Digital scholarship is a constantly evolving and expanding world, meaning new projects are contantly being developed. While at first this may make it a bit confusing, it does have the advantage that projects with similar goals to yours (either in presentation or in dataset) most likely exist, offering a place to start from in terms of project conception and inspiration.

Performing this kind of "environmental scan" (similar to creating an inital research bibliography!) can often prove fruitful. This makes the ability to quickly evaluate a DS project beneficial, offering quick suggestions for tools or techniques you might explore and utilize or avoid entirely.

Attached is short document we use when quickly evaluating a DS project. It is broken down into several sections useful to highlight and consider as you move forward in your own project. Additionally, several links are provided focusing on the evaluation of DH projects, particually as DH is becoming more accepted within disciplines as a means for promotion and tenure.

The DH Awards site contains a great number of excellent DH projects to explore; some of the below are pulled from that resource. There are also those from the BC DS site and many DH group sites from universities across the country openly available to explore.

Mapping

New York Restaurant Keepers (part of NY Immigrant City project out of NYU)

(Harrisburg University / Messiah University)

(University of Iowa)

(ESRI)

Digital Exhibition

(Boston College)

(Dumbarton Oaks)

(audio exhibition)

Data Visualization

(Stanford and Queen Mary University of London, among others)

( Berlin)

Textual Analysis / Annotation

3D

(Michigan)

(Byrn Mawr)

Data and Data Organization

Data is a very general term but includes all the information you have gathered to support the defined goal of your project. Data may be qualitative or textual (e.g. a transcription of a historical document), quantitative or numerical (e.g. a breakdown of racial demographics in Boston between 1940 and 1960), audio/visual (e.g. images, audio recordings, video), spatial (e.g. the locations of excavated ancient Greek theaters), or 3D (e.g. a 3D model of a rum keg from 1776). Data may have accompanying metadata (data about your data) describing where the data comes from and may contain other useful attributes for searching and filtering your dataset.

• What kind(s) of data might my project contain?

• Am I creating a dataset myself or using data that is already accessible? If creating it myself, what work might need to be accomplished to create and organize my dataset?

• What ways might I want the user to engage with the raw data (in terms of searching/filtering/downloading, rather than visualization)?

• Do I need permissions to use any of the data?

Data Presentation and Analysis

Over the course of the next few days, we will be talking about a variety of ways data can be presented. Data presentation can be undertaken in many ways through DS tools, ranging from a tradititional document/image viewer to an interactive map to a data dashboard created through tools like Tableau (and many other ways). The key goal of data presentation is to make your data accessable and comprehensible to the user in order to support the goals and narrative of your project.

A few initial data presentation questions that may be useful as you begin to develop a digital scholarship project:

• How can data visualizations help support the goals of my project?

• How can data visualizations help users understand my dataset better?

• What digital scholarship approaches or tools might be useful for helping me analyze and visualize my data (textual analysis, mapping, charts/graphs, exhibitions, etc)?

Data Contextualization

Sometimes overlooked, data contextualization can be just as important as other aspects of a digital scholarship project. Data contextualization includes all the necessary information a person needs to know about how your data was gathered, what issues and biases it may have, as well as any useful historical or cultural information. It may also describe how your project was constructed on a technical level. Finally, it may also include tradititional scholarly information in the form of citations, bibliographies or further readings.

Some initial questions around data contextualization:

• What innate biases might my data have?

• What does my audience need to know in order to understand my data properly?

• How can my audience learn more about the subject discussed?

More Early Questions to Consider

• What is the goal of my project?

• Who is the prospective audience for my project?

• What areas of DS will be most useful for my project to use to accomplish my goals?