Automated Organization ProfileSiberian Branch-Russian Academy of Sciences, Mel’nikov Permafrost Institute, Yakutsk, Russia
Siberian Branch-Russian Academy of Sciences, Mel’nikov Permafrost Institute, Yakutsk, Russia
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: 0.2 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
In August and September 2013, 17 shallow ocean bottom seismograph (S-OBS) stations and 8 land stations had been deployed on and around Muostakh Island (Laptev Sea, Russia) for a time period of 24 days. The specifically designed underwater recording equipment consists of a low-power digital recorder, a standard 4.5Hz 3-component geophone, and a battery pack. These components are enclosed in a watertight cylindrical container safe for operation down to 100m water depth. Land stations were also equipped with 4.5 Hz 1C-geophones as well as with batteries. All instruments recorded continuously with 200 samples per second (sps). The stations were deployed along two profiles covering a region of 8 km x 8 km. The tilt of the geophone inside the S-OBS influences the sensor characteristics. Since the orientation and tilt at the ocean bottom was unknown, approximately every 24 hours a calibration signal (a sequence of step-functions) was applied to the sensors of the ocean stations. This might be used to recover the actual sensor characteristics (eigenfrequency and damping). The dataset contains 1) a info-folder with a) a README file; b) a file containing the times when calibration signals occurred (format: recorder_ID - date - time); c) the station table (ASCII; recorder_ID - latitude - longitude - (water)depth); d) a map of the region with the locations of the stations; 2) raw CUBE-formatted data; 3) converted mini-seed-formatted data (hourly files).
Authors
- Overduin, Paul ;
- Ryberg, Trond ;
- Kneier, Fabian ;
- Haberland, Christian ;
- Grigoriev, Mikhail