Nature Machine Intelligence

Top Research Topics at Nature Machine Intelligence?

Artificial intelligence, Deep learning, Machine learning, Artificial neural network and Pattern recognition are the subjects of interest in Nature Machine Intelligence. The Artificial intelligence study featured in Nature Machine Intelligence draws connections with the study of Computer vision. The work on Deep learning tackled in Nature Machine Intelligence brings together disciplines like Computational biology and Benchmark (computing).

It focuses on Machine learning as well as the interrelated topic of Field (computer science). Studies on Robot discussed in it link to the field of Human–computer interaction.

• Artificial intelligence (56.75%)
• Deep learning (23.69%)
• Machine learning (17.91%)

What are the most cited papers published in the journal?

• Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (1226 citations)
• From Local Explanations to Global Understanding with Explainable AI for Trees. (640 citations)
• The global landscape of AI ethics guidelines (471 citations)

Research areas of the most cited articles at Nature Machine Intelligence:

The most cited publications facilitate discussions on Artificial intelligence, Deep learning, Artificial neural network, Machine learning and Field (computer science). Most of the Artificial intelligence studies addressed in the published papers also intersect with Pattern recognition. The Random forest studies presented in the published papers fall under the field of Machine learning, but they also have connections to other fields such as Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

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

• Artificial intelligence
• Machine learning
• Artificial neural network

The previous edition focused in particular on these issues:

Artificial intelligence, Deep learning, Machine learning, Artificial neural network and Pattern recognition are among the topics commonly tackled in the journal. The journal tackles research in various disciplines, including Artificial intelligence and Perspective (graphical). The journal aims to bridge the gap between the study of Perspective (graphical) and Health care.

The study on Deep learning presented in Nature Machine Intelligence intersects with the topics under Benchmark (computing). Topics in Machine learning explored in it were investigated in conjunction with research in Scalability and Computational model. While it focused on Artificial neural network, it was also able to explore topics like Feature (machine learning) and Computer engineering.

The most cited articles from the last journal are:

• Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans (95 citations)
• Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators (50 citations)
• Inverse design of nanoporous crystalline reticular materials with deep generative models (44 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.

Top authors and change over time

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

• Jorge Goncalves (5 papers) published 3 papers at the last edition, 1 more than at the previous edition,
• Stephen Cave (5 papers) absent at the last edition,
• Gisbert Schneider (5 papers) absent at the last edition,
• Hai-Tao Zhang (4 papers) published 3 papers at the last edition, 2 more than at the previous edition,
• Ye Yuan (4 papers) published 3 papers at the last edition, 2 more 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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

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

• University of Cambridge (19 papers) published 7 papers at the last edition the same number as at the previous edition,
• Harvard University (18 papers) published 11 papers at the last edition, 7 more than at the previous edition,
• ETH Zurich (16 papers) published 7 papers at the last edition, 3 more than at the previous edition,
• Stanford University (14 papers) published 6 papers at the last edition, 1 more than at the previous edition,
• Imperial College London (12 papers) published 4 papers at the last edition the same number as 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, 5.38% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 31.71% were posted by at least one author from the top 10 institutions publishing in the journal. Another 15.45% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.33% of all publications and 32.52% 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 Pathways in Machine Learning and Artificial Intelligence

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