This document provides an overview and schedule for the course "SBC 361 Research Methods & Comms". The course is a mixture of advanced analytical skills taught in computer labs using the programming language R, and theoretical content covered in lectures and workshops. It includes two workshops on careers in science and popular science writing. Students will complete assignments involving the computer practicals and tutorials, and a mock exam. The schedule details the topics to be covered each week by different professors and teaching staff. It emphasizes the importance of attending classes, completing required work, and doing additional outside reading to succeed in the course.

1. Research Methods & Comms.
y.wurm@qmul.ac.uk
https://wurmlab.github.io

2. Dr. Beth Clare

3. Welcome
to
SBC
361

4. Course
Content
Full
course
description
is
available
on
QM
plus
Overview:
-­‐This
course
is
a
mixture
of
advanced
analytical
skills
and
theoretical
content
Analytical
components
in
the
computer
lab:
-­‐Programming
in
R
-­‐Advanced
statistical
analysis
Workshops:
-­‐One
scheduled
on
Careers
in
Science
-­‐One
scheduled
on
Popular
Science
Writing
Upcoming
Assignments:
-­‐Mock
Exam

5. Course
Content
Week
1
:(Dr.
Wurm):
Programming
in
R,
mock
exam
Week
2
(Dr.
Wurm):
Programming
in
R,
Data
&
reproducibility
Week
3
(Dr.
Michaels):
Workshop
1:
Careers
in
science
Week
3,
5,
10:
Tutorial
Week
4
(Dr.
Clare):
Reasoning
and
Philosophy
Week
6
(Dr.
Michaels):
Fraud
and
Controversy,
Publishing
Industry
Week
8:
Workshop
2:
Popular
Science
Writing
Week
8,
9,
11
(Prof.
Nichols):
P-­‐values,
hypotheses,
probability
Week
12:
TBD…we
may
use
these
or
actually
shift
them
to
be
exam
review
classes
in
semester
B
where
we
would
review
the
course
but
give
additional
help
on
essay
writing
for
exams
(requested
by
previous
years
students).

6. Computer
labs
• Weeks
2,4,6,11
– You
will
be
using
R
(rrrrrr….)
– Weeks
2,4,6
(Dr.
Wurrrrrm)
– Week
11
(Prof.
Nichols)

7. Tutorials
&
Assignments
• Tutorials
:
Weeks
3,6,10
– Practice
writing
essays
–
this
is
your
chance!
– mock
exam
– feedback
– second
attempt
– popular
science
writing
• Assignments:
5
– two
in
computer
practical
(10%,
5%)
– two
in
tutorial
(5%,
5%)
– speaker
questions
–
up
to
1%
bonus
on
the
practical
quiz

8. Mock
Exam
• Huh?
an
exam
already?
• No
preparation
required
–
this
draft
is
to
give
to
your
tutor
to
get
feedback
only,
you
will
then
revise
this
and
draft
two
gets
marked
• Know
who
your
tutor
is
• Today,
Fogg
LT
12
noon
for
45
minutes
• There
is
no
make
up.
If
you
don’t
come
you
can
join
in
the
tutorial
for
general
feedback
and
do
draft
two
but
your
mark
will
be
pegged
to
40%
unless
you
have
an
EC
(e.g.
timetable
conflict)

9. How
to
succeed
in
361
• Come
to
class!
• If
we
tell
you
something
is
required
-­‐
it
is
• Do
outside
reading

11. © Alex Wild & others

13. © National Geographic
Atta leaf-cutter ants

14. © National Geographic
Atta leaf-cutter ants

15. © National Geographic
Atta leaf-cutter ants

17. Oecophylla Weaver ants
© ameisenforum.de

18. © ameisenforum.de
Fourmis tisserandes

19. © ameisenforum.de
Oecophylla Weaver ants

20. © forestryimages.org© wynnie@flickr

21. Tofilski et al 2008
Forelius pusillus

22. Tofilski et al 2008
Forelius pusillus hides the nest entrance at night

23. Tofilski et al 2008
Forelius pusillus hides the nest entrance at night

24. Tofilski et al 2008
Forelius pusillus hides the nest entrance at night

25. Tofilski et al 2008
Forelius pusillus hides the nest entrance at night

26. Avant
Workers staying outside die
« preventive self-sacrifice »
Tofilski et al 2008
Forelius pusillus hides the nest entrance at night

27. Dorylus driver ants: ants with no home
© BBC

28. Animal biomass (Brazilian rainforest)
from Fittkau & Klinge 1973
Other insects Amphibians
Reptiles
Birds
Mammals
Earthworms
Spiders
Soil fauna excluding
earthworms,
ants & termites
Ants & termites

29. We use modern technologies to
understand insect societies.
• evolution of social behaviour
• molecules involved in social behaviour
• consequences of environmental change

32. Big data is invading biology

33. This changes
everything.454
Illumina
Solid...
Any lab can
sequence
anything!

34. http://gregoryzynda.com/ncbi/genome/python/2014/03/31/ncbi-genome.html

35. BIG

36. Big data is invading biology
• Genomics
• Cancer genomics
• Biodiversity assessments
• Stool microbiome sequencing
• Personalized medicine
• Sensor networks - e.g tracking microclimates, recording sounds
• Huge medical studies
• Aerial surveys (Drones) - e.g. crop productivity; rainforest cover
• Camera traps

38. Learning to deal with big data takes time
• Your work last year + at home this year + our 8 hours of practicals.
• QM’s MSc Programs
• Bioinformatics (for biologists)
• Ecological & Evolutionary Genomics (or Biodiversity Informatics)
• EECS: also have some.

40. Practicals
• Aim: get relevant data handling skills
• Doing things by hand:
• impossible?
• slow,
• error-prone,
• Automate!
• Basic programming
• in R
• no stats!

41. Why R?
😳😟
😴😡
😥

42. Practicals: contents
• Groups - ok?
• First 3h practical
• data accessing/subsetting
• search/replace
• regular expressions
• Second 3h practical
• functions
• loops
• Third session:
• 1.5h practical (integrating & revising all skills)
• 1.5h exam
Text search on steroids
Reusable pieces of work
Repeating the same thing many times

44. http://tryr.codeschool.com
just do it.

45. • create a variable that contains the number 35
• create a variable that contains the string “I love tofu”
• give me a vector containing the sequence of numbers
from 5 to 11
• access the second number
• replace the second number with 42
• add 5 to the second number
• now add 5 to all numbers
• now add an extra number: 1999
• can you sum all the numbers?

46. • creating a vector
> my_vector <- c(5, 6, 7, 8, 9, 10, 11)
> my_vector <- 5:11
> my_vector <- seq(from=5, to=11, by=1)
> my_vector
[1] 5 6 7 8 9 10 11
> (10 > 30)
[1] FALSE
> my_vector > 8
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE
> my_vector[my_vector > 8]
9 10 11
> other_vector <- my_vector[my_vector > 8]
> other_vector
9 10 11
> other_vector + 3
• give me a vector containing numbers from 5 to 11 (3 variant

47. • accessing a subset
• of a vector
> big_vector <- 150:100
> big_vector
[1] 150 149 148 147 146 145 144 143 142 141 140 139 138 137 136 1
[20] 131 130 129 128 127 126 125 124 123 122 121 120 119 118 117 1
[39] 112 111 110 109 108 107 106 105 104 103 102 101 100
> big_vector[5]
5
> mysubset <- big_vector[my_vector]
> mysubset
[1] 146 145 144 143 142 141 140
> big_vector > 130
[1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE F
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE F
[49] FALSE FALSE FALSE
> subset(big_vector, big_vector > 140)
[1] 150 149 148 147 146 145 144 143 142 141
> big_vector[big_vector >= 140]
[1] 150 149 148 147 146 145 144 143 142 141 140
> my_vector
[1] 5 6 7 8 9 10 11

48. Regular expressions (regex):
Text search on steroids.

49. who dat?

52. Regular expressions (regex):
Text search on steroids.
Regular expression Finds
David David
Dav(e|(id)) David, Dave
Dav(e|(id)|(ide)|o) David, Dave, Davide, Davo
At{1,2}enborough
Attenborough,
Atenborough
Atte[nm]borough
Attenborough,
Attemborough
At{1,2}[ei][nm]bo{0,1}ro((ugh)|w){0,1}
Atimbro,
attenbrough,
ateinborow
Easy counting, replacing all with “Sir David Attenborough”

53. Regex special symbols
Regular expression Finds Example
[aeiou] any single vowel “e”
[aeiou]*
between 0 and infinity
vowels vowels, e.g.’
“eeooouuu"
[aeoiu]{1,3} between 1 and 3 vowels “oui”
a|i one of the 2 characters “"
((win)|(fail))
one of the two
words in ()
fail

54. More Regex Special symbols
• Google “Regular expression cheat sheet”
• ?regexp
Synonymous with
[:digit:] [0-9]
[A-z] [A-z], ie [A-Za-z]
s whitespace
. any single character
.+ one to many of anything
b* between 0 and infinity letter ‘b’
[^abc] any character other than a, b or c.
( (
[:punct:]
any of these: ! " # $ % & ' ( ) * + , - . /
: ; < = > ? @ [ ] ^ _ ` { |