As data science emerges as a distinct academic discipline, introductory data science (IDS) courses play a key role in shaping students foundational understanding. Often taught by instructors without formal training in data science or pedagogy, these courses present a unique and globally relevant context for examining pedagogical content knowledge (PCK). Drawing on semi-structured interviews with 14 IDS instructors and their course syllabi, this study explores how IDS instructors describe and make sense of their teaching practices, which are analyzed through the lens of PCK. The findings highlight key components of PCK about IDS and offer insights into supporting instructor development. This work contributes to expanding the scope of PCK research into new interdisciplinary domains and ongoing global efforts to build capacity in data science education. It could serve as a starting point for developing a PCK framework specific to IDS.
This study proposes and operationalizes a new typology based on the relative strength of the regional main center (CBD) and subcenters to classify and track changes in urban spatial structure across around 360 metropolitan areas in the U.S. from 2010 to 2020. Using employment and population data, we first applied spatial statistical techniques to identify CBDs and subcenters across metropolitan areas. We then developed composite CBD and subcenter indices and classified metropolitan areas into four categories for 2010 and 2020: monocentric, balanced polycentric, subcenter-led polycentric, and dispersed. The results show that in 2020, subcenter-led polycentric metropolitan areas emerged as the dominant urban spatial structure, accounting for approximately 43% of all cases. The most notable transition between 2010 and 2020 occurred among balanced polycentric MSAs. Of the 48 metropolitan areas in this category, 20 shifted to a monocentric structure, 8 transitioned to a subcenter-led form, and one transitioned to a dispersed form. Overall, the proposed empirical classification framework provides a replicable approach for systematically classifying and tracking changes in metropolitan spatial structure over time. Also, this framework can support future research on urban form, transportation, and regional development by enabling researchers to systematically evaluate how changes in CBDs and subcenters influence metropolitan spatial structure over time.
Abstract Soil liquefaction is a central concern in geotechnical engineering because of its potential to damage infrastructure. Current liquefaction triggering practice relies on semiempirical procedures calibrated from earthquake case histories and surface manifestation labels, which can be biased and unevenly distributed across seismologic, geologic, and geomorphic settings. This paper develops an alternative probabilistic triggering framework anchored in laboratory testing, formulated in the state parameter space, and evaluated using independent piezocone penetration test (CPTu) case-history databases. Cyclic resistance is calibrated across 20 sands using cyclic and monotonic triaxial test data through a hierarchical Bayesian regression that separates within-material dispersion from between-material heterogeneity and propagates specimen-level uncertainty. A CPTu-based procedure is implemented to compute distributions of factor of safety F S and probability of liquefaction P L via Monte Carlo uncertainty propagation, jointly accounting for capacity, demand, and state parameter estimation. Receiver operating characteristic–based evaluation indicates discrimination comparable to, and in some cases exceeding, widely used CPTu procedures. A single-material calibration example shows that incorporating site-representative laboratory data can reduce cyclic resistance uncertainty by an average of 80%. This supports a workflow in which population-level predictions are useful for screening, while material-specific calibration is recommended for critical projects.
BACKGROUND:Dog-assisted interventions (DAIs) to improve human health and wellbeing are increasingly implemented in health, care and educational settings. However, the quality and consistency of reporting of trial design and findings remains poor. Current trial reporting guidelines do not adequately address the unique methodological, ethical, and welfare considerations involved in DAI randomised controlled trials (RCTs). To improve the design and transparent reporting of these trials, we developed SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2025 and CONSORT (Consolidated Standards of Reporting Trials) 2025 extensions for DAI trials. The current paper presents the explanation and elaboration (E&E) for these extensions. METHODS:Using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework, a four-phase consensus approach was conducted: (1) a systematic review; (2) three-round Delphi survey; (3) expert consensus workshops, and (4) checklist finalisation. This E&E paper complements the DAI extensions checklist (reported elsewhere) by providing item-by-item rationales, guidance, and illustrative examples from DAI trials to demonstrate good reporting. RESULTS:The four-phase consensus approach resulted in 69 extensions for DAI RCT reporting (35 applied to SPIRIT; 34 applied to CONSORT). This paper explains the purpose of each extension item and highlights best practice for reporting, based on examples from published DAI protocols and trial reports. CONCLUSION:These DAI extensions provide guidance for the design and reporting of high-quality and transparent DAI RCTs. This E&E paper supports the application of these extensions by offering detailed explanations and examples. Adoption by researchers, journal editors, funders, and ethics committees may enhance the credibility and impact of DAI research.
Although above ground biomass (AGB) inventory provides critical information for sustainable ecosystem management, credible metrics assaying AGB remain a significant challenge. Projecting AGB dynamics, can be enhanced by understanding technological processes and products, thereby isolating the “myths from reality”. Generally, “myths” tacitly presuppose map aptness, whereas “reality” create awareness to proceed with caution by exhibiting imperfections that not only enhance knowledge but create new opportunities for innovation on remedial strategies. Other than data issues (i.e., complexity, heterogeneity, scarcity, accessibility), factors influencing scientific integrity and accuracy in AGB quantification include efficacy of measurement tools, robustness of algorithm, and systemic error in generalized map products smoothened after mosaicking. Here, AGB quantification at variable scales particularly for heterogeneous landscapes are explicated, and limitations of generalization exposed. Consideration is given to the mixed pixels challenge in digital imagery with emphasis on related technology flaws that exacerbate misclassification. The effects of heterogeneity in ground dataset and contribution to product accuracy are explicated. A case study of an unadulterated detailed AGB map output from multi-platform datasets is previewed to show real map aspect prior to generalization which is ordinarily subjective. Future opportunities are advanced for transferable metrics and models that incorporate a minimum data set of key covariates and fusion of multi-platform datasets.
