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The ninth meeting of the Rubber Study Group, held in Ottawa, May 5–9, 1952, was attended by delegations from eighteen countries and territories and observers from the Food and Agriculture Organization, the Organization for European Economic Cooperation, the Organization of American States and the International Rubber Development Committee. Meeting under the chairmanship of the head of the Canadian delegation (Plumptre), delegations exchanged information concerning policies and problems of member governments. A number of short- and long-term problems of special concern to member governments, the producing and manufacturing industries and the rubber trade were discussed and, where necessary, referred to governments for further consideration. Noting that many members of the group were concerned about the uncertainties of rubber production, consumption and price, the study group decided to establish a working party "to consider whether measures designed to prevent burdensome surpluses or serious shortages of rubber are necessary and practicable; to prepare drafts of any agreements required to implement such measures; and to report back to the study group as soon as possible."

The working party established at the ninth meeting of the Rubber Study Group to consider whether measures designed to prevent burdensome surpluses or serious shortages of rubber were necessary or practicable met in July and August 1952 and again in January 1953. The working party, which had decided to focus on a buffer stock type of agreement to apply to natural rubber, concluded that a buffer stock agreement would be practicable, but that the question as to whether it was necessary should be decided at the tenth meeting of the Rubber Study Group, for which a draft agreement was prepared.

Following a decision of the Rubber Study Group at its fourth meeting, a Permanent Secretariat with headquarters in London was established by the Management Committee in 1947, with A. G. Pawson as secretary and W. G. G. Kellett as senior statistician. The secretariat's primary activity was to be to provide the Group with information on the statistical situation and general world economic conditions as related to rubber.

The fifth meeting of the Rubber Study Group, attended by delegates of fifteen nations and observers from several international organizations, was held in Washington from April 26 to 29, 1948.The Group examined the rubber statistical position and made estimates for natural rubber production and total consumption of natural and synthetic rubber during 1948 and provisionally for 1949. Production for 1948 was estimated at approximately 1,390,000 tons with total consumption nearly 1,745,000 tons. Production in 1949 was expected to reach 1,550,000 tons while consumption would remain at the 1948 level.

The sixth meeting of the Rubber Study Group was held in London from March 29 to April 1, 1949, under the chairmanship of Sir Gerard Clauson. Fifteen member nations attended the sessions whose primary object was to review the world rubber situation and discuss problems relating to production, consumption and trade in rubber. Countries attending included Australia, Belgium, the British Colonies, Burma, Canada, Ceylon, Czechoslovakia, Denmark, France, Hungary, Italy, Liberia, the Netherlands and Indonesia, the United Kingdom, and the United States.

The seventh meeting of the Rubber Study Group was held in Brussels from May 2 to 5, 1950 to review the world rubber situation and consider means of expanding consumption and improvement of packing and marketing systems. The meeting was attended by delegations from Australia, Belgium, the United Kingdom colonies, Burma, Canada, Ceylon, Denmark, France, Hungary, Italy, Liberia, the Netherlands, Thailand, the United Kingdom, the United States, the United States of Indonesia, and by observers from the Food and Agriculture Organization and the International Rubber Development Committee.

In the period following the Rubber Study Group's seventh meeting, May 2 to 5, 1950, press reports from western markets indicated that the fear (and the subsequent reality) of a world crisis had lead to heavy stockpiling of rubber and to speculative buying—both of which contributed to higher prices. London financial circles, while expressing the views that the Rubber Study Group's 1950 consumption estimates were too low and that the market could bear the high prices, were reported to have taken a conservative view of the boom, fearing that the unrealistically high prices might force the United States to activate more of its synthetic rubber plants and thus make the position of natural rubber producers less secure. A press release of June 9, 1950, revealed that the United States government had directed the attention of the world's rubber producing and marketing countries to the "serious implications" of the price movements and on July 7 the Reconstruction Finance Corporation reopened three United States synthetic rubber plants with a total annual production capacity of 88,000 tons. There was a definite break in rubber prices on the London market during the week following the release, on August 14, of estimates by the Rubber Study Group Secretariat which showed that the world production of rubber for June had exceeded consumption demands. This development, which press reports indicated to have been welcomed by industry representatives in London, was largely attributed to the cessation of "panic buying".

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.

The third meeting of the International Tin Study Group was held at The Hague from October 25 through 29,1948. Fifty-five delegates and advisors were present to represent the fourteen member governments. The Group discussed common problems in connection with production, consumption and trade and reviewed the world statistical position of tin.

The International Tin Study Group, which parallels in general the other international study groups established for commodities, was created by an international conference held in London in October, 1946. Its functions are to review periodically the world supply and demand position of tin and to make recommendations to the member governments.

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.