Where next for the reproducibility agenda in computational biology?
Background: The concept of reproducibility is a foundation of the scientific method. With the arrival of fast and powerful computers over the last few decades, there has been an explosion of results based on complex computational analyses and simulations. The reproducibility of these results has bee...
| Main Authors: | , , , , |
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| Format: | Journal article |
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BioMed Central
2016
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| _version_ | 1797102666417963008 |
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| author | Lewis, J Breeze, C Charlesworth, J Maclaren, O Cooper, J |
| author_facet | Lewis, J Breeze, C Charlesworth, J Maclaren, O Cooper, J |
| author_sort | Lewis, J |
| collection | OXFORD |
| description | Background: The concept of reproducibility is a foundation of the scientific method. With the arrival of fast and powerful computers over the last few decades, there has been an explosion of results based on complex computational analyses and simulations. The reproducibility of these results has been addressed mainly in terms of exact replicability or numerical equivalence, ignoring the wider issue of the reproducibility of conclusions through equivalent, extended or alternative methods. Results: We use case studies from our own research experience to illustrate how concepts of reproducibility might be applied in computational biology. Several fields have developed ‘minimum information’ checklists to support the full reporting of computational simulations, analyses and results, and standardised data formats and model description languages can facilitate the use of multiple systems to address the same research question. We note the importance of defining the key features of a result to be reproduced, and the expected agreement between original and subsequent results. Dynamic, updatable tools for publishing methods and results are becoming increasingly common, but sometimes come at the cost of clear communication. In general, the reproducibility of computational research is improving but would benefit from additional resources and incentives. Conclusions: We conclude with a series of linked recommendations for improving reproducibility in computational biology through communication, policy, education and research practice. More reproducible research will lead to higher quality conclusions, deeper understanding and more valuable knowledge. |
| first_indexed | 2024-03-07T06:09:12Z |
| format | Journal article |
| id | oxford-uuid:eee51def-324c-4082-b5e4-31f0c424fcae |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:09:12Z |
| publishDate | 2016 |
| publisher | BioMed Central |
| record_format | dspace |
| spelling | oxford-uuid:eee51def-324c-4082-b5e4-31f0c424fcae2022-03-27T11:36:14ZWhere next for the reproducibility agenda in computational biology?Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:eee51def-324c-4082-b5e4-31f0c424fcaeSymplectic Elements at OxfordBioMed Central2016Lewis, JBreeze, CCharlesworth, JMaclaren, OCooper, JBackground: The concept of reproducibility is a foundation of the scientific method. With the arrival of fast and powerful computers over the last few decades, there has been an explosion of results based on complex computational analyses and simulations. The reproducibility of these results has been addressed mainly in terms of exact replicability or numerical equivalence, ignoring the wider issue of the reproducibility of conclusions through equivalent, extended or alternative methods. Results: We use case studies from our own research experience to illustrate how concepts of reproducibility might be applied in computational biology. Several fields have developed ‘minimum information’ checklists to support the full reporting of computational simulations, analyses and results, and standardised data formats and model description languages can facilitate the use of multiple systems to address the same research question. We note the importance of defining the key features of a result to be reproduced, and the expected agreement between original and subsequent results. Dynamic, updatable tools for publishing methods and results are becoming increasingly common, but sometimes come at the cost of clear communication. In general, the reproducibility of computational research is improving but would benefit from additional resources and incentives. Conclusions: We conclude with a series of linked recommendations for improving reproducibility in computational biology through communication, policy, education and research practice. More reproducible research will lead to higher quality conclusions, deeper understanding and more valuable knowledge. |
| spellingShingle | Lewis, J Breeze, C Charlesworth, J Maclaren, O Cooper, J Where next for the reproducibility agenda in computational biology? |
| title | Where next for the reproducibility agenda in computational biology? |
| title_full | Where next for the reproducibility agenda in computational biology? |
| title_fullStr | Where next for the reproducibility agenda in computational biology? |
| title_full_unstemmed | Where next for the reproducibility agenda in computational biology? |
| title_short | Where next for the reproducibility agenda in computational biology? |
| title_sort | where next for the reproducibility agenda in computational biology |
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