Browsing by Author "Juty, Nick"

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    The evolution of standards and data management practices in systems biology
    (EMBO, 2015-12) Stanford, Natalie J.; Wolstencroft, Katherine; Golebiewski, Martin; Kania, Renate; Juty, Nick; Tomlinson, Christopher; Owen, Stuart; Butcher, Sarah; Hermjakob, Henning; Le Novere, Nicolas; Mueller, Wolfgang; Snoep, Jacky; Goble, Carole
    Systems biology involves the integration of multiple heterogeneous data sets, in order to model and predict biological processes. The domain's interdisciplinary nature requires data, models and other research assets to be formatted and described in standard ways to enable exchange and reuse. Infrastructure for Systems Biology Europe (ISBE) is a project to establish essential, centralized services for systems biology researchers throughout the systems biology lifecycle. A key component of ISBE is to support the management, integration and exchange of data, models, results and protocols. To inform further ISBE development, we surveyed the community to evaluate the uptake of available standards, and current practices of researchers in data and model management.
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    Harmonizing semantic annotations for computational models in biology
    (Oxford University Press, 2019) Neal, Maxwell Lewis; Konig, Matthias; Nickerson, David; Mısırlı, Goksel; Kalbasi, Reza; Drager, Andreas; Atalag, Koray; Chelliah, Vijayalakshmi; Cooling, Michael T.; Cook, Daniel L.; Crook, Sharon; De Alba, Miguel; Friedman, Samuel H.; Garny, Alan; Gennari, John H.; Gleeson, Padraig; Golebiewski, Martin; Hucka, Michael; Juty, Nick; Myers, Chris; Olivier, Brett G.; Sauro, Herbert M.; Scharm, Martin; Snoep, Jacky L.; Toure, Vasundra; Wipat, Anil; Wolkenhauer, Olaf; Waltemath, Dagmar
    Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol.Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.
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    Identifiers for the 21st century : how to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data
    (Public Library of Science, 2017-06-29) McMurry, Julie A.; Juty, Nick; Blomberg, Niklas; Burdett, Tony; Conlin, Tom; Conte, Nathalie; Courtot, Melanie; Deck, John; Dumontier, Michel; Fellows, Donal K.; Gonzalez-Beltran, Alejandra; Gormanns, Philipp; Grethe, Jeffrey; Hastings, Janna; Heriche, Jean-Karim; Hermjakob, Henning; Ison, Jon C.; Jimenez, Rafael C.; Jupp, Simon; Kunze, John; Laibe, Camille; Le Novere, Nicolas; Malone, James; Martin, Maria Jesus; McEntyre, Johanna R.; Morris, Chris; Muilu, Juha; Muller, Wolfgang; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Sariyar, Murat; Snoep, Jacky L.; Soiland-Reyes, Stian; Stanford, Natalie J.; Swainston, Neil; Washington, Nicole; Williams, Alan R.; Wimalaratne, Sarala M.; Winfree, Lilly M.; Wolstencroft, Katherine; Goble, Carole; Mungall, Christopher J.; Haende, Melissa A.; Parkinson, Helen
    In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.