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galah is an R interface to the Atlas of Living Australia (ALA;, a biodiversity data repository focussed primarily on observations of individual life forms. It also supports access to some other 'living atlases' that use the same computational infrastructure. The basic unit of data at ALA is an occurrence record, based on the 'Darwin Core' data standard ( galah enables users to locate and download species observations, taxonomic information, or associated media such images or sounds, and to restrict their queries to particular taxa or locations. Users can specify which columns are returned by a query, or restrict their results to observations that meet particular quality-control criteria.


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To get the most value from galah, it is helpful to understand some terminology used by the ALA. Each occurrence record contains taxonomic information, and usually some information about the observation itself, such as its location. In addition to this record-specific information, ALA appends contextual information to each record, particularly data from spatial layers reflecting climate gradients or political boundaries. ALA also runs a number of quality checks against each record, resulting in assertions attached to the record. Each piece of information associated with a given occurrence record is stored in a field, which corresponds to a column when imported to an R data.frame. See show_all_fields() to view valid fields, layers and assertions, or conduct a search using search_fields().

Data fields are important because they provide a means to filter occurrence records; i.e. to return only the information that you need, and no more. Consequently, much of the architecture of galah has been designed to make filtering as simple as possible. Functions with the galah_ prefix offer ways to shape your query call. Each galah_ function allows the user to filter in a different way. Again, the function suffix reveals what each one does. galah_filter, galah_select and galah_group_by intentionally match dplyr's select(), filter() and group_by() functions, both in their name and how they they are used. For example, you can use galah_select() to choose what information is returned as columns. Alternatively, you can use galah_filter() to filter the rows. You can also choose specific taxa with galah_identify() or choose a specific location using galah_geolocate(). By combining different filter functions, it is possible to build complex queries to return only the most valuable information for a given problem.

A notable extension of the filtering approach is to remove records with low 'quality'. ALA performs quality control checks on all records that it stores. These checks are used to generate new fields, that can then be used to filter out records that are unsuitable for particular applications. However, there are many possible data quality checks, and it is not always clear which are most appropriate in a given instance. Therefore, galah supports ALA data quality profiles, which can be passed to galah_filter() to quickly remove undesirable records. A full list of data quality profiles is returned by show_all_profiles().

For those outside Australia, 'galah' is the common name of Eolophus roseicapilla, a widely-distributed Australian bird species.

Package design

In most cases, users will be primarily interested in using galah to return data from one of the living atlases. These functions are named with the prefix atlas_, followed by a suffix describing the information that they provide. For example, users that wish to download occurrence data can use the function atlas_occurrences(). Alternatively, users that wish to download data on each species (rather than on each occurrence record) can use atlas_species() or download media content (largely images) using atlas_media(). Users can also assess how many records meet their particular criteria using atlas_counts() and return a taxonomic tree for a specific clade from one level down to another level (e.g., from family to genus). All functions return a data.frame as their standard format, except atlas_taxonomy() which returns a data.tree.

Functions in galah are designed according to a nested architecture. Users that require data should begin by locating the relevant atlas_ function; the arguments within that function then call correspondingly-named galah_ functions; specific values that can be interpreted by those galah_ functions are given by functions with the prefix search_ or show_all_; desired taxa can be also be identified using search_taxa() and passed within galah_identify() to the taxa argument of atlas_ functions.


For more information on the ALA API, visit If you have any questions, comments or suggestions, please email


Maintainer: Martin Westgate