The Neotoma Paleoecology Database is a community-curated data resource that supports interdisciplinary global change research by enabling broad-scale studies of taxon and community diversity, distributions, and dynamics during the large environmental changes of the past. By consolidating many kinds of data into a common repository, Neotoma lowers costs of paleodata management, makes paleoecological data openly available, and offers a high-quality, curated resource. Neotoma's distributed scientific governance model is flexible and scalable, with many open pathways for participation by new members, data contributors, stewards, and research communities. The Neotoma data model supports, or can be extended to support, any kind of paleoecological or paleoenvironmental data from sedimentary archives. Data additions to Neotoma are growing and now include >3.8 million observations, >17,000 datasets, and >9200 sites. Dataset types currently include fossil pollen, vertebrates, diatoms, ostracodes, macroinvertebrates, plant macrofossils, insects, testate amoebae, geochronological data, and the recently added organic biomarkers, stable isotopes, and specimen-level data. Multiple avenues exist to obtain Neotoma data, including the Explorer map-based interface, an application programming interface, the neotoma R package, and digital object identifiers. As the volume and variety of scientific data grow, community-curated data resources such as Neotoma have become foundational infrastructure for big data science.

A novel feature extracted from a nonlinear time series is presented within the context of vibration-based damage detection in a system. An eight-degree-of-freedom spring-mass-damper 'structure' is considered with damage incurred by a stiffness degradation in one spring. The system is excited with a chaotic input, and by tuning the Lyapunov exponents of the chaotic excitation to the dominant eigenvalue of the structure the dimensionality of the entire system is effectively controlled. Both the input and output are viewed in state space as geometric objects, and the effect of the damage is shown to alter the geometric properties of the corresponding attractors at a local level, which may be captured in construction of the feature. The utility of the feature is compared with that of a number of modal-based features and shown to be superior in resolving capability and in robustness.

A unified approach to attractor reconstruction Louis M. Pecora Code 6362, Naval Research Laboratory, Washington, D.C. 20375 Linda MonizDepartment of Mathematics, Trinity College, Washington, D.C. 20017 Jonathan Nichols Code 5673, Naval Research Laboratory, Washington, D.C. 20375 Thomas L. Carroll In the analysis of complex, nonlinear time series, scientists in a variety of disciplines have relied on a time delayed embedding of their data, i.e., attractor reconstruction. The process has focused primarily on intuitive, heuristic, and empirical arguments for selection of the key embedding parameters, delay and embedding dimension. This approach has left several longstanding, but common problems unresolved in which the standard approaches produce inferior results or give no guidance at all. We view the current reconstruction process as unnecessarily broken into separate problems. We propose an alternative approach that views the problem of choosing all embedding parameters as being one and the same problem addressable using a single statistical test formulated directly from the reconstruction theorems. This allows for varying time delays appropriate to the data and simultaneously helps decide on embedding dimension. A second new statistic, undersampling, acts as a check against overly long time delays and overly large embedding dimension. Our approach is more flexible than those currently used, but is more directly connected with the mathematical requirements of embedding. In addition, the statistics developed guide the user by allowing optimization and warning when embedding parameters are chosen beyond what the data can support. We demonstrate our approach on uni-and multivariate data, data possessing multiple time scales, and chaotic data. This unified approach resolves all the main issues in attractor reconstruction. © 2007 American Institute of Physics. ͓DOI: 10.1063/1.2430294͔What is the requirement for the determination of vector components for the reconstruction of an attractor from time series? This is a problem that has been studied for many years and everyone agrees that the problem consists of finding time delay, embedding dimension, and in the multivariate case which time series to use for each coordinate (although the latter is a much neglected problem). Most work has arbitrarily divided the problem into finding the delay and embedding dimension, separately. This division is the source of many problems. In addition, almost all approaches, and there are many, rely on weak heuristic methods or a choice of arbitrary scales (e.g., what constitutes a false neighbor) which are usually unknown. We view the construction of vectors for attractor reconstruction as a problem in finding a coordinate system to represent the dynamical state. What is mathematically necessary for any good coordinate system is that the coordinates be independent; this requirement is highlighted in Taken's theorem. To this end we develop a statistic to test for general, nonlinear functional dependence called the continuity s...

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