Daniel Nolte

523 citations
15 papers · 402 · h-index 11

Impact in

Papers in

Daniel Nolte

15 papers receiving 398 citations

Peers

Daniel Nolte
Comparison fields: 5 of 43
  • Complementary and Manual Therapy 10
  • Biomedical Engineering 169
  • Surgery 167
  • Epidemiology 79
  • Orthopedics and Sports Medicine 20
Replace Cheng‐Chung Lin with:
Cheng‐Chung Lin Taiwan
Martina Barzan Australia
Jan Van Houcke Belgium
Kanchana Rathnayaka Australia
Joy C. Vroemen Netherlands
Ziyun Ding United Kingdom
Jacqui Hislop-Jambrich Australia
Toshiyuki Kataoka Japan
Sven Herrmann Germany
Ph. Neyret France
Daniel Nolte relative to Cheng‐Chung Lin Taiwan Cheng‐Chung Lin's profile →
Citations per field
00.5×2×4×6×7.2×
Cheng‐Chung Lin · 1×
Citations per year

Countries citing papers authored by Daniel Nolte

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Nolte

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Daniel Nolte, 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 Daniel Nolte Line = papers co-authored together Daniel Nolte links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 201069
2 201763
3 201549
4 201636
5 201832
6 201929
7 202028
8 201926
9 201921
10 201716
11 201914
12 20239
13 20158
14 20131
15 20151

About Daniel Nolte

Daniel Nolte is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering, Surgery, Complementary and Manual Therapy and Epidemiology, having authored 15 papers that have together received 402 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (5 papers), Muscle activation and electromyography studies (4 papers), Anatomy and Medical Technology (4 papers), Shoulder Injury and Treatment (4 papers), Knee injuries and reconstruction techniques (2 papers), Bone fractures and treatments (2 papers), Lower Extremity Biomechanics and Pathologies (2 papers) and Cloud Computing and Resource Management (1 paper). The work is most often cited by research in Complementary and Manual Therapy (10 citations), Biomedical Engineering (169 citations), Surgery (167 citations), Epidemiology (79 citations) and Orthopedics and Sports Medicine (20 citations). Daniel Nolte has collaborated with scholars based in United Kingdom, Germany and New Zealand. Frequent co-authors include Anthony MJ Bull, Angela E. Kedgley, Ziyun Ding, Frank Niemeyer, L. Claes, Ulrich Simon, Christian Klemt, Peter Reilly, Michael Damsgaard and Mark de Zee. Their work appears in journals such as Clinical Biomechanics, Journal of Biomechanical Engineering, IEEE Internet Computing, BioMedical Engineering OnLine and Journal of Biomechanics.

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.

Explore authors with similar magnitude of impact