A project at the London Centre for Nanotechnology (LCN) is making fantastic use of the Pybossa tool (a project of the Open Knowledge Foundation and the Citizen Cyberscience Centre) in a citizen science project called ‘Feynman’s Flowers’, which launched this weekend.
The project asks members of the public to help unlock the secrets of magnetism at the molecular scale, and is powered by our free, open-source, platform for creating and running crowd-sourcing applications that utilise online assistance in performing tasks that require human cognition.
From their press release:
The project’s website invites volunteers from across the world to analyse microscope images of individual molecules, which have characteristic flower shapes. Anyone can take part, and only a few clicks of the computer mouse are required to collect valuable information.
The Feynman’s Flowers project will allow volunteers to measure the position of a molecule in relation to a metal surface to help scientists understand how this can affect the molecule’s properties. Data that volunteers produce will contribute to a research project run by the group of Dr. Cyrus Hirjibehedin at the LCN, in collaboration with Tsinghua University in Beijing and the Citizen Cyberscience Centre.
Currently, the research project is focused on exploring the behaviour of phthalocyanine molecules. In the past, these were used as dyes for fabrics, but scientists now realise that they also have interesting electronic and magnetic properties that make them potentially useful for creating nanoscale devices that can manipulate or store information.
This website is the first project of its kind in this area of physics, applying the power of crowd-sourcing to help understand images created by a scanning tunnelling microscope (STM). Operating at temperatures close to absolute zero (-273˚C), the STM allows scientists to image individual atoms and molecules on surfaces and to explore their fascinating magnetic and electronic properties. Public participation will allow for the analysis of data in ways that previously would not have been possible.