Automated Author ProfileTang, Min
Institute of Zoology
Tang, Min
Institute of Zoology
Current S-Index
0.7
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
0.7
Average Dataset Index per dataset
Total Datasets
1
Total datasets for this author
Average FAIR Score
76.9%
Average FAIR Score per dataset
Total Citations
2
Total citations to the author's datasets
Total Mentions
0
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: 0.7 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
- Bee populations and other pollinators face multiple, synergistically acting threats, which have led to population declines, loss of local species richness and pollination services, and extinctions. However, our understanding of the degree, distribution and causes of declines is patchy, in part due to inadequate monitoring systems, with the challenge of taxonomic identification posing a major logistical barrier. Pollinator conservation would benefit from a high-throughput identification pipeline. 2. We show that the metagenomic mining and resequencing of mitochondrial genomes (mitogenomics) can be applied successfully to bulk samples of wild bees. We assembled the mitogenomes of 48 UK bee species and then shotgun-sequenced total DNA extracted from 204 whole bees that had been collected in 10 pan-trap samples from farms in England and been identified morphologically to 33 species. Each sample data set was mapped against the 48 reference mitogenomes. 3. The morphological and mitogenomic data sets were highly congruent. Out of 63 total species detections in the morphological data set, the mitogenomic data set made 59 correct detections (93·7% detection rate) and detected six more species (putative false positives). Direct inspection and an analysis with species-specific primers suggested that these putative false positives were most likely due to incorrect morphological IDs. Read frequency significantly predicted species biomass frequency (R2 = 24·9%). Species lists, biomass frequencies, extrapolated species richness and community structure were recovered with less error than in a metabarcoding pipeline. 4. Mitogenomics automates the onerous task of taxonomic identification, even for cryptic species, allowing the tracking of changes in species richness and distributions. A mitogenomic pipeline should thus be able to contain costs, maintain consistently high-quality data over long time series, incorporate retrospective taxonomic revisions and provide an auditable evidence trail. Mitogenomic data sets also provide estimates of species counts within samples and thus have potential for tracking population trajectories.
Authors
- Tang, Min ;
- Hardman, Chloe J. ;
- Ji, Yinqiu ;
- Meng, Guanliang ;
- Liu, Shanlin ;
- Tang, Meihua ;
- Yang, Shenzhou ;
- Moss, Ellen D. ;
- Wang, Jiaxin ;
- Yang, Chenxue ;
- Bruce, Catharine ;
- Nevard, Tim ;
- Potts, Simon G. ;
- Zhou, Xin ;
- Yu, Douglas W. ;
- Tan, Meihua
2 Citations0 Mentions77% FAIR1.1 Dataset Index
10.5061/dryad.r71222016