Distant Reader - journal-biomedResInt-cord

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 2021-05-30 by Eric Morgan <emorgan@nd.edu>. The carrel was created using the Distant Reader cord process, and the input was the result of a query applied to a local mirror of CORD, a data set of scholarly articles on the topic of COVID-19. The actual query was: facet_journal:"Biomed Res Int". The results of this query were saved in a cache and transformed into 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 39 item(s) in this carrel, and this carrel is 167,342 words long. Each item in your study carrel is, on average, 4,290 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 48. 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:

cells, virus, study, cell, infection, patients, respiratory, viral, also, rna, protein, used, human, analysis, two, based, using, sequences, results, ifitm, different, disease, nk, detection, viruses, type, i, sars, selection, reported, one, group, pseudoknots, studies, high, ifn, covid, may, table, cases, sequence, infections, time, data, clinical, cov, coronavirus, however, specific, dna

Using the three most frequent words, the three files containing all of those words the most are NK Cells in Mucosal Defense against Infection, A Genome-Wide Analysis of RNA Pseudoknots That Stimulate Efficient −1 Ribosomal Frameshifting or Readthrough in Animal Viruses, and Cell Type-Dependent RNA Recombination Frequency in the Japanese Encephalitis Virus.

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

nk cells, respiratory syndrome, cell lines, convalescent plasma, radix bupleuri, protein sequences, acute respiratory, goose ifitm, type i, nk cell, ssdna mres, tgev pl, preparedness activities, amino acid, severe acute, rna recombination, ssdna mre, respiratory infections, porcine reproductive, viral rna, infectious diseases, natural killer, syndrome virus, biomed research, infection control, healthcare institutions, research international, cord uid, biomed res, syndrome coronavirus, doc id, int doi, res int, respiratory tract, respiratory viruses, dna aptamers, cfu ml, similarity dissimilarity, positive selection, united states, immune response, sabin poliovirus, circular tfo, graphical representation, poliovirus type, porcine epidemic, time pcr, staphylococcus aureus, cell line, vitro selection

And the three file that use all of the three most frequent phrases are Antagonizing Interferon-Mediated Immune Response by Porcine Reproductive and Respiratory Syndrome Virus NK Cells in Mucosal Defense against Infection, and Demographic Variations of MERS-CoV Infection among Suspected and Confirmed Cases: An Epidemiological Analysis of Laboratory-Based Data from Riyadh Regional Laboratory.

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:

sars, covid-19, pcr, ifn, china, cell, table, sequence, rna, infection, wos, work, vero, tumor, trf, tmuv, tgev, tfo, target, taaa, student, ssdna, severe, sepsis, selex, selection, scopus, saudi, sari, sabin, rsv, respiratory, radix, prrsv, protein, preparedness, plasma, pl1, phase, pedv, patient, on1, na_1, mrsa, mre, model, mic, mers, mabs, lod

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 cells, and NGS Nominated CELA1, HSPG2, and KCNK5 as Candidate Genes for Predisposition to Balkan Endemic Nephropathy 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:

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:

patients, respiratory, study - Convalescent Plasma: A Challenging Tool to Treat COVID-19 Patients—A Lesson from the Past and New Perspectives
protein, pseudoknots, sequences - Single-Stranded DNA Aptamers against Pathogens and Toxins: Identification and Biosensing Applications
rna, cells, preparedness - Qualitative Research: Institutional Preparedness During Threats of Infectious Disease Outbreaks
cells, cell, nk - NK Cells in Mucosal Defense against Infection
cells, cancer, tumor - A System Biology-Based Approach for Designing Combination Therapy in Cancer Precision Medicine

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?":

cells, virus, cell, infection, study, patients, protein, analysis, sequences, results, detection, disease, selection, type, studies, infections, cases, viruses, time, group, data, system, activity, number, sequence, coronavirus, plasma, treatment, gene, cancer, target, control, preparedness, effect, therapy, assay, tumor, mres, dna, level, age, authors, influenza, samples, model, proteins, syndrome, expression, children, strains

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:

using, showed, based, reported, include, identified, performed, found, detected, followed, develop, compared, caused, binds, induce, inhibited, selected, considered, infecting, associated, increasing, suggest, provided, described, observed, related, indicated, leading, involved, requiring, obtained, demonstrated, conducted, occur, determined, contain, confirmed, presented, analyzed, tested, treated, given, expressed, reduce, resulting, collected, signaling, mediated, according, needed

Proper Nouns

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

RNA, NK, SARS, IFN, China, PCR, MRE, COVID-19, Table, IFITM3, CoV-2, Radix, Bupleuri, PRRSV, A, S, SELEX, PEDV, MERS, TGEV, CoV, IFITM1, B, TFO, RSV, C, Research, FCoV, II, RT, Figure, DNA, Bupleurum, BEN, mAbs, Sabin, MRSA, IRF3, United, USA, CT, S., ABSM, C6/36, International, M., M, HRSV, CFU, sha

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, we, their, our, they, its, i, them, one, us, itself, his, her, you, he, ifitm3, me, isgf3, irf3-egfp, ifitms, him, ≥100, φðtÞ, she, esat6

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?"

respiratory, viral, human, different, high, clinical, specific, positive, severe, first, new, important, immune, infectious, acute, negative, similar, convalescent, antiviral, significant, porcine, novel, molecular, low, higher, single, previous, several, many, possible, genetic, multiple, early, present, rapid, lower, bacterial, small, innate, common, anti, real, potential, inflammatory, therapeutic, major, bactericidal, natural, medical, free

also, however, well, therefore, respectively, significantly, highly, moreover, recently, first, ssdna, even, furthermore, subsequently, previously, still, together, often, especially, currently, finally, widely, hence, approximately, less, closely, similarly, mostly, frequently, usually, statistically, relatively, rapidly, potentially, single, prior, mainly, generally, effectively, directly, probably, particularly, now, least, already, additionally, much, commonly, nt, worldwide

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.

Thank you for using the Distant Reader.