| 1. |
- Hardisty, Alex R., et al.
(författare)
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BioVeL: A virtual laboratory for data analysis and modelling in biodiversity science and ecology
- 2016
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Ingår i: BMC Ecology. - : Springer Science and Business Media LLC. - 1472-6785. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- © 2016 The Author(s).Background: Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. Results: BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on-line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. Conclusions: Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.
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| 2. |
- Martins Dos Santos, Vitor, et al.
(författare)
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Systems Biology in ELIXIR: modelling in the spotlight
- 2022
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Ingår i: F1000Research. - : F1000 Research Ltd. - 1759-796X .- 2046-1402. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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| 3. |
- Mathew, Cherian, et al.
(författare)
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A semi-automated workflow for biodiversity data retrieval, cleaning, and quality control
- 2014
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Ingår i: Biodiversity Data Journal. - 1314-2836 .- 1314-2828. ; 2:e4221, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- The compilation and cleaning of data needed for analyses and prediction of species distributions is a time consuming process requiring a solid understanding of data formats and service APIs provided by biodiversity informatics infrastructures. We designed and implemented a Taverna-based Data Refinement Workflow which integrates taxonomic data retrieval, data cleaning, and data selection into a consistent, standards-based, and effective system hiding the complexity of underlying service infrastructures. The workflow can be freely used both locally and through a web-portal which does not require additional software installations by users.
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| 4. |
- Obst, Matthias, 1974, et al.
(författare)
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Marine long-term biodiversity assessment suggests loss of rare species in the Skagerrak and Kattegat region
- 2018
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Ingår i: Marine Biodiversity. - : Springer Science and Business Media LLC. - 1867-1616 .- 1867-1624. ; 48:4, s. 2165-2176
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Tidskriftsartikel (refereegranskat)abstract
- © 2017 The Author(s) Studies of cumulative and long-term effects of human activities in the ocean are essential for developing realistic conservation targets. Here, we report the results of a recent national marine biodiversity inventory along the Swedish West coast between 2004 and 2009. The expedition revisited many historical localities that have been sampled with the same methods in the early twentieth century. We generated comparable datasets from our own investigation and the historical data to compare species richness, abundance, and geographic distribution of diversity. Our analysis indicates that the benthic ecosystems in the region have lost a large part of its original species richness over the last seven decades. We find evidence that especially rare species have disappeared. This process has caused a more homogenized community structure in the region and diminished historical biodiversity hotspots. We argue that the contemporary lack of rare species in the benthic ecosystems of the Kattegat and Skagerrak offers less opportunity to respond to environmental perturbations in the future and suggest improving the poor representation of rare species in the region. The study shows the value of biodiversity inventories as well as natural history collections in investigations of accumulated effects of anthropogenic activities and for re-establishing species-rich, productive, and resilient ecosystems.
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| 5. |
- Ostaszewski, Marek, et al.
(författare)
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COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms
- 2021
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Ingår i: Molecular Systems Biology. - : John Wiley & Sons. - 1744-4292 .- 1744-4292. ; 17:10
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Tidskriftsartikel (refereegranskat)abstract
- We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
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| 6. |
- Sonja, Holl, et al.
(författare)
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On specifying and sharing scientific workflow optimization results using research objects
- 2013
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Ingår i: Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science. - Denver, Colorado : ACM. - 9781450325028
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Konferensbidrag (refereegranskat)abstract
- Reusing and repurposing scientific workflows for novel scientific experiments is nowadays facilitated by workflow repositories. Such repositories allow scientists to find existing workflows and re-execute them. However, workflow input parameters often need to be adjusted to the research problem at hand. Adapting these parameters may become a daunting task due to the infinite combinations of their values in a wide range of applications. Thus, a scientist may preferably use an automated optimization mechanism to adjust the workflow set-up and improve the result. Currently, automated optimizations must be started from scratch as optimization meta-data are not stored together with workflow provenance data. This important meta-data is lost and can neither be reused nor assessed by other researchers. In this paper we present a novel approach to capture optimization meta-data by extending the Research Object model and reusing the W3C standards. We validate our proposal through a real-world use case taken from the biodivertsity domain, and discuss the exploitation of our solution in the context of existing e-Science infrastructures.
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| 7. |
- Wilkinson, Mark D., et al.
(författare)
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Comment : The FAIR Guiding Principles for scientific data management and stewardship
- 2016
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 3
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Tidskriftsartikel (refereegranskat)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.
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