Lunchtime Data: talks are back!

Live dataThe Data: series of lunchtime talks is aimed at researchers from all disciplines who generate, gather, rearrange, or repurpose data. The talks will deal with software, services, and techniques to help you make the most of that data. Covering data visualisation tools and projects, and aspects of data management from planning to reuse, these talks are intended both to inspire and to consider the practical requirements of research funders and the issues associated with data sharing. Join us to hear how others are doing things with data, both in Oxford and outside, and come along to tell us about what you are doing.

All talks take place on Fridays from 12:30-13:30 in IT Services, 13 Banbury Road.

Data: DMP Online – a data management planning tool for researchers
29th Jan
A good data management plan provides a solid foundation for research. DMP Online is a web-based tool which makes building a plan straightforward. It offers a range of templates (including those from major funding bodies), along with guidance tailored for Oxford researchers.
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Data: Creating Interactive Data Visualisations with Tableau Public
12th Feb
This talk will introduce Tableau Public – an easy to use, free and powerful tool for creating interactive dashboards and data visualisations that can be shared publicly and embedded in your personal site. All the steps necessary to prepare, visualise and share data through Tableau Public will be covered.
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Data: Creating Interactive Data Visualisations with R and Shiny
26th Feb
R is a tool used by millions of researchers for data analysis and visualisation. Using the free Shiny package, these analyses and visualisations can be published as interactive webpages just using R. This talk will provide the necessary steps to start using Shiny and provide advice on how to learn more.
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Data: Negotiating the archive(s) of UK web space
4th Mar
This presentation will discuss the challenges faced by researchers wishing to work with the archive(s) of UK web space. It will discuss the requirement for scholars to gain an understanding of big data tools and methods, to develop their statistical and technical knowledge, to think about new ethical frameworks for research, and to occupy a sometimes challenging interdisciplinary space.
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