diff --git a/ML-BOM/en/0x20-Design-Model-Component-Metadata.md b/ML-BOM/en/0x20-Design-Model-Component-Metadata.md
index 597cc3ad..7e54a395 100644
--- a/ML-BOM/en/0x20-Design-Model-Component-Metadata.md
+++ b/ML-BOM/en/0x20-Design-Model-Component-Metadata.md
@@ -174,7 +174,7 @@ If the model being described by an ML-BOM is instead hosted in a GitHub reposito
 
 ##### Adding domain-specific identifiers
 
-Organizations that produce BOMs for hardware or software components they produce may have multiple domain-specific identifiers for the same component.  In these cases, it is best practice to register (reserve) an official namespace for these domains with the [CycloneDX Property Taxonomy](), which is the authoritative source of official namespaces used in CycloneDX `properties`.
+Organizations that produce BOMs for hardware or software components they produce may have multiple domain-specific identifiers for the same component.  In these cases, it is best practice to register (reserve) an official namespace for these domains with the [CycloneDX Property Taxonomy](https://github.com/CycloneDX/cyclonedx-property-taxonomy), which is the authoritative source of official namespaces used in CycloneDX `properties`.
 
 ###### Example: domain-specific identifiers
 
diff --git a/ML-BOM/en/0x23-Design-Model-Card-Quantitative-Analysis.md b/ML-BOM/en/0x23-Design-Model-Card-Quantitative-Analysis.md
index cc357bb6..2d1ef64c 100644
--- a/ML-BOM/en/0x23-Design-Model-Card-Quantitative-Analysis.md
+++ b/ML-BOM/en/0x23-Design-Model-Card-Quantitative-Analysis.md
@@ -39,18 +39,18 @@ Benchmarks use standardized datasets to objectively compare model quality, effic
       * *cola* - The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory.
       * *mnli* - The Multi-Genre Natural Language Inference Corpus consists of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis.
   * **Math/STEM tasks**
-    * [GSM8K](https://huggingface.co/datasets/openai/gsm8k) (OpenAII, Grade School Math 8K): a dataset of 8.5K high quality linguistically diverse grade school math word problems.
+    * [GSM8K](https://huggingface.co/datasets/openai/gsm8k) (OpenAI, Grade School Math 8K): a dataset of 8.5K high quality linguistically diverse grade school math word problems.
     * [MATH-500](https://huggingface.co/datasets/HuggingFaceH4/MATH-500) (Hugging Face): Benchmarks specifically designed to evaluate mathematical reasoning.
   * **Coding Tasks**
     * [HumanEval](https://huggingface.co/datasets/openai/openai_humaneval) (OpenAI): used to evaluate the functional correctness of code generated LLMs. It consists of hand-crafted programming problems designed to test reasoning and code synthesis abilities.
     * [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) (Mostly Basic Python Problems ): Benchmarks for evaluating code generation and programming capabilities.
-    * [CodeXGLUE](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement) (MicroSoft, Code Refinement): Used to evaluate a model's ability to remove (i.e., "fix") bugs from Java code (i.e., refine the code) with accuracy being reported as [BLEU](https://learn.microsoft.com/en-us/azure/ai-services/translator/custom-translator/concepts/bleu-score) scores.
+    * [CodeXGLUE](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement) (Microsoft, Code Refinement): Used to evaluate a model's ability to remove (i.e., "fix") bugs from Java code (i.e., refine the code) with accuracy being reported as [BLEU](https://learn.microsoft.com/en-us/azure/ai-services/translator/custom-translator/concepts/bleu-score) scores.
   * **Other Tasks**
     * [IMDB](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews): a large dataset of 50K, highly polarized, movie reviews for NLP sentiment analysis and classification.
 
 * [Computer Vision](0x90-Appendix-A_Glossary.md#computer-vision) (e.g., digital image or video recognition): These benchmarks measure the performance, accuracy, and efficiency of models in tasks like image classification, object detection, segmentation, and tracking. Some example "vision" datasets include:
   
-  * [ImageNet](image-net.org): large-scale dataset for computer vision, featuring over 14 million annotated, high-resolution images across thousands of object categories organized by the [WordNet](https://en.wikipedia.org/wiki/WordNet) hierarchy.
+  * [ImageNet](https://image-net.org): large-scale dataset for computer vision, featuring over 14 million annotated, high-resolution images across thousands of object categories organized by the [WordNet](https://en.wikipedia.org/wiki/WordNet) hierarchy.
   * [MathVista](https://huggingface.co/datasets/AI4Math/MathVista): Used to evaluating math reasoning in Visual Contexts.  It consists of three datasets, *IQTest*, *FunctionQA*, and *PaperQA*, which are tailored to evaluate visual reasoning on puzzle test figures, algebraic reasoning over functional plots, and scientific reasoning with academic paper figures, respectively.
   * [MNIST](https://www.tensorflow.org/datasets/catalog/mnist) (Modified National Institute of Standards and Technology database): a large database of handwritten digits (glyphs) that is commonly used for training various image processing systems.