120 research outputs found
Supplementary descriptions and DNA barcodes of two rarely encountered Trisetacus species (Eriophyoidea, Phytoptidae) associated with Tertiary relict conifers from the Mediterranean region
New records and supplementary morphological descriptions of two rarely encountered Trisetacus species from Pinaceae, T. abietis Postner 1968 and T. cedri (Nalepa 1920), are reported. Trisetacus abietis was found in Abkhazia under the needle epidermis of Abies nordmanniana (Steven) Spach, a conifer endemic to the mountainous Asian coast of the Black Sea. Trisetacus cedri was found in buds of introduced Cedrus deodara(Roxb. ex D. Don) G. Don in Abkhazia and South Africa. It is the only member of Trisetacus known from Cedrus spp. For the first time we provide sequences of two genes (COI and D1âD2 28S) of T. abietis(MN022221, MN025333) and T. cedri (MN022222, MN022223, MN025334, MN025335), along with microphotographs of the damage caused by these mites on their coniferous hosts. Sequences of D1âD2 28S of T. cedri from Abkhazian and South African populations are identical; COI sequences from different populations differ by only one synonymous substitution in a codon for asparagine. Females of T. abietis have long asymmetrical 8/7-rayed empodia, whereas males have shorter symmetrical 6/6-rayed empodia and shorter solenidia Ï I. Similar sexual dimorphism in tarsal appendages was previously reported in Novophytoptus, representing an endoparasitic lineage of phytoptids on monocots. In T. cedri, a âlong formâ and a âshort formâ of both males and females were detected, suggesting a complex life cycle in this species. The evolution of Trisetacus is discussed within the broader context of the molecular phylogenies of Pinaceae and Eriophyoidea, including estimations of divergence times.The Russian Foundation for Basic Research; ZIN RAS (project ĐĐĐĐ-Đ19-119020790133-6) and the Russian Science Foundation.https://www.biotaxa.org/saaam2019Forestry and Agricultural Biotechnology Institute (FABI
Active Brownian Particles. From Individual to Collective Stochastic Dynamics
We review theoretical models of individual motility as well as collective
dynamics and pattern formation of active particles. We focus on simple models
of active dynamics with a particular emphasis on nonlinear and stochastic
dynamics of such self-propelled entities in the framework of statistical
mechanics. Examples of such active units in complex physico-chemical and
biological systems are chemically powered nano-rods, localized patterns in
reaction-diffusion system, motile cells or macroscopic animals. Based on the
description of individual motion of point-like active particles by stochastic
differential equations, we discuss different velocity-dependent friction
functions, the impact of various types of fluctuations and calculate
characteristic observables such as stationary velocity distributions or
diffusion coefficients. Finally, we consider not only the free and confined
individual active dynamics but also different types of interaction between
active particles. The resulting collective dynamical behavior of large
assemblies and aggregates of active units is discussed and an overview over
some recent results on spatiotemporal pattern formation in such systems is
given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte
Image-guided ToF depth upsampling: a survey
Recently, there has been remarkable growth of interest in the development and applications of time-of-flight (ToF) depth cameras. Despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies
Mutationism and the Dual Causation of Evolutionary Change
The rediscovery of Mendel's laws a century ago launched the science that William Bateson called "genetics," and led to a new view of evolution combining selection, particulate inheritance, and the newly characterized phenomenon of "mutation." This "mutationist" view clashed with the earlier view of Darwin, and the later "Modern Synthesis," by allowing discontinuity, and by recognizing mutation (or more properly, mutation-and-altered-development) as a source of creativity, direction, and initiative. By the mid-20th century, the opposing Modern Synthesis view was a prevailing orthodoxy: under its influence, "evolution" was redefined as "shifting gene frequencies," that is, the sorting out of pre-existing variation without new mutations; and the notion that mutation-and-altered-development can exert a predictable influence on the course of evolutionary change was seen as heretical. Nevertheless, mutationist ideas re-surfaced: the notion of mutational determinants of directionality emerged in molecular evolution by 1962, followed in the 1980s by an interest among evolutionary developmental biologists in a shaping or creative role of developmental propensities of variation, and more recently, a recognition by theoretical evolutionary geneticists of the importance of discontinuity and of new mutations in adaptive dynamics. The synthetic challenge presented by these innovations is to integrate mutation-and-altered-development into a new understanding of the dual causation of evolutionary change--a broader and more predictive understanding that already can lay claim to important empirical and theoretical results--and to develop a research program appropriately emphasizing the emergence of variation as a cause of propensities of evolutionary change
Đ Đ°ŃĐżĐŸĐ·ĐœĐ°ĐČĐ°ĐœĐžĐ” ĐŸĐ±Đ»Đ°ŃŃĐ”Đč ŃĐ”ĐșŃŃĐ° Ń ĐżĐ”ŃŃĐŸĐœĐ°Đ»ŃĐœŃĐŒĐž ĐŽĐ°ĐœĐœŃĐŒĐž ĐœĐ° ĐŽĐžĐ°ĐłĐœĐŸŃŃĐžŃĐ”ŃĐșĐžŃ ĐžĐ·ĐŸĐ±ŃĐ°Đ¶Đ”ĐœĐžŃŃ
The aim of the study is to develop a method for detecting areas of text with private data on medical diagnostic images using the Tesseract module and the modified Levenshtein distance.Materials and methods. For threshold filtering, the brightness of the points belonging to the text characters in the images is determined at the initial stage. The dynamic threshold is calculated from the histogram of the brightness of the pixels of the image. Next, the Tesseract module is used for primary text recognition. Based on the tag values from DICOM files, a set of strings was formed to search for them in the recognized text. A modified Levenshtein distance was used to search for these strings. A set of DICOM files of the âDose Reportâ type was used to test the algorithm. The accuracy was assessed by experts marking up blocks of private information on images.Results. A tool has been developed with a set of metrics and optimal thresholds for choosing decisive rules in finding matches that allow detecting areas of text with private data on medical images. For this tool, the accuracy of localization of areas with personal data on a set of 1131 medical images was determined in comparison with expert markup, which is 99.86%.Conclusion. The tool developed within the framework of this study allows identifying personal data on digital medical images with high accuracy, which indicates the possibility of its practical application in the preparation of data sets.ĐŠĐ”Đ»Ń ĐžŃŃĐ»Đ”ĐŽĐŸĐČĐ°ĐœĐžŃ: ŃĐ°Đ·ŃĐ°Đ±ĐŸŃĐșĐ° ĐŒĐ”ŃĐŸĐŽĐ° ĐŸĐ±ĐœĐ°ŃŃĐ¶Đ”ĐœĐžŃ ĐŸĐ±Đ»Đ°ŃŃĐ”Đč ŃĐ”ĐșŃŃĐ° Ń ĐżŃĐžĐČĐ°ŃĐœŃĐŒĐž ĐŽĐ°ĐœĐœŃĐŒĐž ĐœĐ° ĐŒĐ”ĐŽĐžŃĐžĐœŃĐșĐžŃ
ĐŽĐžĐ°ĐłĐœĐŸŃŃĐžŃĐ”ŃĐșĐžŃ
ĐžĐ·ĐŸĐ±ŃĐ°Đ¶Đ”ĐœĐžŃŃ
ĐżŃĐž ĐżĐŸĐŒĐŸŃĐž ĐŒĐŸĐŽŃĐ»Ń Tesseract Đž ĐŒĐŸĐŽĐžŃĐžŃĐžŃĐŸĐČĐ°ĐœĐœĐŸĐłĐŸ ŃĐ°ŃŃŃĐŸŃĐœĐžŃ ĐĐ”ĐČĐ”ĐœŃŃĐ”ĐčĐœĐ°.ĐĐ°ŃĐ”ŃОал Đž ĐŒĐ”ŃĐŸĐŽŃ. ĐĐ»Ń ĐżĐŸŃĐŸĐłĐŸĐČĐŸĐč ŃОлŃŃŃĐ°ŃОО ĐœĐ° ĐœĐ°ŃĐ°Đ»ŃĐœĐŸĐŒ ŃŃапД ĐŸĐżŃДЎДлŃĐ”ŃŃŃ ŃŃĐșĐŸŃŃŃ ŃĐŸŃĐ”Đș, ĐżŃĐžĐœĐ°ĐŽĐ»Đ”Đ¶Đ°ŃĐžŃ
ŃĐžĐŒĐČĐŸĐ»Đ°ĐŒ ŃĐ”ĐșŃŃĐ° ĐœĐ° ĐžĐ·ĐŸĐ±ŃĐ°Đ¶Đ”ĐœĐžŃŃ
. ĐĐžĐœĐ°ĐŒĐžŃĐ”ŃĐșĐžĐč ĐżĐŸŃĐŸĐł ĐČŃŃĐžŃĐ»ŃĐ”ŃŃŃ ĐżĐŸ гОŃŃĐŸĐłŃĐ°ĐŒĐŒĐ” ŃŃĐșĐŸŃŃĐ”Đč пОĐșŃДлДĐč ĐžĐ·ĐŸĐ±ŃĐ°Đ¶Đ”ĐœĐžŃ. ĐалДД ĐŽĐ»Ń ĐżĐ”ŃĐČĐžŃĐœĐŸĐłĐŸ ŃĐ°ŃĐżĐŸĐ·ĐœĐ°ĐČĐ°ĐœĐžŃ ŃĐ”ĐșŃŃĐ° ĐžŃĐżĐŸĐ»ŃĐ·ŃĐ”ŃŃŃ ĐŒĐŸĐŽŃĐ»Ń Tesseract. ĐĐ° ĐŸŃĐœĐŸĐČĐ°ĐœĐžĐž Đ·ĐœĐ°ŃĐ”ĐœĐžĐč ŃŃĐłĐŸĐČ ĐžĐ· DICOM-ŃĐ°ĐčĐ»ĐŸĐČ ŃĐŸŃĐŒĐžŃĐŸĐČĐ°Đ»ŃŃ ĐœĐ°Đ±ĐŸŃ ŃŃŃĐŸĐș ĐŽĐ»Ń ĐżĐŸĐžŃĐșĐ° ĐžŃ
ĐČ ŃĐ°ŃĐżĐŸĐ·ĐœĐ°ĐœĐœĐŸĐŒ ŃĐ”ĐșŃŃĐ”. ĐĐ»Ń ĐżĐŸĐžŃĐșĐ° ŃŃĐžŃ
ŃŃŃĐŸĐș ĐžŃĐżĐŸĐ»ŃĐ·ĐŸĐČĐ°Đ»ĐŸŃŃ ĐŒĐŸĐŽĐžŃĐžŃĐžŃĐŸĐČĐ°ĐœĐœĐŸĐ” ŃĐ°ŃŃŃĐŸŃĐœĐžĐ” ĐĐ”ĐČĐ”ĐœŃŃĐ”ĐčĐœĐ°. ĐĐ»Ń ŃĐ”ŃŃĐžŃĐŸĐČĐ°ĐœĐžŃ Đ°Đ»ĐłĐŸŃĐžŃĐŒĐ° ĐżŃĐžĐŒĐ”ĐœŃĐ»ŃŃ ĐœĐ°Đ±ĐŸŃ DICOM ŃĐ°ĐčĐ»ĐŸĐČ ŃОпа âDose Reportâ ĐŒĐŸĐŽĐ°Đ»ŃĐœĐŸŃŃĐž CT. ĐŃĐ”ĐœĐșŃ ŃĐŸŃĐœĐŸŃŃĐž ĐżŃĐŸĐČĐŸĐŽĐžĐ»Đž ŃĐșŃпДŃŃŃ, ŃĐ°Đ·ĐŒĐ”ŃĐ°ŃŃОД Đ±Đ»ĐŸĐșĐž ĐżŃĐžĐČĐ°ŃĐœĐŸĐč ĐžĐœŃĐŸŃĐŒĐ°ŃОО ĐœĐ° ĐžĐ·ĐŸĐ±ŃĐ°Đ¶Đ”ĐœĐžŃŃ
.РДзŃĐ»ŃŃĐ°ŃŃ. Đ Đ°Đ·ŃĐ°Đ±ĐŸŃĐ°Đœ ĐžĐœŃŃŃŃĐŒĐ”ĐœŃ Ń ĐœĐ°Đ±ĐŸŃĐŸĐŒ ĐŒĐ”ŃŃĐžĐș Đž ĐŸĐżŃĐžĐŒĐ°Đ»ŃĐœŃŃ
ĐżĐŸŃĐŸĐłĐŸĐČ ĐŽĐ»Ń ĐČŃĐ±ĐŸŃĐ° ŃĐ”ŃĐ°ŃŃĐžŃ
ĐżŃĐ°ĐČОл ĐČ ĐœĐ°Ń
ĐŸĐ¶ĐŽĐ”ĐœĐžĐž ŃĐŸĐČĐżĐ°ĐŽĐ”ĐœĐžĐč, ĐżĐŸĐ·ĐČĐŸĐ»ŃŃŃĐžŃ
ĐŸĐ±ĐœĐ°ŃŃжОĐČĐ°ŃŃ ĐŸĐ±Đ»Đ°ŃŃĐž ŃĐ”ĐșŃŃĐ° Ń ĐżŃĐžĐČĐ°ŃĐœŃĐŒĐž ĐŽĐ°ĐœĐœŃĐŒĐž ĐœĐ° ĐŒĐ”ĐŽĐžŃĐžĐœŃĐșĐžŃ
ĐžĐ·ĐŸĐ±ŃĐ°Đ¶Đ”ĐœĐžŃŃ
. ĐĐ»Ń ŃŃĐŸĐłĐŸ ĐžĐœŃŃŃŃĐŒĐ”ĐœŃĐ° ĐŸĐżŃĐ”ĐŽĐ”Đ»Đ”ĐœĐ° ŃĐŸŃĐœĐŸŃŃŃ Đ»ĐŸĐșалОзаŃОО ĐŸĐ±Đ»Đ°ŃŃĐ”Đč Ń Đ»ĐžŃĐœŃĐŒĐž ĐŽĐ°ĐœĐœŃĐŒĐž ĐżĐŸ ŃŃĐ°ĐČĐœĐ”ĐœĐžŃ Ń ŃĐșŃпДŃŃĐœĐŸĐč ŃĐ°Đ·ĐŒĐ”ŃĐșĐŸĐč, ĐșĐŸŃĐŸŃĐ°Ń ŃĐŸŃŃĐ°ĐČĐ»ŃĐ”Ń 99,86%.ĐĐ°ĐșĐ»ŃŃĐ”ĐœĐžĐ”. Đ Đ°Đ·ŃĐ°Đ±ĐŸŃĐ°ĐœĐœŃĐč ĐČ ŃĐ°ĐŒĐșĐ°Ń
ĐœĐ°ŃŃĐŸŃŃĐ”ĐłĐŸ ĐžŃŃĐ»Đ”ĐŽĐŸĐČĐ°ĐœĐžŃ ĐžĐœŃŃŃŃĐŒĐ”ĐœŃ ĐżĐŸĐ·ĐČĐŸĐ»ŃĐ”Ń ĐČŃŃĐČĐ»ŃŃŃ ĐżĐ”ŃŃĐŸĐœĐ°Đ»ŃĐœŃĐ” ĐŽĐ°ĐœĐœŃĐ” ĐœĐ° ŃĐžŃŃĐŸĐČŃŃ
ĐŒĐ”ĐŽĐžŃĐžĐœŃĐșĐžŃ
ĐžĐ·ĐŸĐ±ŃĐ°Đ¶Đ”ĐœĐžŃŃ
Ń ĐČŃŃĐŸĐșĐŸĐč ŃĐŸŃĐœĐŸŃŃŃŃ, ŃŃĐŸ ŃĐșĐ°Đ·ŃĐČĐ°Đ”Ń ĐœĐ° ĐČĐŸĐ·ĐŒĐŸĐ¶ĐœĐŸŃŃŃ Đ”ĐłĐŸ ĐżŃĐ°ĐșŃĐžŃĐ”ŃĐșĐŸĐłĐŸ ĐżŃĐžĐŒĐ”ĐœĐ”ĐœĐžŃ ĐżŃĐž ĐżĐŸĐŽĐłĐŸŃĐŸĐČĐșĐ” ĐœĐ°Đ±ĐŸŃĐŸĐČ ĐŽĐ°ĐœĐœŃŃ
Radioactivity control strategy for the JUNO detector
602siopenJUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day (cpd), therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration to reduce at minimum the impact of natural radioactivity. We describe our efforts for an optimized experimental design, a careful material screening and accurate detector production handling, and a constant control of the expected results through a meticulous Monte Carlo simulation program. We show that all these actions should allow us to keep the background count rate safely below the target value of 10 Hz (i.e. âŒ1 cpd accidental background) in the default fiducial volume, above an energy threshold of 0.7 MeV. [Figure not available: see fulltext.]openAbusleme A.; Adam T.; Ahmad S.; Ahmed R.; Aiello S.; Akram M.; An F.; An Q.; Andronico G.; Anfimov N.; Antonelli V.; Antoshkina T.; Asavapibhop B.; de Andre J.P.A.M.; Auguste D.; Babic A.; Baldini W.; Barresi A.; Basilico D.; Baussan E.; Bellato M.; Bergnoli A.; Birkenfeld T.; Blin S.; Blum D.; Blyth S.; Bolshakova A.; Bongrand M.; Bordereau C.; Breton D.; Brigatti A.; Brugnera R.; Bruno R.; Budano A.; Buscemi M.; Busto J.; Butorov I.; Cabrera A.; Cai H.; Cai X.; Cai Y.; Cai Z.; Cammi A.; Campeny A.; Cao C.; Cao G.; Cao J.; Caruso R.; Cerna C.; Chang J.; Chang Y.; Chen P.; Chen P.-A.; Chen S.; Chen X.; Chen Y.-W.; Chen Y.; Chen Y.; Chen Z.; Cheng J.; Cheng Y.; Chetverikov A.; Chiesa D.; Chimenti P.; Chukanov A.; Claverie G.; Clementi C.; Clerbaux B.; Conforti Di Lorenzo S.; Corti D.; Cremonesi O.; Dal Corso F.; Dalager O.; De La Taille C.; Deng J.; Deng Z.; Deng Z.; Depnering W.; Diaz M.; Ding X.; Ding Y.; Dirgantara B.; Dmitrievsky S.; Dohnal T.; Dolzhikov D.; Donchenko G.; Dong J.; Doroshkevich E.; Dracos M.; Druillole F.; Du S.; Dusini S.; Dvorak M.; Enqvist T.; Enzmann H.; Fabbri A.; Fajt L.; Fan D.; Fan L.; Fang J.; Fang W.; Fargetta M.; Fedoseev D.; Fekete V.; Feng L.-C.; Feng Q.; Ford R.; Formozov A.; Fournier A.; Gan H.; Gao F.; Garfagnini A.; Giammarchi M.; Giaz A.; Giudice N.; Gonchar M.; Gong G.; Gong H.; Gornushkin Y.; Gottel A.; Grassi M.; Grewing C.; Gromov V.; Gu M.; Gu X.; Gu Y.; Guan M.; Guardone N.; Gul M.; Guo C.; Guo J.; Guo W.; Guo X.; Guo Y.; Hackspacher P.; Hagner C.; Han R.; Han Y.; Hassan M.S.; He M.; He W.; Heinz T.; Hellmuth P.; Heng Y.; Herrera R.; Hor Y.K.; Hou S.; Hsiung Y.; Hu B.-Z.; Hu H.; Hu J.; Hu J.; Hu S.; Hu T.; Hu Z.; Huang C.; Huang G.; Huang H.; Huang W.; Huang X.; Huang X.; Huang Y.; Hui J.; Huo L.; Huo W.; Huss C.; Hussain S.; Ioannisian A.; Isocrate R.; Jelmini B.; Jen K.-L.; Jeria I.; Ji X.; Ji X.; Jia H.; Jia J.; Jian S.; Jiang D.; Jiang X.; Jin R.; Jing X.; Jollet C.; Joutsenvaara J.; Jungthawan S.; Kalousis L.; Kampmann P.; Kang L.; Karaparambil R.; Kazarian N.; Khan W.; Khosonthongkee K.; Korablev D.; Kouzakov K.; Krasnoperov A.; Kruth A.; Kutovskiy N.; Kuusiniemi P.; Lachenmaier T.; Landini C.; Leblanc S.; Lebrin V.; Lefevre F.; Lei R.; Leitner R.; Leung J.; Li D.; Li F.; Li F.; Li H.; Li H.; Li J.; Li M.; Li M.; Li N.; Li N.; Li Q.; Li R.; Li S.; Li T.; Li W.; Li W.; Li X.; Li X.; Li X.; Li Y.; Li Y.; Li Z.; Li Z.; Li Z.; Liang H.; Liang H.; Liao J.; Liebau D.; Limphirat A.; Limpijumnong S.; Lin G.-L.; Lin S.; Lin T.; Ling J.; Lippi I.; Liu F.; Liu H.; Liu H.; Liu H.; Liu H.; Liu H.; Liu J.; Liu J.; Liu M.; Liu Q.; Liu Q.; Liu R.; Liu S.; Liu S.; Liu S.; Liu X.; Liu X.; Liu Y.; Liu Y.; Lokhov A.; Lombardi P.; Lombardo C.; Loo K.; Lu C.; Lu H.; Lu J.; Lu J.; Lu S.; Lu X.; Lubsandorzhiev B.; Lubsandorzhiev S.; Ludhova L.; Luo F.; Luo G.; Luo P.; Luo S.; Luo W.; Lyashuk V.; Ma B.; Ma Q.; Ma S.; Ma X.; Ma X.; Maalmi J.; Malyshkin Y.; Mantovani F.; Manzali F.; Mao X.; Mao Y.; Mari S.M.; Marini F.; Marium S.; Martellini C.; Martin-Chassard G.; Martini A.; Mayer M.; Mayilyan D.; Mednieks I.; Meng Y.; Meregaglia A.; Meroni E.; Meyhofer D.; Mezzetto M.; Miller J.; Miramonti L.; Montini P.; Montuschi M.; Muller A.; Nastasi M.; Naumov D.V.; Naumova E.; Navas-Nicolas D.; Nemchenok I.; Nguyen Thi M.T.; Ning F.; Ning Z.; Nunokawa H.; Oberauer L.; Ochoa-Ricoux J.P.; Olshevskiy A.; Orestano D.; Ortica F.; Othegraven R.; Pan H.-R.; Paoloni A.; Parmeggiano S.; Pei Y.; Pelliccia N.; Peng A.; Peng H.; Perrot F.; Petitjean P.-A.; Petrucci F.; Pilarczyk O.; Pineres Rico L.F.; Popov A.; Poussot P.; Pratumwan W.; Previtali E.; Qi F.; Qi M.; Qian S.; Qian X.; Qian Z.; Qiao H.; Qin Z.; Qiu S.; Rajput M.U.; Ranucci G.; Raper N.; Re A.; Rebber H.; Rebii A.; Ren B.; Ren J.; Ricci B.; Robens M.; Roche M.; Rodphai N.; Romani A.; Roskovec B.; Roth C.; Ruan X.; Ruan X.; Rujirawat S.; Rybnikov A.; Sadovsky A.; Saggese P.; Sanfilippo S.; Sangka A.; Sanguansak N.; Sawangwit U.; Sawatzki J.; Sawy F.; Schever M.; Schwab C.; Schweizer K.; Selyunin A.; Serafini A.; Settanta G.; Settimo M.; Shao Z.; Sharov V.; Shaydurova A.; Shi J.; Shi Y.; Shutov V.; Sidorenkov A.; Simkovic F.; Sirignano C.; Siripak J.; Sisti M.; Slupecki M.; Smirnov M.; Smirnov O.; Sogo-Bezerra T.; Sokolov S.; Songwadhana J.; Soonthornthum B.; Sotnikov A.; Sramek O.; Sreethawong W.; Stahl A.; Stanco L.; Stankevich K.; Stefanik D.; Steiger H.; Steinmann J.; Sterr T.; Stock M.R.; Strati V.; Studenikin A.; Sun S.; Sun X.; Sun Y.; Sun Y.; Suwonjandee N.; Szelezniak M.; Tang J.; Tang Q.; Tang Q.; Tang X.; Tietzsch A.; Tkachev I.; Tmej T.; Treskov K.; Triossi A.; Troni G.; Trzaska W.; Tuve C.; Ushakov N.; van den Boom J.; van Waasen S.; Vanroyen G.; Vassilopoulos N.; Vedin V.; Verde G.; Vialkov M.; Viaud B.; Vollbrecht M.C.; Volpe C.; Vorobel V.; Voronin D.; Votano L.; Walker P.; Wang C.; Wang C.-H.; Wang E.; Wang G.; Wang J.; Wang J.; Wang K.; Wang L.; Wang M.; Wang M.; Wang M.; Wang R.; Wang S.; Wang W.; Wang W.; Wang W.; Wang X.; Wang X.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Z.; Wang Z.; Wang Z.; Wang Z.; Waqas M.; Watcharangkool A.; Wei L.; Wei W.; Wei W.; Wei Y.; Wen L.; Wiebusch C.; Wong S.C.-F.; Wonsak B.; Wu D.; Wu F.; Wu Q.; Wu Z.; Wurm M.; Wurtz J.; Wysotzki C.; Xi Y.; Xia D.; Xie X.; Xie Y.; Xie Z.; Xing Z.; Xu B.; Xu C.; Xu D.; Xu F.; Xu H.; Xu J.; Xu J.; Xu M.; Xu Y.; Xu Y.; Yan B.; Yan T.; Yan W.; Yan X.; Yan Y.; Yang A.; Yang C.; Yang C.; Yang H.; Yang J.; Yang L.; Yang X.; Yang Y.; Yang Y.; Yao H.; Yasin Z.; Ye J.; Ye M.; Ye Z.; Yegin U.; Yermia F.; Yi P.; Yin N.; Yin X.; You Z.; Yu B.; Yu C.; Yu C.; Yu H.; Yu M.; Yu X.; Yu Z.; Yu Z.; Yuan C.; Yuan Y.; Yuan Z.; Yuan Z.; Yue B.; Zafar N.; Zambanini A.; Zavadskyi V.; Zeng S.; Zeng T.; Zeng Y.; Zhan L.; Zhang A.; Zhang F.; Zhang G.; Zhang H.; Zhang H.; Zhang J.; Zhang J.; Zhang J.; Zhang J.; Zhang J.; Zhang P.; Zhang Q.; Zhang S.; Zhang S.; Zhang T.; Zhang X.; Zhang X.; Zhang X.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Z.; Zhang Z.; Zhao F.; Zhao J.; Zhao R.; Zhao S.; Zhao T.; Zheng D.; Zheng H.; Zheng M.; Zheng Y.; Zhong W.; Zhou J.; Zhou L.; Zhou N.; Zhou S.; Zhou T.; Zhou X.; Zhu J.; Zhu K.; Zhu K.; Zhu Z.; Zhuang B.; Zhuang H.; Zong L.; Zou J.Abusleme, A.; Adam, T.; Ahmad, S.; Ahmed, R.; Aiello, S.; Akram, M.; An, F.; An, Q.; Andronico, G.; Anfimov, N.; Antonelli, V.; Antoshkina, T.; Asavapibhop, B.; de Andre, J. P. A. 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The Confidence Database
Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4âmillion trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects
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