• Introduction
• Introduction
• Data-driven research
• Data literacy
• Introducing data
• Types of spending data
• Reference data
• Machine-readable and useable data
• Working with data
• The data processing pipeline
• Getting data
• Cleaning data
• Analysis
• Presentation and engagement
• Framing: topic, objective, audience
• Engaging the public online
• Case study: BudgIT
• Working with others
• Why collaborate?
• Using technology in your work
• Countering arguments against publishing data
• Appendices
• About this book
• Privacy guide
• Resources
• Glossary

Why collaborate?

Data-driven research is hard work, but you don't have to go it alone. In this section we focus on how to work with other organizations to accomplish greater things than you could on your own.

CSOs, journalists, and other groups that work with a lot of data encounter a set of similar problems. The problems typically include lack of technical knowledge, duplication of work, lack of wide dissemination of analysis and results, and difficulties creating compelling visual products.

These challenges could be overcome if proper channels of communication existed between these organizations. You can avoid your predecessors' mistakes by learning about their methodologies and the idiosyncrasies in their data. Similarly, if you document how your data has been processed and analyzed, you can save pain for others.

How to collaborate

One of the most important ways to reduce technical barriers in data-driven research is to maintain an active dialogue between researchers and to foster the culture of sharing methodologies and data.

There are many ways to create this dialogue:

• Mailing lists. Creating communication can be as easy as joining or maintaining a list for groups that work on similar topics or leverage similar data. Most webmail services offer free group or mailing list functions (e.g. Google Groups or Yahoo Groups). If you maintain a mailing list, be responsive to messages and active in recruiting members. Email is an extremely low barrier for starting a conversation between organizations.
• Learning resources. If you've already got data products that you want to share, try preparing primers or manuals for other groups that want to start working with datasets that you're particularly experienced with. Turn your existing work into tutorials that others can use to recreate your results.
• Training. You can also organise workshops, meetups, and webinars to familiarise groups with the data you work with. If grassroots issue groups can internalise your data products and present them in a contextually relevant way to their user base, then both groups have gained something from the partnership.
• Internship programmes. Conducting internship programmes for interested individuals and organizations is another good way to facilitate knowledge sharing and communication between organizations.

Consider making a special effort to reach out to groups that work on issues completely different from your own. Especially if these groups represent minority populations or other groups who are marginalized in government spending priorities, they may feel incapacitated to create change in fiscal policy, and your support can have a real impact.

Collaboration can result in a much broader and stronger coalition of organizations that can advocate for fiscal transparency and detailed disclosure of spending data at all levels of government.