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Marco Antonio Correa Varella

Other affiliations: University of Brasília
Bio: Marco Antonio Correa Varella is an academic researcher from University of São Paulo. The author has contributed to research in topics: Psychology & Medicine. The author has an hindex of 13, co-authored 60 publications receiving 2443 citations. Previous affiliations of Marco Antonio Correa Varella include University of Brasília.


Papers
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Journal ArticleDOI
Daniel Conroy-Beam1, David M. Buss2, Kelly Asao2, Agnieszka Sorokowska3, Agnieszka Sorokowska4, Piotr Sorokowski4, Toivo Aavik5, Grace Akello6, Mohammad Madallh Alhabahba7, Charlotte Alm8, Naumana Amjad9, Afifa Anjum9, Chiemezie S. Atama10, Derya Atamtürk Duyar11, Richard Ayebare, Carlota Batres12, Mons Bendixen13, Aicha Bensafia14, Boris Bizumic15, Mahmoud Boussena14, Marina Butovskaya16, Marina Butovskaya17, Seda Can18, Katarzyna Cantarero19, Antonin Carrier20, Hakan Cetinkaya21, Ilona Croy3, Rosa María Cueto22, Marcin Czub4, Daria Dronova17, Seda Dural18, İzzet Duyar11, Berna Ertuğrul23, Agustín Espinosa22, Ignacio Estevan24, Carla Sofia Esteves25, Luxi Fang26, Tomasz Frackowiak4, Jorge Contreras Garduño27, Karina Ugalde González, Farida Guemaz, Petra Gyuris28, Mária Halamová29, Iskra Herak20, Marina Horvat30, Ivana Hromatko31, Chin Ming Hui26, Jas Laile Suzana Binti Jaafar32, Feng Jiang33, Konstantinos Kafetsios34, Tina Kavčič35, Leif Edward Ottesen Kennair13, Nicolas Kervyn20, Truong Thi Khanh Ha19, Imran Ahmed Khilji36, Nils C. Köbis37, Hoang Moc Lan19, András Láng28, Georgina R. Lennard15, Ernesto León22, Torun Lindholm8, Trinh Thi Linh19, Giulia Lopez38, Nguyen Van Luot19, Alvaro Mailhos24, Zoi Manesi39, Rocio Martinez40, Sarah L. McKerchar15, Norbert Meskó28, Girishwar Misra41, Conal Monaghan15, Emanuel C. Mora42, Alba Moya-Garófano40, Bojan Musil30, Jean Carlos Natividade43, Agnieszka Niemczyk4, George Nizharadze, Elisabeth Oberzaucher44, Anna Oleszkiewicz4, Anna Oleszkiewicz3, Mohd Sofian Omar-Fauzee45, Ike E. Onyishi10, Barış Özener11, Ariela Francesca Pagani38, Vilmante Pakalniskiene46, Miriam Parise38, Farid Pazhoohi47, Annette Pisanski42, Katarzyna Pisanski48, Katarzyna Pisanski4, Edna Lúcia Tinoco Ponciano, Camelia Popa49, Pavol Prokop50, Pavol Prokop51, Muhammad Rizwan, Mario Sainz52, Svjetlana Salkičević31, Ruta Sargautyte46, Ivan Sarmány-Schuller53, Susanne Schmehl44, Shivantika Sharad41, Razi Sultan Siddiqui54, Franco Simonetti55, Stanislava Stoyanova56, Meri Tadinac31, Marco Antonio Correa Varella57, Christin-Melanie Vauclair25, Luis Diego Vega, Dwi Ajeng Widarini, Gyesook Yoo58, Marta Zaťková29, Maja Zupančič59 
University of California, Santa Barbara1, University of Texas at Austin2, Dresden University of Technology3, University of Wrocław4, University of Tartu5, Gulu University6, Middle East University7, Stockholm University8, University of the Punjab9, University of Nigeria, Nsukka10, Istanbul University11, Franklin & Marshall College12, Norwegian University of Science and Technology13, University of Algiers14, Australian National University15, Russian State University for the Humanities16, Russian Academy of Sciences17, İzmir University of Economics18, University of Social Sciences and Humanities19, Université catholique de Louvain20, Ankara University21, Pontifical Catholic University of Peru22, Cumhuriyet University23, University of the Republic24, ISCTE – University Institute of Lisbon25, The Chinese University of Hong Kong26, National Autonomous University of Mexico27, University of Pécs28, University of Constantine the Philosopher29, University of Maribor30, University of Zagreb31, University of Malaya32, Central University of Finance and Economics33, University of Crete34, University of Primorska35, Institute of Molecular and Cell Biology36, University of Amsterdam37, Catholic University of the Sacred Heart38, VU University Amsterdam39, University of Granada40, University of Delhi41, University of Havana42, Pontifical Catholic University of Rio de Janeiro43, University of Vienna44, Universiti Utara Malaysia45, Vilnius University46, University of British Columbia47, University of Sussex48, Romanian Academy49, Comenius University in Bratislava50, Slovak Academy of Sciences51, University of Monterrey52, SAS Institute53, DHA Suffa University54, Pontifical Catholic University of Chile55, South-West University "Neofit Rilski"56, University of São Paulo57, Kyung Hee University58, University of Ljubljana59
TL;DR: This work combines this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets and finds that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.
Abstract: Humans express a wide array of ideal mate preferences. Around the world, people desire romantic partners who are intelligent, healthy, kind, physically attractive, wealthy, and more. In order for these ideal preferences to guide the choice of actual romantic partners, human mating psychology must possess a means to integrate information across these many preference dimensions into summaries of the overall mate value of their potential mates. Here we explore the computational design of this mate preference integration process using a large sample of n = 14,487 people from 45 countries around the world. We combine this large cross-cultural sample with agent-based models to compare eight hypothesized models of human mating markets. Across cultures, people higher in mate value appear to experience greater power of choice on the mating market in that they set higher ideal standards, better fulfill their preferences in choice, and pair with higher mate value partners. Furthermore, we find that this cross-culturally universal pattern of mate choice is most consistent with a Euclidean model of mate preference integration.

1,827 citations

Journal ArticleDOI
Hannah Moshontz1, Lorne Campbell2, Charles R. Ebersole3, Hans IJzerman4, Heather L. Urry5, Patrick S. Forscher6, Jon Grahe7, Randy J. McCarthy8, Erica D. Musser9, Jan Antfolk10, Christopher M. Castille11, Thomas Rhys Evans12, Susann Fiedler13, Jessica Kay Flake14, Diego A. Forero, Steve M. J. Janssen15, Justin Robert Keene16, John Protzko17, Balazs Aczel18, Sara Álvarez Solas, Daniel Ansari2, Dana Awlia19, Ernest Baskin20, Carlota Batres21, Martha Lucia Borras-Guevara22, Cameron Brick23, Priyanka Chandel24, Armand Chatard25, Armand Chatard26, William J. Chopik27, David Clarance, Nicholas A. Coles28, Katherine S. Corker29, Barnaby J. W. Dixson30, Vilius Dranseika31, Yarrow Dunham32, Nicholas W. Fox33, Gwendolyn Gardiner34, S. Mason Garrison35, Tripat Gill36, Amanda C. Hahn37, Bastian Jaeger38, Pavol Kačmár39, Gwenaël Kaminski, Philipp Kanske40, Zoltan Kekecs41, Melissa Kline42, Monica A. Koehn43, Pratibha Kujur24, Carmel A. Levitan44, Jeremy K. Miller45, Ceylan Okan43, Jerome Olsen46, Oscar Oviedo-Trespalacios47, Asil Ali Özdoğru48, Babita Pande24, Arti Parganiha24, Noorshama Parveen24, Gerit Pfuhl, Sraddha Pradhan24, Ivan Ropovik49, Nicholas O. Rule50, Blair Saunders51, Vidar Schei52, Kathleen Schmidt53, Margaret Messiah Singh24, Miroslav Sirota54, Crystal N. Steltenpohl55, Stefan Stieger56, Daniel Storage57, Gavin Brent Sullivan12, Anna Szabelska58, Christian K. Tamnes59, Miguel A. Vadillo60, Jaroslava Varella Valentova61, Wolf Vanpaemel62, Marco Antonio Correa Varella61, Evie Vergauwe63, Mark Verschoor64, Michelangelo Vianello65, Martin Voracek46, Glenn Patrick Williams66, John Paul Wilson67, Janis Zickfeld59, Jack Arnal68, Burak Aydin, Sau-Chin Chen69, Lisa M. DeBruine70, Ana María Fernández71, Kai T. Horstmann72, Peder M. Isager73, Benedict C. Jones70, Aycan Kapucu74, Hause Lin50, Michael C. Mensink75, Gorka Navarrete76, Silan Ma77, Christopher R. Chartier19 
Duke University1, University of Western Ontario2, University of Virginia3, University of Grenoble4, Tufts University5, University of Arkansas6, Pacific Lutheran University7, Northern Illinois University8, Florida International University9, Åbo Akademi University10, Nicholls State University11, Coventry University12, Max Planck Society13, McGill University14, University of Nottingham Malaysia Campus15, Texas Tech University16, University of California, Santa Barbara17, Eötvös Loránd University18, Ashland University19, Saint Joseph's University20, Franklin & Marshall College21, University of St Andrews22, University of Cambridge23, Pandit Ravishankar Shukla University24, Centre national de la recherche scientifique25, University of Poitiers26, Michigan State University27, University of Tennessee28, Grand Valley State University29, University of Queensland30, Vilnius University31, Yale University32, Rutgers University33, University of California, Riverside34, Vanderbilt University35, Wilfrid Laurier University36, Humboldt State University37, Tilburg University38, University of Pavol Jozef Šafárik39, Dresden University of Technology40, Lund University41, Massachusetts Institute of Technology42, University of Sydney43, Occidental College44, Willamette University45, University of Vienna46, Queensland University of Technology47, Üsküdar University48, University of Prešov49, University of Toronto50, University of Dundee51, Norwegian School of Economics52, Southern Illinois University Carbondale53, University of Essex54, University of Southern Indiana55, University of Health Sciences Antigua56, University of Illinois at Urbana–Champaign57, Queen's University Belfast58, University of Oslo59, Autonomous University of Madrid60, University of São Paulo61, Katholieke Universiteit Leuven62, University of Geneva63, University of Groningen64, University of Padua65, Abertay University66, Montclair State University67, McDaniel College68, Tzu Chi University69, University of Glasgow70, University of Santiago, Chile71, Humboldt University of Berlin72, Eindhoven University of Technology73, Ege University74, University of Wisconsin–Stout75, Adolfo Ibáñez University76, University of the Philippines Diliman77
01 Oct 2018
TL;DR: The Psychological Science Accelerator is a distributed network of laboratories designed to enable and support crowdsourced research projects that will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability.
Abstract: Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability.

180 citations

Journal ArticleDOI
Kathryn V. Walter1, Daniel Conroy-Beam1, David M. Buss2, Kelly Asao2, Agnieszka Sorokowska3, Agnieszka Sorokowska4, Piotr Sorokowski5, Toivo Aavik6, Grace Akello7, Mohammad Madallh Alhabahba8, Charlotte Alm9, Naumana Amjad10, Afifa Anjum10, Chiemezie S. Atama11, Derya Atamtürk Duyar12, Richard Ayebare, Carlota Batres13, Mons Bendixen14, Aicha Bensafia15, Boris Bizumic16, Mahmoud Boussena15, Marina Butovskaya17, Marina Butovskaya18, Seda Can19, Katarzyna Cantarero20, Antonin Carrier21, Hakan Cetinkaya22, Ilona Croy4, Rosa María Cueto23, Marcin Czub3, Daria Dronova17, Seda Dural19, İzzet Duyar12, Berna Ertuğrul24, Agustín Espinosa23, Ignacio Estevan25, Carla Sofia Esteves26, Luxi Fang27, Tomasz Frackowiak3, Jorge Contreras Garduño28, Karina Ugalde González, Farida Guemaz, Petra Gyuris29, Mária Halamová, Iskra Herak21, Marina Horvat30, Ivana Hromatko31, Chin Ming Hui27, Jas Laile Suzana Binti Jaafar32, Feng Jiang33, Konstantinos Kafetsios34, Tina Kavčič35, Leif Edward Ottesen Kennair14, Nicolas Kervyn21, Truong Thi Khanh Ha20, Imran Ahmed Khilji, Nils C. Köbis36, Hoang Moc Lan20, András Láng29, Georgina R. Lennard16, Ernesto León23, Torun Lindholm9, Trinh Thi Linh20, Giulia Lopez37, Nguyen Van Luot20, Alvaro Mailhos25, Zoi Manesi38, Rocio Martinez39, Sarah L. McKerchar16, Norbert Meskó29, Girishwar Misra40, Conal Monaghan16, Emanuel C. Mora41, Alba Moya-Garófano39, Bojan Musil30, Jean Carlos Natividade42, Agnieszka Niemczyk3, George Nizharadze, Elisabeth Oberzaucher43, Anna Oleszkiewicz3, Anna Oleszkiewicz4, Mohd Sofian Omar-Fauzee44, Ike E. Onyishi11, Barış Özener12, Ariela Francesca Pagani37, Vilmante Pakalniskiene45, Miriam Parise37, Farid Pazhoohi46, Annette Pisanski41, Katarzyna Pisanski47, Katarzyna Pisanski3, Edna Lúcia Tinoco Ponciano, Camelia Popa48, Pavol Prokop49, Pavol Prokop50, Muhammad Rizwan, Mario Sainz51, Svjetlana Salkičević31, Ruta Sargautyte45, Ivan Sarmány-Schuller50, Susanne Schmehl43, Shivantika Sharad40, Razi Sultan Siddiqui52, Franco Simonetti53, Stanislava Stoyanova54, Meri Tadinac31, Marco Antonio Correa Varella55, Christin-Melanie Vauclair26, Luis Diego Vega, Dwi Ajeng Widarini, Gyesook Yoo56, Marta Zat’ková, Maja Zupančič57 
University of California, Santa Barbara1, University of Texas at Austin2, University of Wrocław3, Dresden University of Technology4, Opole University5, University of Tartu6, Gulu University7, Middle East University8, Stockholm University9, University of the Punjab10, University of Nigeria, Nsukka11, Istanbul University12, Franklin & Marshall College13, Norwegian University of Science and Technology14, University of Algiers15, Australian National University16, Russian Academy of Sciences17, Russian State University for the Humanities18, İzmir University of Economics19, University of Social Sciences and Humanities20, Université catholique de Louvain21, Ankara University22, Pontifical Catholic University of Peru23, Cumhuriyet University24, University of the Republic25, ISCTE – University Institute of Lisbon26, The Chinese University of Hong Kong27, National Autonomous University of Mexico28, University of Pécs29, University of Maribor30, University of Zagreb31, University of Malaya32, Central University of Finance and Economics33, University of Crete34, University of Primorska35, University of Amsterdam36, Catholic University of the Sacred Heart37, VU University Amsterdam38, University of Granada39, University of Delhi40, University of Havana41, Pontifical Catholic University of Rio de Janeiro42, University of Vienna43, Universiti Utara Malaysia44, Vilnius University45, University of British Columbia46, Centre national de la recherche scientifique47, Romanian Academy48, Comenius University in Bratislava49, Slovak Academy of Sciences50, University of Monterrey51, DHA Suffa University52, Pontifical Catholic University of Chile53, South-West University "Neofit Rilski"54, University of São Paulo55, Kyung Hee University56, University of Ljubljana57
TL;DR: Using a new 45-country sample (N = 14,399), this work attempted to replicate classic studies and test both the evolutionary and biosocial role perspectives, finding neither pathogen prevalence nor gender equality robustly predicted sex differences or preferences across countries.
Abstract: Considerable research has examined human mate preferences across cultures, finding universal sex differences in preferences for attractiveness and resources as well as sources of systematic cultural variation. Two competing perspectives-an evolutionary psychological perspective and a biosocial role perspective-offer alternative explanations for these findings. However, the original data on which each perspective relies are decades old, and the literature is fraught with conflicting methods, analyses, results, and conclusions. Using a new 45-country sample (N = 14,399), we attempted to replicate classic studies and test both the evolutionary and biosocial role perspectives. Support for universal sex differences in preferences remains robust: Men, more than women, prefer attractive, young mates, and women, more than men, prefer older mates with financial prospects. Cross-culturally, both sexes have mates closer to their own ages as gender equality increases. Beyond age of partner, neither pathogen prevalence nor gender equality robustly predicted sex differences or preferences across countries.

129 citations

Journal ArticleDOI
TL;DR: It is suggested that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when the authors use different extraction methods and correlate and rotate the dimension reduction solution.
Abstract: Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.

74 citations

Journal ArticleDOI
Ke Wang1, Amit Goldenberg1, Charles Dorison2, Jeremy K. Miller3  +470 moreInstitutions (232)
TL;DR: In this paper, the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation, was tested to reduce negative emotions and increase positive emotions.
Abstract: The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world.

54 citations


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30 Apr 1984
TL;DR: A review of the literature on optimal foraging can be found in this article, with a focus on the theoretical developments and the data that permit tests of the predictions, and the authors conclude that the simple models so far formulated are supported by available data and that they are optimistic about the value both now and in the future.
Abstract: Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

2,709 citations

Journal ArticleDOI
20 Apr 1907
TL;DR: For instance, when a dog sees another dog at a distance, it is often clear that he perceives that it is a dog in the abstract; for when he gets nearer his whole manner suddenly changes, if the other dog be a friend as discussed by the authors.
Abstract: ION, GENERAL CONCEPTIONS, SELF-CONSCIOUSNESS, MENTAL INDIVIDUALITY. It would be very difficult for any one with even much more knowledge than I possess, to determine how far animals exhibit any traces of these high mental powers. This difficulty arises from the impossibility of judging what passes through the mind of an animal; and again, the fact that writers differ to a great extent in the meaning which they attribute to the above terms, causes a further difficulty. If one may judge from various articles which have been published lately, the greatest stress seems to be laid on the supposed entire absence in animals of the power of abstraction, or of forming general concepts. But when a dog sees another dog at a distance, it is often clear that he perceives that it is a dog in the abstract; for when he gets nearer his whole manner suddenly changes, if the other dog be a friend. A recent writer remarks, that in all such cases it is a pure assumption to assert that the mental act is not essentially of the same nature in the animal as in man. If either refers what he perceives with his senses to a mental concept, then so do both. (44. Mr. Hookham, in a letter to Prof. Max Muller, in the 'Birmingham News,' May, 1873.) When I say to my terrier, in an eager voice (and I have made the trial many times), "Hi, hi, where is it?" she at once takes it as a sign that something is to be hunted, and generally first looks quickly all around, and then rushes into the nearest thicket, to scent for any game, but finding nothing, she looks up into any neighbouring tree for a squirrel. Now do not these actions clearly shew that she had in her mind a general idea or concept that some animal is to be discovered and hunted? It may be freely admitted that no animal is self-conscious, if by this term it is implied, that he reflects on such points, as whence he comes or whither he will go, or what is life and death, and so forth. But how can we feel sure that an old dog with an excellent memory and some power of imagination, as shewn by his dreams, never reflects on his past pleasures or pains in the chase? And this would be a form of self-consciousness. On the other hand, as Buchner (45. 'Conferences sur la Theorie Darwinienne,' French translat. 1869, p. 132.) has remarked, how little can the hardworked wife of a degraded Australian savage, who uses very few abstract words, and cannot count above four, exert her self-consciousness, or reflect on the nature of her own existence. It is generally admitted, that the higher animals possess memory, attention, association, and even some imagination and reason. If these powers, which differ much in different animals, are capable of improvement, there seems no great improbability in more complex faculties, such as the higher forms of abstraction, and selfconsciousness, etc., having been evolved through the development and combination of the simpler ones. It has been urged against the views here maintained that it is impossible to say at what point in the ascending scale animals become capable of abstraction, etc.; but who can say at what age this occurs in our young children? We see at least that such powers

1,464 citations

Journal ArticleDOI
TL;DR: This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years in single-cell data science.
Abstract: The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

677 citations

Journal ArticleDOI
TL;DR: In this paper, the current status of knowledge on atmospheric microplastics, the methods for sample collection, analysis and detection, and the recommendations for atmospheric micro-plastic sampling and measurement are reviewed.

539 citations