Scientific Programming

2.6k papers and 21.0k indexed citations

About

The 2.6k papers published in Scientific Programming in the last decades have received a total of 21.0k indexed citations. Papers published in Scientific Programming usually cover Artificial Intelligence (646 papers), Computer Networks and Communications (553 papers) and Information Systems (537 papers) specifically the topics of Parallel Computing and Optimization Techniques (273 papers), Distributed and Parallel Computing Systems (235 papers) and Cloud Computing and Resource Management (132 papers). The most active scholars publishing in Scientific Programming are Travis Hirschi, Michael R. Gottfredson, Yong Ding, Xiangyang Kong, Zhong Lin Wang, Rusen Yang, Aytuğ Onan, Rajkumar Buyya, Jia Yu and Robert Griesemer.

In The Last Decade

Scientific Programming

2.1k papers receiving 15.2k citations

Fields of papers published in Scientific Programming

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Scientific Programming. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Scientific Programming.

Countries where authors publish in Scientific Programming

Since Specialization
Citations

This map shows the geographic impact of research published in Scientific Programming. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Scientific Programming with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scientific Programming more than expected).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

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