Automated Author ProfileAyidzoe, Mighty Abra
University of Energy and Natural Resources
Ayidzoe, Mighty Abra
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: 0.3 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset contains 2,001 text entries labeled for toxicity classification. Each entry represents a user-generated comment along with an assigned toxicity label. The dataset is structured into two columns:COMMENT– A text field containing comments written primarily in Akan (Twi). These comments include expressions of gratitude, feedback, conversational messages, and general communication typical of social or online interactions.LABEL– A categorical variable indicating whether the comment is 'toxic' or 'non-toxic'.Current labels present in the dataset: 'non-toxic' (and any others present in the full file, if applicable).Key Features:• Total records: 2,001• Language: Primarily Akan (Twi)• Classification type: Binary toxicity classificationThere are no missing values (both columns have 2,001 non-null entries)Data types: ‘COMMENT’: string and ‘LABEL’`: stringThis dataset can support research in:• Toxic language detection in low-resource languages• Natural Language Processing (NLP) for African languages• Machine learning model training for text classification• Sociolinguistic analysis of online conversational contentThe File Format is CSV file: Toxicity_dataset.csvIt contains two columns: 'COMMENT' and ‘LABEL'
Authors
- Ali, Gifty Suuk ;
- Mensah Kwabena, Patrick ;
- Ayidzoe, Mighty Abra ;
- Ayawli, Ben Belklisi Kwame
This dataset contains 2,001 text entries labeled for toxicity classification. Each entry represents a user-generated comment along with an assigned toxicity label. The dataset is structured into two columns:COMMENT– A text field containing comments written primarily in Akan (Twi). These comments include expressions of gratitude, feedback, conversational messages, and general communication typical of social or online interactions.LABEL– A categorical variable indicating whether the comment is 'toxic' or 'non-toxic'.Current labels present in the dataset: 'non-toxic' (and any others present in the full file, if applicable).Key Features:• Total records: 2,001• Language: Primarily Akan (Twi)• Classification type: Binary toxicity classificationThere are no missing values (both columns have 2,001 non-null entries)Data types: ‘COMMENT’: string and ‘LABEL’`: stringThis dataset can support research in:• Toxic language detection in low-resource languages• Natural Language Processing (NLP) for African languages• Machine learning model training for text classification• Sociolinguistic analysis of online conversational contentThe File Format is CSV file: Toxicity_dataset.csvIt contains two columns: 'COMMENT' and ‘LABEL'
Authors
- Ali, Gifty Suuk ;
- Mensah Kwabena, Patrick ;
- Ayidzoe, Mighty Abra ;
- Ayawli, Ben Belklisi Kwame
This dataset contains 2,001 text entries labeled for toxicity classification. Each entry represents a user-generated comment along with an assigned toxicity label. The dataset is structured into two columns:COMMENT– A text field containing comments written primarily in Akan (Twi). These comments include expressions of gratitude, feedback, conversational messages, and general communication typical of social or online interactions.LABEL– A categorical variable indicating whether the comment is 'toxic' or 'non-toxic'.Current labels present in the dataset: 'non-toxic' (and any others present in the full file, if applicable).Key Features:• Total records: 2,001• Language: Primarily Akan (Twi)• Classification type: Binary toxicity classificationThere are no missing values (both columns have 2,001 non-null entries)Data types: ‘COMMENT’: string and ‘LABEL’`: stringThis dataset can support research in:• Toxic language detection in low-resource languages• Natural Language Processing (NLP) for African languages• Machine learning model training for text classification• Sociolinguistic analysis of online conversational contentThe File Format is CSV file: Toxicity_dataset.csvIt contains two columns: 'COMMENT' and ‘LABEL'
Authors
- Ali, Gifty Suuk ;
- Mensah Kwabena, Patrick ;
- Ayidzoe, Mighty Abra ;
- Ayawli, Ben Belklisi