#' Mechanical, Civil, and Electrical Engineering Curricula Networks
#'
#' A collection of curricular networks for three engineering disciplines across
#' multiple institutions in the United States. Each network is an
#' 'igraph' object representing courses as nodes and prerequisite/corequisite
#' relationships as directed edges.
#'
#' #' @section Author:
#' Olivia Ryan (Virginia Tech)
#'
#' @details
#' Context: These curricula are from 48 of the 50 institutions that awarded the
#' most engineering bachelor’s degrees in 2023, based on ASEE By the Numbers,
#' plus five additional smaller institutions used as counter cases. This data
#' was collected as part of Olivia Ryan’s dissertation.
#'
#' Sampling: Curricular plans were collected from 53 total institutions for
#' Civil, Electrical, and Mechanical engineering programs (n = 155). The
#' curriculum plans in the dataset include information from the 2023, 2024, or
#' 2025 catalog year, depending on which plan was most recently available. Many
#' curriculum plans included upper-level electives or choices throughout the
#' curriculum, which needed to be accounted for to represent an accurate plan of
#' study. A set of rules was applied to determine which electives to include.
#' First, curriculum plan requirements were used to determine whether electives
#' needed to be distributed across specialty areas, and courses were selected to
#' meet those requirements. Next, electives were chosen to align with common job
#' sectors in the field. Within these specialties, electives with the fewest or
#' most common prerequisites were selected to approximate a typical student
#' pathway. To maintain flexibility, two to three electives were left without
#' prerequisites, reflecting options that allow students to take courses in other
#' disciplines.
#'
#' Data structure: The dataset is a list of 155 'igraph' objects. Each object
#' corresponds to a plan of study for a specific discipline, institution, and
#' year. Vertices represent courses with attributes course_name,
#' "course_code", "term", and "credits". Edges represent
#' prerequisite and corequisite relationships with an attribute "type" of
#' "prereq" or "coreq"
#'
#' Limitations: The dataset focuses primarily on large, research-intensive
#' institutions across three engineering disciplines. Users should exercise
#' caution when extrapolating beyond this scope. Although a systematic process
#' was used to determine upper-level electives, selections may not be perfectly
#' consistent across programs and institutions.
#'
#' @format A list of 155 'igraph' objects.
#'
#' @source Collected from institutional websites, Summer 2025.
#'
#' @docType data
#' @keywords datasets
"ThreeEngineeringDisciplines_Ryan"
