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Project Management

Miriam Posner, Project Management: The Very, Very Basics (PDF) and accompanying notes (Word)

Data Visualization

Annotated Bibliography

Text Analysis

Metadata Standards

Copyright and Fair Use


Network Analysis

Usability and User Experience

Steve Krug, Don’t Make Me Think

Balsamiq’s UX Apprentice

Image Analysis Tools

Image analysis, particularly computerized image recognition, is still fairly crude, but a number of tools do allow you to work with images as data.

Image Plot, Image Montage, Image Slice, and Visual Sense
Created by the Lev Manovich-led Software Studies Initiative, these tools allow you to analyze many images at once. Image Plot arranges images on axes according to user-defined parameters, Image Montage arrays tiny thumbnails in a grid, Image Slice provides a “slice” view of an image collection, and Visual Sense is an environment for exploring images as plots, bar graphs, and arrays. The Software Studies Initiative has provided fairly robust documentation. See how Manovich and his team have used these tools in the Projects section of the Software Studies Initiative’s site.

Image Quilts
The brainchild of Edward Tufte, Image Quilts is a simple plugin for the Chrome browser that arrays all the images on a given webpage in a grid. Use it with, for example, a Google Image search for “happiness” to see some of the most popular representations of the concept in a neat grid. Great for teaching.

Feed TinEye an image and it will scour the web for visually similar images.


There are more mapping tools than we can count, but these are some of our current favorites.

Designed to work with the exhibit platform Omeka (see below), Neatline is best for smaller mapping projects for which visual detail and the element of time is important. You can draw polygons on the map and use Neatline’s timeline feature to walk users through changes over time. (You can also use Neatline to annotate an image.) One thing that’s distinctive about Neatline is that every point on your map can be an item from your Omeka collection. So, for example, if you have an Omeka collection that contains works of art, you can automatically plot each one, metadata and all, on your Neatline map. Neatline’s documentation is the best source of information about it. Example.

A mapping platform that’s designed to show how a particular event (like the Olympic relay) or concept has unfolded over time. An easy-to-use interface walks the user from point to point, and each map point can be enhanced with words and pictures. Bonus: Gigapixel, also a Knight Lab project, allows you to do the same thing, but on a picture. Example.

Google Maps Engine
Go from spreadsheet to points on a map with this easy-to-use Google tool. Example.

Harvard WorldMap
The benefit of WorldMap is that you can not only plot your own points; you can also combine them with layers that display other information — for example, population numbers or demographics. It’s a way to see how the points you’re interested in correlate with other geographical information. Example.

Esri Story Maps
Confusingly, this is different from StoryMap. Story Maps are great for things like tours or travel narratives. It’s easy to create map points that incorporate images and text, and you can switch among many different templates. Each point has to be plotted individually, though, so it’s not great if you have a huge amount of data. Example.

This is a pretty easy-to-use mapping platform that also has some advanced features. Uploading a spreadsheet of data is easy (CartoDB can grab latitudes and longitudes for you if you don’t already have them), and mapping data is a matter of a few clicks. CartoDB maps tend to look really nice, and you can custom-style them if you know CSS. But a heads-up that CartoDB only lets you work with 50 MB of data for free; after that, you have to pay. CartoDB’s documentation is excellent. Example.

Developed at the Library of Congress, ViewShare is a terrific, somewhat underused (I think) tool that allows you to visualize a spreadsheet of data in many different ways, including as a map. Its standout feature, in my opinion, is its faceted browsing capability. You can drill down in your data, much the way you drill down from “shoes” to “women’s shoes” to “pumps” on Amazon. Example.

Palladio tutorial (dataset that the tutorial uses)

Timeline Tools

Top 10