Category: Resources

The purpose of this cookbook is to document and discuss the use of RDF in digital humanities. Its focus is specific applications as found in the real world, though a few general principles are suggested. It assumes that you’re vaguely comfortable with RDF and RDFa.

This section contains a directory of digital historians, guidelines for evaluating digital scholarship, an index of digital scholarship, information on the National Endowment for the Humanities Digital Humanities Start-up Grant, Digital History project reviews, and new media tool reviews.

DHCommons is a hub for people and organizations to find projects to work with, and for projects to find collaborators.
 

Zone 1 provides an easy-to-use first line of preservation service for use by anyone at Harvard for any digital content.

Digital Public Library of America…12.27.11 « The Proverbial Lone Wolf Librarian’s Weblog

HypeDyn is a procedural hypertext fiction authoring tool for non-programmers who want to create text-based interactive stories that adapt to reader choice. HypeDyn is free to download and open source, and runs on Linux, MacOS and Windows. You can download HypeDyn from http://www.partechgroup.org/hypedyn/download.html

QueryPic builds a simple visualisation of your search query in the Trove newspaper database. A list of search results is difficult to interpret and offers little context. QueryPic shows you the number of articles matching your query over time, enabling you reframe your questions, pursue hunches, or simply play around.

This book brings together a group of international experts to consider the following key issues:
• What is the role of digital resources in the research life cycle?
• Do the arts and humanities face a ‘data deluge’?
• How are digital collections to be sustained over the long term?
• How is use and impact to be assessed?
• What is the role of digital collections in the ‘digital economy’?
• How is public engagement with digital cultural heritage materials to be assessed and supported?

Real-world data are messy. Relationships between two variables can take on an infinite number of forms, and while one doesn’t see, say, umbrella-shaped data very often, strange things can happen. When scientists talk about correlations or associations between variables, they’re usually referring to one very specific form of relationship–namely, a linear one. The assumption is that most associations between pairs of variables are reasonably well captured by positing that one variable increases in proportion to the other, with some added noise. In reality, of course, many associations aren’t linear, or even approximately so. For instance, many associations are cyclical (e.g., hours at work versus day of week), or curvilinear (e.g., heart attacks become precipitously more frequent past middle age), and so on.

Detecting a non-linear association is potentially just