Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Counting Apples and Oranges With Deep Learning: A Data-Driven Approach
2017322 citationsJnaneshwar Das, Vijay Kumar et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Jnaneshwar Das
Since
Specialization
Citations
This map shows the geographic impact of Jnaneshwar Das's research. 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 Jnaneshwar Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jnaneshwar Das more than expected).
This network shows the impact of papers produced by Jnaneshwar Das. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jnaneshwar Das. The network helps show where Jnaneshwar Das may publish in the future.
Co-authorship network of co-authors of Jnaneshwar Das
This figure shows the co-authorship network connecting the top 25 collaborators of Jnaneshwar Das.
A scholar is included among the top collaborators of Jnaneshwar Das based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Jnaneshwar Das. Jnaneshwar Das is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Das, Jnaneshwar, et al.. (2014). Predicting the speed of a Wave Glider autonomous surface vehicle from wave model data. QUT ePrints (Queensland University of Technology).1 indexed citations
10.
Alsabban, Wesam H., et al.. (2013). Persistent robot tasking for environmental monitoring through crowd-sourcing. QUT ePrints (Queensland University of Technology).2 indexed citations
Das, Jnaneshwar, Frédéric Py, Tom O’Reilly, et al.. (2012). Coordinated Sampling of Dynamic Oceanographic Features with AUVs and Drifters. The International Journal of Robotics Research.5 indexed citations
13.
García‐Olaya, Ángel, Frédéric Py, Jnaneshwar Das, & Kanna Rajan. (2012). An On-line Utility based Multi-criteria Approach for Sampling Dynamic Ocean Fields. IEEE Journal of Oceanic Engineering.4 indexed citations
14.
Das, Jnaneshwar, Frédéric Py, Tom O’Reilly, et al.. (2010). Simultaneous Tracking and Sampling of Dynamic Oceanographic Features with AUVs and Drifters.2 indexed citations
15.
Smith, Ryan N., Jnaneshwar Das, Hordur Heidarsson, et al.. (2010). USC CINAPS Builds bridges : observing and monitoring the southern california bight. QUT ePrints (Queensland University of Technology).10 indexed citations
Das, Jnaneshwar, et al.. (2009). Collective Transport of Robots: Emergent Flocking from Minimalist Multi-robot Leader-following.3 indexed citations
18.
Caron, David A., Beth Stauffer, Carl M. Öberg, et al.. (2009). Networked aquatic microbial observing systems: An overview. QUT ePrints (Queensland University of Technology).2 indexed citations
19.
Smith, Ryan N., Beth Stauffer, Jnaneshwar Das, et al.. (2009). Design and implementation of sensor networks for the observation and research of harmful algal blooms in southern California coastal waters. QUT ePrints (Queensland University of Technology). 5–7.2 indexed citations
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.