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r-cubed: Guiding the overwhelmed scientist from random wrangling to Reproducible Research in R
Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review
Dokumenter
- 10.21105.jose.00122
Forlagets udgivne version, 144 KB, PDF-dokument
DOI
- https://doi.org/10.21105/jose.00122
Forlagets udgivne version
- Luke Johnston
- Helene Bæk Juel, Novo Nordisk Foundation Center for Basic Metabolic Research, Danmark
- Betinna Lengger, Novo Nordisk Foundation Center for Biosustainability, Danmarks Tekniske Universitet, Danmark
- Daniel Rinse Witte
- Hannah Chatwin, Syddansk Universitet, Danmark
- Malene Revsbech Christiansen, Novo Nordisk Foundation Center for Basic Metabolic Research, Danmark
- Anders Aasted Isaksen
The amount of biological data created increases every year, driven largely by technologies such as high-throughput -omics, real-time monitoring, or high resolution imaging in addition to greater access to routine administrative data and larger study populations. This not only presents operational challenges, but also highlights considerable needs for the skills and knowledge to manage, process, and analyze this data.
Along with the open science movement on the rise, methods and analytic processes are also increasingly expected to be open and transparent and for scientific studies to be reproducible.
Unfortunately, training in modern computational skills has not kept pace, which is particularly evident in biomedical research, where training tends to focus on clinical, experimental, or wet-lab skills. The computational learning module we have developed and described below aims to introduce and improve skills in R, reproducibility, and open science for researchers in the biomedical field, with a focus on diabetes research.
The r-cubed (Reproducible Research in R or R3) learning module is structured as a three-day workshop, with five sub-modules. We have specifically designed the module as an open educational resource that: 1) instructors can make use of directly or modify for their own lessons; and, 2) learners can use independently or as a reference after participating in the workshop. All content is available for re-use under CC-BY License.
Along with the open science movement on the rise, methods and analytic processes are also increasingly expected to be open and transparent and for scientific studies to be reproducible.
Unfortunately, training in modern computational skills has not kept pace, which is particularly evident in biomedical research, where training tends to focus on clinical, experimental, or wet-lab skills. The computational learning module we have developed and described below aims to introduce and improve skills in R, reproducibility, and open science for researchers in the biomedical field, with a focus on diabetes research.
The r-cubed (Reproducible Research in R or R3) learning module is structured as a three-day workshop, with five sub-modules. We have specifically designed the module as an open educational resource that: 1) instructors can make use of directly or modify for their own lessons; and, 2) learners can use independently or as a reference after participating in the workshop. All content is available for re-use under CC-BY License.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | 122 |
| Tidsskrift | The Journal of Open Source Education |
| Vol/bind | 4 |
| Nummer | 44 |
| Antal sider | 5 |
| ISSN | 2577-3569 |
| DOI | |
| Status | Udgivet - okt. 2021 |
- Reproducible research, data management and documentation, data management infrastructure, Programming Education
Forskningsområder
Se relationer på Aarhus Universitet Citationsformater
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