New to Open Data Science?  Here is a list of Frequently Asked Questions:

Who can attend the Open Data Science Symposium?

This meeting is open to the public. Join us for this meeting if you are interested in:

• Learning how NIH and other agencies are utilizing new models and funding approaches to support open innovation and open science
• Exploring the challenges, opportunities and implications of a changing biomedical research landscape in which openness is the default across the globe
• Seeking to use open data in your own research and looking for inspiration from international teams who have developed award-winning prototypes
• Watching live demos of all 6 Open Science Prize semi-finalists, and participating in the awards process through casting your vote for your favorite innovation

What is the cost of admission to the Open Data Science Symposium?

The symposium is free to attend; however, space is limited and you must register for the meeting prior to November 18, 2016.  Register Here.

When is the registration deadline?

The registration deadline is November 18, 2016.  Register here.

Will there be registration onsite?

Space is limited at the meeting, so we suggest you register by November 18, 2016.  However, same day registration will be available, space permitting. Register here.

What is Open Science? Open Science is the scholarly approach to make the tools, results, publications and data of scientific experiments freely available to other scientists and the public at large, with the goal of improving the rate and accuracy of scientific discovery.  The FAIR Principles outline specific criteria to help make science open.  

What is Open Data? Open data describes a dataset that is freely available to, and usable by, the community.  In some cases even open data requires an authorization process to access data to maintain privacy or security of the data.  However, that authorization process should be open to all qualified persons.

What is Big Data?  Big data is difficult to define. But is often described with the four Vs of Big Data: Volume, Variety, Velocity, Veracity.

Volume: Big data is Big but there is no set boundary that makes a dataset “Big,” rather than being defined merely by size, Big Data can be defined by the complexity of the data.

Variety: Big Data comes in all types, so understanding what the data are and making them usable to other researchers is very important or else we could lose the benefits of all that work.

Velocity: Big data is generated very quickly.  As methods for generating and collecting data become faster and more efficient, scientists must plan how to store and use that data.

Veracity: The volume, variety and velocity of big data also make Big Data susceptible to errors. Anyone producing big data should work to reduce errors in collection, identify errors after collection and correct errors if possible.

What are the FAIR principles? The FAIR principles are a set of guidelines designed to make data open.   The FAIR principles not only apply to data, but also the tools and infrastructure used to analyze open data.

Findable

In order to be findable, a data object must have a uniquely and persistently identifiable. Thus, the data will require metadata and a persistent unique identifier.

Accessible

Data is always obtainable by humans or computers. Certain types of data may require appropriate authorization to maintain privacy.  

Interoperable (and usable)

Data is interoperable if it can be accessed by computers and used in a meaningful way.

Reproducible

Data is stored with standardized and rich metadata so that steps 1-3 can be repeated in the future.