SYNTHESYS+ will produce tools and methods that will develop methods of collections digitisation, digitisation workflows, and collections access. You can find out more about our three strands of research below.
We will develop an integrated system to support loans, visits, applications for Access, and track outputs. This is primarily a software development, data architecture, and standards implementation task.
This will rebuild the current SYNTHESYS Access request system and expand it to support ‘collection on demand’ requests, institutional visitors and ultimately be constructed with a view to supporting institutional loans. We will adopt a microservices approach to facilitate long term integration with institutional collections management processes and standards (e.g. SPECTRUM) with the aim of transitioning user Access requests over to this system toward the final calls for SYNTHESYS+.
This research will result in an integrated European Loans and Visits System (ELViS). ELViS which will enable smoother, faster, and better access to natural history collections across Europe.
This research will develop the technical infrastructure and capacities of institutions to undertake “collection on demand” requests. This includes molecular sequencing, 3D digitisation and other more challenging forms of digitisation.
Priority will be given to groups of agricultural and ecological interest to increase their discoverability and promote access for new user communities. Activities might include development of standardised tools to enable researchers to study, measure and compare 3D models. This might draw up experience in medical imaging, satellite imagery and potentially involve partners such as Sketchfab.
This research will integrate machine learning, Artificial Intelligence, and human approaches to extract, enhance, and annotate data from digital images and records at scale. Many collections-holding institutions still need to digitise the bulk of their collections. Digitisation takes time and resources. One of the major challenges in digitising massive collections is finding ways of ensuring high-quality collections data can be processed at pace.
We will use new technological approaches, such as computer vision, data mining and machine learning, to rapidly enhance minimal natural history specimen records using images (e.g. of labels, specimens or registers) and unstructured text at scale. These approaches will be largely automated and may support record enhancement by experts as well as members of the public (crowdsourcing).
Possible outcomes may include: