Cameron Neylon

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Iâ€™ve been interested for some time in capturing information and the
context in which that information is created in the lab. The question of
how to build an efficient and useable laboratory recording system is
fundamentally one of how much information is necessary to record and how
much of that can be recorded while bothering the researcher themselves
as little as possible.

The [Beyond the PDF mailing
list](https://groups.google.com/group/beyond-the-pdf) has, since the
meeting a few weeks ago, been partly focused on attempts to analyse
human written text and to *annotate* these as structured assertions, or
nanopublications. This is also the approach that many Electronic Lab
Notebook systems attempt to take, capturing an electronic version of the
paper notebook and in some cases trying to capture all the information
in it in a structured form. I canâ€™t help but feel that, while this is
important, itâ€™s almost precisely backwards. By definition any summary
of a written text will throw away information, the only question is how
much. Rather than trying to capture arbitrary and complex assertions in
written text, it seems better to me to ask what simple vocabulary can be
provided that can express enough of what people want to say to be
useful.

In classic 80/20 style we ask what is useful *enough* to interest
researchers, how much would we lose, and what would that be? This neatly
sidesteps the questions of truth (though not of likelihood) and context
that are the real challenge of structuring human authored text via
annotation because the limited vocabulary and the collection of
structured statements made provides an explicit context.

This kind of approach turns out to work quite well in the lab. In our
blog based notebook we use a [one item-one post
approach](http://cameronneylon.net/blog/evolving-usage-patterns-on-the-southampton-lab-blog-book/)
where every research artifact gets its own URL. Both the verbs, the
procedures, and the nouns, the data and materials, all have a unique
identifier. The relationships between verbs and nouns is provided by
simple links. Thus the structured vocabulary of the lab notebook is
\[Material\] was input to \[Process\] which generated \[Data\] (where
Material and Data can be interchanged depending on the process).Â  This
is not so much 80/20 as 30/70 but even in this very basic form in can be
quite useful. Along with records of who did something and when, and some
basic tagging this actually makes a quite an effective lab notebook
system.

The question is, how can we move beyond this to create a record which is
richer enough to provide a real step up, but doesnâ€™t bother the user
any more than is necessary and justified by the extra functionality that
they're getting. In fact, ideally weâ€™d capture a richer and more
useful record while bothering the user less. A part of the solution lies
in the work that [Jeremy
Frey](http://www.soton.ac.uk/~jgf/Frey/Home.html)â€™s group have done
with blogging instruments. By having an instrument create a record of
itâ€™s state, inputs and outputs, the user is freed to focus on what
their doing, and only needs to link into that record when they start to
do their analysis.

Another route is the approach that [Peter
Murray-Rust](http://blogs.ch.cam.ac.uk/pmr/)â€™s group are exploring
with interactive lab equipment, particularly [a fume
cupboard](http://blogs.ch.cam.ac.uk/pmr/2010/06/17/ami-can-you-help-us-build-a-virtual-chemistry-laboratory-in-second-life/)
that can record spoken instructions and comments and track where objects
are, monitoring an entire process in detail. The challenge in this
approach lies in translating that information into something that is
easy to use downstream. Audio and video remain difficult to search and
worth with. Speech recognition isnâ€™t great for formatting and clear
presentation.

In the spirit of a limited vocabulary another approach is to use a
lightweight infrastructure to record short comments, either structured,
or free text. [A bakery in London](http://twitter.com/#!/albionsoven)
has a switch on its wall which can be turned to one of a small number of
baked good as a batch goes into the oven. This is connected to a very
basic twitter client then tells the world that there are fresh baked
baguettes coming in about twenty minutes. Because this output data is
structured it would in principle be possible to track the different
baking times and preferences for muffins vs doughnuts over the day and
over the year.

The lab is slightly more complex than a bakery. Different processes
would take different inputs. Our hypothetical structured vocabulary
would need to enable the construction of sentences with subjects,
predicates, and objects, but as weâ€™ve learnt with the lab notebook,
even the simple predicate â€œis input toâ€?, â€œis output ofâ€? can be
very useful. "I am doing X" where X is one of a relatively small set of
options provides real time bounds on when important events happened. A
little more sophistication could go a long way. A very simple twitter
client that provided a relatively small range of structured statements
could be very useful. These statements could be processed downstream
into a more directly useable record.

Last week I recorded the steps that I carried out in the lab via the
hashtag [\#tweetthelab](http://twapperkeeper.com/hashtag/tweetthelab).
These free text tweets make a serviceable, if not perfect, record of the
days work. What is missing is a URI for each sample and output data
file, and links between the inputs, the processes, and the outputs. But
this wouldnâ€™t be too hard to generate, particularly if instruments
themselves were actually blogging or tweeting its outputs. A simple
client on a tablet, phone, or locally placed computer would make it easy
to both capture and to structure the lab record. There is still a need
for free text comments and any structured description will not be able
to capture everything but the potential for capturing a lot of the
detail of what is happening in a lab, as it happens, is significant. And
it's the detail that often isn't recorded terribly well, the little bits
and pieces of exactly when something was done, what did the balance
really read, which particular bottle of chemical was picked up.

Twitter is often derided as trivial, as lowering the barrier to shouting
banal fragments to the world, but in the lab we need tools that will
help us collect, aggregate and structure exactly those banal pieces so
that we have them when we need them. Add a little bit of structure to
that, but not too much, and we could have a winner. Starting from human
discourse always seemed too hard for me, but starting with identifying
the simplest things we can say that are also useful to the scientist on
the ground seems like a viable route forward.

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Image via Wikipedia  Iâ€™ve been interested for some time in capturing information and the context in which that information is created in the lab. The question of how to build an efficient and useable laboratory recording system is fundamentally one of how much information is necessary to record and how much of that can be recorded while bothering the researcher themselves as little as possible.

Messaggi di Rogue Scholar

Tweeting the lab