Suchergebnisse

IBISBA's mission as a distributed European research infrastructure is to provide world-class cutting-edge research and innovation services enabling the development of biotechnology as a technology cornerstone of the circular bioeconomy. To achieve this, IBISBA integrates Europe's leading public-operated research infrastructure facilities into a coordinated business environment. The H2020 PRPEP-IBISBA project is designed to finalize the design and prepare IBISBA for future operation. To achieve this the project is organized into several work packages that focus on different aspects of the infrastructure, including its business model, legal structure, and scientific and technological strategy. In this report, we focus on the necessary coordination of IBISBA with the European technical e-Infrastructure landscape. We overview the positioning of EU infrastructures, and their plans, policies and technical solutions. The deliverable primarily focuses on the European Open Science Cloud, the thematic Research Infrastructures and Clusters serving the Biosciences, notably EOSC-Life and ELIXIR. We seek to preliminarily identify digital infrastructure that PREP-IBISBA can exploit, should align with or may contribute to. FAIR initiatives such as FAIRsFAIR, and potential community and commercial infrastructures and their role are briefly discussed. The work was undertaken through a mixture of desk research, participation at EOSC community meetings and workshops, and interviews with key EOSC leaders and service users. This deliverable is an outcome of Task 8.2 (Coordinate with the European technical e-Infrastructure landscape). It informs Task 8.1 (Develop a technical reference service framework, roadmap and evaluation process for the e-tools for the management of EU-IBISBA business processes and FAIR asset stewardship) and its associated deliverable 8.2 (A first technical design report) and Task 8.4 (Assemble a "sandbox" implementation of the service framework for supporting the preparatory stage of EU-IBISBA using candidate ...

Over the past decade we have been working on the concept of Research Objects [1]. We worked at the 20,000 feet level as a new form of FAIR+R scholarly communication and at the 5 feet level as a packaging approach for assembling all the elements of a research investigation, like the workflows, data and tools associated with a publication. Even in our earliest papers [2] we recognised that there was more to success than just using Linked Data. We swung from fancy RDF models and SHACL that specialists could use to simple RO-Crates and JSON-LD that mortal programmers could use. Success depends on a use case need, a community and tools which we found particularly in the world of computational workflows right from the outset. All along there have been politics and that has recently got even hotter. All fun and games!

In 2020, the COVID-19 pandemic resulted in a rapid response from governments and researchers worldwide. As of March 2021, there have been over 122 million confirmed cases, with over 2.7 million people dying as a result of COVID-19. Despite this staggering toll, those who work with pandemic-relevant data often face significant systemic barriers to accessing, sharing or re-using this data. In this paper we report preliminary results for an in-progress qualitative study, where we interviewed data professionals working with COVID-19-relevant data types including social media, mobility, viral genome, testing, infection, hospital admission, and deaths. These data types are variously used for pandemic spread modelling, healthcare system strain awareness, and devising therapeutic treatments for COVID-19. Barriers to data sharing include cost of access to data (primarily certain healthcare sources and mobility data from mobile phone carriers), human throughput bottlenecks, unclear pathways to request access to data, unnecessarily strict access controls and data re-use policies, unclear data provenance, inability to link separate datasources that could collectively create a more complete picture, poor metadata standards, and a lack of computer-suitable data formats.

Background: Data discovery, particularly the discovery of key variables and their inter-relationships, is key to secondary data analysis, and in-turn, the evolving field of data science. Interface designers have presumed that their users are domain experts, and so they have provided complex interfaces to support these "experts." Such interfaces hark back to a time when searches needed to be accurate first time as there was a high computational cost associated with each search. Our work is part of a governmental research initiative between the medical and social research funding bodies to improve the use of social data in medical research. Objective: The cross-disciplinary nature of data science can make no assumptions regarding the domain expertise of a particular scientist, whose interests may intersect multiple domains. Here we consider the common requirement for scientists to seek archived data for secondary analysis. This has more in common with search needs of the "Google generation" than with their single-domain, single-tool forebears. Our study compares a Google-like interface with traditional ways of searching for noncomplex health data in a data archive. Methods: Two user interfaces are evaluated for the same set of tasks in extracting data from surveys stored in the UK Data Archive (UKDA). One interface, Web search, is "Google-like," enabling users to browse, search for, and view metadata about study variables, whereas the other, traditional search, has standard multioption user interface. Results: Using a comprehensive set of tasks with 20 volunteers, we found that the Web search interface met data discovery needs and expectations better than the traditional search. A task × interface repeated measures analysis showed a main effect indicating that answers found through the Web search interface were more likely to be correct (F1,19=37.3, P<.001), with a main effect of task (F3,57=6.3, P<.001). Further, participants completed the task significantly faster using the Web search interface (F1,19=18.0, P<.001). There was also a main effect of task (F2,38=4.1, P=.025, Greenhouse-Geisser correction applied). Overall, participants were asked to rate learnability, ease of use, and satisfaction. Paired mean comparisons showed that the Web search interface received significantly higher ratings than the traditional search interface for learnability (P=.002, 95% CI [0.6-2.4]), ease of use

FAIRplus seeks to establish 'FAIRification' processes that can be used at scale to ensure FAIRness of IMI data. In order to establish, refine and validate FAIRplus FAIRification techniques, four pilot IMI datasets were selected in D1.1. This deliverable describes the outcomes achieved to date when FAIRifying these datasets using the newly established FAIRplus FAIRification process. The FAIRified pilot datasets are now listed in the IMI data catalogue, along with an evaluation of their level of FAIR after FAIRification processes were applied. In this deliverable, we show that FAIRplus FAIRification processes generally increase the level of FAIR for pilot datasets, although no datasets became "completely FAIR" as a result. Level of FAIR is quantified by a series of FAIR indicators, established from a FAIR assessment conducted on each dataset before and after FAIRification. The results of these assessments are linked from the IMI data catalogue. All processes used during FAIRification are documented as recipes in the FAIR Cookbook. ; This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 802750. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation and EFPIA companies.

FAIRplus seeks to establish 'FAIRification' processes that can be used at scale to ensure FAIRness of IMI data. In order to establish, refine and validate FAIRplus FAIRification techniques, four pilot IMI datasets were selected in D1.1. This deliverable describes the outcomes achieved to date when FAIRifying these datasets using the newly established FAIRplus FAIRification process. The FAIRified pilot datasets are now listed in the IMI data catalogue, along with an evaluation of their level of FAIR after FAIRification processes were applied. In this deliverable, we show that FAIRplus FAIRification processes generally increase the level of FAIR for pilot datasets, although no datasets became "completely FAIR" as a result. Level of FAIR is quantified by a series of FAIR indicators, established from a FAIR assessment conducted on each dataset before and after FAIRification. The results of these assessments are linked from the IMI data catalogue. All processes used during FAIRification are documented as recipes in the FAIR Cookbook. ; This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 802750. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation and EFPIA companies.

In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the "bioinformatics way of working". The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB's are built as Python wrappers to provide an interoperable architecture. BioBB's have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments. ; We thank The Barcelona Supercomputing Center for providing all the HPC resources to launch the test executions presented in this manuscript. Te project was supported by the following grants: BioExcel Center of Excellence (Horizon 2020 Framework Programme. Grants 675728, and 823830). Elixir-Excelerate (Horizon 2020 Framework Programme. Grant 676559). Spanish National Institute for Bioinformatics (Institute of Health Carlos III. Grants PT13/0001/0019, PT13/0001/0028, PT17/0009/0001). Spanish Government, Severo Ochoa Grant SEV2015-0493, TIN2015-65316-P, Generalitat de Catalunya. 2014-SGR-1051, 2017-SGR-1110. ; Peer Reviewed ; Postprint (published version)

This document is a report on a portfolio of case/feasibility studies on the ISBE concept. The main objectives of this report are as following: To monitor, assess and where relevant, identify and foster the implementation of case studies, testing whether ISBE methodologies work Compilation and translation of success stories in lay language in the field of the life sciences (i.e. from gene to ecosystems, including, e.g. the Virtual Patient, production of new pharmaceuticals, novel animal and plant breeding systems, industrial production of microbe and algae-based chemicals, etc.). Prepare the science case arguments and evidence to elucidate governments, policy-makers, funders and industry

Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflows have high computational, storage, and/or communication demands, and thus must execute on a wide range of large-scale platforms, from large clouds to upcoming exascale HPC platforms. Workflows will play a crucial role in the data-oriented and post-Moore's computing landscape as they democratize the application of cutting-edge research techniques, computationally intensive methods, and use of new computing platforms. As workflows continue to be adopted by scientific projects and user communities, they are becoming more complex. Workflows are increasingly composed of tasks that perform computations such as short machine learning inference, multi-node simulations, long-running machine learning model training, amongst others, and thus increasingly rely on heterogeneous architectures that include CPUs but also GPUs and accelerators. The workflow management system (WMS) technology landscape is currently segmented and presents significant barriers to entry due to the hundreds of seemingly comparable, yet incompatible, systems that exist. Another fundamental problem is that there are conflicting theoretical bases and abstractions for a WMS. Systems that use the same underlying abstractions can likely be translated between, which is not the case for systems that use different abstractions. More information: https://workflowsri.org/summits/technical

© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nature Genetics 44 (2012): 121-126, doi:10.1038/ng.1054. ; To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision. ; The authors also acknowledge the following funding sources in particular: UK Biotechnology and Biological Sciences Research Council (BBSRC) BB/I000771/1 to S.-A.S. and A.T.; UK BBSRC BB/I025840/1 to S.-A.S.; UK BBSRC BB/I000917/1 to D.F.; EU CarcinoGENOMICS (PL037712) to J.K.; US National Institutes of Health (NIH) 1RC2CA148222-01 to W.H. and the HSCI; US MIRADA LTERS DEB-0717390 and Alfred P. Sloan Foundation (ICoMM) to L.A.-Z.; Swiss Federal Government through the Federal Office of Education and Science (FOES) to L.B. and I.X.; EU Innovative Medicines Initiative (IMI) Open PHACTS 115191 to C.T.E.; US Department of Energy (DOE) DE-AC02- 06CH11357 and Arthur P. Sloan Foundation (2011- 6-05) to J.G.; UK BBSRC SysMO-DB2 BB/I004637/1 and BBG0102181 to C.G.; UK BBSRC BB/I000933/1 to C.S. and J.L.G.; UK MRC UD99999906 to J.L.G.; US NIH R21 MH087336 (National Institute of Mental Health) and R00 GM079953 (National Institute of General Medical Science) to A.L.; NIH U54 HG006097 to J.C. and C.E.S.; Australian government through the National Collaborative Research Infrastructure Strategy (NCRIS); BIRN U24-RR025736 and BioScholar RO1-GM083871 to G.B. and the 2009 Super Science initiative to C.A.S.