Archive for the ‘RSE’ Category

March 15th, 2017

One aspect of my EPSRC Research Software Engineering fellowship is to spread basic good practice in research software to different academic fields. Last year, I was invited to participate in a Reproducible research in Ecology workshop which was part of the 2016 International Society of Behavioural Ecology Conference. My contributions included a talk (your research software correct?) and a workshop on using projects and version control using R and RStudio.

The latest output from this stream of work is a paper in Behavioral Ecology called Striving for transparent and credible research: practical guidelines for behavioral ecologists which discusses various topics including preregistration, open science and, of course, research software practices with shout-outs to initiatives such as Software Carpentry, Research Software Engineering and the Software Sustainability Institute. The lead author is Malika Ihle with contributions from Isabel S Winney, Anna Krystalli and me.

February 27th, 2017

The job title ‘Research Software Engineer’ (RSE) wasn’t really a thing until 2012 when the term was invented in a Software Sustainability Institute collaborations workshop. Of course, there were lots of people doing Research Software Engineering before then but we had around 200 different job titles, varying degrees of support and career options tended to look pretty bleak.  A lot has happened since then including the 2016 EPSRC RSE Fellows, the first international RSE conference and a host of University-RSE groups popping up all over the country.

In my talk, Is your Research Software Correct?, I tell the audience ‘If you need help, refer to your local RSE team. All good Universities have a central RSE team and if yours does not…..I refer you back to the word ‘good” Always leads to healthy debate when talking at an institution that’s yet to get involved :)

Centrally funded, University-wide RSE teams are useful because they offer a way to maintain a pool of expertise that can be costed into grants. It’s the model we are starting to employ at University of Sheffield following its success at trailblazing sites such as UCL and Manchester.

For this model to work, it is vital that we collaborate with researchers on getting RSE time costed into grants. In turn, researchers worry that they are asking funders for ‘something a bit strange’ which might lead to their project being turned down.

Asking for RSE Support in your grant is a Good Idea

There are two main arguments that I use when attempting to alleviate these concerns. The first is that we are quite successful in obtaining RSE funding, even in areas that you might not expect. The second is to point to funding calls where the funding council explicitly recommends RSE costing to be considered where appropriate.

The EPSRC have led the way in the UK with its RSE fellowship call, funding the Software Sustainability Institute (these days its funded by 3 research councils including BBSRC and ESRC) and various other initiatives.

Earlier this month, I was very happy to see that the BBSRC have explicitly mentioned Research Software Engineers in one of their latest calls: Machine Learning to Generate New Biological Understanding. In the call, the BBSRC say:

We note the significant contribution of staff such as Research Software Engineers (see external links) to interdisciplinary computational projects such as machine learning, and supports recognition of their contributions and encourages applicants to cost them appropriately on applications to this highlight.

I feel that this is a great move by the BBSRC and hope to see other funding councils follow their lead in future.

January 12th, 2017

If you are a researcher and are currently writing scripts or developing code then I have a suggestion for you. If you haven’t done it already, get yourself a willing volunteer and send them your code/analysis/simulation/voodoo and ask them to run it on their machine to see what happens. Bonus points are awarded for choosing someone who uses a different operating system from you!

This simple act is one of the things I recommend in my talk Is Your Research Software Correct and it can often help improve both code and workflow.

It quickly exposes patterns that are not good practice. For example, scattered references to ‘/home/walkingrandomly/mydata.dat’ suddenly don’t seem like a great idea when your code buddy is running windows. The ‘minimal tweaking’ required to move your analysis from your machine to theirs starts to feel a lot less minimal as you get to the bottom of the second page of instructions.

Crashy McCrashFace

When I start working with someone new, the first thing I ask them to do is to provide access to their code and simple script called runme or similar that will build and run their code and spit out an answer that we agree is OK. Many projects stumble at this hurdle! Perhaps my compiler is different to theirs and objects to their abuse (or otherwise) of the standards or maybe they’ve forgotten to include vital dependencies or input data.

Email ping-pong ensues as we attempt to get the latest version…zip files with names like get thrown about while everyone wonders where Bob is because he totally got it working on Windows back in 2009.

git clone

‘Hey Mike, just clone the git repo and run the test suite. It should be fine because the latest continuous integration run didn’t throw up any issues. The benchmark code and data we’d like you to optimise is in the benchmarks folder along with the timings and results from our most recent tests. Ignore the papers folder, that just reproduces all of the results from our recent papers and links to Zenodo DOIs’


‘Are you OK Mike?’

‘I’m…..fine. Just have something in my eye’


January 10th, 2017

I work at The University of Sheffield where I am one of the leaders of the new Research Software Engineering function. One of the things that my group does is help people make use of Sheffield’s High Performance Computing cluster, Iceberg.

Iceberg is a heterogenous system with around 3440 CPU cores and a sprinkling of GPUs. It’s been in use for several years and has been upgraded a few times over that period. It’s a very traditional HPC system that makes use of Linux and a variant of  Sun Grid Engine as the scheduler and had served us well.


A while ago, the sysadmin pointed me to a goldmine of a resource — Iceberg’s accounting log. This 15 Gigabyte file contains information on every job submitted since July 2009. That’s more than 7 years of the HPC usage of 3249 users — over 46 million individual jobs.

The file format is very straightforward. There’s one line per job and each line consists of a set of colon separated fields.  The first few fields look like something like this:

The username is field 4 and the number of slots used by the job is field 35. On our system, slots correspond to CPU cores. If you want to run a 16 core job, you ask for 16 slots.

With one line of awk, we can determine the maximum number of slots ever requested by each user.

gawk -F: '$35>=slots[$4] {slots[$4]=$35};END{for(n in slots){print n, slots[n]}}' accounting > ./users_max_slots.csv

As a quick check, I grepped the output file for my username and saw that the maximum number of cores I’d ever requested was 20. I ran a 32 core MPI ‘Hello World’ job, reran the line of awk and confirmed that my new maximum was 32 cores.

There are several ways I could have filtered the number of users but I was having awk lessons from David Jones so let’s create a new file containing the users who have only ever requested 1 slot.

gawk -F: '$35>=slots[$4] {slots[$4]=$35};END{for(n in slots){if(slots[n]==1){print n, slots[n]}}}' accounting > users_where_max_is_one_slot.csv

Running wc on these files allows us to determine how many users are in each group

wc users_max_slots.csv 

3250  6498 32706 users_max_slots.csv

One of those users turned out to be a blank line so 3249 usernames have been used on Iceberg over the last 7 years.

wc users_where_max_is_one_slot.csv 
2393  4786 23837 users_where_max_is_one_slot.csv

That is, 2393 of our 3249 users (just over 73%) over the last 7 years have only ever run 1 slot, and therefore 1 core, jobs.

High Performance?

So 73% of all users have only ever submitted single core jobs. This does not necessarily mean that they have not been making use of parallelism. For example, they might have been running job arrays – hundreds or thousands of single core jobs performing parameter sweeps or monte carlo simulations.

Maybe they were running parallel codes but only asked the scheduler for one core. In the early days this would have led to oversubscribed nodes, possibly up to 16 jobs, each trying to run 16 cores.These days, our sysadmin does some voodoo to ensure that jobs can only use the number of cores that have been requested, no matter how many threads their code is spawning. Either way, making this mistake is not great for performance.

Whatever is going on, this figure of 73% is surprising to me!

Thanks to David Jones for the awk lessons although if I’ve made a mistake, it’s all my fault!

Update (11th Jan 2017)

UCL’s Ian Kirker took a look at the usage of their general purpose cluster and found that 71.8% of their users have only ever run 1 core jobs.



December 12th, 2016

I was in Stockholm last week to give an invited talk at the Workshop on Nordic Big Biomedical Data for Action. I was representing the Software Sustainability Institute and delivered the latest version of my talk Is Your Research Software Correct? screen-shot-2016-12-11-at-12-47-14

It was a great event which introduced me to some nice initiatives going on waaaay up north. Initiatives such as Code Refinery who’s aims align well with those of the UK’s software sustainability Institute. Code refinery was introduced by Radovan Bast — Slide deck at


Other talks included the introduction of a scalable, parallel version of BLAST, Big Data Processing for Genomics and Delivering Bioinformatics Software as Virtual Machine images. I also got chance to geek out with some High Performance Computing and Bioinformatics people over interesting Swedish food.

Slides from most of the talks are available at

October 13th, 2016

I was recently invited to give a talk at the Sheffield R Users Group and decided to give a brief overview of how R relates to other technologies. Subjects included Mathematica’s integration of R, Intel’s compilers, Math Kernel Library and how they can make R faster and a range of Microsoft technologies including R Tools for Visual Studio, Microsoft R Open and the MRAN for reproducibility. I also touched upon the NAG Library, Maple’s code generation for R, GPUs and Spark.

Did I miss anything? If you were to give a similar talk, what might you have included?

September 22nd, 2016

“It gives me great pleasure to welcome you all to the first ever Research Software Engineering conference” 

Rob Haines‘ opening line was met by thunderous applause from 202 people representing 14 different countries with a delegation that included funders, industry, academic researchers and, of course, research software engineers. The hairs on the back of my neck stood up as it dawned on me that this was a historic moment and that I was there when it happened. I wasn’t the only one


I felt like I’d come home and that these people were my tribe…and what a tribe!


Microsoft loves Linux

The conference was a mixture of talks, workshops and networking opportunities with the opening plenary given by Matthew Johnson of Microsoft Research. Microsoft was the gold sponsor for the event and the swag bag included one of these

we're not in Kansas anymore

I reflect on the fact that I’m currently using my Macbook Pro as a Windows 10 machine to access the linux subsystem — we’re not in Kansas anymore!

Microsoft is a keen supporter of the RSE movement although the job title they use is ‘Research Software Development Engineer’, a title they’ve used for several years now. An RSE (or RSDE) does much more software development than a typical researcher and more research than a typical software engineer.

The choice of job role is important since it defines how you are assessed for things such as promotion. This is an issue that some of us are working to address within academia because many RSEs are currently assessed using the same criteria as researchers.


Docker…we need more Docker

The conference included several practical workshops on all sorts of interesting topics but the most popular, by far, was the Docker workshop. It was so oversubscribed that access to the room had to be strictly controlled! Even I wasn’t allowed in and I was on the organising committee!

Fortunately, the materials are freely available on github – 


What a diff’rence a fellowship makes

I attended a discussion workshop called ‘The Role of the Research Software Engineer’ and gave a caffeine fueled lighting talk about the impact my EPSRC RSE fellowship has had within the University of Sheffield over its first six months. Slides are at but you might not get much from them since I like to talk over a set of images for things like this.

The EPSRC RSE Fellowship is the first of its kind and I believe that its had a huge impact on how the role of RSE is perceived by academic institutions. There were only 7 awards, however, so there is still so much more to be done.

Since members of the audience included representatives from various funding bodies, I wanted to help convince them that RSE fellowships are great value for money and they should consider launching their own.

Workshop materials

Here is a list of links to some of the workshop materials. If you know of one I’ve missed, please let me know.


For more information about what happened on the day see the following links

September 5th, 2016

One of the great things about being a Research Software Engineer is the diversity of work you can get involved with. I specialise in smaller interventions which means that I can be working with physicists on Monday, engineers on Tuesday, geneticists on Wednesday….you get the idea.

Last month, I got to work with some Ecologists along with Anna Krystalli. We undertook the arduous journey from Sheffield down to Exeter to deliver talks and workshops at a post-conference symposium on reproducibility in science, organised by Malika Ihle and Isabel Winney, at the International Symposium on Behavioural Ecology.

I gave my talk, Is your research software correct?, and also delivered a workshop on using projects and version control using R and RStudio in the Code Cafe style. For the full write up of the day, see the excellent blog post by Anna over at the Mozilla Science Lab blog.

Updates : More resources

August 24th, 2016

I sometimes give a talk called Is Your Research Software correct (github repo, slide deck) where I attempt to give a (hopefully) entertaining overview of some of the basic issues in modern research software practice and what can be done to make the world a little better.

One section of this talk is a look at some case studies where software errors caused problems in research. Ideally, I try to concentrate on simple errors that led to profound scientific screw-ups. I want the audience to think ‘Damn! *I* could have made that mistake in my code‘.

Curating this talk has turned me into an interested collector of such stories. This is not an exercise in naming and shaming (after all, the odds are that its only a matter of time before I, or one of my collaborators, makes it into the list — why set myself up for a beating?). Instead, it is an exercise in observing the problems that other people have had and using them to enhance our own working practices.

Thus begins a new recurring WalkingRandomly feature.

Excel corrupts genetics data

Today’s entry comes courtesy of a recent paper by Mark Ziemann, Yotam Eren and Assam El-OstaEmail – ‘Gene name errors are widespread in the scientific literature‘ where they demonstrate that the supplementary data files for hundreds of papers in genetics have been corrupted by Microsoft Excel which has helpfully turned gene symbols into dates and floating point numbers.

The paper gives advice to reviewers on how to spot this particular error and the authors have also published the code used for the analysis. I’ve not run it myself so can only attest to its existence, not it’s accuracy.

I’ve not dealt with genetic data directly myself so ask you — what would you have used instead of Excel? (my gut tells me R or Python but I have no details to offer).

Do you have a story to contribute?

If you are interested in contributing a story where a software glitch caused problems in research, please contact me to discuss details.

Update (31st August 2016)

One of the authors of the paper, Mark Ziemann, has written a follow up of the Excel work on his blog:

July 27th, 2016

This post is also published over at the Software Sustainability Institute.

William Stein, lead developer of the computer algebra system, Sage, and its cloud-based spin-off, SageMathCloud, recently announced that he was quitting academia to go and form a company. In his talk, William says ‘I can’t figure out how to create Sage in academia. The money isn’t there. The mathematical community doesn’t care enough. The only option left is for me to build a company.’

His talk is below and slides are at


“Every great open source math library is built on the ashes of someone’s academic career.”

William’s departure is not unique. Here’s a tweet from Wes Mckinney, creator of pandas, one of the essential data science tools for Python.

Contact us

We are looking for similar stories; good research software people who felt that they had to leave academia because there wasn’t enough support, recognition or funding. Equally, we want to hear from you if you think academia is a rewarding environment for software development. Either way, please contact us at