Choosing an Atlas#
The GBIF network consists of a series of ‘node’ organisations who collate biodiversity data from their own countries, with GBIF acting as an umbrella organisation to store data from all nodes. Several nodes have their own APIs, often built from the ‘living atlas’ codebase developed by the ALA.
At present, galah supports the following functions and atlases:
Australia
Austria
Brazil
France
GBIF
Guatemala
Spain
Sweden
Set Organisation#
Set which atlas you want to use by changing the atlas argument in galah.galah_config(). The atlas argument
can accept a a region to select a given atlas, all of which are available
via galah.show_all(atlases=True). Once a value is provided, it will automatically update galah’s server
configuration to your selected atlas. The default atlas is Australia.
If you intend to download records, you may need to register a user profile with the relevant atlas first.
>>> import galah
>>> galah.galah_config(atlas="Spain", email="your-email-here")
Look up Information#
You can use the same look-up functions to find useful information about the Atlas you have set. Available information may vary for each Living Atlas.
>>> galah.galah_config(atlas="Australia")
>>> galah.show_all(datasets=True)
name uri uid
0 ALA Taxonomy List for Species Missing from Conservation Lists part B 27_11_2023 https://collections.ala.org.au/ws/dataResource/dr23929 dr23929
1 "A" Flora EPBC https://collections.ala.org.au/ws/dataResource/dr24170 dr24170
2 "H to O" flora EPBC https://collections.ala.org.au/ws/dataResource/dr24172 dr24172
3 "P to Z" flora EPBC https://collections.ala.org.au/ws/dataResource/dr24173 dr24173
4 10 year trend of levels of organochlorine pollutants in Antarctic seabirds 2003/04 https://collections.ala.org.au/ws/dataResource/dr16247 dr16247
... ... ... ...
5923 Zooplankton abundance in the Kerguelen Axis region 2016 https://collections.ala.org.au/ws/dataResource/dr16244 dr16244
5924 Zooplankton Abundance_Umbrella net_sea ice zone 2007/12 https://collections.ala.org.au/ws/dataResource/dr16265 dr16265
5925 Zooplankton samples from Heron net trawls along the 110E meridian, eastern Indian Ocean, RV Investigator voyage IN2019_V03 (2019) https://collections.ala.org.au/ws/dataResource/dr23120 dr23120
5926 Zooplankton sampling in the coastal waters of south eastern Tasmania, Australia (2009-2015) https://collections.ala.org.au/ws/dataResource/dr15943 dr15943
5927 Zoos Victoria Moth Tracker https://collections.ala.org.au/ws/dataResource/dr22371 dr22371
[5928 rows x 3 columns]
>>> galah.show_all(fields=True)
id description type link
0 _nest_parent_ NaN field NaN
1 _nest_path_ NaN field NaN
2 _root_ NaN field NaN
3 abcdTypeStatus ABCD field in use by herbaria field NaN
4 acceptedNameUsage http://rs.tdwg.org/dwc/terms/acceptedNameUsage field NaN
.. ... ... ... ...
755 multimediaLicence Media filter field media
756 images Media filter field media
757 videos Media filter field media
758 sounds Media filter field media
759 qid Reference to pre-generated query other
[760 rows x 4 columns]
>>> galah.search_all(datasets="year")
name uri uid
0 Elgin Road 3 year observations https://collections.ala.org.au/ws/dataResource/dr661 dr661
1 com plants greater than 50 years https://collections.ala.org.au/ws/dataResource/dr21699 dr21699
2 CoM animal species greater than 50 years.csv https://collections.ala.org.au/ws/dataResource/dr21448 dr21448
3 10 year trend of levels of organochlorine pollutants in Antarctic seabirds 2003/04 https://collections.ala.org.au/ws/dataResource/dr16247 dr16247
4 Coccolithophore assemblages of a 9,000 year old marine sediment core from a climate hotspot in Tasmania, southeast Australia (2018) https://collections.ala.org.au/ws/dataResource/dr23184 dr23184
5 Year-round at-sea movements of fairy prions (Pachyptila turtur) from Kanowna Island, Bass Strait, south-eastern Australia (2017-2020) https://collections.ala.org.au/ws/dataResource/dr23233 dr23233
>>> galah.search_taxa(taxa="Heleioporus")
scientificName scientificNameAuthorship taxonConceptID rank kingdom phylum order family genus issues
0 Heleioporus Gray, 1841 https://biodiversity.org.au/afd/taxa/b63103c4-28f7-44a5-b8d7-df459eeff2d3 genus Animalia Chordata Anura Limnodynastidae Heleioporus noIssue
Download data#
You can build queries as you normally would in galah. For taxonomic queries, use galah.search_taxa() to
make sure your searches are returning the correct taxonomic data.
>>> galah.galah_config(atlas="Australia")
>>> # Returns no data due to misspelling
>>> galah.search_taxa(taxa="vlps")
Empty DataFrame
Columns: []
Index: []
>>> # Returns data
>>> galah.search_taxa(taxa="Vulpes vulpes")
scientificName scientificNameAuthorship taxonConceptID rank kingdom phylum order family genus species vernacularName issues
0 Vulpes vulpes Linnaeus, 1758 https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 species Animalia Chordata Carnivora Canidae Vulpes Vulpes vulpes Fox noIssue
>>> galah.atlas_counts(taxa="Vulpes vulpes", filters="year>2010")
totalRecords
0 97924
Download species occurrence records from other atlases with galah.atlas_occurrences()
>>> galah.atlas_occurrences(taxa="Vulpes vulpes", filters="year>2010", fields=["taxon_name", "year"])
scientificName year
0 Vulpes vulpes 2016
1 Vulpes vulpes 2016
2 Vulpes vulpes 2013
3 Vulpes vulpes 2011
4 Vulpes vulpes 2013
... ... ...
97919 Vulpes vulpes 2018
97920 Vulpes vulpes 2017
97921 Vulpes vulpes vulpes 2018
97922 Vulpes vulpes vulpes 2019
97923 Vulpes vulpes vulpes 2019
[97924 rows x 2 columns]
Complex queries with multiple Atlases#
It is also possible to create more complex queries that return data from multiple Living Atlases. As an example, setting atlases within a loop with galah_config() allows us to return the total number of species records in each Living Atlas in one table.
>>> import galah
>>> import pandas as pd
>>> atlases = ["Australia","Austria","Brazil","France","GBIF","Spain"]
>>> counts_dict = {"Atlas": [], "Total Records": []}
>>> for atlas in atlases:
>>> galah.galah_config(atlas=atlas)
>>> counts_dict["Atlas"].append(atlas)
>>> counts_dict["Total Records"].append(galah.atlas_counts()["totalRecords"][0])
>>> pd.DataFrame(counts_dict)
Atlas Total Records
0 Australia 133620113
1 Brazil 28455094
2 GBIF 2718692883
3 Guatemala 4225159
4 Spain 57380644
5 Sweden 127828551