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.
The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes
20151.9k citationsGydo C. P. van Zundert, João Rodrigues et al.Journal of Molecular Biologyprofile →
The HADDOCK web server for data-driven biomolecular docking
20101.1k citationsSjoerd J. de Vries, Marc van Dijk et al.Nature Protocolsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Marc van Dijk'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 Marc van Dijk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc van Dijk more than expected).
This network shows the impact of papers produced by Marc van Dijk. 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 Marc van Dijk. The network helps show where Marc van Dijk may publish in the future.
Co-authorship network of co-authors of Marc van Dijk
This figure shows the co-authorship network connecting the top 25 collaborators of Marc van Dijk.
A scholar is included among the top collaborators of Marc van Dijk 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 Marc van Dijk. Marc van Dijk is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zundert, Gydo C. P. van, João Rodrigues, Mikaël Trellet, et al.. (2015). The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes. Journal of Molecular Biology. 428(4). 720–725.1942 indexed citations breakdown →
Dijk, Marc van, Koen M. Visscher, Panagiotis L. Kastritis, & Alexandre M. J. J. Bonvin. (2013). Solvated protein–DNA docking using HADDOCK. Journal of Biomolecular NMR. 56(1). 51–63.17 indexed citations
Price, David, et al.. (2012). Representing the spatial variability of rainfall for input to the G2G distributed flood forecasting model: operational experience from the Flood Forecasting Centre. NERC Open Research Archive (Natural Environment Research Council).2 indexed citations
13.
Wassenaar, Tsjerk A., Marc van Dijk, Gijs van der Schot, et al.. (2011). WeNMR: structural biology on the grid. HAL (Le Centre pour la Communication Scientifique Directe). 819.4 indexed citations
14.
Vries, Sjoerd J. de, Marc van Dijk, & Alexandre M. J. J. Bonvin. (2010). The HADDOCK web server for data-driven biomolecular docking. Nature Protocols. 5(5). 883–897.1088 indexed citations breakdown →
15.
Dijk, Marc van, et al.. (2010). The Dutch translation of the revised Childhood Health Assessment Questionnaire: a preliminary study of score distribution.. PubMed. 28(2). 275–80.6 indexed citations
16.
Dijk, Marc van & Alexandre M. J. J. Bonvin. (2009). 3D-DART: a DNA structure modelling server. Nucleic Acids Research. 37(Web Server). W235–W239.263 indexed citations
Weerts, Albrecht, Ferdinand Diermanse, Paolo Reggiani, et al.. (2003). Assessing and quantifying the combined effect of model parameter and boundary uncertainties in model based flood forecasting. EGS - AGU - EUG Joint Assembly. 14564.1 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.