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This is the slides and the speaking manuscript of a presentation given at the event Figshare fest in Amsterdam, Netherlands on June 28th 2017. The presentation is meant to show a case study of organizing research data management in a university both from the management and user perspectives.The presentation describes how the Stockholm University Library team is currently (June 2017) working to manage research data at Stockholm University across different administrative departments. The presentation also includes an overview of the current research data management activities at the Stockholm University with a deeper insight on working with a knowledge hub for research data management based on user feedback.

The main file is performance_correction.html in AAN3_analysis_scripts.zip. It contains the results of the main analyses.See AAN3_readme_figshare.txt: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.se3. 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, Sweden5. 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 INFORMATION1. Licenses/restrictions placed on the data: CC BY 4.02. 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.2020.102954The study was preregistered:https://doi.org/10.17605/OSF.IO/W4U7V3. Links to other publicly accessible locations of the data: N/A4. Links/relationships to ancillary data sets: N/A5. 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.9724280DATA & FILE OVERVIEWFile 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 experimentAAN3_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 dataAAN3_EEG_source_localization_scripts.zip: Python-MNE files needed for source localization. The template MRI is provided in this zip. The files are obtained from the MNE tutorial (https://mne.tools/stable/auto_tutorials/source-modeling/plot_eeg_no_mri.html?highlight=template). Note that the stc folder is empty. The source time course files are not provided because of their large size. They can quickly be generated from the analysis script. They are needed for the 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 INFORMATION1. 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 publication3. 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 WiensDATA-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).

Wikipedia is a well-used source of knowledge. Its reputation has also become better within the academy and formal education, not only being the everyday target for looking up things. However, in order for Wikipedia to be used to its full potential, we must know both how it functions, how we can work with it in a source critical manner, and how we ourselves can contribute to the medium.This presentation is aimed for University teachers who wants to learn how to use Wikipedia as a valuable tool for teaching and learning. It is based upon a workshop that was given to University teachers from all eight CIVIS Universities (Aix-Marseille Université, National and Kapodistrian University of Athens, Universitatea din București, Université libre de Bruxelles, Universidad Autónoma de Madrid, Sapienza Università di Roma, Stockholm University and Eberhard Karls Universität Tübingen).Having a better understanding of Wikipedia and its functions is something that our students would benefit from, and therefore, we must create assignments connected to Wikipedia and its sister platforms. Not only teaching the principles of the platform or the manners of looking at revision history, talk pages and sources, but also how to edit articles, upload pictures to Wikimedia Commons and translate excellent articles from different languages using the built-in tools.