Authentic or Upscale? Place-Based Resources and the Positions of Successful Seafood Restaurants in a Gastronomy Destination

This dataset supports the article "Authentic or Upscale? Place-Based Resources and the Positions of Successful Seafood Restaurants in a Gastronomy Destination". It covers the 36 seafood restaurants nominated by a panel of 13 local experts as the most successful on Santa Catarina Island (Florianópolis, Brazil), a UNESCO Creative City of Gastronomy. Thirty-one restaurants have complete on-site assessments. The dataset contains:(1) on-site attributes of each restaurant (view, origin of the owner, price level, setting, hygiene, cuisine);(2) their calibration into crisp and fuzzy sets (LEGACY, VIEW, BAY, NORTH, FISHER, UPSCALE, HIGH_PRICE, TRADITIONAL_POS);(3) the crisp-set qualitative comparative analysis: descriptives, necessity, truth tables, solutions, and sensitivity analyses;(4) secondary data collected in September 2026 from Google Maps and TripAdvisor listings, restaurant websites, press reports, and business registries;(5) aggregated dictionary coding of Portuguese-language TripAdvisor reviews (authenticity and tradition, sophistication, oysters) for an early sample (up to 2015) and a recent sample, by restaurant;(6) a second observation of the same restaurants on 26 September 2026 (operating status, price per person, and setting coded from listing photographs and descriptions, with the evidence for each code). Files:Dataset_PlaceBasedResources_SeafoodRestaurants.xlsx: complete workbook with raw data, calibration formulas, analyses, secondary data, review coding, and the 2009 to 2026 panel (see the README sheet);coded_data_2009.csv: coded data with English variable names;csqca_input_2009.csv: input file for the R script;panel_2009_2026.csv: one row per restaurant with 2009 position and 2026 status, price, and setting;review_coding.csv: counts of reviews mentioning each category by restaurant and period;analysis_csqca.R and analysis_output.txt: R code (package QCA 3.25) and its output;README.md: codebook and methods. Methods and tools: on-site assessment by a single rater; secondary data collected through a web browser, with listing fields and review pages read by JavaScript routines run in the browser; review coding with regular expressions in Python; statistical tests with SciPy; configurational analysis in R (QCA 3.25). Review texts are not included; only codes and counts are provided.