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About Databrary

Source: vignettes/databrary.Rmd
databrary.Rmd

Databrary is a powerful tool for storing and sharing video data and documentation with other researchers. With the databraryr package, it becomes even more powerful. Rather than interact with Databrary through a web browser, users can write their own code to download participant data or even specific files.

I wrote databraryr so that I could better understand how the site works under the hood, and so that I could streamline my own analysis and data sharing workflows.

Let’s get started.

Registering

Access to most of the material on Databrary requires prior registration and authorization from an institution. The authorization process requires formal agreement by an institution. But you’ll create an account ID (email) and secure password when you register. Then, when you log in with your new credentials, you’ll select an existing institution (if yours is on the list), a new institution (if yours isn’t), or an existing authorized investigator (if you are a student, postdoc, or collaborator) to request authorization from.

Installation

Official CRAN release

  • Install the package from CRAN via install.packages(“databraryr”).

Development release

  • Install the devtools package from CRAN: install.packages("devtools") if you have not already done so.
  • Load devtools into your local environment: library(devtools).
  • Install the databraryr package via install_github("databrary/databraryr"). Required dependencies will be installed at this time.

v0.6.5

  • The latest version of the code is v0.6.5. The v0.6.x code uses the httr2 package under the hood, and it runs much faster than v0.5.x.

First steps

All Databrary API access requires authentication. See the authorized users vignette to learn how to log in. Once you’ve installed the package following one of the above routes, it’s a good idea to check that your installation worked by loading it into your local workspace.

After logging in with login_db(), you can try this command to pull data about one of Databrary’s founders:

# Retrieve metadata about one of Databrary's founders.
user_6 <- databraryr::get_user_by_id(user_id = 6)

tibble::as_tibble(user_6)

Note that this command returns a tibble with columns that include the first name (prename), last name (sortname), affiliation, and ORCID ID if available.

Databrary assigns a unique integer for each registered user on the system. We can create a simple helper function to collect information about a larger group of people.

# Helper function
get_user_as_df <- function(user_id) {
  this_user <- databraryr::get_user_by_id(user_id = user_id)
  if (!is.null(this_user)) {
    tibble::as_tibble(this_user)
  } else {
    NULL
  }
}

# Users 5, 6, and 7 are Databrary's founders
purrr::map(5:7, get_user_as_df, .progress = TRUE) |>
  purrr::list_rbind()

You should see information about Databrary’s three founders.

You can also try seeing what’s new on Databrary. The get_db_stats() command gives you information about the newly authorized people, institutions, and newly uploaded datasets. Try this:

databraryr::get_db_stats("stats")
databraryr::get_db_stats("people")
databraryr::get_db_stats("institutions")
databraryr::get_db_stats("datasets")

Depending on when you run this command and how often, there may or may not be new items.

Next steps

To see more about how to access data on Databrary using databraryr visit the accessing data vignette.

To see how to log in and log out once you have authorization, see the vignette for authorized users.