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content/en/ada.md

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{{</ htmlcode >}}
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The [Australian Data Archive (ADA)](https://ada.edu.au/) is a national service for the collection and preservation of digital research data, similar to the [UK Data Archive (UKDA)](https://www.data-archive.ac.uk/).
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The [Australian Data Archive (ADA)](https://ada.edu.au/) is a national service for the collection and preservation of digital [research data](/item-harmonisation/harmony-a-free-ai-tool-for-cross-cohort-research/), similar to the [UK Data Archive (UKDA)](https://www.data-archive.ac.uk/).
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The ADA provides data access through the [ADA Dataverse](https://dataverse.ada.edu.au/). The collection includes polls on housing conditions in Australian states, political views over time across the country, questions about employment or health, and other datasets that the ADA has collected over the years (such as the Australian election study).
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content/en/ai-in-mental-health.md

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Beyond Harmony, AI is being used in a wide range of mental health research applications:
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* **Predictive Modelling:** AI algorithms can analyze large datasets to identify patterns and predict future outcomes, such as the likelihood of a relapse or the effectiveness of a particular treatment.
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* **Natural Language Processing:** NLP tools can analyze text data, such as social media posts or clinical notes, to gain insights into mental health conditions and identify potential risk factors.
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* **Predictive Modelling:** AI algorithms can analyse large datasets to identify patterns and predict future outcomes, such as the likelihood of a relapse or the effectiveness of a particular treatment.
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* **Natural Language Processing:** NLP tools can analyse text data, such as social media posts or clinical notes, to gain insights into mental health conditions and identify potential risk factors.
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* **Machine Learning:** Machine learning algorithms can be trained on vast amounts of data to develop models that can diagnose mental health conditions with greater accuracy than traditional methods.
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* **Virtual Reality:** AI-powered virtual reality experiences can be used to simulate real-world situations and provide exposure therapy for conditions like anxiety and phobias.
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content/en/blog/ai-meetup.md

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We're pleased to announce that the [AI tool](/psychology-ai-tool/) **Harmony** will be showcased at the upcoming **AICamp AI Meetup in London** on **27 March**.
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You are invited to AICamp's monthly in-person AI meetup in London. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers
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You are invited to AICamp's monthly in-person AI meetup in London. Join us for deep dive tech talks on AI, [GenAI, LLMs](/nlp-semantic-text-matching/how-does-harmony-work/) and machine learning, food/drink, networking with speakers and fellow developers
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Topic: Harmony, Open source AI tool for psychology research
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{{< image src="/images/20240327-thomas-wood-ai-camp-harmony.jpg" alt="Thomas Wood presenting Harmony at AI Camp on 27 March 2024" title="Thomas Wood presenting Harmony at AI Camp on 27 March 2024" >}}
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Abstract: In this talk, Thomas will discuss AI for social sciences research and how to build a research tool with NLP and AI with open source tool Harmony, funded by [Wellcome Trust](https://wellcome.org) with [Social Finance](https://www.socialfinance.org.uk/) and developed with [Ulster University](https://ulster.ac.uk), [UCL](https://ucl.ac.uk) and [Universidade Federal de Santa Maria in Brazil](https://ufsm.br).
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Abstract: In this talk, Thomas will discuss [AI for social sciences research](/item-harmonisation/harmony-a-free-ai-tool-for-cross-cohort-research/) and how to build a research tool with NLP and AI with [open source tool Harmony](/how-can-i-contribute-to-an-open-source-project/), funded by [Wellcome Trust](https://wellcome.org) with [Social Finance](https://www.socialfinance.org.uk/) and developed with [Ulster University](https://ulster.ac.uk), [UCL](https://ucl.ac.uk) and [Universidade Federal de Santa Maria in Brazil](https://ufsm.br).
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{{< card heading="Attend" copy="Sign up on AICamp website" url="https://www.aicamp.ai/event/eventdetails/W2024032710" >}}
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This follows on from the talk on Harmony given by Bettina Moltrecht, PhD last week at [OpenUK](https://openuk.uk/).
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**[Register now](https://www.aicamp.ai/event/eventdetails/W2024032710) and be a part of this exciting event!**
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## See other Harmony events
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* 8 October 2024: [Harmony: a free online tool using LLMs for research in psychology and social sciences](/psychology-ai-tool/aidl-meetup/) at AI|DL London
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* 11 and 12 September 2024: [Harmony at MethodsCon Futures](/ai-in-mental-health/harmony-at-methodscon-futures/
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) in Manchester
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* 2 July 2024: [Harmony: NLP and generative models for psychology research](/psychology-ai-tool/pydata-meetup/) at Pydata London
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* 3 June 2024: [Harmony Hackathon](/hackathon/) at UCL
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* 5 May 2024: [Harmony: A global platform for harmonisation, translation and cooperation in mental health](/ai-in-mental-health/harmony-at-lifecourse-seminar/) at Melbourne Children’s LifeCourse Initiative seminar series.

content/en/blog/aidl-meetup.md

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Speaker: [Thomas Wood](https://freelancedatascientist.net/) ([Fast Data Science](https://fastdatascience.com/))
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Description: Thomas Wood will present our work on Harmony, harmonydata.ac.uk, which is a [free online tool](/psychology-ai-tool/) that uses generative AI and LLMs to help researchers compare items in questionnaires such as [GAD-7](/compare-harmonise-instruments/gad-7-vs-sdq/) (used to measure anxiety), even when they are written in different languages. Harmony is open source under [MIT License](https://github.com/harmonydata/harmony/blob/main/LICENSE) and is written in Python, and uses [HuggingFace Sentence Transformers](/measuring-the-performance-of-nlp-algorithms/) to find similarities between questionnaires.
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Description: Thomas Wood will present our work on Harmony, harmonydata.ac.uk, which is a [free online tool](/psychology-ai-tool/) that uses generative AI and LLMs to help researchers compare items in questionnaires such as [GAD-7](/compare-harmonise-instruments/gad-7-vs-sdq/) (used to measure anxiety), even when they are [written in different languages](/nlp-semantic-text-matching/harmony-on-kufungisisa-a-cultural-concept-of-distress-from-zimbabwe/). Harmony is open source under [MIT License](https://github.com/harmonydata/harmony/blob/main/LICENSE) and is written in Python, and uses [HuggingFace Sentence Transformers](/measuring-the-performance-of-nlp-algorithms/) to find similarities between questionnaires.
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[Harmony Discovery](/discovery/) will soon allow researchers to discover datasets using a vector search.
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[Harmony Discovery](/discovery/) will soon allow researchers to discover datasets using a [vector search](/nlp-semantic-text-matching/how-does-harmony-work/).
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## Where and where to find the AI|DL AI Meetup
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07:10pm - Thomas Wood, Director of [Fast Data Science](https://fastdatascience.com/) [Project Harmony: a free online tool using LLMs for research in psychology and social sciences](https://fastdatascience.com/ai-in-research/aidl-meetup/)
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Thomas Wood will present our work on Harmony, harmonydata.ac.uk, which is a free online tool that uses generative AI and LLMs to help researchers compare items in questionnaires such as GAD-7 (used to measure anxiety), even when they are written in different languages. Harmony is open source under MIT License and is written in Python, and uses [HuggingFace Sentence Transformers](/measuring-the-performance-of-nlp-algorithms/) to find similarities between questionnaires. Harmony will soon allow researchers to discover datasets using a vector search.
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[Thomas Wood](https://fastdatascience.com/team/) will present our work on Harmony (harmonydata.ac.uk), which is a [free online tool](/psychology-ai-tool/) that uses generative AI and LLMs to help researchers compare items in questionnaires such as [GAD-7](https://harmonydata.ac.uk/compare-harmonise-instruments/gad-7-vs-beck-anxiety-inventory/) (used to measure [anxiety](/discover-data/anxiety-datasets-and-studies/)), even when they are written in [different languages](/nlp-semantic-text-matching/harmony-on-kufungisisa-a-cultural-concept-of-distress-from-zimbabwe/). Harmony is [open source](/how-can-i-contribute-to-an-open-source-project/) under MIT License and is written in Python, and uses [HuggingFace Sentence Transformers](/measuring-the-performance-of-nlp-algorithms/) to find similarities between questionnaires. Harmony will soon allow researchers to discover datasets using a vector search.
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07:50pm - Break
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08:00 - Vikram Haridas, Lead Product Manager at Groupon "Implementing AI-Driven Product Innovations: Strategic Insights and Practical Applications"
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Vikram Haridas, from Groupon, will reveal how AI can supercharge product roadmaps. Learn how to balance excitement with realism as you scale AI features and discover practical use cases. Get insights into Groupon's success with AI-powered deal optimization and automated merchant onboarding, and learn how to implement these strategies in your own business.
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Vikram Haridas, from Groupon, will reveal how AI can supercharge product roadmaps. Learn how to balance excitement with realism as you scale AI features and discover practical use cases. Get insights into Groupon's success with AI-powered deal optimisation and automated merchant onboarding, and learn how to implement these strategies in your own business.
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08:40 - Shubhangi Goyal, Data Analyst @ ICS.AI Ltd and Nidhi Agrawal Director @UBS, "Generative AI and its use cases"
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Our session will explore the transformative potential of Generative AI, focusing on its use cases in matching algorithms and its applications in the financial industry. We'll dive into how AI models enhance person-matching processes by analyzing large datasets for customer service, and personalization. Additionally, we’ll examine how Generative AI is revolutionising the financial sector.
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Our session will explore the transformative potential of Generative AI, focusing on its use cases in matching algorithms and its applications in the [financial industry](https://fastdatascience.com/ai-in-finance/). We'll dive into how AI models enhance person-matching processes by analysing large datasets for [customer service](/data-harmonisation/data-standardisation-vs-harmonisation/), and personalisation. Additionally, we’ll examine how Generative AI is revolutionising the financial sector.
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09:10pm - Wrap up, drinks at Angel London
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) in Manchester
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* 2 July 2024: [Harmony: NLP and generative models for psychology research](/psychology-ai-tool/pydata-meetup/) at Pydata London
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* 3 June 2024: [Harmony Hackathon](/hackathon/) at UCL
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* 5 May 2024: [Harmony: A global platform for harmonisation, translation and cooperation in mental health](/harmony-at-lifecourse-seminar/) at Melbourne Children’s LifeCourse Initiative seminar series.
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* 27 March 2024: [Harmony at AI Camp](/upcoming-tech-talk-at-aicamp-meetup/)
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* 5 May 2024: [Harmony: A global platform for harmonisation, translation and cooperation in mental health](/ai-in-mental-health/harmony-at-lifecourse-seminar/) at Melbourne Children’s LifeCourse Initiative seminar series.
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* 27 March 2024: [Harmony at AI Camp](/psychology-ai-tool/aicamp-meetup/)

content/en/blog/back-to-the-future-retrospectively-harmonizing-questionnaire-data.md

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{{< image src="images/blog/blog-pic-1.png" alt="img" >}}
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By identifying, recoding, and testing the equivalence of subsets of [items](/item-harmonisation/harmony-a-free-ai-tool-for-longitudinal-study-in-psychology) from different questionnaires (for guidelines see our previous report), researchers can derive harmonised sub-scales that are directly comparable across studies. Our group has previously used this approach to study trends in mental health across different generations (Gondek et al., 2021), and examine how socio-economic deprivation impacted adolescent mental health across different [cohorts](/item-harmonisation/harmony-a-free-ai-tool-for-cross-cohort-research) (McElroy et al., 2022).
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By identifying, recoding, and testing the equivalence of subsets of [items](/item-harmonisation/harmony-a-free-ai-tool-for-longitudinal-study-in-psychology) from different questionnaires (for guidelines see our previous report), researchers can derive harmonised sub-scales that are directly comparable across studies. Our group has previously used this approach to study [trends in mental health](/ai-in-mental-health/) across different generations (Gondek et al., 2021), and examine how socio-economic deprivation impacted adolescent mental health across different [cohorts](/item-harmonisation/harmony-a-free-ai-tool-for-cross-cohort-research) (McElroy et al., 2022).
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One of the main challenges to retrospectively harmonising questionnaire data is identifying the specific items that are comparable across the measures. In the above example, we used expert opinion to match candidate items based on their content, and used psychometric tests to determine how plausible it was to assume that matched items were directly comparable. Although our results were promising, this process was time-consuming, and the reliance on expert opinion introduces an element of human [bias](https://fastdatascience.com/how-can-we-eliminate-bias-from-ai-algorithms-the-pen-testing-manifesto) – i.e. different experts may disagree on which items match. As such, we are currently working on a [project](https://fastdatascience.com/starting-a-data-science-project) supported by [Wellcome](/ai-in-mental-health/radio-podcast-about-wellcome-data-prize/), in which we aim to develop an online tool, ‘Hamony’, that uses machine learning to help researchers match items from different questionnaires based on their underlying meaning. Our overall aim is to streamline and add consistency and replicability to the harmonisation process. We plan to test the utility of this tool by using it to harmonise measures of mental health and social connectedness across two cohort of young people from the UK and and Brazil.
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content/en/blog/bmc-paper.md

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While the NLP model showed promise in uncovering underlying patterns in the data, it required manual intervention to determine which relationships were truly relevant.
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Our study showed that NLP can be a useful tool to match similar questions from different questionnaires, but it's not perfect and should be used with caution.
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Our study showed that NLP can be a useful tool to match [similar questions from different questionnaires](/data-harmonisation/find-matching-and-common-items-in-questionnaires-and-surveys/), but it's not perfect and should be used with caution.
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## Citing the BMC validation paper
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