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The push to make computer science (CS) education available to all students has been closely followed by increased efforts to collect and report better data on where CS is offered, who is teaching CS, and which students have access to, enroll in, and ultimately benefit from learning CS. These efforts can be highly influential on the evolution of CS education policy, as education leaders and policymakers often rely heavily on data to make decisions. Because of this, it is critical that CS education researchers understand how to collect, analyze, and report data in ways that reflect reality without masking disparities between subpopulations. Similarly, it is important that CS education leaders and policymakers understand how to judiciously interpret the data and translate information into action to scale CS education in ways designed to eliminate inequities. To that end, this article expands on recent research regarding the use of data to assess and inform progress in scaling and broadening participation in CS education. We describe the CAPE framework for assessing equity with respect to the capacity for, access to, participation in, and experience of CS education and explicate how it can be applied to analyze and interpret data to inform policy decisions at multiple levels of educational systems. We provide examples using large, statewide datasets containing educational and demographic information for K-12 students and schools, thereby giving leaders and policymakers a roadmap to assess and address issues of equity in their own schools, districts, or states. We compare and contrast different approaches to measuring and reporting inequities and discuss how data can influence the future of CS education through its impact on policy. 

Prairie dogs (Cynomys spp.) are burrowing rodents considered to be ecosystem engineers and keystone species of the central grasslands of North America. Yet, prairie dog populations have declined by an estimated 98% throughout their historic range. This dramatic decline has resulted in the widespread loss of their important ecological role throughout this grassland system. The 92,060 ha Sevilleta NWR in central New Mexico includes more than 54,000 ha of native grassland. Gunnison's prairie dogs (C. gunnisoni) were reported to occupy ~15,000 ha of what is now the SNWR during the 1960's, prior to their systematic eradication. In 2010, we collaborated with local agencies and conservation organizations to restore the functional role of prairie dogs to the grassland system. Gunnison's prairie dogs were reintroduced to a site that was occupied by prairie dogs 40 years ago. This work is part of a larger, long-term study where we are studying the ecological effects of prairie dogs as they re-colonize the grassland ecosystem. 

Several long-term studies at the Sevilleta LTER measure net primary production (NPP) across ecosystems and treatments. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. The NPP weight data (SEV 157) is obtained by harvesting a series of covers for species observed during plot sampling. These species are always harvested from habitat comparable to the plots in which they were recorded. This data is then used to make volumetric measurements of species and build regressions correlating biomass and volume. From these calculations, seasonal biomass and seasonal and annual NPP are determined. These sampled are then vouchered for use to do analyses of inorganic and organic components such as carbon, nitrogen, and phosphorous as well as and other macro and micro nutrients and organic components such as cellulose and lignin. 

Sevilleta Long-Term Ecological Research Program has monitored plant species cover, height, abundance (counts), and biomass since 1999. This list represents the plant species found at the Sevilleta, including those species featured in long-term datasets on plant abundance and biomass. Species codes have been updated to the most recent taxonomic designations by the U.S. Department of Agriculture (plants.sc.egov.usda.gov), and are listed by their kartez codes, or character and number symbols. 

Patterns of plant biomass partitioning are fundamental to estimates of primary productivity and ecosystem process rates. Allometric relationships between aboveground plant biomass and non-destructive measures of plant size, such as cover, volume, or stem density are widely used in plant ecology. Such size-biomass allometry is often assumed to be invariant for a given plant species, plant functional group, or ecosystem type. Allometric adjustments may be an important component of the short- or long-term responses of plants to abiotic conditions. We used 18 years of size-biomass data describing 85 plant species to investigate the sensitivity of allometry to precipitation, temperature, or drought across two seasons and four ecosystems in central New Mexico, USA. Our results demonstrate that many plant species adjust patterns in the partitioning of aboveground biomass under different climates and highlight the importance of long-term data for understanding functional differences among plant species. 

Patterns of plant biomass partitioning are fundamental to estimates of primary productivity and ecosystem process rates. Allometric relationships between above‐ground plant biomass and non‐destructive measures of plant size, such as cover, volume or stem density are widely used in plant ecology. Such size‐biomass allometry is often assumed to be invariant for a given plant species, plant functional group or ecosystem type.

Allometric adjustment may be an important component of the short‐ or long‐term response of plants to abiotic conditions. We used 18 years of size‐biomass data describing of 85 plant species to investigate the sensitivity of allometry to precipitation, temperature or drought across two seasons and four ecosystems in central New Mexico, USA.

Size‐biomass allometry varied with climate in 65%–70% of plant species. Closely related plant species had similar sensitivities of allometry to natural spatiotemporal variation in precipitation, temperature or drought. Annuals were less sensitive than perennials, and forbs were less sensitive than grasses or shrubs. However, the differences associated with plant life history or functional group were not independent of plant evolutionary history, as supported by the application of phylogenetically independent contrasts.

Our results demonstrate that many plant species adjust patterns in the partitioning of above‐ground biomass under different climates and highlight the importance of long‐term data for understanding functional differences among plant species.

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