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 (Acrostichum speciosum) Mangrove Fern https://collections.ala.org.au/ws/dataResource/dr34493 dr34493
... ... ... ...
15127 Zooplankton sampling in the coastal waters of south eastern Tasmania, Australia (2009-2015) https://collections.ala.org.au/ws/dataResource/dr15943 dr15943
15128 Zoos Victoria Moth Tracker https://collections.ala.org.au/ws/dataResource/dr22371 dr22371
15129 Zosteria fulvipubescens https://collections.ala.org.au/ws/dataResource/dr27880 dr27880
15130 Zosteria fulvipubescens https://collections.ala.org.au/ws/dataResource/dr27881 dr27881
15131 zza https://collections.ala.org.au/ws/dataResource/dr33432 dr33432
[15132 rows x 3 columns]
>>> galah.show_all(fields=True)
id description type link
0 acceptedNameUsage http://rs.tdwg.org/dwc/terms/acceptedNameUsage field NaN
1 acceptedNameUsageID http://rs.tdwg.org/dwc/terms/acceptedNameUsageID field NaN
2 accessRights NaN field NaN
3 annotationsDoi NaN field NaN
4 annotationsUid NaN field NaN
.. ... ... ... ...
852 multimediaLicence Media filter field media
853 images Media filter field media
854 videos Media filter field media
855 sounds Media filter field media
856 qid Reference to pre-generated query other
[857 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 Ocean Sampling Day (OSD) 2014: AUTHORITY-RAW amplicon and metagenome sequencing study from the June solstice in the year 2014 https://collections.ala.org.au/ws/dataResource/dr30174 dr30174
5 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
6 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
7 Jellyfish Database Initiative: Global records on gelatinous zooplankton for the past 200 years, collected from global sources and literature, subset of records from Australian and adjacent seas. (1907-2011) https://collections.ala.org.au/ws/dataResource/dr29550 dr29550
>>> galah.search_taxa(taxa="Heleioporus")
scientificName scientificNameAuthorship taxonConceptID rank matchType kingdom phylum classs order family genus species issues vernacularName
0 Heleioporus Gray, 1841 https://biodiversity.org.au/afd/taxa/4b74df78-ea98-4592-b889-cccfa0c4d514 genus exactMatch Animalia Chordata Amphibia 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")
We were not able to find ['vlps'] in the Australia backbone.
scientificName scientificNameAuthorship ... issues vernacularName
0 vlps ... [noIssue]
[1 rows x 14 columns]
>>> # Returns data
>>> galah.search_taxa(taxa="Vulpes vulpes")
scientificName scientificNameAuthorship taxonConceptID rank matchType kingdom phylum classs order family genus species issues vernacularName
0 Vulpes vulpes Linnaeus, 1758 https://biodiversity.org.au/afd/taxa/2869ce8a-8212-46c2-8327-dfb7fabb8296 species exactMatch Animalia Chordata Mammalia Carnivora Canidae Vulpes Vulpes vulpes [noIssue] Fox
>>> galah.atlas_counts(taxa="Vulpes vulpes", filters="year>2010")
totalRecords
0 121381
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 2019
1 Vulpes vulpes 2013
2 Vulpes vulpes 2019
3 Vulpes vulpes 2016
4 Vulpes vulpes 2020
... ... ...
121376 Vulpes vulpes 2022
121377 Vulpes vulpes 2022
121378 Vulpes vulpes vulpes 2018
121379 Vulpes vulpes vulpes 2019
121380 Vulpes vulpes vulpes 2019
[121381 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","GBIF","Kew","Spain","Sweden","United Kingdom"]
>>> 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 181071285
1 Austria 16511811
2 Brazil 43920796
3 GBIF 3863436880
4 Kew 7731705
5 Spain 59852882
6 Sweden 173433428
7 United Kingdom 362992921