sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
096700cb0ee7ef2997d3ff8692870ec5c0083e2cc70adfff4d094062eae2d938 | Shell | 5,471 | 184 | #!/bin/bash
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); y... |
9b5152e6d53aea5b2c8820adcd43550b6dc5a74195b032d1956a1d558470d8be | Shell | 5,539 | 179 | #!/bin/bash
###Fast genome annotation for use with the JAMS pipeline
##Set environment
#Set Defaults and version
message_use="
fastannotate_JAMS.sh
By John McCulloch
Fast annotates fasta files using PROKKA.
Use: $(basename "$0") [options] -i contigs.fasta
-h Help
-v Version
-i File containing contigs or sequence to ... |
0b97953f483a4be83161292cc9327e861683f8355fe96dbe174d65758ad85dad | Shell | 5,640 | 131 | #!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
MODE="serving_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet serving_cpp_infer: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parser_key... |
333ff7d6ce330da17e7f60f09385e9c7ea914d0ed0135478653d924aa0aacc94 | Shell | 6,084 | 130 | #!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
MODE="paddle2onnx_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet onnx_infer: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parser_key "$... |
889f7ac982ace9ea58658de82ec9c505c9caa6ff9baf8b86682e8a331838eb80 | Shell | 6,098 | 184 | #!/bin/bash
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
# ablation studies for the GVP models on PDBBind regression task
# global variables shared across all runs:
n_gpus=4
num_workers=8
pdbbind_data=/home/ec2-user/SageMaker/efs/data/PIGNet/data/pdbbind_v2019/scoring
residue_featurizer_name=g... |
b09ece448142d6029f57c59bdd735c06c68fd7c4cec01215fe8f83ecaec8f74d | Shell | 6,211 | 210 | #!/usr/bin/env bash
# Execute code in a running marimo session's scratchpad.
# No marimo installation required — talks directly to the HTTP API.
# Usage:
# execute-code.sh [--port PORT] [--session ID] -c "code" # inline code
# execute-code.sh [--port PORT] [--session ID] script.py # code from file
# execute-... |
2cea3c8152ea5c2bb0f61983bc0db310afca142b27c50edcfa507779092eb8ed | Shell | 6,221 | 213 | #!/bin/bash
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
###########
## Propedia
###########
python train.py --accelerator gpu \
--max_epochs 500 \
--precision 16 \
--num_layers 3 \
--node_h_dim 200 32 \
--edge_h_dim 64 2 \
--dataset_name Propedia \
--input_type comp... |
ae52929274d0c87ce6bce1a80084bfeb287fee84d55d0701abccfcf9b0da478f | Shell | 6,798 | 247 | #! /bin/sh
#
# ./getlapack.sh
#
# Troubleshooting note: Remember that this script does not re-download sources if
# it determines that they are already present. When you require a clean slate,
# delete them manually from the temporary directory.
BLAS_VERSION=3.12.0
LAPACK_VERSION=3.5.0
ARPACK_VERSION=3.7.0
# We can'... |
151deb63d514a9dde8a568f7a1e23d28a574aa1c3943c523225f1abd532982d1 | Shell | 6,864 | 221 | #!/bin/bash
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
n_gpus=4
num_workers=8
# data_dir=/home/ec2-user/SageMaker/efs/data/ProtCID/JaredJanssen_Benchmark_thres_10
data_dir=/home/ec2-user/SageMaker/efs/data/ProtCID/JaredJanssen_Benchmark_thres_6
residue_featurizer_name=gin-supervised-contextp... |
0cae5b48daca7f83a9e2a24e29b833e58bec0683379eaa965a571783ca18e59a | Shell | 6,903 | 247 | #!/usr/bin/env bash
set -euo pipefail
usage() {
cat <<'USAGE'
Usage: gha-runtime-prometheus.sh [options]
Fetch gha-runtime-report.json artifacts from recent GitHub Actions runs and
emit Prometheus text metrics for job runtimes.
Options:
--repo REPO GitHub repository, e.g. chroma-core/chroma.
... |
778f7c5bc2741a7b3296f298c344f3aefdb7beafbec553f41568ade7b7a82a15 | Shell | 6,987 | 88 | #!/bin/bash
#SBATCH --time 48:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
#From Encode pipeline, filter out based on replicates
#Need to do for humreffed and chpreffed
echo Filtering based on replicates
bedtools intersect -a ATAC7_CM_Hyb1_chpreffed.merged_peaks.narrowPeak -b ATAC7_CM_H... |
f30ad11bb30b1861e1177e349bd5ba1563b7a72bf5057b8d14517d6b4b9e5f66 | Shell | 7,120 | 216 | #!/usr/bin/env bash
# Build an unsigned arm64 SanPy.app in a version-named dist folder.
set -euo pipefail
cd "$(dirname "$0")"
# shellcheck source=config.sh
source ./config.sh
GIT_STATUS="$(git -C "${REPO_ROOT}" status --porcelain --untracked-files=normal)"
if [[ -n "${GIT_STATUS}" ]]; then
echo "error: refusing to... |
92253547dc147e5a968361849a20902d20bc5fa1a0459fd38939a8434b857bbf | Shell | 7,132 | 82 | #/bin/bash
#####################################################################################
# Description of the document
"""
The example data includes sequencing data for intervals in the mouse genome from GSM753569. It is primarily used as a reference for AIMER usage.
1. test_sorted.bam: The file wh... |
d9219630878b835c6803b01da47f73cd628ad2dfc45ccd14b02a3b50fdd6ee63 | Shell | 7,769 | 153 | #!/bin/bash
# Create an ROI based on a region from HarvardOxford Atlas and a group-level result (cope).
#
# Make executable:
# chmod a+x extract_ROI_HOA_GLM2_SVC.sh # make executable
#
# Execute:
# ./extract_ROI_HOA_GLM2_SVC.sh # just run locally, even just single jobs
#
# EEG/fMRI STUDY, DONDERS INSTITUTE, NIJMEGEN.... |
4aea5c96416f25d4223a0949218a806a4ff67fb676d4f4377054a777ad913faa | Shell | 8,056 | 277 | #!/usr/bin/env bash
###########################################################################
######### ###########
######### mergemultisample ###########
######### @uthor : D Baux david.baux<at>inserm.fr ###########
######### Date : 10/05/2021 ##... |
243172fd042cd2ed18ab0231edd1599b68426c470a4a3c287b626dfdef9ffe8b | Shell | 8,414 | 43 | #!/bin/sh
#just launches GATK selectVariants
srun --job-name=srun_split -N1 -n1 -c24 --partition=monster --account=IURC /usr/bin/java -jar -Xmx48g -Djava.io.tmpdir=tmpjp/ /home/neuro_admin/nenufaar/software/GenomeAnalysisTK/3.6.0/GenomeAnalysisTK.jar -T SelectVariants -R /home/neuro_admin/nenufaar/refData/genome/hg19... |
da2b752595cd40d92ff7690d2a7523e4a64b443947d102526b5bbe796f129fd4 | Shell | 8,441 | 93 | #!/bin/bash
#SBATCH --time 48:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=16G
#First, we iteratively intersect all files removing the -a file as we go.
#This will introduce redundancy
bedtools intersect -a CM_Peaklist_Final_Anno_Humreffed.bed -b HP_Peaklist_Final_Anno_Humreffed.bed MN_Peak... |
68568e23281d5dcff65c5f97b7087b55c3a11bb652d5136328e3b53bde440318 | Shell | 9,501 | 221 | #!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
MODE="cpp_infer"
# parser model_name
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
model_name=$(func_parser_value "${lines[1]}")
echo "ppdet cpp_infer: ${model_name}"
python=$(func_parser_value "${lines[2]}")
filename_key=$(func_parser_key "${lines[3]... |
190ca9f3183222554fef1e4eeff9ded7df0020aafef6085cf02cb148ad44c24f | Shell | 9,602 | 341 | #!/bin/zsh
rm out_f_MTP.csv
rm out_f_MTPI.csv
# set variable
local -A opthash
zparseopts -D -A opthash -- h m: l: p: a:
model=0
len=0
para=4
ar=1.6
LF=$(printf '\\\012_')
LF=${LF%_}
if [[ -n "${opthash[(i)-h]}" ]]; then
echo "Usage : ./mtsim [option]"
echo " -m # : specify model (ex. -m 1 )"
echo " 0:... |
01e0ae36aec9a24fdbbba78758b0205310fcd621ca65afc3c70f45a36ef025dc | Shell | 9,610 | 339 | #!/bin/zsh
# set variable
local -A opthash
zparseopts -D -A opthash -- h m: l: p: a:
model=0
len=0
para=4
ar=1.6
LF=$(printf '\\\012_')
LF=${LF%_}
if [[ -n "${opthash[(i)-h]}" ]]; then
echo "Usage : ./mtsim [option]"
echo " -m # : specify model (ex. -m 1 )"
echo " 0: MT angle fixed"
echo " 1: ... |
bb75cb01cd9ad7e0b4c92fcdfed4dd325714f6506af1311ac2d91f6b409902bc | Shell | 9,719 | 135 | #!/bin/bash
# This script describes each step of the pipeline for training FILET and running it on an actual
# real dataset. Example commands are given, and the input files required to get the pipeline
# started. Thus, if you clone the repository and cannot run through the whole pipeline then
# something is wrong and ... |
2198b53226997898251af262b5555d2578ea3af1e41e70d8e5ae2692a937455c | Shell | 10,072 | 388 | #!/usr/bin/env bash
set -euo pipefail
usage() {
cat <<'USAGE'
Usage: gha-runtime-report.sh [options]
Generate a GitHub Actions runtime report for the current workflow run.
Options:
--repo REPO GitHub repository, e.g. chroma-core/chroma.
Defaults to GITHUB_REPOSITORY.
--r... |
4b202c6f0418a5255e94ecc52370b7ec2f00151521add13bbf24c6fb3c837a33 | Shell | 11,003 | 302 | #!/bin/bash
source test_tipc/utils_func.sh
# set env
python=python
export model_branch=`git symbolic-ref HEAD 2>/dev/null | cut -d"/" -f 3`
export model_commit=$(git log|head -n1|awk '{print $2}')
export str_tmp=$(echo `pip list|grep paddlepaddle-gpu|awk -F ' ' '{print $2}'`)
export frame_version=${str_tmp%%.post*}
ex... |
ce563bc7f0469402a32bf676dd1db3295d7a8f4187eb2cfebd82f3056d19e6a3 | Shell | 11,371 | 185 | #!/bin/bash
# Create ROIs as conjunctions of an anatomical mask (from Harvard-Oxford Atlas; potentially combine multiple smaller regions to one bigger one) and selected clusters above zThreshold in GLM1.
# Used for creating volume-by-volume time-series regressors for TAfT and for extract mean PE estimates from selecte... |
af46881894f66d547ad0c12646e03cb0a917f0fe262705f375b88d83f949e3f8 | Shell | 11,935 | 179 | #!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer'
# 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer',
# 'cpp_infer', 'serving_infer', 'lite_infer', 'paddle2onnx_infer']
MODE=$2
# parse params
datali... |
9904ef28edfe487a7211ad3c8c72b959b1f3cc93f80dfe37877cc2c1126b88f0 | Shell | 12,616 | 251 | #!/bin/bash
PROGNAME="$( basename $0 )"
# Usage
function usage() {
cat << EOS >&2
Usage: ${PROGNAME}
Options:
-c, --coverage Minimum read coverage allowed for the predicted transcripts. (default: 5)
-l, --length Minimum length allowed for the predicted transcripts. (default: 74)
-h, --help ... |
7cd17bec621926ec63318d925253e82c19b172e8075c8b0190ddead4384df04f | Shell | 13,094 | 263 | #!/bin/bash
PROGNAME="$( basename $0 )"
# Usage
function usage() {
cat << EOS >&2
Usage: ${PROGNAME}
Options:
-c, --coverage Minimum read coverage allowed for the predicted transcripts. (default: 5)
-l, --length Minimum length allowed for the predicted transcripts. (default: 74)
-h, --help ... |
4baed0ac10f8f0a77f9e5873bf00d4d1cd867d1bbec81c03f2c8f4e27a9d2436 | Shell | 14,732 | 444 | #!/usr/bin/sh
###########################################################################
######### ###########
######### Meta nenufaar ###########
######### @uthor : D Baux david.baux<at>inserm.fr ###########
######### Date : 12/05/2016 ###########
######### ###########
##########################... |
1921a0d61a706322be16f88236d55661fe7ebd9681c35a6ffb1d2036fcce7f84 | Shell | 15,119 | 408 | #!/bin/bash
PROGNAME="$( basename $0 )"
# Usage
function usage() {
cat << EOS >&2
Usage: ${PROGNAME} [-o <output>] [-g <genome (required)>] [-a <annotation>] [-b <path (required)>] [-i <path (required)>]
Options:
-o, --out Output file name. (default: OUTPUT)
-g, --genome Genome (hg19/hg... |
c5f404d4d5a8b42d1eb7ebd09b05d55fe7021087e7c18d1eda393f6918910a74 | Shell | 16,972 | 502 | #!/bin/sh
# Copyright (C) 2007 University of Oxford
# Authors: Dave Flitney & Stephen Smith
# Part of FSL - FMRIB's Software Library
# http://www.fmrib.ox.ac.uk/fsl
# fsl@fmrib.ox.ac.uk
#
# Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance
# Imaging of the Brain), Department of Clinical... |
7d10e32b4431e1672bf0260bef655954f9604838484a4962773ed0b8882e646e | Shell | 17,044 | 442 | #!/bin/bash
PROGNAME="$( basename $0 )"
# Usage
function usage() {
cat << EOS >&2
Usage: ${PROGNAME} [-o <output>] [-g <genome (required)>] [-a <annotation>] [-b <path (required)>] [-i <path (required)>]
Options:
-o, --out Output file name. (default: OUTPUT)
-g, --genome Genome (hg19/h... |
7539bfa0dfb0e7d9144b97aa55a0c3a64547842329c1c7ea32abc32c25a27f98 | Shell | 18,810 | 375 | #!/bin/bash
source test_tipc/utils_func.sh
FILENAME=$1
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer'
# 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer']
MODE=$2
# parse params
dataline=$(cat ${FILENAME})
IFS=$'\n'
lines=(${dataline})
# The training params
model_nam... |
81bf30a67eeec7dea2642f8b6b9e3803100f08c63ba0612e26585ae23f2cb4b9 | Shell | 19,272 | 159 | #!/bin/bash
#SBATCH --time=72:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=64G
#Hornet on a per chromosome basis.
samtools index rmdup.bam
samtools view -b rmdup.bam chr1 > rmdup.chr1.bam
samtools view -b rmdup.bam chr2 > rmdup.chr2.bam
samtools view -b rmdup.bam chr3 > rmdup.chr3.bam
samto... |
55bb4b278eea023c9dad8c32c4da1b2ce4075639864c604425b672bb9fbf409a | Shell | 19,615 | 162 | #!/bin/bash
#SBATCH --time=72:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=64G
#This script is essentially a way to do Hornet on a per chromosome basis.
samtools index rmdup.bam
samtools view -b rmdup.bam chr1 > rmdup.chr1.bam
samtools view -b rmdup.bam chr2 > rmdup.chr2.bam
samtools view -... |
e2558eddbae395e36521ce4d28a28815b9764642f90282599c20f8069d5add34 | Shell | 24,730 | 827 | #!/bin/bash
function trap_exit_handle() {
echo "(exit_handle)"
if [ -n "${lock_pid_file}" ]; then
rm ${lock_pid_file}
fi
print_stage_status
if [ -n "${PID_PERF_PROFILE}" ]; then
echo "(kill perf-profile)"
kill -- -${PID_PERF_PROFILE}
fi
# kill 0
}
trap trap_exit_han... |
a961ece4ad2ac828b99742226e2dc23e5d412adb3f344a85dd1657cc10f6fd3d | Shell | 25,171 | 779 | #!/bin/bash
source ~/anaconda3/etc/profile.d/conda.sh
conda activate pytorch_p38
# Hierarchical GVP experiments on PDBBind/CASF2016 dataset
# version 29
python train.py --accelerator gpu \
--max_epochs 500 \
--precision 16 \
--num_layers 2 \
--dataset_name PDBBind \
--input_type complex \
-... |
86630ef3d30a4fbd76dc263985a37f2aaf8f1c90c92445cd7b28a04d0de569fb | Shell | 32,511 | 598 | #!/usr/bin/bash
###########################################################################
######### ###########
######### VCF Annotation script ###########
######### @uthor : D Baux david.baux<at>inserm.fr ###########
######### Date : 06/07/2016 ###########
######### ###########
#################... |
986ec56d363fe43810813dcc32d61357582111399f9b045271aec3d58763938a | Shell | 53,731 | 972 | touch GLSS-HF-3016-NB-28D-WGS-0T6U6R.bqsr.txt
touch GLSS-HF-3016-NB-28D-WGS-0T6U6R.realn.mdup.bqsr.bai
touch GLSS-HF-3016-NB-28D-WGS-0T6U6R.realn.mdup.bqsr.bam
touch GLSS-HF-3016-NB-28D-WGS-0T6U6R.realn.mdup.bqsr.bam.md5
touch GLSS-HF-3016-R1-01D-WGS-I9CPLG.bqsr.txt
touch GLSS-HF-3016-R1-01D-WGS-I9CPLG.realn.mdup.bqsr.... |
48c8f25d24809cb331c70172214767c43805e1a147a49be8b32cf373a727297c | Shell | 78,561 | 1,087 | #!/usr/bin/sh
###########################################################################
######### ###########
######### nenufaar ###########
######### @uthor : JP Villemin jpvillemin<at>gmail.com ###########
######### @uthor : D Baux david.baux<at>inserm.fr ###########
######### Date : 11/08/2016 ###... |
0512ea34dd2ba4059d6b2cc07405785d127113bdc3abcab98c4bc37fe55edc3e | Shell | 94,077 | 1,394 | #!/bin/bash
###########################################################################
######### ###########
######### AutoDLML ###########
######### @uthor : D Baux david.baux<at>chu-montpellier.fr ########... |
0a59ff5833bb0abced908146d0d2da3cda271f9de59649450082c7076ee11d40 | Shell | 102,138 | 3,764 | #!/usr/bin/env bash
################################################################################
#
# This Bash script synchronizes multiple copies of the same file in the repository.
# Note that it is not robust against files that have whitespace in their names.
#
# Please keep this file organized in alphabetical o... |
9f56c9e58010d21fdde9aaeb88d03974f8c77856fb47ec8e2d16c17fd4fd0cfb | Shell | 200,000 | 7,005 | # Generated from ltmain.m4sh.
# ltmain.sh (GNU libtool) 2.2.6b
# Written by Gordon Matzigkeit <gord@gnu.ai.mit.edu>, 1996
# Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005, 2006, 2007 2008 Free Software Foundation, Inc.
# This is free software; see the source for copying conditions. There is NO
# ... |
f2d24a1807a627167b9a3a604beea0ed52633a865b0a9daaf5f51147e95d6a99 | Stan | 4,254 | 125 |
functions {
/* Wiener diffusion log-PDF for a single response (adapted from brms 1.10.2)
* Arguments:
* Y: acc*rt in seconds (negative and positive RTs for incorrect and correct responses respectively)
* boundary: boundary separation parameter > 0
* ndt: non-decision time parameter > 0
* bia... |
233e50ea605f116dde61047f099bfbdb64f8db3593c125b9ad95a24172cbce39 | Text | 8 | 1 | # 2in1
|
834241f51777c0565690497e3f57ede86761c4234aa571d4be25e40000e427f8 | Text | 14 | 1 | # rgs_ephaptic |
97f41aba891b00079ac76802eb78d92667d55ab43de06fa5f3a2538a2b622b40 | Text | 29 | 1 | #### **turbo-skin-diagnose**
|
502018bc8119f5e3d788cfb06baf862f33388ad21d84428df1020dc74d3b88ce | Text | 39 | 1 | # WGS plotting and downstream analysis
|
2265af030146ab1535da4e4454884ba0a0c29618b04bf0f193f23737073afc46 | Text | 42 | 2 | # InnateFish
Code to accompany the paper.
|
1cd800a23f5a31bdde1059a6bf944d2398c37d0e0ae65b444021e1e1c0b517b7 | Text | 50 | 2 | # Symmetry-PINNs-
Symmetry PINNs for Solving PDEs
|
a28430e95d18921bd61f2c850c76106a0f8f77a998458b9280fdf641053fcc9d | Text | 56 | 1 | Code for brain tumor detection using Swin Transformer.
|
88760c00febd112b1d14652fd5d8cfec4e8108d15515602ab426353b952d67ec | Text | 75 | 2 | # Sniffles2-Supplement
Supplementary scripts for the Snfifles2 publication
|
97afe825d3ebc2168f502ec2be8e68bd622bbe37c0aef79fe043a0584ba32e58 | Text | 75 | 2 | # BrainMetabolismModeling
Modeling of the energy metabolism of the brain
|
8cc6daa330558b2ec13704ac627f93c238302dae8d90a5d84809d3436463a67d | Text | 103 | 2 | # gtex_tf_activity
This repository contains all of the code for the GTEx TF Activity Shiny Application
|
b15e773d67948a916347d8711a3d00665949f5d2778bd18fddcbb49e348a4b78 | Text | 118 | 3 | # phase_locking
Codebase for analyzing unit phase-locking to local and hippocampal
oscillations in the Goldmine task.
|
69c145000826edcfd54349cffcd437587618247eba890dd477871cec60519d9e | Text | 120 | 1 | # The code used in the article “Using a Deep Generation Network Reveals Neuroanatomical Specificity in Hemispheres”
|
d9f44064f3b4423342f4ad3a467b761480402238e388cb435e4875078c6494bb | Text | 124 | 1 | **Important notice**: pycircstat is deprecated. Please use [pycircstat2](https://github.com/circstat/pycircstat2) instead!
|
17f385c163db70e71a86761536701c27024ed9e35fe0802e63f2e74d1f11142f | Text | 132 | 2 | # Automation-InterRater-Reliability
Automation of the calculation of inter-rater reliability from json/csv files for large datasets
|
fb5492c19ca19ab05713a86a13139e5ccb78718cf40d927a5f079b23b11ee7ce | Text | 135 | 2 | # eLife_2023
Code and source data supporting the results of the manuscript, Kimchi et al. 2023. https://doi.org/10.7554/eLife.89093.1
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743915bb2a0b93836eb10cf0dc7eada72b3255cf61311b25fd540bc44cf03b4d | Text | 151 | 3 | # Description
Source code related to the paper "Proxy-analysis of the genetics of cognitive decline in Parkinson's disease through polygenic scores".
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e7fc8c99937ac56b9d4e89d3d53affd13ebe5926f5a14a15d7a641c4743c453a | Text | 166 | 4 | # Correlation-invariance
Code for paper "Learning what matters: synaptic plasticity with invariance to input correlations" by Carlos Stein Brito, Wulfram Gerstner
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431fe23e64b0801badac280c356c3e9146abdd6a2bd35c8164f86be9a2e853bf | Text | 168 | 3 | # automatic-ecgdiagnosis
the test code and data of the paper "Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis"
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c31259c42ef4d8b5f4b9a82d677ae9959ac0d644af7f63a741f9c2eaa5df7c96 | Text | 175 | 2 | # BrainMetabolismModeling
Code for analysis and figure generation of the article "Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport"
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834ba4a33b2bd090264064f139e9aab07a75b5125fadf740a9a2a17dacb3cc2f | Text | 178 | 1 | This Github Repo is related to our recent publication titled "Non-orthogonal optical multiplexing empowered by deep learning". Some parts of code will be actively uploaded here.
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c87ac41c1d37f158481614390e847333d53170bda161045da132e04ae0209e18 | Text | 180 | 3 | This is not an officially supported Google product.
This repo stores code for the exploration of the theory of interpretable machine learning in https://arxiv.org/abs/2212.11870.
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fb9916d873c9ddfba73d719ca6fcc3176cf3a513cef9c7d3cd0fa31e28bdc560 | Text | 184 | 9 | # Brain_dimensions_ABCD_GenR
This is the code repository for the manuscript:
_**Limited generalizability of multivariate brain-based dimensions of child psychiatric problems**_
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ef5775d588905e1b45a1b5c484f420b53b852a1b22f304e722be9c48b9850582 | Text | 191 | 3 | Build files for resonet (deep residual networks for crystallography trained on synthetic data).
Tutorials and installation info can be found [here](https://smb.slac.stanford.edu/~resonet/).
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3aaca8997514e940da39a10e4fbe5be73f00aa0cd5c4a638c0ae1b3f71215157 | Text | 214 | 2 | # MaloneNatComm2024
MATLAB code used for analysis in "A consistent map in the medial entorhinal cortex supports spatial memory". For assistance with analysis or additional information please contact yi.gu@nih.gov.
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ba4ca3bfe75e8fac9b0a651423f12b08efbd73cf4d635af285ed2afa9a3ffb9c | Text | 238 | 4 | 
# eLife2024
Codes used for behavioral analysis - https://doi.org/10.7554/eLife.95577
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195fcefa228cd4537fe68458644d6d588772bc8541471ca28a05b09cd202a9ee | Text | 248 | 8 | snakedwi
============
.. image:: https://readthedocs.org/projects/snakedwi/badge/?version=latest
:target: https://snakedwi.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
BIDS app and snakemake workflow for dwi pre-processing
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f9d3cd1b0f05741a67bb03cf63aed46c0dc6512f2b43477ad920fbfde8c0a610 | Text | 254 | 10 | # Brainmet-Radiomics
## Source Code
accompanying the paper:
**Limited Capability of MRI Radiomics to Predict Primary Tumor Histology of Brain Metastases in External Validation**
Neuro-Oncology Advances 2024
DOI: 10.1093/noajnl/vdae060
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7c84c2b1fc5c2f5c6206616d54169f7390e78b1efaf77ae189091ed839c0bb41 | Text | 256 | 5 | # Yim-Choe-Bae-et_al-2024
Code and data used for Yim, Choe, Bae *et al*., 2024.
### Preprint
[Yim, Choe, Bae *et al*. Mind of a dauer: Comparative connectomics reveals developmental plasticity. *bioRxiv* (2023)](https://doi.org/10.1101/2023.03.23.533915)
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2c6644f74a8e9861d3d69fb0c5260c02da5882e9e2c306d9bc9ae837be82a5ac | Text | 266 | 5 | Tutorial for learning the continuous-time Monte Carlo dynamics of the Fredrickson-Andersen model, as detailed in "Learning stochastic dynamics and predicting emergent behavior using transformers" (arxiv:2202.08708).
<ins>Requirements</ins>:
PyTorch>=1.9.0
tqdm
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ed370d5748227d80c2c682754a70ae2bd4a7fcf5e263304516fea8bd0e9eb451 | Text | 269 | 6 | # optimal-coupling
This is the code used for the manuscript currently published on bioRxiv:
https://www.biorxiv.org/content/10.1101/2023.03.13.532471v1
Here are provided the python scripts used to obtain the data and shell scripts that set the simulation parameters.
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62515c8c0972e622dde4c806d236b1257d2a7fc4e3992672addf4ee4689ab147 | Text | 270 | 2 | # Algermissen2024NatComms
Analyses scripts used for Algermissen, J., Swart, J. C., Scheeringa, R., Cools, R., & den Ouden, H. E. (2024). Prefrontal signals precede striatal signals for biased credit assignment in motivational learning biases. Nature Communications.
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afc33ce27140c379c791a27f8c9deac2e1e48771f493ed028229e9c4b7ba4989 | Text | 279 | 5 | # Logic_of_Recurrent_Circuits
This code produces key experimental and modeling figures found in:
Oldenburg, I.A., Hendricks, W.D., Handy, G. et al. The logic of recurrent circuits in the primary visual cortex. _Nat Neurosci_ (2024). https://doi.org/10.1038/s41593-023-01510-5
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7f2331c744cbf4301c7a2796982fa6c2bf165760e956fdbcc87dc4c5570956b1 | Text | 299 | 2 | # Neuron_AIS_Analysis
Matlab Program used to tile the original neuron fluorescence images acquired in CZI format, automatically detect and isolate neuron using tubulin fluorescence images, perform harmonic analysis to predict AIS location, and compare to corresponding AnkG/Tau fluorescence images.
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f1fc45f668f2e81a5b66b0a6751fa0087da51b3a413b1e86b236af43b65e73a5 | Text | 303 | 5 | # shh-mben
This repository includes the python and R scripts associated with "The Developmental Basis of SHH Medulloblastoma Heterogeneity" ( https://www.biorxiv.org/content/10.1101/2022.09.02.506110v1.full ).
Information about these analyses and running these scripts can be found in "file_info.txt". |
2963a0642ec74a7f67b0a19a746c568314674c8c8c2ad482c9abbb858d1aa885 | Text | 358 | 7 | # Fast-SNN-PymoNNto-rch
This repository contains the code of the "Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch" (https://doi.org/10.3389/fninf.2024.1331220).
-----
Please check out the well-crafted commentary by Dr. Hans Ekkehard Plesser and the corresponding repository(https://githu... |
5638615a943c737d76660ead8cd99995557608266de2225b460d6472f0be9b01 | Text | 358 | 11 | # DeepDeDust
## Dataset
## Artifact Classifier
A deep CNN network which can be used to classify biological Nuclei and artifcts.
The scikitImage_generator file contains several data pre-processing related functions which can be used on a variety of image datasets.
The data augmenttion file contains various data aug... |
1afda4d55c3c314f8551773c6c207a26c22c0491e4c409044f0067d4924cfac8 | Text | 394 | 9 | # Identifying Content-Invariant Neural Signatures of Phenomenal Magnitude
Code for the paper
## 'Identifying Content-Invariant Neural Signatures of Phenomenal Magnitude'
with Benjy Barnett, Lau Møller Andersen, Steve Fleming, and Nadine Dijkstra.
main.m scripts within the fmri_scripts/ and meg_scripts/ directorie... |
5138f677d936fbe1fc6493c2a3679a2171d50ed332c49f69cb0d4114a0eb3413 | Text | 416 | 4 | # NN_H2Sensing
The official repository containing the machine learning and standard analysis tools used to analyze the nanoplasmonic response of hydrogen gas sensors, introduced in "Neural network enabled nanoplasmonic hydrogen sensors with 100 ppm limit of detection in humid air".
To demo the software, please follow ... |
4093a0a8eb3df1ec94faa19956aa4db975404433d28c0aaf4ec45d7909a3f674 | Text | 445 | 11 | # Overview
This repo provides code for our work on "Nasopharyngeal lymphatic plexus is a hub for cerebrospinal fluid drainage".
# Source code
R script for analysis and producing the results.
# Data availability
The scRNA-seq data of this study are available in the NCBI’s Gene Expression Omnibus under accession codes ... |
827f9f90029f8484e1c3149f2662e419f56f32134de854851e89a2dda8644c3b | Text | 452 | 6 | # Retinol_GWAS_code
Scripts for the Reay et al. GWAS of circulating retinol
Genetic influences on circulating retinol and its relationship to human health
William R. Reay, Dylan J. Kiltschewskij, Maria A. Di Biase, Zachary F. Gerring, Kousik Kundu, Praveen Surendran, Laura A. Greco, Erin D. Clarke, Clare E. Collins, ... |
5386155a80667787cbf66c791d62b1ed216d851db84fa31c0da8f8fc12310679 | Text | 453 | 11 | processed_data.zip contains:
- minimally processed data for behaviour and physiology experiments (i.e., 50 Hz behaviour timeseries, *F* timeseries for imaging ROIs etc.)
- z-stacks for immunohistochemistry
- csv files with values from fits to data
source_data.xlsx contains data for scatter plots and tunning curves in... |
ad3773587e0673c789842bf541cc7593b14697ee11c191adfc06d6d9b5494042 | Text | 453 | 7 | # Meta Pseudo Labels
Code for 'Iterative Pseudo Balancing,' based on Meta Pseudo Labels (https://github.com/kekmodel/MPL-pytorch)
This repository includes a multi-scale implementation of the MPL algorithm, including relevant dataloaders, transformations, and other modules for the application of stem cell microscopy cl... |
838b9be55e6fb3ddd8f801b73ce3f6051f8d6c7de08532f005f79832a629c420 | Text | 457 | 8 | # Meta Pseudo Labels
Code for 'Iterative Pseudo Balancing,' based on Meta Pseudo Labels (https://github.com/kekmodel/MPL-pytorch)
This repository includes a multi-scale implementation of the MPL algorithm, including relevant dataloaders, transformations, and other modules for the application of stem cell microscopy cl... |
3490061da0c1db4008582c15251bcc197e735f99d11786a23d1cd85b89c0f92f | Text | 468 | 7 | # Explainable Hybrid Vision Transformers and Convolutional Network for Multimodal Glioma Segmentation in Brain MRI
## This repository will contain the source code and the pre-trained models for the proposed method, TransXAI, after acceptance of the manuscript "Explainable Hybrid Vision Transformers and Convolutional N... |
2e656a1d7e14ef6001f1d401d93293beecac8dc1a433ebe8ef6f7daca5b37932 | Text | 475 | 23 | ========
Overview
========
Spectral estimation for Markov processes using the Integrated VAC algorithm.
Installation
============
You can install the in-development version with::
git clone https://github.com/chatipat/ivac.git
pip install ./ivac
Documentation
=============
Documentation can be compiled wi... |
2cf2fee7b383640292beade13503ded2f805a428576b47700d82079375819351 | Text | 476 | 11 | # Wrist and Tibia/Shoe Mounted IMU Measurement Results for Gait Analysis
Measurement results for gait analysis registered with wrist, tibia and shoe sensors.
The dataset includes tri-axial acceleration and angular velocity measured during several walks using three gait sensors:
* wrist-worn,
* shoe mounted,
* strapped... |
365d864af437622ae3fae23494d3923fc469fd9b724232f8d9b008259c1acc35 | Text | 479 | 9 | # hse_word_emd_TM
a project to study topic models with neural network elements
This project provides a code for 5 topic models.
Namely. 3 models based on neural networks. 1 model 'Embedding topic modeling', 1 model 'Granulated topic modeling with embedings'.
This project also contains code for calculating the three bas... |
29ce41778c5f1e0e2a421af34578d2994b68da28d9c7a62b2d5b2bb6e66e7691 | Text | 494 | 9 | # TreeMaze
Control software for the TreeMaze behavioral apparatus used in the goal-directed navigation task: Arduino firmware (`ArdPythonSlave*`, `SerialEnabledRewards`) and Python control (`PythonControl`, `PyCmdMessenger`).
Analysis and figure code: [TMA](https://github.com/alexgonzl/TMA)
## Citation
Alexander Gon... |
e57463d741807e382564e87fca66c7a856eea9f860e1eac378d584d42c87c8aa | Text | 502 | 21 | # Dynamical Galerkin approximation with memory
[](https://pypi.org/project/dgamem)
[](https://pypi.org/project/dgamem)
-----
**Table of Contents**
- [Installation](#installation)
- [... |
a2b289157f859f51893aadec59a88dc616394b02d1538c4234df8c514fcc25b2 | Text | 510 | 13 | # QuickSegViewer
Small Python script to quickly visually verify large amounts of medical image segmentation data
# Usage
- 'data' folder containing subfolders 'img' (image data) and 'mask' (segmentation masks) and patients.txt (list of case IDs, one per line)
- filenames start with case ID, image files end with 0000 ... |
7d0e0a10fbe24fb8e40e87ac59f0b40190e97198ea32f3d132cfc6dfe9320180 | Text | 511 | 4 | # entopallium_ephys
Here you can find raw data and the corresponding R-script for analysis and vizualization of neural responses in the Entopallium of young domestic chicks. The raw data are divided into Light incubated and Dark incubated condition. The spikes were summed in 25ms bins for each trial and recorded unit. ... |
b147727157783b9c5b7fda1712535170b0bcce5ae98dc89d356a02892f46a008 | Text | 512 | 6 | # memstim-hh
MEMory STIMulation, a dynamical computational model of theta-nested gamma oscillations w/ theta phase reset interfacing Kuramoto oscillators with Hodgkin-Huxley neurons.
The model described here was used to produce the [following publication in eLife](https://elifesciences.org/articles/87356). If you use ... |
d6871bcb0bcfe2d70d80c625d214943e7f07af9190fea93c44c0f213310b22d7 | Text | 539 | 10 | ## Indebtedness_Gao2021
Codes and behavioral data for
Gao, X., Jolly, E., Yu, H., Liu, H., Zhou, X., & Chang, L. J. (2020). The hidden cost of receiving favors: A theory of indebtedness. bioRxiv.doi: https://doi.org/10.1101/2020.02.03.926295
The data of study 3 (fMRI study), including the unthresholded first-level c... |
049bad95467a214a4e65cdc599fbb8551fae454e5105054a563655483693156a | Text | 585 | 9 | # zapit-CAD
There are two construction routes.
One is to make everything out of ThorLabs parts (although we do not have an example design available at present).
The second is to use our CAD model to machine a compact custom enclosure.
See the [Parts List](Parts_List.md) for a list of parts required.
Detailed constructi... |
6e85f3536c2b0da66daa77d243e9acc70632d80d8b050d1aeff4bb02e55fda13 | Text | 590 | 24 |
# SOMENDER Package
Welcome to the SOMENDER package repository. This Python package allows you to easily integrate SOMENDER into your projects.
## Installation
You can install the SOMENDER package using pip. Run the following command in your terminal:
```bash
pip install SOMENDER==1.1
```
## Getting Started
After... |
e5197dc9557572b86ef5efdb4d0878f9f8762f6c68ee2776a9f2c0fe9b05a882 | Text | 591 | 18 | # MyForestPlot
[](https://www.python.org/)
[](https://pypi.org/project/myforestplot/)
[](https://github.com/toshiakiasakura/myforestplot/b... |
4d71324932831f5e83d763a34a72b693e72643666f6081ff15e8fa25b0394348 | Text | 611 | 7 | # Kinome-wide Virtual Screening by Multi-task Deep Learning
This repository contains code for multi-task deep neural network (MTDNN) model training using aggregated KKB and ChEMBL dataset, LINCS and IDG model prediction, non-deep learning multi-task random forest modeling, and other analyses described in "Kinome-wide ... |
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