Archives Hub Labs Overview
The Archives Hub has been running as a succesful aggregation service for over 20 years. During that time, we have constantly developed our processes, methods, and user experience.
In 2016 we moved to a new system and workflow. This was the beginning of a new chapter in the service. The aim was to establish a way to ingest data easily and at scale from many different sources. At the same time, we wanted a set-up that facilitated innovative development.
We believe that the key to successful innovation is to spend time and effort laying a robust, resilient and sustainble basis. This is what we concentrated on for some years after the migration to a new system. Now we are in a position to look at how we can work more innovatively with all of the amazing content that we have. Data from over 350 institutions, representing a massive wealth of archives held in the UK.
Our intention is to look at how we can work with the data, potentially enhancing it to improve discoverability. We have already done cutting edge work in structuring personal and organisational names, and we are now looking at machine learning as a possible approach, as well as focussing on digital content by developing a prototype data store, and working on image recognition and a IIIF service. We will continue to scan the domain and the technologies available to assess tools and services that may be of use in the future.
We will also continue to work on our user interface, gathering data on use and surveying contributors and users to understand how we can improve the end user experience.
Read more about:
Structuring Names in order to enable name matching
Blog posts from our project: IIIF and Machine Learninng
Exploring IIIF for the Images and Machine Learning Project (30/11/22)
Running Machine Learning in AWS(01/11/22)
Digital Content on Archives Hub (04/10/22)
Assessing Machine Learning Outputs (29/06/22)
Using AI to Write Blog Posts (27/05/22)
Machine Learning: Training the Model (29/03/22)
Machine Learning with Archive Collections (28/02/22)
Images and Machine Learning Project (27/01/22)