Automated Author ProfileHiggins, Derrick
Higgins, Derrick
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: 3.7 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Introduction
ETS Corpus of Non-Native Written English was developed by Educational Testing Service and is comprised of 12,100 English essays written by speakers of 11 non-English native languages as part of an international test of academic English proficiency, TOEFL (Test of English as a Foreign Language). The test includes reading, writing, listening, and speaking sections and is delivered by computer in a secure test center. This release contains 1,100 essays for each of the 11 native languages sampled from eight topics with information about the score level (low/medium/high) for each essay.
The corpus was developed with the specific task of native language identification in mind, but is likely to support tasks and studies in the educational domain, including grammatical error detection and correction and automatic essay scoring, in addition to a broad range of research studies in the fields of natural language processing and corpus linguistics. For the task of native language identification, the following division is recommended: 82% as training data, 9% as development data and 9% as test data, split according to the file IDs accompanying the data set.
Data
The data is sampled from essays written in 2006 and 2007 by test takers whose native languages were Arabic, Chinese, French, German, Hindi, Italian, Japanese, Korean, Spanish, Telugu, and Turkish. The essays are presented in both original raw and tokenized forms and presented in UTF-8 formatted text files. Also included are the prompts (topics) for the essays and metadata about the test takers' proficiency level.
Samples
Please view this original and tokenized samples.
Updates
In July 2014, 1,100 files were added to the corpus, bringing the total number of tokenized and original files to 12,100. All copies distributed after that date contain the full data set.
Portions © 2014 Educational Testing Service, © 2014 Trustees of the University of Pennsylvania
Authors
- Blanchard, Daniel ;
- Tetreault, Joel ;
- Higgins, Derrick ;
- Cahill, Aoife ;
- Chodorow, Martin