This document provides information about various topics related to research methods and communications, including: - Examples of different ant species with corresponding image citations - A graph showing the biomass of different animal groups in the Brazilian rainforest - An overview of how big data is invading biology and the new skills needed to work with large datasets - Details about upcoming practical sessions focused on learning data handling skills in R like formatting, searching/replacing, regular expressions, functions, and loops - Additional tips for writing, career development, experimental design, and reproducible research

1. Research Methods & Comms.
y.wurm@qmul.ac.uk

2. © Alex Wild & others

4. Atta leaf-cutter ants
© National Geographic

5. Atta leaf-cutter ants
© National Geographic

6. Atta leaf-cutter ants
© National Geographic

8. Oecophylla Weaver ants
© ameisenforum.de

9. © ameisenforum.de
Fourmis tisserandes

10. © ameisenforum.de
Oecophylla Weaver ants

11. © wynnie@flickr © forestryimages.org

12. Tofilski et al 2008
Forelius pusillus

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

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

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

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

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

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

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

21. Big data is invading biology

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

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

25. Learning to deal with big data takes time
• New Master’s Programs @ QM:
• Bioinformatics (for biologists)
• Ecological & Evolutionary Genomics (or Biodiversity Informatics)
• Our 6 hours of practicals.

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

27. Practicals: format
• Groups - ok?
• 3h practical this week
• data accessing/subsetting
• search/replace
• regular expressions
• 3h in two weeks
• 2h practical
• functions
• loops
• 1h exam (last hour of practical)

28. http://tryr.codeschool.com

29. Regular expressions:
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){0,1} Atimbro, attenbrough, etc.
Easy counting, replacing all with “Sir David Attenborough”

30. Regular expressions
Synonymous with
d [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: ! " # $ % & ' ( ) * + , - . /
: ; < = > ? @ [ ] ^ _ ` { |
• Google “Regular expression cheat sheet”
• ?regexp

31. Functions
• R has many. e.g.: plot(), t.test()
• Making your own:
tree_age_estimate <- function(diameter, species) {
[...do the magic...
# maybe something like:
growth.rate <- growth.rates[ species ]
age.estimate <- diameter / growth.rate
...]
!
return(age.estimate)
}
> tree_age_estimate(25, “White Oak”)
+ 66
> tree_age_estimate(60, “Carya ovata”)
+ 190

32. “for”
Loop
> possible_colours <- c('blue', 'cyan', 'sky-blue', 'navy blue',
'steel blue', 'royal blue', 'slate blue', 'light blue', 'dark
blue', 'prussian blue', 'indigo', 'baby blue', 'electric blue')
!
> possible_colours
[1] "blue" "cyan" "sky-blue" "navy blue"
[5] "steel blue" "royal blue" "slate blue" "light blue"
[9] "dark blue" "prussian blue" "indigo" "baby blue"
[13] "electric blue"
!
> for (colour in possible_colours) {
+ print(paste("The sky is oh so, so", colour))
+ }
!
[1] "The sky is so, oh so blue"
[1] "The sky is so, oh so cyan"
[1] "The sky is so, oh so sky-blue"
[1] "The sky is so, oh so navy blue"
[1] "The sky is so, oh so steel blue"
[1] "The sky is so, oh so royal blue"
[1] "The sky is so, oh so slate blue"
[1] "The sky is so, oh so light blue"
[1] "The sky is so, oh so dark blue"
[1] "The sky is so, oh so prussian blue"
[1] "The sky is so, oh so indigo"
[1] "The sky is so, oh so baby blue"
[1] "The sky is so, oh so electric blue"

33. xkcd

34. 1.23 (?)

35. More career stuff
• Internships?
•What does PhD mean?
• Basic CV rules

36. Writing
• an essay
• a cover letter
• a reference letter
• (a “new scientist article”)
• a dissertation
• (an abstract)

37. QMUL marking scheme Marking Criteria and Mark Scheme for Essay-style Questions
Levels 5 - 6 Level 6
All Levels
(Desirable in other years)
Evidence of Comprehension Breadth and Depth of Knowledge Irrelevant Material and Errors Synthesis & Balance Originality & Innovation
A+ Outstanding. Deep insight
Outstanding. As much as could be
expected
Absent or minimal Evidence of critical analysis Original ideas and insight
Clear understanding. Shrewd and
appropriate
Extensive. Almost as much as could be
expected
Minimal or absent Astute selection and juxtaposition Some evidence of creative A- Tending to description rather than
interpretation
Extensive Minimal Appropriate selection and combination Some
A-- Sufficient to marshal a well-organised,
direct response
Most key points but not extensive
Perhaps some minor errors and tangential
material
Inappropriate balance, partial synthesis Limited
Sufficient to marshal an organised, direct
response
Not all key points but comprehensive and
accurate
Some minor errors and tangential material Inappropriate balance, partial synthesis Limited
Not a direct response but sufficient for a
logical presentation.
Several omissions but some key points Some errors, tangential material Minimal Minimal
D,E Poor comprehension, muddled
organisation
Major omissions. No key points. A few
basic facts
Major factual errors. Frequently irrelevant None None
F+ Almost none One or two very minor points correct Extensively irrelevant or wrong None None
None
One or two very minor points just about
correct
Extensively irrelevant or wrong None None
F- None No evidence of being better if longer Almost all irrelevant or wrong None None
Nothing written Nothing written Nothing written None None
Notes:
x In order to qualify for an "A-grade" the work must meet most of the indicated criteria.
x Grade to % conversion: A+ = 100; A = 92; A- = 83; A-- = 74; B+ = 68; B = 65; B- = 63; C+ = 58; C = 55; C- = 53; D+ = 49; D = 48; D- = 47; E+ = 44; E = 43; E- = 42; F++ = 39; F+ = 37; F = 27; F- = 17; 0 = 0

38. Important for all: Structure
Clear overall structure?
Separate intro
starts from general points.
announces the structure (paragraphs or major
sections).
One paragraph per idea/point.
Clear structure within each paragraph.
If includes a list: “three lines of evidence suggest
that X. First, ...., Second, ... Finally....”
Clarity of each sentence.
No unnecessary words!
Try to make smooth transitions

39. More writing tips.
• No ping-ponging!
• No unnecessary ideas.
• Eliminate unnecessary words.
• “We have performed X” -- “We did X”
• shorter is better
• Put MS Word in “strict grammar”
• Eliminate jargon.
• write for the “general smart scientists” with little domain
specific knowledge.

40. Writing
• an essay
• a cover letter
• a reference letter
• (a “new scientist article”)
• a dissertation
• (an abstract)

41. Rest of our time together
Experimental design
(Reproducible research)

42. Why consider experimental design?
• If you’re performing experiments
• Cost
• Time
• for experiment
• for analysis
• Ethics
• If you’re deciding to fund? to buy? to approve? to compete?
• are the results real?
• can you trust the data?

43. Main potential problems
• Pseudoreplication
• Confounding factors
• Insufficient data/power
• Inappropriate statistics
Inaccurate Wrong
Misleading