SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles
Authors/Creators
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1.
University of Zurich
Description
SciHyp is a dataset that supports researchers in understanding and identifying hypotheses in scientific literature, serving as a valuable resource across various scientific disciplines. SciHyp provides invaluable insights into the formulation and structure of hypotheses in scientific literature, making it a crucial resource for researchers in various scientific disciplines.
This repository contains the ontology and datasets described in our paper, SciHyp: A Fine-grained Dataset Describing Hypotheses and Their Components from Scientific Articles.
crowd.ttl: The data in this file has been curated utilizing the SciHyp pipeline, which employs a Hybrid-LLM-Crowd methodology described in the paper.
expert.ttl: Contrasting the crowd-sourced data, this file is composed of data curated through expert annotation. It reflects a more specialized and precise perspective, offering insights grounded in expert knowledge and analysis.
scihyp_VoiD.ttl: Serving as a metadata file containing a VoiD/DCAT description (Vocabulary of Interlinked Datasets/Data Catalog Vocabulary).
CrowdAlytics_7.4.owl: This file is the backbone of the dataset, outlining the underlying ontology that defines the structure and relationships within the SciHyp data.
Files
Files
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Additional details
Funding
- Swiss National Science Foundation
- CrowdAlytics: Large-Scale Human-Machine Systems for Data Science 184994