Our dataset contains 2 weeks of approx. 8-9 hours of acceleration data per day from 11 participants wearing a Bangle.js Version 1 smartwatch with our firmware installed. The dataset contains annotations from 4 different commonly used annotation methods utilized in user studies that focus on in-the-wild data. These methods can be grouped in user-driven, in situ annotations - which are performed before or during the activity is recorded - and recall methods - where participants annotate their data in hindsight at the end of the day. The participants had the task to label their activities using (1) a button located on the smartwatch, (2) the activity tracking app Strava, (3) a (hand)written diary and (4) a tool to visually inspect and label activity data, called MAD-GUI. Methods (1)-(3) are used in both weeks, however method (4) is introduced in the beginning of the second study week. The accelerometer data is recorded with 25 Hz, a sensitivity of ±8g and is stored in a csv format. Labels and raw data are not yet combined. You can either write your own script to label the data or follow the instructions in our corresponding Github repository. The following unique classes are included in our dataset: laying, sitting, walking, running, cycling, bus_driving, car_driving, vacuum_cleaning, laundry, cooking, eating, shopping, showering, yoga, sport, playing_games, desk_work, guitar_playing, gardening, table_tennis, badminton, horse_riding. However, many activities are very participant specific and therefore only performed by one of the participants. The labels are also stored as a .csv file and have the following columns: week_day, start, stop, activity, layer Example: week2_day2,10:30:00,11:00:00,vacuum_cleaning,d The layer columns specifies which annotation method was used to set this label. The following identifiers can be found in the column: b: in situ button a: in situ app d: self-recall diary g: time-series recall labelled with a the MAD-GUI Please download our paper for more information: A matter of annotation: an empirical study on in situ and self-recall activity annotations from wearable sensors