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Integrated platform and API for electrophysiological data  [PDF]
Andrey Sobolev,Adrian Stoewer,Aljoscha Leonhardt,Philipp L. Rautenberg,Christian J. Kellner,Christian Garbers,Thomas Wachtler
Frontiers in Neuroinformatics , 2014, DOI: 10.3389/fninf.2014.00032
Abstract: Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information. In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines.
Use of Python in data manipulation and interfacing spreadsheets (Excel)  [cached]
The Python Papers Monograph , 2010,
Abstract: Examines the versatility and strength of python versus spreadsheets, the traditional tool for data crunching. And show how python outdo spreadsheets through its simple and convenient syntax.
Spyke Viewer: a flexible and extensible platform for electrophysiological data analysis  [PDF]
Robert Pr?pper,Klaus Obermayer
Frontiers in Neuroinformatics , 2013, DOI: 10.3389/fninf.2013.00026
Abstract: Spyke Viewer is an open source application designed to help researchers analyze data from electrophysiological recordings or neural simulations. It provides a graphical data browser and supports finding and selecting relevant subsets of the data. Users can interact with the selected data using an integrated Python console or plugins. Spyke Viewer includes plugins for several common visualizations and allows users to easily extend the program by writing their own plugins. New plugins are automatically integrated with the graphical interface. Additional plugins can be downloaded and shared on a dedicated website.
Using Python to Dive into Signalling Data with CellNOpt and BioServices  [PDF]
Thomas Cokelaer,Julio Saez-Rodriguez
Computer Science , 2014,
Abstract: Systems biology is an inter-disciplinary field that studies systems of biological components at different scales, which may be molecules, cells or entire organism. In particular, systems biology methods are applied to understand functional deregulations within human cells (e.g., cancers). In this context, we present several python packages linked to CellNOptR (R package), which is used to build predictive logic models of signalling networks by training networks (derived from literature) to signalling (phospho-proteomic) data. The first package (cellnopt.wrapper) is a wrapper based on RPY2 that allows a full access to CellNOptR functionalities within Python. The second one (cellnopt.core) was designed to ease the manipulation and visualisation of data structures used in CellNOptR, which was achieved by using Pandas, NetworkX and matplotlib. Systems biology also makes extensive use of web resources and services. We will give an overview and status of BioServices, which allows one to access programmatically to web resources used in life science and how it can be combined with CellNOptR.
Python Data Plotting and Visualisation Extravaganza  [cached]
The Python Papers Monograph , 2009,
Abstract: This paper tries to dive into certain aspects of graphical visualisation of data. Specifically it focuses on the plotting of (multi-dimensional) data using 2D and 3D tools, which can update plots at run-time of an application producing or acquiring new or updated data during its run time. Other visualisation tools for example for graph visualisation, post computation rendering and interactive visual data exploration are intentionally left out.
seismic-py: Reading seismic data with Python  [cached]
The Python Papers , 2008,
Abstract: The field of seismic exploration of the Earth has changed dramatically over the last half a century. The Society of Exploration Geophysicists (SEG) has worked to create standards to store the vast amounts of seismic data in a way that will be portable across computer architectures. However, it has been impossible to predict the needs of the immense range of seismic data acquisition systems. As a result, vendors have had to bend the rules to accommodate the needs of new instruments and experiment types. For low level access to seismic data, there is need for a standard open source library to allow access to a wide range of vendor data files that can handle all of the variations. A new seismic software package, seismic-py, provides an infrastructure for creating and managing drivers for each particular format. Drivers can be derived from one of the known formats and altered to handle any slight variations. Alternatively drivers can be developed from scratch for formats that are very different from any previously defined format. Python has been the key to making driver development easy and efficient to implement. The goal of seismic-py is to be the base system that will power a wide range of experimentation with seismic data and at the same time provide clear documentation for the historical record of seismic data formats.
Experiences in Building Python Automation Framework for Verification and Data Collections  [cached]
The Python Papers Monograph , 2010,
Abstract: This paper describes our experiences in building a Python automation framework. Specifically, the automation framework is used to support verification and data collection scripts. The scripts control various test equipments in addition to the device under test (DUT) to characterize a specific performance with a specific configuration or to evaluate the correctness of the behaviour of the DUT. The specific focus on this paper is on documenting our experiences in building an automation framework using Python: on the purposes, goals and the benefits, rather than on a tutorial of how to build such a framework.
Experiences in Building Python Automation Framework for Verification and Data Collections  [cached]
The Python Papers , 2010,
Abstract: This paper describes our experiences in building a Python automation framework. Specifically, the automation framework is used to support verification and data collection scripts. The scripts control various test equipments in addition to the device under test (DUT) to characterize a specific performance with a specific configuration or to evaluate the correctness of the behaviour of the DUT. The specific focus on this paper is on documenting our experiences in building an automation framework using Python: on the purposes, goals and the benefits, rather than on a tutorial of how to build such a framework.
Quantifying periodicity in omics data  [PDF]
Masaru Tomita,Douglas B. Murray
Frontiers in Cell and Developmental Biology , 2014, DOI: 10.3389/fcell.2014.00040
Abstract: Oscillations play a significant role in biological systems, with many examples in the fast, ultradian, circadian, circalunar, and yearly time domains. However, determining periodicity in such data can be problematic. There are a number of computational methods to identify the periodic components in large datasets, such as signal-to-noise based Fourier decomposition, Fisher's g-test and autocorrelation. However, the available methods assume a sinusoidal model and do not attempt to quantify the waveform shape and the presence of multiple periodicities, which provide vital clues in determining the underlying dynamics. Here, we developed a Fourier based measure that generates a de-noised waveform from multiple significant frequencies. This waveform is then correlated with the raw data from the respiratory oscillation found in yeast, to provide oscillation statistics including waveform metrics and multi-periods. The method is compared and contrasted to commonly used statistics. Moreover, we show the utility of the program in the analysis of noisy datasets and other high-throughput analyses, such as metabolomics and flow cytometry, respectively.
Basic Data Analysis and More - A Guided Tour Using Python  [PDF]
O. Melchert
Physics , 2012,
Abstract: In these lecture notes, a selection of frequently required statistical tools will be introduced and illustrated. They allow to post-process data that stem from, e.g., large-scale numerical simulations (aka sequence of random experiments). From a point of view of data analysis, the concepts and techniques introduced here are of general interest and are, at best, employed by computational aid. Consequently, an exemplary implementation of the presented techniques using the Python programming language is provided. The contents of these lecture notes is rather selective and represents a computational experimentalist's view on the subject of basic data analysis, ranging from the simple computation of moments for distributions of random variables to more involved topics such as hierarchical cluster analysis and the parallelization of Python code.
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