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<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Dataviz.Shef RSS Feed]]></title><description><![CDATA[Promoting and building community around data visualisation at the University of Sheffield.]]></description><link>https://dataviz.shef.ac.uk</link><image><url>https://github.com/researchdata-sheffield/dataviz-hub2/blob/development/src/images/author/dataviz.png</url><title>Dataviz.Shef RSS Feed</title><link>https://dataviz.shef.ac.uk</link></image><generator>GatsbyJS</generator><lastBuildDate>Mon, 15 Jul 2024 05:20:43 GMT</lastBuildDate><ttl>1440</ttl><item><title><![CDATA[GlueViz for Heterogeneous data]]></title><link>https://dataviz.shef.ac.uk/blog/18/02/2022/Glue-Viz-for-Heterogeneous-Data</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/blog/18/02/2022/Glue-Viz-for-Heterogeneous-Data</guid><category><![CDATA[blog]]></category><category><![CDATA[Articles]]></category><category><![CDATA[Heterogeneous]]></category><category><![CDATA[Python]]></category><category><![CDATA[GlueViz]]></category><dc:creator><![CDATA[Yu Liang Weng]]></dc:creator><pubDate>Fri, 18 Feb 2022 00:00:00 GMT</pubDate><content:encoded>This is an event blog for the GlueViz workshop hosted by the N8 Centre of Excellence in Computationally Intensive Research (N8 CIR). You can access the training materials from here . Heterogeneous data Heterogeneous data refers to data samples coming from a number of distinct sources which could well be independent or very different to each other. However, modern research often involves exploration and analysis of interrelated heterogeneous data which cannot be done simply by programming scripts. For example, how do you interpret the daily recorded rainfall in the context of hourly recorded…</content:encoded></item><item><title><![CDATA[Assess FAIRness of datasets in ORDA]]></title><link>https://dataviz.shef.ac.uk/visualisation/31/01/2022/ORDA-datasets-FAIRness-assessment</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/visualisation/31/01/2022/ORDA-datasets-FAIRness-assessment</guid><category><![CDATA[visualisation]]></category><category><![CDATA[Library]]></category><category><![CDATA[FAIRness]]></category><category><![CDATA[F-UJI]]></category><dc:creator><![CDATA[Yu Liang Weng]]></dc:creator><pubDate>Mon, 31 Jan 2022 00:00:00 GMT</pubDate><content:encoded></content:encoded></item><item><title><![CDATA[Why Garden? Attitudes and the perceived health benefits of home gardening]]></title><link>https://dataviz.shef.ac.uk/visualisation/27/01/2022/Why-Garden</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/visualisation/27/01/2022/Why-Garden</guid><category><![CDATA[visualisation]]></category><category><![CDATA[IT-Services]]></category><category><![CDATA[shiny]]></category><dc:creator><![CDATA[Joe Molloy]]></dc:creator><pubDate>Thu, 27 Jan 2022 00:00:00 GMT</pubDate><content:encoded></content:encoded></item><item><title><![CDATA[Introducing the new visualisation page]]></title><link>https://dataviz.shef.ac.uk/blog/08/11/2021/New-Visualisation-Page</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/blog/08/11/2021/New-Visualisation-Page</guid><category><![CDATA[blog]]></category><category><![CDATA[News]]></category><category><![CDATA[Dataviz]]></category><category><![CDATA[Research]]></category><dc:creator><![CDATA[Dataviz Team]]></dc:creator><pubDate>Mon, 08 Nov 2021 00:00:00 GMT</pubDate><content:encoded>Data visualisation is a great way to create impact, in support of the University’s vision, and supports the promotion of research. Looking back over the past year, data visualisation has been an effective tool for understanding worldwide trending topics such as climate change, CoVid-19, and elections. Attractive and understandable data visualisations increase the visibility of the University&apos;s research and appeal to different stakeholders. In addition, by sharing any source code, data, and other resources involved in making the visualisation, others can benefit from this openness, allowing…</content:encoded></item><item><title><![CDATA[Overfishing and habitat loss drive range contraction of iconic marine fishes to near extinction]]></title><link>https://dataviz.shef.ac.uk/visualisation/20/10/2021/Marine-Fishes-Near-Extinction</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/visualisation/20/10/2021/Marine-Fishes-Near-Extinction</guid><category><![CDATA[visualisation]]></category><category><![CDATA[School of Biosciences]]></category><category><![CDATA[Ecology]]></category><dc:creator><![CDATA[Yu Liang Weng]]></dc:creator><pubDate>Wed, 20 Oct 2021 00:00:00 GMT</pubDate><content:encoded></content:encoded></item><item><title><![CDATA[Applications of ParaView]]></title><link>https://dataviz.shef.ac.uk/blog/05/10/2021/Paraview</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/blog/05/10/2021/Paraview</guid><category><![CDATA[blog]]></category><category><![CDATA[Articles]]></category><category><![CDATA[ParaView]]></category><category><![CDATA[Visualisation]]></category><dc:creator><![CDATA[Suzana Silva]]></dc:creator><pubDate>Tue, 05 Oct 2021 00:00:00 GMT</pubDate><content:encoded>This post is based on Suzana Silva&apos;s presentation at Research IT Forum: Image processing, techniques and technology. What is ParaView? ParaView is free software that can visualise both observational and numerical data.
<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Dataviz.Shef RSS Feed]]></title><description><![CDATA[Promoting and building community around data visualisation at the University of Sheffield.]]></description><link>https://dataviz.shef.ac.uk</link><image><url>https://github.com/researchdata-sheffield/dataviz-hub2/blob/development/src/images/author/dataviz.png</url><title>Dataviz.Shef RSS Feed</title><link>https://dataviz.shef.ac.uk</link></image><generator>GatsbyJS</generator><lastBuildDate>Thu, 18 Jul 2024 05:20:45 GMT</lastBuildDate><ttl>1440</ttl><item><title><![CDATA[GlueViz for Heterogeneous data]]></title><link>https://dataviz.shef.ac.uk/blog/18/02/2022/Glue-Viz-for-Heterogeneous-Data</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/blog/18/02/2022/Glue-Viz-for-Heterogeneous-Data</guid><category><![CDATA[blog]]></category><category><![CDATA[Articles]]></category><category><![CDATA[Heterogeneous]]></category><category><![CDATA[Python]]></category><category><![CDATA[GlueViz]]></category><dc:creator><![CDATA[Yu Liang Weng]]></dc:creator><pubDate>Fri, 18 Feb 2022 00:00:00 GMT</pubDate><content:encoded>This is an event blog for the GlueViz workshop hosted by the N8 Centre of Excellence in Computationally Intensive Research (N8 CIR). You can access the training materials from here . Heterogeneous data Heterogeneous data refers to data samples coming from a number of distinct sources which could well be independent or very different to each other. However, modern research often involves exploration and analysis of interrelated heterogeneous data which cannot be done simply by programming scripts. For example, how do you interpret the daily recorded rainfall in the context of hourly recorded…</content:encoded></item><item><title><![CDATA[Assess FAIRness of datasets in ORDA]]></title><link>https://dataviz.shef.ac.uk/visualisation/31/01/2022/ORDA-datasets-FAIRness-assessment</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/visualisation/31/01/2022/ORDA-datasets-FAIRness-assessment</guid><category><![CDATA[visualisation]]></category><category><![CDATA[Library]]></category><category><![CDATA[FAIRness]]></category><category><![CDATA[F-UJI]]></category><dc:creator><![CDATA[Yu Liang Weng]]></dc:creator><pubDate>Mon, 31 Jan 2022 00:00:00 GMT</pubDate><content:encoded></content:encoded></item><item><title><![CDATA[Why Garden? Attitudes and the perceived health benefits of home gardening]]></title><link>https://dataviz.shef.ac.uk/visualisation/27/01/2022/Why-Garden</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/visualisation/27/01/2022/Why-Garden</guid><category><![CDATA[visualisation]]></category><category><![CDATA[IT-Services]]></category><category><![CDATA[shiny]]></category><dc:creator><![CDATA[Joe Molloy]]></dc:creator><pubDate>Thu, 27 Jan 2022 00:00:00 GMT</pubDate><content:encoded></content:encoded></item><item><title><![CDATA[Introducing the new visualisation page]]></title><link>https://dataviz.shef.ac.uk/blog/08/11/2021/New-Visualisation-Page</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/blog/08/11/2021/New-Visualisation-Page</guid><category><![CDATA[blog]]></category><category><![CDATA[News]]></category><category><![CDATA[Dataviz]]></category><category><![CDATA[Research]]></category><dc:creator><![CDATA[Dataviz Team]]></dc:creator><pubDate>Mon, 08 Nov 2021 00:00:00 GMT</pubDate><content:encoded>Data visualisation is a great way to create impact, in support of the University’s vision, and supports the promotion of research. Looking back over the past year, data visualisation has been an effective tool for understanding worldwide trending topics such as climate change, CoVid-19, and elections. Attractive and understandable data visualisations increase the visibility of the University&apos;s research and appeal to different stakeholders. In addition, by sharing any source code, data, and other resources involved in making the visualisation, others can benefit from this openness, allowing…</content:encoded></item><item><title><![CDATA[Overfishing and habitat loss drive range contraction of iconic marine fishes to near extinction]]></title><link>https://dataviz.shef.ac.uk/visualisation/20/10/2021/Marine-Fishes-Near-Extinction</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/visualisation/20/10/2021/Marine-Fishes-Near-Extinction</guid><category><![CDATA[visualisation]]></category><category><![CDATA[School of Biosciences]]></category><category><![CDATA[Ecology]]></category><dc:creator><![CDATA[Yu Liang Weng]]></dc:creator><pubDate>Wed, 20 Oct 2021 00:00:00 GMT</pubDate><content:encoded></content:encoded></item><item><title><![CDATA[Applications of ParaView]]></title><link>https://dataviz.shef.ac.uk/blog/05/10/2021/Paraview</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/blog/05/10/2021/Paraview</guid><category><![CDATA[blog]]></category><category><![CDATA[Articles]]></category><category><![CDATA[ParaView]]></category><category><![CDATA[Visualisation]]></category><dc:creator><![CDATA[Suzana Silva]]></dc:creator><pubDate>Tue, 05 Oct 2021 00:00:00 GMT</pubDate><content:encoded>This post is based on Suzana Silva&apos;s presentation at Research IT Forum: Image processing, techniques and technology. What is ParaView? ParaView is free software that can visualise both observational and numerical data.
It has been used in many different research fields, as illustrated in Figure 1, where we see ParaView applied in cosmology and Figure 2, where we see it applied to medical research.
ParaView can readily render the images based on the research data, facilitating visualisation of the data from different perspectives by rotating and post-processing the original data.
Thereby, this…</content:encoded></item><item><title><![CDATA[Improved rice cooking approach to maximise arsenic removal while preserving nutrient elements]]></title><link>https://dataviz.shef.ac.uk/visualisation/22/09/2021/Improved-Rice-Cooking-Approach</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/visualisation/22/09/2021/Improved-Rice-Cooking-Approach</guid><category><![CDATA[visualisation]]></category><category><![CDATA[Department of Geography]]></category><category><![CDATA[School Of Health And Related Research]]></category><category><![CDATA[Inorganic arsenic]]></category><category><![CDATA[Nutrients]]></category><dc:creator><![CDATA[Yu Liang Weng]]></dc:creator><pubDate>Wed, 22 Sep 2021 00:00:00 GMT</pubDate><content:encoded></content:encoded></item><item><title><![CDATA[Alcohol pricing policies are estimated to be more effective at reducing consumption and harm for men than women]]></title><link>https://dataviz.shef.ac.uk/visualisation/06/09/2021/Modeling-the-Effects-of-Alcohol-Pricing-Policies</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/visualisation/06/09/2021/Modeling-the-Effects-of-Alcohol-Pricing-Policies</guid><category><![CDATA[visualisation]]></category><category><![CDATA[School Of Health And Related Research]]></category><category><![CDATA[Alcohol Policy]]></category><category><![CDATA[Gender]]></category><dc:creator><![CDATA[Yu Liang Weng]]></dc:creator><pubDate>Mon, 06 Sep 2021 00:00:00 GMT</pubDate><content:encoded></content:encoded></item><item><title><![CDATA[Statistical Testing]]></title><link>https://dataviz.shef.ac.uk/docs/03/09/2021/LearningPath-Statistical-Modelling-3</link><guid isPermaLink="false">https://dataviz.shef.ac.uk/docs/03/09/2021/LearningPath-Statistical-Modelling-3</guid><category><![CDATA[docs]]></category><dc:creator><![CDATA[Dataviz Team, Jean Russell]]></dc:creator><pubDate>Fri, 03 Sep 2021 00:00:00 GMT</pubDate><content:encoded>Introduction Much of modern research is interested in the relationship between variables.
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<title>datavizhub-tuos [18 Jul 2024 at 05:20]</title>
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