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 26 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 222239 words long. [0] Each item in your study carrel is, on average, 8548.0 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 58.0. [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: like, know, data, just, one, think, right, really, thats, learning, machine, going, different, research, people, im, things, kind, will, get, well, yeah, sort, also, see. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/dh-crc-nd-edu-9570.txt, ./txt/dh-crc-nd-edu-238.txt, and ./txt/dh-crc-nd-edu-3007.txt.

The most frequent two-word phrases (bigrams) include: machine learning, little bit, cross disciplinary, im going, dont know, disciplinary research, right now, computer science, things like, data set, topic modeling, notre dame, crossdisciplinary research, yeah yeah, weve got, data science, research question, different disciplines, know like, make sure, come back, data sets, digital humanities, thats thats, im just, and the three file that use all of the three most frequent phrases are ./txt/dh-crc-nd-edu-9570.txt ./txt/dh-crc-nd-edu-238.txt, and ./txt/dh-crc-nd-edu-3007.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: data, libraries, people, thing, yeah, liked, actual, news, okay, rights, knowing, learn, researcher, knows, worked, collect, difference, differences, differently, digitized, good, goodness, levels, like, likes. 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:

 a knowledge graph by default its linked data its not linked open data and you dont have t
 cpl ils cpl archives events circulation data goes back to a relatively small number of bo
mething that could help you around given data set and you know its a way of saying like ho
s at hospitals we have all sorts of good data that hospitals have batches of data about pe
what infrastructure should academic libraries provide to support machine learning by proce
s will circulate how can the circulation data be integrated with other data sources as i m
s im the associate dean for digital libraries with university of texas hi good morning im 
 tools existing data challenges existing data strategies existing data problem solving wol
t about new tools nlp courses python for data science apply machine learning courses im go
ts how to look at their own data set and data set that theyre using and what five that did
 in the in the program based on how much data you have right and then things tend to clust
nee its very difficult to comprehend the data and what type of data gathering and then som
courses on machine learning and then our data visualization services department is doing w
king about ethics and the ethical use of data or the ethical management of data theres a d
 is something that we can lean into libraries expertise of managing that kind of thing and
 binomial distribution or not in on your data so some of the vocabulary teams is also very
courses on machine learning and then our data visualization services department is doing w
what infrastructure should academic libraries provided support machine learning if each gr
 i could use to further filter down this data set until i had about tables from articles t
r civil and human rights and hasbro libraries from university of notre dame so in which we
oud storage documents very common shared data sets for benchmarking peace assessment and p
ies acquisition and managmement plan for data including related media and permissions web 
ons that frequent item side this is some data this little old now this came from an articl
our even to retrain algorithms using our data someone has to manually draw these boundarie
o we looked at the question how can libraries help with digital literacy particularly in t

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 like, and ./txt/dh-crc-nd-edu-1437.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: like, like, and data. And the respective files would be: ./txt/dh-crc-nd-edu-9570.txt, ./txt/dh-crc-nd-edu-2788.txt, and ./txt/dh-crc-nd-edu-8918.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. like know just - ./txt/dh-crc-nd-edu-775.txt
  2. know like think - ./txt/dh-crc-nd-edu-7421.txt
  3. data research cross - ./txt/dh-crc-nd-edu-7141.txt
  4. project user writing - ./txt/dh-crc-nd-edu-6083.txt
  5. chicago circulation data - ./txt/dh-crc-nd-edu-8918.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 (thing, datum, machine, people, research, library, lot, question, learning, way, project, something, topic, group, problem, time, kind, work, sort, tool, bit, word, example, science, system) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, do, know, go, think, get, use, say, want, see, look, talk, come, work, try, make, take, find, learn, start, mean, need, give, understand) 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 (University, Chicago, Research, AI, Columbia, Google, Eric, Library, Mark, Dame, Notre, Learning, Python, Data, Cross, State, Digital, Disciplinary, Project, Natalie, April, CDS, Department, Family, Meyers) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (we, you, i, it, they, us, them, me, he, she, ''s, itself, yourself, myself, themselves, him, one, ourselves, her, y'', your, ours, yours, bookshelf, mine) 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 (different, other, good, more, little, able, disciplinary, same, new, great, digital, interesting, important, particular, many, big, sure, interested, first, right, next, human, specific, certain, last) and adverbs (so, not, just, then, really, of, here, out, up, also, very, actually, now, kind, back, right, there, more, maybe, sort, well, as, together, in, even) 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.