dtreg

100% AI-free: we did not use any AI technologies in developing this package.

The goal of dtreg is to help users interact with various data type registries (DTRs) and create machine-readable data. Currently, we support the ePIC and ORKG DTRs.

• First, load a DTR schema (an ePIC datatype or an ORKG template) as a Python object.
• Then, create a new instance of the schema by filling in the relevant fields.
• Finally, write the instance as a machine-readable JSON-LD file.

Installation

## install from PyPi:
pip install dtreg

Example

This example shows you how to work with a DTR schema. You need to know the schema identifier; see the help page.

## import functions from the dtreg
from dtreg.load_datatype import load_datatype
from dtreg.to_jsonld import to_jsonld
## import pandas for a dataframe
import pandas as pd
## load the schema with the known identifier
dt = load_datatype("https://doi.org/21.T11969/aff130c76e68ead3862e")
## look at the schemata you might need to use
dt.__dict__.keys() 
## check available fields for your schema
dt.data_item.prop_list 
## create your instance by filling the fields of your choice
## see the help page to know more about the fields
my_label = "my results"
my_df = pd.DataFrame({'A': [1], 'B': [2]})
my_df.name = "dataframe_name"
url_1 = dt.url(label = "URL_1")
url_2 = dt.url(label = "URL_2")
my_inst = dt.data_item(label=my_label,
                       has_expression=[url_1, url_2],
                       source_table=my_df)
## write the instance in JSON-LD format as a string
my_json = to_jsonld(my_inst) 

## the result can be saved as a JSON file
with open('my_file.json', 'w') as f:
    f.write(my_json)

For more information, please see the help page.