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 especially hard to support through traditional university, philanthropic, and private-sector sources. This insight suggests a simple test: to the extent that the NSF allocates funds to genuine public goods as opposed to subsidies on the margin, we ought to see a large difference in the kinds of projects the NSF supports compared to what the "market" sector supports. But what stands out from lists of prominent NSF grants (like the one provided by Moffitt in this symposium) is how similar they look to lists of "good" research produced by today's status quo. If we take public goods theory seriously, what areas of economics should be supported?

We suggest replication studies and support for producing public datasets. Which brings me to Scholar Data, a neat new project that won NIH's competition to create a data-sharing index. That an NIH competition exists at all reflects a broader shift: major funders and journals have increasingly mandated data sharing in recent years—the NIH, for example, introduced a Data Management and Sharing Policy requiring plans for sharing research data, and many journals have made data availability a condition of publication. Scholar Data creates an S-Index—like an H-Index for papers—but instead the S-Index measures a dataset's ease of access, citations, and other mentions. The point is to create a metric to reward the creation of a public good:

Researchers invest years collecting and sharing datasets that underpin reproducibility, transparency, and discovery across every field. Without shared data, findings can't be verified, built upon, or trusted. And yet, the metrics that define academic careers, such as citations, h-index, and impact factor, only count publications. Data sharing goes unrecognized and unrewarded.

This creates a broken incentive: researchers are expected to share data, but get no credit for doing so. The result is that data sharing is treated as a chore rather than a contribution.

Scholar Data and the S-index are aimed at fixing this. By measuring how impactful your datasets are, the S-index gives data sharing the same visibility and recognition as publishing, turning it from an obligation into a career asset. The approach echoes earlier community efforts such as the FAIR data principles—which call for data to be findable, accessible, interoperable, and reusable—by giving researchers a concrete, quantifiable signal for a practice that has long been encouraged but never measured.

Bravo! Your dataset may already be catalogued. You can get credit at ScholarData.

Source: Marginal Revolution