Finish example solutions

Example4_READMETemplate.md

@@ -0,0 +1,81 @@
+This readme file was generated on [YYYY-MM-DD] by Dominik Deffner
+
+
+# GENERAL INFORMATION
+
+**Title of Data set**: Collective incentives reduce over-exploitation of social information in unconstrained human groups
+Description: This repository contains the full data and scripts to reproduce all analyses and figures as well as the experimental game itself used in Deffner, D., Mezey, D., Kahl, B., Schakowski, A., Romanczuk, P., Wu, C. & Kurvers, R. (accepted, in principle, at Nature Communications): “Collective incentives reduce over-exploitation of social information in unconstrained human groups”
+
+
+Author/Principal Investigator Information
+Name: Deffner, Dominik
+ORCID:
+Institution:
+Email:
+Date of data collection: 2022-07-05 to 2022-09-21
+
+Geographic location of data collection: Max Planck Institute for Human Development
+
+Information about funding sources that supported the collection of the data: n/a
+
+# SHARING/ACCESS Data
+
+Licenses/restrictions placed on the data: CC BY 4.0 / Apache License 2.0 
+
+**Links to publications that cite or use the data**: https://psyarxiv.com/p3bj7/ 
+
+Links to other publicly accessible locations of the data: https://github.com/DominikDeffner/VirtualCollectiveForaging/tree/0.1.0?tab=readme-ov-file
+
+Links/relationships to ancillary data sets: n/a
+
+Was data derived from another source? If yes, list source(s): n/a
+
+# DATA & FILE OVERVIEW
+
+**File List**: 
+
+"master_file.r" sources all other scripts and implements the entire project workflow from raw Unity data to plots in the manuscript.
+
+The "Scripts" folder contains all relevant R scripts for data processing and analysis:
+
+- "functions.R" loads all required functions and packages
+- "data_prep.R" runs data preparation script to construct dataframes for analysis from raw Unity outputs
+- "HiddenMarkovModels_prep.R" contructs extended dataframe and stan data list for Social Hidden Markov Decision model
+- "Behavior_prep.R" computes behavioral summary statistics for each player/group and round
+- "Behavior_individual_level.R" runs and plots behavioral analyses at individual level
+- "Behavior_group_level.R" runs and plots behavioral analyses at group level
+- "Behavior_main_plot" produces main behavioral plot (Fig.2)
+- "Behavior_solitary.R" runs and plots behavioral analyses for solo control condition
+- "HiddenMarkovModels_baseline.R" runs baseline Social Hidden Markov Decision model and produces main text plots
+- "Viterbi.r" computes most likely state sequences for all participants through Viterbi algorithm
+- "HiddenMarkovModels_ESM_Plots.R" produces supplementary HMM plots
+- "HiddenMarkovModels_temporal.R" runs and plots temporal Social Hidden Markov Decision model
+- "CollectiveDynamics.R" runs and plots collective visual-spatial dynamics analyses using time-lagged Gaussian-process regressions
+
+The "Stan model code" folder contains stan files for the (baseline and time-varying) Social Hidden Markov Decision model as well as the time-lagged Gaussian-process model
+- "m_SHMDM.stan" is the baseline HMM (called in "HiddenMarkovModels_baseline.R")
+- "m_SHMDM_temporal.stan" is the time-varying HMM (called in "HiddenMarkovModels_temporal.R")
+- "m_temporal_success.stan" is monotonic-effect model for success over time (called in "HiddenMarkovModels_temporal.R")
+- "m_time_laggedGP.stan" is the time-lagged Gaussian-process model to analyze collective visual-spatial dynamics (called in "CollectiveDynamics.R")
+
+The "Data" folder contains all raw data for both "Group" and "Solo" conditions. Each sub-folder contains data from one experimental session. There are separate .txt files for participant demographics and for player and patch data from each round (indexing starts at 0). Variable names should be quite descriptive, but please get in touch in case anything is unclear.
+
+**Relationship between files, if important**: included above
+
+Additional related data collected that was not included in the current data package: n/a
+
+# METHODOLOGICAL INFORMATION
+
+Description of methods used for collection/generation of data:
+
+Methods for processing the data:
+
+Instrument- or software-specific information needed to interpret the data: <include full name and version of software, and any necessary packages or libraries needed to run scripts>
+
+Standards and calibration information, if appropriate:
+
+Environmental/experimental conditions:
+
+Describe any quality-assurance procedures performed on the data:
+
+People involved with sample collection, processing, analysis and/or submission:

documentation.qmd

@@ -267,16 +267,16 @@ You might also suggest to your department/research group/institute/research comm
 
 ::: {.callout-important collapse=true}
 ## Solution: Example 3 
-- 5 section can be filled based on the Example dataset README alone.  
+- 5 sections can be filled based on the Example dataset README alone.  
 - Excluding the "METHODOLOGICAL INFORMATION" section, ~10 of the 19 open fields can be filled based on the information collected in task 2.1. 
 - Find the filled out README template for example 3 [here](/Example3_READMETemplate.md)
-- Note: Only the README file for the OSF repository was taken in consideration.
+- Note: Only the README file for the OSF repository was taken into consideration.
 :::
 
 ::: {.callout-important collapse=true}
 ## Solution: Example 4 
-- X section can be filled based on the Example dataset README alone. 
-- Excluding the "METHODOLOGICAL INFORMATION" section, ~XX of the 19 open fields can be filled based on the information collected in task 2.1. 
+- 4 sections can be filled based on the Example dataset README alone. 
+- Excluding the "METHODOLOGICAL INFORMATION" section, ~11 of the 19 open fields can be filled based on the information collected in task 2.1. 
 - Find the filled out README template for example 4 [here](/Example4_READMETemplate.md)
 :::
 
@@ -405,10 +405,16 @@ To improve the quality and usability of your codebook you can provide it as a JS
 
 ::: {.callout-important collapse=true}
 ## Solution: Example 3
+-  The data files can be open directly in the OSF repository. The column names are not very descriptive. 
+-  However each data file has a corresponding codebook in .json format that explains each of the columns. 
+-  The scripts, how to deploy them and how to generate result from the data is explained in the README-file.
 :::
 
 ::: {.callout-important collapse=true}
 ## Solution: Example 4
+-  The data files are all .log files that can be opened with a standard editor as ;-separated tables. 
+-  Columns are named relatively clear. 
+-  The scripts, how to deploy them and how to generate result from the data is explained in the README-file.
 :::
 
 ## Further Resources: