The data contain the experimental results used in the paper "Digital Identity: The Effect of Trust and Reputation Information on User Judgement in the Sharing Economy" by M. Zloteanu, N. Harvey, D. Tuckett, and G. Livan (https://osf.io/mpvne/). The responses provided by each participant (user) were recorded by the Gorilla platform (http://gorilla.sc/), on which our artificial SE platform was designed. This data was then exported as a CSV file containing all responses from all users. The data included demographics, judgments made on each profile, and other relevant metrics. See screengrab below for how the data files appear. Each row represents one response provided by the user (e.g., ratings for the profile, element selected using their tokens, or responses to the validation question). ![enter image description here][1] The raw data from Gorilla is then processed in Excel to arrange for data input in SPSS. Here, incomplete or incorrect responses are identified and eliminated (see main text and SI for additional details). The data for each user is also transposed to reflect a single line entry per profile. See screengrab below for how the final processed Excel file appears. Each row represents the user’s responses to a single profile. For this particular study, each user has 10 rows reflecting the 10 profiles they rated. ![enter image description here][2] The Excel data is then aggregated per each user to form the final responses used in the data analysis process. In Study 1, for instance, each experimental condition had 10 profiles (as seen in Figure 2). Summing the ratings across all 10 profiles provides the responses of that particular user in their respective experimental condition. These data are then inputted into SPSS. See screengrab below for how this appeared. Each row represents a single user's judgments across the experiment, for their respective condition. The corresponding demographics for that user are also included as well as other relevant metrics. ![enter image description here][3] [1]: https://files.osf.io/v1/resources/ykag6/providers/osfstorage/5bed2f0131c15c001a1afe22?mode=render [2]: https://files.osf.io/v1/resources/ykag6/providers/osfstorage/5bed2f3d897ec60019bd36e7?mode=render [3]: https://files.osf.io/v1/resources/ykag6/providers/osfstorage/5bed2f8b897ec6001abd5978?mode=render The data was then analyzed in SPSS using the appropriate techniques for our hypotheses and experimental design (see the paper for details). The final database files (one for each study) are attached with our submission and have been made available. For anyone wishing to replicate our results, simply copy/open the relevant SAV file into SPSS’s workspace and refer to the SPSS manual for instructions to carry out our chosen manipulations on the data. For example, the first reported analysis in the main text is a one-way ANOVA, where Profile (Hidden, Reveal, or Visible) – named “condition” in the SAV file – is entered as the between-subjects factor of the analysis and Rent decision – named “Rent_tot” in the SAV file – is entered as the dependent measure. Link to SPSS Manual: https://www.ibm.com/support/knowledgecenter/SSLVMB_24.0.0/spss/product_landing.htmlAdditionally, King (2013), 'Discovering Statistics using IBM SPSS Statistics' can provide additional help to SPSS users. **Note**: the user names in the included screengrabs are randomly generated validation codes by Gorilla to keep track of the data, and do not reflect any identifiable information of real people. All data is kept secure and anonymized as per the University’s and EU’s regulations.