Why use DataBasic?

Rahul Bhargava and Catherine D'Ignazio, creators of Databasic, describe how everyone can tell stories with data.

About DataBasic

Accessible to All

Digital technologies have empowered those with disabilities in a variety of ways, and new web standards have made it easier than ever to build new tools that are accessible to all. DataBasic implements those technologies to support screen readers so the visually impaired can start working with data in new and exciting ways.

Doing One Thing Well

There are tons of tools for working with data; many so complex they are intimidating to even start with. We've built each DataBasic tool to focus on doing one thing well, so you know exactly what each can be used for. This lets us concentrate on making that one thing both simple and powerful.

Fitting in to Your Pipeline

Working with data online always involves using a mishmash of tools. We're not going to solve that problem anytime soon. So we've built DataBasic to take in a variety of types of data, and output results in the formats you're used to. Read some content from website and download CSV files of your results. These are just two of the ways we try to introduce you to the way most folks work data online.

Focused on Learning

Sometimes faster isn't better. If you really want to learn how to work with data to find stories to tell, you'll need to use tools that do more than just give you a chart as quickly as possible. That's why we've built DataBasic with learners in mind. Don't know what some technical word means? Hover over it to see a quick definition. Not sure how to use a tool? Try some of the fun sample data like music lyrics and UFO sightings we've included to get started.

Ensuring Your Privacy

With more and more data-centric tools moving online, sometimes it can be hard to tell where your data is going and what will happen with it after you upload it. We store information you upload for only the amount of time it takes us to analyze it, then we delete it. The aggregate results we show you - metadata - are kept for 60 days, and then we delete them. All communications are over https, so other folks can't eavesdrop on the data as you upload it.

By Educators, for Educators

Work with high-schoolers? Journalists? Community groups? Graduate students? Us too!!! That's why we designed and tested the DataBasic tools and activities in our classrooms and in workshops with those audiences. These tools were born out of frustration with things we were trying to use in our undergraduate classes, so we feel your pain.

DataBasic News

DataBasic at Harvard Law’s Systemic Justice Project

by rahulb on February 24, 2017

Rahul was invited again this year to join Professor Jon Hanson’s System Justice course at Harvard, to introduce law students to how to include data within their arguments. The DataBasic activities provided a perfect way to explore asking questions (with WTFcsv) and sketching stories (with WordCounter). The students also got a chance to practice tailoring data-driven arguments to different audiences, using a new participatory activity that we’re still workshopping. Here are the slides: From Data to Argument from rahulbot …

New DataBasic Tool Lets You “Connect the Dots” in Data

by rahulb on February 08, 2017

Catherine and I have launched a new DataBasic tool and activity, Connect the Dots, aimed at helping students and educators see how their data is connected with a visual network diagram. By showing the relationships between things, networks are useful for finding answers that aren’t readily apparent through spreadsheet data alone. To that end, we’ve built Connect the Dots to help teach how analyzing the connections between the “dots” in data is a fundamentally different approach …

Designing Tools for Learners (Not Users)

by kanarinka on December 29, 2016

We (Catherine and Rahul) just co-authored an article in the Journal of Community Informatics called Design Principles, Tools and Activities for Data Literacy Learners. In it, we make the case that most tools that help people work with data prioritize flashy visualizations and outputs rather than helping to scaffold a learning process. This ends up making the process of data analysis like a black box (especially for people from non-technical backgrounds). We pose the question – what …

Designed and Developed by

Principal Investigators:

Rahul Bhargava (MIT Center for Civic Media) & Catherine D'Ignazio (Engagement Lab)

Software development: GitHub

Rahul Bhargava, Catherine D'Ignazio, the Engagement Lab & Stephen Suen

Spanish translation:

Aleszu Bajak, Víctor Rogelio Hernández Marroquín & Mariel García-Montes

Portuguese translation:

Daniel Paz de Araújo with contributions from Emilie Reiser

Spanish data sourcing:

Miguel Paz & Mariel García-Montes

Portuguese data sourcing:

Vivian Guilherme & Daniel Paz de Araújo

This free and open source project would not be possible without the generous support of:
The John S. and James L. Knight Foundation, The Emerson College Faculty Development Fund & The Shuttleworth Foundation

MIT Center for Civic Media
Engagement Lab at Emerson College