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<nettime> How computers broke science...
august on Tue, 10 Nov 2015 18:59:36 +0100 (CET)


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<nettime> How computers broke science...


I thought this article might be somewhat relevant for nettime.  We don't
read too much here about the business of (mostly academic) science.  
                                                                                                                                                                                
TLDR:  point-and-click and closed-source software makes science hard to
reproduce.                                                                                                                                                                    
https://theconversation.com/how-computers-broke-science-and-what-we-can-do-to-fix-it-49938 

-august

--------------

Reproducibility is one of the cornerstones of science. Made popular by
British scientist Robert Boyle in the 1660s, the idea is that a
discovery should be reproducible before being accepted as scientific
knowledge.

In essence, you should be able to produce the same results I did if you
follow the method I describe when announcing my discovery in a scholarly
publication. For example, if researchers can reproduce the effectiveness
of a new drug at treating a disease, that's a good sign it could work
for all sufferers of the disease. If not, we're left wondering what
accident or mistake produced the original favorable result, and would
doubt the drug's usefulness.

For most of the history of science, researchers have reported their
methods in a way that enabled independent reproduction of their results.
But, since the introduction of the personal computer -- and the
point-and-click software programs that have evolved to make it more
user-friendly -- reproducibility of much research has become
questionable, if not impossible. Too much of the research process is now
shrouded by the opaque use of computers that many researchers have come
to depend on. This makes it almost impossible for an outsider to
recreate their results.

Recently, several groups have proposed similar solutions to this
problem. Together they would break scientific data out of the black box
of unrecorded computer manipulations so independent readers can again
critically assess and reproduce results. Researchers, the public, and
science itself would benefit.

Computers wrangle the data, but also obscure it

Statistician Victoria Stodden has described the unique place personal
computers hold in the history of science. They're not just an instrument
-- like a telescope or microscope -- that enables new research. The
computer is revolutionary in a different way; it's a tiny factory for
producing all kinds of new "scopesâ to see new patterns in scientific
data.

It's hard to find a modern researcher who works without a computer, even
in fields that aren't intensely quantitative. Ecologists use computers
to simulate the effect of disasters on animal populations. Biologists
use computers to search massive amounts of DNA data. Astronomers use
computers to control vast arrays of telescopes, and then process the
collected data. Oceanographers use computers to combine data from
satellites, ships and buoys to predict global climates. Social
scientists use computers to discover and predict the effects of policy
or to analyze interview transcripts. Computers help researchers in
almost every discipline identify what's interesting within their data.

Computers also tend to be personal instruments. We typically have
exclusive use of our own, and the files and folders it contains are
generally considered a private space, hidden from public view. Preparing
data, analyzing it, visualizing the results -- these are tasks done on
the computer, in private. Only at the very end of the pipeline comes a
publicly visible journal article summarizing all the private tasks.

The problem is that most modern science is so complicated, and most
journal articles so brief, it's impossible for the article to include
details of many important methods and decisions made by the researcher
as he analyzed his data on his computer. How, then, can another
researcher judge the reliability of the results, or reproduce the
analysis?


[image] Good luck recreating the analysis. US Army
How much transparency do scientists owe?

Stanford statisticians Jonathan Buckheit and David Donoho described this
issue as early as 1995, when the personal computer was still a fairly
new idea.

An article about computational science in a scientific publication is
not the scholarship itself, it is merely advertising of the scholarship.
The actual scholarship is the complete software development environment
and the complete set of instructions which generated the figures.
They make a radical claim. It means all those private files on our
personal computers, and the private analysis tasks we do as we work
toward preparing for publication should be made public along with the
journal article.

This would be a huge change in the way scientists work. We'd need to
prepare from the start for everything we do on the computer to
eventually be made available for others to see. For many researchers,
that's an overwhelming thought. Victoria Stodden has found the biggest
objection to sharing files is the time it takes to prepare them by
writing documentation and cleaning them up. The second biggest concern
is the risk of not receiving credit for the files if someone else uses
them.

A new toolbox to enhance reproducibility


[image] What secrets are within the computer?  US Army
Recently, several different groups of scientists have converged on
recommendations for tools and methods to make it easier to keep track of
files and analyses done on computers. These groups include biologists,
ecologists, nuclear engineers, neuroscientists, economists and political
scientists. Manifesto-like papers lay out their recommendations. When
researchers from such different fields converge on a common course of
action, it's a sign a major watershed in doing science might be under
way.

One major recommendation: minimize and replace point-and-click
procedures during data analysis as much as possible by using scripts
that contain instructions for the computer to carry out. This solves the
problem of recording ephemeral mouse movements that leave few traces,
are difficult to communicate to other people, and hard to automate.
They're common during data cleaning and organizing tasks using a
spreadsheet program like Microsoft Excel. A script, on the other hand,
contains unambiguous instructions that can be read by its author far
into the future (when the specific details have been forgotten) and by
other researchers. It can also be included within a journal article,
since they aren't big files. And scripts can easily be adapted to
automate research tasks, saving time and reducing the potential for
human error.

We can see examples of this in microbiology, ecology, political science
and archaeology. Instead of mousing around menus and buttons, manually
editing cells in a spreadsheet and dragging files between several
different software programs to obtain results, these researchers wrote
scripts. Their scripts automate the movement of files, the cleaning of
the data, the statistical analysis, and the creation of graphs, figures
and tables. This saves a lot of time when checking the analysis and
redoing it to explore different options. And by looking at the code in
the script file, which becomes part of the publication, anyone can see
the exact steps that produced the published results.

Other recommendations include the use of common, nonproprietary file
formats for storing files (such as CSV, or comma separated variables,
for tables of data) and simple rubrics for systematically organizing
files into folders to make it easy for others to understand how the
information is structured. They recommend free software that is
available for all computer systems (eg. Windows, Mac, and Linux) for
analyzing and visualizing data (such as R and Python). For
collaboration, they recommend a free program called Git, that helps to
track changes when many people are editing the same document.

Currently, these are the tools and methods of the avant-garde, and many
midcareer and senior researchers have only a vague awareness of them.
But many undergraduates are learning them now. Many graduate students,
seeing personal advantages to getting organized, using open formats,
free software and streamlined collaboration, are seeking out training
and tools from volunteer organizations such as Software Carpentry,  Data
Carpentry and rOpenSci to fill the gaps in their formal training. My
university recently created an eScience Institute, where we help
researchers adopt these recommendations. Our institute is part of a
bigger movement that includes similar institutes at Berkeley and New
York University.

As students learning these skills graduate and progress into positions
of influence, we'll see these standards become the new normal in
science. Scholarly journals will require code and data files to
accompany publications. Funding agencies will require they be placed in
publicly accessible online repositories.


[image] Example of a script used to analyze data. Author provided
Open formats and free software are a win/win

This change in the way researchers use computers will be beneficial for
public engagement with science. As researchers become more comfortable
sharing more of their files and methods, members of the public will have
much better access to scientific research. For example, a high school
teacher will be able to show students raw data from a recently published
discovery and walk the students through the main parts of the analysis,
because all of these files will be available with the journal article.

Similarly, as researchers increasingly use free software, members of the
public will be able to use the same software to remix and extend results
published in journal articles. Currently many researchers use expensive
commercial software programs, the cost of which makes them inaccessible
to people outside of universities or large corporations.

Of course, the personal computer is not the sole cause of problems with
reproducibility in science. Poor experimental design, inappropriate
statistical methods, a highly competitive research environment and the
high value placed on novelty and publication in high-profile journals
are all to blame.

What's unique about the role of the computer is that we have a solution
to the problem. We have clear recommendations for mature tools and
well-tested methods borrowed from computer science research to improve
the reproducibility of research done by any kind of scientist on a
computer. With a small investment of time to learn these tools, we can
help restore this cornerstone of science.



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