sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7c623f9b5c074421f6ccc6dbf9514fab1831433b21f1ea923ccd19e6993129d8 | Shell | 6,638 | 212 | #!/bin/bash
#
RUN="0";
INSTALL="0";
THREADS="8";
READS1="";
READS2="";
GID="NC_012920.1"
OUTPUT="damage_analysis";
#
################################################################################
#
SHOW_MENU () {
echo " -------------------------------------------------------- ";
echo " ... |
a29e0c47beaf7016ca13f0056079baefe064bcf8dedd40523113fd684a62a311 | Shell | 6,846 | 242 | #!/bin/bash
#
HELP="0";
INSTALL="0";
OUTPUT="G";
RUN="0";
THREADS="8";
READS1="";
READS2="";
HOST="";
VIRUS="";
#
################################################################################
#
SHOW_MENU () {
echo " ------------------------------------------------------- ";
echo " ... |
fb69fc7e2084b2cf24cb449a52697c91c6682a2899eb6aec8f1ff6ca4a1ad422 | Shell | 6,864 | 108 | #!/bin/bash
until [ $# -eq 0 ]
do
name=${1:1}; shift;
if [[ -z "$1" || $1 == -* ]] ; then eval "export $name=true"; else eval "export $name=$1"; shift; fi
done
python ./src/main/python/openresearch/convert_pubmed_dblp_to_anserini_format.py \
--output_folder=${output_folder}/anserini_format/pubmed_title \
--... |
57da77cfbc5ff5cb30b45ec59d14dc25d2e8027639a803900b48b61bdc4ab0eb | Shell | 6,956 | 197 | #!/usr/bin/env bash
# =============================================================================
# run_glm_slurm.sh — Slurm launcher for run_glm.py
#
# Usage:
# bash run_glm_slurm.sh -n <analysis_name> -s 01,10
# bash run_glm_slurm.sh -n <analysis_name> -f subseslist.txt
# =====================================... |
2ef915088340e0ac14de231505a312f31c8b1a4b837c43ebba1387064956d356 | Shell | 7,021 | 185 | #!/bin/bash
#
# ==============================================================================
# | |
# | THIS PROGRAM COMPUTES COMPLEXITY AND RELATIVE COMPLEXITY PROFILES |
# | =======================================================... |
9daa01d7913a58466d604a015f295ad3ca7bf17c24c28e4eac7348c90be82687 | Shell | 7,037 | 180 | #!/usr/bin/env bash
# =============================================================================
# launch_glm_local.sh — Local launcher for run_glm.py
#
# Usage:
# bash launch_glm_local.sh -n <analysis_name> -s 01,10
# bash launch_glm_local.sh -n <analysis_name> -f subseslist.txt
# bash launch_glm_local.sh -... |
395e1aebf89388e2eeba7a563bd8384d9b218ed62a02477bca83f4baea0b39b0 | Shell | 7,134 | 204 | #!/usr/bin/env bash
# =============================================================================
# launch_glm_ips.sh — SGE (qsub) launcher for run_glm.py
#
# Usage:
# bash launch_glm_ips.sh -n <analysis_name> -s 01,10
# bash launch_glm_ips.sh -n <analysis_name> -f subseslist.txt
# bash launch_glm_ips.sh -n <... |
fc656dc264b9039f6ecbe3abb00462fb8f8dc929715edbc7cedf527a00464ac0 | Shell | 7,240 | 181 | ## software versions used in this pipeline:
# sratoolkit v2.9.2 https://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/2.9.2/sratoolkit.2.9.2-centos_linux64.tar.gz
# bwa-mem v0.7.15 https://sourceforge.net/projects/bio-bwa/files/bwa-0.7.15.tar.bz2/download
# picardtools v2.2.2 https://github.com/broadinstitute/picard/releases/tag... |
f84056988d51843a8cf17080842536c3f2ee06f56a42d7c2311420ccf91eef95 | Shell | 7,326 | 195 | #!/bin/bash
#
# INSTALLATION DEPENDENCIES:
#
# sudo apt-get install libopenblas-base
# conda install -c 'bioconda' art
# conda install -c 'bioconda' mummer4
#
RUN_ALL=1;
RUN_TRACES=1;
#
zcat MINIDB.fa.gz > ../src/VDB.fa
cd ../src/
#
if [[ "$RUN_ALL" -eq "1" ]];
then
./TRACESPipe.sh --get-all-aux
#
gto_fasta_ext... |
bcc4bc9f75e1f3cb4c98df15217818b84b8cec39d13cf63ac1b7554b751ce67d | Shell | 7,426 | 200 | #!/usr/bin/env bash
# =============================================================================
# launch_allses_glm_FIR_local.sh — Local launcher for run_allses_glm_FIR.py
# + fir_curve_metrics.py
#
# Fits ONE FIR GLM per subject (all sessions/runs concatenated) for VOTCLOC
# t... |
784930e9cc4a619e6bbf179381ab1274f7eedccdbe123aa7b86c70d71de1b01a | Shell | 7,529 | 74 | #!/bin/bash
set -e
cd ..
# for debugging use `dryrun`, otherwise use `run`
WANDB_MODE=run
GROUP=resize-ablation
ENV=adrian-exodus
EPOCHS=200
BATCH_SIZE=256
TRAIN_TAIL="--dataset casia --group $GROUP --mode $WANDB_MODE --batch_size $BATCH_SIZE --epochs $EPOCHS --env $ENV"
EVAL_TAIL="--eval_config configs/evaluation... |
cb5e0f18d3c1667e81cced6c1a0806a207da6fb30936c861508be67bd0e5d170 | Shell | 7,573 | 72 | #!/bin/bash
set -e
cd ..
# for debugging use `dryrun`, otherwise use `run`
ENV=adrian-exodus
BATCH_SIZE=512
GROUP=$1
ARCH=$2
DATASET=$3
CONFIG=configs/model_configs/$ARCH.yaml
OUTPUT_DIR=$GROUP
#############################################################################################
##########################... |
b2f1f69cfabf7e2fd10e54e4d8c6e445a8e28529c82e6186bb7dde65e0653f08 | Shell | 7,789 | 200 | #!/usr/bin/env bash
# =============================================================================
# launch_tedana_local.sh — Local launcher for run_tedana.py
#
# Usage:
# bash launch_tedana_local.sh -n <analysis> -c <cores> -r <runs> -s sub,ses
# bash launch_tedana_local.sh -n <analysis> -c <cores> -r <runs> -f... |
740d575506eea7cfc4667d0b17697fa92e1d7592df3427bffc402d21f3f4b9d8 | Shell | 7,856 | 169 | #!/bin/bash
# Build the pwiz-sharp Linux packages. Linux counterpart of scripts/installer/build.ps1.
#
# Mirrors the Windows pipeline and its two-variant split:
#
# Windows Linux
# ProteoWizard-Setup-<ver>.exe ProteoWizard-linux-x64-<ver>.tar.gz
# bundles the... |
af657b666cfec0f7e195fac98b83c4e1494985738ff2308525e469287474384e | Shell | 7,870 | 221 | #!/usr/bin/env bash
# =============================================================================
# launch_allses_glm_slurm.sh — Slurm launcher for run_allses_glm.py
#
# Submits ONE job per subject. All sessions for that subject are concatenated
# into a single GLM inside the job. Sessions are resolved in priorit... |
ffed73aabf2724a46a85410ad4dbc65f87b2d59cf8fe99497543c7a37081d41e | Shell | 8,048 | 168 | #!/usr/bin/env bash
# ------------------------------------------------------------------------
# ensure-dotnet.sh - shared dotnet resolution + SDK provisioning for the Linux
# entry points (build.sh, tcbuild.sh). SOURCE this file; do not execute it.
#
# Two separate problems, deliberately kept apart:
#
# resolve_dotn... |
03b8f6f41f8ab3b04944fc6f799f88c2e041f601dc91030fedda75c091211850 | Shell | 8,059 | 225 | #!/usr/bin/env bash
# =============================================================================
# launch_allses_glm_slurm.sh — Slurm launcher for run_allses_glm.py
#
# Submits ONE job per subject. All sessions for that subject are concatenated
# into a single GLM inside the job. Sessions are resolved in priorit... |
d023b1e5be4848fa5de9d48230f6a7091966a830bbe60d91cc3eff839f213fac | Shell | 8,294 | 225 | #!/usr/bin/env bash
# =============================================================================
# launch_power_analysis_slurm.sh — Slurm launcher for power_analysis_ohbm.py
#
# Submits ONE job per subject. All sessions for that subject are concatenated
# into a single 90-run design matrix; the power loop runs in... |
f51f3148d35cb0f45afbcf8e3696b217ce09f1fd09f04b588956fd00d950e99f | Shell | 8,373 | 228 | #!/bin/bash
#
# ==============================================================================
# | |
# | THIS PROGRAM COMPUTES COMPLEXITY AND RELATIVE COMPLEXITY PROFILES | # | ... |
b48ba62c0a68acff90e78baea185b452d97e0e1cfccf3f11c595491ef5edc87d | Shell | 8,977 | 232 | #!/usr/bin/env bash
# =============================================================================
# launch_glm_ips.sh — SGE (qsub) launcher for run_glm.py
#
# Usage:
# bash launch_glm_ips.sh -n <analysis_name> -s 01,10
# bash launch_glm_ips.sh -n <analysis_name> -f subseslist.txt
# bash launch_glm_ips.sh -n <... |
5fa2e3e7b74871586e2cb0d23716d75360fc0a2ec2b3de79db4f0626bc311708 | Shell | 9,109 | 292 | ## software versions used in this pipeline:
# picardtools v2.21.9 https://github.com/broadinstitute/picard/releases/tag/2.21.9
# GATK v3.7 https://console.cloud.google.com/storage/browser/gatk-software/package-archive !Google Cloud Account necessary!
# GATK v4.1.7.0 https://github.com/broadinstitute/gatk/releases/tag/... |
4df662ebb2337a5ae8677a8770552bd4d8ba6301ce6b5ccb2a67c476e7347f0f | Shell | 9,444 | 226 | #!/usr/bin/env bash
# =============================================================================
# launch_glm_local.sh — Local launcher for run_glm.py
#
# Usage:
# bash launch_glm_local.sh -n <analysis_name> -s 01,10
# bash launch_glm_local.sh -n <analysis_name> -f subseslist.txt
# bash launch_glm_local.sh -... |
83019ef7f909bc5f5671ca91046f5841daac5a76b39e426cbeffed29d19cfcad | Shell | 9,530 | 52 | wget -qO- ftp://ftp.ensemblgenomes.org/pub/fungi/release-24/fasta/ashbya_gossypii/dna/*.dna_sm.toplevel.fa.gz | gunzip -c | sed "s/>/>Fungi:ashbya_gossypii:/" >> Fungi.fa
wget -qO- ftp://ftp.ensemblgenomes.org/pub/fungi/release-24/fasta/aspergillus_clavatus/dna/*.dna_sm.toplevel.fa.gz | gunzip -c | sed "s/>/>Fungi:aspe... |
4b05096823c952c7ed78b27e9c46993c2ecc79fe770d1fa27835f806299147d1 | Shell | 9,545 | 217 | #!/usr/bin/env bash
set -euo pipefail
eval "$(micromamba shell hook --shell bash)"
micromamba activate smcore
core start server --addr "$BB_ADDR" > "$ARTIFACT_DIR/bb.log" 2>&1 &
BB_PID=$!
echo "$BB_PID" > "$ARTIFACT_DIR/bb.pid"
python -m simplemind.cli.start_payload_store \
--host "$PAYLOAD_HOST" \
--port "$PAYL... |
2f0a9af3b555023c56aa79ea834ee4c809c27953660b132c3148e015ebbc4bbe | Shell | 9,817 | 246 | #!/bin/bash
#
# ALIGNMENT OF READS
#
# IT REQUIRES THAT THE FOLLOWING FASTQ FILES EXIST In the currect diectory (FOR INPUT):
# o_fw_pr.fq o_rv_pr.fq o_fw_unpr.fq o_rv_unpr.fq
set -o pipefail
FullCall="$0 $*"
ScriptName="$(basename "$0")"
function Log {
msg=$1; shift
lvl=$2; shift
[ -z "$lvl" ] && lvl="ER... |
db1f8171caf7fe2b047e3e9e6d3aba23be11839608734472baacd2bdb269b5b5 | Shell | 10,144 | 230 | #!/usr/bin/env bash
set -euo pipefail
eval "$(micromamba shell hook --shell bash)"
micromamba activate smcore
core start server --addr "$BB_ADDR" > "$ARTIFACT_DIR/bb.log" 2>&1 &
BB_PID=$!
echo "$BB_PID" > "$ARTIFACT_DIR/bb.pid"
python -m simplemind.cli.start_payload_store \
--host "$PAYLOAD_HOST" \
--port "$PAYL... |
51e3699344a6eee9eaf6b31b7f4f1ba4ad5564174d4163371bb5ba936273f052 | Shell | 11,320 | 291 | #!/bin/tcsh -xef
#The user may specify the following individual details, to perform preprocessing of multiple run per subject
#All details change depending on the folder path to follow
set monkey = (m16) #ID of the monkey
set name = (After) #Name of monkey
set session = (s30) #ID of the session
set run = (r1 r2 r3) #... |
981c29c067a26319eed58c342331a6decf0e751cce011e695e8ac7e2fe4d284f | Shell | 11,465 | 282 | #!/usr/bin/env bash
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
ARTIFACT_DIR="$SCRIPT_DIR"
REPO_ROOT="$(cd "$ARTIFACT_DIR/../../../.." && pwd)"
BB_ADDR="${BB_ADDR:-127.0.0.1:18082}"
PAYLOAD_HOST="${PAYLOAD_HOST:-127.0.0.1}"
PAYLOAD_PORT="${PAYLOAD_PORT:-18090}"
ACCOUNTING_CSV="$ARTIFAC... |
1796890a729eff2a1d11d64c90b5c462ad4cd52cc2f87f76ac600697a158ee3a | Shell | 12,005 | 122 | #!/bin/bash
set -e
cd ..
# for debugging use `dryrun`, otherwise use `run`
WANDB_MODE=run
GROUP=ablation_final
ENV=adrian-exodus
EPOCHS=50
BATCH_SIZE=256
TRAIN_TAIL="--dataset casia --group $GROUP --batch_size $BATCH_SIZE --mode $WANDB_MODE --epochs $EPOCHS --env $ENV"
EVAL_TAIL="--group $GROUP --env $ENV --eval_co... |
221ea1b44cf026394dfa1368f7988b95ca46cb1d158c7a478ec8f5e12fb52aa8 | Shell | 12,166 | 297 | #!/usr/bin/env bash
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
ARTIFACT_DIR="$SCRIPT_DIR"
REPO_ROOT="$(cd "$ARTIFACT_DIR/../../../.." && pwd)"
BB_ADDR="${BB_ADDR:-127.0.0.1:18082}"
PAYLOAD_HOST="${PAYLOAD_HOST:-127.0.0.1}"
PAYLOAD_PORT="${PAYLOAD_PORT:-18090}"
ACCOUNTING_CSV="$ARTIFAC... |
3e30324f338213dc8c37960b8e06da1ac6d376d9cb6f6ccd8d4a0b8d4251f4be | Shell | 12,373 | 238 | #!/usr/bin/env bash
# ------------------------------------------------------------------------
# pwiz-sharp build entry point for Linux. Analogue of build.bat; TeamCity
# calls it from tcbuild.sh (Core Linux x86_64, bt17). Runs locally too.
#
# Usage:
# ./build.sh [Debug|Release] [--i-agree-to-the-vendor-licenses]
# ... |
718baaae519c169173da2e6dbda83a1f319a12d9b43677ca47d9e8c10e09c179 | Shell | 13,327 | 127 | #!/bin/bash
set -e
cd ..
# for debugging use `dryrun`, otherwise use `run`
WANDB_MODE=run
GROUP=ablation_final
ENV=adrian-exodus
EPOCHS=50
BATCH_SIZE=256
TRAIN_TAIL="--dataset casia --group $GROUP --batch_size $BATCH_SIZE --mode $WANDB_MODE --epochs $EPOCHS --env $ENV"
EVAL_TAIL="--group $GROUP --env $ENV --eval_co... |
a2b27619181e1a6d10ba540720dfe339fb801c878807718f20274646b6da846a | Shell | 13,699 | 310 | #!/bin/bash
#
# ##############################################################################
# # ========================================================================== #
# # = = #
# # = AUTOMATIC DETECTION OF SAMPLE COMPOSITION IN THE READ... |
b5075524bbebfb9f18f48215ec0705c55ffa29a3f383bf0b5b895324d0be2890 | Shell | 14,209 | 442 | #!/bin/bash
#==============================================================================
# Script: run_prf.sh
# Description: Launch PRF analysis pipeline with configurable steps
# Usage: ./run_prf.sh <cluster> <step> <log_note> <subses_name> [task]
#==================================================================... |
5c299cc663989ed44785450036c81e05bd8d9b5c656219aacfac0783028889bd | Shell | 15,482 | 466 | #!/bin/bash
#
# Vendor reader conversion sweep.
#
# For every vendor test fixture, convert it with msconvert and compare the result against
# the reference mzML checked in beside it with msdiff. This is what the TeamCity "Run
# ProteoWizard tests" step of the container build runs inside the image. It lives in the
# rep... |
0e6958f70317b9d676e8bf5631295d12a5ef460f1794babc21e3dedaba3765d6 | Shell | 17,060 | 355 | #!/bin/sh
# Script: generate_images.sh
# Purpose: Generate Docker and/or Singularity container definition files for a given project
# Description: This script uses neurodocker to create reproducible container definitions
# that include the OS and all dependencies
# Exit immediately if any command fails
#... |
454a6c96f89d0e876e8fdc6d2a0ac682066c59159de005df13b3502192108fff | Shell | 25,077 | 308 | #!/bin/bash
echo " "
echo -e "\e[34m "
echo " ████████╗ ██████╗ █████╗ ██████╗ ███████╗ ███████╗ "
echo " ╚══██╔══╝ ██╔══██╗ ██╔══██╗ ██╔════╝ ██╔════╝ ██╔════╝ ... |
ebda748b1a6e7efe1ffc915770f5e550e76ec3ebb88b6bdf87767a85b475ae3f | Shell | 25,721 | 686 | #!/bin/bash
#
STUDY="data";
RUN="0";
INSTALL="0";
THREADS="8";
PLOTS="0";
READS1="";
READS2="";
FILTER_HUMAN="0";
HUMAN_FASTA="chm13v2.0.fa.gz";
DATABASE="VDB.mfa";
OUTPUT="out_analysis";
MIN_LEN="20";
BWA_N="0.02";
MIN_SIMILARITY="3.0";
MAX_COVERAGE_PROFILE=0;
COVERAGE_MIN_X="0";
COVERAGE_MAX="1000";
COVERAGE_LOG_SCAL... |
f28b6d9bc6856f6eac5e5649199f629d3d90807a4b4081d5526a741d32645b9c | Shell | 37,099 | 33 | #!/bin/bash
#SBATCH --time=00:10:00 # walltime
#SBATCH --ntasks=1 # number of processor cores (i.e. tasks)
#SBATCH --nodes=1 # number of nodes
#SBATCH --mem-per-cpu=16384M # memory per CPU core
#SBATCH --mail-user=petemade@isu.edu
#SBATCH --mail-type=END
# Compatibility variables for PBS. Delete if not needed.... |
bacdb6d1b5bd0d2de03957ad70a3f3c9c044fdd575bb9c9658099a76c365910e | Shell | 37,117 | 33 | #!/bin/bash
#SBATCH --time=00:10:00 # walltime
#SBATCH --ntasks=1 # number of processor cores (i.e. tasks)
#SBATCH --nodes=1 # number of nodes
#SBATCH --mem-per-cpu=16384M # memory per CPU core
#SBATCH --mail-user=petemade@isu.edu
#SBATCH --mail-type=END
# Compatibility variables for PBS. Delete if not needed.... |
f27007dd2ba7ef4487207f8e9ec472e11861abde7800d717095b5f45e6ea281d | Shell | 39,976 | 225 | #!/usr/bin/bash
java -cp `ls target/*-fatjar.jar` io.anserini.search.SearchCollection -threads 16 -index msmarco-v1-passage -topics msmarco-v1-passage.dev -output runs/run.msmarco-v1-passage.bm25.msmarco-v1-passage.dev.base.txt -hits 1000 -bm25
java -cp `ls target/*-fatjar.jar` io.anserini.search.SearchCollection -thr... |
b6b26aee2e41f4abd0af77f9328029bcca283e350ce8969506ab549c311a67d5 | Shell | 86,312 | 2,357 | #!/bin/bash
#
##################################################################################
# ============================================================================== #
# = = #
# = T R A C E S P i p e ... |
74caa224f0d55184412cb43accf224801477055d5865f5ce7dc55ac32d1f0f13 | Text | 6 | 1 | # Bpod |
efc2b9678326dfd6c91b7655791d9618255e9b464ba76019654a322c678cf2a4 | Text | 7 | 2 | # DSC
|
0397eb309b83771a75aac68bfc95cb35a1eb478504f2ee0ed696c66bd9d38e4d | Text | 13 | 1 | look at /beta |
fcdfccc5fabb6c65d189e306e56ca6eb3318d3bb58575a8c3f3d6615d53ec80d | Text | 19 | 1 | # Deep_Brain_SigNet |
e377c375978284afb411692ea2028e9289cece7dee5d05f91eef05043a70ba16 | Text | 49 | 2 | # hardware
Files for laser cutter and 3d printer
|
f36dc83092eb080023010b3fac61f8d13778366cf871733479817768e26f928b | Text | 57 | 2 | # ANMIN
Analytic Optimization of Two Layer ReLU Networks
|
c211a2e388ee44972d483b354a7061fdb5f688a4974e4bec33a263136da97528 | Text | 68 | 2 | # fiberphotometry
Scripts used in Ferguson et al. (2023) eLife doi:
|
c1722a3c9db134ca3804d06e9d8ebe3547ad7a83b3c6925413e84d38bc48baca | Text | 79 | 2 | # NICAG
Code for the paper "Neuro-inspired Continual Anthropomorphic Grasping"
|
757eefbdf6c5e58f3b2f9d9ef6fc53a8bc03370108eb56f61f1e3628439ebc2b | Text | 95 | 2 | # Methods for Deng et al.
Analysis scripts from analyzing Tao Deng et al. nociceptor iPSC data
|
c1f2eaddc0347d6a165d39fc830e6591d53a4841c65587694255948ea9425c49 | Text | 103 | 2 | # NEcalc
source code for the NE score calculator web application at https://lccl.shinyapps.io/NEcalc/.
|
81183957b325dd72fa5071fd71d03294bb1b469707a2d85d8d535f95755d6358 | Text | 118 | 2 | # communities_modelSd
Code from paper « Dimension matters when modeling network communities in hyperbolic spaces ».
|
8618c8d6bcfd033fac1e1b62451784b589f9792bb0f843fed7bfc8e829dc02a4 | Text | 122 | 2 | # batch-selection-experiments
Code to run experiments for event-camera event-batch selection for camera motion estimation
|
f3d0146da4472eea274534dcade9e72bf8ad9817b35f3d8a642833a9d7d8c036 | Text | 125 | 3 | # AxSI
Currently the analysis is available as MATLAB scripts.
Soon, a Python version to be uploaded, including dependencies.
|
dccfb06995801b1b8465a94d8ebe886a5415b56882bbce1f8799f8627500d446 | Text | 136 | 5 | # fcn-core
Fully convolutional neural network core for time series analysis
Described in this paper: https://arxiv.org/abs/2001.05517
|
971a637beeedc2f8aae5f62c6d0674329dbeafca507cf5de996f172515f27e4f | Text | 146 | 4 | # SexRankBrain
This is a repository for SexRankBrain web apps.
The link for SexRankBrain web apps is https://joshiapps.cbu.uib.no/GenderRank_app
|
d50c74ee92f36cd468377d40d653c5c9a36e19bb175bf4fa502429f9f955bfb5 | Text | 149 | 7 | Create a conda environment:
conda create --name zeolites_env python=3.8
Install all the necessary requirements by
pip install -r requirements.txt
|
c716dd579303144b7f571e43bf9a9339c1bdcd73aa09bc8a2acb07dad512a95a | Text | 155 | 2 | # PBM_economic_evaluation
Analysis code for an economic evaluation of increasing utilization of kidneys from deceased donors with primary brain malignancy
|
fad54dcaa44bfa6f40ce7279d51ae14ae68948c9a6f1868551567709055e9eb0 | Text | 163 | 3 | # DELMEP: A deep learning algorithm for automated annotation of motor evoked potential latencies
MEP latency automated annotation using a deep learning algorithm
|
eeb744a7ea275f0f665247656d7ec455bbdbc1949c9346709d3dfa6f3ba20995 | Text | 172 | 2 | # Godbersen2023_eLife
Code from: Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks
|
ffb1b82b5fbdf78ab850576941b227fe6de2642822c141f8c1697dc522defe6b | Text | 201 | 5 | # aortic arch generator
This is the repository accompanying our 2023 IJCARS submission.
**Will be integrated in endovascular Simulation Framework shortly. Will be maintained and documented there.**
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3710b18d0fba1be09c7feb0e522370343f731a0d927afb1689bf0d77fa1786da | Text | 214 | 4 | [](https://zenodo.org/badge/latestdoi/349494821)
# communities_modelSd
Code from paper « Dimension matters when modeling network communities in hyperbolic spaces ».
|
37818c6eda5e1e42000a268b9e023087d5565f023d214795a84c0cd911668fd7 | Text | 245 | 4 | # Crossmodal_Correspondences
Accuracy and reaction time data (reaction time and linear integrated speed-accuracy score) for 44 participants (25 musicians, 19 controls) from a decision reaction time task.
Musicians are coded as 1, controls as 2 |
0f731a1c75bb5a242005a1701b286c392d71b1c75464d1b1b7568a39c3cd2731 | Text | 275 | 4 | # preventing_profiling_ethical_FND
This repository accompanies the paper "Preventing profiling for ethical fake news detection", published in Information Processing & Management
Data splits used for training, validating and testing models can be found in the txt files.
|
4ca513a3599a7c6ab7aaf87338e8dc9aac8876baf0bdb4204b9e24929f8214e9 | Text | 280 | 2 | # TMS-EEG-processing
Analysis and visualization codes from the research artile "Orbitofrontal cortex-hippocampus potentiation mediates relief for depression: A randomized double-blind trial and TMS-EEG study" It is published in Cell Rep Med.(2023) DOI: 10.1016/j.xcrm.2023.101060
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f2130b5a7275861ec4a8f6f9c6d0ece0d2e70571bd3bf634d5b102e92ea81bbd | Text | 288 | 10 | # (2023) Neural dynamics underlying self-control in the primate subthalamic nucleus, eLife 12:e83971.
Authors: Benjamin Pasquereau and Rob Turner
https://doi.org/10.7554/eLife.83971
Matlab codes used to analyze behavioral and neuronal data .
-plot_behavior.m
-plot_ANOVA.m
-plot_PCA.m
|
d41fd08d92dc1eea9a9e795cffdbf31d652278da32d283c4955e030ef4335111 | Text | 290 | 7 | # mnl_seegview
SEEG plotting tools in canonical AC-PC slices
Step-by-step sections in seeg_view_master.m script
Please cite:
Huang H, Valencia GO, Hermes D, Miller KJ (2021) A canonical visualization tool for SEEG electrodes. Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov; 2021:6175-6178. |
a857d7fd24581ac81e418fda5b916b56364c57b73784b8cde2da595ac3ec4c8e | Text | 339 | 3 | # firefly-monkey
This repository contains datasets containing behavioral and neural data from monkeys performing a virtual navigation task. Data are stored according to the data structure defined in https://github.com/kaushik-l/firefly-monkey. Please contact the author of that repository at jl5649@columbia.edu if you ... |
c90c6a5431ac2c76cb666d599a8dc8fe8383533cdd42179e20a4a1f127ccd126 | Text | 342 | 14 | # SCITRAN
Utilities to interact with Flywheel from the client side using the Matlab SDK.
## Installation instructions
See wiki in https://github.com/vistalab/scitran/wiki/Installation
## Usage
See wiki in https://github.com/vistalab/scitran/wiki
## TO-DO
1. Generalize and merge functions in branch garifromafqDim
2. ... |
15ba63dcac587ec974353b96c12c600cab4fc2e6e9cbe7d598ba5e28150637f0 | Text | 354 | 5 | # SliceTimingCorrection
[](https://mybinder.org/v2/gh/poldrack/SliceTimingCorrection/HEAD?filepath=SliceTimingSimulation.ipynb)
This repo contains a notebook outlining issues around slice timing correction and event onsets in fMRIPrep. For more detail, see https://github.... |
3a8621df362d7b106f5dbc1dcd4e0878c21950c3e8f27302f2d6c29f7f33f6a5 | Text | 379 | 7 | # TactileLSN
## Citation ##
Kang, Peng and Banerjee, Srutarshi and Chopp, Henry and Katsaggelos, Aggelos and Cossairt, Oliver (2023) "Boost event-driven tactile learning with location spiking neurons". Front. Neurosci. 17:1127537. doi: 10.3389/fnins.2023.1127537
Please find codes for Hybrid_SRM_FC in main @ d75039b a... |
695f9263fca01af25048978bde2f1822ef928a5c073ef117977a909ce63657e8 | Text | 384 | 5 | # AE-GCN
AE-GCN (Autoencoder-assisted graph convolutional neural network) is a python tool for spatial transcriptomics analysis.
The demo dataset (Invasive Ductal Carcinoma Stained With Fluorescent CD3 Antibody) is available on [10x Genomics website](https://www.10xgenomics.com/resources/datasets/invasive-ductal-carc... |
09c5772d9935d996f0d99f7403df27c5198585ff723b97724b2f95ebcfb70116 | Text | 405 | 9 | #To reproduce the species tree :
#make sure you have iqtree v2.2.0 installed
iqtree -s Concatenaed_alignment.faa -p partitions.txt -B 1000 -m MFP+MERGE
iqtree -s Concatenaed_alignment.faa --date DATE_FILE.txt -te partitions.txt.treefile -p partitions.txt.best_scheme.nex --date-tip 0 --date-ci 100 -o "Callorhinchus_m... |
c06606ddc3834236d3b193849c3c7626c5254661c16e6ff28f019d9db7e52e9b | Text | 406 | 10 | # PNAS_VU595_code-data
Data (.mat) and scripts (.m) used in the 2023 VU595 PNAS paper.
Main script: 'VU595_analysis_PNAS.m'
Supporting scripts: 'SEM_AH.m' & 'FilterLearningCurves.m'
Data files: 'VU595_data_Bard.mat', 'VU595_data_Sindri.mat', 'VU595_data_Igor.mat', 'VU595_data_Reider.mat'
Pleaes contact Dr. Ali Hassani... |
7e39e1b27d2af6bda14221554d7c213be23707632332fd902b431d1d94388bc1 | Text | 424 | 9 | ## Licence
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/... |
34faae8fc058c7cc2e353262be71a0a4a0e5e6c47db4ea33d2390463dda73e1b | Text | 446 | 7 | # ImageJ
These ImageJ macros are used for semi-automated placement of ROIs for synaptic puncta, background subtraction of image series, and averaging of image series for experiments performed in replicate.
Descriptions of the macros and their use can be found within their respective files.
These macros have been pub... |
5df238139011090abdfbd27468a36918699d8f7af1795d7f67e499c0ae2e315d | Text | 451 | 9 | # EnsemblTSSPrediction
[](https://zenodo.org/badge/latestdoi/348648515)
TSS Prediction using Ensembl data.
Dataset: https://universidaddeburgos-my.sharepoint.com/:f:/g/personal/jabarbero_ubu_es/EmhwnagwrBhCiIzVddvoR_sBpV0wNeLfUqJPIA82yBcK0Q?e=I2LCes
The steps to recreat... |
aea79791a7bc38e3c8024924cdf29ae9c193dd624aa45e10792e63f13516305b | Text | 454 | 14 | # Gerhardt et al. 2023
Data repository for DiI-CT: A bimodal neural tracer for X-ray and fluorescence imaging from Gerhardt et al. 2023 (Cell Reports Methods)
Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität zu Berlin, Philippstr. 13, Haus 6, 10115 Berlin
The data provided are:
1. Figure... |
fcd58fcf1c8a6cb482d6fc2a23b9b336c104f2a01b6be151d6220977cc8a5c66 | Text | 458 | 5 | # Multi-synaptic boutons are a feature of CA1 hippocampal connections that may underlie network synchrony
Mark Rigby, Federico Grillo, Benjamin Compans, Guilherme Neves, Julia Gallinaro, Sophie Nashashibi, Gema Vizcay-Barrena, Florian Levet, Jean-Baptiste Sibarita, Angus Kirkland, Roland A. Fleck, Claudia Clopath, Ju... |
bfafe2c7ea5db8bc108b7914daca533c9e29a4f24ae7bc2b3ae953e5773f216e | Text | 463 | 11 | # NEURON_Allen_capacitance
The scripts write to certain folders, so take a look at the scripts before printing and make sure that these folders are present.
Typically:
There is one type of script that runs NEURON (test_allen...,ball_and_stick...,somaonly)
There is one type of script that analyses these results (man... |
eeda74a4842fcf56e01f2a5656bb274f4f8e93655e517f1ccdf934cb30391592 | Text | 494 | 14 | # skeleton-to-MIP 
Generating image stacks from EM neurons for MIP search
See example R code in `FAFB_to_MIPmask.html`
Neuron morphology data available via Virtual Fly Brain: https://fafb.catmaid.virtualflybrain.org/
This script uses the natverse neuroanatomy toolbox: ht... |
505ab1fbc496f664127f81d790766f59d31e41984370ec50dfaec55eba5849bc | Text | 500 | 7 | # wheat_singlecellAtlas
Codes for Asymmetric gene expression and cell type specific regulatory networks in root of bread wheat revealed by single cell multiomics analysis. The datasets of wheat root snRNA-seq are stored at the Beijing Institute of Genomics (BIG) Data Center, Chinese Academy of Sciences, under accession... |
a683bdcc661c7e4cdd17509832f427cfde8c96a1c24828784d52a2db13edb317 | Text | 500 | 10 | # Raw sequencing data
- https://www.ncbi.nlm.nih.gov/bioproject/PRJNA784249
# main R code
- [main_figures_code.ipynb](https://github.com/ellylab/HSCR-scRNAseq-paper/blob/main/main_figures_code.ipynb)
# main R data
- [](https://doi.org/10.5281/zenodo.75219... |
ce67ae645f9ea154b76f4aee43cee62df037040623a1b720640aee6636c65472 | Text | 506 | 7 | # wheat_singlecellAtlas
Codes for Asymmetric gene expression and cell type specific regulatory networks in root of bread wheat revealed by single cell multiomics analysis. The datasets of wheat root snRNA-seq are stored at the Beijing Institute of Genomics (BIG) Data Center, Chinese Academy of Sciences, under accession... |
e8acb8c37c5f630e86978b038be57cb5bb9e6a61a9e079fb431f488f73ad0504 | Text | 518 | 9 | # bp_mood
### Analysis code for the manuscript "Mental health, blood pressure and the development of hypertension" (preprint: https://doi.org/10.1101/2022.11.04.22281936, OSF directory: https://osf.io/638jg).
**File description**
- `ukb_bp_mood.Rmd` code for the main analysis steps
- `ukb_bp_mood.html` markdown file... |
61890a6bd4adcffaf8ea8882ecaf1f4be56ba98568181d89f421fece88634c6f | Text | 537 | 17 | # SFIM_Frontiers_Neuroimaging_QC_Project
For the Frontiers in Neuroimaging special call for papers; code and instructions used for the project.
# Getting the Code
In order to retrieve this code, run:
```
git clone https://github.com/nimh-sfim/SFIM_Frontiers_Neuroimaging_QC_Project.git
```
which you will need to do i... |
a6eb2f8641e0b9dda4c76e8941e3506a1b98da4bc143c0a8ab3f5963f9a4f293 | Text | 537 | 9 |
Python code and notebook for visualizing nascent spots over the whole tissure section from the paper in [Development](https://pubmed.ncbi.nlm.nih.gov/37102683/)
Afzal Z, Lange JJ, Nolte C, McKinney S, Wood C, Paulson A, De Kumar B, Unruh J,
Slaughter BD, Krumlauf R.
Shared retinoic acid responsive enhancers coordinat... |
5ca420412b3fcb1e22f14b834c81ee8023dce2c2ace5b02e63a1bc78a18f2ae1 | Text | 571 | 6 | # hybrid-model
Efficient coding of natural scenes for neural system identification.
This repository contains scripts, notebooks and data [links](https://doi.org/10.5281/zenodo.7656868) related to our manuscript:
Yongrong Qiu, David A. Klindt, Klaudia P. Szatko, Dominic Gonschorek, Larissa Hoefling, Timm Schubert, Lau... |
1e21bfcfeca471850a7118c62f01ba1184af86b9100b2b7de83269bd5e7d9797 | Text | 575 | 9 | # celegans-sensory-code
Written by Albert Lin & Vivek Venkatachalam
From Lin et al., 2022: "Functional imaging and quantification of multi-neuronal olfactory responses in C. elegans"
Code for annotating, tracking, and extracting calcium activity from C. elegans chemosensory neurons, built on software developed for Ve... |
2f88f52ae3937340c3fe25993b98ab29fb7a2eb47e7439f1ebce40d4a5e6b14d | Text | 602 | 6 | # urban_precipitation_downscaling
This repository consists of all the codes and data required to perform urban precipitation downscaling.
High resolution (300m X 300m) urban precipitation data can be generated by using evaluation notebook in the src directory. The trained weight file can be found at data/trained_weig... |
6a788eea8d3f0a7d49ac6620abc921bf6c68888e3f033f3e5aea02c5d5c3c94a | Text | 627 | 18 | INSTALLATION
============
This Python 3 library contains code to reproduce results in
[A modular architecture for transparent computation in Recurrent Neural Networks](https://arxiv.org/pdf/1609.01926.pdf) .
To install it with `pip`, enter the root folder of the project and run:
```
pip install .
```
Examples are p... |
9e7b8fa3cf2c29ed247881d46c8310a1ff1484f92f37c0ff8a489f3d8c016f59 | Text | 637 | 6 | There are two steps corresponding to the eponymous directories, "image_processing" and "solve distributions." Each can be run by following the instructions in the README in the directory. The solve distribution step can also be run without running the image processing first, by using the precomputed input artifacts alr... |
9abf8e7cc010edbb39b146cb9d78829388d8f20f3f603979a35b9bf4a59e09dc | Text | 673 | 13 | # ISS starfish
This repository contains scripts used to convert **ISS data (.czi raw images) into starfish format** , as well as some other scripts used to run Starfish itself
For more information about MATLAB toolbox for Bio-Formats, see https://www.openmicroscopy.org/bio-formats/downloads/ (forked version included ... |
3764f264df720c87ea3482687c51a717f6bd39be0de29ca73021326857f2d4fc | Text | 686 | 17 | # ssSEM-EGL-ML-analysis
EGL ML analysis in ssSEM volume<br>
**egl-ml-200regions-clean-cnn1.ipynb**<br>
1. Input data augmentation<br>
2. Train the CNN<br>
**generate_EGL-ML_boundary_2.ipynb**<br>
1. Use trained model and perform inference on new dataset (tiles from full EM volume)<br>
2. Reconstruct EGL|ML boundary u... |
0827819fa037c66b6f7408deeb942c49eccf7dac6c9158cfe77e215f997271b9 | Text | 696 | 11 | # PAUSE
<center>
<img src="./concept_fig.png?raw=true" width="750">
</center>
Code for the paper "Principled feature attribution for unsupervised gene expression analysis" (PAUSE).
For more information, see our preprint: https://www.biorxiv.org/content/10.1101/2022.05.03.490535v1.
For code to generate the model... |
c1111180e5fdd6c7e7f28e0535206320d26ceee12d51909928b965f54b8a2ee6 | Text | 704 | 16 | Repository related to sampling and bioassays of species predicted byt the model developed in:
Frontiers in Plant Science paper: "Machine Learning Enhances Prediction of Plants as Potential
Sources of Antimalarials" Richard-Bollans et al. (2023) doi: 10.3389/fpls.2023.1173328
## Licence
This work is licensed under a
[... |
9b41344163978dba8446c6d0404f1ab1245f560408a6b1461aaab9691c61d64a | Text | 708 | 7 | # SCZ_PPI
Documentation for analysis scripts used in the SCZ PPI paper:
Hsu YH, Nacu E, Liu R, Kim A, Tsafou K, Petrossian N, Crotty W, Suh JM, Pintacuda G, Riseman J, Martín JM, Malolepsza E, Li T, Singh T, Ge T, Egri SB, Tanenbaum B, Stanclift CR, Apffel AM, Schizophrenia Working Group of the Psychiatric Genomics Co... |
aa47c189adca66cc3bbc2526fde4082afc5a86169aa2e09668642cf2e55973c2 | Text | 771 | 27 | # ADBrainCleanDiagDIA
A Peptide-Centric Quantitative Proteomics Dataset for the Phenotypic Assessment of Alzheimer’s Disease
This package contains the datasets and code to generate the figures included in the scientific data paper:
* Level 0: Raw files
* Level 1: Skyline document grouped by batch
* Level 2: Skyline o... |
73cbfe4e32463eab86bd1cfff90dc6a880e98d1b4640c0822d0e0f0c78dc81ee | Text | 804 | 10 | # HBN_Project
## Organization
Scripts are organized in the following folders: Image processing, analysis. All atlases and templates referenced are disseminated on the NITRC Downloads page for Auto_EACSF: https://www.nitrc.org/projects/auto_eacsf/. README files can be found in each folder, so please see these for addit... |
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