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http://csdms.colorado.edu/wiki/Main_Page
Model repository and data related to earth-surface dynamics modeling. The CSDMS Modeling Tool (CMT) allows you to run and couple CSDMS model components on the CSDMS supercomputer in a user-friendly software environment. Components in the CMT are based on models, originally submitted to the CSDMS model repository, and now adapted to communicate with other models. The CMT tool is the environment in which you can link these components together to run new simulations. The CMT software runs on your own computer; but it communicates with the CSDMS HPCC, to perform the simulations. Thus, the CMT also offers you a relatively easy way of using the CSDMS supercomputer for model experiments. CSDMS deals with the Earth's surface - the ever-changing, dynamic interface between lithosphere, hydrosphere, cryosphere, and atmosphere. They are a diverse community of experts promoting the modeling of earth surface processes by developing, supporting, and disseminating integrated software modules that predict the movement of fluids, and the flux (production, erosion, transport, and deposition) of sediment and solutes in landscapes and their sedimentary basins. CSDMS: * Produces protocols for community-generated, continuously evolving, open software * Distributes software tools and models * Provides cyber-infrastructure to promote the quantitative modeling of earth surface processes * Addresses the challenging problems of surface-dynamic systems: self-organization, localization, thresholds, strong linkages, scale invariance, and interwoven biology & geochemistry * Enables the rapid development and application of linked dynamic models tailored to specific landscape basin evolution (LBE) problems at specific temporal and spatial scales * Partners with related computational and scientific programs to eliminate duplication of effort and to provide an intellectually stimulating environment * Supports a strong linkage between what is predicted by CSDMS codes and what is observed, both in nature and in physical experiments * Supports the imperatives in Earth Science research
Proper citation: Community Surface Dynamics Modeling System (RRID:SCR_002196) Copy
Paleoecology database for plio-pleistocene to holocene fossil data with a centralized structure for interdisciplinary, multiproxy analyses and common tool development; discipline-specific data can also be easily accessed. Data currently include North American Pollen (NAPD) and fossil mammals (FAUNMAP). Other proxies (plant macrofossils, beetles, ostracodes, diatoms, etc.) and geographic areas (Europe, Latin America, etc.) will be added in the near future. Data are derived from sites from the last 5 million years.
Proper citation: Neotoma Paleoecology Database (RRID:SCR_002190) Copy
Privately held company that develops and produces antibodies, ELISA kits, ChIP kits, proteomic kits, and other related reagents used to study cell signaling pathways that impact human health.
Proper citation: Cell Signaling Technology (RRID:SCR_002071) Copy
http://ncmir.ucsd.edu/downloads/jinx.shtm
Jinx was developed to aid in the 3D reconstruction of tomographic datasets acquired with one of the various electron microscopes available at the resource. Tomographic datasets consist of a series of 2D images from which objects of interest are segmented out for the 3D reconstruction. Jinx offers the user a graphical interface to step through each image of the series and facilities to manually trace out objects of interest. It relies on JadeDisplay to support the display of large images with graphical overlays and the JAI libraries for histogram functionality and other types of image manipulations. Jinx is currently under active development and future releases will offer semi-automated segmentation algorithms based on fuzzy logic, level set, and watershed algorithm. This software is free; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or any later version. See the GNU General Public License for more details. For a copy of the GNU General Public License, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA. Sponsors: Jinx presented here was produced at the National Center for Microscopy and Imaging Research at San Diego, which is supported by the National Institutes of Health (NIH) through a National Center for Research Resources program grant P41 RR04050. open source license, GNU general public license
Proper citation: National Center for Microscopy and Imaging Research: Jinx (RRID:SCR_001939) Copy
https://neurograd.ucsd.edu/handbook/prog-requirement/comp-neuro/index.html
The Computational Neuroscience specialization is a new facet of the broader Neuroscience graduate program at UCSD. The goal of the specialization is to train the next generation of neuroscientists with the broad range of computational and analytical skills that are essential to understand the organization and function of complex neural systems. The specialization is intended for students with backgrounds in neuroscience, physics, chemistry, biology, psychology, computer science, engineering, and mathematics. This specialization allows Neuroscience students to concentrate on a focused program of rigorous course work in both the theoretical and experimental aspects of computational neuroscience. Students are encouraged to pursue thesis research that includes both an experimental and a computational component, often arranged by the student as a collaboration between two research groups. The program is focused on these major themes relevant for computational neuroscience research: - Neurobiology of Neural Systems - the anatomy, physiology, and behavior of systems of neurons, with emphasis on basic phenomenology. - Advanced Measurement Tools in Neuroscience - Advanced imaging and recording techniques reflecting the impact of experimental physics on neuroscience. - Algorithms for the Analysis of Neural Data - New algorithms and techniques for analyzing data obtained from physiological recording - Theoretical Basis for Collective Neural Dynamics - A synthesis of approaches from mathematics and physical sciences as well as biology will be used to explore the collective properties and nonlinear dynamics of neuronal systems. Sponsors: This program is supported by the University of California at San Diego.
Proper citation: University of California at San Diego Computational Neuroscience (RRID:SCR_001930) Copy
Original SAMTOOLS package has been split into three separate repositories including Samtools, BCFtools and HTSlib. Samtools for manipulating next generation sequencing data used for reading, writing, editing, indexing,viewing nucleotide alignments in SAM,BAM,CRAM format. BCFtools used for reading, writing BCF2,VCF, gVCF files and calling, filtering, summarising SNP and short indel sequence variants. HTSlib used for reading, writing high throughput sequencing data.
Proper citation: SAMTOOLS (RRID:SCR_002105) Copy
https://github.com/fhcrc/seqmagick/
Software application to expose file format conversion in BioPython in convenient way. Imagemagick like frontend to Biopython SeqIO.
Proper citation: seqmagick (RRID:SCR_024331) Copy
https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html
Software application for linear support vector classification. Used in classification problems.
Proper citation: LinearSVC (RRID:SCR_024741) Copy
https://github.com/CahanLab/singleCellNet
Software tool to classify single cell RNA-Seq data across platforms and across species.
Proper citation: SingleCellNet (RRID:SCR_024742) Copy
https://github.com/jkbonfield/htscodecs/
Software repository implements the custom CRAM codecs used for "EXTERNAL" block types.Custom compression for CRAM custom algorithm written to compress the BAM file format for DNA sequencing data.
Proper citation: Htscodecs (RRID:SCR_024034) Copy
https://github.com/SlicerMorph/SlicerMorph
Open and extensible platform to retrieve, visualize and analyse 3D morphology.Extension to import microCT data and conduct 3D morphometrics in Slicer. Used for data import, visualization, measurement, annotation, and geometric morphometric analysis on 3D data, including volumetric scans (CTs and MRs) and 3D surface scans, all within the 3D Slicer application.
Proper citation: SlicerMorph (RRID:SCR_024674) Copy
Software interface to use R from Python.
Proper citation: rpy2 (RRID:SCR_024701) Copy
http://www.cbcb.umd.edu/software/ELPH/index.shtml
Software tool as general purpose Gibbs sampler for finding motifs in set of DNA or protein sequences.Takes as input a set containing sequences, and searches through them for the most common motif, assuming that each sequence contains one copy of the motif. Used to find patterns such as ribosome binding sites (RBSs) and exon splicing enhancers (ESEs).
Proper citation: ELPH (RRID:SCR_024011) Copy
https://gitlab.com/tjobbertjob/ms-review-paper
Variability analysis of proteomics data used for deep learning.
Proper citation: ms-variability-analysis (RRID:SCR_024531) Copy
https://github.com/PacificBiosciences/unanimity
Software to generate highly accurate single molecule consensus reads.
Proper citation: CCS (RRID:SCR_024379) Copy
https://github.com/mateidavid/fast5
Software C++ library for accessing Oxford Nanopore Technologies sequencing data.
Proper citation: Fast5 Library (RRID:SCR_024023) Copy
https://github.com/TADA-A/TADA-A/tree/master
Software statistical framework for mapping risk genes from de novo mutations in whole genome sequencing studies.
Proper citation: TADA-A (RRID:SCR_024538) Copy
Software provides command line interface and Python API for working with Biological Observation Matrix files.
Proper citation: python-biom-format (RRID:SCR_024193) Copy
http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV/
Software tool for accurate prediction of contacts and long range hydrogen bonding in proteins.
Proper citation: MetaPSICOV (RRID:SCR_024517) Copy
https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MCFLIRT
Software intra-modal motion correction tool designed for use on fMRI time series for linear (affine) inter- and inter-modal brain image registration.
Proper citation: MCFLIRT (RRID:SCR_024792) Copy
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