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FP7 and looking for the truth:FP7 and looking for the truth:mathematicians have it easy!mathematicians have it easy!

Steve QuarrieSteve QuarrieDirector KBMP

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Where does Okvirni Program 7 (FP7) fit in?What is its relevance to young researchers and PhD students?

FP7 has a lot of opportunities for young scientists to improve their research skills.

But the best FP7 money goes to only the very best quality research.

So, how do you ensure that you are doing the best quality research?

That’s what this talk is all about.

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What is the purpose of a research-based post-

graduate degree, such as a PhD?

To do research. WRONG!!!

To learn how to do research. RIGHT!!!

How many supervisors use their PhD students as technicians?

How many PhD students feel as if they are used as technicians?

How many supervisors abandon their post-graduate students?

How many post-graduate students feel as if they are abandoned?

Developing effective research skills needs constant help and support to understand what to do and, more importantly, why.

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The purpose of the post-graduate education is to teach PhD students how to THINK and to become

RELIABLE as independent workers using RESEARCH as the vehicle for these goals.

So, what is RESEARCH all about?What is RESEARCH?

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……A: Znači, vi terate njega da optuži lažne ljude... B: Ko kaže? A: Pa, što mi ne odgovorite onda da li jeste ili niste? B: Ja što s njim razgovaram to vas ne treba da se tiče. A: Znači, nećete da odgovorite da li ste ga terali? B: Ne, ja vam odgovaram šta je. I slušajte me sada, ja nemam nijedan razlog da pričam i da se plašim onog što sam ja sa svojim klijentom razgovarala, uopšte ni jedan jedini razlog, …A: Pa, on je rekao da ste ga terali da lažno svedoči... B: Ma, ne interesuje me.A: Zašto nećete da odgovorite na pitanje da li ste terali vašeg klijenta da lažno... Ako niste terali, znači da X. kao svedok-saradnik laže? B: Ja ne znam šta on priča i ne interesuje me. A: Evo, to je rekao. B: Ma, mene ne interesuje šta on priča. A: Ne, ali da li to znači da on laže?

Start by reading this piece of text:

So, what is the truth?

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A court of law looks for the truth. Research is also looking for the truth!

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Here’s how a court of law would find the truth:

The case of the CanadianThe case of the Canadian Cheese Burglar!Cheese Burglar!

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Here is the cheeseat the centre of this case:

Here is the cheeseat the centre of this case:

Case circumstances

Two suspects were caught within 12 h of a robbery and were found to be in possession of articles taken from a private residence.

The suspects claimed they were innocent of any crime.

Cheddar cheese

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With the help of the home owners, police recovered a small piece of cheddar cheese from the scene within 36 h which showed bite marks, and from which DNA was recovered.

Evidence showed that the suspects possessed stolen property, but there was no evidence connecting the suspects to the crime scene.

The police managed to get blood samples from the two suspects, and DNA was extracted from the blood.

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Here are electropherograms comparing DNA recovered from saliva on the cheese (upper traces) with DNA from a suspect (lower traces) using markers for 9 DNA loci plus Amelogenin, a gender locus probe.

DNA collected from the cheese and suspect was identical at every locus: all peaks were in the same places and of very similar intensities.

Calculating frequency of this genotype in Canadians gave 1 in 1.59 x 1014.

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So, here was the truth.

So, the DNA from the cheese and suspect were identical, despite the denials by the suspects.

- to identify the truth about what really happened.

You should compare yourself with all the participants of a court trial.When doing research you are: Counsel for the prosecution Counsel for the defence All the expert witnesses The judge and (in the UK) also The jury- all at the same time!

So, just like the court case of the Canadian cheese burglar, when doing research you have a lot of responsibility to get it right!

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Instead of looking for the truth, a lot of research appears to be based on proving facts by doing experiments. A fact is regarded to be the result of one or more experiments.

Good quality research is largely based on hypothesis testing. You set up a hypothesis and then design experiments to test it.

Frequently, research programmes seem to be built around the availability of facilities/equipment/chemicals without having a logical research strategy to follow.

However, the truth may not always be easy to find, and the truth may not always be what you think it is!

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Hypothesis [n]1. A concept that is not yet verified but that if true would explain certain facts or phenomena;

2. A proposal intended to explain certain facts or observations

A dictionary definition

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This is a key factor needed for good quality research:• Designing your experiments to ensure that there can be no other explanation that would invalidate the test of your hypothesis.

This leads to ...

• Only then will you know whether your hypothesis is right or wrong!

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The Research Cycle

Hypothesis

Formulate a hypothesis

Hypothesis

Formulate a new hypothesis

Design

Work out how to test it

Carry outDo the experiment and

collect the data

AnalyseProcess the results

Deduce

Interpret the results and make conclusions

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A good research project has to take account of four key components:

- the scale of the project- the cost of the project- the time available for the project- the quality of the results

Each of these factors depends on the others, so they can be considered as a research pyramid …..

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The Research Pyramid

Scale

QualityCost

TimeYou need to adjust Scale,Cost and Time to maximise

Quality

Note that the line joining Quality to Cost is dashed.In fact Quality rarely depends on Cost!

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If you are going to compare one or more treatments with a control, how do you define the control?

Good experimental design is vitally important.

How many replicates of the tests do you need to know whether any differences are significant or not?

How do you design your experiments to get the best test of your hypotheses?

Are they the only controls or should you have several types of control?

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What statistical methods do you plan to use to test whether the data sets are different?

Are there alternative statistical methods that would be better but possible only if you changed the experimental design (eg paired samples)?

You need to think about all of this before you start any experimental work as this will determine your experimental design.

Will you use individual samples, pooled samples or can you use paired samples?

So, the most critical part of the research cycle is experimental design - get this wrong and you are wasting your time!- get this wrong and you are wasting your time!

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The hypothesis:

Ozone is damaging to the yield of wheat plants, and the more ozone you give them, the lower the yield.

[Note that wheat is supposed to be the most sensitive crop to ozone in the UK!]

Here’s an example to challenge you - it’s about plants! This example is from my research programme at Newcastle University which is looking for the truth.

The highest ozone treatment (75 ppb) given in growth chambers to 95 wheat genotypes should have been enough to cause significant damage.

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This is the result of ranking wheat genotypes for yield under ozone (75 ppb) relative to control yields using the original data:

So the hypothesis is WRONG!!Or is it?

Note lots of genotypes with yield stimulated by ozone!!

26 genotypes > 1.1

Yields are expressed as the ratio: ozone-treated yield/control yield.

What is the truthtruth?

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Well, let’s clean up the data to remove values affected by mice eating the ears, and the bags that were spilt on the floor, etc:

So the hypothesis IS STILL WRONG!!Or is it?

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Note lots of genotypes still with yield stimulated by ozone!!

25 genotypes > 1.1

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OK, let’s be clever (let’s pretend we are mathematicians!) and think whether it is realistic for the hypothesis to be wrong, or could there still be mistakes in our dataset?

Ozone treatment started on 20th May.Spikelet production was already completed by 20th May.Therefore spikelets/ear should be the same in both treatments.

But for several genotypes there were some small spikelets/ear, particularly for control plants, as shown here:

Yield in wheat is derived by multiplying the following components:

spikelets/ear x ears/plant x grains/spikelet x weight/grain (klasica/klasu x klasa/biljci x zrna/klasicu x težina/zrnu)

Components are determined at different stages of development - essentially in the order shown above.

Genotype Spikelets SpikeletsNumber Control Ozone

21 17 18

21 17 17

21 14 17

21 11 17

21 16 17

21 17 17

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So, can we just delete the values that look small?

Genotype Spikelets SpikeletsNumber Control Ozone

21 17 18

21 17 17

21 14 17

21 11 17

21 16 17

21 17 17

Possibly, if we can show that they are significantly different from the rest.

It looks as though the most frequent spikelet number for this line is 17.

So, let’s rank all the lines for mean spikelet number, then look at only those lines with spikelet number means from 16.0 to 18.0, i.e. 17.0 plus or minus 1:

DHL Mean Sp no.45 18.748 18.545 18.5

104 18.365 18.065 18.087 18.0

144 18.039 17.810 17.819 17.8

104 17.820 17.787 17.7

114 17.714 17.718 17.758 17.771 17.541 17.519 17.326 17.331 17.352 17.3

102 17.323 17.339 17.348 17.354 17.314 17.258 17.220 17.221 17.290 17.298 17.223 17.032 17.032 17.071 17.08 16.8

22 16.842 16.897 16.898 16.87 16.8

33 16.842 16.897 16.8

143 16.811 16.718 16.7

[Note: data shaded yellow are for the ozone treatment]

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8 9 10 11 12 13 14 15 16 17 18 19 20 21 22Spikelet number per ear

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Then work out a frequency distribution for spikelet number per ear for those genotypes: 81 genotypes (across both treatments), giving in total 476 values for spikelet number per ear.

The range from 15 to 19 (green bars) contains 450 values, which = 95% total values. Therefore, any values outside this range (red bars) can be regarded as significantly different.

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So, let’s see what the picture looks like once we have corrected for variation in spikelets per ear (determined before ozone was given):

So the hypothesis is probably RIGHT after all!!

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Note very few genotypes now with yield stimulated by ozone!!

Now only 11 genotypes > 1.1

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So, the truth from that experiment was that ozone did reduce wheat yields compared with the controls.

Thus, if you have well-designed experiments to give you good quality results, which are analysed and interpreted very carefully, this gives you:

But, this was confirmed only after very careful analysis and interpretation of all the data.

- a valid test of your hypothesis

- something worth writing up for one or more good quality publications

- a sound basis upon which to develop ideas on what to do next: forming your next hypothesis

- but crucially: access to the TRUTH

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Now, plants are difficult to work with because you can never predict what they will do.

That’s why it takes a lot of effort to find the truth.

Mathematics should be less problematic:The answer is either true or it isn’t!

So, you mathematicians have it easy!