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This case study is part of a series of in-depth reports on religiously motivated violent radicalisation - and resilience to it - in 12 countries. The series examines periods in which religious radicalisation and violence has escalated and analyses relevant policy and political discourses surrounding them. While seeking to identify factors that drove radicalisation and violence in each country, the case studies also critically assess programmes of prevention and resilience-building, identifying good practices. This series was produced by GREASE, an EU-funded research project investigating religious diversity, secularism and religiously inspired radicalisation. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 770640.

Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Exploring and quantifying the effect of weather on sales ASDA, one of the biggest supermarket chains in the UK, is interested in how weather affects sales of certain product groups. Understanding the weather-sales relationship would allow ASDA to manage the supply-chain system and distribution in a timely and efficient manner, particularly reducing the risk of food waste for fresh products such as meat while having products in stock as needed. The analysis of the weather-sales relationship builds on a huge dataset which includes daily sales data for 151 product groups in over 600 stores across the UK over 3 years, and daily weather conditions at each store, including mean daily humidity, wind speed, rainfall, snowfall and minimum and maximum daily temperature. The Alan Turing Institute is the UK's national institute for data science and artificial intelligence, with headquarters at the British Library. Data Study Groups are intensive five day collaborative hackathons hosted at the The Alan Turing Institute, which bring together organisations from industry, government, and the third sector, with multi-disciplinary researchers from academia. ASDA, the Data Study Group Challenge Owner, provided the real-world challenge and the data to be tackled by a group of researchers led by two Principal Investigators and a Facilitator. This report is the culmination of that process and is the result of their joint co-authorship.

Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. NCSC: Capturing the purpose of websites This challenge was provided by the National Cyber Security Centre (NCSC), which is tasked with protecting the UK public sector, and aims at improving existing internet search technologies. As the internet grows, there is a need in industry, government and academia to better facilitate website recommendation, semantic search, and domain discovery, as well as to improve the security of the web. For instance, it is not possible to easily find all UK public sector domains with a simple search; nor it is possible for commercial organisations to easily generate a list of potential competitors or suppliers/customers from current search engines. An exciting potential approach to enable the NCSC and other organisations to leverage the latest machine learning algorithms on these challenges is to automatically learn a purposeful compact vector representation of every website or domain on the Web.

Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Smart monitoring for conservation areas WWF (World Wide Fund for Nature) monitors over 250,000 protected areas (e.g. national parks and nature reserves) and thousands of other sites and critical habitats. These sites are the foundation of global natural assets and are central to the preservation of biodiversity and human well-being. Unfortunately, they face increasing pressures from human development. In this challenge, we explore various data science techniques to automatically detect news articles that report emerging threats to key protected areas. We describe a system that identifies such news stories near real-time. This is vital to enable the wider machinery of WWF and the conservation community to engage with governments, companies, shareholders, insurers, and others to help halt the degradation or destruction of key habitats.

Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Automating the evaluation of local government planning applications It is estimated that England requires up to 345,000 new homes to be built each year in order to keep up with population growth. Although housebuilding has increased since reaching a low point after the financial crisis, the annual net supply of new homes would need to increase by more than 40% to reach this number. This lack of new homes contributes to higher mortgages, higher rents, less social housing and wider deprivation. One of the contributing factors is that the current planning application system remains a complex and inefficient task. Every construction project in the UK, including building or extending a house, fitting new windows on a listed building, or chopping down a tree, requires the submission of complicated planning forms and technical drawings. Each one of these documents needs to be manually validated and approved by a planning officer. Over 3.5 million applications are submitted to councils each year. On average, owing to local government budget cuts of 40% over the last 10 years, it takes three weeks for a council to start looking at a planning application. This creates large backlogs and a lack of information for application submitters, leading to additional calls and emails to chase progress which further increase workload on planning officers. At the same time, over a third (1.2 million) of the applications submitted annually are rejected, often owing to the complexity of submitting a correct application and the manual burden in processing them at councils. The overall objective of this work is to move towards the automated detection of common errors in planning applications using ML/AI approaches.

Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Spend Network: Automated matching of businesses to government contract opportunities This report presents the output of a week-long collaboration between Spend Network, and lead academics from the University of Manchester and the University of Oxford that attended the Data Study Group at The Alan Turing Institute. Spend Network is a platform that aims to enable efficient public procure-ment. The goal of this work was to match suppliers to tenders. Our approach consists of building vector representations of suppli-ers and tenders and identifying their compat-ibility with the distance between the respec-tive vectors. In building their representations, we make use of both their textual descriptions and the knowledge of previously awarded con-tracts. We find previous contracts informative of future procurement decisions. Our best re-sults use Correlated Topic Models (Blei et al., 2007) for extracting representations of textual descriptions.

Funding Information: AR received personal fees from Maat Pharma, IML received personal fees from MSD, and Gilead. AA received personal fees from Lilly Foundation, and grants from Grifols and Fisher & Paykel. CEL received personal fees from Bayer, Merck, Aerogen, Biomérieux, ThermoFisher Brahms, and Carmat. SN received personal fees from MSD, Bio-Rad, BioMérieux, Gilead, Fisher and Paykel, and Pfizer. All other authors declare no competing interests. Funding Information: This study was supported in part by a grant from the French government through the « Programme Investissement d'Avenir» (I-SITE ULNE) managed by the Agence Nationale de la Recherche (coVAPid project). Prof. Ignacio Martin-Loeches has been supported by SFI (Science Foundation Ireland), Grant Number 20/COV/0038. The funders of the study had no role in the study design, data collection, analysis, or interpretation, writing of the report, or decision to submit for publication. Publisher Copyright: © 2022, The Author(s). ; Background: Recent multicenter studies identified COVID-19 as a risk factor for invasive pulmonary aspergillosis (IPA). However, no large multicenter study has compared the incidence of IPA between COVID-19 and influenza patients. Objectives: To determine the incidence of putative IPA in critically ill SARS-CoV-2 patients, compared with influenza patients. Methods: This study was a planned ancillary analysis of the coVAPid multicenter retrospective European cohort. Consecutive adult patients requiring invasive mechanical ventilation for > 48 h for SARS-CoV-2 pneumonia or influenza pneumonia were included. The 28-day cumulative incidence of putative IPA, based on Blot definition, was the primary outcome. IPA incidence was estimated using the Kalbfleisch and Prentice method, considering extubation (dead or alive) within 28 days as competing event. Results: A total of 1047 patients were included (566 in the SARS-CoV-2 group and 481 in the influenza group). The incidence of putative IPA was lower in SARS-CoV-2 pneumonia group (14, 2.5%) than in influenza pneumonia group (29, 6%), adjusted cause-specific hazard ratio (cHR) 3.29 (95% CI 1.53–7.02, p = 0.0006). When putative IPA and Aspergillus respiratory tract colonization were combined, the incidence was also significantly lower in the SARS-CoV-2 group, as compared to influenza group (4.1% vs. 10.2%), adjusted cHR 3.21 (95% CI 1.88–5.46, p < 0.0001). In the whole study population, putative IPA was associated with significant increase in 28-day mortality rate, and length of ICU stay, compared with colonized patients, or those with no IPA or Aspergillus colonization. Conclusions: Overall, the incidence of putative IPA was low. Its incidence was significantly lower in patients with SARS-CoV-2 pneumonia than in those with influenza pneumonia. Clinical trial registration The study was registered at ClinicalTrials.gov, number NCT04359693. ; publishersversion ; published