At Harmony, we’re working on a new feature aimed at making data discovery and exploration - through the use of meta-data - even more efficient and intuitive To ensure this feature addresses real needs, we’re conducting co-design sessions with researchers, data-managers and other users, allowing us to develop a tool that solves real-life user requirements.
The Co-Design Approach: Co-design allows us to build this feature with direct input from those who will use it most. Through these sessions, users and stakeholders participate in shaping the tool, providing detailed feedback, identifying challenges, and contributing their own insights. This process is collaborative by design, creating a feedback loop that helps us address pain points in data discovery and visualisation while ensuring usability.
What problem does Harmony Discovery solve? Researchers can struggle to find datasets and often need something specific, such as a longitudinal study measuring anxiety and household income. Existing repositories don’t always go into the details of which variables have been captured, and finding variables can be a difficult process with a basic keyword search. Harmony Discovery indexes studies, datasets, and variables using the same cutting-edge vector representation used in our harmonisation tool, so that a search for “anxiety” will return studies with the question items “I feel anxious”, “I often feel worried”, and even items in other languages. This is made possible with the latest developments in AI and large language models (LLMs), which allow us to index and retrieve items based on semantic content rather than just matching words.
Key Goals for the New Feature: While the feature is still evolving, our main objective is to understand how researchers search and discover research data and how Harmony can support the decision making process especially when trying to compare meta-data of large or complex datasets. From early feedback, we’ve noted specific needs for adaptability and efficiency in data discovery and comparison, and we’re integrating these priorities into the design of the tool.
Get Involved: If you’re a researcher or data manager interested in participating in our co-design workshops, we’d love to hear from you! Get in touch with us via email, you can also sign up to our Discord server and newsletter and follow us on LinkedIn to stay connected with the latest updates and community discussions.
Harmony is an open source tool for social science research.
Train your own Large Language Model to parse PDFs and win up to £1000 in vouchers! Join a competition to train a machine learning model to improve Harmony’s PDF parsing. You don’t need to have trained a machine learning model before. Register on DOXA AI Enter the competition on DOXA AI by fine tuning your own model and improve Harmony! Join our Discord Join the Harmony Discord server. Check out the 🏅「matching-challenge」 channel!
Harmony at GenAI and LLMs night at Google London on 10 December 2024 Above: video of the AICamp meetup in London on 10 December 2024. Harmony starts at 40:00 - the first talk is by Connor Leahy of Conjecture We have presented the AI tool Harmony at the GenAI and LLMs night at Google London on 10th December organised by AI Camp at Google Cloud Startup Hub. AI Camp and Google hosted two deep dive tech talks on AI, GenAI, LLMs and machine learning, with food/drink, networking with speakers and fellow developers.