This dataset presents statistics from the Census of Governments, School System Finances conducted every five years (years ending in '2' and '7') and from the Annual Survey of School System Finances in intermediate years. These programs identify the scope and nature of the nation's state and local government education sector providing authoritative benchmark figures public elementary and secondary education finances, including revenues, expenditures, debt, and assets of school systems. Data are shown for the fiscal year for the given reference period.

This dataset presents statistics from the Census of Governments, Public Pensions conducted every five years (years ending in '2' and '7') and from the Annual Survey of Public Pensions in intermediate years. These programs identify the scope and nature of the nation's state and local government sector providing authoritative benchmark figures of state- and locally-administered defined benefit public pensions, including revenue, expenditures, financial assets, and membership information. Data are shown for the fiscal year for the given reference period.

This dataset presents statistics from the Census of Governments, State and Local Government Finances conducted every five years (years ending in '2' and '7') and from the Annual Survey of State and Local Government Finances in intermediate years. These programs identify the scope and nature of the nation's state and local government sector providing authoritative benchmark figures of public finance, including revenue, expenditures, and debt. Data are shown for the fiscal year for the given reference period.

This dataset presents statistics from the Census of Governments, Organization conducted every five years (years ending in '2' and '7') when available, and the Annual Government Organization counts beginning in 2025. This program identifies the scope and nature of the nation's government sector and provides official counts of U.S. governments. Data are shown for the fiscal year for the given reference period.

Numerical values of data points and figure generation scripts for manuscript "Sawtooth wave adiabatic passage in a grating magneto-optical trap" submitted for publication and available on ArXiv (2602.06230).

These data are associated with the paper “Quantifying rheology and microstructure of hydrating Portland-Limestone Cement blends to address additive manufacturing needs” by the same authors [1]. The data are associated with in situ dielectric rheological small-angle neutron scattering (rheo-SANS) studies of Portland limestone cement (PLC) blends and Ordinary Portland Cement (OPC) control [2]. The rheo-SANS data were collected on the NIST Center for Neutron Research (NCNR) VSANS instrument [3] as a function of cement hydration time.

We compare the calculated, quantum-based output of a radio-frequency Josephson arbitrary waveform synthesizer (RF-JAWS) with its measured output that is calibrated through the established NIST RF power traceability path, a necessary first step towards quantum-based RF metrology. An array of

This paper evaluates the short-term and long-term performance of a new type of dc secondary voltage standard at 10 V by direct comparison to a programmable Josephson voltage standard. Allan deviation measurements indicated a noise floor of 4.5 nV, enabling potential new applications in voltage metrology and possibly reducing uncertainty along the dissemination chain from primary calibration laboratories to calibration laboratories.

This publication contains the data presented in the associated proceeding paper ”Pushing the Envelope in Mechanical Characterization of Copper in Hybrid-Bonding Patterns for Advanced Packaging: from Instrumented Indentation to Multimodal Atomic Force Microscopy” submitted for publication in the proceedings of the 2026 IEEE 76th Electronic Components and Technology Conference.
The datasets were obtained in single-step and multi-step indentations using instrumented indentation (IIT) and atomic force microscope (AFM) on hybrid-bonding copper at room temperature.

SpherIndent is a modular, open-source software package with graphical user interface (GUI) for analysis of quasi-static spherical indentation experiments conducted in single-step or multi-step loading sequences. All components are developed in Python programming language with the GUI built on Tkinter. The software package is essentially constructed using libraries including NumPy, Pandas, SciPy, Sklearn, Matplotlib, and OpenCV. Data storage, from raw to processed and analyzed data (including figures) is handled via Pickle with traceability, efficient retrieval and sharing in mind.