Harmony training workshop

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Transforming data management with the Harmony Data Tool: A hands-on introduction

We’re excited to be partnering with UK Data Service to deliver a practical workshop on how to best use our tools. The session will take place online on 29 November.

With live demonstrations of the Harmony Data Tool’s key functionalities, participants will leave with a clear understanding of how this tool can improve their data management processes which will help improve the efficiency and accuracy of longitudinal data analysis. Sign up for the session.

The session will be presented by Harmony project leaders: Bettina Moltrecht, PhD from the UCL Centre for Longitudinal Studies at UCL, Eoin McElroy from Ulster University and Thomas Wood from Fast Data Science.

Register for the session with UKDS

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See also our past events

22 November 2024: Harmony at Women In Data™️ London Chapter
30 October 2024: Onboarding webinar for DOXA AI competition
8 October 2024: Harmony: a free online tool using LLMs for research in psychology and social sciences at AI|DL London
11 and 12 September 2024: Harmony at MethodsCon Futures in Manchester
2 July 2024: Harmony: NLP and generative models for psychology research at Pydata London
3 June 2024: Harmony Hackathon at UCL
5 May 2024: Harmony: A global platform for harmonisation, translation and cooperation in mental health at Melbourne Children’s LifeCourse Initiative seminar series.
27 March 2024: Harmony at AI Camp
17 August 2023: Harmony and TIDAL workshop

Introducing Harmony Meta: A Simpler Way to Discover Research Metadata

Introducing Harmony Meta: A Simpler Way to Discover Research Metadata Finding the right data is the foundation of good research, but it shouldn’t be the hardest part of the process. We are pleased to announce the beta release of Harmony Meta, a search tool built to help researchers find datasets and study information more efficiently. This first version is now live, and we are inviting the research community to test it and help us shape its future.

Improving Harmony's PDF extraction with user testing

Since we built Harmony, a common complaint has been that it frequently identifies the wrong questions in PDFs. The original algorithm for finding questions in PDFs was a mixture of rule based heuristics and some hand coded logic to look for e.g. lines in the document which begin with numbers. This was very fragile and worked fine on short questionnaires such as the GAD-7, but failed on larger documents. We decided to run a competition with our partner DOXA AI where members of the public could train their own model to extract questions from PDFs.