Automated Author ProfileA. Kovalev, Maxim
A. Kovalev, Maxim
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: 1.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The ratio of bacterial taxa in each soil collection region and by season are presented in Supplementary Table 1 (normalized read counts).Supplementary Tables 2, 3 provide the predicted relative abundance (in percents) of genes encoding for various enzymes (IUBMB Enzyme Nomenclature), according to PICRUSt2 and MicFunPred predictions, respectively. It is worth noting that abundance values can be higher than 100% in cases where a gene have multiple copies in genomes.Supplementary Table 4 shows the predicted abundance of genes encoding for various known proteins, according to KEGG Orthology nomenclature (predicted with PICRUSt2).Supplementary Table 5 contains data on the availability of chemical reactions in the microbial community (inferred by MicFunPred; MetaCyc nomenclature), including those catalyzed by the encoded enzymes.Supplementary Table 6 shows the relative availability of metabolic pathways (MetaCyc nomenclature ) as predicted by PICRUSt2.
Authors
- I. Popchenko, Mikhail ;
- S. Gladysh, Natalya ;
- A. Kovalev, Maxim ;
- V. Volodyn, Vsevolod ;
- S. Krasnov, George ;
- S. Bogdanova, Alina ;
- I. Shuvalova, Anastasia ;
- A. Zheglov, David ;
- O. Monastyrskaia, Mariia ;
- L. Bolsheva, Nadezhda ;
- S. Fedorova, Maria ;
- V. Kudryavtseva, Anna
The ratio of bacterial taxa in each soil collection region and by season are presented in Supplementary Table 1 (normalized read counts).Supplementary Tables 2, 3 provide the predicted relative abundance (in percents) of genes encoding for various enzymes (IUBMB Enzyme Nomenclature), according to PICRUSt2 and MicFunPred predictions, respectively. It is worth noting that abundance values can be higher than 100% in cases where a gene have multiple copies in genomes.Supplementary Table 4 shows the predicted abundance of genes encoding for various known proteins, according to KEGG Orthology nomenclature (predicted with PICRUSt2).Supplementary Table 5 contains data on the availability of chemical reactions in the microbial community (inferred by MicFunPred; MetaCyc nomenclature), including those catalyzed by the encoded enzymes.Supplementary Table 6 shows the relative availability of metabolic pathways (MetaCyc nomenclature ) as predicted by PICRUSt2.
Authors
- I. Popchenko, Mikhail ;
- S. Gladysh, Natalya ;
- A. Kovalev, Maxim ;
- V. Volodyn, Vsevolod ;
- S. Krasnov, George ;
- S. Bogdanova, Alina ;
- I. Shuvalova, Anastasia ;
- A. Zheglov, David ;
- O. Monastyrskaia, Mariia ;
- L. Bolsheva, Nadezhda ;
- S. Fedorova, Maria ;
- V. Kudryavtseva, Anna