Category: Publication

New Paper: Reproducibility with Administrative Data

Our journal article [1] looks at encouraging good practice to enable reproducible analysis of data analysis workflows. This is a result of a collaboration between social scientists and a computer scientist with the ADRC-Scotland. Abstract: Powerful new social science data resources are emerging. One particularly important source is administrative data, which were originally collected for organisational […]

Our journal article [1] looks at encouraging good practice to enable reproducible analysis of data analysis workflows. This is a result of a collaboration between social scientists and a computer scientist with the ADRC-Scotland.

Abstract: Powerful new social science data resources are emerging. One particularly important source is administrative data, which were originally collected for organisational purposes but often contain information that is suitable for social science research. In this paper we outline the concept of reproducible research in relation to micro-level administrative social science data. Our central claim is that a planned and organised workflow is essential for high quality research using micro-level administrative social science data. We argue that it is essential for researchers to share research code, because code sharing enables the elements of reproducible research. First, it enables results to be duplicated and therefore allows the accuracy and validity of analyses to be evaluated. Second, it facilitates further tests of the robustness of the original piece of research. Drawing on insights from computer science and other disciplines that have been engaged in e-Research we discuss and advocate the use of Git repositories to provide a useable and effective solution to research code sharing and rendering social science research using micro-level administrative data reproducible.

[1] [doi] C. J. Playford, V. Gayle, R. Connelly, and A. J. Gray, “Administrative social science data: The challenge of reproducible research,” Big Data & Society, vol. 3, iss. 2, 2016.
[Bibtex]
@Article{Playford2016BDS,
abstract = {Powerful new social science data resources are emerging. One particularly important source is administrative data, which were originally collected for organisational purposes but often contain information that is suitable for social science research. In this paper we outline the concept of reproducible research in relation to micro-level administrative social science data. Our central claim is that a planned and organised workflow is essential for high quality research using micro-level administrative social science data. We argue that it is essential for researchers to share research code, because code sharing enables the elements of reproducible research. First, it enables results to be duplicated and therefore allows the accuracy and validity of analyses to be evaluated. Second, it facilitates further tests of the robustness of the original piece of research. Drawing on insights from computer science and other disciplines that have been engaged in e-Research we discuss and advocate the use of Git repositories to provide a useable and effective solution to research code sharing and rendering social science research using micro-level administrative data reproducible.},
author = {Christopher J Playford and Vernon Gayle and Roxanne Connelly and Alasdair JG Gray},
title = {Administrative social science data: The challenge of reproducible research},
journal = {Big Data \& Society},
year = {2016},
OPTkey = {},
volume = {3},
number = {2},
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month = dec,
url = {http://journals.sagepub.com/doi/full/10.1177/2053951716684143},
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HCLS Tutorial at SWAT4LS 2016

On 5 December 2016 I presented a tutorial [1] on the Heath Care and Life Sciences Community Profile (HCLS Datasets) at the 9th International Semantic Web Applications and Tools for the Life Sciences Conference (SWAT4LS 2016). Below you can find the slides I presented. The 61 metadata properties from 18 vocabularies reused in the HCLS Community […]

On 5 December 2016 I presented a tutorial [1] on the Heath Care and Life Sciences Community Profile (HCLS Datasets) at the 9th International Semantic Web Applications and Tools for the Life Sciences Conference (SWAT4LS 2016). Below you can find the slides I presented.

The 61 metadata properties from 18 vocabularies reused in the HCLS Community Profile are available in this spreadsheet (.ods).

[1] M. Dumontier, A. J. G. Gray, and S. M. Marshall, “Describing Datasets with the Health Care and Life Sciences Community Profile,” in Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2016), Amsterdam, The Netherlands, 2016.
[Bibtex]
@InProceedings{Gray2016SWAT4LSTutorial,
abstract = {Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting HCLS community profile covers elements of description, identification, attribution, versioning, provenance, and content summarization. The HCLS community profile reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets. The goal of this tutorial is to explain elements of the HCLS community profile and to enable users to craft and validate descriptions for datasets of interest.},
author = {Michel Dumontier and Alasdair J. G. Gray and M. Scott Marshall},
title = {Describing Datasets with the Health Care and Life Sciences Community Profile},
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month = dec,
address = {Amsterdam, The Netherlands},
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HCLS Tutorial at SWAT4LS 2016

On 5 December 2016 I presented a tutorial [1] on the Heath Care and Life Sciences Community Profile (HCLS Datasets) at the 9th International Semantic Web Applications and Tools for the Life Sciences Conference (SWAT4LS 2016). Below you can find the slides I presented. The 61 metadata properties from 18 vocabularies reused in the HCLS Community […]

On 5 December 2016 I presented a tutorial [1] on the Heath Care and Life Sciences Community Profile (HCLS Datasets) at the 9th International Semantic Web Applications and Tools for the Life Sciences Conference (SWAT4LS 2016). Below you can find the slides I presented.

The 61 metadata properties from 18 vocabularies reused in the HCLS Community Profile are available in this spreadsheet (.ods).

[1] M. Dumontier, A. J. G. Gray, and S. M. Marshall, “Describing Datasets with the Health Care and Life Sciences Community Profile,” in Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2016), Amsterdam, The Netherlands, 2016.
[Bibtex]
@InProceedings{Gray2016SWAT4LSTutorial,
abstract = {Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting HCLS community profile covers elements of description, identification, attribution, versioning, provenance, and content summarization. The HCLS community profile reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets. The goal of this tutorial is to explain elements of the HCLS community profile and to enable users to craft and validate descriptions for datasets of interest.},
author = {Michel Dumontier and Alasdair J. G. Gray and M. Scott Marshall},
title = {Describing Datasets with the Health Care and Life Sciences Community Profile},
OPTcrossref = {},
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booktitle = {Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2016)},
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month = dec,
address = {Amsterdam, The Netherlands},
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HCLS Community Profile for Dataset Descriptions

My latest publication [1] describes the process followed in developing the W3C Health Care and Life Sciences Interest Group (HCLSIG) community profile for dataset descriptions which was published last year. The diagram below provides a summary of the data model for describing datasets which covers 61 metadata terms drawn from 18 vocabularies. [1] M. Dumontier, A. […]

My latest publication [1] describes the process followed in developing the W3C Health Care and Life Sciences Interest Group (HCLSIG) community profile for dataset descriptions which was published last year. The diagram below provides a summary of the data model for describing datasets which covers 61 metadata terms drawn from 18 vocabularies.Overview of the HCLS Community Profile for Dataset Descriptions

[1] [doi] M. Dumontier, A. J. G. Gray, S. M. Marshall, V. Alexiev, P. Ansell, G. Bader, J. Baran, J. T. Bolleman, A. Callahan, J. Cruz-Toledo, P. Gaudet, E. A. Gombocz, A. N. Gonzalez-Beltran, P. Groth, M. Haendel, M. Ito, S. Jupp, N. Juty, T. Katayama, N. Kobayashi, K. Krishnaswami, C. Laibe, N. {Le Novère}, S. Lin, J. Malone, M. Miller, C. J. Mungall, L. Rietveld, S. M. Wimalaratne, and A. Yamaguchi, “The health care and life sciences community profile for dataset descriptions,” PeerJ, vol. 4, p. e2331, 2016.
[Bibtex]
@article{Dumontier2016HCLS,
abstract = {Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the {W3C} Semantic Web for Health Care and the Life Sciences Interest Group ({HCLSIG}) identified Resource Description Framework ({RDF}) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of {FAIR} data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.},
author = {Dumontier, Michel and Gray, Alasdair J.G. and Marshall, M Scott and Alexiev, Vladimir and Ansell, Peter and Bader, Gary and Baran, Joachim and Bolleman, Jerven T and Callahan, Alison and Cruz-Toledo, Jos{'{e}} and Gaudet, Pascale and Gombocz, Erich A and Gonzalez-Beltran, Alejandra N. and Groth, Paul and Haendel, Melissa and Ito, Maori and Jupp, Simon and Juty, Nick and Katayama, Toshiaki and Kobayashi, Norio and Krishnaswami, Kalpana and Laibe, Camille and {Le Nov{`{e}}re}, Nicolas and Lin, Simon and Malone, James and Miller, Michael and Mungall, Christopher J and Rietveld, Laurens and Wimalaratne, Sarala M and Yamaguchi, Atsuko},
doi = {10.7717/peerj.2331},
issn = {2167-8359},
journal = {PeerJ},
month = aug,
title = {The health care and life sciences community profile for dataset descriptions},
volume = {4},
pages = {e2331},
year = {2016},
url = {https://peerj.com/articles/2331/}
}

The FAIR Principles herald more open, transparent, and reusable scientific data

Today, March 15 2016, the FAIR Guiding Principles for scientific data management and stewardship were formally published in the Nature Publishing Group journal Scientific Data. The problem the FAIR Principles address is the lack of widely shared, clearly articulated, and broadly applicable best practices around the publication of scientific data. While the history of scholarly […]

FAIR Article PosterToday, March 15 2016, the FAIR Guiding Principles for scientific data management and stewardship were formally published in the Nature Publishing Group journal Scientific Data. The problem the FAIR Principles address is the lack of widely shared, clearly articulated, and broadly applicable best practices around the publication of scientific data. While the history of scholarly publication in journals is long and well established, the same cannot be said of formal data publication. Yet, data could be considered the primary output of scientific research, and its publication and reuse is necessary to ensure validity, reproducibility, and to drive further discoveries. The FAIR Principles address these needs by providing a precise and measurable set of qualities a good data publication should exhibit – qualities that ensure that the data is Findable, Accessible, Interoperable, and Reusable (FAIR).

The principles were formulated after a Lorentz Center workshop in January, 2014 where a diverse group of stakeholders, sharing an interest in scientific data publication and reuse, met to discuss the features required of contemporary scientific data publishing environments. The first-draft FAIR Principles were published on the Force11 website for evaluation and comment by the wider community – a process that lasted almost two years. This resulted in the clear, concise, broadly-supported principles that were published today. The principles support a wide range of new international initiatives, such as the European Open Science Cloud and the NIH Big Data to Knowledge (BD2K), by providing clear guidelines that help ensure all data and associated services in the emergent ‘Internet of Data’ will be Findable, Accessible, Interoperable and Reusable, not only by people, but notably also by machines.

The recognition that computers must be capable of accessing a data publication autonomously, unaided by their human operators, is core to the FAIR Principles. Computers are now an inseparable companion in every research endeavour. Contemporary scientific datasets are large, complex, and globally-distributed, making it almost impossible for humans to manually discover, integrate, inspect and interpret them. This (re)usability barrier has, until now, prevented us from maximizing the return-on-investment from the massive global financial support of big data research and development projects, especially in the life and health sciences. This wasteful barrier has not gone unnoticed by key agencies and regulatory bodies. As a result, rigorous data management stewardship – applicable to both human and computational “users” – will soon become a funded, core activity within modern research projects. In fact, FAIR-oriented data management activities will increasingly be made mandatory by public funding bodies.

The high level of abstraction of the FAIR Principles, sidestepping controversial issues such as the technology or approach used in the implementation, has already made them acceptable to a variety of research funding bodies and policymakers. Examples include FAIR Data workshops from EU-ELIXIR, inclusion of FAIR in the future plans of Horizon 2020, and advocacy from the American National Institutes of Health. As such, it seems assured that these principles will rapidly become a key basis for innovation in the global move towards Open Science environments. Therefore, the timing of the Principles publication is aligned with the Open Science Conference in April 2016.

With respect to Open Science, the FAIR Principles advocate being “intelligently open”, rather than “religiously open”. The Principles do not propose that all data should be freely available – in particular with respect to privacy-sensitive data. Rather, they propose that all data should be made available for reuse under clearly-defined conditions and licenses, available through a well-defined process, and with proper and complete acknowledgement and citation.This will allow much wider participation of players from, for instance, the biomedical domain and industry where rigorous and transparent data usage conditions are a core requirement for data reuse.

“I am very proud that just over two years after the meeting where we came up with the early FAIR Principles. They play such an important role in many forward looking policy documents around the world and the authors on this paper are also in positions that allow them to follow these Principles. I sincerely hope that FAIR data will become a ‘given’ in the future of Open Science, in the Netherlands and globally”, says Barend Mons, Professor in Biosemantics at the Leiden University Medical Center.

Validata: An online tool for testing RDF data conformance

Validata is an online web application for validating an RDF document against a set of constraints. This is useful for data exchange applications or ensuring conformance of an RDF dataset against a community agreed standard. Constraints are expressed as a Shape Expression (ShEx) schema. Validata extends the ShEx functionality to support multiple requirement levels. Validata […]

Validata is an online web application for validating an RDF document against a set of constraints. This is useful for data exchange applications or ensuring conformance of an RDF dataset against a community agreed standard. Constraints are expressed as a Shape Expression (ShEx) schema.
Validata extends the ShEx functionality to support multiple requirement levels. Validata can be repurposed for different deployments by providing it with a new ShEx schema.

The Validata code is available from https://github.com/HW-SWeL/Validata. Existing deployments are available for:

Paper published at SWAT4LS2015.

CIM Best Paper

Our paper [1] presenting a framework for terminology mappings won one of two best paper awards at the First Workshop on Context, Interpretation and Meaning (CIM2014). The other award went to the paper by Amy Guy from the University of Edinburgh. Kerstin Forsberg from AstraZeneca presented the paper. You can find her slides on slideshare and embedded below. A […]

Our paper [1] presenting a framework for terminology mappings won one of two best paper awards at the First Workshop on Context, Interpretation and Meaning (CIM2014). The other award went to the paper by Amy Guy from the University of Edinburgh.

Kerstin Forsberg from AstraZeneca presented the paper. You can find her slides on slideshare and embedded below.

[1] S. Hussain, H. Sun, G. B. L. Erturkmen, M. Yuksel, C. Mead, A. J. G. Gray, and K. Forsberg, “A Justification-based Semantic Framework for Representing , Evaluating and Utilizing Terminology Mappings,” in Context. Interpret. Mean., Riva del Garda, Italy, 2014.
[Bibtex]
@inproceedings{Hussain2014CIM,
abstract = {Use of medical terminologies and mappings across them are consid- ered to be crucial pre-requisites for achieving interoperable eHealth applica- tions. However, experiences from several research projects have demonstrated that the mappings are not enough. Also the context of the mappings is needed to enable interpretation of the meaning of the mappings. Built upon these experi- ences, we introduce a semantic framework for representing, evaluating and uti- lizing terminology mappings together with the context in terms of the justifica- tions for, and the provenance of, the mappings. The framework offers a plat- form for i) performing various mappings strategies, ii) representing terminology mappings together with their provenance information, and iii) enabling termi- nology reasoning for inferring both new and erroneous mappings. We present the results of the introduced framework using the SALUS project where we evaluated the quality of both existing and inferred terminology mappings among standard terminologies.},
address = {Riva del Garda, Italy},
author = {Hussain, Sajjad and Sun, Hong and Erturkmen, Gokce Banu Laleci and Yuksel, Mustafa and Mead, Charles and Gray, Alasdair J G and Forsberg, Kerstin},
booktitle = {Context. Interpret. Mean.},
file = {:Users/Alasdair/Documents/Mendeley Desktop/2014/Hussain et al. - A Justification-based Semantic Framework for Representing , Evaluating and Utilizing Terminology Mappings.pdf:pdf},
title = {{A Justification-based Semantic Framework for Representing , Evaluating and Utilizing Terminology Mappings}},
year = {2014}
}

ISWC2014 In-use Paper

Slides for my ISWC2014 In-use track paper [1] are available below. Paper abstract: When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked […]

Slides for my ISWC2014 In-use track paper [1] are available below.

Paper abstract:

When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied.

In this paper, we present an approach to enable applications to choose the equivalence criteria to apply between datasets. Thus, supporting multiple dynamic views over the Linked Data. For chemical data, we show that multiple sets of links can be automatically generated according to different equivalence criteria and published with semantic descriptions capturing their context and interpretation. This approach has been applied within a large scale public-private data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

[1] [doi] C. R. Batchelor, C. Y. A. Brenninkmeijer, C. Chichester, M. Davies, D. Digles, I. Dunlop, C. T. A. Evelo, A. Gaulton, C. A. Goble, A. J. G. Gray, P. T. Groth, L. Harland, K. Karapetyan, A. Loizou, J. P. Overington, S. Pettifer, J. Steele, R. Stevens, V. Tkachenko, A. Waagmeester, A. J. Williams, and E. L. Willighagen, “Scientific Lenses to Support Multiple Views over Linked Chemistry Data,” in The Semantic Web – ISWC 2014 – 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I, 2014, pp. 98-113.
[Bibtex]
@inproceedings{iswc2014,
author = {Colin R. Batchelor and
Christian Y. A. Brenninkmeijer and
Christine Chichester and
Mark Davies and
Daniela Digles and
Ian Dunlop and
Chris T. A. Evelo and
Anna Gaulton and
Carole A. Goble and
Alasdair J. G. Gray and
Paul T. Groth and
Lee Harland and
Karen Karapetyan and
Antonis Loizou and
John P. Overington and
Steve Pettifer and
Jon Steele and
Robert Stevens and
Valery Tkachenko and
Andra Waagmeester and
Antony J. Williams and
Egon L. Willighagen},
title = {Scientific Lenses to Support Multiple Views over Linked Chemistry
Data},
booktitle = {The Semantic Web - {ISWC} 2014 - 13th International Semantic Web Conference,
Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part {I}},
month = oct,
year = {2014},
pages = {98--113},
url = {http://dx.doi.org/10.1007/978-3-319-11964-9_7},
doi = {10.1007/978-3-319-11964-9_7},
}

ISWC 2014

ISWC 2014 is taking place on the shores of Lake Garda, Italy. However, I won’t have much time to relax on the lake. Look out for my tweets (@gray_alasdair). My conference activities start on Sunday 19 October with the first workshop on Context, Interpretation and Meaning (CIM2014), which together with Harry Halpin (W3C) and Fiona […]

ISWC 2014 is taking place on the shores of Lake Garda, Italy. However, I won’t have much time to relax on the lake. Look out for my tweets (@gray_alasdair).

My conference activities start on Sunday 19 October with the first workshop on Context, Interpretation and Meaning (CIM2014), which together with Harry Halpin (W3C) and Fiona McNeill (Heriot-Watt University) I am a chair. We have managed to put together an interesting selection of 5 papers – two focusing on the context of links, two on the interpretation of alignments and one on the meaning of mappings. I am a co-author on this final paper, but Kerstin Forsberg will be presenting the work [1]. We also have an exciting panel session in store with Aldo Gangemi (CNR), Paul Groth (VU University of Amsterdam) and Harry Halpin.

Also taking place on Sunday is the Linked Science Workshop (LISC). Together with Simon Jupp and James Malone of the EBI we have a paper on modelling the provenance for linksets of convenience [2]. A linkset of convenience is one that does not model the underlying science correctly, but provides a convenient shortcut for linking data. An example from the world of biology is a linkset that directly links genes with their protein product.

On Monday I will be working with the W3C RDF Stream Processing (RSP) Community Group. We have been having regular phone meetings for the last year and have made great progress towards defining a common community model for RDF streams and a query language for processing them. The group will largely be attending the Stream Ordering Workshop and the Semantic Sensor Networks Workshop.

Tuesday is the first day of ISWC, and it is going to be a busy one for me. In the morning I will be presenting the Open PHACTS paper on our work enabling scientific lenses for chemistry data [3]. In the evening I will be at the poster and demonstration session showing off the Open PHACTS VoID Editor [4].

Finally, I am organising the Lightning Talks session on the last day of the conference. This is a session where you can present late breaking results or responses to work presented in the conference. Talks will be 5 minutes each and abstracts can be submitted until 8.30 am on Thursday.

After ISWC I think I’m going to need a break.

[1] S. Hussain, H. Sun, G. B. L. Erturkmen, M. Yuksel, C. Mead, A. J. G. Gray, and K. Forsberg, “A Justification-based Semantic Framework for Representing , Evaluating and Utilizing Terminology Mappings,” in Context. Interpret. Mean., Riva del Garda, Italy, 2014.
[Bibtex]
@inproceedings{Hussain2014CIM,
abstract = {Use of medical terminologies and mappings across them are consid- ered to be crucial pre-requisites for achieving interoperable eHealth applica- tions. However, experiences from several research projects have demonstrated that the mappings are not enough. Also the context of the mappings is needed to enable interpretation of the meaning of the mappings. Built upon these experi- ences, we introduce a semantic framework for representing, evaluating and uti- lizing terminology mappings together with the context in terms of the justifica- tions for, and the provenance of, the mappings. The framework offers a plat- form for i) performing various mappings strategies, ii) representing terminology mappings together with their provenance information, and iii) enabling termi- nology reasoning for inferring both new and erroneous mappings. We present the results of the introduced framework using the SALUS project where we evaluated the quality of both existing and inferred terminology mappings among standard terminologies.},
address = {Riva del Garda, Italy},
author = {Hussain, Sajjad and Sun, Hong and Erturkmen, Gokce Banu Laleci and Yuksel, Mustafa and Mead, Charles and Gray, Alasdair J G and Forsberg, Kerstin},
booktitle = {Context. Interpret. Mean.},
file = {:Users/Alasdair/Documents/Mendeley Desktop/2014/Hussain et al. - A Justification-based Semantic Framework for Representing , Evaluating and Utilizing Terminology Mappings.pdf:pdf},
title = {{A Justification-based Semantic Framework for Representing , Evaluating and Utilizing Terminology Mappings}},
year = {2014}
}
[2] S. Jupp, J. Malone, and A. J. G. Gray, “Capturing Provenance for a Linkset of Convenience,” in Proceedings of the 4th Workshop on Linked Science 2014 – Making Sense Out of Data (LISC2014) co-located with the 13th International Semantic Web Conference (ISWC 2014), Riva del Garda, Italy, 2014, pp. 71-75.
[Bibtex]
@inproceedings{Jupp2014,
address = {Riva del Garda, Italy},
author = {Jupp, Simon and Malone, James and Gray, Alasdair J G},
booktitle = {Proceedings of the 4th Workshop on Linked Science 2014 - Making Sense Out of Data (LISC2014)
co-located with the 13th International Semantic Web Conference (ISWC 2014)},
publisher = {CEUR},
month = oct,
volume = {1282},
pages = {71-75},
title = {{Capturing Provenance for a Linkset of Convenience}},
url = {http://ceur-ws.org/Vol-1282/lisc2014_submission_7.pdf},
year = {2014}
}
[3] [doi] C. R. Batchelor, C. Y. A. Brenninkmeijer, C. Chichester, M. Davies, D. Digles, I. Dunlop, C. T. A. Evelo, A. Gaulton, C. A. Goble, A. J. G. Gray, P. T. Groth, L. Harland, K. Karapetyan, A. Loizou, J. P. Overington, S. Pettifer, J. Steele, R. Stevens, V. Tkachenko, A. Waagmeester, A. J. Williams, and E. L. Willighagen, “Scientific Lenses to Support Multiple Views over Linked Chemistry Data,” in The Semantic Web – ISWC 2014 – 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I, 2014, pp. 98-113.
[Bibtex]
@inproceedings{iswc2014,
author = {Colin R. Batchelor and
Christian Y. A. Brenninkmeijer and
Christine Chichester and
Mark Davies and
Daniela Digles and
Ian Dunlop and
Chris T. A. Evelo and
Anna Gaulton and
Carole A. Goble and
Alasdair J. G. Gray and
Paul T. Groth and
Lee Harland and
Karen Karapetyan and
Antonis Loizou and
John P. Overington and
Steve Pettifer and
Jon Steele and
Robert Stevens and
Valery Tkachenko and
Andra Waagmeester and
Antony J. Williams and
Egon L. Willighagen},
title = {Scientific Lenses to Support Multiple Views over Linked Chemistry
Data},
booktitle = {The Semantic Web - {ISWC} 2014 - 13th International Semantic Web Conference,
Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part {I}},
month = oct,
year = {2014},
pages = {98--113},
url = {http://dx.doi.org/10.1007/978-3-319-11964-9_7},
doi = {10.1007/978-3-319-11964-9_7},
}
[4] C. Goble, A. J. G. Gray, and E. Tatakis, “Help me describe my data: A demonstration of the Open PHACTS VoID Editor,” in ISWC 2014 – Poster Demos, Riva del Garda, Italy, 2014, pp. 1-4.
[Bibtex]
@inproceedings{Goble2014,
address = {Riva del Garda, Italy},
author = {Goble, Carole and Gray, Alasdair J G and Tatakis, Eleftherios},
booktitle = {ISWC 2014 – Poster Demos},
month = oct,
pages = {1--4},
title = {{Help me describe my data: A demonstration of the Open PHACTS VoID Editor}},
year = {2014}
}