Author: Martin Westgate
Date: 2026-02-11

# Accessing sensitive data

#### Martin Westgate

#### 2026-02-11

`galah` provides access to biodiversity information stored by GBIF and its’ partner nodes. While much of this information is shared freely and without restriction, there are a number of cases where this is not appropriate, such as the locations of:

- threatened species, whose locations are protected by law in many jurisdictions
- species at risk from poachers, particularly for wildlife trade

This leads to a problem: data that is more open helps scientists and policy-makers understand and conserve biodiversity, but also generates threats to those species. To balance these concerns against one another, it is common to ‘obfuscate’ sensitive data, which consists of providing location information at a lower spatial resolution, making it harder to locate threatened species on the ground.

# How ALA handles sensitive data

While many data providers solve this problem themselves in a range of ways - such as by not sharing data at all, randomising some locations, or reducing the number of decimal places of their locations - others provide high-precision data on the understanding that it will only be made available to specific users, and then only by written agreement. In these cases, the ALA displays the obfuscated data publicly, but retains the original data for use for specific purposes. Researchers can request access to the original data via the National Framework for the Sharing of Restricted Access Species Data in Australia, more simply known as the ‘RASD framework’ (https://www.rasd.org.au).

If your access to sensitive data is approved by the provider(s) in question, you can use ‘galah’ to access that sensitive data.

# Using authentication

You have two choices for how to call authentication in your queries. The simplest way is to ‘switch on’ authentication using `galah_config()`:

    galah_config(authenticate = TRUE)

This is straightforward, but you’ll have to remember to switch `authentication` to `FALSE` again once you’re done. A safer choice is to use the `authenticate()` function in-pipe:

    df <- galah_call() |>
      identify("Mammalia") |>
      filter(year == 2025) |>
      authenticate() |>
      collect()

It doesn’t matter where in the pipe you place `authenticate()`, as it will only trigger your browser to open once you run `collect()`. Whichever method you use, you’ll notice a page pop up in your default browser, encouraging you to sign in. Once you have done so, you should be redirected back to your chosen IDE to continue working.

# Deciding what sensitive data to access

In the the ALA, sensitive data are stored in bespoke fields, which have an existing field name prefixed with `sensitive_`. They cannot be requested directly. Instead, if you request a field that has a sensitive counterpart, both the public and sensitive version of that field will be returned.

public_field | restricted_field
--- | ---
eventID | sensitive_eventID
eventDate | sensitive_eventDate
eventTime | sensitive_eventTime
month | sensitive_month
day | sensitive_day
locality | sensitive_locality
locationRemarks | sensitive_locationRemarks
decimalLatitude | sensitive_decimalLatitude
decimalLongitude | sensitive_decimalLongitude
footprintWKT | sensitive_footprintWKT
verbatimEventDate | sensitive_verbatimEventDate
verbatimLocality | sensitive_verbatimLocality
verbatimCoordinates | sensitive_verbatimCoordinates
verbatimLatitude | sensitive_verbatimLatitude
verbatimLongitude | sensitive_verbatimLongitude

So to download data, we might try something like:

    result <- galah_call() |>
        filter(species_list_uid == "dr491") |>
        collect()

    > result
    # A tibble: 487 × 12
       recordID   scientificName taxonConceptID decimalLatitude sensitive_decimalLat…¹ decimalLongitude
       <chr>      <chr>          <chr>                    <dbl>                  <dbl>            <dbl>
     1 00825ab0-… Caladenia vul… https://id.bi…           -37.8                     NA             145.
     2 0094e7df-… Caladenia vul… https://id.bi…           -37.7                     NA             141.
     3 00d3a4e3-… Caladenia vul… https://id.bi…           -39.6                     NA             147.
     4 02305849-… Caladenia vul… https://id.bi…           -38.0                     NA             145.
     5 02e28dc1-… Caladenia vul… https://id.bi…           -37.7                     NA             145.
     6 068b4c68-… Caladenia vul… https://id.bi…           -37.7                     NA             141.
     7 07b5356b-… Caladenia vul… https://id.bi…           -37.0                     NA             143.
     8 0807fcbe-… Caladenia vul… https://id.bi…           -37.9                     NA             145.
     9 093df7f4-… Caladenia vul… https://id.bi…           -37.9                     NA             145.
    10 0942b709-… Caladenia vul… https://id.bi…           -38.4                     NA             145.
    # ℹ 477 more rows
    # ℹ abbreviated name: ¹​sensitive_decimalLatitude
    # ℹ 6 more variables: sensitive_decimalLongitude <dbl>, eventDate <dttm>,
    #   sensitive_eventDate <lgl>, basisOfRecord <chr>, occurrenceStatus <chr>, dataResourceName <chr>
    # ℹ Use `print(n = ...)` to see more rows

    > colnames(result)
     [1] "recordID"                   "scientificName"             "taxonConceptID"
     [4] "decimalLatitude"            "sensitive_decimalLatitude"  "decimalLongitude"
     [7] "sensitive_decimalLongitude" "eventDate"                  "sensitive_eventDate"
    [10] "basisOfRecord"              "occurrenceStatus"           "dataResourceName"