Can We Find the Code? An Empirical Study of Google Scholar's Code Retrieval
March 02, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Shi-Shun Chen
arXiv ID
2503.01031
Category
cs.DL: Digital Libraries
Cross-listed
cs.IR
Citations
1
Venue
arXiv.org
Last Checked
3 months ago
Abstract
Academic codes associated with research papers are valuable resources for scholars. In specialized fields outside computer science, code availability is often limited, making effective code retrieval essential. Google Scholar is a crucial academic search tool. If a code published in the paper is not retrievable via Google Scholar, its accessibility and impact are significantly reduced. This study takes the term "accelerated degradation" combined with "reliability" as an example, and finds that, for papers published by Elsevier, only GitHub links included in abstracts are comprehensively retrieved by Google Scholar. When such links appear within the main body of a paper, even in the "Data Availability" section, they may be ignored and become unsearchable. These findings highlight the importance of strategically placing GitHub links in abstracts to enhance code discoverability on Google Scholar.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Digital Libraries
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Measuring academic influence: Not all citations are equal
R.I.P.
π»
Ghosted
The Open Access Advantage Considering Citation, Article Usage and Social Media Attention
R.I.P.
π»
Ghosted
A Bibliometric Review of Large Language Models Research from 2017 to 2023
R.I.P.
π»
Ghosted
On the Performance of Hybrid Search Strategies for Systematic Literature Reviews in Software Engineering
R.I.P.
π»
Ghosted
A Systematic Identification and Analysis of Scientists on Twitter
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted