[Esip-preserve] Fwd: PLOS Computational Biology: Ten Simple Rules for Reproducible Computational Research

[Lynnes, Christopher S. (GSFC-6102)]

Those rules may be simple, in that they are written pithily, but there is a world of complexity beneath nearly every single one of them.  Particularly for scientists that do not have robust data, workflow and configuration management systems to rely on.

If only there were a cross-platform, easy-to-install, easy-to-manage "science management system" designed for individual scientists, with full support for managing:
o archiving
o provenance
o workflows
o analysis results
o annotations
o versions (of every artifact above)
o secure public access

How hard could it be?  :-)

On Oct 28, 2013, at 8:37 AM, Curt Tilmes <Curt.Tilmes at nasa.gov<mailto:Curt.Tilmes at nasa.gov>>
 wrote:

-------- Original Message --------
Subject:        PLOS Computational Biology: Ten Simple Rules for Reproducible Computational Research
Date:   Sun, 27 Oct 2013 07:54:56 -0600
From:   Steve Aulenbach <saulenbach at usgcrp.gov><mailto:saulenbach at usgcrp.gov>

PLOS Computational Biology: Ten Simple Rules for Reproducible Computational Research
http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285;jsessionid=4AFC0E022E4769E5856CC5BB897EF6F9

"Replication is the cornerstone of a cumulative science [1]. However, new tools and technologies, massive amounts of data, interdisciplinary approaches, and the complexity of the questions being asked are complicating replication efforts, as are increased pressures on scientists to advance their research [2]. As full replication of studies on independently collected data is often not feasible, there has recently been a call for reproducible research as an attainable minimum standard for assessing the value of scientific claims [3]. This requires that papers in experimental science describe the results and provide a sufficiently clear protocol to allow successful repetition and extension of analyses based on original data [4].

[...]

We here present ten simple rules for reproducibility of computational research. These rules can be at your disposal for whenever you want to make your research more accessible—be it for peers or for your future self."



  *   Rule 1: For Every Result, Keep Track of How It Was Produced<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s2>
  *   Rule 2: Avoid Manual Data Manipulation Steps<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s3>
  *   Rule 3: Archive the Exact Versions of All External Programs Used<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s4>
  *   Rule 4: Version Control All Custom Scripts<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s5>
  *   Rule 5: Record All Intermediate Results, When Possible in Standardized Formats<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s6>
  *   Rule 6: For Analyses That Include Randomness, Note Underlying Random Seeds<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s7>
  *   Rule 7: Always Store Raw Data behind Plots<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s8>
  *   Rule 8: Generate Hierarchical Analysis Output, Allowing Layers of Increasing Detail to Be Inspected<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s9>
  *   Rule 9: Connect Textual Statements to Underlying Results<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s10>
  *   Rule 10: Provide Public Access to Scripts, Runs, and Results<http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003285#s11>

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--
Dr. Christopher Lynnes     NASA/GSFC, Code 610.2    phone: 301-614-5185
"The future is already here--it's just not very evenly distributed." Wm. Gibson



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