sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
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e96285a10821ea3916be682d084a1fe1e3676add37f62e3ae0af97037566a875 | Shell | 29,634 | 875 | # main script functions
function filter_reference_files() {
echo "Preprocessing reads: $READS"
if [ ! -d "$OUTPUT_NAME/temp_files/refs" ]
then
mkdir -p "$OUTPUT_NAME/temp_files/refs"
fi
# filter GTF by ensembl id
grep $ENSG_ID $ANNOTATION > $OUTPUT_NAME/temp_files/refs/filt_chr.gtf
ANNOTATION_FILT=$OUTP... |
a105af23e182672ee1c75f81c77dbebbc0bdd05b56e0acd962df0e53e65cada7 | Shell | 39,765 | 1,084 | #!/bin/bash
source /home/julien/epipax.config
# usage
# fastqToBigWig.sh <full path to file>.NO.EXTENSION
# fastqToBigWig.sh /brcwork/FASTQ/ICM_ChIP/BC_ICM_H3K4me3
# fastqToBigWig.sh /brcwork/FASTQ/ICM_ChIP/BC_ICM_RNA_rep1
# extension in these cases MUST BE "_R1.fastq.gz"
# UPDATE 28 Feb 2018: remove --local option f... |
16d49d12d71a7e94e1e51d9211290527523b0d0119cab06d898459eae2fe9713 | Shell | 44,714 | 1,467 | #! /bin/bash
REL_TOL_NVE=1e-8
REL_TOL_NVT=5e-3
UNITS=lj
LMPDIR=/Users/xwb17127/Work/code/lammps
SRCDIR=$LMPDIR/src
BUILDDIR=$LMPDIR/build
DAY=$(grep -e 'LAMMPS_VERSION' $SRCDIR/version.h | awk '{print $3}' | sed 's/"//g')
MONTH=$(grep -e 'LAMMPS_VERSION' $SRCDIR/version.h | awk '{print $4}' | sed 's/"//g')
YEAR=$(... |
6645b3f2c9deee49fa12cef120b4b6166f13513b128c024d95996c2de97422a6 | Shell | 74,672 | 718 | maf-convert axt scaffold00001_pilon_pilon_mm10.smiCra1.maf > scaffold00001_pilon_pilon_mm10.smiCra1.axt
maf-convert axt scaffold00002_pilon_pilon_mm10.smiCra1.maf > scaffold00002_pilon_pilon_mm10.smiCra1.axt
maf-convert axt scaffold00003_pilon_pilon_mm10.smiCra1.maf > scaffold00003_pilon_pilon_mm10.smiCra1.axt
maf-conv... |
59f57df0c952a3d6483ff55e6581df61e96bb548b73d2d09a36d9745adef5ccc | Shell | 77,717 | 713 | chainSort scaffold00001_pilon_pilon_mm10.smiCra1.chain > scaffold00001_pilon_pilon_mm10.smiCra1.sorted.chain
chainSort scaffold00002_pilon_pilon_mm10.smiCra1.chain > scaffold00002_pilon_pilon_mm10.smiCra1.sorted.chain
chainSort scaffold00003_pilon_pilon_mm10.smiCra1.chain > scaffold00003_pilon_pilon_mm10.smiCra1.sorted... |
17ac881b4b0b6a07f1e86701ea27d8fe15e75b617a507eb27d3b78522200c8fe | Shell | 124,808 | 3,107 | #!/bin/bash
# Kaapana control helper: run `./kaapanactl.sh deploy|install|report [options]` to deploy the platform,
# prepare servers, or gather microk8s diagnostics without touching other scripts manually.
# if unusual home dir of user: sudo dpkg-reconfigure apparmor
set -euf -o pipefail
function main() {
init_co... |
3b672a59dba64248cd3729170e3c832e348c978a62785a87955192b58f9d6f2b | Shell | 130,858 | 719 | RepeatMasker -q -xsmall smiCra1/scaffold00001_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -hmmer_dir /home/lecook/.conda/envs/wga/bin/
RepeatMasker -q -xsmall smiCra1/scaffold00002_pilon_pilon.fa -default_search_engine hmmer -trf_prgm /home/lecook/.conda/envs/wga/bin/trf -... |
1903a9e1dca93b1fc6718adf43801730929f8b0503da3540906bc78f312b252f | Shell | 200,000 | 520 | lastz_32 /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.fa[multiple] /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1_RM/scaffold00001_pilon_pilon.fa H=2000 K=2400 L=3000 Y=9400 --format=maf > /data/projects/punim0586/lecook/chipseq-pipeline/cross_spe... |
748e5cbb28a40ff6e45fe9309fe826b3fef1d3bc02446766a3c4b30fbbcea65c | Shell | 200,000 | 585 | axtChain -linearGap=loose -scoreScheme=../../bin/GenomeAlignmentTools/HoxD55.q scaffold00001_pilon_pilon_mm10.smiCra1.axt /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/mm10.2bit /data/projects/punim0586/lecook/chipseq-pipeline/cross_species/data/genomes/smiCra1.2bit scaffold00001_pilon_pil... |
bebd6119eff96982c4813583fa9d0ec6f4b5aa59f41e193174a9e922b4358e58 | Shell | 200,000 | 6,980 |
# libtool (GNU libtool) 2.4
# 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 Free Software Foundation, Inc.
# This is free software; see the source for copying conditions. There is NO
# warranty; not even f... |
f8b5fa4442f4403f94cdea7cb017095abcc651f6f4bf9bab5f606152928f76d6 | Shell | 200,000 | 5,091 | mkdir -p n01440764
mkdir -p n01443537
mkdir -p n01484850
mkdir -p n01491361
mkdir -p n01494475
mkdir -p n01496331
mkdir -p n01498041
mkdir -p n01514668
mkdir -p n01514859
mkdir -p n01518878
mkdir -p n01530575
mkdir -p n01531178
mkdir -p n01532829
mkdir -p n01534433
mkdir -p n01537544
mkdir -p n01558993
... |
5e685a8b7f2c750768281ea886c280c0f9606920bba19e7e20ed35c6710905b7 | Stan | 2,019 | 45 | // Bayesian hierarchical meta-regression.
//
// y_i ~ normal(x_i' beta + theta_{g(i)}, sigma_i) i = 1..N
// theta_g ~ normal(0, tau) g = 1..K
//
// The Stan User's Guide random-effects meta-analysis model (Measurement Error
// and Meta-Analysis) with that guide's extension to obse... |
f16436437ef65ace8bf0265a9422d2438e9589859355c8fe4954f6b0ef729f62 | Stan | 5,134 | 146 | // generated with brms 2.22.0
functions {
/* compute correlated group-level effects
* Args:
* z: matrix of unscaled group-level effects
* SD: vector of standard deviation parameters
* L: cholesky factor correlation matrix
* Returns:
* matrix of scaled group-level effects
*/
matrix scale_r_cor(m... |
ae2f2827ecf96e40ba797dc847cf7b6b2c51da6f09458a37447b9620ed663c3a | Stata | 290 | 9 | insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/iolib/tests/stata_dates.csv"
format datetime_c %tc
format datetime_big_c %tC
format date %td
format weekly_date %tw
format monthly_date %tm
format quarterly_date %tq
format half_yearly_date %th
format yearly_date %ty
|
289411a6984507f131d182573ffd330de86efc5314338a771919ce0402cc6bbf | Stata | 433 | 19 | insheet using "/home/skipper/statsmodels/statsmodels-skipper/scikits/statsmodels/datasets/macrodata/macrodata.csv", double clear
gen qtrdate=yq(year,quarter)
format qtrdate %tq
tsset qtrdate
gen lgdp = log(realgdp)
gen lcons = log(realcons)
gen linv = log(realinv)
gen gdp = D.lgdp
gen cons = D.lcons
gen inv = D.linv... |
4cabd8f34a08fe1831cb8436df18ed4345c72b33f684633e431d62862a43f320 | Stata | 595 | 16 | * Stata do file for getting test results
insheet using "/home/skipper/statsmodels/statsmodels-skipper/statsmodels/datasets/macrodata/macrodata.csv", double clear
gen qtrdate=yq(year,quarter)
format qtrdate %tq
tsset qtrdate
ac realgdp, gen(acvar)
ac realgdp, gen(acvarfft) fft
corrgram realgdp
matrix Q = r(Q)'
svmat Q, ... |
386c690eaa918ccaaaad2769b8264e10bf659166017cc06a09c4a8f0df756ca4 | Stata | 620 | 20 | insheet using /Users/fulton/projects/statsmodels/statsmodels/datasets/macrodata/macrodata.csv, clear
gen tq = yq(year, quarter)
format tq %tq
tsset tq
cusum6 cpi m1, rr(rr) cs(cusum) lw(lw) uw(uw) cs2(cusum2) lww(lww) uww(uww) noplot
outsheet rr-lww using results_rls_stata.csv, comma replace
// Section for restrict... |
b0d4fed719b833a061bae10180438047e2f15c58c0b5de8bd1900aa48ac43f28 | Stata | 745 | 22 | use https://www.stata-press.com/data/r12/wpi1, clear
gen dwpi = D.wpi
// Estimate an AR(3) via a state-space model
// (to test prediction, standardized residuals, predicted states)
constraint 1 [dwpi]u1 = 1
constraint 2 [u2]L.u1 = 1
constraint 3 [u3]L.u2 = 1
sspace (u1 L.u1 L.u2 L.u3, state noconstant) ///
(u2... |
1ac308764e5035bb1007d60012be82a5fac708ef1ce63ca3f9f4d995e13b2638 | Stata | 845 | 31 | webuse lutkepohl2, clear
tsset
// AR(1)
// For results output, do:
// matrix list e(b)
// matrix list e(Sigma)
// for loglike, aic, sbic, hqic, fpe:
// ereturn list
// for non-Lutkepohl IC, do:
// estat ic
// matrix list r(S)
var dln_inv if qtr<=tq(1978q4), lags(1)
estat ic
var dln_inv if qtr<=tq(1978q4), lags(1) luts... |
faa62e083048066c54d12dc76927d5f1a001e9f2f2e1bf7e5814b8c2f53ddf88 | Stata | 1,323 | 52 | *IBD analyses
clear
clear mata
clear matrix
set more off
set maxvar 32000
capture log close
cd A:\IBD_project\IBD_simulation_data\
import delimited A:\IBD_project\IBD_simulation_data\netProductionFluxes_IBD.csv, clear
gen _varname="__"+metabolite_vmh_id
xpose, clear varname
drop in 1/2
rename _varname... |
64d10ca4f20f7d9fcdd1645553442fc5b91b305c4ff3bffda6d6a3389231cc71 | Stata | 1,538 | 49 | *tSNE analyses
*Johannes Hertel
clear
clear mata
clear matrix
set more off
capture log close
set maxvar 40000
cd A:\AGORA_2_New\Files_for_Johannes_revision\raw_data_revision\tSNE_Distances
log using A:\AGORA_2_New\Files_for_Johannes_revision\results\logs\tSNE_distances.log, replace
import delimited "A... |
b7dd4ede5109dc6b9483e1589e8f0d54bd526988a337b32dffc1fde473974ab9 | Stata | 2,050 | 56 | // Example 1: ARIMA model
use https://www.stata-press.com/data/r12/wpi1, clear
arima wpi, arima(1,1,1) vce(opg)
arima wpi, arima(1,1,1) vce(oim)
arima wpi, arima(1,1,1) vce(robust)
arima wpi, arima(1,1,1) diffuse vce(opg)
arima wpi, arima(1,1,1) diffuse vce(oim)
// Estimate via a state-space model
constraint 1 [D.wpi]... |
e18e34b9b9d65e72f6fc1847f3541603a66fe57bd9af87b2d9616eeac81a91ff | Stata | 2,155 | 78 | /* Run Survival models and save results
Author: Josef Perktold
based on example from Stata help
*/
clear
*basic Kaplan-Meier
capture use "E:\Josef\statawork\stan3.dta", clear
if _rc != 0 webuse stan3
capture save "E:\Josef\statawork\stan3.dta"
capture erase surf.dta
sts list, saving("surf")
use "E:\Josef\statawork\... |
33b5ed26037f1b355f4d37fdc48ce2b78bdff74c286a56610afd518525197912 | Stata | 2,295 | 70 | clear
insheet using results_realgdpar_stata.csv
keep value
gen lgdp = log(value)
gen dlgdp = lgdp - lgdp[_n-1]
gen quarter = _n
tsset quarter
// Estimate an ARMA(3,0)
arima dlgdp if ~missing(dlgdp), arima(3,0,0) noconstant
matrix b = e(b)
matrix b = (b[1,1..3],1,1,1,b[1,4]^2)
// Estimate via a state-space model
cons... |
e550d6f439d0f95325956a53abeed66a70d00f2492ed4be826acd8229528ae47 | Stata | 2,394 | 87 | * Data preparation for integrating metabolome data with AGORA2 community models using Yachida et al. 2019 data
* Johannes Hertel
clear
clear mata
clear matrix
set more off
capture log close
set maxvar 50000
cd A:\AGORA_2_New\Files_for_Johannes_revision\processed_data
import delimited "A:\AGORA_2_New\Fi... |
9a8c41dd13ed59a915df2046eaf9d559020acba644e3409accbee171af4aaffa | Stata | 2,613 | 143 | // Local level
clear
input x t
10.2394 1
1 2
1 3
1 4
1 5
1 6
1 7
1 8
1 9
1 10
end
tsset t
matrix define b0 = (8.253, 1.993)
ucm x, model(llevel) from(b0) iterate(0)
// e(ll) = -23.9352603142740605
disp %20.19g e(ll)
// Local linear trend
clear
input x t
10.2394 1
4.2039 2
6.123123 3
1 4
1 5
1 6
1 7
1 8
1 9
1 10
end
... |
4e9679ae399b3391a1f5ce85c583b2112b6d52b64aac67db83c2a5a420c5f816 | Stata | 4,095 | 155 | *Secretion spaces/ Uptake Spaces via PCAs
* Johannes Hertel
*generates raw figures S3A, S3B
clear
clear mata
clear matrix
set more off
set maxvar 32000
capture log close
cd A:\AGORA_2_New\Files_for_Johannes_revision\processed_data
import excel "A:\AGORA_2_New\Files_for_Johannes_revision\raw_data_revi... |
4709c83100ccf6ed11d5eec35dbf1b8b95ae6584171245026e689d1d1edc6c30 | Stata | 6,097 | 185 | * IBD Sulfur species diversity script
clear
clear mata
clear matrix
set more off
set maxvar 32000
capture log close
cd A:\IBD_project\IBD_simulation_data
log using manuscript_analyses.log, replace
use data_merge_all.dta
merge 1:1 id using reactions.dta
drop _merge
tab stratification, gen(group_)
... |
faa2a86cd2e07377a238ab73c9522511c67443b6674550fd4970feb284d977d7 | Stata | 6,313 | 148 | webuse lutkepohl2, clear
tsset
// VAR(1)
dfactor (dln_inv dln_inc dln_consump = , ar(1) arstructure(general) noconstant covstructure(unstructured)) if qtr<=tq(1978q4)
estat ic
// These are predict in-sample + forecast out-of-sample (1979q1 is first out-of sample obs)
predict predict_1, dynamic(tq(1979q1)) equation(dl... |
41178c1d2c08ebbbc888d2c12dd323cbcec30a214902d46d8b9095c73a1ab4bb | Stata | 6,522 | 154 | *Revision AGORA2 analyses - gene added/removed - comparison JD vs AED - accuracy drug prediction
*Johannes Hertel
*generates figures S1, S7
clear
clear mata
clear matrix
set more off
set maxvar 32000
capture log close
cd A:\AGORA_2_New\Files_for_Johannes_revision\processed_data
import excel "A:\AGORA_... |
0d4c1a5bf8fa4f17da4906de965a1b679f9a928f998a200a44e0cdc69d5b8635 | Stata | 6,661 | 273 | // Dataset
use https://www.stata-press.com/data/r12/wpi1, clear
rename t time
set more off
set maxiter 100
// 25, 43, 48, 52
// Create data for exog test
gen x = (wpi - floor(wpi))^2
// Create data for deterministic trend tests
gen c = 1
gen t = _n
gen t2 = t^2
gen t3 = t^3
// Dummy column for saving LLFs
gen mod ... |
7dc9434d3c193f2bf143c974edaea73fedc4e941fadfc6362befe9d3c6d8cc76 | Stata | 7,037 | 149 | // Helpful commands:
// matrix list e(b)
// matrix d = vecdiag(e(V))
// matrix list d
webuse lutkepohl2, clear
tsset
// use lutkepohl2_s12, clear
// tsset qtr
// Dynamic factors
dfactor (dln_inv dln_inc dln_consump = , noconstant ) (f = , ar(1/2)) if qtr<=tq(1978q4)
// These are predict in-sample + forecast out-of-... |
37802097b685c491b2a125c4014527a6e227832e370eb4d11dc3eef8ae0eb3f6 | Stata | 8,308 | 268 | *Analyses CRC simulated drug metabolites
clear
clear mata
clear matrix
set more off
set maxvar 32000
capture log close
cd A:\AGORA_2_New\Files_for_Johannes_revision\raw_data_revision
import delimited A:\AGORA_2_New\Files_for_Johannes_revision\raw_data_revision\AGORA2_CRC_Objectives_JD.txt, varnames(1... |
33c6c4471664084bae63a40669ed01fc0895dce9f3858f7c85a64b121adeb925 | Stata | 13,516 | 447 | clear
tempname filename
local filename = "M:\josef_new\stata_work\results_glm_poisson_weights.py"
insheet using M:\josef_new\eclipse_ws\statsmodels\statsmodels_py34_pr\statsmodels\datasets\cpunish\cpunish.csv
generate LN_VC100k96 = log(vc100k96)
generate var10 = 1 in 1
replace var10 = 1 in 2
replace var10 = 1 in 3
rep... |
eb2373b4ff0afde18eb218051474ddb4b6ef45360e9d1936181bf38055810ce2 | Stata | 17,676 | 441 | *Performance comparison across resources
*Johannes Hertel
clear
clear mata
clear matrix
set more off
capture log close
cd A:\AGORA_2_New\Files_for_Johannes_revision\results\logs
*NJC19
log using comparison_NJC19.log, replace
import delimited "A:\AGORA_2_New\Files_for_Johannes_revision\raw_data_revisio... |
8a59691d04c1202e9674cd68d4976b80d28ec35a2d98ebfe70d9f300a7a5afdd | Stata | 28,119 | 698 | *data preparation AGROA2 Yachida integration for in silico in vivo pattern analyses
clear
clear mata
clear matrix
set more off
capture log close
set maxvar 32000
cd A:\AGORA_2_New\Files_for_Johannes_revision\processed_data
use "CRC_AGORA2_merged.dta"
local n=0
local list_met=""
foreach j of varlist _C... |
0265fe240649c1a3a17dc109e0f8f2a6f52d4f0106e7e3eff565c0845d8be70b | Text | 13 | 1 | 🧬🧬🧬
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4007d844a9da2c495eaedd6f699eacbe67aaa96a9f62796e4862a5251f17607a | Text | 15 | 1 | # eSNPKaryotype |
eb82b68b8bf2e8c8fb8b23bc9e07799088345777ac63fd69f7412033f3f18353 | Text | 16 | 1 | # 1_UCR_project
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3f930472c91e8a869c384bc987a636ac8627cde82f9b8df9774dbb461a4a5344 | Text | 18 | 1 | # LMX1A-astrocyete |
1284ce51a5a0534743c588e253704a904f7d0b79b4b966d19b3e85c8726b04b2 | Text | 20 | 1 | # Caudate-scMultiome |
6a2610979659e83341e88cd28e57f7c6883d81294884ee0598a2295a879b58ea | Text | 20 | 1 | # ReliableFC_release |
8a564fd9a6f0941e51dfe37ba332570db67c028331a1115b89ce163de43d524e | Text | 20 | 2 | # GBMSpatialOmics
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970ff85023b3b0d2acaa82f2c481036487ed0913735d5e20703faba61a3b2271 | Text | 20 | 1 | # SmartNanoparticles |
0ba9bddc040b5edbfda7b3b8be01667f802ef5538bf73fe306fd1909645afa2d | Text | 23 | 1 | # preterm-ExecuteSNF.CC |
b7e9dfefde1e1a64de804eb4516c1f56f86e86af0b02bba22fcefbde6a65d3f6 | Text | 24 | 1 | # parabrachial-hub-2025
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527c97408a198961aec1547ae0c2114d125c76dceef18b5fc12eba2150dd00b8 | Text | 50 | 2 | # MG-Diff
A novel molecular graph diffusion model
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a3efe09533a3d1c11ddfa047aefec9c58c96de69b0eebff881b7dbb82d6b43ef | Text | 65 | 2 | # mimic-iv-code
Code and discussion around the MIMIC-IV database
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b4d68b1020d73ddde8932b409d07a8eeea5b70e47f28cfa77a4d715c80ba325c | Text | 66 | 1 | # Continuous Variable Quantum Solver for Complementarity Problems
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d4715b1bc3515a2d5e19cc6d1d535e26b137cb628e49354981daedc730be7c83 | Text | 83 | 2 | # Scepter
Spatiotemporal dynamic analysis framework for studying brain activities.
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dab12268efe943eff89c75d966db68eb7aede3238871bdabd6af508b58fe117d | Text | 86 | 1 | Code for Gruber et al., (https://www.biorxiv.org/content/10.1101/2022.09.30.510181v3)
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b962370bb5fa804180ad41e3a9db78ceb4e81acea169d91a1c7317e63debd965 | Text | 93 | 2 | # BiRScope
Software for Birefringence Microscope @ Biomedical Optics Lab | Boston University
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10c4eba6e63c51eb843b778ee637bd623e59305ed5024d93588f0884a41507d6 | Text | 97 | 2 | # attentional_switching
Code and data for the paper on attentional switching in larval zebrafish
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0dcc0af770dfec20651417e61130ea7308d034cbd4d5899850b16960c8c0d25d | Text | 101 | 2 | # ALTOL_ms
ASSR music attention Alternate Overlap Machine-learning repeated-splitting SVM manuscript
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39604f683c9ecec0f311c26f01f6b7a959b54a6d6390eb38500e48ee9cf31bf5 | Text | 101 | 2 | ## Visit our website to learn more about microdrives:
https://buzsakilab.github.io/3d_print_designs/
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f4642901ae73d90a3301a570b05a2e309055869114931a0075560403a58cb653 | Text | 107 | 4 | # MusicPlast
Old vs Young adults, trained via MusicPlast.
Pre and post training averaged FSG (EEG) files |
5eebb9411b5d60387158d6738ac5b5c4a68e041f44241b7f131964b5322494e9 | Text | 112 | 2 | # Perfusion_quality
This repository contains code regarding the brain perfusion quality evaluation manuscript.
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1f9e6c6d434d0fe9e87527deb5ae03829cabfb7eeb29123f1b5fa9f3c0f7c2a4 | Text | 122 | 2 | # Cross-expression manuscript code files
Custom code scripts used to analyze the data in the cross-expression manuscript.
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0b5f9cd74ad9a272b8c692f0b6bf0dddea55e0226466dc92779a0585b08e15f5 | Text | 125 | 1 | SDUST2022GRA.nc is the global marine free air gravity anomaly models covering 80°S~82°N and 0~360°E on 1′×1′ grids.
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d7ac12a8158d831a87eed4b753cb39cabd86e32bde82889c0b10dbc81a10caea | Text | 128 | 2 | # Wilson_CPDfingerprint
Scripts for the Wilson CPDfingerprint paper (identifying transcription factor binding sites using Weka)
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ce997c20b28a968641199ed955b50c9730ee7390f0bf4718f1eed066c7fbf7d9 | Text | 139 | 4 | # altmotifs
Alternative motifs in healthy individuals
This directory contains scripts for analyzing tandem repeats in general population.
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38410d91a6c4308ed7715735a0b70f9033bf2a14a90d982332315b8a0913ffac | Text | 150 | 2 | # scell_CSF_NSCLC_BrainMets_ICI
Cellular Dynamics in Cerebrospinal Fluid with Lung Cancer Brain Metastases during Immune Checkpoint Inhibitor Therapy
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09270714b132892e1d30a723638d5037709ddb30d8c614899e954bd96d2160d6 | Text | 161 | 3 | scripts and data to create figures 3 and 6 in
Dockès J, Oudyk K, Torabi M, de la Vega AI, Poline JB. Mining the neuroimaging literature. eLife. 2024 Apr 9;13.
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1e1635411e35b1a2d7ea1d9d2823625816e53457dcd5f548e01e3cd52d146a69 | Text | 161 | 2 | # PLOS_biology_2025
Codes for data processing for paper "Recurrent circuits encode de novo visual center-surround computations in the mouse superior colliculus"
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c4ea70f3dc272674b6c108c444582236c428297ceb02c8c62c93e0d5235ee617 | Text | 162 | 2 | # CTPredict
CTPredict: A multimodal multitask deep learning model for predicting stroke lesion and functional outcomes using 4D CTP imaging and clinical metadata
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c4a71302032fa1fc9e868359cdff492500e4bf1f43461b40833d462ccddd55bc | Text | 169 | 6 | # spikeA
Analysis of electrophysiological data in python
The documentation is in the [docs](https://github.com/kevin-allen/spikea/tree/master/docs/main.md) directory. |
5f107b05f47a38a930027f410bb02acd0cdc1c9e135b7ee04f80fdc3016a8bae | Text | 172 | 3 | R code for figure 7
Code for behavior figures can be found here > https://doi.org/10.5281/zenodo.11396729
Figure numbers are subject to change upon review and publication |
301ac717462ae6419726f46e1c58f4e772ab4cdad37994fd365193185b1a5d89 | Text | 175 | 6 | <!-- # Fam_circuit_model -->
Code for Fig2B-E, FigS2, using model described in Fig1.
Entry: fam_effects.sh
Specify cifar saving path and project directory in fam_effects.sh |
992514ae6020c560c3ccf808065576d48dc4312eb105da50884bbc2dd5f6a51f | Text | 184 | 7 | # large-scale-ibma
`Warning:` README is currently in progress. Please check back later.
Large-scale automated IBMA
Tutorial: https://github.com/neurostuff/2025-ohbm-ibma-neurovault
|
5f705437dcfe935595b3674b2ab18ac74e69841e9f49d7f0e96779eb7f2f7e47 | Text | 203 | 3 | Code used to create the results and figures in the "Participant Demographics" section of
Dockès J, Oudyk K, Torabi M, de la Vega AI, Poline JB. Mining the neuroimaging literature. eLife. 2024 Apr 9;13. |
f757a6156a9ab16192a6a861c422be8d87d1bf3cc680ab0ca1049755430314fa | Text | 209 | 3 | This repository contains data associated with the paper:
Garg, C., & Salahuddin, S. (2025). Efficient Optimization Accelerator Framework for Multistate Ising Problems. ArXiv. https://arxiv.org/abs/2505.20250
|
55550124014b5b4aef29f2fbe8b950fb042381b364ff6bd8222900b56cd6881a | Text | 212 | 3 | # commsbio-heartbeat-perception
This repository contains the EEG data underlying the Communications Biology publication "Heartbeat perception is causally linked to frontal delta oscillations" by Haslacher et al. |
f0a687ee17da81a0dfb977a235c1ad4ed44d34edc30e64cd467b3c152690be24 | Text | 218 | 2 | # Microglia
Programmes used to analyse microglial surveillance (Surveillance folder) and ramification (GUISholl folder), for Madry, Kyrargyri, Arancibia-Carcamo, Jolivet, Kohsaka, Bryan and Attwell (2017) Neuron paper
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5b23770a8ffc1f002a9518136a852d2aa894ca6cbccbdb036f84756f3dd2e20c | Text | 222 | 3 | # Welcome to the NDA Collection 3165 DCAN Labs ABCD-BIDS Documentation Repository
## The full documentation lives here: [https://nda-abcd-collection-3165.readthedocs.io/](https://nda-abcd-collection-3165.readthedocs.io/)
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a829f4845c68ea4ab1cb598627c056234b74f9944889a8949e8c9b55dceec671 | Text | 222 | 4 | # meta-GRN-Root-Epidermis
Code for analysis of the meta Gene Regulatory Network (meta-GRN) proposed in
Cellular patterns in Arabidopsis root epidermis emerge from gene regulatory network and diffusion dynamical feedback
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eea60eed73ee356401d135330897c96840f9f52cd1cf9209189c5fe5db4f72ee | Text | 235 | 7 | # brainregister
Python package for elasitx-based registration of mouse brain images to the Allen CCFv3.
This package has now been moved to int-brain-lab:
[int-brain-lab:brainregister](https://github.com/int-brain-lab/brainregister)
|
7fc116c622bcb8a41d62d630f17b660448d550ff64afbf2adbc2122818a8e323 | Text | 251 | 11 | # Testosterone induced singing in adult female canaries
## Project:
Data and analyses
## Description:
This project includes the codes, protocols, and etc. for study neural mechanisms of testosterone induced singing in adult female canaries.
|
e22057e361fe9a0e90979bb6805e022fd30b9ffbc5dac209cc5c38ee3d7a7bcc | Text | 259 | 2 | # PICSL Histology Annotation Server (PHAS)
This project provides a web-based framework for annotating histology slides both for anatomical segmentation and training deep learning algorithms. Please see documentation at https://picsl-histoannot.readthedocs.io
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88b7c69f906f212e908c381f3338d349d84b300c312d3dd4f2842c24a2f4abaf | Text | 268 | 1 | R code used to run statsitical analyses for "More than chronic pain: behavioural and psychosocial protective factors predict lower brain age in adults with/at risk of knee osteoarthritis over two years": https://academic.oup.com/braincomms/article/7/5/fcaf344/8251081
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05e888570885db5217fe5749281f05722e05ea31c4d56f026205b0635a278056 | Text | 270 | 6 | Nuclear Plotter
Requires the python packages listed in requirements.txt.
GUI for finding marked cell nuclei and determining whether they are in the epithelium or not.
User sets thresholds for identifying nuclei of interest and classifying different epithelial tissue.
|
2d27185454b9485b1a49137ce15c8a2921bbc1c7e740e8ad42fd61dea0af3f5b | Text | 271 | 2 | # Ultra low field MRI: reliability & correspondence to high-field MRI
Code for "Ultra-low-field brain MRI morphometry: test-retest reliability and correspondence to high-field MRI" (Imaging Neuroscience, 2025: https://direct.mit.edu/imag/article/doi/10.1162/IMAG.a.930)
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4168face3612ece5f523a0c6e3c98b769310be2acbfcb488e11f676077db9ff6 | Text | 273 | 5 | This repository contains example scripts and scripts for generating key figures in the Virlogeux Bollu et al 2025 Cell Reports paper.
Code is compatible with Matlab 2021b or higher. All code has example data.
Please reach out to tpbollu@gmail.com for any clarifications.
|
1720cd838f617711b06c8986fcdd7a627190c158f42037e076149ce463f42240 | Text | 281 | 5 | # Perinatal alcohol exposure and Circadian Rhythms
Hello, this is a repository for all the code used in the study "Prenatal alcohol exposure dysregulates the expression of clock genes and alters rhythmic behaviour in mice" in Dr. Olga Valverde's lab at PRBB (Barcelona, Spain).
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3861a210d6c93a880ea3f3d2b09068432d1648634b3c1185adac0a03886c715b | Text | 285 | 12 | CEEMDAN:
https://perso.ens-lyon.fr/patrick.flandrin/emd.html
PDO calculation:
https://github.com/ZijieZhaoMMHW/Cal_CM
System requirements for running machine learning in Python:
- python3.7
- keras2.2.5
- tensorflow-cpu1.15
All remaining analyses were performed in MATLAB R2023a.
|
c394eab2091f65353112d82a791a996bf96423fad0974b5ada0e798e4c1f7d1f | Text | 288 | 9 | # xarray-behave
Install a working conda installation (see [here](https://docs.conda.io/en/latest/miniconda.html)).
```shell
conda env create -n xb -y -f https://raw.githubusercontent.com/janclemenslab/xarray-behave/refs/heads/master/env/xb.yml
```
See `demo.ipynb` for usage examples.
|
302624144f9f8883d675b87fe38333e48536fdb91d31e26bddadecaa2c19c2ad | Text | 295 | 6 | # PrimaVoice-Toolbox
The PrimaVoice toolbox is a Matlab pipeline for primate fMRI data analysis. It can be used to perform every step of the analysis, from brain segmentation, to representational similarity analysis.
# Usage
Please refer to PrimaVoice_Toolbox_manual.odt (work in progress...)
|
3281aee0db9be3e6acc5a77f85a3c0b9423d8c1eb9f816852c51c80a87d61934 | Text | 296 | 5 | **Sex/Gender Predictions**
This repository contains the Jupyter Notebook based implementation of the work described in: [citation to be updated].
This implementation was used to generate and evaluate predictions of sex and gender based on functional connectivity data using the ABCD dataset.
|
91618560e304f137f7fd501a4e35ac1a4127fc79ef78923a40a66942e3419979 | Text | 301 | 2 | # multiomics-drug-response-resistance-tl
Code for drug response prediction and resistance analysis using transfer learning with multi-omics data (gene expression, mutation, CNA) from GDSC, TCGA, and PDX. Includes deep neural network models and pathway-level insights for key cancer chemotherapeutics.
|
41403a453c3e54377b9d6eca5301a69ebff2197c461b68feabdf6ecab25e52a9 | Text | 305 | 9 | # tTest
Compute t-test given two input files
Synopsis: Return mean, sd and p-value from t-test comparison
Usage: vga_tTest <file1> <file2>
Notes: <file1> and <file2> contain single-column data without headings. Edit Makefile to point to the boost library and, if applicable, to the include directory.
|
350b1187cd12d15503a9cfd0800a768755c5a6689d12a8f908f25b89e58dfaf2 | Text | 323 | 5 | # Effective Network Inference Using Transfer Entropy
This repository contains the scripts necessary to reproduce the results for the paper *Inferring effective networks of spiking neurons using a continuous-time estimator of transfer entropy*.
These scripts depend on the [JIDT](https://github.com/jlizier/jidt) packag... |
74268d19ec9e1931a5124afbedad73474cb5a8ac61ed2860df93ed01d0ed2568 | Text | 328 | 6 | # FigureGround
Figure-ground segmentation and analysis of natural scenes
Requirements: Medical Imaging Toolbox in Matlab, Figure/ground dataset from Zenodo, UT Austin dataset
Note: If you want to recompute optic flow from scratch, do not try to load in RAW files, but instead you'll need to load in the PNG files from ... |
e8fe8d9727b4f4e70b43a2c39bc2ec55c10f314155fd6f5a6af12b181fdd4a8c | Text | 330 | 5 | This repository provides the source code for training the recurrent neural networks investigated in the following paper:
Kim Christopher M, Chow Carson C, Averbeck Bruno B (2024) Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks eLife 13:RP103660
https://doi.org/10.7554/eLife.... |
e96ab0e73abb3c843fecaac412e3640c73ceac99e5cdf18ce7c79185489164e0 | Text | 331 | 5 | # Intraopmap (Publication)
Accompanying code for the publication "Integrating direct brain stimulation with the human connectome (https://doi.org/10.1093/brain/awad402).
Data can be downloaded from Zenodo: https://zenodo.org/records/10439149
Please do not forget to cite our work if you use the data and/or the code her... |
3c041f035e7a1674a309d675e4ec605e96354f93f7a788c13243fee5f4f05b7e | Text | 332 | 4 | ## Repository for Cavernoma Study
- First, download MNI reference volume *MNI152_T1_1mm_brain.nii.gz* from [HCPPipeline](https://github.com/Washington-University/HCPpipelines/tree/master/global/templates)
- ComputeStatistics.py for normalization and computation of cavernoma statistics (volume, mean signal and standard... |
b7e9f68f474bc922075f8c21fdcee29f847b6e8a24706fbce47397e34bf0689a | Text | 336 | 3 | Analysis and plotting of data for _The gut microbiome promotes mitochondrial respiration in the brain of a Parkinson’s disease mouse model_ by LH Morais et al. (2024) https://www.biorxiv.org/content/10.1101/2024.12.18.629251v1.
Cite the code: [](https://doi.org/10.5281/zen... |
55d00c81523a4d8084a36696cc631af5082c590b4e917333ac543c93a7f7203f | Text | 337 | 7 | # scripts or pseudo-codes for CAB AutoMapper pipelines for RNA-seq, ATAC-seq, CUT&Tag
## steps
1. run run_fastq_screen.sh to check contamination
2. run xenograft_cleansing.py to extract human-speficic reads to a new fastq file for xenograft samples
3. run specfic standard workflow according to data type
![workflow... |
fb7e6423a94f63b000090eb22a0e94a5446f39358036e710896dc1caf1af11bf | Text | 338 | 12 | # circles2024
Code for Alfonso-Gonzalez et al.).
Structure:
- circRNA detection & saturation analysis: `CIRI2`
- circRNA quantification: `snakemake.CIRIquant`
- circRNA binding, RIP-seq: `RIP-seq.eisa.junction_counts`
- intron binding, iCLIP: `iCLIP.Intron_analysis`
- Signal tracks for circRNA back-splice junctions:... |
eb0b8c3e3c5b59a05d523f03ef7b53c5bc5f77d93487b539c2c3596fe48e2908 | Text | 339 | 12 | Libraries needed PyTorch scipy numpy matplotlib Versions can be found in pip_installed_libs.txt
To Run the code:
1. cd Experiments
2. python graph_coloring_pt_vhyper.py
3. python graph_coloring_vectorized_pt.py
4. python graph_coloring_vectorized.py
5. python graph_coloring_vhyper.py
6. python tsp_ising.py
7. python t... |
21cedeec22e3f991c3bc565146313d5262d92052088060bda19995faae62e82a | Text | 341 | 17 |
## Environments
Install packages under conda env
```bash
rdkit, pytorch, numpy networkx scikit-learn, ase, ogb, torch_cluster, torch_scatter, torch_sparse, torch-geometric
```
## Dataset Processing
```bash
python add_xyz.py
python GEOM_dataset_preparation.py --n_conf 3
```
## Training
```bash
python ... |
e179ad722062cdd6c1fb53a915de8c51d04f78edb69f91d5e3fd5ff8a9168b82 | Text | 343 | 5 | This repository contains code pertaining to Marquina-Solis et al. 2024
The MATLAB-based code is used to detect multiple _C. elegans_ animals outside circular bacterial lawns seeded on agar plates.
Questions about this code can be directed towards Javier Marquina-Solis at javier.marquina.s@gmail.com or Cori Bargmann a... |
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