Nirwan Sharma

23 papers receiving 301 citations

Peers

Nirwan Sharma
Comparison fields: 5 of 78
  • Ecological Modeling 158
  • Social Psychology 96
  • Ecology, Evolution, Behavior and Systematics 61
  • Computer Science Applications 14
  • Human-Computer Interaction 12
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Kerstin Kremer Germany
Barbara Kieslinger Austria
Daniel Dörler Austria
Yaela N. Golumbic Israel
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Maria Aristeidou United Kingdom
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Citations per year

Countries citing papers authored by Nirwan Sharma

Since Specialization
Citations

This map shows the geographic impact of Nirwan Sharma'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 Nirwan Sharma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nirwan Sharma more than expected).

Fields of papers citing papers by Nirwan Sharma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nirwan Sharma. 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 Nirwan Sharma. The network helps show where Nirwan Sharma may publish in the future.

Co-authors

The 25 scholars most cited alongside Nirwan Sharma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Nirwan Sharma Line = papers co-authored together Nirwan Sharma links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201675
2 201545
3 201938
4 201835
5 201922
6 201620
7
Natural Language Generation for Nature Conservation: Automating Feedback to Help Volunteers Identify Bumblebee Species
201213
8 202012
9 201910
10 20178
11
Capturing mink and data : Interacting with a small and dispersed environmental initiative over the introduction of digital innovation
20137
12 20207
13
Development of a Map-matching Algorithm for Rural Passenger Information Systems via Mobile Phones and Crowd-Sourcing
20126
14 20165
15 20124
16
Designing a Mobile Device for Pre-hospital Care
20132
17 20222
18 20241
19 20231
20 20241

About Nirwan Sharma

Nirwan Sharma is a scholar working on Ecological Modeling, Social Psychology, Artificial Intelligence, Computer Vision and Pattern Recognition and Genetics, having authored 24 papers that have together received 317 indexed citations. Recurring topics across this work include Species Distribution and Climate Change (12 papers), Animal and Plant Science Education (9 papers), Data Visualization and Analytics (5 papers), Natural Language Processing Techniques (4 papers), Insect and Arachnid Ecology and Behavior (4 papers), Plant and animal studies (3 papers), Lexicography and Language Studies (2 papers) and Second Language Acquisition and Learning (2 papers). The work is most often cited by research in Ecological Modeling (158 citations), Social Psychology (96 citations), Ecology, Evolution, Behavior and Systematics (61 citations), Computer Science Applications (14 citations) and Human-Computer Interaction (12 citations). Nirwan Sharma has collaborated with scholars based in United Kingdom, Poland and Sweden. Frequent co-authors include René van der Wal, Advaith Siddharthan, Chris Mellish, H Anderson, Ana Frankenberg‐Garcia, Robert Lew, Jonathan C. Roberts, Richard Comont, Laura Colucci‐Gray and Ben Darvill. Their work appears in journals such as Citizen Science Theory and Practice, Conservation Biology, Scientific Reports, International Journal of Science Education Part B and Journal of Environmental Management.

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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