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**Published as:** Behnke, M., Buchwald, M., Bykowski, A. et al. Psychophysiology of positive and negative emotions, dataset of 1157 cases and 8 biosignals. **Sci Data** 9, 10 (2022). **Overview:** This database consists of recordings of 1157 cases, collected across seven studies, a continuous record of self-reported affect, along with several biosignals. We measured the psychophysiological reactivity to emotional stimuli using electrocardiogram (ECG), impedance cardiogram (ICG), electrodermal activity (EDA), hemodynamic measures, e.g., systolic blood pressure (SBP), diastolic blood pressure (DBP), cardiac output (CO), total peripheral resistance (TPR), respiration trace (Resp), and skin temperature (Temp). We experimentally elicited a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. **Dataset Structure:** The data repository consists of seven ZIP-compressed directories (folders), one for each study, e.g., "study1" directory was compressed to "" archive file, "study2" was compressed as ""), etc. Each of these directories contains a set of CSV files with psychophysiological information for particular subjects. Name of each of these CSV files follows a consistent naming convention, i.e.: "S<study_id>_P<participant_id>_<phase_name>.csv”, where “S” stands for study, “P” for participants, and “<study_id>” & “<particpant_id>” are natural numbers indicating study and unique participant identifiers; and “<phase_name>” is the name of the phase of an experiment, e.g., “Baseline”, or “Amusement1”. The description of all experimental-phase labels is explained in the metadata spreadsheet. All psychophysiological signals recorded during the experiment for each individual are available in a single CSV datafile named " S<study_id>_P<participant_id>_All.csv". Detailed information is presented in the manuscript. **Single File Structure:** Each of the CSV files in the dataset has a 9-line header, i.e., each file's first nine rows start with a hash sign ("#"). In the header, file metadata is available, including: 1. ID of the study as a variable "Study_name", e.g., "Study 7"; 2. participant's ID within the study as a variable "Subject_ID", e.g., "119"; 3. participant's age as a variable "Subject_Age", e.g., "23"; 4. participant’s sex coded as man = 0, woman = 1, as a variable “Subject_Sex”; 5. participant’s height in centimeters as variable “Participants_Height”, e.g. “178”; 6. participant’s weight in kilograms as variable “Participants_Weight”, e.g. “74”; 7. channel/sensor name as a variable "Channel_Name", e.g., "timestamp", "affect", "ECG", "dzdt", "dz", "z0", or "marker"; 8. category of the data in each column as a variable "Data_Category", e.g., "timestamp", "data", and "marker" 9. units of the measurement as a variable "Data_Unit", e.g.: “second”, “millivolt”, or “ohm”; 10. sample rate of data collection as a variable "Data_Sample_rate", e.g.: "1000Hz", or "beat to beat"; 11. name of the device (manufacturer) used for data collection as a variable "Data_Device", e.g., "LabChart 8.19 (ADInstruments, New Zealand)", "Response Meter (ADInstruments, New Zealand)", or "ECG (Vrije Universiteit Ambulatory Monitoring System, VU-AMS, the Netherlands)". Following the header, each CSV file contains 7-12 columns, depending on the study. For studies in which data were gathered from more channels, there are more columns in CSV files. Sensor names used in all studies are consistent across all CSV files (see metadata file). The first column of the data table (except for the header) contains timestamps, as provided by a clock on the main data acquisition (logging) computer – the timestamp format is time in seconds. In the last column, there is a marker that identifies the specific phase of the experiment. The metadata file provides a full explanation of the stimulus IDs used to mark the specific phase of the experiment, e.g.: "-1" indicates the experimental baseline, while "107" indicates neutral film clip "The Lover". The columns in between timestamp and marker contain the physiological data (see the Manuscript, Table 2 for details). **GitHub repository:** Scripts for converting data from proprietary acquisition software formats into consistent CSV files, as well as IPython Jupyter Notebooks presenting how to load the data from POPANE CSV file into Python Pandas DataFrame structure are available at the following GitHub repository: --- **Cite as:** Behnke, M., Buchwald, M., Bykowski, A. et al. Psychophysiology of positive and negative emotions, dataset of 1157 cases and 8 biosignals. Sci Data 9, 10 (2022).
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