Research Challenges in Data Science

A SICSA Data Science Theme Event

Location:  The Data Lab, 15 South College Street, Edinburgh, EH8 9AA.

Event Date: 23 of November 2017

Registration Deadline:9 of November 2017 (Two weeks before the event).

Suggested Areas: Data mining and processing, Machine learning, Data visualisation, Trust and explanation of algorithms, data providence and privacy, linked data, and applications.  

Background

Data Science is increasingly being accepted as a critical underpinning resource and technology that is crucial to the wellbeing and prosperity of nations. It has been, and will continue to be the source and enabler of large-scale disruptive commercial and social change and is the subject of considerable investment by Scottish Government, Westminster and the EU.

Three years ago SICSA held the inaugural meeting of the Data Science theme that surveyed the research challenges as seen at that time (https://www.well-sorted.org/explore/SICSADataScience). Much has changed since then and also partly in response to informal conversations with such organisations as the Data Lab, Scottish Enterprise and EPSRC we see a need to refresh the Scottish Data Science Research Challenges from the point of view of researchers.   

Research challenges in data science

So join us at the SICSA event in November in Edinburgh to contribute to, and help form the SICSA Data Science Research Challenges.

We see this as being a highly inclusive and active event that will be driven by SICSA members. To this end, we will use Well Sorted (a simple remote card-sorting tool) to allow you to both contribute concise descriptions of research challenges and to structure these into groups that will be used to drive the generation of Research Challenge areas. The aim is to generate a short document that will provide a concise overview of what the theme sees as the major data science research challenges that should be addressed over the next five years.

As part of the activity, this meeting will help to nail how the Data Science theme will distribute some of its funds to SICSA researchers for future specialised data science activities and events. Look out for news very soon on the data science mailing list (below) about how you can help to shape that.

This meeting is open to all SICSA researchers (but space is limited so it will be first come first serve). It will be held in the Data Lab, 15 South College Street. The provisional outline of the event is:

10.00 Registration and coffee

10.30 Introduction talk

11.00 Expanding the data science challenges (breakout sessions)

12.00 60s plenary feedbacks from the challenges

12.30 Cross challenge communication and links generation

13.00 Lunch break (networking)

13.45 Invited talk

14:15 Refining and consolidating the data science challenges

15:00 60s plenary feedbacks from challenges consolidation

15:30 Next steps, summary, and SICSA opportunities

16.00 Close

Sponsors                   

This event is funded by Scottish Informatics and Computer Science Alliance (SICSA) (http://www.sicsa.ac.uk), with help and premises provided by the Data Lab (http://www.thedatalab.com).

Costs

The event registration and lunch are free of charge to registered attendees. Travel costs to the event must be met by attendees.

Travel

Participants will need to make their own travel arrangements. By train, the nearest station is Edinburgh Waverley, which is 10 minutes’ walk from the event location. See National Rail Enquiries (http://www.nationalrail.co.uk) for train information. For those coming from further afield, information about travel to and from Edinburgh Airport is available (http://www.edinburghairport.com/transport-links). A taxi to the city centre from the airport costs about 20GBP to 22GBP one way. There is also an excellent express bus from the airport called Airlink that terminates in the city centre and costs 7.50GBP for a return journey. The journey to the airport requires approximately 30 minutes from the city centre of actual journey time (add a little more during the rush hours).