Data Management Dashboard
=========================

The Data Management Dashboard provides a read-only overview of project and
dataset organization, metadata coverage, BIDS-like structure, FAIR readiness,
and general data-management health.

It can scan a complete NIM Studio project, a raw BIDS dataset, a derivative
dataset, or an existing study folder created outside NIM Studio.

The Dashboard does not generate BIDS metadata and does not reorganize files. It
is intended as an assessment and reporting tool. :contentReference[oaicite:0]{index=0}

Overview
--------

The Dashboard recursively inspects the selected root and summarizes:

* Total files and storage size
* Participants
* Sessions
* Datatypes and modalities
* Tasks and suffixes
* Existing metadata files
* NIfTI and DICOM files
* Sidecar JSON coverage
* Derivative datasets
* Analysis directories
* Output and publication directories
* Empty folders
* BIDS-like and non-BIDS-like filenames

The selected root is classified as one of the following:

* Project
* Study
* Raw dataset
* Derivative dataset

Dashboard Scores
----------------

After scanning, NIM Studio displays four high-level indicators:

``Health``
   General dataset organization and structural completeness.

``FAIR``
   An indicative assessment of Findability, Accessibility,
   Interoperability, and Reusability.

``Metadata``
   Presence and coverage of important metadata files and sidecars.

``BIDS``
   The proportion of files and structures that appear to follow BIDS-like
   naming and organization.

The dashboard also displays separate FAIR indicators for:

* Findable
* Accessible
* Interoperable
* Reusable

These scores are practical readiness indicators generated by NIM Studio. They
are not external certifications or formal institutional assessments.
:contentReference[oaicite:1]{index=1}

Dataset Health Assessment
-------------------------

The Dashboard checks for features such as:

* Presence of ``dataset_description.json``
* Presence of required dataset description fields
* Presence of ``participants.tsv``
* Sidecar JSON coverage
* Empty directories
* Presence of a licence file
* Presence of ``CITATION.cff``
* Derivative provenance through ``GeneratedBy``
* BIDS-like file naming coverage

Findings are grouped into:

``Healthy``
   Components that appear complete or well organized.

``Improve``
   Non-critical areas where the dataset could be strengthened.

``Critical``
   Important missing project or dataset components.

The assessment is heuristic and should be interpreted as a structured review,
not as a complete BIDS or FAIR validation.

Views
-----

The right-hand report panel provides several views:

``Dataset Health``
   Summarizes healthy components, suggested improvements, and critical
   findings.

``FAIR Report``
   Describes the basis for the Findable, Accessible, Interoperable, and
   Reusable scores.

``Data Flow``
   Summarizes the relationship between source data, raw datasets,
   derivatives, analyses, outputs, and publication material.

``Export Summary``
   Produces a consolidated project or dataset report.

Reports can be printed or exported as PDF.

How to Use the Dashboard
------------------------

#. Open the **Data Management Dashboard** module.

#. Click **Browse**.

#. Select the root that you want to assess.

   Suitable roots include:

   * A complete NIM Studio project
   * A raw BIDS dataset root
   * A derivative dataset
   * A study-level folder
   * An existing research project created outside NIM Studio

#. Click **Scan Dashboard**.

#. Wait for the scan to complete.

#. Review the summary showing:

   * Detected dataset type
   * Participants
   * Sessions
   * Files
   * Datatypes
   * Storage size

#. Review the four score cards.

#. Open the **Dataset Health** view and address critical findings first.

#. Review the FAIR report and identify missing documentation, metadata, or
   provenance information.

#. Use the **Data Flow** view to verify that raw data, derivatives, analyses,
   and outputs are separated clearly.

#. Export the report as PDF when documentation or team review is required.

Recommended Workflow for a New Project
--------------------------------------

For a newly created project:

#. Build the project structure using the Project Builder.
#. Create the dataset architecture using the Dataset Builder.
#. Add or transform research data.
#. Generate and review metadata with the Metadata Builder.
#. Scan the completed project using the Data Management Dashboard.
#. Address missing metadata, licence, citation, provenance, and structural
   findings.
#. Re-scan the project after corrections.
#. Export the final report as project documentation.

Recommended Workflow for an Existing Project
---------------------------------------------

For an existing project:

#. Create a verified backup before making any corrections.
#. Scan the highest relevant project or dataset root.
#. Review how NIM Studio classified the root.
#. Inspect critical findings and metadata coverage.
#. Check for mixed raw and derivative data.
#. Review sidecar coverage.
#. Review derivative ``GeneratedBy`` information.
#. Inspect empty folders and inconsistent output locations.
#. Make corrections manually or with the relevant NIM Studio modules.
#. Re-run the Dashboard to compare the updated scores.
#. Export the report for project records or team discussion.

What the Dashboard Does Not Do
------------------------------

The Data Management Dashboard does not:

* Create or edit metadata.
* Rename files.
* Move files.
* Delete files.
* Convert datasets into BIDS.
* Guarantee complete BIDS compliance.
* Certify FAIR compliance.
* Evaluate scientific data quality.
* Validate imaging acquisition quality.
* Confirm GDPR or ethics compliance.
* Determine whether data may legally be shared.
* Replace institutional data-management review.
* Replace the official BIDS Validator.

The Dashboard describes the organizational state that it can detect from the
selected local directory.