This repository contains the data and code for the paper "Does the use of unusual combinations of datasets contribute to greater scientific impact?". You can access the paper via the following link: (https://www.pnas.org/doi/epub/10.1073/pnas.2402802121).

Citation: Yu, Yulin, and Daniel M. Romero. "Does the use of unusual combinations of datasets contribute to greater scientific impact?." Proceedings of the National Academy of Sciences 121.41 (2024): e2402802121.

Contents:

DataComb_Full.csv: This file includes all the final cleaned metadata used for analysis, with each row representing a single paper. The final dataset was created by integrating, cleaning, and quantifying variables from three large-scale datasets: Altmetric data: Available free of charge to researchers at Altmetric（https://www.altmetric.com/research-access/）. Publication records: Extracted from OpenAlex, a publicly accessible dataset（https://docs. openalex.org/how-to-use-the-api/api-overview. ICPSR Data Citation: ICPSR/mica-data-descriptor](https://github.com/ICPSR/mica-data-descriptor. For access to raw or additional data from Altmetric or OpenAlex, please use their APIs or contact Digital Science directly.

1-Result_Visualization_Main.ipynb: Contains the code to replicate the main figures and statistical analyses presented in the paper.

2-QuantifyDataComb.ipynb: This notebook includes the code used to quantify the atypical combination of datasets.