On 26 June 2018, the new TU Delft Research Data Framework Policy was approved by TU Delft’s Executive Board. The Framework Policy is an overarching policy on research data management for TU Delft as a whole and it defines the roles and responsibilities at the University level. In addition, the Framework provides templates for faculty-specific data management policies.
From now on, the deans and the faculty management teams, together with the Data Stewards, will lead the development of faculty-specific policies on data management which will define faculty-level responsibilities.
If you are working at TU Delft and if you would like to be involved in the development of faculty-specific policies, please do get in touch with the relevant Data Steward.
The full text of the policy (pdf) is available below.
The 4TU.Centre for Research Data announces its report on research data management within the 4TU Research Centres.
Over the last few months, the 4TU.Centre for Research Data had the chance to make contact and to speak with several of the Scientific Directors of the 4TU Research Centres about research data management. The report published today highlights the findings from these contacts and conversations.
A citable version of the report is available on OSF Preprints (DOI: 10.17605/OSF.IO/SGFTW).
1. Research data management is not addressed at a strategic level by the 4TU Research Centres, but left to individual research groups or to individual researchers connected to the Centres.
2. Within the 4TU Research Centres, there is a broad range of attitudes towards data and a broad range of data types and characteristics, including large datasets; commercially sensitive datasets; privacy and ethical concerns regarding data; software and its sustainability.
3. Software sustainability is an important and much discussed topic, but there are currently no standards or systematic way of looking after software.
4. Research on human subjects and datasets including personally identifiable information or sensitive personal information are more prominent than might be expected in engineering and the technical sciences. Lack of transparency and reproducibility of scientific results can be an issue in these areas because the underlying datasets are often not available.
An Opportunity to Collaborate
Research data management is increasingly viewed as an important part of high-quality research. International and national funding bodies now mandate institutions and researchers to make data available. Data sharing is predicated on good research data management and has the potential to make scientific research more transparent, open, and efficient. In view of these principles and developments, the 4TU.Centre for Research Data wishes to maintain and deepen its links with the 4TU Research Centres and to support the Centres in various aspects of research data management.
Written by: Mary Donaldson and Vessela Ensberg
On the 21st February 2018, a Birds of a Feather session was held as part of the 13th International Digital Curation Conference in Barcelona on ‘Data management costing in grants’. The session was proposed and chaired by Marta Teperek of TU Delft.
The session proposal recognised that ‘many research funders now require that research data is properly managed and shared. Consequently, many agree for the costs of data management to be budgeted in grant proposals. This is necessary for the sustainability of data management activities. So why is this not a normality yet?’
Identifying the problems
We identified two main sources for data management not being included in the grant proposal budget: lack of awareness among researchers as to what funds they can request and lack of available support at research institutions.
Among all the usual suspects for the reasons why Research Data Management (RDM) activities are not costed into grant proposals
- researchers prefer to ask for money for other purposes
- researchers are not aware which costs are eligible
- researchers believe that RDM costs should come from award overhead
Identifying solutions to researchers’ issues
Some of the RDM activities we identified as eligible for funding are
- transcription of interviews
- data anonymization
- data curation assistance (outside of existing central posts)
We acknowledge that some of these activities are already included in research proposals as parts of the normal research process, and a specialist, such as a data curator, maybe difficult to hire for a less than full-time post. Growing the list of examples and viable options is likely key to having data management included in grant budgets.
As we moved on from discussing why grants don’t often contain data management costing, we strayed into the related territory of institutional issues. Those included
- worries about ‘double dipping’ for RDM costs, especially when trying to recover staffing costs
- need for training for research admin staff who are directly involved in application processes; high staff turn-over in these positions
- lack of a centralised system which tracks all grant applications or lack of communication between the Office coordinating the grant awards and RDM services
- preservation costs being incurred after the award has been closed
- lack of a pool of ‘expert’ staff which can be hired out to research projects
Identifying solutions to institutional issues
Institutional issues can be addressed by investment in the processes. In particular, Utrecht University and the University of Glasgow gave examples of addressing communication and training of research support staff. The RDM team at Glasgow investigating the possibility of adding a check-box to the central grant review system to indicate that funding for RDM has been costed and included in the application. Utrecht also provides consultations on data management costs and is experimenting with a pool of data managers who can be hired from the library for a certain amount of time to work on specific projects. The library is funding these positions but hopes to be able to recover up to 75% of the cost of each position from research projects in the future.
We also looked for lessons learned from the Open Access for publications. Funders have experimented with different models to pay for the more mature requirement for open access to publications in recent years. We explored whether these models could be adapted to help with the requirement for data management and sharing of research data. The first model we discussed was the FP7 pilot for open access where eligible projects were entitled to apply to a central pot of money, provided certain conditions were met. This pilot is due to end this week (28th Feb 2018), and has encountered administrative issues. In the UK, Research Councils UK (RCUK) have provided large research-intensive institutions with a block grant award to pay for Open Access charges for eligible articles. At the end of the pilot, RCUK will accept longer embargo periods. While we felt that centralized pots of money might work to support data management, the administrative burden of this funding is high.
To summarize, institutions can consider the following options to boost up data management inclusion in the grant budget.
- An institution should have a centralized grant administration system. These systems can be adapted to ensure data management is included in the budget.
- RDM should provide more advocacy with researchers using vocabulary the researchers understand and relate to. RDM should match researchers with resources to support costing of RDM activities.
- Providing seed funding to researchers for legacy projects. These might help researchers engage better with RDM and consider their needs earlier in the process on subsequent projects.
- Institutions should consider having a core team of RDM specialists (data curators, statisticians etc) whose time can be bought out by grants, in the way that technicians already are in the life sciences.
- Provide in-depth training for technical or other support staff to enable them to deliver data management for a project. This would provide regular subject-specific RDM support for projects and help build capacity in departments.
However, despite all the ways in which institutions could help improve and support costing for RDM activities, we felt that tackling funders to better support this process would be more effective than each institution having to develop their own solutions. We also thought that funders should be alerted that in cases in which they only require an outline plan at the time of application, by the time the award is made and a more detailed plan is developed, the opportunity to identify and cost data management activities has passed
Proposed funder interventions
- Improve review process for data management plans. Check for discrepancies between the RDM activities promised and the resources requested.
- Provide a clear statement with examples about acceptable and fundable data management activities.
- Indicate the proportion of each grant award expected to be spent on RDM activities.
This could be expressed as a percentage, or a range (to avoid the figure itself from becoming a point for argument) and would signal to researchers that funders don’t see RDM as a waste of money that could better be spent on generating more research data.
- Make it clear who in the funding body is the person /role to contact to discuss RDM issues. RDM requirements are still new enough that clarification is regularly required.
- Fund more data re-use.
For researchers, the cost/benefit analysis of making research data available is difficult to assess. Issuing calls specifically to encourage re-use of datasets would improve the understanding of data re-use and drive demand for shared datasets, helping tip the scales in favour of sharing data.
Ultimately, better alignment of funder RDM requirements would make it simpler for researchers to comply. It was mentioned that Research Data Alliance RDA had tried to get a funder working group together. Perhaps this is something Science Europe could also be involved with.
Jisc have funded a project in the UK to produce centralised guidance by July on the following:
- What do different funders require in terms of RDM?
- What do different funders require in terms of data sharing?
- What are different funders willing to pay for?
- How should funding for RDM be justified in grant applications?
- How can funds for RDM be used by institutions?
- Utrecht University data management costing guidance: https://www.uu.nl/en/research/research-data-management/guides/costs-of-data-management
- RDM: http://www.uu.nl/rdm
- Data management costing tool and checklist: http://www.data-archive.ac.uk/media/247429/costingtool.pdf
- ‘Funder requirements for datasets’ project (Jisc-funded): https://rdmfunderrequirements.wordpress.com/
Authors: Alastair Dunning, Marta Teperek, Anke Versteeg, Wilma van Wezenbeek
This is a joint response from TU Delft Library to the public consultation on the draft version of the VSNU Code of Conduct for Research Integrity.
- The focus on the relationship of research data to research integrity is welcomed.
- There are some inconsistencies in good practice in data management that need to be ironed out.
- The VSNU may need to consider the implications of researchers that frequently work with companies that do not have equivalents to this code.
- Reporting on research is increasingly done via other channels than traditional journals, e.g. via platforms, preprint servers or blogs. The paragraphs on assessment and reporting are still very much focused on the traditional ways of communicating about research.
- With the upcoming GDPR, is there enough being said about the need for researchers to be increasingly aware of their own role in how they share their own personal information and what tools or applications they use and share this information with?
- The document currently does not discuss the importance of management and sharing of source code used to create, process or analyse the data. However, most research projects have now a computational element and the ability to validate and reproduce research results often relies on the availability of the supporting source code. Results of a recent survey revealed that 92% of academics use research software in their research. Therefore, if the Code of Conduct is to be relevant and applicable to the current research practices, the issues associated with managing and sharing software/code need to be addressed.
- Terms such as “data”, “research material” and “sources” need to be defined.
1. Preamble; paragraph 4 Remove “large” in “the growing importance of the way large data files are used and managed” – this is applicable to all data files, and not only to big data.
2.2.7 Many private institutions will not have subscribed to the code, and may not even have these guarantees in place. It is good that this issue is mentioned in the code, but it will have implications for universities and their researchers that work with private companies, particularly smaller companies.
2.3.9 The reference to Citizen Science seems rather cavalier, and perhaps deserves more detail. Research projects can involve thousands of citizen scientists; sometimes they may come from non-western countries, with different ethical expectations/norms etc.
2.4, footnote 7 – Students (such as masters’ students) are excluded from this code of research. But what happens if work done by masters’ student (eg preliminary data collection) is integrated into research?
4.2 Overarching comments to the section on “design” standards:
- Make extra emphasis on transparency by design and the need for planning data management and sharing from the start.
- The ethical and societal issues of fair use and access to research results need to be addressed at the design stage of research experiments. As discussed in the recent issue of Science magazine, research should aim to “ensure that those societies providing and collecting the data, particularly in resource-limited settings, benefit from their contributions”.
4.2.9 What is meant by “joint research”? Should this not apply to any funded or commissioned research?
4.2.12 The research should not be accepted if agreements outlined in point 4.2.9 are not defined and signed by all partners.
4.3.21 “To your discipline” is a bit weak. Consider “appropriate for your discipline and methodology” instead.
4.3.22 Emphasise that all data underpinning an article should be FAIR. The statement is weak at the moment.
4.4 Given that source code is often necessary for validation and reproducibility of research results, it is crucial that availability of source code used to create, process or analyse data is also discussed.
4.4.26 Given the fact that we want all contributors to be acknowledged properly (“author” is not always the right word for this), could we add “and processing” before the data?
4.4.30 Methods and protocols necessary to verify and reproduce research results should be made available.
4.4.37 Rephrase to “Always provide references and attribution when reusing research materials, including research data and code”. It is crucial that any reused research outputs are properly cited and the original authors properly attributed. In addition, the phrasing “that can be used for meta-analysis or the analysis of pooled data” was limiting the scope of the reuse and should be omitted.
4.4.40 More emphasis is needed to ensure that research data and code supporting your findings are available for scrutiny. In addition, emphasise that research outputs should be made as open as possible, as closed as necessary.
5.4 As discussed before, ensure that good practices for managing and sharing research software are also discussed.
5.4.12 Research Infrastructure is the wrong phrase. Rather: “Ensure that proper data management is embedded in the research lifecycle and that the necessary support is provided.”
5.4.13 & 5.4.14 These need clarification. At present they are contradictory (ie should data be stored permanently vs data should be stored for a period appropriate for the discipline. Again, the appeal to disciplinary practice might be incorrect, eg one can have very different data in the same discipline. “Archived in the long term” would be a better phrase than stored permanently.
5.14.15 There is an appeal to the FAIR principle earlier in the document. It should be repeated here.
5.5.17 Does this refer to commercial funders/industry partners as well?
6.3 Under “other measures”, if a retraction would be valid as measure, this should also apply to the underlying data.
This week, we are presenting at the International Digital Curation Conference 2018 in Barcelona.
This presentation can be downloaded from Zenodo.
The pre-print version of the practice paper accepted for the conference is available on OSF Preprints.
Title: From Passive to Active, From Generic to Focused: How Can an Institutional Data Archive Remain Relevant in a Rapidly Evolving Landscape?
Authors: Maria J. Cruz, Jasmin K. Böhmer, Egbert Gramsbergen, Marta Teperek, Madeleine de Smaele, Alastair Dunning
Abstract: Founded in 2008 as an initiative of the libraries of three of the four technical universities in the Netherlands, the 4TU.Centre for Research Data (4TU.Research Data) provides since 2010 a fully operational, cross-institutional, long-term archive that stores data from all subjects in applied sciences and engineering. Presently, over 90% of the data in the archive is geoscientific data coded in netCDF (Network Common Data Form) – a data format and data model that, although generic, is mostly used in climate, ocean and atmospheric sciences. In this practice paper, we explore the question of how 4TU.Research Data can stay relevant and forward-looking in a rapidly evolving research data management landscape. In particular, we describe the motivation behind this question and how we propose to address it.
The framework is entitled “Impact for a better society” and “openness” is listed as one of the four major guiding principles. The principle of openness was apparent already during the consultation phase of the framework: “more than 600 internal and external stakeholders have been actively participating” in the process.
The purpose of the strategic framework is “to serve as a high-level compass that will guide decision-making bodies at all levels within our university in the years ahead”. But what does the framework really mean for Open Science? In this blog post, I highlighted the key quotations from the strategic framework which are likely to have the highest impact on future Open Science developments at TU Delft.
Impact for a better society
First, Open Science fits neatly with the overall title of the framework “Impact for a better society”. The framework states in the preface that “societal impact and academic excellence can be mutually reinforcing”. And this is indeed the case. Open Science means that research results can be accessed and re-used by everyone in the society, including the members of the public. TU Delft also wishes to increase its societal engagement by “promoting public participation in scientific research (‘citizen science’). Which is all deeply in line with the principles of Open Science.
Open Access publishing
Within Open Access publishing, TU Delft wishes to first develop a stronger awareness among its researchers. Second, the strategic framework also emphasises the need for a sustainable transition to Open Access publishing and it thus includes the commitment to “reducing costs for Open Access publishing by negotiating journal subscriptions with publishers.” At the same time, TU Delft will explore “new ways to present and disseminate knowledge”, which will not necessarily rely on publishing via the traditional scientific journals. Finally, researchers are encouraged “to serve on relevant Editorial Boards”, suggesting that TU Delft researchers take an active part in shaping publishers’ policies.
The importance of good data management and sharing is also stipulated in the strategic framework. TU Delft wishes to stimulate the sharing of research data, and it realises that in order to achieve this, researchers need to be provided “with the necessary support, for example by appointing data stewards and data engineers within all faculties who advise researchers in managing their data.”
In addition, TU Delft will implement a “policy for research data, and enable researchers to control their own research data in accordance with this policy.” And, quite importantly, the strategic framework states that TU Delft wants to “involve researchers in contributing to TU Delft’s policy for research data management.”
Finally, the strategic framework recognises the importance of the new EU General Data Protection Regulation, and will “set up an integrity policy that protects scientific data and personal data in line with the EU directives.”
Software is an integral part of research and is necessary for research reproducibility. It is therefore not surprising that the commitment to open source software has been stated in several locations in the strategic framework. First, TU Delft will develop “best practices for working with open source software, for example in relation to copyright and archiving of source code” and “facilitate a central place of support for researchers who want to use open source software.” Furthermore, TU Delft stresses the importance of communities in raising awareness and reinforcing good practice. It will, therefore, create “an open source software community with active ambassadors.”
Rewards for Open Science
The Strategic Framework is aiming at recognising the engagement with Open Science by changing the ways in which researchers are evaluated. TU Delft wants to include a more explicit recognition of “engagement with Open Science and Open Education” in yearly R&O evaluation cycles. To facilitate this, TU Delft supports “(inter) national initiatives aimed at finding alternative indicators that positively value open access publications” and is “collaborating with (inter)national leaders in the field of non-traditional metrics.”
Supporting researchers in their transition to Open Science
Importantly, TU Delft recognises that researchers need to be professionally supported in order to ensure that the objectives of the strategic framework can be successfully met. Therefore, it aims to “improve the quality of [its] professional services” and wants to provide researchers with a clear, ‘one-stop-shop’ contacts for requests which should be “simple and effective”, “digital where possible, and personalised where needed”.
TU Delft also plans to appropriately recognise and reward those supporting researchers in their transition to Open Science. TU Delft will “take the lead in national initiatives aimed at extending the job classification for support staff with positions that support recent developments, such as data stewards that advise researchers in managing their (open) research data”.
Strategy for Open Education was also widely mentioned in the framework. The one-page summary outlines TU Delft’s commitment to “promote and facilitate Open Education”, which is then followed by a declaration: “we wholeheartedly support Open Education and want to make Open Educational Resources part of our educational policy”. To achieve this, TU Delft will support lecturers and students in the use of open education resources and will encourage “lecturers to publish their educational material under an open license”
Importantly, TU Delft also wishes to appropriately reward those engaged in Open Education activities. It wishes to strengthen a culture “in which education and teaching receive more appreciation and recognition” and “will refine [its] HR policy so that it will offer further scope for professional development and career opportunities within education”. In addition, as part of its educational policy, TU Delft wants to make “open education part of the basic teaching qualification programme and the evaluation criteria of courses.”
Last, the framework also states that TU Delft has the ambition to replace “commercial textbooks by open resources in all BSc programmes as much as possible.”
How important is the strategic framework?
So how important is the framework? Will the statements be really implemented?
To answer these questions I will conclude with the final quotation from the framework: “this framework is more than a formal requirement; it is our moral responsibility”.
In 2016 TU Delft embarked on a new project aiming to comprehensively address research data management needs in a disciplinary manner. Rector Magnificus Karel Luyben announced the TU Delft Open Science programme with these words: “The world is facing challenges that our university of technology alone cannot meet.” Good research data management is a necessary prerequisite for effective data sharing and greater openness. Therefore, research data stewardship was recognised at TU Delft as a key component of its Open Science programme.
In research data management there tend to be very few (if any) one-size fit all solutions. At TU Delft data management concerns and recommended workflows will be different for researchers studying social behaviours of people in cities and for researchers collecting and analysing live-time weather data or working on 3D printing projects. In order to be truly relevant to diverse types of research, data management advice needs to be discipline-specific and thus those advising researchers need to have discipline-specific expertise.
Additionally, ensuring any lasting cultural change is not just about technology and expertise but, perhaps more importantly, about communication and trust. Relationships between researchers and those who advise them on data management practice need to be developed over time and by allowing people to get to know each other and to work closely together.
As a result, we came up with the idea of a one year project where discipline-specific Data Stewards will be appointed at each one of the eight TU Delft faculties. Alignment between activities of individual Data Stewards will be ensured by a dedicated Data Stewardship Coordinator leading the project from the Library, which was outlined in their roles and responsibilities.
What are the objectives of the Data Stewardship project?
A long-term objective of the project is to ensure that researchers across all the disciplines supported at TU Delft adhere to good research data management practice in their day-to-day work. This is, of course, a difficult task to achieve and quite unrealistic during the period of a one year project.
We have therefore split the project into several short, medium and long-term objectives, which would allow us to realistically assess the progress of the project and, at the end of the one year period, decide whether it indeed helps improve research data management practice and provides value to the research community at TU Delft. Some of these goals are discussed below.
There are several short-term goals of the project, which we would like to achieve by the end of 2017. First, there needs to be a mechanism which will create active links between the researchers, Data Stewards and the Library in order to ensure consistent and aligned messages. In addition, we need to develop a framework within which Data Stewards could effectively work together. Some of the challenges which will need to be addressed are actually not so much different from challenges faced by researchers. Data Stewards who will be based at different faculties will have to be able to effectively exchange their files, they need to develop a robust system for version control and data backup, as well establish effective ways of communicating with each other and with the research community.
Second, Data Stewards will join the project with a various degree of expertise of the different aspects of research data management. Therefore, all Data Stewards will need to complete an intense training programme. Some of that training will be externally provided (Essentials 4 Data Support), whereas other sessions will be delivered by local experts (about 4TU.Centre for Research Data or on the local Data Management Plan support service). Completion of the training programme will ensure that all Stewards are equipped with the necessary knowledge and skills to advise their faculty researchers.
Third, in order to judge the progress of the project, it is necessary to develop and agree on an effective set of metrics, which would allow deciding whether the project is moving towards its goal of improving good research data management practice. We will write more about this in future blog posts.
Finally, and crucially, Data Stewards need to get to know the research community they are supporting, not only to get a thorough understanding of the discipline-specific needs of their communities, but also to start building trust necessary for them to establish themselves as the ‘go to people’ and the first points of contact whenever advice on data management is needed.
There are two main mid-term goals (beginning – mid-2018). First, we would like to develop faculty-level policies on research data management, as well as an overarching TU Delft’s research data policy framework. Many institutions, in particular in the UK, have central institutional policies on research data management. However, our rationale for deciding on a different approach was similar to the rationale behind the whole Data Stewardship project: we believe that one size fits all solutions for research data management are difficult to implement in practice and come with a risk of becoming too aspirational and detached from the day-to-day practice. Our aim is to create tools which would give faculties sufficient flexibility to decide themselves on their most practical local solutions and expectations and to create policies, which could be truly implemented by the research community. We also thought that ensuring faculty’s leadership in policy development will result in a greater ownership and engagement with research data management.
Our second mid-term goal is to put our set of agreed assessment metrics to test: to obtain first meaningful data on the development of the project and to enable the assessment of its progress. This should happen by mid-2018 and this would also allow any necessary iterations to be devised and implemented.
As explained before, the ultimate goal of the project is to ensure that researchers at TU Delft adhere to good data management practice on a day-to-day basis (2018 and beyond). However, a more realistic goal for a one-year project is to assess whether the proposed solution of having dedicated Data Stewards network is indeed working and whether it leads to improvements in data management practice with the use of agreed metrics.
The ultimate evidence of the success of the project would be however the judgement of the research community itself. Will researchers perceive Data Steward as their trusted source of necessary data management expertise? If so, one would expect to see Data Stewards to become permanent, key faculty staff members. And if subject-specific expertise is the solution for good data management practice, perhaps one day the presence of a dedicated Data Steward in every research group will become the normal thing, similarly as the presence of Lab Managers or Project Managers nowadays.
We will post regular updates on this project here, so watch this space!