Automated Author ProfileP, Kumaresan
VIT University0000-0001-5563-8325
P, Kumaresan
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: 8.4 (sum of 6 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The historical agricultural yield data, encompasses records from 1997 to 2020, spanning 27 Indian states and 3 Union Territories. It comprises 19,689 instances, each described by ten distinct attributes. The dataset captures six agricultural seasons and includes data on 55 unique crop types cultivated across diverse agro-climatic regions of India
Authors
- V, Ramesh ;
- P, Kumaresan
The historical agricultural yield data, encompasses records from 1997 to 2020, spanning 27 Indian states and 3 Union Territories. It comprises 19,689 instances, each described by ten distinct attributes. The dataset captures six agricultural seasons and includes data on 55 unique crop types cultivated across diverse agro-climatic regions of India
Authors
- V, Ramesh ;
- P, Kumaresan
This dataset is created using data from Kaggle, an open source website for data. The dataset consists of 2940 facial images of children between male and female participants, divided into two primary classes: Autistic and Non-Autistic. The dataset is organized into three folders with each folder containing the images of both classes. The data has been curated for research purposes to improve the classification accuracy of autism detection models.
Authors
- R, Thillaikarasi ;
- P, Kumaresan
This dataset is created using data from Kaggle, an open source website for data. The dataset consists of 2940 facial images of children between male and female participants, divided into two primary classes: Autistic and Non-Autistic. The dataset is organized into three folders with each folder containing the images of both classes. The data has been curated for research purposes to improve the classification accuracy of autism detection models.
Authors
- R, Thillaikarasi ;
- P, Kumaresan
This dataset is created using data from Kaggle, an open source website for data. The dataset consists of 2940 facial images of children between male and female participants, divided into two primary classes: Autistic and Non-Autistic. The dataset is organized into three folders with each folder containing the images of both classes. The data has been curated for research purposes to improve the classification accuracy of autism detection models.
Authors
- R, Thillaikarasi ;
- P, Kumaresan
This dataset is created using data from Kaggle, an open source website for data. The dataset consists of 2940 facial images of children between male and female participants, divided into two primary classes: Autistic and Non-Autistic. The dataset is organized into three folders with each folder containing the images of both classes. The data has been curated for research purposes to improve the classification accuracy of autism detection models.
Authors
- R, Thillaikarasi ;
- P, Kumaresan