HathiTrust Integrations

The HathiTrust is a digital library with over 16 million volumes digitized by a partnership of over 120 research institutions and libraries.

The digitized holdings include both in-copyright and public domain works. Full-text access is given to the public domain works and works still in copyright can be searched through the catalog.

The HathiTrust Research Center (HTRC) gives access to both public domain and in-copyright text for computational analysis by members of partner institutions. Research carried out with these resources must follow principles of non-consumptive use, essentially ensuring the original text of each volume cannot be reconstructed.

The HTRC and InPhO Topic Explorer facilitate non-consumptive use through three integrations:

  1. HTRC Algorithms provide a push-button way to run the Topic Explorer on a remote machine and export model files for further analysis on a personal computer.
  2. HTRC Data Capsules provide a secure virtual machine for resarchers wanting to derive their own datasets.
  3. HTRC Extracted Features allow for training of topic models on a personal computer through word counts.

Each of these methods relies on collection management for keeping track of HathiTrust IDs. The InPhO Topic Explorer also provides metadata management to display titles in the browser and links to the fulltext views at HathiTrust.

Non-consumptive Research

Research conducted with in-copyright HathiTrust resources must adhere to the principles of non-consumptive research, as defined by the 2010 Authors Guild v. Google ammended settlement agreement:

“Non-Consumptive Research” means research in which computational analysis is performed on one or more Books, but not research in which a researcher reads or displays substantial portions of a Book to understand the intellectual content presented within the Book.”

Non-consumptive research includes image analysis and text extraction, textual analysis and information extraction, linguistic analysis, automated translation, and indexing and search.

Working with Collections

Any work with the HathiTrust requires management of HathiTrust IDs. The simplest form of collection management involves a simple text file consisting of identifiers:


With this list of identifiers, volumes can be downloaded for use in the HTRC Data Capsules or via the HTRC Extracted Features capacity.

HahtiTrust Collection Builder

Members of HathiTrust partner institutions can login and create curated collections of HathiTrust materials using the Collection Builder. Non-members have access to public collections through the interface. These collections can then be modeled using the InPhO Topic Explorer.

Collections can be exported via the HTRC Workset Toolkit to produce a txt file that can then be used with the HTRC Extracted Features or within the HTRC Data Capsules.

  1. Install the HTRC Workset Toolkit: pip install htrc

  2. Select a HathiTrust collection.

  3. Copy the collection URL.

  4. Run the following command to get a volume list, remembering to put the URL in quotes:

    htrc export "https://babel.hathitrust.org/cgi/mb?a=listis;c=2027705310" > volumes.txt

Working with HTRC Algorithms

The InPhO Topic Explorer can be used without locally installing any software through the HTRC Algorithms portal. This page will run the Explorer over a HathiTrust Collection on a remote machine with a preset prep phase that guarantees non-consumptive use.

  1. Sign in to HTRC Analytics.
  2. Import a collection as a HTRC Workset.
  3. Go to HTRC Algorithms using the top navigation.
  4. Select the “InPhO Topic Explorer” algorithm.
  5. Select the newly imported collection.
  6. Run the algorithm, optionally customizing parameters.

At the end of execution, an interactive summary visualization will be shown that groups similar topics together. Hovering over each circle in the visualization will display the top 10 words in the topic.


Unlike with the HTRC Data Capsules, output files do not have to undergo non-consumptive review and are immediately available. Three files are available for download:

This file can be used with the topicexplorer import workset.tez to launch the full visualization via topicexplorer launch or to analyze the models further using topicexplorer notebook
This file contains the topics for each model trained, the top 10 terms in each topic, and their probabilities.
This file contains information that drives

Working with HTRC Data Capsules

The HTRC Data Capsules are virtual machines with secure access to the fulltext data files via the Data API. The InPhO Topic Explorer is pre-installed on each data capsule, and can be run with any parameter settings, unlike the HTRC Algorithms mode.

Within the Data Capsule, the following command can be used to automate download of a workset and guidance through the init-prep-train workflow:

htrc run topicexplorer "https://babel.hathitrust.org/cgi/mb?a=listis;c=2027705310"

Analyses performed in HTRC Data Capsules must be reviewed for compliance with non-consumptive use before result files can be exported.

See also

HTRC Data Capsule Tutorial
A tutorial on basic usage of the HTRC Data Capsule.

Working with HTRC Extracted Features

The HTRC Extracted Features dataset contains word counts for 15.7 million volumes of public domain and in-copyright works. These word counts are already a non-consumptive use, so the extracted features can be downloaded to any computer.

To use the InPhO Topic Explorer with extracted features:

  1. Create a text file with volume IDs, possibly using the htrc export command on a HathiTrust collection URL.

  2. Use the --htrc flag on the Topic Explorer to download volumes from the extracted features dataset and construct a corpus object:

    topicexplorer init --htrc volumes.txt

    where volumes.txt is the name of the file containing volume IDs.

Adding HathiTrust Metadata

The topicexplorer metadata --htrc command will add volume titles and links to the HathiTrust Page Turner fulltext view to the InPhO Topic Explorer document view.

The metadata command may be run on datasets computed via HTRC Algorithms, in Data Capsules, and using Extracted Features.