Gene function curation via Gene Ontology (GO) annotation is a common task among Model Organism Database (MOD) groups. Due to its manual nature, this task is time-consuming and labor-intensive, and thus considered one of the bottlenecks in literature curation. There have been many previous attempts of automatic identification of GO terms and associated information from full text. However, few systems have delivered an accuracy that is comparable to human annotators. One recognized challenge in developing such systems is the lack of marked passagelevel evidence text that provides the basis for making GO annotations. To this end, we aim to create a corpus that includes the GO evidence text along with the three essential elements of GO annotations: 1) a gene or gene product, 2) a GO term and 3) a GO evidence code. To ensure our results are consistent with real-life GO annotation data, we recruited a team of eight professional GO curators from the biocuration community, and asked them to follow their routine GO annotation protocols. With the aid of a web-based annotation tool, our annotators marked up