CoCon: A Data Set on Combined Contextualized Research Artifact Use

Tarek Saier, Youxiang Dong, Michael Färber
3/27/2023
cs.DLcs.CL

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

In the wake of information overload in academia, methodologies and systems for search, recommendation, and prediction to aid researchers in identifying relevant research are actively studied and developed. Existing work, however, is limited in terms of granularity, focusing only on the level of papers or a single type of artifact, such as data sets. To enable more holistic analyses and systems dealing with academic publications and their content, we propose CoCon, a large scholarly data set reflecting the combined use of research artifacts, contextualized in academic publications' full-text. Our data set comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications. We additionally formalize a link prediction task for "combined research artifact use prediction" and provide code to utilize analyses of and the development of ML applications on our data. All data and code is publicly available at https://github.com/IllDepence/contextgraph.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{saier2023cocon,
  title  = {CoCon: A Data Set on Combined Contextualized Research Artifact Use},
  author = {Tarek Saier and Youxiang Dong and Michael Färber},
  year   = {2023},
  eprint = {2303.15193},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.15193v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2303.15193.svg)](https://codeofpaper.com/paper/2303.15193)

Discussion