Computers and Geosciences

Top Research Topics at Computers & Geosciences?

Computers & Geosciences focuses on Algorithm, Artificial intelligence, Data processing, Data mining and Software. Algorithm study tackled is connected to the field of Fortran. While it focused on Artificial intelligence, it was also able to explore topics like Machine learning, Computer vision and Pattern recognition.

Discussions in Computers & Geosciences are anchored in the subject of Data processing and the similar topic of Geographic information system.

• Algorithm (14.94%)
• Artificial intelligence (9.77%)
• Data processing (9.51%)

What are the most cited papers published in the journal?

• FCM: The fuzzy c-means clustering algorithm (3903 citations)
• CONISS: a FORTRAN 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares (2259 citations)
• SUPCRT92: a software package for calculating the standard molal thermodynamic properties of minerals, gases, aqueous species, and reactions from 1 to 5000 bar and 0 to 1000 ° C (2086 citations)

Research areas of the most cited articles at Computers & Geosciences:

The published articles primarily focus on research topics in Algorithm, Data processing, Data mining, Artificial intelligence and Mineralogy. While work presented in the most cited papers provide substantial information on Algorithm, it also covers topics in Statistics and Fortran. The works on Data processing tackled in the most cited articles bring together disciplines like Software and Geographic information system.

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

• Artificial intelligence
• Statistics
• Operating system

The previous edition focused in particular on these issues:

The discussions in the journal mainly cover the fields of Artificial intelligence, Mineralogy, Reinforcement learning, Event (relativity) and Joint (geology). It facilitated discussions that integrated Mineralogy and Disequilibrium. It tackles research in Event (relativity) and various other disciplines, including Deep learning, Microseism and Seismology.

The most cited articles from the last journal are:

• MudrockNet: Semantic segmentation of mudrock SEM images through deep learning (1 citations)
• Updating geostatistically simulated models of mineral deposits in real-time with incoming new information using actor-critic reinforcement learning (0 citations)
• Joint event location and velocity model update in real-time for downhole microseismic monitoring: A deep learning approach (0 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 Computers & Geosciences (based on the number of publications) are:

• Eulogio Pardo-Igúzquiza (30 papers) absent at the last edition,
• John C. Butler (28 papers) absent at the last edition,
• Clayton V. Deutsch (26 papers) absent at the last edition,
• Gordon R. J. Cooper (25 papers) absent at the last edition,
• Qiuming Cheng (23 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.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Computers & Geosciences (based on the number of publications) are:

• Chinese Academy of Sciences (101 papers) absent at the last edition,
• United States Geological Survey (94 papers) absent at the last edition,
• China University of Geosciences (Wuhan) (79 papers) absent at the last edition,
• Geological Survey of Canada (77 papers) absent at the last edition,
• Stanford University (70 papers) absent at the last 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 2022 edition, 60.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 0.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 50.00% of all publications and 50.00% 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.