Gerhard Mayer

15.8k total citations · 1 hit paper
15 papers, 3.5k citations indexed

About

Gerhard Mayer is a scholar working on Molecular Biology, Spectroscopy and Information Systems and Management. According to data from OpenAlex, Gerhard Mayer has authored 15 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 12 papers in Spectroscopy and 1 paper in Information Systems and Management. Recurrent topics in Gerhard Mayer's work include Advanced Proteomics Techniques and Applications (11 papers), Metabolomics and Mass Spectrometry Studies (6 papers) and Mass Spectrometry Techniques and Applications (6 papers). Gerhard Mayer is often cited by papers focused on Advanced Proteomics Techniques and Applications (11 papers), Metabolomics and Mass Spectrometry Studies (6 papers) and Mass Spectrometry Techniques and Applications (6 papers). Gerhard Mayer collaborates with scholars based in Germany, United Kingdom and United States. Gerhard Mayer's co-authors include Juan Antonio Vizcaíno, Henning Hermjakob, Florian Reisinger, Yasset Pérez‐Riverol, Johannes Griss, Tobias Ternent, Rui Wang, José A. Dianes, Ilias Lavidas and Attila Csordás and has published in prestigious journals such as Nucleic Acids Research, Analytical Chemistry and Molecular & Cellular Proteomics.

In The Last Decade

Gerhard Mayer

14 papers receiving 3.5k 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.

2016 update of the PRIDE database and its related tools

2015 3.0k citations Juan Antonio Vizcaíno, Attila Csordás et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gerhard Mayer Germany	12	2.4k	835	304	291	290	15	3.5k
Noemí del‐Toro United Kingdom	13	2.5k 1.0×	641 0.8×	333 1.1×	327 1.1×	309 1.1×	17	3.7k
Tobias Ternent United Kingdom	8	2.6k 1.1×	731 0.9×	369 1.2×	359 1.2×	348 1.2×	9	3.9k
Gregory L. Finney United States	7	2.7k 1.1×	1.4k 1.7×	256 0.8×	245 0.8×	198 0.7×	7	3.9k
Yunping Zhu China	28	2.5k 1.1×	712 0.9×	402 1.3×	321 1.1×	230 0.8×	112	4.1k
Antonio Fabregat United Kingdom	10	1.8k 0.8×	453 0.5×	334 1.1×	186 0.6×	220 0.8×	15	2.9k
Mathias Walzer Germany	13	3.2k 1.3×	796 1.0×	518 1.7×	467 1.6×	316 1.1×	20	4.9k
Nicholas Shulman United States	14	3.1k 1.3×	1.5k 1.8×	282 0.9×	316 1.1×	241 0.8×	21	4.6k
David García‐Seisdedos Spain	10	2.6k 1.1×	431 0.5×	441 1.5×	439 1.5×	297 1.0×	16	4.2k
Songfeng Wu China	19	1.7k 0.7×	517 0.6×	246 0.8×	252 0.9×	210 0.7×	50	2.8k
Jeffrey C. Silva United States	23	3.2k 1.4×	1.4k 1.6×	276 0.9×	308 1.1×	305 1.1×	32	4.5k

Countries citing papers authored by Gerhard Mayer

Since Specialization

Citations

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

Fields of papers citing papers by Gerhard Mayer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gerhard Mayer

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

All Works

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15 of 15 papers shown

Mayer, Gerhard, et al.. (2023). Trends in Aeronautical Flight Test Telemetry. Journal of Aerospace Sciences and Technologies. 252–258.

Hoffmann, Nils, Gerhard Mayer, Canan Has, et al.. (2022). A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics. Metabolites. 12(7). 584–584. 17 indexed citations

Hoffmann, Nils, Timo Sachsenberg, Jürgen Hartler, et al.. (2019). mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics. Analytical Chemistry. 91(5). 3302–3310. 41 indexed citations

Vizcaíno, Juan Antonio, Gerhard Mayer, Simon Perkins, et al.. (2017). The mzIdentML Data Standard Version 1.2, Supporting Advances in Proteome Informatics. Molecular & Cellular Proteomics. 16(7). 1275–1285. 42 indexed citations

Turewicz, Michael, Michael Kohl, Maike Ahrens, et al.. (2017). BioInfra.Prot: A comprehensive proteomics workflow including data standardization, protein inference, expression analysis and data publication. Journal of Biotechnology. 261. 116–125. 19 indexed citations

Mayer, Gerhard, Christian Quast, Janine Felden, et al.. (2017). A generally applicable lightweight method for calculating a value structure for tools and services in bioinformatics infrastructure projects. Briefings in Bioinformatics. 20(4). 1215–1221. 2 indexed citations

Deutsch, Eric W., Sandra Orchard, Pierre‐Alain Binz, et al.. (2017). Proteomics Standards Initiative: Fifteen Years of Progress and Future Work. Journal of Proteome Research. 16(12). 4288–4298. 56 indexed citations

Mayer, Gerhard, Katrin Marcus, Martin Eisenacher, & Michael Kohl. (2016). Boolean modeling techniques for protein co-expression networks in systems medicine. Expert Review of Proteomics. 13(6). 555–569. 3 indexed citations

Mayer, Gerhard, Christoph Stephan, Helmut E. Meyer, et al.. (2015). ProCon — PROteomics CONversion tool. Journal of Proteomics. 129. 56–62. 12 indexed citations

10.

Vizcaíno, Juan Antonio, Attila Csordás, Noemí del‐Toro, et al.. (2015). 2016 update of the PRIDE database and its related tools. Nucleic Acids Research. 44(D1). D447–D456. 2962 indexed citations breakdown →

11.

Deutsch, Eric W., Juan Pablo Albar, Pierre‐Alain Binz, et al.. (2015). Development of data representation standards by the human proteome organization proteomics standards initiative. Journal of the American Medical Informatics Association. 22(3). 495–506. 36 indexed citations

12.

Mayer, Gerhard, Andrew R. Jones, Pierre‐Alain Binz, et al.. (2013). Controlled vocabularies and ontologies in proteomics: Overview, principles and practice. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1844(1). 98–107. 26 indexed citations

13.

Mayer, Gerhard, Luisa Montecchi‐Palazzi, David Ovelleiro, et al.. (2013). The HUPO proteomics standards initiative- mass spectrometry controlled vocabulary. Database. 2013(0). bat009–bat009. 57 indexed citations

14.

Walzer, Mathias, Da Qi, Gerhard Mayer, et al.. (2013). The mzQuantML Data Standard for Mass Spectrometry–based Quantitative Studies in Proteomics. Molecular & Cellular Proteomics. 12(8). 2332–2340. 47 indexed citations

15.

Jones, Andrew R., Martin Eisenacher, Gerhard Mayer, et al.. (2012). The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results. Molecular & Cellular Proteomics. 11(7). M111.014381–1. 142 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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