Publications

What is a Publication?
61 Publications visible to you, out of a total of 61

Abstract (Expand)

ional workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.

Authors: Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober

Date Published: 2020

Publication Type: Journal

Abstract (Expand)

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.

Authors: M. L. Neal, M. Konig, D. Nickerson, G. Misirli, R. Kalbasi, A. Drager, K. Atalag, V. Chelliah, M. T. Cooling, D. L. Cook, S. Crook, M. de Alba, S. H. Friedman, A. Garny, J. H. Gennari, P. Gleeson, M. Golebiewski, M. Hucka, N. Juty, C. Myers, B. G. Olivier, H. M. Sauro, M. Scharm, J. L. Snoep, V. Toure, A. Wipat, O. Wolkenhauer, D. Waltemath

Date Published: 22nd Mar 2019

Publication Type: InBook

Abstract (Expand)

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.

Authors: M. L. Neal, M. Konig, D. Nickerson, G. Misirli, R. Kalbasi, A. Drager, K. Atalag, V. Chelliah, M. T. Cooling, D. L. Cook, S. Crook, M. de Alba, S. H. Friedman, A. Garny, J. H. Gennari, P. Gleeson, M. Golebiewski, M. Hucka, N. Juty, C. Myers, B. G. Olivier, H. M. Sauro, M. Scharm, J. L. Snoep, V. Toure, A. Wipat, O. Wolkenhauer, D. Waltemath

Date Published: 22nd Mar 2019

Publication Type: Book

Abstract (Expand)

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.

Authors: M. L. Neal, M. Konig, D. Nickerson, G. Misirli, R. Kalbasi, A. Drager, K. Atalag, V. Chelliah, M. T. Cooling, D. L. Cook, S. Crook, M. de Alba, S. H. Friedman, A. Garny, J. H. Gennari, P. Gleeson, M. Golebiewski, M. Hucka, N. Juty, C. Myers, B. G. Olivier, H. M. Sauro, M. Scharm, J. L. Snoep, V. Toure, A. Wipat, O. Wolkenhauer, D. Waltemath

Date Published: 22nd Mar 2019

Publication Type: Booklet

Abstract

Not specified

Author: Alan Williams

Date Published: 16th Jan 2019

Publication Type: Not specified

Abstract

Not specified

Authors: Sebastian Schmelzle, Thomas van de Kamp, Michael Heethoff, Vincent Heuveline, Philipp Lösel, Jürgen Becker, Felix Beckmann, Frank Schluenzen, Jörg U. Hammel, Andreas Kopmann, Wolfgang Mexner, Matthias Vogelgesang, Nicholas T. Jerome, Oliver Betz, Rolf Beutel, Benjamin Wipfler, Alexander Blanke, Steffen Harzsch, Marie Hörnig, Tilo Baumbach

Date Published: 7th Sep 2017

Publication Type: Not specified

Abstract

Not specified

Editor:

Date Published: 1st Sep 2017

Publication Type: Journal

Powered by
(v.1.15.0-main)
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH