Automated Organization Profile4Points-1Cap
4Points-1Cap
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization'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: 3.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
EN — DescriptionThis dataset accompanies the preprint Advanced Goldbach Toolkits and provides numerical diagnostics for the binary Goldbach problem. The CSV aggregates and/or samples outputs from our certified pipeline: counts G(n)G(n)G(n), Hardy–Littlewood main-term estimates S(n) n/(logn)2S(n),n/(\log n)^2S(n)n/(logn)2, fit ratios, short-interval variance indicators, residue-class information (mod 210), and risk scores R(n)R(n)R(n) to prioritize “hard cases.” Column semantics and usage examples are described in the project README and the Methods section of the preprint.License: CC0 1.0 to encourage reuse and independent replication.Links: this dataset is marked as Supplement to the Zenodo record for Advanced Goldbach Toolkits.Suggested keywords / Mots-clés: Goldbach; prime pairs; Hardy–Littlewood; Bombieri–Vinogradov; Elliott–Halberstam; short intervals; variance; residue classes; reproducibility; CSV.
Authors
- Durand, Serge
EN — DescriptionThis dataset accompanies the preprint Advanced Goldbach Toolkits and provides numerical diagnostics for the binary Goldbach problem. The CSV aggregates and/or samples outputs from our certified pipeline: counts G(n)G(n)G(n), Hardy–Littlewood main-term estimates S(n) n/(logn)2S(n),n/(\log n)^2S(n)n/(logn)2, fit ratios, short-interval variance indicators, residue-class information (mod 210), and risk scores R(n)R(n)R(n) to prioritize “hard cases.” Column semantics and usage examples are described in the project README and the Methods section of the preprint.License: CC0 1.0 to encourage reuse and independent replication.Links: this dataset is marked as Supplement to the Zenodo record for Advanced Goldbach Toolkits.Suggested keywords / Mots-clés: Goldbach; prime pairs; Hardy–Littlewood; Bombieri–Vinogradov; Elliott–Halberstam; short intervals; variance; residue classes; reproducibility; CSV.
Authors
- Durand, Serge