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Register a new userThe SpeX Prism Library Analysis Toolkit (SPLAT): A Data Curation Model
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I describe our teams development of the SpeX Prism Library Analysis Toolkit (SPLAT), a combined spectral data repository for over 2500 low-resolution spectra of very low mass dwarfs (late M, L and T dwarfs), and Python-based analysis toolkit. SPLAT was constructed through a collaborative, student-centered, research-based model with high school, undergraduate and graduate students and regional K-12 science teachers. The toolkit enables spectral index analysis, classification, spectrophotometry, atmosphere model comparison, population synthesis, and other analyses. I summarize the current components of this code, sample applications, and future development plans.
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Norm-conserving pseudopotentials are used by a significant number of electronic-structure packages, but the practical differences among codes in the handling of the associated data hinder their interoperability and make it difficult to compare their results. At the same time, existing formats lack provenance data, which makes it difficult to track and document computational workflows. To address these problems, we first propose a file format (PSML) that maps the basic concepts of the norm-conserving pseudopotential domain in a flexible form and supports the inclusion of provenance information and other important metadata. Second, we provide a software library (libPSML) that can be used by electronic structure codes to transparently extract the information in the file and adapt it to their own data structures, or to create converters for other formats. Support for the new file format has been already implemented in several pseudopotential generator programs (including ATOM and ONCVPSP), and the library has been linked with Siesta and Abinit, allowing them to work with the same pseudopotential operator (with the same local part and fully non-local projectors) thus easing the comparison of their results for the structural and electronic properties, as shown for several example systems. This methodology can be easily transferred to any other package that uses norm-conserving pseudopotentials, and offers a proof-of-concept for a general approach to interoperability.
In high-energy physics, with the search for ever smaller signals in ever larger data sets, it has become essential to extract a maximum of the available information from the data. Multivariate classification methods based on machine learning techniques have become a fundamental ingredient to most analyses. Also the multivariate classifiers themselves have significantly evolved in recent years. Statisticians have found new ways to tune and to combine classifiers to further gain in performance. Integrated into the analysis framework ROOT, TMVA is a toolkit which hosts a large variety of multivariate classification algorithms. Training, testing, performance evaluation and application of all available classifiers is carried out simultaneously via user-friendly interfaces. With version 4, TMVA has been extended to multivariate regression of a real-valued target vector. Regression is invoked through the same user interfaces as classification. TMVA 4 also features more flexible data handling allowing one to arbitrarily form combined MVA methods. A generalised boosting method is the first realisation benefiting from the new framework.
SPLAT-VO is a powerful graphical tool for displaying, comparing, modifying and analyzing astronomical spectra, as well as searching and retrieving spectra from services around the world using Virtual Observatory (VO) protocols and services. The development of SPLAT-VO started in 1999, as part of the Starlink StarJava initiative, sometime before that of the VO, so initial support for the VO was necessarily added once VO standards and services became available. Further developments were supported by the Joint Astronomy Centre, Hawaii until 2009. Since end of 2011 development of SPLAT-VO has been continued by the German Astrophysical Virtual Observatory, and the Astronomical Institute of the Academy of Sciences of the Czech Republic. From this time several new features have been added, including support for the latest VO protocols, along with new visualization and spectra storing capabilities. This paper presents the history of SPLAT-VO, its capabilities, recent additions and future plans, as well as a discussion on the motivations and lessons learned up to now.
We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The librarys ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at https://github.com/giotto-ai/giotto-tda.
Gravitational waves are minute ripples in spacetime, first predicted by Einsteins general theory of relativity in 1916. Gravitational waves from rapidly-rotating neutron stars, whose shape deviates from perfect axisymmetry, are a potential astrophysical source of gravitational waves, but which so far have not been detected. The search for this type of signals, also known as continuous waves, presents a significant data analysis challenge, as their weak signatures are expected to be buried deep within the instrumental noise of the LIGO and Virgo detectors. The OctApps library provides various functions, written in Octave, intended to aid research scientists who perform searches for continuous gravitational waves.
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