World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
6396
World Ranking
9774
National Ranking
24

Mark Gahegan publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Mark Gahegan sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 140 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Mark Gahegan D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Mark Gahegan sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Mark Gahegan is affiliated with the University of Auckland in New Zealand. Their research spans multiple domains, with a primary focus on computer science and social sciences, involving 15 and 9 publications respectively in these fields. The subfields of their work include molecular biology, geography, planning and development, artificial intelligence, signal processing, and information systems.

The scientist's research topics cover various areas such as geographic information systems studies, data management and algorithms, RNA and protein synthesis mechanisms, RNA research and splicing, RNA modifications and cancer, scientific computing and data management, and semantic web and ontologies.

Mark Gahegan has published papers in a range of venues, notably in arXiv (Cornell University) with three publications, Transactions in GIS with two, and one publication respectively in BMC Bioinformatics, Annals of GIS, and Sensors.

Frequent collaborators include Benjamin Adams, Jidong Zhang, Бо Лю, Zhihan Wang, and Klaus Lehnert, indicating a collaborative research network involving multiple co-authors over successive projects.

The following are some of the recent papers attributed to Mark Gahegan, detailing their titles, publication years, and publication venues:

  • Spatially explicit models for exploring COVID-19 lockdown strategies, 2020, Transactions in GIS
  • DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites, 2022, BMC Bioinformatics
  • Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand, 2021, Sensors
  • GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS, 2025, Annals of GIS
  • A gastric cancer recognition algorithm on gastric pathological sections based on multistage attention-DenseNet, 2021, Concurrency and Computation Practice and Experience

Best Publications

  • Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know

    Alan M. MacEachren;Anthony Robinson;Susan Hopper;Steven Gardner

  • Geospatial Cyberinfrastructure: Past, present and future

    Chaowei Phil Yang;Robert Raskin;Michael F. Goodchild;Mark Gahegan

  • Geovisualization for knowledge construction and decision support

    A.M. MacEachren;M. Gahegan;W. Pike;I. Brewer

  • Visual Semiotics a Uncertainty Visualization: An Empirical Study

    A. M. MacEachren;R. E. Roth;J. O'Brien;B. Li

  • A Typology for Visualizing Uncertainty

    Judi R. Thomson;Elizabeth G. Hetzler;Alan MacEachren;Mark N. Gahegan

  • Biodiversity data should be published, cited, and peer reviewed

    Mark J. Costello;William K. Michener;Mark Gahegan;Zhi-Qiang Zhang

  • GeoVISTA studio: a codeless visual programming environment for geoscientific data analysis and visualization

    Masahiro Takatsuka;Mark Gahegan

  • Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach

    Diansheng Guo;Mark Gahegan;Alan M. MacEachren;Biliang Zhou

  • The Integration of Geographic Visualization with Knowledge Discovery in Databases and Geocomputation

    Mark Gahegan;Monica Wachowicz;Mark Harrower;Theresa-Marie Rhyne

  • Introducing GeoVISTA Studio: an integrated suite of visualization and computational methods for exploration and knowledge construction in geography

    Mark Gahegan;Masahiro Takatsuka;Mike Wheeler;Frank Hardisty

  • A framework for the modelling of uncertainty between remote sensing and geographic information systems

    Mark Gahegan;Manfred Ehlers

  • Proximity operators for qualitative spatial reasoning

    Mark Gahegan

  • On the Application of Inductive Machine Learning Tools to Geographical Analysis

    Mark Gahegan

  • Cybertools and archaeology

    Dean R. Snow;Mark Gahegan;C. Lee Giles;Kenneth G. Hirth

  • ICEAGE: Interactive Clustering and Exploration of Large and High-Dimensional Geodata

    Diansheng Guo;Donna J. Peuquet;Mark Gahegan

  • Four barriers to the development of effective exploratory visualisation tools for the geosciences

    Mark Gahegan

  • Is inductive machine learning just another wild goose (or might it lay the golden egg)

    Mark Gahegan

  • Data structures and algorithms to support interactive spatial analysis using dynamic Voronoi diagrams

    Mark Gahegan;I Lee

  • Beyond ontologies: Toward situated representations of scientific knowledge

    William Pike;Mark Gahegan

  • Beyond Tools: Visual Support for the Entire Process of GIScience

    Mark Gahegan

  • Geospatial Data Mining and Knowledge Discovery

    May Yuan;Barbara P. Buttenfield;M. N. Gahegan;Harvey Miller

Frequent Co-Authors

Alan M. MacEachren
Alan M. MacEachren Pennsylvania State University
Geoff West
Geoff West Curtin University
Gillian Dobbie
Gillian Dobbie University of Auckland
Tim Stockwell
Tim Stockwell University of Victoria
Stefanie Vandevijvere
Stefanie Vandevijvere University of Auckland
Jan M. Lindsay
Jan M. Lindsay University of Auckland
Brendon A. Bradley
Brendon A. Bradley University of Canterbury
Brent Yarnal
Brent Yarnal Pennsylvania State University
Niklaus J. Grünwald
Niklaus J. Grünwald Oregon State University
Yong-Hwan Lee
Yong-Hwan Lee Seoul National University

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