Reproducible Research§


This is, much like research itself, and the art of eating spaghetti without soiling yourself, work-in-progress.

This page is not as general as it should be. It is biased towards audio signal processing, audio engineering, spatial audio reproduction and auditory perception. However, many of the ideas presented here can be applied more widely.

Other collections of similar information:




The open definition

Definition by Wikipedia:

Open science is the movement to make scientific research, data and dissemination accessible to all levels of an inquiring society, amateur or professional. It encompasses practices such as publishing open research, campaigning for open access, encouraging scientists to practice open notebook science, and generally making it easier to publish and communicate scientific knowledge. […] In modern times there is debate about the extent to which scientific information should be shared. The conflict is between the desire of scientists to have access to shared resources versus the desire of individual entities to profit when other entities partake of their resources.

Definition by Wikipedia:

Open research is research conducted in the spirit of free and open source software. Much like open source schemes that are built around a source code that is made public, the central theme of open research is to make clear accounts of the methodology freely available via the internet, along with any data or results extracted or derived from them. This permits a massively distributed collaboration, and one in which anyone may participate at any level of the project.

Especially if the research is scientific in nature, it is frequently referred to as open science. Open research can also include social sciences, the humanities, mathematics, engineering and medicine.

Definition by Wikipedia:

Open data is the idea that some data should be freely available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control. The goals of the open data movement are similar to those of other “open” movements such as open source, open hardware, open content, and open access.

Definition by Wikipedia:

Open science data is a type of open data focused on publishing observations and results of scientific activities available for anyone to analyze and reuse.

Definition by Wikipedia:

Open notebook science is the practice of making the entire primary record of a research project publicly available online as it is recorded. This involves placing the personal, or laboratory, notebook of the researcher online along with all raw and processed data, and any associated material, as this material is generated. The approach may be summed up by the slogan ‘no insider information’. It is the logical extreme of transparent approaches to research and explicitly includes the making available of failed, less significant, and otherwise unpublished experiments; so called ‘dark data’.

Definition by Wikipedia:

Open access (OA) refers to online research outputs that are free of all restrictions on access (e.g. access tolls) and free of many restrictions on use (e.g. certain copyright and license restrictions). Open access can be applied to all forms of published research output, including peer-reviewed and non peer-reviewed academic journal articles, conference papers, theses, book chapters, and monographs.

Two degrees of open access can be distinguished: gratis open access, which is online access free of charge, and libre open access, which is online access free of charge plus various additional usage rights.


Reproducible Research vs. Non-Reproducible Research?


reproducible vs. easily reproducible


online material as supplement to traditional publications

Vandewalle et al. distinguish six degrees of reproducibility:

  1. The results can be easily reproduced by an independent researcher with at most 15 min of user effort, requiring only standard, freely available tools (C compiler, etc.).

  1. The results can be easily reproduced by an independent researcher with at most 15 minutes of user effort, requiring some proprietary source packages (MATLAB, etc.).

  1. The results can be reproduced by an independent researcher, requiring considerable effort.

  1. The results could be reproduced by an independent researcher, requiring extreme effort.

  1. The results cannot seem to be reproduced by an independent researcher.

  1. The results cannot be reproduced by an independent researcher.

While I don’t agree with all details (especially the over-concrete time specifications and the overly vague effort metrics), I like the general idea.

Replicability vs. Reproducibility§

Great overview: Language Log: Replicability vs. reproducibility — or is it the other way around?

Wikipedia thinks it’s the same:

Reproducibility is the ability of an entire experiment or study to be duplicated, either by the same researcher or by someone else working independently. Reproducing an experiment is called replicating it. Reproducibility is one of the main principles of the scientific method.

Chris Drummond claims they are different:

Reproducibility requires changes; replicability avoids them. Although reproducibility is desirable, I contend that the impoverished version, replicability, is one not worth having.

Roger D. Peng also claims that they are different, but uses slightly different definitions:

The replication of scientific findings using independent investigators, methods, data, equipment, and protocols has long been, and will continue to be, the standard by which scientific claims are evaluated. However, in many fields of study there are examples of scientific investigations that cannot be fully replicated because of a lack of time or resources. In such a situation, there is a need for a minimum standard that can fill the void between full replication and nothing. One candidate for this minimum standard is “reproducible research”, which requires that data sets and computer code be made available to others for verifying published results and conducting alternative analyses.

Victoria Stodden defines them slightly differently (and throws in a third concept – “repeatability”):

We can reserve the term “replicability” for the regeneration of published results from author-provided code and data. […] Reproducibility is a more general term, implying both replication and the regeneration of findings with at least some independence from the code and/or data associated with the original publication. Both refer to the analysis that occurs after publication. A third term, “repeatability,” is sometimes used in place of reproducibility, but this is more typically used as a term of art referring to the sensitivity of results when underlying measurements are retaken.


Here are few guidelines which may (or may not) help to make your work more reproducible:

make everything public (and each step of it)

At some point, every aspect of your work should be publicly accessible. And not only the parts which (you think) are most interesting … every single bit and every single step. This way it will be easiest for others to reproduce your work.

You may not want to publish everything from the very beginning, which leads to the next point …

release early

This is borrowed from the Open Source movement, but it’s also applicable here. Even if you feel it’s not finished yet, just make it public! Because if you wait too long, you’ll probably never release it …

If you release early, you also give others the chance to comment on your work and to suggest improvements before you think it’s “finished” (which may never happen).

make stuff public by default

In case of doubt, make it public! Keep things only for yourself if there is a good reason. And even if there is a reason now, you should think about making it public later (e.g. after publication of a related paper).

think about others

Don’t just think about how great your results are, also think about how you can make it as easy as possible for others to reproduce them.

use tools that others can use, too

If you have a choice, prefer tools that are available to other researchers, too.

Of course, often expensive equipment is needed in research, and sometimes only few laboratories have even the theoretical possibility to reproduce your experiments. We have to live with that.

When it comes to software, there is often an alternative to expensive programs, sometimes the free ones are even better. Try to choose software that is accessible to most people, and try to use software that runs on different operating systems.

use open source software

TODO: content

specify a license

If provide something to the public and don’t specify a license, said public may have a hard time using the thing legally. With everything you publish, you should also tell people what they may and may not do with it.

But remember: the more restrictions you impose, the more freedom you take away from people who want to use your work. You can waive all your rights (at least with regard to copyright law), you can request attribution, you can demand that derived works must be published under the same conditions as the original work (a.k.a. share-alike), you can forbid commercial use, …

Try these links to help you choose an appropriate license:

For more details, have a look there:

Licensing your research, webinar with Brandon Butler:

bring research and teaching closer together

Every research starts from some existing knowledge.

TODO: more arguments

Today’s students are tomorrow’s researchers.

What Should be Reproducible?§

Short answer: everything!

But let’s be a bit more verbose. Ideally, the whole research process should be reproducible. The following list shows things that can (and should!) be made reproducible. There are also some tools mentioned that may help, see below for links to more software and libraries.

All this is of course very much dependent on the research area. Some points may apply to your area, others won’t.

collecting ideas

Ideas are the core of any research activity. They are also one of the main resources needed by researchers (besides funding). Understandably, many researcher are reluctant to make their ideas public before they reap their fruits themselves.

But at a later time, e.g. after a publication, there may not be a reason anymore to keep the ideas a secret. Also, some researchers (mostly the good ones) have more ideas than they could possibly work on. In this case they should make their “vacant” ideas public for other researchers to work on.

In the era of the world-wide-web there are countless possibilities to share your ideas, no need to give any pointers here, you’ll find something.

symbolic derivations

In many areas, deriving equations is the daily drill of a researcher. In traditional publications, however, only a limited amount of space can be used for equations, so typically only a few steps of the derivation are shown or even only the final resulting equation.

This can make it very time-consuming for other researchers to reproduce and build on your results. Ideally, for every published equation the complete and detailed derivation should also be publicly available.

You can create nice equations using LaTeX documents, but also some blogging systems support entering math equations. IPython also supports nice-looking equations (using MathJax).


numeric calculations, simulations, visualizations, plots

TODO: NumPy, SciPy, matplotlib, Mayavi, …

cluster computing

TODO: IPython


TODO: settings, logs, software, pre-/post-processing scripts

experimental apparatus
TODO: detailed description, drawings, photos, detailed list of devices ant

the used configuration, …

TODO: software (ideally open source), scripts, configuration files, data

files, …

statistical evaluation

TODO: raw data, all scripts

TODO: pandas, R


Three points from

  1. data theft

  2. not patentable once published

  3. data deluge


The following is a completely subjective selection of open-source software. This is not at all exhaustive, there are a lot of alternatives, both commercial and non-commercial.



Why Python?

The chief reason is that it’s just a beautiful programming language. And it’s versatile … so the two reasons are its beauty and versatility … and its extensive standard library, therefore the three reasons to use Python are its beauty, versatility and extensive standard library … and a sheer unimaginably humongous number of third-party libraries and extensions.

Let’s just say amongst the reasons to choose Python are such diverse elements as beauty, versatility, extremely useful standard library and tons of third-party stuff.

For more information, watch this:

Scientific Python (SciPy)

This is a collection of many software projects: NumPy, SciPy, matplotlib, IPython, SymPy, pandas, Mayavi, PyTables, and many more …

See also my introduction to Python, NumPy, IPython, …



TikZ, gnuplot, beamer


See Getting Started with Git.

More Software§

There’s always more …




Publication Tools§


IJulia (example notebook)






Online Services§

IPython/Jupyter Notebook Viewer

Binder (Turn a GitHub repo into a collection of interactive notebooks)


Bitbucket (free unlimited accounts for academic users)

figshare, connecting Github and figshare





my experiment

re3data (Registry of Research Data Repositories)

RADAR - Research Data Repository

Open Science Framework



PubPeer (post publication peer review)

PubMed Commons (post publication peer review) (discontinued, see

CKAN (Open Source data portal platform)

sciety (curated preprints)

Peer Community in


F1000Research (life sciences)

Scientific Data -


The ReScience Journal

Peer Community Journal


Patrick Vandewalle, Jelena Kovačević, Martin Vetterli, Reproducible Research in Signal Processing, IEEE Signal Processing Magazine Volume 26, Issue 3, 2009.

Robert Gentleman, Duncan Temple Lang, Statistical Analyses and Reproducible Research, Journal of Computational and Graphical Statistics Volume 16, Issue 1, 2007.

Bruce G. Charlton, Peer usage versus peer review, BMJ Volume 335, Issue 7617, 2007.

Arturo Casadevall, Ferric C. Fang, Reproducible Science, Infection and Immunity Volume 78, Issue 12, 2010.

Jonathan B. Buckheit, David L. Donoho, WaveLab and Reproducible Research, in Wavelets and Statistics, Springer, 1995.

Darrel C. Ince, Leslie Hatton, John Graham-Cumming, The Case for Open Computer Programs, Nature Volume 482, 2012.

Nature special Challenges in Irreproducible Research, 2010-2013.

Fernando Pérez, Brian E. Granger, John D. Hunter, Python: An Ecosystem for Scientific Computing, Computing in Science Engineering, Volume 13, Issue 2, 2011.

Peter Suber, Open Access, MIT Press, 2012.

Peter Suber, Gratis and libre open access, SPARC Open Access Newsletter, issue #124, 2008.

Peter Suber, Knowledge Unbound: Selected Writings on Open Access, 2002–2011, MIT Press, 2016.

John P. A. Ioannidis, Why Most Published Research Findings Are False, PLoS Med 2(8): e124. doi:10.1371/journal.pmed.0020124, 2005.

Detailed comment to the above:

Chris Drummond, Replicability is not Reproducibility: Nor is it Good Science, Proc. of the Evaluation Methods for Machine Learning Workshop at the 26th ICML, 2009.

Ian P. Gent, The Recomputation Manifesto, Unpublished position paper, Version 1.9479, 2013.

Michael Woelfle, Piero Olliaro, Matthew H. Todd, Open science is a research accelerator, Nature Chemistry, Volume 3, Issue 10, 2011.

Radovan Vrana, Open science, open access and open educational resources: Challenges and opportunities, International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015.

Yale Law School Roundtable on Data and Code Sharing, Reproducible Research: Addressing the Need for Data and Code Sharing in Computational Science, Computing in Science & Engineering, Volume 12, Issue 5, 2010.

Toronto International Data Release Workshop Authors, Prepublication Data Sharing, Nature 461, no. 7261, 2009.

Rinze Benedictus, Frank Miedema, and Mark W. J. Ferguson, Fewer Numbers, Better Science, Nature News, Volume 538, Issue 7626, 2016.

J. Wilsdon et al., The Metric Tide: Report of the Independent Review of the Role of Metrics in Research Assessment and Management, 2015.

Barak A. Cohen, Point of View: How should novelty be valued in science?, 2017.

D. Cicchetti, The reliability of peer review for manuscript and grant submissions: A cross-disciplinary investigation, 1991.

J. Bollen et al., From funding agencies to scientific agency, 2014.

J. Bollen et al., An efficient system to fund science: from proposal review to peer-to-peer distributions, 2017.

B. Alberts et al., Self-Correction in Science at Work, Science Vol. 348, Issue 6242, pp. 1420-1422, 2015

B. A. Nosek et al., Promoting an Open Research Culture, Science Vol. 348, Issue 6242, pp. 1422-1425, 2015

Mary C. Murphy et al., Open science, communal culture, and women’s participation in the movement to improve science, Proceedings of the National Academy of Sciences, 2020

Thomas H. Berquist, Peer Review: Is the Process Broken?, American Journal of Roentgenology, Volume 199, Issue 2, 2012

Melinda Baldwin, Peer Review, Encyclopedia of the History of Science 2020