27/10/2021 – AI3SD Autumn Seminar III: Data Science 4 Chemistry

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This seminar forms part of the AI3SD Online Seminar Series that will run across the autumn (from October 2021 to December 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 Data Science 4 Chemistry.

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07/07/21 – Skills4Scientists: Research Data Management

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This event is part of the virtual Skills 4 Scientists series run by the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Physical Sciences Data-Science Service (PSDS). This session will focus on an introduction to coding in python, using Jupyter notebooks and Anaconda. This course is designed to allow you to follow along with the content and examples as the course goes, but you will also be provided with course material to allow you to cover it again after the live event.

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03/12/2020 – Failed it to Nailed it! How to get data sharing right! – Data Citations & Publishing

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This event is the last of our four online events in our current '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 publishing and data citations. There will be talks on data publishing, data citations and supporting information for publications. We will also be running an interactive panel session with all of the speakers for discussion around the different issues in these areas.

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05/11/2020 – Failed it to Nailed it! How to get data sharing right! – Data Standards and Guidelines

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…

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05/11/2020 – Failed it to Nailed it! How to get data sharing right! – Data Standards and Guidelines

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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.

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22/10/2020 – Failed it to Nailed it! How to get data sharing right! – Dealing with Data: Tips & Tricks

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…

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22/10/2020 – Failed it to Nailed it! How to get data sharing right! – Dealing with Data: Tips & Tricks

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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.

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07/07/2020 – AI4Good @ WebSci20

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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.

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