A Force to be reckoned with

Force2015 logoOn Monday and Tuesday of this week (12th-13th January) I attended the Force2015 conference on research communications and e-scholarship. The conference was the successor to two US-based events entitled ‘Beyond the PDF’ – but happily for me, Force2015 was handily located in Oxford, in a venue about five minutes’ walk from my office.

A major aim of the conference was to bring together people from a wide range of different sectors – researchers, publishers, funders, librarians, and more – so as one might expect, the programme covered a wide range of topics. Chris Lintott’s fascinating keynote on citizen science got things off to a strong start, but for me the most interesting discussion happened in the latter part of Tuesday morning, when there was a vision session (a series of flash talks where conference attendees had five minutes to present their idea for improving scholarly communication), followed by a panel session on academic credit.

Although these two sessions started from somewhat different perspectives, a common theme very rapidly emerged: that the way in which the outcomes of research are presented and assessed needs to change. The primary unit of academic communication (and the thing that matters most in terms of CV points for researchers) is still the traditionally published journal article. However, text-based articles aren’t the only result of scholarly endeavour, and we need to find new ways of enabling other research outputs – data, software, multimedia objects, and more – to become part of the formal research record. Alongside that, we need to rethink the way in which researchers are credited for the work they do (composing the actual text of an article is only one part of the scholarly process), and the value that is placed on each role. This echoes much of what the research data management community has been saying for some years now, though with an even broader scope – I hadn’t, for example, previously fully appreciated the importance of software as a research output in some fields.

However, while there was much useful debate, I was personally rather disappointed that non-science disciplines weren’t better represented, both here and elsewhere on the program. Social sciences popped up occasionally, but all too many of the sessions barely even acknowledged that the humanities existed. For a conference about the future of research communications to argue that the current model of scientific publishing doesn’t represent how research in that field actually works is entirely legitimate and much needed. But for much discussion at the same conference to proceed as if scientific research were the only sort that takes place is more than a little worrying.

At one point things almost seemed to be veering in the direction of claiming that papers weren’t really important at all, or that they were merely advertising for the real content. A question from the audience drawing attention to this produced some hasty backtracking, and assurances that the significance of the interpretation and conclusions provided by the text wasn’t being overlooked. Nevertheless, I couldn’t help feeling that the whole shape of the discussion might have been different if there’d been someone on the panel putting the perspective of the philosopher or the historian. (We were told a couple of times that Force11, the organization behind the conference, is making an effort to be more inclusive and to cover a wider range of disciplinary views: we can only hope that these labours will have borne more fruit by the time Force2016 rolls around.)

On a more positive note, I was at the conference with my Online Research Database Service (ORDS) hat on, with a poster and an accompanying demo. It was good to have the opportunity to show the system off to a group of interested people, and pleasing to get some excited responses. A major part of the reason for developing ORDS was to provide researchers with a straightforward way of sharing their data, both with collaborators and with the public, with a view to allowing the data to be recognized as a key resource in its own right – so it’s nice to feel we’re doing our bit to help bring about a revolution in scholarly communication.

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Is your software open or fauxpen?

 

Is your software project open or “fauxpen”? Are there barriers in place preventing external developers from contributing? Barriers to commercial uptake? Barriers to understanding how the software or the project itself works?

These are the kind of questions that the OSS Watch team, in partnership with Pia Waugh, developed the Openness Rating to help you find out.

Using a series of questions covering legal issues, governance, standards, knowledge sharing and market access, the tool helps you to identify potential problem areas for users, contributors and partners.

We’ve used the Openness Rating at OSS Watch for several years as a key part of our consultancy work, but this is the first time we’ve made the app itself open for anyone to use.

It requires a fair bit of knowledge to get the most out of it, but even at a basic level its useful for highlighting questions that a project needs to be able to answer. If you have a software project developed within your research group, then you can use the app to get an idea of where the barriers might be. Likewise, you can use it if you’re considering contributing to a software project, for example when evaluating a platform to use as the basis of work in a research project.

Some of the questions do require a bit more specialist knowledge, but you can contact our team via email at researchsupport@it.ox.ac.uk to get help.

Get started with the Openness Rating tool.

Photo by Alan Levine used under CC-BY-SA.

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Data Visualisation Talks

Data visualisation - eyeOn Thursdays of Hilary term 2015, the Research Support team will be hosting a new series of lunchtime talks about data visualisation.

This series of hour-long talks is designed to provide an accessible overview of a series of data visualisation tools and projects. Join us to discuss your own methods and problems and to hear how others are handling visualisation both in Oxford and outside.

The talks will be held at IT Services in the 13 Banbury Road building, beginning at 12:30pm and concluding at 1:30pm. The series is co-hosted by the IT Learning Programme.

Researchers working with data may also be interested in another Research Support team talk series – Things to do with Data.

Hilary Term 2015 Programme

29th January, 2015 (week 2) – Data visualisation: in Blender
This session will look at case studies of academic use of 3D modelling tool Blender as a means of visualising research data.
Further details and booking

5th February, 2015 (week 3) - Data visualisation: Digital mapmaking using geographic information systems (GIS)
This session will look at what GIS is and what it can do for you.
Further details and booking

19th February, 2015 (week 5) – Data visualisation: visualising network data
This talk will discuss the use of software tools Gephi and NodeXL as a means of visualising network data.
Further details and booking

26th February, 2015 (week 6) – Data visualisation: visual analytics at NATO
Margaret Varga, chairman of the NATO (North Atlantic Treaty Organisation) Research Task Group on visual analytics, talks about new approaches to data visualisation.
Further details and booking

5th March, 2015 (week 7) – Data visualisation: data visualisation in Javascript
This talk will focus on practical examples of the use of Javascript for in-browser data visualisation.
Further details and booking

12th March, 2015 (week 8) – Data visualisation: visual vignettes: examples of R graphics in demography

This talk will present a series of vignettes on using R graphics in population research. The focus will be on the flexibility of the R graphics framework including its ability to deal with novel demands that arise both during data exploration and in result communication.
Further details and booking

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3-month report: September to November 2014

Highlights 

As part of the DiXiT project our ER, Magdalena Turska, has been working with the TEI Consortium’s Mellon-funded TEI Simple project. Her work has involved the migration of the reference corpora into the TEI Simple tagset as well as prototyping an implementation of the TEI Simple Processing Model. The latter she presented at the TEI Conference in Chicago. The TEI Simple Processing Model aims to allow general specification of intended processing scenarios targeting multiple output formats by using extensions to the TEI ODD customisation language. Magdalena also travelled to Romania spending a week at the headquarters of DiXiT partner SyncRo Soft SRL implementing some additional features to the oXygen XML Editor’s TEI framework. Magdalena will be returning to Romania for a longer period in 2015. She also took the lead in organising and teaching an ‘Introduction to TEI’ workshop, assisted by James Cummings, on behalf of DiXiT in Warsaw in October that was very well received and resulted in a number of potential future partnerships. Upcoming plans for Oxford’s contributions to the DiXiT project include the analysis and publications of the results of Magdalena’s survey of publication infrastructures, continued implementation of the TEI Simple Processing Model, and preparation for DiXiT Camp 3 to be held in February in Borås, Sweden where she will again be doing some additional teaching.

Luke Norris, Ken Kahn and the fishing prototype created using the MIT app inventor

Luke Norris, Ken Kahn and the fishing prototype created using the MIT app inventor

ORDS ELS Update 1.0.6 has been released and fixes a number of software bugs. There are ten full research projects and 14 trial projects in the system at the moment, which is good progress towards the target of 20 full projects by September 2015.

The Things to do with data series is running again and we will soon release recordings of these talks online.

The Lecture capture project was funded and we will deliver a work package that will evaluate current solutions i.e. for recording and sharing lectures in a way that people can attend remotely and the footage can be shared afterwards with voice matched up with presentation slides.

The Oxford Innovation platform was launched for IT Services, Libraries and Museum staff. Our team contributed many ideas and comments and we look forward to finding out which are funded.

Luke Norris completed his 1 week work experience placement from Woodgreen school in Witney. Luke has already decided he wants to be a game programmer. Luke investigated tools for creating a game that fisherman (and other stakeholders) would play to design a common pool resource institition (aka sustainable fishing in light of climate change and the bleaching of coral reef that is happening very rapidly all around the world). Luke is 15 and in the last year of his GCSEs.

Progress against plans for last 3 months

Engagement statistics, September to November 2014

Engagement statistics, September to November 2014

  1. Meriel is leading our communications plan and we have requested a series of changes to the research support page on the IT Services website.
  2. The ORDS early life support project is underway and the team have just submitted release 1.0.6. We have also initiated the process to handover application ownership to the software solutions team.There are currently ten projects in the system.
  3. Current projects:
    1. VALS is a project that aims to provide “virtual placements” for computing students where they work with mentors on open source projects. So far 64 open source organisations have contributed 237 potential placements.
    2. WebCMS project has been put on hold until January 2015 but we are supporting the project by conducting requirements gathering and analysis exercises.
    3. DiXiT is a 3 year Marie Curie ITN where Oxford is employing Magdalena Turska for 20 months to look at scholarly digital edition publication infrastructure.
  4. We submitted the following project proposals to the research committee:
    1. OxLangCloud would provide online access for research purposes to members of the University, as well as authenticated and authorized users from other HEIs, to the large and growing number of textual resources managed by the
      University.
    2. Live Data would create a pilot data visualisation service for the research community at Oxford. The project will demonstrate how data sets can be visualised to promote public understanding of research.
    3. Participant Data would investigate how we can support academic researchers who need to maintain a database of participant details e.g. in order to conduct longitudinal social science studies, invite people in for psychology experiments or conduct vaccinatation trials.
    4. Redds would scope a deposit process for archiving databases created in ORDS
  5. We’re waiting to find out our role on the StaaS project i.e. supporting the selection of a tool that would make it easy for researchers to store data
  6. We decided not to look into whole lab RDM solution at this stage, and we have instead decided to focus on a project with software solutions that would deliver a coherant set of webservices for support research requests, with a particularl eye on more advanced requests e.g. making research data sets available for search, browse and visualisation.
  7. The communications plan is set up and we are submitting articles regularly e.g. to the medical sciences newsletter and IT Services communications
  8. We have not been able to implement the changes we need to make to the IT Services website because of the recent severe security issues that have hit Drupal instances.
  9. We ran a 3 hour meeting with service teams across IT Services who provide support for research i.e. research support, ITLP, software solutions, ARC team. The main outcomes are:
    1. Research support team to setup and implement a single point of contact for researchers and ensure that IT Services offers a high quality advice, support and guidance service for researchers who request IT-related advice.
    2. To change the research support page on the IT Services website to reflect the full range of services we provide i.e. ARC, ITLP, Crowdsourcing, Software Selection,

Plans for next 3 months

  1. Update research support service reporting based on what is requested by the Research Committee
  2. Deliver or continue ongoing projects: ORDS ELS, VALS, WebCMS, DiXiT
  3. Start new projects if funded i.e. OxLangCloud, Live Data, Participant data and Redds
  4. Plan our work on the lecture capture project that has just received funding
  5. Create a new wall of faces page within the Openspires site to feature researchers interested in the openness agenda, and create a new documentary style video focused on research data at Oxford.
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Where do Oxford researchers manage the source code for their software?

I’ve been taking a look around lately at the various places where researchers are keeping the source code for their software.

Its not an exhaustive survey by any means (though maybe we should do one of those), but it seems that there are two common options.

Octocat - the mascot of Github

Github is, as you would expect, a very popular place to host source code. Here you can find the Micron Oxford Bioimaging Unit, for example, the Oxford Clinical Trials Unit, and the Oxford Internet Institute. Its also where IT Services hosts its own open source projects. Even the New College JCR has its own space on Github!

GitHub is a good choice given its well known, has good supporting services such as issue tracking and website hosting, and lets you register an organisation as the owner of multiple projects. It also allows a small number of private repositories for free as well as unlimited public repositories.

The gitlab mascot

However, for research groups that need to manage private code repositories, or want to host the code locally, GitLab seems to be a popular option. GitLab provides many of the supporting services that you find on GitHub, such as issue tracking, but can be hosted locally with no limit on the number of private repositories, and can even be integrated with other services such as LDAP. You can find GitLab installations at Oxford in Mathematics, at the FMRIB, and the Bodleian.

Subversion logo

There are also a few Subversion repositories around; we use one in IT Services for managing our websites (among other things), and there’s one in Computer Science. Given that these are primarily for internal use I suspect there are quite a few more out there we aren’t aware of.

If you’d like help choosing where to host software source code for your research group, send us an email at researchsupport@it.ox.ac.uk

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How to: create a bubble chart from a Google Spreadsheet using D3.js

Earlier in this series I discussed how to get data out of a Google Spreadsheet in JSON format using an API call, and how to convert the JSON data into an array. Now I’m going to talk about how to visualise the data as a bubble chart on a web page, using the fantastically powerful JavaScript library D3.js, aka Data Driven Documents.

For this exercise I’ve created a Google Spreadsheet representing some information about a fictional group of people with a count of their interactions. You can see the spreadsheet here.

Following the instructions in the previous How To guides we can get this data using JSONP; you can see the result for yourself here.

So, having got the source data, how are we going to visualise it?

Well, the first step is to transform the data once again into a structure that is more suitable for the D3.js techniques we want to use. In this case we’re creating a bubble chart using a method called d3.layout.pack(). This takes a tree structure of objects, and fits them into a volume based on the value property of each leaf node. In our example, the value we’re interested in is the number of interactions – so team members with more interactions will be represented by larger bubbles within the visualisation.

So how do we do that? Well, the easiest approach is to iterate over each row in the data, and create an object for it with a name, a value and a group. (The group property in this case is the team the person belongs to.) These “leaf” objects can then be added to a “root” object to make a tree in JavaScript.

The code for this looks like so:

    var root = {};
    root.name = "Interactions";
    root.children = new Array();
    for (i=0;i<dataframe.length;i++){
      var item = {};
      item.name = dataframe[i][0];
      item.value = Number(dataframe[i][1]);
      item.group = dataframe[i][2];
      root.children.push(item);
    }

So, taking it one line at a time – we create a root object, give it a name, and create a new empty array inside it called children. We then we go through each row in the dataframe and create an item object for each one, mapping the name, value and group properties to the correct columns in the spreadsheet. Each item is added to the children array.

We now have a tree of objects, each of which has a name, a value and  a group.

How do we create a nice-looking bubble chart with them?

First we set up the d3.layout.pack function so it can calculate the size and position of the bubbles. We do this using:

var bubble = d3.layout.pack().sort(null).size([960,960]).padding(1.5);

If you were to now call …

bubble.nodes(root)

… and take a look at the output, you would see each “leaf” object  now has several new properties for “x”, “y” and “r”. The “x” and “y” properties are where within the chart to position the bubble, for the object and the “r” property is the radius of the bubble.

(How this is actually drawn is up to you – you could equally well take this information and draw the whole thing using hexagons or squares or spheres. But lets stick to circles for now.)

Next we need to create a graphic for the chart in our HTML page. D3 can make this for us like so:

    var svg = d3.select("body")
                .append("svg")
                .attr("width",960)
                .attr("height", 960)
                .attr("class","bubble");

For each “leaf” we then need to create a graphical element. D3.js uses a very clever approach for this:


    var node = svg.selectAll(".node")
                  .data(bubble.nodes(root)
                  .filter(function(d){ return !d.children;}))
                  .enter()
                  .append("g")
                  .attr("class","node")
                  .attr("transform", function(d) { return "translate(" + d.x + "," + d.y + ")"; });

The key thing here is the data() method. We pass this the bubble layout we created earlier, and ask it to create the nodes based on our root object. (We also filter out the root node itself as we’re not interested in drawing that, just the individual leaf nodes.) The enter() method is then called for each leaf node in the tree, which appends a <g> element to the <svg> element in our HTML document, and applies the transform property to it to place it at the correct x and y coordinates within the chart.

This still doesn’t draw anything interesting, so lets make some circles for each node, and give them a label:

   var colour = d3.scale.category10();
   node.append("circle")
       .attr("r", function(d) { return d.r; })
       .style("fill", function(d) { return colour(d.group); });
   node.append("text")
       .attr("dy", ".3em")
       .style("text-anchor", "middle")
       .text(function(d) { return d.name; });

The result of all this is a nice diagram! Click to view it full size; you can also see the live version here.

A bubble chart

The complete source code for this How To guide can be found on Github.

If you’d like to know more about data visualisation, you can get in touch with us at researchsupport@it.ox.ac.uk.

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How to: convert Google Spreadsheet JSON data into a simple two-dimensional array

In a previous post I explained how to extract JSON data from a Google Spreadsheet via an API call.

However, when you actually get the data, the JSON isn’t really in the kind of structure you would imagine. Instead of a matrix of rows and columns, Google returns an RSS-style linear feed of “entries” for all of the cells!

So how to convert that into something that you can use in D3.js or R?

We need to iterate over each entry in the feed, and push the values into an array, moving to a new “line” in the array each time we get to a cell that is at the beginning of a row in the spreadsheet. I’ve written a JavaScript function to do the work necessary; you can get the code on Github.

Running this function we can then get the values from the resulting array using something like:

data[1][5]

Note that the function doesn’t differentiate the labels from a header row (which is something you’d commonly see, and which R would usually expect) so there is definitely room for improvement in the function.

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How to: get data out of a Google spreadsheet using JSONP

Google Drive spreadsheets are a great way to collect data using their handy forms, but the visualisation tools sometimes aren’t sufficient. What if you wanted to do a visualisation using d3.js for example?

Google has an API for obtaining the data using JSONP; this means that the data is exposed in JSON format using a callback function – this gets around the “Same Origin Policy” restriction on accessing data from a different web domain.

To do this, you need to:

  1. Make your spreadsheet public
  2. Get the ID of your spreadsheet and worksheet
  3. Include a script tag calling the API
  4. Write a callback function to use the data

1. Make your spreadsheet public

In Google Drive, go to File > Publish to the web … and click Publish. You can only obtain data from a sheet that is publicly readable.

2. Get the ID of your spreadsheet and worksheet

This isn’t as obvious as it sounds. Your spreadsheet URL will contain some sort of long identifier, but this isn’t the only information you need – you also need the shorter worksheet ID as well.

You can find the worksheet number by calling a URL constructed like so:

https://spreadsheets.google.com/feeds/worksheets/your-spreadsheet-id/private/full

Note that you must be logged in to Google Drive to do this, or the URL will return nothing at all!

Calling this URL will return an RSS feed that will contain something like this:

<entry>
<id>https://spreadsheets.google.com/feeds/worksheets/your-spreadsheet-id/private/full/o10c0rt</id>
<updated>2014-10-08T11:35:31.493Z</updated>
<category scheme="http://schemas.google.com/spreadsheets/2006" term="http://schemas.google.com/spreadsheets/2006#worksheet"/>
<title type="text">Form Responses 1</title>

The information you need is in the <id> tag. The last part of the id is the worksheet identifier.

3.  Include a script tag calling the API

In your HTML, include a script tag, like so:

<script src="https://spreadsheets.google.com/feeds/cells/your-spreadsheet-id/your-worksheet-id/public/values?alt=json-in-script&callback=sheetLoaded"></script>

Obviously you need to replace “your-spreadsheet-id” and “your-worksheet-id” with the values from the previous step.

4. Write a callback function to use the data

In your javascript code you need to implement the callback function named in the script tag, so in the above example we need to do something like:

function sheetLoaded(spreadsheetdata) {
 // do something with spreadsheet data here
 console.log(spreadsheetdata);
}

Job done! Now you can actually start doing the clever D3 visualisation part…

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Research at Risk: Report from the Jisc Co-Design Workshop

On the 22nd of September, I was invited to a Jisc Co-Design event on “Research at Risk”, with participants from organisations such as UCISA, RLUK, RUGIT, DCC, and of course some universities, including yours truly representing both the University of Oxford, and also as a special bonus the University of Bolton.

What follows are my completely informal and unofficial notes of the event.

Looking for the Gaps

This was about the need to properly map the entire architecture for RDM to identify where the gaps and joins are to inform decision making at different levels.

One issue we face is that many research data management solutions are barely past the prototype stage. Rather than build completely new services, it would make more sense to look at the solutions that are closest to matching requirements, such as CKAN and HYDRA, and work together to make them complete. The OSS Watch report on RDM tools highlighted the fact that many of the tools developed had very poor sustainability prospects, linked to the fact that they were developed with a small local user base and without long term sustainability planning. The next step could be to focus on a few solutions and ensure they are fit for purpose and sustainable.

Likewise, on the storage side there is already OwnCloud, which several institutions are interested in developing further. As an open source project, we can work on this collaboratively to ensure we have a good solution, while Jisc can work on the matching service offering for it for institutions that don’t have their own data center. Anyway, more on this later.

At a higher level, this whole area seems to be about taking stock of where we are now, which seems a pretty sensible thing to do.

What we know

Similar to the previous topic, but really about putting together the advice, guidance and lessons learned. UCISA were very keen on this one.

An interesting thing I learned about here was the “4Cs” cost exchange project that Jisc (or DCC, I wasn’t sure which) are engaged in, which seems to be principally about baselining IT costs against peers, including in areas such as RDM.

The Case for RDM

There seemed to be consensus that there is a gap between ideology and practice, and that while there is plenty of talk around mandates from the Research Councils and journals, there hasn’t really been very much from the researcher perspective, and this is something that needs to be addressed. So making the case, not from a mandate perspective, but from a benefits to researchers perspective.

One issue here is the different demands on research data depending on whether the intent is compliance, validation, reuse, or engagement. To make data truly reusable requires more effort than simply dumping it in an archive, but also can yield more benefits to researchers.

Changing Culture

This was seen as probably a good thing longer-term, but it wasn’t clear exactly what it would involve, or what role Jisc would play. For example, the previous three items taken together might constitute actions leading towards culture change. This also encompassed areas such as treating RDM as a professional skill and providing support for developing its practice. Another practical area is information sharing between institutions.

Making data count

This idea was all to do with metrics and measures, though it wasn’t clear what those metrics might look like. There could be some progress by combining existing measures and sources, such as DataCite, and then seeing where that leads.

Simplifying Compliance

There was an amusing comparison between RDM compliance and Health and Safety. However, we have the current situation where compliance is not standardised between the Research Councils, or between the Councils and the journals that mandate RDM. Help and support on compliance is also outdated, or difficult to find.

Another topic we discussed was something I’ve dubbed (wearing my University of Bolton hat) as “barely adequate research infrastructure for institutions that only give half a toss” – basically, many Universities are not research intensive and do not have dedicated resource in either Library or IT Services to support RDM, or even Open Access. Instead, a simple hosted solution with a reasonable subscription rate would be absolutely fine.

What was interesting is that some of the research intensive universities were also keen on this idea – can we just have ePrints+CKAN+DataCite+etc all set up for us, hosted, Shibbolized, configured to meet whatever the Research Councils want, and ready to just bung on a University logo?

Simplifying Data Management Plans (DMP)

There seemed to be a general feeling that it isn’t clear who should be writing DMPs, or why they should be doing it. In some cases it seems that research support staff are producing these instead of researchers, which seems sensible. The general feeling is that creating a DMP is something you do for someone else’s benefit.

Some institutions have been customising DMPOnline. Interestingly, one area that gets explored is “model DMPs” or “copy and paste”. I somewhat cheekily suggested a button that, once pressed, generates a plausible-sounding DMP that doesn’t actually commit you to anything.

In any case, if compliance requirements are simplified and standardised (see above) then this would also in effect simplify the needs for DMPs.

Other ideas explored included being able to export a DMP as a “data paper” for publication and peer review, though I’m not sure exactly how that contributes to knowledge.

So again we have the issue of what’s in it for researchers, and the tension between treating RDM as a hoop to jump through, or something with intrinsic benefit for researchers.

Metadata

There was a case made for this by DCC (Correction – Actually it was Neil Jacobs – Thanks Rachel!), which is basically around standardising the metadata profile for archiving research data, working on DataCITE, CRIS, PURE, ORCID, achieving consensus on a core schema and so on.

This sparked off a debate, my own contribution being “it may be important for some, but don’t start here” which seemed to resonate with a few people.

There was also the interesting area of improving the metadata within the data itself – for example making the labels within data tables more explanatory to support reuse – rather than just adding more citation or discovery metadata.

Storage as a service

This was the only major “techie” discussion, and it was interesting to see how much convergence there was between the Universities present at the event. So we had the issue of how we work with Dropbox (which many researchers really like), through to how we make best use of cloud storage services as infrastructure.

I asked whether Jisc had met with DropBox to discuss potential collaboration and apparently they have, though it seems not with great success. This is a pity as one potential “win” would be for researchers to be able to make use of the DropBox client tools, but synchronised with a UK data centre, or even institutional data centres.

Another interesting dimension was that several institutions have been looking into OwnCloud as a Dropbox replacement, and there was strong interest in collaborating to add any missing capabilities to OwnCloud (its open source) to bring it up to parity. Maybe thats something Jisc could invest in.

Preservation

I hadn’t met Neil Grindley before, and was surprised to see he bore more than a passing resemblance to the late SF author Philip K Dick. But anyway, onto the topic.

Preservation (and managed destruction) is one of those topics that people are either passionate about, or sends them into a kind of stupefied trance. I’m one of the latter I’m afraid. Its probably very important.

The only thing I can add to this is that the issue of preserving not just the data, but the software needed to process it, is not something that has been considered as part of the scope of this programme by Jisc.

Its nice also that they are considering using hashes to verify data integrity.

The Voting

Using the ultra scientific method of putting numbered post-it notes onto sheets of paper, the ranking of ideas looked like this:

Activity area (Raw data) Number of votes 1 2 3 4 5
Looking for the gaps 224535343 9 0 2 3 2 2
What we know so far 5245154 7 1 1 0 2 3
Case for sharing research data 1144221211 10 5 3 0 2 0
Changing the culture of research 4 1 0 0 0 1 0
Measuring the impact 215125 6 2 2 0 0 2
Simplifying compliance 34232333411 11 2 2 5 2 0
Simplifying data management planning 255355213 9 1 2 2 0 4
Data about data 35525 5 0 1 1 0 3
Sharing the costs of data storage 32444 5 0 1 1 3 0
Data for the future 12541143 8 3 1 1 2 1
Interestingly enough, although “Storage” wasn’t ranked highly, it was the topic that seemed to spark the most discussion amongst the university representatives after the event closed, and several of us pledged to work together in future to collaborate on our various approaches to solve these issues,

Funding?

Of course, it being a Jisc event, we wanted to know if there was going to be any funding!
The good news is, that as well as funding a number of larger projects already through capital funding (e.g. BrissKit), there are plans afoot for a “Research Data Spring” competition for innovation projects, I guess following a similar pattern to the successful Summer of Student Innovation competition but targeted at researchers and IT staff in universities.

More!

If you’d like to know more about this event, and read the “official” notes, then just get in touch with us at researchsupport@it.ox.ac.uk.
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Jisc Summer of Student Innovation

This summer, Jisc ran its second Summer of Student innovation.  Via the Jisc Elevator, students can pitch their idea to improve the student experience using technology.  Successful projects receive £5000 and mentorship over the summer to develop and realise their idea.  Around 20 projects were received funding this year, covering student induction, study tools, learner feedback, and open access.  Some of the successful projects included Open Access Button, Lingoflow, and Vet-Revise.

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For the second year running, members of Research Support were invited to attend the SOSI Summer School events, providing advice and guidance on technical implementation, legal issues and business models to the projects.  Advice provided this year included introducing version control, good software engineering practices, identifying potential commercial partners, exploring different sustainability options and business models, and assessing technical feasibility of software designs.  If your research group could use advice in these or related areas, please contact researchsupport@it.ox.ac.uk to discuss your needs.

Image Credit: Innovation Lab

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