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 338 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 581,724 words long.  Each item in your study carrel is, on average, 1,778 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 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 54.  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 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: 
library, information, will, may, data, text, open, libraries, content, use, search, digital, one, software, services, eric, june, people, like, source, http, books, musings, morgan, april, august, number, things, lease, time, process, next, web, access, march, internet, well, december, january, july, september, infomotions, used, system, using, university, create, database, html, librarianship
The most frequent two-word phrases (bigrams) include:
eric lease, open source, lease morgan, source software, alex catalogue, digital humanities, infomotions mini, digital library, generation library, next generation, library catalogs, code lib, great books, text mining, notre dame, plain text, public library, linked data, open access, eric morgan, internet archive, word cloud, open library, document frequency, internet resources, library catalog, inverse document, morgan infomotions, ngc lib, com musings, term frequency, named entities, library federation, frequency inverse, google books, steve cisler, archival description, encoded archival, technology trends, documents using, top technology, morgan eric, text version, digital public, extracting parts, tableau public, charting graphing, google onebox, clustering documents, author eric
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, archives, library, texts, information, http, books, words, post, data, searches, software, open, people, book, content, service, university, html, marc, mylibrary, searching, files, like, web, xml, file, indexed, search, services, users, accessible, archived, collection, computers, database, greatness, included, including, indexer, metadata, ockham, text, thing, applications, cataloging, clients, databases, describing, document
And now word clouds really begin to shine:
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 ASIS&T Bulletin on open source software – Infomotions Mini-Musings is most about that word.
If your study carrel could be summed up in three words ("topics") then those words and their significantly associated titles include:
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 helps you answer what questions; "What is discussed in this collection?":
library, information, text, content, data, people, software, libraries, services, source, number, things, time, system, internet, process, web, search, database, books, user, access, book, word, computer, documents, xml, document, file, date, use, words, resources, files, list, collection, librarianship, way, index, example, metadata, systems, texts, records, results, tools, items, catalog, application, term
An enumeration of the verbs 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:
is, are, be, was, have, do, were, has, used, using, create, use, make, been, created, get, described, based, provide, called, does, see, creating, given, need, find, include, had, think, know, want, read, widgets, put, published, done, intended, written, take, being, named, did, made, searching, search, found, making, go, archiving, am
An extraction of proper nouns helps you determine the names of people and places in your study carrel.
june, april, august, eric, library, lease, march, january, july, december, september, musings, morgan, university, may, october, november, alex, digital, marc, catalogue, google, february, archives, librarian, browse, perl, oai, mini, infomotions, blog, mylibrary, next, open, libraries, artist-, serials, books, public, |, code4lib, search, notre, dame, html, tfidf, pmh, ead, archive, mr.
An analysis of personal pronouns 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?"
it, i, you, they, my, your, we, their, he, our, them, its, his, me, she, us, itself, themselves, her, myself, one, yourself, him, ourselves, himself, ’s, ''s, y, ‘, yours, http://example.org/rome, x-, money", dickens’, email.cgi, http://example.org/france, mine, mtx, rdf::redland::query->new, record->property, thee, thy, wgs84_pos, ''em, ^_aeng, bookshelf, dspace-dev.anu.edu.au/dspace-xmlui/handle/1030.58/19507, em, ftp://ftp.lib.ncsu.edu/pub/stacks/alex/alex-950224-tagged.txt, h*ll
Below are words cloud of your study carrel's proper & personal pronouns.
Learning about a corpus's adjectives and adverbs 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?"
open, other, more, many, digital, such, next, same, large, available, new, great, electronic, first, good, few, subject, public, 8), much, possible, previous, simple, particular, different, able, similar, plain, full, bibliographic, own, free, most, specific, traditional, hypertext, necessary, difficult, important, easy, useful, recent, second, scholarly, current, greater, various, better, local, single
not, then, more, as, well, very, also, just, only, up, most, n''t, so, here, really, first, too, much, out, now, even, instead, finally, consequently, n’t, thus, still, again, specifically, together, never, on, back, always, rather, often, necessarily, simply, there, usually, easily, almost, above, increasingly, away, about, probably, all, once, especially