There aren’t many sci-fi movies as cleverly written and executed as the 1983 Hollywood classic WarGames, directed by John Badham and written by Lawrence Lasker and Walter F. Parkes. But it might not have been as good as it is if not for the input of the British AI scientist Donald Michie (1923-2007) and his knowledge of game-learning machines, especially computers that can play noughts and crosses.

Who was Donald Michie?

Donald Michie was a codebreaker at Bletchley Park (BP) during the second world war; after the war, a geneticist and later an artificial intelligence (AI) pioneer who founded the first department for machine intelligence in the world at the University of Edinburgh. He made significant contributions to all three fields that he worked in and was instrumental in institutionalising AI and machine learning (ML) research in the UK, and abroad.

A major part of his AI research focused on machines that could play games. However, one of his most famous achievements in this subfield of AI is MENACE (Machine Educable Noughts and Crosses Engine), a mechanical computer made of matchboxes and beads that could learn to play noughts and crosses. MENACE is now considered one of the first working examples of, what ML researchers call, reinforcement learning. This eventually led to the development of the BOXES algorithm by Michie and R. A. Chambers for learning of dynamic systems.

Therefore, it is no surprise that Universal Studios reached out to him when working on WarGames.

Dangerous games

If you haven’t seen it, be warned that there will be spoilers ahead.

WarGames tells the story of a young computer hacker, David Lightman (Matthew Broderick) who accidentally gains access to a US military supercomputer that controls the launch of nuclear warheads. He starts playing games with it thinking that it’s a game-playing machine, which makes the supercomputer run real war-game simulations and almost launch the nuclear warheads. Fortunately, Lightman, along with Prof. Stephen Falken (John Wood), the scientist who designed the supercomputer, makes it play a game of noughts and crosses against itself, which leads the machine to conclude that a Third World War involving nuclear warheads cannot be won, thus stopping it from launching the missiles.

It might be an exaggeration to call it sci-fi as it hits a little too close to reality than most sci-fi movies would. Although there is no public record of any supercomputer malfunctioning and almost launching nuclear warheads to start a world war, there have been a few mishaps in United States alone that have almost caused nuclear detonations, and one incident in 1983 when a Soviet early warning system gave a false alarm of a missile launch by the US.

The fear of the Cold War escalating to a direct nuclear war was alive and well when WarGames was released. However, that was only a part of the reason why the movie became a box office success and a cult classic. It was the presence of an ‘intelligent’ supercomputer that could play games and treat global thermonuclear war as a game, and act on its own to potentially end the world as we know it, that made WarGames such a compelling film.

Michie’s concern

WarGames tapped into a real concern that was prevalent at that time, regarding the increasing inscrutability and influence of large computers that were being used by big companies and government organisations for various purposes. This was something that Michie too was very concerned about and he points out in his correspondence regarding the script consultancy with Universal Studios:

We are all in danger from the current emergence of super-computers inscrutable to man. Very few are yet alert to the seriousness and nature of the peril.

This is not surprising coming from Michie, who had already been discussing the social risks of AI for over a decade by this point. In fact, he was the one who organised the first global conference on the social impact of AI in 1972 at Serbelloni in Italy¹. In the years since then, Michie had raised some of his concerns regarding increasingly complex and sophisticated computers in several publications despite being a pioneer in the field. It makes sense why he was enthusiastic to collaborate on this sci-fi movie project, which was initially titled The Genius.

Main suggestions

“WarGames Correspondence with Universal Studios”, 1981. (Add MS 88958/3/54). Reproduced with permission of the estate of Donald Michie.

In his correspondence, Michie points out five pages worth of technical inaccuracies in the draft script. Most of them are small details regarding the terminologies that are spoken about and technical issues in how the technology is used in various scenes. Michie recommended a lot of minor changes, for instance using the term “printer” instead of “teleprinter,” which was no longer used among the computing community by the 1980s. However, he also pointed out major technical errors, such as that a program once terminated would not have a fragment of itself accidentally re-animated when the computer goes online as was shown in one of the scenes in the draft script.

Although some of Michie’s minor suggestions have been taken into account by the scriptwriters, we can see that most of the technical inaccuracies he pointed out are still there in the final cut of the movie. This includes the premise of Michie’s main suggestion: to consider changing the climax sequence where the machine is self-playing a 3-in-a-row noughts and crosses to “learn” that it’s a game that cannot be won. Michie recommended changing it a game called 5-IN-A-ROW (sometimes called Peggity) as it would take much longer for the computer to self-learn how to play compared to noughts and crosses, which can be learned by a fast computer within seconds. This, in his view, would make the long climax sequence where the computer is learning to play the game more realistic. Moreover, Michie pointed out that the supercomputer “would try to establish the ‘no win’ property by logical proof,” rather than brute-forcing through various playing strategies as is shown in the movie. He states that a computer as sophisticated as the one shown in the movie would not use a ‘try everything’ strategy to learn how to play a game.

Crucial contribution to the script

However, one key suggestion of Michie’s that was taken into account by the studio and the scriptwriters was the technique by which David and Prof. Falken make the computer play itself. In the draft script given to Michie, the computer “somehow” starts self-playing, presumably without a prompt. Michie suggested changing the number of players to “zero” to make the computer play itself. This is what happens in the movie.

Michie also recommends changing the way the supercomputer abruptly stops trying to guess the launch codes of the missiles, as it was in the draft script. He instead suggests that the filmmakers show the computer generalise from the self-play sequence that ‘nuclear wars are not winnable’ and apply this to terminate the war game it was playing. He recommends David or someone else instructing the computer to do this, although this was not taken into account. In the film, the computer automatically generalises from the noughts and crosses self-playing that various real-world nuclear war-game simulations all end up with no winners.

Although Michie’s main suggestion to change the 3-in-a-row game to 5-in-a-row was ignored by the filmmakers, he himself points out in the correspondence that it is not necessary to fix this as ‘there is a risk of distracting from the gripping simplicity of the ending by fussing over side-issues.’ It shows his concern for how well laypeople could receive the movie and its main theme of the futility of nuclear war, rather than focusing too much on the technical inaccuracies or inconsistencies in the script.

Aswin Valsala Narayanan

The Donald Michie Papers at the British Library comprise two separate tranches of material gifted to the Library in 2004 and 2008. They contain correspondence, notes, notebooks, offprints and photographs and are available to researchers.

Aswin Valsala Narayanan is as PhD student at the University of Leeds and the British Library. He is on an WRoCAH Collaborative Doctoral Award researching the Donald Michie Archive, exploring Michie's work as an artificial intelligence researcher in post-war Britain.

[1] Rosamund Powell, “The ‘Artificial Intelligentsia’ and Its Discontents: An Exploration of 1970s Attitudes to the ‘Social Responsibility of the Machine Intelligence Worker,’” BJHS Themes, September 19, 2023, pp.1–15, https://doi.org/10.1017/bjt.2023.8.

There is a good chance that AI research in Britain would not have evolved the way it did had Alan Turing been a great chess player. As a matter of fact, he couldn’t hold a candle to the chess masters, such as Hugh Alexander and Harry Golombek, who were in his Enigma codebreaking team at Bletchley Park (BP). As per one account, Turing once played himself into such a mess that his opponent Golombek turned the board around and tried to save the game for him.

However, Turing’s mediocrity in chess proved to be a blessing for one of the youngest codebreakers at BP, Donald Michie, who was just 18-years-old when he was recruited to the Government Code and Cypher School. Despite having no mathematical background, he proved to be a quick learner and eventually became a key member of Testery — Major Ralph Tester’s team attempting to break the ‘Fish’ codes using manual hand methods. It was during his time at Testery that he met and befriended Turing, who had developed Turingery — one of the manual methods used by codebreakers of Testery.

Donald Michie c. 1974 (Add MS 89072/1/5). Reproduced with permission of the estate of Donald Michie.

While Turing was no match for his chessmaster teammates, in young Michie, he found a partner who could give him a competitive game. They met every week at a pub in Wolverton not too far away from BP. This friendship and weekly chess session proved to be life-changing for Michie.

According to Michie, their conversations invariably centred around ideas of learning machines and mechanising chess, ideas formative to his interest and eventual career in machine intelligence.

Turing and Michie remained friends after the war.

Post-war games and game-playing machines

In the period of 1947-48, Michie and the mathematician Shaun Wylie, another colleague at BP, developed a chess-playing algorithm, or a machine on paper — a ‘paper machine’, so to speak. This was basically a collection of machine-rules to decide the next move using the opponent’s move as input. The ‘MACHIAVELLI’ (so named after creators Michie and Wylie), was created independently of the paper-machine that Turing and his friend David Champernowne developed, named ‘TUROCHAMP.’

I. J. ‘Jack’ Good, who was friends with both Michie and Turing, too took part in the BP discussions of ‘chess-playing’ machines. In fact, Jack Copeland notes that Good recalls Turing speaking about the concept of mechanizing chess in 1941, before Michie’s time at BP. Like Michie, Good was interested in both chess and ‘learning’ machines. He was an excellent chess player apart from being a brilliant mathematician. Good was aware of the challenge that Michie and Wylie made to Turing and Champerowne’s chess-machine, and suggested a way in which MACHIAVELLI could be beaten in a letter he wrote to Turing in 1948:

I visited Oxford last week-end. Donald showed me a 'chess machine' invented by Shaun and himself. It suffers from the very serious disadvantage that it does not analyse more than one move ahead. I am convinced that such a machine could play a very poor game, however accurately it scored the position with respect to matter and space. In fact it could easily be beaten by playing 'psychologically', i.e. by taking into account the main weakness of the machine. This could be done by deliberately complicating the position and entering into combinations.

The correspondences show how the discussions on mechanizing chess from the BP years, evolved during the post-war period. However, these were not just the result of Michie and Turing’s acquaintance with chess players during their time at BP.

They were also shaped very much by the kind of codebreaking work they were involved in.

Colossus [c 1944]. The first-of-its-kind digital codebreaking was used for ‘guided’ searches to break German messages encoded using the Lorenz teleprinter cipher. (c) Crown copyright

 Both Michie’s and Turing’s chess-playing machines involved taking the opponent’s move as input and then creating an output move in response. Both of the algorithms involved ‘searching’ for a good move after considering various possible single moves by ranking them based on variables such as the safety of the piece, and the value of opponent’s pieces that could be captured. This was a guided search for ‘moves’ or ‘solutions’ using an evaluating – or ‘heuristic’ – function to narrow the number of possibilities rather than completing an exhaustive general search through all possible moves. It was conceptually very similar to the machine-aided searches that codebreakers at BP had to conduct to break the German ciphers. Michie’s job, being a key member of Newmanry team that broke the Lorenz cipher using the digital codebreaking machine Colossus, was to come up with solutions to narrow down the daily codebreaking searches of the machines. Such a problem-solving technique that uses a heuristic function to estimate hopeful paths to a solution would eventually be termed as "heuristic search". Michie himself would go on to make a significant contribution to developing and popularising this computational technique during the 1960s with his graph traverser program co-developed with J. E. Doran.

Going through Michie’s archives, we can find that research into chess-machines remained relevant to his entire machine intelligence and AI research career, even during the period that he worked as a geneticist. For instance, during the 1950s he had played MACHIAVELLI against biologist John Maynard Smith’s SOMA (Smith’s One-Move Analyser) in matchup refereed by Maynard Smith’s eldest son. A detailed description of how the machines performed was given in the 1961 New Scientist that Michie wrote together with Maynard Smith: ‘Machines that play games’.

Importance of chess to AI and machine learning

Chess became central to Michie’s research in the 1970s, when his research program was curtailed by the University of Edinburgh in the aftermath of the Lighthill Report that drastically defunded AI research in the UK. Chess endgames research was something Michie could work on with the limited funding he had post-Lighthill. However, for Michie, chess was not just a convenient and playful way to engage his interest in machine intelligence, as he clearly puts it in a 1980 draft titled ‘A representation for pattern knowledge in chess end-games’. In it, he addresses the question of whether those involved in computer chess are just ‘fooling around with the taxpayer’s money,’ and argues that ‘no other equally apposite material is readily available for investigating certain scientific issues of importance.’

This is also a point he emphasised in his 1966 paper, ‘Game-playing and game-learning automata’, referring to Turing’s interest in machines that could play games,:

It is sometimes thought that Turing’s interest in mechanised game playing was the spare time frivolity of a man who reserved his serious thoughts for worthier topics. That was not the case. He had the conviction that the development of high-speed digital computing equipment would make possible the mechanisation of human thought processes of every kind, and that games constituted an ideal model system in which studies of machine intelligence could first be developed.

Chess, for Michie, was the “fruit-fly” of AI research that was perfect for “studying the representation and measurement of knowledge in machines.’’ In another 1980 article ‘Chess with Computers’, he describes in detail why the strategic game is ideal for AI research and its “chief advantages”:

…chess constitutes a well-defined and formalized domain; it challenges the highest levels of intellectual capacity over a wide range of cognitive functions logical concept-formation, calculation, rote-learning, analogical thinking, deductive and inductive reasoning, and so forth; a detailed corpus of chess knowledge has accumulated over centuries in chess instructional works and commentaries; a generally accepted numerical scale for performance is available in the USCF rating system; and finally, the game can readily be decomposed into sub-games which can be subjected to intensive separate analysis.

Michie’s focus on chess-endgames research would eventually contribute to the development of the groundbreaking Iterative Dichotomiser 3 (ID3) learning algorithm for generating decision trees by J. Ross Quinlan, who was one of the many brilliant researchers Michie mentored during his career. In Quinlan’s 1986 paper, he acknowledges Michie’s role in the endeavour:

ID3 (Quinlan, 1979, 1983a) is one of a series of programs developed from CLS in response to a challenging induction task posed by Donald Michie, viz. to decide from pattern-based features alone whether a particular chess position in the King-Rook vs King-Knight endgame is lost for the Knight's side in a fixed number of ply.

This shows how chess shaped the fields of AI and machine learning; chess endgames research also played a key role in the projects of many of Michie’s PhD students from the 1970s and 80s, including Stephen Muggleton, Alan Shapiro and Tim Niblett’s pioneering work on induction methods in machine learning.

Nevertheless, the relationship between chess and AI research remains severely underexplored in the history of AI.

Posted by Aswin Valsala Narayanan

Further reading:

Donald Michie, “Alan Turing’s Mind Machines,” February 8, 2008, https://doi.org/10.7551/mitpress/7626.003.0005.

Herbert A. Simon and Allen Newell, “Heuristic Problem Solving: The Next Advance in Operations Research,” Operations Research 6, no. 1 (1958): 1–10.

Donald Michie, “Game-Playing and Game-Learning Automata,” in Advances in Programming and Non-Numerical Computation (Elsevier, 1966), 183–200.

Donald Michie, “Chess with Computers,” Interdisciplinary Science Reviews 5, no. 3 (January 1, 1980): 215–27, https://doi.org/10.1179/isr.1980.5.3.215.

J. R. Quinlan, “Induction of Decision Trees,” Machine Learning 1, no. 1 (March 1, 1986): 81–106, https://doi.org/10.1007/BF00116251.

The Donald Michie Papers at the British Library comprise two separate tranches of material gifted to the Library in 2004 and 2008. They contain correspondence, notes, notebooks, offprints and photographs and are available to researchers.

Aswin Valsala Narayanan is as PhD student at the University of Leeds and the British Library. He is on an WRoCAH Collaborative Doctoral Award researching the Donald Michie Archive, exploring Michie's work as an artificial intelligence researcher in post-war Britain.

The fiftieth anniversary of the foundation of the British Library is an opportunity to look back at the leading role the Library and its parent bodies played in introducing computerised information retrieval for science and medicine to the UK. Between 1965 and 1975 experiments in searching databases of medical research were carried out in partnership with the US National Library of Medicine (NLM)  together with computer scientists and medical users in the UK. Following the success of these experiments the Library launched BLAISE (British Library Automated Information Service)  as a national public service in 1977.

The NLM began publishing Index Medicus, an index of medical journal articles, in 1879. In 1960 printing was computerised and the machine readable data on tape became available for information retrieval. A publicly available US service, MEDLARS (Medical Literature Analysis and Retrieval System) opened in 1963 with MEDLINE  (MEDLARS online) going live in 1971. [1]

 In 1965 the NLM agreed with the National Lending Library for Science and Technology [2] to supply tapes in exchange for MEDLARS records of UK medical literature. With these tapes in hand the Office of Science and Technology Information [3]  funded Newcastle University to develop a retrieval package based on NLM’s IBM software to run on the university’s English Electric computer. Subsequent projects in 1973-74 tested the online environment and current awareness services with medical researchers and librarians in Leeds and Manchester over an online telephone link. [4]

The next step in service delivery was to establish online access to the NLM. University College London had set up a link to the US through ARPANET, the early version of the internet [5], and in 1973 British Library Research & Development [3] along with other public bodies, joined this network. This programme was historically significant as the first international communication over the internet. Project STEIN (Short Term Experimental Information Network) involved sixteen centres (e.g. the Royal Post-Graduate Medical School) each with its own terminal and trained intermediary.  The number of users (362) and searches (1217) was substantial and the study confirmed the need for intermediaries who were experienced in using the system and formulating searches. The clinicians and researchers who accompanied each session evaluated the results and gave feedback. Despite difficulties with telecoms, satisfaction was high as searches delivered articles that were new together with articles that were familiar to the users, thus increasing their confidence in the search. [6]

These encouraging results led the Library in 1977 to launch BLAISE, a fully supported public service providing Medline and databases for toxicology and cancer. Tapes were delivered monthly from Washington by diplomatic bag to a computer bureau in Harlow to run on an IBM 370 machine with NLM’s ELHILL retrieval software. Mounting tested software on an established bureau service meant that BLAISE went live within a year. Users benefited from the dedicated BLAISE PSS (Packet Switched Service) network and a support team that provided training, documentation and a help desk, alongside document supply from the British Library Lending Division at Boston Spa.[7] At first researchers and clinicians used Medline for checking references or keeping up to date but it has since become an essential tool for the evidence based medicine community to generate systematic reviews and contribute to the Cochrane Library.[8] From 1977 the Library was the sole provider of NLM databases in the UK but in a political decision in 1982 NLM, as a federal agency, was required to release its products to US online providers. With the ensuing competition BLAISE was no longer able to support a UK based service and it was relaunched as BLAISE-LINK, a UK portal for online access to NLM. Within a few years customers moved over to commercial online hosts and BLAISE-LINK closed. 

Today, the Library continues online healthcare with the publication of AMED (Allied and Complementary Medicine Database). This database supplements the coverage of Medline in areas such as alternative medicine, palliative care and rehabilitation. [9]

We have come a long way in fifty years.  In 1973 searching involved expensive telecoms and computer access, clumsy equipment  (who now remembers audio-acoustic couplers?) minimal records, complex Boolean search strings and the need for skilled medical librarians to navigate all these obstacles. Now, there is free access to the internet and PubMed, open access full text and sophisticated relevance searching empowering every user. Information has exploded:  in 1976, Medline and its associated files had 3.5 million records, by 2022, PubMed had 35 million. [10] 

References [BL shelfmark]

All URLs accessed on 7 July 2023.

[1] MEDLINE History. https://www.nlm.nih.gov/medline/medline_history.html

[2] The National Lending Library for Science and Technology (NLLST) was the predecessor of the British Library Lending Division and later, the Document Supply Centre. The service is currently available as British Library On Demand.

Barr, D. P. The National Lending Library for Science and Technology. Postgraduate Medical Journal. 42.493 (1966): 695. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2466097/pdf/postmedj00407-0003.pdf

[3] The Office of Science and Technology Information (OSTI) was the predecessor of British Library Research & Development which promoted and funded R&D by the UK library and information community until its merger with the Library and Information Commission in 1999.

Baxter, P. "The role of the British Library R&D department in supporting library and information research in the United Kingdom." Journal of the American Society for Information Science 36.4 (1985): 276. https://www.proquest.com/openview/77a69cbd42dd0412f39b217892f95ac2/1?pq-origsite=gscholar&cbl=1818555 

[4] Barraclough, E. Information Retrieval, its origins in Newcastle. http://history.cs.ncl.ac.uk/anniversaries/40th/webbook/infoRetrieval/index.html

Harley, A. J., and Elizabeth D. Barraclough. MEDLARS information retrieval in Britain. Postgraduate medical journal 42.484 (1966): 69. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2465839/pdf/postmedj00398-0003.pdf

[5] Kirstein, P. T. "Early experiences with the Arpanet and Internet in the United Kingdom." IEEE Annals of the History of Computing 21.1 (1999): 38-44. https://citeseerx.ist.psu.edu/doc/10.1.1.112.8527

Computer History – Internet history of the 1970s.  https://www.computerhistory.org/internethistory/1970s/

[6]  Holmes, P. A description of the British Library’s short-term experimental information network project. pp 231-237 - 1st International On-line Information Meeting, London 13-15 December 1977 / organised by On-line Review, the international journal of on-line information systems. (1977). Oxford ; New York: Learned Information. [available in the British Library at shelfmark 2719.x.4085 ]

Holmes, P. (1978). On-line information retrieval: An introduction and guide to the British Library's short-term experimental information network project / P.L. Holmes. Vol.2, Experimental use of medical information services. (Research and development reports (British Library) ; no.5397). London: British Library Research and Development Department. [available in the British Library at shelfmark 2113.560000F BLRDR 5397 ]

Trials were also made with other scientific and engineering databases on the Lockheed Dialog system.

(7) Holmes, P. L. The British Library Automated Information Service (BLAISE). Online Review 3.3 (1979): 265-274. https://www.emerald.com/insight/content/doi/10.1108/eb024003/full/html       

BLAISE also provided bibliographic databases for the British National Bibliography and the Library of Congress, finally closing in 2002.

[8] McKibbon, K. A. Evidence-based practice. Bulletin of the medical library association 86.3 (1998): 396. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC226388/pdf/mlab00092-0108.pdf

Cochrane Library. https://www.cochranelibrary.com/about/about-cochrane-reviews

[9] Allied and Complementary Medicine Database (AMED) https://www.ebsco.com/products/research-databases/allied-and-complementary-medicine-database-amed

[10] Miles, W. (1982). A history of the National Library of Medicine : The nation's treasury of medical knowledge. (NIH publication ; no.82-1904). Bethesda, Md.: U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health, National Library of Medicine. [p.386 -3.5 m records, 1976] https://collections.nlm.nih.gov/bookviewer?PID=nlm:nlmuid-8218545-bk

PubMed Milestone - 35 Millionth Journal Citation Added. https://www.nlm.nih.gov/pubs/techbull/nd22/brief/nd22_pubmed_milestone.html

Further reading

Bourne, C., & Hahn, Trudi Bellardo. (2003). A history of online information services, 1963-1976, Cambridge, Mass. ; London: MIT. [Available in the British Library on open shelf: Humanities 2 Reading Room HUR 025.04]

Written by Richard Wakeford (Science Reference Specialist, Retired). Richard was a member of the BLAISE support team, 1981-1984.

We are happy to announce that this week we have acquired the Wiley Digital Archives of several major learned societies. The collections currently available are those from the New York Academy of Sciences, the British Association for the Advancement of Science, the Royal Geographical Society, the Royal Anthropological Institute of Great Britain and Ireland, and the Royal College of Physicians. The database also includes scientific material from major British universities, digitised as part of the BAAS project.

Information in the archives includes field notes on Hausa Islamic law, beginners' lessons in the Mole language spoken in parts of Ghana, research for a government investigation into early-Victorian mine ventilation, reports on an earthquake in Erzerum, Turkey in 1859, a recipe for a "very rare and excellent" seventeenth-century "wound drink", and a huge range of maps. The Royal College of Physicians collections include a number of digitised incunabula and medieval printed books. For those items which might be harder to read, automated transcriptions are available.

Unfortunately the database cannot currently be used from outside the Library, but we are open again and any reader with an interest in the history of science is highly recommended to come in and try it out.

Left: Trofim Lysenko in 1938. Picture in public domain. Right: Donald Michie c. 1980s. (Add MS 88958/5/4). Reproduced with permission of the estate of Donald Michie.

In August 1957, a 33-year-old Donald Michie travelled across Europe to visit Moscow. The journey was a remarkable one. Driving through Germany and Poland in a 1948 Standard drop head coupé with his friend from Oxford, John Matheson, the pair had lively encounters with enthusiastic locals, a Polish hitchhiker, and even an offer for their car from a film director in Russia.ⁱ

Whilst visiting the Institute of Genetics in Moscow, Michie had a chance encounter with Trofim Lysenko, the infamous Soviet geneticist. Seizing the opportunity, a five-hour interview between the two and Lysenko’s colleagues ensued, with a transcript and reports following in British publications over the following 12 months. What had started out as the tour of a young socialist had turned into a golden chance to meet and interrogate the man at the centre of one of the greatest scientific controversies of the twentieth century.

The British scientific community was rocked in the 1940s and ‘50s by the rise of Lysenko to Director of the Institute of Genetics in Moscow. His theories and methods (both scientific and as a political figure) prompted resignations from scientific societies, radio broadcasts and journal articles denigrating him, and no small degree of infighting as people attempted to separate the emerging Cold War political divide from the scientific merits (or demerits) of his work. Michie, as a young geneticist forging his career in this time, found himself at the heart of this.

Lysenko was a neo-Lamarckist, arguing that characteristics acquired in response to the environment an organism lives in could then be passed on to future generations. The traditional view of the 1950s, based on the work of Gregor Mendel, was that the environment’s role was limited to accelerating or slowing down random mutations of genes. Lysenko’s belief in this view was not the only factor in driving controversy. The international scientific community was also concerned by the state endorsement of his science within the Soviet Union, prompting the disappearance, side-lining, or death of prominent critics, such as N. I. Vavilov. Lysenko’s precise liability remains an issue of contention to this day.

Michie’s visit to the Institute of Genetics. Left to right: Kosikov, Ružica Glavinic, John Matheson, Trofim Lysenko, Nuzhdin, Anne McLaren and Donald Michie. Reproduced with permission of the estate of Donald Michie.

Michie was carving out his career in genetics in the 1950s. By 1953, he had finished his DPhil in mammalian genetics under the supervision of E. B. Ford at Oxford. He then moved to work as a researcher at UCL alongside notable figures such as J. B. S. Haldane, Michie’s second wife and celebrated biologist Anne McLaren, and future Nobel Prize winner Peter Medawar. Michie had already dipped his toe in the waters of the Lysenko debate in a remarkable exchange of letters to an obscure rabbit breeders’ magazine, Fur and Feather, showing himself unafraid to side with controversy as he argued in favour of testing Lysenko’s theories.ⁱⁱ

First page of Donald Michie, ‘Interview with Lysenko’, Soviet Science Bulletin, V (1 & 2, 1958), 1-10. Add MS 89202/11/6

The interview with Lysenko revolved around a major theme from Michie: would Lysenko be prepared to share his methods, publish work in English and permit exchanges of personnel with Western institutions? Michie’s belief was that differences between the West and Soviet Union could be overcome through collaboration and openness, fostering a spirit of sharing knowledge. Lysenko agreed with the sentiment, responding:

I do not agree with this division into Western genetics and Soviet genetics. Science is unitary. I believe, and my colleagues believe, that science knows no frontiers.ⁱⁱⁱ

Both Michie and Lysenko argued for letting scientific results win the debate, however they understood the obstacles in the way of that outcome rather differently. Lysenko saw bad faith and entrenched attitudes from Western scientists, believing them unwilling to entertain the possibility of Soviet scientists producing good research. Michie saw barriers to accessibility, such as the poor understanding of the Russian language in the West. He criticised the stubbornness of Lysenko and his colleagues to share their techniques and offer work for publication in English journals, whilst also castigating Western scientists for not engaging with the science and testing it rigorously and with an open mind.

Ultimately, Michie concluded from his meeting with Lysenko:

The only certain remedy that I can see is to reunite the genetical profession in a single scientific brotherhood irrespective of politics, nationality or genetical creed. … In more definite terms, Soviet and East European biologists must be willing to publish in Western journals and vice versa.^iv

The question which follows is: Did Michie’s interview impact Lysenko’s reputation in Britain?

The short answer is probably not. For instance, Michie drew upon Lysenkoist scientists in a remarkable 1958 essay reflecting on 100 years since Charles Darwin’s On the Origin of Species.^v The references to Lysenkoists were not well-received by reviewers, with them finding Michie’s piece out of step with the tone of the other essays in the collection. Lysenko’s reputation was, at least in the late 1950s, still entrenched negatively in the Western scientific world.

Shortly after these interventions, Michie drifted away from the world of genetics to pursue his long-standing interest in computers and artificial intelligence following his move to the University of Edinburgh in 1958. As such, his contributions on Lysenko petered out. He would go on to become one of the pioneers of artificial intelligence research in the United Kingdom. Never one to shy away from controversial topics, he found himself at the centre of the heated Lighthill debate in the 1970s concerning the funding of AI projects.

Lysenko’s reputation has largely remained contentious in the UK. Whilst there have been attempts to rehabilitate his science and separate it from his political reputation, such as by Chinese scientist Yongsheng Liu in the early 2000s, there is still a great deal of baggage associated with Lysenko.

Reflecting on the Lysenko controversy nearly 50 years later, Michie remarked:

Perhaps history is not after all a documented story of what probably happened. Rather, perhaps history is whatever tale of mystery and imagination becomes in the end too embedded to set straight.^vi

Whilst this may have been one tale which Michie could not set straight, his open-mindedness and commitment to scientific exchange as an early-career researcher are admirable and fascinating to see in the face of such a controversial and fraught debate.

Matt Wright

Sources and Further Reading
Michie, D., ‘The Moscow Institute of Genetics’, Discovery, October 1957, pp. 432-434, p. 434. Available in Add MS 89202/11/6.
Michie, D., ‘Interview with Lysenko’, Soviet Science Bulletin, V (1 & 2, 1958), 1-10, p. 4. Available in Add MS 89202/11/6.
Michie, D., ‘The Third Stage in Genetics’, in A Century of Darwin, ed. By S. A. Barnett, (London: Heinemann, 1958), pp. 56-84.
Donald Michie to Judith Field, 14 July 2005, in London, British Library, uncatalogued digital collection.

Matt Wright is a PhD student at the University of Leeds and the British Library. He is on an AHRC Collaborative Doctoral Partnership researching the Donald Michie Archive, exploring his work as a geneticist and artificial intelligence researcher in post-war Britain.

Donald Michie at the British Library
The Donald Michie Papers at the British Library comprises of three separate tranches of material gifted to the library in 2004 and 2008. They consist of correspondence, notes, notebooks, offprints and photographs and are available to researchers through the British Library’s Explore Archives and Manuscripts catalogue at Add MS 88958, Add MS 88975 and Add MS 89072.

i Details of Michie’s trip driving across Europe in a 1948 Standard drop head coupé are available in Add MS 88958/3/21.
ii These letters are available in the Donald Michie archive: Add MS 88958/3/20.
iii Donald Michie, ‘Interview with Lysenko’, Soviet Science Bulletin, V (1 & 2, 1958), 1-10, p. 4. Available in Add MS 89202/11/6.
iv Donald Michie, ‘The Moscow Institute of Genetics’, Discovery, October 1957, pp. 432-434, p. 434.
v For more details, see Donald Michie, ‘The Third Stage in Genetics’, in A Century of Darwin, ed. By S. A. Barnett, (London: Heinemann, 1958), pp. 56-84.
vi Donald Michie to Judith Field, 14 July 2005, in London, British Library, uncatalogued digital collection.

The past year has seen many a new word popping up in our languages: Furlough (from the Dutch ‘verlof’ or paid leave), social distancing, lockdown, you name it. Most of these have ‘gone viral’, just like the virus itself. And just like the virus itself the word ‘virus’ mutated over time.

The word ‘virus’ was long known in science, but it was not used to describe the pathogen we know it to be. That was the work of Dutch biologist Martinus Willem Beijerinck.

Portrait of Martin Willem Beijerinck, Wikipedia Commons

Beijerinck was the third of three scientists who had worked on the tobacco mosaic disease, an infection that could devastate whole crops. He continued the work done on the disease by Adolf Mayer, former Director of the Agricultural Experiment Station at the Agricultural School in Wageningen where he himself was based. Meyer found that if a bacterium was the cause, there was something strange going on but he could not figure out what it was.

Portrait of Adolf Meyer in 1875. Wikipedia Commons

The next step in the right direction was made by Russian botanist Dmitrii Ivanovsky He concluded that the tobacco mosaic disease is caused by something much smaller than a bacterium, because it had slipped through the finest filters of the time, that no bacterium could cross.

He published his findings in several publications, amongst which was an article entitled ‘Die Pockenkrankheit der Tabakspflantze’, published in Mémoires de l'Académie Impériale des Sciences de Saint-Pétersbourg in 1890.

Dmity Ivanovski, from a USSR postage stamp celebrating the centenary of his birth

Apparently this was not picked up by our third man, Beijerinck. He conducted similar research on the tobacco mosaic disease as Ivanovsky had done, but concluded there had to be a new form of infectious agent. Because it was soluble in water Beijerinck called it Contagium vivum fluidum and he called the pathogen ‘virus’ to distinguish it from bacteria.

He also suggested the new idea that viruses were only capable of reproducing in cells of other organisms. His hypothesis was confirmed a few years later, when electron microscopes became available. I am not sure whether Beijerinck lived to see this new type of kit, because he died in 1931, the year it was invented.

Title page of 'Verzamelde geschriften van M. W. Beijerinck ter gelegenheid van zijn 70sten verjaardag…' The Hague, 1940. 10761.i.33

Marja Kingma, Curator Germanic Collections.

References and further reading:

Beijerinck, Martinus Willem, Verzamelde geschriften van M. W. Beijerinck ter gelegenheid van zijn 70sten verjaardag ... uitgegeven door zijne vrienden en vereerders. (Delft, 1921-1940.) 6 vols. Shelfmark 12260.l.13.

Iterson Jr. , G. van, Dooren de Jong, L.E. den, Kluyver, A.J., Maritinus Willem Beijerinck. His life and his work. The Hague, 1940. Shelfmark 10761.i.33 Separate publication in English translation of part 2 of vol. 6 of 'Verzamelde geschriften'. Another edition was published in 1983 by Science Tech in Madison, Wisconsin. Shelfmark 85/11941

Iwanowski, D. (1892). "Über die Mosaikkrankheit der Tabakspflanze". Bulletin Scientifique Publié Par l'Académie Impériale des Sciences de Saint-Pétersbourg / Nouvelle Serie III (in German and Russian). 35: pp. 67–70. Translated into English in Johnson, J., Ed. (1942) Phytopathological classics No. 7, pp. 27–-30 Neither item held by the BL.

Zaitlin, Milton. The Discovery of the Causal Agent of the Tobacco Mosaic Disease. In: Discoveries in plant biology / S.D. Kung and S.F. Yang (Eds.). Hong Kong, 1998, Chapter 7, pp 105-110. Available at https://www.apsnet.org/edcenter/apsnetfeatures/Documents/1998/ZaitlinDiscoveryCausalAgentTobaccoMosaicVirus.pdf.

We are very happy to hear that zbMATH, one of the most important bibliographic databases in the field of mathematics, is now freely available to all online. The database is run by FIZ Karlsruhe, the European Mathematical Society and the Heidelberg Academy of Arts and Sciences, and the funding to make it free to all was provided by the Joint Science Conference, the German national government organisation for science research funding and policy.

The database covers mathematics books and scholarly articles comprehensively since 1868, with some items from considerably earlier. It includes material from the paper abstracts journals Jahrbuch über die Fortschritte der Mathematik (1868-1945) and Zentralblatt für Mathematik (1931-2013). It can be searched by author and subject as normal, but also includes searching by mathematical formula and the subject-specific Mathematics Subject Classification. It includes not just abstracts, but independent reviews of the significance of important articles, although some of these are in German rather than English. It also has both forward and backward citation data. Where possible links to the online full-text item are provided.

The administrators are currently working on developing an API to allow content from zbMATH to be used in other digital information systems on an open access basis.

Anybody with an interest in mathematics is heartily recommended to try it out.

18th July 2020 is the three hundredth anniversary of the birth of Gilbert White, the "parson-naturalist" best known for his pioneering work on the natural history and history of his parish of Sherborne, Hampshire. A number of posts are appearing on different British Library blogs to celebrate, but this post will discuss his influence on science to this day.

Stained glass window commemorating White in Selborne church, showing St Francis of Assisi preaching to the birds. All the birds shown in the window are mentioned in White's writings. Photograph by Si Griffiths under a CC BY-SA 3.0 licence.

Prior to White's work most scientific biology was based around the study of dead or captive animals in scientists' studies. White, who has been described as "the first ecologist" preferred to observe the animals and plants around his home, over long periods of time. These practices inspired Charles Darwin, whose observations of the finches of the Galapagos Islands initially inspired his thoughts about evolution by natural selection. On a more popular scale, White's influence is seen by some as creating birdwatching as a hobby.

Although more laboratory-centric biologists have occassionally dismissed White-style naturalism as dilatanttish or twee, it has become increasingly important since the mid-twentieth-century, especially in the study of environmental conditions, and of animal behaviour - "ethology".

One of the oldest sites of long-term nature-observation studies in Britain has been Wytham Woods in Oxfordshire. Nicknamed the "laboratory with leaves", it was donated to Oxford University in 1942 by Colonel Raymond ffenell, although some observation had been carried out there since the 1920s. Colonel ffennell was a member of the wealthy and socially prominent German Jewish Schumacher family, who had become rich through his involvement in the South African gold-mining industry, and adopted his wife's surname to avoid anti-German prejudice during World War I. Ever since, a host of research projects have been carried out there on all kinds of animals and plants, as well as climate and soil conditions.

One of the most important discoveries to have been made through long-term environmental observation was the discovery of the damage caused to the environment by acid rain in North America, which came from Gene Likens' observational work at the Hubbard Brook Experimental Forest in New Hampshire, beginning in the 1960s. 

Equipment cabinet at Hubbard Brook containing apparatus used for continuous monitoring of a stream's pH. Used non-commercially with permission of USDA Forest Service.

A listing of current long-term environmental observation sites is maintained by the International Long Term Ecological Research Network (ILTER) on their database DEIMS-SDR (Dynamic Ecological Information Management System - Site and Dataset Registry). See also the review article by Hughes and others with links to many examples.

The modern science of animal behaviour, or ethology, was developed in the 1930s by Nikolaas Timbergen, Konrad Lorenz, and Karl von Frisch. All three did most of their research on domestic or captive animals, but the discipline would later see the importance of long-term observation of the behaviour of wild animals in their natural habitats. Three of the most famous practitioners of this were the so-called "Trimates", known for their observations of wild apes - Jane Goodall with chimpanzees in Tanzania, Dian Fossey with gorillas in Zaire and Rwanda, and Birute Galdikas with orang-utans in Indonesia. Another example which has achieved fame outside science, although not yet enough, is Dave Mech's disproof, from observations of wild wolves in Minnesota, of the outdated "alpha wolf" model of social dynamics in wolf packs, which has influenced a great deal of beliefs about dog-training and even human interactions, but was derived from observations of what turned out to be disfunctional behaviour in captive animals.

It is also possible to follow in White's footsteps yourself, by taking part in a citizen science project based on observing nature in your garden or in your wider local area. The Countryside Jobs Network maintains a list of opportunities, which aren't just in rural areas.

We hope that you look a bit more closely at the nature around you this weekend!

Your experience of the COVID-19 pandemic could be an important contribution to science. Researchers from diverse disciplinary backgrounds are keen to learn about your stories, insights, routines, thoughts and feelings. While some projects would be eager to receive diaries in the narrative style of Samuel Pepys or John Evelyn, others want more specific information in survey format.

Illustration: Graham Newby, The British Library: Lockdown Rooms (3rd June 2020)

Citizen science engages self-selected members of the public in academic research that generates new knowledge and provides all participants with benefits. The engagement can vary from data gathering or participatory interpretation to shared research design. Different forms of citizen science can be referred to as public science, public participation in scientific research, community science, crowd-sourced science, distributed engagement with research and knowledge production, or trans-disciplinary research that integrates local, indigenous and academic knowledge.

Contributing to citizen science projects sustains a sense of control, sense of belonging (empowering feelings in and after isolation) and sense of being useful which are particularly important in uncertain times. According to the UK Environment Observation Framework, self-measured evidence is more trusted by people, and organisations that draw on data generated through citizen science are more trusted. Trust is linked to transparency. Better understanding of how scientific knowledge is produced, and having a role and responsibility in shaping the knowledge production process, are likely to enable citizen scientists to re-frame the often-uneasy relationship between society and science.

Scale is a distinctive feature of citizen science. The more people are engaged, the more comprehensive an understanding can be reached about the researched topic. The featured COVID-19 Symptom Study has become the largest public science project in the world in a matter of weeks:  3,881,488 citizen scientists are involved as of 18th June 2020. Big data allowed medics to develop an artificial intelligence diagnostic that can predict the likelihood of having COVID-19 based on the symptoms only: a vital tool indeed when testing is limited.

The citizen science initiatives highlighted here, COVID-19 Symptom Study, COVID-19 and You, and COVID Chronicles, may inspire you to contribute to them or find other projects where you can take an active role in developing better understanding of current and future epidemics.

COVID-19 Symptom Study
https://COVID.joinzoe.com/data
Epidemiology
Institutions: King's College London, ZOE
Launched: 25th March 2020
Your contribution helps you and researchers understand COVID-19 and the dynamics of the pandemic (UK, USA).
How: Submit your physical health status regularly.

COVID-19 and You
https://nquire.org.uk/mission/COVID-19-and-you/contribute
Social sciences
Institutions: The Open University, The Young Foundation
Launched: 7th April 2020
Your contribution helps you and researchers understand how COVID-19 is affecting households and communities across the world.
How: Fill in an online survey with choices and narratives.

In addition to supporting current research, your contribution could add to future inquiries as well. Collecting and archiving short personal stories ensures authentic data will be available when researchers in the future look back to us now with their research questions. Reliable data should be collected now, while we are still living in unprecedented times. It is especially important to record the experiences of people from less privileged backgrounds, in contrast to earlier pandemics where the voices of all but the upper and middle classes, and the political, legal and scholarly elite, have often been lost to history. COVID Chronicles, an archival initiative, is doing just that. COVID Chronicles is a joint project: BBC 4 PM collects and features some of the stories and The British Library archives them all for future academic inquiries.

COVID Chronicles
https://www.bbc.co.uk/news/entertainment-arts-52487414
History, social sciences
Institutions: BBC Radio 4, The British Library
Launched: 30th April 2020
Your contribution helps you and future researchers understand how people experience the COVID-19 pandemic in their daily life, at a personal level.
How: Submit a mini-essay (about 400 words) to BBC Radio 4 PM via e-mail: pm at bbc dot co dot uk. Your essay will be archived by The British Library and made available for future research.

The gradually easing lockdown and the anticipated long journey of national and global recovery generate a growing appetite to record, reflect on and analyse the COVID-19 epidemic's influence on our life. Not all "citizen science" projects observe high standards of privacy and ethical responsibility, however. Before joining in any research with public participation, consider the principles of citizen science suggested by the European Citizen Science Association and the questions below:

Five questions before joining a citizen science initiative

1. Can you contact the researchers and the institution(s) they belong to with your questions and concerns?
2. Is the research approach clear to you? In order words, is it clear to you what happens to your contribution, how it shapes the investigation and what new knowledge is expected?
3. Is your privacy protected? In other words, is the privacy policy clear to you, including how you can opt out any time and be sure that your data are deleted?
4. Are you contacted regularly about the progress of the research you are contributing to?
5. Are you gaining new transferable skills, new knowledge, insights and other benefits by participating in the research?

Further reading:

Bicker, A., Sillitoe, P., Pottier, J. (eds) 2004. Investigating Local Knowledge: New Directions, New Approaches. Aldershot : Ashgate.
BL Shelfmark YC.2009.a.7651, Document Supply m04/38392

Citizen Science Resources related to COVID-19 pandemic (annotated list) https://www.citizenscience.org/COVID-19/
[Accessed 18th June 2020]

Curtis, V. 2018. Online citizen science and the widening of academia: distributed engagement with research and knowledge production. Basingstoke, Hampshire: Palgrave Macmillan.
Available as an ebook in British Library reading rooms.

Open University. 2019. Citizen Science and Global Biodiversity  (free online course) https://www.open.edu/openlearn/science-maths-technology/citizen-science-and-global-biodiversity/content-section-overview?active-tab=description-tab
[Accessed 18th June 2020]

Sillitoe, P. (ed). 2007. Local science vs global science: approaches to indigenous knowledge in international development. New York : Berghahn Books.
BL Shelfmark YC.2011.a.631, also available as an ebook in British Library reading rooms.

Written by Andrea Deri, Science Reference Team

Contributions from Polly Russell, Curator, COVID Chronicles, and Phil Hatfield, Head of the Eccles Centre for American Studies, are much appreciated.

By Susan Guthrie, Maja Maricevic and Catriona Manville

Earlier this year, the British Library and RAND Europe hosted a roundtable discussion on how research outputs – the different ways research can be disseminated – are changing. It brought together representatives from research funders, publishers, research institutes, government and universities to explore the issue and its implications.

Workshop participants discussed RAND Europe’s recent study for Research England that showed that researchers currently produce a diversity of output forms, the range of which is likely to increase. Although researchers expect to continue to produce journal articles and conference contributions, they also want and plan to diversify the outputs they produce, with a particular focus on those aimed at a wider, non-academic audience.

The British Library also presented its current work and experience in collecting, preserving and making accessible a range of research outputs such as research data, web and social media, as well as new and evolving output formats.

The discussion addressed the following five questions:

How do we define and identify a research output?

There are many different types of outputs from research, from traditional journal articles and books to more diverse examples such as computer code, artworks, blogs, datasets and peer review contributions. One of the challenges is to identify which are actually outputs for dissemination, and which represent a stage in the development of research on the pathway to producing those outputs. An example of the latter is a Github repository for managing and storing revisions of projects, which may be fluid and changing on an ongoing basis. Other products – for example social media exchanges – are a fixed point but may not represent a researcher’s final perspective on a topic, rather the emergence and discussion of views and ideas. This fluid and dynamic mix of different media emerging over time makes it challenging to understand what is a ‘research output’ as traditionally defined. 

Where does responsibility lie?

Research is increasingly global and research outputs may span national borders – hence, drawing lines between what is and what is not ‘UK research’ is not straightforward. There is a limit on the extent to which a full record of all research endeavour can be provided. Different stakeholders – libraries, funders, institutions, publishers – can either look to shape and drive desirable changes in behaviour or respond to changes as they emerge from the ‘bottom up’. Funders in particular have the potential to drive researcher actions through the use of incentives.

How do we manage quality control?

As the range and nature of outputs broaden, questions emerge around how to assess the quality of the outputs and decide what is part of the scientific record. Peer review, the current approach, has its weaknesses. A key test of the quality and rigour of research is the extent of uptake and use by the academic community over time. In that sense, the change in types of outputs makes little difference to the ultimate assessment of their quality. However, as the volume of research products increase, alongside increasing concerns over reproducibility, fake news and the reliability of evidence, being able to point to legitimate and reliable sources may be of increasing value.

Do we have the support infrastructure for now and the future?

The growing diversity of research outputs creates new challenges in relation to the complex infrastructure needed to support their review, dissemination and storage across different players in the field e.g. funders, publishers and libraries. Identifying areas in which an intervention could make systems more efficient and futureproof could help but needs to be better understood. Securing digital platforms for sharing and collaborating on research could be part of these interventions, as could increasing digital archiving for discovery and access.

What are some possible solutions?

Permanent digital links to research outputs, which act as unique IDs for outputs to enable their consistent identification and referencing, may be a key part of the solution. Ensuring their consistent use, however, is a potential challenge and an important route forward to help make this problem more tractable. Participants discussed the successful example of DataCite in establishing an international solution. AI may also be part of the solution, in terms of discoverability of outputs. However, there are potential risks associated with this, such as biases, and a lack of knowledge around the way information is curated and presented by algorithms (for example, when using Google Scholar). Linked to these technological solutions is the need for data literacy, within and beyond the research community, as well as creating a culture of openness and transparency across all stages of the research cycle.

The changing nature of research outputs has the potential to affect a wide range of organisations and people in the sector. Joined-up thinking and action could help. As the diversity of research outputs increases, we have to make choices. We can either be reactive, responding to needs and challenges as they emerge, or proactive, to help shape and guide the nature and effective preservation of research outputs. A more proactive stance could help drive research towards better practice in information storage, sharing and communication, but requires early action and shared goals at a sector level. Continued dialogue and sharing of views on this topic could be important to make sure these issues are appropriately and adequately addressed.

Dr Susan Guthrie and Dr Catriona Manville are research leaders in science and innovation policy at RAND Europe. Maja Maricevic is head of higher education and science at the British Library.