Scholar Data
In our paper, A Skeptical View of the National Science Foundation’s Role in Economic Research, Tyler and I point out that if the NSF is doing what it should it ought to be doing very different things than other funders: Public goods theory tells us that the National Science Foundation should support activities that are […] The post Scholar Data appeared first on Marginal REVOLUTION .
In their paper, Tyler and the author highlight the National Science Foundation's (NSF) responsibility to support activities that are difficult to fund through traditional sources. The NSF should prioritize genuine public goods over marginal subsidies. The researchers propose testing the NSF's allocation of funds by comparing the types of projects supported by the NSF to those supported by the private sector.
Unfortunately, the prominent NSF grants appear to be similar to those produced by the current academic status quo, contradicting public goods theory.
The authors suggest two specific areas of economics that should be supported: replication studies and the creation of public datasets. Scholar Data is a new project that won the competition to create a data-sharing index. This project aims to develop an S-Index, similar to the H-Index for papers, but measures a dataset's accessibility, citations, and other mentions. The goal is to create a metric to reward the creation of public goods.
Researchers spend years collecting and sharing datasets, which are crucial for reproducibility, transparency, and discovery across various fields. However, data sharing goes unnoticed and unrewarded in the current metric system. Metrics such as citations, H-Index, and Impact Factor only consider publications. This creates a broken incentive, as researchers are expected to share data but receive no credit for doing so. Consequently, data sharing is treated as a chore rather than a valuable contribution.
Scholar Data and the S-Index aim to address this issue. By measuring the impact of your datasets, the S-Index provides data sharing with the same visibility and recognition as publishing. This transforms data sharing from an obligation into a career asset. The article concludes by encouraging researchers to check if their dataset is already catalogued and to credit it on ScholarData.
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