This event was the third of the `Failed it to Nailed it' online data seminar series. The event was hosted online through a zoom conference. The event ran for approximately…
This event is the third of four events in our 'Failed it to Nailed it - Getting Data Sharing Right' series. In this event we aim to provide information and advice on different areas of data governance and legislation. The event includes talks on different aspects of this including the ethical dimensions of research data management and different types of legislation with respect to personal and non personal data. We will also be including an interactive breakout session using a set of Moral IT cards developed by researchers at the University of Nottingham: https://lachlansresearch.com/the-moral-it-legal-it-decks/
This seminar was the first of ten in our AI3SD Winter Seminar Series. This seminar was hosted online via a zoom webinar. The event was themed around Topology and its…
This seminar forms part of the AI3SD Online Seminar Series that will run across the winter (from November 2020 to April 2021). This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. The theme for this seminar is Topology and Applications in Chemistry.
This event was the second of the `Failed it to Nailed it' online data seminar series. The event was hosted online through a zoom conference. The event ran for approximately…
This event is the second of four events in our 'Failed it to Nailed it - Getting Data Sharing Right' series. This event aims to provide best practice advice on different types of data standards, including metadata, open data and linked data. There will be three talks by experts in these areas, followed by a discussion panel on data standards.
This event was the first of the `Failed it to Nailed it' online data seminar series. The event was hosted online through a zoom conference. The event ran for approximately…
This event is the first of four events in our 'Failed it to Nailed it - Getting Data Sharing Right' series. This event aims to provide general advice on a range of aspects of research data management including handling and sharing data. We have two talks by experts in research data management and data wrangling. The event will also feature a panel comprised of Early Career Researchers who will each provide their top tips for handling data, and reflect on the lessons they have learned about data throughout their career so far.
There is now broad recognition within the scientific community that the ongoing deluge of scientific data is fundamentally transforming academic research. Turing Award winner Jim Gray referred to this revolution as “The Fourth Paradigm: Data Intensive Scientific Discovery’. Researchers now need tools and technologies to manipulate, analyze, visualize, and manage vast amounts of research data. This talk will begin by reviewing the challenges posed by the explosive growth of experimental and observational data generated by large-scale facilities such as the Diamond Synchrotron and the CryoEM Facilities at the Rutherford Appleton Laboratory. Increasingly, scientists are beginning to use sophisticated machine learning and other AI technologies both to automate parts of the data pipeline and also to find new scientific discoveries in the deluge of experimental data. In particular, ‘Deep Learning’ neural networks have already transformed several areas of computer science and research scientists are now exploring their use in analyzing their ‘Big Scientific Data’. The talk concludes with a vision of how this ‘AI for Science’ agenda can be truly transformative for experimental scientific discovery.
Since the 1980’s the development of novel antibiotics has dramatically reduced. This, combined with the ever-increasing prevalence of antibiotic resistance in bacteria, means that some bacterial strains have now been identified that are resistant to treatment with all known classes of antibiotic currently available. Supramolecular Self-associating Amphiphiles (SSAs) are a novel class of amphiphilic salts that contain an uneven number of covalently linked hydrogen bond donating and accepting groups, meaning that they are ‘frustrated’ in nature. The hydrogen-bonded, self-associative properties for members of this class of over 70 compounds synthesised to date have been extensively studied in the gas phase, solution state, solid state and in silico. Through these studies we have shown correlations between certain physicochemical properties that maybe predicted by simple, low-level, high-throughput, easily accessible computational modelling. In addition, members from this class of compound have been shown to kill a variety of different bacteria, including those with known antibiotic resistance (e.g. Methicillin Resistant Staphylococcus aureus (MRSA)). These initial studies have highlighted within the supramolecular chemistry community a vast amount of experimental data, not yet accessed by AI/machine learning. Could data sets such as these be the next targets of interest for this community? Is there room for a consortium or community led approach to solving predictive modelling within this branch of chemistry.