The easiest method by far to download and run the application is to use pip to install it

pip install dao-scripts

Then, you can use this script using the command daoa-cache-scripts or dao-scripts

Enter in your terminal (git must be installed) and write down:

git clone https://github.com/Grasia/dao-scripts

After that, move to the repository root directory with:

cd dao-scripts

All code has been tested on Linux, but it should work on Windows and macOS, 'cause it just uses the Python environment.

So, you must install the following dependencies to run the tool:

• python3 (3.10 or later)
• python3-pip

Now, install the Python dependencies:

pip3 install -r requirements.txt

If you don't want to share Python dependencies among other projects, you should use a virtual environment, such as virtualenv.

If you want all the data used in the app, you can just use:

dao-scripts

this will create a folder called datawarehouse with a lot of files in Apache's arrow format.

You can import those files to pandas with read_feather. For example:

pd.read_feather('datawarehouse/aragon/apps.arr')

If you don't want all the data (and it can take a lot of time), you have a lot of options available to select whichever data you want. The full --help output is

usage: daoa-cache-scripts [-h] [-V] [-p [{aragon,daohaus,daostack} ...]]
                          [--ignore-errors | --no-ignore-errors] [-d] [-f] [-F] [--skip-daohaus-names]
                          [-n {mainnet,arbitrum,xdai,polygon} [{mainnet,arbitrum,xdai,polygon} ...]]
                          [-c COLLECTORS [COLLECTORS ...]] [--block-datetime BLOCK_DATETIME]
                          [-D DATAWAREHOUSE]

Main script to populate dao-analyzer cache

options:
  -h, --help            show this help message and exit
  -V, --version         Displays the version and exits
  -p [{aragon,daohaus,daostack} ...], --platforms [{aragon,daohaus,daostack} ...]
                        The platforms to update. Every platform is updated by default.
  --ignore-errors, --no-ignore-errors
                        Whether to ignore errors and continue (default: True)
  -d, --debug           Shows debug info
  -f, --force           Removes the cache before updating
  -F, --delete-force    Removes the datawarehouse folder before doing anything
  --skip-daohaus-names  Skips the step of getting Daohaus Moloch's names, which takes some time
    -n {mainnet,arbitrum,xdai,polygon} [{mainnet,arbitrum,xdai,polygon} ...], --networks {mainnet,arbitrum,xdai,polygon} [{mainnet,arbitrum,xdai,polygon} ...]
                        Networks to update. Every network is updated by default
  -c COLLECTORS [COLLECTORS ...], --collectors COLLECTORS [COLLECTORS ...]
                        Collectors to run. For example: aragon/casts
  --block-datetime BLOCK_DATETIME
                        Get data up to a block datetime (input in ISO format)
  -D DATAWAREHOUSE, --datawarehouse DATAWAREHOUSE
                        Specifies the destination folder of the datawarehouse

You can select the platform to download data about with the --platform selector. Let's download only data for daostack and aragon:

daoa-cache-scripts --platforms daostack aragon

You can select the chain to get data from with the --networks switch. For example, to get data only for xdai network, you can do:

daoa-cache-scripts --networks xdai

DAO-Analyzer is developed under the umbrella of multiple research projects:

• Chain Community, funded by the Spanish Ministry of Science and Innovation (RTI2018‐096820‐A‐I00) and led by Javier Arroyo and Samer Hassan
• P2P Models, funded by the European Research Council (ERC-2017-STG 625 grant no.: 75920), led by Samer Hassan.
• DAOapplications, funded by the Spanish Ministry of Science and Innovation (PID2021-127956OB-I00) and led by Javier Arroyo and Samer Hassan

You can just cite one of our publications:

Javier Arroyo, David Davó, Elena Martínez-Vicente, Youssef Faqir-Rhazoui, and Samer Hassan (2022). "DAO-Analyzer: Exploring Activity and Participation in Blockchain Organizations.". Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing (CSCW'22 Companion). ACM, 193–196.

Or, if you want to explicitly cite the application:

Arroyo, Javier, Davó, David, Faqir-Rhazoui, Youssef, & Martínez Vicente, Elena. (2023). DAO Analyzer. Zenodo. https://doi.org/10.5281/zenodo.7669689