J. Schümann

1.7k total citations · 1 hit paper
9 papers, 991 citations indexed

About

J. Schümann is a scholar working on Pulmonary and Respiratory Medicine, Radiation and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, J. Schümann has authored 9 papers receiving a total of 991 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Pulmonary and Respiratory Medicine, 8 papers in Radiation and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in J. Schümann's work include Radiation Therapy and Dosimetry (8 papers), Advanced Radiotherapy Techniques (6 papers) and Medical Imaging Techniques and Applications (3 papers). J. Schümann is often cited by papers focused on Radiation Therapy and Dosimetry (8 papers), Advanced Radiotherapy Techniques (6 papers) and Medical Imaging Techniques and Applications (3 papers). J. Schümann collaborates with scholars based in United States. J. Schümann's co-authors include Joseph Perl, Harald Paganetti, Bruce Faddegon, Jae‐Gook Shin, Jungwook Shin, Pyungwon Ko, A. Limosani, Deog Ki Hong and José Ramos‐Méndez and has published in prestigious journals such as Physics in Medicine and Biology, Medical Physics and AIP conference proceedings.

In The Last Decade

J. Schümann

9 papers receiving 982 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

TOPAS: An innovative proton Monte Carlo platform for research and clinical applications

2012 799 citations Joseph Perl, Jae‐Gook Shin et al. Medical Physics profile →

Peers

J. Schümann

PRM

RADIA

RNMI

EEE

MC

E. Paige Abel United States

D. Meer Switzerland

José Ramos‐Méndez United States

J. Lambert Australia

J.M. Bordy France

Zhiyan Xiao United States

Liliana Stolarczyk Poland

Ben Clasie United States

Th. Haberer Germany

S. Psoroulas Switzerland

E. Paige Abel United States

J. Schümann

617 ×0.7

PRM

611 ×0.7

RADIA

258 ×1.2

RNMI

146 ×0.8

EEE

108 ×1.5

MC

Citations per year, relative to J. Schümann J. Schümann (= 1×) peers E. Paige Abel

Countries citing papers authored by J. Schümann

Since Specialization

Citations

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

Fields of papers citing papers by J. Schümann

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Schümann

This figure shows the co-authorship network connecting the top 25 collaborators of J. Schümann. A scholar is included among the top collaborators of J. Schümann based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with J. Schümann. J. Schümann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Perl, Joseph, et al.. (2015). Improved efficiency in Monte Carlo simulation for passive-scattering proton therapy. Physics in Medicine and Biology. 60(13). 5019–5035. 9 indexed citations

2.

Ramos‐Méndez, José, Joseph Perl, J. Schümann, et al.. (2015). A framework for implementation of organ effect models in TOPAS with benchmarks extended to proton therapy. Physics in Medicine and Biology. 60(13). 5037–5052. 6 indexed citations

3.

Schümann, J., et al.. (2013). Experimental validation of the TOPAS Monte Carlo system for passive scattering proton therapy. Medical Physics. 40(12). 100 indexed citations

4.

Perl, Joseph, Jae‐Gook Shin, J. Schümann, Bruce Faddegon, & Harald Paganetti. (2012). TOPAS: An innovative proton Monte Carlo platform for research and clinical applications. Medical Physics. 39(11). 6818–6837. 799 indexed citations breakdown →

5.

Schümann, J., Harald Paganetti, Jae‐Gook Shin, Bruce Faddegon, & Joseph Perl. (2012). Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4. Physics in Medicine and Biology. 57(11). 3281–3293. 43 indexed citations

6.

Shin, Jae‐Gook, Joseph Perl, J. Schümann, Harald Paganetti, & Bruce Faddegon. (2012). A modular method to handle multiple time-dependent quantities in Monte Carlo simulations. Physics in Medicine and Biology. 57(11). 3295–3308. 21 indexed citations

7.

Schümann, J., Joseph Perl, Jungwook Shin, Bruce Faddegon, & Harald Paganetti. (2011). TU‐E‐BRB‐05: Streamlining Monte Carlo Dose Calculations for Routing Clinical Use in Proton Therapy. Medical Physics. 38(6Part29). 3767–3767. 1 indexed citations

8.

Perl, Joseph, et al.. (2011). TU-C-BRB-08: TOPAS: A Fast and Easy to Use Tool for Particle Simulation. Medical Physics. 38(6Part28). 3754–3755. 6 indexed citations

9.

Schümann, J., A. Limosani, Pyungwon Ko, & Deog Ki Hong. (2008). Improved Measurement of Inclusive Radiative B-meson decays. AIP conference proceedings. 342–344. 6 indexed citations

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.

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