Marjan Sjerps

1.2k total citations
60 papers, 843 citations indexed

About

Marjan Sjerps is a scholar working on Genetics, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Marjan Sjerps has authored 60 papers receiving a total of 843 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Genetics, 11 papers in Artificial Intelligence and 9 papers in Molecular Biology. Recurrent topics in Marjan Sjerps's work include Forensic and Genetic Research (23 papers), Molecular Biology Techniques and Applications (7 papers) and Forensic Fingerprint Detection Methods (6 papers). Marjan Sjerps is often cited by papers focused on Forensic and Genetic Research (23 papers), Molecular Biology Techniques and Applications (7 papers) and Forensic Fingerprint Detection Methods (6 papers). Marjan Sjerps collaborates with scholars based in Netherlands, United Kingdom and New Zealand. Marjan Sjerps's co-authors include A. Kloosterman, Gabriel Vivó‐Truyols, Martin Lopatka, Charles E.H. Berger, Peter Vergeer, Ronald Meester, Patsy Haccou, Reinoud D. Stoel, Ivo Alberink and Peter J. Schoenmakers and has published in prestigious journals such as Food Chemistry, Biometrics and Journal of Chromatography A.

In The Last Decade

Marjan Sjerps

58 papers receiving 805 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marjan Sjerps Netherlands 17 303 169 159 103 96 60 843
Cédric Neumann United States 18 364 1.2× 191 1.1× 111 0.7× 256 2.5× 35 0.4× 59 913
David A. Stoney United States 14 336 1.1× 111 0.7× 79 0.5× 128 1.2× 16 0.2× 36 752
Anders Nordgaard Sweden 10 173 0.6× 103 0.6× 148 0.9× 52 0.5× 18 0.2× 32 483
Alex Biedermann Switzerland 26 895 3.0× 533 3.2× 356 2.2× 238 2.3× 44 0.5× 112 2.1k
J.A. Lambert United Kingdom 14 613 2.0× 246 1.5× 231 1.5× 130 1.3× 10 0.1× 28 1.2k
Keith Inman United States 13 261 0.9× 45 0.3× 143 0.9× 46 0.4× 14 0.1× 22 554
Simon Baechler Switzerland 13 155 0.5× 40 0.2× 89 0.6× 46 0.4× 20 0.2× 29 461
R. Cook United Kingdom 11 340 1.1× 117 0.7× 89 0.6× 131 1.3× 9 0.1× 17 743
Simon J. Walsh Australia 17 727 2.4× 49 0.3× 505 3.2× 82 0.8× 10 0.1× 65 1.1k
Glenn Langenburg United States 14 287 0.9× 54 0.3× 29 0.2× 206 2.0× 20 0.2× 23 624

Countries citing papers authored by Marjan Sjerps

Since Specialization
Citations

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

Fields of papers citing papers by Marjan Sjerps

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marjan Sjerps

This figure shows the co-authorship network connecting the top 25 collaborators of Marjan Sjerps. A scholar is included among the top collaborators of Marjan Sjerps 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 Marjan Sjerps. Marjan Sjerps is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ramos, Daniel, et al.. (2024). An overview of log likelihood ratio cost in forensic science – Where is it used and what values can we expect?. Forensic Science International Synergy. 8. 100466–100466. 3 indexed citations
2.
Vink, Monique, et al.. (2024). A template Bayesian network for combining forensic evidence on an item with an uncertain relation to the disputed activities. Forensic Science International Synergy. 9. 100546–100546.
3.
Vergeer, Peter, et al.. (2024). From data to a validated score-based LR system: A practitioner’s guide. Forensic Science International. 357. 111994–111994. 4 indexed citations
4.
Vink, Monique & Marjan Sjerps. (2023). A collection of idioms for modeling activity level evaluations in forensic science. Forensic Science International Synergy. 6. 100331–100331. 1 indexed citations
5.
Visser, Richard G. F., et al.. (2022). Evidential value of duct tape comparison using loopbreaking patterns. Forensic Science International. 332. 111178–111178. 7 indexed citations
6.
Sjerps, Marjan, Ivo Alberink, Richard G. F. Visser, & Reinoud D. Stoel. (2022). The evidential strength of a combination of corresponding class features in tire examination. Forensic Science International. 337. 111351–111351. 1 indexed citations
7.
Sjerps, Marjan, et al.. (2019). Combining evidence in complex cases - a practical approach to interdisciplinary casework. Science & Justice. 60(1). 20–29. 10 indexed citations
8.
Meuwly, Didier, et al.. (2017). Performance Study of a Score‐based Likelihood Ratio System for Forensic Fingermark Comparison. Journal of Forensic Sciences. 62(3). 626–640. 40 indexed citations
9.
Sjerps, Marjan, et al.. (2017). Evaluating evidence in linked crimes with multiple offenders. Science & Justice. 57(3). 228–238. 6 indexed citations
10.
Kokshoorn, Bas, et al.. (2016). Cell type determination and association with the DNA donor. Forensic Science International Genetics. 25. 97–111. 13 indexed citations
11.
Curran, James M., et al.. (2015). A probabilistic approach for the interpretation of RNA profiles as cell type evidence. Forensic Science International Genetics. 20. 30–44. 22 indexed citations
12.
Berger, Charles E.H. & Marjan Sjerps. (2015). International Conference on Forensic Inference and Statistics 2014. UvA-DARE (University of Amsterdam). 2015(3). 96–97.
13.
Meester, Ronald, et al.. (2014). Het gebruik van schakelbewijs; juridische en kans-theoretische gezichtspunten. UvA-DARE (University of Amsterdam). 2014(5). 153–167. 1 indexed citations
14.
Kloosterman, A., et al.. (2014). Error rates in forensic DNA analysis: Definition, numbers, impact and communication. Forensic Science International Genetics. 12. 77–85. 66 indexed citations
15.
Sjerps, Marjan, et al.. (2014). Modelling crime linkage with Bayesian networks. Science & Justice. 55(3). 209–217. 21 indexed citations
16.
Haraksim, Rudolf, et al.. (2013). Assignment of the evidential value of a fingermark general pattern using a Bayesian network. UvA-DARE (University of Amsterdam). 212. 1–11. 3 indexed citations
17.
Sjerps, Marjan, et al.. (2009). Anti-doping researchers should conform to certain statistical standards from forensic science. Science & Justice. 49(3). 214–215. 4 indexed citations
18.
Meester, Ronald & Marjan Sjerps. (2008). Why the Effect of Prior Odds Should Accompany the Likelihood Ratio When Reporting DNA Evidence. SSRN Electronic Journal. 1 indexed citations
19.
Meester, Ronald & Marjan Sjerps. (2003). The Evidential Value in the DNA Database Search Controversy and the Two‐Stain Problem. Biometrics. 59(3). 727–732. 29 indexed citations
20.
Sjerps, Marjan, et al.. (1995). A Dutch population study of the STR loci HUMTHO1, HUMFES/FPS, HUMVWA31/1 and HUMF13A1, conducted for forensic purposes. International Journal of Legal Medicine. 108(3). 127–134. 15 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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