Search
Research methods
Projects
Ground truth for mental health data science
Linking digital footprint data in the UK birth cohorts to guide the next generation of algorithms for mental health and wellbeing research
AI for multiple long-term conditions: Research Support Facility
Developing data standards, best practice and community around AI for multiple long term conditions research
WAYS: What aren’t you seeing?
Enhancing everyday visualisation practice through generative and evaluative design tools for data scientists
Detecting and understanding harmful content online
Developing benchmarks and datasets for online harms researchers, and guidance for practitioners using tools to detect online harms
Programmes
Publications and software
Diachronic word embeddings from 19th-century British newspapers
Diachronic word embeddings trained on a 4.2-billion-word corpus of 19th-century British newspapers, using Word2Vec...
Pedrazzini, Nilo & Barbara McGillivray. (2022). Diachronic word embeddings from 19th-century British newspapers [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7181682
Towards a quantitative research framework for historical disciplines
McGillivray, B., Wilson, J. and Blanke, T. (2019). Towards a quantitative research framework for historical disciplines. In Proceedings of the Workshop on Computational Methods in the Humanities 2018, Lausanne, Switzerland, June 4-5, 2018
Mining the UK Web Archive for Semantic Change Detection
Semantic change detection (i.e., identifying words whose meaning has changed over time) started emerging...
Tsakalidis, A., Bazzi, M., Cucuringu, M., Basile, P., & McGillivray, B. Mining the UK Web Archive for Semantic Change Detection. Proceedings of INTERNATIONAL CONFERENCE RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING, https://doi.org/10.17863/CAM.43534
Towards a quantitative research framework for historical disciplines
The ever-expanding wealth of digital material that researchers have at their disposal today, coupled...
McGillivray, B., Wilson, J. and Blanke, T. (2019). Towards a quantitative research framework for historical disciplines. In Proceedings of the Workshop on Computational Methods in the Humanities 2018, Lausanne, Switzerland, June
Quantitative Historical Linguistics.
A Corpus Framework
Jenset, G. B. and McGillivray, B. (2017). Quantitative Historical Linguistics. A Corpus Framework, Oxford: Oxford University Press.
Events
The Northern Data Debates: Rage against the (technology) machine
Wednesday 22 Jun 2022
Time: 17:00 - 20:00
The BigScience philosophy of building open AI
Tuesday 14 Jun 2022
Time: 16:00 - 17:00
On the dangers of stochastic parrots
Thursday 08 Jul 2021
Time: 16:00 - 17:15
Methodological challenges in the translation of data and evidence into policy during COVID-19
Friday 21 May 2021
Time: 16:00 - 17:00
Methodological challenges and approaches in COVID-19 epidemiology
Friday 23 Apr 2021
Time: 16:00 - 17:00
Building a future for the urban analytics blueprint
Wednesday 16 Oct 2019 - Friday 18 Oct 2019
Time: 10:00 - 17:00
Vocal interactivity in-and-between humans, animals and robots
Thursday 29 Aug 2019 - Friday 30 Aug 2019
Time: 09:00 - 17:00
Measuring information exposure in dynamic and dependent networks
Monday 22 Jul 2019
Time: 10:00 - 16:00
Urban analytics at the interface between environment, human health and behaviour
Tuesday 02 Jul 2019 - Thursday 04 Jul 2019
Time: 09:00 - 18:00
Impact stories
Interest groups
Bridging machine learning and behaviour models
How can we bridge data-driven and theory-driven behavioural models?
Omics data generation and analysis group
Developing rigorous and robust data science methods for curation, collection and computation of omic data sets
Economic data science
How can we use modern computational and statistical methods to analyse economic data?
People
Professor Jon Crowcroft
Researcher at Large
Bastian Greshake Tzovaras
Senior Researcher
Abdulrahman Abdullatif Alsayed
Enrichment Student
Ankita Garg
Enrichment Student
Jiawen Liang
Enrichment Student
Anda Solea
Enrichment Student
Scarlett Syme
Enrichment Student
Shahina Begum
Enrichment Student