Emotion recognition in text is a complex and evolving field that has garnered considerable interest. This paper addresses the pressing need to explore and experiment with new corpora annotated with emotions. We identified several corpora presented since 2018. We restricted this study to English single-labeled data. Nevertheless, the datasets vary in source, domain, topic, emotion types, and distributions. As a basis for benchmarking, we conducted emotion detection experiments by fine-tuning a pretrained model and compared our outcomes with results from the original publications. More importantly, in our efforts to combine existing resources, we created a unified corpus from these diverse datasets and evaluated the impact of training on that corpus versus on the training set for each corpus. Our approach aims to streamline research by offering a unified platform for emotion detection to aid comparisons and benchmarking, addressing a significant gap in the current landscape. Additionally, we present a discussion of related practices and challenges. Our code and dataset information are available at https://github.com/a-koufakou/EmoDetect-Unify. We hope this will enable the NLP community to leverage this unified framework towards a new benchmark in emotion detection.