Now you can share your harmonisations with your colleagues with a simple share link! We’ve added Firebase authentication to Harmony. You can log in with Google, Github or Twitter, and then you can see all your previous harmonisation work. You can even share your work with colleagues on LinkedIn, Twitter, or via URL. You’ll then be taken to the dialogue box in your chosen platform, where you can share your work as normal.
Привет Гармония! 哈莫尼可以让中英文和谐！ שלום הרמוני Harmony peut aussi harmoniser les instruments en français. We’re happy to share some exciting news with you. Harmony now supports at least 8 languages: Portuguese, French, German, Spanish, Russian, Chinese, and Hebrew. This means that you can use Harmony to compare and harmonise questionnaire data across studies that are written in different languages. I evaluated Harmony’s ability to match the GAD-7 in 11 languages to the English version.
On Thursday, August 17th, 2023, the Harmony and TIDAL teams teamed up to run a workshop at University College London to allow researchers to try out their software tools. The workshop was attended by researchers interested in using these tools to study child and adolescent mental health, and other areas in social science research, from the effects of gambling addiction to asking questions about nature vs nurture in twin studies.
Here’s a quick start guide to running Harmony. These instructions are for the complete version of Harmony including the graphical browser-based tool which is available online at https://harmonydata.ac.uk/app/. If you only need the Python or R libraries, or the REST API, please refer to our Github page. You will need to first download and install a couple of programs that Harmony needs to run. You need a computer with at least 16 GB RAM – in other words, a fairly high-end computer.
Semantic text matching is a task in natural language processing involving estimating the semantic similarity between two texts. For example, if we had to quantify the similarity between “I feel nervous” and “I feel anxious”, most people would agree that these are closer together than either sentence is to “I feel happy”. A semantic text matching algorithm would be able to place a number on the similarity, such as 79%.