World's Best Scientists 2026 revealed!
Tim Wilhelm Nattkemper

Tim Wilhelm Nattkemper

D-Index & Metrics

Computer Science

D-Index
35
Citations
4324
World Ranking
11810
National Ranking
584

Tim Wilhelm Nattkemper 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 Tim Wilhelm Nattkemper 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: 212 publications — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Tim Wilhelm Nattkemper 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 Tim Wilhelm Nattkemper 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Tim Wilhelm Nattkemper is affiliated with Bielefeld University in Germany. Their research primarily focuses on the field of Environmental Science, with significant work within its subfields including Molecular Biology, Ecology, Artificial Intelligence, Mechanical Engineering, and Water Science and Technology.

The main topics addressed by Nattkemper's work are:

  • Coral and Marine Ecosystems Studies
  • Genomics and Phylogenetic Studies
  • Water Quality Monitoring Technologies
  • Diatoms and Algae Research
  • Species Distribution and Climate Change
  • Identification and Quantification in Food
  • Advanced Neural Network Applications

The scientist has contributed to various publication venues, frequently publishing in:

  • Zenodo (CERN European Organization for Nuclear Research)
  • Frontiers in Marine Science
  • PLoS ONE
  • arXiv (Cornell University)
  • Scientific Reports

Recent papers authored or coauthored by Nattkemper include:

  • "The quest for seafloor macrolitter: a critical review of background knowledge, current methods and future prospects" (2020, Environmental Research Letters)
  • "Repositories for Taxonomic Data: Where We Are and What is Missing" (2020, Systematic Biology)
  • "Deep learning-based diatom taxonomy on virtual slides" (2020, Scientific Reports)
  • "A Digital Twin concept for the prescriptive maintenance of protective coating systems on wind turbine structures" (2021, Wind Engineering)
  • "Making marine image data FAIR" (2022, Scientific Data)

Frequent collaborators in their research include:

  • Daniel Langenkämper
  • Martin Zurowietz
  • Bánk Beszteri
  • Michael Kloster
  • Torben Möller

Best Publications

  • Phylogenetic classification of short environmental DNA fragments

    Lutz Krause;Naryttza N. Diaz;Alexander Goesmann;Scott Kelley

  • TACOA: taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach.

    Naryttza N. Diaz;Lutz Krause;Alexander Goesmann;Karsten Niehaus

  • BIIGLE 2.0 - Browsing and Annotating Large Marine Image Collections

    Daniel Langenkämper;Martin Zurowietz;Timm Schoening;Tim Wilhelm Nattkemper

  • Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN.

    Timm Schoening;Melanie Bergmann;Jörg Ontrup;James Taylor

  • Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding

    Jennifer M. Durden;Timm Schoening;Franziska Althaus;Ariell Friedman

  • A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections

    T.W. Nattkemper;H.J. Ritter;W. Schubert

  • MeltDB 2.0–advances of the metabolomics software system

    Nikolas Kessler;Heiko Neuweger;Anja Bonte;Georg Langenkämper

  • An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data

    T. Twellmann;O. Lichte;T.W. Nattkemper

  • Use of machine-learning algorithms for the automated detection of cold-water coral habitats: a pilot study

    Autun Purser;Melanie Bergmann;Tomas Lundälv;Jörg Ontrup

  • Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods

    Tim W. Nattkemper;Bert Arnrich;Oliver Lichte;Wiebke Timm

  • A neural network architecture for automatic segmentation of fluorescence micrographs

    Tim Wilhelm Nattkemper;Heiko Wersing;Walter Schubert;Helge Ritter

  • Human vs. machine: evaluation of fluorescence micrographs

    Tim Wilhelm Nattkemper;Thorsten Twellmann;Helge Ritter;Walter Schubert;Walter Schubert

  • Repositories for Taxonomic Data: Where We Are and What is Missing.

    Aurélien Miralles;Aurélien Miralles;Teddy Bruy;Teddy Bruy;Katherine Wolcott;Katherine Wolcott;Mark D Scherz

  • GISMO--gene identification using a support vector machine for ORF classification

    Lutz Krause;Alice C. McHardy;Tim Wilhelm Nattkemper;Alfred Pühler

  • Learning to Classify Organic and Conventional Wheat - A Machine Learning Driven Approach Using the MeltDB 2.0 Metabolomics Analysis Platform.

    Nikolas Kessler;Anja Bonte;Stefan Albaum;Paul Mäder

  • Detection of suspicious lesions in dynamic contrast enhanced MRI data

    T. Twellmann;A. Saalbach;C. Muller;T.W. Nattkemper

  • Tumor feature visualization with unsupervised learning.

    Tim Wilhelm Nattkemper;A Wismuller

  • Current and future trends in marine image annotation software

    Jose Nuno Gomes-Pereira;Vincent Auger;Kolja Beisiegel;Robert Benjamin

  • Automatic segmentation of digital micrographs: a survey.

    Tim W. Nattkemper

  • An in situ probe for on-line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning.

    Ning Wei;Jia You;Karl Friehs;Erwin Flaschel

  • Libraries of synthetic stationary-phase and stress promoters as a tool for fine-tuning of expression of recombinant proteins in Escherichia coli.

    Gerhard Miksch;Frank Bettenworth;Karl Friehs;Erwin Flaschel

  • MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration

    Martin Zurowietz;Daniel Langenkämper;Brett Hosking;Henry A. Ruhl;Henry A. Ruhl

  • Breast MRI data analysis by LLE

    C. Varini;T.W. Nattkemper;A. Degenhard;A. Wismuller

Frequent Co-Authors

Helge Ritter
Helge Ritter Bielefeld University
Melanie Bergmann
Melanie Bergmann Alfred Wegener Institute for Polar and Marine Research
Karsten Niehaus
Karsten Niehaus Bielefeld University
Alexander Goesmann
Alexander Goesmann University of Giessen
Martin O. Leach
Martin O. Leach Royal Marsden NHS Foundation Trust
Nasir M. Rajpoot
Nasir M. Rajpoot University of Warwick
Axel Wismüller
Axel Wismüller University of Rochester
Anke Becker
Anke Becker Philipp University of Marburg
Daniel O.B. Jones
Daniel O.B. Jones National Oceanography Centre
Henry A. Ruhl
Henry A. Ruhl Monterey Bay Aquarium Research Institute

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