The input and discussions from Data Analytics State of Nation event has contributed immensely to shaping CAMS Data Analytics thematic area. We have identified several gaps between industry and academia, by CAMS. Working towards addressing these challenges the event working group proposed the creation of two steering committees that will drive short term and long-term projects.
- Data Analytics Teaching and Training (DATT) steering committee.
- Data Analytics Network and Infrastructure (DANI) steering committee.
Data Analytics Teaching and Training (DATT)
Teaching the basic principles of data analytics to chemists is paramount to providing future proofed and desirables skills across the workforce in many industries (transferable skills). Currently, most students don’t learn data analytics with the appropriate statistics until at research level. Logic, coding, modelling, critical analysis, quantifying and dealing with uncertainty, importance of metadata, data management (FAIR and ALCOA), workflow and pattern recognition are interdisciplinary, transferable skills. It is not just students who need access to developing these skills, but also those already in the workforce. A stronger collaboration between industry and academia is needed to ensure that the right material is taught to all those who needed it.
This committee is aimed to building that connection between academia and industry to work on the following areas/tasks, aiming towards delivering outputs over a two- and five-year period:
- Curate database of teaching and training available to upskill already employed workforce.
- Compile a list of CAMS industry leads interested in voluntary teaching
- Matchmaking of industry expertise with current academic teaching requirements
- Industry to work with academic institutions to inform degree course design, active learning, regulation (laws) FAIR and ALCOA.
- Creating two-way education / learning opportunities for industry and academia. Identifying and engaging with “change champions” within organisations and universities to ensure this initiative is adopted long term.
- Collaborative design of short courses on key principles of data analytics, which can be aimed at both degree students and continued professional development in workforces. Perhaps offering an accreditation/certificate
- Developing opportunities for academics second on short term industry placements, an industry exchange, to develop understanding of working practices to help better focus teaching on areas of interest.
- Develop a broad range of talks and seminars which industry and academics can present at university event, chemistry society meetings, outreach days, etc., and deliver these.
- Data access, creating data sets and closing the disconnect between bench level and innovation.
- Build connections for student placement/summer placement opportunities.
- Royal Society Fellows (?)
Data Analytics Network and Infrastructure (DANI)
There is currently no research network dedicated to data analytics within the chemistry sector. CAMS data analytics could be the seed point for developing this network and pull together those from a range of disciplines.
This committee will facilitate this network, creating excellent opportunities to develop the following identified areas of interest, but not limited to these:
- Arrange and deliver quarterly TechEx meetings (CAMS wide with invited guest speakers) – an online webinar series where CAMS PhDs, PostDocs, and partners can present their work in a short talk to other CAMS members, across the lines. Each quarter, one line will select several speakers giving short presentations, with a longer keynote speaker.
- Develop hackathons, where groups and individuals can attend (in person) to focus on a single task/set of data. A session could be an industry challenge set out by a company, or academic institute. For example, a contact in the NHS could provide a data set and set the challenge of managing and/or visualising the data.
- Need to show usefulness of their own data. Bring their own data and we can help with how they can show its usefulness.
- Advice to how we can obscure proprietary information
- Open discussion groups in the pre-competitive space. This would aid in the development of standardised vocabularies, metadata standards, and infrastructure. Bring in people from other disciplines.
- Develop a database of challenges posed across domains and the tools which have been in solving these. This opens the line for communication, and we can potentially get support and guidance from RSC.
- Develop unified infrastructure/resources for cross platform integration.
- Create a social presence for the network (Twitter, LinkedIn etc) with resource sharing and as industry-academic network
- Create and execute annual awards to individuals (DA champions) who actively advocate DA in their work e.g. FAIR practice, prompting use in their departments/institutes, raising awareness etc.
