Distant Reader Study Carrel

About your study carrel

This page outlines the breadth & depth of your "study carrel" -- the results & analysis of your Distant Reader submission. Peruse the content of this page, and then consider learning how to dig deeper by reading the Distant Reader Study Carrel Cookbook. If you want "just the facts", then consider reading this text's synopsis.

Size & scope

First, the simple things. Your study carrel was created through the submission of a [SINGLE URL|FILE OF URLS|FILE FROM YOUR COMPUTER|ZIP FILE]. This ultimately resulted in a collection of 1101 item(s). The original versions of these items have been saved in a cache, and each of them have been transformed & saved as a set of plain text files. All of the following analysis has been done against these plain text files.

Your study carrel is 4118452 words long. [0] Each item in your study carrel is, on average, 3741.0 words long. [1] If you dig deeper, then you might want to save yourself some time by reading a shorter item. On the other hand, if your desire is for more detail, then you might consider reading a longer item. The following histograms and box plots illustrate the overall size of your study carrel.


On a scale from 0 to 100, where 0 is very difficult and 100 is very easy, your documents have an average readability score of 36.0. [2] Consequently, if you want to read something more simplistic, then consider a document with a higher score. If you want something more specialized, then consider something with a lower score. The following histograms and box plots illustrate the overall readability of your study carrel.

Word frequencies

By merely counting & tabulating the frequency of individual words or phrases, you can begin to get an understanding of your carrel's "aboutness". Excluding "stop words", some of the more frequent words include: digital, information, library, access, research, will, electronic, metadata, data, libraries, journal, university, use, journals, open, may, also, one, new, content, web, project, preservation, work, services. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/dare-uva-nl-3912.txt, ./txt/dare-uva-nl-1362.txt, and ./txt/www-clir-org-5560.txt.

The most frequent two-word phrases (bigrams) include: open access, digital library, digital preservation, digital libraries, dublin core, dlib magazine, electronic journals, electronic publishing, web site, scholarly communication, institutional repositories, digital object, et al, research libraries, digital objects, information science, peer review, digital repositories, intellectual property, institutional repository, full text, scholarly publishing, electronic journal, national library, university library, and the three file that use all of the three most frequent phrases are ./txt/ec-europa-eu-3530.txt ./txt/dare-uva-nl-3912.txt, and ./txt/digital-scholarship-org-3114.txt.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:




Sets of keywords -- statistically significant words -- can be enumerated by comparing the relative frequency of words with the number of times the words appear in an entire corpus. Some of the most statistically significant keywords in your study carrel include: libraries, http, library, metadata, journals, journal, researcher, researchers, data, information, digital, service, publish, repository, electronically, publisher, projects, published, digitized, digitally, new, user, article, electronic, project. And now word clouds really begin to shine:


Through the use of a concordance -- a keyword-in-context tool, or a "poor man's search engine" -- you can see how words are used in relation to other words. Here is a random sample of concordance entries using the two most significant keyword as input:

t the virtual resources provided by libraries is substantial between half and threefifths 
y extend the collections of holding libraries licencing conditions are better but issues s
nagement system used in uk academic libraries has also recently been implemented at the re
tpdl theory and practice of digital libraries poznań poland august sigir special interest
n techniques for historical digital libraries has received a great deal of attention recen
rce distributed among the minds and libraries of the world as well as the phenomena of nat
e commercialized web challenges for libraries and democracy edited by bettina fabos item s
 remains an open question we do see libraries getting together to define a small number of
ok patten matdl integrating digital libraries into scientific practice journal of digital 
ir materials the survey of research libraries reveals the challenges and opportunities of 
ngs of older articles obtained from libraries involve articles originally obtained through
ostateedu colorado state university libraries fort collins co â search for more papers by
tegy document from emory university libraries for to states the digital production and man
crawford w exceptional institutions libraries and the pareto principle american libraries 
as within a particular community eg libraries commercial entities scientific communities w
versity and university of tennessee libraries over threefourths of the scientists say they
number of departments including the libraries education and curatorialabout meaningful way
adhtml highwire stanford university libraries highwire press home page httpwwwhighwireorg 
international conference on digital libraries will be digital libraries information manage
ts for representing text in digital libraries designers sometimes face a choice between a 
e it covered issues such as digital libraries electronic publishing the internet and onlin
tpwwwarlorg association of research libraries includes listserv for electronic reservists 
e biggest issue of all is a digital libraries one how do we preserve big data collections 
ng and maintaining internet virtual libraries and allowing users great flexibility in acce
s of european conference on digital libraries pages pp trondheim norway httpeprintsecssoto

Topic modeling

Topic modeling is another popular approach to connoting the aboutness of a corpus. If your study carrel could be summed up in a single word, then that word might be digital, and ./txt/digital-scholarship-org-2982.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: digital, access, and electronic. And the respective files would be: ./txt/worldcat-org-1386.txt, ./txt/www-uic-edu-482.txt, and ./txt/digital-scholarship-org-3325.txt.

If your study carrel could be summed up in five topics, and each topic were each denoted with three words, then those topics and their most significantly associated files would be:

  1. digital metadata library - ./txt/www-dlib-org-950.txt
  2. journals journal electronic - ./txt/www-uic-edu-5994.txt
  3. research repositories repository - ./txt/www-indicare-org-1497.txt
  4. library vol information - ./txt/crl-acrl-org-3175.txt
  5. http www digital - ./txt/digital-scholarship-org-5346.txt

Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:

Nouns & verbs

Through an analysis of your study carrel's parts-of-speech, you are able to answer question beyonds aboutness. For example, a list of the most frequent nouns (library, journal, information, access, research, metadata, university, article, service, repository, user, system, project, content, resource, web, author, collection, digital, e, datum, work, publication, preservation, publisher) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, use, do, provide, make, include, publish, base, develop, see, create, need, support, require, find, take, work, allow, give, identify, become, describe, offer, exist) helps you learn what actions take place in a text or what the things in the text do. Very frequently, the most common lemmatized verbs are "be", "have", and "do"; the more interesting verbs usually occur further down the list of frequencies:



Proper nouns & pronouns

An extraction of proper nouns (Library, University, Digital, Information, Vol, Access, Research, Journal, Open, Libraries, Electronic, Science, |, National, Publishing, Metadata, OAI, Magazine, Core, Project, Lib, Dublin, OA, Technology, Preservation) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (it, we, they, i, them, you, us, he, itself, themselves, one, she, me, y, him, ourselves, ‘, her, himself, ''s, yourself, em, myself, ’s, surf) enables you to answer at least two questions: 1) "What, if any, is the overall gender of my study carrel?", and 2) "To what degree are the texts in my study carrel self-centered versus inclusive?" Below are words cloud of your study carrel's proper & personal pronouns.

proper nouns


Adjectives & adverbs

Learning about a corpus's adjectives (digital, other, electronic, such, available, new, many, open, more, scholarly, different, large, high, important, good, institutional, academic, first, current, full, same, most, technical, possible, online) and adverbs (not, also, more, only, as, well, however, most, even, so, up, now, very, then, out, often, still, just, e.g., rather, currently, already, here, thus, much) helps you answer how questions: "How are things described and how are things done?" An analysis of adjectives and adverbs also points to a corpus's overall sentiment. "In general, is my study carrel positive or negative?"




[0] Once upon a time, a corpus of a million words was deemed large.

[1] To put this into context, the typical scholarly journal article is about [NUMBER] words long, Shakespeare's Hamlet is [NUMBER] words long, and the Bible is [NUMBER] words long.

[2] In this case, a Flesch readability score is being calculated. It is based on things like the number of words in a document, the lengths of the words, the number of sentences, the lengths on the sentences, etc. In general children's stories are have lower Flesch scores while insurance documents and doctoral dissertations have higher scores.

[3] "Stop words" are sometimes called "function words", and they are words which carry little or no meaning. Every language has stop words, and in English they include but are not limited to "the", "a", "an", etc. A single set of stop words has been used through out the analysis of your collection.

[4] Concordances are one of the oldest forms of text mining, first developed in the 13th century to "read" religious documents.

[6] An unsupervised machine learning process, topic modeling is a very popular text mining operation. Assuming that a word is known by the company it keeps, topic modeling identifies sets of keywords denoted by their centrality in the text. Words which are both frequent as well as in close proximity to each other are considered significant.