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 (Appendix 2) Stratigraphic distribution of key and potential stratigraphic calcareous nannofossil markers in the upper Campanian–Maastrichtian of ODP Hole 122-762C https://collections.ala.org.au/ws/dataResource/dr29981 dr29981
... ... ... ...
10141 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
10142 Zooplankton sampling in the coastal waters of south eastern Tasmania, Australia (2009-2015) https://collections.ala.org.au/ws/dataResource/dr15943 dr15943
10143 Zoos Victoria Moth Tracker https://collections.ala.org.au/ws/dataResource/dr22371 dr22371
10144 Zosteria fulvipubescens https://collections.ala.org.au/ws/dataResource/dr27880 dr27880
10145 Zosteria fulvipubescens https://collections.ala.org.au/ws/dataResource/dr27881 dr27881
[10146 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
... ... ... ... ...
1105 multimediaLicence Media filter field media
1106 images Media filter field media
1107 videos Media filter field media
1108 sounds Media filter field media
1109 qid Reference to pre-generated query other
[1110 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 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/dr30212 dr30212
6 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
7 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
8 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 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 105576
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 2016
2 Vulpes vulpes 2021
3 Vulpes vulpes 2014
4 Vulpes vulpes 2022
... ... ...
105571 Vulpes vulpes 2017
105572 Vulpes vulpes 2016
105573 Vulpes vulpes vulpes 2018
105574 Vulpes vulpes vulpes 2019
105575 Vulpes vulpes vulpes 2019
[105576 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 150121628
1 Austria 9173597
2 Brazil 37089463
3 France 160368606
4 GBIF 3125039095
5 Portugal 16043865
6 Spain 59764520
7 Sweden 160190282
8 United Kingdom 299207653