sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8ad24a02ea25f69dab839b24bdc96ad39b0092d7d22fadf5f40132f527949f7a | Shell | 12,284 | 274 | #!/bin/bash
# This script creates the data directories for us to run a syllable-based system
# in the directory with the same name as this, but with the -syllables suffix.
# dependency and build a LM on the phone level. We get the syllable sequences from one
# of the existing systems' alignments.
set -e ... |
5f95ee1d03b0e8ea286776c1475a1654cd3936863dd92d0268e7d8a0e945d7f7 | Shell | 12,288 | 263 | #!/usr/bin/env bash
# Cook, package and inspect a CARLA UE 5.8 package.
#
# bash package.sh scope what the configured cook scope is
# bash package.sh build cmake --build ... --target package[-CONFIG]
# bash package.sh inspect check the ARTIFACT for the failures a green build h... |
382f95c37b22b8f2af2962a7bac68694e84e13543fcbb3f507114239c82ad477 | Shell | 12,317 | 234 | #!/usr/bin/env bash
# Copyright 2016 Vimal Manohar
# Apache 2.0
#
# This script trains a LM on the Broadcast News transcripts.
# It is based on the example scripts distributed with PocoLM.
# It will first check if pocolm is installed and if not will process with installation
set -e
set -o pipefail
set -u
stage=0... |
1ca1e5e67e9f51324078b02e05fafa7297de7945762b1e617254110674f48c41 | Shell | 12,400 | 261 | #!/usr/bin/env bash
# Copyright (c) 2017 Johns Hopkins University (Author: Yenda Trmal, Shinji Watanabe)
# Apache 2.0
export LC_ALL=C
# Begin configuration section.
words_file=
train_text=
dev_text=
oov_symbol="<UNK>"
# End configuration section
echo "$0 $@"
[ -f path.sh ] && . ./path.sh
. ./utils/parse_options.s... |
c3380636467ceabed56e19bad8534922f09753729e8c4d8a6e6b2bbb4a9f1a55 | Shell | 12,405 | 267 | #!/bin/bash
# Stop on error
set -e
## species file name, conda environment name
SPECIES_FILE_BASENAME=aquas_chipseq_species.conf
CONDA_ENV=aquas_chipseq
## build index or not
BUILD_BWT2_IDX=0
BUILD_BWA_IDX=1
## show help
if [[ "$#" -lt 2 ]]; then
echo
echo "This script installs data for genome [GENOME] on a d... |
72b03538b673048ed7ff8407603a04b7dea97a4ebb7dbdba02a78df18bcf56b2 | Shell | 12,515 | 316 | #!/bin/bash
# Copyright 2020 Idiap Research Institute (Srikanth Madikeri)
# chain2 recipe for monolingual systems for BABEL
set -e -o pipefail
# First the options that are passed through to run_ivector_common.sh
# (some of which are also used in this script directly).
stage=-1
nj=30
train_set=train
gmm=tri5 #... |
ce9c02ae814fa5b961b4b5a1c35ce8270179e43d69f30ee9f2a06266bdbd7852 | Shell | 12,613 | 307 | #!/usr/bin/env bash
# same as 1n but replacing proportional-shrink with l2-regularize.
# Also applied similar changes from 1i to 1j:
# changed to fast-lstmp layer
# changed num-chunk-per-minibatch to be variable
# added extra_left_context_initial=0
# and extra_right_context_final=0
# The results match those from 1n.
... |
fac17dced398f635856636ff0b6f2a4177e0d930d7062a0a90ded76d40b8db9d | Shell | 12,661 | 381 | #!/bin/bash -u
# Copyright 2012 Arnab Ghoshal
#
# Copyright 2016 by Idiap Research Institute, http://www.idiap.ch
#
# See the file COPYING for the licence associated with this software.
#
# Author(s):
# Bogdan Vlasenko, February 2016
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may n... |
a9076341cc8814e673b3fe60215cf50da3665dda75dfb834ec348eaa9a06bd63 | Shell | 12,679 | 305 | #!/usr/bin/env bash
# Copyright 2021 Xiaomi Corporation (Author: Yongqing Wang)
# Seasalt AI, Inc (Author: Guoguo Chen)
# Apache 2.0
# This script is copied from mini_librispeech/s5
# 1b is as 1a but adding SpecAugment and removing dropout (which, in
# combination with SpecAugment, no longer seemed t... |
66ae2202558a05d73f298f44c68b1327613301e4337a304b0867a8dc265c326e | Shell | 12,684 | 309 | #!/usr/bin/env bash
# same as tdnn_lstm_1o but use backstitch training.
# Also num-epochs and l2-regularize are tuned for best performance.
# local/chain/tuning/run_tdnn_lstm_bs_1a.sh --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
# local/chain/compare_wer_general.sh sdm1 tdnn_lstm_bs_1a_... |
ed3a140c8c7d99bd2669d2bcbb1bc86f058004c9ca1545513b3f89af114ab4ca | Shell | 12,698 | 318 | #!/usr/bin/env bash
# run_cnn_tdnn_1a.sh is modified from run_tdnn_1b.sh but taking
# the xconfig from mini-librispeech's run_cnn_tdnn_1a54.sh; only
# reducing the bottleneck-dim from 96 to 64, which is the value
# the run_tdnn1b.sh script here has. Results are better.
# local/chain/compare_wer.sh exp/chain/tdnn... |
2e3cadaf4c8a669a296ac6218b9f981542079b6fcef864df1be54bd2aa444c19 | Shell | 12,709 | 263 | #!/usr/bin/env bash
set -e
# based on run_tdnn_7b.sh in the swbd recipe
# configs for 'chain'
stage=7 # assuming you already ran the xent systems
train_stage=-10
get_egs_stage=-10
dir=exp/chain/tdnn_7b
decode_iter=
# training options
num_epochs=4
remove_egs=false
common_egs_dir=
num_data_reps=3
min_seg_len=
xent_... |
1f51fba4ef5cf24f62584395f1955d2e652f628fc80134c118781173583549cf | Shell | 12,806 | 302 | #!/usr/bin/env bash
# 1k is same as 1j but with smaller delay on the first lstm layer
# there is a 37% increase in training time 11hrs vs 8hrs and the gains are modest
# Results with flags : --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned
#System tdnn_lstm1j_sp_bi_ihmali_ld5 td... |
04bbe7d555c441a1565457feb836e18b879dfdb922ebb204bb4ad9343c8439da | Shell | 12,911 | 313 | #!/usr/bin/env bash
# Copyright 2016 Pegah Ghahremani
# This script can be used for training multilingual setup using different
# languages (specifically babel languages) with no shared phones.
# It will generates separate egs directory for each dataset and combine them
# during training.
# In the new multilingual tr... |
67e60b9f8107aed2d099143e4d5c8402cd7de4c41b0845f284befcbe326f8cb6 | Shell | 12,913 | 306 | #!/usr/bin/env bash
# 1j is same as 1i but with changes related to fast-lstmp layer
# changed num-chunk-per-minibatch to be variable
# added extra_left_context_initial=0
# and extra_right_context_final=0
# These changes are similar to those between swbd's run_tdnn_lstm_1{c,d}.sh
# recipes
# Results with flags : --mic... |
4d2c1ce31fa4363ac5bed1da7acbb74736940d4cd1cfc6c4a85989e8438e4eb3 | Shell | 12,932 | 366 | #! /usr/bin/env bash
set -euo pipefail
# Pin the renderer version used by this integration. A floating latest tag can
# change its gRPC surface underneath the checked-in stubs.
NUREC_IMAGE_DEFAULT="nvcr.io/nvidia/nre/nre-ga:26.04.01"
PYTHON_EXECUTABLE=python
# Default test scene from the public HuggingFace dataset; -... |
c8a1cc2613998f46d34699bc6516c9980ca583ccb663e8e386e5a778ad03cdd5 | Shell | 12,937 | 344 | #!/bin/bash
set -e
set -o pipefail
. conf/common_vars.sh || exit 1;
. ./lang.conf || exit 1;
type=dev10h
dev2shadow=dev10h.uem
eval2shadow=eval.uem
data_only=false
fast_path=true
skip_kws=false
skip_stt=false
max_states=150000
wip=0.5
. utils/parse_options.sh
if [ $# -ne 0 ]; then
echo "Usage: $(basename $0) --t... |
7607996e5bd632a7e0334f5ca1a8e40d4549efe0cb8c643fc3157853ab405895 | Shell | 13,023 | 349 | #!/bin/bash
set -e
set -o pipefail
. conf/common_vars.sh || exit 1;
. ./lang.conf || exit 1;
type=dev10h
dev2shadow=dev10h
eval2shadow=eval
data_only=false
fast_path=true
skip_kws=false
skip_stt=false
max_states=150000
wip=0.5
. utils/parse_options.sh
if [ $# -ne 0 ]; then
echo "Usage: $(basename $0) --type (dev... |
f5073f2d125a6cd5d06a0c9a6432974fd727aa637385e78a53e961698e9131ff | Shell | 13,076 | 53 | #!/bin/bash -u
# Copyright 2020 Prisyach Tatyana (STC-innovations Ltd)
# Apache 2.0.
segments=$1
dset=$2
dir_chime6=$3
json=$4
if [ $dset == "dev" ]; then
awk -F "_|-| " -v dir_chime6=$dir_chime6 -v S02="false" -v S09="false" '
BEGIN { printf"%s","{\n \"alias\": {\n \"dev\": [\n ... |
883336e0a6a27520e858c92fa8709fc7a1594a11fa9630e5f7089aae50fe0656 | Shell | 13,109 | 313 | #!/usr/bin/env bash
# Copyright 2016-18 Vimal Manohar
# Apache 2.0
set -e
set -o pipefail
# This script demonstrates how to use out-of-domain WSJ models to segment long
# audio recordings of HUB4 with raw unaligned transcripts into short segments
# with aligned transcripts for training new ASR models.
# The overa... |
59e013d0ad7f18e4317b07d0cdc9a902ab58b23c9dc8ddab675729bd79ac10d5 | Shell | 13,173 | 303 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# Same as run_tdnn_opgru_1a.sh, but replacing Norm-OPGRU with LSTMP.
# Also Batchnorm in TDNN layers does not reduce the WER in Fisher+SWBD, so in run_tdnn_lstm_1a.sh,
# I just apply renorm component in TDN... |
74aa44a59d03207c53080a303ef4e035c4eae4bad88bd888738fc8b64d01a624 | Shell | 13,200 | 362 | #!/usr/bin/env bash
#
# This script decodes raw utterances through the entire pipeline:
# Feature extraction -> SAD -> Diarization -> TS-VAD diarization -> GSS enhancement -> ASR
#
# Copyright 2017 Johns Hopkins University (Author: Shinji Watanabe and Yenda Trmal)
# 2019 Desh Raj, David Snyder, Ashish Aro... |
b39c0306d9479c67c0a33ffbd70b7dab579348d3800c4ab2215c24118a777713 | Shell | 13,254 | 340 | #!/usr/bin/env bash
#
# Chime-6 Track 2 baseline. Based mostly on the Chime-5 recipe, with the exception
# that we are required to perform speech activity detection and speaker
# diarization before ASR, since we do not have access to the oracle SAD and
# diarization labels.
#
# Copyright 2017 Johns Hopkins Universit... |
c1e9283c9146266f87d8030c21e1b6e59190316e99afff6e84b9851c4bcd4c65 | Shell | 13,287 | 329 | #!/usr/bin/env bash
# This is a chain-training script with TDNN+LSTM neural networks.
# This script is based on local/chain/tuning/run_tdnn_lstm_1i.sh, but adding
# the reverberated IHM data into the train set.
# This script obtains better results on IHM, SDM and MDM tasks.
# Please see RESULTS_* for examples of comm... |
7a34fa2ade7b782ef4c8bac260237378470210b4c35172444d7547c38bb14949 | Shell | 13,319 | 283 | #!/usr/bin/env bash
# 2016 Modified by Takafumi Moriya at Tokyo Institute of Technology
# for Japanese speech recognition using CSJ.
# Copyright 2012-2014 Brno University of Technology (Author: Karel Vesely)
# Apache 2.0
# This example script trains a bottleneck feature extractor with
# 'Universal Context' topolog... |
fea634cc0838c3648462bd1776c770df8118141175a99d8b9510b6edb1d9a480 | Shell | 13,454 | 296 | #!/bin/bash
# Copyright 2013 Daniel Povey
# Apache 2.0.
# This script computes iVectors in the same format as extract_ivectors_online.sh,
# except that they are actually not really computed online, they are first computed
# per speaker and just duplicated many times.
# This is mainly intended for use in decodin... |
c4cca20d3ae6c659397d365849ce57d0a76358d91826b077a4a57f5ee3a7ffc3 | Shell | 13,529 | 247 | #!/bin/bash
#SBATCH --job-name=images
#SBATCH --time=03:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=2
#SBATCH --mem-per-cpu=2gb
#SBATCH --account=iacc_nbc
#SBATCH --qos=pq_nbc
#SBATCH --partition=investor
# Outputs ----------------------------------
#SBATCH --output=/home/data/abcd/abcd-hispanic-... |
d384dbc85330cfc5f12fe07fe80d86522a8b7a4b40d76354134cf32a90c60f32 | Shell | 13,624 | 312 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# Similar to swbd\s5c\local\chain\tuning\run_tdnn_lstm_1e.sh
# Difference between tdnn_lstm_1a and tdnn_lstm_1b:
# chunk width 150 140,100,160
# xent_regularize 0.025 0.01
# minibatch 6... |
f4c6caceccbce1e39e605deb2bcd501bdb229617240268aacb2c0ad3073445ce | Shell | 13,941 | 310 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# Similar to tdnn_lstm_1e (from egs/swbd/s5c).
# Difference between tdnn_opgru_1a and tdnn_opgru_1b:
# chunk width 150 140,100,160
# xent_regularize 0.025 0.01
# minibatch 64 64,32
# ... |
b3e2f0f562ee567a0f5ecb9ccfc96aa3003d55d03231eba6f5b7262e01d92336 | Shell | 13,944 | 310 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# This is based on TDNN_LSTM_1b (from egs/swbd/s5c), but using the NormOPGRU to replace the LSTMP,
# and adding chunk-{left,right}-context-initial=0
# Different from the vanilla OPGRU, Norm-OPGRU adds batch... |
bc16d9da8e0d482103d7ad38b4b50da5a42c7aa39d91664e85ad55d3de65aeb4 | Shell | 13,955 | 334 | #!/usr/bin/env bash
# cnn_tdnn_lstm_1b is based on cnn_tdnn_lstm_1a, but adding dropout and
# proportional-shrink with value 5
# SDM
# Results with flags : --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned \
# ./local/chain/compare_wer_general.sh sdm1 tdnn_lstm1j_sp_bi_ihmali_ld5 cnn_tdnn_ls... |
3115940af66a8270d4560727d39d3602900f28a1255503200b2e34ad3494d308 | Shell | 14,013 | 363 | #!/usr/bin/env bash
# This is not necessarily the top-level run.sh as it is in other directories. see README.txt first.
tri5_only=false
sgmm5_only=false
denlats_only=false
data_only=false
[ ! -f ./lang.conf ] && echo 'Language configuration does not exist! Use the configurations in conf/lang/* as a startup' && exit... |
972e2754ee2f9490ac35e877ef06ba97d76253d6a5d51914ed17c5b1b854b08b | Shell | 14,046 | 350 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# This is based on the run_tdnn_opgru_1a.sh, but using backstisch (http://www.danielpovey.com/files/2017_interspeech_backstitch.pdf)
# together with per-frame dropout.
# System tdnn_opgru_1a_s... |
b554169388b64168101b9d3875f0f416ab835c28730b990e5a85bdacdf7c833e | Shell | 14,055 | 349 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# This is based on the run_tdnn_opgru_1b.sh, but using backstitch interval 1 and value 0.3.
# System tdnn_opgru_1a_sp_ihmali tdnn_opgru_1b_sp_ihmali tdnn_opgru_1c_sp_ihmali
# WER on dev ... |
06b7ad54a05088beec8e5811fddb963d46c32b95eac12f4b259cc54045f35da6 | Shell | 14,135 | 349 | #!/usr/bin/env bash
# Copyright 2017 University of Chinese Academy of Sciences (UCAS) Gaofeng Cheng
# Apache 2.0
# This is based on the run_tdnn_lstm_1j.sh, replacing the LSTM with the proposed Norm-OPGRU.
# Different from the vanilla OPGRU, Norm-OPGRU adds batchnorm in its output (forward direction)
# and renorm in ... |
f769236fc4e6d4f7086af28428891c627dbb061d5ccebbc4cf8c4ce6620afbc3 | Shell | 14,178 | 340 | #!/usr/bin/env bash
# cnn_tdnn_lstm_1a is based on tdnn_lstm_1j, but adding the cnn front end, and
# replacing all renorm in tdnn layers with batchnorm
# SDM
# Results with flags : --mic sdm1 --use-ihm-ali true --train-set train_cleaned --gmm tri3_cleaned \
# ./local/chain/compare_wer_general.sh sdm1 tdnn_lstm1j_sp_... |
48cae98b07545b4eedbb0567a75759fb96af31314ed646e3599c09328f92850f | Shell | 14,269 | 334 | #!/usr/bin/env bash
# This script is based on swbd 7q TDNN-F recipe
# with resnet-style skip connections, more layers,
# skinnier bottlenecks, removing the 3-way splicing and skip-layer splicing,
# and re-tuning the learning rate and l2 regularize. The configs are
# standardized and substantially simplified.
# The a... |
d1bb192d62af292468313c4fcacdc6e88540b46dd845d51bd654dc494473de94 | Shell | 14,309 | 344 | #!/usr/bin/env bash
# This is not necessarily the top-level run.sh as it is in other directories. see README.txt first.
tri5_only=false
sgmm5_only=false
data_only=false
[ ! -f ./lang.conf ] && echo 'Language configuration does not exist! Use the configurations in conf/lang/* as a startup' && exit 1
[ ! -f ./conf/co... |
d140d3c75c54937f8f185cf76c703370352524bd034837ba0f5649930d71d51a | Shell | 14,333 | 344 | #!/usr/bin/env bash
# This is not necessarily the top-level run.sh as it is in other directories. see README.txt first.
tri5_only=false
sgmm5_only=false
data_only=false
[ ! -f ./lang.conf ] && echo 'Language configuration does not exist! Use the configurations in conf/lang/* as a startup' && exit 1
[ ! -f ./conf/co... |
522ff46e0d5533079f016f807c39fa9c129a9fb0fe5a0fe7831b1398b4d5cdf1 | Shell | 14,382 | 336 | #!/usr/bin/env bash
. ./cmd.sh ## You'll want to change cmd.sh to something that will work on your system.
## This relates to the queue.
# This is a shell script, but it's recommended that you run the commands one by
# one by copying and pasting into the shell.
case 0 in #goto here
1)
;; ... |
536d94d2189e2d48db20bea9d54c6b4c65a876bd8190212e40ca1bec0306f0c6 | Shell | 14,964 | 343 | #!/usr/bin/env bash
# cnn_tdnn_lstm_1c is based on cnn_tdnn_lstm_1b, but using smaller dropout-schedule
# and larger decay-time option(40).
# After testing different combinations of dropout-schedule('0,0@0.20,0.15@0.50,0',
# '0,0@0.20,0.3@0.50,0' or without), proportional-shrink(with or without) and
# decay-time opt... |
87b1910a2d6fdb25fb42a427018f24e9adcb6962d783e858c5ccc684f15593cd | Shell | 14,970 | 347 | #!/usr/bin/env bash
# It's best to run the commands in this one by one.
. ./cmd.sh
. ./path.sh
mfccdir=mfcc
set -e
rescore=true
# check for kaldi_lm
which get_word_map.pl > /dev/null
if [ $? -ne 0 ]; then
echo "This recipe requires installation of tools/kaldi_lm. Please run extras/kaldi_lm.sh in tools/" && exit 1;
... |
d7d726b406009454b5c19dd93aff6dc1be52f5058f68d5b9e8c4834f10d90d53 | Shell | 14,998 | 386 | #!/usr/bin/env bash
# This is not necessarily the top-level run.sh as it is in other directories. see README.txt first.
tri5_only=false
sgmm5_only=false
denlats_only=false
data_only=false
morfessor=true
tag_percentage=0.1
[ ! -f ./lang.conf ] && echo 'Language configuration does not exist! Use the configurations in... |
684259dd22c7b0417b1a9b85e4a6b7cdc9258d1f176aefd13f47d41e1be0f25c | Shell | 15,103 | 377 | #!/usr/bin/env bash
# this is a TDNN+LSTM chain system.
# It was modified from local/nnet3/tuning/run_tdnn_lstm_lfr_1a.sh with
# reference to ../../tedlium/s5_r2/local/chain/run_tdnn_lstm_1e.sh.
# Note: we're using the same hidden-layer sizes as
# ../../tedlium/s5_r2/local/chain/run_tdnn_lstm_1e.sh despite the
# fact... |
782fa447cd60784a279afbaf1e4e8f526c1ce5d0233037590c3e6d8d41e8ec4e | Shell | 15,125 | 358 | #!/usr/bin/env bash
# Copyright Johns Hopkins University (Author: Daniel Povey, Vijayaditya Peddinti) 2015. Apache 2.0.
# This script generates the ctm files for dev_aspire, test_aspire and eval_aspire
# for scoring with ASpIRE scoring server.
# It also provides the WER for dev_aspire data.
iter=final
mfccdir=mfcc_r... |
4755e853c7017fa4e7571078ec097eb883b62d346d31043d21d37a921da9eb54 | Shell | 15,250 | 396 | #!/usr/bin/env bash
# This was modified from wsj/local/chain/tunning/run_tdnn_1e.sh to be
# used in Chime4.
#This is the result using all 6 channels:
# exp/chain/tdnn1a_sp/best_wer_blstm_gev.result
# -------------------
# best overall dt05 WER 4.34% (language model weight = 7)
# -------------------
# dt05_simu WER: 4... |
f0ad5f0423be2118f888d7d8eb6c3acc6907663b0c7afb93a8d271642c3ba0f4 | Shell | 15,254 | 316 | #!/usr/bin/env bash
# Copyright 2017 Johns Hopkins University (Author: Daniel Garcia-Romero)
# 2017 Johns Hopkins University (Author: Daniel Povey)
# 2017-2018 David Snyder
# 2018 Ewald Enzinger
# 2018 Zili Huang
# Apache 2.0.
#
# See ../README.txt for more info on... |
f4aea71df3c0f2ec0a99f5bbf8b9f8bbbdf6b0da3851ce4408899169c957f26d | Shell | 15,704 | 360 | #!/usr/bin/env bash
# This is a chain-training script with TDNN+LSTM neural networks.
# This script is similar to local/chain/multi_condition/tuning/run_tdnn_lstm_1a.sh,
# but updated to use new l2-regularize options and fast-lstmp with decay-time.
# It uses the reverberated IHM data in the train set.
# This script ob... |
543dc4c9d29622d546f33023223e7ee628366edf04cf50101ecfda1fa22a8ae0 | Shell | 15,812 | 409 | #!/usr/bin/env bash
set -e
# Copyright 2009-2012 Microsoft Corporation Johns Hopkins University (Author: Daniel Povey)
# Apache 2.0.
if [ $# -ne 2 ]; then
printf "\nUSAGE: %s <corpus-directory>\n\n" `basename $0`
echo "The argument should be a the top-level WSJ corpus directory."
echo "It is assumed that ther... |
2586c7910a90fefa8926a8faa127051597cc3a5c07c5387f0514b195f30efb12 | Shell | 15,893 | 352 | #!/usr/bin/env bash
# This is not necessarily the top-level run.sh as it is in other directories. see README.txt first.
tri5_only=false
[ ! -f ./lang.conf ] && echo "Language configuration does not exist! Use the configurations in conf/lang/* as a startup" && exit 1
[ ! -f ./conf/common_vars.sh ] && echo "the file ... |
4712e5203a80b289a8dda02bcbaf5f0ab5b7c44592ace9296a295ba504f73f6c | Shell | 16,685 | 402 | #!/usr/bin/env bash
# Copyright 2015-2016 David Snyder
# 2015 Johns Hopkins University (Author: Daniel Garcia-Romero)
# 2015 Johns Hopkins University (Author: Daniel Povey)
# 2017 Radboud University (Author: Emre Yilmaz)
# Apache 2.0.
#
# See README.txt for mor... |
584f42211df0c95d61b59de5b4b4b292c8961f97f8588484a3ba9718d953a086 | Shell | 16,742 | 344 | #!/usr/bin/env bash
# This (1l.sh) is the same as 1i but with per-frame dropout on LSTM layer
# It is a regular (non-fast) LSTM with per-frame dropout on [i, f, o] gates of the LSTM,
# the dropout-adding place is "place4" in paper : http://www.danielpovey.com/files/2017_interspeech_dropout.pdf.
# We have tried both 4-... |
545a5f8de309342ae73ab7f9aa447d4bd733c4ddce993903d46836937b713c19 | Shell | 16,937 | 457 | #!/bin/bash
# This runs the complete GeneNetwork processing pipeline
set -e
set -u
module load Python
#### Only lines that should be changed ####
# TMPDIR for writing files
TMPDIR=
# original expression table
expression_file=
# Directory to write output to
output_dir=
# Directory to write configuration files to (wil... |
59df96ea54473e88aa35eb13e1fc320ea5b85d2ce01c34db847393b7ea538c2b | Shell | 16,976 | 615 | #!/bin/bash
VERSION="0.0.0 test"
# trap keyboard interrupt (control-c)
trap control_c SIGINT
function setPath {
cat <<SETPATH
--------------------------------------------------------------------------------------
Error locating ANTS
-------------------------------------------------------------------------------... |
44e4fc0cc8b4a741f156fbc048e80423dc8dab9678bfca0596a7930bfbf1f55b | Shell | 17,004 | 352 | #!/usr/bin/env bash
# This (1m.sh) is the same as 1j but with per-frame dropout on LSTM layer
# It is a fast LSTM with per-frame dropout on [i, f, o] gates of the LSTM,
# the dropout-adding place is "place4" in paper : http://www.danielpovey.com/files/2017_interspeech_dropout.pdf.
# We have tried both 4-epoch and 5-ep... |
542f89e914563aa156eab3dc1826c60f15f82095d80eed5c3bab5aa21983d60c | Shell | 17,313 | 458 | #!/usr/bin/env bash
#
# JHU-CLSP submission for Chime-6 Track 2.
# This recipe uses the ASR from the s5b_track1 recipe. We also replace the
# baseline SAD with one that uses posterior fusion from the array outputs,
# and the baseline AHC diarization with VB-based overlap assignment.
#
# This is essentially a decoding... |
56a3e86b8bb83ea6253c09b5be4a5434302857def432d699d1c14afc15f682d8 | Shell | 17,392 | 497 | #!/bin/bash
set -e
set -o pipefail
. conf/common_vars.sh || exit 1;
. ./lang.conf || exit 1;
dir=dev10h.pem
kind=
data_only=false
fast_path=true
skip_kws=false
skip_stt=false
skip_scoring=false
max_states=150000
extra_kws=true
vocab_kws=false
tri5_only=false
wip=0.5
echo "run-4-test.sh $@"
. utils/parse_options.s... |
3c760d60dfcb7706c4344b99fb98eb34d926c68cab215d1dfbeaa0f19134dc01 | Shell | 17,487 | 499 | #!/usr/bin/env bash
set -e
set -o pipefail
. conf/common_vars.sh || exit 1;
. ./lang.conf || exit 1;
dir=dev10h.pem
kind=
data_only=false
fast_path=true
skip_kws=false
skip_stt=false
skip_scoring=false
extra_kws=true
vocab_kws=false
tri5_only=false
wip=0.5
echo "run-4-test.sh $@"
. utils/parse_options.sh
if [ $# ... |
9cb9499e68b4eb9aacd0f3144a66e956f7c167f56c705e46a26ed5336596ef74 | Shell | 17,832 | 367 | #!/usr/bin/env bash
# Answer "is feature X usable here, and is there a vetted procedure for it".
#
# support.sh matrix the support table (no CARLA needed)
# support.sh broken only what is broken or removed — read before promising
# support.sh probe inspect THIS machine: tre... |
6a1b2baad640dacb8266c1c529c16e4bdc81d8b7a374c88c65ab181618050f19 | Shell | 17,994 | 428 | #!/usr/bin/env bash
# Copyright 2017 Vimal Manohar
# Apache 2.0
# This script is semi-supervised recipe with 100 hours of supervised data
# and 250 hours unsupervised data with naive splitting.
# Based on "Semi-Supervised Training of Acoustic Models using Lattice-Free MMI",
# Vimal Manohar, Hossein Hadian, Daniel Po... |
a1bcc87f7c7dd8d49ab528570643a7d28564c23f5b7443a35a9a9f00ef4a6e41 | Shell | 18,711 | 138 | find /Users/gazelle/Documents/Dropbox/Research/NeuroML/neuroml-db/www/NeuroMLmodels/NMLCH000001/ -name "fileOut*" -exec python createChartJS.py "{}" \;
find /Users/gazelle/Documents/Dropbox/Research/NeuroML/neuroml-db/www/NeuroMLmodels/NMLCH000002/ -name "fileOut*" -exec python createChartJS.py "{}" \;
find /Users/gaze... |
90f900f72732e1cdba26763194d4871f1778f5a4b945fd780348ba94acfc7c2f | Shell | 18,986 | 448 | #!/usr/bin/env bash
#
# Copyright 2018 Nagendra Kumar Goel,
# Saikiran Valluri, Govivace.Inc - Apache 2.0
# The script is organized as below.
# First we train the baseline LSTMP-TDNN config chain model for few epochs on the (Fisher+swbd)-english data,
# Then, we perform SVD based refactoring of all the Af... |
42820d50e8d9cdf60dcd0fd894fdf6f1525b87a994f54a6e3b86bd2120691320 | Shell | 19,208 | 404 | #!/usr/bin/env bash
# Copyright 2017-2018 David Snyder
# 2017-2018 Matthew Maciejewski
#
# Apache 2.0.
#
# This recipe demonstrates the use of x-vectors for speaker diarization.
# The scripts are based on the recipe in ../v1/run.sh, but clusters x-vectors
# instead of i-vectors. It is similar to the x-vect... |
1f29de0a2e2bad7fcf266ae442f259fde6413ad2dd3cd5172a608415126524c4 | Shell | 19,232 | 447 | #!/usr/bin/env bash
# Copyright 2017 Vimal Manohar
# Apache 2.0
# This script is semi-supervised recipe with around 50 hours of supervised data
# and 250 hours unsupervised data with naive splitting.
# Based on "Semi-Supervised Training of Acoustic Models using Lattice-Free MMI",
# Vimal Manohar, Hossein Hadian, Dan... |
ac12102d79f5bb43f26610131176ef4971c1256536d2060c78b9a83443f6bec1 | Shell | 19,295 | 446 | #!/bin/bash
# chain2 recipe for monolingual systems for BABEL
# Copyright 2016 Pegah Ghahremani
# Copyright 2020 Srikanth Madikeri (Idiap Research Institute)
# This script is used to train multilingual LF-MMI system with a multi-task training
# setup.
# local.conf should exists (check README.txt), which contains conf... |
43912087d88d13e17a4bf12c01f4531440b091a3bca010abd12ea42457aced11 | Shell | 19,757 | 374 | #!/usr/bin/env bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
# Copyright 2014 Vimal Manohar
# Apache 2.0.
# This script, which will generally be called from other neural-net training
# scripts, extracts the training examples used to train the neural net (and also
# the validation examples used... |
2db21db879a93d5d92e0637afa1bcef3feec0b431db0e9e8b534f086d52ff438 | Shell | 19,784 | 374 | #!/usr/bin/env bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey)
# Copyright 2014 Vimal Manohar
# Apache 2.0.
# This script, which will generally be called from other neural-net training
# scripts, extracts the training examples used to train the neural net (and also
# the validation examples used... |
8a4536061291f5809b093f7af780300dbe110e8ae2bf643d08dff4d4b1dfc416 | Shell | 22,102 | 411 | #!/usr/bin/env bash
# Download and make alignment pipeline
# This script clones the Molgenis Compute pipeline for alignin FastQ files and
# modifies the protocols and parameter files for analysis of Brain eQTL data.
# Makes samplesheet dependent parameter files.
# NOTE: This script changes paths in a parameters.csv t... |
a04e0a414ace285884b0dfb602bc4c72feb4035a9a5b956355e7f93babed3c16 | Shell | 22,130 | 397 | #!/usr/bin/env bash
# This script clones the Molgenis Compute pipeline for alignin FastQ files and
# modifies the protocols and parameter files for analysis of Brain eQTL data.
# Makes samplesheet dependent parameter files.
# NOTE: This script changes paths in a parameters.csv that are specific to our clusters
# ... |
3e18b191611ffb537039534f766d4da47b8d6cc3c3bcd171bf117b2664fc9480 | Shell | 22,564 | 425 | #!/usr/bin/env bash
# Download and make alignment pipeline
# This script clones the Molgenis Compute pipeline for alignin FastQ files and
# modifies the protocols and parameter files for analysis of Brain eQTL data.
# Makes samplesheet dependent parameter files.
# NOTE: This script changes paths in a parameters.csv th... |
36ad558c4e490e5c119368a7e2ef15b232114e5ef10e3f7a0659b5c901748b11 | Shell | 22,883 | 422 | #!/bin/bash
# Copyright 2020 Ivan Medennikov (STC-innovations Ltd)
# Apache 2.0
# This script trains TS-VAD model using the same training data
# as in the baseline acoustic model.
. ./path.sh
. ./cmd.sh
# Begin configuration section.
stage=0
train_stage=-10
srand=0
# Training options
num_epochs=2
lrate=0003
l2=0.... |
18f79274e685c8beb7715db7c9de83e3c4435e2969a78440428e14bab475157a | Shell | 24,413 | 403 | #!/usr/bin/env bash
# This script clones the Molgenis Compute pipeline for alignin FastQ files and
# modifies the protocols and parameter files for analysis of Brain eQTL data.
# Makes samplesheet dependent parameter files.
# NOTE: This script changes paths in a parameters.csv that are specific to our clusters
# ... |
dfdad0c9ce30943cef581570eeee2c592fb45f5642cedbf7d4473fe509d97e1c | Shell | 24,755 | 438 | #!/usr/bin/env bash
# Download and make quantification pipeline
# This script clones the Molgenis Compute pipeline for alignin FastQ files and
# modifies the protocols and parameter files for analysis of Brain eQTL data.
# Makes samplesheet dependent parameter files.
# NOTE: This script changes paths in a parameters.... |
4124efef9151a69e1c9d5c4d7ffce1217700dcc9f28ef39e68e9127ecc9be3ef | Shell | 24,948 | 560 | #!/usr/bin/env bash
# Copyright 2014 Johns Hopkins University (authors: Daniel Povey, Yenda Trmal)
# 2014 Guoguo Chen
# Apache 2.0.
# This script takes an input lexicon (e.g. lexicon.txt) and generates likely
# out of vocabulary words from it, with their associated spellings. It outputs
# two files: lexi... |
ee5941438f1879e054d5b2d927e55e7d8dab960598c915dee963520b4a57bf77 | Shell | 25,442 | 571 | #!/usr/bin/env bash
# Copyright 2014 Johns Hopkins University (authors: Daniel Povey, Yenda Trmal)
# 2014 Guoguo Chen
# 2015 MIT Lincoln Labs (author: Fred Richardson)
# Apache 2.0.
# This script takes an input lexicon (e.g. lexicon.txt) and generates likely
# out of vocabulary words from it, w... |
e07671fe2cd50c229a6131ca6942b2dc8c6eafbfe558c7bd6e83956bf33919c8 | Shell | 25,655 | 572 | #!/usr/bin/env bash
# Copyright 2014 Johns Hopkins University (authors: Daniel Povey, Yenda Trmal)
# 2014 Guoguo Chen
# 2015 MIT Lincoln Labs (author: Fred Richardson)
# Apache 2.0.
# This script takes an input lexicon (e.g. lexicon.txt) and generates likely
# out of vocabulary words from it, w... |
341b6362966704ec82d16e758e9d67249fb4a3af7a13762785a8ee102fcc3598 | Shell | 27,236 | 762 | #!/usr/bin/env bash
set -e
set -o pipefail
. conf/common_vars.sh || exit 1;
. ./lang.conf || exit 1;
dir=dev10h.pem
kind=
data_only=false
fast_path=true
skip_kws=false
skip_stt=false
skip_scoring=
extra_kws=true
vocab_kws=false
tri5_only=false
wip=0.5
nnet3_model=nnet3/tdnn_sp
chain_model=
parent_dir_suffix=_cleane... |
b8101ccdb27c4884e8ece24abd027ff483bafb8d90e90cfe4c6c51820938721c | Shell | 27,477 | 493 | #!/bin/bash
# This runs the complete GeneNetwork processing pipeline
set -e
set -u
module load Python
module load RPlus
#### Only lines that should be changed ####
# TMPDIR for writing files
TMPDIR=
# Directory to write output to
output_dir=
# Directory to write configuration files to (will be put in $project_dir/co... |
dd6bb0a408d190f84e31a3a4175c4600e282f6e24b1ce1e50d8ac08a3f7b4bb3 | Shell | 32,072 | 70 | ####
# Note
mdkir data
cd data
# L2/3IT
curl -o L2=3IT.h5ad "https://corpora-data-prod.s3.amazonaws.com/d87621ed-e37a-4124-92c9-b13acb511068/local.h5ad?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATLYQ5N5XX7PGJMPI%2F20220905%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20220905T055931Z&X-Amz-Expires=604800&X-A... |
9d17d37d6d83b3868d3b791fd3232eaa89b429fe73c1bce03e9b4186f60e4b98 | Shell | 33,350 | 369 | #1
TITLE=BulkBcells-S
FASTQ1_1=/mnt/lab_data/kundaje/users/pgreens/projects/hematopoiesis/data/atac_seq/fastq/marson_data/pooled/"$TITLE"-a-R1.fastq.gz
FASTQ1_2=/mnt/lab_data/kundaje/users/pgreens/projects/hematopoiesis/data/atac_seq/fastq/marson_data/pooled/"$TITLE"-a-R2.fastq.gz
FASTQ2_1=/mnt/lab_data/kundaje/users/p... |
bd59e4be38eb7dc16a73e3ac11f7f6a6cf3ab3dce237b4fce8e13ef503dc7160 | Shell | 33,667 | 203 | # to call it:
# swarm -f ./swarm50_imagewisedecoding.sh -g 20 -t 18 --time=2:00:00 --logdir=./logdir
module load matlab;matlab -nodesktop -r "step3d_validation_pairwise_decoding200('/data/HemiMotion/THINGS/THINGS-MEG-bids','/data/HemiMotion/THINGS/toolboxes',1,1,50)"
module load matlab;matlab -nodesktop -r "step3d_val... |
677ca8b3357822818d780cc8519d732b461711e362e8228fe2701aa290c7a12b | Shell | 34,985 | 702 | #!/usr/bin/env bash
#
# install_autoware.sh - fetch/install Autoware for use with CARLA (UE5.8, native ROS2)
#
# Modes:
# --source (default) source build: clone autowarefoundation/autoware at a
# release tag, vcs import, rosdep install, colcon build with
# BOUNDED parallelism (this m... |
1371065bd9d0f1ed13096218b6dc4f6ba88d56afbfafa50edb6d6fc3909faf95 | Shell | 49,335 | 573 | blender & disown; python manage.py save_morphology_data NMLCL000003; pkill -9 blender;
blender & disown; python manage.py save_morphology_data NMLCL000085; pkill -9 blender;
blender & disown; python manage.py save_morphology_data NMLCL000097; pkill -9 blender;
blender & disown; python manage.py save_morphology_data NML... |
5e45ff919ee4c9d6667b19840c18fb83162fa34ed72c878290ff578fec73e217 | Shell | 51,774 | 1,243 | #!/bin/bash
# This code has been made available under the GPL version 3. The GPL
# applies SOLELY to this code as the authors have no ownership of the
# source atlases. For more information on the GPLv3, see:
# https://github.com/EmoryPcvdLab/EagleVAC/blob/main/LICENSE
#============================ OVERRIDES ========... |
6e8919a2eacf2b37c804e4d8ca3337f48363d14943b7182d644d425c205ef675 | Shell | 66,273 | 456 | #!/usr/bin/env bash
# Copyright 2015 Johns Hopkins University (author: Vijayaditya Peddinti)
# Apache 2.0
# This script downloads the impulse responses from http://www.openairlib.net/
# It downloads only impulse responses and not sinesweep responses
#==============================================
download=true
sampli... |
77c6629affa5241ef72e40e2c36bd3ba4a4f99fdbadd47e991b6ce88bb20f1d5 | Shell | 75,314 | 1,438 | #!/usr/bin/env bash
#
# run_carla_autoware.sh -- orchestrate CARLA (UE5.8, native ROS2) + Autoware.
#
# Modes:
# classical : TIER IV-validated classic stack
# ros2 launch autoware_launch e2e_simulator.launch.xml
# vehicle_model:=sample_vehicle sensor_model:=awsim_sensor_kit
# ... |
1df4cf4a84d69530e94502d25faf9e79172097d87a208ac9d8c0f6fbc1f8183d | Shell | 104,305 | 615 | # to call it:
# swarm -f ./swarm.sh -g 240 -t 18 --time=24:00:00 --logdir=./logdir
module load matlab;matlab -nodesktop -r "step3e_validation_pairwise_decoding1854('/data/HemiMotion/THINGS/THINGS-MEG-bids','/data/HemiMotion/THINGS/toolboxes',1,1,153)"
module load matlab;matlab -nodesktop -r "step3e_validation_pairwise... |
a05b4f59ebe11fd2a64558255b2cf76b573457137713e4dd6ef585e04ad36f14 | Shell | 138,079 | 1,670 | #!/bin/bash
#rsync -t -av --progress /srv/gsfs0/scratch/leepc12/run/imk_chipseq_surya_121-150 nandi.stanford.edu:/srv/scratch/leepc12/
#cd /srv/gsfs0/scratch/leepc12/run/imk_chipseq
#find . -wholename '*/true_reps/*_rep1-rep2.IDR0.05.filt.narrowPeak.gz'
#grep -rl 'rror' --include=*.BDS.log | grep -v 'Done report'
#gre... |
b5e763d5d2265cd05f72ee668fef8505c9b2bcc0b152f79896dcdb7c33ea3de2 | Shell | 200,000 | 2,418 | #1 , 3029328166, order: 3, svr: scg, old #: 38
NTH=6; SUFFIX="CHIPseq.ATF2.GM12878.EXPID_ENCSR000BQK"
FASTQ1=$DATA/DREAM_challenge/CHIPseq.ATF2.GM12878.EXPID_ENCSR000BQK.BSID_ENCBS110ENC.BSREP2.TECHREP1.FILEIDENCFF000NSG.unpaired.fastq.gz
FASTQ2=$DATA/DREAM_challenge/CHIPseq.ATF2.GM12878.EXPID_ENCSR000BQK.BSID_ENCBS110... |
f7889d10726e752d078bb2b0de72eb5dfc155889f909c7fe5335d9ab9888b082 | Shell | 200,000 | 2,623 | cat RNAseq/R3509_C0GTHACXX_TGACCA_L008_R1_001.fastq.gz RNAseq/R3509_C3V3YACXX_TGACCA_L003_R1_001.fastq.gz RNAseq/R3509_C3V3YACXX_TGACCA_L002_R1_001.fastq.gz RNAseq/R3509_C0GTHACXX_TGACCA_L007_R1_001.fastq.gz > RNAseq_merged/R3509.R1.fastq.gz
cat RNAseq/R3509_C0GTHACXX_TGACCA_L008_R2_001.fastq.gz RNAseq/R3509_C3V3YACXX_... |
7fde0c4669570e29514d4fc6a46e7377d60ad2dba4bba772523b3b4ac1eb5ad3 | Stan | 842 | 47 | data {
int<lower=1> N;
real x[N];
real y[N];
int<lower=1> id[N];
int<lower=1> Ninds;
}
parameters {
real a0;
real a[Ninds];
real<lower=0> SD_a;
real d0;
real d[Ninds];
real<lower=0> SD_d;
real c0;
real c[Ninds];
real<lower=0> SD_c;
real<upper=d0> b0;
real<upper=d[Ninds]> b[Ninds];
rea... |
451398e182ce1b752106f7c6d963df260fe13023d8b653c5845bc020f9a0eef8 | Stata | 634 | 15 | ***STATA code developed by Larisa Tereshchenko <tereshl@ccf.org>
***January 2022
***use STATA 17
use "CONFIDENTIAL_pos_retests_merged.dta"
*** adjusted preventable fraction
logistic hosp_omicron i.sex c.age_in_yrsx i.wave_pos c.time i.vaccine i.sympt_omicron
pwcompare wave_pos vaccine, effects
logistic hos... |
8a995b639bd41c9f4374d80bf2c570bed8c2f78625e12c2d2bf9651bc2c4be8e | Stata | 6,475 | 189 | ***Analysis of the data for MESA MS #AM084
***by Larisa Tereshchenko <tereshch@ohsu.edu>
***April-May 2021
***use STATA 16.1
help concord
***calculate heart rate
gen HR1 = 60000/ RRinterval_ms1
gen HR2 = 60000/ RRinterval_ms2
gen HR3 = 60000/ RRinterval_ms3
label variable HR1 "heart rate ECG1, bpm"
label ... |
aef7b197bdd9bf4758a08eb68ff178a476141b450e0813fd5c308b23a607dbd5 | Stata | 6,998 | 196 | ***STATA code developed by Larisa Tereshchenko <tereshch@ohsu.edu>
***for analysis of the JHS approved manuscript proposal P1224
***January 2020
***use STATA 16
set seed 123456789
splitsample , generate(sample) split(0.6 0.2 0.2)
label values sample lbsample
label define lbsample 1 "Training" 2 "Testing" 3"V... |
513a69c8671e8341aade89ac77fb6843de30c74b27c1297f3a694dcc1671db5e | Stata | 9,050 | 112 | ***Analysis of the data for HCHS/SOL MS 0924
***by Larisa Tereshchenko <tereshl@ccf.org>
***May 2023
***use STATA 17
***
*** setup survey analysis:
svyset PSU_ID [pweight=WEIGHT_FINAL_NORM_OVERALL], strata(STRAT) vce(linearized) singleunit(missing)
***prevalence
svy linearized, subpop(included) : proportion... |
43d0c5d4d0082759eb8f5fb233173e4c88390d331c7c930629ba2160fe3b22ec | Stata | 12,348 | 268 | ***SMART AV data analysis
***code developed by Larisa Tereshchenko <tereshch@ohsu.edu>
***March-June 2020
***use Stata 16
***I acknowledge use of code developed by Schonlau and Zou for the random forests algorithm.
***I acknowledge use of BRAIN Stata module to provide neural network [by Thorsten Doherr].
***see ... |
9ee31f389f52d4b53491f8c5b296e5e7e1a45831092a80365f2d35c0cb308b09 | Stata | 13,144 | 131 | ****CaVaR-Co19 study data analysis
***STATA 17
***July 2022
***by Larisa Tereshchenko <TERESHL@ccf.org>
***imputed each ECG variable one-by-one
mi set mlong
mi xtset, clear
mi stset, clear
mi reshape wide WilsonSVG_uVms RR_interval_ms m3beat ECGdate , i( study_id) j( timecat)
mi register imputed ECGdate1- m3... |
1dbbcfd617cb9f0d1087eb5f1d1c019cae1369b2bafd9d48fc9f7e44ec4d1879 | Stata | 14,690 | 313 | *** Covd19 study data analysis
*** Stata17
***October 2021
***by Larisa Tereshchenko
use"...OCTRI dataset.dta", replace
sort MRN_CD
sum MRN_CD
quietly by MRN_CD : gen dup = cond(_N==1,0,_n)
drop if dup>1
sum MRN_CD
clear
***November 19 2021
use "...RedCapExport_10_24_21.dta"
gen covid_positive = 1 if ... |
e53c5489d57efd84808450c2f9f856649bd01b093042eaa21440dd5ad7ee3ded | Stata | 23,152 | 168 | ****analysis of ALLHAT dataset provided by BioLINCC
***by Larisa Tereshchenko <tereshch@ohsu.edu>
***May-June 2020
***use STATA 16 or later
***causal mediation analysis developed by VanderWeele et al.
gen GFRdyn = gfrm48 - blgfr
gen GFRdyn24 = gfrm24 - blgfr
replace GFRdyn = GFRdyn24 if GFRdyn==.
sum GFRdyn ... |
ebaa66cc3f4d1420ee054028ca7effee1b8995d1fe0d80a52567d4d953f88439 | Stata | 28,398 | 344 | ***STATA code developed by Larisa Tereshchenko <tereshch@ohsu.edu>
***for analysis of the JHS approved manuscript proposal P1224
***January 2020
***use STATA 16
use "data file in your computer"
****preparation
***calculate frontal QRS-T angle
gen fQRS_T = abs(R-T)
sum fQRS_T
replace fQRS_T = 360 - fQRS_... |
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