Update: we've extended the survey to April 30th to allow for more responses!

What are we studying?

There's a long history of crowdsourcing projects at cultural heritage institutions (GLAMS, or galleries, libraries, archives and museums; including citizen science and citizen history projects). More recently, GLAMs have experimented with machine learning or 'AI' to create, enrich or enhance data about collections. However, some projects struggle to integrate the data created or enriched by online volunteers and/or machine learning into collections management and discovery platforms (catalogues, for short, but we mean it very inclusively).

We're seeking to understand the barriers and successes for projects incorporating enriched data into catalogues or other core systems by gathering information on the types of data, tools and processes used by project teams. We hope these results will help organisations, software suppliers and projects with the work of integrating enriched data appropriately into collections systems.

This survey should take about 15 minutes to complete.

Who can take this survey?

We're interested in the experiences of anyone who's worked on crowdsourcing or machine learning projects to enrich collections data. We're particularly interested in hearing from projects in Europe, Asia and Africa.

This survey is designed so that more than one person can respond for any institution or project, especially for large or complex organisations. We also welcome responses from inactive projects, and past project teams. Please feel free to collaborate with colleagues, or provide individual responses. If you can't provide a comprehensive answer for a question, feel free to provide a partial response from your own perspective. If you have more than one significant project, you may wish to do the survey once for each project.

The survey is particularly designed for people working in collecting institutions (libraries, archives, museums, etc) with their own catalogues, but we also welcome responses from projects that create or enrich data through e.g. research or community projects working with data from GLAMs, or 'roundtripping' records to return enhanced data to a catalogue.

What will we do with the results, and where can you access them?
We will share the results of this research on the Collective Wisdom website (https://collectivewisdomproject.org.uk), in blog posts by the British Library and Zooniverse, and in conference papers or journal publications. If you give us your email address and permission to email you, we'll send you the results by email.

The survey has been designed so that you can answer anonymously if you wish and will not be identifiable by those with access to the data. There is also an opportunity to provide contact data if you wish, but we will not publish this information.

Who are we? And who can you contact with questions?

You can email us (Mia Ridge, Sam Blickhan and Meghan Ferriter) via digitalresearch@bl.uk. We were the investigators on the AHRC-funded Collective Wisdom project. After launching our white paper 'Recommendations, Challenges and Opportunities for the Future of Crowdsourcing in Cultural Heritage: a White Paper' last year we've re-convened to explore this question, as it's so fundamental to ensuring the benefits of crowdsourcing and AI work.

A blog post with further background and a PDF of all the questions is at https://collectivewisdomproject.org.uk/survey-integrating-volunteer-and-ai-enriched-metadata-into-collections-systems/

The survey is open until April 30th, but we encourage you to complete it sooner!