Parts of a DS Project
Some Initial questions to consider when developing a DS project
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 Data may be qualitative or textual (e.g. a transcription of a historical document), quantitative or numerical (e.g. a breakdown of racial demographics in Boston between 1940 and 1960), audio/visual (e.g. images, audio recordings, video),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)?
Are opensource tools for accomplishing my goals already available, or will I need funding for custom development?
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?
Do I have or need a project team to accomplish my goals?
What specific technologies or expertise will I need for my project to be considered a success? Are there members of my project team with those skills, or do I need to develop them.
Do I have any copyright concerns?
What is the timeline for my project? Are there benchmarks I could put in place to ensure the project is moving along at an appropriate pace?
Do I have or need funding for my project?
Where will my project be hosted/what kind of support am I looking for (institutional vs personal)?
Last updated