Unit 2 Using Open Data
Go back to Open Data Management in Agriculture and Nutrition Online Course
Learning objectives
This unit provides a foundation in how to discover and use open data, assess data quality, information on how to analyse, visualise, and reference the data. At the end of this unit, you will be able to:
- identify services provides access to open data
discover downloadable and hidden data
evaluate data sources, identify the provenance of data
apply number of techniques to analyse the data
choose appropriate methods to visualise the data
identify steps in policy cycle
understand the value of identifiers in citing data
Lesson 2.1: Discovering open data
The lesson aims to provide a foundation in how to discover and access data that is available on the web. From data downloads and data service providers to publishers of linked open data, this lesson will cover the complete toolkit to obtain the right data from the web, faster. After studying this lesson, you should be able to list different types of services that provide access open data, list different methods by which data can be accessed from these services, explain the difference between these types of services, use the different types of services to access open data, discover downloadable and hidden data, identify whether a data source is open data, and describe the advantages and disadvantages of these services.
Lesson 2.2: Quality and provenance
The lesson aims to teach how to assess the quality of data, describe methods to evaluate usability of open data, provide information in identifying provenance of datasets and describe what useable data is. After studying this lesson, you should be able to describe and apply the factors that affect usability of open data, use the tools that help you evaluate usability of open data such as refining, schema validation, cross check your data agains other data sources, identify the provenance of open datasets, and describe what useable data is from different points of view.
Lesson 2.3: Data analysis and visualisation
The lesson aims to provide a foundation in how to prepare, analyse and present findings from data. After studying this lesson, you should be able to explain why data needs to be analysed, prepare data for analysis, apply number of techniques to analyse data, define the risks with analysing different types of data, explain the purpose of data visualisation, choose an appropriate visualisation for data, and evaluate the effectiveness of a number of different data visualisations.
Lesson 2.4: Open data in policy cycles
The lesson aims to enable learners to use open data to inform and drive policy and to evaluate its effectiveness. After studying this lesson, you should be able to describe the steps in policy cycle, apply open data to the steps in the policy cycle, and evaluate the effectiveness of using open data to inform and drive policy.
Lesson 2.5: Referencing data
The lessons aims to provide information on principles applies how to discover and reference scientific data. After studying this lesson, you should be able to explain the importance of citation, list the key features that citation provides and explain each, understand the value of adding persistent identifiers in the data exchange workflow, and identify existing good practices for the use of persistent identifiers.
Webinar. Using Open Data
Slides: Online on Slide Share (click here for .pdf)
Webinar recording: Online onYouTube
About the webinar: In this webinar we will look at how you obtain and use open data. We will look at the key role of search engines and how you establish rust in the data you find. The webinar will also look at the quality of data and how to clean and prepare data for analysis. Finally the session will look at how you can quickly visualise cleaned data and the applications of this in the agriculture sector.
Presenter: Dr David Tarrant, Learning Skills Lead at Open Data Institute
David joined the ODI from the University of Southampton where he was a Lecturer in the Web and Internet Science Group. He was responsible for creating the world's first undergraduate course in open data. Since joining the ODI David has put in place key educational content that has helped transform governments and unlock over $15m for startups. Additionally David has applied his data science skills to build policy making tools for open data leaders, including the Open Data Barometer visualisation. This tool has been used to guide policy development and allow leaders to compare and contrast their open data initiatives with other similar initiatives globally.
Audiovisual Learning MaterialsUnderstanding the Web of Data by Dave Tarrant (The Open Data Institute)
Direct link: https://vimeo.com/129197209
