Data Literacy Training

Participation in the open movement requires data literacy. The Mekong region is characterized by low data literacy, and not all people in the region have equal access to opportunities to build these skills.Implemented by Open Development Thailand, a series of data literacy training has been developed for specific sectors in the Mekong to more effectively participate in data collection, aggregation and dissemination.

The Data Literacy Certification Program is a 15-day training (5 consecutive weekdays per month for three months), which emphasizes on using data to tell a story, finding reliable data sources, and cleaning, analyzing, and communicating insights from the data. This includes hands-on activities using examples, datasets, and data analysis to explore potential barriers to improved welfare and inclusion. 

Our target groups include indigenous and marginalized peoples, and women and youth groups in Thailand. 

Exercise hours during the data literacy training in Chiang Mai. Photo by Open Development Thailand. Licensed CC-BY-SA 4.0

Exercise hours during the data literacy training in Chiang Mai. Photo by Open Development Thailand. CC BY-SA 4.0

This program has been localized to meet the local context of Thailand. EWMI-ODI and training team would like to express gratitude to the original program of  World Bank’s Data Literacy Program, and advisors who supported the curriculum improvement for Thailand. 

 

Module 1: From Evidence to Stories

This component introduces data and how you could use it to create forest stories. It will also explain what open data is and how it affects policy at the national level. Through the analysis of a number of case studies, participants will explore how data from various sectors have been used to create data-driven stories. Key topics include:
>> How Data is Used for Public Interest Stories
>> What is Data?
>> Data Storytelling at Its Best
>> Sector Specific Data Stories
>> Indigenous Data Sovereignty 

Module 2: ​Finding Data, Data Collection and Data Formats  

This component introduces basic knowledge of data formats, the skills to find data online, sample tools that are used for collecting data, and the concepts to transform data into stories. Starting with a review of data formats, the unit moves on to techniques used to find, convert and process data that is in different formats, and how to develop a hypothesis and questions for a data story. Selected open-source tools will be introduced to you in case you require tools supporting your data collection, such as Mapeo – Mobile (only available in Android at the moment). Key topics include:
>> Common Data Formats
>> Finding Data Online
>> Data Collection Tool: Mapeo
>> Collecting Data with KoBoCollect
>> Thai OCR
>> Using Open Data Portals (ODM/ODT) 
>> Alternative Data Sources

Module 3: Understanding Data

This component will introduce basic concepts of data organization and cleaning as well as questions to help you evaluate the source of the data. It will also cover basic calculations and an introduction to statistics. The ethics and potential pitfalls of working with data will also be covered. Key topics include:
>> Organising Data
>> Simple Maths Revision
>> Summarizing and Simplifying Data Insights
>> Google Sheets Peer Teaching
>> Essential Statistics 

Module 4: Visualizing Data 

This component will introduce the basics of effective communication with data visualization, focusing on best practices in visually communicating data, emphasizing on techniques and tools that could be used to convey knowledge and information through visual stories, not just dry statistics. Participants will be trained in a handful of data visualization and dashboard software including Datawrapper, Flourish, and Google Data Studio or Tableau.
>> Selecting the Correct Chart
>> Design and Color Basics
>> Introducing Datawrapper
>> Making Maps with Datawrapper
>> Introducing Flourish
>> Infographic
>> Introducing Piktochart
>> How to Design Dashboard
>> Google Data Studio: The Basic
>> Google Data Studio: Mapping
>> Introduction to Tableau 

Module 5: Thinking Like a Data Journalist

Using all the data skills learned in the previous modules, participants will be divided into groups based on their interests and will dive into the relevant datasets in order to produce stories by using these datasets. Participants will be throughout guided by the expert in this process.
>> Evaluating Data Interpretation
>> User Centered Design Basics
>> Tips for Data Visualization 
>> Juxtapose, RAW, TimelineJS, StorylineJS, Onodo

Module 6: Data Security and Digital Rights

Working with data in the digital age raises concerns about the privacy and rights of data owners. Key online security practices will be recommended when using technologies, including smart phones, computers, datasets, and the Internet in their daily activities. Participants will learn about threat modeling, risk assessment, and how to ensure data security.   
>> Digital Security and Privacy
>> Data Ethics

Relevant story:

 

Data literacy is the ability to read, understand, work with, analyze, and argue with data. It is also the ability to derive meaningful information from data. Data literacy is not simply the ability to read text since it requires quantitative and analytical skills (for example: mathematical and statistical) involving reading and understanding data. Hence, with increased data literacy, one will be able to produce more insightful and evidence-based stories.


Note of licensing:

The Open Development Data Literacy training materials are based on the original Introduction to the Data Literacy training manual by Eva Constantaras developed under the Sudan Evidence-Base Program (EBP), a partnership among the Ministry of Finance and Economic Planning, the   United Kingdom’s Department for International Development (DFID), and the World Bank. The Myanmar version of the manual was localized by Yan Naung Oak and Phandeeyar, with support from United Kingdom’s Foreign, Commonwealth & Development Office (FCDO),Australian Aid, Ministry of Foriegn Affairs of Denmark, and the Ministry of Foreign Affairs of Finland.  The Cambodia version of the manual was localized by Yan Oak and Open Development Cambodia, with support from USAID’s Cambodia Civil Society Strengthening Project. Additional modules and materials for this version were included to provide greater context to differing thematic issues and needs within the Mekong, including Indigenous Data Sovereignty and applications such as MAPEO with the support of Digital Democracy, developed by Saowalak Jingjunvisut and Open Development Mekong, with support from Voices for Mekong Forests Project and other donors.The materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

You are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material
  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Sample attribution:
This lesson was adapted from the World Bank’s Introduction to Data Literacy training manual by Eva Constantaras, and adapted by Yan Oak, Open Development Cambodia and Open Development Initiative,  and is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

 

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