Biology Reference
In-Depth Information
Computer Program”). But other recent scholarship seems to downplay both
the novelty of “big data” and the distinction between “hypothesis-driven” and
“hypothesis-free” biology (see Leonelli, “Introduction”).
41. DeLisi, “Computers in Molecular Biology,” 47.
42. Franklin, “Exploratory Experiments.”
43. Brown and Botstein, “Exploring the New World.”
44. On the increasing importance of hypothesis-free biology, see Kell and
Oliver, “Here Is the Evidence.”
45. Strasser, “Data-Driven Sciences.”
46. Georges Cuvier, Louis Agassiz, and Charles Darwin, for instance, all
dealt with large numbers of objects and paper records. Calling all this “data,”
however, seems to impose a present-day category on historical actors. The
Ox-
ford English Dictionary
offers two defi nitions of “data”: one based on philoso-
phy (facts forming the basis of reasoning) and one from computing (symbols
on which operations are performed). Suggesting that natural history deals with
“data” runs the risk of confusing or confl ating these two defi nitions.
47. Bacon, “The New Atlantis.”
Chapter Three
1. One way to appreciate the special value of sequence data is to examine
the Bermuda rules. In 1996 and 1997, a set of meetings among the genomics
community established rules for how and when data were to be shared and
who was entitled to use them. These rules recognized the unique epistemic
status of data (as something less than knowledge) and attempted to prevent
the direct exchange of data for fi nancial value.
2. Davis, “Sequencing the Human Genome,” 121. Davis compared the
HGP with Nixon's ill-fated “war on cancer” of the 1970s.
3. Davis, “Sequencing the Human Genome,” 21.
4. Shapin, “House of Experiment.”
5. At the start of my fi eldwork, it was usually my practice to ask infor-
mants and interviewees who else among their friends, colleagues, and profes-
sional acquaintances I should talk to. However, the separation of the two
groups made it such that, having begun talking to computational biologists, it
was diffi cult to gain an introduction to a bioinformatician.
6. One bioinformatics blog, written by a graduate student, half-jokingly di-
vided bioinformatics into six different “career paths”: Linux virtuoso (“the LV
performs all their research at the command line: vi edited bash scripts chained
together using shell pipes”), early adopter (“always working on the latest area
of research, system biology synthetic biology, personal genomics”), old school
(“blinkered to change in tools and technology, the Old School is doing their
analysis in Fortran on a Windows 95 Pentium II”), data miner (“their everyday
tools are mixed effect regression, hidden Markov models, and the fearsome
neural gas algorithm”), perfect coder (“produces code like poetry and, after
a fi ve second glance, even your dog knows what the script does”), wet-lab
bioinformatician (“while others have their heads in the clouds thinking about
theories and algorithms, the WB is getting his hands dirty with real data as it is
