Paula Martinez Lavanchy, the Research Data officer coordinating RDM training initiatives at TU Delft and 4TU.ResearchData co-organized the ‘First meeting of National Research Data & Software training coordination’ together with Mateusz Kuzak from the Netherlands eScienceCenter and Carlos Teijeiro Barjas from SURFsara.
This is her report about the event that took place on 3 March 2020 at SURF in Utrecht.
How did this initiative start?
In October 2019, TU Delft published its Vision for Research Data and Software Management Training. This ambitious plan aims at covering the different training needs that we consider relevant for researchers (including PhD candidates) at different stages of their research. This is a live document that needs constant evaluation and adjustment. So, since its publication, my task as the coordinator of RDM training activities has been to implement this vision and to investigate the challenges that can be encountered and possible solutions.
One of the first evident challenges is, how can we ensure that we are able to provide all the training we think is relevant for researchers in a sustainable way?
At the end of 2019, in a meeting with Mateusz Kuzak from the Netherlands eScienceCenter and Carlos Teijeiro from SURFsara, we discussed all our ideas for developing and organizing training at our respective institutions and already found some ways to collaborate. But, we also thought that the sustainability issue of RDM (including software) training is probably common at different institutions and that there are other challenges that we could be solving collaboratively within the training coordinators community. So, we decided to go national!
What was the meeting about?
We contacted everybody that we knew was involved in providing or coordinating training in the field of Research Data and Software skills at the Dutch Universities and other organizations and call for a meeting to:
- Network and exchange knowledge about the training activities at different research institutions around the Netherlands
- Identify the challenges and the needs for training
- Identify collaboration opportunities
The session took place on 3 March 2020 and 18 participants (including us – the three hosts) from 12 institutions joined for a whole afternoon to discuss training.
Here you can see the introduction slides of the meeting: https://doi.org/10.5281/zenodo.3712254
We initiated the session with 5 min pitches from each institution to know what type of training is in place. After that, we collected challenges encountered in RDM-generic training, Software-related training and Discipline-specific training.
This first part of the discussion provided useful information about the different approaches towards training for RDM-related topics. Sometimes training on RDM is provided under a broad topic like Research Reproducibility or Open Science, and sometimes it is split to focus on specific RDM-related tasks such as Data Management Planning or Data Publication.
At this point, I already identified with whom I should be exchanging experiences more in-depth about similar courses organized at TU Delft. It is in my to-do list to discuss with colleagues from the Centre for Digital Scholarship Leiden if and how to join forces to provide Data Carpentries for Social Sciences, which demonstrated to be very relevant for researchers at TU Delft Faculty of Architecture. I am also hoping to exchange knowledge with colleagues from VU Amsterdam about their approach to training on versioning control (training on Git). And, I could also get some more colleagues engaged in participating in our Code Refinery Train-the-Trainer event planned after the summer.
When discussing challenges, it was easy to identify commonalities such as lack of trainers and helpers for the training sessions, the low motivation of researchers to attend the generic RDM courses if they are not mandatory, the lack of practical exercises and applied material, or the the lack of awareness about innovative ways to provide RDM training.
Then, the most exciting part came: Is there room for collaboration to approach the challenges? Are we interested in collaborating? If yes, how can we collaborate?
A collaborative future
In the last part of the meeting, we brainstormed about ideas to work collaboratively on training. Everybody agreed that there is a lot of potential to work we could collaborate on in different areas.
Some example of collaborative efforts that were mentioned:
- National pool of trainers for domain-specific, data type-specific and/or software to exchange between institutions.
- Database of trainers profiles – in case institutions want to hire trainers or facilitate the exchange of trainers within institutions.
- Coordination of The Carpentries efforts – having a national coordinator of The Carpentries (or training in general) that can support people in creating a community around training, certifying instructors, and the development of training materials.
- Exchange of course materials from the different institutions in The Netherlands – focus on making them FAIR, exchange course description, modules, visual material and exercises (especially practical exercises).
- Training in developing open training materials in a collaborative manner (using The Carpentries framework)
- Train-the-Trainers programme – support staff providing training needs pedagogical skills and to learn creative ways of providing the content/exercises. In addition, there is also a need to learn about tools that researchers use for RDM in order to be able to teach about them.
In the next few weeks, we need to identify if some of these ideas could be organized within established initiatives already existing in the Netherlands e.g. LCRDM, NPOS, RDNL, etc. It is also necessary to discuss how the resources can be organized to continue the coordination of the Research Data & Software training at a national level.
The ideas will be shared with the initial group we contacted and we can decide as a community the best way to move forward.
On 20th September, TU Delft and 4TU.ResearchData welcomed visitors from the iPres conference. It introduced them to TU Delft’s approach to building a culture of good research data management. TU Delft has taken an approach that is systematic (by involving many related stakeholders across the university) but also pragmatic (that acknowledges the different motivations, or lack of them, that researchers have in managing their data well).
It also included the Lego game for teaching the importance of documentation in research data.
The following presentations were given
Written by Marta Teperek, Esther Plomp, Yasemin Turkyilmaz-van der Velden
The Electronic Lab Notebook (ELN) project at TU Delft is now well under way. It was officially kicked-off during a meeting which took place on 18 April 2019 and it will last for 1 year. During this time, interested TU Delft researchers will be able to try out two different ELN products: ResearchSpace (RSpace) and eLABjournal. At the moment, 37 researchers from three different TU Delft faculties (Applied Sciences – AS); Mechanical, Maritime and Materials Engineering – 3mE; Civil Engineering and Geosciences – CEG) are participating in the trial.
Why was this project started?
Discussions about ELNs at TU Delft were initiated back in March 2018, when several ELN providers, as well as researchers interested in moving away from paper to digital lab note keeping, came along to the event on the topic. Feedback gathered during and after the event demonstrated the interest among researchers at TU Delft in ELNs.
Subsequently, a dedicated ELN working group was created by colleagues from the library, ICT and Faculties of AS and 3mE (Susan Branchett, Esther Maes, Esther Plomp, Marta Teperek and Yasemin Turkyilmaz-van der Velden). The group gathered some key requirements for ELN products (based on the needs indicated by the research community at TU Delft and consultations with colleagues at other universities) and shortlisted two suppliers (RSpace and eLABjournal) which were able to best meet these key requirements. All functionalities were described in details by the providers on 18 April and both products are now available to interested TU Delft researchers for testing.
Photo from the ELN kick-off meeting on 18 April 2019
What will happen next?
The ELN working group will gather feedback from researchers testing the two products during and after the trial:
- Early evaluation (1-3 months into the project) is currently taking place by gathering feedback about the initial user experience via informal meetings with researchers.
- Mid-term evaluation (6 months into the project) is scheduled to happen during a dedicated consultation meeting with ELN users at TU Delft on Tuesday 24th of September.
- Final evaluation of the two products, which might inform a future tender process, will happen at the end of April 2020, at the end of the trial.
Subsequently, the ELN working group will develop recommendations for the next steps aiming at providing ELN solutions at TU Delft. Summary of these recommendations will be published on the Open Working blog and shared with interested stakeholders (researchers, ICT, library, faculties).
How can I get involved?
If you are a TU Delft researcher and would like to take part in the trial, please get in touch with Esther Plomp or Yasemin Turkyilmaz-van der Velden, Data Stewards from the Faculty of AS and 3mE, respectively.
On 27 and 28 February 2019, I attended the NSF FAIR Hackathon Workshop for Mathematics and the Physical Sciences research communities held in Alexandria, Virginia, USA. I travelled to the event at the invitation of the TU Delft Data Stewards and with the generous support of the Hackathon organisers, Natalie Meyers and Mike Hildreth from the University of Notre Dame.
Participants were encouraged to register and assemble as duos of researchers and/or students along with a data scientist and/or research data librarian. I was invited, as a data librarian with a research background in the physical sciences, to form a duo with Joseph Weston, a theoretical physicist by background and a scientific software developer at TU Delft, who is also one of the TU Delft Data Champions.
I presented about the Hackathon at the last TU Delft Data Champions meeting. The presentation is available via Zenodo. All the presentations and materials from the FAIR Hackathon are also publicly available. The FAIR data principles are defined and explained here. This blog post aims to offer some of my views and reflections on the workshop, as an addition to the presentation I gave at the Data Champions meeting on 21 May 2019.
The grand vision of FAIR
The workshop’s keynote presentation, given by George Strawn, was one the highlights of the event for me. His talk set clearly and authoritatively what is the vision behind FAIR and the challenges ahead. Strawn’s words still ring in my head: “FAIR data may bring a revolution on the same magnitude as the science revolution of the 17th century, by enabling reuse of all science outputs – not just publications.” Drawing parallels between the development of the internet and FAIR data, Strawn explained: “The internet solved the interoperability of heterogeneous networks problem. FAIR data’s aspiration is to solve the interoperability of heterogeneous data problem.” One computer (“the network is the computer”) was the result of the internet, one dataset will be FAIR’s achievement. FAIR data will be a core infrastructure as much as the internet is today.
“The internet solved the interoperability of heterogeneous networks problem. FAIR data’s aspiration is to solve the interoperability of heterogeneous data problem.” — George Strawn
Strawn warned that it isn’t going to be easy. The challenge of FAIR data is ten times harder to solve than that of the internet, intellectually but also with fewer resources. Strawn has strong credentials and track record in this matter. He was part of the team that transitioned the experimental ARPAnet (the precursor to today’s internet) into the global internet and he is part of the global efforts trying to bring about an Internet of FAIR Data and Services. In his view, “scientific revolution will come because of FAIR data, but likely not in a couple of years but in a couple decades.”
Researchers do not know about FAIR
Strawn referred mainly to technical and political challenges in his presentation. One of the challenges I encounter in my daily job as a research data community manager is not technical in nature but rather cultural and sociological: how to get researchers engaged with FAIR data and how to make them enthusiastic to join the road ahead? Many researchers are not aware of the FAIR principles, and those who are, do not always understand how, or are willing, to put the principles into practice. As reported in a recent news item in Nature Index, the 2018 State of Open Data report, published by Digital Science, found that just 15% of researchers were “familiar with FAIR principles”. Of the respondents to this survey who were familiar with FAIR, only about a third said that their data management practices were very compliant with the principles.
The workshop tried to address this particular challenge by bringing together researchers in the physical sciences, experts in data curation and data analysts, FAIR service providers and FAIR experts. About half of the participants were researchers, mainly in the areas of experimental high energy physics, chemistry, and materials science research, at different stages in their careers. Most were based in the US and funded by NSF.
These researchers were knowledgeable about data management and for the most part familiar with the FAIR principles. However, the answers to a questionnaire sent to all participants in preparation for the Hackathon, shows that even a very knowledgeable and interested group of participants, such as this one, struggled when answering detailed questions about the FAIR principles. For example, when asked specific questions about provenance metadata and ontologies and/or vocabularies, many respondents answered they didn’t know. As highlighted in the 2018 State of Open Data report, interoperability, and to a lesser extent re-usability, are the least understood of the the FAIR principles. Interoperability, in particular, is the one that causes most confusion.
There were many opportunities during the workshop to exchange ideas with the other participants and to learn from each other. There was much optimism and enthusiasm among the participants, but also some words of caution, especially from those who are trying to apply the FAIR principles in practice. The PubChem use case “Making Data Interoperable”, presented by Evan Bolton from the U.S. National Center for Biotechnology Information, was a case in point. It could be said, as noted by one of the participants, that the chemists “seem to really have their house in order” when it comes to metadata standards. Not all communities have such standards. However, when it comes to “teaching chemistry to computers” – or put in other words, to make it possible for datasets to be interrogated automatically, as intended by the FAIR principles – Bolton’s closing slide hit a more pessimistic note. “Annotating and FAIR-ifying scientific content can be difficult to navigate”, Bolton noted, and it can feel like chasing windmills. “Everything [is] a work in-progress” and “what you can do today may be different from tomorrow”.
What can individual researchers do?
If service providers, such as PubChem, are struggling, what are individual researchers to do? The best and most practical thing a researcher can do is to obtain a persistent identifier (e.g. a DOI) by uploading data to a trusted repository such as the 4TU.Centre for Research Data archive, hosted at TU Delft, or a more general archive such as Zenodo. This will make datasets at the very least Findable and Accessible. Zenodo conveniently lists on its website how it helps datasets comply with the FAIR principles. The 4TU.Centre for Research Data, and many other repositories, offer similar services when it comes to helping make data FAIR.
I am grateful to the University of Notre Dame for covering my travel costs to the MPS FAIR Hackathon. Special thanks to Natalie Meyers from the University of Notre Dame, and Marta Teperek, Yasemin Turkyilmaz-van der Velden and the TU Delft Data Stewards for making it possible for me to attend.
Maria Cruz is Community Manager Research Data Management at the VU Amsterdam.
Written by Shalini Kurapati and Marta Teperek
Training needs: research computing skills for open science
In addition to good data management, software sustainability is important for open science.
In accordance with the survey conducted by the Software Sustainability Institute in 2014, 7 out of 10 researchers rely on code for their research. Sharing research data without the supporting code often makes research impossible to reproduce. Good documentation and version control have been highlighted as major contributors to sustainable software. In addition, earlier workshops and survey results indicated that researchers need training on good code writing and code management practices and version control.
Similarly, TU Delft-wide survey on data management needs revealed that 32% of researchers were interested in training on version control and 18% specifically in software carpentry workshops.
What are The Carpentries?
The Carpentries “teach foundational coding, and data science skills to researchers worldwide.” That’s a community-based organisation, which maintains and develops curricula for three different types of workshops: software carpentry, data carpentry, and library carpentry. Detailed and structured lesson plans are available on GitHub and they are delivered by a network of carpentry instructors.
An important element of The Carpentries is that in order to deliver a workshop, instructors need to be certified. The certification process puts a particular emphasis on the pedagogical skills of the instructors.
First software carpentry at TU Delft
TU Delft hosted the first software carpentry workshop on 29 November 2018 as a pilot before officially joining The Carpentries. We had around 30 researchers participating (and another 45 on the waiting list!). The participants were from four faculties at TU Delft: Civil Engineering and Geosciences, Applied Sciences, Technology Policy & Management, and Architecture and Built Environment. We had three instructors and four helpers in the room.
The GitHub pages with the lesson materials are publicly available and can be found here: https://mariekedirk.github.io/2018-11-29-Delft/ All participants were asked to bring their laptops along and to install some specific software. No prior programming knowledge was required. Collaborative notes were taken with Etherpad.
During the workshop, participants downloaded a prepared dataset and they worked with that dataset through the two days. They learnt task automation using Unix shell, version control using git, and python programming using jupyter notebooks.
The Carpentries have a special way of organising feedback. Participants receive red and green post-it notes and use them to indicate problems / completion of tasks during the whole course. Similarly, after the end of each day, the participants are asked to indicate all the plus sides and negatives of the workshop on green and red post-it notes, respectively.
The feedback from the participants after the workshop helped us evaluate the training. The participants were overwhelmingly appreciative of the instructors and helpers and seem to have enjoyed the training. Some of the participants felt that the pace of the workshop was fast and they did not have time to experiment with the data set. Some others wished to get a more personal approach and to actually get an opportunity to work with their own disciplinary datasets.
Plans for the future
The waiting list for the workshop was very long and we had to disappoint more than 45 researchers who didn’t manage to get their spot on the day. In addition, faculty graduate schools have been willing to give course credits for PhD students who attend this workshop, which made the course even more attractive to attend for PhD students. Therefore, to meet the demand, we are planning to organise four more workshops in 2019: two workshops at TU Delft, one in Eindhoven and one in Twente. We will continue to monitor the number of interested researchers and if the need arises, we might consider scheduling some additional courses.
In addition, to increase our capacity in delivering carpentry training, some of the TU Delft’s data stewards and data champions will attend the training to become instructors. We hope to have this instructor training organised in April.
To address the feedback about the pace of the course, we will be more selective and include fewer exercises in our future workshops to ensure that the participants get the chance to experiment and play with their datasets and scripts.
In order to provide some more tailored support to researchers who have started to code but need some additional support to make it work, or who might have attended a carpentry workshop but are not sure how to apply the learning into practice, we will host dedicated coding walk-in hours consultations starting in January 2019.
So… watch out for the next carpentry workshop – scheduled for Spring 2019!
Authors: Heather Andrews, Maria Cruz, Angus Whyte, Yasemin Turkyilmaz-van der Velden, Shalini Kurapati
To read Part 1 of the blog post follow this link.
Researchers at all levels should be equipped with skills relevant to open science and FAIR data, and the practice of these skills should be effectively rewarded and recognised. This much is clear and has been recently highlighted in the “Turning FAIR into reality” Report of the European Commission FAIR Data Expert Group. Indeed, that report states “there is an urgent need to develop skills in relation to FAIR data” and “metrics and indicators for research contributions need to be reconsidered and enriched to ensure they act as compelling incentives for Open Science and FAIR.”
To change the academic rewards system, it is necessary to define and agree upon the skills researchers need to have at different stages of their careers. This was the goal of the workshop “It’s time for open science skills to count in academic careers” held at TU Delft on 26 September 2018. This post forms the second part of the report of the the workshop. Here we present the outcomes of workshop, based on the interactive group work described below. The results of this work will be applied in EOSCpilot, which is laying the groundwork for skills development in the European Commission’s European Open Science Cloud.
Overview of the hands-on workshop
The participants were divided into four groups. Each group focused on a specific career level according to the European Commission’s framework for research careers:
- R1: First Stage Researcher (up to the point of PhD)
- R2: Recognized Researcher (PhD holders or equivalent who are not yet fully independent)
- R3: Established Researcher (researchers who have developed a level of independence.)
- R4: Researchers – Leading Researcher (researchers leading their research area or field)
The above mentioned groups were led by Yasemin Turkyilmaz, Ellen Verbakel, Maria Cruz and Alastair Dunning, respectively. The registered participants of the one day event included researchers from all career levels, librarians, data stewards, and policymakers. However R4 researchers were unavailable for the hands-on workshop.
Each group received a list of nine pre-defined open science skills together with a detailed explanation for each skill. The group activity for each of the groups was divided into two parts. In the first part, the groups were asked to shortlist a maximum of 4 skills most relevant to their respective career level (R1-R4). Subsequently, for each skill, they wrote down on post-it notes: 1. Why is this skill relevant to researchers at this career level? 2. What would be the evidence that researchers at this career level have these skills and can apply it in practice? (in other words: what does a person applying the skill do?) 3. What support (from support staff, service providers) will researchers need to apply this skill?
After displaying their ideas on whiteboards, the groups were given 5 minutes each to present a summary of their main findings to the other groups. This was followed by a short discussion followed with questions from all the groups. Below is the summary of the findings of each of the four groups.
R1 group activity summary:
The group focused on early career researchers. This group of researchers is dependant on the researchers at higher positions in the academic ladder. At the same time, often this group of researchers is perceived as being the ‘bold’ ones able to take initiatives and amenable to change. It was also recognised that R1 researchers can benefit greatly from more training and more information on open science possibilities. The four skills shortlisted by this group were:
1) Adherence to the FAIR data and code principles during and after research. The group added the term ‘code’ to this skill as well. The group reasoned that the FAIR principles represent what is necessary to have sustainable sharing and archiving of both data and code, which is important for verifiability of research. The group recognised that in order to adhere to the FAIR principles, early career researchers have to receive training as an inherent part of the curriculum. They also stressed that good supervision and getting good examples would facilitate this skill.
2) Securing funding for open science/support. The group reasoned that receiving specific training on awareness of funding opportunities for Open Science (e.g. funding for Open Access publishing) as well as preparation to secure such grants would support early career researchers to be more independent from their respective supervisors and practise open science more freely. They proposed that this could be achieved in collaboration with grant support offices and graduate schools.
3) Awareness and adherence to relevant ethical and legal policies. This skill was found to be important to overcome fear and uncertainties about ethical and legal requirements. This can make early career researchers more confident when discussing with senior colleagues the parameters these requirements set around sharing their research outputs. The group proposed Q&A catalogues which could help early career researchers better understand complex terms such as codes of conduct, legal terms, informed consent etc.
4) Recognizing and acknowledging the contribution of others. The group found it important for early career researchers to know how to properly cite data, code and methods; and how to acknowledge collaborators, technicians in the lab, etc. The group argued that if people are properly acknowledged, then they are more willing to contribute again, which is a great source of motivation for early career researchers. They expect University and Faculty policies and training to be instrumental for this skill. These should also promote the standards for using persistent identifiers, and the CRediT taxonomy for acknowledging who has contributed what to a publication.
R2 group activity summary
This group saw R2 researchers as typically those at the postdoctoral level. The group thought that postdocs were seen as researchers who are not in charge of funding nor leading projects like type 3 and type 4 researchers. Postdocs were seen as researchers focused on making effective collaborations, and working on building up their reputation. The four skills proposed were:
1) Recognising and acknowledging the contribution of others. Recognition was perceived as the main driver for postdoctoral researchers, as well as for researchers working with postdocs. The group thought that researchers need a policy framework that enforces proper recognition and receive training on how to get recognition (e.g. setting up and using ORCID).
2) Making use of open data from others. The group considered this skill as important for verifiability, and as an effective way to start collaborations with other researchers. However, to put this skill into practice, researchers need to know how to search for datasets and assess their quality. Support staff should define the standards for high quality open data, provide support in data curation and give training to researchers on best open data practices.
3) Adherence to good code management practices. This skill was also considered important for verifiability purposes and to stimulate others to reuse the code. It is seen as a quality stamp for the respective code creator, which improves the researcher’s reputation. In order to get this skill, researchers would benefit from training on version control and on writing proper code documentation. It was also suggested that researchers could learn more about these matters from research software engineers.
4) Using or developing research tools open for reuse by others. The group felt that standardisation is crucial to enable effective collaborations. Thus, researchers should receive training on the use of platforms such as github and training on metadata standards.
R3 group activity summary
This group proposed 3 skills, unlike the other groups that considered the maximum number of 4 skills (proposed by the workshop organizing committee). Largely because it was felt that the skill ‘being a role model in practicing open science’ would by definition cover most of the practical skills on the list. The group considered this to be the most important skill for R3 researchers. These researchers are already established in their careers and their fields (already developing and leading some projects), but are still very much involved in the day-to-day practice of research (e.g. they still acquire data or write software). As such, they can lead by example and influence not only earlier career researchers, particularly those they directly supervise, but also more senior colleagues. R3 researchers are very aware of the obstacles which researchers encounter when trying to change how research groups work. The proposed skills were the following:
1) Being a role model in practising open science. Stage 3 researchers were seen as researchers who are still very active in research, and but who also have a close relation with senior colleagues, the researchers in higher positions. This gives them the opportunity to establish how researchers are evaluated within their team. Stage 3 researchers have an influential role within their team, e.g. in the hiring and promotion decisions within the team. In order to do this, it was acknowledged that stage 3 researchers need support from the R4 researchers. This was a key point of discussion, because even though stage 3 researchers are seen as big influencers in the academic ladder, they still depend on the stage 4 researchers and funding committees. In relation to this skill, it was also felt that R3 researchers should not only lead by example in the way they practice open science, but should also directly influence others by speaking about it. In short, practice what they preach, and preach what they practice.
2) Securing funding for open science/support. Stage 3 researchers are involved in hiring people and applying for funding. When applying for funding for example, they should be explicit about how open science will be carried out throughout the project. When hiring, they should include open science requirements in the hiring criteria. The group also recognised that for this to happen effectively, funders need to be willing to provide funding to pay for the costs of open science activities associated with projects, and research grant offices need to advise researchers on how to include these costs in their grant applications.
3) Recognizing and acknowledging the contribution of others. The group felt this is an important area where R3 researchers can lead by example. In addition, R3 researchers are usually still building up a network and collaborations, and to do this effectively, recognition is always necessary.
R4 group activity summary
The group considered project leaders as researchers who are usually less involved in the day-to-day research practices of research. As project leaders they may be in charge of project management and involved in designing research projects, policies and regulations, vision and strategy. Nevertheless, it was acknowledged that researchers at this senior career stage still needed substantial support from their institution in order to put their vision into practice. Having this in mind, the group shortlisted the following skills:
1) Being a role model in practising open science. As project leaders, stage 4 researchers can influence a broader community (not only the researchers in their project, but also funding committees, other project leaders, executive boards, etc.). They can promote change in daily practices, but also in research policies. Stage 4 researchers could become open science role models by promoting and discussing it with their network. Advocating for open science during meetings and conferences; participating in policy development, and changing the practices within their respective groups. In order to do this, they need platforms and tools at their institutions, they also need recognition for advocating for open science, and they need support from their team members.
2) Recognising and acknowledging the contribution of others. Just like in the other groups, this group found it relevant for researchers to recognise everyone’s contribution in a project; recognising not only the scientific staff but also the support staff (laboratory technicians, data managers, etc.). In order to do it, the careers of the support staff should also be recognised for example, by creating new job profiles for data managers, data stewards, etc.
3) Developing a vision and strategy on how to integrate OS practices in the normal practice of doing research. This skill was found to help create the link between principles and actual practice. Stage 4 researchers usually work on the ‘big pictures’ of research, and thus, they have to have a vision and strategy to steer their research and project members. In doing so, they should have the advice of support staff, to ensure the feasibility of their vision. They should also have clear information about who does/can-do what within their institution, and about financial possibilities for them to turn their vision into reality.
4) Awareness and adherence to relevant ethical and legal policies. This skill was seen as relevant because senior researchers are accountable for their project team’s behaviour. Any risk of ethical and/or legal infringement will jeopardise the reputation not only of the project leader, but also of their entire group and, quite likely, the institution they belong to. Thus, it is important for stage 4 researchers to establish procedures dealing with ethical and legal issues. In order to properly do this, the institution should provide researchers with integrated support. The role of each support staff member should be well-defined (and well-informed to the researcher), there should be effective communication within the support staff, and the workflows through which the researchers can receive support need to be clearly stated.
Overall workshop summary
Overall, all groups stressed the importance of peer to peer learning: everyone can contribute to changing cultures and daily practices. All groups also agreed that proper infrastructure and policy support from institutions is required for researchers to truly implement open science practices.
Finally, recognition was seen as one of the main drivers for both scientific and non-scientific staff participating in a research project and all groups stressed the importance of proper recognition of open science practices.
The ideas and discussion generated during the workshop have given us a rich corpus of information to reflect on the workshop objectives and to envision a road map for the future to implement these ideas and discussions. The workshop outputs will be applied in EOSCpilot to help focus its Skills Framework on the key skills identified, the rationale for these, and the mapping of skills to researcher career stages together with the support requirements. Watch this space for progress and updates.
And finally a motto for everyone: change is in your hands! Everyone can contribute to change of practice in their own spheres of influence.
Authors: Shalini Kurapati, Marta Teperek, Maria Cruz, Angus Whyte
Disclaimer: In the spirit of openness and transparency, we would like to share that Shalini Kurapati wrote parts of this blog post based on the zenodo record of the presentations even though she wasn’t present during the event. Her account was verified by the remaining authors who were present.
To read Part 2 of this blog post follow this link.
Open Science is not always easy – skills are urgently needed
Open science is becoming a ubiquitous and recurring theme in the current academic environment. Researchers are increasingly expected to publicly share their research outputs (data, code, models etc.) as well as their publications. This often requires considerable effort from researchers to manage and curate their research outputs to make them shareable. But are these efforts appropriately rewarded? Emphasising the number of publications in high impact factor journals as the only valuable metric for academic promotion and hiring won’t motivate researchers to practise open science.
There is a lot of interest in changing the reward system to better align it with the actions researchers are expected to take towards more open research practices, for instance, the OSPP Rewards WG. Making sure that researchers have the right skills to do that is the other side of that coin. To change the rewards system, we have to understand and identify the skills researchers should be rewarded, and recognise that these may change at different stages of their academic careers. This was precisely the goal of the workshop on 26 September 2018 that we organised at TU Delft jointly with the EOSCpilot. The EOSCpilot project is laying the groundwork for the European Open Science Cloud, and wants to offer a framework for institutions and others to develop the skills needed for researchers, data stewards, and others who support research to help put open science into practice.
The workshop was aptly titled “It’s time for open science skills to count in academic careers” (#openskills18). The workshop format combined presentations on related topics with interactive group work in the afternoon. In this post, we summarise the presentations and in a separate blog post we’ll present the outcomes of the workshop and will reflect on the key findings /thoughts on future steps.
The aim and format of the workshop was presented by Valentino Cavalli of LIBER and EOSCPilot. In his welcome note, Mr.Cavalli explained barriers to open science in the European and wider academic context. These barriers include a culture of disincentives, fragmentation between infrastructures, interoperability issues and access to computational resources. He highlighted that the workshop would focus on the culture of disincentives, which has to be changed such that researchers at all careers levels are equipped with relevant skills and suitably rewarded for putting them into practice
The opening talks were delivered by Ms. Anne de Vries (PhD students Network Netherlands), Prof. Bartel Van de Walle (TU Delft) and Mr. Rinze Benedictus (UMC Utrecht).
Ms. Anne de Vries shared the perspectives of the eurodoc, the European council of doctoral candidates and junior researchers on open science policy and practices. She stated that it is important to identify and train open science skills for early career researchers based on their disciplines. Early career researchers should also be made aware on how to make their outputs FAIR and how open science skills will not only take science forward but also positively influence their careers. She also reflected that senior staff should support early career researchers in practising open science and thus also need appropriate education and training.
Prof. Bartel van de Walle spoke on the open science policies and practical examples from his domain of information management during humanitarian crisis response. He also presented the challenges of implementing open science due to the inertia in research institutions that are often resistant to change. He insisted that open science is not just a requirement of funding agencies but is the right way forward to democratise science and achieve the UN’s sustainable development goals. He also pointed out the waves of change and indicated an example of successful implementation of open science policies in practices like McGill University’s Neurological institute and hospital. He concluded his speech by saying that open science is just science done right.
Mr. Rinze Benedictus delivered a powerful message with his talk: institutions should not equate the impact of a research work to an impact factor of a journal. He displayed the reinforcing loop of how authorship in high impact journals is an incentive for researchers for receiving more funding and recognition and to continue with the cycle of publishing to increase citation scores. He showed the damning evidence from The Lancet and Nature about the reproducibility crisis in science due to the earlier said focus on publishing to establish scholarship. While referring to global initiatives such as the DORA to change attitudes among institutions and individual researchers, he gave a concrete example of how UMC Utrecht is implementing good practices in rewarding researchers. For instance, at evaluation meetings at UMC, a researcher would be asked “How did you arrive at your research question and what are your next steps”? Rather than the traditional “what is your measurable output”.
Dr. Simon Kerridge (CASRAI) gave a talk on the CreDIT taxonomy. The problem that CreDIT tries to solve is that the current authorship criteria in publication doesn’t give sufficient recognition for various contributions of researchers. In addition, authorship credit alone doesn’t support accountability for the research results. He stated that since science is increasingly a team effort, credit needs to be given where due to incentivise researchers for their unique contribution. He explained that the CreDiT taxonomy aims to offer a role based credit systems, where the contributors can assign themselves credit for 14 tasks: writing, supervision, review, data analysis, project management and so on. Finally, he presented the vision for the future of increasing the awareness of the CreDiT taxonomy and to create feedback mechanism to evaluate future versions and to link it platforms like ORCID and Crossref.
The closing remarks of the workshop were provided by Ms. Anette Björnsson (European Commission) and by Mr. Kevin Ashley (Digital Curation Center & EOSCPilot).
Ms. Anette Björnsson reflected on the current initiatives within the European Commission towards changing academic rewards. She highlighted the importance of several recent reports produced by EC Working Groups: Evaluation of Research Careers fully acknowledging Open Science Practices, the report on Next Generation Metrics and Turning FAIR Data into reality. All of them influence the current thinking at the European Commission and also help shape the mission and vision of the European Open Science Cloud. She also stressed that large collaborative efforts at the European level require cooperation and consensus between all EU Member States, which often require time and patience. The situation is no different when it comes to the implementation of policies and changing practices in open science: individual Member States are at different stages of implementation and have varying levels of infrastructure and personnel currently available to them. However, Ms.Björnsson ensured us that while sometimes slower than desired, change is coming. Given that EOSC is a collaborative, pan-European endeavour, the chances are that changes brought with the EOSC will also be more effective and sustainable long-term.
Mr. Kevin Ashley then continued reflecting on the discussions which took place throughout the day, and in particular, the points raised by researchers during the interactive workshop part. He stressed that the common priority to all researchers, regardless of their career stages seems to be to get the recognition they deserve for Open Science activities. He reflected that there are (numerous) barriers to practical implementation of Open Science and to rewarding those practising Open Science appropriately, but that these barriers should not stop anyone from changing the status quo. As Dr. Maria Cruz beautifully summarised in her tweet, based on Mr. Ashley’s words: It’s possible to change the academic rewards system. It’s possible for PhD students. It’s possible for senior researchers. And it’s possible for institutions.
The format, content and outcomes of the hands-on workshop during the event, together with some reflections and thoughts on next steps are published in a separate blog post.
- All presentations of the speakers can be viewed and downloaded here.
This blog post was written and originally published by Loek Brinkman on his own blog.
On the 26th of September, I participated in the event “Time for open science skills to count in academic careers!”, organised by the European Open Science Cloud Pilot (EOSCPilot) and the 4TU.Centre for Research Data. The goal was to define open science skills that we thought should be endorsed (more) in academic career advancement.
The setting was nice: we were divided in four groups, representing different stages of academic careers (from PhD to full professor) and discussed which open science skills are essential for each career stage. What I liked about the event was that the outcomes of the discussion were communicated to representatives of EOSCpilot and the European Commission. So I’m optimistic that some of the recommendations will, in time, affect European research policies regarding career advancement.
On the other hand, I think we might be skipping a step here. Open science is often talked about as a good thing that we should all strive for (in line with the (in)famous sticker present on many laptops of open science advocates: “Open Science: just science done right”), as though open science is a goal on itself. To me, this doesn’t make a lot of sense. There is no clear definition of open science. It is an umbrella term covering many aspects, e.g. open access, open data, open code, citizen science and many more. So, in practice, people use various definitions of open science that in- or exclude some of the aforementioned aspects of open science, and differ in how these aspect should be prioritised. That means that while many people are in favour of open science, they may disagree greatly on what they think should be addressed first and how.
I don’t see open science as a goal. I see open science as a means to achieve a goal. I think, we should first agree on the goal: specify what we want to change or improve. The way I see it, the goal is to make science more efficient – to achieve more, faster. Starting from this goal, several sub-goal can be defined, such as:
(1) making science more accessible,
(2) making science more transparent & robust,
(3) making science more inclusive.
Open science can be a means to achieve these subgoals. Depending on how you prioritise the subgoals, you might be more interested in (1) open access, (2) open data and code, or (3) citizen science, respectively.
It is not too difficult to come up with a list of open science skills for academics, and it would be awesome if those skills would be endorsed more in academic career advancement. But we first need to define the goals we want to achieve, before we can start to prioritise the means by which these can be achieved. If the endorsement of open science skills can be aligned with the overall goals, then we are well on our way to make science more efficient.
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)
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.
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.
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.
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
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.
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.
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.
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.
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:
- How can the advice by Hut, van de Giesen & Drost be put in practice?
- Any additional advice?
- How can researchers, RSEs, and data stewards work together towards implementing the advice?
- 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.
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.