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.
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 139 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 405191 words long.  Each item in your study carrel is, on average, 2915.0 words long.  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 61.0.  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.
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: information, electronic, university, computer, library, page, systems, will, libraries, access, use, users, journal, review, internet, system, one, user, search, network, may, also, publicaccess, new, available.  Using the three most frequent words, the three files containing all of those words the most are ./txt/pr-v2n02-bailey1.txt, ./txt/pr-v4n02-wiggins.txt, and ./txt/pr-v3n08-rawlins.txt.
The most frequent two-word phrases (bigrams) include: computer systems, publicaccess computer, systems review, university libraries, email message, electronic journal, rights reserved, listservuhupvm uhedu, libraries university, electronic journals, research libraries, individual scholars, message must, reserved copying, commercial use, electronic information, must appear, use requires, copied material, requires permission, state university, public access, worldwide web, information technology, last name, and the three file that use all of the three most frequent phrases are ./txt/pr-index.txt ./txt/pr-v6n01-bailey-networkbased.txt, and ./txt/pr-v7n05-harter-impact.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, university, information, page, electronic, pages, users, user, informal, network, networks, journal, journals, networking, electronically, informed, search, internet, new, publisher, publishers, searches, searching, access. 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:
s vital to a committee in this case libraries such as osu kent state university and the un lassifications for example in a dewey library books on desktop publishing will be in two o g a wide range of possible services libraries have an important role to play in the provis t users of information technology the library staff can contribute to the development of q iggest single article suppliers are libraries via the longpopular and fairly effective int ew is copyright c by the university libraries university of houston all rights reserved co n put it questions and reports from libraries indicated a transition from paper to electro ing the best training and support the library can offer to its patrons systems staff shoul es growing student body my friend the library director has added cdrom and subsidized onli skepticism among librarians and other library staff for starters internet searchers must b exploring the possible uses of html a library can identify needs that can only be met with torial board not only a pillar of the library and information science communities paul als able from the university of houston libraries gopher server point your gopher client at in ew is copyright c by the university libraries university of houston all rights reserved co is obtained from commercial sources libraries may need to restrict remote access to it iro e can libraries protect public access library journal november lowry anita k electronic te rcial vendor and merge these with the library catalog we have two projects of this type un s individual scholars and libraries libraries are authorized to add the journal to their c ext and field searchable database the library is presently investigating the software view ools on cdrom etc so that experienced library users can do some of their own reference wor du get caplan prv n fmail my academic library is in the market for a new integrated librar drive on november the medical school library gave a presentation to sophomore medical stu state university paul garwig textiles library north carolina state university laura n gasa e library as a physical entity to the library as an information system accessible from the computer inc walt crawford research libraries group lorcan dempsey university of bath pat
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 information, and ./txt/pr-v4n05-crawford.txt is most about that word.
If your study carrel could be summed up in three words ("topics") then those words might be: information, electronic, and university. And the respective files would be: ./txt/pr-v3n08-rawlins.txt, ./txt/pr-v7n05-harter-impact.txt, and ./txt/pr-v3n01-tonta.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:
Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:
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, computer, university, user, access, page, journal, system, network, file, document, review, internet, systems, search, database, text, mail, _, server, public, =, article, -----------------------------------------------------------------) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, use, do, provide, make, include, send, need, get, retrieve, require, find, see, follow, create, give, base, take, develop, publish, search, add, distribute, 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:
An extraction of proper nouns (University, Page, Access, Computer, Systems, Review, Public, Library, Information, Libraries, Gopher, Houston, PACS, Electronic, |, BITNET, HTML, Research, Journal, MAIL, F, Internet, Copyright, C, Rights) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (it, we, they, i, you, them, us, he, me, itself, one, themselves, she, yourself, him, ''s, ourselves, myself, ohiolink, her, himself, herself, ours, >, bookshelf) 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.
Learning about a corpus's adjectives (electronic, other, available, new, many, such, more, good, technical, first, online, different, commercial, most, large, individual, same, important, scholarly, local, possible, academic, several, public, bibliographic) and adverbs (not, also, more, only, as, even, up, well, however, most, then, now, so, very, out, e.g., just, already, still, often, rather, currently, much, first, here) 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?"
 Once upon a time, a corpus of a million words was deemed large.
 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.
 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.
 "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.
 Concordances are one of the oldest forms of text mining, first developed in the 13th century to "read" religious documents.
 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.
Eric Lease Morgan <firstname.lastname@example.org>
June 11, 2019