Research Data vs Research Data

As I’ve been looking closer at various requirements for Orbital, as well as other research data management projects, it’s becoming increasingly apparent that Orbital has taken a different tack when it comes to defining what research data actually is. Whilst not a problem, it does lead to a certain disconnect when talking to people with a different idea about what data means.¬†When it comes to storing data the disconnect is even bigger, caused by people experiencing problems breaking the transit format of the data away from the data itself. In true engineering/computing style, it’s time for an analogy. I’m using sweets because hey, sweets are awesome.

Sugar! Sugar!

Imagine a tube of Smarties (or sugar-coated chocolate beans of choice). When I talk about research data I’m talking about the individual smarties, the individual nuggets of information. You could tip 100 tubes of smarties into a bowl and you’d just end up with a big pile of smarties. You could then go through and sort the smarties by colour, or perform some other type of organisation. Since you’ve got the individual smarties out of their containers it’s a lot easier to see a whole overview and work with them all at once.

 

Taking this approach makes sense to me, because if I want to throw in a couple of bags of Peanut M&Ms I can do without suddenly having a tube saying “Smarties” which contains nuts. I can still sort my pile of sweets into colours, or into types. I can orient them by the little letters on top. I could throw in a handful of jelly beans and a bar of chocolate broken into squares, and then order by sugar content, colour, and number of artificial flavours. The possibilities are quite literally limited only by my tolerance for sugar highs.

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Pivoting Around

As part of Orbital’s development we need to keep what we’re doing on track, and ensure that what is produced is actually what people are after. We’re building the project using agile development methods, which mean that instead of generating a load of documentation and exacting requirements up front and then building software, we generate a basic set of requirements, start developing and then return to look at new or changed requirements at regular intervals.

Keeping tabs on this kind of thing requires a management tool, and in our case we’re using the wonderful Pivotal Tracker, and here’s why.

Pivotal allows us to break down user requirements (gathered through a variety of means, including meetings, surveys, observation and so-on) into discreet bundles called ‘stories’, each of which represents something that a user needs (or wants) to be able to do with the final product. An example may be “project administrators must be able to assign roles to project users”, or “users must be able to manually add a data point”. By creating these stories it starts to become clearer what actually needs to be done.

From there we can start to fully analyse each of these stories, providing them with information such as a ‘score’ of how difficult to achieve each story will be, or including sub-tasks for actual development purposes. Stories can be assigned to various people based on who needs to be involved, and go through a clearly defined workflow of being started, being finished, being delivered in a product version and being approved by the customer.

On top of this management of user stories we can also pack out Pivotal with higher-level package deliverables and deadlines, along with bug reporting and general project chores. Once we’ve got all these things into the Tracker we’re able to re-order them as priorities shift, giving us an instant overview of what’s happening in the current iteration (a 2-week long development cycle) as well as what’s going to be happening in future iterations. At this point, Pivotal Tracker comes into its own with something called ’emergent planning’.

Emergent planning takes a look at how we’re actually performing in terms of crunching through our list of user stories and dynamically adjusts which stories we’re going to be tackling in upcoming iterations. If we’re doing well we begin to see more points worth of development per iteration, and if we’re slipping then Tracker gives us fewer. Since we’ve told Pivotal what needs to happen before certain deadlines are met (when we ordered stories and tasks), and since Pivotal knows roughly how fast we’re working, it’s easy to see if we’re predicted to hit or miss development milestones.

Want to see what we’re up to? Our Pivotal Tracker project is open for you all to see.

Let’s Look At Data

Last week Joss and I had a chat with one of our engineering researchers about the kinds of data he handles in his research. This was an incredibly useful meeting, leading to a whole bunch of notes on data types, requirements and workflows. The one I’m taking a look at today is the flow that data takes from its source, through storage and processing, and into a useful research conclusion.

The existing workflow looks something like that shown above. Source data is manually transferred (often using ‘in-the-clear’ methods) from its point of origin to local storage on a researcher’s machine, where it will reside on the hard disk until it’s used. From there the data is processed (Engineering love using MATLAB, as do a lot of other science disciplines, so that’s the example here) and potentially the results of that analysis are recombined with the local storage for further work. At some point the processing will arrive at conclusions for the data, and from those an output can be drawn.

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Welcome aboard, this is your developer speaking…

Hi everyone! For those who don’t know me (this is my first blog post for Orbital) I’m Nick Jackson, the lead web developer for the project. I’ll be blogging about all kinds of bits and pieces, ranging from discussion of user requirements through to the rationale for features, how certain bits of Orbital fit together with the wider world, overviews of technologies that we’re using and the minutiae of implementation.

If you’re interested in this kind of stuff at an institutional level you may also be interested in following my other blog, where I discuss things I’m working on in a wider context along with the occasional opinion piece on technology in education.

For those of you who enjoy Twitter you can find me at @jacksonj04, and don’t forget that you can keep up with the latest from Orbital at #OrbitalMRD. If you’re more a face-to-face person then you can find me most days at the University of Lincoln’s Brayford campus, and I’m open to being plied with coffee.