Louhos have generated a tool called Sorvi with the aim of making R based statistical computational tools and methods traditionally used by scientists available for people wrangling all sorts of data sets from government and finance to weather and more.

Sorvi combines these resources by providing a centralized collection of general-purpose open-source tools for data manipulation, analysis and and visualization. The project currently focuses on Finnish open data sets, but has far wider applications.

Members of the Louhos team hard at work.

The hackday project focused on formulating documentation that will be easy for newcomers and two use cases for a regular user and developer to illustrate the abilities of the tools.

The team made significant advances in completing and improving documentation during the day, if you’d like to explore, check out the Louhos homepage and have a go with the tools yourself!

This morning saw the start of the world’s biggest ever open knowledge meeting and following an inspiring opening session 40 odd hackers descended on the OKFest MAKE space to collaborate on building apps and tools to open up scientific and cultural data sets. The open science crowd was about 15 strong and quickly settled on three projects to focus on throughout the day.

We had our friendly mascot Chuff to help us on the way, sporting his own conference badge:

Creating a pyBOSSA App: Feynman’s Flowers

Daniel Lombraña González and Quentin Mazars-Simon are leading development of Feynman’s Flowers, a pyBOSSA app to crowdsource measurement of how individual molecules stick to surfaces. Read on for more details!

Spintronics with Individual Molecules

Example of a single atom diagram (Helium atom)

Traditional electronic devices work by moving charge around a circuit. This has produced astounding results over the last half century, but we are now at a point where further reducing the size of circuit elements is difficult because it would create too much heat in too small a space.

Our research group is studying magnetic molecules to understand how they can be used to make the smallest possible “spintronic” devices, in which charge (electronic) and spin (magnetic) properties can be used together. In the future, this would allow us to make devices that can do more and also use less energy.

As a simple example, we may be able to use the magnetic orientation of one molecule (the direction in which its internal compass needle points) to store a single bit (0 or 1) of information: this would potentially increase the storage density on hard drives by 100x.

How You Can Help: Measure How Molecules Stick to Surfaces

Using a special kind of “microscope”, operating at close to absolute zero temperature and based on the quantum mechanical principle of tunneling, we can measure single molecules on surfaces.

Many of a molecule’s magnetic properties are determined by how it binds (sticks) to the surface. For example, a group in Japan found that the magnetic stability energy of a molecule could change by 50% just by a 15˚ rotation in its binding angle.

It is therefore crucial for us to measure the distribution of binding angles of a particular molecule on a given surface. This will allow us to compare our results with theoretical predictions to better understand their properties. That’s where you come in …

The App

Data on the centre of attached molecules and their angle must be determined by hand as there is no reliable algorithm. the Feynman’s Flowers app will allow users to determine the centre of the attached molecule and its angle allowing researchers to analyse the data digitally.

What Did We Do?

We’re nearly there! Daniel and Quentin have the app online and with a few tweaks it should be fully up and running within the next day!

You can have a go and also check out the code if you’re keen to see under the bonnet or adapt the code to your own needs.

[Atom diagram from Wikimedia Commons under CC-BY-SA 3.0. All other images under CC-BY]

The third hackday project aims to explore the links between corporations and researchers for the areas of organic food, but more widely research into pharmaceuticals, cosmetics, food and other consumer products. By extracting author and funding data from the full text open access literature, funding links become clearer allowing visualisation which can be manipulated into patterns based on study outcomes to identify areas with particularly high positive result publication biases which may be influenced by commercial interests.

How to Do It?

Luckily the OKFN has several tools at its disposal to make this happen:

Our Open Biblio project has developed tools to extract full text literature (PubCrawler) and convert files into bibliographic metadata formats such as BibJSON whereby data can be easily manipulated and facet browsed (BibServer).
Therefore, we are extracting a subset of the open access BioMedCentral articles.

The next step will be to extract author information and affiliation (illustrated using Ahmet et al., 2011), which brings up a new problem of reconciling affiliations where no canonical list of institutions exists. Much discussion ensued about the possibility of using pyBOSSA and merging several publicly available lists of institutions from authoritative sources as a starting point.

We will also require funding data and competing interests sections from the articles, which can be mined using natural language processing tools to extract corporate names (possibly utilising Open Corporates) and references to other funders e.g. research councils and charities.

Once extracted this information would be entered into a database for faceted searching by funder, institution and author along with standard bibliographic metadata e.g. list all articles funded by GlaxoSmithKline and published in UK institutions in 2011.

What Can We Do With It?

This would lay the ground work for linking to other data sets and further elucidating patterns in corporate funding of research. For example, abstracts and key words could be used to browse by topic and a further pyBOSSA app could be generated to type the articles by a positive or negative result, or even graded e.g. relative risk of a new clinical treatment. This could be used to look at positive publishing bias in different areas of science and for work through different funders – which appear to be the worst affected areas? Which funders very rarely publish negative results?

Many studies on publication bias already exist in clinical trials but they are often limited to a few hundred articles e.g. Friedman and Richter, 2004, whereas the automated methodology described above could examine large portions of the literature. There are also different types of publication biases e.g. citation biases for positive articles which could possibly be investigated on a larger scale using these techniques than is currently possible, particularly involving Open Citation tools.

Incorporating geographic information on institutions and keyword analysis could reveal hot spots of research and other useful information for funders and research managers.

Hard at work hacking with the OKF Okapi

What Did We Do?

We completed liberating BMC articles by crawling the site and using parsers from BibTex to BibJSON. By the end of the week we expect to have 140k articles uploaded to BibServer including author, affiliations, abstracts and more.