World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
6595
World Ranking
13878
National Ranking
890

John Darlington 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 John Darlington 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 139 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

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

John Darlington 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 John Darlington sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 30 D-Index — 3rd percentile

3% of scientists in this discipline score the same or lower.

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

Overview

John Darlington is affiliated with Imperial College London in the United Kingdom and specializes in social sciences with a focus on interdisciplinary subfields including sociology and political science, communication, epidemiology, economics and econometrics, and health.

Their research spans multiple topics, particularly in the areas of misinformation and its impacts, public relations and crisis communication, data-driven disease surveillance, vaccine coverage and hesitancy, media studies and communication, housing market and economics, and economic and environmental valuation.

John Darlington has collaborated frequently with several coauthors, including Niko Yiannakoulias, Catherine E. Slavik, Charlotte Buttle, Shelby L. Sturrock, and Nikolaos Yiannakoulias.

The scientist's publications appear in diverse venues such as the Journal of Medical Internet Research, Health & Place, International Journal of Disaster Risk Science, Social Science & Medicine, and JMIR Infodemiology.

Their recent scholarly papers include:

  • Experimental Evidence for Coverage Preferences in Flood Insurance, 2022, International Journal of Disaster Risk Science
  • Examining Tweet Content and Engagement of Canadian Public Health Agencies and Decision Makers During COVID-19: Mixed Methods Analysis, 2021, Journal of Medical Internet Research
  • Has public health messaging during the COVID-19 pandemic reflected local risks to health?: A content analysis of tweeting practices across Canadian geographies, 2021, Health & Place
  • Open government data, uncertainty and coronavirus: An infodemiological case study, 2020, Social Science & Medicine
  • Negative COVID-19 Vaccine Information on Twitter: Content Analysis, 2022, JMIR Infodemiology

John Darlington has also contributed to book publications, including one titled Amongst the Ruins, published by Yale University Press in 2023.

Best Publications

  • A Transformation System for Developing Recursive Programs

    R. M. Burstall;John Darlington

  • Parallel Programming Using Skeleton Functions

    John Darlington;A. J. Field;Peter G. Harrison;Paul H. J. Kelly

  • A system which automatically improves programs

    J. Darlington;R. M. Burstall

  • ALICE a multi-processor reduction machine for the parallel evaluation CF applicative languages

    John Darlington;Mike Reeve

  • Co-creation and user innovation

    Thierry Rayna;Ludmila Striukova;John Darlington

  • A synthesis of several sorting algorithms

    John Darlington

  • An experimental program transformation and synthesis system

    John Darlington

  • Functional Skeletons for Parallel Coordination

    John Darlington;Yike Guo;Hing Wing To;Jin Yang

  • ICENI: An Open Grid Service Architecture Implemented with Jini

    Nathalie Furmento;William Lee;Anthony Mayer;Steven Newhouse

  • A Semantic Similarity Measure for Semantic Web Services

    Jeffrey Hau;William Lee;John Darlington

  • Parallel skeletons for structured composition

    John Darlington;Yi-ke Guo;Hing Wing To;Jin Yang

  • An Architecture for Distributed Enterprise Data Mining

    Jaturon Chattratichat;John Darlington;Yike Guo;S. Hedvall

  • Some transformations for developing recursive programs

    R. M. Burstall;John Darlington

  • Algorithm classification through synthesis

    Keith L. Clark;John Darlington

  • Unlocking the potential of public sector information with semantic web technology

    Harith Alani;David Dupplaw;John Sheridan;Kieron O'Hara

  • ICENI: optimisation of component applications within a Grid environment

    Nathalie Furmento;Anthony Mayer;Stephen McGough;Steven Newhouse

  • Workflow Enactment in ICENI

    Stephen McGough;Laurie Young;Ali Afzal;Steven Newhouse

  • Scheduling Architecture and Algorithms within the ICENI Grid Middleware

    Laurie Young Stephen McGough;Steven Newhouse;John Darlington

  • Brief An algorithm for constrained nonlinear optimization under uncertainty

    J. Darlington;C. C. Pantelides;B. Rustem;B. A. Tanyi

  • The Unification of Functional and Logic Languages.

    John Darlington;A. J. Field;Helen Pull

  • Structured parallel programming

    J. Darlington;M. Ghanem;H.W. To

  • Capacity planning and scheduling in Grid computing environments

    Ali Afzal;A. Stephen McGough;John Darlington

Frequent Co-Authors

Yike Guo
Yike Guo Hong Kong Baptist University
William E. Lee
William E. Lee Imperial College London
Nigel Shadbolt
Nigel Shadbolt University of Oxford
Berç Rustem
Berç Rustem Imperial College London
Constantinos C. Pantelides
Constantinos C. Pantelides Imperial College London
Paul J. Valdes
Paul J. Valdes University of Bristol
Robert Marsh
Robert Marsh University of Southampton
Neil R. Edwards
Neil R. Edwards The Open University
Timothy M. Lenton
Timothy M. Lenton University of Exeter
John W. Polak
John W. Polak Imperial College London

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