
The Turing Way Practitioners Hub is an extension of The Turing Way that engages and involves industry experts, called Experts in Residence (EiRs), from partnering organisations. This initiative aims to advance their efforts in promoting best practices in data science and AI, especially around open source, open data and reproducibility. The second cohort of the Practitioners Hub was launched in September 2024: https://www.turing.ac.uk/turing-way-practitioners-hub/eirs. The Practitioners Hub serves as a platform for cross-sector collaboration, knowledge exchange, and strategic partnerships, spanning various sectors and data science initiatives, including but not limited to the small and medium-sized (SME) enterprises in the BridgeAI network. By leveraging a cohort-based approach, we build a shared understanding of open source principles, open data practices, cross-sector collaboration, research reproducibility, and ethical considerations in the context of data science and AI. EiRs are supported in developing case studies with stakeholders in their networks, sharing insights into the successes and challenges associated with the adoption of AI within their respective sectors. This is combined with customised training, expert consultation, and collaborative opportunities, which contribute to the improvement and adoption of data science practices that enhance the quality, viability, and real-world impact of data science and AI technologies in their sectors.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
