Authors (in alphabetical order): Maria Cruz (VU), Marc Galland (UvA), Carlos Martinez (NL eScience Center), Raúl Ortiz (TU Delft), Esther Plomp (VU), Anita Schürch (UMCU), Yasemin Türkyilmaz-van der Velden (TU Delft)

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Based on the contributions from workshop participants (in alphabetical order): Joke Bakker (University of Groningen), Jochem Bijlard (The Hyve), Mattias de Hollander (NIOO-KNAW), Joep de Ligt (UMCU), Albert Gerritsen (Radboud UMC) Thierry Janssens (rivm), Victor Koppejan (TU Delft), Brett Olivier (Vrije Universiteit Amsterdam), Raúl Ortiz (TU Delft), Esther Plomp (Vrije Universiteit Amsterdam), Jorrit Posthuma (ENPICOM), Anita Schürch (UMCU)


On 2 October 2018, Maria Cruz (VU), Marc Galland (UvA), Carlos Martinez (NL eScienceCenter), and Yasemin Türkyilmaz-van der Velden (TU Delft) facilitated a workshop titled “Software Reproducibility – The Nuts and Bolts”, as part of the DTL Communities@Work 2018 event held in Utrecht, the Netherlands.

Besides the four organisers, there were 24 workshop participants, including researchers, research software engineers/developers, data stewards and others in research support roles.

Below we summarise the background and rationale for the workshop, key discussions and insights, and recommendations. The description of the workshop setup, including information about the participants gathered via Mentimeter, can be found at the end of this report.

The listed authors include the four organisers and the workshop participants who actively contributed to the report. Workshop participants who agreed to be acknowledged for their contributions are also listed.

Rationale for the workshop

The starting point for the workshop was a paper published in Water Resources Research by Hut, van de Giesen and Drost (2017), which argues that carefully documenting and archiving code and research data may not enough to guarantee the reproducibility of computational results. Alongside the use of the current best practices in scientific software development, these authors recommend close collaboration between scientists and research software engineers (RSEs) to ensure scientists are aware of the latest computational advances, most notably the use of containers (e.g. Docker) and open interfaces.

As happened in a previous similar workshop held at TU Delft on 24 May 2018, the participants discussed the merits of these recommendations and how they could be put into practice; and also what role the various stakeholders (researchers, research software engineers, research institutions, data stewards and other research support staff) could play in this regard.

In this second edition of the workshop, the participants also made recommendations for actions that could be taken at the national level in the Netherlands to raise the awareness of software sustainability and reproducibility and to implement the advice from the paper and the workshop. The key discussion points and insights from these discussions and the ensuing recommendations are summarised below, based on information recorded during the workshop within a collaborative google document.

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In this report we define reproducibility and reusability of software as follows. Reproducibility is focused on being able to reproduce results obtained in the past – that is, use the same data and the same software to reach the same result (a docker image may be good enough for this). Reusability is concerned with using the software on a different context than it was used before; this could be as simple as using the same software with different data or it may require modification of the original software (docker images may or may not be sufficient for software reusability).

Key discussion points and insights on the advice by Hut, van de Giesen and Drost

Sound but too technical advice

Overall, the groups felt that the advice was sound but too technically focussed, particularly if it is aimed at researchers. Researchers should not need to concern themselves with containers and open API’s, which are too technical to implement. The advice also fails to consider and recognise deeper cultural issues, such as: the lack of awareness on the topic of reproducibility and reusability of research software; the lack of relevant training, tools and support; and the diversity of code.

Concerns regarding the use of containers

Docker may not necessarily be easy to use if you are not a software developer or research software engineer. There was also the concern that containers, although helpful, should not be used to mask bad coding practices. The use of containers also makes it difficult to upgrade the software. Containers make it easier to distribute software on the short term, but to make software sustainable someone needs to understand how to update and build a new container. This is a role for the research software engineers, not the researchers, as there are no tools that are easy to use that allow for re-use of software in different containers. The other issue that was raised was whether Docker and other platforms would still exist in 20 years’ time.

Not all code is equal

Not all software is meant to be maintained or reused. High software quality, version management, code review, etc. will all help with reproducibility and reusability, but at some point in time the software might not be sustainable anymore. Is this necessarily bad? Code from 10 years ago will probably need to be rewritten in newer languages. Defining the scope of code will help determine the level of reproducibility and reusability requirements. In particular, it is important to differentiate between single-use scripts and pipelines that are used repeatedly and/or by different people. While the former do not need to be highly maintained, the latter need to be extensively reviewed and tested. Commercial software is also an issue. In some fields of research, many scientists use Excel or MATLAB. Commercial software is often closed source, making it difficult to test, review and publish it; and sometimes the publication of the code is also not possible for IP or confidentiality reasons.

Training and raising awareness

How much are researchers aware of the reproducibility crisis? Researchers need to be aware of the key features and concepts behind reproducibility and reusability of research software. These concepts are more important than any particular techniques. The first step should thus be raising awareness of these issues. People who are already aware of the reproducibility crisis and of practices conducive to reproducibility and their practical benefits have a responsibility to raise awareness within their department/group/colleagues. Researchers also need to be aware of the possibilities and best practices in order to apply them. Training is important in this regard. Having the right tools and support is also essential. Researchers need to know who to contact for help and support and how to find the right tools.

Code review

There should be code review sessions involving all the interested parties. Code review could be similar to peer review and be done at the institutional or departmental level. Working together on software increases the quality of code, particularly if it is reviewed by multiple stakeholders. Sharing the experience and the knowledge gained from these code review sessions more widely would provide a way to advertise and advocate for the best practices in software development.

Community building

Building a community behind a particular tool or piece of software was also seen as a good way to ensure that code is maintained and upgraded. If the software is out there and there is interest in it, people will maintain it. Being a part of such community may not necessarily require specific expertise and technical involvement. A user of a tool can very well contribute to the community by raising issues without needing to have specific knowledge about the code.

Good practices in scientific software development

Good coding practices should be publicly available and widely advertised. Building software should start with clearly documented use cases, and these use cases should define the entry points for the code. Materials and methods should include parameters for any executable. The environment configuration should also be added alongside the code to make it reproducible. For software to be redeployable on different platforms (also through time), it needs to be well documented, including open data and workflows. You need to be able to understand what the purpose of the experiment was and how it was done, and how the data was processed, if that is relevant. Version control and releases with DOIs are also important. Testing with proper positive and negative controls, integration, and validation are also critical to re-using software.

The roles of data stewards, RSEs and researchers

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RSEs as ambassadors for software reproducibility

While researchers should lead when it comes to reproducibility, data stewards could help raise the awareness of this important issue and of the best practices for software reproducibility. RSEs often in support roles, standing between the researchers and their software – have a key role to play as ambassadors and should be part of the driving force behind efforts towards software reproducibility. In particular, they should be creating and maintaining software development guidelines. Research support roles, including those of data stewards and RSEs, should be more clearly defined and rewarded; these roles should not be seen or performed as just a side activity. RSEs should be actively involved in the research design and publication process, and should not been seen solely as a supporter of the researcher, but as a collaborator. Unfortunately, the current funding schemes do not reward these activities.

Cross-expertise speed-networking

Communication and interaction between the three key stakeholders (researchers, RSEs and data stewards) was seen as a shared responsibility. However, setting up cross-expertise speed-networking events could be an easy way to connect researchers, data stewards and RSEs, and to encourage collaboration. This type of initiatives could be implemented at institutional, national and/or even at international level. At the institutional level, a central service desk could work as a hub to connect researchers to research support experts. Encouraging collaboration by helping researchers connect with available experts provides a way to avoid redundant solutions to similar problems. For collaborations to be fruitful, however, researchers need to understand the perspective of RSEs and data stewards, and vice versa. Domain-specificity is another barrier that can block the collaboration between data stewards, RSEs and researchers.

How to encourage reproducibility in computational research?

As said earlier, researchers should lead when it comes to reproducibility. However, they may not always be interested in reproducibility, as reproducibility does not always guarantee good science. Researchers need to be intrinsically stimulated to document and review their code and to follow the best practices in software management and development. Publishing a methods or software paper that includes easy-to-reuse, high-quality software will help researchers get more citations. User friendly tools that help with software management and reproducibility will also stimulate use by researchers. 

Key recommendations

Reproducibility should be enforced from the top down

Journals and funders, in particular NWO, should enforce their policies. There should be funding for reproducibility; there should also be standards and requirements and appropriate audits. Data management plans as well as software sustainability plans are essential to ensure best practices. The funders need to become more aware of software sustainability and the needs for software management. For FAIR data there are funding opportunities, but these are not available for FAIR software. There is a need to make good practices in science the de facto standard. FAIR (both for data and software) should be the rule and no longer the exception. There should also be more recognition about publishing data and code, not only papers.

A leading role for national platforms

National platforms, such as the Netherlands eScience Center, should also be responsible and lead the research community into making software and data sustainability a recognised element of the research process. There is also a need among the research community for more knowledge and awareness about the NL eScience Center and the possibilities for collaborations between researchers and RSEs. In this respect, the Netherlands eScience Center should also take the lead in promoting collaboration between RSEs and researchers.

Community building as a bottom-up approach

Besides a top-down approach, building communities from the bottom up was also recommended as a way to connect researchers with relevant research support experts. The Dutch Techcentre for Life Sciences (DTL), for example, could set up a platform to connect individual researchers with software experts. This could be in the form of national cross-expertise speed-networking events or a forum. The NL-RSE initiative could also play a role in this regard and could help raise awareness of the issues around software reproducibility and sustainability.

Training

It is crucial to educate early career researchers, who have the time and interest. Courses and trainings are needed at the universities and at the national level. Researchers should be made aware of good practices for software development and software engineering at the earliest stages of their careers, including at the bachelor and master level.

Additional information

Workshop setup

The workshop session lasted two hours. It started with the organisers introducing themselves, followed by a short survey of the audience using Mentimeter, led by Yasemin Türkyilmaz-van der Velden. Maria Cruz then gave a presentation setting the scene, providing information on reproducibility and summarising the paper and the suggestions by Hut, van de Giesen & Drost (2017). Marc Galland gave a short presentation on software sustainability from the researcher’s point of view, and Carlos Martinez Ortiz gave his perspective on the same subject from the research software engineer’s point of view.

The audience was then split into four groups, with the organisers each joining a group to help facilitate the discussion. Each groups was allotted 45 minutes to answer the following questions within a collaborative google document:

  1. How can the advice by Hut, van de Giesen & Drost be put in practice?
  2. Any additional advice?
  3. How can researchers, RSEs, and data stewards work together towards implementing the advice?
  4. What needs to happen at the national level in the Netherlands to raise awareness of research software reproducibility and help implement the above or any of your ideas and recommendations?

About the participants

We asked a few questions to the audience, using Mentimeter, to get familiar with their background and their experiences with research software. As seen in the responses below, we had a mixed audience of researchers, research software engineers, data stewards, and people in other research support positions. As expected from a DTL conference, which focussed on the life sciences, most participants had a research background within this area, ranging from biomedical sciences to bioprocess engineering and plant breeding. All participants had experience with research software.

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Almost all participants agreed that there is a reproducibility crisis in science, reflecting the high level of awareness among the audience of this important issue. Before moving to the presentation about software reproducibility, we asked the participants what came to their mind about this topic. The answers, which ranged from version control, documentation and persistent identifiers to Git, containers, and Docker, clearly show that the audience was already very familiar with the topic of software reproducibility. In line with this, when we asked what they were doing themselves in terms of software reproducibility, we received very similar answers, with version control taking the lead among the answers to both questions.

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