DAC and CRS code lists

core

Files Size Format Created Updated License Source
17 2MB csv zip 2 months ago OECD
The DAC Secretariat maintains various code lists which are used by donors to report on their aid flows to the DAC databases. In addition, these codes are used to classify information in the DAC databases. Here you can find these codes republished in a machine readable format. They’re fetched read more
Download

Data Files

File Description Size Last changed Download Other formats
dac-members [csv] DAC members 910B dac-members [csv] dac-members [json] (910B)
multilateral-donors [csv] Multilateral donors 1kB multilateral-donors [csv] multilateral-donors [json] (1kB)
non-dac-donors [csv] Non-DAC donors 682B non-dac-donors [csv] non-dac-donors [json] (682B)
private-donors [csv] Private donors 104B private-donors [csv] private-donors [json] (104B)
agencies [csv] Agencies 36kB agencies [csv] agencies [json] (36kB)
nature-of-submission [csv] Nature of submission 491B nature-of-submission [csv] nature-of-submission [json] (491B)
recipients [csv] Recipients 11kB recipients [csv] recipients [json] (11kB)
channel-codes [csv] Channel codes 46kB channel-codes [csv] channel-codes [json] (46kB)
collaboration-types [csv] Collaboration types 2kB collaboration-types [csv] collaboration-types [json] (2kB)
flow-types [csv] Flow types 2kB flow-types [csv] flow-types [json] (2kB)
finance-types [csv] Finance types 11kB finance-types [csv] finance-types [json] (11kB)
finance-type-categories [csv] Finance type categories 502B finance-type-categories [csv] finance-type-categories [json] (502B)
aid-types [csv] Aid types 15kB aid-types [csv] aid-types [json] (15kB)
aid-type-categories [csv] Aid type categories 3kB aid-type-categories [csv] aid-type-categories [json] (3kB)
sectors [csv] Sectors 117kB sectors [csv] sectors [json] (117kB)
sector-categories [csv] Sector categories 23kB sector-categories [csv] sector-categories [json] (23kB)
dac-and-crs-code-lists_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 261kB dac-and-crs-code-lists_zip [zip]

dac-members  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

multilateral-donors  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

non-dac-donors  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

private-donors  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

agencies  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
donor_name_en 2 string
donor_name_fr 3 string
agency_code 4 string
agency_name_en 5 string
agency_name_fr 6 string
acronym 7 string

nature-of-submission  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

recipients  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string
income_group 4 string
geography 5 string

channel-codes  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
category 1 string
code 2 string
name_en 3 string
acronym_en 4 string
name_fr 5 string
acronym_fr 6 string

collaboration-types  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

flow-types  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
description_en 3 string
name_fr 4 string
description_fr 5 string

finance-types  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
category 1 string
code 2 string
name_en 3 string
description_en 4 string
name_fr 5 string
description_fr 6 string

finance-type-categories  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
name_fr 3 string

aid-types  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
category 1 string
code 2 string
name_en 3 string
description_en 4 string
name_fr 5 string
description_fr 6 string

aid-type-categories  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
description_en 3 string
name_fr 4 string
description_fr 5 string

sectors  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
category 1 string
code 2 string
voluntary_code 3 string
name_en 4 string
description_en 5 string
name_fr 6 string
description_fr 7 string

sector-categories  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
code 1 string
name_en 2 string
description_en 3 string
name_fr 4 string
description_fr 5 string

dac-and-crs-code-lists_zip  

This is a preview version. There might be more data in the original version.

Read me

goodtables.io

The DAC Secretariat maintains various code lists which are used by donors to report on their aid flows to the DAC databases. In addition, these codes are used to classify information in the DAC databases.

Here you can find these codes republished in a machine readable format. They’re fetched from an excel file available on the OECD website.

Preparation

You will need: python 3.x

Run the following to download and convert the data from XLS to CSV:

pip install -r requirements.txt
python scraper.py

License

This material is licensed by its maintainers under the Public Domain Dedication and License.

Import into your tool

In order to use Data Package in R follow instructions below:

install.packages("devtools")
library(devtools)
install_github("hadley/readr")
install_github("ropenscilabs/jsonvalidate")
install_github("ropenscilabs/datapkg")

#Load client
library(datapkg)

#Get Data Package
datapackage <- datapkg_read("https://pkgstore.datahub.io/core/dac-and-crs-code-lists/latest")

#Package info
print(datapackage)

#Open actual data in RStudio Viewer
View(datapackage$data$"dac-members")
View(datapackage$data$"multilateral-donors")
View(datapackage$data$"non-dac-donors")
View(datapackage$data$"private-donors")
View(datapackage$data$"agencies")
View(datapackage$data$"nature-of-submission")
View(datapackage$data$"recipients")
View(datapackage$data$"channel-codes")
View(datapackage$data$"collaboration-types")
View(datapackage$data$"flow-types")
View(datapackage$data$"finance-types")
View(datapackage$data$"finance-type-categories")
View(datapackage$data$"aid-types")
View(datapackage$data$"aid-type-categories")
View(datapackage$data$"sectors")
View(datapackage$data$"sector-categories")
View(datapackage$data$"dac-and-crs-code-lists_zip")

Tested with Python 3.5.2

To generate Pandas data frames based on JSON Table Schema descriptors we have to install jsontableschema-pandas plugin. To load resources from a data package as Pandas data frames use datapackage.push_datapackage function. Storage works as a container for Pandas data frames.

In order to work with Data Packages in Pandas you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-pandas

To get Data Package run following code:

import datapackage

data_url = "https://pkgstore.datahub.io/core/dac-and-crs-code-lists/latest/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# to see datasets in this package
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

In order to work with Data Packages in Python you need to install our packages:

$ pip install datapackage

To get Data Package into your Python environment, run following code:

import datapackage

dp = datapackage.DataPackage('https://pkgstore.datahub.io/core/dac-and-crs-code-lists/latest/datapackage.json')

# see metadata
print(dp.descriptor)

# get list of csv files
csvList = [dp.resources[x].descriptor['name'] for x in range(0,len(dp.resources))]
print(csvList) # ["resource name", ...]

# access csv file by the index starting 0
print(dp.resources[0].data)

To use this dataset in JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use code snippet below:

  const {Dataset} = require('data.js')

  const path = 'https://pkgstore.datahub.io/core/dac-and-crs-code-lists/latest/datapackage.json'

  const dataset = Dataset.load(path)

  // get a data file in this dataset
  const file = dataset.resources[0]
  const data = file.stream()

In order to work with Data Packages in SQL you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-sql
$ pip install sqlalchemy

To import Data Package to your SQLite Database, run following code:

import datapackage
from sqlalchemy import create_engine

data_url = 'https://pkgstore.datahub.io/core/dac-and-crs-code-lists/latest/datapackage.json'
engine = create_engine('sqlite:///:memory:')

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'sql', engine=engine)

# to see datasets in this package
storage.buckets

# to execute sql command (assuming data is in "data" folder, name of resource is data and file name is data.csv)
storage._Storage__connection.execute('select * from data__data___data limit 1;').fetchall()

# description of the table columns
storage.describe('data__data___data')
Datapackage.json