Using the dataset ID, return all records associated with the data. At present, only returns the dataset in an unparsed format, not as a data table. This function will only download one dataset at a time.

get_chroncontrol(x, chronology = 1, verbose = TRUE, add = FALSE)



A single numeric chronology ID, a vector of numeric dataset IDs as returned by get_dataset or a download or download_list object.


When download objects have more than associated chronology, which chronology do you want? Default is 1.


logical, should messages on API call be printed?


logical, should this chron control be added to the download object?


This command returns either an object of class "try-error" containing the error returned from the Neotoma API call, or a full data object containing all the relevant information required to build either the default or prior chronology for a core. When download or download_list objects are passes, the user can add the chroncontrol to the download object explicitly, in which case the function will return a download with chroncontrol embedded.

This is a list comprising the following items:


A table describing the collection, including dataset information, PI data compatable with get_contact and site data compatable with get_site.


Dataset information for the core, primarily the age-depth model and chronology. In cases where multiple age models exist for a single record the most recent chronology is provided here.

If Neotoma returns empty content, either the control table or the associated metadata (which happens in approximately 25% of cases) then the data.frames are returned with NA content.


+ Neotoma Project Website: + API Reference:


Simon J. Goring [email protected]


if (FALSE) { # The point of pulling chronology tables is to re-build or examine the # chronological information that was used to build the age-depth model for # the core. You can do this by hand, but the `write_agefile` function works # with `download` objects directly. three_pines <- get_download(get_dataset(get_site("Three Pines Bog"), datasettype = "pollen")) pines_chron <- get_chroncontrol(three_pines) # Spline interpolation: model <- smooth.spline(x = pines_chron[[1]]$chron.control$depth, y = pines_chron[[1]]$chron.control$age) new_ages <- predict(model, x = three_pines[[1]]$sample.meta$depth) }