#SWDchallenge: visualize THIS data!

UPDATE: It seems we may have overcomplicated things this time around. Let’s simplify!

You can download the file with country-to-country donations here. Create a visual to ANSWER ONE QUESTION: WHO DONATES? (Related subquestions you may also answer: How are donations distributed across countries? Who donates to whom? Are there any patterns, for example some group of countries tends to donate only to some specific group of other countries?)

SHARE: Tweet your graph(s) or post publicly and email the link to SWDchallenge@storytellingwithdata.com.

NEW EXTENDED DEADLINE: Friday, March 15th (midnight PST).

You’re of course welcome to do more (original full instructions follow), but our hopes are that simplifying will boost participation and we’ll get enough content to push some important data viz research forward!


There is no single “right” way to graph data. Any data can be visualized multiple ways, and variant views of the data allow us to see different things. This is one of the reasons I believe data visualization is so much fun—it sits at an intersection between science and art. There is science to it: there are best practices and guidelines that make sense to follow more often than not. But there is also an artistic component, which is really interesting. This means two different people approaching the same data visualization challenge may do so in two totally different ways. Extrapolating that idea… many different people approaching the same data visualization challenge may do so in many totally different ways.

Or will they? Let’s see.

This is a brief prelude to the March #SWDchallenge: visualize a predefined dataset to answer specific questions. Not only will it be interesting to see the differences and similarities of the various views created, but the hope is that the output will help with some practical data visualization research as well.

This month’s challenge is in partnership with Enrico Bertini. Enrico is Associate Professor at NYU, where he studies, researches, and teaches data visualization. In his own words: Some of my research aims at better understanding visualization practice from an empirical standpoint. What prevents people from creating effective visualizations? What elements of a visualization make it hard for people to read or interpret the information? How do we go about researching these questions? This little experiment is a first step towards answering these questions. We want to see how many different ways people find to answer the same question with the same dataset. Also, we want to figure out how to evaluate them. What makes a visualization better than another? How can we measure it?

While a number of previous challenges have encouraged you to try something new, the aim this time will be effectiveness. The goal is to see whether we can judge the quality of a visualization by how easy it is to answer the questions we ask you to answer with your visualization. We recognize that “easy” can be defined in many ways, so for purposes here it will be defined primarily by two aspects: 1) how hard is it to interpret or make sense of? and 2) how accurately can you extract information out of it?

Ready to take part? We hope so. Not only is this a great low-risk opportunity to practice effectively visualizing data, but it will also help push some important research forward. Following are all of the specifics. We look forward to seeing what you create!

the challenge

Create visuals from provided data to effectively answer specific questions.

GET THE DATA. We’ll be using AidData for this challenge. Follow these easy steps:

  • Hit “Download” from the AidData main page.

  • Unzip the file.

  • The file you’ll want is called: AidDataCoreThin_ResearchRelease_Level1_v3.1.csv.


In this dataset, each row represents a financial transaction between two countries. Attributes include: Year, Donor, Recipient, Commitment Amount, and Coalesced Purpose Name. There is a README file in the folder you downloaded with a glossary explaining the meaning of each variable. Note that the full dataset also includes international organizations other than countries.

This is a large dataset (over a million rows): if you’d prefer to work with something smaller, Enrico created a version that only includes donations between countries that you can download directly here.

ANSWER THE QUESTIONS. Create a graph or set of graphs to effectively answer the following questions:

  1. WHO DONATES? How are donations distributed across countries? Who donates to whom? Are there any patterns, for example some group of countries tends to donate only to some specific group of other countries? Or maybe some countries tend to receive only from a specific set of countries?

  2. HOW MUCH DO THEY DONATE? How much do countries donate and receive? Who donates the most/least? Are there countries that donate and also receive? How does the amount donated/received by country change over time?

  3. WHY DO THEY DONATE? What purposes do the donations serve? Do countries tend to send (or receive) donations for specific reasons? For instance, is it possible that some countries tend to receive/send certain type of donations whereas other receive/send different types?

Build your solution with any tool you like. This could be a single graph, multiple graphs organized in your preferred layout or even an interactive dashboard if you prefer. Remember that your primary goal is effectiveness. For research purposes, submissions will be assessed in terms of how easy it is to answer the outlined questions (both in terms of difficulty of interpretation and ease and accuracy of extracting information). While it won’t be possible to provide individual feedback, we will plan to share any findings once the analysis is complete.

SHARE ON TWITTER using the hashtag #SWDchallenge. Unlike previous challenges, you do not need to email us—this month’s challenge will be conducted entirely online. This is due to constraints on collecting data from human subjects: we need to have your solution submitted in the public domain. If you aren’t on Twitter or would like to say more about your solution, share in any public forum (LinkedIn, blog post, etc) and send an email to SWDchallenge@storytellingwithdata.com with the link so we know where to find it (no need to email if you post to Twitter).

DEADLINE: Sunday, March 10th by midnight PST.

We look forward to seeing what you come up with! Stay tuned for the recap post in the second half of the month. Check out the #SWDchallenge page for past challenge details and recaps.