wanyu/IteraTeR_v2 huggingface dataset:
Original:  Intellectuals were thus lured into criticizing the leadership so they could be arrested later, and many were sent to hard labor camps (Gulags), following the model of Mao Tse-tung's Hundred Flowers campaign in URL Freedom of speech, freedom of assembly and other basic civilian freedoms were soon revoked after the government's attempt of destroying the literary movement.
Revised:  Intellectuals were thus lured into criticizing the leadership so they could be arrested later, and many were sent to hard labor camps (Gulags), following the model of Mao Tse-tung's Hundred Flowers campaign in China. Freedom of speech, freedom of assembly and other basic civilian freedoms were soon revoked after the government's attempt of destroying the literary movement.
Edit intent: coherence
Original:  For a population count Y with mean µ and variance var (Y), Taylor's law is written,
Revised:  For a population count Y with mean µ and variance Var (Y), Taylor's law is written,
Edit intent: fluency
Original:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Revised:  For a population count with mean and variance , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Edit intent: clarity
Original:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Revised:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Edit intent: fluency
Original:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Revised:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Edit intent: clarity
Original:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Revised:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between and , is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Edit intent: clarity
Original:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Revised:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in and and is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Edit intent: fluency
Original:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Revised:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in and . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Edit intent: coherence
Original:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Revised:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both and are subject to error. where m is the sample mean and s2 is the variance.
Edit intent: fluency
Original:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Revised:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where is the sample mean and s2 is the variance.
Edit intent: fluency
Original:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and s2 is the variance.
Revised:  For a population count Y with mean µ and variance Var(Y) , Taylor's law is written , where r is the Pearson moment correlation coefficient between log(s2) and log m, f is the ratio of sample variances in log(s2) and log m and φ is the ratio of the errors in log(s2) and log m . Ordinary least squares regression assumes that φ = ∞. This tends to underestimate the value of b because the estimates of both log(s2) and log m are subject to error. where m is the sample mean and is the variance.
Edit intent: fluency
Original:  No comment on the distribution can be made if k = 0. where var(r) is the variance of r . Let K be a measure of the species abundance (organisms per unit area).
Revised:  No comment on the distribution can be made if k = 0. where is the variance of r . Let K be a measure of the species abundance (organisms per unit area).
Edit intent: clarity
Original:  No comment on the distribution can be made if k = 0. where var(r) is the variance of r . Let K be a measure of the species abundance (organisms per unit area).
Revised:  No comment on the distribution can be made if k = 0. where var(r) is the variance of . Let K be a measure of the species abundance (organisms per unit area).
Edit intent: fluency
Original:  No comment on the distribution can be made if k = 0. where var(r) is the variance of r . Let K be a measure of the species abundance (organisms per unit area).
Revised:  No comment on the distribution can be made if k = 0. where var(r) is the variance of r . Let be a measure of the species abundance (organisms per unit area).
Edit intent: coherence
Original:  Based on this model the constants a and b were derived and a table prepared relating the values of P and m Uses The predicted estimates of m from this equation are subject to bias and it is recommended that the adjusted mean ( ma ) be used instead where SE(IP) is the standard error of the index of patchiness, var(k) is the variance of the parameter k and q is the number of quadrats sampled.. where D is the dispersal index, n is the number of units per sample and N is the number of samples.
Revised:  Based on this model the constants a and b were derived and a table prepared relating the values of P and mUses Uses The predicted estimates of m from this equation are subject to bias and it is recommended that the adjusted mean ( ma ) be used instead where SE(IP) is the standard error of the index of patchiness, var(k) is the variance of the parameter k and q is the number of quadrats sampled.. where D is the dispersal index, n is the number of units per sample and N is the number of samples.
Edit intent: clarity
Original:  Based on this model the constants a and b were derived and a table prepared relating the values of P and m Uses The predicted estimates of m from this equation are subject to bias and it is recommended that the adjusted mean ( ma ) be used instead where SE(IP) is the standard error of the index of patchiness, var(k) is the variance of the parameter k and q is the number of quadrats sampled.. where D is the dispersal index, n is the number of units per sample and N is the number of samples.
Revised:  Based on this model the constants a and b were derived and a table prepared relating the values of P and m The predicted estimates of m from this equation are subject to bias and it is recommended that the adjusted mean ( ma ) be used instead where SE(IP) is the standard error of the index of patchiness, var(k) is the variance of the parameter k and q is the number of quadrats sampled.. where D is the dispersal index, n is the number of units per sample and N is the number of samples.
Edit intent: fluency
Original:  Based on this model the constants a and b were derived and a table prepared relating the values of P and m Uses The predicted estimates of m from this equation are subject to bias and it is recommended that the adjusted mean ( ma ) be used instead where SE(IP) is the standard error of the index of patchiness, var(k) is the variance of the parameter k and q is the number of quadrats sampled.. where D is the dispersal index, n is the number of units per sample and N is the number of samples.
Revised:  Based on this model the constants a and b were derived and a table prepared relating the values of P and m Uses The predicted estimates of m from this equation are subject to bias and it is recommended that the adjusted mean ( ma ) be used instead where is the standard error of the index of patchiness, var(k) is the variance of the parameter k and q is the number of quadrats sampled.. where D is the dispersal index, n is the number of units per sample and N is the number of samples.
Edit intent: clarity
Original:  Based on this model the constants a and b were derived and a table prepared relating the values of P and m Uses The predicted estimates of m from this equation are subject to bias and it is recommended that the adjusted mean ( ma ) be used instead where SE(IP) is the standard error of the index of patchiness, var(k) is the variance of the parameter k and q is the number of quadrats sampled.. where D is the dispersal index, n is the number of units per sample and N is the number of samples.
Revised:  Based on this model the constants a and b were derived and a table prepared relating the values of P and m Uses The predicted estimates of m from this equation are subject to bias and it is recommended that the adjusted mean ( ma ) be used instead where SE(IP) is the standard error of the index of patchiness, is the variance of the parameter k and q is the number of quadrats sampled.. where D is the dispersal index, n is the number of units per sample and N is the number of samples.
Edit intent: fluency
Original:  Chikani ChameliVaishali Samant She got nominated for Gadbad Gondhal in Ambarnath Marathi Film Festival in 2017 as Best Singer (Female).
Revised:  Chikni ChameliVaishali Samant She got nominated for Gadbad Gondhal in Ambarnath Marathi Film Festival in 2017 as Best Singer (Female).
Edit intent: fluency
Original:  Her "Aika Dajiba", "Kombadi Palali", "Nad khula" and "Durchya Ranat" songs are popular and a hit .
Revised:  .
Edit intent: coherence
Original:  Career Samant's debut song Aika Dajiba made sensation all over India.
Revised:  Career
Edit intent: clarity
Original:  She has sung for music composers like A. R. Rahman in Lagaan, Taal (film)  Saathiya (film).
Revised:  She has sung for music composers like A. R. Rahman in Lagaan, Taal (film) and Saathiya (film).
Edit intent: coherence
Original:  She has also sung for bollywood films like Padmashree Laloo Prasad Yadav, Girlfriend, Eight, Malamaal Weekly, Tujhe Meri Kasam, Chetna, Dil Jo Bhi Kahe, Traffic Signal, Chamku, Mirch and so on .
Revised:  She has also sung for bollywood films like Padmashree Laloo Prasad Yadav, Girlfriend, Eight, Malamaal Weekly, Tujhe Meri Kasam, Chetna, Dil Jo Bhi Kahe, Traffic Signal, Chamku, Mirch .
Edit intent: coherence
Original:  She won  MTV Asia music awards, Singapore in 2004.
Revised:  She won the MTV Asia music awards, Singapore in 2004.
Edit intent: fluency
Original: Chikni ChameliVaishali Samant She got nominated for Gadbad Gondhal in Ambarnath Marathi Film Festival in 2017 as Best Singer (Female).
Revised: Chikni ChameliVaishali Samant She was nominated for Gadbad Gondhal in Ambarnath Marathi Film Festival in 2017 as Best Singer (Female).
Edit intent: clarity
Original:  She has sung over 2000 songs in Marathi. . Career She has sung for music composers like A. R. Rahman in Lagaan, Taal (film) and Saathiya (film).
Revised:  She has sung over 2000 songs in Marathi. Career She has sung for music composers like A. R. Rahman in Lagaan, Taal (film) and Saathiya (film).
Edit intent: fluency
Original:  She won the MTV Asia music awards , Singapore in 2004.
Revised:  She won the MTV Asia music awards in 2004.
Edit intent: clarity
Original:  Chikni ChameliVaishali Samant She was nominated for Gadbad Gondhal in Ambarnath Marathi Film Festival in 2017 as Best Singer (Female).
Revised:  She was nominated for Gadbad Gondhal in Ambarnath Marathi Film Festival in 2017 as Best Singer (Female).
Edit intent: coherence
Original:  "C'est La Vie" Home Delivery (film) 2008 Chamku 2010 Mirch "Zindagi Tu Hi Bata" 2016 Majha Naav Shivaji "Dil Yeh Mera" 2018 Gadbad Gondhal "Sang Na", "Alis Tu" Music Director Yanda Kartavya Aahe Galgale Nighale Mast Challay Amcha
Revised:  "C'est La Vie" Home Delivery (film) 2008 Chamku 2010 Mirch "Zindagi Tu Hi Bata" 2016 Majha Naav Shivaji "Dil Yeh Mera" 2018 Gadbad Gondhal "Sang Na", "Alis Tu" Yanda Kartavya Aahe Galgale Nighale Mast Challay Amcha
Edit intent: coherence
Original:  "C'est La Vie" Home Delivery (film) 2008 Chamku 2010 Mirch "Zindagi Tu Hi Bata" 2016 Majha Naav Shivaji "Dil Yeh Mera" 2018 Gadbad Gondhal "Sang Na", "Alis Tu" Music Director Yanda Kartavya Aahe Galgale Nighale Mast Challay Amcha
Revised:  "C'est La Vie" Home Delivery (film) 2008 Chamku 2010 Mirch "Zindagi Tu Hi Bata" 2016 Majha Naav Shivaji "Dil Yeh Mera" 2018 Gadbad Gondhal "Sang Na", "Alis Tu" Music Director 
Edit intent: coherence
Original: Career She has sung for music composers like A. R. Rahman in Lagaan, Taal (film) and Saathiya(film) .
Revised: Career She has sung for music composers like A. R. Rahman in Lagaan, Taal and Saathiya .
Edit intent: fluency
Original:  Pyaar Hai 2004 Girlfriend (2004 film) "Suno To Jaana Jaana" 2005 Dil Jo Bhi Kahey...
Revised:  Pyaar Hai 2004 Girlfriend "Suno To Jaana Jaana" 2005 Dil Jo Bhi Kahey...
Edit intent: coherence
Original:  "C'est La Vie" Home Delivery (film) 2008 Chamku 2010 Mirch "Zindagi Tu Hi Bata" 2016 Majha Naav Shivaji "Dil Yeh Mera" 2018 Gadbad Gondhal "Sang Na", "Alis Tu"
Revised:  "C'est La Vie" Home Delivery 2008 Chamku 2010 Mirch "Zindagi Tu Hi Bata" 2016 Majha Naav Shivaji "Dil Yeh Mera" 2018 Gadbad Gondhal "Sang Na", "Alis Tu"
Edit intent: coherence
Original: Playback singing Year Movie Song 2000 Alaipayuthey "Yaro Yarodi" 2001 Lagaan "Radha Kaise Na Jale" 2003 Kaise Kahoon Ke...
Revised: Playback singing Year Film Song 2000 Alaipayuthey "Yaro Yarodi" 2001 Lagaan "Radha Kaise Na Jale" 2003 Kaise Kahoon Ke...
Edit intent: clarity
Original:  FLD mainly drew its support from the Issa  clan.
Revised:  FLD mainly drew its support from the Issa (clan) clan.
Edit intent: fluency
Original:  FLD mainly drew its support from the Issa (clan) clan.
Revised:  FLD mainly drew its support from the Issa clan.
Edit intent: fluency
Original:  In the struggle of Independence the Front for the Liberation of Djibouti sabotaged the Addis Ababa - Djibouti Railway in the 1977 near Dewele  When the France give Djibouti it Independence.
Revised:  In the struggle of Independence the Front for the Liberation of Djibouti sabotaged the Addis Ababa - Djibouti Railway in the 1977 near Dewele . When the France give Djibouti it Independence.
Edit intent: fluency
Original:  The military struggle was actively supported by the government of Somalia in  60s and 70s.
Revised:  The military struggle was actively supported by the government of Somalia in the 60s and 70s.
Edit intent: fluency
Original: Biography Zhang was born in Pingjiang County, Hunan Province, with Hakka ancestry from Pingyuan County, Guangdong Province.
Revised: Biography Zhang was born in Pingjiang County, Hunan Province, with ancestry from Pingyuan County, Guangdong Province.
Edit intent: clarity
Original:  He celebrated his 100th birthday in October 2014 and died on September 3, 2015, just over a month before his 101st birthday.
Revised:  He celebrated his 100th birthday in October 2014 and passed away on September 3, 2015, just over a month before his 101st birthday.
Edit intent: clarity
Original:  Religious skepticism is not the same as atheism or agnosticism  and some religious skeptics are deists  .
Revised:  Religious skepticism is not the same as atheism or agnosticism , and some religious skeptics are deists .
Edit intent: fluency
Original:  Religious skepticism is not the same as atheism or agnosticism, and some religious skeptics are deists (or theists who reject the prevailing organized religion , they encounter, or even all organized religion).
Revised:  Religious skepticism is not the same as atheism or agnosticism, and some religious skeptics are deists (or theists who reject the prevailing organized religion they encounter, or even all organized religion).
Edit intent: fluency
Original:  This a significant evolution from his earlier writings wherein he was more accommodating to religion 
Revised:  This a significant evolution from his earlier writings wherein he was more accommodating to religion .
Edit intent: fluency
Original:  Its origin is in the Greek  skeptikos, meaning inquiring, reflective or one that doubts .
Revised:  Its origin is in the Greek word skeptikos, meaning inquiring, reflective or one that doubts .
Edit intent: clarity
Original:  The majority of skeptics are agnostics and atheists, but there are also a number of religious people that are skeptical of religion.
Revised:  majority of skeptics are agnostics and atheists, but there are also a number of religious people that are skeptical of religion.
Edit intent: coherence
Original:  Modern religious skepticism The term has morphed into one that typically emphasizes scientific and historical methods of evidence.
Revised:  History The term has morphed into one that typically emphasizes scientific and historical methods of evidence.
Edit intent: clarity
Original:  There are still echoes of early Greek skepticism in the way some current thinkers question the intellectual viability of belief in the divine.
Edit intent: clarity
Original:  Selected historical occurrences Socrates was raised in polytheistic society in which the gods were not omnipotent and required sacrifice and ritual.
Revised:  Socrates was raised in polytheistic society in which the gods were not omnipotent and required sacrifice and ritual.
Edit intent: clarity
Original:  In Japan, Yamagata Bantō (d.1821) declared that "in this world there are no gods, Buddhas, or ghosts, nor are there strange or miraculous things"       .
Revised:  In Japan, Yamagata Bantō (d.1821) declared that "in this world there are no gods, Buddhas, or ghosts, nor are there strange or miraculous things" . .
Edit intent: fluency
Original:  Socrates ' conception of the divine was that they were always benevolent, truthful, authoritative, and wise.
Revised:  Socrates ' conception of the divine was that the gods were always benevolent, truthful, authoritative, and wise.
Edit intent: fluency
Original:  This a significant evolution from his earlier writings wherein he was more accommodating to religion.
Edit intent: coherence