Using Globus as a data store for the Canadian Open Neuroscience Portal

This use case shows how the Canadian Open Neuroscience Portal (CONP) disseminates data as DataLad datasets using the Globus network with git-annex, a custom git-annex special remote, and Datalad. It demonstrates

  1. How to enable the git-annex Globus special remote to access files content from,

  2. The workflows used to access datasets via the Canadian Open Neuroscience Portal (CONP),

  3. An example of disk-space aware computing with large datasets distributed across systems that avoids unnecessary replication, eased by DataLad and git-annex.

The Challenge

Every day, researchers from different fields strive to advance present state-of-the-art scientific knowledge by generating and publishing novel results. Crucially, they must share such results with the scientific community to enable other researchers to further build on existing data and avoid duplicating work.

The Canadian Open Neuroscience Portal (CONP) is a publicly available platform that aims to remove the technical barriers to practicing open science and improve the accessibility and reusability of neuroscience research to accelerate the pace of discovery. To this end, the platform will provide a unified interface that – among other things – enables sharing and open dissemination of both neuroscience data and methods to the global community. Managing the scientific data ecosystem is extremely challenging given the amount of new data generated every day, however. CONP must take a strategic solution to allow researchers to

  • dynamically work on present data,

  • upload new versions of the data, and

  • generate additional scientific work.

An underlying data management system to achieve this must be flexible, dynamic and light-weight. It would need to have the ability to easily distribute datasets across multiple locations to reduce the need of re-collecting or replicating data that is similar to already existing datasets.

The Datalad Approach

CONP makes use of Datalad as a data management tool to enable efficient analysis and work on datasets: Datalad minimizes the computational cost of holding full storage of datasets versions, it allows files in a dataset to be distributed across multiple download sources, and to be retrieved on demand only to save disk space. Therefore, it is common practice for researchers to both download and publish research content in a dataset format via a CONP, which provides them with a vast dataset repository.

Basic principles of DataLad for new readers

If you are new to DataLad, the introduction of the handbook and the chapter DataLad datasets can give you a good idea of what DataLad and its underlying tools can to, as well as a hands-on demonstration. This findoutmore, in the meantime, sketches a high-level overview of the principles behind DataLad’s data sharing capacities.

Datalad is built on top of Git and git-annex, and enables data version control. A one-page overview can be found in section What you really need to know.

git-annex is a useful tool that extends Git with the ability to manage repositories in a lightweight fashion even if they contain large amounts of data. One main principle of git-annex lies storing data that should not be stored in Git (e.g., due to size limits) in an annex. In its place, it generates symbolic links (symlinks) to these annexed files that encode their file content. Only the symlinks are committed into Git while git-annex handles data management in the annex. A detailed explanation of this process can be found in the section Data integrity, but the outcome of it is a light-weight Git repository that can be cloned fast and yet contains access to arbitrarily large data managed by git-annex.

In the case of data sharing procedures, annexed data can be stored in various third party hosting services configured as special remotes. When retrieving data, git-annex requests access to the primary data source storing those files to retrieve actual files content when the user needs it.

The workflows for users to get data are straightforward: Users log into the CONP portal and install Datalad datasets with datalad install -r <dataset>. This gives them access to the annexed files (as mentioned in the findoutmore above, large files replaced by their symlinks). To request the content of the annexed files, they simply download those files locally in their file system using datalad get path/to/file. So simple!

On a technical level, under the hood, git-annex needs to have a connection established with the primary data source, the special remote, that hosts and provides the requested files’ contents. In some cases, annexed files are stored in Globus is an efficient transfer files system suitable for researchers to share and transfer files between so called endpoints, locations in where files get uploaded by their owners or get transferred to, that can be either private or public. Annexed file contents are stored in such Globus endpoints. Therefore, when users download annexed files, Globus communicates with git-annex to provide access to files content. Given this functionality, we can say that Globus works as a data store for git-annex, or in technical terms, that Globus is configured to work as a special remote for git-annex. This is possible via the git-annex backend interface implementation for Globus called git-annex-globus-remote developed by CONP. In conjunction, CONP and the git-annex-globus-remote constitute the building blocks that enable access to datasets and its data: CONP hosts small-sized datasets, and is the data store that (large) file content can be retrieved from.

To sum up, CONP makes a variety of datasets available and provides them to researchers as Datalad datasets that have the regular, advantageous Datalad functionality. All of this exists thanks to the ability of git-annex and Datalad to interface with special remote locations across the web such as to request access to data. In this way, researchers have access to a wide research data ecosystem and can use and reuse existing data, thus reducing the need of data replication.


Globus as git-annex data store

A remote data store exists thanks to git-annex (which DataLad builds upon): git-annex uses a key-value pair to reference files. In the git-annex object tree, large files in datasets are stored as values while the key is generated from their contents and is checked into Git. The key is used to reference the location of the value in the object tree[1]. The object-tree (or keystore) with the data contents can be located anywhere – its location only needs to be encoded using a special remote. Therefore, thanks to the git-annex-globus-remote interface, provides git-annex with location information to retrieve values and access files content with the corresponding keys. To ultimately enable end users’ access to data, git-annex registers Globus locations by assigning them to Globus-specific URLs, such as globus://dataset_id/path/to/file. Each Globus URL is associated with a the key corresponding to the given file. The use of a Globus URL protocol is a fictitious mean to assign each file of the dataset a unique location and source and therefore, it is a wrapper for additional validation that is performed by the git-annex-globus-remote to check on the actual presence of the file within the Globus transfer file ecosystem. In other words, the ‘Globus URL’ is simply an alias of an existing file located on the web and specifically available in Registration of Globus URLs in git-annex is among the configuration procedures carried out on an administrative, system-wide level, and users will only deal with direct easy access of desired files.

With this, Globus is configured to receive data access requests from git-annex and to respond back if data is available. Currently, the git-annex-globus-remote only supports data download operations. In the future, it could be useful for additional functionality as well. When the globus special remote gets initialized for the first time, the user has to authenticate to using ORCID , Gmail or a specific Globus account. This step will enable git-annex to then initialize the globus special remote and establish the communication process. Instructions to use the globus special remote are available at Guidelines specifying the standard communication protocol to implement a custom special remote can be found at

An example using Globus from a user perspective

It always starts with a dataset, installed with either datalad install (manual) or datalad clone (manual).

$ datalad install -r <dataset>
$ cd <dataset>

In order to get access to annexed data stored on, users need to install the globus-special-remote. If it is the first time using Globus, users will need to authenticate to by running the git-annex-remote-globus setup command:

$ pip install git-annex-remote-globus
# if first time
$ git-annex-remote-globus setup

After the installation of a dataset, we can see that most of the files in the dataset are annexed: Listing a file with ls -l will reveal a symlink to the dataset’s annex.

$ ls -l NeuroMap_data/cortex/mask/mask.mat
 cortex/mask/mask.mat -> ../../../.git/annex/objects/object.mat

However, without having any content downloaded yet, the symlink currently points into a void, and tools will not be able to open the file as its contents are not yet locally available.

$ cat NeuroMap_data/cortex/mask/mask.mat
  NeuroMap_data/cortex/mask/mask.mat: No such file or directory

However, data retrieval is easy. At first, users have to enable the globus remote.

$ git annex enableremote globus
 enableremote globus ok
 (recording state in git...)

After that, they can download any file, directory, or complete dataset using datalad get (manual):

$ datalad get NeuroMap_data/cortex/mask/mask.mat
 get(ok): NeuroMap_data/cortex/mask/mask.mat (file) [from globus...]

$ ls -l NeuroMap_data/cortex/mask/mask.mat
 cortex/mask/mask.mat -> ../../../.git/annex/objects/object.mat

$ cat NeuroMap_data/cortex/mask/mask.mat
 # you can now access the file !

Downloaded! Researchers could now use this dataset to replicate previous analyses and further build on present data to bring scientific knowledge forward. CONP thus makes a variety of datasets flexibly available and helps to disseminate data. The on-demand availability of files in datasets can help scientists to save disk space. For this, they could get only those data files that they need instead of obtaining complete copies of the dataset, or they could locally datalad drop (manual) data that is hosted and thus easily re-available on after their analyses are done.


The README at provides an excellent and in-depth overview of how to install and use the git-annex special remote for