dtreg 1.0.0

Interact with Data Type Registries and Create Machine Actionable Data

dtreg

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. For instance, the schema “inferential test output” requires the ePIC datatype with the DOI https://doi.org/21.T11969/74bc7748b8cd520908bc. For the ORKG, please use the ORKG template URL, such as https://incubating.orkg.org/template/R855534.

## 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.T11969/74bc7748b8cd520908bc")
## look at the schemata you might need to use
dt.__dict__.keys() 
## check available fields for your schema
dt.inferential_test_output.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.inferential_test_output(label = my_label,
                                     has_description = [url_1, url_2],
                                     has_format = 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.