---
title: "Creating A Hub Package: ExperimentHub or AnnotationHub"
author: "Valerie Obenchain, Lori Shepherd, and Kayla Interdonato"
date: "Modified: Jan 2025. Compiled: `r format(Sys.Date(), '%d %b %Y')`"
output:
  BiocStyle::html_document:
    toc: true
vignette: >
  % \VignetteIndexEntry{Creating A Hub Package: ExperimentHub or AnnotationHub}
  % \VignetteEngine{knitr::rmarkdown}
  % \VignetteEncoding{UTF-8}
---


# Overview

First, one must decide if an ExperimentHub or AnnotationHub package is
appropriate. 


The `AnnotationHubData` package provides tools to acquire, annotate, convert
and store data for use in Bioconductor's `AnnotationHub`. BED files from the
Encode project, gtf files from Ensembl, or annotation tracks from UCSC, are
examples of data that can be downloaded, described with metadata, transformed
to standard `Bioconductor` data types, and stored so that they may be
conveniently served up on demand to users via the AnnotationHub client. While
data are often manipulated into a more R-friendly form, the data themselves
retain their raw content and are not normally filtered or curated like those in
[ExperimentHub](http://bioconductor.org/packages/ExperimentHub/).
Each resource has associated metadata that can be searched through the
`AnnotationHub` client interface.


`ExperimentHubData` provides tools to add or modify resources in
Bioconductor's `ExperimentHub`. This 'hub' houses curated data from courses,
publications, or experiments. It is often convenient to store data to be used in
package examples, testings, or vignettes in the ExperimentHub. The resources can
be files of raw data or more often 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. 


It is advisable to create a separate package for annotations or experiment data
rather than an all encompassing package of data and code. However, it is
sometimes understandable to have a Software package that also serves as the
package front end for the hubs. Although this is generally not recommended; if
you think you have a use case please reach out to hubs@bioconductor.org to
confirm before proceeding with a single package rather than the accompanied
package approach.  






# Setting up a package to use a Hub





## New Hub package

Related resources are added to `AnnotationHub` or `ExperimentHub` by
creating a package. The package should minimally contain the resource metadata,
man pages describing the resources, and a vignette. It may also contain supporting
`R` functions the author wants to provide. This is a similar design to the
existing `Bioconductor` experimental data packages or annotation packages except
the data is stored in Microsoft Azure Genomic Data Lake or other publicly accessibly
sites (like Amazon S3 buckets or institutional servers) instead of
the `data/` or `inst/extdata/` directory of the package. This keeps the package
light weight and allows users to download only necessary data files.


Below are the steps required for creating the package and adding new resources:


### Notify `Bioconductor` team member

The man page and vignette examples in the package will not work until
the data are available in `AnnotationHub` or `ExperimentHub`. If you are not
hosting the data on a stable web server (github and dropbox does not suffice), you should
look into a stable option. We highly recommend zenodo; other options can
include cloudflare, S3 buckets, mircorsoft azure data lake, or an institutional
level server. If you do not have access to a secure location, you can reach out
to `Bioconductor` team member to discuss at hubs@bioconductor.org. Adding data
to the live location will also require reaching out to hubs@bioconductor.org. To
have the data live in the appropriate hub, the metadata.csv file will have to be
created (See inst/extdata section below) and the description file of the package
will need to be accurate. 

### Building the package

When a resource is downloaded from one of the hubs the associated
package is loaded in the workspace making the man pages and vignettes readily
available. Because documentation plays an important role in understanding these
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.

#### `inst/extdata/`

- `metadata.csv`:
  This file contains the metadata in the format of one row per resource
  to be added to the Hub database (each row corresponds to one data file
  uploaded to publically hosted data server). 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
  `ExperimentHubData::makeExperimentHubMetadata` or
  `AnnotationHubData::makeAnnotationHubMetadata`. See
  ?`ExperimentHubData::makeExperimentHubMetadata` or
  ?`AnnotationHubData::makeAnnotationHubMetadata` for details.
  Ensuring that the above function runs without ERROR is also a validation step
  for the metadata file.

  An example data experiment package metadata.csv file can be found [here](https://github.com/Bioconductor/GSE62944/blob/master/inst/extdata/metadata.csv)
  
    If necessary, metadata can be broken up into multiple csv files instead
    having of all records in a single "metadata.csv". The requirement is the
    necessary required columns and using csv format.


#### `inst/scripts/`

- `make-data.R`:
  A script describing the steps involved in making the data object(s). It can be
  code, pseudo-code, or text but should include 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. Output of the script should be files on disk ready to be pushed to
  data server. If data are to be hosted on a personal web site instead of
  Microsoft Azure Genomic Data Lake, this file
  should explain any manipulation of the data prior to hosting on the web
  site. For data hosted on a public web site with no prior manipulation this
  file is not needed. For experimental data objects, 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 Microsoft Azure Genomic Data Lake". 

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


#### `vignettes/`

- One or more vignettes describing analysis workflows or use cases. It could
  minimally show how to access the resources from the hub.


#### `R/`


- `R/*.R`: Optional. Functions to enhance data exploration.


**For ExperimentHub resources only:**
- `zzz.R`: Optional. You can include a `.onLoad()` function in a zzz.R file that
  exports each resource name (i.e., metadata.csv field `title`) into a function. This allows the data
  to be loaded by name, e.g., `resource123()`.

    ```{r, eval=FALSE}
    .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).


#### `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 \alias
  entry for each resource title either on the package man page or individual
  man pages. While this is optional, it is strongly recommended.

- resource man pages:
  Resources can be documented on the same page, grouped by common type
  or have their own dedicated man pages. Man page(s) should describe the
  resource (raw data source, processing, QC steps) and demonstrate how the data
  can be loaded through the standard hub interface.
  
  Data can be accessed via the standard ExperimentHub or AnnotationHub interface with
  single and double-bracket methods. Queries are often useful for finding
  resources. For example you could replace packagename with the name of this package
  being developed, e.g.,

    ```{r, eval=FALSE}
    library(ExperimentHub)
    eh <- ExperimentHub()
    myfiles <- query(eh, "PACKAGENAME")
    myfiles[[1]]        ## load the first resource in the list
    myfiles[["EH123"]]  ## load by EH id
    ```

    NOTE: As a developer, resources should be accessed within your package using
    the Hub id, e.g., `myfiles[["EH123"]].

    You can use multiple search queries to further filter resources. For
    example, replace "SEARCHTERM*" below with one or more search terms that
    uniquely identify resources in your package.

    ```
    library(AnnotationHub)
    hub <- AnnotationHub()
    myfiles <- query(hub, "SEARCHTERM1", "SEARCHTERM2")
    myfiles[[1]]  ## load the first resource in the list
    ```

- **ExperimentHub packages only** If a `.onLoad()` function is used to export each resource as a function
  also document that method of loading, e.g.,

    ```{r, eval=FALSE}
    resourceA(metadata = FALSE) ## data are loaded
    resourceA(metadata = TRUE)  ## metadata are displayed
    ```
- 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.


#### `DESCRIPTION` / `NAMESPACE`

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


- If making an Experiment Data Hub package, the biocViews should contain terms
  from
  [ExperimentData](http://bioconductor.org/packages/release/BiocViews.html#___ExperimentData)
  and should also contain the term `ExperimentHub`.

  If making an Annotation Hub package, the biocViews should contain terms from
[AnnotationData](http://bioconductor.org/packages/release/BiocViews.html#___AnnotationData)
and should also contain the term `AnnotationHub`.

  If the case where a software package was appropriate rather than a separate
  annotation or experiment data package, the biocViews term should include only
  [Software](http://bioconductor.org/packages/release/BiocViews.html#___Software)
  terms but must include either `AnnotationHubSoftware` or
  `ExperimentHubSoftware`.
  

### Data objects

Large data are not formally part of the software package and are stored separately in
a publicly accessible hosted site.


### Confirm Valid Metadata

When you are satisfied with the representation of your resources in
your metadata.csv (or other aptly named csv file) the `Bioconductor` team
member will add the metadata to the production database. Confirm the metadata
csv files in inst/extdata/ are valid by by running either
ExperimentHubData::makeExperimentHubMetadata() or
AnnotationHubData::makeAnnotationHubData() on your package. Please address any
warnings or errors.


### Package review

Once 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](https://github.com/Bioconductor/Contributions) for
review. The package should be submitted without any of the data that is now
located remotely. This keeps the package light weight and minimal size while still
providing access to key large data files now stored remotely. If the data files
were added to the github repository please see [removing large data files and
clean git
tree](http://bioconductor.org/developers/how-to/git/remove-large-data/) to
remove the large files and reduce package size.

Many times these data package are created as a supplement to a software
package. There is a process for submitting [multiple package under the same
issue](https://github.com/Bioconductor/Contributions#submitting-related-packages).






## Additional resources to existing Hub package

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

* The titles for the new versions should be unique and not match the title of
  any resource currently in the Hub. Good practice would be to
  include the version and / or genome build in the title. If the title is
  not unique, the `AnnotationHub` or `ExperimentHub` object will list multiple
  files with the same title. The user will need to use 'rdatadateadded' to
  determine which is the most current or infer from the id numbers which could
  lead to confusion. Let the core team member know if any previously available
  version should be "removed" by adding a 'rdatadateremoved'. 

* Make data available on a publicly accessible site.

* Update make-metadata.R with the new metadata information

* Generate a new metadata.csv file. The package should contain
  metadata for all versions of the data in ExperimentHub or AnnotationHub so the
  old file should remain.  When adding a new version it might be helpful to
  write a new csv file named by version, e.g., metadata_v84.csv, metadata_85.csv
  etc. If using a single metadata.csv file, please add new or updated entries to
  the end of the file.
  
* Bump package version and commit to git

* Notify hubs@bioconductor.org that an update is ready and
  a team member will add the new metadata to the production database;
  new resources will not be visible in AnnotationHub or ExperimentHub until
  the metadata are added to the database.

Contact hubs@bioconductor.org or maintainer@bioconductor.org with any
questions.






## Converting a non AnnotationHub annotation package or non ExperimentHub
   experiment data package to utilizing the Hub.

The concepts and directory structure of the package would stay the same.
The main steps involved would be

1. Restructure the inst/extdata and inst/scripts to include metadata.csv and
make-data.R as described in the section above for creating new packages. Ensure the
metadata.csv file is formatted correctly by running
`AnnotationHubData::makeAnnotationHubMetadata()` or
`ExperimentHubData::makeExperimentHubMetadata()` on your package.

2. Add biocViews term "AnnotationHub" or "ExperimentHub" to DESCRIPTION (or
"AnnotationHubSoftware", "ExperimentHubSoftware" if appropriate).

3. Upload the data to a publicly accessible site and remove the
data from the package. See the section on "Storage of Data Files" below.

4. Once the data is officially added to the hub, update any code to utilize
AnnotationHub or ExperimentHub for retrieving data.

5. Push all changes with a version bump back to Bioconductor
git.bioconductor.org location







# Bug fixes

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

## Update the resource

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

* Notify hubs@bioconductor.org that you want to replace the data
  and make the files available: see section "Uploading Data to Microsoft Azure
  Genomic Data Lake".

* If a file is replaced on the data lake directly, the old file will no longer be
  accessible. This could affect reproducibility of end users' research if the
  old file has already been utilized. This approach should be done with caution.


## 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 and has to be done manually on the database by a core team member.

* Update make-metadata.R and regenerate the metadata.csv file if necessary

* Bump the package version and commit to git

* Notify hubs@bioconductor.org that you want to change the metadata for
  resources. The core team member will likely need the current AH/EH ids for the
  resources that need updating and a summary of what fields in the metadata file
  changed. **NOTE:** Large changes to the metadata may require the core team
  member to remove the resources entirely from the database and re-add resulting
  in new AH/EH ids.






# Remove resources

Removing resources should be done with caution. The intent is that resources in the
Hubs be 'reproducible' research 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 AnnotationHub/ExperimentHub
please contact hubs@bioconductor.org or maintainer@bioconductor.org for
assistance.

When a resource is removed from ExperimentHub or AnnotationHub two things happen:
the 'rdatadateremoved' field is populated with a date and the 'status'
field is populated with a reason why the resource is no longer available. Once
these changes are made, the `ExperimentHub()` or `AnnotationHub()` constructor
will not list the resource among the available ids. An attempt to extract the resource with
'[[' and the EH/AH id will return an error along with the status message. The
function `getInfoOnIds()` will display metadata information for any resource
including resources still in the database but no longer available.

In general, resources are only removed when they are no longer available
(e.g., moved from web location, no longer provided etc.).

To remove a resource from `AnnotationHub` contact hubs@bioconductor.org
or maintainer@bioconductor.org.






# Versioning

Versioning of resources is handled by the maintainer. If you plan to provide
incremental updates to a file for the same organism / genome build, we
recommend including a version in the title of the resource so it is easy
to distinguish which is most current. We also would recommend when uploading the
data to genomic data lake or your publicly accessible site to have a directory structure
accounting for versioning.

If you do not include a version, or make the title unique in some way,
multiple files with the same title will be listed in the `ExperimentHub` or
`AnnotationHub` object. The user will have to use the 'rdatadateadded' metadata field
to determine which file is the most current or try an infer from ids which can
lead to confusion.





# Visibility

Several metadata fields control which resources are visible when
a user invokes ExperimentHub()/AnnotationHub(). Records are filtered based on these criteria:

- 'snapshotdate' >= the date of the Bioconductor release being used
- 'rdatadateadded'  >= today's date
- 'rdatadateremoved' is NULL / NA
- 'biocVersion' is <= to the Bioconductor version being used

Once a record is added to ExperimentHub/AnnotationHub it is visible from that point forward
until stamped with 'rdatadateremoved'. For example, a record added on
May 1, 2017 with 'biocVersion' 3.6 will be visible in all snapshots >=
May 1, 2017 and in all Bioconductor versions >= 3.6.

A special filter for OrgDb is utilized in AnnotationHub. Only one OrgDb is available per
release/devel cycle. Therefore contributed OrgDb added to a devel cycle are
masked until the following release. There are options for debugging these masked
resources. See `?setAnnotationHubOption`





# Storage of Data Files

The data should not be included in the package. This keeps the package light
weight and quick for a user to install. This allows the user to investigate functions and
documentation without downloading large data files and only proceeding with the
download when necessary. When at all possible data should be hosted on a
publicaly accessible site designated by the package maintainer. If this is not
possible contact a core team member at hubs@bioconductor.org to request options
for hosting. 

## Hosting Data on a Publicly Accessible Site

Data can be accessed through the hubs from any publicly accessible site. The
metadata.csv file[s] created will need the column `Location_Prefix` to indicate
the hosted site. See more in the description of the metadata columns/fields
below but as a quick example if the link to the data file is
`ftp://mylocalserver/singlecellExperiments/dataSet1.Rds` an example breakdown of
the `Location_Prefix` and `RDataPath` for this entry in the metadata.csv file
would be `ftp://mylocalserver/` for the `Location_Prefix` and
`singlecellExperiments/dataSet1.Rds` for the `RDataPath`. Github and Dropbox are not an
acceptable hosting platform for data. We highly recommend zenodo; other
possiblities include cloudflare, S3 buckets, microsoft data lakes, or possible a
server located at your home institution.

## Uploading Data to Microsoft Azure Genomic Data Lake

In some cases we may allow access to a `Bioconductor` Microsoft Azure Genomic
Data Lake. Instead of providing the data files via dropbox, ftp, github, etc. we will grant
temporary access to S3 directory where you can upload your data for
preprocessing.

If interesting in hosting on Bioconductor, please email
hubs@bioconductor.org. You should provide the following information:

1. Description or link to package and description of hosted data. Include why
existing data already provided in similar packages or hub is not appropriate and
why you would like to host your own.

2. Why a location like zenodo is not appropriate and/or verification that you do
not have access to an institutional level hosting location or something like S3
bucket or Azure is not available.

3. The number and size of files to be hosted and the total size that is to be
uploaded.


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 hubs@bioconductor.org to continue the
process. To add the data officially the data will need to be uploaded and the
metadata.csv file will need to be created in the github repository.



### R Interface with R package 

In some cases we may allow access to a `Bioconductor` Microsoft Azure Genomic
Data Lake. Please email hubs@bioconductor.org to obtain necessary
information. Examples below we assume the data on your system is in a directory
call YourLocalDataDir and will use the following that would be provided by core
team:

1. username: hubtest1
2. key: 102da5beeebe1339ef50dd9138589d8e46a354d1ad69a7b909f165d265f38a33
3. core team bucket name: userdata

A helper R package has been created to assist with upload called
BiocHubsIngestR; It is currently on
[github](https://github.com/Bioconductor/BiocHubsIngestR). A contributor can use
the following commands in R to upload data:

```
## install package
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("Bioconductor/BiocHubsIngestR")

## set up authentication
BiocHubsIngestR::auth(username = "hubtest1", password = "102da5beeebe1339ef50dd9138589d8e46a354d1ad69a7b909f165d265f38a33")

## Upload data
BiocHubsIngestR::upload("/Local/Path/To/YourLocalDataDir", bucket="userdata")

```


### Command Line via AWS S3 CLI

In some cases we may allow access to a `Bioconductor` Microsoft Azure Genomic
Data Lake. Instead of providing the data files via dropbox, ftp, github, etc. we will grant
temporary access to S3 bucket where you can upload your
data for preprocessing. The command line interface for upload is through AWS S3 Command Line
Interface. You should install the [AWS CLI](https://aws.amazon.com/cli/) on your
machine. Please email hubs@bioconductor.org to obtain necessary
information. Examples below we assume the data on your system is in a directory
call YourLocalDataDir and will use the following that would be provided by core
team:

1. username: hubtest1
2. key: 102da5beeebe1339ef50dd9138589d8e46a354d1ad69a7b909f165d265f38a33
3. core team bucket name: userdata

To set up credentials on your system you would use the command
`aws configure --profile <username>`. It will take you through prompts for AWS
Access Key Id, AWS Secret Access Key, Default region name, and Default output
format. Using our example information it would be something like the following:

```
> aws configure --profile hubtest1
AWS Access Key ID:  hubtest1
AWS Secret Access Key: 102da5beeebe1339ef50dd9138589d8e46a354d1ad69a7b909f165d265f38a33
Default region name: <leave blank>
Default output format: <leave blank>

```

You would then be able to access the userdata that was set up for you. You can
use `s3 cp` to upload data. Use with recursive to upload directories. The
general form will be

```
aws --profile <username>
--endpoint-url https://<username>.hubsingest.bioconductor.org/
s3 cp --recursive <path to yourlocal directory>
s3://<coreteam bucket name>/<local directory name>
```
So using our example data:

```
aws --profile hubtest1
--endpoint-url https://hubtest1.hubsingest.bioconductor.org/
s3 cp --recursive /path/to/YourLocalDataDir
s3://userdata/YourLocalDataDir

```

You can check the upload with `s3 ls`. With our example data it would look
something like

```
aws --profile hubtest1
--endpoint-url https://hubtest1.hubsingest.bioconductor.org/
s3 ls --recursive s3://userdata/
```


In general, all files should be in a folder that matches your package name. Only upload data files;
subdirectories are optionally okay to include to distguish versions or
characteristics of the data (i.e species, tissue types). Do not upload your
entire package directory (i.e DESCRIPTION, NAMESPACE, R/, etc.)

Once the upload is complete, email hubs@bioconductor.org to continue the
process. To add the data officially the data will need to be uploaded and the
metadata.csv file will need to be created in the github repository.


### Utilizing the Bioconductor Docker container

Coming soon!


# Validating

The best way to validate record metadata is to  read inst/extdata/metadata.csv
(or aptly named csv file in inst/extdata) using the
`AnnotationHubData::makeAnnotationHubMetadata()` or
`ExperimentHubData::makeExperimentHubMetadata()`. If that is successful the
metadata should be valid and able to be entered into the database.




# Example metadata.csv file and more information

As described above the metadata.csv file (or multiple metadata.csv files) will
need to be created before the data can be added to the database. To ensure
proper formatting one should run `AnnotationHubData::makeAnnotationHubMetadata`
or `ExperimentHubData::makeExperimentHubMetadata`
on the package with any/all metadata files, and address any ERRORs that
occur. Each data object uploaded to data server should have an entry (row) in the metadata
file. Briefly, a description of the metadata columns required:

* Title: 'character(1)'. Name of the resource. This can be the exact file name
  (if self-describing) or a more complete description. For ExperimentHub resources, 
  the title can be used to generate the function name to load the resource, so
  spaces should be avoided in this case.
* Description: 'character(1)'. Brief description of the resource, similar to the
  'Description' field in a package DESCRIPTION file.
* BiocVersion: 'character(1)'. The first Bioconductor version the resource was
  made available for. Unless removed from the hub, the resource will be
  available for all versions greater than or equal to this field. This generally
  is the current **devel version** of Bioconductor.
* Genome: 'character(1)'. Genome. Can be NA.
* SourceType: 'character(1)'. Format of original data, e.g., FASTA, BAM, BigWig,
  etc. 'AnnotationHubData::getValidSourceTypes()' list currently acceptable values. If nothing seems
  appropiate for your data reach out to hubs@bioconductor.org
* SourceUrl: 'character(1)'. Location of original data files. Multiple urls
  should be provided as a comma separated string. If the data is simulated we
  recommend putting either a lab url or the url of the Bioconductor package.
* SourceVersion: 'character(1)'. Version of original data.
* Species: 'character(1)'. Species. For help on valid species see
  'getSpeciesList, validSpecies, or suggestSpecies.' Can be NA.
* TaxonomyId: 'character(1)'. Taxonomy ID. There are checks for valid taxonomyId
  given the Species which produce warnings. See GenomeInfoDb::loadTaxonomyDb()
  for full validation table. Can be NA.
* Coordinate_1_based: 'logical'. TRUE if data are 1-based. Can be NA.
* DataProvider: 'character(1)'. Name of company or institution that supplied the
  original (raw) data.
* Maintainer: 'character(1)'. Maintainer name and email in the following format:
  Maintainer Name <username@address>.
* RDataClass: 'character(1)'. R / Bioconductor class the data are stored in,
  e.g., GRanges, SummarizedExperiment, ExpressionSet etc. If the file is loaded
  or read into R what is the class of the object.
* DispatchClass: 'character(1)'. Determines how data are loaded into R. The
  value for this field should be 'Rda' if the data were serialized with 'save()'
  and 'Rds' if serialized with 'saveRDS'. The filename should have the
  appropriate 'rda' or 'rds' extension. There are other available DispathClass
  types and the function 'AnnotationHub::DispatchClassList()' will output a
  matrix of currently implemented DispatchClass and brief description of
  utility. If a predefine class does not seem appropriate contact
  hubs@bioconductor.org. An all purpose DispathClass is `FilePath` that
  instead of trying to load the file into R, will only return the path to the
  locally downloaded file.
* Location_Prefix: 'character(1)'. **Do not** include this field if data are stored
  in the Bioconductor Data Lake; it will be generated automatically. If data will
  be accessed from a location other than Bioconductor's Microsoft Data Lake
  location,  this field should be the base url.
* RDataPath: 'character(1)'.This field should be the remainder of the path to
  the resource. The 'Location_Prefix' will be prepended to 'RDataPath' for the
  full path to the resource.  If the resource is stored in Bioconductor's AWS S3
  buckets, it should start with the name of the package associated with the
  metadata and should not start with a leading slash. It should include the
  resource file name.
* Tags: 'character() vector'.  'Tags' are search terms used to define a subset
  of resources in a 'Hub' object, e.g, in a call to 'query'. Multiple 'Tags' are
  specified as a colon separated string, e.g., tags for two resources would look
  "tag1:tag2". The tags in the database are a combination of any individually
  listed 'Tag' provided here in the metadata file as well as any biocViews terms
  listed in the DESCRIPTION of the package
  

Any additional columns in the metadata.csv file will be ignored but could be
included for internal reference.

More on Location_Prefix and RDataPath. These two fields make up the complete
file path url for downloading the data file. If using the Bioconductor Microsoft
Azure Genomic Data Lake the Location_Prefix should not be included in the metadata file[s] as this field
will be populated automatically.  The RDataPath will be the directory structure
you uploaded to the Data Lake. If you uploaded a directory `MyAnnotation/`, and that
directory had a subdirectory `v1/` that contained two files `counts.rds` and
`coldata.rds`, your metadata file will contain two rows and the RDataPaths would
be `MyAnnotation/v1/counts.rds` and `MyAnnotation/v1/coldata.rds`.  If you
host your data on a publicly accessible site you must include a base url as the
`Location_Prefix`. If your data file was at
`ftp://myinstiututeserver/biostats/project2/counts.rds`, your metadata file will
have one row and the `Location_Prefix` would be `ftp://myinstiututeserver/` and
the `RDataPath` would be `biostats/project2/counts.rds`.


This is a bad example because these annotations are already in the hubs but it
should give you an idea of the format for AnnotationHub. Let's say I have a package myAnnotations
and I upload two annotation files for dog and cow with information extracted from
ensembl to Bioconductor's Data Lake location.  You would want the following saved as a csv (comma seperated output)
but for easier view we show in a table:


Title | Description | BiocVersion | Genome | SourceType | SourceUrl | SourceVersion | Species | TaxonomyId | Coordinate_1_based | DataProvider | Maintainer | RDataClass | DispatchClass | RDataPath
---------------|----------------------------------------------|-----|-------------|-----|----------------------------------------------------------------------------------------------------|------------|-------------|------|------|---------|-------------------------------------------------------|-----------|----------|--------------------------------------------------------------------------
Dog Annotation | Gene Annotation for Canis lupus from ensembl | 3.9 | Canis lupus | GTF | ftp://ftp.ensembl.org/pub/release-95/gtf/canis_lupus_dingo/Canis_lupus_dingo.ASM325472v1.95.gtf.gz | release-95 | Canis lupus | 9612 | true | ensembl | Bioconductor Maintainer <maintainer@bioconductor.org> | character | FilePath | myAnnotations/canis_lupus_dingo.ASM325472v1.95.gtf.gz
Cow Annotation | Gene Annotation for Bos taurus from ensemble | 3.9 | Bos taurus | GTF | ftp://ftp.ensembl.org/pub/release-74/gtf/bos_taurus/Bos_taurus.UMD3.1.74.gtf.gz | release-74 | Bos taurus | 9913 | true | ensembl | Bioconductor Maintainer <maintainer@bioconductor.org> | character | FilePath | myAnnotations/Bos_taurus.UMD3.1.74.gtf.gz




This is a dummy example but hopefully it will give you an idea of the format for
ExperimentHub.
Let's say I have a package myExperimentPackage and I upload two files one a
SummarizedExperiments of expression data saved as a .rda and the other a sqlite
database both considered simulated data.
You would want the following saved as a csv (comma seperated output)
but for easier view we show in a table:


Title | Description | BiocVersion | Genome | SourceType | SourceUrl | SourceVersion | Species | TaxonomyId | Coordinate_1_based | DataProvider | Maintainer | RDataClass | DispatchClass | RDataPath
---------------|----------------------------------------------|-----|-------------|-----|----------------------------------------------------------------------------------------------------|------------|-------------|------|------|---------|-------------------------------------------------------|-----------|----------|--------------------------------------------------------------------------
Simulated Expression Data  | Simulated Expression values for 12 samples and 12000 probles | 3.9 | NA   | Simulated | http://mylabshomepage | v1 | NA | NA | NA | http://bioconductor.org/packages/myExperimentPackage | Bioconductor Maintainer <maintainer@bioconductor.org> | SummarizedExperiment | Rda | myExperimentPackage/SEobject.rda
Simulated Database | Simulated Database containing gene mappings | 3.9 | hg19 | Simulated | http://bioconductor.org/packages/myExperimentPackage | v2 | Home sapiens | 9606 | NA | http://bioconductor.org/packages/myExperimentPackage | Bioconductor Maintainer <maintainer@bioconductor.org> | SQLiteConnection | SQLiteFile | myExperimentPackage/mydatabase.sqlite


Let's also show an example if you were using zenodo to host the data:



Title | Description | BiocVersion | Genome | SourceType | SourceUrl | SourceVersion | Species | TaxonomyId | Coordinate_1_based | DataProvider | Maintainer | RDataClass | DispatchClass | RDataPath | Location_Prefix
---------------|----------------------------------------------|-----|-------------|-----|----------------------------------------------------------------------------------------------------|------------|-------------|------|------|---------|-------------------------------------------------------|-----------|----------|--------------------------------------------------------------------------|--------------------------------------------------------------------------
Simulated Expression Data  | Simulated Expression values for 12 samples and 12000 probles | 3.9 | NA   | Simulated | http://mylabshomepage | v1 | NA | NA | NA | http://bioconductor.org/packages/myExperimentPackage | Bioconductor Maintainer <maintainer@bioconductor.org> | SummarizedExperiment | Rda | records/2353526/files/SEobject.rda | https://zenodo.org/
Simulated Database | Simulated Database containing gene mappings | 3.9 | hg19 | Simulated | http://bioconductor.org/packages/myExperimentPackage | v2 | Home sapiens | 9606 | NA | http://bioconductor.org/packages/myExperimentPackage | Bioconductor Maintainer <maintainer@bioconductor.org> | SQLiteConnection | SQLiteFile | records/2353526/files/mydatabase.sqlite | https://zenodo.org/
