Basic Reports


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 494 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 1,039,502 words long. [0] Each item in your study carrel is, on average, 2,855 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 illustrate the overall size of your study carrel.

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histogram of sizes
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box plot of sizes

Readability

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 50. [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 illustrate the overall readability of your study carrel.

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histogram of readability
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box plot of readability

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: [3]

library, data, will, new, one, like, libraries, work, information, use, time, also, may, people, open, digital, code, research, web, get, first, university, august, search, content, us, now, project, make, just, year, org, government, post, many, using, services, job, users, http, user, well, system, see, access, way, need, support, public, based

Using the three most frequent words, the three files containing all of those words the most are ./txt/zbw-eu-3802.txt, ./txt/zbw-eu-1775.txt, and ./txt/www-zotero-org-2468.txt.

The most frequent two-word phrases (bigrams) include:

government will, code lib, social media, open source, new york, linked data, proactive chat, human resources, united states, digital library, copy link, march february, retrieved august, january december, li li, october september, job search, february january, april march, july june, august july, november october, fiscal year, september august, december november, june may, may april, continue reading, information technology, tweet embed, reply retweet, href https, retweet retweeted, retweeted like, like liked, embed tweet, public library, open library, digital preservation, personal data, open access, xff xff, michael brown, likes reply, academic libraries, library association, retweets likes, data protection, last year, york times

And the three file that use all of the three most frequent phrases are ./txt/zbw-eu-3802.txt ./txt/zbw-eu-1775.txt, and ./txt/www-zotero-org-2468.txt.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:

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unigrams
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bigrams

Keywords

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, data, library, news, http, people, archived, github, librarian, lita, new, users, web, librarians, august, https, jobs, likely, privacy, universities, user, worked, amp, like, likes, posts, project, searching, social, works, book, close, inform, record, researcher, researchers, service, archives, archiving, books, consenter, continue, digital, digitization, diverse, dlf, mydata, oclc, open, opening

And now word clouds really begin to shine:

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keywords

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 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:

  1. data - www-kantei-go-jp-6045
  2. library - White Librarianship in Blackface: Diversity Initiatives in LIS – In the Library with the Lead Pipe
  3. 2014 - Shooting of Michael Brown - Wikipedia

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, data, 2019 - ZBW Labs
  2. like, new, just -
  3. government, data, information - www-kantei-go-jp-6045
  4. chat, like, library - Rain Delay Theater: The Podcast - Pete Alonso speaks his mind
  5. 2014, august, retrieved - Ferguson unrest - Wikipedia

Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:

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topic model

Noun & 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 helps you answer what questions; "What is discussed in this collection?":

data, library, time, people, information, libraries, work, web, year, content, ►, way, users, research, job, project, system, user, post, search, development, %, things, years, resources, chat, service, services, use, community, software, part, access, world, number, librarians, page, source, support, students, media, article, book, systems, business, case, end, example, list, metadata

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, has, ’s, were, been, had, get, make, using, use, see, based, ’m, did, used, does, want, ’ve, being, need, think, find, read, know, including, ’re, work, take, found, working, made, provide, retrieved, create, go, help, am, posted, going, following, look, doing, said, got, looking

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nouns
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verbs

Proper Nouns

An extraction of proper nouns helps you determine the names of people and places in your study carrel.

library, august, university, new, may, government, june, july, |, march, april, october, november, december, september, digital, libraries, google, january, data, open, ferguson, february, brown, tweet, lita, information, japan, research, twitter, ala, project, york, code4lib, technology, search, services, dlf, wordpress, journal, archives, association, ’s, news, wikipedia, michael, wilson, wikidata, post, librarian

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?"

i, it, we, you, our, their, my, they, your, me, them, he, us, its, his, her, she, themselves, itself, myself, him, yourself, ’s, one, ourselves, ‘, em, y, himself, mine, sh, herself, ya, yours, ''s, oneself, theirs, zu, https://t.co/cpdmhwfuf92, participants’, u, y'', hers, yum, ’em, ''em, bookshelf, elf, h3, https://case.law/trends/

Below are words cloud of your study carrel's proper & personal pronouns.

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proper nouns
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pronouns

Adjectives & Verbs

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?"

new, more, other, many, such, first, digital, open, good, social, available, different, public, full, own, same, last, most, few, local, free, important, much, academic, human, next, able, current, great, possible, personal, better, long, recent, little, online, high, specific, large, best, old, small, big, particular, easy, necessary, proactive, real, interested, useful

not, n’t, up, also, so, out, more, just, now, here, as, well, then, only, even, very, really, still, most, back, in, too, much, together, often, on, n''t, however, first, always, again, already, never, ago, there, actually, off, all, down, rather, yet, instead, recently, especially, later, probably, further, enough, far, about

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adjectives
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adverbs