Collaboration, schmollaboration

Whenever someone asks me what my research project is about, I usually open by saying we’re calling it collaborative knowledge discovery from data. That’s a nice, convenient way of putting it in a nutshell, but it immediately calls for some elaboration, especially on the meaning of the term “collaborative”. Technically, any activity that involves two or more people working together toward a common goal is collaborative, but this definition doesn’t get us very far, because in knowledge discovery you typically have at least someone who knows about the technology and someone who knows about the application domain. It’s not unheard of for one person to know about both, but still, I think it’s safe to say that collaboration is the rule rather than the exception here.

To narrow it down a bit, the kind of collaboration we’re talking about is remote and synchronous. In other words, the participants are not located in the same place, but they can all simultaneously edit whatever it is they’re collaborating on and see the effects of each other’s edits in real time. This implies that there must be some kind of online environment where the collaboration takes place; think something like Google Docs or MS Office Online, only for KDD artifacts such as datasets, algorithms and processing pipelines.

Even this is not a particularly novel idea in itself, as there are collaboration platforms already available where you can develop just these sorts of things. Therefore in KDD-CHASER we’re upping the ante even further by focusing specifically on collaborative knowledge discovery from personal data, driven by the data owner who cannot be assumed to have any particular technology or domain expertise. It’s a bit of a niche, which of course makes our eventual results somewhat less generalisable, but it also makes it considerably easier to spot opportunities for novel research contributions.

To me, the most interesting problems here are not necessarily associated with knowledge discovery as such but with the things that need to happen before knowledge discovery can take place. After all, from the data owner’s perspective the point of collaborating with experts is basically to have the actual discovery done by people who are better equipped for it in terms of skills and tools. This doesn’t mean, however, that the data owner’s role in the collaboration is limited to being a passive data source; on the contrary, it is the data owner’s needs that drive the entire process of collaborative KDD in the special case we’re considering.

The first problem that a data owner may encounter on the way to a successful collaboration is that they don’t even know anyone they could collaborate with, so the first thing the collaboration platform should do is provide a matchmaking service that brings together people who have data with people who have the right sort of expertise to help turn it into something more valuable. After the matchmaking follows the really interesting part: negotiation. What kind of knowledge is the data owner interested in? What is actually achievable, given the available data and the extent to which the data owner is willing to share it with the expert? What is the expert expecting to get in compensation for their efforts? The collaborators need to find the answers to such questions among themselves, and the collaboration platform should support them in this.

The bare minimum is to provide the collaborators with some kind of communication channel, but this is something that would be required anyway, and it’s hardly a research problem from a computing standpoint. However, there’s a lot more to negotiation than just talking, and I’m interested to see what I might do to help things along in this area. Privacy, for example, is traditionally close to my heart and something that I want to address also here, because one of the things to be determined through negotiation is how much of their data the data owner is prepared to trust their collaborators with, considering that the latter may be the KDD equivalent of someone they just matched with on Tinder.

It’s been pretty clear from the start that whatever we manage to accomplish in my current project, it’s not going to be a comprehensive solution to all the problems of collaborative KDD, even within the niche we’ve carved for ourselves. What we can realistically shoot for, though, is a model that shows us what the collaboration process looks like and gives us an understanding of where the major problems are. The software I’m building will basically be a collection of candidate solutions to a select few of these problems, and it will hopefully be something I can continue to build on when my MSCA fellowship is over.

Far side of the world

Things are getting quite busy again, as the project has come to a stage where I need to be producing some publications on early results while also doing implementation work to get more solid results, not to mention thinking seriously about where my next slice of funding is going to come from. Any one of these could consume all of my available time if I allowed it to, and it’s not always easy to motivate yourself to keep pushing when the potential returns are months away at best. What is all too easy, however, is to neglect things that are not strictly necessary – blogging, for example, but I’m determined to write at least one new post each month, even if it’s only because it makes for a welcome respite from the more “serious” work.

One thing that can help a great deal in maintaining motivation is if you have something nice in the not-too-distant future to look forward to, and as it happens, I have quite a biggie: the paper I submitted in January got accepted to the IEEE Congress on Evolutionary Computation, which will be held in Wellington, New Zealand. It’s a bit of a strange event for me to attend; while I do find the field very interesting, my professional experience of it, not counting some courses I took years ago when I was a doctoral student in need of credits, is limited to having been a reviewer for CEC once. However, there is a special session there on the theme of “Ethics and Social Implications of Computational Intelligence”, and this is something I have done actual published work on. It’s also one of the themes I wanted to address in my current project, so that’s that box ticked I guess. Besides, visiting NZ has been on my bucket list for quite a while, so I could hardly pass up the opportunity.

So, a small fraction of my time this month has been spent at the very pleasant task of making travel plans. Wellington lies pretty much literally on the opposite side of the globe from Dublin, so even in this day and age travelling there is something of an operation. It’s not cheap, obviously, but that’s not really a problem, thanks to my rather generous MSCA fellowship budget. The main issue is time: the trip takes a minimum of 27 hours one way, and the “quick” option leaves you with precious little time to stretch your legs between flights. I didn’t exactly relish this idea, so I ended up choosing an itinerary that includes a 12-hour stopover in Sydney on the outbound journey. This should give me a chance to take a shower, reset my internal clock and yes, also go have a look at that funny-looking building where they do all the opera.

It would make little sense to go all that way just for a four-day conference, so after CEC I’m going to take some personal time and spend part of my summer holiday travelling around NZ (even though it will be actually winter there). I still want to spend a couple of weeks in Finland as well, so I have to be frugal with my leave days and efficient in how I use my limited time. Therefore I’m going to be mostly confined to the North Island, although I am planning to take a ferry across Cook Strait to Picton and back – the scenery of the Marlborough Sounds is supposed to be pretty epic. On the North Island I’m going to stop in Auckland and Rotorua before coming back to Wellington; between Auckland and Rotorua, the Hobbiton movie set is a must-see for a Tolkien reader and Lord of the Rings film fan such as myself.

As for the conference, I’m very much looking forward to the plenary talk by my countryman Prof. Risto Miikkulainen on “Creative AI through Evolutionary Computation”. The idea of machines being creative is philosophically challenging, which is part of why this talk interests me, but I’m also intrigued by the practical potential. The abstract mentions techy applications such as neural network architecture design, but personally, I’m particularly interested in artistic creativity – in fact, when I was doing those evolutionary computation courses at my alma mater, I toyed with the idea of a genetic algorithm that would serve as a songwriting aid by generating novel chord progressions. Apart from the plenaries, the conference programme is still TBA, but it’s always good to have a chance to meet and exchange views with people from different cultural and professional backgrounds, and since Wellington is apparently the undisputed craft beer capital of NZ, I’m expecting some very pleasant scholarly discussions over pints of the nation’s finest brews.

Getting fit, bit by bit

I’ve been making decent progress on my software, and while it’s no good yet for any kind of data analysis, it can already be used to do a number of things related to the management of datasets and collaborations. I may even unleash the current incarnation upon some unsuspecting human beings soon, but for now, I’m using myself as my first guinea pig, so I’ve started wearing one of the Fitbits I bought myself (or rather, for my project) for Christmas. From the perspective of my research, the reason for this is that I need to capture some sample data so I can get a look at what the data looks like when it’s exported from the Fitbit cloud into a file, but I’m also personally interested in seeing firsthand what’s happened in fitness trackers since the last time I wore one, which was quite a few years ago and then also for research purposes.

Back then I wasn’t hugely impressed, but it seems that by now these gadgets have advanced enough in terms of both functionality and appearance that I would consider buying one of my own. My initial impression of the Fitbit was that it’s quite sleek but not very comfortable; no matter how I wore it, it always felt either too loose or too tight. However, it seems that I either found the sweet spot or simply grew accustomed to it because it doesn’t bother me that much anymore, although most of the time I am still aware that it’s there. I’m probably not wearing it exactly as recommended by the user manual, but I can’t be bothered to be finicky about it.

By tapping on the screen of the device I can scroll through my basic stats: steps, heart rate, distance, energy expenditure and active minutes. More information is available by launching the Fitbit app; this is where I see, for example, how much sleep the device thinks I’ve had. Here I could also log my weight and what I’ve eaten if I were so inclined. Setting up the device and the app so that they can talk to each other takes a bit of time, but after that the device syncs to the app without any problems, at least on Windows. However, for some reason the app refused to acknowledge that I’m wearing the Fitbit on my right wrist rather than my left; this setting I had to make on the website to make it stick. The website is also where I export my data, which is quick and straightforward to do, with a choice between CSV or Excel for the data format.

The accuracy of the data is not really my number one concern, since I’m interested in the process of collaborative data analysis rather than the results of the analysis. However, on a personal note again, it is interesting to make observations on how the feedback I get from the device and the app relates to how I experience those aspects of my life that the feedback is about. For example, I can’t quite escape the impression that the Fitbit is flattering me, considering how consistently I’ve been getting my daily hour or more of activity even though in my own opinion I certainly don’t exercise every day. On the other hand, I do get a fair bit of walking done on a normal working day, including a brisk afternoon walk in the park next to the university campus whenever I can spare the time, so I guess it all adds up to something over the course of the day.

Based on my fairly brief experience, I can already see a few reasons for the rising popularity of wearables such as the Fitbit. Even if the accuracy of the data in terms of absolute values leaves something to hope for, presumably the device is at least reasonably consistent with itself over time, so if there are any rising or falling trends in your performance, they should be visible in the data. To make the product more friendly and fun to use, the developers have used a host of persuasion and gamification techniques; for example, there are various badges to be earned, with quirky names like “Penguin March”, and occasionally the device gets chatty with me, offering suggestions such as “take me for a walk?”. When I reach the daily magic number of ten thousand steps, the Fitbit vibrates a little silent congratulatory fanfare on my wrist.

In terms of what I need to carry out my project, the Fitbit will definitely serve: setting it up, syncing it and exporting the data all seem to work without any big hassle. As for whether I’m going to get one for myself, I would say that it’s now more likely than before that I will get some kind of wearable – not necessarily a Fitbit, but one that will give me the same kind of information anyway. Having this opportunity to try out a few different ones is an unexpected perk of the project that I now suddenly welcome, even though I wasn’t particularly interested in these devices when I was applying for the grant.

Getting engaged

Besides research, one of the things I’m supposed to be doing as a Marie Curie research fellow is learning new things. Of course, that’s a good thing to be doing regardless of what other things you do, but in the case of the fellowship, it’s expected of me by the funder that I spend my time in Dublin doing things that will help me develop myself and my career prospects. I’ve already learned quite a few useful new things through my research work here, but I’ve also attended a number of training courses and workshops on various topics, and last week I had the opportunity to go to a particularly interesting one dealing with engaged research.

I learned about the workshop from the education and public engagement manager of the Insight Centre, who sent me an email about it and recommended that I sign up. I wasn’t previously familiar with the concept of engaged research, but as I was reading the description of the workshop, it soon became clear that it applies to quite a few, perhaps most, of the research projects I’ve been involved in over the course of my career so far. The gist of how this concept is defined is that it describes research where the individuals or organisations for whom the research is relevant are engaged to be involved in it, not merely as recipients of the eventual results but as co-creators of them. In my case the engaged partners have mostly come from industry, but they could also represent the public sector, civil society or the general public.

The workshop was facilitated by people from Campus Engage, a network that aims to “promote civic and community engagement as a core function of Higher Education on the island or Ireland”. Since Finnish universities have had social influence as their so-called third mission (research and education being the first two) for quite a years now, this statement also rings very familiar. A few days before the workshop, we were requested to fill out a survey with questions about our background and what sort of lessons we were particularly hoping to take home, which the facilitators then used to tailor the content to the interests of those attending the training.

A whole day of training can get very boring if it’s not well planned and executed, but there was no such problem here, as the presentations given by the facilitators were interspersed with discussions of our own questions and experiences, as well as small group activities. The latter involved, for example, studying an extract from a research project proposal and coming up with ways to improve it in terms of stakeholder engagement. One of the things I was hoping the training would give me was information and ideas on how to develop the engagement aspect of my own proposals, and this certainly qualified, although strictly speaking it was perhaps more of a cautionary example of how it’s not meant to be done. We did get more constructive planning tools as well, such as the logic model, a way of planning for long-term impact by laying out the path there as a series of if-then relationships starting with an analysis of the current situation.

Another thing about the workshop that I enjoyed was that we discussed some actual real-world cases of community engagement in action. A particularly interesting one was Access Earth, a mobile app that can be used by people with accessibility needs to find and rate hotels and restaurants by criteria such as accessible parking and wide doors. Clearly the key to successful implementation and deployment of such an application is engaging the people who are going to use it, both to get the design right and to collect the data on the accessibility of various places around the world, and one of the facilitators of the workshop has been working on the project as a community engagement advisor. The app is available worldwide, so the potential impact on the lives of people with disabilities is big – an inspiring example of what engagement is in practice and what it can accomplish.

Philosophical ruminations vol. 1

Holidays are over and I’m back from the Finnish winter wonderland in Ireland, who seems to retain an appreciable fraction of her famous forty shades of green even in the middle of winter. No sign of the pre-Christmas frenzy anymore – I’ve been working at a fairly leisurely pace for these past few weeks, enjoying the luxury of being able to take the time to have a good think about what I’m doing before I actually do it. The only deadline of immediate concern was the extended deadline of the conference for which I was preparing a paper before the holidays, and since I didn’t dare rely on there being an extension, I all but finished the manuscript during the break, so there wasn’t much left for me to do to it after I got back on the clock.

Since things are not so hectic now, I thought this would be a good time for a post discussing a topic that’s not directly concerned with what’s going on in my project at the moment. When I started the blog, my intention was that one of the themes I would cover would be the philosophical dimension of knowledge discovery, and there’s a certain concept related to this that’s been on my mind quite a lot lately. The concept is known as epistemic opacity; that’s epistemic as in epistemology – the philosophical study of knowledge – and opacity as in, well, the state of not being transparent (thanks, Captain Obvious).

I ran into this concept in a paper by Paul Humphreys titled “The philosophical novelty of computer simulation methods”, published in the philosophy journal Synthese in 2009. Humphreys puts forward the argument that there are certain aspects of computer simulations and computational science that make them philosophically novel as methods of scientific enquiry, and one of these aspects is their epistemic opacity, which he defines as follows:

[…] a process is epistemically opaque relative to a cognitive agent X at time t just in case X does not know at t all of the epistemically relevant elements of the process. A process is essentially epistemically opaque to X if and only if it is impossible, given the nature of X, for X to know all of the epistemically relevant elements of the process.

That’s a bit of a mouthful, but the gist of it – as far as I understand it – is that computer simulations are opaque in the sense that there is no way for a human observer to fully understand why a given simulation behaves the way it does. This makes it impossible to verify the outcome of the simulation using means that are independent of the simulation itself; a parallel may be drawn here with mathematics, where there has been criticism of computer-generated proofs that are considered non-surveyable, meaning that they cannot be verified by a human mathematician without computational assistance.

The philosophical challenge here arises from the fact that since we have no means to double-check what the computer is telling us, we are effectively outsourcing some of our thinking to the computer. To be fair, we have been doing this for quite some time now and it seems to have worked out all right for us, but in the history of science this is a relatively new development, so I think the epistemologists can be excused for still having some suspicions. I doubt that anyone is suggesting we should go back to relying entirely on our brains (it’s not like those are infallible either), but I find that in any activity, it’s sometimes instructive to take a step back and question the things you’re taking for granted.

The algorithms used in knowledge discovery from data can also be said to be epistemically opaque, in the sense that while they quite often yield a model that works, it’s a whole different matter to understand why it works and why it makes the mistakes that it does. And they do make mistakes, even the best of them; there’s no such thing as a model that’s 100% accurate 100% of the time, unless the problem it’s supposed to solve is a very trivial one. Of course, in many cases such accuracy is not necessary for a model to be useful in practice, but there is something about this that the epistemologist in me finds unsatisfying – it feels like we’re giving up on the endeavour to figure out the underlying causal relationships in the real world and substituting the more pedestrian goal of being able to guess a reasonably accurate answer with adequate frequency, based on what is statistically likely to be correct given loads and loads of past examples.

From a more practical point of view, the opacity of KDD algorithms and the uncertainty concerning the accuracy of their outputs may or may not be a problem, since some users are in a better position to deal with these issues than others. Traditionally, KDD has been a tool for experts who are well aware of its limitations and potential pitfalls, but it is now increasingly being packaged together with miniaturised sensors and other electronics to make a variety of consumer products, such as the wearable wellness devices I’m working with. The users of these products are seldom knowledge discovery experts, and even for those who are, there is little information available to help them judge whether or not to trust what the device is telling them. The net effect is to make the underlying algorithms even more opaque than they would normally be.

Now, I presume that by and large, people are aware that these gadgets are not magic and that a certain degree of skepticism concerning their outputs is therefore warranted, but it would be helpful if we could get some kind of indication of when it would be particularly good to be skeptical. I suspect that often it’s the case that this information exists, but we don’t get to see it basically because it would clutter the display with things that are not strictly necessary. Moreover, this information is lost forever when the outputs are exported, which may be an issue if they are to be used, for instance, as research data, in which case it would be rather important to know how reliable they are. I’d be quite interested in seeing a product that successfully combines access to this sort of information with the usability virtues of today’s user-friendly wearables.

Dear Santa

Now that I’ve managed to clear away all of the stressful and/or boring stuff that was keeping me busy, time to do something fun: Christmas shopping! After the break my project is going to be almost halfway through, and although it will be a good while yet before I’m ready to start conducting user tests, it’s time to start getting serious about recruiting participants. After all, the tests are supposed to be about analysing the participants’ data, so they can’t just walk in at their convenience – I need them to spend some time collecting data first, and to do that, they’ll need something to collect the data with.

Our initial idea was to recruit people who are already using a sleep monitor of some kind, and I’m sure we’ll be able to find at least a few of those, but naturally we’ll have a bigger pool of candidates if we have a few devices available to loan to people who don’t have one of their own. Also, it’s obviously useful for me to play with these devices a bit so I can get a better idea of what sort of data they generate and what’s the best way to export it if I want to use it for my research (which I do). Besides, I’m hardly going to spend my entire expense budget on travel even if I go out of my way to pick the most remote conferences I can find to submit papers to.

So I didn’t need to worry too much about what I can afford – one of the many great things about the MSCA fellowship – but that doesn’t mean that the choice of what to buy was straightforward, because the range of consumer products capable of tracking sleep is, frankly, a little bewildering. Some devices you wear on your body, some you place in your bed and some at the bedside, and although I soon decided to narrow down my list of options by focusing on wearables, that still left me with more than enough variety to cope with. Some of these gadgets you wear on your wrist, while others go on your finger like a ring, and the wrist-worn ones range from basic fitness bracelets to high-end smartwatches that will probably make you your protein smoothie and launder your sports gear for you if you know how to use them.

One thing that made the decision quite a lot easier for me is that the manufacturers of fitness bracelets now helpfully include all of their sleep tracking functionality in models that are near the low end of the price spectrum, and since I’m only interested in sleep data, there was no need to ponder if I should go with the inexpensive ones or invest in bigger guns. Also, I had a preference for products that don’t make you jump through hoops if you want to export your data in a CSV file or similar, so I looked at the documentation for each of my candidates and if I couldn’t find a straight answer on how to do that, I moved on. In the end I settled on three different ones: the Fitbit Alta HR, the Withings Steel, and the Oura Ring.

What I particularly like about this trio is that each of these models represents a distinct style of design: the Fitbit is a modern bracelet-style gadget, whereas the Withings looks more like a classic analog wrist watch, and the Oura is, well, a ring. I can thus, to a certain extent, cater for my study participants’ individual stylistic preferences. For example, I’m rather partial toward analog watches myself, so I’d imagine that for someone like me the design of the Withings would have a lot of appeal.

Today’s my last day at work before the Christmas break, and things are wrapping up (no pun intended) very nicely. The orders for the sleep trackers went out last week, this morning I submitted the last of my (rather badly overdue) ethics deliverables to the European Commission, and just minutes ago I came back from my last performance with the DCU Campus Choir for this year. The only thing that may impinge on my rest and relaxation over the next couple of weeks is that there’s a conference deadline coming up immediately after my vacation and I’m quite eager to submit, but I shouldn’t need to worry about that until after New Year. Happy holidays, everyone!

Busy times

With the end-of-year holidays approaching, things tend to get busy in a lot of places, not just in Santa’s workshop. My life in Ireland is no exception: there are five major work-related (or at least university-related) things that I’ve been trying my best to juggle through November, with varying success. Many of these will culminate over the next two weeks or so, so after that I’m hoping it will be comparatively smooth sailing till I leave for my well-deserved Christmas break in Finland. The blog I’m not even counting among the five and I’ve been pretty much neglecting it, so this post is rather overdue, and also a welcome break from all of the more pressing stuff that I should really be working on right now.

One area where I’ve had my hands full is data protection, where it seems that whenever a document is finished, there’s always another one to be prepared and submitted for evaluation. Getting a green light from the Research Ethics Committee was a big step forward, but there’s now one more hurdle left to overcome in the form of a Data Protection Impact Assessment. I’m very much learning (and making up) all of this as I go along, and the learning curve has proved a rather more slippery climb than I expected, but I’m getting there. In fact, I’m apparently one of the first to go through this process around here, so I guess I’m not the only one trying to learn how it works. I hope this means that things will be easier for those who come after me.

Meanwhile, I’ve been preparing to give my very first lecture here at DCU – thankfully, just one guest lecture and not a whole course, but even that is quite enough to rack my nerves. It is a little strange that this should be the case, even after all the public speaking I’ve had to do during my fifteen-plus years in research, but the fact of the matter is that it does still feel like a bit of an ordeal every time. Of course it doesn’t help that I’m in a new environment now, and also I’ll be speaking to undergraduate students, which is rather different from giving a presentation at a conference to other researchers. Still, I’m not entirely unfamiliar with this type of audience, and I can recycle some of the lecture materials I created and used in Oulu, so I think I’m going to be all right.

Speaking of conferences, I’m serving in the programme committee of the International Conference on Health Informatics for the second year running and the manuscript reviewing period is currently ongoing, so that’s another thing that’s claimed a sizable chunk of my time recently. Somewhere among all of this I’m somehow managing to fit in a bit of actual research as well, although it’s nowhere near as much as I’d like, but I guess we’ve all been there. The software platform is taking shape towards a minimum viable product of sorts, and I have a couple of ideas for papers I want to write in the near future, so there’s a clear sense of moving forward despite all the other stuff going on.

So what’s the fifth thing, you ask? Well, I’ve rekindled my relationship with choral singing by joining the DCU Campus Choir, having not sung in a proper choir since school. Despite the 20-year gap (plus a bit), I haven’t had much trouble getting into it again: I can still read music, I can still hit the bass notes, and I don’t have all that much to occupy myself in the evenings and weekends so I have plenty of time to learn my parts (although I’m not sure how happy my neighbours are about it). The material we’re doing is nice and varied, and the level of ambition is certainly sufficient, as it seems like we’re constantly running out of rehearsal time before one performance or other. Our next concert will be Carols by Candlelight at DCU’s All Hallows campus on the evening of Monday the 10th of December, so anyone reading this who’s in town that day is very warmly welcome to listen!

Sleepytime

I recently obtained approval for my research from the DCU Research Ethics Committee, so I’m now officially good to go. This might seem like a rather late time to be getting the go-ahead, considering that I’ve been doing the research since February, but so far the work has been all about laying the foundations of the collaborative knowledge discovery software platform (for which I’m going to have to come up with a catchy name one of these days). This part of the project doesn’t involve any human participants or real-world personal data, so I’ve been able to proceed with it without having to concern myself with ethical issues.

As a matter of fact, if it were entirely up to me, the ethics application could have waited until even later, since it will be quite a while still before the platform is ready to be exposed to contact with reality. However, the Marie Curie fellowship came with T&Cs that call for ethics matters to be sorted out within a certain time frame, so that’s what I’ve had to roll with. I’d never actually had to put together an application like this before, so perhaps it was about time, and presumably it won’t hurt that some important decisions concerning what’s going to happen during the remainder of the project have now been made.

One of the big decisions I’d been putting off, but couldn’t anymore, was the nature of the scenario that I will use to demonstrate that the software platform is actually useful for the purpose for which it’s intended. This will be pretty much the last thing that happens in the project, and before that the software will have been tested in various other ways using, for example, open or synthetic data, but eventually it will be necessary to find some volunteers and have them try out the software so I can get some evidence on the workability of the software in a reasonable approximation of a real-world situation. It’s hardly the most controversial study ever, but it’s still research on human subjects and there will be processing of personal data involved, so things like research ethics and the GDPR come into play here and need to be duly addressed.

What I particularly needed a more precise idea about was the data that would be processed using the software platform. In the project proposal I said that this would be lifelogging data, but that can mean quite a few different things, so I needed to narrow it down to something specific. Of course it wouldn’t make sense to develop a platform for analysing just one specific kind of data, so as far as the design and implementation of the software is concerned, I have to pretend that the data could be anything. However, the only way I can realistically expect to be able to carry out a meaningful user test where the users actually bring their own data is by controlling the type of data they can bring.

There were a few criteria guiding the choice of the type of data to focus on. For one thing, the data had to be something that I knew to be already available at some sources accessible to me, so that I could run some experiments on my own before inflicting the software on others. Another consideration was the availability of in-house expertise at the Insight Centre: I’ve never done any serious data mining myself, having always looked at things from more of a software engineering perspective, so it was important that there would be someone close by who knows about the sort of data I intend to process and can help me ensure that the platform I’m building has the right tools for the job.

When I discussed this issue with my supervisor, he suggested sleep data – I’m guessing not least because it’s a personal interest of his, but it does certainly satisfy the above two criteria. Furthermore, it also satisfies a third one, which is no less important: there are many different devices in the market that are capable of tracking your sleep, and these are popular enough that it shouldn’t be a hopeless task to find a decent number of users to participate in testing the software. The concept of lifelogging if often associated with wearable cameras such as the Microsoft SenseCam, but these are much more of a niche product, making photographic data a not very attractive option – which it in fact was anyway because of the privacy implications of various things that may be captured in said photographs, so we kind of killed two birds with one stone there.

Capturing and analysing sleep data is something of a hot topic right now, so in terms of getting visibility for my research, I guess it won’t hurt to hop on the bandwagon, even though I’m not aiming to develop any new analysis techniques as such. Interestingly, the current technology leader in wearable sleep trackers hails from Oulu, Finland, the city where I lived and worked before joining Insight and moving to Dublin. There’s been quite a lot of media buzz around this gadget recently, from Prince Harry having been spotted wearing one on his Australian tour to Michael Dell announcing he’s decided to invest in the company that makes them. I haven’t personally contributed to the R&D behind the product in any way, but I feel a certain amount of hometown pride all the same – Nokia phones may have crashed and burned, but Oulu has bounced back and is probably a lot better off in the long run, not depending so heavily on a single employer anymore.

A Solid foundation for social apps?

Tim Berners-Lee recently posted an open letter on the web, announcing the launch of Solid, a new technology platform that he and his team at MIT have been working on for the past few years, to the wider online community. Like a lot of people these days, he’s not too happy about the way our personal data is being controlled and exploited by providers of online services, but when the father of the web is telling you how it’s not gone the way he intended, you may want to prick up your ears even if you personally have no problem with the way things are. Not only that, but when he says he’s come up with something that we can use to set things right, it’s probably worth checking out.

We’ve all seen the headlines that result when a company with a business model based on aggregating and monetising personal data gets negligent or unscrupulous with the data in its possession, but these incidents are really just symptoms of a more fundamental issue concerning the architecture of basically every popular online social application out there. Even if we imagine a perfect world of ideal application providers that are completely open and honest about how they use your data and never suffer any security breaches, the fact remains that they, not you, control the data you’ve given them. You still own it, yes, but they control it.

Why is this an important distinction? The answer has to do with the coupling of your data with the specific services you’re using: you can’t have one without the other. As a result, your data is broken up into pieces that are kept in separate bins, one for each service, even when it would be really to helpful to have it all in the same place. If you want to use several services that all use the same data, you have to upload it to each one separately, and that’s assuming that you have or can get the data in a reusable format, which isn’t always the case. It would make a lot more sense to have just a single copy of the data and permit the services to access that – within privacy parameters that you have complete control of – and it would be even better if you could move your data to a different location without breaking all those services that depend on it.

Sound good? Well, the people behind Solid apparently want you to be able to do just that. Their proposed solution is based on decoupling data from applications and storing it in units called PODs (short for personal online data store). Applications built on the Solid platform can access the data in your POD if you give them permission to do so, but they don’t control the data, so they can’t impose any artificial restrictions on how you use, combine and reuse data from different sources. The end-users of Solid are thus empowered to make the best possible use of their data while retaining full control of what data they disclose and to whom, which is very much what I’m aiming for in my own research; I can easily see collaborative knowledge discovery as an app implemented on Solid or some similar platform.

So that’s the theory, anyway. What about reality? I can’t claim to have examined the platform in great depth, but at least on the surface, there are a number of things that I like about it. It’s built on established W3C specifications in what looks like a rather elegant way where linked data technologies are used to identify data resources and to represent semantic links between them – for example, between a photo published by one user and a comment on the photo posted by another. Besides your data, your POD also holds your identity that you use to access various resources, somewhat like you can now use your Google or Facebook credentials to log in to other vendors’ services, but without the dependence on a specific service to authenticate your identity. Of course, you still need to get your Solid POD from somewhere, but you’re free to choose whichever provider suits you best, or even to set up your own Solid server if you have the motivation and the means.

Whether Solid will catch on as a platform for a new class of social web apps is not just a matter of whether it is technically up to the challenge, though. The point of social media is very much to have everyone in your social network using the same applications, so the early adopters won’t have much of an impact if their friends decide that it’s just so much more convenient to keep using the apps where they already have all their connections and content rather than to switch platforms and build everything up all over again – which, of course, is precisely the sort of thinking the providers of those apps are counting on and actively reinforcing. People like me may give Solid a go out of sheer curiosity, but I suspect that the majority can’t be bothered unless there are Solid apps available that let them do things they really want to do but haven’t been able to before. Taking control of your own data is a noble principle for sure, but is it enough to attract a critical mass of users?

Then there’s the question of how the Solid ecosystem will work from a business perspective. The supply of interesting applications is going to be quite limited unless there’s money to be made by developing them, and presumably the revenue-generation models of centralised social apps can’t be simply dropped in a decentralised environment such as Solid without any modifications. We pretty much take it for granted now that we can “pay” for certain kinds of services through the act of using them and generating data for the service provider to use as raw material for services that the provider’s actual customers will pay good money for, but would – and should – this work if the provider could no longer control the data? On the other hand, would we be willing to pay for these services in cash rather than data, now that we’ve grown so used to getting them for “free”? Then again, there was a time when it was not at all clear how some of today’s multi-billion-dollar companies were ever going to turn a profit, so maybe we just need the right sort of minds to take an interest for these things to get figured out.

It’s also worth noting that Solid is by no means the only project aiming to make the web less centralised and more collaborative. There is a substantial community of researchers and developers working on solutions to various problems in this area, as evidenced by the fact that Solid is but one of dozens of projects showcased at the recent DWeb Summit in San Francisco, so it may well turn out that even if Solid itself won’t take off, some other similar thing will. I won’t be betting any money on any of the contenders just yet, but I probably will get myself a Solid POD to play with so I can get a better idea of what you can do with it.

Tips for MSCA hopefuls

This year’s call for MSCA Individual Fellowship applications closed recently, with a grand total of 9,830 applications received – apparently a record number for MSCA and in fact for Horizon 2020 in general. Good luck to everyone who submitted! Soon after I started my own fellowship at the Insight Centre, I was invited by one of the people who helped me prepare my proposal to participate in a seminar on MSCA and speak on my experiences as a successful candidate. The presentation I gave there was quite well received, so I thought I’d share my little tips and tricks in the blog as well, even though the timing isn’t arguably the greatest, given that it won’t be until sometime in the spring that the next call opens.

First of all, if you’re considering applying but having some doubts, I heartily recommend that you go through with it. Although technically MSCA fellowships are H2020 projects, which may sound a bit frightening, the proposal process is actually quite lightweight, with the length of the research plan limited to ten pages and the budget being a simple function of which country you’re going to, how many months you will spend there and whether you have a family. The same goes for how the projects are managed, so you don’t need to worry that you’ll end up spending an inordinate portion of your precious research time cranking out deliverables instead of generating results. So, without further ado, here are my top 5 tips for would-be MSCA fellows:

1. Find the right host

I’ve already mentioned in a previous post that it boosts your chances considerably if the strengths of your prospective host complement yours. It certainly doesn’t hurt if there’s someone at the host institution – ideally, your prospective supervisor – that you already know and have developed a rapport with, but you shouldn’t get too hung up on that particular point; what really matters from the reviewers’ point of view is whether the place where you are proposing to carry out your project is the best possible environment for that project. Consider what the host can offer you in terms of things such as training, research infrastructure and potential collaborators, and make sure that you have a persuasive argument that comes across in your proposal. Also, keep in mind that there is expected to be two-way knowledge transfer between the researcher and the host, so it’s not just about what you can get from the host – it’s also about what you can bring to the host.

2. Get all the help you can

The most important part of the proposal is the actual research to be done – objectives, methodology, etc. – and that’s all up to you (plus, to a certain extent, your supervisor of course, but they’re likely to have quite a few things on their plate besides this). However, for everything else, don’t hesitate to take advantage of any support that the host institution can offer you in preparing the proposal. The odds are that there are people there who have done this sort of thing before and know what reviewers look for in a proposal in terms of facts and figures, hosting arrangements, available research services and so forth. They may also have access to external experts and offer to send your proposal to them for feedback, and I think it goes without saying that you should accept such an offer. What I found particularly useful was ideas on how to communicate my research results to non-academic audiences, since my first instinct (and I’m pretty sure I’m not alone in this) is to just write papers for journals and conferences and let others worry about public relations, and this cost me some crucial points when I applied for the first time.

3. It’s all about you

This is another thing I touched upon in that earlier post: MSCA fellowships are unusual, if not unique, in that their impact is measured in terms of the career development of the fellow as a European researcher. Therefore you should consider starting not with the question “What do I want to study?” but with “What do I want to be?” The answer won’t give you your research topic, but it will affect the way you go about choosing one and developing a plan around it. Do you want to work in academia or industry? In what sort of role? Or maybe you’re interested in starting your own company? Whatever your target is, state it clearly in the proposal and make sure that everything else in the proposal – research activities, training, etc. – is aligned with that target. If you’re not quite sure what you want and would prefer to keep your options open, pick a career goal anyway and pretend that you do know that’s where you’re headed; there’s nothing wrong with changing your mind later, but it doesn’t look good if you don’t seem to have any sort of long-term vision of your career. Of course, if you’ve come up with a work plan that can support multiple career paths equally well, it shouldn’t hurt if you point this out in the proposal.

4. Give details generously

This is really a more general formulation of the previous point: it can be tempting to keep things a bit vague, but every bit of vagueness will make your proposal seem that much less convincing – and remember, the bar is high and competition fierce, so every little bit counts. This goes for your career objectives, but other things as well; for example, when describing how you plan to disseminate the results of your research, try to come up with tentative titles for the papers you’re going to write and to identify specific journals and conferences where you will aim to publish those papers. If you can name some likely co-authors, even better, and it’s also good to consider how you will measure the impact of your dissemination and communication activities (e.g., number of paper citations, number of people reached). Likewise, in your implementation plan, provide as much detail as you can (without breaking the page limit) on things such as work breakdown, timetables, deliverables and milestones; in the real world, you won’t be expected to follow that plan to the letter, but you do need to demonstrate in the proposal that there’s a clear path from where you are now to where you want to be at the end of the project.

5. Focus your efforts right

Having only ten pages to explain your research plan in full detail is a blessing but also a curse, because rationing out those ten pages between the things you want to say may prove quite a challenge. To have an idea of where you should be concentrating your best efforts, keep always in mind the three evaluation criteria and their weights relative to your overall score: excellence counts for 50%, impact for 30% and implementation for 20%, so it’s a good rule of thumb to allot 5, 3 and 2 pages for the corresponding proposal sections, respectively. However, if you’re working on a revision of a proposal that didn’t get funding the first time around, you also need to consider your previous evaluation scores, because the law of diminishing returns applies as your score for a given criterion approaches 5. So, if you did very well on excellence but not quite as well on the other two criteria, you’re likely to get a bigger increase in your total score for the same amount of effort if you focus on impact and implementation, even though excellence weighs as much as the other two combined. You’ll definitely want to improve any criterion score lower than 4, and the verbal feedback in the evaluation report should give you a pretty good idea of how you can do that.

So that’s it! I hope you found these tips useful and will come back to them when it’s time to start preparing an application for the next MSCA IF call.