sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e3afcfa0ce8135b03be897fc29e991a64bd8a9094a9ed56cd8ef0d5d28bccc05 | Shell | 4,177 | 47 | # Training implicit network + NeuralODE for Cubic example
mkdir -p ./Results/Cubic2D/
########################################################
################ Batch time 4 ##########################
########################################################
python Cubic2D_Implicit_NeuralODE.py --num_epochs 10000 --noi... |
109d930952126c4d1b1076920930d4f556671c78aa81d3843571c33d9c2635f0 | Shell | 4,189 | 47 | # Training implicit network + NeuralODE for Cubic example
mkdir -p ./Results/Cubic2D/
########################################################
################ Batch time 2 ##########################
########################################################
#python Cubic2D_Implicit_NeuralODE.py --num_epochs 10000 --no... |
ef27201055820e5d219d927c592c00717f87218238371f293f0d342a171b6e95 | Shell | 4,301 | 101 | #!/bin/bash
# This is an example of a script that will three post-processing ciftify functions using singularity exec
# The three functions are:
# ciftify_clean_img - to denoise and smooth the scans
# ciftify_PINT_vertices - to run PINT
# cifti_vis_PINT - to build PINT qc pages
# the inputs are:
# subject -> the s... |
3961fc0819df532e852d1df749bdfde72858c60edca224620858fa853c579e33 | Shell | 4,381 | 76 |
#!/bin/sh
# User must supply the directory name.
dir=$1; currdir=`pwd`
top=$2
# Retrieve number of clusters:
nclusters=`sort -nbr -k2,2 ${currdir}/${dir}/SeuratResults/Cell_Clusters.tsv | head -n1 | cut -f2`
echo "===> Clusters 0 to ${nclusters} were found."
echo ""
# Enter input directory and create output di... |
181d7c88d6af0cc643dd1859120e997c2a373b4f97bc5fdff1cfcab411322db5 | Shell | 4,422 | 103 | #!/bin/bash
# This is an example of a script that will three post-processing ciftify functions using singularity exec
# The three functions are:
# ciftify_clean_img - to denoise and smooth the scans
# ciftify_PINT_vertices - to run PINT
# cifti_vis_PINT - to build PINT qc pages
# the inputs are:
# subject -> the s... |
57209e759875eeace063bedbac14849599e667d5cdb577a38703dfdf4122cef6 | Shell | 4,457 | 29 | htseq-count -f bam -r pos -s no -t exon -m union /data/NCATS_ifx/data/mRNASeq/ISB009/bam/Sample_VJ4001-10_S24/VJ4001-10_S24_hg38Aligned.sortedByCoord.out.bam /fdb/GENCODE/Gencode_human/release_27/gencode.v27.annotation.gtf > /data/NCATS_ifx/data/mRNASeq/ISB009/htseq/VJ4001-10_S24_htseq_counts.txt
htseq-count -f bam -r... |
7cfa92751dcfe2e430a3c3bfe27db00fd2cd29bc73381da8e4de9184f728f3e8 | Shell | 4,540 | 123 | #!/bin/bash
set -e
export NVARCH=x86_64
export NVIDIA_REQUIRE_CUDA="cuda>=11.1 brand=tesla,driver>=418,driver<419 brand=tesla,driver>=440,driver<441 driver>=450"
export NV_CUDA_CUDART_VERSION=11.1.74-1
export NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-1
export NV_ML_REPO_ENABLED=1
export NV_ML_REPO_URL=https://developer.d... |
63f4826f3147da7051c62cd880f1aaea11f908ae8bd8b3ba0f630fecb4a062f2 | Shell | 5,114 | 124 | #!/bin/bash
# This is an example of a script that will generate seedcorrelation maps for the last PINT analysis bit..
# The three functions are:
# want to use ciftify_surface_rois to also output a roi dlabel file
# want to used ciftify_surface_rois to output a network cortical rois dlabel file
# want to also the cont... |
4603dcfc2931d6f0dd0ce0cb92ec4c000a77307b5741649f653a108544e306dc | Shell | 5,133 | 117 | #!/bin/bash
# #############
# set this to the directory containing antsRegistration
ANTSPATH=/usr/local/ants/bin/
# ITK thread count
ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=1
export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS
# Check args
if [ $# -lt 1 ]; then
echo "USAGE: $0 <sbj>"
exit
fi
sbj=$1
ses=$2
task=$3
echo Sbjec... |
f3c6f7424f65b13afa9ba9d42e32a0106be8ce4facc874b1a8d31f0166d47456 | Shell | 5,178 | 30 | java -jar $TRIMMOJAR PE -phred33 /data/NCATS_ifx/data/mRNASeq/ISB009/VJ4001-10_S24_L006_R1_001.fastq.gz /data/NCATS_ifx/data/mRNASeq/ISB009/VJ4001-10_S24_L006_R2_001.fastq.gz -baseout VJ4001-10_S24.fastq.gz ILLUMINACLIP:/usr/local/apps/trimmomatic/Trimmomatic-0.36/adapters/TruSeq3-PE-2.fa:2:30:10 LEADING:10 TRAILING:5 ... |
4d1f1ba83835fd3eb21cdcf399292523e67363293ce1b1dc7bc4cdc941b3b238 | Shell | 5,395 | 88 | ## This script generates two test FASTQ datasets:
## 1. An undemultiplexed dataset representing several barcoded samples with molecular indexing.
## This dataset represents raw data from a MiSeq run following the GUIDE-Seq protocol
## described in Tsai et al., 2014 (PMID XXXX)
## 2. A two sample dataset conta... |
14661539540661bcd6ea283e5f169a6c47c3b11256a9d13ffa705b759ad45387 | Shell | 5,933 | 119 | #!/bin/bash
# This is an example of a script that will three post-processing ciftify functions using singularity exec
# The three functions are:
# ciftify_clean_img - to denoise and smooth the scans
# ciftify_PINT_vertices - to run PINT
# cifti_vis_PINT - to build PINT qc pages
# the inputs are:
# subject -> the s... |
bbace75173b011a01b9f1524edd535b3434d2edb182882be37061945241b9e40 | Shell | 6,087 | 100 |
expr_prefix="backup"
# config_names=cifar10_32_100_hybrid_conv_256s16k3l12
# config_names="cifar10_32_100_hybrid_conv_128down2k3l12 cifar10_32_100_hybrid_conv_128down4k3l9 cifar10_32_100_hybrid_conv_128s16k3l12 cifar10_32_100_hybrid_conv_128s16k7l12 cifar10_32_100_hybrid_conv_512down2k3l12"
# config_names=${config_nam... |
40afb6e57fb63f4284536226dfa1bf95f44e8e2f7cf0df5144b96e77877eeb96 | Shell | 6,277 | 171 | #!/bin/sh
# User must supply the directory name.
dir=$1; currdir=`pwd`
markerslist=/working/lab_julietF/mainaB/scRNAseq/LiteratureMarkers/Markers_Revised_Annotation
AverageRows=/working/lab_julietF/mainaB/Scripts/AverageRows.R
MakeMDS=/working/lab_julietF/mainaB/Scripts/MakeMDS.R
# Enter input directory and crea... |
2be806df4758f1d2af6ed0c1033e038d6d627aae906e0a63c992c76da724079d | Shell | 6,641 | 181 |
#!/bin/sh
############################################
############################################
# #
# **GRADE in PBS Cluster** #
# General RNAseq Analysis for #
# Differential Expression #
# ... |
e60c8d286a4d0010e2c8cf223a7587590e05e23fe2c6e49af2cc788d89108cd2 | Shell | 6,666 | 175 |
#!/bin/sh
############################################
############################################
# #
# **GRADE in PBS Cluster** #
# General RNAseq Analysis for #
# Differential Expression #
# ... |
bc96ea283bd171844820d026e899405bb5c0f866e6ecd4b4feca00ec04fe3f58 | Shell | 6,718 | 179 |
#!/bin/sh
############################################
############################################
# #
# **GRADE in PBS Cluster** #
# General RNAseq Analysis for #
# Differential Expression #
# ... |
683ffd256c543ac9ebe91e902895e84374dacdf0215cea8ecebaad35d6e4082c | Shell | 6,856 | 78 |
# expr_prefix="conv_fc_comparison"
expr_prefix="pure_hybrids" # pure_hybrids, measure_test
# config_names="cifar10_32_100_hybrid_conv_128s4k3l12 cifar10_32_100_hybrid_conv_128s8k3l12 cifar10_32_100_hybrid_conv_128s16k3l12 cifar10_32_100_hybrid_conv_128s4k3l36" #cifar10_32_100_hybrid_conv_128s4k3l12 cifar10_32_100_hyb... |
7fa20889a7133c67ab3b39c2f38f7729370c0fb25c6caf2f580e0f4884cfbeff | Shell | 6,950 | 218 | #!/bin/bash
#
# deps:
# pip install py-spy
# https://github.com/benfred/py-spy
#
# pip install memray
# https://github.com/bloomberg/memray
#
# example usage:
# remove all files in default output dir (use zsh N glob to avoid error) if dir empty
# then run doprofile script
# generate sppedscope json for sp... |
8d3dacd3c28c1993c0630bcdd49f95432a3c4c75b7ac0dffc7ee5a5d573659c6 | Shell | 6,959 | 184 |
#!/bin/sh
############################################
############################################
# #
# **GRADE in PBS Cluster** #
# General RNAseq Analysis for #
# Differential Expression #
# ... |
6973864c372f42fe8a3b2fc71b4c00dc62f8feada6d7583f0d2f56cb48bff9f9 | Shell | 7,060 | 183 |
#!/bin/sh
############################################
############################################
# #
# **GRADE in PBS Cluster** #
# General RNAseq Analysis for #
# Differential Expression #
# ... |
fc234c85d5fc098f7a7c6f5192e5ce3cba5278d4db9c868481d0f089b9efd188 | Shell | 7,343 | 215 | #!/usr/bin/env bash
#
# PyNIDM QueryAI Demo Script
# ===========================
# Demonstrates the AI-assisted SPARQL query tool for NIDM files.
# The tool translates natural-language questions into SPARQL queries,
# resolves DataElement URIs from the data, and executes locally via rdflib.
#
# The script automatically... |
539867d30a001e1cfc166bbf0f570e30bfda36a3bc563aa8ab9a06c852745308 | Shell | 7,464 | 191 |
#!/bin/sh
############################################
############################################
# #
# **GRADE in PBS Cluster** #
# General RNAseq Analysis for #
# Differential Expression #
# ... |
7debd6b71223eee7687e404cde8f45c191ffba79f3be22adee3b9fff5dc9bd8b | Shell | 7,491 | 81 | #!/bin/bash
TITLE=A628T;SPECIES=hg19;
FASTQ1_1=/srv/gsfs0/projects/seq_center/Illumina/PublishedResults/2016/feb/160205_MONK_0465_BC8C8DACXX/L3/160205_MONK_0465_BC8C8DACXX_L3_TAAGGCGA_1_pf.fastq.gz
FASTQ1_2=/srv/gsfs0/projects/seq_center/Illumina/PublishedResults/2016/feb/160205_MONK_0465_BC8C8DACXX/L3/160205_MONK_046... |
f2a51bfb992c365c5fbb25a787960bfe3dc6a689f71316ab7fa548b5d0205de5 | Shell | 7,566 | 192 |
#!/bin/sh
############################################
############################################
# #
# **GRADE in PBS Cluster** #
# General RNAseq Analysis for #
# Differential Expression #
# ... |
1bc87d3d9b37cb33fa24c4bc8727d23d41b24580880983552a179311740298e4 | Shell | 7,734 | 85 | #!/bin/bash
SUFFIX=Ct-1h; WORK=/srv/scratch/shared/nandi/projects/training-camp-2016/run/$SUFFIX; mkdir -p $WORK; cd $WORK
FASTQ1_1=/srv/scratch/shared/nandi/projects/training-camp-2016/data/fastqs/Ct-1h_S5_L001_R1_001.fastq.gz
FASTQ1_2=/srv/scratch/shared/nandi/projects/training-camp-2016/data/fastqs/Ct-1h_S5_L001_R2... |
fea2181778a67070dec926f308c4f39ed937a64951c88b94b99d73634d694980 | Shell | 8,277 | 210 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
1ccdae6e2c709ed21a25afdaa00ae1f68f3ef71a3087257ef77ce2e0f0e6477f | Shell | 8,319 | 210 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
e601dd94e5f7c395945ae220b1005ae8fe79902c60ec7d74adf69ef652f833a4 | Shell | 8,499 | 219 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
bf2883bc6cace61d65a2df83d2f922e92fd5927dcbb61722586f685c4599ca7a | Shell | 8,538 | 150 |
expr_prefix="pure_hybrids" # measure_test pure_hybrids
# config_names=${config_names}" cifar10_32_100_phybrid_conv_32s4k3l12A_adam cifar10_32_100_phybrid_conv_32s4k5l12A_adam cifar10_32_100_phybrid_conv_32s4k7l12A_adam"
# config_names=${config_names}" cifar10_32_100_phybrid_conv_32s8k3l12A_adam cifar10_32_100_phybrid... |
28c280cae1e4ed2798642beccfa1d1803155943f3ac065c04827fc1ccfa924c1 | Shell | 8,581 | 220 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
3b3c3bb6f2df6fa51c4bafa57f5d1e46ddc00b2235ad6580cf038a9c44027823 | Shell | 8,629 | 216 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
fe92ceb866cc867cd3b06c4eaa7430c9093057cedc15c41f9c65fd6f0a1cb902 | Shell | 8,700 | 199 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
f1f2f78b8a9bb3157bd3205408980a60a1745d7905099291c5647857781921db | Shell | 8,819 | 219 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
f444827f819b31166b7adb46c46743ea92b4ff433a49d8409844e85d1adb50f3 | Shell | 9,131 | 224 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
3a6bc84fbfb4af757b413fee94fb9c05e3b5249a7863f858270577d8a1fd7682 | Shell | 9,250 | 149 | expr_prefix="normalization" #mlp measure_test pure_hybrids normalization
# config_names=${config_names}" cifar10_32_100_phybrid_fc_3072l9_bn cifar10_32_100_phybrid_fc_3072l12_bn"
# config_names=${config_names}" cifar10_32_100_phybrid_fc_3072l6_ln cifar10_32_100_phybrid_fc_3072l9_ln cifar10_32_100_phybrid_fc_3072l12_ln... |
9ecc485a882e587c6484d22287eedad12a14414edfbc8d2362d309d2af17dc58 | Shell | 9,476 | 225 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
701a1fa11dae63bdd10ed391263a10296bc419a88fabb046d9aa02f2bc16648a | Shell | 9,485 | 215 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
54f1c69a58a8f94362a82774d097460570616a85c8ff8550a9bbc762fa91ffcf | Shell | 9,531 | 194 | #!/bin/bash
# This is an example of a script that will three post-processing ciftify functions using singularity exec
# The three functions are:
# ciftify_clean_img - to denoise and smooth the scans
# ciftify_PINT_vertices - to run PINT
# cifti_vis_PINT - to build PINT qc pages
# the inputs are:
# subject -> the s... |
93d6e65ab03eeb7e9b3d0b3d719f4f2d2e8ad515bcfafcef53a4b0bb853cb9e6 | Shell | 9,850 | 229 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
1219f66ab8d4e06787e8e66b8e17a12d1b6c256191a1c4807d36206552e8eeff | Shell | 10,368 | 101 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
#################################... |
c46c4a5f177494de8b0730448a9a54fd003c2eafea02cf6d777d70172ad622d8 | Shell | 10,647 | 234 | #!/bin/bash
# This is an example of a script that do all the post-processing ciftify functions using singularity exec
# The three functions are:
# ciftify_clean_img - to denoise and smooth the scans
# ciftify_PINT_vertices - to run PINT
# cifti_vis_PINT - to build PINT qc pages
# extracting the subcortical timeseries... |
b2c4b24688bcdbad216841322d932608b4b1706326133f8ff3a8e11dc5a975a1 | Shell | 11,085 | 30 | cd /data/NCATS_ifx/data/mRNASeq/ISB009 && mkdir -p bam/Sample_VJ4001-10_S24 && STAR --runThreadN $SLURM_CPUS_PER_TASK --genomeDir /fdb/STAR_current/GENCODE/Gencode_human/release_27/genes-75 --sjdbOverhang 75 --outSAMunmapped Within --outFilterType BySJout --outFilterMultimapNmax 20 --outFilterMismatchNmax 999 --outFilt... |
2d9f1cf409295868002038e073507374358ca919e1ad93381ec4915f662a4731 | Shell | 11,181 | 252 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
864d8c94a2c349459ffeadea28d2ae3d73d2a1bb6be0d2ccdc160c337d1e9ba5 | Shell | 11,456 | 242 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
663770c988754ca9eb33a915be6fff923ae5d29ff7fc72b90907bd5a41599f62 | Shell | 12,080 | 82 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
#################################... |
4919a95ebe11dc4413a75f9d584040cd100297268f66c5f4d2bf3e5805cb4909 | Shell | 12,183 | 237 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
ad3af89802252f48cb5380170a73e8d302a717b70f9bb7cb0220d504ff97d33b | Shell | 12,238 | 241 |
#!/bin/sh
############################################
############################################
# #
# **GRADE in PBS Cluster** #
# General RNAseq Analysis for #
# Differential Expression #
# ... |
e9483d9d20c74c6e107cc84368206da991462686faa504d6254c43db5f07b727 | Shell | 12,245 | 340 | #! /bin/bash
#
# docker2singularity.sh will convert a docker image into a singularity
# Must be run with sudo to use docker commands (eg aufs)
#
# NOTES:
# If the docker image uses both ENTRYPOINT and CMD the latter will be ignored
#
# KNOWN ISSUES:
# Currently ENTRYPOINTs and CMDs with commas in the arguments are not ... |
512d7ecd026b141869fa40a1323a7c29cc7c67d36fafbf1cf5899e34165deeb2 | Shell | 12,338 | 251 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
d4ed1718bbe109ffdb2f755569048eebcdda319c2ccf7afe9f24c9ae5fd0a827 | Shell | 12,395 | 267 | #!/bin/bash
# Stop on error
set -e
## species file name, conda environment name
SPECIES_FILE_BASENAME=bds_atac_species.conf
CONDA_ENV=bds_atac
## build index or not
BUILD_BWT2_IDX=1
BUILD_BWA_IDX=0
## show help
if [[ "$#" -lt 2 ]]; then
echo
echo "This script installs data for genome [GENOME] on a directory [... |
4bb0e0535e578b42fe514ebf1bb50091d5463e9a8d80832b1d3afe0c9143728b | Shell | 12,598 | 156 | #!/bin/bash
channels=$1
conda activate gfcn
if [[ $channels == "4ch" ]]
then
echo "runing 4ch"
# echo " training losses case GFCNC"
# echo "GFNC BCE"
# python training.py -s GISLES2018 -n GFCNC -b 4 -c BCElogistic -O False -t True -N 10 --id BCElogistic4Ch -lr 1E-4 -g 100 --mod TMAX CBF CBV MTT --weight 5.5 >> gf... |
b17e31da03e455c9b8d8e634770ceff239071188fbe0ac0363e8a1b3055ea0ce | Shell | 13,387 | 261 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
68e4c82a8757eac4f9c9cdee693503cb09ac37111c416605e972ab0e704e21f2 | Shell | 14,083 | 265 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
d7c4229a0c1cf9fe6b722f491dcc66e0ec73952e58ba2b2fad0b60cd3854b474 | Shell | 14,537 | 259 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
dfdb6430fc27af44f5f379768e8d6dd3b29f17761ac36cf7b39f6d83cc799a98 | Shell | 14,984 | 175 |
expr_prefix="measure_test"
# config_names=cifar10_32_100_hybrid_conv_256s16k3l12
# config_names="cifar10_32_100_hybrid_conv_128down2k3l12 cifar10_32_100_hybrid_conv_128down4k3l9 cifar10_32_100_hybrid_conv_128s16k3l12 cifar10_32_100_hybrid_conv_128s16k7l12 cifar10_32_100_hybrid_conv_512down2k3l12"
# config_names=${conf... |
1aca1493f445c7aab40a1f8297ef2d79baf0d68416b93e0655b9132cb17b7556 | Shell | 18,212 | 282 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
320c7d076b26c48e8cd4484b808daa69b8a278b2712d95843456195db4c09338 | Shell | 20,023 | 203 | # Training implicit network + NeuralODE for Cubic example
mkdir -p ./Results/Cubic2D/
########################################################
################ Batch time 2 ##########################
########################################################
python Cubic2D_Implicit_NeuralODE.py --num_epochs 10000 --noi... |
36ddf63c23772a1d31ec379a4513e898736aa5ab1c2be6b17074d1ae415a0174 | Shell | 20,857 | 374 | find . -type f -name '*.fq.gz' -exec bash -c 'mv "$1" "${1/.fq.gz/.fastq.gz}"' -- {} \;
WORK_ROOT_OLD=/srv/scratch/leepc12/run/atac_old/atac_Hardison
mkdir -p $WORK/out/align/rep1
mkdir -p $WORK/out/align/rep2
cp -pr $WORK_ROOT_OLD/$SUFFIX/out/rep1/*.fastq.gz $WORK/out/align/rep1
cp -pr $WORK_ROOT_OLD/$SUFFIX/out/rep... |
65ea60b46f655fccd304ed7191914362589fc43183bfaea516a1fa06ea1fb135 | Shell | 22,979 | 281 |
expr_prefix="normalization" # pure_hybrids
# config_names="cifar10_32_100_hybrid_conv_256s16k3l12 cifar10_32_100_hybrid_conv_256s16k7l12 cifar10_32_100_hybrid_conv_512s4k1l9 cifar10_32_100_hybrid_conv_512s4k1l15"
# config_names2="cifar10_32_100_hybrid_conv_512s4k3l9 cifar10_32_100_hybrid_conv_512s4k3l15 cifar10_32_10... |
4525d9f8f94f79599b95adda7dfaa8bcf2d80e81c6b455eb2f183c1f6d77bc1f | Shell | 24,146 | 801 | #!/bin/bash
function buildTemplate {
iter=$1
#build template from $((iter-1)) data
iter_out=$work_folder/${seg_name}_iter$iter
iter_prev=$work_folder/${seg_name}_iter$((iter-1))
mkdir -p $iter_out
all_target_segs=$iter_out/in_target_segs.nii.gz
template_prob_seg=$iter_out/temp... |
42f5321e3a3f4182c5631fdc234b7accf4c4de3decaedfb0ef2c7bca25ade5cc | Shell | 25,399 | 319 | #!/bin/sh
######################################################
# #
# ** DENTAL (de Novo Transcriptome Assembly Line) ** #
# #
######################################################
##################################... |
57a253621a8f3fd399731ad645712504f14f5045bf066496757df12c67a89f3b | Shell | 30,434 | 185 | # This bash script can be used to regenerate all test output when a global change happens.
# It needs to be launched from the root folder.
# test_bed_and_gtf_tracks:
pgt --tracks ./pygenometracks/tests/test_data/bed_and_gtf_tracks.ini --region X:3000000-3300000 --trackLabelFraction 0.2 --width 38 --dpi 130 -o ./pygenom... |
5291b4da70685d24cd03eb7712471f5032e012970cbf4f1acd7f037a90690334 | Shell | 60,641 | 1,496 | #!/bin/bash
# rendered script-it from rest_master_170223.sh
# generated: 2017/03/31 -- 15:20:31 by jviviano.
set -e
export DIR_MODULES=/archive/code/epitome
export DIR_DATA=/external/miklos/epitome
export DIR_EXPT=EXP
export DATA_TYPE=REST
export ID=run1
export SUB=${1}
if [ -z ${1} ]; then
echo "Usage:"
ec... |
5f2223304c6af63875be70e54a144db821792548a13e3281f2908146ef7f3a1e | Shell | 60,705 | 1,499 | #!/bin/bash
# rendered script-it from rest_master_170223.sh
# generated: 2017/03/31 -- 15:20:31 by jviviano.
# edited: 2017/11/23 -- by edickie for COBRE dataset
set -e
export DIR_MODULES=/archive/code/epitome
export DIR_DATA=/external/SchizConnect/COBRE/epitome
export DIR_EXPT=COBRE
export DATA_TYPE=REST
export ID=... |
46c51d3cc1b43595bf0f59f5f89c6b3355c539d757758b7752b3be51f78834db | Shell | 200,000 | 6,976 |
# libtool (GNU libtool) 2.4.2
# Written by Gordon Matzigkeit <gord@gnu.ai.mit.edu>, 1996
# Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005, 2006,
# 2007, 2008, 2009, 2010, 2011 Free Software Foundation, Inc.
# This is free software; see the source for copying conditions. There is NO
# warranty; no... |
e3ce63de6f71f677fb6639490fa6d40bd9b9988eefd4705ea089a9b50f1c3089 | Shell | 200,000 | 6,975 |
# libtool (GNU libtool) 2.4.2
# Written by Gordon Matzigkeit <gord@gnu.ai.mit.edu>, 1996
# Copyright (C) 1996, 1997, 1998, 1999, 2000, 2001, 2003, 2004, 2005, 2006,
# 2007, 2008, 2009, 2010, 2011 Free Software Foundation, Inc.
# This is free software; see the source for copying conditions. There is NO
# warranty; no... |
615529a17ff6c4f6202cdd163ad8396b896279cd9d9a77fd1956a24f587dedab | Stan | 300 | 16 | data {
int<lower=0> n;
vector[n] phase;
vector[n] amplitude;
}
parameters {
real beta1;
real beta2;
real<lower=sqrt(beta1^2 + beta2^2)> beta0;
real<lower=0> sigma;
}
model {
target += normal_lpdf(amplitude | beta0 + beta1 * cos(phase) + beta2 * sin(phase), sigma);
} |
dd25391c058406dbfb6cdaa59748a54958676ac7d8346244c2687c4f26795b96 | Stan | 431 | 22 | data {
int<lower=0> N;
vector[N] swa;
vector[N] ce;
}
parameters {
real<lower=0> s;
real<lower=0> r;
real<lower=0, upper=4> t;
real<lower=0, upper=1> u;
real log_sigma;
}
transformed parameters {
real sigma = exp(log_sigma); // Jeffreys prior is uniform on log(sigma)
}
model {
... |
25fd13679ffab35b73fd636c269d71e5ed6b329ee12f8a07c204a8226477eee3 | Stata | 454 | 21 | **Running repeated measure ANOVA with the data from Matlab
*In Matlab: run "second_level_Deborah_with_stats".
*Rename variables
rename var2 cond1
rename var3 cond2
rename var4 cond3
rename var5 cond4
rename var6 Participant
reshape long cond, i(Participant) j(outcome)
rename cond EEG
*Run ANOVA
... |
5dfa467d49d263c988fa2eb389adbc7ba02854aaa73d4ae8715485dc6f748ca4 | Stata | 487 | 21 | **Running repeated measure ANOVA with the data from Matlab
*In Matlab: run "second_level_Deborah_with_stats".
*Copy tstata into the Data Editor
*Rename variables
rename var2 cond1
rename var3 cond2
rename var4 cond3
rename var5 cond4
rename var6 Participant
reshape long cond, i(Participant) j(outcome)... |
2c05aed4180bda770803edf0b7f8827c86eb001e1da1135d7f9e961adb796545 | Stata | 579 | 15 | * To run Repeated-Measure ANOVA for Study 2 and Study 4 behavioral data.
clear
*import data
import delimited "C:\Users\OWNER\Dropbox\Berkeley postdoc\Mes experiences\Near Miss\Narrow Escape EEG\Writing the paper\Behavioral part\Curves for stata\NE_curves_for_anova_stata.csv"
*Rename variables
rename subj_i... |
348437bba6100203db506f88e24e06869e150e52ba8f7806c6e1e02b85756903 | Text | 12 | 1 | # schizo2023 |
2f1f28f5290e418e1c15650f40dbea92e582606924c4dc95129744d29f51ad62 | Text | 17 | 2 | # MRABeta tools
|
2ebeb1a1008df9a648909091101d1c5616a82719621f513d06f745254b665e76 | Text | 18 | 1 | # neuro-ontologies |
e19ed26eab41d522c7ccf1d021ea6050bf78725aa35030701de02ffc65776ef4 | Text | 20 | 1 | # Varin_2023_NatComm |
c4068a5a191df030a26332888525755fe19f8583043fde777778417d1c7e98a6 | Text | 41 | 2 | # surfmorph
Surface morphometry BIDS app
|
8286c46ae5ebb30ba8c80e0cb73cbe98cef7f872250cdc3f13d07ecf9d117de9 | Text | 50 | 2 | # NeuralCollapse
An algorithm for Neural Collapse
|
e369855ccaa18c1295b9f27353ca5aa734f9a87550d4cfd14ee6b1ae3c9b504a | Text | 52 | 2 | # energetics-atrium
Modeling atrial cell energetics
|
6815405756aec8e8eb921a46ff820c863cdb941e0b5e91eed67213043f38a506 | Text | 53 | 3 | # Watanabe_Kadohisa_Kusunoki_Buckley_Duncan
Raw data |
67b9171704a4fd97f2145e26b2eff2ed3b58e838ebae0684ef6bce0ad1150e36 | Text | 67 | 2 | # bbi-review-2023
Monroe et al (in review) Brain body interactions
|
7b27f7f4a47dd0d5c3e3a41dcae591d65c77a78805bb0a0eee31a12c10d1fc64 | Text | 68 | 2 | # SMAE-NN
Here you find a neural network to sort food by food group
|
c0a5700beef40f77607ee40ee4f748d6b8dc5ab17fd1139e2fec1b5ea89cc28a | Text | 100 | 2 | # Jovanovic_methods
Preprocessing and analysis pipeline for Jovanovic et al. astrocytes manuscript.
|
87663b97b8aa31a361533dcd05ea0f04a8e567d95d5f82793c94e6a6416c218d | Text | 120 | 6 | # crp_scripts
Canonical Response Parameterization Code
Read the file kjm_CRP_code_and_data_description.pdf
kjm 8/2022
|
5cbc9732c28e00099e54f10417414934a0f601055941c52e0fcf33d08ae732aa | Text | 121 | 2 | # plz
"plz" is a small and simple, gui-based task-runner for python, developed primarily for metabolomics data analysis.
|
bedf2e5c996d08fc8c2a10d4aa676d1f9573c73e4a49f99e13a9d55fa6560cbf | Text | 135 | 2 | # real-time-indoor-localization-catadioptric-vision-sensor
Real-Time Indoor Localization with a Catadioptric Vision Sensor source code
|
e1921334ecf16b34959fd842a8dfa36af84807472d260f1f7672076f7979adf1 | Text | 145 | 2 | # MultimodalCorr
This code is for PLSR among neuroiamging, gene expression, and behavior features and the sptial correlation between brain maps.
|
4aacf05251906ac820f198be284115c245b0a8741c5834e7cc2a2b8ec3fedf92 | Text | 146 | 5 | # arduino-scripts
A compilation of useful C++ scripts, mainly for lab work (Arduino Uno/Teensy).
Let me know if you have issues using the code.
|
6b50433c9f807c77a0f5d80c2d94489d82c9729e45506d791cbd7050d0c149cd | Text | 170 | 2 | # NetBPAbrain
This repository will contain the Network analysis for evaluating the impact of Bisphenol A (BPA) during the neuronal human embryo development by using iPSC
|
b5ec955d0d352cc5005b7d41c83ee6b8684bcb7bed5c50c15e25a0857c93d4af | Text | 185 | 3 | # SwineCoVadaptation
Risk assessment of pigs as possible intermediate host role for coronaviruses with a deep learning predictor.
Data was uploaded to Zenodo (10.5281/zenodo.18993097).
|
ca47b8fd01c211365952b982e12529f3cb2e40d4ed1a13b5b8cc4e4065c9371e | Text | 191 | 2 | # proj-emosymp
code for analyzing the fMRI study on IAPS picture-induced somatic symptoms in healthy controls and patients with functional somatic syndromes (Bogaerts, Van den Houte, et al).
|
5efd97477e789a1a2340630d1fe72bc086f9c44151cf4fdcbf662a9069c2e248 | Text | 204 | 5 | # Operant-Box-Design-Files
## Inner box assembly
<p align="center" width="100%">
<img src="https://user-images.githubusercontent.com/71041273/112739518-46667f00-8f75-11eb-9356-108044e2183b.gif">
</p>
|
9e9095b7dd3666df6365145e54674d28b495e243e72c6e536f78a61890f151cc | Text | 209 | 6 | # ArborTools
Matlab app that provides a semi-automated way to segment and analyze 3D arbors in 3D image stacks.
See the PDF manual for more information. BY-NC-SA 4.0 license.
David C Alston
UofL Neurobiology
|
9b52598c486ecee7d6654a21caec33e010c63be6ae10720362e42aa5d833517e | Text | 217 | 1 | This repository provides scripts for the analysis in "Shen et al. Brain and molecular mechanisms underlying the nonlinear association between close friendships, mental health, and cognition in children. eLife, 2023."
|
6dd9e9a1c431e103566732fddd69b6a1b27c31e6ed271f1ca847719121c84584 | Text | 223 | 3 | # Sentinel-S1-S2-ML-patches-workflow
This Python workflow prepares Sentinel-1 and Sentinel-2 layer stacks described in the article "Machine learning-ready remote sensing data for Maya archaeology" by Kokalj et al. (2023).
|
0ca7b5bb4a5031b7791b2ee3c3abedabeba94f23985ec8e6f8030bd27957dcba | Text | 242 | 6 | # ratpack
Pack of vector rats (Long Evans, mostly) for use in publications, illustrations, etc.
This is a work in progress. More rats will be added as time permits.
|
b3b33e7bc12a844034b2d03c5152e93b14d3428994ef9b4142771e0e7221818b | Text | 254 | 8 | # grayspace
If you have any questions, please reach out to the corresponding author (contact information below):
Julia C. Harris
Department of Psychology
University of Wisconsin-Milwaukee
2441 E. Hartford Ave.
Milwaukee, WI 53211
harri585@uwm.edu
|
2589549575f4dbed1364b2fa74f9ae11cf930cf947b2b3af7c2f70453d754654 | Text | 272 | 4 | # chemPredPD
An open source code (including demo) repository to predict mechanistic subtypes of Parkinson’s using patient-derived stem cell models.
Data used for the codes are deposited in doi.org/10.5281/zenodo.7419421
Instruction is included in the README.docx file.
|
3a04d838fa9c54f9248694cc495b0d0dacb4fc1d89946d6c690dccefa9034b50 | Text | 277 | 6 | # OptogeneticExcitability
Simulate single cell in a 3d light field with arbitrarily opsin distribution
# Models
Base models are migliore cell models from paper of migliore 2018
## Model/MiglioreModels
https://live-papers.brainsimulation.eu/#bee280cc-8184-4380-a2cb-a74b131de611 |
a4e5c5a4d0ec8280c468c71dbd21330ef6449def4c582ee109eaf0de3ac83223 | Text | 277 | 2 | # epileptic-brain-states
Code and variables for the paper Dallmer-Zerbe, I., Jajcay, N., Chvojka, J. et al. Computational modeling allows unsupervised classification of epileptic brain states across species. Sci Rep 13, 13436 (2023). https://doi.org/10.1038/s41598-023-39867-z
|
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