Rolf J.F. Ypma

1.3k total citations
28 papers, 824 citations indexed

About

Rolf J.F. Ypma is a scholar working on Artificial Intelligence, Genetics and Epidemiology. According to data from OpenAlex, Rolf J.F. Ypma has authored 28 papers receiving a total of 824 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 9 papers in Genetics and 7 papers in Epidemiology. Recurrent topics in Rolf J.F. Ypma's work include Forensic and Genetic Research (5 papers), Neural dynamics and brain function (3 papers) and Influenza Virus Research Studies (3 papers). Rolf J.F. Ypma is often cited by papers focused on Forensic and Genetic Research (5 papers), Neural dynamics and brain function (3 papers) and Influenza Virus Research Studies (3 papers). Rolf J.F. Ypma collaborates with scholars based in Netherlands, United Kingdom and United States. Rolf J.F. Ypma's co-authors include W. Marijn van Ballegooijen, Jacco Wallinga, Edward T. Bullmore, Mikail Rubinov, Charles Watson, Arjan Stegeman, Arnaud Bataille, John Suckling, G. Koch and Michael G. Hart and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and The American Naturalist.

In The Last Decade

Rolf J.F. Ypma

25 papers receiving 811 citations

Peers

Rolf J.F. Ypma

EPIDE

GENET

Scott P. Layne United States

Xiaolong Zhang China

Theodore Kypraios United Kingdom

Eugene Skepner United States

Ronald E. Blanton United States

Philip A. Eckhoff United States

Anita Schmid United States

Laurent Hébert‐Dufresne United States

Lauren M. Childs United States

Thomas Fletcher United States

Scott P. Layne United States

Rolf J.F. Ypma

108 ×0.4

163 ×0.9

EPIDE

54 ×0.3

GENET

322 ×2.2

183 ×1.5

Citations per year, relative to Rolf J.F. Ypma Rolf J.F. Ypma (= 1×) peers Scott P. Layne

Countries citing papers authored by Rolf J.F. Ypma

Since Specialization

Citations

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

Fields of papers citing papers by Rolf J.F. Ypma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rolf J.F. Ypma

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

All Works

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20 of 20 papers shown

Durán, Juan M., et al.. (2024). From understanding to justifying: Computational reliabilism for AI-based forensic evidence evaluation. Forensic Science International Synergy. 9. 100554–100554.

Ypma, Rolf J.F., et al.. (2024). Fusing linguistic and acoustic information for automated forensic speaker comparison. Science & Justice. 64(5). 485–497. 2 indexed citations

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

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

Bleka, Øyvind, et al.. (2024). ‘Low’ LRs obtained from DNA mixtures: On calibration and discrimination performance of probabilistic genotyping software. Forensic Science International Genetics. 73. 103099–103099. 2 indexed citations

Benschop, Corina C.G., et al.. (2022). Development and validation of a fast and automated DNA identification line. Forensic Science International Genetics. 60. 102738–102738. 6 indexed citations

Ypma, Rolf J.F., et al.. (2022). Objectifying evidence evaluation for gunshot residue comparisons using machine learning on criminal case data. Forensic Science International. 335. 111293–111293. 8 indexed citations

Morrison, Geoffrey Stewart, Ewald Enzinger, Vincent Hughes, et al.. (2021). Consensus on validation of forensic voice comparison. Science & Justice. 61(3). 299–309. 47 indexed citations

Ypma, Rolf J.F., et al.. (2021). Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles. Forensic Science International Genetics. 56. 102632–102632. 17 indexed citations

10.

Ypma, Rolf J.F., et al.. (2021). Calculating LRs for presence of body fluids from mRNA assay data in mixtures. Forensic Science International Genetics. 52. 102455–102455. 15 indexed citations

11.

Vergeer, Peter, Ivo Alberink, Marjan Sjerps, & Rolf J.F. Ypma. (2020). Why calibrating LR-systems is best practice. A reaction to “The evaluation of evidence for microspectrophotometry data using functional data analysis”, in FSI 305. Forensic Science International. 314. 110388–110388. 11 indexed citations

12.

Benschop, Corina C.G., et al.. (2019). Automated estimation of the number of contributors in autosomal short tandem repeat profiles using a machine learning approach. Forensic Science International Genetics. 43. 102150–102150. 25 indexed citations

13.

Ypma, Rolf J.F., Rachel Moseley, Rosemary Holt, et al.. (2016). Default Mode Hypoconnectivity Underlies a Sex-Related Autism Spectrum. Biological Psychiatry Cognitive Neuroscience and Neuroimaging. 1(4). 364–371. 58 indexed citations

14.

Donker, Tjibbe, Thijs Bosch, Rolf J.F. Ypma, et al.. (2016). Monitoring the spread of meticillin-resistant Staphylococcus aureus in The Netherlands from a reference laboratory perspective. Journal of Hospital Infection. 93(4). 366–374. 9 indexed citations

15.

Ypma, Rolf J.F. & Edward T. Bullmore. (2016). Statistical Analysis of Tract-Tracing Experiments Demonstrates a Dense, Complex Cortical Network in the Mouse. PLoS Computational Biology. 12(9). e1005104–e1005104. 25 indexed citations

16.

Ypma, Rolf J.F., Tjibbe Donker, W. Marijn van Ballegooijen, & Jacco Wallinga. (2013). Finding Evidence for Local Transmission of Contagious Disease in Molecular Epidemiological Datasets. PLoS ONE. 8(7). e69875–e69875. 11 indexed citations

17.

Enserink, Remko, Rolf J.F. Ypma, Gé Donker, Henriëtte A. Smit, & Wilfrid van Pelt. (2013). Infectious Disease Burden Related to Child Day Care in the Netherlands. The Pediatric Infectious Disease Journal. 32(8). e334–e340. 37 indexed citations

18.

Ypma, Rolf J.F., et al.. (2013). Viral Substitution Rate Variation Can Arise from the Interplay between Within-Host and Epidemiological Dynamics. The American Naturalist. 182(4). 494–513. 18 indexed citations

19.

Ypma, Rolf J.F., Hester Korthals Altes, Dick van Soolingen, Jacco Wallinga, & W. Marijn van Ballegooijen. (2013). A Sign of Superspreading in Tuberculosis. Epidemiology. 24(3). 395–400. 45 indexed citations

20.

Ypma, Rolf J.F., Marcel Jonges, Arnaud Bataille, et al.. (2012). Genetic Data Provide Evidence for Wind-Mediated Transmission of Highly Pathogenic Avian Influenza. The Journal of Infectious Diseases. 207(5). 730–735. 54 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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