whats-eric-reading
Introduction
This is a Distant Reader "study carrel", a set of structured data intended to help the student, researcher, or scholar use & understand a corpus.
This study carrel was created on 2021-03-08 by Eric Morgan <emorgan@nd.edu>. The carrel was created using the Distant Reader zip2carrel process, and the input was a Zip file locally cached with the name input-file.zip. Documents in the Zip file have been saved in a cache, and each of them have been transformed & saved as a set of plain text files. All of the analysis -- "reading" -- has been done against these plain text files. For example, a short narrative report has been created. This Web page is a more verbose version of that report.
All study carrels are self-contained -- no Internet connection is necessary to use them. Download this carrel for offline reading. The carrel is made up of many subdirectories and data files. The manifest describes each one in greater detail.
Size
There are 32 item(s) in this carrel, and this carrel is 435,292 words long. Each item in your study carrel is, on average, 13,602 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 charts illustrate the overall size of the carrel.
Readability
On a scale from 0 to 100, where 0 is very difficult and 100 is very easy, the documents have an average readability score of 56. 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 charts illustrate the overall readability of the carrel.
Word Frequencies
By merely counting & tabulating the frequency of individual words or phrases, you can begin to get an understanding of the carrel's "aboutness". Excluding "stop words", some of the more frequent words include:
https, data, ai, research, org, learning, one, library, people, www, collections, new, metadata, machine, report, figure, also, project, time, digital, use, work, oclc, intelligence, will, university, information, index, com, libraries, many, may, entity, collection, artificial, support, well, systems, see, number, doi, model, services, two, used, us, first, participants, linked, based
Using the three most frequent words, the three files containing all of those words the most are Transitioning to the Next Generation of Metadata, Transforming Metadata into Linked Data to Improve Digital Collection Discoverability: A CONTENTdm Pilot Project, and stanford-ai-2021.
The most frequent two-word phrases (bigrams) include:
machine learning, artificial intelligence, index report, linked data, intelligence index, ai index, et al, research support, oclc research, united states, org cdmld, cdmld screenshots, digital collection, archival discovery, pdf https, html https, focus group, deep learning, org abs, disciplinary research, social interoperability, line graph, project team, research data, ff qbxq, transforming metadata, data visualization, data management, org research, scientific collections, training data, improve digital, collection discoverability, group members, report figure, next generation, one another, support services, university research, research enterprise, hanging together, line vec, natural language, new york, campus partnerships, data model, ai skills, computer vision, academic graph, north america
And the three file that use all of the three most frequent phrases are stanford-ai-2021 wiegand-cultures-2021, and prudhomme-taking-2021.
While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:
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 the carrel include:
learning, university, research, machine, library, oclc, datum, system, september, metadata, libraries, entity, edge, digital, data, yoshimura, word, wikipedia, wikibase, whitechapel, white, way, visualization, usda, type, time, technology, table, support, street, stepney, startup, stanford, st., scientific, scholar, report, reading, process, proceedings, preservation, pmss, plague, place, people, partnerships, participant, parish, page, notre
And now word clouds really begin to shine:
Topic Modeling
Topic modeling is another popular approach to connoting the aboutness of a corpus. If the study carrel could be summed up in a single word, then that word might be data, and is most about that word.
If the study carrel could be summed up in three words ("topics") then those words and their significantly associated titles include:
- data - Lighting the Way: A Preliminary Report on the National Forum on Archival Discovery and Delivery
- ai - stanford-ai-2021
- people - defoe-plague-1722
If the 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:
- data, https, org - Lighting the Way: A Preliminary Report on the National Forum on Archival Discovery and Delivery
- ai, 20, research - stanford-ai-2021
- people, plague, time - defoe-plague-1722
- collections, https, org - schindel-economic-2020
- entity, edge, bootleg -
Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:
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?":
e, %, data, o, n, t, i, r, p, a, c, people, research, h, s, time, l, collections, project, learning, work, information, library, d, metadata, systems, entity, model, v, number, machine, participants, f, services, figure, libraries, entities, use, support, example, collection, source, part, way, m, process, plague, knowledge, discovery, b
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, be, was, are, were, have, had, do, has, been, used, see, based, using, being, did, make, use, linked, •, given, said, made, take, say, go, provide, found, come, including, came, learning, help, know, went, need, related, find, include, think, get, following, called, died, create, ’s, working, improve, does, work
Proper Nouns
An extraction of proper nouns helps you determine the names of people and places in your study carrel.
_, ai, �, research, index, university, report, intelligence, artificial, data, •, learning, library, figure, b, forum, machine, digital, metadata, ta, bootleg, al, libraries, united, oclc, wikibase, m, w, contentdm, s, chart, u, national, states, n, group, -, new, ml, cross, a, collection, social, et, st., london, academic, international, conference, information
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, they, we, i, their, them, you, our, he, its, your, his, my, us, me, themselves, him, her, itself, she, one, ’s, himself, myself, ourselves, yourself, herself, thy, λ, thee, ours, mine, α, thyself, theirs, t, ibqm, auto-, `ikr?qh2f, #f[mb+f/`, [ensure, ၯஒ,ࡢᄝࡢმ, zbmath,19, y’, yourselves, yours, yolov5, yolov2, x, wikicite,48
Below are words cloud of your study carrel's proper & personal 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?"
other, such, more, new, many, different, digital, same, -, first, great, several, public, institutional, specific, good, large, important, own, satisfied, poor, available, next, possible, particular, few, social, academic, human, dead, common, most, little, able, last, high, best, significant, deep, open, local, full, better, sick, similar, whole, larger, much, future, recent
not, also, so, more, only, as, up, well, then, very, out, most, even, now, here, together, just, often, n’t, indeed, e.g., again, however, much, first, rather, in, too, away, especially, still, there, down, all, somewhat, really, before, particularly, already, sometimes, extremely, always, yet, perhaps, far, thus, instead, never, on, at
Next steps
There is much more to a study carrel than the things outlined above. Use this page's menubar to navigate and explore in more detail. There you will find additional features & functions including: ngrams, parts-of-speech, grammars, named entities, topic modeling, a simple search interface, etc.
Again, study carrels are self-contained. Download this carrel for offline viewing and use.
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