In a previous post, several months ago, we talked about Chaos and the Mandelbrot Set: an innovation brought about by the advent of computers.

In this post, we’ll talk about a present-day innovation that is promising similar levels of disruption: Open Data.

Open Data is data that is, well, open, in the sense that it is accessible and usable by anyone. More precisely, the Open Definition states:

A piece of data is open if anyone is free to use, reuse, and redistribute it – subject only, at most, to the requirement to attribute and/or share-alike

The point of this post is to share some of the cool resources I’ve found, so the reader can take a look for themselves. In a subsequent post, I’ll be sharing some of the insights I’ve found by looking at a small portion of this data. Others are doing lots of cool things too, especially visualisations such as those found on http://www.informationisbeautiful.net/ and https://www.reddit.com/r/dataisbeautiful/.

Sources

One of my go-to’s is data.gov.uk. This includes lots of government-level data, of varying quality. By quality, I mean usability and usefulness. For example, a lat-long might be useful for some things, a postcode or address for other things, or an administrative boundary for yet others. This means it can be very hard to “join” the data together, as the way they store something like “location” is many different ways. I often find myself using intermediate tables that map lat-long into postcodes etc., which takes time and effort (and lines of code).

Another nice meta-source of datasets is Reddit, especially the datasets subreddit. There is a huge variety of data there, and people happy to chat about it.

For sample datasets, I use the ones that come with R, listed here. The big advantage with these is they are neat and tidy, so they don’t have missing values etc and are nicely formatted. This makes them very easy to work with. These are ideal for trying out new techniques, and are often used in worked examples of methods which can be found online.

Similarly useful are the kaggle datasets, which cover loads of things from US election polls to video games sales. If you are inclined they have competitions which can help structure your exploration.

A particularly awesome thing if you’re into social data is the European Social Survey. This dataset is collected through a sampled survey across Europe, and is well established. It is conducted every 2 years, since 2002, and contains loads of cool stuff from TV watching habits to whether people voted. It is very wide (ie lots of different things) and reasonably long (around 170,000 respondents), so great fun to play with. They also have a quick analysis tool online so you can do some quick playing without downloading the dataset (it does require signing up by email for a free login).

Why is Open Data disruptive?

Thinking back to the start of the “information age”, the bottleneck was processing. Those with fast computers had the ability to do stuff noone else could do. Technology has made it possible for many people to get access to substantial processing power for very cheap.

Today the bottleneck is access to data. Google is making their business around mastering the world’s data. Facebook and twitter are able to exist precisely because they (in some sense) own data. By making data open, we start to be able to do really cool stuff, joining together seemingly different things and empowering anyone interested. Not only this, but in the public sector, open data means citizens can better hold government officials to account: no bad thing. There is a more polished sales pitch on why open data matters at the Open Data Institute (and they also do some cool stuff supporting Open Data businesses).

Some dodgy stuff

There are obviously concerns around sharing personal data. Deepmind, essentially a branch of Google at this point, has very suspect access to unanonymised patient data. Google also recently changed the rules, making internet browsing personally identifiable:

We may combine personal information from one service with information, including personal information, from other Google services – for example to make it easier to share things with people you know. Depending on your account settings, your activity on other sites and apps may be associated with your personal information in order to improve Google’s services and the ads delivered by Google.

Source: https://www.google.com/policies/privacy/

We’ve got to watch out, and as ever be mindful about who and what we allow our data to be shared with. Sure, this usage of data makes life easier… but at what privacy cost.

Data matters. A great example of a smart use of data is genetic sequencing. This involves 3 billion base pairs, although scientists only know what around 1% of these do. The arguably most important ones are to do with creating proteins. By looking at people with traits, diseases or ancestry, scientists have been able to pick out those sets of genes which seem to match with those attributes. For example, breast cancer risk is 5 times higher if you have a mutation in either of the tumour-suppressing BRCA1 and BRCA2 genes.

Due to science, there are now commercial providers of DNA sequencing available, such as 23andme. They market this as a way to discover more about your ancestry and any genetic health traits you might want to watch out for. To try this out, I bought a kit to see how they surfaced the data in an understandable way. The process itself is really easy, you just give them money and post a tube of your spit to them.

After a few weeks wait for them to process it, you can look at your results. Firstly, you have your actual genetic sequencing. This is perhaps really only of interest (or any use) to geneticists. As part of their service, 23andme pull out the “interesting” parts of the DNA which have been shown (through maths/biology) to correspond to particular traits or ancestry.

They separate this out into:

• Health:
  • Genetic risks
  • Inherited conditions
  • Drug response
  • Traits (eg hair colour or lactose tolerance)
• Ancestry:
  • Neanderthal composition
  • Global ancestry (together with a configurable level of “speculativeness”)
  • Family tree (to find relatives who have used the service too)

Part of what is smart about this service is that while it uses DNA as underlying data, it almost entirely hides this from the end user. Instead, they see the outcome for them. They have realised that people don’t care about a sequence like “agaaggttttagctcacctgacttaccgctggaatcgctgtttgatgacgt” but they do care about whether they have a higher risk of Alzheimer’s. Because some of these things are probabilistic, they also put a 1*-4* scale of “Confidence”: again this is easy to read at a glance. It isn’t very engaging, but it looks something like this:

Perhaps more visually interesting is the ancestry stuff. Apologies that my ancestry isn’t very exciting:

I hope this has been interesting. Commercial DNA sequencing is a real success story not just for biochemistry and genetics, but also for the industrialisation of these processes and the mathematics and software that makes it possible. The thing that is especially cool, according to me at least, is the ability to make something as complex as genetics accessible, understandable and useful.