Retrieval-Augmented Generation (RAG) is a widely used strategy in Large-Language Models (LLMs) to extrapolate beyond the inherent pre-trained knowledge. Hence, RAG is crucial when working in data-sparse fields such as Chemistry. The evaluation of RAG systems is commonly conducted using specialized datasets. However, existing datasets, typically in the form of scientific Question-Answer-Context (QAC) triplets or QA pairs, are often limited in size due to the labor-intensive nature of manual curation or require further quality assessment when generated through automated processes. This highlights a critical need for large, high-quality datasets tailored to scientific applications. We introduce ChemLit-QA, a comprehensive, expert-validated, open-source dataset comprising over 1,000 entries specifically designed for chemistry. Our approach involves the initial generation and filtering of a QAC dataset using an automated framework based on GPT-4 Turbo, followed by rigorous evaluation by chemistry experts. Additionally, we provide two supplementary datasets: ChemLit-QA-neg focused on negative data, and ChemLit-QA-multi focused on multihop reasoning tasks for LLMs, which complement the main dataset on hallucination detection and more reasoning-intensive tasks.