wanyu/IteraTeR_full_sent huggingface dataset:
Original: Scientists Scientists In Rough Chronological Order Scientist Democritus Aristotle Aristarchus of Samos Eratosthenes Hipparchus Nicolaus Copernicus Tycho Brahe Galileo Galilei Johannes Kepler Isaac Newton Category:Wikipedia books on
Revised: Scientists Scientists In Rough Chronological Order Scientist Democritus Aristotle Aristarchus of Samos Eratosthenes Hipparchus Nicolaus Copernicus Tycho Brahe Galileo Galilei Johannes Kepler Isaac Newton Scientist Society of Mexican American Engineers and Scientists Hispanic Scientist of the Year Award List of African-American inventors and scientists Male Scientists Albert Einstein Albert Turner Bharucha-Reid Alfred Wegener Arlie Petters Benjamin Banneker Bruce C. Heezen Carl Sagan Charles Darwin Charles Henry Turner (zoologist) Drummond Matthews Eduard Suess Emmett Chappelle Enrico Fermi Ernest Everett Just Fernando Caldeiro Frederick Vine Friedrich Mohs Galileo Galilei George Edward Alcorn Jr. George Robert Carruthers George Washington Carver Georges Lemaître Harold Amos Harry Hammond Hess Hermann von Helmholtz Inge Lehmann Isaac Newton James Cook James Hutton James McLurkin Jan Oort Johannes Kepler John Dabiri John Harrison Joseph L. Graves Leonardo da Vinci Louis Pasteur Luigi Galvani Michael Faraday Neil deGrasse Tyson Nicolaus Copernicus Niels Bohr Norman L. Bowen Percival Lowell René Descartes St. Elmo Brady Sylvester James Gates Tycho Brahe Warren M. Washington Female Scientists Women in science Organization for Women in Science for the Developing World Ada Yonath Adriana Ocampo Alice Ball Ana Maria Rey Annie Easley Annie Jump Cannon Annie Russell Maunder Antonia Novello Cecilia Payne-Gaposchkin Chien-Shiung Wu Dian Fossey Dorothy Vaughan Elizabeth Blackburn Grace Hopper Henrietta Swan Leavitt Inés Cifuentes Katherine Johnson Laura Bassi Lise Meitner Lydia Villa-Komaroff Marie Curie Maria Margaretha Kirch Marie Maynard Daly Marie Tharp Mary Jackson (engineer) Nora Volkow Rachel Carson Sally Ride Willie Hobbs Moore Ancient Scientists Archimedes Aristarchus of Samos Aristotle Democritus Eratosthenes Euclid Hipparchus Ibn al-Haytham Ptolemy Theophrast
Edit intent: meaning-changed
Original:  Organization for Women in Science for the Developing World  Ada Yonath Adriana Ocampo Alice Ball Ana Maria Rey Annie Easley Annie Jump Cannon Annie Russell Maunder Antonia Novello Cecilia Payne-Gaposchkin Chien-Shiung Wu Dian Fossey Dorothy Vaughan Elizabeth Blackburn Grace Hopper Henrietta Swan Leavitt Inés Cifuentes Katherine Johnson Laura Bassi Lise Meitner Lydia Villa-Komaroff Marie Curie Maria Margaretha Kirch Marie Maynard Daly Marie Tharp Mary Jackson (engineer)
Revised:  Organization for Women in Science for the Developing World Association for Women in Science Ada Yonath Adriana Ocampo Alice Ball Ana Maria Rey Annie Easley Annie Jump Cannon Annie Russell Maunder Antonia Novello Cecilia Payne-Gaposchkin Chien-Shiung Wu Dian Fossey Dorothy Vaughan Elizabeth Blackburn Grace Hopper Henrietta Swan Leavitt Inés Cifuentes Katherine Johnson Laura Bassi Lise Meitner Lydia Villa-Komaroff Marie Curie Maria Margaretha Kirch Marie Maynard Daly Marie Tharp Mary Jackson (engineer)
Edit intent: fluency
Original:  That same person on a bicycle, on the same ground, with the same power output, can travel at using an ordinary bicycle, so in these conditions the energy expenditure of cycling is one-third that of walking  . Uphill and downhill speeds vary according to the slope of the incline and the effort of the rider.
Revised:  That same person on a bicycle, on the same ground, with the same power output, can travel at using an ordinary bicycle, so in these conditions the energy expenditure of cycling is one-third that of walking the same distance . Uphill and downhill speeds vary according to the slope of the incline and the effort of the rider.
Edit intent: clarity
Original:  Without pedaling downhill a bicycle rider can easily reach speeds of 20 - 40 km/h down a gentle \% 5 slope and dangerous speeds exceeding 50 km/h on steeper inclines.
Revised:  Without pedaling downhill a bicycle rider can easily reach speeds of 20-40 km/h down a gentle \% 5 slope and dangerous speeds exceeding 50 km/h on steeper inclines.
Edit intent: fluency
Original:  Without pedaling downhill a bicycle rider can easily reach speeds of 20 - 40 km/h down a gentle \% 5 slope and dangerous speeds exceeding 50 km/h on steeper inclines.
Revised:  Without pedaling downhill a bicycle rider can easily reach speeds of 20 - 40 km/h down a gentle 5 slope and dangerous speeds exceeding 50 km/h on steeper inclines.
Edit intent: clarity
Original:  Without pedaling downhill a bicycle rider can easily reach speeds of 20 - 40 km/h down a gentle \% 5 slope and dangerous speeds exceeding 50 km/h on steeper inclines.
Revised:  Without pedaling downhill a bicycle rider can easily reach speeds of 20 - 40 km/h down a gentle \% 5 \% slope and speeds exceeding 50 km/h on steeper inclines.
Edit intent: clarity
Original:  Energy output Active humans can produce between 1.5 Watts /kg (untrained), 3.0 W/kg ( threshold for the medical definition of “fit” ), and 6.6 W/kg (top-class male athletes).
Revised:  Energy output Active humans can produce between 1.5 Watts per kilogram of body mass (untrained), 3.0 W/kg ( threshold for the medical definition of “fit” ), and 6.6 W/kg (top-class male athletes).
Edit intent: meaning-changed
Original:  Energy output Active humans can produce between 1.5 Watts /kg (untrained), 3.0 W/kg ( threshold for the medical definition of “fit” ), and 6.6 W/kg (top-class male athletes).
Revised:  Energy output Active humans can produce between 1.5 Watts /kg (untrained), 3.0 W/kg ( fit ), and 6.6 W/kg (top-class male athletes).
Edit intent: clarity
Original:  He earned a B.S. degree in Conservation and Resource Studies in 1984, and then an M.S. and a Ph.D.  in 1993, all from the University of California, Berkeley.
Revised:  In 1984, Gelobter received a B.S. degree in Conservation and Resource Studies in 1984, and then an M.S. and a Ph.D. in 1993, all from the University of California, Berkeley.
Edit intent: meaning-changed
Original:  He earned a B.S. degree in Conservation and Resource Studies in 1984, and then an M.S. and a Ph.D.  in 1993, all from the University of California, Berkeley.
Revised:  He earned a B.S. degree in Conservation and Resource Studies from the University of California, Berkeley. He has an M.S. and a Ph.D. in 1993, all from the University of California, Berkeley.
Edit intent: coherence
Original:  He earned a B.S. degree in Conservation and Resource Studies in 1984, and then an M.S. and a Ph.D.  in 1993, all from the University of California, Berkeley.
Revised:  He earned a B.S. degree in Conservation and Resource Studies in 1984, and then an M.S. and a Ph.D. , which he earned in 1993, all from the University of California, Berkeley.
Edit intent: meaning-changed
Original:  He earned a B.S. degree in Conservation and Resource Studies in 1984, and then an M.S. and a Ph.D.  in 1993, all from the University of California, Berkeley.
Revised:  He earned a B.S. degree in Conservation and Resource Studies in 1984, and then an M.S. and a Ph.D. in 1993, from the University of California, Berkeley.
Edit intent: clarity
Original: Category: New Zealand Anglicans Category:New Zealand religious leaders Category:New Zealand Māori people Category:Primates of New Zealand Category:Bishops of Aotearoa Category:1972 births Category:Living peop
Revised: Category: 21st-century Anglican bishops in New Zealand Category:New Zealand Māori people Category:Primates of New Zealand Category:Bishops of Aotearoa Category:1972 births Category:Living peop
Edit intent: clarity
Original: Phylogenetic convolutional neural networks Phylogenetic convolutional neural network (Ph-CNN) is a novel deep neural network architecture proposed by Fioranti et al. for clasification of Metagenomics databased on the convolutional neural networks.
Revised: Phylogenetic convolutional neural networks Phylogenetic convolutional neural network (Ph-CNN) is a novel convolutional neural network architecture proposed by Fioranti et al. for clasification of Metagenomics databased on the convolutional neural networks.
Edit intent: clarity
Original: Phylogenetic convolutional neural networks Phylogenetic convolutional neural network (Ph-CNN) is a novel deep neural network architecture proposed by Fioranti et al. for clasification of Metagenomics databased on the convolutional neural networks. Their approachtries to endow Phylogenetic data with a vicinity notion using patristic distance to select k-neighborhoods for each OTU .
Revised: Phylogenetic convolutional neural networks Phylogenetic convolutional neural network (Ph-CNN) is a novel deep neural network architecture proposed by Fioranti et al. to classify metagenomics data. In this approach, phylogenetic data is endowed with patristic distance (the sum of the lengths of all branches connecting two operational taxonomic units) to select k-neighborhoods for each OTU .
Edit intent: clarity
Original:  Their approachtries to endow Phylogenetic data with a vicinity notion using patristic distance to select k-neighborhoods for each OTU . Each OTU and his neighborhood is processed with a convolutional filter to mimic the CNN workflow .
Revised:  Their approachtries to endow Phylogenetic data with a vicinity notion using patristic distance to select k-neighborhoods for each OTU , and each OTU and its neighbors are processed with convolutional filters .
Edit intent: coherence
Original:  According to Lin and colleagues, machine learning requires a great computing power, but the development of fast supercomputers and web servers has made access to these tools easier nowadays. A major challenge in the characterization of differences in the microbiome composition across groups of samples is the high dimensionality of microbiome datasets, which significantly reduces the power of current approaches for identifying true differences and increases the chance of false discoveries.
Revised:  According to Lin and colleagues, machine learning requires a great computing power, but the development of fast supercomputers and web servers has made access to these tools easier . The high dimensionality of microbiome datasets is a major challenge in the characterization of differences in the microbiome composition across groups of samples is the high dimensionality of microbiome datasets, which significantly reduces the power of current approaches for identifying true differences and increases the chance of false discoveries.
Edit intent: coherence
Original:  A major challenge in the characterization of differences in the microbiome composition across groups of samples is the high dimensionality of microbiome datasets, which significantly reduces the power of current approaches for identifying true differences and increases the chance of false discoveries.
Revised:  A major challenge in characterizing differences in the microbiome composition across groups of samples is the high dimensionality of microbiome datasets, which significantly reduces the power of current approaches for identifying true differences and increases the chance of false discoveries.
Edit intent: fluency
Original:  A major challenge in the characterization of differences in the microbiome composition across groups of samples is the high dimensionality of microbiome datasets, which significantly reduces the power of current approaches for identifying true differences and increases the chance of false discoveries.
Revised:  A major challenge in the characterization of differences in the microbiome composition across groups of samples ; this significantly limits the power of current approaches for identifying true differences and increases the chance of false discoveries.
Edit intent: coherence
Original:  Despite their importance for processing the great amount of information from environmental samples, the development of machine learning tools related to metagenomics has been focused on the study of gut microbiota and the relationship with digestive diseases, such as ; Inflammatory bowel disease (IBD), Clostridioides difficile infection (CDI), colorectal cancer and Diabetes , with the aim for obtaining better approaches of diagnosis and treatments for those pathologies.
Revised:  Despite their importance for processing the great amount of information from environmental samples, the development of machine learning tools related to metagenomics has been focused on the study of gut microbiota and the relationship with digestive diseases, such as inflammatory bowel disease (IBD), Clostridioides difficile infection (CDI), colorectal cancer and Diabetes , with the aim for obtaining better approaches of diagnosis and treatments for those pathologies.
Edit intent: fluency
Original:  Inflammatory bowel disease (IBD), Clostridioides difficile infection (CDI), colorectal cancer and Diabetes , with the aim for obtaining better approaches of diagnosis and treatments for those pathologies.
Revised:  Inflammatory bowel disease (IBD), Clostridioides difficile infection (CDI), colorectal cancer and diabetes , with the aim for obtaining better approaches of diagnosis and treatments for those pathologies.
Edit intent: fluency
Original:  Inflammatory bowel disease (IBD), Clostridioides difficile infection (CDI), colorectal cancer and Diabetes , with the aim for obtaining better approaches of diagnosis and treatments for those pathologies.
Revised:  Inflammatory bowel disease (IBD), Clostridioides difficile infection (CDI), colorectal cancer and Diabetes , with the aim of obtaining better approaches of diagnosis and treatments for those pathologies.
Edit intent: fluency
Original:  There are a lot of examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data  e.g. 16S rRNA or whole-genome sequencing (WGS)  by the use of methods such as ;
Revised:  There are many examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data e.g. 16S rRNA or whole-genome sequencing (WGS) by the use of methods such as ;
Edit intent: coherence
Original:  There are a lot of examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data  e.g. 16S rRNA or whole-genome sequencing (WGS)  by the use of methods such as ;
Revised:  There are a lot of examples of algorithms developed to classify microbial communities according to the health condition of the host, regardless of the type of sequence data e.g. 16S rRNA or whole-genome sequencing (WGS) by the use of methods such as ;
Edit intent: coherence
Original:  There are a lot of examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data  e.g. 16S rRNA or whole-genome sequencing (WGS)  by the use of methods such as ;
Revised:  There are a lot of examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data , e.g. 16S rRNA or whole-genome sequencing (WGS) by the use of methods such as ;
Edit intent: fluency
Original:  There are a lot of examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data  e.g. 16S rRNA or whole-genome sequencing (WGS)  by the use of methods such as ;
Revised:  There are a lot of examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data e.g. 16S rRNA or whole-genome sequencing (WGS) , by the use of methods such as ;
Edit intent: fluency
Original:  There are a lot of examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data  e.g. 16S rRNA or whole-genome sequencing (WGS)  by the use of methods such as ;
Revised:  There are a lot of examples of algorithms developed for trying to classify microbial communities according to the health condition of the host, regardless of the type of sequence data e.g. 16S rRNA or whole-genome sequencing (WGS) by the use of methods such as 
Edit intent: fluency
Original:  For example, Fioravanti and colleagues in 2018 worked in the development of an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks .
Revised:  For example, in 2018 worked in the development of an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks .
Edit intent: clarity
Original:  For example, Fioravanti and colleagues in 2018 worked in the development of an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks .
Revised:  For example, Fioravanti and colleagues in 2018 , Fioravanti et al developed an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks .
Edit intent: clarity
Original:  For example, Fioravanti and colleagues in 2018 worked in the development of an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks .
Revised:  For example, Fioravanti and colleagues in 2018 worked in the development of an algorithm called Ph-CNN to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks .
Edit intent: coherence
Original:  For example, Fioravanti and colleagues in 2018 worked in the development of an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks .
Revised:  For example, Fioravanti and colleagues in 2018 worked in the development of an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms (to distinguish healthy and sick patients) by using phylogenetic trees and convolutional neural networks .
Edit intent: meaning-changed
Original:  For example, Fioravanti and colleagues in 2018 worked in the development of an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks . As a result, they can establish the classification between healthy and sick patients .
Revised:  For example, Fioravanti and colleagues in 2018 worked in the development of an algorithm called Ph-CNN . They use this algorithm to classify data samples from healthy patients and patients with IBD symptoms by the use of phylogenetic trees and convolutional neural networks .
Edit intent: coherence
Original: Legacy A high-temperature superconductor levitating above a magnet His doctoral studies at IIT Bombay gave Kumar  opportunity to work alongside noted physicists such as Krityunjai Prasad Sinha and Ram Prakash Singh and during his days at the University of British Columbia, he studied condensed matter physics with Maurice Pryce.
Revised: Legacy A high-temperature superconductor levitating above a magnet His doctoral studies at IIT Bombay gave Kumar the opportunity to work alongside noted physicists such as Krityunjai Prasad Sinha and Ram Prakash Singh and during his days at the University of British Columbia, he studied condensed matter physics with Maurice Pryce.
Edit intent: fluency
Original:  He has published four books, Interaction-Magnetically-Ordered-Solids, coauthored with his mentor, Krityunjai Prasad Sinha, Invitation to Contemporary Physics, Deterministic Chaos: Complex Chance out of Simple Necessity and Quantum Transport in Mesoscopic Systems: Complexity and Statistical Fluctuations which is a monograph in the field of mesoscopic physics.
Revised:  Kumar has published four books, Interaction-Magnetically-Ordered-Solids, coauthored with his mentor, Krityunjai Prasad Sinha, Invitation to Contemporary Physics, Deterministic Chaos: Complex Chance out of Simple Necessity and Quantum Transport in Mesoscopic Systems: Complexity and Statistical Fluctuations which is a monograph in the field of mesoscopic physics.
Edit intent: fluency
Original:  He had been involved with the committees for the review of research institutions which included Tata Institute of Fundamental Research and Physical Research Laboratory and has been the chairman of the committee of the Council of Scientific and Industrial Research for selecting Shanti Swarup Bhatnagar Prize winners.
Revised:  Kumar had been involved with the committees for the review of research institutions which included Tata Institute of Fundamental Research and Physical Research Laboratory and has been the chairman of the committee of the Council of Scientific and Industrial Research for selecting Shanti Swarup Bhatnagar Prize winners.
Edit intent: fluency
Original:  In the 19th century versions, Manas is the leader of the Nogay people, while in versions dating after 1920, Manas is a Kyrgyz and a leader of the Kyrgyz. kiner , Shirin & Sims-Williams, Nicholas.
Revised:  In the 19th century versions, Manas is the leader of the Nogay people, while in versions dating after 1920, Manas is a Kyrgyz and a leader of the Kyrgyz. Akiner , Shirin & Sims-Williams, Nicholas.
Edit intent: fluency
Original: A legend is a genre of folklore that consists of a narrative featuring human actions perceived or believed both by teller and listeners to have taken place within human history. Narratives in this genre may demonstrate human values, and possess certain qualities that give the tale verisimilitude. Legend , for its active and passive participants may include miracles. Legends may be transformed over time, in order to keep them fresh and vital. Many legends operate within the realm of uncertainty, never being entirely believed by the participants, but also never being resolutely doubted. Legends are sometimes distinguished from myths in that they concern human beings as the main characters rather than gods, and sometimes in that they have some sort of historical basis whereas myths generally do not.
Revised: A Legend is Ali Osama
Edit intent: coherence
Original: The United Nations Mission for Justice Support in Haiti (MINUJUSTH) is a peacekeeping mission in Haiti mandated by the United Nations Security Council through Resolutions 2350 (2017) and 2410 (2018).
Revised: The United Nations Mission for Justice Support in Haiti (MINUJUSTH) was a peacekeeping mission in Haiti mandated by the United Nations Security Council through Resolutions 2350 (2017) and 2410 (2018).
Edit intent: style
Original:   MINUJUSTH will have up to 1,275 members of the police force as well as correction officers and international civilians, but no military personnel.
Revised:  It was the successor to MINUSTAH (the United Nations Stabilisation Mission in Haiti). MINUJUSTH will have up to 1,275 members of the police force as well as correction officers and international civilians, but no military personnel.
Edit intent: meaning-changed
Original: Marine life This Huge Display contains over 40 species sharks, one of them is a 7m basking shark, in additions to hundreds of fish, crabs, cephalopods, crustations , sea turtles, Dolphins and a rare Monk seal.
Revised: Marine life This huge display contains over 40 species sharks, one of them is a 7m basking shark, in additions to hundreds of fish, crabs, cephalopods, crustations , sea turtles, Dolphins and a rare Monk seal.
Edit intent: fluency