World's Best Scientists 2026 revealed!
F1000Research
H-index 51

F1000Research

2046-1402

Published by: Taylor & Francis

https://f1000research.com/

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Biology and Biochemistry 162 248 395 32
Medicine 513 304 442 34

Additional Metrics

Number of Best Scientists*: 1119
Documents by Best Scientists*: 1453
Top 100 Ranked Scientists*: 34
SCIMAGO H-index: 119
SCIMAGO SJR: 0.537
Impact Factor: N/A

Overview

Top Research Topics at F1000Research?

F1000Research was organized to reinforce research efforts on Open peer review, Neuroscience, Physiology, Immunology and Computational biology. It focuses on research topics in Open peer review within the context of other fields like Anesthesiology, Internal medicine, Artificial intelligence, Open data and Cell biology. Topics in Internal medicine explored in F1000Research were investigated in conjunction with research in Oncology and Cardiology.

It links adjacent topics like Open data with Open science. F1000Research focuses on Neuroscience as well as the interrelated topic of Cognitive psychology. The studies tackled, which mainly focus on Physiology, apply to Alternative medicine as well.

  • Open peer review (42.93%)
  • Neuroscience (22.73%)
  • Physiology (22.00%)

What are the most cited papers published in the journal?

  • Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences (1486 citations)
  • A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor (660 citations)
  • FastQ Screen: A tool for multi-genome mapping and quality control. (393 citations)

Research areas of the most cited articles at F1000Research:

The published papers are organized to reinforce research efforts on Neuroscience, Immunology, Computational biology, Disease and Bioinformatics. The journal publications tackle research in various disciplines, including Neuroscience and Open peer review. In addition to Computational biology research, the published papers aim to explore topics under Open science, Genomics, Bioconductor, Gene and Workflow.

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

  • Internal medicine
  • Gene
  • Disease

The previous edition focused in particular on these issues:

F1000Research primarily focuses on research topics in Internal medicine, Computational biology, Coronavirus disease 2019 (COVID-19), Artificial intelligence and Pandemic. Internal medicine study tackled is connected to the field of Gastroenterology. The work on Coronavirus disease 2019 (COVID-19) presented in the journal focuses on Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in particular.

Discussions in the journal are anchored in the subject of Artificial intelligence and the similar topic of Machine learning.

The most cited articles from the last journal are:

  • Sustainable data analysis with Snakemake. (51 citations)
  • SARS-CoV-2 and the role of airborne transmission: a systematic review (10 citations)
  • Limited containment options of COVID-19 outbreak revealed by regional agent-based simulations for South Africa (10 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 F1000Research (based on the number of publications) are:

  • Adriana Bankston (89 papers) published 18 papers at the last edition, 14 less than at the previous edition,
  • Kaja Abbas (57 papers) absent at the last edition,
  • Iza Ciglenecki (43 papers) absent at the last edition,
  • Kevin J. Black (41 papers) absent at the last edition,
  • Alcides Chaux (39 papers) absent at the last 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 F1000Research (based on the number of publications) are:

  • University of Oxford (111 papers) published 12 papers at the last edition, 9 less than at the previous edition,
  • University College London (108 papers) published 21 papers at the last edition, 1 less than at the previous edition,
  • Harvard University (106 papers) published 7 papers at the last edition, 2 less than at the previous edition,
  • University of Toronto (103 papers) published 8 papers at the last edition, 6 less than at the previous edition,
  • National Institutes of Health (91 papers) published 6 papers at the last edition, 6 less 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, 26.09% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 15.86% were posted by at least one author from the top 10 institutions publishing in the journal. Another 3.20% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.45% of all publications and 66.50% 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.

Education, Training and Key Skills for Researchers

As with any profession, education plays a crucial role in preparing individuals for a career in research. Aspiring researchers often begin with a Bachelor's of Science degree in a field such as biology, chemistry, or similar scientific disiplines. This foundational level of education provides a basic understanding of scientific principles and methodologies, which are essential for conducting sound research. However, in many cases, a higher level of education may be necessary. This is particularly true for those interested in more specialized fields or those who aspire to leadership roles in research. A Master's or Doctoral degree can provide in-depth knowledge in a specific field of research, as well as practical experience through hands-on research projects. Beyond formal education, additional training can further enhance a researcher's skillset. This can take the form of workshops, seminars or continuing education courses on topics such as data analysis, experimental design, or cutting-edge research technologies. Joining professional organizations can also provide opportunities for networking and professional development. One key skill inherent to successful research is the ability to critically analyze and interpret data, while effectively communicating the results and implications to the wider scientific community. Practiced writing and presentation skills are therefore invaluable for any budding researcher. Another important aspect of maintaining a successful research career is staying updated with current advancements in the chosen field. This can be accomplished by regularly attending scientific conferences and meetings, and routinely reading relevant scientific literature. For those seeking to become medical assistants or allied healthcare professionals, specialized training programs are also available. For instance, a useful reference could be the list of the best medical assistant training in Maryland.

Top Publications

  • Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

    Kristen Maynard;Leonardo Collado Torres;Lukas M Weber;Cedric Uytingco

    (2020)
    596 Citations
  • ProteinBERT: A universal deep-learning model of protein sequence and function

    Dan Ofer;Nadav Brandes;Michal Linial;Nadav Rappoport

    (2021)
    473 Citations
  • All disease begins in the (leaky) gut: role of zonulin-mediated gut permeability in the pathogenesis of some chronic inflammatory diseases.

    Alessio Fasano

    (2020)
    348 Citations
  • The impact of the COVID-19 pandemic on self-harm and suicidal behaviour: update of living systematic review

    Ann John;Emily Eyles;Emily Eyles;Roger T Webb;Roger T Webb;Chukwudi Okolie

    (2020)
    185 Citations
  • The impact of the COVID-19 pandemic on self-harm and suicidal behaviour: update of living systematic review [version 2; peer review: 1 approved, 2 approved with reservations]

    Ann John;Emily Eyles;Roger T. Webb;Chukwudi Okolie

    (2020)
    184 Citations
  • Predictors of COVID-19 severity: a systematic review and meta-analysis

    Mudatsir Mudatsir;Jonny Karunia Fajar;Jonny Karunia Fajar;Laksmi Wulandari;Gatot Soegiarto

    (2020)
    166 Citations
  • Recent advances in managing and understanding Stevens-Johnson syndrome and toxic epidermal necrolysis.

    Akito Hasegawa;Riichiro Abe

    (2020)
    130 Citations
  • The essentials of developmental apoptosis.

    Anne K Voss;Anne K Voss;Andreas Strasser;Andreas Strasser

    (2020)
    130 Citations

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