Daniel Lang

15.0k total citations
54 papers, 2.5k citations indexed

About

Daniel Lang is a scholar working on Plant Science, Molecular Biology and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Daniel Lang has authored 54 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Plant Science, 32 papers in Molecular Biology and 9 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Daniel Lang's work include Plant Molecular Biology Research (21 papers), Photosynthetic Processes and Mechanisms (12 papers) and Genomics and Phylogenetic Studies (10 papers). Daniel Lang is often cited by papers focused on Plant Molecular Biology Research (21 papers), Photosynthetic Processes and Mechanisms (12 papers) and Genomics and Phylogenetic Studies (10 papers). Daniel Lang collaborates with scholars based in Germany, United States and United Kingdom. Daniel Lang's co-authors include Ralf Reski, Stefan A. Rensing, Andreas Zimmer, Wolfgang Frank, Sandra Richardt, Yves Van de Peer, Annette Hohe, Tomoaki Nishiyama, Mitsuyasu Hasebe and Luiz Gustavo Guedes Corrêa and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and The Plant Cell.

In The Last Decade

Daniel Lang

53 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Lang Germany 30 1.8k 1.5k 487 125 114 54 2.5k
Patrick Gallois United Kingdom 32 2.5k 1.4× 2.2k 1.5× 190 0.4× 71 0.6× 113 1.0× 59 3.5k
Prem L. Bhalla Australia 37 3.2k 1.8× 2.7k 1.8× 390 0.8× 91 0.7× 250 2.2× 147 4.4k
Karsten Liere Germany 20 866 0.5× 1.8k 1.1× 143 0.3× 127 1.0× 123 1.1× 29 1.9k
Kristi R. Harkins United States 18 2.1k 1.1× 1.6k 1.1× 447 0.9× 112 0.9× 246 2.2× 27 2.7k
Erik Richly Germany 13 796 0.4× 1.8k 1.2× 201 0.4× 380 3.0× 279 2.4× 16 2.2k
Oliver Drechsel Germany 15 709 0.4× 2.4k 1.5× 686 1.4× 282 2.3× 573 5.0× 25 2.8k
Erwin Heberle‐Bors Austria 47 4.7k 2.6× 4.8k 3.1× 336 0.7× 55 0.4× 169 1.5× 136 6.3k
Megumi Iwano Japan 37 3.9k 2.2× 4.1k 2.7× 1.5k 3.0× 126 1.0× 248 2.2× 74 5.1k
Eva Sundberg Sweden 35 2.6k 1.4× 2.4k 1.6× 264 0.5× 42 0.3× 130 1.1× 52 3.1k
M. Virginia Sanchez‐Puerta Argentina 24 649 0.4× 1.5k 1.0× 354 0.7× 378 3.0× 114 1.0× 63 1.9k

Countries citing papers authored by Daniel Lang

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Lang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Lang

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Lang. A scholar is included among the top collaborators of Daniel Lang 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 Daniel Lang. Daniel Lang 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.
Chitimia‐Dobler, Lidia, Gerhard Dobler, Daniel Lang, et al.. (2025). Distribution and Genotypic Landscape of Tick-Borne Encephalitis Virus in Ticks from Latvia from 2019 to 2023. Pathogens. 14(9). 950–950.
2.
Hoernstein, Sebastian N. W., Andreas Schlösser, Nico van Gessel, et al.. (2024). A snapshot of the Physcomitrella N-terminome reveals N-terminal methylation of organellar proteins. Plant Cell Reports. 43(10). 250–250. 4 indexed citations
3.
Chitimia‐Dobler, Lidia, Andrea Springer, Daniel Lang, et al.. (2024). Molting incidents of Hyalomma spp. carrying human pathogens in Germany under different weather conditions. Parasites & Vectors. 17(1). 70–70. 5 indexed citations
4.
Bestehorn, Malena, et al.. (2023). Increased Vaccination Diversity Leads to Higher and Less-Variable Neutralization of TBE Viruses of the European Subtype. Vaccines. 11(6). 1044–1044. 2 indexed citations
5.
Lang, Daniel, Lidia Chitimia‐Dobler, Malena Bestehorn, et al.. (2022). The Emergence and Dynamics of Tick-Borne Encephalitis Virus in a New Endemic Region in Southern Germany. Microorganisms. 10(11). 2125–2125. 8 indexed citations
6.
Keilwagen, Jens, Heike Lehnert, Thomas Berner, et al.. (2019). Detecting Large Chromosomal Modifications Using Short Read Data From Genotyping-by-Sequencing. Frontiers in Plant Science. 10. 1133–1133. 19 indexed citations
7.
Juhász, Angéla, Tatiana Belova, Iris Fischer, et al.. (2018). Genome mapping of seed-borne allergens and immunoresponsive proteins in wheat. Science Advances. 4(8). eaar8602–eaar8602. 105 indexed citations
8.
Gardiner, Laura‐Jayne, Ryan Joynson, Jimmy Omony, et al.. (2018). Hidden variation in polyploid wheat drives local adaptation. Genome Research. 28(9). 1319–1332. 35 indexed citations
9.
Stevenson, Sean R., Yasuko Kamisugi, Chi H. Trinh, et al.. (2016). Genetic analysis of Physcomitrella patens identifies ABSCISIC ACID NON-RESPONSIVE (ANR), a regulator of ABA responses unique to basal land plants and required for desiccation tolerance. The Plant Cell. 28(6). tpc.00091.2016–tpc.00091.2016. 74 indexed citations
10.
Lind, Christof, Ingo Drèyer, Kimitsune Ishizaki, et al.. (2015). Stomatal Guard Cells Co-opted an Ancient ABA-Dependent Desiccation Survival System to Regulate Stomatal Closure. Current Biology. 25(7). 928–935. 127 indexed citations
11.
Schuette, Scott, et al.. (2015). Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource. BMC Bioinformatics. 16(1). 89–89. 12 indexed citations
12.
Hieno, Ayaka, Hushna Ara Naznin, Mitsuro Hyakumachi, et al.. (2013). ppdb: plant promoter database version 3.0. Nucleic Acids Research. 42(D1). D1188–D1192. 59 indexed citations
13.
Lang, Daniel, Benjamin Weiche, Gerrit Timmerhaus, et al.. (2010). Genome-Wide Phylogenetic Comparative Analysis of Plant Transcriptional Regulation: A Timeline of Loss, Gain, Expansion, and Correlation with Complexity. Genome Biology and Evolution. 2. 488–503. 118 indexed citations
14.
Rensing, Stefan A., et al.. (2010). Generation-Biased Gene Expression in a Bryophyte Model System. Molecular Biology and Evolution. 28(1). 803–812. 37 indexed citations
15.
16.
Qudeimat, Enas, Glen L. Wheeler, Daniel Lang, et al.. (2008). A P IIB -type Ca 2+ -ATPase is essential for stress adaptation in Physcomitrella patens. Proceedings of the National Academy of Sciences. 105(49). 19555–19560. 86 indexed citations
17.
Kamisugi, Yasuko, Μ. v. Stackelberg, Daniel Lang, et al.. (2008). A sequence‐anchored genetic linkage map for the moss, Physcomitrella patens. The Plant Journal. 56(5). 855–866. 30 indexed citations
18.
Richardt, Sandra, Daniel Lang, Ralf Reski, Wolfgang Frank, & Stefan A. Rensing. (2007). PlanTAPDB, a Phylogeny-Based Resource of Plant Transcription-Associated Proteins. PLANT PHYSIOLOGY. 143(4). 1452–1466. 69 indexed citations
19.
20.
Rensing, Stefan A., Daniel Lang, & Ralf Reski. (2003). In silico prediction of UTR repeats using clustered EST data. 117–122. 3 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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