Centre de Biophysique Moléculaire

Konrad Hinsen

Among the many characteristics that distinguish programming languages, static vs. dynamic typing is one of the most debated ones. The main advantages claimed by the advocates of static typing are that compile-time type checks make code more robust and that static typing allows a compiler to do better optimizations.

Static typing and code clutter

This is a contribution to the Challenge problem: Fearless extensibility by the Malleable Systems Collective.

As superheroes know very well, with great power comes great responsibility. Malleable systems offer a lot of power to their users. In exchange, users have to take responsibility for the system they have tailored to their needs, since there is nobody else to blame.

Modular malleability

I suspect that most people in the Western world (at least) are realizing that we are living in interesting times. News of floodings, droughts, and wildfires are ever more frequent. We hear that this is due to climate change, which most governments promise to fight, but don't. Our economies keep growing, but our quality of life is not improving. Digital tools are ever more prominent in our lives, but don't make us happy either.

Going for robustness

Dear software engineers,  Many  of you were horrified at the sight of the C++ code that Neil Ferguson and his team wrote to simulate the spread of epidemics. I feel with you. The only reason why I am less horrified than you is that I have seen a lot of similar-looking code before. It is in fact quite common in scientific computing, in particular in research projects that have been running for many years.

An open letter to software engineers criticizing Neil Ferguson's epidemics simulation code

With the increasing importance of parallel computers, ranging from multi-core desktop machines to massively parallel machines such as IBM's BlueGene, functional programming could well become an important technique for scientific software development, as it facilitates program transformations (including those for automatic or semi-automatic parallelization) considerably.

Functional programming for scientific computing

This is a follow-up to my earlier post entitled "Going for robustness", focusing on scientific research.

Going for robustness: science

A much cited essay by Bret Victor, "Explorable Explanations", argues for supporting and encouraging

 active reading

in communicating ideas. Explanatory text should thus be complemented by interactive visualizations and computational demonstrations, allowing the reader to actively engage with the ideas. If you haven't read Victor's essay yet, please do so now, and then come back here. It's not very long.

Explorable explorable explanations

My most recent paper submission (preprint available) is about improving the verifiability of computer-aided research, and contains many references to the related subject of reproducibility. A reviewer asked the same question about all these references: isn't this the same as for experiments done with lab equipment? Is software worse? I think the answers are of general interest, so here they are.

Scientific software is different from lab equipment

A few years ago, I discovered Mike Caulfield's The Garden and the Stream: A Technopastoral and understood why I wasn't happy with my blog. Blogs are streams, timelines of posts. Each post has a timestamp, and is considered "finished". Later changes are technically possible, but culturally limited to corrections. A blog post is considered a published essay, and therefore comes with a date of publication.

Welcome to my digital garden!

Two NumPy-related news items appeared on my Twitter feed yesterday, just a few days after I had accidentally started a somewhat heated debate myself concerning the poor reproducibility of Python-based computer-aided research.

A plea for stability in the SciPy ecosystem

Every time I teach a class on parallel computing with Python using the multiprocessing module, I wonder if multiprocessing is really mature enough that I should recommend using it. I end up deciding for it, mostly because of the lack of better alternatives. But I am not happy at all with some features of multiprocessing, which are particularly nasty for non-experts in Python. That category typically includes everyone in my classes.

Teaching parallel computing in Python

Today I have published the first release of ActivePapers for Python, available on PyPI or directly from the Mercurial repository on Bitbucket. The release coincides with the publication of my first scientific paper for which the complete code and data is in the supplementary material, available through the J. Chem. Phys. Web site or from Figshare.

ActivePapers for Python

One of the more interesting things I have been playing with recently is Pharo, a modern descendent of Smalltalk. This is a summary of my first impressions after using it on a small (and unfinished) project, for which it might actually turn out to be very helpful. The first time I read about Smalltalk was in the August 1981 issue of Byte magazine.

Exploring Pharo

Since the dawn of computer programming, software developers have been aware of the rapidly growing complexity of code as its size increases.

Literate computational science

This post is a follow-up to my previous one, Deconstructing the Mastodon client. My topic is a scenario that traditional Mastodon clients handle rather badly, wheres my home-grown solution handled it very well: lengthy and branching conversations. Such conversations happen all the time on social networks. Someone posts an interesting question or observation, which is commented by many others.

Following branching conversations on Mastodon

Ever since I joined Twitter in 2011, and then moved to Mastodon in 2022, I have been unhappy with the timeline view proposed by both of these communication platforms as their main interface. Now I have finally done something about it: I wrote my own Mastodon client. Or perhaps rather a non-client, because the concept of "the client" is a big part of what I disliked.

Deconstructing the Mastodon client

Over the last years, an interesting metaphor for information and knowledge curation is beginning to take root. It compares knowledge to a landscape in which it identifies in particular two key elements: streams and gardens. The first use of this metaphor that I am aware of is this essay by Mike Caulfield, which I strongly recommend you to read first.

The landscapes of digital scientific knowledge

In my last post, I have discussed the two main types of scientific models: empirical models, also called descriptive models, and explanatory models. I have also emphasized the crucial role of equations and specifications in the formulation of explanatory models. But my description of scientific models in that post left aside a very important aspect: on a more fundamental level, all models are stories.

The structure and interpretation of scientific models, part 2

It is widely recognized by now that software is an important ingredient to modern scientific research. If we want to check that published results are valid, and if we want to build on our colleagues' published work, we must have access to the software and data that were used in the computations.

What can we do to check scientific computation more effectively?

In his 1962 classic "The Architecture of Complexity", Herbert Simon described the hierarchical structure found in many complex systems, both natural and human-made. But even though complexity is recognized as a major issue in software development today, the architecture described by Simon is not common in software, and in fact seems unsupported by today's software development and deployment tools.

Wanted: a hierarchically modular software architecture

The plea for stability in the SciPy ecosystem that I posted last week on this blog has generated a lot of feedback, both as comments and in a lengthy Twitter thread. For the benefit of people discovering it late, here is a summary of the main arguments and my reply to them.

Stability in the SciPy ecosystem: a summary of the discussion

We all know that software deployment in a research environment can be a pain, but knowing this as a fact is not quite the same as experiencing it in reality. Over the last days, I spent way more time that I would have imagined on what sounds like a simple task: installing a scientific application written in Python on a Linux machine for use by a group of students in a training session.

A rant about software deployment in 2015

It's hard to find an aspect of modern life that is not influenced in some way by software. Some of it is very visible, for example the Web browser I start on my computer. Other software is completely invisible, such as the software controlling my car's diesel engine. Some software is safety critical, for example flight control software in airplanes. Other software is used in a much more futile way, such as playing games.

There is no such thing as software development

A recurrent theme in computational science (and elsewhere) is the need to combine machine-readable information (which in the following I will call "facts" for simplicity) with a narrative for the benefit of human readers.

From facts to narratives

Yesterday I participated in the International workshop “Software, Pillar of Open Science”, organized by the French Committee for Open Science. In the course of the various presentations and discussions (both in public and during coffee breaks), I realized that something has been absent from such events all the time: the vast majority of scientists.

The low-hanging fruit in computational reproducibility

The importance of reproducibility in computer-aided research (and elsewhere) is by now widely recognized in the scientific community. Of course, a lot of work remains to be done before reproducibility can be considered the default. Doing computational research reproducibly must become easier, which requires in particular better support in computational tools. Incentives for working and publishing reproducibly must also be improved.

From reproducible to verifiable computer-aided research

Think of all the things you hate about using computers in doing research. Software installation. Getting your colleagues' scripts to work on your machine. System updates that break your computational code. The multitude of file formats and the eternal need for conversion. That great library that's unfortunately written in the wrong language for you. Dependency and provenance tracking. Irreproducible computations.

Composition is the root of all evil

Welcome to the last post on this WordPress blog. I have set up a new blog for all my future writing. The reason for the move is that the user interface at WordPress is changing all the time without ever getting better. I like to write my posts on my own computer using Emacs, rather than typing into a rudimentary editing window on a Web site. This is not completely impossible with WordPress, but more hassle than it's worth.

This blog is moving!

Over the last few years, I have spent a lot of time thinking, speaking, and discussing about the reproducibility crisis in scientific research. An obvious but hard to answer question is: Why has reproducibility become such a major problem, in so many disciplines? And why now?

The industrialization of scientific research

A few days ago, I noticed this tweet in my timeline:  That sounded like a good read for the weekend, which it was. The main argument the author makes is that C remains unsurpassed as a system integration language, because it permits interfacing with "alien" code, i.e. code written independently and perhaps even in different languages, down to assembly.

Why Python does so well in scientific computing

The recent announcement of clojure-py made some noise in the Clojure community, but not, as far as I can tell, in the Python community. For those who haven't heard of it before, clojure-py is an implementation of the Clojure language in Python, compiling Clojure code to bytecode for Python's virtual machine. It's still incomplete, but already usable if you can live with the subset of Clojure that has been implemented.

Python becomes a platform

Yesterday I participated (as a visitor) in the kickoff meeting for OpenDreamKit, where one recurrent topic of discussion was notebooks, both Jupyter and Sage, including the question if they could be brought together. This reminded me of a recent blog post by Kirill Pomogajko entitled "Why I don't like Jupyter". And it reminded me of my own long-term project of integrating Jupyter with my ActivePapers system for reproducible research.

Beyond Jupyter: what's in a notebook?

Three years ago, I first looked at the then-very-new language Julia. Back then, I concluded that there were many interesting features, but also regretted too much bad Matlab influence in the array handling. A hands-on Julia tutorial in my neighborhood was a good occasion to take another look at this language, which has evolved quite a bit since 2012, and continues to evolve rapidly.

Another look at Julia

Another EuroSciPy conference is over, and like last year it was very interesting. Here is my personal list of highlights and comments. The two keynote talks were particularly inspiring. On Saturday, Marian Petre  reported on her studies of how people in general and scientists in particular develop software.

EuroSciPy 2011

Regular visitors to my blog have probably noticed that it looks different now. However, the visual changes are only a side effect of a more profound change: I now use a different static site generator, coleslaw.

This blog gets a facelift

One of my hobby projects over the last months has been the exploration of monads. Monads are packages consisting of a data structure and associated control structures that are used as abstractions in functional programming. They were popularized by the Haskell language, where they play a central role in introducing side effects (such as I/O) in a controlled way into a language that is otherwise purely functional.

Monads in Clojure

A recent article in "The Atlantic" has been the subject of many comments in my Twittersphere. It's about scientific communication in the age of computer-aided research, which requires communicating computations (i.e. code, data, and results) in addition to the traditional narrative of a paper.

Scientific communication is a research problem

It's the season when everyone writes about the past year, or even the past decade for a year number ending in 9. I'll make a modest contribution by summarizing my experience with Pharo after one year of using it for projects of my own. My first contact with Pharo happened a bit more than one year ago, when I signed up for the Pharo MOOC in October 2018. But following a MOOC means working on exercice problems defined by someone else.

Pharo year one

While reading the final report of the reproducibility workshop at XSEDE14, I noticed a statement that I encounter frequently in discussions about reproducible research:       In the interest of clarity, let me start by pointing out that within the systematic terminology that I am trying to adopt (see this post for an explanation), I will write "bitwise replicability" from now on, as the problem falls into the technical domain (getting the same

Why bitwise reproducibility matters

A while ago I wrote about my ideas for a successor of today's computational notebooks. Since then I have made some progress on a prototype implementation, which is the topic of this post. Again I have made a companion screencast so that you can get a better idea of how all this works in practice.

The computational notebook of the future (part 2)

The other day I was looking at the release notes for the recently published release 1.8 of NumPy, the library that is the basis for most of the Scientific Python ecosystem.

Python as a platform for reproducible research

Calculators are among the most popular applications for smartphones, and therefore it is not surprising that the Google Play Store has more than 1000 calculators for the Android platform. Having used HP's scientific calculators for more than 20 years, I picked RealCalc when I got my Android phone and set it to RPN mode. It works fine, I have no complaints about it. But I no longer use it because I found something much more powerful.

The ultimate calculator for Android and iOS

Data science is usually considered a very recent invention, made possible by electronic computing and communication technologies. Some consider it the  fourth paradigm of science, suggesting that it came after three other paradigms, though the whole idea of distinct paradigms remains controversial. What I want to point out in this post is that the principles of data science are much older than most of today's practitioners imagine.

Data science in ancient Greece

Two currently much discussed issues in scientific computing are the sustainability of research software and the reproducibility of computer-aided research. I believe that the communities behind these two ideals should work together on taming their common enemy: software collapse. As a starting point, I propose an analysis of how the risk of collapse affects sustainability and reproducibility.

Sustainable software and reproducible research: dealing with software collapse

Programmers, scientific and otherwise, spend a lot of time discussing which programming languages, libraries, and development tools to use. In such discussions, the notion of a programming paradigm is rarely mentioned, and yet it is a very fundamental one. It becomes particularly important for parallel and concurrent programming, where the most popular languages and libraries do not necessarily provide the best programming paradigm.

Scientific computing needs deterministic programming paradigms

In discussions about computational reproducibility (or replicability, or repeatability, according to the preference of each author), I often see the argument that reproducing computations may not be worth the investment in terms of human effort and computational resources. I think this argument misses the point of computational reproducibility. Obviously, there is no point in repeating a computation identically. The results will be the same.

Reproducibility does not imply reproduction

Now that the birch pollen season is definitely over, I can draw some conclusions from a two-year experiment with the impressive sample size of one - myself. As you will see, my topic is not so much the experiment itself, but the circumstances in which it happened. I have been allergic to birch pollen for more than thirty years.

The compartmentalization of knowledge

I have been using mobile (pocket size) computers for about 15 years, starting with the Palm Pilot. Currently I use an Android smartphone (Samsung Galaxy S). While mobile devices are mostly used for consulting rather than for entering information, text entry has always been a hot topic of debate. Apple's Newton Messagepad, probably the first mobile computing device in the modern sense, pursued the ambitious goal of handwriting recognition.

Text input on mobile devices

Over the last few years, I have repeated a little experiment: Have two scientists, or two teams of scientists, write code for the same task, described in plain English as it would appear in a paper, and then compare the results produced by the two programs. Each person/team was asked to do a maximum amount of verification and testing before comparing to the other person's/team's work.

Which mistakes do we actually make in scientific code?

The importance of reproducibility in computational science is being more and more recognized, which I think is a good sign. However, I also notice a lot of confusion about what reproducibility means exactly, and also confusion about the difference (if any) between reproducibility and replicability.

Reproducibility, replicability, and the two layers of computational science

A two-hour train journey provided the opportunity to watch the video recording of the Panel with Guido van Rossum at the recent PyData Workshop. The lengthy discussion about PEP 225 (which proposes to add additional operators to Python that would enable to have both elementwise and aggregate operations on the same objects, in particular for providing both matrix and elementwise multiplication on arrays with a nice syntax) motivated me

Binary operators in Python

One question I have been thinking about in the context of reproducible research is this: Why is all stable software technology old, and all recent technology fragile? Why is it easier to run 40-year-old Fortran code than ten-year-old Python code? A hypothesis that comes to mind immediately is growing code complexity, but I'd expect this to be an amplifier rather than a cause.

The rise of community-owned monopolies

A recent paper in PLOS One made some noise in my twittersphere over the Christmas days. It compares the productivity of writing scientific documents using Microsoft Word and using LaTeX, and concludes that Microsoft Word is so clearly superior that, in the interest of saving taxpayers' money, scientific publishers should abandon LaTeX to allow authors to become more productive.

Drawing conclusions from empirical science

Regular readers of this blog may have noticed that I am not very happy with today's state of computational notebooks, such as they were pioneered by Mathematica and popularized by more recent free incarnations such as Jupyter, R markdown, or Emacs/OrgMode. In this post and the accompanying screencast (my first one!), I will explain what I dislike about today's notebooks, and how I think we can do better.

The computational notebook of the future

Why do people write computer programs? The answer seems obvious: in order to produce useful tools that help them (or their clients) do whatever they want to do. That answer is clearly an oversimplification. Some people write programs just for the fun of it, for example.

The roles of computer programs in science

New programming languages are probably invented every day, and even those that get developed and published are too numerous to mention. New programming languages developed specifically for science and engineering are very rare, however, and that's why such a rare event deserves some publicity.

Julia: a new language for scientific computing

In a recent blog post, Titus Brown asks if software is a primary product of science, and basically says "no" (but do read the post for the details). A blog-post length reply by Daniel Katz comes to the opposite conclusion (again, please read the post before continuing here). I left a short comment on Titus' blog but also felt compelled to expand this into a blog post of its own - so here it is.     Titus introduces a useful criterion for what

Software in scientific research

Like all information with a complex structure, scientific knowledge evolves over time. New ideas turn into validated models, and are ultimately integrated into a coherent body of knowledge defined by the concensus of a scientific community. In this essay, I explore how this process is affected by the ever increasing use of computers in scientific research.

The lifecycle of digital scientific knowledge

Malleable systems are software systems that are designed to be modified and extended by their users, eliminating the usually strict borderline between developers and users.

Emacs as a malleable system

Over the last few months I have been exploring the Racket language for its potential as a language for computational science, and it's time to summarize my first impressions. Why Racket? There are essentially two reasons for learning a programing language: (1) getting acquainted with a new tool that promises to get some job done better than with other tools, and (2) learning about other approaches to computing and programming.

Exploring Racket

About two years ago I wrote a post about why and how I abandoned Apple's iCal for my agenda management and moved to Emacs org-mode instead. Now I am in the process of making the second step in the same direction: I am abandoning Apple's Address Book and starting to use the "Big Brother DataBase", the most popular contact management system from the Emacs universe.

Bye bye Address Book, welcome BBDB

In an earlier post, I mentioned the Nix package management system as a candidate for ensuring reproducibility in computational science.

The Nix package manager in computational science

A while ago I was chatting with two users of my Molecular Modelling Toolkit (MMTK), a library for molecular simulations written in Python. One of them asked me what I would do differently if I were to write MMTK today. That's an interesting question, but not the kind of question I can answer in a sentence or two, so I promised to write a blog post about this. Here it is.  First, a bit of history.

Lessons from sixteen years of molecular simulation in Python

This morning I read C. Titus Brown's blog post on how science could be so much better if scientitic data and the software used to work with it were openly available for reuse. One problem he mentions, like many others have done before, is the lack of incentive for publishing anything else but standard scientific papers. What matters for a scientist's career and for grant applications is papers, papers, papers.

Integrating scientific software and datasets into the citation record

I have been using Macintosh computers since 2003, and overall I have been happy with the personal information management (PIM) tools provided by Apple: AddressBook, Mail, Safari (for bookmark management). The one tool I have never liked is iCal. Its user interface is fine for consulting my agenda, but entering information is too complicated and the todo-list management is particularly clumsy.

Bye bye iCal, welcome org-mode

Many people are asking for my opinion on the recent impressive success of AlphaFold at CASP14, perhaps incorrectly assuming that I am an expert on protein folding. I have actually never done any research in that field, but it's close enough to my research interests that I have closely followed the progress that has been made over the years. Rather than reply to everyone individually, here is a public version of my comments.

Some comments on AlphaFold

A coffee break conversion at a scientific conference last week provided an excellent illustration for the industrialization of scientific research that I wrote about in a recent blog post. It has provoked some discussion on Twitter that deserves being recorded and commented on a more permanent medium. Which is here. I was chatting with a colleague who I have been meeting at such occasions for about 15 years.

Industrialization of scientific software: a case study

SciPy 2015 is over, meaning that many non-participants like myself are now busy catching up with what happened by watching the videos.

The future of the Scientific Python ecosystem

The release of NumPy 1.9 a few days ago was a bit of a revelation for me.  For the first time in the combined history of NumPy and its predecessor Numeric, a new release broke my own code so severely thatI don't see any obvious way to fix it, given the limited means I can dedicate to software maintenance.

The state of NumPy

This post was motivated by Ian Gent's recomputation manifesto and his blog post about it. While I agree with pretty much everything said there, there is one point that I strongly disagree with, and here I'd like to explain the reasons in some detail.

Platforms for reproducible research

The question if living beings, in particular those of our own species, possess "free will", and how it works if it exists, has recently become fashionable again. The new idea that brought the topic back into discussion was that our sense of free will might just be an illusion.

Science and free will

Altmetrics is one of the hotly debated topics in the Open Science movement today. In summary, the idea is that traditional bibliometric measures (citation counts, impact factors, h factors, ...) are too limited because they miss all the scientific activity that happens outside of the traditional journals.

A critical view of altmetrics

One of the books I read during my summer vacation is the recently published second edition of Rupert Sheldrake's "A New Science of Life". It is one of the most controversial books in science, having been both praised and condemned; a review of the first edition in the renowned science journal

 Nature

concluded that this book should be burnt! The question that Sheldrake addresses in this book is where form comes from.

Sheldrake's New Science of Life

Thirty years after my first contact with computational (ir)reproducibility, I am happy to note that many things have improved. Reproducibility, computational and otherwise, is increasingly recognized as an important aspect of scientific quality control, and mostly considered worth striving for.

Why computational reproducibility matters

When the Greek philosopher Heraclitus pronounced his famous "πάντα ῥεῖ" (everything flows), he most probably was not thinking about software. But it applies to software as much as to other aspects of life: software is in perpetual change, being modified to remove bugs, add features, and adapt it to changing environments.

Unifying version control and dependency management for reproducible research

A few days ago, Google announced its experimental project Open Source Insights, which permits the exploration of the dependency graph of Open Source software. My first look at it ended with a disappointment: in its initial stage, the site considers only the package universes of Java, JavaScript, Go, and Rust. That excludes most of the software I know and use, which tends to be written mainly in C, C++, Fortran, and Python.

The dependency hubs in Open Source software

There is an important and ubiquitous process in scientific research that scientists never seem to talk about. There isn't even a word for it, as far as I now, so I'll introduce my own: I'll call it

 knowledge distillation

. In today's scientific practice, there are two main variants of this process, one for individual research studies and one for managing the collective knowledge of a discipline.

Knowledge distillation in computer-aided research

Most scientists have found out by now that a lot has been going wrong with scientific publishing over the years. In many fields, scientific journals are no longer fulfilling what used to be their primary role: disseminating and archiving the results of scientific studies.

A first experience with Open Access publishing

It is often said that science rests on two pillars, experiment and theory. Which has lead some to propose one or two additional pillars for the computing age: simulation and data analysis. However, the

 real

two pillars of science are observations and models. Observations are the input to science, in the form of numerous but incomplete and imperfect views on reality. Models are the inner state of science.

The structure and interpretation of scientific models

This weekend I attended the EuroSciPy 2010 conference in Paris, dedicated to scientific applications of the programming language Python. This was the third EuroSciPy conference, but the US-based SciPy conference has been a regular event for many years already, and recently SciPy India joined the crowd. It looks like Python is becoming ever more popular in scientific computing. Next year, EuroSciPy will take place in Paris again.

EuroSciPy 2010

A while ago I described why migrated my agendas from iCal to orgmode. To sum it up, my main motivation was to gain more freedom in managing my information: where iCal imposes a rigid format for events and insists on storing them in its own database, inaccessible to other programs, orgmode lets me mix agenda information with whatever else I like in plain text files. Today's story is a similar one, but without the happy end.

A rant about mail clients

I have received a number of questions and remarks about my keynote talk at EuroSciPy 2010, ranging from questions about technical details to an inquiry about the release date of Python 4.0! Rather than writing lengthy replies to everyone, I try to address all these issues here.

The future of Python

How can we document software and computational analyses in such a way that others can convince themselves of their validity, and build on them for their own work? The question has been around for many years, and a number of attempts have been made to provide partial answers. This post provides a brief review and describes my own tentative answer, inviting you to play with it.  Explainable AI is a hot topic today.

Explaining software and computational methods

A few days ago, a discussion in my Twitter timeline caught my attention. It was about a very high-level model for the process of scientific research whose conclusions included the affirmation that reproducibility does not improve the convergence of the research process towards truth.

Is reproducibility good for scientific progress? (a paper review)

A few months ago I decided to take a closer look at Eclipse, since several people I know seemed to be quite fond of it. I had tried it earlier on my old iBook G4, but quickly abandoned it because it was much too slow. But my new MacBook Pro should be able to handle it.  Last week I finally decided to retire my Eclipse installation.

Eclipse experiences

A few years ago, I decided to adopt the practices of reproducible research as far as possible within the technical and social constraints I have to live with. So how reproducible is my published code over time? The example I have chosen for this reproducibility study is a 2013 paper about computing diffusion coefficients from molecular simulations. All code and data has been published as an ActivePaper on figshare.

Reproducible research in the Python ecosystem: a reality check

The last two days I participated in the "Executable Papers workshop" at this year's ICCS conference. It was not just another workshop among the many ICCS workshops. The participants had all submitted a proposal to the "Executable Paper Grand Challenge" run by Elsevier, one of the biggest scientific publishers. On the first day, the nine finalists presented their work, and on the second day, the remaining accepted proposals were presented.

Executable Papers

Computational reproducibility has become a topic of much debate in recent years. Often that debate is fueled by misunderstandings between scientists from different disciplines, each having different needs and priorities. Moreover, the debate is often framed in terms of specific tools and techniques, in spite of the fact that tools and techniques in computing are often short-lived.

The four possibilities of reproducible scientific computations

Yesterday a blog post by Cyrille Rossant entitled "Moving away from HDF5" caught my eye. My own tendency at the moment is to use HDF5 more and more, so I was interested in why someone else would want to do the opposite. Here is my conclusion after reading his post, plus some ideas about where scientific data management is or should be heading in my opinion.

On HDF5 and the future of data management

At the recent SciCodes Symposium, I brought up the question of reviewing research software during the panel discussion. One panelist then raised the question of

 why

we should review research software. I found this question surprising at first, but I do agree that it deserves an answer. Here is mine.

Konrad Hinsen's blog