C. Titus Brown

I would prefer recommending a stable build process: — a Docker image can be just like having a VM image or that one PC in the corner of the lab nobody is sure is unneeded. It's far better than having nothing or just the result file but it still has the possibility of needing to reverse-engineer the internal state and given how fast the Docker world moves I would not want to bet on format compatibility 5 years out. Docker could be that stable build process but it requires the additional assertion that there wasn't, say, a truckload of changes made using `docker exec` or a bunch of customizations to files which were copied into the image. Simply putting a note on the source repo which says that might be enough. (I really like what C Titus Brown has written about reproducibility in computation research over the years: http://ivory.idyll.org/blog/tag/reproducibility.html)

You shouldn't be using kwargs! That is also well known to be bad practice (or it should be anyway). https://medium.com/codex/stop-using-kwargs-as-method-arguments-for-goo… http://ivory.idyll.org/blog/on-kwargs.html Give me another one.

One more related link on the same subject, "The top 10 reasons why blog posts are better than scientific papers": http://ivory.idyll.org/blog/2017-top-ten-reasons-blog-posts.html * there is a "maybe satire" tag added by the author to this post.

http://ivory.idyll.org/blog/ is a good one if you're into bioinformatics and academic programming in general.

Well, the context here is discussion of the problem of unreplicable research. I do get the impression (including from your post and reply) that computational biologists need to get their act together on that front; see http://ivory.idyll.org/blog/ for one researcher in that field who sometimes blogs on this theme.

I never said that they were. I'm trying to help Joeboy understand why sexual content can trigger legitimate fears. Note that at this very conference, there was other inappropriate stuff going on: http://ivory.idyll.org/blog/pycon-2013-and-codes-of-conduct.html So although I agree that Adira was in no immediate danger, I think she was right to see jokes like that as being part of an environment that alienates women and enables problematic behavior.

It depends what you're interested in. I found these two the most interesting: http://pyvideo.org/video/1796/simplecv-computer-vision-using-python http://ivory.idyll.org/blog/2013-pycon-awesome-big-data-algorithms-tal… (No video posted yet?)

It makes me sad that HN reinforces the fact that women have a hard time participating in s/tech conferences/tech forums/g PyCon was 20% female this year which was a remarkable milestone. Along with this milestone came an amplification of why it was so hard for PyCon to reach that milestone. This was not an isolated issue; Titus' post provides a good summary of the general harassment women faced at PyCon http://ivory.idyll.org/blog/pycon-2013-and-codes-of-conduct.html And, yeah, blame your employer for making a poor judgement call on this. If I were you I wouldn't want to work for a place that fires people over Internet drama anyway.

Various people, e.g. Titus Brown, have been trying to take a different approach. Titus doesn't practice open notebook science, but he does try and practice "replication". More on replication: http://ivory.idyll.org/blog/apr-12/replication-i.html The paper gets it own website: http://ged.msu.edu/papers/2012-diginorm/; which includes arXic preprint, data and code repositories and even an AMI with everything loaded. Basically eveything you need to replicate the work in the paper.

Titus Brown also a blog post on this earlier this year "The top 10 reasons why blog posts are better than scientific papers" http://ivory.idyll.org/blog/2017-top-ten-reasons-blog-posts.html and it generated similar discussion. There are a few elements they emphasize. One is what the blog format enables that traditional publishing doesn't support. Those are things like having real-time feedback and comments, being able to version and making the blog post interactive, rather than a static document. Another element to the format is a lack of gatekeepers, so it can be quickly disseminated and disseminated by anyone, so there aren't barriers to participation in the scientific discourse. Another is norms and expectations. In blogging, it is more the norm that data and code are open. Open is still possible in traditional publishing; it just isn't yet the norm. A new format however, enables new norms and it's easier to set them from the start, than try to revise existing ones. Finally, there's the element of 'correctness'. Going through peer review and being in a traditional journal certainly doesn't ensure that the paper is correct. You can just look at retractions to see that http://retractionwatch.com/2011/08/11/is-it-time-for-a-retraction-inde… However it would be interesting to see more evidence around whether the blog format does ultimately lead to more 'correct' conclusions, on the whole, and not just for the posts that lead to a lot of discussion.

Sometimes people will distinguish between "replicating a result" and "reproducing an experiment" – with the differences more prominent when you are dealing with straight data. Replicating a result simply means you confirm that the results aren't made up – you start with what they say they started with, you get the result they say. Reproducing an experiment might mean working from their conclusions, not trying to repeat every detail, and seeing if you come up with the same result – you might not if you mess up the experiment, but you might also confirm the interpretation of the result because you've eliminated aspects that the theory considers insignificant. With results derived strictly from data this is a clearer line, as repeating the process exactly is quite possible (the spreadsheet in this case is the "process"). This might seem trivial, but it's pretty common that even repeating the purported process is very hard. From a techy point of view this post helped me see the distinction: http://ivory.idyll.org/blog/replication-i.html

> It's not sexist to make jokes about the word "dongle" sounding like "dong". It is sexist to create a sexualized environment at a tech conference, because the consequences of that environment hurt women much more than they hurt men; it's a failure to show consideration to women. mr-hank has admitted that he made a sexual joke about big dongles and apologized for it. Making sexual jokes about big dongles is creating a sexualized environment. More evidence that a sexualized environment existed at PyCon (though I think the organizers were doing an awesome job and it was probably much better than other large tech conferences!): http://ivory.idyll.org/blog/pycon-2013-and-codes-of-conduct.html "First, I came to PyCon with two women colleagues, one of whom was harassed nearly constantly by men, albeit on a low level. Both of them are friendly people who are willing to engage at both a personal and a technical level with others, and apparently that signals to some that they can now feel free to comment on "hotness", proposition them, and otherwise act like 14 year old guys."

This piece seems to have touched a nerve in the bioinformatics community, though I have no idea why. Much of what is said here is obvious to anyone working in academic research that requires programming expertise. Yes, industry typically pays more than academia. Yes, most molecular biologists cannot code and rely on bioinformatics support. Yes, biological data is often noisy. Yes, code in bionformatics is often research grade (poorly implemented, poorly documented, often not available). These are all good points that have been made many times more potently by others in the field like C. Titus Brown (http://ivory.idyll.org/blog/category/science.html). But they are not universal truths and exceptions to these trends abound. Show me an academic research software system in any field outside of biology that is functional and robust as the UCSC genome browser (serving >500,000 requests a day) or the NCBI's pubmed (serving ~200,000 requests a day). To conclude from common shortcomings of academic research programming that bioinformatics is "computational shit heap" is unjustified and far from an accurate assessment of the reality of the field. From looking into this guy a bit (who I've never heard of before today in my 10+ years in the field), my take on what is going is here is that this is the rant of a disgruntled physicist/mathematician is a self-proclaimed perfectionist (https://documents.epfl.ch/users/r/ro/ross/www/values.html), who moved into biology but did not establish…

Somebody observed (around here or reddit?) recently that there are no unemployed mathematicians. By that, s/he probably meant statistics/probability, linear algebra and diff EQ's as good things to be good at: http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-lea… ===================== FP, there's pretty strong interest in erlang, ocaml, haskell, F#, scala , clojure. Also scheme/CL . http://stackoverflow.com/questions/805429/learning-scala-or-haskell http://www.slideshare.net/brweber2/functional-concepts-for-oop-develop… http://enfranchisedmind.com/blog/posts/what-killed-lisp-could-kill-has… http://ivory.idyll.org/blog/jun-10/functional-programming-languages

And some interesting links from my bookmarks: * Intermediate and Advanced Software Carpentry in Python:http://ivory.idyll.org/articles/advanced-swc/ * Beej's Guide to Network Programming:http://beej.us/guide/bgnet/output/html/singlepage/bgnet.html * How To Use Linux epoll with Python:http://scotdoyle.com/python-epoll-howto.html * 6.006: Introduction to Algorithms, using Python:http://courses.csail.mit.edu/6.006/spring11/notes.shtml * Python beginner's mistakes:http://zephyrfalcon.org/labs/beginners_mistakes.html * Python Gotchas:http://pythonconquerstheuniverse.wordpress.com/2008/06/04/python-gotch… * Python Conquers The Universe 1. Command-line syntax, some basic concepts:http://bit.ly/r07qvK 2. Unicode for dummies:http://bit.ly/oMFvd8 3. debugging in Python:http://bit.ly/rmEQiK * Pypy - Tutorial: Writing an Interpreter with PyPy, Part 1 : http://morepypy.blogspot.com/2011/04/tutorial-writing-interpreter-with… - Global Interpreter Lock, or how to kill it:http://morepypy.blogspot.com/2011/06/global-interpreter-lock-or-how-to… * Be careful with exec and eval in Python: http://lucumr.pocoo.org/2011/2/1/exec-in-python/ * Guido van Rossum:Interfaces or Abstract Base Classes? http://www.artima.com/weblogs/viewpost.jsp?thread=92662