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Using DANDI

DANDI allows you to work with stored neurophysiology data in multiple ways. You can search, view, and download files, all without registering for a DANDI account. As a registered user, you can also create these collections of data along with metadata and publish them to the DANDI platform.

Dandisets

DANDI stores cellular neurophysiology data in Dandisets.

A Dandiset is a collection of assets (files and their metadata) and metadata about the collection.

  • A Dandiset is organized in a structured manner to help users and software tools interact with it.
  • Each Dandiset has a unique persistent identifier that you can use to go directly to the Dandiset (e.g. https://identifiers.org/DANDI:000004). You can use this identifier to cite the Dandiset in your publications or provide direct access to a Dandiset.

Quick Start

If you are new to DANDI, all you need is an Internet connection to use the DANDI Web application to view and download files from a public Dandiset. Registration is not required.

To view a specific public Dandiset and download one of its files:

  1. At the top of the DANDI Web application, click PUBLIC DANDISETS to see all Dandisets currently available in the archive. You can sort them by name, identifier, or date of modification.

  2. Search for a specific Dandiset by contributor name, modality, or species.

  3. Click a Dandiset to open its landing page and view important information such as contact information, description, license, access information and keywords, and simple statistics.

  4. From the right side of the Dandiset landing page, click FILES to see a list of all folders and files for that Dandiset. Click the download icon download_file_icon to download a specific file. Note: To download an entire Dandiset, you will need to follow the instructions in the Download section to install and use the DANDI Python client tool.

Next steps

Although anyone on the Internet can view and download public Dandisets, registered users can also create Dandisets, upload data, and publish the Dandiset to generate a DOI for it. See the sections that follow for more detailed information about the DANDI project, as well as instructions on how to work with public Dandisets or to create and publish you own as a registered user.

Dandiset Actions

The DANDI project contains the DANDI Web application, the DANDI Python client tool, and the DANDI JupyterHub instance. These tools can be used to perform actions on Dandisets.

dandiset_activity

You can learn more about the Dandiset actions in separate sections:

Tools to interact with DANDI

DANDI Web application

The DANDI Web application allows you to:

  • Search across all public Dandisets
  • Download data from public Dandisets
  • Create a new Dandiset and provide metadata
  • Publish your Dandiset

DANDI Python client

The DANDI Python client allows you to:

  • Download Dandisets and individual subject folders or files
  • Organize your data locally before upload
  • Upload Dandisets

Before you can use the DANDI Python client, you have to install the package with pip install dandi in a Python 3.8+ environment.

You should check the Dandi Debugging section in case of any problems.

Dandihub analysis platform

Dandihub provides a JupyterHub instance in the cloud to interact with the data stored in DANDI.

To use the hub, you will need to register for an account using the DANDI Web application. Note that Dandihub is not intended for significant computation, but provides a place to introspect Dandisets and to perform some analysis and visualization of data.

Technical limitations

  • File name/path: There is a limit of 512 characters for the full path length within a dandiset.
  • Volume and size: There is a limit of 5TB per file. We currently accept any size of standardized datasets, as long as you can upload them over an HTTPS connection. However, we ask you contact us if you plan to upload more than 10TB of data.

Citing DANDI

You can add the following statement to the methods section of your manuscript.

Data and associated metadata were uploaded to the DANDI archive [RRID:SCR_017571] using the Python command line tool (https://doi.org/10.5281/zenodo.3692138). The data were first converted into the NWB format (https://doi.org/10.1101/2021.03.13.435173) and organized into a BIDS-like (https://doi.org/10.1038/sdata.2016.44) structure.

You can refer to DANDI using any of the following options: