Many university research data collections are anchored by traditional datasets. In finance and accounting, CRSP stock prices and Compustat financial statements have supported decades of research on what firms report and how markets react. As more data has become available, researchers have broadened how they study markets: building on that foundation with measures of what customers, workers, and places are doing, through foot traffic data, card spending, job postings, rents, climate risk, and other alternative data sources.

Meeting this expanded research demand requires managing data that is both varied and comprehensive: serving an economist, a marketing scholar, a policy analyst, a public health researcher, or an urban planner. That is the role Dewey plays alongside a library's foundational collections, bringing alternative data from 40+ providers onto one platform with one license, along with robust documentation and helpful support resources. We make it easy for researchers to find and access novel datasets, as well as combine multiple datasets towards diverse research questions.

How WRDS opened the door to foundational finance datasets

WRDS became the home for regulatory-grade financial data, providing access to fundamental datasets like CRSP and Compustat that have been driving economics research for decades.

A brief history: In 1960, Merrill Lynch gave the University of Chicago $50,000 to answer what sounded like a simple question: what has been the average rate of return on investments in common stocks? James Lorie and Lawrence Fisher expected the work to take a year. It took four years and about $250,000, mostly because so little about the data was defined or standardized yet. As Lorie later put it, "We thought that a common stock is a common stock is a common stock; but it isn't." (Lorie, 1965). Their work established the Center for Research in Security Prices (CRSP) and produced the first comprehensive study of stock market returns (Fisher & Lorie, 1964). Alongside it, Standard & Poor's established Compustat in 1962 as a database of company financial statements (Linnainmaa & Roberts, 2018). Joining the two through their permanent identifiers (PERMNO and GVKEY) became a workhorse of finance and accounting research.

As these datasets’ popularity grew among academics, Wharton Research Data Services (WRDS) was developed in 1993 to support faculty research at Wharton (CRL). It brought CRSP, Compustat, and other vendor databases together on one standardized platform, with linking tools, documentation, sample programs, and support from doctoral-level specialists. Today WRDS reports over 50 data vendors, used by about 75,000 people at 530+ institutions in 38 countries (WRDS). 

This widespread access has made CRSP and Compustat the shared foundation of finance research. As one study of more than 14,000 finance journal articles found, they are the most commonly used commercial databases, appearing in roughly 30% of cases and nearly twice that in top-5 journals (Berninger et al., 2022). That study also found that 74% of empirical papers rely on at least one common commercial database. Shared data makes results easier to compare, replicate, and build on, but it also means the same variables are tested again and again. In any empirical field, multiple comparisons inflate false positives and require statistical correction, a recommendation echoed by a study of hundreds of proposed factors explaining stock returns (Harvey, Liu, and Zhu, 2016). Replication and validation remain essential work, and new questions keep emerging as markets and the economy evolve. Both benefit from new variables and data sources, especially when they are linked to the foundation this literature has built.

Applying multiple data sources to answer one research question

Alternative datasets provide new perspectives, increased granularity, and real-life measurements that expand research potential, particularly when combined with traditional datasets. In finance, for example, researchers studying public retailers might start with revenue filings, then examine mobility data for customer traffic (Noh, So & Zhu, 2024), job postings for hiring rates (Campello, Kankanhalli & Muthukrishnan, 2024), credit card transactions for stock return predictions (Agarwal, Qian & Zou, 2021), or lobbying records for political influence valuation (Borisov, Goldman & Gupta, 2016). These pairings add valuable layers beyond traditional data alone. And some studies use this combination of sources to gain a better understanding of the big picture, such as combining credit card spending, small business revenue, and job postings to follow the economic impact of a pandemic in near real time (Chetty et al., 2024).

Beyond economics, this approach has also spread across disciplines, with researchers joining datasets by firm, geography, industry categories, and other cross-functional variables. Recent research spans from tracking the rental market after climate disasters (Carmona et al., 2026), to linking foot traffic with neighborhood mental health (Chandra Mouli et al., 2026) and public safety (Moritz et al., 2026). These studies add diverse points of view to new and old questions, which relies on having several datasets within reach at once. Few research budgets stretch to licensing several datasets separately, so affordable access to many sources in one platform goes a long way.

How Dewey supports academic research with alternative data

Dewey makes data access easy, not only for many of the researchers mentioned above, but also for researchers across 500+ universities, and the librarians managing this growing demand for data (Carroll, Bergstrom & Hulbert, 2026). No single platform meets every research need, and the strongest data collections work like a portfolio (Barry, 2025). Currently, 100% of the universities with an institutional Dewey subscription also subscribe to WRDS. Dewey adds the alternative data layer that researchers across disciplines are interested in. WRDS gave researchers a shared front door to traditional datasets; Dewey opens the next door to alternative data, allowing researchers to ask new questions, or evaluate old ones from a fresh angle.

Within the Dewey catalog, hundreds of datasets and dozens of providers cover a wide range of categories, including: foot traffic, consumer spending, job postings and salaries, private company financials, real estate, climate risk, web traffic, healthcare prices, lobbying records, and more. The average subscribed university draws on about 25 providers and 110 distinct datasets in its first year (see more usage stats in this institutional subscription trends). And new providers are added at no extra cost, so the collection keeps growing within the same subscription. That breadth extends a library's data resources to researchers across departments, from business and economics, to marketing, real estate, public policy, psychology, health, urban planning and computational social sciences. 

For library teams, Dewey also makes the logistics simple:

• SSO, user approval controls, and usage statistics by group and dataset
• Licenses that cover teaching, research, and publication
• Added data services like Connect, for managing datasets your institution licenses directly on the same platform
• Robust documentation and support resources for data discovery, evaluation, and data workflows for every technical skill level
• Onboarding support with a typical go-live within two weeks of procurement approval

Set your researchers up for success and expand your university’s data access with Dewey. As an addition to WRDS, Dewey provides not only the novel perspective layer to financial research, but also opens up new research opportunities across departments. Institutions with WRDS can license compliance focused,  regulatory-grade data providers, primarily in the financial space. And Dewey unlocks hundreds of datasets on one platform with a modern UX. This combination gives researchers across your institution the ability to extend the application of those traditional datasets with premium commercial and alternative datasets across disciplines. 

Want to learn more about how Dewey can broaden your university’s research potential? Set up a demo, or browse our catalog! 

Happy researching!

Resources

• Check whether your university subscribes
• Explore and sample datasets in the Dewey catalog
• The power of alternative data for academic research
• Getting the most value from the Dewey subscription
• Evaluating data subscriptions amid funding cuts
• Librarians: book a call with our team

References

Agarwal, S., Qian, W., & Zou, X. (2021). Disaggregated sales and stock returns. Management Science, 67(11), 7167–7183. https://doi.org/10.1287/mnsc.2020.3813

Barry, E. (2025, April 29). How data providers are meeting the needs of modern business libraries. Dewey. https://www.deweydata.io/blog/how-data-providers-are-meeting-the-needs-of-modern-business-libraries

Berninger, M., Kiesel, F., & Schnitzler, J. (2022). Commercial data in financial research [Working paper]. SSRN. https://doi.org/10.2139/ssrn.3943132

Borisov, A., Goldman, E., & Gupta, N. (2016). The corporate value of (corrupt) lobbying. The Review of Financial Studies, 29(4), 1039–1071. https://doi.org/10.1093/rfs/hhv048

Campello, M., Kankanhalli, G., & Muthukrishnan, P. (2024). Corporate hiring under COVID-19: Financial constraints and the nature of new jobs. Journal of Financial and Quantitative Analysis, 59(4), 1541–1585. https://doi.org/10.1017/S0022109023000522

Carmona, G., Mukhija, V., Dominguez-Villegas, R., Bonilla, M., Barajas, S., Palomino, X. M., & Lua Martel, A. G. (2026, February 25). Rebuilding for whom? An analysis of tenant demographics, the rental market, affordability, and the rebuilding efforts in Altadena. UCLA Latino Policy & Politics Institute. https://latino.ucla.edu/research/rebuilding-for-whom/

Carroll, E., Bergstrom, T., & Hulbert, I. G. (2026, May 14). US library survey 2025: Under pressure. Ithaka S+R. https://doi.org/10.18665/sr.325599

Center for Research Libraries. (n.d.). Wharton Research Data Services (WRDS). eDesiderata. Retrieved October 13, 2026, from https://edesiderata.crl.edu/resources/wharton-research-data-services-wrds

Chandra Mouli, S., Zhang, T., Chen, Z., Rajagopalan, S., Maddock, J. E., Nasir, K., Dong, W., & Al-Kindi, S. (2026). Neighborhood greenspace visits and mental health: Insights from mobility data across nine U.S. metropolitan areas. Frontiers in Public Health, 14, 1731243. https://doi.org/10.3389/fpubh.2026.1731243

Chetty, R., Friedman, J. N., Stepner, M., & the Opportunity Insights Team. (2024). The economic impacts of COVID-19: Evidence from a new public database built using private sector data. The Quarterly Journal of Economics, 139(2), 829–889. https://doi.org/10.1093/qje/qjad048

Fisher, L., & Lorie, J. H. (1964). Rates of return on investments in common stocks. The Journal of Business, 37(1), 1–21. https://crsp.org/crsp_pdf/journal-of-business-rates-of-return-on-investments-in-common-stocks-january-2014-2

Harvey, C. R., Liu, Y., & Zhu, H. (2016). …and the cross-section of expected returns. The Review of Financial Studies, 29(1), 5–68. https://doi.org/10.1093/rfs/hhv059

Linnainmaa, J. T., & Roberts, M. R. (2018). The history of the cross-section of stock returns. The Review of Financial Studies, 31(7), 2606–2649. https://doi.org/10.1093/rfs/hhy030

Lorie, J. H. (1965). Current controversies on the stock market [Address to the American Statistical Association, September 8, 1965]. Selected Papers No. 20, Graduate School of Business, University of Chicago. https://crsp.org/crsp_pdf/history-james-lorie-benchmarks-for-research-quality-data

Moritz, M., [REMAINING AUTHORS NEEDED]. (2026). The effect of citywide improvement in street lighting on the use of public space by community residents. Justice Quarterly. Advance online publication. https://doi.org/10.1080/07418825.2026.2712831

Noh, S., So, E. C., & Zhu, C. (2024). Financial reporting and consumer behavior [Working paper]. SSRN. https://doi.org/10.2139/ssrn.3932590

Wharton Research Data Services. (n.d.). WRDS. Retrieved October 13, 2026, from https://wrds-www.wharton.upenn.edu/pages/

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