iridium – postgraduate evaluation of MANTRA RDM training (2) – Sharing,preservation and licensing unit

From Jack:

The final module of the MANTRA online research data management training is entitled Sharing, Preservation and Rights. The second of two new modules (the last one being Data Protection, Rights and Access) focus on the back end of the research lifecycle.  In this instance, when working on a project the main focus for the researcher will be gathering the data and achieving outputs, there may be little focus initially beyond this. Once work has been completed preservation and sharing may be one of great importance to ensure the greatest possible impact; if research is intended to be cumulative and part of a community then making research data available should be of priority. However, for some there may be restrictions to the extent they make data available and limits to how others are able to use it. These are also covered in this module.

The module outlines the benefits of sharing research data. There are benefits for the researcher their self (scientific integrity, funder requirements and preservation for one’s own future use) and the research community more widely (teaching, impact, collaboration and public record).  Whilst for the most part we may take on good faith the validity of outputs published in journals and other academic papers the module outlines some high profile instances of how some results have been fabricated by researchers. They argue then that making the data available upon outputs are based ensures legitimacy of research and conduct of openness.

Whilst outlining the importance of preserving data for future reuse the difficulties and potential problems of maintaining it over time are highlighted. Rapid changes in file formats and obsolete storage methods are cited as potential future issues for access. Though this may pose an undue hindrance one’s research activities I see it to emphasise the importance or proper and correctly managed data preservation. Reasons are given for placing data into repositories with emphasis. A further emphasis of the module is that whilst for the most part it focuses on the creator of data, also recognises the position of the secondary data user and provides help for them.

For further guidance on data preservation and best practice the recommended reading  of DCC Curation Reference Manual ( provides in-depth curation techniques split into several chapters (some still in development).

This final module of the MANTRA training completes a comprehensive yet straightforward beginner’s guide to research data management. Having reviewed the content of several online data management guides recently the University of Edinburgh learning units are the ones I would be recommending as an introduction for fellow postgraduate researchers and equally anybody with related interest in research data management.

MANTRA available from: (CC-by licensed)

iridium – postgraduate student evaluation of MANTRA RDM training – Sharing, Preservation and Licensing unit

From Blanca:

Probably I have blogged before about how useful MANTRA training units are and how much I enjoy then.  This month MANTRA released its new unit called “Sharing, preservation & licensing” which is no different from the other units in terms of how effectively it manages to get the message across.

More than that, I believe this to be a dramatic unit. Leaving aside specific barriers for sharing data such as not sharing because of commercial purposes, keeping subjects confidentiality and data ownership (all these barriers may or may not have solutions), there are other reasons which are linked to how the data has been managed during its lifecycle. This unit provides some dramatic examples of why you should share your data and how you need to treat your data from the moment you first get it.

One of the examples this is unit provides is an animated cartoon, which I found hilariously frustrating (putting myself in the shoes of a researcher who wants to re-use some data and finds herself at the mercy of the owner of the data). Problems such as backing up (which are the perils of using physical devices for storage on the short and long term?), appropriate formats (what do you do if the software you used for manipulating your data becomes unsupported?  What does this mean for future users?), and metadata recording (Do you want other researchers to be depending on you to interpret your data? Are you actually going to be available during the whole life cycle of the data?).

This simple animated cartoon made me reflect on the fact that besides barriers such as the ones mentioned above, some barriers are created by the very researcher and having an effective research data management plan can help you take the decision of sharing or not your data, and how you want to share it. In any case, how your data has been managed should not be a barrier for sharing it.

How the data is managed is effectively important.  This unit presents impressive real cases of data fabrication and falsification, these cases are truly unbelievable and I can just think, why would somebody put his/her reputation on the line in such way? The consequences are simply terrifying.

The unit also mentions the benefits of sharing your data, which may bring various rewards such as scientific integrity, increased impact in terms of primary and secondary publications, it may allow collaboration between data users and data creators, it may be the source of some other innovative unrelated research based on the same data,…, there are indeed various benefits and perhaps more importantly the researcher maximises transparency and accountability of his/her research while at the same time he/she complies with funders’ requirements.

Making your data shareable is not an easy task; there are several things to take into account, specially the need to define how you want your data to be re-used? This unit introduces Open data licensing briefly, a topic which I would possible like to see more developed in another unit.

In general, this is a really useful unit which I genuinely enjoyed reading.

MANTRA unit available from:

iridium – ‘core’ institutional research data management plan development

Research data management plan authoring is a key part of our draft institutional RDM policy and good practice. Most RCUK funders (apart from EPSRC, currently) require a formal RDMP. NERC now require a pre- and post award RDMP.  These types of templates are available in the DMP Online system.

We wanted to write an institutional RDMP within iridium for research projects that do not have a Funder mandated template (a fair proportion, ~66% of research projects?).  This was to be as easy to complete by end user as possible (i.e. low time burden for researchers) and to be used across Faculties (disciplines) if possible.

What are the ‘essential’ RDMP questions (a ‘core plan’, Donnelley, 2012)? We reviewed several RCUK RDMP templates from different disciplines for similarities, but also distinctive and pertinent questions. Also we had project specific criteria together with good practice from DATUM RDMP template with strong actions and review (‘active plan‘) emphasis.

It was decided to pursue a post-award RDMP template approach for projects without a mandate plan, as less it was burden to write ‘core’ plans for projects that were not awarded in the end and maximise uptake. We noted need for key aspects of RDMP planning to be brought forward in pre-award processes and RIM systems (such as ethics which is already strongly monitored institutionally, but also including RDM costs/atypical data volume size (plus extended curation duration?)). For example, recommending for questions/planning to be a ‘flag’/check-box in  a ‘minimal’ (‘ultra-minimal’?) RDMP check-list in existing RIM systems/pre-award Faculty peer review process.

—- —- —

iridium institutional template post-award RDMP v5 [DRAFT]

This template is for projects that DO NOT have a Funder mandated research data management (RDM) plan. Funding body requirements relating to the creation of a research data management plan are available from …

{ Our institution RIM system MyProjects contains research project administration data (see below). In the long term it would be useful to have this imported and auto-populated into a RDMP direct from RIM system. This aligns to the ‘header’ information in the DMP Online template }

Proposal Type:
Proposal Title:
Proposal Short Title:
… ….  … …. etc.

Contact details of named individuals (Role/Name/Unit):

MyProjects Owner:

Date of creation of this plan:
Plan version/supersedes:

 Aims and purpose of plan: … …

[SCOPE NOTES: Guidance on completion of this plan is available from …. ‘DCC 1.x references link to additional guidance provide by the Digital Curation Centre]

1 Introduction and Context
1.1 Introduction and Context
[DCC 1.2]: Short description of the project’s fundamental aims and purpose
[DCC 1.3.2(re-worded)]: Describe how you have considered the Newcastle University RDM institutional policy and any Faculty/research group guidelines, together with any other policy-related dependencies:
[From RC template] Document the RDM advice you have sought on planning your proposed project, including any consultation with projects using similar methods.
[DCC 10.2]: Glossary of terms
2 Data Types, Formats, Standards and Capture Methods
2.1 Data Types, Formats, Standards and Capture Methods
[SCOPE NOTE – for further guidance on ‘data’ definitions and the capture of non-digital data, please see XYZ]
[DCC 2.1]: Give a short overview description of the data being generated or reused in this research
[SCOPE NOTE – for further guidance on ‘open’ file formats, please see …]
[DCC 2.3.3(re-worded)]: Which open file formats will you use, and why?
DCC 2.3.4: What criteria and/or procedures will you use for Quality Assurance/Management?
[SCOPE NOTE – for further guidance on ‘Quality Assurance/Management, please see …]
DCC 2.5.1: Are the datasets which you will be capturing/creating self-explanatory, or understandable in isolation?
[DCC 2.5.2]: If you answered No to [DCC 2.5.1], what contextual details are needed to make the data you capture or collect meaningful?
[DCC 2.5.3]: How will you create or capture these metadata?
[DCC 2.5.4]: What form will the metadata take?
3A Ethics
3A Ethics
HAVE YOU COMPLETED A NEWCASTLE UNIVERSITY ETHICS APPLICATION?[YES] [NO] [NOT APPLICABLE] REFERENCE NUMBER:{ We already have strong RIM/institutional check points for ethics, we don’t want to duplicate information gathering, thus this section is brief. }
3B Intellectual Property
3B Intellectual Property
[SCOPE NOTE – for further guidance Intellectual Property/licensing, please see …]
[DCC 3.2.1]: Will the dataset(s) be covered by copyright or the Database Right? If so give details in DCC 3.2.2, below.
[DCC 3.2.2]: If you answered Yes to [DCC 3.2.1], Who owns the copyright and other Intellectual Property?
[DCC 3.2.3]: If you answered Yes to [DCC 3.2.1], How will the dataset be licensed?
4 Access, Data Sharing and Re-Use
4.1 Access, Data Sharing and Re-Use
[From Research Council template] Are there issues of consent, confidentiality (including commercial), anonymisation and other ethical considerations?
[From RC templates] What are the main risks to data security/ confidentiality?
[DCC 4.2.3]: Are there any embargo periods for political/commercial/patent reasons?
[DCC 4.2.4]: If you answered Yes to DCC 4.2.3, Please give details.
[DCC 4.3.1]: Which groups or organisations are likely to be interested in the data that you will create/capture?
[DCC 4.3.2]: How do you anticipate your new data being reused?
[DCC 5.3.2]: How will you implement permissions, restrictions and/or embargoes?
[DCC 4.1.1]: Are you under obligation or do you have plans to share all or part of the data you create/capture?
[DCC 4.1.3]: If you answered Yes to DCC 4.1.1, How will you make the data available?
[DCC 4.1.4]: If you answered Yes to DCC 4.1.1, When will you make the data available?
[DCC 4.1.5]: If you answered Yes to DCC 4.1.1, What is the process for gaining access to the data?
[From RC template] What will be the responsibilities of data sets users (for example as detailed in a ‘Statement of Agreement’)?
[SCOPE NOTE – for further guidance responsibilities of data sets users and ‘Statement of Agreement’ wording, please see ….]
[DCC 4.1.6]: Will access be chargeable?
5 Short-Term Storage and Data Management
5.1 Short-Term Storage and Data Management
[DCC 5.1.1]: Where (physically) will you store the data during the project’s lifetime?
[DCC 5.1.2]: What media will you use for primary storage during the project’s lifetime?
[From RC template] What is the anticipated (‘ballpark’ figure) of data volume that will be collected? Will this vary after processing?
[DCC 5.2.1]: How will you back-up the data during the project’s lifetime?
[DCC 5.2.2]: How regularly will back-ups be made?
Has the back-up process been tested and successfully validate?
Who is responsible for back-up process?
[DCC 5.3.1]: How will you manage access restrictions and data security during the project’s lifetime?
6 Deposit and Long-Term Preservation
6.1 Deposit and Long-Term Preservation
[DCC 6.1]: What is the long-term strategy for maintaining, curating and archiving the data?
[SCOPE NOTE – for further guidance curation and archiving of data sets, please see …]
[DCC 6.2.1]: Will or should data be kept beyond the life of the project?
What is your deletion policy? Will data sets be deleted? When, by whom and how will they be identified?
[DCC 6.2.2]: If you answered Yes to DCC 6.2.1, How long will or should data be kept beyond the life of the project?
[DCC 6.2.3]: If you answered Yes to DCC 6.2.1, What data centre/ repository/ archive have you identified as the long-term place of deposit?
What is the anticipated (‘ballpark’ figure) of data volume that will be archived?
[DCC 6.2.7]: Will transformations be necessary to prepare data for preservation and/or data sharing?
[SCOPE NOTE – for further guidance data set transformations, please see …]
[DCC 6.2.8]: If you answered Yes to DCC 6.2.7, what transformations will be necessary to prepare data for preservation / future re-use?
[DCC 6.3.3]: Will you include links to published materials and/or outcomes?
[SCOPE NOTE – for further guidance on include links to published materials and/or outcomes, including the Research Data Catalogue, please see …]
[DCC 6.3.4]: If you answered Yes to [DCC 6.3.3], please give details.
[DCC 6.3.5]: How will you address the issue of persistent citation?]
[SCOPE NOTE – for further guidance persistent citation, please see …]
[DCC 6.4.1]: Who will have responsibility over time for decisions about the data once the original personnel have gone?
7 Resourcing
7.1 Resourcing
[DCC 7.1]: Outline the staff/organisational roles and responsibilities for research data management
[DCC 7.2]: How will data management activities be funded during the project’s lifetime?
[DCC 7.3]: How will longer-term data management activities be funded after the project ends?
Describe how funding for RDM has been specifically been costed into funding application (where appropriate).
[SCOPE NOTE – for further guidance on costings for RDM, please see …]
8 Adherence and Review
8.1 Adherence and Review
[DCC 8.1.1]: How will adherence to this data management plan be checked or demonstrated?
[DCC 8.1.2]: Who will check this adherence?
[DCC 8.2.1]: When will this data management plan be reviewed?
[SCOPE NOTE – for further guidance on review points for for RDM plans, please see …]
[DCC 8.2.2]: Who will carry out reviews?
9 Actions Required
9.1 Actions Required
Please list actions and timelines against named individuals identified as a result of completing this RDM plan.
For example please indicate additional hardware, software and relevant technical expertise, support and training that is likely to be needed and how it will be acquired.
For any deferred or unanswered questions outline how you plan to seek advice.
Action: / Responsibility: / Review Date:-: / -: / -:-: / -: / -:
Signature Date
Print name Role/Institution
Signature Date
Print name Role/Institution
Signature Date
Print name Role/Institution



© Northumbria University School of Computing, Engineering & Information Sciences, 2012 cc: by-nc-sa DATUM DMP template

© Newcastle University, iridium project, 2012 cc: by-nc-sa

— — — —-

We are currently evaluating end user acceptance of this draft plan, time required to complete and support required to assist with writing.

iridium – postgrad evaluation of MANTRA RDM training – Sharing, Preservation and Licensing unit

From Amy.

The new unit from the MANTRA Data Management Training programme focuses on Sharing, Preservation and Licensing, which follows on well from the previous unit on Data Protection, Rights and Access. The module took about an hour to get through, making notes as I went, and I found it a useful introduction to a topic that I know fairly little about.

The unit discusses the reasons for and against sharing research data and the benefits that can be enjoyed by researchers who do decide to share data. Other guides that I have read on this topic seem to offer a more one-sided view of the debate as they are trying to encourage researchers to share data. While this is understandable, and ultimately the aim of increasing awareness will be that more researchers share more data, it can sometimes make the source appear slightly less credible. For this reason, I was really pleased that this unit included a section on the barriers to sharing research data. For the issue of confidentiality it offered the solution of anonymisation, but it also recognised that financial and ownership issues are sometimes capable of preventing sharing altogether. By recognising that not all research data can be shared, its advice on data that can be shared became more realistic.

The unit provides extensive benefits of sharing research data including scientific integrity, meeting funder requirements, increasing research impact and preserving data for personal future use. This is all underlined by the examples given of real-life cases where the repercussions of not properly preserving/sharing data have caused problems. The unit gives an example of a postgraduate research student whose project was spoiled because they could not access the relevant data. While this is useful, the point is underlined far more seriously by the examples given of researchers who were accused of falsifying data and not having the records to back up their research. One of the benefits given that I could identify with the most was the impact that sharing data can have on teaching. The unit suggests that using research data in teaching is a good way to teach students how to collect and analyse data. Also, in my experience as a student, some of the most interesting teaching sessions I have had were those when lecturers talked about their current or recent projects and showed us data that they had collected for these. It made teaching much more closely related to research and made us, as students, feel more involved with what was going on in the University than when you feel like you’re just being taught from a set syllabus.

The unit also covers issues on licensing and introduces Open Data Commons as a source of guidance and licences that are conformant with the principles set out in the Open Knowledge Foundation’s definition of open knowledge. The unit definitely succeeded in its aims as the information provided, combined with the activities which outlined key terms and definitions, were useful to me as a postgraduate student in consideration of my own research, but also in consideration of data that I am using that belongs to someone else.


iridium – postgrad evaluation of MANTRA RDM training – Data protection, rights and access unit

From Blanca.

Today I had the opportunity to explore the “Data protection, rights and access” unit of MANTRA. This is a quite new unit which offers plenty of relevant and essential concepts.

Firstly, it discusses the concept of ethics and how ethical requirements need to be taken into consideration with planning a RDM. Ethics, is a serious issue, specially when it involves people. Most of the examples and RDM strategies discussed over the unit concern data about people.

Essential concepts this unit focusses on are privacy, consent and confidentiality. The first step towards an ethical research would be to obtain consent from your research subjects (This way people are given the right to take decisions on the use of their personal data). Next, the researcher needs to make sure he/she will guarantee the protection of subject’s privacy, to do so, the researcher will need to outline confidentiality strategies (this is an agreement between the researcher and the research subjects on how his/her identifiable private information will be handled, managed and disseminated).

Besides ethics, the unit makes relevance on how important are legal considerations for RDM. The 1998 Data Protection Acts regulates personal data handling. Failure to comply with these regulations can incur in extremely severe consequences for organisations and individuals, the unit provides a series of crude examples about it. Even huge institutions such as the NHS are not exempt!

Next, the unit provides with some very useful anonymisation techniques (masking data so that no person identifiers are present), a document with some examples is provided.

Finally, the unit discusses what a are “Intellectual Property Rights” and “Freedom of Information.”

Intellectual property (IP) is all about the creation of the mind. Laws try to make sure owners of these creations are granted with certain exclusive rights when it comes to commercialisation of their creation. There are 2 categories: Industrial property (includes patents, trademarks…) and Copyrights (for literary and artistic works). On the other hand, Freedom of Information (FoI) is about providing the public the right to access information from public bodies.

In general, I found this unit to be quite vast in content. The approach it takes for the explanation of the concepts is really good and concise. However, it didn’t have as many interactive parts as previous units. The unit also provides some other recommended resources.”

MANTRA Data protection, rights and access unit:

iridium – workshop talk and dissemination at JISC Progress Meeting, Nottingham

The iridium project presented at the JISC MRD02 Progress Meeting in Nottingham. The two day schedule from the event is here, together with the Programme introductory/close slides.

Workshop topics were:

  • Institutional RDM policies; developing an institutional strategy and an ‘EPSRC’ roadmap
  • Managing active data: storage, access, academic dropbox services
  • Data management planning: developing good practice and providing effective support
  • Data repositories and storage: options for repository service solutions
  • Training & guidance
  • Triage and handover: what to keep and where to entrust it? Selection and appraisal, deposit and handover
  • Business case: covering roles, responsibility, costing, sustainability, advocacy etc
  • Data catalogues: metadata profiles, identifiers

Individual projects were encouraged to contextualise presentations around the following themes:

[1] “what has worked/is working”
[2] “what lessons you have learned and how generalisable these may be”
[3] “what challenges remain”
[4] “how such challenges may be approached and what your institution/project intends to do”
[5] “what DCC / MRD activity you think may help make the challenge more tractable”

iridium ‘support’ presentation within ‘Training & Guidance’ session:

iridium presentation thumbnail

iridium presentation

iridium_JISC_Progress_25_10_2012_v4_web_sml_LW [.pdf]

We also presented two posters, one on the research data catalogue proof-of-concept and the second on our thematic analysis requirements gathering.

Other project presentations from the Programme are available here.

iridium – early findings on research data management planning (approaches, tools and writing plans)

Below is brief summary of some resources, findings and discussions on research data management plans (DMP) that have been noted along the way since the project start up. This has been collected from several activities and events such as iridium support team use of the MANTRA RDM online training package, project RDM tools assessment, together with attendance at the JISC Meeting (Disciplinary) Challenges in Research Data Management Planning Workshop and the DCC Roadshow North East.


“Research data management refers to all aspects of creating, housing, delivering, maintaining, and archiving and preserving data. It is one of the essential areas of responsible conduct of research.” – MANTRA

“Plans typically state what data will be created and how, and outline the plans for sharing and preservation, noting what is appropriate given the nature of the data and any restrictions that may need to be.” – DCC


  • to assist in planning the research data management (RDM) aspects of your research
  • to assist you in making RDM decisions
  • to identify the RDM actions required
  • to highlight areas that need further thought
  • to provide a record of decisions made and actions taken

Attribution: Northumbria University School of Computing, Engineering & Information Sciences, 2012. CC-BY-SA


The benefits of managing your data include:

  • Meeting funding body grant requirements.
  • Ensuring research integrity and reproducibility.
  • Increasing your research efficiency.
  • Ensuring research data and records are accurate, complete, authentic and reliable.
  • Saving time and resources in the long run.
  • Enhancing data security and minimising the risk of data loss.
  • Preventing duplication of effort by enabling others to use your data.
  • Complying with practices conducted in industry and commerce.


Local DMP practice/DAF survey results

From our survey (128 projects), findings were were 23% of projects have a formal research data management plan for institutional as a whole, with further 33% having a partial RDM plan (by Faculty split suggested a slightly higher proportion in line with a likely higher proportion of Research Council awards). I expect this is similar across sector? Open Exeter project reported ‘few researchers have experience of completing a data management plan ‘ from their DAF survey.


Institutional policies on DMP, some examples (see also DCC website):

Edinburgh, point 3:

Lincoln (draft), point 4:

Warwick , point 7:

Funder polices on DMP:

Various requirements at application and funded project stages. For exampe:




See also DCC mappings across 6 funder policies to generic DCC Checklist (July 2011)

Training and guidance on research data management planning

External institutional support pages guidance:

MANTRA training package covers DMP.

Advocacy for why DMP is important:

“… the role of data management for a new researcher as being one of those essential skills that you really ought to get at the same time as you learn how to handle your references, as you understand methodology, as you get to grips with the theory that is going to set the frame by which you do your research. And it sits alongside those and it’s equal to them …” – Professor Jeff Haywood, Vice Principal, CIO & Librarian, University of Edinburgh talks about the role of of data management for  PhD students and early career researchers

“… it actually gives you a really good framework and for my postgrads now I am pointing them towards that and saying hey,  you know, take a look at that because it will help you to think about how you’re going to gather your data and how you are going to look after it from  the beginning to the end of the project. It gives you a framework to deal with it rather than realizing too late that you  haven’t done some things that you should have done and  therefore you’ve made your life and perhaps actually cause problems for you with the use of data subsequently or sharing your data is made that more difficult.” – Professor Jeff Haywood, Vice Principal, CIO & Librarian, University of Edinburgh talks about the role of data management for  PhD students and early career researchers

Attribution: EDINA and Data Library, University of Edinburgh. Research Data MANTRA [online course].

Also, available as a video.

DCC resources:

Discussion on DMP approaches and reviewing the styles of questions,  format, how ‘active’ in approach

Oxford DMPOnline Project wrote on and discussed the detail of research data management plans – very interesting reading. They discussed the concept of ‘plan questions’, ‘project questions’, ‘data questions’ and common issues they found when reviewing DMPs – such as compound questions, duplicates,  and individual plan unique questions [link to table XLS]. On DMP style they noted – discursive versus concise, ‘metadata’ versus ‘data’ questions, option to add possible responses and overall gaps in DMP scope and plans that lead to quantified expected data sizes/acquisition rates (resulting in actionable identification of requirements that can be report to a central service provider, as a result of the plan).

The conclusions should be read:

“.. difficult work, since there are many possible questions ..”

“.. avoid asking ambiguous questions ..”

“.. avoid asking for the same or similar information multiple times..”

“..unique questions not covered by the DMPonline ..”

“.. all of the available question sets have drawbacks ..”

“.. in terms of comprehensiveness, the best may be the enemy of the good enough..”

“.. devise and standardize the best possible set of questions for different constituencies of user ..”

[and more …]

DMPs for different audiences – from targeted plans to template author background ‘bias’/priorities

Life-cycle stage specific: from (conception?), pre-award, post-award, to post-project.

Postgrad research project versus PI bidding for new funding.

Curator/archiver versus researcher orientated.

DMP online authoring tools or offline Word/PDF templates

Online tool has many useful advanced staging, customisation & collaboration features.

Online systems:

DMPOnline – pre-award, post-award, post-project, templates for Research Council/major funders, default templates, post-grad, etc. Features – add additional questions from DCC checklist, save, share/collaborate, copy, export to Office files, etc.


  • Introduction and Context
  • Data Types, Formats, Standards and Capture Methods
  • Ethics and Intellectual Property
  • Access, Data Sharing and Re-Use
  • Short-Term Storage and Data Management
  • Deposit and Long-Term Preservation
  • Resourcing
  • Adherence and Review

DCC website/DMPOnline

DMPOnline tools training:

DMPOnline advocated by MRC (ref 14), etc.

Institutional customisation or tailoring for local use available.
GitHub code: (Ruby on Rails/MySQL)

Offline templates (Word/PDF format):

Some users do not like online systems, are overwhelmed by array of features/customisation options and just want a ready to go familiar Office document to type into.


Shotton 20 Questions [CC:BY 3.0]

Bath 360 (postgrad-specific):

DMPTPsych(York) (postgrad?):


Wellcome Trust:

Wider DMP discussions

  • extent of pre-population of template with default institutional information to aid researcher versus reducing actual thinking/planning for RDM
  • experience in information banks of DMPs, shared pool, ‘successful’ DMP
  • DMP online tools – metadata transfer protocols between systems/integration in existing RIM systems
  • DMP training needs, online/in person, embedding with training – ‘dual service engagement‘ (i.e. Monash), see DCC ‘support researchers with DMP
  • DMP embedding in existing institutional processes, internal peer review, funder review, DMP fields (i.e. data size) resulting a RIM system flag or automatic central service trigger
  • time requirements for writing a plan – minimal plans/resources required to support/advise/review DMP
  • DMP auditing – institutional, Funding Council, etc.
  • wider use as knowledge/information base for forward institutional planning, storing DMP (or parts of ) with an  archived data set, re-use to support metadata population

JISC Research Data Management Planning Projects

Strand B: DATUM, DMPSPsych, History DMP, etc.

Next steps for iridium project:

Reporting on initial user testing of DMPOnline and other templates, authoring a local DMP template and hosting options.

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