A comprehensive, community-curated database of integer sequences (e.g., 1, 1, 2, 3, 5, 8, …). Each sequence entry typically includes formulas, references, code, and links to related sequences.
Use this resource when you encounter a numerical pattern and want to identify it, find known properties, or see where it appears in the literature.
Useful for : Number theory, combinatorics, discrete mathematics, recreational math, and student projects exploring patterns.
The DLMF is the modern, online successor to Handbook of Mathematical Functions with Formulas, Graphs and Mathematical Tables by Abramowitz and Stegun. It provides definitions, identities, graphs, and tables for special functions (e.g., Bessel functions, gamma function, elliptic integrals) along with references and links to software.
Useful for : Advanced calculus, complex analysis, mathematical physics, and any work needing accurate numerical values or identities for special functions.
The LMFDB is a research-grade database of L-functions, modular forms, elliptic curves, number fields, and related objects. Entries include invariants, examples, and links between objects.
Useful For : Number theory and arithmetic geometry courses or projects; exploring concrete examples of objects that appear in advanced pure mathematics.
polyDB and related resources provide data on polytopes, matroids, and other discrete geometric structures.
Useful For : Graph theory, discrete geometry, extremal combinatorics, and constructing challenging examples or counterexamples for coursework and research.
The House of Graphs offers a curated collection of “interesting” graphs, including extremal examples and known counterexamples.
Useful For : Graph theory, discrete geometry, extremal combinatorics, and constructing challenging examples or counterexamples for coursework and research.
MacTutor History of Mathematics is on online project oirganized and sponsored by the University of St Andrews in Scotland. It contains biographical data, topics in the history of mathematics, and other mathematical topics.
Wolfram|Alpha a search engine that computes answers using Wolfram's breakthrough technology & knowledgebase, and is relied on by millions of students & professionals in disciplines like math, science, nutrition, history, geography, engineering, mathematics, linguistics, sports, finance, music and more.
The pi page Curiosities of and trivia about the number π.
A searchable database of mathematicians’ “academic family trees,” documenting advisor–student relationships, thesis titles, institutions, and degree years. Users can trace lineages back through multiple generations of advisors.
Good for: History of mathematics projects, department talks, and exploring the development of mathematical schools of thought.
The American Mathematical Society’s annual surveys collect detailed data on mathematics and statistics departments in the U.S. and Canada. Topics include employment, salaries, hiring, degrees awarded, and demographic distributions.
Good for: Institutional research, studies of diversity and hiring in mathematics, and student projects on the structure of the mathematical profession.
The SED is a national census of doctoral recipients from U.S. institutions. Public data tables include breakdowns by field (including mathematics and statistics), gender, race/ethnicity, citizenship, time-to-degree, and post-graduation plans.
Good for: Long-term trends in doctoral education, demographic studies in the mathematical sciences, and research on academic career pathways.
The National Assessment of Educational Progress (NAEP) is often called “The Nation’s Report Card.” The NAEP Data Explorer provides access to national and state-level mathematics achievement data for U.S. students (grades 4, 8, and 12) along with background variables.
Good for: U.S. math education research, equity and achievement gap studies, policy analysis, and statistics or data science assignments using real educational data.
TIMSS offers international assessment data on mathematics and science achievement at grades 4 and 8 across dozens of countries, with accompanying contextual data (curriculum, instructional practices, school characteristics). Public-use datasets are available for download.
Good for: Cross-national comparisons of math performance, studies of curriculum and teaching practices, and advanced methods courses needing rich multilevel data.
PISA assesses 15-year-olds’ competencies in reading, mathematics, and science every three years. The PISA datasets include student-level test scores plus extensive background information on students, families, and schools.
Good for: Research on mathematics literacy, socio-economic factors in education, international comparisons, and quantitative methods courses.
