Category: Notes

Informal Notes from LCRDM Brainstorming Meeting

As mentioned in a previous blog post, I attended a brainstorming meeting aiming at building on the existing achievement of the LCRDM (Landelijk Coördinatiepunt Research Data Management).

The new proposed model for the LCRDM consists of three levels. (I don’t think the draft model has been published yet)

  • A Steering Group with general oversight of LCRDM
  • A set of Expert Groups with knowledge in particular topics (eg Infrastructure, Training, Metadata, Data Stewardship)
  • A series of ad hoc Task and Finish Groups (drawn broadly from across RDM expertise in the Netherlands) who work on specific issues as directed by the Expert Groups

The model was tested out at the meeting with members seeing how some pertinent example issues (Data Stewardship, Data Ownership and Interoperability) would work

Pros uncovered

  • The model is dynamic; new topics can be addressed quickly
  • LCRDM will be open to a wide range of participants
  • The full range of RDM issues can also be tackled.
  • Ad hoc nature of Task and Finish groups makes it easier for those who can only make smaller contributions to be involved

Cons / Issues to be addressed

  • Difficulty in keeping up with the large number of outputs produced
  • Lack of strategic alignment between the many Task and Finish groups
  • Model leads to a focus on smaller problems and not overarching national challenges (this is a key problem in my mind)
  • Relationship to other bodies (NPOS, UKB, EOSC) still to be clarified.
  • Role and responsibilities for sustaining outputs (whether tools, advice or services)  

Two other points raised with Ingeborg Verheul at the end

  • Can we please have a mailing list for RDM participants in Netherlands?
  • If relevant for those taking part, can Task and Finish groups take place in English as well as Dutch

A follow up meeting will be organised later in the Spring

TU Delft Library’s response to the public consultation on draft version of VSNU Code of Conduct for Research Integrity

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.

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General Points

  • 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.

Specific Notes

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.

Views on Data Stewardship – report of preliminary findings at TPM faculty

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Date: 29 January 2018
Author: Marta Teperek, Data Stewardship Coordinator, TU Delft Library


Executive summary

Qualitative interviews with nine researchers at the Faculty of Technology, Policy and Management (TPM) at TU Delft were undertaken in order to get an understanding of data management needs at the faculty in advance of appointing a dedicated Data Steward. The purpose of this was to aid the recruitment of the Data Steward and to define the skills and experience of an ideal candidate, as well as help deciding on the work priority areas for the Data Steward. The results of this research can be also used as a point in time reference to monitor changes in data management practice at the faculty.

The main data management challenges identified were: handling personal sensitive research data; working with big data, managing and sharing commercially confidential information and software management issues. Despite the diversity of problems, some common issues were identified as well: the need for improving daily data management practice, as well as the need for revising workflows for students’ research data. With the exception of one researcher, who was in opposition to the Data Stewardship project, all other researchers expressed their support for the project and welcomed the idea of having a dedicated Data Steward at the faculty.

Additionally, several follow up actions were already undertaken as a follow up of these interviews:

  • the Data Stewardship Coordinator was invited to give two talks about Data Stewardship to two different groups of researchers;
  • a member of the Research Data Support from the Library team was asked to deliver a training course for students;
  • the Data Stewardship Coordinator was asked to discuss the best way of rolling our data management training for PhD students at TPM in coordination with the TPM Graduate School.

Given that the financial allocation for the Data Steward at TPM faculty is currently at 0,5 FTE for the first year and 1,0 FTE for the two subsequent years (until December 2020), it is recommended that the first year is spent on continuing and extending this research to better understand the needs of the faculty. It is suggested that at the same time, the Data Steward starts addressing the most urgent data management needs at TPM faculty, in particular, the development of a data management policy, as well as the development of solutions and recommendations for working with personal sensitive research data.

The two subsequent years could be devoted to developing resources and solutions for the remaining problems and for critical evaluation of the project and its effect on data management practice at the faculty. This approach should provide the faculty with enough resources and information to decide on the best strategy for Data Stewardship beyond December 2020.


Introduction

Data stewardship has been recognised internationally as a key foundation of future science. Carlos Moedas from the European Commision (EC) said that Open Science “is a move towards better science, to get more value out of our investment in science and to make research more reproducible and transparent. (…) Recent advances such as the discovery of the Higgs boson and gravitational waves, decoding of complex genetic schemas, climate change models, all required thousands of scientists to collaborate (…) on data. And that implies that research data are findable and accessible and that they are interoperable and reusable”. In support of this, the EC anticipated that about 5% of research expenditure should be spent on properly managing and stewarding data. Barend Mons, the Chair of EC high level expert group on the European Open Science Cloud, estimated that 500.000 Data Stewards will be needed in Europe to ensure effective research data management. Consequently, all NWO and H2020 projects starting from 2017 onwards must create a Data Management Plan and are required to make their data open. In addition, the European Open Science Cloud promises new tools and related EC strategy papers suggest new rewards and grant funding schemes (such as FP9) to benefit those practising open science.

TU Delft’s College van Bestuur (CvB) made a strategic decision to be a frontrunner of this global move and a dedicated Data Stewardship programme was initiated. The long-term goal of this programme is to comprehensively address research data management needs across the whole campus in a disciplinary manner. To achieve this, subject-specific Data Stewards are to be appointed at every TU Delft faculty. Strategic funding from the CvB was allocated to support 0,5 FTE of a Data Steward per Faculty until December 2018, and 1,0 FTE of a Data Steward per Faculty for two years from January 2019 to December 2020. Subsequently, faculties are to decide how to best address their researcher data management needs.

In 2017 the first Data Stewards were appointed at three TU Delft faculties: Faculty of Electrical Engineering, Mathematics and Computer Science, Faculty of Civil Engineering and Geosciences and Faculty of Aerospace Engineering. At the beginning of 2018, Data Stewards are to be appointed at the five remaining faculties, including the Faculty of Technology, Policy and Management (TPM).

In order to facilitate the recruitment decision over the appointment of a Data Steward at TPM faculty, the Data Stewardship Coordinator was set out to investigate the faculty’s research data management needs. Qualitative interviews were undertaken with TPM researchers in autumn 2017, which led to the identification of four main data management issues, specific to the types of research done, and revealed some common data problems for the faculty overall. The report below describes the key findings of this research and makes some recommendations for the future work of a Data Steward at TPM Faculty.

Methodology

Semi-structured qualitative interviews were conducted with four full professors, three associate professors and two assistant professors in September and October 2017. Initial interviewees were selected and approached by the Data Stewardship Coordinator based on their online profile content to ensure a representation of the different research methodologies used across the faculty as well as representation of all three TPM’s departments: Engineering Systems and Services, Multi-Actor Systems and Values, Technology and Innovation. In addition, one researcher was interviewed as a result of a recommendation from the initial interviewee, and two other interviewees were suggested by the Secretary General of the faculty.

All interviewees were informed that interview findings will be used to create a preliminary report on data management needs at the faculty and that the report might be made publicly available. Interviewees were assured that no information will be directly attributed to them and that they will not be named in the report. Interviewees agreed for the interview notes, including personal information, to be shared internally with the Secretary General of the faculty.

Interviews lasted for 30 – 60 minutes. Interviews were not recorded, and instead, notes of key discussion points were taken by the interviewer during the interview.

Categories of data management issues

Diverse nature of research topics at TPM suggested that researchers could have different data management needs. Nine interviews conducted so far revealed that this was indeed the case and identified four top data management issues: handling personal sensitive research data; working with big data, software management issues and managing and sharing commercially confidential information.

Handling personal sensitive research data

Questions about handling of personal sensitive research data were from across the whole research lifecycle: starting with experimental design and ensuring that only the minimum necessary data about people were collected and the right consent forms were in place, all the way through to data anonymisation and deciding which parts of data could be made publicly available, which could be shared only under managed access conditions, and which datasets should never be shared. Researchers also mentioned difficulties of working with sensitive data on a daily basis – the need to use secure servers, encryption to share the data and to ensure that only authorised partners have access to data. Some discussed the challenges of working with sensitive information in fieldwork conditions, especially if the data was politically contentious.

Interviewers wished to have more guidance about recommended workflows and policies, as well as practical support for finding the right storage solutions and means for sharing data with collaborators. In addition, better support was required at the experimental design stage: deciding on the minimal amount of personal information to be collected and drafting the right consent forms. Finally, many expressed the need for resources which could help them with data anonymisation and to manage the risks and benefits of making datasets publicly available.

All these concerns seemed particularly pressing in light of the new EC Data Protection Regulation, coming into force in May 2018. Some interviewees feared that they were unprepared for the new regulation and felt they had not received sufficient information about the impact of the new regulation on their research.

Challenges of working with big data

Challenges of working with big data were mainly related to infrastructure limitations. For researchers working with very large files simple aspects of data management become a difficulty. For example, due to ever-increasing storage requirements for big datasets, many researchers were unable to backup their data. This consequently led to occasional irretrievable data loss. Due to large volumes, big datasets were rarely archived, raising reproducibility concerns. In addition, many researchers had to use third-party computing services in order to effectively process their data. These often resulted in issues associated with very slow data transfer.

Working with big datasets, especially those which needed to be dynamically updated, also meant challenges for data publishing. Many data repositories providers did not offer options for big data sharing and had strict limitations on the maximum size of files. In addition, publishing of big datasets often meant substantial costs and it was often more cost-effective to simply re-generate the data when needed.

Software management issues

The third issue was with software management. In general, researchers did not have policies within their research groups on how software should be managed, annotated and shared. Often the very platforms for software management differed within the same research group. Some researchers felt they did not have sufficient time to annotate their software properly and that their colleagues, especially students, did not have the right skills to effectively work with tools which could help them manage their software better. One researcher mentioned missed commercialisation opportunity due to the fact that the software developed by the group was not understandable to anyone outside the group, including the third party interested in commercialisation.

Interviewees mentioned that due to lack of appropriate skills amongst researchers, there was a need for professional service support in data science. In addition, many suggested that training on the use of software management tools (such as Git, Subversion or Jupyter Notebooks) would be useful, in particular for students. Several wished to receive more information about methods for software archiving and for getting citation credit for code publishing.

Managing and sharing commercially confidential information

Working with commercially confidential data also proved problematic. First, there were tensions between sharing data for the sake of reproducibility, and the need to protect third party’s commercial interests. Interviewees mentioned that navigating between the different contractual clauses could be difficult. One researcher admitted that the inability to share research data obtained from commercial partners made it more difficult to publish papers due to the fact that some journals now required that research data supporting publications was made publicly available. Another researcher felt that collaborating with industry negatively affected the progress of his academic career because commercial clauses consequently meant fewer papers published. That researcher thought that when it came to academic promotions, commercial collaborations were valued less than the number of published articles.

Common data management problems

In addition to data management issues related to the type of research conducted, some common problems mentioned by almost all the interviewees were identified as well. These were related to improving daily data management practice, and to better data management procedures for students.

Daily data management practice

Problems related to daily data management practice concerned issues such as designing a data backup strategy and adhering to it, good file and folder naming, as well as issues with version control. These problems were shared also by researchers who based their research primarily on literature reviews. Overall, very few interviewees established workflows for good data management which would be followed by entire research groups. Most of the time it was down to individuals as to whether data was properly managed or not. Many researchers expressed the wish to improve their data management practice and to attend appropriate training.

Students’ data management practice

Almost all interviewees said that data management practice amongst students needed to be improved and that data management training should be part of the Graduate School’s curriculum. Training needs were related to both awareness of general principles, such as data backup, as well as knowledge of specific techniques and practices, such as data science skills and software management tools.

In addition, one interviewee expressed his concern about the fact that PhD students were not required to archive their research data at the time of graduation. This, he believed, led to research reproducibility concerns and potential reputational damages. The researcher suggested that all PhD students should be required to archive their research data before leaving TU Delft. This view was shared by researchers from the TPM Policy Analysis section (see ‘Follow up actions undertaken’).

An additional concern regarding students’ data was raised during the meeting with researchers from the Engineering Systems and Services department (see the section ‘Follow up actions undertaken’). When discussing research data ownership, researchers mentioned that according to TU Delft regulations, research data collected by Master students belonged to the students, and not to TU Delft. As a result, in several cases, Master students left TU Delft and took all their research data with them, without leaving a copy with their TU Delft supervisors. Researchers believed that this was a concerning and a serious issue from the research integrity and research continuity point of view. To avoid similar issues occurring in the future and to overcome the unfavourable regulation, supervisors now avoided offering participation in valuable, larger projects to Master students.

Views on Data Stewardship

With the exception of one researcher, who was in strong opposition to the Data Stewardship project, all other researchers welcomed the project and thought that there were data management needs at the faculty which could be addressed by the Data Steward.

The researcher with negative views on the Data Stewardship project thought that appointing a dedicated staff member to support researchers in data management was counterproductive. That researcher believed that a Data Steward would “develop guidelines (…) and hold meetings to raise awareness etc.” instead of solving “any actual operational issue”. He also suggested that a quantitative survey should be done to define the common practices and to decide whether any corrective steps needed to be taken. Interestingly, despite the negative attitude in general, the researcher agreed that there were issues with data management which needed to be solved and thought that training in data management for all PhD students was particularly needed.

Another researcher who welcomed the overall idea of the Data Stewardship project raised his concern about the number of resources allocated to the project and suggested that care was taken to ensure that the project would not result in new compliance expectations.

All remaining researchers were enthusiastic about the project and identified numerous data management issues with which they hoped that a Data Steward could help. These included:

  • Advice on data management workflows and best practices (such as data backup, version control, file and folder naming)
  • Advice on data sharing and citation
  • Advice on working with different types of confidential data (such as personal sensitive and commercially sensitive data)
  • Support in designing strategies for sustainable code management
  • Advice on code sharing and citation
  • Help with managing funders’ and publishers’ expectations
  • Training on data and software management, in particular for PhD students

Follow up actions undertaken

As a result of the initial interviews with researchers at TPM, several actions were undertaken, which might suggest that interviewed researchers were genuinely interested in data management issues. First, the Data Stewardship Coordinator was invited to give two presentations about the Data Stewardship project: to researchers from the Department of Engineering Systems and Services, and to researchers from the Policy Analysis section of the Multi-Actor Systems Department. Second, one of the interviewed researchers asked members of the Research Data Services team to deliver a workshop to his students about using data repositories. Third, one of the interviewees made a suggestion to connect with the TPM’s Graduate School to discuss the possibilities of rolling out data management training for PhD students.

In addition, the Data Stewardship Coordinator initiated discussions with other faculties to determine whether the issues around Master students’ research data ownership were also problematic at other faculties and whether the problem should be tackled centrally or not. The Furthermore, the Research Data Services team started liaising with the Human Research Ethics Committee to ensure alignment between research ethics and data management guidelines and policies.

Recommendations

This preliminary report identifies several areas where data management practices at TPM faculty could be improved with the help of a Data Steward. However, given the preliminary nature of these findings and the risk that they might not be representative of the whole faculty, it is recommended that the work of the newly appointed Data Steward is initially focused on a more in-depth investigation of data management needs. While qualitative interviews should be continued, a quantitative survey at the faculty is also needed, in agreement with the advice of the interviewee who was negative about the Data Stewardship project. Indeed, results of quantitative surveys conducted at the three faculties that already have Data Stewards proved to be valuable for measuring the scale of data management issues and deciding on priority actions. The thorough investigation of data management needs will allow the faculty to decide how to prioritise them. Finally, understanding the faculty-specific requirements will inform the development of a faculty data management policy.

In addition, given the fact that many researchers interviewed expressed uncertainties about the recommended procedures for working with personal sensitive data and that the new EC Data Protection Regulation becomes legally binding in May 2018, it is suggested that development of recommendations and training for working with personal sensitive data is also prioritised. This work should be done in collaboration with other teams at TU Delft: the Data Protection Officer, the Research Data Support team at the Library, the ICT team and the Human Research Ethics Committee.

Subsequent two years during which the Data Steward will be appointed at 1,0 FTE could be solely devoted to developing solutions for the remaining priority data management needs and also to evaluating the project. Comprehensive evaluation of the project should help the faculty make an informed decision on how to take the Data Stewardship forward after the end of 2020.


Acknowledgements

I would like to thank: all researchers who agreed to participate in my interviews for their time and valuable feedback; Martijn Blaauw for interviewee suggestions and introduction to the faculty; Alastair Dunning and Heather Andrews for comments on this report.


Citeable version

A citable version of this report is available on the Open Science Framework: https://osf.io/8ce5v

 

 

Connecting data and literature: the Scholix initiative – some notes and reflections

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Today I attended the “Connecting data and literature: the Scholix initiative” webinar organised by the Research Data Alliance (RDA). Adrian Burton, the Director of Services at the Australian National Data Service, gave an overview of the Scholix initiative. He is one of the authors of this paper that describes Scholix in detail.

Scholix brings together publishers, data centres and service providers (e.g. Crossref, DataCite) to address the problem of linking datasets and publications on a global scale. Instead of each player coming up with their own solution, the idea is that all the interested parts work together to solve a common problem.

As decribed on the Scholix website: “Scholix stands for “(a Framework for) Scholarly Link Exchange”. Scholix is not an organisation. It is the consensus achieved by a number of organisations — journal publishers, data centres, global service providers — to create an open global information ecosystem to collect and exchange links between research data and literature. The Scholix recommendations are the output of the joint Research Data Alliance/ICSU World Data System Data Publishing Services Working Group.”

4TU.ResearchData (or rather 3TU.DataCentrum) is listed as one of the organisations that has been involved in the working group and related projects. I am not sure what our involvement was or is, but I learned today that there are three ways in which we can participate.

1. Join the RDA-WDS Scholarly Link Exchange (Scholix) Working Group

It is possible to join to “simply stay in touch with the latest developments.” But we could also help expand and document the Scholix Guidelines.

2. Get the Scholix information using the open DLI API 

DLI stands for Data-Literature Interlinking Service.

This could be interesting for us to do because it would allows us to query for any known links in the literature to our datasets.  We could see which papers are citing our datasets and could even enrich our archive with links to those papers.

3. Feed our data-literature link information to an existing Scholix hub 

We may already be doing this through DataCite, which is one of the Scholix hubs. The metadata used for DOI is made available via DataCite and DLI APIs. This metadata needs to be enriched with links between the literature (related resources) and the data.

 

Something to note, which was discussed during the webinar’s Q&A, is that this is an initiative that is getting started, so it is not yet comprehensive. For example, not all data centres registering DOIs with DataCite  include links to the literature in their metadata. Additionally, not all data centres use DOIs. This is common is astronomy, for example. Burton mentioned that they are reaching out to these communities.

LIBER conference, July 5-7, 2017, in Patras (Greece)

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Every year LIBER (Ligue des Bibliotheques Europeennes de Recherche, Association of European Research Libraries) organizes a conference. The theme of the 47th annual conference was ‘Libraries Powering Sustainable Knowledge in the Digital Age’. The conference venue was the Conference and Cultural Centre of Patras, Greece.

Prior to the conference were meetings of working groups, general assembly, editorial groups and even more. This annual conference facilitates LIBER members to meet and gives the opportunity to network and get informed of the newest developments in the field.

Last February Essentials 4 Data Support submitted an abstract on how the FAIR guidelines are incorporated in the course. In Patras we heard that about 100 abstracts were submitted and only 30 were invited to present at the conference. We are very proud of that! We were invited to present in the Student-Centered Services session on Friday.

We explained how the course was developed and what the main starting points of the course are. Thereafter the FAIR guidelines were explained and following to that was examined on how these guidelines meet the course. The starting point was: what do we teach the students, so they can support the researcher to be FAIR? How can the data supporter help the researcher to be FAIR and how can they use the Essentials 4 Data Support content in this matter? We proved that many parts of the course are FAIRproof!

I visited a lot of sessions, which gave me an overview of the matters that are now playing a role in the library world. It is too much to list here what I have heard, some important insights:

  • European Commission is now rewriting the copyright law. There are some dangerous developments. It will be very tight and give lots of restrictions to use/link data/publications to another. LIBER is now very active in fighting this!
  • many presentations on collaboration, like COAR
  • Citizen Science from University College of London
  • Text and Data Mining, http://www.futuretdm.eu/ A project on TDM, which focuses on practical issues in TDM, like user guidelines, stakeholders guidelines on licenses, university policies, data management, legal issues. After that, we had a practical exercise, which showed me that TDM is not yet in Essentials 4 Data Support. We should integrate a paragraph on TDM in the course.
  • a very inspiring talk of CERN on Risk aversion narrows the future of libraries. CERN has a long tradition of Open Access and is a researcher-centred organisation.

Takeaways from the DCC Workshop on Supporting Open Research in H2020

1. It’s possible to be in the ORD pilot and not open any data
2. Helps to re-frame ORD Pilot as ‘Data Management Pilot’
3. Blogpost on opportunity costs to Open Science:   http://brunalab.org/blog/2014/09/04/the-opportunity-cost-of-my-openscience-was-35-hours-690/
4. Contact your H2020 national contact point for guidance and practical information
5. Interesting view on depositing in multiple locations: a single location may not provide all that is needed
6. Use re3data.org for finding a repository. Be aware of the differences regarding use of persistent identifiers (DOIs) or repository certification.
7. Using the H2020 DMP compliance rubric can be helpful for reviewers.
8. It’s not clear yet how the EC is evaluating the DMPs and the FAIR principles, it’s a learning process.
9. LCRDM is looking into the feasibility of DMPOnline for Dutch universities. Marjan Grootveld is member of this subgroup. Should RDS be part of this?
10.Monitoring functionality in DMPOnline is limited. Will be extended in future.

All of the presentation slides are now available via the DCC website: http://www.dcc.ac.uk/events/workshops/supporting-open-research-horizon-2020