There’s a young, bubbling ecosystem of platforms and ideas that seek to fundamentally change the process of publication. At the core of this is updating the position of peer review (good slides at https://zenodo.org/record/7040997) But, many outside the open science system are unaware of this, assuming that the journey is complete when are 100% of journals are open access.
How to achieve change? There are discussions over vision policies, strategies, seed funding, sustainability. The words used are different, but the notion of a fusion of top down and bottom up approaches is popular. (One good example at https://zenodo.org/record/7025049)
Change is not just about the world of science. For the benefits of open science to blossom, much better dialogue is needed with the broader public. Without dialogue, the risk of misunderstanding of (open) science will grow. (See https://zenodo.org/record/7038872 for more)
5. And nothing will really change without changing the incentives – how do we reward and recognise all staff involved in (open) science? (Dutch approach – https://recognitionrewards.nl/)
Digitalisation offers a lot of opportunities for researchers to boost their research: automation of repetitive tasks, collection of more (complex) data, increase in computing capacity, re-use of data and code plus new ways to collaborate with colleagues within and outside of the research group.
At TU Delft, Library Research Data Services and ICT/Innovation are working closely together to define a vision and strategy to improve the support for developing digital competencies amongst the research community. In this process, we are looking at workshops and activities that are already being organised to assess their impact and possible improvements. With that aim in mind, a survey was developed to be sent to former participants of Data and Software Carpentry workshops. Our survey was based on the standard questions of the long-term impact survey that The Carpentries use, but we added questions on the actual usage of the tools taught and additional learning needs on the topics that were addressed in the workshops.
The survey was sent to 315 former participants of Data and Software Carpentry workshops held by TU Delft between 2018 and the summer of 2021. We received 45 responses. In addition, the feedback from both instructors/helpers and participants over the years was considered in this report.
Demographics and Scope
Most of the respondents were PhD candidates (37 out 45) that attended the workshops during their first or second year (21 out of 37). Respondents came from all faculties, but the majority from Applied Sciences (8), Civil Engineering and Geosciences (6), Architecture and the Built Environment (6) and Mechanical, Maritime and Materials Engineering (6).
Most respondents provided feedback on the software carpentry workshops (30 of 45).
Main Takeaways from Carpentries
The top three takeaways from the carpentry workshops, according to the respondents, were:
Using programming languages including R, Python, or the Command Line to automate repetitive tasks (16)
Making code reusable (12)
Using scripts and queries to manage large data sets (10)
The workshop(s) helped them to improve their efficiency, data analysis and data management skills.
It should be noted that eight respondents wrote that they are not using any of the tools. The main reason for that was that they use alternative tools that better match their practice or that are easier to use on an ad hoc basis.
Relevance of the content
Most of the respondents found programming with Python (partly or very) relevant. But there is a lot of debate on the level of Python being taught. Although it was communicated that the level is very basic beginner, a recurrent note (also in the surveys right after the Software Carpentry Workshops) was being made that the topics addressed are too basic and the pace of the course too slow for those who want to refresh. The reason they still attended was that in order to get the full Graduate School credits for this course, everyone should attend all parts of the carpentry. In an online setting this seemed to result in less engagement in the breakout rooms.
Figure 1 Response to the question ‘How relevant was the content of the Software Carpentry for your research’? (n=22).
Data Carpentry Genomics
In the Genomics carpentry the command line part was found most relevant to the participants, closely followed by data wrangling.
Figure 2 Response to the question ‘How relevant was the content of Data Carpentry Genomics for your research?'(n=3)
Data Carpentry Social Sciences
In this data carpentry the data analysis and visualization with R is most valued. The relevance (and eventual) use of OpenRefine was questioned. None of the respondents (n=5) in the long-term survey reported using OpenRefine in their current work.
Figure 3 Response to the question ‘How relevant was the content of Data Carpentry Social Sciences for your research?'(n=5)
Use of tools that are taught in the carpentries
In the survey, respondents were asked about their usage of the tools that were taught in the workshops. Did they start using the tools and to what extent, or did they quit using the tools or never even started using them. On each tool they were also asked if and how they would like to advance their skills.
The use of Unix was picked up by a good number of respondents, those that didn’t use Unix mentioned that it did not serve their purpose. Two respondents stated that they did not feel confident enough to use it. Additional materials for self-paced learning or help with how to apply it in their work might help them get started.
It appeared that the respondents that already used Python increased their usage from occasional to more frequent. Those who reported not using Python stated that they use an alternative tool. Both frequent and occasional Python-users would like to improve their skills either by attending more advanced workshops, self-paced materials or talking with peers. Examples of topics they are interested in include data analysis, (big) data management, packaging, Jupyter Notebooks, libraries (including scikit and numba), handling large databases and automating tasks.
Only three people responded to the usage of R question, one of them started using R from that moment on, the other two report that they were using a different tool to do their data management. The respondent that used R said that additional training, learning materials and talking to peers would be appreciated to improve the skills.
Amongst the respondents the use of Git increased. Those who didn’t use the tool (7 respondents) said either that it didn’t fit their purpose (4 respondents) or that they did not feel confident enough to start using it (3 respondents). This last group would like access to additional learning materials, guided practice or consultation on how to apply Git in their own work. Most of the Git users would love to improve their skills in various ways. Topics that are mentioned are dealing with complex files, comparing different versions of code, collaborative use of Git and building more confidence using Git.
None of the respondents (n=5) used OpenRefine after the workshop. Three of them stated that the tool doesn’t fit their purpose, while two respondents would like to have additional materials or consultation on how to apply it in their work.
The use of spreadsheets remained about the same after the workshop. And the respondents did not feel the need to improve their skills.
None of the respondents (n=3) had been using this prior to the workshop. After the workshop two of them started using it and they would like to learn more. No specific topics were mentioned. The respondent that did not start using cloud computing stated that it did not fit his/her purpose.
Additional training on research data management or software development
Respondents could tick multiple boxes in this question about additional training needs. The top five training topics were:
Basic programming – 14 votes
Modular code development – 13 votes
RDM workflows for specific data types – 13 votes
Software versioning, documentation and sharing – 11 votes
Software testing – 10 votes
Impact of attending the carpentry workshops
Most respondents agreed that attending the carpentries had an impact on their work. They gained confidence in working with data, made their analysis more reproducible, were motivated to seek more knowledge about the tools, advanced their career, improved their research productivity and improved their coding practices. The only topic where there was a wide spread of (dis)agreement was on the impact of the carpentry workshops on the professional recognition of their work.
Recommendation of the Carpentry workshops
Most respondents recommended or would recommend the carpentry workshops to their colleagues.
From the survey we learned that the carpentries fulfill an important role in the introduction of tools that help members of the research community to carry out their work more efficiently, but additional means and support are necessary. The translation of the carpentry materials to the daily practice can be challenging, causing some to quit or not even start using the tools. In the discussions with observers, helpers, and instructors of the software carpentry workshop we identified the need to link the carpentry topics more clearly to the research workflow, in order to increase the understanding of the ‘why’ we teach these topics and tools.
And of course..
We (TU Delft) would like to thank all those who participated in the survey….
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