Auditing One Collection: A Quick-Start Guide

This is the narrow path: you have one collection (or a rough idea of one) and you want to know when its files actually change. If you’re curious about the broader exploration that shaped this package’s other functions – collection discovery, S3 region gotchas, provider-specific metadata quirks – see vignette("cmr-landscape") instead. This article sticks to the five steps that matter for a single collection.

library(cmrlifecycle)

Step 1: find your collection

If you already have a short_name, skip to Step 2. Otherwise, cmr_find_collections() does a free-text search across CMR’s collection metadata (title, abstract, science keywords, platforms, instruments, …):

cmr_find_collections(keyword = "sea surface temperature", provider = "POCLOUD",
                      page_size = 5, granule_count = FALSE) |>
  dplyr::select(short_name, entry_title, provider, version)
#> 5 of 500 total matching collections returned (page 1) -- raise `page_size` (CMR max 2000), narrow `keyword`/`provider`, or use cmr_all_collections() to see more.
#> # A tibble: 5 × 4
#>   short_name                  entry_title                       provider version
#>   <chr>                       <chr>                             <chr>    <chr>  
#> 1 OSTIA-UKMO-L4-GLOB-REP-v2.0 GHRSST Level 4 OSTIA Global Hist… POCLOUD  2.0    
#> 2 MUR-JPL-L4-GLOB-v4.1        GHRSST Level 4 MUR Global Founda… POCLOUD  4.1    
#> 3 H09-AHI-L3C-ACSPO-v2.90     GHRSST L3C NOAA/ACSPO Himawari-0… POCLOUD  2.90   
#> 4 MUR25-JPL-L4-GLOB-v04.2     GHRSST Level 4 MUR 0.25deg Globa… POCLOUD  4.2    
#> 5 MODIS_T-JPL-L2P-v2019.0     GHRSST Level 2P Global Sea Surfa… POCLOUD  2019.0

This example uses this vignette’s running example throughout: MUR-JPL-L4-GLOB-v4.1, GHRSST’s daily global sea-surface-temperature analysis.

Step 2: is it actually alive, and what does it cover?

Before auditing anything, two quick sanity checks. First, the collection’s own declared metadata:

cmr_find_collections(short_name = "MUR-JPL-L4-GLOB-v4.1") |>
  dplyr::select(temporal_start, temporal_end, ends_at_present, granule_count)
#> # A tibble: 1 × 4
#>   temporal_start           temporal_end ends_at_present granule_count
#>   <chr>                    <chr>        <lgl>                   <int>
#> 1 2002-05-31T21:00:00.000Z <NA>         FALSE                    8901

granule_count > 0 confirms this collection actually has UMM-G granules to audit at all – some CMR “collections” don’t (see vignette("cmr-landscape")’s AADC example). temporal_start is usually reliable; ends_at_present/temporal_end are declared metadata that vary a lot in reliability by provider, so don’t lean on them alone –

cmr_collection_liveness("MUR-JPL-L4-GLOB-v4.1")
#> # A tibble: 1 × 5
#>   short_name           provider lookback_days recent_granule_count is_live
#>   <chr>                <chr>            <dbl>                <int> <lgl>  
#> 1 MUR-JPL-L4-GLOB-v4.1 <NA>                60                   59 TRUE

cmr_collection_liveness() checks real granule activity in a recent window directly, which is the more trustworthy “is this still going” signal. recent_granule_count capped at your page_size (default 2000) just means “at least that many” – raise lookback_days for low-cadence collections (annual composites, etc.) that might show is_live = FALSE over a short window despite being fine.

Step 3: run the audit

cmr_audit_revisions() sweeps a date range in 3-month chunks and returns one row per granule, with the full revision/production lifecycle. This example covers a little over a year to keep the vignette quick to build; drop from further back for a fuller picture (MUR itself goes back to 2002):

rev <- cmr_audit_revisions("MUR-JPL-L4-GLOB-v4.1", from = Sys.Date() - 400)
#> 2025-09-10 .. 2025-12-09 : 92 granules
#> 2025-12-10 .. 2026-03-09 : 91 granules
#> 2026-03-10 .. 2026-06-09 : 93 granules
#> 2026-06-10 .. 2026-09-09 : 93 granules
#> 2026-09-10 .. 2026-10-15 : 34 granules
rev |>
  dplyr::select(gran_date, n_rev, insert_date, update_date, production,
                prod_lag) |>
  head()
#> # A tibble: 6 × 6
#>   gran_date  n_rev insert_date              update_date      production prod_lag
#>   <date>     <int> <chr>                    <chr>            <chr>         <dbl>
#> 1 2025-09-10    18 2025-09-19T07:52:25.822Z 2025-09-19T07:5… 2025-09-1…        9
#> 2 2025-09-11    18 2025-09-20T07:52:05.545Z 2025-09-20T07:5… 2025-09-2…        9
#> 3 2025-09-12    18 2025-09-21T07:52:12.661Z 2025-09-21T07:5… 2025-09-2…        9
#> 4 2025-09-13    18 2025-09-22T07:52:05.087Z 2025-09-22T07:5… 2025-09-2…        9
#> 5 2025-09-14    18 2025-09-23T07:51:54.806Z 2025-09-23T07:5… 2025-09-2…        9
#> 6 2025-09-15    18 2025-09-24T07:51:41.460Z 2025-09-24T07:5… 2025-09-2…        9

The column dictionary, briefly (full detail in the README):

column	what it tells you
`n_rev`	how many times the CMR metadata record was touched – often bulk provider events, not evidence the file changed
`insert_date`/`update_date`	UMM `ProviderDates` – first publish / latest metadata touch
`production`	`DataGranule.ProductionDateTime` – when the science file itself was generated. This is the churn-immune signal
`*_lag`	each date above minus `gran_date`, in days

range(rev$n_rev)
#> [1]  2 22

If that range is wide (as it typically is for actively-maintained GHRSST products), don’t read it as “this file was regenerated that many times” – cross-check against prod_lag instead, which is immune to metadata churn.

Step 4: look at it

plot_cmr_lifecycle(rev)

Two diagnostic plots: CMR revision count vs granule date, and production lag in days vs granule date

Two things to look for:

Revision count vs. granule date (top plot): a staircase pattern (roughly constant counts jumping at specific dates across many granules) usually means n_rev is tracking a bulk metadata event, not per-granule data changes.
Production lag vs. granule date (bottom plot): expect a flat band at a stable finalization lag for settled/older dates, with smaller lags only at the frontier (recent dates still on a near-real-time product, not yet replaced by the finalized file).

When your collection doesn’t play by MUR’s rules

Two things assumed above don’t hold universally:

Opaque GranuleUR, no embedded date. gran_date defaults to parsing a leading YYYYMMDD out of granule_ur – true for GHRSST-style products, not universal. Some collections (e.g. MODIS swath products via LAADS) use an opaque GranuleUR like "LAADS:9975745835". cmr_audit_revisions() warns (not errors) if gran_date comes back entirely NA; the fix is to derive it from temporal_start instead:

rev_swath <- cmr_audit_revisions("MOD021KM",
  from = as.Date("2026-08-01"), to = as.Date("2026-08-02"),
  gran_date_fun = function(df) as.Date(df$temporal_start))

Too many granules worldwide. Swath products can produce hundreds of granules a day globally. cmr_find_granules()/cmr_audit_revisions() take an optional bounding_box (c(west, south, east, north)) or polygon filter to subset to a region of interest instead:

cmr_find_granules("MOD021KM", as.Date("2026-08-01"), as.Date("2026-08-01"),
                   bounding_box = c(140, -45, 155, -35))

If your collection is cloud-hosted

Check region/s3_credentials_endpoint before relying on s3_url for compute placement – s3:// links are only cheap from matching-region compute, and require an Earthdata Login credential exchange regardless of region:

cmr_find_collections(short_name = "MUR-JPL-L4-GLOB-v4.1") |>
  dplyr::select(region, s3_credentials_endpoint)
#> # A tibble: 1 × 2
#>   region    s3_credentials_endpoint                                
#>   <chr>     <chr>                                                  
#> 1 us-west-2 https://archive.podaac.earthdata.nasa.gov/s3credentials

vignette("cmr-landscape") covers why this matters in more detail.

Cheat sheet

library(cmrlifecycle)

# 1. find it (skip if you know short_name)
cmr_find_collections(keyword = "...")

# 2. sanity check
cmr_collection_liveness("your-short-name")
cmr_find_collections(short_name = "your-short-name")

# 3. audit
rev <- cmr_audit_revisions("your-short-name", from = as.Date("2024-01-01"))

# 4. look
plot_cmr_lifecycle(rev)

# only if needed:
# - opaque GranuleUR:  gran_date_fun = function(df) as.Date(df$temporal_start)
# - swath volume:      bounding_box = c(west, south, east, north)
# - cloud compute:     check region / s3_credentials_endpoint first