Supporting Open Science
at the Atlas of Living Australia

outline

about

who we are

about

who we are

	Dr Shandiya Balasubramaniam
	Dr Dax Kellie
	Dr Martin Westgate

	Dr Amanda Buyan
	Ms Juliet Seers

about

principles

about

data life-cycle

code

what we build

code

open science package suite

	galah
	potions

	galaxias
	corella
	delma

code

galah: access data from the GBIF node network

code

galah: tidy syntax

galah_call() |>
  filter(genus == "Perameles",
         basisOfRecord == "HumanObservation",
         year == 2025) |>
  group_by(species) |>
  count() |>
  collect()

# A tibble: 5 × 2
  species                count
  <chr>                  <int>
1 Perameles nasuta         444
2 Perameles fasciata       128
3 Perameles gunnii          81
4 Perameles pallescens      54
5 Perameles bougainville     3

code

galah: reproducible workflows

galah_config(email = "martinjwestgate@gmail.com")

df <- galah_call() |>
  filter(genus == "Perameles",
         basisOfRecord == "HumanObservation",
         year == "2025") |>
  select(occurrenceID, eventDate, species, occurrenceStatus) |>
  collect(mint_doi = TRUE)

slice_head(df, n = 3)

# A tibble: 3 × 4
  occurrenceID                      eventDate           species occurrenceStatus
  <chr>                             <dttm>              <chr>   <chr>           
1 https://naturemapr.org/sightings… 2025-01-14 06:47:00 Perame… PRESENT         
2 https://naturemapr.org/sightings… 2025-01-12 08:17:00 Perame… PRESENT         
3 https://naturemapr.org/sightings… 2025-01-09 09:27:00 Perame… PRESENT         

code

galah: access sensitive data (coming soon)

authenticate()

galah_call() |>
  filter(species_list_uid == "dr650") |>
  collect()

code

galaxias: format & publish biodiversity data

code

galaxias: assumptions about scientists

code

galaxias: format to Darwin Core

my_data_dwc <- df |>
  set_occurrences(occurrenceID = composite_id(location_id, 
                                              sequential_id()),
                  basisOfRecord = "humanObservation") |> 
  set_coordinates(decimalLatitude = latitude, 
                  decimalLongitude = longitude) |>
  set_datetime(dmy(date)) |>
  set_scientific_name(scientificName = species, 
                      taxonRank = "species")

my_data_dwc

# A tibble: 2 × 8
  location_id occurrenceID basisOfRecord    decimalLatitude decimalLongitude
  <chr>       <chr>        <chr>                      <dbl>            <dbl>
1 A           A-01         humanObservation           -35.3             149.
2 B           B-02         humanObservation           -35.3             149.
# ℹ 3 more variables: eventDate <date>, scientificName <chr>, taxonRank <chr>

workflows

what we recommend

workflows

ALA labs: home

workflows

ALA labs: posts

workflows

data cleaning

future

where we’re going

future

themes

future

artificial intelligence & machine learning

• ML widely used for sound and image classification
• AI will enable new applications and change expectations
• AI unlikely to replace detailed data work, and may lower barriers to coding

future

biodiversity indicators

• Indicators have traditionally been difficult to build and productionize
• This is changing with improvements to workflows (GEO-BON, galaxy-e) and data standards (DwC-DP)
• Unclear how active ALA should be in this space

future

data quality

• Traditionally treated as a simple error detection problem
• In practice, many problems stem from meaningful complexity (taxonomic, morphological, computational)
• Our capacity to identify anomalous observations should increase with higher data volumes