The following post is from Mark Hahnel, founder of the Science 3.0 network and member of the Open Knowledge Foundation’s Working Group on Open Data in Science.
Scientific publishing as it stands is an inefficient way to do science on a global scale. A lot of time and money is being wasted by groups around the world duplicating research that has already been carried out. FigShare allows you to share all of your data, negative results and unpublished figures. In doing this, other researchers will not duplicate the work, but instead may publish with your previously wasted figures, or offer collaboration opportunities and feedback on preprint figures:
Upload all of your negative data.
Upload all of your preliminary data.
….Upload all of your data!
Data can currently be uploaded as a figure or as a dataset. The figures can be linked to datasets and vice-versa. Figures can include images, graphs, photographs and many other formats.
There is no need to register (You still can of course!). You can log in with any of the following social media accounts:
FigShare also gives you the ability to easily share your figures and datasets via a host of social media platforms through ‘share buttons’ on every page.
FigShare is a permanent database of your research. To further ensure this, FigShare is supported by ‘Systems Institute‘. Systems Institute is a not for profit which is providing ongoing support for the hosting of FigShare as it expands. This also allows FigShare to make backups of all of your data each and every day.
FigShare is being developed with the great work done by the Open Knowledge Foundation in mind. Ongoing converstaions with them about their CKAN project mean that we are all pulling in the same direction, and all data stored witin FigShare will be listed on a new CKAN science group.
The beta release of FigShare is a fully functional data sharing platform. We will continue to develop the features of the site to bring you even more tools to help get your data to a global audience. If you have any suggestions or feature requests please get in touch.
A special thanks to all those who helped with beta testing:
Pawel Szczesny, Heather Piwowar, Anthony Williams, Steve Koch, Jean-Claude Bradley, Cameron Neylon, Egon Willighagan, Rosie Redfield, Carl Boettiger, Alejandro Tamayo, and Martin Johnson.
If you have any questions or comments, please leave them below and we will endeavour to get back to you asap. Alternatively, get in touch via email at mark@figshare.com, @figshare on twitter, or through the FigShare facebook page.
I think this is potentially a great idea, and I realise it is in the early stages. However, looking at the site and thinking about the concept, we have yet to see answers to questions about the “findability” of datasets, and also about determining their provenance. How much should I trust a dataset on Figshare? How do I determine experimental conditions, calibration, etc?
If this is to be easy enough for many to use, then it is unlikely that depositors will spend as much time describing the provenance of their data as they would for an article. Even less so for negative results! Are there ways to capture provenance information automatically?
On “findability”, I’m not just thinking about search tools (although datasets and figures intrinsically need some metadata for searching, see the previous para). I’m thinking of the way social sites like twitter, friendfeed or even facebook let me find the information of interest to me from millions or billions of candidates, through a group of those i’m interested in. I guess this is also about scalability…
But nevertheless, this is an interesting and potentially very useful development.