FIGSHARE METADATA


Categories


- Biological psychology
- Neuroscience and physiological psychology
- Auditory awareness and consciousness
- Performance


Keywords


- EEG
- ERP
- consciousness
- awareness
- neural correlates
- auditory awareness negativity
- controlling for performance


References


- https://doi.org/10.17605/OSF.IO/W4U7V
- https://github.com/stamnosslin/mn
- https://doi.org/10.17045/sthlmuni.4981154.v3


GENERAL INFORMATION


1. Title of Dataset:
Open data: Is auditory awareness negativity confounded by performance?


2. Author Information
    A. Principal Investigator Contact Information
            Name: Stefan Wiens
            Institution: Department of Psychology, Stockholm University, Sweden
            Internet: https://www.su.se/profiles/swiens-1.184142
            Email: sws@psychology.su.se


    B. Associate or Co-investigator Contact Information
            Name: Rasmus Eklund
            Institution: Department of Psychology, Stockholm University, Sweden
            Internet: https://www.su.se/profiles/raek2031-1.223133
            Email: rasmus.eklund@psychology.su.se
    
    C. Associate or Co-investigator Contact Information
            Name: Billy Gerdfeldter
            Institution: Department of Psychology, Stockholm University, Sweden
            Internet: https://www.su.se/profiles/bige1544-1.403208
            Email: billy.gerdfeldter@psychology.su.se




3. Date of data collection: 
Subjects (N = 28) were tested between 2018-12-03 and 2019-01-18.


4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden


5. Information about funding sources that supported the collection of the data:
Swedish Research Council / Vetenskapsrådet (Grant 2015-01181)
Marianne and Marcus Wallenberg (Grant 2019-0102)


SHARING/ACCESS INFORMATION


1. Licenses/restrictions placed on the data: CC BY 4.0


2. Links to publications that cite or use the data: Eklund R., Gerdfeldter B.,  & Wiens S. (2020).  Is auditory awareness negativity confounded by performance? Consciousness and Cognition. https://doi.org/10.1016/j.concog.2020.102954


The study was preregistered:
https://doi.org/10.17605/OSF.IO/W4U7V


3. Links to other publicly accessible locations of the data: N/A


4. Links/relationships to ancillary data sets: N/A


5. Was data derived from another source? No
    
6. Recommended citation for this dataset: Eklund R., Gerdfeldter B., & Wiens S. (2020). Open data: Is auditory awareness negativity confounded by performance? Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.9724280




DATA & FILE OVERVIEW


File List:
The files contain the raw data, scripts, and results of main and supplementary analyses of the electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.


AAN3_experiment_scripts.zip: contains the Python files to run the experiment


AAN3_rawdata_EEG.zip: contains raw EEG data files for each subject in .bdf format (generated by Biosemi)


AAN3_rawdata_log.zip: contains log files of the EEG session (generated by Python)


AAN3_EEG_scripts.zip: Python-MNE scripts to process and to analyze the EEG data


AAN3_EEG_source_localization_scripts.zip: Python-MNE files needed for source localization


AAN3_analysis_scripts.zip: R scripts to analyze the data. The main file is performance_correction.html. It contains the results of the main analyses.


AAN3_results.zip: contains summary data files, figures, and tables that are created by Python-MNE and R.


METHODOLOGICAL INFORMATION


1. Description of methods used for collection/generation of data:
The auditory stimuli were two 100-ms tones (f = 900 Hz and 1400 Hz, 5 ms fade-in and fade-out).
The experiment was programmed in Python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mn 
The EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and saved in .bdf format.
For more information, see linked publication.


2. Methods for processing the data:
We computed event-related potentials and source localization. See linked publication


3. Instrument- or software-specific information needed to interpret the data:
MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html# 
Rstudio used with R (R Core Team, 2016): https://rstudio.com/products/rstudio/ 
Wiens, S. (2017). Aladins Bayes Factor in R (Version 3). https://www.doi.org/10.17045/sthlmuni.4981154.v3 


4. Standards and calibration information, if appropriate:
For information, see linked publication.


5. Environmental/experimental conditions:
For information, see linked publication.


6. Describe any quality-assurance procedures performed on the data:
For information, see linked publication.


7. People involved with sample collection, processing, analysis and/or submission:


- Data collection: Rasmus Eklund with assistance from Billy Gerdfeldter.
- Data processing, analysis, and submission: Rasmus Eklund and Stefan Wiens


DATA-SPECIFIC INFORMATION:
All relevant information can be found in the MNE-Python and R scripts (in EEG_scripts and analysis_scripts folders) that process the raw data. For example, we added notes to explain what different variables mean.


The folder structure needs to be as follows:
AAN3 (main folder)
--->data
--->--->bdf (AAN3_rawdata_EEG)
--->--->log (AAN3_rawdata_log)
--->--->raw (empty)
--->MNE (AAN3_EEG_scripts)
--->R (AAN3_analysis_scripts)
--->results (AAN3_results)
--->source (AAN3_EEG_source_localization_files)


To run the MNE-Python scripts:
Anaconda was used with MNE-Python 0.20 (see installation at https://mne.tools/stable/index.html# ).
For Downsample_AAN3, ICA_raw_AAN3, Preprocess_AAN3, Make_inverse_operator_AAN3.py, BehaviorTables_AAN3, and PlotSource, the complete scripts should be run (from anaconda prompt).
For Analysis_AAN3, one section at the time should be run (from Spyder).