I was invited to speak at the National Higher Education Faculty Research Summit in Sydney on May 22 about our Research Data Repository project. The conference promises to provide a forum for exploration.

Explore

• Sourcing extra grant funding and increasing revenue streams

• Fostering collaboration and building successful relationships

• Emerging tools and efficient practices for maintaining research efficacy and integrity

• Improving your University’s research performance, skills and culture to enable academic excellence

My topic is “Introducing a data deposit management plan to the research community at UWS”. This relates directly to the conference theme I have highlighted, on emerging tools and practice. My strategy for this presentation, given that we’re at a summit, is to stay above 8000m, use a few metaphors, and discuss the strategy we’re taking at UWS rather than dive too deeply into the sordid details of projects. As usual, these are my notes; I hope these few paragraphs will be more useful than just a slide deck, but this is not a fully developed essay.

In very general terms, we have divided our data storage into two parts: the working Research Data Storage service where people get things done, collect data and work with it and the archival Research Data Repository part where stable, citable published data sets are looked after (by the library) for the long term.

This talk is not going to be all about architecture diagrams but here’s one more, from a recent project update showing two examples of applications that will assist researchers in working with data. One very important application is HIEv, the central data capture/management platform for the Hawkesbury Institute for the Environment. This is where research teams capture sensor data, research support staff work to clean and package the data, researchers develop models and produce derived data and visualisations. We’re still working out exactly how this will work as publications using the data start to flow, but right now data moves from the working space to the archival space, and thence to the national data discovery service, see this example of weather data – (unfortunately the data set is not yet openly available for this one, I think it should be, and I’ll be doing what I can to make it so).

The other service shown on this diagram is Dropbox.com. We’d be hard pressed to stop researchers from using this service – it comes up in just about every consultation meeting. Researchers themselves must take responsibility for making sure that services like this are appropriate given their data management obligations under funder agreements and codes of practice. For those projects where Dropbox.com is appropriate we plan to let researchers invite the Research Data Store to share their stuff, thus creating a managed, backed-up copy at the university, and opening the way for us to provide useful services over the data (coming soon).

Yes, we have a web page about research data management, with some basic advice and links to more resources, but putting up web pages does not effect the kind of culture change needed to establish research data management, data re-use and data citation. As our Research Office head, Gar Jones, says this will be a change similar to the introduction of Human and Animal ethics management which will take several years to roll out.

This is a concrete path on the Werrington South (Penrith) campus of the University of Western Sydney. The path is there because people kept walking through the garden bed, which was in between where the shuttle bus stops and where they wanted to be, at the library. As I said at a similar conference for IT-types last year:

Groups like mine work in the gap between the concrete and the goat track, my job is to encourage the goats.

And once we’ve encouraged the goats to make new paths, we need to get the university infrastructure people to come and pave the paths.

What do research administrators and IT directors need to be thinking about?

• Changes in the research landscape – more emphasis on data reuse and citation, increasing emphasis on defensible research mean data will become as important as citations

• Providing access to publications and data so it can be reused.

• (e)Research infrastructure in general, where collaboration must not be constrained by the boundaries of individual institutional networks and firewalls.

Any others?

The Australian National Data Service runs a data-discovery service designed to advertise data for reuse.

As research organisations, we want to have infrastructure for data management, and a culture of data management that involves forward planning, and data re-use. So the next section of the talk is about how we need to:

• Stop the fat multinational-publisher tail from wagging the starving research dog. Ensure research funded by us is accessible and usable by us.

• Understand our researchers and their habits, so we can help them take on this new data management responsibility (actually it’s not a new responsibility, but many have simply been paying no attention to it, in the absence of any obvious reason to do so).

• Sort out the metadata mess most universities are swimming in.

Now for the big picture stuff.

Open Free scholarship is coming? (Just beyond that ridge)

OA is a Good Thing,

Which will:

• Reduce extortionate journal pricing.

• Provide equitable access to research outputs to the whole world.

• Open Access to publications and Coming Soon: Open Access to data.

• Promote Open Science and Open Research.

• Drive huge demand for data management, cataloguing, archiving, publishing services

http://aoasg.org.au/

There are competing models for open access. Bizarrely the discussion is often framed as a contest between ‘Green’ and ‘Gold’. It’s a lot like the State of Origin Rugby League, a contrived but popular-in-obscure-corners of the world contest where the ‘Blues’ and ‘Maroons’ run repeatedly into each other. In both State of Origin and Open Access, the current winners are large media companies. Add least being an Open Access advocate doesn’t give you head injuries.

Green OA refers to author-deposited pre-publication versions of research articles. Gold means that the published version itself is ‘Open’ for some ill-defined definition of open, often at a cost of thousands of dollars, out of the researcher’s budget. Green or Gold, a lot of so-called Open Access publishing operates with no formal legal underpinnings, that is, without copyright-based licenses. For example when I deposited a Green version of a paper I had written here, and wrote to the publisher asking them to clarify copyright and licensing issues I got no reply.

We have a brief window now to try to build services for research data management that do have a solid legal basis and avoid following some of the OA movements missteps but this is not trivial (1).

I have used a variant of the above dog picture before to talk about identity management. This dog has a name but it’s a terrible way to find out about him as he has a much more famous namesake.

Like the rest of us, this dog has all sorts of identifying names and numbers – a microchip number linked to a database, an ID assigned by the RSPCA, patient numbers at veterinary practices, which may be linked to more than one human, phone numbers on his tag etc. Point is, it’s much worse for researchers than for dogs – identities are maintained all over the place. Foley and Kochalko put it like this:

While much has changed since the days of David Livingstone, we continue to struggle with associating individuals with their works accurately and unambiguously. Author name ambiguity plagues science and scholarship: when researchers are not properly identified and credited for their work, dead-ends and information gaps emerge. The impact ripples throughout the ecosystem, compromising collaboration networks, impact metrics, “smarter” research allocations, and the overall discovery process. Name ambiguity also weighs on the system by creating significant hidden costs for all stakeholders. (2)

To do metadata management well we need to make sure that we sort out all sorts of naming and identifying issues, dealing correctly with potential causes of confusion, multiple people with the same name, people with multiple names over time, and simultaneously, name variants. Even where there are agreed subject codes like the Field of Research codes that are heavily used in research measurement exercises they can get mixed us as different databases use different variants.

At Rochester university, when they installed an institutional repository the team conducted ethnographic research on their research community (3). We have not gone that far, but our Research Data Repository project does try to pay attention to what researchers do as part of their current work, and to fit new processes into existing ones.

For example, the above scenario tries to capture the interactions that would happen when a researcher is required by a journal to deposit data before publication. We spend a lot of time talking to the Office of Research Services (ORS) and research librarian team about how we can fit in with their existing processes, and how to minimise negative impacts on research groups. Research Offices are used to responding to changing regulatory environments so adding new fields to forms etc is straightforward. Changing IT services is much harder; the ITS is much bigger than ORS, new services need to be acquired, provisioned and documented, and the service desk team has to be taught new processes.

This is a rather a substantial issue to try to talk about in a discussion about research data management and repositories, but it’s essential to keep an eye on the big picture. We know that scholarship has to change, publishing has to change, but we don’t know how. We need to develop strategies for how we want it to change. Some examples of where this is important:

• Policy on ‘ownership’ of intellectual property rights over data needs to be established. This is not as simple as it is for publications, as data are not always subject to copyright (1).

• Data citation is going to be an important metric.

New models are needed. People like Alex Holcombe from Sydney uni are developing them:

Science is broken; let’s fix it. This has been my mantra for some years now, and today we are launching an initiative aimed squarely at one of science’s biggest problems. The problem is called publication bias or the file-drawer problem and it’s resulted in what some have called a replicability crisis.

When researchers do a study and get negative or inconclusive results, those results usually end up in file drawers rather than published. When this is true for studies attempting to replicate already-published findings, we end up with a replicability crisis where people don’t know which published findings can be trusted.

To address the problem, Dan Simons and I are introducing a new article format at the journal Perspectives on Psychological Science (PoPS). The new article format is called Registered Replication Reports (RRR).  The process will begin with a psychological scientist interested in replicating an already-published finding. They will explain to we editors why they think replicating the study would be worthwhile (perhaps it has been widely influential but had few or no published replications). If we agree with them, they will be invited to submit a methods section and analysis plan and submit it to we editors. The submission will be sent to reviewers, preferably the authors of the original article that was proposed to be replicated. These reviewers will be asked to help the replicating authors ensure their method is nearly identical to the original study.  The submission will at that point be accepted or rejected, and the authors will be told to report back when the data comes in.  The methods will also be made public and other laboratories will be invited to join the replication attempt.  All the results will be posted in the end, with a meta-analytic estimate of the effect size combining all the data sets (including the original study’s data if it is available). The Open Science Framework website will be used to post some of this. The press release is here, and the details can be found at the PoPS website.

http://alexholcombe.wordpress.com/2013/03/03/registered-replication-reports-are-open-for-submissions/

This seems like a positive note on which to end. Hundreds of researchers are trying to fix scholarship, they’re the ones we need to talk to about what a data repository or a data management plan should be.

1. Stodden V. The Legal Framework for Reproducible Scientific Research: Licensing and Copyright. Computing in Science Engineering. 2009;11(1):35–40.

2. Foley MJ, Kochalko DL. Open Researcher and Contributor Identification (ORCID). 2012 [cited 2013 May 21]; Available from: http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1133&context=charleston

3. Lindahl D, Bell S, Gibbons S, Foster NF. Institutional Repositories, Policies, and Disruption. 2007 [cited 2013 May 21]; Available from: http://open.bu.edu/xmlui/handle/2144/919

Steven Hayes from Arts eResearch at the University of Sydney invited me to visit their group and talk about running open source software projects, as they are making their Heurist (semantic database-of-everything) software open source. This was more of a conversation than a presentation, but I prepared a few ‘slides’ to remind me of which points to hit. Here are my notes. The focus here was not on why go open source, or open source in general, it was about doing it in a small university-based team. Comments about how various uni open source projects run would be appreciated.

I have been involved in creating two sizeable code-bases both released by the University of Southern Queensland as open source. They had very different histories. I’ll talk about both and how they run, although actually one of them doesn’t run any more in any meaningful way.

*Thanks to Ron Ward, Oliver Lucido, Linda Octalina, Duncan Dickinson, Greg Pendlebury, Daniel de Byl, Bron Chandler, Tim McCallum, Cynthia Wong, Jason Zejfert, Sally MacFarlane, Caroline Drury, Pamela Glossop, Warwick Milne, Sue Craig, Vicki Picasso, Dave Huthnance, Shirley Reushle and the late Alan Smith who made, tested, championed and supported these projects. Thanks also to funding from the Australian government via ANDS, ARROW and other streams. Sorry if I forgot anyone.

(At this point I wanted to check that everyone knows what Open Source means, making sure that we all understand how Richard Stallman made software free using copyright law. Whoever holds the copyright in a bit of software, which is likely to be whoever wrote it, or their employer can control distribution by using a licence, a legal instrument. Stallman’s insight was that a licence could be used to enforce sharing, openness and freedom: you can use this stuff I created provided you promise to share it with other people (that’s not a quote). Oh, and people working in this space should also understand the difference between Free and Open Source [1].

But I forgot.)

Me, I’m not sure that all these scenarios Fogel lists are failures at all, there are lots of reasons to release code and they are not all necessarily about building a substantial community:

We tend not to hear very much about the failures. Only successful projects attract attention, and there are so many free software projects in total^[2] that even though only a small percentage succeed, the result is still a lot of visible projects. We also don’t hear about the failures because failure is not an event. There is no single moment when a project ceases to be viable; people just sort of drift away and stop working on it. There may be a moment when a final change is made to the project, but those who made it usually didn’t know at the time that it was the last one. There is not even a clear definition of when a project is expired. Is it when it hasn’t been actively worked on for six months? When its user base stops growing, without having exceeded the developer base? What if the developers of one project abandon it because they realized they were duplicating the work of another—and what if they join that other project, then expand it to include much of their earlier effort? Did the first project end, or just change homes?

It’s important to work out why you are going to release software as Open Source – think about the audience. One very important audience is you, yourself. If you work on code as part of your job, then your employment contract may well mean that your employer owns the copyright. Do you want to be able to continue using it in your next job? Show potential employers? Making it open source helps your future self.

I know this first hand.

Universities are not as stable as they seem, or you may hope. At the Australian Digital Futures Institute at USQ we began by hosting code repositories and websites internally. I reasoned that the university would be a good bet for maintaining persistence of these resources.

But then one Gilly Salmon came to our institute to be the new professor, decided, along with the rest of the senior leadership team that there was altogether too much making the digital future going on in the Australian Digital Futures Institute, too much technology. They let just about all the technical staff go, no matter how useful they were to the organisation, or how pregnant they happened to be (we’re a relationship brand, the director of marketing told me, so we shouldn’t be continuing to develop software to deliver award-winning distance-ed services).

Web sites that would still have value are just gone from public view, including, ironically the PILIN project site, which was about persistent identifiers. Even the ICE website which is full of useful stuff for USQ itself now appears to be only accessible via the Wayback machine. They’re still using it but they turned off the website anyway, the code, however, is sitting on Google code so we all still have access to it.

This sort of thing happens all the time. For a couple of us, the NextEd refugees, this was the second redundancy associated with USQ. Kids, it is prudent to make sure that any code you might want to re-use later in your career is released under an open licence, and documentation, web sites etc likewise under creative commons. Think of it as a professional escape pod.

The ReDBOX project survived this ADFI shut down, because it had been open source from the beginning but further funding had to be redirected to another university which was willing to host the building of a digital future.

Oh, and the answer to most questions is on Stack Exchange. I decided that this list was worth using as a starting point for discussion.

http://programmers.stackexchange.com/questions/51553/checklist-for-starting-an-open-source-project

More from Havoc P

After those priorities I’d say:

• documentation (this saves you work on the mailing list… make a FAQ from your list posts is a simple start)

• try to do things in a “normal” way (don’t invent your own build system or use some weird one, don’t use 1-space indentation, don’t be annoyingly quirky in general because it adds learning curve)

• promote your project. marketing marketing marketing. You need some blogs and news sites and stuff like that to cover you, and then when people show up interested, you need to talk to them and be sure they get it working and look at their patches. Maybe mention your project in the forums for related projects.

  [Yes, this is a huge one. One of the big differences between ReDBOX, which is no hit, but has a solid user base and ICE which never made it out of USQ is that Vicki Picasso from Newcastle Uni and I marketed the hell out of ReDBOX early to a very specific community of user-organisations. We needed a community so the software would have a sustainable base, so we designed the software for the community and sought input on the design as broadly as we could.

  With ICE, I talked about it to lots of the wrong people and didn’t sell it to the right ones, other distance ed unis, but that was partly because it conferred a competitive advantage on USQ. This comes back to the point above about success vs failure – there’s more than one way to succeed.]

• always review and accept patches as quickly as humanly possible. Immediately is perfect. More than a couple days and you are losing lots of people.

• always reply to email about the project as quickly as humanly possible.

• create a welcoming/positive/fun atmosphere. don’t be a jerk. say please and thank you and hand out praise. chase off any jackasses that turn up and start to poison the community. try to meet people in person when you can and form bonds.

Running an Open Source project from a university dev team by Peter (pt) Sefton is licensed under a Creative Commons Attribution 3.0 Unported License.

Repositories! (What are they good for?)

Georgina Edwards has invited me to Intersect NSW to give a talk to the software engineering team about repositories in eResearch. There were also quite a few eResearch analysts in attendance, not to mention a couple of members of the senior management team. (Just in case you’re wondering, the answer to the question in the title is not “absolutely nothing”).

Here are my notes, with embedded slides, which I put together on the train to and from the CBD (ie quick and dirty).

The summary: repository means a lot of different things, but the main sense I talked about with the Intersect team was ‘data-store component’. I tried to cover why using a repository in an eResearch project might be important because repositories can provide a lot of ready-made functionality, particularly in the area of digital preservation, but also access to indexing services and content-transcoding to generate new formats from things ingested. I talked about one aid for thinking about repository services which I think is useful – the Repository Micro-services framework from the California Digital Libraries, and ran through some of the repository frameworks that people in the eResearch.au world might encounter.

The liveliest discussion was around RDF, the Resource Description Framework, and what it’s good for. I made the assertion that RDF was the best practice approach to storing metadata, allowing for built-in extensibility. RDF uses URIs as names for both things and relations, which reduces ambiguity and aids interoperability. But I think it’s important to draw the line between RDF as a good way to do metadata, and annotation and the assumption that an RDF query language (via an RDF triple-store) is always going to be needed or even work. I’m sceptical about the promise of RDF as some kind of super semantic world-wide web of knowledge you can query for the answer to anything, but it’s clearly a good way to do metadata – there’s no excuse for inventing a new metadata schema that is not RDF based these days.  (Use the comments if you want to discuss).

By Dr Peter Sefton (University of Western Sydney) with Ms Caroline Drury (University of Southern Queensland).

On Wednesday 5^th Dec I (Peter) visited the Japanese Digital Repository Federation at their invitation and expense, to talk about how our respective repository communities are organised.  I’d like to thank the DRF for this opportunity to make the brief trip to Tokyo. Caroline was invited but was unable to make it. The DRF folks have put up a summary of the meeting, in Japanese. Note that while my comments on that page are listed as “CAIRSS” I was not representing CAIRSS (the CAUL (Council of Australian University Librarians) Institutional Repository Support Service), I attended as a member of the Australian/Australasian repository community. I also attended the DRF international conference in 2009 on a similar basis, when I did happen to be associated with CAIRSS, so the organisers knew me. I did talk a fair bit about CAIRSS, in the context of other projects in Australia.

Before I went I polled the CAIRSS-list to find out if there were any questions people would like answered – more on that below.

First, a bit about me and repositories:

• I was the technical lead for the Regional Universities Building Research Infrastructure Collaboratively (RUBRIC) project which was hosted by the University of Southern Queensland (and the de-facto project manager for several months during the project establishment phase).

• I led a small team at USQ subcontracting to the ARROW project during 2008, providing technical support to ARROW, and repository services to small Higher Education institutions in Australia.

• I worked on USQ’s successful bid to host the first CAIRSS repository support service in Australia and acted as a senior strategist for the service, for example working on guides such as the one on how to get into Google Scholar et al, and negotiating major changes to repository infrastructure such as the closure of the Australian Digital Theses search service and its subsumption into the National Library of Australia’s Trove service.

• I was not involved in running the second version of CAIRSS from 2011-2012 but I have remained part of the repository community in Australia and attended the 2012 community day where I spoke about trends in repository software in the context of organisational governance.

• I am on the conference committee for the Open Repositories series of conferences (from 2011) – the call for papers for the 2013 conference is just out.

I’m leading a brief session at the CAIRSS community days today (CAIRSS is the national repository support service for Australasia). The title is “Emerging Repository Software”, but I thought I’d turn that around and propose that the future of institutional repositories is to fade into the background.

Here are my notes for the session.

Take this screenshot of the Griffith University repository. See here’s the default browse-screen for publications.

Just in case these Griffith people get big heads, I do have to point out that while I think this service points to the future of the Institutional Repository as an embedded part of the research information systems of the university, the work’s not all done yet.

This stuff is hard to get right. The hardest bit is getting good quality metadata so things do join up.

There are two new kinds of repository we’re seeing in Australia, thanks to investment from the Australian National Data Service.

1. There are many “Data Capture” systems for researchers to manage data early in its lifecycle.

2. These feed into Data Catalogues or Data Repositories – there’s a lot of terminological confusion here because of the way the funding streams have been structured.

   Data capture Systems

   See the ANDS list of DC projects. Here’s one I selected at random:

   

   It’s difficult to get useful information about many of these projects.

There are many data capture projects, and all of them will presumably need to be hooked up to systems like the Griffith Research Hub at some stage.

Culture and climate

I was invited to attend the planning day for the Institute for Culture and Society (ICS) at the University of Western Sydney, to talk about the eResearch team at UWS, discuss collaboration tools, and show a few useful, relevant examples of eResearch in the humanities.

Here are some rough notes for the discussion.

For eResearch I will talk about our small eResearch website, and on the subject of collaboration tools I’ll be evasive.

Like the guy in this video I used to think it would be a good idea to arrange icons in the OS X dock by how often I used them, or maybe by type. But I found that whatever ordering I used I would have trouble finding things. I know that iTunes is a blue circle, but so is Skype, and Safari (I rarely use it but sometimes I want to test something) – so the task of finding the app I want meant scanning for blue circles and often zooming my attention to the wrong one – I hate to say it, but they all look the same to me those pictures.

So, I now arrange them by shape, then colour. To seek, zoom in to the right shape-group. It’s easy to see the differences because they’re side by side rather than spread out.

Circles and circle-like things:

’

Squarish things:

Love those Apple apps that are so easy to tell apart:

Oh, and might as well arrange things with letter-icons in alphabetical order.

Et volia!

Alf (Andrew) Leahy and I were recently in Melbourne for the NeCTAR Über Dojo event.

By coming to this two day event you will be able to go back to your institution with a signed certificate showing that you’ve been ‘black belted’ as a Cloud expert, specifically we’ll train and qualify you in using the following tools and data in the Cloud:

• How to use image management tools for production level VM management on the Cloud, i.e. Puppet or Chef

• How to use libraries to access storage data APIs, e.g. SWIFT/S3, NFS, Object Store, etc.

• We’ll also use this event to get you the experts to tell us what new features we need for all these new toys (I mean infrastructure ;-)

The climax of this thing was an Iron Chef-style challenge. This is an initial rough post about our two hour hack (which we cleaned up over a few more hours post-event). Andrew will write up the event for the UWS eResearch blog. Thanks to Remko Duursma and Craig Barton for letting use their code and data for the demo.

The night before the challenge I asked Remko and Craig if we could use some sample data and R code:

Andrew (Alf) and I are at a workshop in Melbourne – I was wondering if I can use this as a demo tomorrow – ie it will appear on screen but not be made available over the net. The short notice is because the idea has only emerged today.

The thing we’d be demoing would be to _simulate_ the following:

• Data set + scripts like the attached is sitting in a web based repository.

• Repository offers user the opportunity to download the package OR – Get an interactive R-Studio shell where they can re-run the data

  • Use clicks a “see with interactive shell” link.

  • Our server:

    • Fires up a virtual server in the cloud

    • Installs the server version of R-Studio

    • Creates a user account 

    • Pushes the data package onto the new server

    • Unpacks the package

    • Sets up R-studio to run Knitr on the main script to create HTML

    • sends a link to the R-Studio to the user (maybe by email cos this might take a few minutes)

• The user can see the plots etc, and will have access to the R environment to tweak things

Copyright  Peter Sefton and Andrew Leahy, 2012. Licensed under Creative Commons Attribution-Share Alike 2.5 Australia. <http://creativecommons.org/licenses/by-sa/2.5/au/>

Fostering Collaboration across Institutions

• Improving the collaborative capacity of research data

• Making data discoverable and connected

• Security and IP

Remember, data don’t collaborate…

… people collaborate.

There are two kinds of people

• Researchers

• And people who are here on earth to help them

At UWS, we have started mapping out these relationships and there will be a lot more of this.

Watching the Alpha Geeks

Tim O’Reilly said in 2002:

So often, signs of the future are all around us, but it isn’t until much later that most of the world realizes their significance. Meanwhile, the innovators who are busy inventing that future live in a world of their own. They see and act on premises not yet apparent to others. In the computer industry, these are the folks I affectionately call “the alpha geeks,” the hackers who have such mastery of their tools that they “roll their own” when existing products don’t give them what they need.

The alpha geeks are often a few years ahead of their time. They see the potential in existing technology, and push the envelope to get a little (or a lot) more out of it than its original creators intended. They are comfortable with new tools, and good at combining them to get unexpected results.

Please, walk on the grass

Personal attributes of an Alpha Collaborator:

• Shows up

• Speaks up

• Keeps up

• Blogs / publishes

• Can operate as an individual, not a role

Collaborative research model v0.1

Peter (pt) Sefton ‏@ptsefton

After extensive ethnographic research on @MatToddChem at pub last night: my model of collaboratively doing science pic.twitter.com/2E4hkK47

Contribution from Mat

Matthew Todd ‏@MatToddChem

@ptsefton most interesting. If it’s open, one does not need to define, in advance, who the researchers are. #openscience

Peter (pt) Sefton ‏@ptsefton

@MatToddChem Too hard given my skills with diagramming tool

 Peter (pt) Sefton ‏@ptsefton

.@MatToddChem But seriously, I gathered openness of data & process and advertising via social networks were key success factors

Matthew Todd ‏@MatToddChem

@ptsefton Correct, meaning that anyone can join, including people you don’t know at the start, outside regular circle.

An _open_ collaboration model v0.1

An example of open science

In April 2010 a request was posted to a (closed, 2,500-member) process chemistry networking forum¹² on LinkedIn for suggestions, but also for people who might be willing to contribute more materially. This stimulated 20 comments (from 11 different people) and four private e-mails (via the website). None of these contributors were previously known to us. From the advice and offers, we chose to send one gram of racemic PZQamine to a Dutch contract research organization, Syncom, which arrived in mid-May. On 25 May, the company posted the identification of several chiral columns and conditions that enabled the baseline separation of the PZQamine enantiomers, permitting an assay for the effectiveness of any resolution attempts. On 25 August the company posted a lead chiral acid that had been identified (actually two months earlier) that effected the resolution of PZQamine. The company was not paid for this work.

Woelfle, Michael, Piero Olliaro, and Matthew H. Todd. “Open Science Is a Research Accelerator.” Nature Chemistry 3, no. 10 (2011): 745–748. http://www.nature.com/nchem/journal/v3/n10/full/nchem.1149.html

Another community of alpha-collaborators  

Fraction of astronomical papers published with one, two, three, four or more authors. CREDIT: Robert Simpson

 http://radar.oreilly.com/2012/08/data-mining-the-literature.html

Making data collaboration-ready

All we have to do is get data sets into RDA, right?

ANDS-wins

The latest ANDS newsletter has a number of success stories about data-driven collaboration. Read it.

Example from the ANDS newsletter

Repository of Antibiotic Resistance Cassettes

The repository (www2.chi.unsw.edu.au:8080/rac) allows researchers to submit data on potential cassettes online. This information is reviewed by staff at the Centre for Infectious Diseases and Microbiology, and any new cassettes are provided with accurate names, archived and the knowledge made available. The database can then be searched online and the cassettes annotated from anywhere in the world. At present, it has regular users in 15 countries, though primarily in Australia.

http://www.ands.org.au/newsletters/newsletter-2012-13.pdf

Nascent UWS project: Reproducible Research publications

(Sorry, but it’s complicated)

Making data linkable – give everything a URI

Please, don’t say “IP”

Don’t talk about “owning” data

Use language appropriate to the various kinds of intellectual property that researchers are dealing with.

See: Did you say you “own” this data? You keep using that word. I do not think it means what you think it means.

Making data collaboration-ready?

• The alpha-collaborators are telling us Remove all “IP” related issues by using open licenses or go public-domain.

• If that’s not possible, seek tools that allow the right people to work together.
  (Thanks NeCTAR with your cloud and labs and tools)

• Do data management well. Eg use linked-data approached to metadata.

  (Sorry if you were expecting that talk, maybe next time)

• And finally:

  If in doubt and you need a collaboration tool: use a mailing list.