Also at the retreat were:

  • Julia Jennings, University of Albany
  • Christine Jones
  • Diego Ramiro-Farinas, Centre for Human and Social Sciences (CCHS) of the Spanish National Research Council (CSIC)

Interoperability and FAIRness through a novel combination of Web technologies

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] 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] [doi] 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}
}

Supporting Dataset Descriptions in the Life Sciences

Seminar talk given at the EBI on 5 April 2017.

Abstract: Machine processable descriptions of datasets can help make data more FAIR; that is Findable, Accessible, Interoperable, and Reusable. However, there are a variety of metadata profiles for describing datasets, some specific to the life sciences and others more generic in their focus. Each profile has its own set of properties and requirements as to which must be provided and which are more optional. Developing a dataset description for a given dataset to conform to a specific metadata profile is a challenging process.

In this talk, I will give an overview of some of the dataset description specifications that are available. I will discuss the difficulties in writing a dataset description that conforms to a profile and the tooling that I’ve developed to support dataset publishers in creating metadata description and validating them against a chosen specification.

Smart Descriptions & Smarter Vocabularies (SDSVoc) Report

In December 2016 I presented at the Smart Descriptions and Smarter Vocabularies workshop on the Health Care and Life Sciences Community Profile for describing datasets, and our validation tool (Validata). Presentations included below.

The purpose of the workshop was to understand current practice in describing datasets and where the DCAT vocabulary needs improvement. Phil Archer has written a very comprehensive report covering the workshop. A charter is being drawn up for a W3C working group to develop the next iteration of the DCAT vocabulary.

Shapeshifting LOD Cloud

A new version of the Linked Open Data (LOD) cloud has been produced and it shows quite a shift from the previous version. It is great to see the LOD cloud continue to grow both in scale and diversity.

(You can click on the image to get to an interactive version of the cloud with links to the DataHub entries.)

LOD Cloud January 2017

LOD Cloud January 2017

Previously DBPedia and GeoNames were the centre of the LOD universe. While DBPedia still remains an important linking dataset, it is now clear that there are clusterings within application domains. This is most significant in the life sciences.

LOD Cloud August 2014

LOD Cloud August 2014

Attribution: “Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/”