Scott Edmunds

**Researchers release a treasure trove of data on the developing retina,
pushing the boundaries of neuroscience publishing by presenting it
dynamically and reproducibly.**  
A [new
paper](http://dx.doi.org/10.1186/2047-217X-3-3 "Carmen paper"){target="_blank"
rel="noopener"} in *GigaScience* today demonstrates a major step forward
for reproducible research and public data-sharing in the neurosciences
with the publication and
[release](http://dx.doi.org/10.5524/100089 "GigaDB electrophysiology data"){target="_blank"
rel="noopener"} of a huge cache of electrophysiology data resources.
Important for studying visual development, many groups have been using
[multielectrode
array](http://en.wikipedia.org/wiki/Multielectrode_array "MEA wikipedia page"){target="_blank"
rel="noopener"} recordings to look at developmental changes and the
effects of various genetic defects on the spontaneous activity of the
retina. We've written
[previously](http://blogs.biomedcentral.com/gigablog/2013/05/09/the-difficulties-sharing-neuroscience-data-can-data-publishing-help/ "Neuroscience data publishing problems blog"){target="_blank"
rel="noopener"} about the difficulties in sharing neuroscience data, and
to date there are not many publicly available electrophysiology
datasets. In this new study, researchers made available 366 recordings
from 12 electrophysiology studies collected between 1993 and 2014,
spending several months to read the data from different formats and
convert and annotate them in a standardized and interoperable manner as
[HDF5](http://en.wikipedia.org/wiki/Hierarchical_Data_Format "Hierarchical Data Format wikipedia page"){target="_blank"
rel="noopener"} files. The authors hope the data will serve both as an
example for developing future standards, as well as being used to
address new scientific questions.

**A tribute to CARMEN**  
![](http://gigasciencejournal.com/blog/wp-content/uploads/2014/03/275px-Henri-Lucien_Doucet_-_Carmen-138x300.jpeg){.alignright
.size-medium .wp-image-2034 loading="lazy" decoding="async" width="138"
height="300"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2014/03/275px-Henri-Lucien_Doucet_-_Carmen-138x300.jpeg 138w, http://gigasciencejournal.com/blog/wp-content/uploads/2014/03/275px-Henri-Lucien_Doucet_-_Carmen.jpeg 275w"
sizes="(max-width: 138px) 100vw, 138px"}We've written extensively on the
importance of workflow management systems promoting reproducible
research, and following a similar example to our series of papers
utilizing our Galaxy platform, this study takes advantage of the [CARMEN
virtual
laboratory](http://www.carmen.org.uk/ "CARMEN homepage"){target="_blank"
rel="noopener"} for neurophysiology. CARMEN is an eScience project put
together by a consortium of British universities to enable sharing and
collaborative exploitation of data, analysis code and expertise along
the complete lifecycle of neurophysiology data. Tragically, Prof Colin
Ingram, the lead investigator for the CARMEN project [passed
away](http://incf.org/newsroom/highlights/professor-colin-ingram "Colin Ingram obit"){target="_blank"
rel="noopener"} while this paper was undergoing review, and the authors
have included a
[dedication](http://www.gigasciencejournal.com/content/3/1/3#sec1 "Dedication"){target="_blank"
rel="noopener"} in this paper to his memory, as the project was
initiated with his discussion and support.

On top of the 1GB of supporting data and code being available from our
[GigaDB
database](http://dx.doi.org/10.5524/100089 "GigaDB electrophysiology data"){target="_blank"
rel="noopener"} and [CARMEN
portal](https://portal.carmen.org.uk/#link=URN:LSID:portal.carmen.org.uk:metadata:36156 "CARMEN data link"){target="_blank"
rel="noopener"}, the authors also produced the paper in a dynamic
matter, creating it using R and the
[Knitr](http://yihui.name/knitr "Knitr homepage"){target="_blank"
rel="noopener"} application. Following the reproducible research
paradigm, this allows readers to see and use the code that generated
each figure and table and know exactly how the results were calculated,
adding confidence in the research output and allowing others to easily
build upon previous work.

In a similar manner to our [previous Q&A
articles](http://gigasciencejournal.com/blog/tag/qa/ "Q&A's in GigaBlog"){target="_blank"
rel="noopener"}, we asked first author Dr Stephen Eglen from the
University of Cambridge (with input from the other authors), to explains
how this kind of work can encourage practices that enhance openness and
reproducibility. We will also follow up in a [later posting on dynamic
documents](http://blogs.biomedcentral.com/gigablog/2014/04/16/qa-on-dynamic-documents/ "Q&A on dynamic documents"){target="_blank"
rel="noopener"}, and taking advantage of our open peer-review process
(see the pre-publication history
[here](http://www.gigasciencejournal.com/content/3/1/3/prepub "CARMEN paper reviewers comments"){target="_blank"
rel="noopener"}) we discuss with the referees how easy it was to review
and recreate the work in the paper.

**Q&A with Dr Stephen Eglen**

**With PLOS [tightening their data availability
policies](http://blogs.plos.org/everyone/2014/03/08/plos-new-data-policy-public-access-data/ "PLOS data policy change"){target="_blank"
rel="noopener"} (making them closer [to
ours](https://academic.oup.com/gigascience/pages/editorial_policies_and_reporting_standards "GigaScience supporting data policies"){target="_blank"
rel="noopener"}) there has been some [debate and
push-back](http://drugmonkey.wordpress.com/2014/02/25/plos-is-letting-the-inmates-run-the-asylum-and-this-will-kill-them/ "Wacalloons polemic"){target="_blank"
rel="noopener"} recently on the feasibility of enforcing this, so do you
have anything to say on this issue? You (and the authors of the papers
providing data) have gone to great effort to make data and code
available, and the methods transparent. Do you think it is worth it, and
what can we do to encourage other authors to follow?**

[![SEmugshot](http://gigasciencejournal.com/blog/wp-content/uploads/2014/03/SEmugshot.jpeg){.alignleft
.size-full .wp-image-1046 loading="lazy" decoding="async" width="173"
height="214"}](http://gigasciencejournal.com/blog/wp-content/uploads/2014/03/SEmugshot.jpeg)Our
view is that wherever possible, data and materials related to a
publication should be shared at the same time the article is made
available. For some fields, such as genomics, data sharing is
commonplace and straightforward. In Neuroscience, however, public
sharing of data is not routine. One way of encouraging neuroscientists
to share their data is to provide some form of academic credit. Data
papers in journals such as *Gigascience* are therefore one way forward.

As funding agencies now require data to be shared, we hope that we will
see an increase in data being made available. It will be interesting to
see how the funders assess compliance with data sharing.

**Why is the reproducible research paradigm important to you, and how
does this paper address that? What would you hope future users of these
resources will be able to do with them?**

Many computer programs currently used in research, e.g. for analyzing
data, are quite complicated and insufficiently described in academic
papers. The reproducible research (RR) paradigm encourages people to
encapsulate all the materials needed to write a paper in a format that
can be easily shared. When reading articles written using reproducible
research techniques, it is comforting to know that you can go, see and
use the code that generates each figure and table to know exactly how
something was calculated. This adds confidence in the research output,
and allows others to easily build upon previous work.

The RR paradigm also helps keeping documents consistent and up-to-date.
As a simple example, the legend of Figure 1 includes the phrase "We
currently have 366 recordings in the repository, occupying 298 MB on
disc". Neither of those numbers are hard-coded in the article, but are
dynamically computed by examining the files in our database. While we
were writing our paper, we kept adding new datasets and these numbers
always reflected the current size of the database.

We cannot say for sure, but we hope that our repository will continue to
grow over the years. Furthermore, we hope the data will serve both as an
example for developing future standards, as well as being used to
address new scientific questions. We are currently working on an article
developing new methods using some of these data as test cases.

**Can you give a little insight about how you put the paper together?
How long did it take you to collect and curate the data? As you used
Knitr to make it a dynamic literate programming document, how different
was this to putting together a regular static document? Was it more
effort and did it take you any longer than usual?**

Converting and curating the data took a few months, especially as the
data from colleagues came in varying different formats, mostly
text-based. However, it seems there are only so many ways of describing
spike-time data and the process got easier with each new dataset we
converted. Checking the data was more difficult and in some cases
required brief discussions with our colleagues.

Writing the paper with
[KnitR](http://yihui.name/knitr "Knitr homepage"){target="_blank"
rel="noopener"} was actually fairly straightforward. The KnitR framework
is a pleasure to use, and highly recommended to anyone wishing to learn
about reproducible research, especially, but not exclusively, for the R
programming language. One of its key features is that it can cache
computations, so that it only performs some task if it thinks the
corresponding code in the article has changed. Furthermore, in the
longer term we think it saves time --- as all of the details about what
you computed need to be specified in the document, you have a natural
record of what you did. You therefore don't need to keep a record
elsewhere, where it would probably not get updated, or you may forget
you even had. Following our example above, by writing a dynamic
document, you can also avoid worrying about what to change (e.g. a
figure legend) when you add or remove new datasets.

**It was very sad to hear about the passing of Prof Colin Ingram at the
end of last year, and you have added a dedication in the paper about
this. Do you have anything you would like to say on top of this, and
about the legacy of his CARMEN project?**

Colin was passionate about the CARMEN project from the start. As
Principal Investigator of the project and experimentalist himself, he
was a strong advocate of seeing the Carmen platform used by
neuroscientists for data analysis. It was him who initially suggested we
undertake this study on retinal waves and he gave us strong and generous
support all along. We hope to be able to carry his dream on and keep
developing the platform, making it attractive to a broader community of
basic and clinical neuroscientists.

UPDATE 26/4/14: Updated to include link to [subsequent related
posting](http://blogs.biomedcentral.com/gigablog/2014/04/16/qa-on-dynamic-documents/ "Q&A on dynamic documents"){target="_blank"
rel="noopener"} on dynamic documents. This work has also just been
highlighted in the April edition of the *Nature Neuroscience* [NeuroPod
podcast where you can
hear](http://www.nature.com/neurosci/neuropod/index-2014-04-25.html "April Neuropod podcast"){target="_blank"
rel="noopener"} an interview with Stephen talking about the feelgood
factor of sharing your data.

### **References**

[1.](http://dx.doi.org/10.1186/2047-217X-3-3 "Carmen paper"){target="_blank"
rel="noopener"} Eglen, SJ; Weeks, M; Jessop, M; Simonotto, J; Jackson,
T; Sernagor, E. A data repository and analysis framework for spontaneous
neural activity recordings in developing retina. *GigaScience* 2014,
**3**:3
[http://dx.doi.org/10.1186/2047-217X-3-3](http://dx.doi.org/10.1186/2047-217X-3-3 "Carmen paper"){target="_blank"
rel="noopener"}  
[2.](http://dx.doi.org/10.5524/100089 "GigaDB electrophysiology data"){target="_blank"
rel="noopener"} Eglen, SJ; Weeks, M; Jessop, M; Simonotto, J; Jackson,
T; Sernagor, E. (2014): Supporting material for "A data repository and
analysis framework for spontaneous neural activity recordings in
developing retina". GigaScience Database.
[http://dx.doi.org/10.5524/100089](http://dx.doi.org/10.5524/100089 "GigaDB electrophysiology data"){target="_blank"
rel="noopener"}

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<strong>
 Researchers release a treasure trove of data on the developing retina, pushing the boundaries of neuroscience publishing by presenting it dynamically and reproducibly.
</strong>
A new paper in
<em>
 GigaScience
</em>
today demonstrates a major step forward for reproducible research and public data-sharing in the neurosciences with the publication and release of a huge cache of electrophysiology data resources.

Messaggi di Rogue Scholar

CARMEN, reproducible research and push-button papers