Authors (listed in alphabetical order by the first name): Arthur Newton, Diana Popa, Esther Plomp, Heather Andrews, Jeff Love, Kees den Heijer, Lora Armstrong,Nicolas Dintzner, Santosh Ilamparuthi, Yan Wang, Yasemin Turkyilmaz-van der Velden
Advancing data stewardship
The Data Stewards team at TU Delft has finished another busy year. As changes and remote working became the new norm, the team carried on the success with great adaptability and maturity in 2021. In this report, we review the activities done in the past year and acknowledge the achievements as a team.
More transitions and finally a complete & bigger team!
In February, we made the team complete by welcoming Diana Popa, the new Data Steward of the Faculty Architecture and the Built Environment.
Sadly in April, we had to start saying farewell to Kees den Heijer, the Data Steward of the Faculty of Civil Engineering and Geosciences. Kees was one of the first Data Stewards at the start of the Data Stewardship program. Thankfully that Kees’s new position is within the Research Data Services (RDS) team at the library and he could still offer essential RDM support to the faculty before the new Data Steward is in place. Nicolas Dintzner, the Data Steward of the Faculty Technology, Policy and Management, also kindly helped with some requests with ethical approval required.
From the end of May till the end of September, the coordinator Yan Wang was on maternity leave. The coordination was done in a joint effort by the whole team and colleagues from the RDS team. The team’s internal coordination was shared by the Data Stewards. Some Data Stewards were taking lead in coordinating with other research support teams according to their engagement and interests in relevant topics.
In October, the team was finally complete. We are proud to have Armstrong and Newton on board. Lora Armstrong is the new Data Steward of the Faculty of Civil Engineering and Geosciences. Arthur Newton is the first Data Steward of QuTech. Now the sky is not even the limit anymore!
Due to COVID restrictions, the team has continued working virtually during the year, and we did not have a chance to have a proper farewell and welcome.
Strengthening connections with other research support teams
The RDM support provided by the Data Stewards team is a joint effort with valuable input from numerous research support teams. We have established good connections with many of them since the start of the data stewardship. In 2021 we further strengthened the link with those who are already working with us and reached out to others to build up more connections.
The privacy team, Human Research Ethics Committee (HREC), and ICT innovation have been our close partners in the university-level Personal Research Data (PRD) workflow. The communication between the Data Stewards and these teams became further regulated on a bi-weekly or monthly basis during last year.
The Data Stewards team has also established closer contact with the Innovation and Impact Centre (IIC). The IIC has teams of grant officers and project managers who help researchers with grant proposals and project coordination. In collaboration with the former Open Science community engagement manager Emmy Tsang, a few data stewards contributed to a series of collaborative activities:
A mini-workshop on Data and IP for project managers
The Data Stewards have been in close collaboration with the DCC team on various activities since the start of the DCC at TU Delft. Since October 2021, the coordinators of both teams were in place and started to keep each other updated.
Team achievements across all faculties
Despite different disciplinary demands among faculties, there are a few common activities performed by DSs across all faculties. As a team, we continued to provide the following types of support which form the foundation of the RDM support from the team.
Data management consultation
Consultation is still the main channel to deliver the RDM support. From all faculties, more than 1200 requests from researchers were received in 2021. This shows another significant increase (approximately 50%) compared to the requests received in 2020. While Data Management Plans (DMPs) were still the majority of the requests, there was a broad range of questions on data storage, sharing, licensing, privacy, tooling, and others.
Training & Education
Data Stewards continue to get involved in RDM training at both the faculty and university level. Many Data Stewards contributed to the software carpentry and data carpentry workshops as instructors, helpers,s or coordinators. RDM training at the faculty level is provided in various formats, such as informative sessions at the individual, group, or department level, RDM courses for PhDs, or discipline-specific training workshops.
Policy & Strategy
The team has been working on publishing and implementing faculty-level data management policies since 2019. In early 2021, all faculties (except QuTech whose Data Steward only started in October 2021 but already started working on the policy draft since then) have published their policies. The team also provided valuable input and facilitated the consultation of the TU Delft Research Software Policy and the Guidelines on Research Software which were published in March 2021.
In addition to the above-mentioned common achievement of the whole team, each Data Steward further expanded their disciplinary support according to faculty needs.
Faculty of Aerospace Engineering
Disciplinary RDM support
Co-developed the ASCM Code Initiative which has consisted of developing an instructing wiki and giving training sessions on proper coding practices for ASCM researchers. This is expected to continue this year and probably expand to more faculty sections.
Performed specific training sessions for different groups to improve their coding management skills (version control).
1 Astronomy Data Carpentry workshop (at the national level in collaboration with Netherlands Institute for Space Research SRON, Leiden Sterrewacht, and Netherlands eScience Center), and 4 code management workshops (at the faculty level).
Expanded the faculty Data Champions from 13 (one left TU Delft) in 2020 to 19 in 2021
Instructor in Train the Trainer FAIR and Reproducible Code workshop for 4TU Data Stewards.
Led sessions on Data Curation for the Consorci de Serveis Universitaris de Catalunya
Invited speaker at 8 (online) events showcasing Data Stewardship at TU Delft and recommended research data management practices (for Helis Academy, Graz University of Technology, Universitat Oberta de Catalunya, Universidad Nacional de Costa Rica, LA Referencia, Universidad Internacional de Ciencia y Tecnología de Panamá and Agencia Nacional de Investigación y Desarrollo de Chile).
Session organizer at the RDA’s 17th & 18th Plenary Meetings
Invited as a guest on the R2OS (Road to Open Science) podcast from Utrecht University
Part of the team that created and published the FAIRly Open After Dark podcast series, which dealt with Open Science, FAIR Data, and Academia in general from the perspectives of different stakeholders.
Participated in the Health RI conference
Data Stewardship coordination
Collaborated with the privacy team and HREC on aligning workflow for research projects that handle personal data.
Faculty of Industrial Design and Engineering
Disciplinary RDM support
Co-taught two sessions of ‘Ethics and Research Data Management’ module for the IDE Ph.D. research school
Co-developed and taught a BSc course on ‘Data as a Design Material’
Developed and taught a module on ‘Responsible IoT Design’ for the BSc course on ‘Software-Enabled Products’
Co-authored a user research report from a survey and interviews on the topic of specialist usage of digital collections
Event and community engagement
Attended multiple Design & Digital/Computational Humanities conferences and events, most notably those from DH Benelux featuring practices and practitioners from the Benelux Regions and Dutch Design Week in Eindhoven
Faculty of Mechanical, Maritime, and Materials Engineering
Disciplinary RDM support
Further developed and regularly delivered Data Management Plan Training for 3mE Ph.D. students
Contributed to the development of and taught at the MSc course ‘Introduction to Engineering Research’
Taught at the BSc programme ‘Clinical Technology’
Co-developed and co-delivered the Workshop on FAIR for Material Design
Member of a cross-TU Delft working group (involving the Library and ICT) about Electronic Lab Notebooks (ELN)s which:
Offers ELN licenses to interested researchers
Organization of regular community events
Event and community engagement
Organized and presented (departmental) information sessions about Research Data Management, Research Software Policy, and the Open Science Program
Co-organized and presented an Open Science Session for 3mE Ph.D. students
Member of the 4TU.ResearchData FAIR and Reproducible Code, Privacy and GDPR, Engagement and Education working groups
Co-chair of RDA working group: Discipline-specific Guidance for Data Management Plans
Was an invited speaker or a session organizer at seven (inter)national conferences / training sessions / webinars (RDA’s 17th & 18th Plenary Meetings, NWO Life Conference, Material Pioneers Webinar, National Turkish and Hacettepe University Research Data Symposiums, Turkish Open Access Week) and acted as a Programme Committee member of two conferences (National Turkish and Hacettepe University Research Data Symposiums)
Khodiyar, Varsha; Laine, Heidi; O’Brien, David; Rodriguez-Esteban, Raul; Turkyilmaz-van der Velden, Yasemin; Baynes, Grace; et al. (2021): Research Data: The Future of FAIR White paper. figshare. Journal contribution. https://doi.org/10.6084/m9.figshare.14393552.v1
Data Stewardship coordination
Initiated set up a guidance document about data management and open science sections in Horizon Europe proposals in collaboration with the Open Science Community Manager and Data Stewards
Took part in a collaboration with the TU Delft Innovation & Impact Center to create awareness about the Open Science and Research Data Management requirements in Horizon Europe and presented/contributed to two information sessions
Collaborate with the privacy team and HREC on aligning workflow for research projects that handle personal data.
The QuTech Data Steward started in October 2021. Within the short period left in the year, he already contributed to the training at the university level, actively provided RDM consultation, and reached out to most research teams in the faculty. Furthermore, he has drafted the QuTech Data Management policy and planned to have the faculty consultation (this policy has been published in April 2022).
A few lessons learned from this year can help guide us for the coming year(s). We should do better in documenting our Data Stewards’ knowledge base on RDM-related information within the organization as a guide for both researchers and Data Stewards. This would especially be helpful for new DS on boarding.
We are still in the process of further shaping the Data Steward work scope to handle increasing and more complex RDM demands. Besides all the RDM support activities, the team has also been actively engaged in discussions about the Data Stewardship model, the profile, and the career paths for Data Stewards. Every Data Steward is encouraged to explore their own ‘Data Stewardship’ within the faculty. This does not just include working on disciplinary RDM support, but also exploring organizational solutions to sustain and expand the Data Stewardship support within the faculty. Faculty level Data Stewardship implies that a senior role of the current faculty Data Steward should play. This requires a corresponding recognition regarding the Data Steward profile and progress paths. We are all motivated to do more, meanwhile appreciate the rewards and recognition of the work we do. There is definitely more room for attention and efforts on the professional growth of Data Steward and Data Stewardship.
There was considerable focus on AI, and its implications for research and research support, at the Surf Research Week in Utrecht this week (10 May 2022). Here’s a brief set of bullet points on the discussions I noted:
There’s too much focus on the negative examples of AI. How can we do more to demonstrate the positive benefits? And ally that to greater transparency in how AI functions.
Also, how can we intelligently reflect on the applied uses of AI? AI should not be viewed as a panacea for all social and research challenges; we need to gain the critical insight to understand when it is (and is not) the right methodology to be applied.
AI changes the nature of how research projects are organised. Also: if AI permits researchers to make discoveries about things they were not even aware of, should we even think of AI itself as a collaborator in research projects, rather than a methodology that we use?
In any case the need for collaboration between people with different knowledge becomes even stronger with AI. There are wide-ranging skills that are needed to deploy AI within a project – not just in technical terms, but in terms of data science and management, and embedding the ethical context. In particular, ensuring an ethical footing for AI projects with potentially profound social implications should involve the right philosophical expertise. The related need for management and archival skills in curating and documenting the datasets that underpin AI.
And also efficiencies and expertise provided by high-quality software engineering can drastically time and money when it comes to deploying the computer power needed for AI. A familiar discussion arose – should AI expertise be situated locally or nationally? Should it be generic or focussed on specific subject areas?
The tension within the AI community between acceleration and regulation. On the one hand, global challenges desperately required the novel ideas that AI can provide – let’s move quickly! On the other hand, we need standardisation to be able to deploy AI in a sustainable and regulation to provide the necessary ethical context. Let’s get this sorted out.
Are they distinctions between AI algorithms and datasets that area developed within the research community, and buying AI as a service from third parties? What does the latter mean for issues such as reproducibility, ethics? What are the implications for university procurement departments if a whole university wants to use AI as a platform.
(Thanks to SURF for organsing the event, and to the panellists and workhsop speakers who contributed so many ideas Emily Sullivan (Tu/E), Nanda Piersma (HvA), Maarten de Rijke (University of Amsterdam), Damian Podareanu (SURF), Antal van den Bosch (Meertens Institute) Sascha Caron (Nikhef) Matthieu Laneuville (SURF)
When I started as a Data Steward at the Faculty of Applied Sciences I attended the Essentials 4 Data Support course to learn more about research data management support. I was therefore happy to accept Eirini Zormpa’s invitation to discuss my Data Steward journey with the participants of the Essentials 4 Data Support course. Together with Zafer Öztürk from Twente University we shared our experiences during the data supporter panel on the 14th of April. This blog post is a summary of what I highlighted during the panel.
The Essentials 4 Data Support course is an introductory course about how to support researchers with the management and sharing of research data. The course materials are helpful to gain an overview of what research data management support entails. The course also provided an opportunity to link up with peers (such as Lena Karvovskaya) and meet experts (such as Petra Overveld).
In December 2018 I started as the Data Steward at the Faculty of Applied Sciences. In my first couple of months I had the privilege to be peer-mentored by Yasemin Türkyilmaz-van der Velden, who showed me the ropes of data management support. Initially, I had to get to know the position, the workings of the faculty, my new colleagues and the researchers I was now supporting.
In this first year I worked together with Yasemin on our Faculties Research Data Management Policies, based on the TU Delft Research Data Framework Policy. This was an arduous process, as we visited all departments of our faculties. The policy was discussed with many stakeholders, including PhD candidates. In the beginning of 2020 the Applied Sciences Policy on Research Data Management was officially released! Yasemin and I also worked together in the Electronic Lab Notebook pilot that took place at TU Delft resulting in TU Delft licences for RSpace and eLABjournal.
In 2019 I followed a Software Carpentry Workshop to learn basic programming skills so I could better support researchers with any software support questions. I later took the train-the-trainer course and became a Carpentries Instructor myself. By being a Carpentries instructor I can teach basic programming lessons set up by the Carpentries community. With the pandemic we had to shift these trainings online, and I coordinated these workshops for a year (2020-2021).
Over the years, I also increasingly supported researchers with Open Science questions. This is an aspect of the role that I very much enjoy and currently try to expand upon. My role differs somewhat from the other Data Stewards at TU Delft: we each have our own preferences and areas of expertise next to data support (such as software, ethics, or personal data). Another difference is my involvement in a side project focused on PhD duration. At TU Delft and at my faculty we try to reduce the amount of time that PhD candidates take to finish their PhD project. While the official duration for a Dutch PhD is four years, the majority of PhD candidates take much longer. This often means that they have to finish the project in their unpaid free time. As someone who has spent seven years on a PhD project I can say that finishing your PhD next to a full time job is no joke.
As a Data Steward I’m also a connection point in the university network. This allows me to address researcher’s questions myself or to connect them with the expert that they need.
My position at the Faculty itself allows for close contact with researchers. Before the pandemic I regularly hopped between their offices to help them with any questions. At the Faculty I’m embedded in the support team where I work together with the Faculty Graduate School and the Information Coordinator. I’m in regular contact with project officers, managers and researchers from all levels at the faculty.
As part of the Data Stewards team I meet the other Data Stewards once a week (virtually) and we communicate through Slack/Teams.
I’m also in contact with colleagues from the Library and the Digital Competence Center, either through collaborative work or because they are the experts that can address questions from researchers.
Sometimes I reach out to central experts from the Human Research Ethics Committee, the Privacy Team and ICT Security when needed.
Next to my activities as a Data Steward at TU Delft, I’m also involved in several other initiatives that are revolving around data and open research:
Since 2020 I’ve been a contributor to The Turing Way. I have primarily written about Research Data Management and contributed a Data Steward case study.
Since 2021 I’m a mentor of the Open Life Science programme, which is now also offered for credits for the PhD candidates of my Faculty. In this 16 week mentor programme you will learn about open science practices and apply them practically to your own project.
I’m the Open Research Ambassador and Secretary General of IsoArcH, a disciplinary specific data repository for isotope data.
Over the years I very much enjoyed writing blogs like this one, summarising my experiences of conferences, activities and learnings.
I very much enjoy the Data Steward role, for various reasons:
I support researchers in making their research more transparent.
I work with amazing colleagues and collaborators
I meet new people interested in similar topics.
I can continuously develop and learn new skills.
I have a lot of autonomy over my working activities and schedule.
A lot of this is made possible by a supportive manager, and many individuals that I learned from along the way.
“Create the world you want, and fill it with the opportunities that matter to you.”
– Alicia Keys
My tips for people just starting in a data support role:
Accept that things can take more time than you originally anticipated. Starting in a new role will take some time to adjust and achieving cultural change in university processes will not happen overnight.
The downside of being able to create your own opportunities is that there might be a lot of things that you want to do. Even if everything seems important or fun to do, it could mean that you will end up with too much on your plate. Sometimes it is good to say no to shiny opportunities.
In whatever you do I would recommend you to not take the road alone and seek out others to collaborate with, or ask feedback from. Exchanging expertise and experience will not only be more efficient, it will make the road more worthwhile to walk.
We are pleased to announce that our article “Time to re-think the divide between academic and support staff” has been just published: https://www.nature.com/articles/d41586-022-01081-8. The article speaks about the negative consequences of the divide between academic and professional support staff, and argues that this divide no longer makes sense as it is not conducive to a successful and effective research process.
By publishing this article, we hope to raise awareness about these problems, start discussions within the community and start identifying the steps which have to be taken to stop the divide. We would welcome your comments and reflections on the topic.
We also wanted to use this opportunity to express our gratitude to Jeff Love, Melanie Imming, Alastair Dunning and Shalini Kurapati for their crucial input and support throughout the process of conceiving this article. Their comments and reflections on the early drafts of the article, as well as the numerous constructive discussions we have had with them, were invaluable to us.
Finally, we also wanted to thank Connie Clare, Manuel Garcia, Hans de Jonge, Lena Karvovskaya, Esther Plomp, Diana Popa, Mark Schenk, Jeroen Sondervan, Emmy Tsang, Yasemin Turkyilmaz-van der Velden and Jose Urra for their comments and suggestions on an early draft of the manuscript.
A Data Article (also known as a Data Paper/Note/Release, or Database article) is a publication that is focused on the description of a dataset. It uses the traditional journal article structure, but focuses on the data-collection and methodological aspects and generally not on the interpretation or discussion of the results. Data articles are in line with the FAIR principles, especially since most publishers will encourage you to share the data through a data repository. The benefit of a Data Article is that your output will be peer reviewed, something which is generally not the case for datasets that are archived on data repositories. It also facilitates recognition for datasets through research assessment procedures that are more traditionally focused on publication output. Publishing a data paper will therefore increase the visibility, credibility and usability of the data, as well as giving you credit as a data producer (The Turing Way Community 2022).
Options to publish a Data Article
Below you can find some journals that publish data articles. The costs information was collected in February 2022.
Emmy argued that we (universities, libraries, the research and educational communities) need to make much more values-informed choices about the type of infrastructure we build and invest in; not just blindly reverting to commercial infrastructures because it seems the most convenient.
A first step of this process is actually realising what infrastructures are being funded and supported. Without a clear map of what a library or a university pays for, it’s difficult to make concrete actions. But even this tricky. Responsibility for contracts, services, tools tend to spread over many different people within an organisation
Authors: Esther Plomp, Emmy Tsang, Emma Henderson and Delwen Franzen
This blogpost summarises a discussion session held during the AIMOS2021 conference (1 Dec – 08:30-9:30 AM UTC). During the discussion, we focused on what our institutes and departments could do to improve the awareness of Open Science practices and support the change towards a more open research culture. We started our session with some of the questions that the participants were currently struggling with, and some of our (not so) success stories:
The Delft University of Technology (the Netherlands) already has a lot of policies and support roles in place that support Open Science practices. There is the Open Science Programme with a dedicated Community Manager that also supports the building and growth of the TU Delft Open Science Community. At the Faculty level, Data Stewards provide support for research data and software management and sharing. Thanks to these Data Stewards, the faculties each have their own Data Management policy.
The Flinders University (Adelaide, Australia) is working on policy changes and has an Open Science training in place.
The BIH QUEST Center (Charité – Universitätsmedizin Berlin, Germany) has developed a pilot dashboard that provides an up-to-date overview of several metrics of open and responsible research at the Charité.
Having dedicated roles or policies for Open Science and Data Management is crucial to drive effective change in research practises, but not every institute has these resources. While the uptake of Open Science practises in the last five to ten years has increased, there is also still a lot of frustration at the local level. Not everyone has the time to pay attention to or is enthusiastic about Open Science developments, and participants indicated that some principal investigators did not care about replicability in research. If bachelor/master students are following training on open research practices, they are equipped to take this aspect into account when selecting a supervisor for their PhD research (see also Emily Sena’s contributions in the AIMOS 2021 Panel Discussion on “How to start a revolution in your discipline”). While some institutions offer Open Science training, sometimes the uptake is low. During the session we struggled with some of these obstacles and discussed the following four questions in more detail:
How can you make the case for hiring professionals that support Open Science practices?
It helps if other institutes have examples of professional support roles, especially if there is visible impact in the uptake of Open Science practices. A great example of this is the UK Reproducibility Network (UKRN). The UKRN is actively involved in supporting institutions in setting up roles that focus on increasing reproducibility of research, by connecting stakeholders to share best practices and by providing expert advice.
To build the case for the institution to prioritise investment in Open Science, it is often helpful to illustrate to institutional leadership the effects of (inter)national funders’ commitments to Open Science. Funder mandates on data management planning and sharing are now commonplace (for example, the European Commission, NWO, NIH) and are directly impacting the institution’s researchers.
It was also noted that support from institutional/faculty leadership alone was often not sufficient: the establishment for these roles should also be driven by the needs of the researchers. Ideally, there is alignment in these bottom-up needs and top-down strategic decisions.
How do you set up an Open Science policy at your institute?
To set up an Open Science policy, you may be more successful if you tackle the variety of different aspects of Open Science separately. Open Science is a very broad concept and it may be complicated to address Open Access, Data, Software, Education, Engagement in a single policy.
Stakeholder engagement is essential when setting up a policy. You should make sure that the policy represents various interests at your institution. Stakeholder mapping is a helpful exercise that could help one understand who to talk to, how and at what stage of policy development. While it may take time to actively engage all of your stakeholders, in the end your policy will be more practically applicable and supported. At the same time, it is also an opportunity to engage in conversation with your stakeholders with this topic, as an upcoming policy that would affect them creates a sense of urgency. It is helpful to run your policy past procedural check points (such as Human Research Ethics committees).
How do we incentivise/reward researchers practising Open Science?
One way to incentivise researchers to practise Open Science is setting up Awards:
The University of Surrey organised a showcase event on Open Research and Transparency, where researchers from any discipline could present their case studies in 20 minutes. The presentations were followed by an award ceremony and afterwards the case studies were listed on the website.
While it is important to recognise the efforts of individual researchers in practising Open Science, there are discussions on whether incentivising them with awards is the best approach (see Lizzie Gadd’s post ‘How (not) to incentivise open research’ and Anton Akhmerov’s Twitter thread).
How do you get more people onboard in practising Open Science?
In order to gain more support for Open Science practices, it helps if there are practical examples. It is not always clear from hypothetical or abstract statements what can be done on a daily basis to make research practices more open.
It was noted that it is easier to start at the beginning of the research career with learning about open research practises, for example, during undergraduate or early graduate school training. Once the students have gained more knowledge, they can also demonstrate to their supervisors that these practices are beneficial. However, it cannot just be up to PhD candidates to drive these changes as they are in a hierarchical relationship with their supervisors. Supervisors should also receive training and support to adjust their practices.
The Seven Deadly Sins of Psychology: A Manifesto for Reforming the Culture of Scientific Practice by Chris Chambers
Science Fictions by Stuart Ritchie
Bad Pharma by Ben Goldacre
This blogpost is written based on contributions by the session participants: Peter Neish (The University of Melbourne, @peterneish), Delwen Franzen (BIH QUEST Center for Responsible Research, @DelwenFranzen), Jen Beaudry (Flinders University, @drjbeaudry), Emma Henderson (University of Surrey, @EmmaHendersonRR), Fiona Fidler (University of Melbourne, @fidlerfm), Nora Vilami and Pranali Patil.
Some people think that digital skills in research focus on learning how to program (Python, R, C++, MATLAB, etc.) or use digital tools to automate recurring tasks, but it entails a lot more.
Becoming a digitally-skilled researcher requires more than ‘just’ learning to use individual tools. It is like becoming a star chef: It does not suffice to know how to use the different cooking appliances (knife, mixer, oven, stove, etc). You also need to know how to run a kitchen efficiently, making sure all prepared ingredients for the dish come together on a plate at the right time without mixing up steps in the recipe that affect the final quality of the dish. To summarize, it is essential to consider, plan and prepare all steps and aspects of the research process workflow at the beginning of each research project.
The potential drawbacks
Implementing best practices in using digital tools requires a significant change in workflow to achieve efficiency and good quality outcomes. If not, code and other scientific outputs can be lost or become unusable by others in the future. Think about the master student who had done a great research and successfully graduated. However, after the student has left, the successors cannot find or re-use the developed code and have to start from scratch. So, the valuable contribution to the project is lost, and the continuity of the work is disturbed.
Also, if researchers do not document the actions and steps during the research project, they may need to figure out things twice when it comes to publication. Reproducibility of the results largely depends on good digital skill practices. So, how could one make sure that the research artefacts remain useful for society and successors?
The Open Science community formulated four main principles or the best practices helping in this process. Those are the “FAIR” principles, which stands for “Findable, Accessible, Interoperable and Reusable”. So, let us analyze a typical life cycle of research software or code creation and see how to build FAIR principles and essential digital skills into your research project.
What “digital skill” ingredients do you need during your project?
At the start of a research project, it helps to have a good overview of the elements in the workflow in relation to the various stages of your project. In this section, we provide a roadmap that could help you to plan your work.
Step 1 – Preparation Phase:
Defining what you need to build and what tools will be used is essential.
Analysis of the project requirements – What research questions do you need to answer, and what results do you want to achieve? Breaking down big questions into smaller problems/deliverables often helps in building a more modular code in the long run.
Investigation of the available codebases – Can you build your project on an existing platform, codebase or use available algorithms, for example, or do you have to “start from scratch”? It is often more efficient to re-use available resources, but you always need to check the licenses and conditions before using, copying or modifying the code of someone.
Learning about the best practices for Research Software development – a high-level understanding of the best practices allows you to avoid the common pitfalls and makes your work more efficient.
Choosing the platform, programming language and the concepts for the code produced – is the last preparation step. You are now ready to start the actual work.
Step 2 – Research project:
Creating the code or research software is only one piece in the whole story. The other essential aspects one should consider are:
Data Management – consider the best practices for data management at the beginning of the project by drawing up a Data Management Plan, which will detail how the data is structured, stored, and archived. Having a Data Management Plan will save you a lot of work and time later. Data Stewards at the faculties are available to provide you with all the support, training and information required (https://www.tudelft.nl/library/research-data-management/r/support/data-stewardship/contact)
Backing up the code – think about the storage with periodic automated backup or set up the backup routine yourself. If you use TUDelft research drive or SurfDrive, your code and data are automatically backed up for you. If you use your laptop or external hard drive, you can lose data if the storage drive is damaged. When storing in the cloud, make sure that your credentials are secured, and you will always be able to retrieve them if forgotten.
Documentation – code by itself can be great but not (re)usable by others if no documentation is attached. It might be challenging to remember and understand what the code is doing a year later. So, having proper documentation is a valuable step in making your code reusable by yourself and others.
Metadata to describe your code and results – metadata can be as broad and descriptive as possible. It may contain information about the code creation (author, date, OS, configurations) and describe when and how to use it. Adding appropriate metadata can make your code more findable.
Use of Version Control – this is an essential part of any research project. It allows you to see and manage changes to files over time, keep track of those modifications and ease the collaboration and co-creation of the code for you and your colleagues. The use of GitLab, GitHub or other version control systems ensures that you can always go back to the previous version of your code if something went wrong at the current state. It enhances the reproducibility of the research produced.
Testing / Distribution – you should build tests into the code at various stages to make debugging easier, mitigate potential errors and ensure that you and others can use your code without errors and reproduce results.
Security and Privacy – you often need to build some security features or choose the framework with the built-in security to keep classified and sensitive data well-protected and keep vulnerabilities out of your system.
Step 3 – Publishing and Sharing
Now, the code has been built, and the first results are obtained. It is a perfect moment to celebrate, but this is not the end of the story. Now think about the sharing and archiving of your results. If you would like the community to use your results, your code should have a license, be stored where others can find it, have explicit metadata attached to it and possess unique identifiers. But no worries, if you have followed the FAIR principles, you are well-covered.
Licensing – Whether you want others to (re)use your code or you are thinking about patenting your software, you should choose a license for it. The most common software license models are Public domain, Permissive, LGPL, Copyleft and Proprietary – they are different types of licenses varying from completely open to fully restricted.
Often if you are developing software openly, e.g. on GitHub/GitLab, the advice is to choose a license at the beginning. This also has implications for registering the software as per the Research Software policy.
Citations – Citing the sources you used acknowledges and gives credit to the authors. It also allows others to learn more about the previous work your project is built upon. To make your code more citable, it is worth adding a citation file (CFF) to your repository (https://citation-file-format.github.io/)
Publishing – there are many platforms on which you can share and publish your code, e.g. GitHub or SourceForge. Publishing and sharing your project on these platforms can attract collaboration and increase visibility. Please remember that the code or any digital object should have a Digital Object Identifier (DOI) to make it easier to find or cite. If the data/code cannot be shared, you can still share the metadata in a repository so that others can find your project and request access to it.
Archiving – when the project is over, you may want to archive your code in a repository to access it in the future. Code can be archived at, for example, 4TU.ResearchData or at Zenodo.
Digitization brings a lot of opportunities to researchers to do more advanced research and collaborate with others. But it requires adjustments to the workflow, development of a common language and learning skills to effectively use new tools that come available. The good news is that at TU Delft, we have training courses and excellent support available through the Digital Competence Center (DCC, https://dcc.tudelft.nl/ ) and Data Stewards that can help you run your kitchen as a star chef in the digital age.
Who are we?
Meta Keijzer-de Ruijter:
Meta has a background in Chemical Engineering and Corporate Education. She spent more than 10 years in the ICT Innovation department developing digital assessment in education at TU Delft. Recently, she became a project manager of the FAIR Software project within the Open Science Program. Together with colleagues in ICT Innovation and Research Support at the Library, she set up the Digital Competence Center (DCC) support team. She currently investigates the needs for digital skills for researchers.
Masha has got her PhD in Physics at TUDelft and recently joined the ICT department as an Innovation Analyst. She focuses on supporting researchers in challenging ICT related requests.
It is the first in the series of blog posts in which we want to talk about the work we do to support the researchers as the Innovation Department and DCC team to reflect on the things we come across.