sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
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99ab6198fcfa93d30cb5f792df060e7bbd19f08e877f6d5d4e9bd1e38ed5dc64 | Stata | 32,075 | 461 | ***STATA code for analysis of ARIC manuscript #3333
***by Larisa Tereshchenko, MD, PhD <tereshch@ohsu.edu>
***use STATA 16
***January-March 2020
use "file in a folder"
***Table 1
tabulate female score, chi2 column
tabulate cvdV1 score, chi2 column
tabulate white score, chi2 column
oneway bmiV1 score, tabula... |
6c51813c8b380f512797b0259acdb7584053142bffc23ff3b7d20b2a4f6f606a | Stata | 44,953 | 318 | ****analysis of ARIC approved manuscript #3293
***by Larisa Tereshchenko <tereshch@ohsu.edu>
***February 2019
***use STATA 15 or later
***Cox and competing risk models using wide file
*** generate standardized exposure variables for all 9 variables, in all participants together
sum QRS_TmV1
gen z_QRS_TV1 = (QRS... |
c8341165c63d1996fa7d40bc4229f76937cbb63d2018ae521471f0d98abf05b3 | Stata | 48,394 | 858 | ***Analysis of the ALLHAT data provided by BioLINCC
***by Larisa Tereshchenko <tereshl@ccf.org>
***November 2023
***use STATA 18.0
***
tab rzgroup, nolab
gen chlor = 1 if RZGROUP == 2
recode chlor (.=0) if RZGROUP ==1 | RZGROUP ==3
gen lisin = 1 if RZGROUP ==4
recode lisin (.=0) if chlor ==0
tab lisin RZG... |
14a2d0adaef8d18963916dd5d2eafa29faa636f20a2c64a2ad38798dea3ab401 | Stata | 57,617 | 354 | *****analysis of ARIC approved manuscript #3293
***by Larisa Tereshchenko <tereshch@ohsu.edu>
***February 2019
***use STATA 15 or later
***Time-updated Cox and competing risk models using long data file
gen trSVGaz = (SVGaz*2)+360
gen trpSVGaz = (pSVGaz*2)+360
sum QRS_Tm
gen z_QRS_T = (QRS_Tm - r(mean))/r(... |
e29370f8161cce8e0511b2144012be06306eb39adb50115e81e9a179ce81627f | Stata | 137,129 | 918 | ***Analysis of the data for ARIC MS 3292
***by Larisa Tereshchenko <tereshch@ohsu.edu>
***August 2019
***use STATA 15
***use long data file for time-updated survival analysis
gen trSVGaz = (SVGaz*2)+360
gen trpSVGaz = (pSVGaz*2)+360
sum QRS_Tm
gen z_QRS_T = (QRS_Tm - r(mean))/r(sd)
sum SVGel
gen z_SVGe... |
ea834d32cfe5315b4125a080d81eaef51751ffe9b4878ce8e5875a1aa1fd6d4c | Stata | 143,276 | 1,150 | ***Analysis of the data for ARIC MS 3292
***by Larisa Tereshchenko <tereshch@ohsu.edu>
***August 2019
***use STATA 15
***use wide data file
*** generate standardized exposure variables for all 9 variables, in all participants together
sum QRS_TmV1
gen z_QRS_TV1 = (QRS_TmV1 - r(mean))/r(sd)
sum SVGelV1
gen z... |
fdf5b3b293bf7d23b47cba10144c2e44e7f58d31b44023bb615573a083fba6c1 | Text | 7 | 1 | # MADNN |
32a367a3494dafe4cd955d6cc3daaaeee0e8b2bdc989954cd84f0e1d64042de6 | Text | 17 | 1 | # GCPDS.databases |
a7ea67bafa17cdcc3da1b88e18de027de1c414d0c8a62300e1a673548866827b | Text | 19 | 1 | # hypacadd_notebook |
69e05e36fdd1d49b87afbf790dc2d0588514c46a3bf73ef5dbcbf975c84f1783 | Text | 31 | 1 | # alzheimer-biomarker-study-XAI |
99661a4c2c55a65e1b50baa58ede1a7dc82fd740c1961434f1a269e9e5002d9e | Text | 38 | 2 | # Bagattini_Cid-Fernandex_et_al_2023
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aefc3601697329d169780c4b44bd59b2b3199758576db961c4004322d1c15b11 | Text | 41 | 2 | # IHC
Immunohistochemistry analysis code
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4092f5a7fd22f411ce6143f3b2f7826b35758effe2608b8e840e55171ea5b594 | Text | 47 | 2 | # Uzbek-ASR
Automatic Uzbek Speech Recognition
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89d5072a6da2bb1bc9b8801d593c94441cde39847841eb7c7f76cd0b1180b4a7 | Text | 58 | 2 | # OT_RBM
Assign fiber orientation to a biventricular mesh
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316ed8d55aef45a88ad4757bbca7f9ce41322815e775e2235d142b38f1323e98 | Text | 59 | 2 | # FTD_PRODIA
Analysis of proteomics data of FTD subtypes
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3f45aced9c99494ac5617be4958f5dec8b3104dd073c75616d4c807f610ce791 | Text | 60 | 1 | # Aligned matching: Improving small object detection in SSD
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0d6b6b9b8f30f4c26bd58582bca5c8a819801d0efc708b5be461486c504ffeec | Text | 69 | 2 | # VikingPlot
The original Viking morphology viewer written in Matlab
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05a4f2fac376f627df48380adea7746511a3cca07dcd748d3fb853ea6600704b | Text | 72 | 2 | # aor
Datasets and machine learning models for ammonia electrooxidation
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4131989118a120b1a0ba62b772d3e392b051ba4fc237aefc536c94a36a9b77a8 | Text | 73 | 1 | # DEVELOPMENT OF PYAFQ HAS MOVED TO https://github.com/tractometry/pyAFQ
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2a029fa15192fd66efd0e956b2dd81c68dc6ce6d0dce225dec1dd70cb109c5fd | Text | 75 | 1 | # Training code for the mpox virus convolutional neural network (MPXV-CNN)
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683a62be33e678911626754ae5d2889a7b49b9f2e5a078b6ecedaa8330a2c77d | Text | 88 | 3 | # Brain AutoML
This repository contains a list of AutoML related models and libraries.
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ef32a445e996d11b92d1afac467df151345836e1f5b5c8e932e37202c3920462 | Text | 89 | 2 | # clustering_Ca_imaging
Time Series Clustering on Fluorescence Data from In Vivo Imaging
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5ea4ae4512f16f8144c401ff8ccf01c2198a416c94379dc0536c3f66a9b02496 | Text | 93 | 2 | # Pubertal_TDA
Scripts to generate TDA features from adolescent brain functional connectomes
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0ee41a1ad5b90983b1634b1b1b8d701eeaf6ca90e92f9bd66ff4355dce6972c2 | Text | 116 | 2 | # motif4node
A Quantitative Evaluation of Topological Motifs and Their Coupling in Gene Circuit State Distributions
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89577d025775aadeab470fc54c88000e8215a6d1a932aa0fd46ec825a59030e2 | Text | 117 | 2 | # EEC_scRNA
Hayashi et al. 2023 - Enteroendocrine cell lineages that differentially control feeding and gut motility
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8dae28d52e21ca4664a836b3f0473137125231f1486d774fe5a4b75b519c9e3b | Text | 120 | 3 | # TreeExplorationOfDefoNet
This project is funded, in part, by the NSF Grant #OAC-2112606 and the Ohio Soybean Council
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ba58b4fe020b22e91723bc400eee8dab9e4d768b9cac112cc09036355143fc66 | Text | 121 | 2 | # mriqc-sops
MRI-SOPs (Standard operation procedures) for quality control of functional MRI data with MRIQC and fMRIPrep
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db21a7dfdb2281fa026ed34c27f3caaf336807d8502ba22bdf8db00d0158e6c4 | Text | 125 | 2 | # Domanski_CurrentBiology_2013
Code used in the analysis of data featured in Domanski, Kucewicz et al., Current Biology 2013
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ccfa16414bfb73e6324637e7b46dcf291f9d82b9fc22f86e19007260159bd84d | Text | 127 | 2 | # Code_for_Brain_Isoform
Figure codes for the manuscript: Hidden genetic regulation of human complex traits via brain isoforms
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9bbbf61c673a46dd2c4d2783690e0b61bd8af7705956f6640e3294dc49b3d2b7 | Text | 129 | 3 | # NeuLay
Accelerating Network Layouts Using Graph Neural Networks
(Csaba Both, Nima Dehmamy, Rose Yu, Albert-László Barabási)
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39d8b72f216defb293d29c0da1cb5d591c5039db28bf531ef38c09e470de5595 | Text | 131 | 6 | # BraTS-2021
### README in progress
https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1091850/full
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b5bb26b57a1fb742e4645acb2c77f9085bf98f78a6f432456816e193b32832e0 | Text | 131 | 4 | # printz_nat_comm_2023
[](https://doi.org/10.5281/zenodo.7607067)
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0dd2999b727f3f3d451b9e14b5caae3e374b134128ff27c398cf42616278db6c | Text | 133 | 2 | # synthetic-contrast-enhancement
Projects related to synthetic contrast enhancement of computed tomography scans using deep learning
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5b75e3ea3951f0580788301df777d2573d70ec39f2fd0b61a6d1694817f04962 | Text | 133 | 3 | # RNN_learnDA
An RNN-based reinforcement learning model
associated with: https://www.biorxiv.org/content/10.1101/2021.05.31.446464v2
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d2f36286a9e35e92b18747860dc6dd7d98f26c86565a8403a2f5e9d0abe0d5b3 | Text | 137 | 3 | # NeuronMask
Fiji pipeline for denoising, segmenting and masking of 2D/3D neuron datasets for subsequent branch and organelle analysis.
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bd2a3c65787ae596fc133d12580de9d9bd8f2467e58b691816958ad2678a620d | Text | 153 | 5 | # AI for Radiograph Classification
Preprint: https://arxiv.org/abs/2204.13857
Models: https://gitlab.com/raniere-phd/ai-for-radiograph-classification/
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51982bf106a450d7252b8d7f9e16cf569002eb1f5fa493450b5bf33fc23457ca | Text | 159 | 2 | # abcd-hispanic-via
A repository for analyzing the Vancouver Acculturation Index Questionnaire responses from Hispanic/Latinx participants in the ABCD dataset
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7bf94d9fa19f8c55a6ba679eb71c7915f62a81e807aa8a1c2f816c2b68f52e11 | Text | 165 | 2 | # single cell RNA-seq (scRNA-seq) of U5 human Neural Stem Cells (U5-hNSCs)
This repository contains all the code to analyze the U5-hNSCs from O'Connor et al., 2020.
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ae63d6892aebe5525b77f2ca81d6b1243808ee59ad778d5e0ff9b7d8247fbc1b | Text | 165 | 2 | # OE_Bonsai_network_sync
Python and Matlab code to synchronize OpenEphys and Bonsai using network events. It can be used to synchronize ephys and video acquisition.
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09a7bd4f771c46fd993cb7b63c0e12cf4e69ac22f2136953d3b72cc6f1292d40 | Text | 168 | 2 | # C4A-ABCD
Code accompanying the manuscript "Multi-ethnic phenome-wide association of complement component 4 variation with psychiatric and brain phenotypes in youth"
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ad520594a6e722cab5b792b8329eb4a62c21f6a39855a70888a88f00bed65d7b | Text | 178 | 6 | Data and Code used in :
"Axis convergence in C. elegans embryos"
Archit Bhatnagara, Michael Nestlerb, Peter Groß, Mirna Kramarc, Mark Leaver, Axel Voigt, Stephan
W. Grill
2023
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00e29f4c7d3684a12ec6b510061e157e56980f68da3876d0014b8ea2474d95ac | Text | 179 | 2 | # fMRI-transcriptomics-cortisol
Analysis of transcriptional and cell type signatures of brain areas affected by a loss of cortisol ultradian rhythm, combining fMRI and AHBA data.
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2f63d898fbf34ee42ecc14ec0f6256b4236d6b5fc86c8323ecdf7cd0e3a573cc | Text | 186 | 10 |
Docker neo4j server with VFB production DB loaded
To run:
```docker run -p:7474:7474 -p 7687:7687 virtualflybrain/docker-vfb-neo4j-productiondb```
To access:
http://localhost:7474
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8786edad5474eaff5aed2c1007118e45723aa9871a9fd80523b463e86a0d2f7e | Text | 189 | 2 | # MAGOHMAGOHB
Collection of scripts used in "The paralogues MAGOH and MAGOHB are oncogenic factors in high grade gliomas and safeguard the splicing of cell division and cell cycle genes."
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dcb53513742e5292dbb0afbbba7a8e7e708ed47dfa67005da533e90d8a301b8d | Text | 189 | 2 | The files here are the ones present on 4/27/18 (last modified substantially before that).
This is the model code accompanying Zarr & Brown, https://www.biorxiv.org/content/10.1101/779322v1
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1a5d4b2a9f500bd0ff51d04e78ecdc988f51abcfa089b0a0147b8d35e38f1945 | Text | 194 | 4 | # dHEC-model
Modelling of the reference frame of the ventroquism aftereffect
PLAYGROUND.m is the main file in this folder, and according to our impression it is easily readable and modifiable.
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2ee2532adfbbac0b6b0f78df6ca6c4f4bea9ae560038185f3945163ed36350dc | Text | 197 | 5 | # Preprocessing_and_QC
The flowchart of preprocessing and quality control of fMRI images.
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e60cc505d6fb0d3f24bc95ccfbd6c6cdcac9fb4dfa9f9c97b8750e3f568787e1 | Text | 197 | 6 |
Scripts related to the current version of our manuscript entitled:
Neural connectivity molecules best identify the heterogeneous clock and dopaminergic cell types in the Drosophila adult brain
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7d1d9b6ba2a4d675feb4d8889667ed0492822d42d8331a6065cbdb236b7b8a1f | Text | 205 | 5 |
Toolbox for Electrodermal Activity (EDA) analysis
_________________________________________________
Information and last version downloads can be found in https://github.com/mateusjoffily/EDA/wiki.
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cd565f02eca4c8b1550d58a10fd07b09a02525d6e6fd94518e8c11cd5deddd6d | Text | 213 | 5 | The open-source implementation of the paper:<br />"**A Fully-mapped and Energy-efficient FPGA Accelerator for Dual-function AI-based Analysis of ECG**"
- The paper is under review.
Thank you for your attention.
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3e46778643909495dcdb4b7c89826b00538a996c1c15b26bb7d4aeab48b4bbf1 | Text | 218 | 2 | # Entropy-Detecting-Information-Relays-in-Deep-Neural-Networks
Code to generate the data and perform analysis for that publication, contains the code to compute information relay decomposition using a greedy algorithm
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354fc8bf1671e5d75a83f1eccf4e0400b24d6564f83ca795d3e7e52f5854f709 | Text | 221 | 4 | # kk_en_corpora
This is the parallel corpora for the English - Kazakh language pair for the neural machine translation (NMT)
[](https://zenodo.org/badge/latestdoi/541750148)
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95428e705a3f0e53bd74bd7d6a2e3456e2c10d130b2be3d4374effa2bda014b6 | Text | 222 | 5 | group1：-cJUN-CRS-serum 1,-cJUN-CRS-serum 4, -cJUN-CRS-serum 5
group2: -cJUN+CRS-serum 8,-cJUN+CRS-serum 12, -cJUN+CRS-serum 15
group3: +cJUN+CRS-serum 25,+cJUN+CRS-serum 26, +cJUN+CRS-serum 27
FC:1.5；pvalue:0.05 |
31842240132c900e54a662055960970f3f7e32f6bd05ed2824342e2ad90b314d | Text | 226 | 6 | # Radiotherapy_Quality_Control_API
API for quality control in radiotherapy
URL of the API : https://share.streamlit.io/aureliencd/radiotherapy_quality_control_api/main/API_CQ_patient_predictor.py
More details coming soon...
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4395b7df5fc01e63e982b6f507d098af765b5b1b5f8ff881e238782ed80cdfe1 | Text | 234 | 3 | # spatially-resolved-TDP
This repository has been archived and is no longer maintained. The code is provided for historical reference and may contain unpatched or unknown vulnerabilities. It should not be used in production systems.
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0c997808017f518d4f12fc55f5174370177d2d7b7a11359460332c3cea89e0e2 | Text | 239 | 7 | # Longitudinal-drug-synergy-assessment
Preprocessed data and model binaries are available under:
https://drive.google.com/drive/folders/1IjWGbgy5mTJCFF5tIPJFfgeSuvMz6XgD?usp=sharing
Image data is available from the authors upon request
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f62ec9b623c5d2f5b5080eedfa9b7f10bd8c19cf139e020429a273a06685ee3e | Text | 239 | 7 | # printz_nat_comm_2023
1.0:
[](https://doi.org/10.5281/zenodo.7607227)
Latest:
[](https://zenodo.org/badge/latestdoi/596478067)
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f4a82040891bd282198a59e8970cf27f2cfba5277c18d5144c881fd655715023 | Text | 244 | 2 | This repository contains custom MATLAB software used to analyze data and produce Figures presented in the eLife paper :
"Neural circuits underlying habituation of visually evoked escape behaviors in larval zebrafish", Fotowat and Engert 2023.
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1c729a712299926d88da8e464ffe836954acb9f23f180a5521e6f78a5c3b3cff | Text | 246 | 3 | ## Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
Training and test datasets and general scripts for training the models.
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a2f323d179ba6257ac4dcc723b10e8868b3e670cfce811e8a1e8a87d0aa0db47 | Text | 254 | 2 | # Complexity-of-cortical-wave-patterns-of-the-mouse-cortex
The codes for Nat. Comm. paper entitled by "Complexity of cortical wave patterns of the wake mouse cortex". The corresponding data has been deposited in "https://doi.org/10.5281/zenodo.7574791".
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b216de3ef2f2dd51a83981695dffdeb8c80e5fc1c9c63e579f02b0e09d3e5b1e | Text | 254 | 2 | # Complexity-of-cortical-wave-patterns-of-the-mouse-cortex
The codes for Nat. Comm. paper entitled by "Complexity of cortical wave patterns of the wake mouse cortex". The corresponding data has been deposited in "https://doi.org/10.5281/zenodo.7426115".
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c3f40991d083d352c86660457f7d2cbfec9ed4e4e8d06515ee65d8e204e1abb3 | Text | 274 | 7 | [](http://www.molgenis.org/jenkins/job/systemsgenetics/)
Systems Genetics
================
See: http://www.molgenis.org/systemsgenetics/
Or see the readme files of the different modules
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343716052905987fc7732431adf9551bf406d588c7408255c4b4e97d2ae2dda8 | Text | 281 | 5 | # THINGS-data
**THINGS-data** is a multimodal, large-scale collection for studying natural object representations in human brain and behavior.
👉 **Data, documentation, and how to cite:** [https://vicco-group.github.io/THINGS-data/](https://vicco-group.github.io/THINGS-data/)
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89eabec06ba6d68dff12593934cb443fcad69a7b7bbef96343810e4226860123 | Text | 287 | 5 | # Article parsing code:
# E. K. G. D. Ferreira, D. S. D. Lara & G. F. Silveira, Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm. Scientific Reports volume 13, Article number: 2596 (2023)
https://doi.org/10.1038/s41598-023-29694-7
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bfe9d3a19ceff7dccfd6980243d6a5c8f8bc18d8e1376e18226fec8d7c24ce42 | Text | 287 | 9 | # SAUNet_HCM_segmentation
This page provide the code for training and testing SAUNet and Unet models for segmentation tasks.
Report bugs and provide suggestions by sending email to:
Author and maintainer: Zeinab Navidi (zeinab.nvd@gmail.com)
Or open a new issue on this Github page.
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7d7ca208d3bac7a38c2ebc76ecc094d9125de4b70573be371a1bda00e9adf5ba | Text | 300 | 5 | # Prenatal methadone exposure selectively alters protein expression in primary motor cortex: Implications for synaptic function
The following repo cotains the raw datasets and python scirpts used to analyze and create the figures for Haggerty et al. 2023
https://doi.org/10.3389/fphar.2023.1124108
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becd591782e8c628d1029a45713a350f4cc982351098621f4601233ccf52ff74 | Text | 310 | 7 | 
# Non-Local Means Denoiser
NLM Denoiser is a program for applying 2D and 3D Non-Local Means denoising filters to grayscale 2D/3D tif files.
# Downloads
Releases including windows binaries are located here: https://gitlab.com/jesseds/nlm/-/releases
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01876e83101104dbb809331060a172a9f8db8fd7560bdae8022430f400b94524 | Text | 313 | 9 | sparse-filtering
================
Unsupervised feature learning based on sparse-filtering
This implements the method described `Jiquan Ngiam, Pang Wei Koh,
Zhenghao Chen, Sonia Bhaskar, Andrew Y. Ng:
Sparse Filtering. NIPS 2011: 1125-1133`
and is based on the Matlab code provided in the supplementary material
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01ec77db54fd5de4b2cd9476af60649dc07a6f9c408d272dae6d1c56144a3631 | Text | 332 | 4 | # NiftyPAD
## Novel Python Package for Quantitative Analysis of Dynamic PET Data
Jiao, J. et al. NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data. *Neuroinform* (2023). [link](https://doi.org/10.1007/s12021-0... |
81aafa5df25fbeaf655151ba8ed2c0f3ef1a1e12301bef5b6dc7f236debbab1c | Text | 338 | 5 | The Evo-MD code used for the optimization of curvature sensing peptides.
The core of the code was written by Jeroen Methorst. Problem-specific modules were written by Niek van Hilten.
When using this code, please cite:
Van Hilten, N.; Methorst, J.; Verwei, N.; Risselada, H.J., Science Advances. 2023, 9(11). DOI: 10.11... |
2e81d70b9dfc254f73e14fa09e94b7f3896fedefc86719c529812aa31d3db3c6 | Text | 339 | 3 | # MultiNet
Code for the multimodal architecture presented in: *Ostertag, C., Beurton-Aimar, M., Visani, M., Urruty, T., & Bertet, K. (2020, November). Predicting Brain Degeneration with a Multimodal Siamese Neural Network. In 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)... |
3f1e7f200e54f0447f9f60dafed64f7bd97a996da938705763f10e5f0dfa31a1 | Text | 356 | 18 | # YOLOv4-tiny-attention-Pytorch
---
## Environment
torch==1.2.0
## How to train
### Train
1. make your data sets
labels in VOCdevkit/VOC2007/Annotation
images in VOCdevkit/VOC2007/JPEGImages
2. run voc_annotation.py to get 2007_train.txt and 2007_val.txt for training
3. Training by running train.py
4. P... |
3546d3db428ddcd95b587268f7c0e5eea09a1b3c1b67eb39ccf994f44f8a0980 | Text | 358 | 7 | # LC4_code
analysis and modeling code for LC4 and target descending neurons
There are 3 R scripts, run "LC4_startup.R" first.
Then "LC4_proc.R" (mostly analysis of LC4) and "LC4_target_proc.R" (mainly analysis of descending neurons) can be run independently.
Sections for plotting figures in the paper are named as, e... |
c6ff3b576b05cafcec2255852f6a80ac2c56acae680beecbf57ebd886219fed7 | Text | 367 | 8 |
Scripts related to the current version of our manuscript entitled:
[](https://doi.org/10.5281/zenodo.7387695)
[Neural connectivity molecules best identify the heterogeneous clock and dopaminergic cell types in the Drosophila adult brain](https://www.bior... |
8669b5451e0cc4d3f980d065d665969af2c70358627ec9736da15e6fd80d98ac | Text | 368 | 8 | # Neural Induction Gene Regulatory Network
The java code was used to integrate time-course RNAseq and NanoString expression data and epigenetic data to model the GRN for neural induction.
For citation, please refer to the paper below.
"A gene regulatory network for neural induction."
Trevers KE, LU HC et al. Elife.... |
5a1c08b276d946edfb7f43cdf5c46f49db9480af97252a63e2891b7855d06e9c | Text | 377 | 2 | # coding_gene_function_annotation
These scripts are used to annotate the function of coding genes in our "Genetic mapping and molecular mechanism behind color variation in the Asian vine snake" research. (Chen-Yang Tang†, Xiaohu Zhang†, Xiao Xu†, Shijie Sun, Changjun Peng, Meng-Huan Song, Chaochao Yan, Huaqin Sun, Ming... |
3c041b7f4801d4429b2773b0e6cd8dccb66af8c8fc2b83d412d296142ff10756 | Text | 388 | 6 | # Stat92E-paper-Wilson-et-al-2022
This repository contains materials associated with the publication entitled: Drosophila Stat92E signaling following pre-exposure to ethanol.
Contents include:
1) .stl file of vapor chamber arena
2) ffmpeg code used to record and pre-process videos with a USB WebCam
3) R code used... |
f8f7728a10d0206f796fd9e534c0a57aedf2c4ffdcc06627128d8a211c561660 | Text | 398 | 6 | # TectalLNP
Linear-nonlinear-Poisson OT network simulation (Zylbertal, A & Bianco, IH (2023). 'Recurrent network interactions explain tectal response variability and experience-dependent behaviour'. eLife, https://doi.org/10.7554/eLife.78381)
Based on Pillow et al., 2008 (https://github.com/pillowlab/GLMspiketools)
R... |
97ee2879318a3d961df90235323741b01f10a17696f22d0c823f0b9698520f65 | Text | 427 | 20 | # Analyses of the innexins of ctenophores
## 01-PHYLOTOCOL
planned analyses specified at the start of the project
## 02-SCRIPTS
scripts
## 04-INITIAL_PHYLOGENY
Initial phylogeny (superceded by 05-EXPANDED_PHYLOGENY that includes more outgroups)
## 05-EXPANDED_PHYLOGENY
Main phylogeny that produced figure 2
## 06-B... |
863bbf9bfab1af646846774b585c70fb062bb9506ce539858c4745dff4c4355c | Text | 432 | 7 | # Seq-N-Slide: sequencing data analysis pipelines
[](https://zenodo.org/badge/latestdoi/66501450)
Automated workflows for common sequencing-based (Illumina) protocols, such as RNA-seq, ChIP-seq, ATAC-seq, WGBS/RRBS methylation, whole genome/exome/targeted variant detection... |
253b6295b891a96252361d215cc9e82ffc0b8b88ed5ff9f0028f5d7a5d44487f | Text | 446 | 9 | # continuous-monitoring-tracking
Created by Sarah Frisken, Brigham and Women's Hospital.
Funded by NIH grant # EB027134-01.
Please cite the following publication:
Sarah F. Frisken, Parikshit R. Juvekar, Wenya Linda Bi, Alexandra J.
Golby, Mapping the progression of resection continuously during brain
tum... |
8fe95bd40298a01f417fe4e7110b5cc04ff7b90914ddd1b850e6a7f7f1913b4a | Text | 469 | 11 | # SEGVAE
A supplementary code for Symbolic expression generation via Variational Auto-Encoder paper.
# Getting started
1. Clone or download this repo. `cd` yourself to it's root directory.
2. Grab or build a working python enviromnent. [Anaconda](https://www.anaconda.com/) works fine.
3. Install packages from `requi... |
08ca129d42c01ff527a53d70ab776f39bb4ba4ea11440f75eb248802fe60c087 | Text | 490 | 21 | # GCPDS - visualizations
## Installation
```python
pip install -U git+https://github.com/UN-GCPDS/python-gcpds.visualizations.git
```
<div class='gcpds-images0'>
[](notebooks/01-connectivity_circosplot.html)
[](noteb... |
9208ec3f351bbb809a37089fa76cb5e30c8d233b2440927a83122bedf6ec6c1e | Text | 491 | 20 | ## What is this project used for?
- The **BraInZ** IamgeJ macro is used measure the invasiveness of tumour spheroids into *ex vivo* brain slices in the Z direction.
## How to use the file:
### Option 1:
- Open Fiji ImageJ Software.
- Drag and drop .ijm file.
### Option 2:
- Open Fiji ImageJ Software.
- Plugin > Ma... |
4caa9d81a47ae43e4c936a07060faa169c78ec7b6674c3dd92b098bc793bb6ce | Text | 504 | 11 | 
#
![PiPy](https://img.shields.io... |
0422761f24840521a6ead14ee9681bec52c50ebbe15682bd2a890a61568a46d7 | Text | 505 | 7 | # Si-ZEO22 Dataset
Dataset consisting of 350,000 DFT single point energy calculations from 219 different pure silica zeolite topologies for training machine learning force fields.
Configurations taken from snapshots of NVT- and NPT-MD runs from 298 K – 1000 K, full details available at DOI: ...
Initial structures tak... |
56599374bbf855af97f38fb9709d5e69820d3a31f5c4e327837236827dcab5f6 | Text | 506 | 1 | This program takes two data sets, each containing several predictors and one outcome variable. It uses one dataset for training and another for testing, and then uses the second dataset for training and first one for testing. It also predicts the outcomes within a dataset using leave-one-out cross-validation. PRedictio... |
fda5b5a613e49e6421b8dce2862c2362e7711699980a52b29c50ae7d9d32e7f7 | Text | 507 | 8 | # FOC_PMSM
Simulink model of a Field Oriented Control scheme applied to a Permanent Magnet Synchronous Motor
To Run the model, download and open both files. First run the "Motor_Parameter.m" to initialize the model internal values.
Open in Simulink the file "FOCsimulation" and choose among the various pre-set input ca... |
7977730f2812245467a3ecb87dee51ed402f4e1c648edb19a3b8a62a6ad8cd82 | Text | 517 | 13 | [](https://travis-ci.org/VirtualFlyBrain/owlery-vfb)[](https://hub.docker.com/r/virtualflybrain/owlery-vfb/)
# owlery-vfb
Owlery server loaded with VFB.owl
``... |
3f0eb2667e8e0b5ac2f359d1ac21ca8bd26f913c5c281538f72260d6a003c544 | Text | 533 | 15 | NiftyPAD
========
Neuroimaging for the Prevention of Alzheimer's Disease.
|DOI| |Licence|
A novel Python package for quantitative analysis of dynamic PET data.
.. image:: https://raw.githubusercontent.com/JJiao/NiftyPAD/master/images/Fig1.jpg
.. |DOI| image:: https://img.shields.io/badge/doi-10.1007/s12021--022--0... |
b2366132d59200f628a81b5829bacd792df6098dbfa55319acf00fb70d5377fa | Text | 536 | 13 | # SUFI
An automated approach to spectral unmixing of fluorescent images
## Downloading instructions
1. Download or clone SUFI github repository
2. Download example datasets from https://doi.org/10.6084/m9.figshare.13577840.v6
## Running instructions
1. Extract contents of downloaded example zip files into appropriat... |
3125d2f9d062bf333fb2b75db230ecc821b29db88aa4d32d69753292ba8c328e | Text | 546 | 11 | # PCovNet+
---
1. Clone the repository
2. Install the dependencies using pip:
```
pip install -r requirements.txt
```
3. Follow the instructions for downloading [Phase 1](https://github.com/farhanfuadabir/PCovNet/blob/main/data/raw/phase1/readme.txt) and [Phase 2](https://github.com/farhanfuadabir/PCovNet/blob/main/d... |
a7bd1cc9d27bc4cd30ce4e98d14e26c1febbb8bacbd64be2d17b8d874e8cf687 | Text | 550 | 9 | # xrd-symmetry-prediction
This repository is an additional materials for :
Symmetry prediction and knowledge discovery from X‐ray diffraction patterns using an interpretable machine learning approach.
Suzuki, Y., Hino, H., Hawai, T., Saito, K., Kotsugi, M. & Ono, K.
<i>Scientific Reports</i> (2020).
[https://na... |
06bbebb6639a3c5bf2546e7b555b4bc236b589c244fbebc2bde1078534efeaa4 | Text | 561 | 7 | # MSU Soft Sensors Research #
Tools created and maintained by MSU Athlete Engineering research concerning soft robotic sensors (SRSs).
## Items and Descriptions ##
* **gait-biomechanics-tools** - Python and R scripts developed to preprocess and analyze MOCAP/SRS experimental data.
* **SSTK-labkit-v1** - Hardware desig... |
6ff0567afbcf10b9cd11b8385077725f7f5c1cbc7f447950b95dba91ec2089ff | Text | 587 | 2 | # TOFEWSI-ECOSYS-ANN
The codes here train and validate artificial neural network models with fire weather index variables and modelled hydrology variables by a process-based model "ecosys" as features in providing seasonal fire forecast over Riau, Sumatra, Indonesia. The codes form part of the Towards a Fire Early Warn... |
e4627f5282f06867f92b03c45c6038b098ee537d931f8edb0ca6ec5309ffbbfe | Text | 595 | 15 | # painn-sli
This is the source code for paper [Neural Network Potentials for Accelerated Metadynamics of Oxygen Reduction Kinetics at Au-Water Interfaces](https://pubs.rsc.org/en/content/articlelanding/2023/sc/d2sc06696c)
<details open>
<summary>How to use</summary>
```bash
pip install -U .
```
* See `train.py` in `s... |
9b909edabdd2d0241905f80e6fde6520d6cc71fa9abdd3efc95f4b252d6512f9 | Text | 607 | 9 | # Conditional Ablation of Glucocorticoid and Mineralocorticoid Receptors from Cochlear -Supporting Cells Reveals Their Differential Roles for Hearing Sensitivity and Dynamics of Recovery from Noise-Induced Hearing Loss
This is the repository for data processing applications used to compile data presented in the paper:... |
634f52b3a1e00e1aba8c89647a3a57f2784e1955724b512553efbe70417215eb | Text | 639 | 10 | This repository contains all the data employed in the publication "neural fatigue by passive induction".
it contains the anatomical and raw functional images of the brain of every participant, along with the masks and arrays with the timings and conditions for analysis in SPM.
also the behavioral models and correla... |
f9572853ef6ad26e9a2b93568534484942065cdef6856bd9907cfd047aa3befc | Text | 645 | 17 | # Pixel RNN
Theano implementation of the Diagonal BiLSTM and Pixel CNN models from Pixel Recurrent Neural Networks (van der Oord et al.) on binarized MNIST.
Not yet implemented:
- Residual connections
- 256-way softmax output
The Diagonal BiLSTM gets a dev-set NLL of 82.12 after 100K iterations (at which point it st... |
b68796739ce08bf7aee33c70b479f2b12411fa298f39cd20b3aaaf258456a683 | Text | 656 | 22 | # Neural knowledge assembly in humans and neural networks
This repository contains the official implementation of the neural network simulations of the [Neuron](https://www.cell.com/neuron/home) paper "Neural knowledge assembly in humans and neural networks".
## Requirements
Experiments are implemented in python us... |
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