################################################################################################################################################ ################################################################################################################################################ Data collected during the performance STEM4Youth: Games xViladecans in Viladecans on May 12th, 2017. The participants played a public goods game. There were 6 experimental stations, one per individual, spatially distributed so that participants could not see each other. Also, they were rigorously prevented from talking or signaling one another. To further guarantee that potential interactions among players did not influence the results of the experiment, the assignment of players’ partners was completely random. All of the participants played through Citizen Social Lab, a web interface specifically developed for the experiment. The participants were shown a brief tutorial, but were not given any clue. They were informed that they had to make decisions under different conditions and against different opponents. We made sure the interface be the most simple and understandable to ensure the correct understanding of the tasks. Also, the interface was the same for everybody. We made sure to avoid the research be upsetting or harmful for the participants by presenting the experiment as a game and playful activity. One researcher closely monitored each session to guarantee the experimental protocol be strictly followed. Yet, the researchers provided help when required. All participants in the experiment signed an informed consent to participate and no association was ever made between their real names and the results, in agreement with the Spanish Law for Personal Data Protection. This procedure was approved by the Ethics Committee of Universitat de Barcelona, and all methods were performed in accordance with the relevant guidelines and regulations. The experiment consist on a collective-risk dilemma, a particular case of a public goods game with threshold. The dataset is formed by 5 tables (each field is described below). 1. user.csv: data of valid users. 2. session.csv: data of each session. 3. userround.csv: data of users in each round 4. round.csv: data of rounds ################################################################################################################################################ The fields marked * are specified in the file survey QuestionsAnswersSurvey.xls 1. user.csv ------------------ Fields: id: participant's identifier is_robot: Is a bot (1: bot, not valid user 0: valid user) gender gender (M: Male F: Female) age* enquesta1 > pr2* employment* enquesta2 > pr1* civil_status* enquesta2 > pr2* studies* enquesta2 > pr3* residence postal_code postal code origin origin (Spain, Others) country country session_id session's identifier ended participant end the game (1) endowment endowment (20,30,40,50,60) end_capital savings winnings winnings num_selec number of selections (ideally 10) enquesta_final_XX* final survey questions pr1 to pr13 bots number of bot's answers 2. session.csv -------------- Fields: id session's identifier num_session number of session num_rounds total rounds (default:10) num_players total players (default:6) win_if_fails win if do not achieve the target (1) else (0) goal_reached goal reached (1) and unequal (1) comments comments 3. userronda.csv ---------------- Fields: id userronda's identifier has_selected participant has selected (1) bot (0) option_selected participant's choice ([0-4]) round_id round's identifier user_id participant's identifier 4. ronda.csv ------------ Fields: id round's identifier num_round num_round start_pot distance to the goal at the round's beginning end_pot distance to the goal at the round's ending session_id session's identifier