Automated Author ProfileBlanco, Eva
Universidade da Coruña0000-0002-5007-0897
Blanco, Eva
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: 4.1 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
DescriptionThis dataset contains restaurant reviews from TripAdvisor for five European cities, capturing detailed information on users, restaurants (items), and reviews. It offers a comprehensive view of user experiences, opinions, and restaurant attributes.Data StructureUser InformationuserId: Unique identifier for each user (hashed).name: Display name or username.location: User's location (city and country).Restaurant Information (Items)itemId: Unique identifier for each restaurant.name: Restaurant name.city: City where the restaurant is located.priceInterval: Price range.url: Link to the restaurant’s TripAdvisor review page.rating: Average rating score for the restaurant.type: List of cuisine types (e.g., [Spanish, Mediterranean]).Review InformationreviewId: Unique identifier for each review.userId: Corresponding user who wrote the review.itemId: Restaurant associated with the review.title: Title of the review summarizing the user’s impression.text: Full text of the review describing the user’s experience.date: Date when the review was posted.rating: Numerical score (typically from 0 to 50, where 50 represents the highest satisfaction).language: Language of the review.images: List of URLs pointing to images uploaded by the user (if available).url: Link to the full review on TripAdvisor.Code exampleimport pandas as pdcity = "Barcelona"# Load restaurantsitems = pd.read_pickle(f"{city}/items.pkl")# Load usersusers = pd.read_pickle(f"{city}/users.pkl")# Load reviewsreviews = pd.read_pickle(f"{city}/reviews.pkl")
Authors
- Pablo Pérez-Núñez ;
- Blanco, Eva ;
- Bolon-Canedo, Veronica ;
- Beatriz Remeseiro
DescriptionThis dataset contains restaurant reviews from TripAdvisor for five European cities, capturing detailed information on users, restaurants (items), and reviews. It offers a comprehensive view of user experiences, opinions, and restaurant attributes.Data StructureUser InformationuserId: Unique identifier for each user (hashed).name: Display name or username.location: User's location (city and country).Restaurant Information (Items)itemId: Unique identifier for each restaurant.name: Restaurant name.city: City where the restaurant is located.priceInterval: Price range.url: Link to the restaurant’s TripAdvisor review page.rating: Average rating score for the restaurant.type: List of cuisine types (e.g., [Spanish, Mediterranean]).Review InformationreviewId: Unique identifier for each review.userId: Corresponding user who wrote the review.itemId: Restaurant associated with the review.title: Title of the review summarizing the user’s impression.text: Full text of the review describing the user’s experience.date: Date when the review was posted.rating: Numerical score (typically from 0 to 50, where 50 represents the highest satisfaction).language: Language of the review.images: List of URLs pointing to images uploaded by the user (if available).url: Link to the full review on TripAdvisor.Code exampleimport pandas as pdcity = "Barcelona"# Load restaurantsitems = pd.read_pickle(f"{city}/items.pkl")# Load usersusers = pd.read_pickle(f"{city}/users.pkl")# Load reviewsreviews = pd.read_pickle(f"{city}/reviews.pkl")
Authors
- Pablo Pérez-Núñez ;
- Blanco, Eva ;
- Bolon-Canedo, Veronica ;
- Beatriz Remeseiro