A major challenge with combining administrative and survey data sets is addressing their structural differences. The following is an overview of the differences in key structural features of administrative and survey data (table 1).

The linkage effort combines two main data sources. The first is data from the SED, which is an annual census conducted since 1957 of all individuals receiving a research doctorate from an accredited U.S. institution in a given academic year. The SED is sponsored by NCSES within the National Science Foundation (NSF) and by three other federal agencies: the National Institutes of Health, Department of Education, and National Endowment for the Humanities. The SED collects information on the doctorate recipient's educational history, demographic characteristics, and postgraduation plans. Results are used to assess characteristics of the doctoral population and trends in doctoral education and degrees (Chang et al. 2022, 2019).

The second data source is the UMETRICS data submitted to IRIS by IRIS member universities (Nicholls et al. 2020). These longitudinal data files can provide direct information about the levels and stability of research funding at multiple levels—individual, research field, program, department, and institutional. The data files include university financial and personnel administrative data pertaining to sponsored project expenditures at each member university during a given year, drawn directly from sponsored projects, procurement, and human resources data systems on each IRIS university’s campus. Individual campus files are deidentified, cleaned, and aggregated by IRIS to produce the core collection files. The 2020 release (https://iris.isr.umich.edu/research-data/2022datarelease-summarydoc/) includes transactions from about 440,000 unique federal and nonfederal awards, including wage payments to about 700,000 individuals as well as transactions to about 900,000 unique vendors (both organizations and individuals). In addition, about 23,600 unique organizations or institutions received subawards from IRIS universities transferring their prime awards. About 36,000 unique awards were used by IRIS universities as the funding source to transfer subawards to subrecipients. Overall, award direct expenditures total to $100 billion.

The technical challenge lies in the fact that, like many data sets that need to be linked, the overlap between the two allows linkage but differences exist because the two data sets cover different populations, cover different time periods, and have different identifiers. The two data sets also have different confidentiality protocols. Briefly, the SED, as is often the norm with survey data, draws from a fixed cross-sectional frame consisting of PhDs on or about the time that they graduate. Measures such as “funding source” are predefined before the survey is administered. UMETRICS data, as is often the norm with administrative data, are longitudinal, with a frame that varies depending on the number of universities participating and the number of individuals being paid on research grants. In keeping with most administrative data, measures such as “graduate student” or “paid on a research grant” are not predefined and must be characterized by the analysts.

Although this linkage benefits both the SED and UMETRICS, the focus of this report is to determine the extent to which it is possible to enhance SED data with UMETRICS data. Therefore, the SED is used as the frame, and the study determined how many individuals from UMETRICS institutions can be linked to doctorate recipients in the SED. Image 1 provides a schematic of the combined data sets and illustrates the organizing framework for the report. As illustrated in image 1, the linkage between doctorate recipients in the SED and university employees funded by research grants is expected for those who are classified as graduate students in UMETRICS data and received their doctorates in academic years 2014–17.

It is important to note that there is no gold standard information on how many doctorate recipients are supported by federal research funding—or indeed, how many individuals in total are supported by federal research funding (Fealing et al. 2011; Marburger 2005, 2011; Reedy, Teitelbaum, Litan 2011). The SED simply asks the source of financial support^{​The questionnaire for the 2017 SED is available at the SED survey website at https://www.nsf.gov/statistics/srvydoctorates/surveys/srvydoctorates-2017.pdf.} (image 2). The question does not ask the respondent about federal or nonfederal funding, much less the agency that provided the funding, or the intensity or the stability of funding. The only known characteristic is the proportion of doctorate recipients who reported receiving support for each source of financial support. For example, 64% of doctorate recipients who graduated in academic year 2017 reported receiving a research assistantship.

The SED is protected by a number of federal statutes. These include the Privacy Act of 1974, as amended, and the National Science Foundation Act of 1950, as amended, which provide for the security and privacy of personally identifiable statistical data maintained by NSF and the federal government. Sections of these laws make unlawful the disclosure or improper use of restricted use data. Violators are subject to a fine or imprisonment.^{​The NCSES restricted use data procedures guide is available at https://nsf.gov/statistics/license/forms/pdf/ncses_license_guide_august_2011.pdf.}

IRIS has developed the necessary institutional and governance mechanisms necessary to support such data sharing, as well as new linkage tools. In particular, it receives confidential data from its member institutions (depositors). The membership agreement^{​The IRIS membership and data transfer agreement is available at https://iris.isr.umich.edu/wp-content/uploads/2021/08/IRIS-New-Membership-and-DUA-v.-5.0-August-2021.pdf.} requires that IRIS not disclose identifiable institution materials or identifiable personal materials without a depositor’s permission. IRIS agrees to use reasonable administrative, technical, and physical safeguards to prevent use or disclosure of the data. All data are encrypted in transmission (including via Web interface) and stored at no less than advanced encryption standard 128-bit level encryption, and they will use industry-standard and up-to-date security tools and technologies, such as antivirus protections and intrusion detection methods in performing its obligations. All exports are reviewed for statistical disclosure limitation conformance before approval to release is granted.

All work was conducted using state of the art protocols within the Coleridge Initiative’s Administrative Data Research Facility (ADRF). The ADRF is a secure cloud-based environment that was developed initially as a pilot to inform the decision-making of the Commission on Evidence-Based Policymaking and has since grown to be a platform used by multiple agencies (Coleridge Initiative 2021). The ADRF was established using a federally approved and standardized approach for the certification of secure cloud-based platforms called FedRAMP. The authorization to operate certification has been issued to the ADRF by the Department of Agriculture and by the National Science Foundation. It has an authorization to use by the Department of Health and Human Services. The data are hosted in the Amazon Web Services GovCloud, which complies with a wide range of security requirements, such as the Department of Justice’s Criminal Justice Information Systems Security Policy, U.S. International Traffic in Arms Regulations; Export Administration Regulations, and Department of Defense Cloud Computing Security Requirements Guide for Impact Levels 2, 4, and 5.

The operational approach is to apply the “five safes” framework (“safe projects,” “safe people,” “safe settings,” “safe data,” “safe exports”) to data protection (Arbuckle, Ritchie 2019; Desai, Ritchie, Welpton 2016). That framework provides automated and structured workflows, which allow data owners to manage approvals (“safe projects”). It is designed to automate researcher onboarding by streamlining search for and discovery of relevant data and their approvals for access. It tracks data access and use by analysts and researchers through an automated data stewardship application (“safe people”). The FedRAMP approval is the “safe settings.” A collaboration with Amazon Web Services resulted in the development of a stand-alone Windows application to simplify the hashing of data elements—the transformation of direct identifiers like name and social security number to a meaningless string of letters and numbers—prior to being transmitted to the ADRF^{​Information on ADRF is available at https://coleridgeinitiative.org/administrative-data-research-facility.} (“safe data”). That application is open source and is provided free of charge to any interested users. The export module automates the export review process (“safe output”).

The ADRF also has provided a data stewardship application that provides automated and structured workflows. The application allows data owners to manage approvals, track data access, and oversee use by analysts and researchers. It is also designed to automate researcher onboarding by streamlining search and discovery of relevant data and approvals for access. In so doing, the data stewardship app reduces administrative burden, improves resources utilization, and provides stewards with essential reports on data access and usage.

To prevent secondary reidentification, outputs on cells that contain information with fewer than 10 individuals or 3 institutions were suppressed.

The SED is an annual census of all individuals receiving a research doctorate from almost all accredited U.S. institutions in a given academic year (Marburger 2005). The survey response rate exceeds 90%. At the end of survey data collection phase, institutional coordinators are contacted to obtain information on a small number of critical SED data items for nonrespondents from their institution. Therefore, the SED data files do contain all individual doctorate recipients. The years used for this report include academic years 2014 through 2017.

The SED collects information on the doctorate recipient's educational history, demographic characteristics, and postgraduation plans. Imputation is not performed for missing survey data items except for the month value used in calculation of the age at doctorate and time to degree variables. No direct question is asked on federal research funding—the level, source, or stability. The question that asks respondents to indicate whether each of the following was a source of financial support for your educational and living expenses during graduate school includes research assistantship as an option.

The SED includes information on the name and the year and month of birth for more than 41,000 respondents^{​These data are deidentified using a hashing algorithm, and access is restricted to authorized and indemnified individuals.} who received research doctorates from the subset of 21 active IRIS universities and who provided both personally identifiable information (PII) and at least 4 years of data prior to the close of the academic year 2017. The institution was used as a blocking variable.

The UMETRICS data comprises administrative records drawn directly from university sponsored projects, procurement, and human resources data systems that track direct cost expenditures at the level of monthly transactions for nearly 450,000 federal and nonfederal sponsored project grants. UMETRICS data include monthly records for more than 720,000 faculty, staff, students, and postdoctoral trainees employed on those grants. Currently, data are available for 75 campuses at 33 universities representing about 41% of total academic R&D spending in the United States (Chang et al. 2022); the time coverage varies across universities and ranges from 3 months to 18 years.

The core data elements for the purpose of this project are data on employees and on research grants (awards). The employee file provides information about the individuals working on research-related grants at IRIS member universities. The employee file has an observation for each employee on each award for every pay period they worked in that award. A pay period is typically 2 weeks but may vary by university. The employee file connects directly with the award file by the grant IDs. Although all individuals who charge time to federal or nonfederal research grants are included in the data, the unit of record is a payment to an individual on an award in a pay period. Thus, individuals routinely appear in multiple periods, on multiple awards. Although each employee should have only one entry per award–account number pairing per month, an employee could work on several awards concurrently or sequentially. An additional file containing the hashed PII for each person in the award file was provided and used for linkage. This file contained the unique ID for each person that linked to the core employee file. Twelve of the 33 IRIS member universities either did not provide PII, did not provide PII on students, or did not have sufficient coverage for linkage; therefore, this linkage was only performed on 21 of the 33 IRIS member universities.

The award file is the centerpiece of the UMETRICS data and directly connects to other files, thus making it a fundamental source for record linkages. This file contains transaction data on every sponsored project that has direct or overhead (commonly referred to as indirect) expenditures during the period covered in the file. This file can be considered an award profile in that types of sponsors can be identified through a federal grant indicator, Catalog of Federal Domestic Assistance number, funding source names, and grant administering suborganization units on campus. The file includes all funded awards that IRIS universities received during a given year. Awards include (but are not limited to) both federal and nonfederal research-related awards and nonresearch-related activities such as work-study programs.

The analytical file was constructed to maximize the potential for linkage. The earliest data available begins in 2001; however, the number of reporting universities is sparse in the earlier years, with fewer than half of the universities reporting before 2013. The greatest consistent coverage is for the academic years 2014–17. Only 21 active IRIS universities provide both PII data and have at least 4 years of available data prior to the close of the academic year 2017. As shown in image 3, each of the 21 universities being examined in this pilot project are represented during this time; 19 universities have full coverage, and 2 universities are missing only 1 year of data.

Because the focus of this project is to link UMETRICS data to SED data, it is useful to construct a measure of each individual’s occupation from the employee file. To construct this variable, a file containing the unique job names and descriptions was extracted from the files submitted from the institutions. An occupation classification was assigned to each job or job description (Ikudo et al. 2020). It is important to note that in order to protect the employee's privacy and confidentiality, this process was applied to the job description, and the data was later joined back with the individuals in the secure environment. This means that none of the personal information on work history was taken into account when assigning the occupation classification and is a possible source of classification noise.

As noted above, 21 institutions permitted the use of year of birth and name for linking purposes. The institution name was used as a blocking variable. Although technically only graduate students in UMETRICS should be linked to SED respondents, the noise in the occupational classification meant that it was not a reliable block key.