All applicants applying to the call need to carefully consider if they require support from CoSeC. All applicants requesting computational support from CoSeC must contact CoSeC:firstname.lastname@example.org by 13 May 2022 to discuss their requirements. Applicants are expected to develop their proposal in collaboration with CoSeC.
The Computational Science Centre for Research Communities, CoSeC, provides computational science support for a number of scientific communities funded by EPSRC, MRC and BBSRC, and organised in Collaborative Computational Projects (CCPs) and High End Computing (HEC) Consortia. This programme of work is carried out by staff at the Daresbury and Rutherford Appleton Laboratories. The nature of the support provided differs depending on the needs of the communities:
• Development of theory, algorithms, and software: This is a key element of support for many current projects, resulting in long-term, continued expansion and updating of the software programs. It may include the consolidation of existing codes into a more sustainable community software package.
• Maintenance, distribution, license management, dissemination and demonstration of software.
• User support and training: This includes individual support and training as well as help to organise events such as summer schools and study weekends, and contribute to the teaching and preparation of training materials.
• Collaboration on scientific projects.
• Porting, optimisation, and benchmarking for HPC and new architectures.
• Management of scientific data: This includes activities such as, the development of visualisation and workflow management tools, database and curation, and verification and validation activities.
• Co-ordination and networking support: Assist the consortia Chairs with planning and reporting of the network, and provide administrative support for community activities such as scientific and executive meetings and visitor programmes. Support is also available for project websites.
The CoSeC team is embedded in the Scientific Computing Department (SCD) of the Science and Technology Facilities Council (STFC). The Department has specialist expertise in related, relevant fields such as computational mathematics, software engineering, data management, machine learning, etc. Some relevant activities are summarised here:
Physical Science Data Infrastructure (PSDI):
This recent initiative aims to enable researchers in the physical sciences to handle data more easily by connecting the different data infrastructures they use. PSDI will connect and enhance existing infrastructure in Physical Sciences. We envisage this to be an expansion of the Physical Science Data-science Service (PSDS), a National Research Facility supported by EPSRC which supports UK researchers across the physical sciences by enabling access to data resources, both commercial and open source.
Computational Mathematics Group
SCD's Computational Mathematics group has particular strengths in the fields of numerical linear algebra and continuous optimization. The Group has a threefold mission: to support the mathematical needs of STFC's experimental Facilities and associated bodies; to develop and distribute library-quality mathematical software; and to carry out world-leading research in the field of numerical analysis.
The Scientific Machine Learning GroupThe Scientific Machine Learning (SciML) Group within STFC focusses on the development and application of machine learning techniques for advancing fundamental science. In doing that, the group works with various facilities within STFC, various universities, and US labs, covering different domains of science, including particle physics, space, environmental, molecular and material sciences. The group is also spearheading the development of a novel benchmarking suite for assessing machine learning ecosystems applied to scientific data. The Data & Software Engineering GroupThe Data & Software Engineering Group's main activities centre around the design, implementation and support of a wide range of software enabling researchers to catalogue and discover their experimental data; capture analysis workflows; make links between the different research outputs such as data, publications and software; support the research software development lifecycle; and use, promote and develop best practices around research software.