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 122 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 26356541 words long. [0] Each item in your study carrel is, on average, 299506.0 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 79.0. [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, books, public, libraries, new, will, work, one, state, school, may, book, mr, librarian, miss, city, association, york, made, use, year, time, many, first, li. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/dh-crc-nd-edu-8544.txt, ./txt/dh-crc-nd-edu-3399.txt, and ./txt/dh-crc-nd-edu-9949.txt.

The most frequent two-word phrases (bigrams) include: public library, new york, li brary, public libraries, library association, state library, library journal, library school, library work, high school, american library, united states, free library, per cent, library commission, st louis, york state, library building, li braries, york city, school library, public li, school libraries, pub lic, library service, and the three file that use all of the three most frequent phrases are ./txt/dh-crc-nd-edu-4353.txt ./txt/dh-crc-nd-edu-9949.txt, and ./txt/dh-crc-nd-edu-5969.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: mrs, meets, com, johns, timely, colleges, little, citi, greatness, best, workings, life, chicago, includ, shall, tioned, librari, years, williams, free, committees, placing, ings, librarians, listing. 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:

 simplify our routine in public libraries mrs pearl williams kelley spoke of the simplific
ary additional for a building from mr and mrs e c thompson making a total gift of from alu
d be ashamed to ask for minnesota st paul mrs helen j mccaine librarian i can say that we 
j c terrell of fort worth d vicepresident mrs reese wilson of dallas secretary phineas l w
cox r l ball clara m hurd clara c giddins mrs j w sparhawk miss jennie l weeks jno e cochr
s situated one square from the courthouse mrs anna isley is in charge as librarian the nor
e course in the history of libraries with mrs powells book as a text book are invited to c
over n h p l caroline h garland in downes mrs william f lucy deane in p l canton mass down
ary chief ln co lumbia college l new york mrs melvil dewey exln wellesley college w th st 
pson college library for a book fund from mrs stillman iowa city public library march for 
oines la cope jessie e des moines la cope mrs lana h ln state l des moines la crandall f a
ade to the committee for a new edition of mrs mcdonald jones magazines for the small libra
ected from funds left by will of the late mrs eunice denio was formally dedicated and tran
ding room society st louis park vols from mrs t b walker st paul from carnegie cor poratio
ear under the country to which it belongs mrs austins nantucket scraps a description of th
was followed by discussion on the part of mrs c f stoutenborough of plattsmouth presi dent
n the childrens room which is down stairs mrs maude henning reported for the piano townshi
neral asst l as soc portland ore norville mrs marguerite i rolla mo norwich conn otis l im
 earhart emily m haynes charles e janvrin mrs j a jones kathrine mcalarney euphemia d macr
ard geo s in state l hartford conn godard mrs geo s hartford conn goldberg bessie head cat
ciation meetings open shelf system osborn mrs lyman p biography osterhout library report o
ing l f l commission harrisburg pa hodges mrs c w anne l dubois waverley place schenectady
lightful program of music was rendered by mrs fanny anstutz roberts pianist mrs frank k bi
re haute ind emeline fairbanks memorial l mrs sallie c hughes in terry daisy in south br p
d of the book phillips broughton carrie l mrs blanton north carolina library association r

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/dh-crc-nd-edu-114.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: library, library, and library. And the respective files would be: ./txt/dh-crc-nd-edu-3220.txt, ./txt/dh-crc-nd-edu-6718.txt, and ./txt/dh-crc-nd-edu-5460.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. library books public - ./txt/dh-crc-nd-edu-4008.txt
  2. library public books - ./txt/dh-crc-nd-edu-3220.txt
  3. library state asst - ./txt/dh-crc-nd-edu-5460.txt
  4. library books mr - ./txt/dh-crc-nd-edu-6211.txt
  5. library lib books - ./txt/dh-crc-nd-edu-4663.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, book, l., work, school, a., librarian, state, p., year, public, new, n., city, association, time, miss, list, york, mr., child, c., tion, e., j.) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, make, do, give, take, e, read, use, find, say, see, know, follow, come, send, hold, print, show, go, receive, include, get, keep, issue) 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 (L., Library, P., New, N., Public, A., Miss, Mr., C., E., J., York, W., State, M., Y., H., F., S., Association, R., LIBRARY, D., B.) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (it, we, i, they, he, them, you, us, she, him, me, itself, themselves, one, himself, her, ourselves, myself, herself, y, yourself, yours, ours, im-, ly) 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 (public, good, other, new, many, great, such, large, more, first, special, li-, small, general, free, same, high, present, american, few, last, much, own, little, old) and adverbs (not, so, only, also, very, now, as, most, out, more, well, up, then, even, too, here, far, much, just, about, thus, however, always, still, never) 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.