Considering the ethical implications of digital collaboration in the Food Sector

The Internet of Food Things Network+ (IoFT) and the Artificial Intelligence and Augmented Intelligence for Automated Investigation for Scientific Discovery Network+ (AI3SD) brought together an interdisciplinary multi-institution working group to create an ethical framework for digital collaboration in the food industry. This will enable the exploration of implications and consequences (both intentional and unintentional) of using cutting-edge technologies to support the implementation of data trusts and other forms of digital collaboration in the food sector. This article describes how we identified areas for ethical consideration with respect to digital collaboration and the use of Industry 4.0 technologies in the food sector and describes the different interdisciplinary methodologies being used to produce this framework. The research questions and objectives that are being addressed by the working group are laid out, with a report on our ongoing work. The article concludes with recommendations about working on projects in this area

AI3SD Video: Writing an ethics application

This talk forms part of the Skills4Scientists Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Physical Sciences Data-Science Service (PSDS). This series ran over summer 2021 and aims to educate and improve scientists skills in a range of areas including research data management, python, version control, ethics, and career development. This series is primarily aimed at final year undergraduates / early stage PhD students. This video was the second talk in the Skills4Scientists #7 - Ethical Research Session, which focussed on several areas of ethical research including discussions on why ethics is important and how to write an ethics application

AI3SD Series: Summer Seminar Series 2020

This is a record of all of the talks from the AI3SD Summer Seminar Series 2020. All of our speakers were invited to be interviewed and where that interview has taken place, the records of the talks listed below also contain links to the related interviews. The following talks took place in this series:1. Drug Repositioning for COVID-19 - Professor John Overington (Medicines Discovery Catapult) - http://dx.doi.org/10.5258/SOTON/P00462. InChI: Measuring the molecules - Professor Jonathan Goodman (University of Cambridge) - https://doi.org/10.5258/SOTON/P00473. Design Fiction as a Method, and why we might use it to consider AI - Dr Naomi Jacobs (University of Lancaster) - http://dx.doi.org/10.5258/SOTON/P00484. Neural Networks and Explanatory Opacity - Dr Will McNeill (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P0049 5. Dimensionality in Chemistry: Using Multidimensional data for Machine Learning - Dr Ella Gale (University of Bristol) - http://dx.doi.org/10.5258/SOTON/P00506. Quantum Computing: A Guide for the Perplexed - Professor Andy Stanford Clark (IBM) - Video available on request (email: [email protected])7. Artificial Intelligence’s new clothes? From General Purpose Technology to Large Technical System - Dr Simone Vannuccini (University of Sussex) & Ms Ekaterina Prytkova (Friedrich Schiller University Jena) - http://dx.doi.org/10.5258/SOTON/P0051 8. Smart Cleaning & COVID-19 - Dr Nicholas Watson (University of Nottingham) - http://dx.doi.org/10.5258/SOTON/P00579. The Bluffers Guide to Symbolic AI - Dr Louise Dennis (University of Manchester) - http://dx.doi.org/10.5258/SOTON/P005810. Machine Learning for Early Stage Drug Discovery - Professor Charlotte Deane (University of Oxford) - http://dx.doi.org/10.5258/SOTON/P005611. Using Artificial Intelligence to Optimise Small-Molecule Drug Design - Dr Nathan Brown (Benevolent AI) - Video not available12. On the Basis of Brain:Neural–Network–Inspired Changes in General Purpose Chips - Ms Ekaterina Prytkova (Friedrich Schiller University Jena) & Dr Simone Vannuccini (University of Sussex) - http://dx.doi.org/10.5258/SOTON/P005513. Supramolecular antimicrobials – the next target for AI/Machine Learning? - Dr Jennifer Hiscock (University of Kent) - http://dx.doi.org/10.5258/SOTON/P005414. On the Basis of Brain:Neural–Network–Inspired Changes in General Purpose Chips - Ms Ekaterina Prytkova (Friedrich Schiller University Jena) & Dr Simone Vannuccini (University of Sussex) - http://dx.doi.org/10.5258/SOTON/P005315. Supramolecular antimicrobials – the next target for AI/Machine Learning?:Dr Jennifer Hiscock (University of Kent)16. AI for Science: Transforming Scientific Research:Professor Tony Hey (STFC

Machine Intelligence Showcase Report 2018

This event was run by the Centre for Machine Intelligence (CMI) and was designed to be a showcase of the work that is being conducted in different areas of machine learning at the University of Southampton. This was the first event of its kind for the CMI as it was only launched earlier this year. This event was a full day showcase, hosted at the University of Southampton. The programme was made up of a number of presentations, ranging from AI for social good to robots. These were all run one after the other so it was possible to attend each talk. There was plenty of time for networking, as there was both a lunch and drinks session included as part of the day. Lunch was held during the poster session so attendees could eat and explore the range of posters displaying work from PhD students and Postdoctoral researchers in ECS relating to machine learning. There was also another opportunity to view posters later in the afternoon during the drinks session, and a specific “speed dating” session was held to facilitate students and postdoctoral researchers making contact with relevant companies and research organisations

Understanding and defining the academic chemical laboratory’s requirements: Approach and scope of digitalization needed

When considering the requirements of a chemistry lab, it is important to remember that chemistry is a multifaceted discipline with many different sub domains, and the potential to be used in conjunction with other scientific disciplines in interdisciplinary research. This section will detail the different aspects of addressing the lab requirements: Including the contrasting types of chemistry lab, barriers to adoption of increased digitisation, the different stages of digitisation and the level of digitisation requirements at each stage. Approaches to digitising the lab will be covered in the next section

What influence would a cloud based semantic laboratory notebook have on the digitisation and management of scientific research?

Electronic laboratory notebooks (ELNs) have been studied by the chemistry research community over the last two decades as a step towards a paper-free laboratory; similar work has also taken place in other laboratory science domains. However, despite the many available ELN platforms, their uptake in both the academic and commercial worlds remains limited. This thesis describes an investigation into the current ELN landscape, and its relationship with the requirements of laboratory scientists. Market and literature research was conducted around available ELN offerings to characterise their commonly incorporated features. Previous studies of laboratory scientists examined note-taking and record-keeping behaviours in laboratory environments; to complement and extend this, a series of user studies were conducted as part of this thesis, drawing upon the techniques of user-centred design, ethnography, and collaboration with domain experts. These user studies, combined with the characterisation of existing ELN features, informed the requirements and design of a proposed ELN environment which aims to bridge the gap between scientists' current practice using paper lab notebooks, and the necessity of publishing their results electronically, at any stage of the experiment life cycle. The proposed ELN environment uses a three-layered approach: a notebook layer consisting of an existing cloud based notebook; a domain specific layer with the appropriate knowledge; and a semantic layer that tags and marks-up documents. A prototype of the semantic layer (Semanti-Cat) was created for this thesis, and evaluated with respect to the sociological techniques: Actor Network Theory and the Unified Theory of Acceptance and Use of Technology. This thesis concludes by considering the implications of this ELN environment on broader laboratory practice. The results of the user studies in this thesis have underscored laboratory scientists' attachment to paper lab notebooks; however, even though paper lab notebooks are currently unlikely to be replaced by a system of digitised experimental records, laboratory scientists are not opposed to using technology that facilitates high-level integration, management and organisation of their records. This thesis therefore identifies areas of improvement in current laboratory data management software

AI3SD PhenoHarmonIS Conference Report 2018

PhenoHarmonIS is an invite only week long workshop run every two years that is focused on harmonizing agronomic data using semantic web technologies. The scientific domains that were represented in this workshop are conservation, breeding, crop traits, agronomy and agro-ecology. The invited participants ranged from agronomists in many different areas of agriculture to data scientists and ontologists working in the agricultural domain. The participants were encouraged to provide feedback on the standards and tools that they use in their data management and analysis to identify which tools are useful, and where the issues and gaps currently lie. The workshop was also intended to assess progress made since the last PhenoHarmonIS conference in 2016, to see what tools and standards have been adopted and developed by the community. <br/

AI3SD Video: Intro to LaTeX

This talk forms part of the Skills4Scientists Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Physical Sciences Data-Science Service (PSDS). This series ran over summer 2021 and aims to educate and improve scientists skills in a range of areas including research data management, python, version control, ethics, and career development. This series is primarily aimed at final year undergraduates / early stage PhD students. This video was the first talk in the Skills4Scientists #3 - Version Control and LaTeX Session, which focussed on focus on teaching the basics of LaTeX and version control

AI3SD Series: Winter Seminar Series 2020/21

This is a record of all of the talks from the AI3SD Winter Seminar Series 2020/21. All of our speakers were invited to be interviewed and where that interview has taken place, the records of the talks listed below also contain links to the related interviews. The following talks took place in this series:1. Topology: From shapes to numbers - Professor Jacek Brodzki (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P00892. New theoretical and data-driven approaches to the study of molecular conformational spaces and energy landscapes – Dr Ingrid Membrillo Solis (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P00883. The Shape of Data in Chemistry – Insights Gleaned from Complex Solutions and Their Interfaces – Professor Aurora Clark (Washington State University) - http://dx.doi.org/10.5258/SOTON/P00874. Natural Language Processing in AI-driven Drug Discovery: What it is, why it matters and how (not) to do it – Dr Sia Togia (Benevolent AI) - Video not available5. New Trends in Drug Discovery – Robotics &amp; AI – Dr Martin-Immanuel Bittner (Arctoris) - http://dx.doi.org/10.5258/SOTON/P00866. An Open Competition of People and Machines to Develop Predictive Models for Antimalarial Drug Discovery – Professor Matthew Todd (University College London) - http://dx.doi.org/10.5258/SOTON/P00857. Interpretable machine learning for materials design and characterization – Dr Keith Butler (STFC) - http://dx.doi.org/10.5258/SOTON/P00848. When charge transport data are a worm – a transfer learning approach for unsupervised data classification – Professor Tim Albrecht (University of Birmingham) - http://dx.doi.org/10.5258/SOTON/P00839. Prediction in organometallic catalysis – a challenge for computational chemistry – Dr Natalie Fey (University of Bristol) - http://dx.doi.org/10.5258/SOTON/P009310. Data-driven materials discovery for functional applications – Professor Jacqui Cole (University of Cambridge) - http://dx.doi.org/10.5258/SOTON/P0082 11. Outlier Detection in Scientific Discovery – Dr Jo Grundy (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P008112. Machine learning for electronically excited states of molecules – Dr Julia Westermayr (University of Warwick) - http://dx.doi.org/10.5258/SOTON/P008013. Machines Learning Chemistry – Professor Jonathan Hirst (University of Nottingham) - http://dx.doi.org/10.5258/SOTON/P009214. Preserving Structural Motifs in Machine-Learning Approaches to Modeling Water Clusters – Dr Jenna A. Bilbrey (Pacific Northwest National Laboratories) - http://dx.doi.org/10.5258/SOTON/P007915. Semantic Web in Scientific Research – Possibilities &amp; Practices – Dr Samantha Kanza (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P007816. Ontologies, Natural Language, Annotation and Chemistry – Dr Colin Batchelor (Royal Society of Chemistry) - http://dx.doi.org/10.5258/SOTON/P007717. H2020 Project Onto Trans – Dr Alexandra Simperler (Goldbeck Consulting) - http://dx.doi.org/10.5258/SOTON/P007618. Unifying Machine Learning and Quantum Chemistry: From Deep Learning of Wave Functions to ML/QM-hybrid methods – Dr Reinhard Maurer (University of Warwick) - http://dx.doi.org/10.5258/SOTON/P007519. Accelerating structure prediction methods for materials discovery: Professor Graeme Day (University of Southampton) - http://dx.doi.org/10.5258/SOTON/P007420. High-Throughput Approaches for the Discovery of Supramolecular Organic Materials: Fusing Computational Screening with Automated Synthesis – Dr Becky Greenaway (Imperial College London) - Video not available 21. Generating a Machine-Learned Equation of State for Fluid Properties – Professor Erich Müller (Imperial College London) - http://dx.doi.org/10.5258/SOTON/P007322. Machine Learning with Causality: Solubility Prediction in Organic Solvents and Water – Dr Bao Nguyen (University of Leeds) - http://dx.doi.org/10.5258/SOTON/P007223. Machine learning for biological sequence design – Dr Lucy Colwell (University of Cambridge) - http://dx.doi.org/10.5258/SOTON/P009024. Machine learning applications for macro-molecular X-ray crystallography at Diamond – Dr Melanie Vollmar (Diamond) - http://dx.doi.org/10.5258/SOTON/P009125. Using convolutional neural networks to enable neoantigen load as a biomarker of cancer immunotherapy – Dr Felicia Ng (AstraZeneca) - Video not available26. Can Lattice Theory Help Find a Cure for Paralysis? – Dr Nicola Richmond (GlaxoSmithKline) - Video not availabl

AI3SD Video: Cultivating your Web Presence

This talk forms part of the Skills4Scientists Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Physical Sciences Data-Science Service (PSDS). This series ran over summer 2021 and aims to educate and improve scientists skills in a range of areas including research data management, python, version control, ethics, and career development. This series is primarily aimed at final year undergraduates / early stage PhD students. This video was the second talk in the Skills4Scientists #6 - Careers 1 Session, which focussed on on several areas of careers advice that will be useful to you as you complete your studies and begin your careers