Distant Reader - journal-sciTotalEnviron-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:"Sci Total Environ". 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 86 item(s) in this carrel, and this carrel is 225,225 words long. Each item in your study carrel is, on average, 3,817 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 52. 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:

covid, sars, cov, air, water, health, temperature, study, pm, data, virus, also, transmission, coronavirus, using, wastewater, high, used, cases, pollution, time, human, china, based, analysis, viruses, concentration, due, may, disease, risk, number, model, lockdown, studies, quality, different, i, pandemic, environmental, use, spread, method, treatment, urban, waste, average, factors, viral, samples

Using the three most frequent words, the three files containing all of those words the most are SARS-CoV-2 in the environment: Modes of transmission, early detection and potential role of pollutions, Unlocking the surge in demand for personal and protective equipment (PPE) and improvised face coverings arising from coronavirus disease (COVID-19) pandemic – Implications for efficacy, re-use and sustainable waste management, and A chronicle of SARS-CoV-2: Part-I - Epidemiology, diagnosis, prognosis, transmission and treatment.

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

air pollution, air quality, novel coronavirus, public health, confirmed cases, journal pre, acute respiratory, respiratory syndrome, particulate matter, coronavirus disease, relative humidity, severe acute, air pollutants, wastewater treatment, human health, total environ, sci total, doc id, environ doi, cord uid, climate change, viral rna, world health, health organization, waste management, average temperature, epidemic spread, water quality, lockdown period, health risks, syndrome coronavirus, drinking water, raw sewage, primary concentration, united states, population density, wind speed, meteorological factors, death rate, enveloped viruses, protective equipment, risk assessment, temperature indices, solar radiation, nucleic acid, health risk, social distancing, airborne transmission, action labs, new york

And the three file that use all of the three most frequent phrases are A health-based assessment of particulate air pollution in urban areas of Beijing in 2000–2004 Exceedances and trends of particulate matter (PM2.5) in five Indian megacities, and Impact of city lockdown on the air quality of COVID-19-hit of Wuhan city.

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:

covid-19, sars, china, rna, cov-2, wuhan, water, virus, india, waste, pcr, tropomi, temperature, ppe, particle, new, mediterranean, italy, covid, beijing, africa, Δili, york, wsud, wildfire, wastewater, vulnerability, u.s., transmission, taiwan, surface, stormwater, sna, size, simulation, sediment, sea, saharan, rowan, qmra, population, pnc, pmf, pm2, piba, pesticide, people, peg, paulo, pathogen

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 2020, and Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: A comparison between MCDM and SWAT models 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:

sars, cov, 2020 - Applicability of polyethylene glycol precipitation followed by acid guanidinium thiocyanate-phenol-chloroform extraction for the detection of SARS-CoV-2 RNA from municipal wastewater
covid, temperature, 2020 - High-temperature indices associated with mortality and outpatient visits: Characterizing the association with elevated temperature()()
air, pm, pollution - Exceedances and trends of particulate matter (PM2.5) in five Indian megacities
2020, waste, 10 - A review on microbial contaminants in stormwater runoff and outfalls: Potential health risks and mitigation strategies
water, pollen, farmers - A multi-actor, participatory approach to identify policy and technical barriers to better farming practices that protect our drinking water sources

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?":

air, water, temperature, virus, study, data, transmission, health, wastewater, cases, time, pollution, coronavirus, viruses, concentration, analysis, number, lockdown, risk, studies, -p, model, quality, disease, pandemic, method, treatment, samples, factors, concentrations, waste, rate, population, infection, period, exposure, humidity, days, detection, cities, effects, spread, °, use, results, impact, outbreak, areas, effect, patients

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:

used, show, based, including, reported, increased, confirmed, compared, found, reduced, associated, provide, considering, caused, estimate, follow, indicating, affected, observed, related, identifying, suggest, detected, made, applied, required, determined, collected, developed, take, given, obtained, assess, leads, needed, analyzed, measured, containing, present, evaluating, calculate, spreading, control, represents, improve, tested, performed, decreasing, predicted, known

Proper Nouns

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

SARS, CoV-2, COVID-19, China, J, PM, RNA, Wuhan, Fig, India, Health, PPE, March, Beijing, Coronavirus, Italy, PCR, C, Table, Journal, Total, CoV, Liu, Wang, RT, April, Environ, World, TROPOMI, CO, Sci, DOI, Africa, January, US, sha, New, Pre, February, Organization, Spain, National, AOD, Li, Hubei, Air, United, OMI, MERS, Action

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, its, our, they, i, them, us, he, one, themselves, itself, his, your, you, she, ourselves, her, yourself, u, cpq_064, covid-19, 's

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?"

high, covid-19, human, different, environmental, viral, urban, higher, average, significant, low, respiratory, potential, public, daily, positive, non, first, global, meteorological, new, novel, specific, relative, severe, important, local, due, total, large, similar, medical, long, spatial, infectious, lower, many, acute, primary, several, available, previous, natural, major, current, airborne, possible, various, effective, early

also, however, therefore, well, respectively, significantly, still, highly, even, especially, mainly, moreover, furthermore, first, relatively, directly, often, particularly, much, potentially, generally, worldwide, currently, approximately, hence, finally, now, yet, overall, usually, recently, commonly, widely, typically, rather, likely, less, far, similarly, rapidly, better, already, previously, positively, mostly, additionally, globally, clearly, effectively, statistically

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.