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This section will cover the EcoPlots API Dashboard (EcoPlots API Query Dashboard) and will run through examples of how to use the dashboard to extract and visualise data.

The API Dashboard is another way that users can query data stored within EcoPlots using an API. The dashboard provides a lightweight interface for EcoPlots APIs to query datasets hosted on the EcoPlots Platform. The interface offers standard pre-defined query patterns to retrieve data from multiple data sources based on simple selection criteria. Users can use this dashboard to construct queries and get a code snippet to access data in any programming language. The interface provides sample data for a constructed query. Hence, users must use generated code snippets to retrieve the complete dataset for any constructed query. Alternatively, users can use the EcoPlots search dashboard to perform more detailed search and download data.

To navigate to the dashboard, go to the EcoPlots home page (https://ecoplots.tern.org.au/) and click on the (blue star) tab in the menu bar and select the API Query Dashboard. For more information and detailed instructions on using the API Query Dashboard, you can select the (blue star) button, which will generate the following screen:

Now we can go through the steps of selecting and extracting data. User can query the data by either “Dataset” or “Region” (as seen below).

API_dashboard_search.png

Once a selection has been made, you can use additional search parameters: Datasource, Feature Type, Survey sites and Observable properties to narrow down the results.

In the below example, we are using the “Dataset” query endpoint and have selected the TERN Ecosystem Processes datasource. For this basic example, we are interested in extracting all observable properties involving above-ground biomass, stem diameter, stem height and species' scientific names.

example_search_dataset.png

Once the search parameters have been selected, click on the (blue star) button and the following screen will be displayed.

Here we can either select the data format (either CSV or GeoJson) (blue star) . In this example, we have selected the CSV option. In the grey “search body” section, we can see the query results showing the respective URLs of the “dataset” and the observable properties (above-ground biomass, stem diameter, stem height and species' scientific names.

To access the data, there are two methods: 1) select the API button (blue star) or 2) select the Execute Data Query button (blue star) . The choice of which method to use will depend on the amount of resulting observations from the data query which we will go over in the examples below.

If the query produces large amounts of data (>10,000 observations/rows), the best way to extract the data is using the the code snippet produced after selecting the API button (blue star). Here we see that a code generator appears with the dataset and observable properties and the user has the option to customise the programming language, variant and output format.

csv_code_snippet.png

In this example , we have selected the programming language R, CSV output and httr variant. Now we can copy the code snippet by pressing the copy snippet button(blue star). In R studio, we can paste this snippet. It is important to note that you will need to enter your own individual API-key into the code.

library(httr)

## you will need to enter your own individaul API key ##
headers = c(
  'X-Api-Key' = 'your-api-key',
  'Content-Type' = 'application/json'
)

body = '{
  "query": {
    "dataset": [
      "http://linked.data.gov.au/dataset/tern-ecosystem-processes"
    ],
    "observed_property": [
      "http://linked.data.gov.au/def/tern-cv/56195246-ec5d-4050-a1c6-af786fbec715",
      "http://linked.data.gov.au/def/tern-cv/5701d307-bbea-415a-8857-fbf27d5b93ed",
      "http://linked.data.gov.au/def/tern-cv/c3d26c6f-91b7-4627-91e6-2147fa44ad03",
      "http://linked.data.gov.au/def/tern-cv/c3111898-8404-4306-9599-e7866a749de7"
    ]
  }
}';

res <- VERB("POST", url = "https://ecoplots.tern.org.au/api/v1.0/data/tern-ecosystem-processes?dformat=csv", body = body, add_headers(headers))

cat(content(res, 'text'))

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