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Database of Cross-Linguistic Norms, Ratings, and Relations for Words and Concepts

Installation of the database

Get started

We recommend using your terminal to carry out the following instructions. We (strongly) advise you to install a virtual environment. In addition, you need GIT and Python (version 3) on your system.

Install pynorare

The NoRaRe database uses a Python package called pynorare (as a dependency pyconcepticon will be installed automatically). The Python pachage can be installed with PyPi. Use either pip or pip3.

$ pip install pynorare

Clone norare-data GIT repository

To access the NoRaRe data sets, you need to clone the norare-data GIT repository into a folder of your choice. Navigate or create a folder where you want to store the norare-data repository.




Clone the repository by typing:

$ git clone

Clone concepticon-data GIT repository

The NoRaRe database is linked to Concepticon. To access all data sets and perform the mapping of your own data, you need to download the concepticon-data GIT repository.

$ git clone


To check if the installation worked and see the available commands of the pynorare package, type:

$ norare

Show NoRaRe statistics

For example, you can get statistic of the distribution of the Concepticon identifiers by typing the following command. Note that you should add the path to a specific clone of concepticon-data, because you are not within the root of the concepticon-data. The same is possible for the norare-data repository if you are working with multiple clones. You can also use the catalog.ini file to specify the path (see 'Define repository paths' section).

$ norare --repos=/PATH/TO/concepticon-data --norarepo=/PATH/TO/norare-data stats

List NoRaRe data sets

To see all available data sets, navigate into the norare-data folder and type:

$ norare --repos=/PATH/TO/concepticon-data ls

Download a NoRaRe data set

By typing the following command, the original data set will be downloaded into the raw folder. It is only locally stored.

$ norare --repos=/PATH/TO/concepticon-data download Abdaoui-2017-EmoLex

Define repository paths

To make your live easier, you can define the default path to concepticon-data in a catalog.ini file (Linux users can follow the desciption in this blog post).

For Mac users: Open the catalog.ini file in a text editor of your choice and add the following lines.

concepticon = /PATH/TO/concepticon-data 

If you can't find the catalog.ini file, create a directory mkdir /Users/YOURNAME/Library/Application\ Support/cldf/ and add it to the cldf folder.

Mapping procedure

Create a file

You need to derive a dataset first:

from pynorare.dataset import NormDataSet
class Dataset(NormDataSet):
    id = "Author-Year-Keyword"

Note that the id is important, as it will determine the name of your file.

Then you can define a download function for this dataset:

    def download(self):

Note that there different options how data sets are usually stored. Use either self.download_file or self.download_zip after you defined the function.

After that, you define a map function:

    def map(self, write_file=True):

Data sets are often stored in different file formats. To define the sheet, change the following line according to the file format of your target data set. Use either .get_excel or .get_csv for text files.

    sheet = self.get_excel('DATASET.xlsx', 0, dicts=True)

If the data comes in a straightforward structure with table headers in the first row, you only need to define the mappings depending on the language of the words in your data set.


Create a metadata.json file

The metadata file is meant to provide additional information to your data set. It is also used to define the column names. For a comprehensive description of the possible namespace terms see the CSVW documentation. You can also follow the standards of the metadata.json files for other data sets. Note that you can specify the old "titles" and new column names with "name" as follows:

            "name": "ENGLISH",
            "datatype": "string",
            "titles": "word"

Indicate the data "datatype" of each column: integer, float, string.

Don't forget to add the Conceticon columns:

            "name": "CONCEPTICON_ID",
            "datatype": "integer"
            "name": "CONCEPTICON_GLOSS",
            "datatype": "string"

Download and map

Make sure that you have stored the and metadata.json files according to the schema provided for the other data sets and created a raw folder in your data set folder. If you are not already, navigate to the norare-data folder and type the following commands into your terminal:

$ norare download YOUR-DATASET-ID
$ norare map YOUR-DATASET-ID

The raw file should be stored in the raw folder and a new .tsv should occur in your data set folder.


You can validate your data set by typing:

$ norare validate YOUR-DATASET-ID


Tjuka, Annika, Robert Forkel, and Johann-Mattis List. 2022. Linking Norms, Ratings, and Relations of Words and Concepts Across Multiple Language Varieties. Behavior Research Methods 54. 864–884.

Tjuka, Annika. “Adding Data Sets to NoRaRe: A Guide for Beginners,” in Computer-Assisted Language Comparison in Practice, 11/08/2021,

Tjuka, Annika. “Comparing NoRaRe Data Sets: Calculation of Correlations and Creation of Plots in R,” in Computer-Assisted Language Comparison in Practice, 24/11/2021,