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.
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.
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
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
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
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.
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.
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.
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.