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 āThe Fourth Paradigm: Data Intensive Scientific Discoveryā. 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, āDeep Learningā neural networks have already transformed several areas of computer science and research scientists are now exploring their use in analyzing their āBig Scientific Dataā. The talk concludes with a vision of how this āAI for Scienceā agenda can be truly transformative for experimental scientific discovery.
This year theĀ AI3SD Network+Ā (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) ran a workshop at theĀ WebSci20Ā Conference that took place online due to COVID-19 Restrictions. A full report…
This year the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) will be running a workshop at the WebSci20 Conference in Southampton, UK. Artificial and Augmented Intelligence systems have the potential to make a real difference in the scientific discovery domain however this brings a new wealth of ethical and societal implications to consider with regards to this research (e.g. human enhancement, algorithmic biases, risk of detriment). This workshop looks to explore the ethical and societal issues centered around using intelligent technologies (Artificial Intelligence, Augmented Intelligence, Machine Learning, and in general Semantic Web Knowledge Technologies) to further scientific discovery, with a strong consideration of data ethics and algorithmic accountability. Advances in technology and software are rarely inherently bad in themselves, however that unfortunately does not preclude them from being subverted to ill intent by others; furthermore, as demonstrated by the examples above, even an unintentional lack of care towards ethical codes and algorithmic accountability can lead to societal and ethical implications of scientific discovery. It is our responsibility as researchers to consider these issues in our research; are we conducting studies ethically? What ethical codes can we put in place for scientific discovery research to mitigate against ethical and societal issues. These are really important issues, and they require an interdisciplinary focus between scientists, social scientists and technical experts in order to be comprehensively addressed.
This is a joint meeting between the Dial-a-Molecule, Directed Assembly and AI3SD (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) Networks. The meeting will examine the state of the art and future opportunities in the use of Artificial Intelligence to predict the outcome of unknown chemical reactions, and consequently design optimum synthetic routes to desired molecules. A wide variety of AI approaches will be illustrated including expert systems, statistical methods, mechanism based and Machine Learning. The meeting will also consider: Data sourcing, sharing, and quality. Automated experimentation to generate reaction knowledge. Theoretical calculations to enrich or replace experimental data. The meeting will include talks to introduce the breadth of the area to all participants. Discussion sessions and opportunities to develop collaborations will be a key aspect of the meeting.
In early April 2019, Dr Samantha Kanza and Dr Nicola Knight attended the Paperless Lab Academy. This was a fascinating conference with a strong focus on Research Data Management and…
On the 13th March Dr Samantha Kanza and Dr Nicola Knight divided and conquered to attend both the Pistoia Alliance Annual Conference (specifically their Lab of the Future session) and…
The Frey Group at University of Southampton (including Jeremy and Samantha) helped out at this event. It was designed to help physical chemists find the best software tools for their…