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
... ... ... ...
14206 Zooplankton sampling in the coastal waters of south eastern Tasmania, Australia (2009-2015) https://collections.ala.org.au/ws/dataResource/dr15943 dr15943
14207 Zoos Victoria Moth Tracker https://collections.ala.org.au/ws/dataResource/dr22371 dr22371
14208 Zosteria fulvipubescens https://collections.ala.org.au/ws/dataResource/dr27880 dr27880
14209 Zosteria fulvipubescens https://collections.ala.org.au/ws/dataResource/dr27881 dr27881
14210 zza https://collections.ala.org.au/ws/dataResource/dr33432 dr33432
[14211 rows x 3 columns]
>>> galah.show_all(fields=True)
id description type link
0 abcdTypeStatus ABCD field in use by herbaria field NaN
1 acceptedNameUsage http://rs.tdwg.org/dwc/terms/acceptedNameUsage field NaN
2 acceptedNameUsageID http://rs.tdwg.org/dwc/terms/acceptedNameUsageID field NaN
3 accessRights NaN field NaN
4 annotationsDoi NaN field NaN
... ... ... ... ...
1100 multimediaLicence Media filter field media
1101 images Media filter field media
1102 videos Media filter field media
1103 sounds Media filter field media
1104 qid Reference to pre-generated query other
[1105 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 issues
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.
Empty DataFrame
Columns: []
Index: []
>>> # 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 119654
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 2014
1 Vulpes vulpes 2022
2 Vulpes vulpes 2014
3 Vulpes vulpes 2022
4 Vulpes vulpes 2016
... ... ...
119649 Vulpes vulpes 2021
119650 Vulpes vulpes 2017
119651 Vulpes vulpes vulpes 2018
119652 Vulpes vulpes vulpes 2019
119653 Vulpes vulpes vulpes 2019
[119654 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 167267751
1 Austria 16511810
2 Brazil 39612239
3 GBIF 3779369492
4 Kew 7569372
5 Spain 59852878
6 Sweden 171831902
7 United Kingdom 312057008