Getting Started with openneuro

Why openneuro?

OpenNeuro is the largest open neuroimaging data repository, hosting thousands of BIDS-formatted datasets spanning MRI, EEG, MEG, and more. Accessing these datasets programmatically — searching by modality, downloading specific subjects, managing local caches — typically requires juggling GraphQL queries, S3 URLs, and file-system bookkeeping.

The openneuro package handles all of that so you can focus on your analysis. With a few function calls you can search the catalogue, inspect dataset contents, and download exactly the files you need.

Load the package

library(openneuroR)

Find datasets

Search the OpenNeuro catalogue. Results come back as a tibble:

ds <- on_search(limit = 10)
ds[, c("id", "name", "n_subjects")]

#> # A tibble: 10 x 3
#>    id        name                              n_subjects
#>    <chr>     <chr>                                  <int>
#>  1 ds000001  Balloon Analog Risk-taking Task           16
#>  2 ds000002  Classification Learning                   17
#>  3 ds000003  Rhyme Judgment                            13
#>    ...

You can filter by modality:

mri <- on_search(modality = "MRI", limit = 25)
head(mri$id)

#> [1] "ds000001" "ds000002" "ds000003" "ds000005" "ds000006" "ds000007"

Inspect a single dataset:

meta <- on_dataset("ds000001")
meta

Inspect versions and files

Each dataset has one or more snapshots (versioned releases):

snaps <- on_snapshots("ds000001")
snaps

#> # A tibble: 3 x 3
#>   tag    created                    size
#>   <chr>  <chr>                      <chr>
#> 1 1.0.0  2018-02-02T00:00:00.000Z   2.1 GB
#>   ...

List files from the latest snapshot:

files <- on_files("ds000001")
head(files)

#> # A tibble: 6 x 3
#>   filename                   size     directory
#>   <chr>                      <chr>    <lgl>
#> 1 dataset_description.json   1.2 kB   FALSE
#> 2 participants.tsv           800 B    FALSE
#>   ...

Download data

Download specific files to your local cache:

res <- on_download(
  id = "ds000001",
  files = c("dataset_description.json", "participants.tsv"),
  quiet = FALSE
)
res

Download by subject IDs — the package normalises bare numbers and "sub-" prefixed IDs automatically:

res_sub <- on_download(
  id = "ds000001",
  subjects = c("01", "02"),
  include_derivatives = FALSE
)

Use a regex to select subjects by pattern:

res_rx <- on_download(
  id = "ds000001",
  subjects = regex("sub-0[1-5]")
)

on_cache_info()
on_cache_list()

# Clear one dataset from cache
on_cache_clear("ds000001", confirm = FALSE)

Discover derivatives

Many OpenNeuro datasets have community-contributed derivative outputs (e.g. fMRIPrep, MRIQC). You can list them:

derivs <- on_derivatives("ds000001")
derivs[, c("dataset_id", "pipeline", "source")]

Inspect available output spaces for a derivative:

spaces <- on_spaces(derivs[1, ])
spaces

Download derivative outputs for specific subjects and spaces:

on_download_derivatives(
  dataset_id = "ds000001",
  pipeline = "fmriprep",
  subjects = c("01", "02"),
  space = "MNI152NLin2009cAsym"
)

Next steps

• vignette("openneuro-fmriprepper-e2e") — a full end-to-end workflow that downloads an OpenNeuro dataset and processes it with fMRIPrep via fmriprepper.
• ?on_search, ?on_download, ?on_derivatives — detailed function documentation.