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

1. Open the Data Management Dashboard module.

2. Click Browse.

3. 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

4. Click Scan Dashboard.

5. Wait for the scan to complete.

6. Review the summary showing:

   • Detected dataset type

   • Participants

   • Sessions

   • Files

   • Datatypes

   • Storage size

7. Review the four score cards.

8. Open the Dataset Health view and address critical findings first.

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

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

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

Recommended Workflow for a New Project

For a newly created project:

1. Build the project structure using the Project Builder.

2. Create the dataset architecture using the Dataset Builder.

3. Add or transform research data.

4. Generate and review metadata with the Metadata Builder.

5. Scan the completed project using the Data Management Dashboard.

6. Address missing metadata, licence, citation, provenance, and structural findings.

7. Re-scan the project after corrections.

8. Export the final report as project documentation.

Recommended Workflow for an Existing Project

For an existing project:

1. Create a verified backup before making any corrections.

2. Scan the highest relevant project or dataset root.

3. Review how NIM Studio classified the root.

4. Inspect critical findings and metadata coverage.

5. Check for mixed raw and derivative data.

6. Review sidecar coverage.

7. Review derivative GeneratedBy information.

8. Inspect empty folders and inconsistent output locations.

9. Make corrections manually or with the relevant NIM Studio modules.

10. Re-run the Dashboard to compare the updated scores.

11. 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.