Scipediacontent: Scipediacontent moved page Draft Content 597293968 to Barghi et al 2018a

2021-02-03T15:21:51Z

Scipediacontent moved page Draft Content 597293968 to Barghi et al 2018a

Scipediacontent: Created page with " == Abstract == Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that in..."

2021-02-03T15:21:48Z

Created page with " == Abstract == Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that in..."

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== Abstract ==

Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their computational and storage requirements. We present a method to predict the computational reproducibility of data analysis pipelines in large population studies. We formulate the problem as a collaborative filtering process, with constraints on the construction of the training set. We propose 6 different strategies to build the training set, which we evaluate on 2 datasets, a synthetic one modeling a population with a growing number of subject types, and a real one obtained with neuroinformatics pipelines. Results show that one sampling method, "Random File Numbers (Uniform)" is able to predict computational reproducibility with a good accuracy. We also analyze the relevance of including file and subject biases in the collaborative filtering model. We conclude that the proposed method is able to speedup reproducibility evaluations substantially, with a reduced accuracy loss.

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1809.10139 http://arxiv.org/abs/1809.10139]

* [http://arxiv.org/pdf/1809.10139 http://arxiv.org/pdf/1809.10139]

* [http://xplorestaging.ieee.org/ielx7/8610059/8621858/08622095.pdf?arnumber=8622095 http://xplorestaging.ieee.org/ielx7/8610059/8621858/08622095.pdf?arnumber=8622095],
: [http://dx.doi.org/10.1109/bigdata.2018.8622095 http://dx.doi.org/10.1109/bigdata.2018.8622095]

* [https://dblp.uni-trier.de/db/journals/corr/corr1809.html#abs-1809-10139 https://dblp.uni-trier.de/db/journals/corr/corr1809.html#abs-1809-10139],
: [https://academic.microsoft.com/#/detail/2962866071 https://academic.microsoft.com/#/detail/2962866071]

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Barghi et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 597293968 to Barghi et al 2018a

Scipediacontent: Created page with " == Abstract == Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that in..."