cordis-rank

Rank a given company / institute based on the EC contribution using the Cordis dataset

Project maintained by fabriziomiano Hosted on GitHub Pages — Theme by mattgraham

Cordis Rank

A tool to rank a company/institute based on EC contributions using Cordis dataset.

Requirements

The tool has been tested on Ubuntu 18.04, Windows 10, and mac OS Catalina. It requires

Python 3.6+
pandas
pytest

Setup

It’s a typical python3 setup. Once you installed Python 3.6+ , open a terminal, e.g. in your $HOME directory and follow these steps

Clone the repo

git clone https://github.com/fabriziomiano/cordis-rank.git

Install `virtualenv`

Ubuntu 18.04:

sudo apt install -y python3-venv

mac OS Catalina:

xcode-select --install
sudo easy_insall virtualenv

Windows 10

virtualenv is shipped with the Python3.6+ installation setup

Then, let’s create a new directory in e.g. $HOME/.envs/cordis-rank

mkdir -p ~/.envs/cordis-rank

Create and activate the virtual environment

Assuming you’re still in a terminal in your $HOME directory

Ubuntu & mac OS

python3 -m venv ~/.envs/cordis-rank
source ~/.envs/cordis-rank/bin/activate

Windows

python3 -m venv cordis-rank
cordis-rank\Scripts\activate.bat

Check that now you have (cordis-rank) at the beginning of your command line

Update pip and install the requirements in requirements.txt

pip install --upgrade pip
pip install -r requirements.txt

You’re now ready to run it

Configuration

Although the tool accepts user input parameters, the file constants.py contains a number of constans that can be modified according to the type of data to use or analysis to carry out. In particular, here are some of the parameters:

COMPANY_NAME: the name of the company to rank
ACTIVITY_TYPE_FILTER: e.g. “PRC” to consider only companies
APPLY_PRC_FILTER: boolean: apply the activity-type filter if True
BUDGET_COLUMN_NAME: name of the budget / EC contribution column, e.g. “ecContribution”
INTERESTING_COLUMNS: the list of columns to filter the raw Cordis dataset with
DEFAULT_LOCAL_DATA_PATH: the default path of the Cordis dataset in this repo
DEFAULT_URL: the default URL used to get the Cordis 2020 CSV file
COLUMNS_MAP: a dict to rename the processed data frame to pretty print ranking results

How to run

Navigate to the working copy of the repo you previously cloned
```
cd cordis-rank
```

Run the tool by giving

python rank.py

An initialization output should show up, saying

config - [INFO] - --------------------------------------------------
config - [INFO] - Initializing with the following configuration
config - [INFO] - Check constants.py to change any of the following
config - [INFO] - --------------------------------------------------
config - [INFO] - COMPANY_NAME: THE UNIVERSITY OF SUSSEX
config - [INFO] - ACTIVITY_TYPE_FILTER: HES
config - [INFO] - APPLY_ACTIVITY_FILTER: True
config - [INFO] - --------------------------------------------------
config - [INFO] - Assuming an input dataset with the following features
config - [INFO] - --------------------------------------------------
config - [INFO] - BUDGET_COLUMN_NAME: ecContribution
config - [INFO] - COMPANY_COLUMN_NAME: name
config - [INFO] - ACTIVITY_COLUMN_NAME: activityType
config - [INFO] - COUNTRY_COLUMN_NAME: country
config - [INFO] - --------------------------------------------------
config - [INFO] - Fallback data sources
config - [INFO] - --------------------------------------------------
config - [INFO] - DEFAULT_URL: https://cordis.europa.eu/data/cordis-h2020organizations.csv
config - [INFO] - DEFAULT_LOCAL_DATA_PATH: cordis-h2020organizations.csv
config - [INFO] - --------------------------------------------------

at the end of which you will be prompted to whether download the data or run on a local CSV file

Read Cordis data_tools from URL? [y/n]: n

in this example the cordis-h2020organizations.csv file within this repo (leave blank)

Data file path (default: cordis-h2020organizations.csv):

and you should get the following output

data_tools - [INFO] - Reading data_tools from cordis-h2020organizations.csv
data_tools - [INFO] - Data frame loaded in 0.5 seconds

then, if you set the activity-type filter to true in constants.py, you’ll get a message informing you about the filter being applied

data_tools - [INFO] - Considering only activityType = HES

lastly, you should get the following results

printer - [INFO] - --------------------------------------------------
printer - [INFO] - Ranking:
 Rank       Company / Institute Country  EC Contribution
0   124  THE UNIVERSITY OF SUSSEX      UK      43154405.56
printer - [INFO] - --------------------------------------------------
printer - [INFO] - Overall company budget: 43154405.56
printer - [INFO] - Company Ranking: 124 out of 1753
printer - [INFO] - Done

Note: if you choose to read the data from the default 2020 Cordis URL:

https://cordis.europa.eu/data/cordis-h2020organizations.csv

the process may take a while as pandas need to download the data. Furthermore, the final results may vary, as the CSV file might have been updated with respect to the one in this repo.

That’s it!

Tests

To run the tests from the home of the repo, e.g. $HOME/cordis-rank, simply run

pytest

Tests may take a while as the data have to be downloaded twice to run the various fixtures.

Do not forget to rerun the test if you change any of configuration parameters in constants.py