Terms of Reference for the Development of a Course on Data FAIRness, Data Friction, and Open Data Editor

Work description: Development of an online course on Data FAIRness, Data Friction, and Open Data Editor
Location: Global (remote).                                 
Type of contract: Consultancy
Duration: 8 weeks
Delivery deadline: 13 December 2024

Introduction

This Terms of Reference outlines the requirements for developing an online course focused on data FAIRness, data friction, and using the Open Data Editor (ODE).

The course will target non-technical users who work with tabular data formats (e.g., Excel, Google Sheets, CSV files), helping them understand key data principles and best practices and learn how to improve the quality of their data. 

About Open Data Editor

Open Data Editor (ODE) is an app to make it easier for people with little to no technical skills to work with data. ODE helps users detect mistakes in their datasets and correct them in no time. It also checks that all the necessary information is there for other people to use those datasets, hence increasing the quality of the data that is produced and consumed.

It is being developed in the framework of Frictionless Data, an initiative at OKFN producing a collection of standards and software for the publication, transport, and consumption of data.

• Documentation website: https://opendataeditor.okfn.org 
• Github repository: https://github.com/okfn/opendataeditor 

Open Data Editor will have its first stable release on 1 December 2024.

Objective

The objective of the course is to provide non-tech users with practical knowledge on data best practices, address common friction points in tabular data, and ensure data quality and compliance with the FAIR principles. The course will also teach users how to use ODE to implement these concepts.

Target Audience

The course is designed for non-technical individuals with no programming skills who regularly work with tabular data in Excel, Google Sheets, or CSV formats. They need help with data validation and improving data quality but lack technical expertise. The assumption is that they are not familiar with basic data concepts.

Course Structure

The course will be divided into two main sections:

1. General Part: 
Data Best Practices, Data Friction, and FAIRness

Content:

This introductory module will cover:

• Key data principles: understanding data quality, consistency, and the value of well-structured data.
• The concept of data friction: common issues faced when working with tabular data (e.g., missing data, format inconsistencies, and validation challenges).
• Introduction to the FAIR data principles (Findable, Accessible, Interoperable, Reusable).
• Best practices for improving data quality and reducing data friction.

Format:

• Engaging videos explaining concepts.
• Gamified exercises to reinforce learning.
• Case studies/examples demonstrating real-world data friction issues and how they can be resolved.

2. Tutorial: 
Using ODE to solve data friction problems

Content:

• This module will provide hands-on tutorials on using the ODE to apply the concepts from the first module.  
• Introduction to the ODE interface and features.
• Step-by-step guide on uploading, validating, and cleaning tabular data in ODE.
• Addressing data friction: examples of how ODE can fix common tabular data issues (e.g., missing values, format inconsistencies).
• How to ensure data complies with FAIR principles using ODE.
• A glossary of terms commonly used when working with data (e.g. validation, description) 

Format:

• Interactive video tutorials with on-screen walkthroughs of ODE.
• Practical exercises using real datasets for validation and cleanup in ODE.
• Gamified challenges where users complete specific tasks in ODE to progress.

Deliverables

1. A detailed outline for each course module, including videos, exercises, quizzes, and challenges.
2. Professionally produced videos for the general module and ODE tutorial, which are engaging, concise, easy-to-follow, breaking down complex concepts into digestible bits.
3. Practical, hands-on exercises and ready-to-use datasets for users to practise data validation in ODE with real-world examples. The exercises should assess learning and reinforce key concepts.
4. Gamification elements that challenge and motivate learners, and make the course interactive.

Please note that all the materials will be reused and adapted,  so every digital asset must be modifiable using non-proprietary software. 

We expect the consultancy to deliver a fully functional, mobile-friendly, ready-to-use online course by the end of the project.

We are interested in low-budget creative approaches that ensure broad accessibility of the course. 

Timeline

The project is expected to be completed within 8 weeks:

• W1: Inception and brainstorm with Open Data Editor team.
• W2: Finalisation of course structure and approval of the content plan.
• W3-5: Creation of video content, quizzes, and practical exercises.
• W6: Development of the gamification elements.
• W7: External review of the course content + review and feedback from ODE team.
• W8: Release.

The course has to be released by 13 December 2024.

Payment Schedule

Payment will be structured as follows:

• 20% upon project start.
• 40% upon submission and approval of draft content.
• 40% upon final delivery and approval.

Submission Process

If you’d like to apply, please send us to: jobs@okfn.org:

• A brief proposal outlining your approach to developing the course.
• A timeline and a quote, including all costs and fees, in USD.
• Examples of similar work you have undertaken.
• Two references from clients you have provided services to in the last 24 months.

Please submit your application by 21 October 2024 at 12 pm CEST.

Do you have questions? 

We will host 2 live sessions of one hour online to answer all questions: 

• 10 October 2024 - 15:00 UTC. Register here.
• 11 October 2024 - 9:00 UTC. Register here.

At Open Knowledge Foundation we are committed to being a diverse and inclusive workplace and aim to cultivate and sustain a diverse, equitable, and inclusive team. We value and encourage diversity because a range of experiences and perspectives enriches our work and strengthens our ability to address complex challenges. Applicants from communities that are under-represented in our workplace - ethnic minorities, women, people with disabilities, and LGBTI+ individuals are encouraged to apply.

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