text improvements

Author: Celine Lewerentz

docs/overview/concepts/data_collection_validation.md

@@ -6,30 +6,24 @@ myst:
     "keywords": "OpenSPP, data collection, data validation, data minimization, consent, social protection"
 ---
 
-# Data Collection and Validation
+# Data collection and validation
 
-OpenSPP, with its comprehensive approach to data management, emphasizes the importance of data collection and rigorous validation procedures. This article delves into how OpenSPP approaches these critical aspects, ensuring data integrity and usefulness.
+OpenSPP, with its comprehensive approach to data management, emphasizes the importance of data collection and rigorous validation procedures. This article presents how OpenSPP approaches these critical aspects while ensuring data integrity and usefulness.
 
-## Data Collection
+## Data collection
 
-1. Adhering to Data Minimization Principles
-   OpenSPP is designed to adhere to the principle of data minimization. This means it only collects data that is essential for the intended purpose, avoiding any unnecessary accumulation of information. This approach is crucial for maintaining efficiency and reducing the risks associated with data storage and processing.
+1. **Adhering to data minimization principles:** OpenSPP is designed to adhere to the principle of data minimization. This means that it only collects data essential for the intended purpose, avoiding any unnecessary accumulation of information. This approach is crucial for maintaining efficiency and reducing the risks associated with data storage and processing.
 
-2. User-Centric Data Collection
-   OpenSPP places significant emphasis on user consent and control. The platform incorporates mechanisms ensuring that user consent is obtained for data collection, aligning with privacy norms and regulations. This ensures compliance with global data protection standards.
+2. **User-centric data collection:** OpenSPP places significant emphasis on user consent and control. The platform incorporates mechanisms ensuring that user consent is obtained for data collection, aligning with privacy norms and regulations. This ensures compliance with global data protection standards.
 
-3. Versatile Input Methods
-   Recognizing the varied environments in which it operates, OpenSPP supports multiple data input methods. This versatility enables effective data collection in diverse scenarios by interacting with other data sources and applications allow data to be collected and pushed OpenSPP via APIs.
+3. **Versatile input methods:** Recognizing the varied environments in which it operates, OpenSPP supports multiple data input methods. This versatility enables effective data collection in diverse scenarios by interacting with other data sources and applications via APIs to allow data to be collected and pushed to OpenSPP.
 
-## Data Validation
+## Data validation
 
-1. Input Validation Protocols
-   OpenSPP implements input validation protocols where data inputs are strongly typed, sanitized, and parameterized to ensure that they meet predefined criteria and formats. This process is crucial in preventing malicious data entry, which can lead to vulnerabilities or corrupt data sets.
+1. **Input validation protocols:** OpenSPP implements input validation protocols where data inputs are strongly typed, sanitized, and parameterized to ensure that they meet predefined criteria and formats. This process is crucial in preventing malicious data entry, which can lead to vulnerabilities or corrupt data sets.
 
-2. Consistent Data Integrity Checks
-   OpenSPP incorporates routine data integrity checks. These checks are designed to verify the accuracy and consistency of data over time, ensuring that it remains reliable and uncorrupted. This is particularly important in long-term operations where data integrity is paramount.
+2. **Consistent data integrity checks:** OpenSPP incorporates routine data integrity checks. These checks are designed to verify the accuracy and consistency of data over time, ensuring that it remains reliable and uncorrupted. This is particularly important in long-term operations where data integrity is paramount.
 
-3. Automated and Manual Validation Processes
-   OpenSPP utilizes a combination of automated and manual validation processes. While automated systems efficiently handle large volumes of data, manual checks are employed for complex or sensitive data sets where human oversight is essential.
+3. **Automated and manual validation processes:** OpenSPP utilizes a combination of automated and manual validation processes. While automated systems efficiently handle large volumes of data, manual checks are employed for complex or sensitive data sets where human oversight is essential.
 
 The effectiveness of a social protection program heavily relies on its ability to accurately collect and validate data. By implementing a blend of data collection practices and validations, OpenSPP ensures that the data it handles is both reliable and respectful of user privacy.

docs/overview/concepts/data_protection.md

@@ -6,15 +6,15 @@ myst:
     "keywords": "OpenSPP, data protection, privacy, security, GDPR, accountability, social protection"
 ---
 
-# Data Protection
+# Data protection
 
 ## Introduction
 
 The protection of Personally Identifiable Information (PII) has become a fundamental requirement for governments when implementing digital public infrastructure. Data is constantly being collected, processed, and shared within a country as well as across borders, making it crucial to implement strong data protection measures. While regulations such as the General Data Protection Regulation (GDPR) provide legal frameworks (or similar frameworks specific to the country), the focus should remain on the principles of responsible data management to build trust and ensure long-term digital security.
 
 Data protection is not just a legal obligation. It is essential for maintaining privacy, security, and trust when Digital Public Infrastructure is implemented. Governments that fail to implement adequate safeguards risk exposing sensitive information, leading to financial losses, reputational damage, and legal consequences. Individuals, too, must be aware of how their data is used and have control over their personal information.
 
-## Key Principles of Data Protection
+## Key principles of data protection
 
 Effective data protection relies on several core principles, including:
 
@@ -26,7 +26,7 @@ Effective data protection relies on several core principles, including:
 6. **Confidentiality and integrity** - Data security must be prioritized through encryption, access controls, and cybersecurity best practices. Unauthorized access or breaches can have serious consequences.
 7. **Accountability and compliance** - Businesses and institutions must take responsibility for ensuring data protection. Regular audits, training, and clear policies help reinforce compliance.
 
-## Implementing Strong Data Protection Measures
+## Implementing strong data protection measures
 
 With the increasing importance of digital public infrastructure, Governments/organizations must take a proactive approach to protecting personal data. As an example, the General Data Protection Regulation (GDPR) establishes a framework that enforces strict standards for data collection, processing, and storage. Failure to comply with GDPR can lead to severe financial penalties, reputational damage, and loss of consumer trust. All stakeholders including governments, individuals all have a role in ensuring data is handled responsibly. By embedding security-by-design and privacy-by-design principles governments/organizations can minimize risks and uphold the rights of individuals.
 
@@ -45,7 +45,7 @@ Key measures include:
 
 The list above is a starting point but by no means exhaustive. Every government/organization has unique needs and risks, so these measures should be adapted and expanded based on context.
 
-## Exceptions to the Right to Erasure
+## Exceptions to the right to erasure
 
 While individuals have the right to request the deletion of their personal data, certain situations require data to be retained for legal, public interest, or security reasons. These exceptions ensure that critical government functions, research, and legal obligations are not disrupted.
 

docs/overview/concepts/extensibility.md

@@ -6,19 +6,19 @@ myst:
     "keywords": "OpenSPP, extensibility, customization, Odoo, modular architecture, inheritance, social protection"
 ---
 
-# Customisable, Configurable, and Extensible
+# Customisable, configurable, and extensible
 
 OpenSPP is a highly adaptable digital social protection information system designed to improve the {term}`efficiency` and {term}`effectiveness` of social protection programs in low and middle-income countries. Thanks to its foundation on the widely used ERP platform, Odoo, OpenSPP offers unparalleled customizability, configurability, and extensibility. This page aims to give technical personnel a thorough understanding of these key features.
 
 ## Customisable
 
 OpenSPP's customizability is deeply rooted in its underlying Odoo framework, an open-source ERP platform known for its modular architecture and flexibility. This section will delve into the details of how Odoo empowers the customizability of OpenSPP.
 
-### Modular Architecture
+### Modular architecture
 
 Odoo's modular architecture enables developers to create, change, and extend functionalities by working with individual modules. Each module encapsulates specific features, making it easier to develop, support, and upgrade the system without disrupting its core functionality. In OpenSPP, this modular approach allows countries to implement custom solutions that address their unique social protection requirements.
 
-### Inheritance and Overrides
+### Inheritance and overrides
 
 Odoo provides a powerful inheritance mechanism, which lets new modules extend, change, or override existing functionalities without altering the original code. This allows OpenSPP to be adapted to specific needs while preserving the ability to receive updates and enhancements in the future.
 
@@ -44,7 +44,7 @@ OpenSPP's configurability allows implementers to adapt the system's functionalit
 
 OpenSPP's extensibility is in part a direct result of its foundation on the Odoo framework, which has been designed with extensibility and adaptability in mind. This section will explore the various aspects of Odoo that make OpenSPP a highly extensible solution.
 
-### Large Ecosystem
+### Large ecosystem
 
 Odoo boasts a vast ecosystem of pre-built modules and third-party applications developed by its extensive community. This wealth of resources enables OpenSPP users to access a wide range of functionalities and integrations, helping them address specific needs without starting from scratch. With over 15,000 modules available, the possibilities for extending OpenSPP are nearly endless.
 

docs/overview/concepts/index.md

@@ -12,49 +12,26 @@ These concepts explain the theoretical foundations and design principles behind
 
 ## Architecture and design
 
-**Digital public infrastructure**
 
-{doc}`Digital public infrastructure <digital_public_infrastructure>`
-Essential components of DPI and how OpenSPP aligns with DPI principles through modular, interoperable architecture.
+**{doc}`Digital public infrastructure <digital_public_infrastructure>`**: Essential components of DPI and how OpenSPP aligns with DPI principles through modular, interoperable architecture.
 
-**Integrated beneficiary registry**
+**{doc}`Integrated beneficiary registry <integrated_beneficiary_registry>`**: Key components of an IBR, its advantages, and its relationship with social registries.
 
-{doc}`Integrated beneficiary registry <integrated_beneficiary_registry>`
-Key components of an IBR, its advantages, and its relationship with social registries.
-
-**Extensibility**
-
-{doc}`Extensibility <extensibility>`
-How OpenSPP's Odoo foundation enables customization through modular architecture and inheritance.
+**{doc}`Extensibility <extensibility>`**: How OpenSPP's Odoo foundation enables customization through modular architecture and inheritance.
 
 ## Data management
 
-**Registry key concepts**
-
-{doc}`Registry key concepts <registry_key_concepts>`
-Best practices for organizing data with a minimalistic approach and the four main registry structure components.
-
-**Registrant concepts**
+**{doc}`Registry key concepts <registry_key_concepts>`**: Best practices for organizing data with a minimalistic approach and the four main registry structure components.
 
-{doc}`Registrant concepts <registrant_concepts>`
-Core terminology for individuals, groups, group memberships, and their relationships.
+**{doc}`Registrant concepts <registrant_concepts>`**: Core terminology for individuals, groups, group memberships, and their relationships.
 
-**Data collection and validation**
-
-{doc}`Data collection and validation <data_collection_validation>`
-Data minimization, user consent, versatile input methods, and validation processes.
+**{doc}`Data collection and validation <data_collection_validation>`**: Data minimization, user consent, versatile input methods, and validation processes.
 
 ## Security and governance
 
-**Data protection**
-
-{doc}`Data protection <data_protection>`
-Principles of lawfulness, data minimization, and accountability within DPI context.
-
-**User management**
+**{doc}`Data protection <data_protection>`**: Principles of lawfulness, data minimization, and accountability within DPI context.
 
-{doc}`User management <user_management>`
-Framework for controlling system access and safeguarding user data with two management approaches.
+**{doc}`User management <user_management>`**: Framework for controlling system access and safeguarding user data with two management approaches.
 
 ```{toctree}
 ---