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Nature Methods
H-index 139

Nature Methods

1548-7091

Published by: Springer

https://www.nature.com/nmeth/

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Biology and Biochemistry 7 396 391 104

Additional Metrics

Number of Best Scientists*: 1225
Documents by Best Scientists*: 792
Top 100 Ranked Scientists*: 63
SCIMAGO H-index: 408
SCIMAGO SJR: 17.251
Impact Factor: 32.1

Overview

Top Research Topics at Nature Methods?

Computational biology, Genetics, Cell biology, Biophysics and Bioinformatics are among the topics commonly tackled in Nature Methods. Nature Methods explores topics in Computational biology which can be helpful for research in disciplines like Transcriptome, Gene expression, RNA, Proteomics and Molecular biology. Nature Methods addresses concerns in Proteomics which are intertwined with other disciplines, such as Proteome and Mass spectrometry.

Genome, Gene, Genomics and DNA sequencing are among the concentrations of Genetics that garnered much attention in it. The Cell biology research dealing mostly with Stem cell is the focus of the journal. Some problems in Biophysics that were presented in Nature Methods overlapped with concepts under Fluorescence and Microscopy.

The studies on Microscopy discussed can also contribute to research in the domains of Resolution (electron density) and Fluorescence-lifetime imaging microscopy.

  • Computational biology (25.16%)
  • Genetics (16.91%)
  • Cell biology (13.08%)

What are the most cited papers published in the journal?

  • NIH Image to ImageJ: 25 years of image analysis (30501 citations)
  • Fiji: an open-source platform for biological-image analysis (27838 citations)
  • Fast gapped-read alignment with Bowtie 2 (25423 citations)

Research areas of the most cited articles at Nature Methods:

The most cited publications mostly deal with topics like Genetics, Computational biology, Cell biology, Bioinformatics and Microscopy. The Computational biology research tackled in the journal publications is interrelated with Proteomics which concerns subjects like Proteome. The most cited articles explore topics in Bioinformatics which can be helpful for research in disciplines like Software and Mass spectrometry.

What topics the last edition of the journal is best known for?

  • Gene
  • DNA
  • Enzyme

The previous edition focused in particular on these issues:

The journal investigates studies in Computational biology, Artificial intelligence, Cell, Deep learning and Microscopy. Computational biology research featured in it incorporates concerns from various other topics such as Gene expression profiling, RNA, Transcriptome, Genome and Chromatin. The work on Artificial intelligence tackled in it brings together disciplines like Software, Computer vision and Pattern recognition.

The studies in Microscopy featured incorporate elements of Microscope, Resolution (electron density) and Fluorescence-lifetime imaging microscopy. The Fluorescence-lifetime imaging microscopy study featured falls within the wider field of Optics.

The most cited articles from the last journal are:

  • nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation (223 citations)
  • Cellpose: a generalist algorithm for cellular segmentation (146 citations)
  • Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. (85 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing in Nature Methods (based on the number of publications) are:

  • Vivien Marx (185 papers) published 12 papers at the last edition the same number as at the previous edition,
  • Allison Doerr (185 papers) absent at the last edition,
  • Nicole Rusk (183 papers) absent at the last edition,
  • Michael Eisenstein (154 papers) absent at the last edition,
  • Nina Vogt (129 papers) published 10 papers at the last edition, 13 less than at the previous edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing in Nature Methods (based on the number of publications) are:

  • Harvard University (300 papers) published 22 papers at the last edition, 8 more than at the previous edition,
  • Howard Hughes Medical Institute (223 papers) published 7 papers at the last edition, 8 less than at the previous edition,
  • Max Planck Society (220 papers) published 13 papers at the last edition, 7 less than at the previous edition,
  • Stanford University (206 papers) published 10 papers at the last edition, 7 less than at the previous edition,
  • Massachusetts Institute of Technology (152 papers) published 9 papers at the last edition, 1 more than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 27.44% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 39.90% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.15% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 23.32% of all publications and 32.64% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities in Nature Methods Fields

While the research topics in Nature Methods are fascinating, it's also worth noting the career paths they can lead to. For example, computational biology and bioinformatics are essential skills in the field of medical billing and coding. These professionals use their specialized knowledge to process patient data, code it appropriately, and send it to insurance companies for reimbursement.

If you're interested in combining a love for biology with a practical career, becoming a medical biller and coder could be a great fit. To start, it's important to have a strong foundation in biology and computer science, and then acquire specific training in medical coding systems. For those based in Rhode Island, you can check the average salary for medical biller in rhode island.

In conclusion, the research topics covered in Nature Methods not only contribute to academic knowledge but also open doors for exciting career opportunities in various fields ranging from academia to healthcare and more.

Top Publications

  • Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm.

    Haoyu Cheng;Gregory T. Concepcion;Xiaowen Feng;Haowen Zhang

    (2021)
    3048 Citations
  • DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.

    Vadim Demichev;Vadim Demichev;Christoph B Messner;Spyros I Vernardis;Kathryn S Lilley

    (2020)
    1525 Citations
  • Feature-based molecular networking in the GNPS analysis environment.

    Louis-Félix Nothias;Louis-Félix Nothias;Daniel Petras;Daniel Petras;Robin Schmid;Kai Dührkop

    (2020)
    1453 Citations
  • NicheNet: modeling intercellular communication by linking ligands to target genes

    Robin Browaeys;Wouter Saelens;Yvan Saeys

    (2020)
    1267 Citations
  • Fast and accurate long-read assembly with wtdbg2.

    Jue Ruan;Heng Li;Heng Li

    (2020)
    1187 Citations
  • Single-cell chromatin state analysis with Signac.

    Tim Stuart;Avi Srivastava;Shaista Madad;Caleb A. Lareau

    (2021)
    998 Citations
  • Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices

    Saleh Alseekh;Asaph Aharoni;Yariv Brotman;Kevin Contrepois

    (2021)
    993 Citations
  • Squidpy: a scalable framework for spatial omics analysis

    (2022)
    792 Citations
  • Benchmarking atlas-level data integration in single-cell genomics

    (2021)
    743 Citations
  • Orchestrating Single-Cell Analysis with Bioconductor

    Robert A. Amezquita;Aaron T. L. Lun;Aaron T. L. Lun;Etienne Becht;Vince J. Carey

    (2020)
    698 Citations

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Best Scientists Contributing to This Journal

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