Daan A. Weits

2.8k total citations · 1 hit paper
24 papers, 2.0k citations indexed

About

Daan A. Weits is a scholar working on Plant Science, Biochemistry and Molecular Biology. According to data from OpenAlex, Daan A. Weits has authored 24 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Plant Science, 6 papers in Biochemistry and 5 papers in Molecular Biology. Recurrent topics in Daan A. Weits's work include Plant responses to water stress (21 papers), Plant Stress Responses and Tolerance (10 papers) and Lipid metabolism and biosynthesis (6 papers). Daan A. Weits is often cited by papers focused on Plant responses to water stress (21 papers), Plant Stress Responses and Tolerance (10 papers) and Lipid metabolism and biosynthesis (6 papers). Daan A. Weits collaborates with scholars based in Italy, Germany and Netherlands. Daan A. Weits's co-authors include Joost T. van Dongen, Francesco Licausi, Pierdomenico Perata, Beatrice Giuntoli, Monika Kosmacz, Federico M. Giorgi, Laurentius A. C. J. Voesenek, Sandro Parlanti, Heike Riegler and Rainer Hoefgen and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Daan A. Weits

24 papers receiving 2.0k citations

Hit Papers

Oxygen sensing in plants is mediated by an N-end rule pat... 2011 2026 2016 2021 2011 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daan A. Weits Italy 17 1.8k 622 324 182 168 24 2.0k
Beatrice Giuntoli Italy 19 2.1k 1.2× 710 1.1× 370 1.1× 280 1.5× 159 0.9× 30 2.5k
Angelika Mustroph Germany 24 2.5k 1.4× 790 1.3× 247 0.8× 373 2.0× 50 0.3× 37 2.7k
Sandro Parlanti Italy 12 943 0.5× 326 0.5× 108 0.3× 123 0.7× 50 0.3× 12 1.1k
Seok Keun Cho South Korea 24 1.5k 0.9× 1.5k 2.3× 51 0.2× 22 0.1× 125 0.7× 40 2.2k
Hanna Jańska Poland 23 649 0.4× 1.3k 2.2× 48 0.1× 79 0.4× 51 0.3× 58 1.6k
Cécile Raynaud France 29 1.8k 1.0× 1.8k 2.9× 53 0.2× 42 0.2× 77 0.5× 58 2.5k
Xiaofeng Fang China 23 1.1k 0.6× 1.3k 2.0× 91 0.3× 25 0.1× 49 0.3× 47 1.9k
Simon Stael Belgium 21 1.4k 0.8× 1.3k 2.0× 43 0.1× 24 0.1× 96 0.6× 43 2.0k
Quan‐Sheng Qiu China 20 1.5k 0.8× 844 1.4× 59 0.2× 33 0.2× 121 0.7× 52 1.9k
Johan Edqvist Sweden 23 842 0.5× 1.1k 1.7× 158 0.5× 45 0.2× 56 0.3× 33 1.5k

Countries citing papers authored by Daan A. Weits

Since Specialization
Citations

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

Fields of papers citing papers by Daan A. Weits

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daan A. Weits. 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 Daan A. Weits. The network helps show where Daan A. Weits may publish in the future.

Co-authorship network of co-authors of Daan A. Weits

This figure shows the co-authorship network connecting the top 25 collaborators of Daan A. Weits. A scholar is included among the top collaborators of Daan A. Weits 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 Daan A. Weits. Daan A. Weits 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.
Khan, Mohammad Shahneawz, Antoine Wallabrègue, Edward Smith, et al.. (2025). Hypoxia‐activated fluorescent probes as markers of oxygen levels in plant cells and tissues. New Phytologist. 247(6). 2998–3009. 1 indexed citations
2.
Jiménez, Juan de la Cruz, Angelika Mustroph, Ole Pedersen, Daan A. Weits, & Romy Schmidt. (2024). Flooding stress and responses to hypoxia in plants. Functional Plant Biology. 51(4). 2 indexed citations
3.
Herzog, Max, et al.. (2024). Tools to understand hypoxia responses in plant tissues. PLANT PHYSIOLOGY. 197(1). 2 indexed citations
4.
Pantazopoulou, Chrysoula K., Sara Buti, Chi Tam Nguyen, et al.. (2023). Mechanodetection of neighbor plants elicits adaptive leaf movements through calcium dynamics. Nature Communications. 14(1). 5827–5827. 9 indexed citations
5.
Kunkowska, Alicja B, Gerold J. M. Beckers, Christian Meyer, et al.. (2023). Target of rapamycin signaling couples energy to oxygen sensing to modulate hypoxic gene expression in Arabidopsis. Proceedings of the National Academy of Sciences. 120(3). e2212474120–e2212474120. 36 indexed citations
6.
Carbonare, Luca Dalle, Luca Rindi, Fabio Bulleri, et al.. (2023). Dim artificial light at night alters gene expression rhythms and growth in a key seagrass species (Posidonia oceanica). Scientific Reports. 13(1). 10620–10620. 6 indexed citations
7.
Weits, Daan A., Lina Zhou, Beatrice Giuntoli, et al.. (2022). Acquisition of hypoxia inducibility by oxygen sensing N‐terminal cysteine oxidase in spermatophytes. Plant Cell & Environment. 46(1). 322–338. 20 indexed citations
8.
Weits, Daan A., Joost T. van Dongen, & Francesco Licausi. (2020). Molecular oxygen as a signaling component in plant development. New Phytologist. 229(1). 24–35. 95 indexed citations
9.
Mariotti, Lorenzo, et al.. (2020). Auxin is required for the long coleoptile trait in japonica rice under submergence. New Phytologist. 229(1). 85–93. 29 indexed citations
10.
Iacopino, Sergio, et al.. (2020). An Improved HRPE-Based Transcriptional Output Reporter to Detect Hypoxia and Anoxia in Plant Tissue. Biosensors. 10(12). 197–197. 19 indexed citations
11.
Valeri, María Cristina, Giacomo Novi, Daan A. Weits, et al.. (2020). Botrytis cinerea induces local hypoxia in Arabidopsis leaves. New Phytologist. 229(1). 173–185. 50 indexed citations
12.
Weits, Daan A., Alicja B Kunkowska, Zoe Nemec Venza, et al.. (2019). An apical hypoxic niche sets the pace of shoot meristem activity. Nature. 569(7758). 714–717. 167 indexed citations
13.
Shukla, Vinay, et al.. (2019). A Ratiometric Sensor Based on Plant N-Terminal Degrons Able to Report Oxygen Dynamics in Saccharomyces cerevisiae. Journal of Molecular Biology. 431(15). 2810–2820. 24 indexed citations
14.
Niccolini, Luca, et al.. (2019). Hypoxic Conditions in Crown Galls Induce Plant Anaerobic Responses That Support Tumor Proliferation. Frontiers in Plant Science. 10. 56–56. 48 indexed citations
15.
Schmidt, Romy, Martin Fulda, Melanie Paul, et al.. (2018). Low-oxygen response is triggered by an ATP-dependent shift in oleoyl-CoA in Arabidopsis. Proceedings of the National Academy of Sciences. 115(51). E12101–E12110. 66 indexed citations
16.
White, Mark D., Maria Klecker, Richard J. Hopkinson, et al.. (2017). Plant cysteine oxidases are dioxygenases that directly enable arginyl transferase-catalysed arginylation of N-end rule targets. Nature Communications. 8(1). 14690–14690. 180 indexed citations
17.
Schmidt, Romy, et al.. (2017). Oxygen Sensing and Integrative Stress Signaling in Plants. PLANT PHYSIOLOGY. 176(2). 1131–1142. 83 indexed citations
18.
Weits, Daan A., Beatrice Giuntoli, Monika Kosmacz, et al.. (2014). Plant cysteine oxidases control the oxygen-dependent branch of the N-end-rule pathway. Nature Communications. 5(1). 3425–3425. 305 indexed citations
19.
Licausi, Francesco, Monika Kosmacz, Daan A. Weits, et al.. (2011). Oxygen sensing in plants is mediated by an N-end rule pathway for protein destabilization. Nature. 479(7373). 419–422. 612 indexed citations breakdown →
20.
Licausi, Francesco, Daan A. Weits, Bikram Datt Pant, et al.. (2010). Hypoxia responsive gene expression is mediated by various subsets of transcription factors and miRNAs that are determined by the actual oxygen availability. New Phytologist. 190(2). 442–456. 140 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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