Automated Author ProfileWeigel, Detlef
Weigel, Detlef
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 70.6 (sum of 75 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The ankyrin-repeat transmembrane protein ACD6 was identified two decades ago, but its exact biochemical function has remained enigmatic. Chen et al. show that ACD6 is a bona fide regulator of calcium influx using heterologous systems and calcium reporters in plants, and that this activity can be further enhanced by a MHA1L peptide ligand.
Authors
- Chen, Jun-bin ;
- Li, Lei ;
- Kim, Jonghum ;
- Neuhaeuser, Benjamin ;
- wang, mingyu ;
- Thelen, Michael ;
- Hilleary, Richard ;
- Chi, Yuan ;
- Wei, Luyang ;
- Venkataramani, Kavita ;
- Exposito-Alonso, Moises ;
- Liu, Chang ;
- Keck, Jakob ;
- Barragan, A. Cristina ;
- Schwab, Rebecca ;
- Lutz, Ulrich ;
- Pei, Zhenming ;
- He, Sheng-Yang ;
- Ludewig, Uwe ;
- Weigel, Detlef ;
- Zhu, Wangsheng
The ankyrin-repeat transmembrane protein ACD6 was identified two decades ago, but its exact biochemical function has remained enigmatic. Chen et al. show that ACD6 is a bona fide regulator of calcium influx using heterologous systems and calcium reporters in plants, and that this activity can be further enhanced by a MHA1L peptide ligand.
Authors
- Chen, Jun-bin ;
- Li, Lei ;
- Kim, Jonghum ;
- Neuhaeuser, Benjamin ;
- wang, mingyu ;
- Thelen, Michael ;
- Hilleary, Richard ;
- Chi, Yuan ;
- Wei, Luyang ;
- Venkataramani, Kavita ;
- Exposito-Alonso, Moises ;
- Liu, Chang ;
- Keck, Jakob ;
- Barragan, A. Cristina ;
- Schwab, Rebecca ;
- Lutz, Ulrich ;
- Pei, Zhenming ;
- He, Sheng-Yang ;
- Ludewig, Uwe ;
- Weigel, Detlef ;
- Zhu, Wangsheng
Supplementary Material contains:
Tables S1 - S5Figures S1 - S15Files S1 - S5Supplemental Legends
Authors
- Rowan, Beth ;
- Heavens, Darren ;
- Feuerborn, Tatiana ;
- Tock, Andrew ;
- Henderson, Ian ;
- Weigel, Detlef
Through the lens of evolution, climate change is an agent of natural selection that forces populations to change and adapt, or face extinction. Current assessments of the risks associated with climate change, however, do not typically take into account that natural selection can dramatically impact the genetic makeup of populations. We made use of extensive genome information in Arabidopsis thaliana and measured how rainfall-manipulation affected the fitness of 517 natural lines grown in Spain and Germany. This allowed us to directly infer selection at the genetic level. Natural selection was particularly strong in the hot-dry Spanish location, killing 63% of lines and significantly changing the frequency of ~5% of all genome-wide variants. A significant proportion of this selection over variants could be predicted from the climate (mis)match between experimental sites and the geographic areas where variants are found (R2=29-52%). Field-validated predictions across the species range indicated that Mediterranean and Western Siberia populations — at the edges of the species' environmental limits — currently experience the strongest climate-driven selection, and Central Europeans the weakest. With rapidly increasing droughts and rising temperatures in Europe, we forecast a wave of directional selection moving North, putting many native A. thaliana populations at evolutionary risk.
Authors
- Exposito-Alonso, Moises ;
- Weigel, Detlef
No description available
Authors
- Vasseur, François ;
- Fouqueau, Louise ;
- De Vienne, Dominique ;
- Nidelet, Thibault ;
- Violle, Cyrille ;
- Weigel, Detlef
Through the lens of evolution, climate change is an agent of natural selection that forces populations to change and adapt, or face extinction. Current assessments of the risks associated with climate change, however, do not typically take into account that natural selection can dramatically impact the genetic makeup of populations. We made use of extensive genome information in Arabidopsis thaliana and measured how rainfall-manipulation affected the fitness of 517 natural lines grown in Spain and Germany. This allowed us to directly infer selection at the genetic level. Natural selection was particularly strong in the hot-dry Spanish location, killing 63% of lines and significantly changing the frequency of ~5% of all genome-wide variants. A significant proportion of this selection over variants could be predicted from the climate (mis)match between experimental sites and the geographic areas where variants are found (R2=29-52%). Field-validated predictions across the species range indicated that Mediterranean and Western Siberia populations — at the edges of the species' environmental limits — currently experience the strongest climate-driven selection, and Central Europeans the weakest. With rapidly increasing droughts and rising temperatures in Europe, we forecast a wave of directional selection moving North, putting many native A. thaliana populations at evolutionary risk.
Authors
- Exposito-Alonso, Moises ;
- Weigel, Detlef
Supplementary Material contains:
Tables S1 - S5Figures S1 - S15Files S1 - S5Supplemental Legends
Authors
- Rowan, Beth ;
- Heavens, Darren ;
- Feuerborn, Tatiana ;
- Tock, Andrew ;
- Henderson, Ian ;
- Weigel, Detlef
Additional file 5. R code used to predict fruit number from inflorescence skeleton descriptors, with cross-validation approach to train and test different models and training population size.
Authors
- Franรงois Vasseur ;
- Bresson, Justine ;
- Wang, George ;
- Schwab, Rebecca ;
- Weigel, Detlef
Additional file 4. ImageJ macro used to extract inflorescence skeleton descriptors from top-view images of plant inflorescence.
Authors
- Franรงois Vasseur ;
- Bresson, Justine ;
- Wang, George ;
- Schwab, Rebecca ;
- Weigel, Detlef