This will be a hybrid event . When registering can you please select whether you will attend virtually (via Zoom) or in person at the Wellcome Trust Building, Euston Road, London.
We look forward to welcoming you for our third Frontiers meeting, to build our shared vision for our next quinquennium (2023-2028). This meeting provides the next collaborative opportunity for our community and partners to shape our vision, strategy and delivery.
We are now over halfway through our first quinquennium (5-year funding period), and preparations for our quinquennial review by our core funders are well underway. Through a series of collaborative meetings, we are coming together as a national research institute, crossing disciplines and organisations to co-develop an innovative vision for the second quinquennium of HDR UK (2023-2028).
Who should attend?
This workshop is open to all members of the HDR UK community as well as current and future partners. We would welcome and encourage all to attend to contribute at this key stage, as this is integral to our current and future work to unite the UK’s health data to enable discoveries that improve people’s lives.
This meeting will be held via Zoom and in person. Please register to receive the joining details.
If you have not received the joining instructions email by 11.30am today, please check your junk and ‘other’ folders in the first instance. The email will be from “Caroline Cake and Andrew Morris”. If you still do not have it, please email firstname.lastname@example.org
R for Health Data Science: from clinicians who code to Shiny interventions
27 May 2022 at 10:00 am
Riinu Pius will present on R for Health Data Science: from clinicians who code to Shiny interventions on Friday 27th May at 10:00 UK time. Please click here for more details and to register.
Data Access & Discovery – June
10 June 2022 at 11:00 am
Join us at the next Data Access and Discovery event on Friday 10 June at 11am. Theme: Reproducible Data Science The recent Goldacre review highlighted the importance of reproducibility in data...