1 Overview

ExperimentHubData provides tools to add or modify resources in Bioconductor’s ExperimentHub. This ‘hub’ houses curated data from courses, publications or experiments. The resources are generally not files of raw data (as can be the case in AnnotationHub) but instead are R / Bioconductor objects such as GRanges, SummarizedExperiment, data.frame etc. Each resource has associated metadata that can be searched through the ExperimentHub client interface.

2 New resources

Resources are contributed to ExperimentHub in the form of a package. The package contains the resource metadata, man pages, vignette and any supporting R functions the author wants to provide. This is a similar design to the existing Bioconductor experimental data packages except the data are stored in AWS S3 buckets instead of the data/ directory of the package.

Below are the steps required for adding new resources.

2.1 Notify Bioconductor team member

The man page and vignette examples in the software package will not work until the data are available in ExperimentHub. Adding the data to AWS S3 and the metadata to the production database involves assistance from a Bioconductor team member. Please read the section “Uploading Data to S3”.

2.2 Building the software package

When a resource is downloaded from ExperimentHub the associated software package is loaded in the workspace making the man pages and vignettes readily available. Because documentation plays an important role in understanding these curated resources please take the time to develop clear man pages and a detailed vignette. These documents provide essential background to the user and guide appropriate use the of resources.

Below is an outline of package organization. The files listed are required unless otherwise stated.

2.2.1 inst/extdata/

  • metadata.csv: This file contains the metadata in the format of one row per resource to be added to the ExperimentHub database. The file should be generated from the code in inst/scripts/make-metadata.R where the final data are written out with write.csv(..., row.names=FALSE). The required column names and data types are specified in AnnotationHub::readMetadataFromCsv(). See ?readMetadataFromCsv for details.

2.2.2 inst/scripts/

  • make-data.R: A script describing the steps involved in making the data object(s). This includes where the original data were downloaded from, pre-processing, and how the final R object was made. Include a description of any steps performed outside of R with third party software. It is encouraged to serialize Data objects with save() with the .rda extension on the filename but not strictly necessary. If the data is provided in another format an appropriate loading method may need to be implemented. Please advise when reaching out for “Uploading Data to S3”.

  • make-metadata.R: A script to make the metadata.csv file located in inst/extdata of the package. See ?readMetadataFromCsv for a description of expected fields and data types. readMetadataFromCsv() can be used to validate the metadata.csv file before submitting the package.

2.2.3 vignettes/

  • One or more vignettes describing analysis workflows.

2.2.4 R/

  • zzz.R: Optional. You can include a .onLoad() function in a zzz.R file that exports each resource name (i.e., title) into a function. This allows the data to be loaded by name, e.g., resouce123().

    .onLoad <- function(libname, pkgname) {
        fl <- system.file("extdata", "metadata.csv", package=pkgname)
        titles <- read.csv(fl, stringsAsFactors=FALSE)$Title
        createHubAccessors(pkgname, titles)

    ExperimentHub::createHubAccessors() and ExperimentHub:::.hubAccessorFactory() provide internal detail. The resource-named function has a single ‘metadata’ argument. When metadata=TRUE, the metadata are loaded (equivalent to single-bracket method on an ExperimentHub object) and when FALSE the full resource is loaded (equivalent to double-bracket method).

  • R/*.R: Optional. Functions to enhance data exploration.

2.2.5 man/

  • package man page: The package man page serves as a landing point and should briefly describe all resources associated with the package. There should be an entry for each resource title either on the package man page or individual man pages.

  • resource man pages: Resources can be documented on the same page, grouped by common type or have their own dedicated man pages.

  • document how data are loaded: Data can be accessed via the standard ExperimentHub interface with single and double-bracket methods, e.g.,

    eh <- ExperimentHub()
    myfiles <- query(eh, "PACKAGENAME")
    myfiles[[1]]        ## load the first resource in the list
    myfiles[["EH123"]]  ## load by EH id
  • If a .onLoad() function is used to export each resource as a function also document that method of loading, e.g.,

    resourceA(meta = FALSE) ## data are loaded
    resourceA(meta = TRUE)  ## metadata are displayed


  • The package should depend on and fully import ExperimentHub. If using the suggested .onLoad() function, import the utils package in the DESCRIPTION file and selectively importFrom(utils, read.csv) in the NAMESPACE.

  • Package authors are encouraged to use the ExperimentHub::listResources() and ExperimentHub::loadResource() functions in their man pages and vignette. These helpers are designed to facilitate data discovery within a specific package vs within all of ExperimentHub.

2.3 Data objects

Data are not formally part of the software package and are stored separately in AWS S3 buckets. The author should follow instructions in the section “Uploading Data to S3”.

2.4 Metadata

When you are satisfied with the representation of your resources in make-metadata.R (which produces metadata.csv) the Bioconductor team member will add the metadata to the production database.

2.5 Package review

Once the data are in AWS S3 and the metadata have been added to the production database the man pages and vignette can be finalized. When the package passes R CMD build and check it can be submitted to the package tracker for review.

3 Add additional resources

Metadata for new versions of the data can be added to the same package as they become available.

Contact or with any questions.

4 Bug fixes

A bug fix may involve a change to the metadata, data resource or both.

4.1 Update the resource

  • The replacement resource must have the same name as the original and be at the same location (path)

  • Notify that you want to replace the data and make the files available: see section “Uploading Data to S3”.

4.2 Update the metadata

New metadata records can be added for new resources but modifying existing records is discouraged. Record modification will only be done in the case of bug fixes.

  • Notify that you want to change the metadata

  • Update make-metadata.R with modified information and regenerate the metadata.csv file if necessary

  • Bump the package version and commit to git

5 Remove resources

Removing resources should be done with caution. The intent is that ExperimentHub be a ‘reproducible’ resource by providing a stable snapshot of the data. Data made available in Bioconductor version x.y.z should be available for all versions greater than x.y.z. Unfortunately this is not always possible. If you find it necessary to remove data from ExperimentHub please contact or for assistance.

When a resource is removed from ExperimentHub the ‘status’ field in the metadata is modified to explain why they are no longer available. Once this status is changed the ExperimentHub() constructor will not list the resource among the available ids. An attempt to extract the resource with ‘[[’ and the EH id will return an error along with the status message.

6 Uploading Data to S3

Instead of providing the data files via dropbox, ftp, etc. we will grant temporary access to an S3 bucket where you can upload your data. Please email for access.

You will be given access to the ‘AnnotationContributor’ user. Ensure that the AWS CLI is installed on your machine. See instructions for installing AWS CLI here. Once you have requested access you will be emailed a set of keys. There are two options to set the profile up for AnnotationContributor

  1. Update your .aws/config file to include the following updating the keys accordingly:
[profile AnnotationContributor]
output = text
region = us-east-1
aws_access_key_id = ****
aws_secret_access_key = ****
  1. If you can’t find the .aws/config file, Run the following command entering appropriate information from above
aws configure --profile AnnotationContributor

After the configuration is set you should be able to upload resources using

aws --profile AnnotationContributor s3 cp test_file.txt s3://annotation-contributor/test_file.txt --acl public-read

Please upload the data with the appropriate directory structure, including subdirectories as necessary (i.e. top directory must be software package name, then if applicable, subdirectories of versions, …)

Once the upload is complete, email to continue the process

7 Validating

The best way to validate record metadata is to read inst/extdata/metadata.csv with ExperimentHubData::readMetadataFromCsv(). If that is successful the metadata are ready to go.