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Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format
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Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format Example of Nature format
Sample paper formatted on SciSpace - SciSpace
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open access Open Access ISSN: 280836 e-ISSN: 14764687
recommended Recommended

Nature — Template for authors

Publisher: Nature
Categories Rank Trend in last 3 yrs
Multidisciplinary #1 of 110 -
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 4773 Published Papers | 271357 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 08/07/2020
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Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

42.778

1% from 2018

Impact factor for Nature from 2016 - 2019
Year Value
2019 42.778
2018 43.07
2017 41.577
2016 40.137
graph view Graph view
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insights Insights

  • Impact factor of this journal has decreased by 1% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

56.9

12% from 2019

CiteRatio for Nature from 2016 - 2020
Year Value
2020 56.9
2019 51.0
2018 55.7
2017 53.7
2016 49.2
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table view Table view

insights Insights

  • CiteRatio of this journal has increased by 12% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

15.993

14% from 2019

SJR for Nature from 2016 - 2020
Year Value
2020 15.993
2019 14.047
2018 16.345
2017 17.875
2016 18.389
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 14% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

9.249

5% from 2019

SNIP for Nature from 2016 - 2020
Year Value
2020 9.249
2019 8.82
2018 9.448
2017 8.647
2016 7.901
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has increased by 5% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

open access Open Access e-ISSN: 23648228

Springer

CiteRatio: 2.3 | SJR: 0.407 | SNIP: 0.889
open access Open Access ISSN: 19326203
recommended Recommended

PLOS

CiteRatio: 5.3 | SJR: 0.99 | SNIP: 1.349
open access Open Access ISSN: 17419174 e-ISSN: 17419182

Inderscience Publishers

CiteRatio: 3.3 | SJR: 0.44 | SNIP: 1.047
open access Open Access ISSN: 20452322
recommended Recommended

Nature

Impact factor: 4.379 | CiteRatio: 7.1 | SJR: 1.24 | SNIP: 1.377
Nature

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Nature

Nature

Nature is a weekly international journal publishing the finest peer-reviewed research in all fields of science and technology on the basis of its originality, importance, interdisciplinary interest, timeliness, accessibility, elegance and surprising conclusions. Nature also pr...... Read More

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Bibliography Name
Naturemag Citation
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Citation Type
Numbered (Superscripted)
25
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Bibliography Example
Beenakker, C. W. J. Specular andreev reflection in graphene. Phys. Rev. Lett. 97, 067007 (2006). URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Journal Article DOI: 10.1038/227680A0
Cleavage of Structural Proteins during the Assembly of the Head of Bacteriophage T4
Ulrich K. Laemmli1
15 Aug 1970 - Nature

Abstract:

Using an improved method of gel electrophoresis, many hitherto unknown proteins have been found in bacteriophage T4 and some of these have been identified with specific gene products. Four major components of the head are cleaved during the process of assembly, apparently after the precursor proteins have assembled into some ... Using an improved method of gel electrophoresis, many hitherto unknown proteins have been found in bacteriophage T4 and some of these have been identified with specific gene products. Four major components of the head are cleaved during the process of assembly, apparently after the precursor proteins have assembled into some large intermediate structure. read more read less

Topics:

Bacteriophage T5 (61%)61% related to the paper, Head morphogenesis (59%)59% related to the paper, Gap junction assembly (57%)57% related to the paper, Viral Tail Proteins (56%)56% related to the paper, Bacteriophage phi 6 (56%)56% related to the paper
229,303 Citations
open accessOpen access Journal Article
Cleavage of structural proteins during the assemble of the head of bacterio-phage T4
01 Jan 1970 - Nature

Abstract:

Using an improved method of gel electrophoresis, many hitherto unknown proteins have been found in bacteriophage T4 and some of these have been identified with specific gene products. Four major components of the head are cleaved during the process of assembly, apparently after the precursor proteins have assembled into some ... Using an improved method of gel electrophoresis, many hitherto unknown proteins have been found in bacteriophage T4 and some of these have been identified with specific gene products. Four major components of the head are cleaved during the process of assembly, apparently after the precursor proteins have assembled into some large intermediate structure. read more read less

Topics:

Cleavage (embryo) (57%)57% related to the paper
203,017 Citations
Journal Article DOI: 10.1038/354056A0
Helical microtubules of graphitic carbon
Sumio Iijima1
NEC1
01 Nov 1991 - Nature

Abstract:

THE synthesis of molecular carbon structures in the form of C60 and other fullerenes1 has stimulated intense interest in the structures accessible to graphitic carbon sheets. Here I report the preparation of a new type of finite carbon structure consisting of needle-like tubes. Produced using an arc-discharge evaporation meth... THE synthesis of molecular carbon structures in the form of C60 and other fullerenes1 has stimulated intense interest in the structures accessible to graphitic carbon sheets. Here I report the preparation of a new type of finite carbon structure consisting of needle-like tubes. Produced using an arc-discharge evaporation method similar to that used for fullerene synthesis, the needles grow at the negative end of the electrode used for the arc discharge. Electron microscopy reveals that each needle comprises coaxial tubes of graphitic sheets, ranging in number from 2 up to about 50. On each tube the carbon-atom hexagons are arranged in a helical fashion about the needle axis. The helical pitch varies from needle to needle and from tube to tube within a single needle. It appears that this helical structure may aid the growth process. The formation of these needles, ranging from a few to a few tens of nanometres in diameter, suggests that engineering of carbon structures should be possible on scales considerably greater than those relevant to the fullerenes. On 7 November 1991, Sumio Iijima announced in Nature the preparation of nanometre-size, needle-like tubes of carbon — now familiar as 'nanotubes'. Used in microelectronic circuitry and microscopy, and as a tool to test quantum mechanics and model biological systems, nanotubes seem to have unlimited potential. read more read less

Topics:

Single-Walled Nanotube (57%)57% related to the paper, Colossal carbon tube (56%)56% related to the paper, Carbon nanotube (56%)56% related to the paper, Mechanical properties of carbon nanotubes (56%)56% related to the paper, Carbon nanofiber (55%)55% related to the paper
36,871 Citations
Journal Article DOI: 10.1038/30918
Collective dynamics of small-world networks
Duncan J. Watts1, Steven H. Strogatz1
04 Jun 1998 - Nature

Abstract:

Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random.... Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices. read more read less

Topics:

Complex network (67%)67% related to the paper, Evolving networks (65%)65% related to the paper, Network motif (62%)62% related to the paper, Synchronization networks (61%)61% related to the paper, Barabási–Albert model (58%)58% related to the paper
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35,972 Citations
Journal Article DOI: 10.1038/NATURE14539
Deep learning
Yann LeCun1, Yann LeCun2, Yoshua Bengio3, Geoffrey E. Hinton4, Geoffrey E. Hinton5
28 May 2015 - Nature

Abstract:

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug di... Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. read more read less

Topics:

Deep learning (59%)59% related to the paper, Object detection (53%)53% related to the paper, Cognitive neuroscience of visual object recognition (51%)51% related to the paper, Abstraction (linguistics) (50%)50% related to the paper
33,931 Citations