We present a manually curated and annotated, multidisciplinary dataset of 15,262 sentences from research articles (abstract and main text) that can be used for transformer-based extraction from scholarly publications of three types of entities: 1) research methods, named entities of variable length, 2) research goals, entities that appear as textual spans of variable length with mostly fixed lexico-syntactic-structure, and 3) research activities, entities that appear as textual spans of variable length with complex lexico-syntactic structure. We explore the capabilities of our dataset by using it for training/fine-tuning various ML and transformer-based models. We compare our finetuned models as well as LLM responses (chatGPT 3.5) based on 10-shot learning, by measuring F1 scores in token-based, entity-based strict and entity-based partial evaluations across interdisciplinary and discipline-specific datasets in order to capture any possible differences in discipline-oriented writing styles. Results show that fine tuning of transformer-based models significantly outperforms the performance of few- shot learning of LLMs such as chatGPT, highlighting the significance of annotation datasets in such tasks. Our dataset can also be used as a source for linguistic linked data by itself. We demonstrate this by presenting indicative queries in SPARQL, executed over such an RDF knowledge graph.