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 614 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 1700820 words long. [0] Each item in your study carrel is, on average, 2770.0 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 45.0. [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: data, research, information, management, digital, researchers, science, university, sharing, library, access, services, metadata, libraries, use, journal, also, new, curation, will, open, available, may, preservation, article. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/www-ifla-org-8334.txt, ./txt/www-iassistdata-org-9857.txt, and ./txt/www-data-archive-ac-uk-8023.txt.

The most frequent two-word phrases (bigrams) include: research data, data management, data sharing, et al, data curation, google scholar, digital curation, open access, data citation, data sets, vol iss, data services, data reuse, digital preservation, scientific data, iss vol, social science, new window, data repositories, open data, information science, view article, doi httpdoiorg, science data, flickr digital, and the three file that use all of the three most frequent phrases are ./txt/www-ifla-org-8334.txt ./txt/www-ala-org-1727.txt, and ./txt/www-data-archive-ac-uk-8023.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, researcher, researchers, scholar, library, sciences, https, libraries, metadata, http, service, accessed, policy, information, journals, science, projects, repository, research, shared, www, digital, accessibility, citations, informed. 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:

kshop authors furthermore publishing raw data provides additional advantages to the owner 
participant was responsible for longterm data produced by institutional field research he 
ouglass k aydinoglu au wu l read e et al data sharing by scientists practices and percepti
ifficulty of tracking their users i used data citation tracking to identify people who hav
ac eab cd b serco consulting uk research data service report and recommendations to hefce 
 manage and curate create a directory of data sets generated at the wqfs and make this dir
d links to additional guides on research data management and curation no release date regu
in figure the system uses a tabdelimited data file derived from the excel spreadsheet that
tive influence on the intention to reuse data β p however the availability of metadata st
services raises additional challenges to data preservation as static files are replaced by
sions in circulation with open access to data becoming the norm around the world this prob
re help with the storage and transfer of data including data migration as well as assistan
ion of data and produced a demonstration data journal which used overlay mechanics to crea
ip briney has written a useful primer on data management for researchers which provides pr
sising postgraduate training in research data management laura molloy kellie snow doi http
more ro researcherlibrary collaborations data repositories as a service for researchers jo
a preexisting swo class edams format and data have been fully merged and can be found with
that most articles do not refer to these data sets in a section indexed by wos calling int
 the dda are different in kind these are data produced from research processes and which m
ields require more time to analyze their data before allowing third parties to access the 
jc enyedy n little science confronts the data deluge habitat ecology embedded sensor netwo
lian national data service ands research data australia national library of australia trov
ing requirements and characteristics the data sharing policies were ranked using a rubric 
g more research publications and related data will be produced therefore an institutional 
on and dissemination of digital research data the case of dans the digital curation of eth

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 data, and ./txt/doi-org-530.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: data, data, and data. And the respective files would be: ./txt/dx-doi-org-688.txt, ./txt/doi-org-1261.txt, and ./txt/www-ifla-org-6358.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. data research http - ./txt/dx-doi-org-688.txt
  2. data research management - ./txt/doi-org-516.txt
  3. data research sharing - ./txt/doi-org-1699.txt
  4. data research library - ./txt/www-ifla-org-6358.txt
  5. data digital information - ./txt/www-charleswbaileyjr-name-6253.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 (datum, data, research, information, library, science, management, university, researcher, service, journal, article, policy, access, repository, project, sharing, metadata, citation, curation, preservation, study, digital, dataset, publication) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, have, use, provide, include, do, share, make, develop, publish, support, require, base, need, create, identify, access, describe, find, work, see, manage, take, cite, exist) 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 (Data, Research, University, Digital, Science, Information, Journal, Library, al, et, Scholar, Vol, DOI, National, Libraries, Management, Google, Curation, Search, |, Open, International, J., M., UK) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (it, we, they, you, i, them, us, he, she, themselves, itself, me, one, y, ‘, her, him, ourselves, yourself, myself, yours, ’s, himself, ours, pp) 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 (other, available, such, new, digital, scientific, more, open, different, many, good, social, academic, specific, long, important, large, institutional, current, high, public, first, repository, technical, main) and adverbs (not, also, more, well, only, as, however, e.g., often, most, then, very, so, even, up, out, here, currently, now, already, therefore, thus, rather, still, just) 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.