(a not entirely serious output of International Data Week 2016*, see also Should there be an Oath for Scientists and Engineers? and A Hippocratic Oath for life scientists (based on the Modern Hippocratic Oath, written in 1964 by Louis Lasagna, Academic Dean of the School of Medicine at Tufts University)
I swear to fulfill, to the best of my ability and judgment, this covenant:...
I will respect the hard-won research gains of those researchers in whose steps I walk, and gladly share such knowledge as is mine with those who are to follow.
I will make no assertion without evidence.
I will apply, for the benefit of all, all measures which are required to preserve and make usable research data, avoiding those twin traps of data hoarding and unhelpful data description.
I will remember that there is art and craft to data management as well as science, and that humans as well as machines need to be able to interpret and use the data, now and in the future.
I will welcome opportunities to say "I know not," never failing to call in my colleagues when the skills of another are needed to assist in data sharing, dissemination or management.
I will respect the privacy of those who provide personal or sensitive data to me, for their problems are not disclosed to me that the world may know. Most especially must I tread with care in matters of life and death, not only of humanity, but also of the global ecosystem. Above all, I must not play at God (even if I create a data management system or infrastructure that allows me to do so).
I will remember that I do not manage a stream of bytes, but a whole story of data collection, analysis and interpretation. My responsibility includes these related research objects (such as software, workflows, project plans, etc.), if I am to care adequately for the data and the results and conclusions resulting from it.
I will prepare for data management in advance whenever I can, simply because it will make my life, and others’, easier.
I will remember that I remain a member of society, with special obligations to all my fellow human beings, as well as the research record.
If I do not violate this oath, may I enjoy life and art, respected while I live and remembered with affection thereafter. May I always act so as to preserve the finest traditions of my calling and may I long experience the joy of research and help make the world a better place.
* yes, it's been a while since I wrote this, or have blogged, for that matter! But I've decided to pick this blog up again and figure that this bit of fluff is a good place to start.
Friday, 20 April 2018
Monday, 22 May 2017
- "Should we cite preprints?" - Green Tea and Velociraptors
- Agrees with my "cite what you use" rule of thumb
- "Preprints won’t just publish themselves: Why we need centralized services for preprints" - Collaborative Knowledge Foundation
- Neylon C, Pattinson D, Bilder G and Lin J. On the origin of nonequivalent states: How we can talk about preprints [version 1; referees: 1 approved]. F1000Research 2017, 6:608 (doi: 10.12688/f1000research.11408.1)
- Really interesting article that proposes a model that distinguishes the characteristics of the object, its “state” (the external, objectively determinable, characteristics), from the subjective “standing” (the position, status, or reputation) granted to it by different communities.
- Baldwin, Melinda, "In referees we trust?", Physics Today 70, 2, 44 (2017); doi: http://dx.doi.org/10.1063/PT.3.3463
- Fascinating article about the history of academic journal peer review, and the societal pressures that have made peer review the "gold standard" of academic credibility, with some discussion of how it's creaking at the seams.
- "Does It Matter Whose Name Appears After the © When Using Creative Commons?" - Todd Carpenter (The Scholarly Kitchen)
- "Citation Performance Indicators — A Very Short Introduction" - Phil Davis (The Scholarly Kitchen)
- "Satire in Scholarly Publishing" - COPE
- A satirical article made it into a serious review article - COPE (Committee on Publication Ethics) give their judgement on the case. TL;DR - always fully read the papers you're citing!
- "Journal accepts bogus paper requesting removal from mailing list" - The Guardian
- A tale of a predatory open access journal accepting a paper (with lovely diagrams) which just repeated the words: "Get me off your ******* mailing list"
Tuesday, 2 May 2017
Two little aliens stowed away for this trip, and were very pleased that the venue was all space themed.
RDA Plenary 9 was held in Barcelona, in April 2017. I made my usual bunch of scrappy notes, which I've tidied up and added links and commentary (in italics) for those who are interested.
- Ideas spreadsheet for suggestions on how to coordinate and communicate across RDA groups
- has to be an actionable suggestion - no moaning!
- closed for suggestions now, but you can see what was proposed
WG RDA/WDS Scholarly Link Exchange
Interesting stuff and presenting things that are approaching maturity and could be useful and usable systems in the future.
- All about linking research objects
- Scholix information model:
- mandatory: for link information package: publication date, link publisher. For source and target object: identifier and object type
- other optional metadata includes link provider, relationship type, license URL of link information package (for link information package), title, creator, publication date, publisher (for source/target objects)
- DLI service available as a prototype
- automatically picks up stuff from DataCite
- Scopus using the DLI system to find links to data
- information available for preview users
- wishlist for Scopus includes: clearer information on where data is stored, ability to retrieve richer metadata...
- Scopus planning on doing data citation counts in the future
- Scholix plans on collecting every link possible, not just citations
- information about datasets in the text of papers, needs to be mined out and extracted - some publishers doing this
- community focus groups within the WG - working on documents to answer the main questions "why?" "how?" FAQs - hoping to have them produced in the next 3 months or so
- use cases - how data centres can contribute artile links to DataCite = use "relatedIdentifier" property in DataCite metadata schema
- Scholix doesn't say whether the dataset or the article is open, or about the licensing of the objects being linked
How to give credit to scientists for their involvement in making data & samples available for sharing
Unfortunately seemed to spend too much time rehashing old data citation, data publication and data metrics arguments.
- BRIF - Bioresource Research Impact Factor
- Data metrics and reward systems - table 3 in report
- Analysis of metadata records in DataCite reveals that not all records are complete.
- Consensus and standardisation of metadata needed
- Top data creator in DataCite is a mycologist
- WG RDA / TD Metadata Standards for attribution of physical and digital collections stewardship already exists. Reasearch Data Provenance IG already exists.
- Focussing very much on data publication as a method for giving credit - too much overlap with existing WG/IGs
- CoBRA short checklist for citation of bioresources in scientific journal articles
- IGSN is now in DataCite metadata schema as relatedIdentifierType
IG RDA/WDS Certification of Digital Repositories
Started with presentations, then we broke out into groups to discuss certain questions and responses in the self certification process. I also got photographed by the official photographer.
- Core Trustworthy Data Repository Requirements incude:
- explicit mission, licenses, continuity plan, disciplinary and ethical norms, adequate funding (3-5 years) and qualified staff, expert guidance, integrity and authenticity of the data, relevence and understandability, documented processes and procedure, long-term preservation
IG RDA/WDS Publishing Data
A key topic of this session was trying to figure out the next direction the IG should take... unfortunately still to be determined
- WG on Data Fitness for use - just starting - see below
- OECD-GSF CODATA project: business models for sustainable research data repositories
- Niso recommendation on assessment of scholarly research - non traditional metrics
- Where to take the IG?
- think about where scholarly publishing is going in the future. New publishing models - preprint repositories, open peer-review...
IG Data policy standardisation and implementation
Came from a BoF last plenary, but now an official IG - this meeting primarily about what already exists
- UK Concordat on open research data
- IG primary objective - define a common framework for research data policy allowing for different requirements, different levels of commitment and acknowledging disciplinary differences
- Journal research data policy registry
- Complying with funder policy is what researchers give as their motivation to share data, but researchers find it hard to comply with policy
- Springer Nature Research Data Policy framework
- A Data Citation Roadmap for Publishers
- Do studies of quantitative results of the impact of data sharing exist? Citation benefits?
- doing studies, but insufficient evidence as yet.
- Suggestion that the Belmont Forum is bringing together people for standardising policies...?
Software Source Code focus group
Good discussion in this BoF - though mainly asking questions rather than providing answers
- Statement of the problem clear - need software for scientific reproducibility. But don't have suitable repositories/ontologies for source code.
- differences between scientific software and open source? Can we learn from open source developers?
- is RDA a suitable venue for this work? Anything else going on in this area?
- Mailing lists and bug tracking chains are important sources of information about the code
- Software as knowledge, versus software as an instrument in the process
- Docker - focussing on re-run-abilty
- Archives do throw things out - so saving all the commits might not be possible/practical
- Open Source software - don't know when it starts what it will turn into - often safer to archive everything and then throw things out later.
- Distinction between code as knowledge and reproducibility
- Cost of storage, curation and maintainence of the metadata
- Reproducibility IG working on this a bit
- Difference between replicability and reproducibility
- Docker image not enough for reproducibility - as we need to be able to modify the source code
- Don't get the chance to read a scientific article's first five drafts. People don't want to share their first drafts. Might put people off sharing.
- first drafts of literature don't usually get shared, until the person writing them becomes famous, in which case people are interested
- Rely on top layers overlaying archival? e.g. overlay journal
- Work being done on software citation - in/out of scope? Connected to metadata
- Notes from the session
WG RDA/WDS Assessment of Data Fitness for Use
New WG - meeting primarily about the criteria that can be used to assess data fitness for (re)use.
- Looking at individual data sets
- Needs to be efficient, high impact and visibility
- Data quality: "degree to which a set of characteristics of data fulfills requirements" (ISO900)
- any data are usable as long as they fit the requirements
- Criteria 1
- inherent properties: objectively verifiable/measurable e.g. validity of used methodologies, completeness of metadata
- non-inherent propertise: subjective assessments
- Criteria 2: properties directly related to data objects/ data accessibility/ data management processes
- FAIR data principles
- FAIRness Index - a collection of metrics to assess adherence to the FAIR principles
- DANS FAIR badge scheme - going through testing at the moment
- reusability as the resultant of the other 3 (F+A+I)/3=R
- scores for F,A,I as 1 to 5
- publish number of user reviews, archivist assessments, downloads
- mapping of reusable criteria to other F/A/I criteria
- examples of star values criteria for each F/A/I
- Online questionnaire system developed for reviewers of datasets
- planning on creating a neutral website to assess datasets FAIRDAT.org (DAT = data assessment tool)
- Issues with asssessing multi-file datasets (with files in different formats), quality of metadata (how to evaluate when metadata is insufficient versus rich), how to define use of standard vocabularies
Friday, 26 August 2016
Standing on the Digits of Giants: Research data, preservation and innovation - ALPSP seminar, London, 8 March 2016
ALPSP seminar, London, 8 March 2016
I was asked to present at an Association of Learned and Professional Society Publishers seminar, back in March this year. You can found my presentation slides here, and the audio of my presentation here.
I've info-dumped my notes on the various talks below, but to sum up, it was a very interesting seminar that seemed to go down well with an audience of primarily publishers, many of whom were getting to grips with this whole data thing for the first time.
William Killbride, Digital Preservation Coalition
* "Access is not an event, it's a process"
* Standing on someone's shoulders is quite precarious! We need a stable and secure platform - but how do we make one?
* Solutions for digital preservation need to be put in place at the beginning of the lifecycle
* Discussions with publishers can get bogged down in Open Access issues
* Small publishers hold the content that's most at risk
* We need action on Open Access! We've talked about it lots already
* International profile is important
Mark Thorley, NERC
* The digital, networked world is a real game changer. Peopel want on-line access now and for free. And anyone can "publish" anything on the web
* Open research is not an admin overhead
* The data revolution is replaying the printing revolution established by Gutenberg's mechanical, moveable type
* ICSU's report "Open Data in a Big Data World"
* Open research costs money - we have to learn to live with that
* Technology is the "easy bit" - people are complicated!
Robert Gurney, University of Reading
* The cloud approach is developing fast in environmental data - visualisation of data (especially large quantities of data) is very important
* Infrastructure as a service provides easy access to resources
* Problems in Big Data - volume, variety, veracity
* The Belmont Forum
* is set up to allow common cross-national calls. Their data policy and principles are published on the web
* is establishing a data and e-Infrastructure coordination office
* creating a common enhanced data plan
* planning scoping workshops and international calls for case studies and to share infrastructure and develop best practice
* NERC are leading the effort on cross-disciplinary training curriculum to expand human capacity. This will involve the UN training agency, and there will be an open call for a training champion
* The Belmont Forum implementation plan is published
Phil Jones, Digital Science
* We are moving from cottage industry to industrial scale science, but funding structures are more set up to support cottage industry science.
* Valen, Blanchat, figshare, 2015 - Survey of data policies for funders across the UK and USA
* Open Academic Tidal Wave is moving from recommendations to enforcement
* Data repositories have different approaches - structured versus unstructured
* Publishers only have a limited window of time to engage with researchers during the research workstream - but new tools are coming out to allow publishers to interactwith researchers across a greater time
* If we want compliance, the simpler we can make the tools to do it, the better
Peter Burnhill, EDINA
* Increasingly more references to the wild web, not just back to other articles
* Scholarly record always has a fuzzy edge
* Libraries no longer have e-collections, only e-connections
* Mostly big publisher content being archived - but we don't know if the small stuff is being archived. Research libraries archiving stuff aren't going for the long tail of stuff published by small publishers
* Reference rot = link rot + content drift
* analysed ~ 1 million URI links - tested if URIs still worked, is there a "memento" of that reference in the "archived web"
* ~75% not archived within 14 days of publication
* Klein 2014, PLOS One - "Scholarly Context Not Found: One in Five Articles Suffers from Reference Rot"
* rotten references mean defective articles!
* author workflow - note taking software, working with Zotero
* Publishers should accept robust links in cited reference, avoid reference rot by triggering archiving of snapshots and inserting Hiberlinks/robust links at the point of ingest into submission system.
Mike Taylor, Elsevier
* Research data metrics - interest has exploded in past few years
* NISO - data metrics recommendations - set up 3 working groups
* "metrics for non-traditional outputs" group
* recommending report dataset download usage by using COUNTER compliant formulations, and that funders support repositories to do this
* Elsevier is adapting its research infrastructure to deal with research data
* much easier to set up new products than adapt existing systems!
* Ambitions for next year:
* most Elsevier journals promoting data publishing with data policies
* submission system to support data citations and data submissions
* communicate what's being done
* Data metrics part of the value loop encouraging researchers to make their data available. (Also including data)
* Metrics based on data citation will be happening in the near future, as soon as the infrastructure is built
* Not just one metric!
* article level metrics
* journal level metrics
* the more metrics, the harder it is to hide things - multiple metrics give multiple points of view
Josh Brown, Orcid
* CRediT schema - update ORCID schema to include other research roles e.g. data etc.
* Contributor type badges
* need PIDs for organisations
* issues with versioning, identifier equivalence, granularity, changes over time, making cultural changes mainstream
* all research activities need to be taken into account
* we can't reward it if we don't recognise it
* we won't recognise it if we can't agree on what it is
Matthew Addis, Arkivium
* direct benefit to researchers in getting involved with digital preservation
* tools and services exist now that allow researchers to get on and do digital preservation
* 44% of links to Astronomy data broken after 10 years
* Researchers only really get judged on how much grant money thay bring in, and how many publications - digital preservation will help with both these
* Lots of tools and models out there, but not particularly helpful for most researchers. Too much choice!
* do the bare minimum to get benefits from digital preservation - parsimonious preservation
* know what you have - understand the formats, catalogue the data
* put it somewhere safe
* link rot - how to address it?
* Droid - file format identification tool, can generate xml/pdf reports. Metadata includes links to PRONOM - technical registry for file formats
* checksums - useful to establish if data has been lost/corrupted. Tools e.g. exactly - creates BagIt manifest of files
* ADMIRe survey at Nottingham
* make lots of copies to keep stuff safe - put them in places like institutional repositories...
* links are important. DOIs are dependent on URLs, which are as brittle as any URLs - lots of links compensate for reference rot
Wendy White, University of Southampton
* PIs as change agents - collaboration with academic leadership to enact changes
* collaboration - e.g. capturing information about equipment and facilities
* Risk of garbage in and pretty visualisations out
* Quick wins - embedding DOIs, CC0 metadata
* Zika initiative - engage with lots of other smaller initiatives as well e.g. greynet.org
* Networks of repositories - institutional repositories working with international and national disciplinary repositories
* Not making enough of theses data - encourage more theses to have data made available
* Library triaged research data services - consultancy, engagement with editors, advice, workshops
* Different training models - pick and mix, intense and seasonal, integrated pathways (what we want!), emergency boost (help panicking people)
* Southampton reviewing curricula - modules on data analysis, ethics and research methods are good areas to discuss data management
* PhD students are great agents for change - passionate advocates
* Embedded librarians inside research teams iutility.ac.uk
* Research data - more than management!
* An archive isn't a thing, it's a strategy
Peter Doorn - DANS
* Lots of different types of data journals and data papers
* Data paper describes the research context of a dataset
* Presentation of a data paper should look attractive - more user-friendly than the view of the dataset in the archive
* Variety of interactive data visualisation - make the data more alive
* publishing data in Mendeley data - Elsevier aren't making it obligatory to publish data in Mendeley Data
Friday, 1 July 2016
Old books in my local second hand bookshop
The COPE (Committee on Publication Ethics) Seminar: An Introductions to Publication Ethics, was held on Friday 13th May 2016, in Oxford.
Being fairly new to this being an editor business, and the workshop being so local, I took the opportunity to go, and found it all really useful. Not only from my perspective as someone in charge of a journal, but also from the data management and publication point of view. A lot of the issues raised during the workshop, like attribution, authorship, plagiarism etc. are just as easily applied to datasets as they are to journal articles.
The workshop was a mixture of talks and discussion sessions, where we were given examples of actual cases that COPE had been told about, and we had to discuss and decide what the best course of action was. Then we were told what the response from the COPE members was in those particular cases - reassuringly we were pretty much in agreement in all cases!
Key notes that I jotted down during the day include:
- Retractions of papers are growing at a rate faster than publications
- An emerging area of concern is the growth of fake peer reviewers
- Ethical guidelines for peer reviewers are available on the COPE website, along with other guidelines
- Similarly, there are flowcharts on the COPE site to guide you through what to do if you suspect an ethical problem
- Report for the Nuffield Council on Bioethics on the culture of scientific research
- Academy of Medical Sciences - Reproducibility and reliability of biomedical research
- Some authors will put in white quotation marks around text to get around plagiarism detection software
The main take home message for me was that COPE have a lot of resources on their website, all free to use.
Astrolabes at the Museum of the History of Science, Oxford
It was a pretty standard workshop format - lots of talks, but there were a wide variety of speakers, coming from a wide spread of backgrounds, which really helped make people think about the issues involved in data visualisation. I particularly enjoyed the interactive demonstrations from the speakers from the BBC and the Financial Times - both saying things that seem really obvious in retrospect, but are worth remembering when doing your own data visualisations (like keep it simple, and self contained, and make sure it tells a story).
For those who are interested, I've copied my (slightly edited) notes from the workshop below. Hopefully they'll make sense!
Richard O’Beirne (Digital Strategy Group, Oxford University Press)
- What is a figure? A scientific result converted into a collection of pixels
- Steep growth in "data visualisation" in Web of Science, PubMed
- Data visualisation in Review: Summary, Canada 2012
- Infographics tell a story about datasets
- Preservation of visualisations is an issue
- OUP got funding to identify suitable datasets to create visualisations (using 3rd party tools) and embed them in papers
Mark Hahnel (figshare)
- Consistency of how you get to files on the internet is key
- Institutional instances of figshare now happening globally e.g. ir.stedwards.edu / stedwards.figshare.com
- Making files available in the internet allows the creation of a story
- How do you get credit? Citation counts? Not being done yet
- Files on the internet -> context -> visualisation
- Data FAIRport initiative - to join and support existing communities that try to realise and enable a situation where valuable scientific data is ‘FAIR’ in the sense of being Findable, Accessible, Interoperable and Reusable
- Hard to make visualisations scale!
- Open data and APIs make it easier to understand the context behind the stories
- Whose responsibility is it to look after these data visualisations?
- Need to make files human and machine readable - add sufficient metadata!
- Making things FAIR just allows people to build on stuff that has gone before - but it's easy to break if people don't share
- How to deal with long-tail data? Standardisation...
John Walton (Senior Broadcast Journalist, BBC News)
- Example of data visualisation of number of civilians killed by month in Syria
- Visualisation has to make things clear - the layer of annotation around a dataset is really important
- Most interactive visualisations are bespoke
- It's helpful to keep things simple and clear!
- Explain the facts behind things with data visualisation, but not just to people who like hard numbers - also include human stories
- Lots of BBC web users are on mobile devices - need to take that into account
- Big driver for BBC content is sharing on social media - BBC spend time making the content rigourous and collaborating with academia
- Jihadism: tracking a month of deadly attacks- during the month there was about 600 deaths and ~700 attacks around the world
- Digest the information for your audience
- Keep interaction simple - remember different devices are used to access content
Rowan Wilson (Research Technology Specialist, University of Oxford)
- Creating cross walks for common types of research data to get it into Blender
- People aren't that used to navigating around 3 dimensional data - example imported into Minecraft (as sizeable proportion of the population are comfortable with navigating around that environment)
- Issues with confidentiality and data protection, data ownership, copyright and database rights, open licenses are good for data, but should consider waiving hard requirement for attribution, as cumbersome attribution lists will put people off using data
- Meshlab - tool to convert scientific data into Blender format
Felix Krawatzek (Department of Politics and International Relations, University of Oxford)
- Visualising 150 years of correspondence between the US and Germany
- Letters (handwritten/typed) need significant resource and time to process them before they can be used
- Software produced to systematically correct OCR mistakes
- Visualise the temporal dynamics of the letters
- Visualisation of political attitudes
- Can correlate geographic data from the corpus with census data
- Always questions about availability of time or resources
- Crowdsourcing projects that tend to work are those that appeal to people's sense of wonder, or their human interest. Get more richly annotated data if can harness the power of crowds.
- Zooniverse created a byline to give the public credit for their work in Zooniverse projects
Andrea Rota (Technical Lead and Data Scientist. Pattrn)
- Origin of the platform: the Gaza platform - documenting atrocities of war, humanitarian and environmental crises
- "improving the global understanding of human evil"
- Not a data analysis tool - for visualisation and exploration
- Data in google sheets (no setup needed)
- Web-based editor to submit/approve new event data
- Information and computational politics - Actor Network Theory - network of human and non-human actors - how to cope with loss
- Pattrn platform for sharing of knowledge, data, tools and research, not for profit
- Computational agency - what are we trading in exxchange for short term convenience?
- "How to protect the future web from its founders' own frailty" Cory Doctorow 2016
- Issues with private data backends e.g. dependency on cloud proprietary systems
- Computational capacity - where do we run code? Computation is cheap, managing computation isn't easy
Alan Smith (Data Visualisation Editor, Financial Times)
- Gave a lovely example of bad chart published in the Times, and how it should have been presented
- Visuals need to carry the story
- Avoid chart junk!
- Good example of taking an academic chart and reformatting them to make the story clearer
- Graphics have impact on accompanying copy
- Opportunity to "start with the chart"
- Self-contained = good for social media sharing
- Fewer charts, but better
- Content should adapt to different platforms
- The Chart Doctor - monthly column in the FT
- Visualisation has a grammar and a vocabulary, it needs to be read, like written text
Scott Hale (Data Scientist, Oxford Internet Institute, University of Oxford)
- Making existing tools easy to use, online interfaces to move from data file to visualisation
- Key: make it easy
Alejandra Gonzalez-Beltran (Research Lecturer, Oxford e-Research Centre)
- All about Scientific Data journal
- Paper on survey about reproducibility - "More than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments."
- FAIR principles
- isaexplorer to find and filter data descriptor documents
Philippa Matthews (Honorary Research Fellow, Nuffield Department of Medicine)
- Work is accessible if you know where to look
- Lots of researcher profiles on lots of different places - LinkedIn, ResearchFish, ORCID,...
- Times for publication are long
- Spotted minor error with data in a supplementary data file - couldn't correct it
- Want to be able to share things better - especially entering dialogue with patients and research participants
- Want to publish a database of HBV epitopes - publish as a peer-reviewed journal aricle, but journals wary of publishing a live resource
- my response to this was to query the underlying assumption that at database needs to be published like a paper - again a casualty of the "papers are the only true academic output" meme.
- Public engagement - dynamic and engaging rather than static images e.g. Tropical medicine sketchbook
Cute bollard at Helsinki airport
The 3rd LEARN (Leaders Activating Research Networks) workshop on Research Data Management, “Make research data management policies work” was held in Helsinki on Tuesday 28th June. I was invited wearing my CODATA hat (as Editor-in-Chief for the Data Science Journal) to give the closing keynote about the Science International Accord "Open Data in a Big Data World".
The problem with doing closing talks is that so much of what I wanted to say had pretty much already been said by someone during the course of the day - sometimes even by me during the breakout sessions! Still, it was a really interesting workshop, with excellent discussion (despite the pall that Brexit cast over the coffee and lunchtime conversation - but that's a topic for another time).
There were three breakout session possibilities, of which the timings meant that you could go to two of them.
I started with Group 3: Making possible and encouraging the reuse of data: incentives needed. This is my day job - taking data in from researchers, making it understandable and reusable, and figuring out ways to give them credit and rewards for doing so. And my group has been doing this for more than 2 decades, so I'm afraid I might have gone off on a bit of a rant. Regardless, we covered a lot, though mainly the old chestnuts of the promotion and tenure system being fixated on publications as the main academic output, the requirements for standards (especially for metadata - acknowledging just how difficult it would be to come up with a universal metadata standard applicable to all research data), and the fact that repositories can control (to a certain extent) the technology, but culture change still needs to happen. Though there were some positives on the culture change - I noted that journals are now pushing DOIs for data, and this has had an impact on people coming to us to get DOIs.
Next breakout group I went to was Group 1: Research Data services planning, implementation and governance. What surprised me in this session (maybe it shouldn't have) was just how far advanced the UK is when it comes to research data management policies and the likes, in comparison to other countries. This did mean that me and my other UK colleagues did get quizzed a fair bit about our experiences, which made sense. I had a bit of a different perspective from most of the other attendees - being a discipline-specific repository means that we can pick and choose what data we take in, unlike institutional repositories, who have to be more general. On being asked about what other services we provide, I did manage to name-drop JASMIN, in the context of a UK infrastructure for data analysis and storage.
I think the key driver in the UK for getting research data management policies working was the Research Councils, and their policies, but also their willingness to stump up the cash to fund the work. A big push on institutional repositories was EPSRC's putting the onus on research institutions to manage EPSRC-funded research data. But the increasing importance of data, and people's increased interest in it, is coming from a wide range of drivers - funders, policies, journals, repositories, etc.
I understand that the talks and notes from the breakouts will be put up on the workshop website, but they're not up as of the time of me writing this. You can find the slides from my talk here.