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 (http://www.dcc.ac.uk/resources/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: http://datalib.edina.ac.uk/mantra/preservation.html (CC-by licensed)

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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: http://datalib.edina.ac.uk/mantra/preservation.html

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.

See MANTRA http://datalib.edina.ac.uk/mantra/preservation.html

iridium – evaluation of DataStage and DataBank research data management tools from DataFlow project

DataFlow project background:

DCC catalogue record: http://www.dcc.ac.uk/resources/external/datastage

Two tools

(a) DataStage, for researchers to manage their research data locally.

DataFlow lets researchers save their work to a DataStage file system that appears as a mapped drive on their computer, a lightweight system requiring them to install no special software on their computers.

More details: http://www.dataflow.ox.ac.uk/index.php/datastage/users/researchers

(b) DataBank, to preserve and publish valuable research.

DataStage is a secure personalized ‘local’ file management environment for use at the research group level, appearing as a mapped drive on the end-user’s computer.

More details:  http://www.dataflow.ox.ac.uk/index.php/databank 

Firstly, it’s great that the DataFlow team have released this system openly for re-use. Below are some of our findings.

From a local technical infrastructure assessment:

Ubuntu is not our standard Linux platform (which is Red Hat/CentOS). It would almost certainly be possible to port the Dataflow packages to CentOS (and feed this back to the main project) or use Ubuntu as an appliance (but this would mean that the systems used for this would not be managed by our standard configuration system). Either option comes with a reasonably significant cost.

The feeling that we got from installation (testing prior to 24 July 2012) is that the system is in the early stages of its lifecycle and our assessment is that Dataflow is not yet of sufficient maturity to deploy in production at Newcastle. It would be worth re-evaluating this decision at a later time, this would be prioritised against end users who have tried the system i.e. the more that they liked it, the more worthwhile putting resources into trying it again/working with the DataStage developers.

In terms of initial user testing (in early August 2012/and on ‘v0.3.1rc2’ Oxford installation), initial feedback was:

User testing – DataStage:

Users liked the feature specification of what it offered as a tool (desktop integration through mapped drives, web access aiding working from home, do not need a designated computer for their research work, setting of different access writes (private, public, and collaborative) and the ‘invite to share’ options. System interface is fine, basic yet functional and could be ‘skinned’ to institutional brand. Uploading documents/data files is straightforward.

My opinion was if an institution had no existing RDM systems, it would be a very useful ‘bootstrap’ system providing a simple functional system.

Seamless integration of a data file staging system/VRE with the user desktop (ideally through ‘drag & drop’/mapping over existing user networked drives) and through web access are key features that are top of an ‘average’ researchers wish list.

Making sure research data sets can be appended with an appropriate level of metadata in ‘data staging’ RDM tools (or perhaps later in lifecycle as practical?), so that metadata can flow through to an eventual data catalogue/or national repository is important RDM requirement. Thus, making sure that this function is provided to researchers is important to flag and DataStage/DataBank are a good approach to this.

I thought more data file re-use metadata capture would have been an option in DataStage (noting manifest/Zip package upload feature), pulling in automatically from individual data file itself (that’s probably me being simplistic on technical aspects?) ahead of the DataBank stage?

We noted that not all users are comfortable or had success in Windows drive mapping (network path errors), so some end user support would be needed. Users have high expectations on usability – ‘as easy as DropBox’.

Error messages while testing – access forbidden, 505/405, ‘submit as data package’ – where an entered/saved password was looping? (more helpful customisation of error messages, such as ‘this problem normally occurs because of x, y or z – wrong password, wrong file path, etc.’. (rather than ‘Error 505’/’Error 404’ would be helpful.

User testing – DataBank

 Liked:

– Simple, clean functional interface – again could be ‘skinned’ to instituitional brand.

– Current search/’on-off’ filters was good

– Assigning a DOI/RDF were useful RDM specific features.

– Licensing/embargo fields

– Simple admin interface

– CSV/JSON exports are useful

– Rest API was documented

 Suggestions:

– Clarifying, who was intended user audience for DataBank? Researcher or archivist?

– Terminology – not understood by user testers – ‘Silo’, ‘Mediator’, ‘Aggregate’ – obviously this could be changed easy.

– RDF and click through access to XML schema was confusing for our testers (they were not archivist, librarians, metadata experts – who would probably appreciate this function – i.e. package/manifest upload/explore)

– A basic tagging interface/fields to populate the RDF/XML for none specialists would be more friendly

– Again frequent error messages (404 not found/ 500 Internal Server Error, ‘Add manifest’ gives 505)

Documentation for DataStage/DataFlow researcher end users:

User documentation for researchers seemed a little sparse (I think the project/developers noted it is a work in progress i.e. https://github.com/dataflow/RDFDatabank/wiki). More end user documentation would facilitate wider take up. To note, technical installation documentation was more detailed with screen shares, etc.

We look forward to further DataFlow project developments.

DataFlow user forum is at: https://groups.google.com/forum/?fromgroups=#!forum/dataflow-users

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: http://datalib.edina.ac.uk/mantra/dataprotection.html

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.

Definitions

“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

Purpose:

  • 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

http://www.northumbria.ac.uk/static/5007/ceispdf/dmpguide.pdf

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

Benefits:

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.

– MANTRA

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.

Policy

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

Edinburgh, point 3: http://www.ed.ac.uk/schools-departments/information-services/about/policies-and-regulations/research-data-policy

Lincoln (draft), point 4: https://github.com/lncd/RDM-Policy/blob/master/Lincoln%20RDM%20Policy.md

Warwick , point 7: http://www2.warwick.ac.uk/services/rss/researchgovernance_ethics/research_code_of_practice/datacollection_retention/reseatch_data_mgt_policy

Funder polices on DMP:

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

ESRC: http://www.esds.ac.uk/create/esrc/dataman/and http://ukdaresearchdatamanagement.blogspot.co.uk/

NERC: http://www.nerc.ac.uk/research/sites/data/dmp.asp?cookieConsent=A

MRC: http://www.mrc.ac.uk/Ourresearch/Ethicsresearchguidance/datasharing/DMPs/index.htm

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:

http://www.admin.ox.ac.uk/rdm/dmp/plans/

http://www.ed.ac.uk/schools-departments/information-services/services/research-support/data-library/research-data-mgmt/data-mgmt/why-research-data-policy

http://www.gla.ac.uk/services/datamanagement/creatingyourdata/dataplanning/

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]. http://datalib.edina.ac.uk/mantra

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 …]

http://datamanagementplanning.wordpress.com/2012/03/27/dmp-questions-comparisons-and-conclusions/

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.

DCC/DMPOnline:

  • 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:

http://www.dcc.ac.uk/webfm_send/879
http://www.dcc.ac.uk/webfm_send/881
http://www.screenr.com/Syo

DMPOnline advocated by MRC (ref 14), etc.

Institutional customisation or tailoring for local use available.
GitHub code: https://github.com/DigitalCurationCentre/DMPOnline (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.

DATUM: http://www.northumbria.ac.uk/sd/academic/ceis/re/isrc/themes/rmarea/datum/action/outputs/?view=Standard

Shotton 20 Questions http://datamanagementplanning.wordpress.com/2012/03/07/twenty-questions-for-research-data-management/ [CC:BY 3.0]

Bath 360 (postgrad-specific): http://blogs.bath.ac.uk/research360/2012/03/postgraduate-dmp-template-first-draft/

DMPTPsych(York) (postgrad?): http://www.dmtpsych.york.ac.uk/docs/pdf/dmpt_guidance.pdf

MRC: http://www.mrc.ac.uk/Utilities/Documentrecord/index.htm?d=MRC008617

Wellcome Trust: http://www.wellcome.ac.uk/About-us/Policy/Spotlight-issues/Data-sharing/Guidance-for-researchers/index.htm

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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