Bibliografia

Björn Grüning i altres (2018). Practical Computational Reproducibility in the Life Sciences. Cell Systems6(6), p. 631-635. https://docs.anaconda.com/free/anacondaorg/user-guide/

Carole Goble i altres (2020). FAIR Computational Workflows. Data Intelligence, 2, p. 108-121.

Mark D. Wilkinson i altres (2016). The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific Data, 3. 160018

Serghei Mangul i altres (2019). Challenges and Recommendations to Improve the Installability and Archival Stability of Omics Computational Tools. PLoS Biology, 17, e3000333.

Sean P. Kane, Karl Matthias (2023). Docker: Up & Running (3rd Edition). O’Reilly Media.

Victoria Stodden i altres (2018). An Empirical Analysis of Journal Policy Effectiveness for Computational Reproducibility. Proceedings of the National Academy of Sciences, 115, p. 2.584-2.589.

Wade L. Schulz i altres (2016). Use of Application Containers and Workflows for Genomic Data Analysis. Journal of Pathology Informatics7(53). https://doi.org/10.4103/2153-3539.197197 (2016).

Yang-Min Kim i altres (2018). Experimenting with Reproducibility: A Case Study of Robustness in Bioinformatics. Gigascience, 7, giv077.

Yuxing Yan, James Yan (2018). Hands-On Data Science with Anaconda. Packt Publishing https://docs.docker.com/

Zachary D. Stephens i altres (2015). Big Data: Astronomical or Genomical? PLoS Biology, 13, e1002195.