Summary of your 'study carrel' ============================== This is a summary of your Distant Reader 'study carrel'. The Distant Reader harvested & cached your content into a collection/corpus. It then applied sets of natural language processing and text mining against the collection. The results of this process was reduced to a database file -- a 'study carrel'. The study carrel can then be queried, thus bringing light specific characteristics for your collection. These characteristics can help you summarize the collection as well as enumerate things you might want to investigate more closely. This report is a terse narrative report, and when processing is complete you will be linked to a more complete narrative report. Eric Lease Morgan Number of items in the collection; 'How big is my corpus?' ---------------------------------------------------------- 76 Average length of all items measured in words; "More or less, how big is each item?" ------------------------------------------------------------------------------------ 6496 Average readability score of all items (0 = difficult; 100 = easy) ------------------------------------------------------------------ 51 Top 50 statistically significant keywords; "What is my collection about?" ------------------------------------------------------------------------- Top 50 lemmatized nouns; "What is discussed?" --------------------------------------------- 7844 particle 2320 virus 1769 air 1400 size 1288 concentration 1224 cell 1098 study 1038 flow 1027 aerosol 1008 % 935 time 859 surface 847 filter 838 velocity 815 diameter 782 system 780 effect 772 protein 739 method 711 model 703 rate 699 number 682 nanoparticle 673 sampling 671 deposition 657 result 646 distribution 606 membrane 602 sample 571 vaccine 571 infection 538 droplet 533 nm 499 efficiency 493 μm 485 figure 476 environment 466 process 455 room 449 value 443 source 432 transmission 432 exposure 428 m 421 case 420 datum 417 dust 417 bioaerosol 408 mask 401 sampler Top 50 proper nouns; "What are the names of persons or places?" -------------------------------------------------------------- 1318 al 1111 et 970 . 623 Fig 353 VLP 257 RNA 202 m 185 SARS 182 Table 180 s 167 C 166 ¼ 147 NanoTrap 145 Eq 133 RVFV 119 NT 111 T 109 D 107 COVID-19 105 B 102 CFD 101 USA 101 NPs 100 bioaerosol 100 PM 100 HEPA 99 NT53 93 PCR 93 OR 89 A 87 LAF 87 DV 87 CoV-2 85 Aerosol 80 Health 79 u 78 VEEV 76 MMT 75 IEM 73 siRNA 73 Air 71 N 69 mg 69 Particle 68 −1 68 China 67 L 67 K 64 lm 64 M Top 50 personal pronouns nouns; "To whom are things referred?" ------------------------------------------------------------- 1573 it 813 we 476 they 146 i 142 them 31 itself 28 themselves 24 one 19 us 16 he 11 you 8 s 2 u 2 il-1β 2 em 1 yourself 1 tonometry 1 she 1 ourselves 1 is~0.1 1 imagej 1 's Top 50 lemmatized verbs; "What do things do?" --------------------------------------------- 17978 be 2463 have 2049 use 1001 show 532 include 495 increase 490 base 432 find 388 reduce 384 induce 377 contain 375 provide 365 compare 362 do 357 measure 353 follow 347 generate 342 see 318 consider 316 obtain 316 cause 309 result 305 determine 302 make 301 give 295 produce 292 observe 291 describe 279 collect 255 detect 251 take 249 demonstrate 248 associate 246 require 237 apply 234 perform 229 form 226 indicate 217 allow 215 report 215 affect 214 become 213 develop 210 exhale 201 lead 199 depend 198 suggest 198 bind 194 know 192 remain Top 50 lemmatized adjectives and adverbs; "How are things described?" --------------------------------------------------------------------- 1232 not 1026 high 976 also 822 such 812 airborne 809 large 787 small 754 viral 710 - 675 other 625 low 614 different 611 human 591 more 563 only 523 however 508 well 486 indoor 481 respiratory 353 like 351 most 345 as 340 surgical 338 therefore 327 specific 319 important 318 negative 300 very 296 same 284 less 275 experimental 273 then 272 long 271 several 266 many 266 first 261 various 259 biological 253 non 247 single 244 total 240 bacterial 234 so 224 significant 223 similar 213 even 210 new 209 average 208 immune 207 further Top 50 lemmatized superlative adjectives; "How are things described to the extreme?" ------------------------------------------------------------------------- 125 most 70 high 56 low 46 least 44 good 38 Most 28 large 17 great 10 small 10 bad 7 strong 4 simple 3 short 3 long 3 late 2 ω 2 safe 2 fast 2 early 2 broad 1 ±5 1 wide 1 weak 1 poor 1 old 1 near 1 fit 1 dusty 1 dry 1 cool 1 common 1 big 1 \10 1 \.040 1 -Separate Top 50 lemmatized superlative adverbs; "How do things do to the extreme?" ------------------------------------------------------------------------ 226 most 25 least 12 well 1 vrna 1 lowest 1 highest 1 -tag Top 50 Internet domains; "What Webbed places are alluded to in this corpus?" ---------------------------------------------------------------------------- 12 doi.org 3 orcid.org 2 creativecommons.org 2 creat 1 www.ncbi.nlm.nih.qov 1 www.ncbi.nlm.nih.gov 1 www.nature.com 1 www.frontiersin.org 1 www.epa.gov 1 www.code-saturne.org 1 www 1 textbookofbacteriology.net 1 smartairfilters.com 1 pubs.acs.org 1 imagej.nih.gov 1 github.com 1 dx.doi.org 1 clinicaltrias.gov 1 clinicaltrials.gov Top 50 URLs; "What is hyperlinked from this corpus?" ---------------------------------------------------- 6 http://doi.org/10.1101/2020.07.12.20152157 2 http://doi.org/10.1038/s41598-020-72865-z 2 http://creat 1 http://www.ncbi.nlm.nih.qov/genbank/ 1 http://www.ncbi.nlm.nih.gov 1 http://www.nature.com/srep 1 http://www.frontiersin.org/articles/10.3389/fvets 1 http://www.epa.gov/microbes/moldtech.htm 1 http://www.code-saturne.org 1 http://www 1 http://textbookofbacteriology.net/staph.html 1 http://smartairfilters.com/cn/en/ 1 http://pubs.acs.org/doi/10.1021/acsnano.0c03252 1 http://orcid.org/0000-0003-2613-1669 1 http://orcid.org/0000-0001-8692-5116 1 http://orcid.org/0000-0001-5844-1341 1 http://imagej.nih.gov/ij/ 1 http://github.com/sophiamersmann/molecular-counting 1 http://dx.doi.org/10.1016/j.jmb.2012.06.029 1 http://doi.org/10.1101/2020.07 1 http://doi.org/10.1007/s42757-019-0016-z 1 http://doi.org/10.1007/s12273-020-0623-4 1 http://doi.org/10.1007/s00417-020-04815-4 1 http://creativecommons.org/licenses/by/4.0/ 1 http://creativecommons.org/licenses/by/ 1 http://clinicaltrias.gov 1 http://clinicaltrials.gov/ct2/search/index Top 50 email addresses; "Who are you gonna call?" ------------------------------------------------- 1 timcook007@gmail.com 1 enric.robine@cstb.fr Top 50 positive assertions; "What sentences are in the shape of noun-verb-noun?" ------------------------------------------------------------------------------- 13 particles do not 11 particles are not 8 particles are capable 7 particles are able 7 particles were then 5 concentration is less 5 flow does not 5 particles are likely 4 model is not 4 nanoparticles are not 4 particles are more 4 particles are usually 4 particles does not 4 particles have also 4 particles is not 4 particles were also 4 particles were capable 4 particles were less 4 results are consistent 4 sampling is not 3 % using colony 3 aerosol generating medical 3 diameter is larger 3 distribution is usually 3 filter is close 3 method is more 3 particles are difficult 3 particles are present 3 particles have previously 3 particles is also 3 particles is mainly 3 particles were not 3 rate is q 3 rate is very 3 result is consistent 3 results are not 3 sizes is not 3 velocity is less 3 velocity is much 3 velocity is very 2 % increased risk 2 % is acceptable 2 aerosol generating dental 2 aerosols containing legionella 2 aerosols have also 2 aerosols including radon 2 air is lower 2 air was insufficient 2 cells is lethal 2 cells were cultured Top 50 negative assertions; "What sentences are in the shape of noun-verb-no|not-noun?" --------------------------------------------------------------------------------------- 2 model is not only 2 nanoparticles are not generally 2 nanoparticles is not large 2 particles do not necessarily 1 % is not so 1 air is not stagnant 1 concentration are not only 1 concentration had no effect 1 concentration was not enough 1 concentrations produced no additional 1 deposition is not directly 1 deposition is not necessarily 1 effects are not significant 1 flow is not laminar 1 method is not stable 1 methods are not well 1 model is not reflective 1 particle is not enough 1 particles are not already 1 particles are not completely 1 particles are not necessarily 1 particles are not only 1 particles are not readily 1 particles are not sensitive 1 particles are not successfully 1 particles are not uniformly 1 particles do not only 1 particles does not remarkably 1 particles has not yet 1 particles is not difficult 1 particles is not only 1 particles were not morphologically 1 protein is not as 1 results are not instantaneous 1 results are not particularly 1 sampling is not explicitly 1 sampling is not more 1 size did not considerably 1 sizes is not obvious 1 sizes is not significant 1 study was not able 1 system is not only 1 time is not long 1 velocity are not available 1 virus are not well 1 virus had no impact 1 virus is not clear 1 virus was no different 1 viruses cause no cytopathic A rudimentary bibliography --------------------------