DataSet for SNE-TAP

DATA - SNE_TAP============== Layout------S1/ Real dataset from one semester (25 lecturers, 153 classes, 13 courses, 10 time slots).SNE_synthetic_S2_S5_v2/ Data/S2 .. S5/ Synthetic datasets of increasing size. generate_s2_s5_v2.py Generator for S2-S5 (random seed 42). Each instance folder contains six spreadsheets with the same schema-------------------------------------------------------------------data_T.xlsx One row per lecturer: id, name, min_class, max_class, ideal_class (minimum / maximum / expected teaching load).data_Sub.xlsx One row per course: Id, Subject (course code).sp22_to_import.xlsx One row per class to assign: CourseId, Class (section code), Subject, SubjectId, Slot (M1-M5 / E1-E5), SlotId, Dept, Room.data_Rating.xlsx Teaching-quality matrix, lecturers (rows) x courses (columns), 0-10 (0 = not qualified).data_FSlot_S.xlsx Course-preference matrix, lecturers x courses, 0-10.data_FSlot_T.xlsx Time-slot preference / availability matrix, lecturers x 10 slots, 0-10 (0 = unavailable). A (lecturer, class) pair is usable when the lecturer is qualified for the course andavailable in the class time slot. Anonymization-------------S1 is a real dataset that has been fully anonymized. The lecturer names in data_T.xlsx(for example "Wesley Maddox") are randomly generated pseudonyms: they are NOT real namesand do not identify any real person. No personally identifiable information is included.The synthetic S2-S5 names are likewise random placeholders.