HTRC Extracted Features
HTRC Extracted Features datasets consist of metadata and derived data elements that have been extracted from volumes in the HathiTrust Digital Library. The dataset is periodically updated, including adding new volumes and adjusting the file schema. When we update the dataset, we create a new version. The current version is v.2.0.
The basics
A great deal of useful research can be performed with features extracted from the full text volumes. For this reason, we generate and share a dataset called the HTRC Extracted Features. The current version of the dataset is Extracted Features 2.0. Each Extracted Features file that is generated corresponds to a volume from the HathiTrust Digital Library. The files are in JSON-LD format.
An Extracted Features file has two main parts:
Metadata
Each file begins with bibliographic and other metadata describing the volume represented by the Extracted Features file.
Features
Features are notable or informative characteristics of the text. The features include:
- Token (word) counts that have been tagged with part-of-speech in order to disambiguate homophones and enable a greater variety of analyses
- Various line-level information, such as the number of lines with text on each page, and a count of characters that start and end lines on each page
- Header and footer identification for cleaner data.
Within each Extracted Features file, features are provided per-page to make it possible to separate text from paratext. For instance, feature information could aid in identifying publishers' ads at the back of a book.
Examples and tutorials
Tools for working with HTRC Extracted Features
- HTRC Feature Reader (Python)
- Hathidy (R) – this tool is community-built and not supported by HTRC
- Topic Explorer Extracted Features integration
The versions
Version 1.5
NOTE: this dataset has been superseded by Extracted Features versions above.
Version 0.2
NOTE: this dataset has been superseded by Extracted Features versions above.
Partner-created derived datasets
HTRC has partnered with researchers to create other derived datasets from the HathiTrust corpus. Follow the links below to learn more and access the data.