Skip to content

Latest commit

 

History

History
183 lines (136 loc) · 10.5 KB

13_upload.md

File metadata and controls

183 lines (136 loc) · 10.5 KB

Create Dandisets and Upload Data

You can create a new Dandiset at http://dandiarchive.org. This Dandiset can be fully public or embargoed according to NIH policy. When you create a Dandiset, a permanent Dandiset ID is automatically assigned to it.

To prevent the production server from being inundated with test Dandisets, we encourage developers to develop against the development server (https://gui-staging.dandiarchive.org/). Note that the development server should not be used to stage your data. On the production server, all data are uploaded as draft and can be adjusted before publishing. The development server is primarily used by users learning to use DANDI or by developers.

The instructions below will alert you to where the commands for interacting with these two different servers differ slightly.

Install DANDI Client

  1. To create a new Dandiset and upload your data, you need to have a DANDI account. See the Create a DANDI Account page.
  2. Log in to DANDI and copy your API key. Click on your user initials in the top-right corner after logging in. Production (dandiarchive.org) and staging (gui-staging.dandiarchive.org) servers have different API keys and different logins.
  3. Locally:

    1. Create a Python environment. This is not required, but strongly recommended; e.g. miniconda, virtualenv.

    2. Install the DANDI CLI into your Python environment:

       pip install -U dandi

    3. Store your API key somewhere that the CLI can find it; see Storing Access Credentials.

Store Access Credentials

There are two options for storing your DANDI access credentials.

  1. DANDI_API_KEY Environment Variable

    • By default, the DANDI CLI looks for an API key in the DANDI_API_KEY environment variable. To set this on Linux or macOS, run:

        export DANDI_API_KEY=personal-key-value

    • Note that there are no spaces around the "=".

  2. keyring Library

    • If the DANDI_API_KEY environment variable is not set, the CLI will look up the API key using the keyring library, which supports numerous backends, including the system keyring, an encrypted keyfile, and a plaintext (unencrypted) keyfile.

    • Specifying the keyring backend

      • You can set the backend the keyring library uses either by setting the PYTHON_KEYRING_BACKEND environment variable or by filling in the keyring library's configuration file.
      • IDs for the available backends can be listed by running keyring --list.
      • If no backend is specified in this way, the library will use the available backend with the highest priority.
      • If the DANDI CLI encounters an error while attempting to fetch the API key from the default keyring backend, it will fall back to using an encrypted keyfile (the keyrings.alt.file.EncryptedKeyring backend). If the keyfile does not already exist, the CLI will ask you for confirmation; if you answer "yes," the keyring configuration file (if it does not already exist; see above) will be configured to use EncryptedKeyring as the default backend. If you answer "no," the CLI will exit with an error, and you must store the API key somewhere accessible to the CLI on your own.

    • Storing the API key with keyring

      1. You can store your API key where the keyring library can find it by using the keyring program: Run keyring set dandi-api-dandi key and enter the API key when asked for the password for key in dandi-api-dandi.

      2. If the API key isn't stored in either the DANDI_API_KEY environment variable or in the keyring, the CLI will prompt you to enter the API key, and then it will store it in the keyring. This may cause you to be prompted further; you may be asked to enter a password to encrypt/decrypt the keyring, or you may be asked by your operating system to confirm whether to give the DANDI CLI access to the keyring.

Register a Dandiset

Register a Dandiset to generate an identifier.

  1. After logging in to https://dandiarchive.org, click the NEW DANDISET button in the top right corner.
  2. You will be asked to enter basic metadata for your dataset:
    1. Title (i.e. name)
    2. Description (i.e. abstract)
    3. License
  3. The dataset identifier will be created; we will call this <dataset_id>.

Standardize your dataset

The data within Dandisets should be standardized to the NWB, and/or BIDS data standards. Standardize with NWB files (and optionally with the BIDS standard), if your dataset includes electrophysiology, optical physiology, tracking, or stimlus data. Standardize with the BIDS standard, if your dataset includes MRI or microscopy data. Below are instructions on how to organize your data into these standards, and create your Dandiset.

  1. NWB dataset or BIDS dataset with NWB files

    1. Convert your data to NWB 2.1+ in a local folder. Let's call this <source_folder>. We suggest beginning the conversion process using only a small amount of data so that common issues may be spotted earlier in the process. This step can be complex depending on your data. NeuroConv automates conversion to NWB from a variety of popular formats. nwb-overview.readthedocs.io points to more tools helpful for working with NWB files, and BIDS converters if you are preparing a BIDS dataset containing NWB files. Feel free to reach out to us for help.

    2. Check your files for NWB Best Practices by installing the NWBInspector (pip install -U nwbinspector) and running

           nwbinspector <source_folder> --config dandi

    3. Thoroughly read the NWBInspector report and try to address as many issues as possible. DANDI will prevent validation and upload of any issues labeled as level 'CRITICAL' or above when using the --config dandi option. See Validate NWB Files for more information about validation criteria for uploading NWB files and which are deemed critical. We recommend regularly running the inspector early in the process to generate the best NWB files possible. Note that some autodetected violations, such as check_data_orientation, may be safely ignored in the event that the data is confirmed to be in the correct form; this can be done using either the --ignore <name_of_check_to_suppress> flag or a config file. See the NWB Inspector CLI documentation for more details and other options, or type nwbinspector --help. If the report is too large to efficiently navigate in your console, you can save a report using

       nwbinspector <source_folder> --config dandi --report-file-path <report_location>.txt

    4. Once your files are confirmed to adhere to the Best Practices, perform an official validation of the NWB files by running:

       dandi validate --ignore DANDI.NO_DANDISET_FOUND <source_folder>
      • If you are having trouble with validation, make sure the conversions were run with the most recent version of dandi, PyNWB and MatNWB.**

    5. Now, prepare the Dandiset folder used for upload:

       dandi download https://dandiarchive.org/dandiset/<dataset_id>/draft
 cd <dataset_id>
 dandi organize <source_folder> -f dry
 dandi organize <source_folder>
      • The dandi organize steps should not be used if you are preparing a BIDS dataset with the NWB files.
      • Renaming files with dandi organize can be customized with the --required-field option.
      • If you have an issue using the dandi CLI, see the Debug page.

  2. BIDS dataset

    1. Convert your data to BIDS standard in a local folder. Let's call this <source_folder>. We suggest beginning the conversion process using only a small amount of data so that common issues may be spotted earlier in the process. This step can be complex depending on your data. BIDS converters automate conversion to BIDS from a variety of popular formats and the BIDS specification provides more information on the BIDS standard. Feel free to reach out to us for help.

    2. Once your files are confirmed to adhere to the BIDS standard, perform an official validation of the BIDS files using the BIDS validator.

    3. Download the Dandiset folder used for upload:

       dandi download https://dandiarchive.org/dandiset/<dataset_id>/draft
 cd <dataset_id>

    4. Move your <source_folder>/ content (i.e. BIDS dataset) into the Dandiset folder.

    5. Check your files with the BIDS Validator and try to address as many issues as possible.

       dandi validate --ignore DANDI.NO_DANDISET_FOUND <source_folder>

    6. Dandisets with Zarr assets currently cannot be versioned and published. We are actively working on enabling this feature.

Upload data

  1. Validate the Dandiset folder, and begin upload:

     dandi validate .
 dandi upload

  2. Note:

    1. Upload to the development server with the -i option, e.g. dandi upload -i dandi-staging.
    2. Validation is also done during upload, but ensuring compliance using validate prior to upload helps avoid interruptions of the lengthier upload process due to validation failures.
    3. If you have an issue using the dandi CLI, see the Debug page.

Add Dandiset metadata

  1. Add metadata by visiting your Dandiset landing page at https://dandiarchive.org/dandiset/<dataset_id>/draft and clicking on the METADATA link.