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PyMVPA

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"PyMVPA is a Python module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known as decoding or MVPA analysis. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run."

Gwyddion

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Gwyddion is a modular program for SPM (scanning probe microscopy) data visualization and analysis. Primarily it is intended for analysis of height fields obtained by scanning probe microscopy techniques (AFM, MFM, STM, SNOM/NSOM), however it can be generally used for any other height field and image analysis, for instance for analysis of profilometry data.

It aims to provide multiplatform modular program for 2D data analysis that could be easily extended by modules and plug-ins.

EGADSS

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EGADSS (Evidence-based Guideline and Decision Support System) is an open source tool that is designed to work in conjunction with primary care Electronic Medical Record (EMR) systems to provide patient specific point of care reminders in order to aid physicians provide high quality care. EGADSS is designed as a stand alone system that would respond to requests from existing Electronic Medical Records such as Wolf, Med Access, and MedOffIS to provide patient specific clinical guidance based on its internal collection of guidelines.

OBsched

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Occupancy in certain hospital patient care units is impacted by procedure scheduling policies and practices. For example, intensive care unit occupancy is strongly related to open heart surgery schedules. Similarly, occupancy in obstetrical postpartum units is impacted by the daily number of scheduled labor inductions and cesarean sections. That was the motivation for this project.

OBsched is a set of optimization models and supporting software for exploring the relationship between patient scheduling and nursing unit occupancy in hospitals.

PyEEG

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A Python function library to extract EEG feature from EEG time series in standard Python and numpy data structure. Features include classical spectral analysis, entropies, fractal dimensions, DFA, inter-channel synchrony and order, etc.

WEKA

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Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Brainstorm

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Brainstorm is a collaborative open-source Matlab application dedicated to magnetoencephalography (MEG) and electroencephalography(EEG) data visualization, processing and cortical source estimation.
The intention is to make a comprehensive set of tools available to the scientific community involved in MEG/EEG experimental research.
For physicians and researchers, the interest of this software package resides in its rich and intuitive graphic interface, which does not require any programming knowledge.

MassChroQ

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MassChroQ (Mass Chromatogram Quantification) software performs quantification of data obtained from mass-spectrometry techniques. It is particularly well suited for peptide quantification of LC-MS (Liquid Chromatography - Mass Spectrometry) data. It performs chromatographic alignment, XIC extraction, peak detection and quantification on identified peptides, with or without isotopic labeling, on high or low resolution data and it takes into account peptide or protein fractionation.

Ogles2

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Ogles2 is an interactive slice and volume visualization and analysis tool based on Open Inventor / Coin3D. Ogles2 allows for reproducing the workflow of frame based stereotactic neurosurgery. In the long run it strives for being an open source stereotactic planning and analysis system. Ogles2 is NOT APPROVED FOR CLINICAL USE.

ParaView

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ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView's batch processing capabilities.

ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of terascale as well as on laptops for smaller data.