Web API

Introduction

A Web API offers programmatic access to the content of a web application.

The Django-REST-API provides an in-browser interface for interacting with the Web API, but generally one will access the data from a script in a language like Python.

Organization

The data is highly nested (entries tend to link to another entry), so the current organization attempts to decrease data sparsity by returning complicated objects as separate entities that are linked via an ID rather than having their contents listed explicitly and redundantly everywhere they are relevant.

Currently, the API has the following endpoints:

Viewing the Study List (/api/studies/)
Viewing an individual Study (/api/studies/{id}/)
Viewing a list of Targets (/api/targets/)
Viewing a list of Methods (/api/methods/)
Viewing a list of Units (/api/units/)
Viewing a list of Experimental Model Locations (/api/locations/)

Only publicly accessible Studies (those that are Signed Off and marked Unrestricted) and Discoverable Studies (Studies for which only a subset of metadata is publicly accessible) are available in the API.

Study List (/api/studies/)

For every Public and/or Discoverable Study, the following are returned:

access_status
id
url link to the api details page (so a user can click the link rather than typing in the id)
name
created_on
modified_on
data_group
center
pi
contact_person
study_types (TOX, CC, etc.)
start_date (in ISO yyyy-mm-dd)
description

Study Details (/api/studies/{id}/)

For an individual Study, the following fields are returned. Fields marked with an asterisk (*) are provided for Public and Discoverable Studies, other fields are only present for publicly available Studies.

access_status*

id*

name*

created_on*

modified_on*

data_group*

center*

pi*

contact_person*

study_types (TOX, CC, etc.)*

start_date (in ISO yyyy-mm-dd)*

description*

study_description_image*

supporting_data*

processed_data_files
omic_data_files
assay_plate_data_files
groups: matches group ids to:
1. mps_model
2. mps_model_version
3. compounds: a list which contains:
  1. compound
  2. supplier
  3. lot
  4. receipt_date
  5. concentration
  6. concentration_unit
  7. addition_time
  8. addition_time_in_minutes
  9. duration
  10. duration_in_minutes
  11. addition_location
4. cells: a list which contains:
  1. cell_sample
  2. biosensor
  3. density
  4. density_unit
  5. passage
  6. addition_time
  7. addition_time_in_minutes
  8. addition_location
5. settings: a list which contains:
  1. setting
  2. value
  3. unit
  4. addition_time
  5. addition_time_in_minutes
  6. duration
  7. duration_in_minutes
  8. addition_location
items: matches item ids to:
1. group_id
2. name
3. scientist
4. notebook
5. notebook_page
6. notes
assays: matches assay ids to:
1. target
2. method
3. unit
data: a list of data points containing:
1. item_id
2. assay_id
3. sample_location
4. value
5. cross_reference
6. time
7. time_in_minutes
8. notes
9. assay_plate_id
10. assay_well_id
11. replicate
12. excluded
13. replaced
14. update_number

Study Component Endpoints

Study Component lists include an id, name and description.

Viewing a list of Targets (/api/targets/)
Viewing a list of Methods (/api/methods/)
Viewing a list of Units (/api/units/)
Viewing a list of Experimental Model Locations (/api/locations/)

Python Example


            # This library will help you make requests
            

            # There are other similar libraries, requests is popular but not built-in to python
            

            # requests is a library that will help you make API requests
            

            import urllib.request
            

            # Processing the data can be done with the standard JSON parser
            

            import json
            

            

            # In this example, we get the study list:
            

            # Just have a string of the url for the API study list
            

            study_list_url = 'https://biosystics-ap.com/api/studies/'
            

            # And make a request with the library
            

            # This response should contain a string of the response
            

            study_list_response = urllib.request.urlopen(study_list_url)
            

            # We can parse it with json
            

            # Now it will be comprised of Python objects rather than strings
            

            study_list_parsed = json.loads(study_list_response.read())
            

            # This will print the first study in the list
            

            print(study_list_parsed[0])
            

            

            # Something similar can be done with particular studies
            

            study_id = 23
            

            study_url = 'https://biosystics-ap.com/api/studies/{}/'.format(study_id)
            

            study_response = urllib.request.urlopen(study_url)
            

            study_parsed = json.loads(study_response.read())
            

            # This will print the study's name
            

            print(study_parsed.get('name'))

In-Browser Interface

The In-Browser Interface provided by Django-Rest-Framework can be accessed by visiting the URLs one would use in a script directly in the browser.

URLs pointing to something in the API should be clickable.

You might notice that copying and pasting the data is somewhat difficult in the default display. Clicking the down arrow next to the GET button (which effectively acts as a refresh button) gives one the opportunity to select the "json" format. Selecting json will show the raw json to your browser (which may or may not attempt to make it more presentable). From here, it should be easier to copy and paste the data.

Click here for the API list of Studies