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 283 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 471964 words long. [0] Each item in your study carrel is, on average, 1668.0 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 59.0. [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: library, libraries, information, digital, article, web, use, internet, new, current, will, cites, access, one, research, may, technology, university, data, copyright, roy, tennant, electronic, online, rt. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/currentcites-org-1442.txt, ./txt/currentcites-org-183.txt, and ./txt/currentcites-org-9613.txt.

The most frequent two-word phrases (bigrams) include: current cites, roy tennant, cc html, open access, html contributors, digital library, httpsunsiteberkeleyeducurrentcites cc, rights reserved, electronic publishing, research libraries, california berkeley, library university, sunsite manager, cites issn, bailey jr, higher education, contributors charles, tennant current, leo robert, robert klein, first monday, information technology, digital libraries, open source, new york, and the three file that use all of the three most frequent phrases are ./txt/currentcites-org-2094.txt ./txt/currentcites-org-94.txt, and ./txt/currentcites-org-7777.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, library, http, information, web, digital, https, internet, news, digitized, new, informative, article, articles, data, digitization, april, online, researchers, technologies, access, january, july, march, research. 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:

people spent less than minutes in the library and twothirds did not know what they wanted 
hat involve moving to automation in a library for instance procedures to define specs and 
s libraries respecting the privacy of library users this is done by drawing up a credible 
ibraries march disaster strikes drake library at suny brockport when a system glitch vapor
nce systems individual scholars and libraries libraries are authorized to add the journal 
dekalb county a suburb of atlanta the library system uses the shame factor as a deterrent 
liar with the common apis relevant to library and information studies work reference libra
ns for the education profession and libraries are exciting and challenging young people ar
ulates that the vision of the digital library has so favored a selfconcept as a accessible
arch engines and other entities about library patrons who are using library computers libr
report summarizes the findings of the library publishing services strategies for success p
writes the experience of the national library of australia shows that tagging is a good th
g organizations academic and public libraries nonprofits and commercial organizations rt h
ober edited by teri andrews rinne the library university of california berkeley issn https
 personal electronics and the library library technology reports april httpwwwalatechsourc
 reference services across campus new library world the future of reference is both more r
ion infrastructure to support digital library collections this article is to some degree a
nal collaboration and supportinternal library services branch libraries and collections al
hart teri rinne roy tennant digital libraries information technology society networks netw
but in a nutshell arl believes that libraries can advocate for better license terms withou
e percent of materials expenditures libraries spend percent of their print book budgets on
ss digitization programs will have on library book collections and she offers recommendati
tal panel part one weave a journal of library user experience httpquodlibumicheduwweave vi
ist from archiveorg compares academic library websites at two different points in time nam
using distributed objects for digital library interoperability stanford university atkins 

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 library, and ./txt/currentcites-org-4440.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: library, http, and internet. And the respective files would be: ./txt/currentcites-org-1751.txt, ./txt/currentcites-org-94.txt, and ./txt/currentcites-org-5338.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. http library www - ./txt/currentcites-org-94.txt
  2. library libraries http - ./txt/currentcites-org-7028.txt
  3. internet information berkeley - ./txt/currentcites-org-6420.txt
  4. library libraries http - ./txt/currentcites-org-1751.txt
  5. library http digital - ./txt/currentcites-org-9793.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, information, article, web, internet, technology, author, access, issue, user, university, journal, research, copyright, service, use, system, cites, roy, tennant, book, rt, current, project, librarian) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, use, make, provide, include, find, take, create, base, get, need, see, know, describe, go, read, give, publish, offer, look, require, say, work) 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, Cites, Roy, Tennant, University, RT, Current, Information, Digital, Libraries, Research, Google, Journal, California, Technology, New, Electronic, ISSN, Access, March, April, January, June, Publishing, May) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (it, they, you, we, he, i, them, us, she, one, itself, themselves, me, yourself, him, ''s, himself, ourselves, myself, her, ''em, herself, y, ours, yours) 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, new, good, other, such, many, more, open, electronic, available, current, online, useful, interesting, public, academic, first, high, important, commercial, most, large, scholarly, same, last) and adverbs (not, also, well, more, out, up, as, only, so, just, now, even, most, here, then, very, still, rather, however, often, much, too, on, necessarily, far) 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.