[open-science] "open science" definition?

Peter Murray-Rust pm286 at cam.ac.uk
Mon Oct 13 05:54:50 UTC 2014

This is not a new issue. Ten years ago Jean-Claude Bradley and others
including me discussed this at length and we decided then that "Open
Science" was too broad a term to be useful in guiding practice. For many
people the laboratory aspects of science and its communication are
sufficiently different that one term didn't cover them. We also were
worried that "Open Access" had become so vague, variable and dliuted that
it could mean whatever you wanted. The same c/would happen for science.

  Because of that J-C coined the term "Open Notebook Science" for the
parctice of science with immediate visibility to the whole world. The term
is precise ("no insider knowledge"), valuable in practice and becoming
increasingly used. Other such as Mat Todd adopted essentially the same
philosophy for Open Drug Discovery.

I have attempted to summarise some of this, including snippets of  J-C's
writings, in presentations such as
http://www.slideshare.net/petermurrayrust/osbrazil "Open Data and Open
Notebook Science"

On Mon, Oct 13, 2014 at 4:32 AM, Tom Roche <Tom_Roche at pobox.com> wrote:

> Jenny Molloy Sun, 12 Oct 2014 19:57:06 +0100 [1]
> >> my feeling is that ensuring that the knowledge and tools created
> through your research are available as per the Budapest Open Access
> Initiative (or Berlin Declaration), Open Knowledge Definition, Free
> Software Definition plus similar implementations for hardware and more
> specialised tools like seeds, cell lines, reagents, other materials (not
> all of which exist but most are being worked on) just about covers open
> scientific knowledge from a legal/technical perspective.
> Tom Roche Sun, 12 Oct 2014 19:38:26 -0400 [2]
> > That sounds about right[.] I suspect we can *quickly* get 95% of a
> useful Open-Science Definition 1.0 just by composing definitions of the
> main parts: open input data/assimilation (e.g., the Open Definition[3]),
> open processing (e.g. (computationally), open-source code in public
> repositories), open output data/analysis (e.g., open-access publishing--the
> OD seems applicable here as well).
> Just to be blindingly obvious (unless the following exposes flaws to which
> I am currently blind):
> I assert that we can, to a first approximation, model a scientific study
> like a classic *x pipeline[4]:
> * input > transform > output
> * open output (data or analysis) from any one study may become an input
> for any number of subsequent studies
> That scientific studies seek to rigorously create and test empirical
> hypotheses is extraneous to our purpose, which is to characterize the
> openness of studies. Similarly, that input data is often subject to
> previous assimilation[5] and output data is usually subject to subsequent
> analysis (e.g., figure creation) is immaterial: for purposes of this model,
> all operations on data can be lumped into the single step=transform.
> Furthermore, for characterization of openness, inputs and outputs are
> equivalently data. Unless I'm missing something, the Open Definition (or
> similar) should apply to both--no?
> That leaves characterization of the openness of a study's transform(s). To
> a first approximation, we can separately characterize every transform as
> non- or computational. For computational science, I'm assuming we could
> leverage prior art on openness of
> * source codes and their repositories
> * source platforms (e.g., the hardware, OS, or other software required to
> run the sources)
> I know much less about openness of non-computational protocols (I'm just a
> coder who works on environmental models) but assume (absent contradiction
> from someone who actually *knows* about this space :-) that their openness
> has been defined by one or more domain experts: in the worst case, their
> openness could be defined, as with computational transforms, in terms of
> their reproducibility.
> Am I missing something?
> FWIW, Tom Roche <Tom_Roche at pobox.com>
> [1] https://lists.okfn.org/pipermail/open-science/20141012/003550.html
> [2] https://lists.okfn.org/pipermail/open-science/20141012/003551.html
> [3] http://opendefinition.org/od/
> [4] https://en.wikipedia.org/wiki/Pipeline_%28Unix%29
> [5] https://en.wikipedia.org/wiki/Data_assimilation
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Peter Murray-Rust
Reader in Molecular Informatics
Unilever Centre, Dep. Of Chemistry
University of Cambridge
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