Information Structures

Database schema, data formats, and live visualization of PRISM's information architecture supporting the MaLDReTH II research data lifecycle mapping initiative.

Live Database Statistics

0

Tool Interactions

72

Exemplar Tools

12

Lifecycle Stages

33

Tool Categories

Database Schema Architecture

maldreth_stages

id	`INT`
name	`VARCHAR(50)`
description	`TEXT`
position	`INT`
color	`VARCHAR(7)`

tool_categories

id	`INT`
name	`VARCHAR(200)`
description	`TEXT`
stage_id	`INT`

exemplar_tools

id	`INT`
name	`VARCHAR(100)`
description	`TEXT`
url	`VARCHAR(500)`
provider	`VARCHAR(200)`
is_open_source	`BOOLEAN`
is_active	`BOOLEAN`
auto_created	`BOOLEAN`
import_source	`VARCHAR(100)`
license	`VARCHAR(100)`
github_url	`VARCHAR(500)`
notes	`TEXT`
created_via	`VARCHAR(100)`
is_archived	`BOOLEAN`
created_at	`DATETIME`
updated_at	`DATETIME`
stage_id	`INT (NULL)`
category_id	`INT (NULL)`

tool_interactions

id	`INT`
interaction_type	`VARCHAR(100)`
lifecycle_stage (deprecated)	`VARCHAR(50) NULL` Auto-computed from source/target tools (Nov 2025)
description	`TEXT`
technical_details	`TEXT`
benefits	`TEXT`
challenges	`TEXT`
examples	`TEXT`
contact_person	`VARCHAR(100)`
organization	`VARCHAR(100)`
email	`VARCHAR(100)`
priority	`VARCHAR(20)`
complexity	`VARCHAR(20)`
status	`VARCHAR(20)`
submitted_by	`VARCHAR(100)`
submitted_at	`DATETIME`
auto_created	`BOOLEAN`
is_archived	`BOOLEAN`
source_tool_id	`INT`
target_tool_id	`INT`

Relationships

maldreth_stages → tool_categories (1:many)

tool_categories → exemplar_tools (1:many)

exemplar_tools → tool_interactions (1:many as source)

exemplar_tools → tool_interactions (1:many as target)

Field Legend

Primary Key - Unique identifier

Foreign Key - References another table

DATA_TYPE - Column data type and constraints

Schema Design Principles

Hierarchical Structure: Stages → Categories → Tools → Interactions
Flexible Relationships: Many-to-many tool interactions
MaLDReTH Compatibility: Enhanced fields for tool providers and import tracking

Audit Trail: Timestamps and source tracking for all tools
Extensible Design: Support for new interaction types and tool metadata
Data Integrity: Foreign key constraints and validation

Interaction Types Distribution

No interaction data available yet.

Lifecycle Stage Activity

No stage data available yet.

CSV Import/Export Format

PRISM supports CSV import/export with the following standardized format. All fields marked with Required must be present.

Field Name	Type	Required	Description
`Source Tool`	String	Required	Name of the source tool (must exist in database)
`Target Tool`	String	Required	Name of the target tool (must exist in database)
`Interaction Type`	Enum	Required	Type from predefined list (API Integration, Data Exchange, etc.)
`Lifecycle Stage` (deprecated)	Enum	Deprecated	MaLDReTH lifecycle stage (PLAN, COLLECT, ANALYSE, etc.) Auto-computed from source/target tools as of November 2025. If provided in CSV, will be ignored.
`Description`	Text	Optional	Detailed description of the interaction
`Technical Details`	Text	Optional	Implementation details (API, protocols, formats)
Additional optional fields: Benefits, Challenges, Examples, Contact Person, Organization, Email, Priority, Complexity, Status, Submitted By

Demo CSV Available

A comprehensive demo CSV file is included in the repository with sample interactions from the MaLDReTH II working session.

View Demo CSV Upload CSV

MaLDReTH Research Data Lifecycle Stages

1. CONCEPTUALISE

9 tools

To formulate the initial research idea or hypothesis, and define the scope of the research project and the data component/requirements of that project.

2. PLAN

9 tools

To establish a structured strategic framework for management of the research project, outlining aims, objectives, methodologies, and resources required for data collection, management and analysis.

3. FUND

0 tools

To identify and acquire financial resources to support the research project, including data collection, management, analysis, sharing, publishing and preservation.

4. COLLECT

9 tools

To use predefined procedures, methodologies and instruments to acquire and store data that is reliable, fit for purpose and of sufficient quality to test the research hypothesis.

5. PROCESS

8 tools

To make new and existing data analysis-ready. This may involve standardised pre-processing, cleaning, reformatting, structuring, filtering, and performing quality control checks on data.

6. ANALYSE

5 tools

To derive insights, knowledge, and understanding from processed data.

7. STORE

6 tools

To record data using technological media appropriate for processing and analysis whilst maintaining data integrity and security.

8. PUBLISH

4 tools

To release research data in published form for use by others with appropriate metadata for citation (including a unique persistent identifier) based on FAIR principles.

9. PRESERVE

5 tools

To ensure the safety, integrity, and accessibility of data for as long as necessary so that data is as FAIR as possible.

10. SHARE

3 tools

To make data available and accessible to humans and/or machines.

11. ACCESS

7 tools

To control and manage data access by designated users and reusers.

12. TRANSFORM

7 tools

To create new data from the original, for example by migration into a different format or by creating a subset.

Technical Implementation

Database Technology

PostgreSQL (Production)
SQLite (Development)
SQLAlchemy ORM
Flask-Migrate for schema management

API Endpoints

/api/v1/tools - Tools catalog
/api/v1/interactions - Interactions data
/export/interactions/csv - CSV export
/upload/interactions/csv - CSV import

Data Validation

Duplicate detection on import
Tool name validation
Interaction type enumeration
Data integrity constraints

Integration Features

Curation interface for data quality
Real-time statistics and visualization
Heroku cloud deployment
GitHub Actions CI/CD pipeline

View All Data Upload CSV Export CSV GitHub Repository