Research Intelligence & Collaboration Platform

Website Portfolio Copy — Historical System + Modern V2 Reconstruction

Project positioning

This project originated in 1396 SH (2017–2018) as part of an institutional initiative to create a structured digital environment for connecting researchers, scholars, academic institutions, and traditional knowledge communities.

The institutional concept and program belonged to the commissioning organization. My contribution focused on the research, software architecture, platform evaluation, localization, prototyping, implementation, testing, and iterative redevelopment of the software platform.

The surviving proposal documentation includes a large comparative study of existing research and expert-network platforms, a staged implementation roadmap, an initial target dataset of 1,000 researchers for the pilot phase, and a feature-comparison process based on 33 identified capabilities.

Classification

Period: 1396 SH / approximately 2017–2018
Domain: Research Collaboration / Expert Networks / Knowledge Management
Project Type: Institutional Research Platform / Social Knowledge Network
Original Organization: Astan Quds Razavi institutional context
Status: Functional pilot completed; extensively tested; multiple implementation generations produced
Historical Technology: UNA-based prototype + custom Laravel management layer + localized/custom development
Later Reimplementation: .NET implementation, followed by a Laravel implementation
Modern V2 Direction: Research Intelligence & Collaboration Platform
Portfolio Relevance: High
Suggested CV Relevance: Tier A / Core Architecture Evidence

Website Card

Title:
Research Intelligence & Collaboration Platform

One-line Description:
A research networking and knowledge platform designed from comparative analysis of 13 systems and 33 capabilities, combining researcher profiles, collaboration, projects, expert discovery, and institutional knowledge workflows.

Categories:

Enterprise Applications
Research Platforms
Knowledge Management
Solution Architecture
Legacy Modernization
Search & Discovery
Collaboration Systems
Public / Institutional Software
Product Engineering

Technology Tags — Historical:
UNA · Laravel · .NET · PHP · Web Application · Localization · Researcher Profiles · Collaboration

Technology Tags — V2 Direction:
Laravel · REST API · PostgreSQL · Redis · OpenSearch / Elasticsearch · Docker · RBAC · Audit Logs · Queues · Knowledge Graph Concepts

Overview

The project began as an attempt to create a digital environment in which researchers and subject-matter experts could be discovered, connected, organized into thematic groups, and engaged around real research questions and institutional problems.

The requirement was significantly broader than building a conventional social network.

The proposed platform combined:

researcher identity,
scientific and professional profiles,
publications,
research interests,
institutional affiliations,
project collaboration,
expert discovery,
thematic working groups,
messaging and interaction,
research outputs,
files and resources,
knowledge organization,
feedback collection,
and structured problem-solving workflows.

Before implementation, a comparative study was performed across 13 domestic and international platforms. The analysis produced 33 target capabilities used as a requirements framework for the proposed system.

The surviving proposal also defined an implementation roadmap beginning with a pilot platform, followed by the entry of approximately 1,000 researcher records, structured feedback collection, feature refinement, thematic working groups, public rollout, and continuous analysis of usage patterns.

The Original Challenge

The underlying problem was not simply the absence of another website.

Research information was fragmented across institutions, individual profiles, publications, projects, and isolated information systems.

A useful platform needed to answer questions such as:

Who is working on a particular topic?
What has a researcher published?
Which projects are currently active?
Which researchers have overlapping interests?
Which experts may be relevant to a specific problem?
What organizations are connected to a field?
Which resources and research outputs already exist?
Can a group of researchers collaborate around a structured problem?
Can activity and feedback be analyzed to improve the network itself?

The system therefore had characteristics of several product categories at the same time:

Researcher Directory
        +
Professional Network
        +
Research Collaboration Platform
        +
Project Workspace
        +
Knowledge Repository
        +
Expert Discovery System
        +
Institutional Problem-Solving Platform

This combination made the project primarily an information-architecture and software-architecture problem, not merely a website-development task.

Requirements Engineering & Comparative Analysis

One of the strongest parts of the project was the research performed before implementation.

The proposal reviewed 13 existing platforms with partially overlapping purposes.

The systems examined included domestic and international examples such as:

research collaboration platforms,
expert databases,
researcher directories,
academic networks,
institutional research portals,
knowledge-broker platforms,
and researcher social networks.

The comparison was not limited to visual appearance.

Features were extracted and evaluated systematically.

The resulting requirements set contained 33 capabilities.

Examples of capability areas included:

researcher search,
advanced filtering,
geographic search,
academic-rank filtering,
institutional filtering,
researcher profiles,
scientific history,
publications,
active projects,
collaborators,
research interests,
messaging,
following,
groups,
public/private collaboration spaces,
project resources,
file sharing,
blogging,
expert Q&A,
events,
reports,
analytical views,
knowledge organization,
and research-output discovery.

This research became the functional basis for the platform rather than choosing a technology first and adapting the problem to it.

Historical Implementation Strategy

Phase 1 — Rapid Functional Prototype

For the first complete prototype, UNA was used as an existing social/community platform foundation.

The purpose of using UNA was not to treat the ready-made system as the final architecture.

Instead, it provided a fast way to expose users to a broad range of social and collaboration capabilities so that real feedback could be collected.

The platform was:

configured,
localized,
extended,
customized,
integrated with additional custom development,
and paired with a separate Laravel-based management layer.

This allowed a broad functional surface to be demonstrated before committing to a final custom implementation.

Why UNA Was Used

Using an existing platform at the prototype stage reduced the cost of answering the wrong questions.

Rather than spending months implementing every possible interaction model from scratch, the prototype could expose users to features such as:

profiles,
connections,
content,
groups,
communication,
community interactions,
and social-network patterns.

Users could then compare the experience with other professional networks they already knew and provide concrete feedback.

In modern product-development language, UNA functioned as a high-fidelity functional prototype platform.

Its purpose was:

validate workflows and collect requirements before finalizing the custom system.

Parallel Laravel Management Layer

A separate Laravel-based layer was developed alongside the UNA environment.

This provided room for institution-specific functionality and management processes that did not naturally belong inside the off-the-shelf community platform.

The resulting architecture was therefore not simply:

Install UNA
→ Change Theme
→ Deliver

It was closer to:

UNA Functional Prototype
        +
Localization
        +
Custom Development
        +
Parallel Laravel Management Layer
        +
Institution-Specific Workflows
        +
User Testing
        +
Feedback Collection

This distinction is important because the objective was not to deliver a branded template.

The objective was to discover and validate the correct product before implementing the custom system.

Testing and Feedback

The pilot was completed and subjected to detailed testing.

Testing was used not only to detect implementation defects but also to evaluate the functional model.

Users were able to experience:

the prototype platform,
familiar professional-network concepts,
researcher-oriented capabilities,
and the proposed collaboration workflows.

Feedback from this process influenced the subsequent implementation.

The project therefore followed an iterative development pattern:

Research
   ↓
Requirements Extraction
   ↓
Functional Prototype
   ↓
Localization & Customization
   ↓
Testing
   ↓
User Feedback
   ↓
Architecture Revision
   ↓
Custom Reimplementation

For its period, this was much closer to product discovery and iterative software engineering than to a conventional one-pass institutional website project.

Custom Reimplementation

After the prototype and feedback stage, the system concept was implemented again as a custom application.

Two later implementation generations were produced:

a .NET-based implementation, and
a subsequent Laravel-based implementation.

The important engineering point is that the final direction no longer depended on UNA as the application core.

UNA had served its purpose as a requirements-validation and interaction prototype.

The later implementations represented a transition toward a platform whose domain model and behavior could be controlled directly.

Functional Domain Model

The platform can be understood through several primary domains.

1. Researcher Identity

Each researcher may have a structured profile containing information such as:

identity,
biography,
academic background,
institutional affiliations,
expertise,
research interests,
educational history,
scientific experience,
awards,
inventions,
publications,
projects,
and professional activity.

The profile is not merely a social-media profile.

It functions as a structured expert record.

2. Publications & Research Outputs

Researchers can be connected to outputs such as:

books,
papers,
articles,
theses,
reports,
inventions,
research products,
project outputs,
and uploaded resources.

This enables the network to model not only people but also evidence of their work.

3. Research Projects

Projects provide a collaboration context.

A project may connect:

researchers,
resources,
files,
discussions,
activities,
outputs,
notes,
and project events.

This transforms the system from a directory into a working research environment.

4. Expert Discovery

Search and filtering were central to the original comparative study.

Relevant dimensions included:

name,
research topic,
specialization,
academic rank,
institution,
geography,
and professional characteristics.

A modern implementation can extend this into a dedicated expert-discovery engine.

5. Thematic Working Groups

The institutional roadmap included organizing researchers into thematic groups.

This introduces a hierarchy such as:

Research Network
    ↓
Thematic Domain
    ↓
Working Group
    ↓
Researchers
    ↓
Problems / Projects / Outputs

Such a structure supports both discovery and coordinated work.

6. Problem-Solving Workflows

The broader vision included presenting real problems to experts and using structured groups to investigate them.

A modern interpretation could model:

Problem
  ↓
Domain Classification
  ↓
Expert Matching
  ↓
Working Group
  ↓
Discussion / Evidence
  ↓
Research Tasks
  ↓
Recommendations / Outputs

This is substantially more valuable than generic social-network activity.

My Role

My role covered the software and product-engineering side of the initiative.

Responsibilities included:

comparative research of existing platforms,
requirements extraction,
analysis of candidate capabilities,
functional platform design,
prototype strategy,
UNA implementation and customization,
localization,
custom software development,
Laravel development,
development of a parallel management layer,
user-feedback-driven refinement,
detailed functional testing,
architecture iteration,
custom .NET reimplementation,
and subsequent Laravel reimplementation.

The institutional initiative itself was larger than the software project and involved multiple people and proposals.

The software implementation described here represents my technical contribution to that broader program.

What Made the Project Difficult

1. Ambiguous Product Category

The product could not be described accurately as only:

a social network,
a researcher directory,
a project-management tool,
or a knowledge base.

It needed elements from all of them.

This required careful domain modeling.

2. Researcher Data Is Highly Structured

A researcher profile may include relationships to:

institutions,
publications,
fields,
collaborators,
projects,
awards,
events,
inventions,
and research topics.

A simplistic user-profile table would not be sufficient for a mature implementation.

3. Search Is a Core Product Capability

The system only becomes useful when the right expert can be discovered efficiently.

Search therefore belongs to the core architecture rather than being a secondary UI feature.

4. Collaboration and Discovery Are Different Problems

Finding an expert and working with that expert are separate workflows.

The system had to support both:

Discovery
→ Connection
→ Collaboration
→ Output

5. Prototype Technology vs Final Architecture

The fastest technology for discovering user requirements is not necessarily the best technology for the final system.

UNA was useful for functional exploration.

A custom application was more appropriate once requirements became clearer.

Recognizing that distinction was one of the key architectural decisions in the project.

Outcome

A complete functional pilot was produced and tested.

The project progressed beyond a presentation-only concept and included actual implementation, localization, customization, testing, feedback collection, and later custom redevelopment.

The institutional procurement / selection outcome did not result in this implementation becoming the selected final organizational platform.

For portfolio purposes, the important engineering evidence is therefore:

substantial comparative research,
requirements engineering,
a working prototype,
user validation,
multiple implementation iterations,
and full custom redevelopment.

The project should not be represented as an organization-wide production deployment.

Evidence Available

Current evidence includes:

the original institutional proposal,
a 100+ page comparative analysis document,
screenshots of evaluated systems,
feature-by-feature research,
the staged implementation roadmap,
documentation of the 1,000-researcher pilot-data plan,
the extracted 33-feature comparison framework,
and historical project knowledge of the implemented prototype and later reimplementations.

Where surviving code, databases, screenshots, or builds are available, they can be added to the evidence library separately.

V2 — Modern Research Intelligence & Collaboration Platform

The modern rebuild should preserve the original problem while replacing the historical implementation.

The central concept becomes:

A research-intelligence platform that models people, expertise, publications, institutions, projects, topics, and problems as connected knowledge entities.

The system should not be rebuilt as another generic social network.

V2 Product Vision

A modern platform can combine five major capabilities:

Researcher Intelligence
        +
Knowledge Graph
        +
Expert Search
        +
Research Collaboration
        +
Problem-Solving Workflows

This creates a much clearer product identity than the historical “researcher social network” framing.

V2 Core Domains

Identity & Expertise

researcher profiles,
institutional affiliations,
academic positions,
research interests,
expertise areas,
verified profile fields,
career history.

Research Outputs

publications,
books,
datasets,
patents,
theses,
reports,
software,
project outputs.

Institutions

universities,
laboratories,
research centers,
organizations,
departments,
research groups.

Topics & Taxonomy

hierarchical research fields,
keywords,
controlled vocabularies,
topic relationships,
expertise classification.

Projects

research projects,
collaborators,
tasks,
resources,
files,
discussions,
outputs,
milestones.

Problems / Challenges

institutional problems,
research questions,
open challenges,
domain classification,
expert recommendations,
working groups,
proposed solutions.

V2 Knowledge Graph

One of the strongest modern improvements would be explicit relationship modeling.

For example:

Researcher
 ├── WORKS_AT ────────> Institution
 ├── EXPERT_IN ───────> Topic
 ├── AUTHORED ────────> Publication
 ├── WORKED_ON ───────> Project
 ├── COLLABORATED_WITH > Researcher
 └── CONTRIBUTED_TO ──> Challenge

This enables discovery questions that are difficult to answer with traditional relational screens alone.

Examples:

Find researchers in a field who have collaborated with a specific institution.
Find experts connected to a topic through both publications and projects.
Discover researchers who have never worked together but have strongly overlapping expertise.
Identify institutions with complementary capabilities.
Find existing outputs relevant to a newly submitted challenge.

A relational database can remain the system of record while graph-oriented projections or search indexes provide advanced discovery.

V2 Proposed Architecture

                    ┌─────────────────────────┐
                    │     Web / Mobile UI     │
                    └────────────┬────────────┘
                                 │
                    ┌────────────▼────────────┐
                    │       REST / API        │
                    └────────────┬────────────┘
                                 │
        ┌────────────────────────┼────────────────────────┐
        │                        │                        │
┌───────▼────────┐     ┌────────▼────────┐      ┌────────▼────────┐
│ Identity       │     │ Collaboration   │      │ Research        │
│ & Profiles     │     │ & Projects      │      │ Intelligence    │
└───────┬────────┘     └────────┬────────┘      └────────┬────────┘
        │                        │                        │
        └────────────────────────┼────────────────────────┘
                                 │
                    ┌────────────▼────────────┐
                    │      Domain Layer       │
                    └────────────┬────────────┘
                                 │
       ┌─────────────────────────┼─────────────────────────┐
       │                         │                         │
┌──────▼──────┐          ┌───────▼────────┐       ┌────────▼────────┐
│ PostgreSQL  │          │ Search Engine  │       │ Redis / Queues  │
│ System Data │          │ OpenSearch     │       │ Cache / Jobs    │
└─────────────┘          └────────────────┘       └─────────────────┘

Optional graph projections can be added if relationship exploration becomes a first-class requirement.

V2 Recommended Technical Stack

Backend

A strong implementation path:

Laravel
PHP 8.x
REST / JSON API
Service / domain-oriented application structure
queues
scheduled jobs
event-driven internal workflows

Laravel is particularly appropriate here because the historical project already evolved toward Laravel and the framework provides a strong productivity/performance balance for a large business application.

Data

PostgreSQL — system of record
Redis — cache, sessions, queues
OpenSearch / Elasticsearch — researcher, publication, and expertise search
optional graph database or graph projection if advanced relationship traversal justifies it

Frontend

Possible approaches:

Laravel + Livewire for a cohesive server-driven platform,
or a dedicated SPA frontend if very rich interaction is required.

The decision should be based on interaction complexity rather than trend.

Infrastructure

Docker
reverse proxy
isolated environments
queue workers
scheduler
object storage
automated backups
CI/CD
structured logs
health checks

Search Architecture

Search should be treated as an independent subsystem.

Indexed entities may include:

Researchers
Publications
Institutions
Topics
Projects
Challenges
Research Outputs

A researcher search document could contain:

{
  "name": "...",
  "institutions": [],
  "expertise": [],
  "topics": [],
  "publications": [],
  "projects": [],
  "locations": [],
  "academic_rank": "...",
  "collaboration_signals": []
}

This enables filtering, ranking, faceting, and expert discovery without placing excessive load on transactional queries.

Matching & Recommendation

A modern V2 can support explainable matching.

Examples:

Researcher ↔ Researcher

Based on:

topic overlap,
complementary expertise,
shared institutions,
project history,
publication relationships.

Challenge ↔ Expert

Based on:

expertise,
publications,
relevant projects,
domain classification,
previous contribution history.

Researcher ↔ Project

Based on:

required skills,
research interests,
availability,
prior experience.

Recommendations should remain explainable:

“Suggested because this researcher has published in X, worked on Y, and is affiliated with Z.”

This is preferable to opaque recommendation scoring.

RBAC & Governance

The platform requires a serious authorization model.

Possible roles:

Visitor
Researcher
Verified Researcher
Working Group Member
Working Group Coordinator
Institution Manager
Research Administrator
Platform Administrator

Authorization may additionally depend on resource membership.

Example:

Project Member
→ View internal resources

Project Coordinator
→ Manage members and project workflow

Institution Manager
→ Manage institutional profile

Administrator
→ Platform governance

Verification

Researcher networks require trust.

V2 can include verification workflows for:

institutional affiliation,
researcher identity,
academic profile,
publication ownership,
project membership.

Every verified element should preserve:

verification source,
verifier,
timestamp,
status,
audit history.

Auditability

Administrative and high-impact operations should be auditable.

Examples:

profile verification,
role changes,
publication reassignment,
project membership changes,
content moderation,
institution ownership,
challenge assignment.

Audit trails are particularly important for an institutional research platform.

Privacy & Data Protection

A modern implementation must separate:

public profile data,
registered-user data,
group-only data,
project-private information,
administrative metadata.

Researchers should have control over visibility where institutional rules permit.

The platform should support:

explicit visibility settings,
consent-aware contact information,
export capabilities,
retention policies,
access logging for sensitive administrative operations.

Collaboration Workspaces

Each project or research group can have its own collaboration space.

Possible components:

members,
activity stream,
discussion,
resources,
files,
references,
tasks,
milestones,
outputs,
meetings,
decision log.

This gives the social network an operational purpose.

Challenge Management

A dedicated challenge workflow could become one of the platform's strongest differentiators.

Example lifecycle:

Draft
  ↓
Submitted
  ↓
Classified
  ↓
Expert Discovery
  ↓
Working Group Formation
  ↓
Research / Discussion
  ↓
Recommendation
  ↓
Review
  ↓
Published / Closed

This directly connects expertise discovery to institutional problem solving.

Research Output Lifecycle

Research outputs should not simply be uploaded as files.

A structured model can capture:

title,
type,
authors,
institution,
project,
publication date,
topics,
identifiers,
source,
abstract,
attachments,
relationships to other outputs.

This creates reusable knowledge instead of isolated documents.

Observability

Production V2 should include:

structured logging,
exception tracking,
queue monitoring,
search-index health,
job failures,
API latency,
database metrics,
user-activity aggregates,
uptime monitoring.

Operational visibility is part of architecture, not an afterthought.

V2 Testing Strategy

The original system already had a strong emphasis on detailed functional testing.

A modern rebuild can formalize that further.

Automated Tests

domain tests,
feature tests,
API tests,
authorization tests,
search indexing tests,
queue/job tests,
integration tests.

Workflow Tests

Critical flows:

researcher onboarding,
profile verification,
expert discovery,
project creation,
group membership,
challenge assignment,
publication linking,
privacy enforcement.

Regression Tests

Every historical bug fixed during modernization should ideally become a regression test.

Data Migration

If surviving legacy data exists, migration should be explicit.

Suggested pipeline:

Legacy Export
   ↓
Staging Tables
   ↓
Normalization
   ↓
Identity Resolution
   ↓
Validation
   ↓
Import
   ↓
Search Reindex
   ↓
Verification

Ambiguous researcher identities should never be merged automatically without sufficient evidence.

Engineering Highlights

Performed broad comparative analysis across 13 research and expert-network systems.
Extracted a structured set of 33 functional capabilities to guide system requirements.
Built and localized a complete high-fidelity UNA-based functional prototype.
Added custom software and a parallel Laravel management layer rather than relying on theme-level customization.
Used real user testing and feedback to refine the product model.
Reimplemented the system as a custom .NET application and subsequently again in Laravel.
Treated off-the-shelf software as a prototyping instrument rather than allowing it to dictate the final domain architecture.
Designed the platform around researcher identity, expertise, publications, projects, collaboration, search, and structured problem solving.
Created a foundation suitable for modern search, recommendation, knowledge-graph, and research-intelligence capabilities.

Career Evidence

This project provides strong evidence for:

Solution Architecture
Application Architecture
End-to-End Ownership
Requirements Engineering
Product Engineering
Legacy Modernization
Enterprise Applications
Search / Information Retrieval
Knowledge Management
Data Modeling
System Integration
Localization
Research Platforms
Iterative Product Development
User Feedback & Validation
Institutional Software
Technical Documentation

CV Relevance

Tier A — CV Core

This is suitable as a core portfolio project for architecture-oriented roles because it demonstrates more than implementation.

It shows a complete sequence:

Problem Discovery
→ Competitive Research
→ Requirements Extraction
→ Prototype Strategy
→ Implementation
→ Localization
→ Testing
→ User Feedback
→ Architecture Revision
→ Custom Reimplementation
→ Modernization

That sequence is directly relevant to:

Solution Architect
Application Architect
Technical Lead
Senior Full-Stack / Platform Engineering roles

Interview Story

Situation

An institutional initiative required a digital platform capable of connecting researchers, organizing scientific expertise, and supporting structured collaboration across academic and traditional knowledge communities.

Task

The software problem had not yet been reduced to a clear product specification. Multiple domestic and international systems offered different subsets of the required capabilities.

Action

I analyzed 13 comparable systems, extracted 33 candidate capabilities, built and localized a complete UNA-based functional prototype, added custom development and a parallel Laravel management layer, conducted detailed testing and user-feedback validation, and then implemented custom versions using .NET and later Laravel.

Result

The work produced a tested functional platform and a much clearer understanding of the required domain model and workflows. Although the implementation was not selected as the final institutional platform, the project generated substantial evidence in requirements engineering, platform evaluation, iterative development, system architecture, and custom software delivery.

Safe Public Positioning

Because this was an institutional project, the public portfolio should avoid publishing:

proprietary source code,
private databases,
researcher personal data,
internal credentials,
confidential administrative documents,
internal organizational decisions.

Safe material includes:

the general problem,
the public-facing architecture,
technology choices,
personal responsibilities,
abstracted workflows,
publicly shareable proposal material,
recreated V2 diagrams,
synthetic/demo datasets.

Intellectual Property Positioning

Recommended website wording:

The broader institutional initiative and its organizational objectives belonged to the commissioning organization. My role focused on research, software architecture, prototyping, localization, implementation, testing, and iterative redevelopment of the digital platform.

This is stronger and more credible than implying ownership of the entire institutional program.

Suggested Website Hero Copy

Research Intelligence & Collaboration Platform

A researcher-network and knowledge platform originally developed from a systematic comparison of 13 existing systems and 33 identified capabilities.

The project combined expert discovery, researcher profiles, publications, projects, working groups, collaboration, and institutional problem-solving workflows.

A high-fidelity UNA-based prototype was first used to validate functionality and collect user feedback. The system was then reimplemented as custom software, first with .NET and subsequently with Laravel.

The modern V2 concept evolves the same domain into a research-intelligence platform centered on structured expertise data, advanced search, collaboration workspaces, explainable expert matching, knowledge relationships, governance, and auditable workflows.

Short Website Card Copy

Research Intelligence & Collaboration Platform

Designed and prototyped a researcher-network platform based on comparative analysis of 13 systems and 33 functional capabilities, followed by custom .NET and Laravel implementations.

Alternative One-Liners

Architecture-oriented

Designed a research-intelligence and collaboration platform spanning expert discovery, researcher profiles, project workflows, advanced search, and institutional knowledge management.

Product-oriented

Converted comparative research across 13 platforms into a tested researcher-network product, then iterated from an UNA prototype to custom .NET and Laravel implementations.

Modernization-oriented

Reframed an early researcher social-network concept into a modern research-intelligence platform with structured expertise, search, collaboration, matching, and knowledge relationships.

Suggested V2 Repository Structure

research-intelligence-platform/
├── app/
│   ├── Domain/
│   │   ├── Researchers/
│   │   ├── Institutions/
│   │   ├── Publications/
│   │   ├── Projects/
│   │   ├── Topics/
│   │   ├── Challenges/
│   │   └── Collaboration/
│   ├── Application/
│   ├── Infrastructure/
│   └── Policies/
├── database/
│   ├── migrations/
│   ├── factories/
│   └── seeders/
├── routes/
├── tests/
│   ├── Feature/
│   ├── Unit/
│   └── Integration/
├── docker/
├── docs/
│   ├── architecture/
│   ├── adr/
│   └── api/
└── README.md

Cover Direction

Title

Research Intelligence & Collaboration Platform

Subtitle

Expert Discovery · Knowledge Graph · Research Networks · Collaborative Problem Solving

Visual Direction

A premium technology cover showing:

interconnected researcher nodes,
publication and institution relationships,
expertise clusters,
a search/discovery interface,
project collaboration spaces,
graph relationships,
analytics,
knowledge flow.

Avoid reproducing historical institutional branding.

The cover should communicate:

People
+
Knowledge
+
Research
+
Relationships
+
Collaboration
+
Intelligence

rather than a generic social-media interface.

Final Portfolio Statement

This project is valuable not because a particular framework was used.

Its strongest evidence is the engineering process.

A vague institutional requirement was converted into a structured software problem by researching existing systems, extracting capabilities, building a functional prototype, validating it with users, and repeatedly refining the implementation.

The technology changed from an off-the-shelf social platform to custom .NET and Laravel applications.

The core problem remained the same:

How can people, expertise, publications, projects, institutions, and real-world problems be connected in a system that helps researchers discover one another and work together?

The modern V2 rebuild answers that question with a cleaner domain model, stronger search, explicit governance, explainable matching, collaboration workspaces, auditability, and research-intelligence capabilities.

That progression—from research and requirements through prototype, validation, architecture, implementation, and modernization—is the central value of the project as professional engineering evidence.