Science

First parasites, now online harrassment – how has transparency harmed you lately?

An interesting post at Political Science Replication:

Getting the idea of transparency all wrong

Following an article in the New England Journal of Medicine, which portrayed scientists who re-use data as parasites, we now hear more on this from Nature. Apparently, data transparency is a menace to the public. The Nature comment “Don’t let transparency damage science” claims that the research community must protect authors from harassment by replicators. The piece further infects the discussion about openness with more absurd ideas that don’t reflect reality, and it leads the discussion backwards, not forward. 

Read more at https://politicalsciencereplication.wordpress.com/2016/01/29/getting-the-idea-of-transparency-all-wrong/

Duty Calls, Science

I am a research parasite. Got a problem with that?

In case you wondered what’s wrong with biomedical research, just read this editorial on data sharing by Longo and Drazen in the New England Journal of Medicine, a leading journal in the field. What you will find is a desperate attempt to take data hostage and to enforce co-authorships for people who didn’t make any intellectual contributions.

But let’s take it one step at a time. What did Longo and Drazen actually say? They think there are major problems with sharing data fully, timely and openly.

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Science

Science Stories – Reproducibility

If you think I am serious about reproducibility, you should see my wife.

In this movie by the Royal Society she is explaining the issue to David Spiegelhalter. That is Sir David Spiegelhalter, FRS etc etc.

Published on 22 Dec 2015. We need mathematical help to tell the difference between a real discovery and the illusion of one. Fellow of the Royal Society and future President of the Royal Statistical Society, Sir David Spiegelhalter visits Dr Nicole Janz to discuss reproducibility in scientific publications.

 Way to go!

Florian

Science

“Five selfish reasons to work reproducibly” published

 

Wohoo! Genome Biology just published my piece on “Five selfish reasons to work reproducibly” (which I have talked about before).

And so, my fellow scientists: ask not what you can do for reproducibility; ask what reproducibility can do for you! Here, I present five reasons why working reproducibly pays off in the long run and is in the self-interest of every ambitious, career-oriented scientist.

Go check it out at http://www.genomebiology.com/2015/16/1/274

I am a bit sad, though, that they cut this über-geeky joke I used to illustrate how tightly the tools of reproducibility have to be linked with routine practice:

in-case-of-fire-1-git-commit-2-git-push-3-leave-building2

Florian

Career, Science

Why science needs continuous leadership support

Hello, my fellow PIs, here is a question for you: Did you get trained well for your job?

Silly question, of course you did. Years of study and examinations culminating in a PhD have obviously trained you well in all things science.

But that’s not what I mean. Details of experiments and algorithms –what you learn in a PhD– are only a small part of a PI’s job. Once you start leading a group, the tough nuts to crack are people-problems.

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Creativity

Lit with a ghoulish inner light — Three Oncologists for Halloween

The scariest picture I have seen this Halloween (or maybe even ever) is Ken Currie’s eerie portrait Three Oncologists:

The Three Oncologists are Professor RJ Steele, Professor Sir Alfred Cuschieri and Professor Sir David P Lane of the Department of Surgery and Molecular Oncology, Ninewells Hospital, Dundee. *

In the Guardian Kathleen Jamie writes:

It’s a portrait, but far from flattering. (…) The three men are lit with a ghoulish inner light; they seem to be haunting the threshold between life and death. (…)

Furthermore, they hold their tools or means: Steele raises his gloved and bloodstained hands, Cuschieri holds a surgeon’s implement, Lane carries a paper. Whose sentence is written there?

As we grow more able to say the word “cancer” out loud and more of us survive it, thanks in no small part to our surgeons and physicians, this painting will become a historical record of an emotional state, as well as honouring three esteemed medics.

But it will still send a shiver down the spine.

It sure will.

Florian

Science

Inferring tumour evolution 7 – the roots of metastasis

Series on Tumour Evolution

What makes a cancer deadly is not necessarily the growth at the location where it started (the primary tumour) but its spread through the body to other organs and tissues (called metastasis). Better understanding the metastatic process is one of main reasons we are interested in inferring cancer evolution.

Today I would like to summarize and discuss two recent papers on cancer phylogenetics and metastasis. The first paper is the comprehensive review by Naxerova and Jain in Nature Reviews Clinical Oncology titled “Using tumour phylogenetics to identify the roots of metastasis in humans.” The second paper is an Opinion paper by Hong, Shpak and Townsend in Cancer Research  titled “Inferring the origin of metastases from cancer phylogenies.”

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Science

Sustaining reproducibility

Our paper on tumor evolution in ovarian cancer (see here) came with a nice knitR file to reproduce the survival results, which I used as an example in my recent talk about reproducibility (see here).

I thought that was a nice test scenario to see if I could reproduce the results I got more than a year ago.

How reproducible am I?

Downloading the Rnw from the journal webpage (link) was easy, but -of course- it didn’t run through smoothly.

LaTeX failed and there were several R error messages.

The joys and frustrations of reproducibility

First of all, I had linked to a BibTeX file instead of just copying the bibliography in to the Rnw as I should have done.

Second, I ran into problems with the survival analysis, because one of the packages had changed.

rms::survplot() used to allow plotting a survfit object through survplot.survfit() function. However, this function has been deprecated as of version 4.2.

Luckily I found an easy workaround, just use npsurv() instead of survfit().

The updated Rnw is here on my webpage:
http://www.markowetzlab.org/supplements/journal.pmed.1001789.s020-v2.Rnw

Together with a PDF so you can see what the output should look like.
http://www.markowetzlab.org/supplements/journal.pmed.1001789.s020-v2.pdf

Take-home message for me: Even with a knitR file I did myself, reproducibility is not a one-click thing.

To make reproducibility sustainable I would have to check all published analysis scripts in regular intervals (e.g. once every year or every 6 months). Am I prepared to do this? And for how long?

Florian

Science

Inferring tumour evolution – clones, again

Series on Tumor Evolution
How do you know the leaders in your field? Because they get invited by
Nature Medicine and Nature Biotechnology to a fancy place owned by the Volkswagen Foundation and write a report about it. For example, this one in the latest issue of Nature Medicine titled Toward understanding and exploiting tumor heterogeneity.

What did they discuss? Lots of things. But I got stuck already in the very first topic:

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