Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
5 | 0B | csv zip | 3 years ago |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
anti_beps_measures | 2kB | csv (2kB) , json (22kB) | ||
blacklist | 4kB | csv (4kB) , json (36kB) | ||
fair_taxation | 5kB | csv (5kB) , json (48kB) | ||
tax_transparency | 8kB | csv (8kB) , json (58kB) | ||
datapackage_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 22kB | zip (22kB) |
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
jurisdiction | 1 | string (default) | |
Passes Implementation of Anti-Beps Measures | 2 | string (default) | |
Member of the Inclusive Framework on BEPS | 3 | string (default) | |
Signatory of the MLI | 4 | string (default) | |
Implements ATAD | 5 | string (default) |
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
jurisdiction | 1 | string (default) | |
blacklisted? | 2 | string (default) | |
Passes Criterion Tax Transparency | 3 | string (default) | |
Passes Criterion Fair Taxation | 4 | string (default) | |
Passes Criterion Anti Beps | 5 | string (default) | |
Listed solely on criterion two | 6 | string (default) | |
Income Level | 7 | string (default) | |
OECD member, G20 member, EU candidate or Financial Center? | 8 | string (default) |
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
jurisdiction | 1 | string (default) | |
passes fair taxation | 2 | string (default) | |
is a sink of a conduit (with sink centrality value) | 3 | string (default) | |
witholding tax rate on dividends | 4 | string (default) | |
has a zero percent rate? | 5 | string (default) | |
has a preferential tax regime? | 6 | string (default) | |
Corporate tax rate (2017) | 7 | number (default) | |
Corporate tax rate ranking | 8 | string (default) | |
Has transfer pricing rules? | 9 | string (default) | |
has thin cap rules? | 10 | string (default) | |
has CFC rules? | 11 | string (default) |
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
jurisdiction | 1 | string (default) | |
Passes Tax Transparency | 2 | string (default) | |
Passes Criterion 1.1 | 3 | string (default) | |
MCAA signatory | 4 | string (default) | |
CRS by MCAA or Bilateral Network | 5 | string (default) | |
Number of Automatic exchange relations | 6 | number (default) | |
Passes Criterion 1.2 | 7 | string (default) | |
Global Forum Rating | 8 | string (default) | |
Rating Schedule | 9 | string (default) | |
Passes Criterion 1.3 | 10 | string (default) | |
Multilateral Convention Signatory | 11 | string (default) | |
Number of treaties with EU Countries that contain paragraph 4 & 5 | 12 | number (default) | |
Has Sufficient Treaty Network | 13 | string (default) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/opendatafortaxjustice/blacklist
data info opendatafortaxjustice/blacklist
tree opendatafortaxjustice/blacklist
# Get a list of dataset's resources
curl -L -s https://datahub.io/opendatafortaxjustice/blacklist/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/opendatafortaxjustice/blacklist/r/0.csv
curl -L https://datahub.io/opendatafortaxjustice/blacklist/r/1.csv
curl -L https://datahub.io/opendatafortaxjustice/blacklist/r/2.csv
curl -L https://datahub.io/opendatafortaxjustice/blacklist/r/3.csv
curl -L https://datahub.io/opendatafortaxjustice/blacklist/r/4.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/opendatafortaxjustice/blacklist/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$resources$name)
# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
if(json_data$resources$datahub$type[i]=='derived/csv'){
path_to_file = json_data$resources$path[i]
data <- read.csv(url(path_to_file))
print(data)
}
}
Note: You might need to run the script with root permissions if you are running on Linux machine
Install the Frictionless Data data package library and the pandas itself:
pip install datapackage
pip install pandas
Now you can use the datapackage in the Pandas:
import datapackage
import pandas as pd
data_url = 'https://datahub.io/opendatafortaxjustice/blacklist/datapackage.json'
# to load Data Package into storage
package = datapackage.Package(data_url)
# to load only tabular data
resources = package.resources
for resource in resources:
if resource.tabular:
data = pd.read_csv(resource.descriptor['path'])
print (data)
For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):
pip install datapackage
To get Data Package into your Python environment, run following code:
from datapackage import Package
package = Package('https://datahub.io/opendatafortaxjustice/blacklist/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
print(resource.read())
If you are using JavaScript, please, follow instructions below:
Install data.js
module using npm
:
$ npm install data.js
Once the package is installed, use the following code snippet:
const {Dataset} = require('data.js')
const path = 'https://datahub.io/opendatafortaxjustice/blacklist/datapackage.json'
// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
const dataset = await Dataset.load(path)
// get list of all resources:
for (const id in dataset.resources) {
console.log(dataset.resources[id]._descriptor.name)
}
// get all tabular data(if exists any)
for (const id in dataset.resources) {
if (dataset.resources[id]._descriptor.format === "csv") {
const file = dataset.resources[id]
// Get a raw stream
const stream = await file.stream()
// entire file as a buffer (be careful with large files!)
const buffer = await file.buffer
// print data
stream.pipe(process.stdout)
}
}
})()