Get the Data

The data is accessible using rsync. Rsync should be installed already on your Mac or Linux system; Windows users can use it through Cygwin.

Sample Files

A sample of 100 extracted feature files is available for download through your browser: sample-EF201801.zip.

Also, thematic collections are available to download: DocSouth (85 volumes), EEBO (244 volumes), ECCO (423 volumes).

Rsync

Rsync will synchronize files from our servers to your system.  The Rsync module for the extracted features dataset is at data.analytics.hathitrust.org::features-2018.01/

Note: Make sure to include the final period (.) when running your command in order to sync the files to your current directory, or else provide the path to the local directory of your choosing where you would like the files to by synced to. 

It is possible to sync any feature file in the following manner:

rsync -av data.analytics.hathitrust.org::features-2018.01/{PATH-TO-FILE} .

A full listing of all the files is available in the following location:

rsync -azv data.analytics.hathitrust.org::features-2018.01/listing/file_listing.txt .

A full listing of all the volume IDs in the dataset is available in the following location:

rsync -azv data.analytics.hathitrust.org::features-2018.01/listing/ids.txt .

To rsync paths listed in text file:

rsync -av --files-from FILE.TXT data.analytics.hathitrust.org::features-2018.01/ .

The data is kept in a pairtree directory structure, allowing you to infer the location of any file based on its HathiTrust volume identifier. Rather than learning the pairtree specification, we recommendation using our command line htid2rsync tool:  

    htid2rsync {VOLUMEID} | rsync --files-from - data.analytics.hathitrust.org::features-2018.01/ 

You can also use the workset download script generator to create a shell script that lists the rsync commands for every volume in an HTRC workset, and then run it:

   sh <name of file>.sh

You can sync into a single folder, throwing away the directory structure, by adding --no-relative to the rsync command:

rsync -av --no-relative --files-from FILE.TXT data.analytics.hathitrust.org::features-2018.01/ . 

Tips

• Not including a path will sync all files: this is possible, but remember that the data is 4 Terabytes! Only download all of it if you know what you are doing.

• The fastest way to sync is by pointing at exactly the files that you want. Use the htrc-ef-all-files.txt file if you want the paths to all the EF files.
• Have a custom list of volumes and want to get the file paths for it? We have created two options to help:
  • Uploading the volume list to the HTRC Portal as a workset allows you to download a shell script. Details.
  • The HTRC Feature Reader installs a command line utility, htid2rsync, and Python functions for doing the same. Details. 

Workset Download

To download the Extracted Features data for a specific workset in HTRC Analytics, there is an algorithm that generates the Rsync download script, the Extracted Features Download Helper.

HTRC Feature Reader

We provide a Python library called the HTRC Feature Reader which simplifies many of the activities that you may want to perform with EF Dataset. 

The best way to start with the HTRC Feature Reader is to read the introductory tutorial.

Acknowledgements

This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute, and in part by the Indiana METACyt Initiative. The Indiana METACyt Initiative at IU is also supported in part by Lilly Endowment, Inc.

This release has been made possible, in part, by the National Endowment for the Humanities: Celebrating 50 Years of Excellence. Any views, findings, conclusions, or recommendations expressed in this release do not necessarily represent those of the National Endowment for the Humanities.

Questions

How are tokens parsed?

Hyphenation of tokens at end of line was corrected using custom code. Apache OpenNLP was used for sentence segmentation, tokenization, and part of speech (POS) tagging. No additional data cleaning or OCR correction was performed.

OpenNLP uses the Penn Treebank POS tags.

Can I use the page sequence as a unique identifier?

The seq value is always sequential from the start. Each scanned page of a volume has a unique sequence number, but it is specific to the current version of the full text. In theory, updates to the OCR that add or remove pages will change the sequence. The practical likelihood of changes in the sequence is low, but uses of the page as an id should be cautious.

Extracted Features 2.0 includes a hash that can help track when page content has changed.

Where is the bibliographic metadata? Who wrote the book?; When was it published, etc.?

This dataset is foremost an extracted features dataset, with minimal metadata included as a convenience. For additional metadata information, i.e. subject classifications, etc., HT offers Hathifiles, which can be paired to our feature dataset through the volume id field.

The metadata that is included in this data includes MARC metadata from HathiTrust and additional information from Hathifiles:

• imprint: extracted from MARC field 260, with MARC 260 subfield $a pulled from the HathiTrust MARC record, and 260 $b and 260 $c from Hathifiles.
• language: extracted from MARC control field 008 from Hathifiles.
• pubDate: extracted from Hathifiles. See also: details on HathiTrust's rights-determination.
• oclc: OCLC number extracted from Hathifiles.

Additionally, schemaVersion and dateCreated are specific to this feature dataset.

What do I do with beginning- or end-of-line characters?

The characters at the start and end of a line can be used to differentiate text from paratext at a page level. For instance, index lines tend to begin with capitalized letters and end with numbers. Likewise, lines in a table of contents can be identified through arabic or roman numerals at the start of a line.

What is the difference between the header, body, and footer sections?

Because repeated headers and footers can distort word counts in a document, but also help identify document parts, we attempt to identify repeated lines at the top or bottom of a page and provide separate token counts for those forms of paratext. The "header" and "footer" sections will also include tokens that are page numbers, catchwords, or other short lines at the very top or bottom of a page. Users can of course ignore these divisions by aggregating the token counts for header, body, and footer sections.

Known Issues

The current EF dataset has two known issues:

• Chinese and Japanese tokens sometimes have a zero-width space character, \u200b, at the start or end of the token. 

This issue is fixed in Extracted Features 2.0. 

• The character count information is “beginCharCounts” and “endCharCount”

    This issue is fixed in Extracted Features 2.0.

Has a fix been released yet? No. Watch this space or adapt your code.