Merge pull request #8 from pautva/collaboration-toolkit

updated toolkit page

src/content/toolkit/best-practices-for-decision-support-tools.md

@@ -9,7 +9,7 @@ tags:
  - Decision Support Tools
  - Best Practices
  - Design Guidelines
-category: User Research, Design and Prototyping
+category: Collaboration
 featured: false
 ---
 

src/content/toolkit/choosing-scientific-methods.md

@@ -0,0 +1,87 @@
+---
+title: Choosing Scientific Methods
+publishDate: 2024-07-08 00:00:00
+# img: /co-creation-toolkit/assets/illustrations/nldp-farmer.svg
+img: /co-creation-toolkit/assets/toolkit/covers/toolkit-cover-17.svg
+img_alt: Cover description
+description: Common scientific methods used in the decision support tools and how to choose the most appropriate one for your project.
+tags:
+ - Scientific Methods
+ - Decision Support Tools
+ - Data Analysis
+category: Design and Prototyping
+featured: false
+---
+
+### Overview
+
+#### Who is this guidance for?
+
+This page provides **developers**, **designers**, and **product owners** with an overview of scientific methods commonly used in decision support tools, along with guidance on selecting the most appropriate method for their specific use case.
+
+
+> #### Summary
+> 
+> - **Problem and Decision Type Matter**: Start by clearly outlining the problem your decision support tool (DST) will address and the type of decision it will support (operational, tactical, strategic).
+> - **Tailor to Your Data**: analyse the data available (structured vs. unstructured, complexity, etc.) as this influences suitable scientific methods.
+> - **Output and User Interaction**: Determine what output the DST should provide (e.g., recommendations, visualisations) and how much user control is needed.
+> - **Match Method to Requirements**: Common decision support methods include rule-based systems, statistical methods, machine learning, simulation, and optimisation. Select the one that aligns best with your problem, data, and output needs.
+> - **Technical Factors and User Expertise**: Consider your technical resources and assess the technical sophistication of potential users to ensure the DST's complexity is appropriate.
+
+### How to Choose?
+
+#### 1. Define the Problem and Decision Type
+
+* **Problem Clarity**: Clearly outline the problem the DST is intended to address. Is it complex, straightforward, highly structured, or unstructured?
+* **Decision Type**: Identify the nature of the decision:
+    * Operational: Routine, day-to-day decisions.
+    * Tactical: Mid-level decisions focusing on resource allocation and planning.
+    * Strategic: High-level, long-term decisions impacting the organization's direction.
+
+#### 2. Consider the Data Landscape
+
+* **Data Availability**: Evaluate the type and quality of data you have access to (structured vs. unstructured, internal vs. external).
+* **Data Complexity**: Assess the volume and intricacy of the data. Will it involve simple calculations or complex analysis?
+
+#### 3. Determine the Desired Output and Level of User Interaction
+
+* **Output Needs**: What type of output does the DST need to provide (recommendations, reports, visualisations, etc.)?
+* **User Interaction**: How much control and interactivity do users require? Should it be a simple recommendation engine or a system that allows for exploration and what-if scenarios?
+
+#### 4. Match Methods to Your Requirements
+
+Here are common decision support techniques and when they are most suitable:
+
+* **Rule-Based Systems**: Ideal for structured problems with clear if-then logic.
+* **Statistical Methods**: Effective for analyzing historical data, identifying trends, and forecasting (e.g., regression analysis).
+* **Machine Learning**: Suited for complex problems where patterns aren't easily defined by rules. Can adapt as new data becomes available (e.g., classification, clustering, deep learning).
+* **Simulation**: Useful for testing "what-if" scenarios and understanding the impact of potential decisions in a risk-free environment.
+* **Optimisation**: Helps find the best solution when there are multiple constraints and objectives.
+* **Physics-based or Environmental Models**: Leverage the laws of physics and domain-specific knowledge to simulate real-world systems and their behaviours. These models are valuable for complex environmental decision-making where cause-and-effect relationships are well understood and need to be explicitly represented (e.g., assessing the impact of infrastructure projects on ecosystems).
+
+#### 5. Consider Your Assumptions and User Expertise
+
+* **Transparency of Assumptions and Uncertainty**: Explicitly communicate the assumptions underlying models or analysis. Provide users with a clear understanding of potential uncertainties and their impacts on the decision-making process.
+* **User Sophistication**: Assess the technical expertise of potential users. The DST's complexity should match their skills.
+
+#### Important Considerations
+
+* **Explainability**: Some methods (like rule-based systems) are transparent, while others (like deep neural networks) can be more of a "black box." Consider the need for explainability in your decision-making process.
+* **Bias**: Watch out for potential bias in your data, as this might carry over into your DST's outputs.
+* **Technical Resources**: Do you have in-house expertise or need external development or science support? Are there computational limitations?s
+* **Validation**: Rigorously test and validate your DST using real-world data to ensure it yields accurate and helpful results.
+
+#### Example 
+
+A product owner wants to develop a DST to support mineral exploration decisions.
+
+* **Problem**: Prioritise areas for further exploration based on the likelihood of finding valuable mineral deposits.
+* **Data**: Historical geological surveys, geochemical data, geophysical measurements, remote sensing data, etc.
+* **Output**: Risk/potential score indicating the probability of a commercially viable mineral deposit within the area.
+* **Method**: A machine learning model (e.g., random forest or neural network) might be appropriate as it can analyse complex relationships between geological variables and known mineral deposit locations.
+
+Remember: The best method for your decision support tool is **the one that most effectively addresses the specific nature of your decision problem, available data, and desired outputs**.
+
+### Links
+
+* [Find more details in the Guidance for Decision Support Tools guidance document](https://drive.google.com/file/d/1HyczxRNE2pCbw3n1ayYcHkmghjvrATqc/view?usp=sharing)

src/pages/case-studies/[...slug].astro

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   .content :global(ul) {
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   .content :global(ul li) {
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   .content :global(ol) {
     list-style-type: decimal; /* Custom bullet style */
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src/pages/toolkit/[...slug].astro

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   .content :global(ul) {
     list-style-type: square; /* Custom bullet style */
     padding: 0; /* Remove default padding */
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+    margin: 20px 0 0 20px; /* Add left margin to indent the list */
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   .content :global(ul li) {
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   .content :global(ol) {
     list-style-type: decimal; /* Custom bullet style */
     padding: 0; /* Remove default padding */
-    margin: 0 0 0 20px; /* Add left margin to indent the list */
+    margin: 20px 0 0 20px; /* Add left margin to indent the list */
   }
 
   .content :global(ol li) {