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Tim Wilhelm Nattkemper

Tim Wilhelm Nattkemper

D-Index & Metrics

Computer Science

D-Index
35
Citations
4324
World Ranking
11812
National Ranking
583

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
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
Lutz Krause
Lutz Krause University of Queensland

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