Part 2. The FLOAT Method
2.3 Locate
Locate a Data Source
When you are preparing for a data-driven project, you should take into account (1) if an available dataset has already been constructed related to your research topic, (2) how long it will take to create a custom dataset from scratch to fulfill your needs, and/or (3) how much labor and time will be needed to locate your data.
Our current moment constitutes an exciting and daunting time to look for and retrieve information. With so many search engines, publicly available datasets, and data scraping capabilities, our experiences locating useful data can be truly extraordinary and time-consuming. While the possibilities for locating items are wide open, it is necessary to place parameters on searches so that you are pursuing efficient searches. Ultimately, “locating data” can involve anything from downloading information from an online archive to having expert knowledge and being able to use specific technical skills to gather the data.
Tips for Locating Data
- Think about your deadline. Then, work backward thinking about how much time you would like to devote to collecting the information and the steps to process it such as transcribing texts, arranging digital materials, and coding information.
- It’s helpful to think about who might have collected the data you’re looking for such as governmental bodies, organizations, business/trade groups, or commercial entities and see what data they have available
Smart Data for the Humanities
Many humanists hear “data” and assume they do not have it. Many are surprised to learn that humanities data can take many forms, including images, music, poetry, short stories, and more. Even though there might not be a single dataset devoted to your research project, there are different ways to compile information together.
In “Smart Data for Digital Humanities” (February 18, 2017) Marci Lei Zeng provides a useful model for thinking about how we collect data pertinent to our research. Zeng defines “smart data” as “the way in which different data sources (including Big Data) are brought together, correlated, analyzed, etc., to be able to feed decision-making and action processes.”
“Smart data” is one method through which we might think about how to transform the unstructured data—such as heritage materials into not only machine-readable but also machine-processable resources—that are crucial to academic disciplines.
There are several different data collecting capabilities currently at our disposal. Researchers can locate data from already existing sources and databases. One important reason to look for compilations or sets of research data would be to cut down on the amount of labor in a given project. Using already compiled data, researchers can also extend previous studies over time, geography, or other parameters by combining multiple data sets and even locate individual facts or pieces of information that may be contained in a dataset.
For instance, in the clas