Imagine a world where AI can effortlessly unlock the secrets hidden within millions of scientific papers, connecting groundbreaking discoveries and accelerating research like never before. That future is closer than you think. A team of researchers has just unveiled SciER, a powerful new dataset designed to supercharge AI's ability to understand and extract critical information from scientific literature. Why is this such a big deal? Currently, scientific knowledge is trapped in a sea of unstructured text. Sifting through countless articles to find relevant datasets, experimental methods, and research tasks is a Herculean effort for scientists. Existing AI tools struggle to comprehend the complex relationships within these documents, hindering their ability to truly assist researchers. SciER tackles this head-on. By meticulously annotating 106 full-text scientific publications, the team has created a rich, detailed dataset encompassing over 24,000 entities and 12,000 relationships. Unlike previous datasets focusing on abstracts or limited sections, SciER captures the nuances of scientific language across entire documents, leading to more comprehensive AI understanding. This focus on full-text annotation allows SciER to identify subtle but crucial links between datasets, methods, and tasks, including relations like "trained-with," "evaluated-with," and "benchmark-for." This granular level of detail empowers AI models to make more informed connections, uncovering hidden patterns and potentially sparking new research avenues. The team rigorously tested both cutting-edge supervised and large language models (LLMs) using SciER, uncovering valuable insights. They found that LLMs, while promising, still lag behind supervised methods in accurately extracting relationships. However, the research showed that structuring the task as a pipeline, where entities are identified before relationships are extracted, significantly boosts LLM performance. This suggests exciting possibilities for integrating LLMs into scientific data processing workflows. SciER opens doors to a future where AI becomes an indispensable research partner. Imagine AI systems recommending relevant datasets for your experiments, suggesting alternative methods based on previous studies, or even identifying promising research directions by connecting seemingly disparate findings. While challenges remain in fully harnessing AI's potential for scientific discovery, SciER marks a critical step towards unlocking the vast knowledge trapped within scientific literature.