Identifying rates at which birders engage with different species can inform the impact and efficacy of conservation outreach scientific use community-collected biodiversity data. Species that are thought to be “charismatic” often prioritized in conservation, previous researchers have used sociological experiments digital records estimate charisma indirectly. In this study, we take advantage community science efforts as another record human engagement animals reveal observer biases directly,...

Participatory science (or ‘citizen science') records are becoming increasingly useful for wildlife monitoring due to their volume and spatiotemporal coverage. Statistical analysis of these data can be challenging the many sources sampling heterogeneity that need accounted for. Many previous studies characterize variability across entire participatory datasets, such as spatial in effort or species preferences. User‐level behavior is less well studied, but it may just important dataset‐level...

Traditional data science education often omits training on research workflows: the process that moves a scientific investigation from raw to coherent question insightful contribution. In this paper, we elaborate basic principles of reproducible analysis workflow by defining three phases: Exploratory, Refinement, and Polishing Phases. Each phase is roughly centered around audience whom decisions, methodologies, results are being immediately communicated. Importantly, each can also give rise...

The Consultative Committee for Mass and related quantities (ccm), of the International weights measures (cipm), has recently declared readiness community to support redefinition international system units (SI) at next meeting General Conference on Weights Measures (cgpm) scheduled November, 2018.

Hierarchical statistical models are important in applied sciences because they capture complex relationships data, especially when variables related by space, time, sampling unit, or other shared features. Existing methods for maximum likelihood estimation that rely on Monte Carlo integration over latent variables, such as Expectation Maximization (MCEM), suffer from drawbacks efficiency and/or generality. We harness a connection between sampling-stepping iterations and stochastic gradient...

An errors-in-variables regression method is presented as an alternative to the ordinary least-squares computation currently employed for determining calibration function force measuring instruments from data acquired during calibration. A Monte Carlo uncertainty evaluation also presented. The corresponding (which we call measurement function, often called analysis in gas metrology) necessary subsequent use of calibrated device measure force, and associated evaluation, are derived results....

What are the challenges and best practices for doing data-intensive research in teams, labs, other groups? This paper reports from a discussion which researchers many different disciplines departments shared their experiences on data science domains. The issues we discuss range technical to social, including with getting same computational stack, workflow pipeline management, handoffs, composing well-balanced team, dealing fluid membership, fostering coordination communication, not...

Abstract Understanding variation in species occupancy is an important task for conservation. When assessing patterns over multiple temporal seasons, it recommended to visit at least a subset of sites times within season during period closure account observation biases. However, logistical constraints can inhibit re‐visitation season, resulting the use single‐visit multi‐season models. Some have suggested that autocorrelation space and/or time provide “fractional replication” separately...

What actions can we take to foster diverse and inclusive workplaces in the broad fields around data science? This paper reports from a discussion which researchers many different disciplines departments raised questions shared their experiences with various aspects diversity, inclusion, equity. The issues discuss include fostering interpersonal small group dynamics, rules codes of conduct, increasing diversity less-representative groups disciplines, organizing events for long-term efforts...

The National Academies report, "Reproducibility and Replicability in Science," outlines values broad goals for reproducible replicable scientific research, emphasizing the importance of its availability transparency. Confronted with these goals, we examine what it means research material to be transparent, available, reproducible. As early-career researchers, advocate focus placed on reusable extensible which is naturally achieved when reproducible, open. When understand inner workings a...

Abstract Small datasets comprising observations made under conditions of repeatability or reproducibility pervade the practice measurement science. Many laboratories typically will make only one determination, occasionally they two, and rarely three more replicate determinations same measurand. Interlaboratory comparisons, including key meta-analyses, often involve a handful participants. These limitations pose considerable challenges to production reliable uncertainty evaluations. This...

A bstract Identifying which species are perceived as charismatic can improve the impact and efficiency of conservation outreach, receive more funding have their needs prioritized (9; 17; 13). Sociological experiments studying animal charisma relied on stated preferences to find correlations between hypothetical “willingness pay” or “empathy” for a species’ size, color, aesthetic appeal (51; 13; 16). Recognizing increasing availability digital records public engagement with animals that...

Data scientists rely on many technical skills and the ability to reason about data solve problems.As educators grapple with how prepare students in this new field, they are faced identifying both what a student must know should be able do by end of their science education, also collect evidence those abilities.We present way unite coordinate individual efforts toward training well-rounded scientists: portfolio that highlights strong communication.This structuring classroom assignments...

In 2022, the Journal of Statistics and Data Science Education (JSDSE) instituted augmented requirements for authors to post deidentified data code underlying their papers. These changes were prompted by an increased focus on reproducibility open science (NASEM 2019). A recent review availability practices noted that "such policies help increase published literature, as well make a larger body available reuse re-analysis" (PLOS ONE, 2024). JSDSE values accessibility it endeavors share...

Participatory science (or "citizen science") records are becoming increasingly useful for wildlife monitoring due to their volume and spatiotemporal coverage. However, statistical analysis using these data can be challenging the many sources of bias that need corrected. Many previous studies characterize sampling biases across entire participatory datasets, such as spatial heterogeneity in effort or species preferences. User-level behavior is less well studied, but it may just important...

Abstract Deborah Nolan and Sara Stoudt present a framework for learning the art of statistical storytelling

Since coverage intervals are widely used expressions of measurement uncertainty, this contribution reviews as defned in the Guide to Expression Uncertainty Measurement (GUM), and compares them against principal types probabilistic that commonly applied statistics science. Although formally identical conventional confdence for means, GUM interprets more if they were Bayesian credible intervals, or tolerance intervals. We focus, particular, on a common misunderstanding about derived from...

Turnover is a fact of life for any project, and academic research teams can face particularly high levels people who come go through the duration project. In this article, we discuss challenges turnover some potential practices helping manage it, computational- data-intensive projects. The topics include establishing implementing data management plans, file format standardization, workflow process documentation, clear team roles, check-in check-out procedures.

A traffic generation model is a stochastic of the data flow in communication network. These models are useful during development telecommunication technologies and for analyzing performance capacity various protocols,algorithms, network topologies. We present here two modeling approaches simulating internet traffic. In our models, we simulate length interarrival times individual packets, discrete unit transfer over internet. Our first approach based on fitting to known theoretical...