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| == Structure == | | == Structure == |
− | It is important that your article have a beginning, middle, and end. This sounds simple, but if often hard to actually put into practice. | + | It is important that your article have a beginning, middle, and end. Your article should have a story arc that allows the reader to put the article into context, follow you through the topic, and come to some sort of resolution. |
| | | |
| === Introduction === | | === Introduction === |
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| === Conclusion === | | === Conclusion === |
| The article needs to have a clean end to it. You need to summarize your topic, restate the takeaways, and maybe have a call to action depending on the objective of the article. | | The article needs to have a clean end to it. You need to summarize your topic, restate the takeaways, and maybe have a call to action depending on the objective of the article. |
| + | |
| + | == Important Elements == |
| + | There are some accompanying elements to articles that need to be thought of as well. |
| + | * Abstract: Every article needs an abstract that will be displayed to allow readers who land on the front page of the DSN to get a general sense of the topic. These should aim to be about 40-50 words long. |
| + | * Accessibility: All images need to be accompanied by descriptive text for assistive devices to use. Also, a description should accompany the image to allow the reader to understand it. All images should be WCAG 2.0 AAA compliant. You can use [https://webaim.org/resources/contrastchecker/ this tool] to test images for minimum contrast levels. |
| | | |
| = Other Resources = | | = Other Resources = |
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| * [https://www.ordnancesurvey.co.uk/business-government/products/case-studies/stroke-nhs Ordnance Survey data helps hospital open in Sumerset] | | * [https://www.ordnancesurvey.co.uk/business-government/products/case-studies/stroke-nhs Ordnance Survey data helps hospital open in Sumerset] |
| * [https://datasciencecampus.ons.gov.uk/extracting-visualising-and-identifying-emerging-important-terminology-from-patent-collections/ Extracting, visualising and identifying emerging important terminology from patent collections] | | * [https://datasciencecampus.ons.gov.uk/extracting-visualising-and-identifying-emerging-important-terminology-from-patent-collections/ Extracting, visualising and identifying emerging important terminology from patent collections] |
| + | * [https://www.capitalone.com/tech/open-source/open-source-global-attribution-mapping-for-neural-networks/ Global Attribution Mapping] |
| | | |
| == Full Length Articles == | | == Full Length Articles == |
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| === Examples === | | === Examples === |
| * Predicting energy efficiency using machine learning. Short introduction: [https://datasciencecampus.ons.gov.uk/can-machine-learning-be-used-to-predict-energy-performance-scores/ Can machine learning be used to predict energy performance scores?] Long follow up: [https://datasciencecampus.ons.gov.uk/projects/using-machine-learning-to-predict-energy-efficiency/ Using machine learning to predict energy efficiency] | | * Predicting energy efficiency using machine learning. Short introduction: [https://datasciencecampus.ons.gov.uk/can-machine-learning-be-used-to-predict-energy-performance-scores/ Can machine learning be used to predict energy performance scores?] Long follow up: [https://datasciencecampus.ons.gov.uk/projects/using-machine-learning-to-predict-energy-efficiency/ Using machine learning to predict energy efficiency] |
| + | [[Category:Data Science]] |