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2cb5e847ecb2115b8bff89b9c74248fea26a24600d803f906a1288242cb575ee | Text | 9 | 1 | # PathCLR |
2ff7aa7e655546e8d7499c356491824c42db21f825b22a31530a993d8dbc7d51 | Text | 12 | 1 | # Perrj-code |
83f948c6d2f9a570c688a63370b9e9e42a74bdb157546184c3f108f51725b54f | Text | 13 | 2 | # LOFT-CBF
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2e59ff4219cd8358c10c1e34d823524a095d080df2fb31e2b2dcf68f75c21a90 | Text | 16 | 1 | # Benchmark-RBP
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bda73281b24b061f19019a0ed3ccd350391031a6ff70826c91d015611db7c9e2 | Text | 16 | 1 | # hcpd_adversity |
4f4719e60924ab65a5f8117bcab26e8b956147ecb0be80b60f3267514bc90c7c | Text | 17 | 1 | # Directionalnet
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e0311ff37bddedb8190162d965a2806e7b9e99390c2ec04eac748fec98931700 | Text | 18 | 1 | # Actions-training |
745870be0d62caa9b030d7d79b31725c1cadd4c07a300a7c063d49b0cec382eb | Text | 19 | 1 | # Insight_Risk_NAcc |
8131801eb2f78dc54abf4b0d9aecdae36f896a2fac992e3727594f4567413a02 | Text | 19 | 1 | # CppRosBridge_demo |
5cfc34de05e35684754d8f8ab3769c0d43898850fd1b7f97e4bf08aab56cb738 | Text | 20 | 1 | Testing file upload
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e3dc372c5ca28b0f7cfce5894d8cba3efedb09f48868694d391ff8c5a4486fbf | Text | 22 | 1 | # Cel_dup_development
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d10ce4250c2c0a3c7c25c040710e908319afe080e82a19a5683bdacf165c3cf1 | Text | 24 | 2 | # Phosphenes_occipital
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5d469fe9ecf17dc2ea394a924db0d58945985c242b212adaaf5853535f759462 | Text | 25 | 1 | # phi_psi_prediction_FCNN |
d70d1289c78b9acccdd564ee2c0ecb9d216dbf659b5f8d71d339694fe12da05e | Text | 27 | 1 | # asd_infants_eeg_con_paper |
dc4a630b764825b57182ea8ad1fd90a1bed2bbe0697ed98b2998bcab0563c57a | Text | 27 | 1 | # CircularArenaTrackingCode |
9b8e527efb330ad126aefbb636c23f614c2e2baf416dab23eb7da77770458d42 | Text | 32 | 1 | To plot FigS7 panels run FigS7.m |
fef554393f77f7ee77a768e42b2a65b37dc20ab5f84dc8f63c8f2db4b91ef37f | Text | 32 | 2 | To plot Fig 3 panels
Run Fig3.m |
083e431799034bc06529028693fce9c489932c0e6244ff732ae949c2ebea1470 | Text | 34 | 2 | # assortative-mating
Mating model
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477ff7da579f74237186e258f70dd9f289e3a22a77744ca5dbbd4cc96d3109f8 | Text | 42 | 1 | To plot supplementary figure 4 run FigS4.m |
b3b4f1753669275e40655701964024ca9c63ba4dcb00a2dfb6afe38d897adc80 | Text | 42 | 1 | # deep-linear-network-with-prior-knowledge |
cfcf59eb476b2edcfb8b8ef3fb2599755928496ba82113747d68a943e6c021ed | Text | 43 | 2 | To plot Fig 6 I to K panels
Run Fig6_I_K.m |
c02e183cb9080f3d9582f9f7d9a3531eb040d79e5fe51d50259dae83071449f3 | Text | 47 | 2 | # GRU-Dpp
A PyTorch implementation of GRU-D++.
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319c06c5efc8d2cafe3fa1b55006065144e5b034e0fd223ea552da1c23dfaf51 | Text | 48 | 2 | # CWIT-calc
CWIT scandinavian norms calculator
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7990cac2ae2c98ec56b7b1495487ca633fb3015689adf28d02cc9d2e2b8f71d7 | Text | 52 | 2 | # simsef_py
Python code for SIMSEF acquisition tool
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0a66a8770056ee578d6c4ad82c2b90ed6510b99be822a08806a36fc789350b81 | Text | 56 | 2 | # SleepScore
Automatic and manual sleep scoring package
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29d980bfd17539939d297c005d88177ec41033ee781cf3820986286b91dbf46f | Text | 57 | 1 | Analysis scripts for the whole mouse brain MERFISH atlas
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a660f0bd600499b10ca8638d412f95f102cb713ad7976faf8d303dc846ef627a | Text | 58 | 2 | # Fetal-T2star-Recon
Pipeline for fetal body T2* analysis
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beadfcbe2b943849c5f634430ea080cef829861253d983dc36ec1029c85d89ba | Text | 61 | 2 | # article-ptic-suppl
Supplementary material for PTIC article
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928e1ad1f1adba289ee7f8cb596c9105249eabd9b61c1eafe2849830fc1bd701 | Text | 64 | 2 | # hanganu-etal-2014
A notebook to replicate Hanganu et al, 2014
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a9ea5d20b06a348ae98fe28519834fa4df6bbadb9e08b7b8a20c2d1af148359b | Text | 71 | 2 | # V1-HVA-oscillations
Data analysis scripts of V1 and HVA oscillations
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1d5a73b1ca586bbd63e26a375d378023bdee703d1f717e2b4c13dd81f918526f | Text | 89 | 2 | ## Manifold Fitting
This is a repository containing several projects on manifold fitting. |
15d7b27b23d7c6130ae4af227f741b175c9e00c7df7f242e62615e6166b4799a | Text | 93 | 2 | # Hyper-Aging
Simulation of triadic multigenerational interactions using the Kuramoto model.
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f81dc04f549cbc6ccf950ba3b9276f16ebf29bea0783f080bedb9427ba1c8987 | Text | 94 | 3 | Optimized code for segmentation of neurons.
export LD_LIBRARY_PATH=/home/frandi/.local/lib64
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1cb1ab402035e8a2785bf8110db3e43584908057cbfc35144ef6d5f690e57ec6 | Text | 98 | 2 | # Single nucleus RNA-seq of the rhesus macaque nucleus accumbens after chronic morphine exposure
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e51707d902b8deabec7bf22650fdecccf2f50c7c1007358621a6ff383bcf238c | Text | 98 | 2 | # PerPsych
An iPadOS-based Open-source Neuropsychological Software for Time Perception Assessment
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5beb10aea366cad010e23904714b94404ebe19fda1359fd5d088e3cb4b0f5c91 | Text | 99 | 4 | All documentation for Trinity is provided at the Trinity website:
http://trinityrnaseq.github.io
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6cbd2b7a8db7190377c5c9da1ce59fcbe4fd77fa228d6771f3b6b5de2de3054f | Text | 102 | 3 | # 4xLTP-igor
Igor Pro data acquisition procedure for Lee lab
Move the file to Igor Procedures folder.
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7b404eddfd8b188c6da92b726ecffe106d58dc5059d851c1187f4077c32657cf | Text | 102 | 2 | # CoPImmunoPD_Clustering
Clustering analysis of single cell RNA seq data for the project CoPImmunoPD.
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c2ccfee5bdcaf126dc4506f597f24db485c3226be4fe65a6f503e7bd82825eea | Text | 104 | 2 | # BrainComm_mammalian_evolution
Implementation of brain communication analyses across mammalian species
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a95e3d9b6b212d77231ced4f47c9c6a2dd71d32e776bdc70dececa91218a3c20 | Text | 121 | 7 | # gabor_fit
Fitting Gabor Filters to Receptive fields
After cloning the repository, run
```
python setup.py develop
```
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b83dcaf807507d61ea806869af6c1b03ad1e26ab50be14680e007c33211e87d1 | Text | 122 | 1 | Analysis code relating to Ghazaryan et al. 2023: Cortical time-course of evidence accumulation during semantic processing
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88c5b1e512baded55ed64da6b2069834ee5ed25c98740d2fb1285601592e5fab | Text | 129 | 2 | # Functional network analysis
R tools for creating and evaluating functional brain networks from microscopic brain imaging data.
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25d26764758e86341d053917f49498e7202c6cb9925d35a9b88b5b7d7fc9bf87 | Text | 133 | 2 | # npj_Brain-Tumor-Stem-Cells-Following-a-Suborbital-Rocket-Flight
Data analysis for rocket flight and cystic tumor area calculations
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ad75d4b8d7b8f4116c547c1a2aa08d23e7cfb724c7971ffc9e168f21e8109aec | Text | 135 | 2 | # HierarchicalClusteringALS
Code used to replicate the preprocessing and hierarchical clustering in the hierarchical clustering paper.
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29d93f9c7d4b85e6205411ce59823a98a401c4273592e6ee950f9c4db59f907d | Text | 137 | 6 | # A simple object recognition memory scorer
- Using OpenCV and PyQt
- Acquires data from cameras supported by OpenCV (e.g. Point Grey) |
2b3ed6d2c75f7bdd996bf0bdedf1c4f9b46350927add5c28a51e8166731cb17b | Text | 138 | 6 | # telewave
Inspired by work on automated PPT
[Access it here](https://gjeuken.github.io/telewave/)
[Instructions here](instructions.md)
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3db5acbcf2ff081cf8ab434e1fb01a727d72af0969e45019cf4943ec792f9136 | Text | 142 | 3 | # Abramson_code
Code to supplement the submission of Time or distance: predictive coding of Hippocampal cells, by Shai Abramson et al., 2022
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c6106a00284d46d30d41be9cea5e72e0634e354a774cd680be55b69741dfafce | Text | 147 | 4 | # Baruchin-et-al-2023
Analysis code accompanying the paper
This repository contains all processing code pertaining to the analysis in the paper.
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e42a85718c5e7e03ce81edfdf96b8e937b8ea8d016efdaa1dd4aeb602de551a4 | Text | 151 | 1 | # Understanding the Post-Pandemic spread of COVID-19 dynamics due to Under-Reporting, Vaccine Efficiency and Social Behavior via Mathematical Modeling
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f9a9f2a4ef48504ee728a61e39a9b799c9b696daa6d44102f7fed04039d9ff59 | Text | 153 | 3 | To plot Fig 5 panels
Run Fig5.m
SVM results were calculated using Acc_SVM5.m function using the selected time intervals obtained with SVM_models_LvsR.m |
605be573675607b8109c6b71dd14ad2eb6321aad14472339fce90e09732d8f47 | Text | 157 | 4 | > [!CAUTION]
> **This is not the official repository and it is not guaranteed to be maintained**.
>
> For the official 👉 https://github.com/adosar/moxel.
|
45e2237a3a298c285cb455bfef7f4b58489d3aa677277320774f84296335bf78 | Text | 159 | 4 | [](https://doi.org/10.5281/zenodo.8009939)
# simsef_py
Python code for SIMSEF acquisition tool
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fa6a1443e414dd0c7605d1e04f867603825fc77ecdd7ef46a29a656bfc773ac6 | Text | 163 | 2 | Include \fiji-win64\Fiji.app\scripts\importTrackMateTracks.m to the Matlab path and run
[tracks, md] = importTrackMateTracks('C:\Users\bbnc\Desktop\_Tracks.xml') |
e1fcef215c87ca7ff22fa5d2f69d03fdfb25dc77044237dace14de161515976c | Text | 166 | 2 | # Bashkirova_Zonal_OR_2023
Code for Bashkirova et al., Opposing, spatially-determined epigenetic forces impose restrictions on stochastic olfactory receptor choice.
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73e32dc22f82e550edf6b5f5c7da51c3dc54433185ac25ce6b7b9e1bba9d0224 | Text | 167 | 2 | # VisualCortexGradient
This is a github repository for examining the pattern, function, and development of gradients in the human visual cortex based on the HCP data.
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9e09ca61c46e58cac5f8741ee77cdbf4196ecb482d393db38d374322ee659ccf | Text | 167 | 2 | # Cardiac_Motor_TMS_EEG
The experimental script and analysis code that were used for the ms, "Cardiac Activity Impacts Cortical Motor Excitability" can be found here.
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4cca4fc005319ffd98f23a42bc703e29deb64e45dfd05525d943793cab7168e6 | Text | 169 | 2 | # infQC_pub
Code and trained model described in the article "Automated Motion Artifact Detection in Early Pediatric Diffusion MRI Using a Convolutional Neural Network".
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79b4643fd995f6997a2608434e8db899c2c2db9e7f4762a34163e60677f3f912 | Text | 172 | 2 | # 16p11.2-Organoid-scRNA-seq
Data and Code for publication titled “Patient brain organoids identify a link between the 16p11.2 copy number variant and the RBFOX1 gene”
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d50b09d729661d7fcfb4a598b9c653266b43fa4e9746f46c703c2ab9b20c7926 | Text | 175 | 2 | # HumanHypoDev2Adult
Code base for the paper "Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus"
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7bd8f624521d5e03be3cd9652f4cfe1ce2d0f51fe16321ab77dc4e3554b7378c | Text | 176 | 2 | # MicrosEEG_Data_Analysis
This repository contains code for the manuscript entitled "Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain".
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7c4be9752d697b906b2d5552dbee8f52b492ded2b6ad0135ef9c09676bbf8154 | Text | 176 | 2 | # IMPS_IMAGEN
Respository of the Institute for Medical Psychology and Medical Sociology, University Medical Centre Kiel, for Notebooks and Scripts used for the IMAGEN project.
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9df7848220099e099e4750b6730a61c84a2d2e89956e8324918715b65f1f5818 | Text | 176 | 2 | # tFUS_Inhibition_Elife
Repository for Fine et al, 2024 Transcranial focused ultrasound to human rIFG improves response inhibition through modulation of the P300 onset latency
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4d593aa2e333d565756f2e9ce233fe2e1ceceba7834bc9362ed47fb158fd8a67 | Text | 177 | 4 | # migraine-meta
R codes to carry out the migraine subtype analyses reported in
migraine GWAS by [Hautakangas et al. 2022](https://www.nature.com/articles/s41588-021-00990-0).
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9dd714c43a880253e131688cf71555c578d8187a0a9eb0786981c7644465d7b8 | Text | 182 | 4 | # deeprl-timecells
Code base for the deep RL time cell paper (D Lin, ZX Huang, BA Richards)
Run codes from /deeprl-timecells directory, eg. `sbatch sbatch_sh_files/run_training.sh`
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468df2bd60b533afa315184e72d0670c5c879be0388d50b5e5e4d226ee60ab05 | Text | 186 | 4 | To plot panels of Fig 2 and suppl Fig 2
Run Fig2.m
To plot panels of suppl Fig 3
Run Fig2pyrvsIN.m selecting pyr or IN options (significance has to be manually changed in the figure). |
9a1644edbbbe97048fbd8bb156b1dc2990d923ee5c187459967c480ab6f4d994 | Text | 198 | 4 | [](https://zenodo.org/badge/latestdoi/657152016)
# CoPImmunoPD_Clustering
Clustering analysis of single cell RNA seq data for the project CoPImmunoPD.
|
49f2d8bc415a487c55b59ccfdfeeff4b3b64ada757d28e787ef4d6c1b8019544 | Text | 199 | 5 | To plot Fig 7 A to G panels
Run Fig7_A_G.m
To plot Fig 7 H to K panels
Run Fig7_H_K
SVM parameters were calculated using Acc_SVM5.m with the relevant time intervals obtained with SVM_GL_SmplChc.m |
1ed6f218d410797bf41292d000d4ad52f6777f2715cb71f020ca7153c203b223 | Text | 201 | 4 | <img align="right" width="250" height="250" src="https://github.com/teamnbc/MouSpine/blob/main/pics/Mouspine.png">
# MouSpine
An annotation tool to measure 3D spinal curvature in mouse micro-CT data.
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77bf0602ed59f270e2d9a988e01a12ef812b32b96f1fe748edd3d1238137fdd8 | Text | 204 | 4 | # neurotransmitter_networks
Creates neurotransmitter networks using
* Pearson correlation
* Iterative random forest (iRF) [uses this repo](https://github.com/Yu-Group/iterative-Random-Forest/tree/master)
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e158aa2d98b5df51f50282abfa6966f2f1498663694a294dd7e5a6cd27bb78e2 | Text | 204 | 4 | # Xception_ornithopod
PLOS ONE: Classification of large ornithopod dinosaur footprints using Xception transfer learning
The trained data is downloadable from **https://doi.org/10.5281/zenodo.10056871**
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1b549d9ce90821855802f7726fc750d648821e43195518824c08522e4b254f83 | Text | 221 | 9 | # nwn_ol
Code for the paper ``Online dynamical learning and sequence memory with neuromorphic nanowire networks''.
Need to add documentations.
Code have not been tested extensively.
Edemame is private at the moment.
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2d865cc119c4fd685a713a4376efc6a1fb6831a6dbcc2af2c16ef8b2d1a061a9 | Text | 221 | 6 | # Single-cell expression predicts neuron specific protein homeostasis networks
Biorxiv: https://doi.org/10.1101/2023.03.14.532571 \
Contact: Sebastian Pechmann\
Email: sebastian@pechmannlab.net\
Web: www.pechmannlab.net
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85e811154a70703cf50d978c297c26e6d28e8d11fe976ca3f2766f31634e4d12 | Text | 226 | 4 | # PC-DCN-different-size-inputs
Code for the paper "Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs"
Shuting Wu, Asem Wardak, Mehak M. Khan, Christopher H. Chen and Wade G. Regehr*
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8c993eb1aa69b67337a1f3e904b9b637bee18a85373b23ce5c24a0965d5f75fe | Text | 226 | 16 | # Barrier GNN - Predict
## GNN loading and predicting reaction barriers
Example code to load trained GNN models for the prediction of HAT energy barriers in collagen.
## Installation
* Download
* `pip install -e ./`
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9fb1e0325090f749ef35bd643172098aa2b4c653381029d8d0eeaea9ba5f2fab | Text | 233 | 2 | # Prenatal-substance-exposure-child-and-adolescent-health
The effects of prenatal exposure of four common substances on child and adolescent health in the context of environment, genetics and the brain: Evidence from the ABCD Study
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c9bbb295aeddf5f288513475975a4c6f17f34e6a5bf6086124bc4f85f44223ba | Text | 238 | 5 | # FSQC
Code for Image-rating App, generation of FSQC images, and analysis for Bedford et al, 2023 (Imaging Neuroscience: https://doi.org/10.1162/imag_a_00022)
Full protocol available at: dx.doi.org/10.17504/protocols.io.kxygx9m6wg8j/v1
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928819ab7c3d35cd4a6a6d50e2f80cf49ef7d63b60832a2a8e5a474fe616b279 | Text | 243 | 2 | # Simplicial-Metaplexes
This repository contains the code to generate genral simplicial metaplexes. It was used to generate the results from the article "What's in a simplicial complex? A metaplex-based approach to its structure and dynamics"
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bf99d76879a322aa35e81a4f6f298c49b95438440c40d8d529dcabcb5b7717f4 | Text | 243 | 5 | # Publications
This is a public repository to store publication related analyses for reproducible research
## Electrical field causes volumetric changes in the human brain
See details in the ECTEFvsVolume folder: open EF_volume_published.org
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284b7b53a883ebaaa4f11e50ad3540330b597647c2f5acf3934c14bc43e82215 | Text | 247 | 4 | To plot FigS5 panels A to G run FigS5_A_G.m
SVM parameters were calculated using Acc_SVM5.m with the relevant time intervals obtained with SVM_GL_SmplChc.m
To plot FigS5 panels H to L run FigS5_H_L.m
Comment line 44 and 46 and uncomment line 45 |
8acc1e73844bb546a75c398827e33e058fccf33f2f773e048c8f2bfab42f7fa7 | Text | 247 | 4 | To plot FigS6 panels A to G run FigS6_A_G.m
SVM parameters were calculated using Acc_SVM5.m with the relevant time intervals obtained with SVM_GL_SmplChc.m
To plot FigS6 panels H to L run FigS6_H_L.m
Comment line 44 and 45 and uncomment line 46 |
a335d02ff8c647139e3fb33edaef8d56441d9023b79bd7192c1b220b698be86e | Text | 256 | 9 | 1. The lmer function in R4.1.2 software (https://www.r-project.org/) is used to perform mixed effect model.
2. AHBA processing
https://github.com/BMHLab/AHBAprocessing
3. using PLS to correlate slope and gene expression
4. enrichment analysis
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8d5acae0d1ce5e0ff588d1a0005033f801e6be314969a37d9efbacc9b9798233 | Text | 259 | 4 | # UncertaintyInPhase
Repository to run analyses described in paper: "Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty "
Please read the legend file in each folder for details on the different files.
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06d89eb33ddc88550d24f1ba722adaf3585e02f6dc3586fff66e1168d9c74bd0 | Text | 260 | 4 | # MyoChip
David Hardman
Cell image analysis tools for MyoChip muscle-on-a-chip project
Includes ImageJ image pre-processing macros, cell tracking matlab code for live images and cell quality quantification metrics from static stained (and brightfield) images.
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38ad614368b51cb71ad793f27a66a174a2458318bfad6c6bba0d68318bc9375c | Text | 262 | 5 | # Pyraman: working with Raman spectra in Python
Pyraman can be used to aid baseline removal in spectra, processing of Raman
images (reads Renishaw file format) and extract basic features, such as peak
ratios, which can also be mapped as image for Raman images.
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a78b4544f083e14c347f49e60d04f4f08a2fc94f8adac4bc5d66f84a6063101d | Text | 265 | 7 | # HTStream Project HAS MOVED!!!
Please immediately begin using HTStream at [https://github.com/s4hts/HTStream](https://github.com/s4hts/HTStream). This copy is for historical purposes only and will no longer be kept up to date.
Thanks!
--- HTStream developers
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02270fe867d1738f93485d5dc3b074a15e1dc235596fe0bb34346d7647e7ff12 | Text | 270 | 4 | ## A Monochromatically Excitable Green-Red Dual-Fluorophore Fusion Incorporating a New Large Stokes Shift Fluorescent Protein
Corrresponding KNIME and Cellprofiler workflows
DOI: 10.1021/acs.biochem.3c00451
URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC10765376/
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79fa1761b10863f12b79f79057e7cd35efde7d377196e6b242224895b1dfcd88 | Text | 271 | 4 | # micro_ecog_phoneme_decoding
The folders contain necessary m-files to process micro-ecog recordings and decode speech features.
Obtain data files from corresponding authors to run the decoder files.
Refer phonemeDecoder.m to perform decoder analysis from the manuscript
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0db7efffe7520d000ada2d892aef252eb056682812ad742bb806727e0e8e6558 | Text | 275 | 3 | # Cabral-Calderin_et_al_eLife_2024
This repository includes the MATLAB code used in the paper: "Behavioral entrainment to rhythmic auditory stimulation can be modulated by tACS depending on the electrical stimulation field properties" by Cabral-Calderin et al, eLife, 2024.
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ada091b0d940285cbc484f67e9d4464ecb389de140169a7d30cd3d487c3e419c | Text | 280 | 4 | # PredictionAbeta
This repository includes codes for
Yuichiro Yada and Honda Naoki, "Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates", npj Systems Biology and Applications 9, 59 (2023). https://doi.org/10.1038/s41540-023-00321-5
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23ab8cfaaeb59c50d2c263fd2f79a58c691c0abf51cf6d8645deb1b9dbd0ec86 | Text | 282 | 3 | # meg-phantom-perceptPC
Code related to the paper "Combining Magnetoencephalography with Telemetric Streaming of Intracranial Recordings and Deep Brain Stimulation – a Feasibility Study"
by M. Fahimi Hnazaee, M. Sure, G. O'Neill, G. Leogrande, A. Schnitzler, E. Florin, V. Litvak
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e9ac70889a8a3183c583750b73b92ae414aa13e4424e542311b420f92afb7241 | Text | 282 | 4 | # DYFNet (Deep Yeast Fusion Network)
This is a CNN to classify yeast vacuoles as partially or fused based on fluorescence images.
Annotated data used for our [paper](https://doi.org/10.1101/2023.02.27.530171) is freely available [here](https://www.doi.org/10.5281/zenodo.8070003).
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310e73cf30c914b6e279a9872df94505e9f4e5424986003404e49979db2248ac | Text | 289 | 4 | # Use examples
A number of use examples that show how to use PyRates to perform numerical integration, parameters sweeps, and code generation.
The latter is used to interface other tools that provide dynamical systems analysis methods and apply them to the models implemented in PyRates.
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1489ebdda79f31a250b3ea05b4e5e5b122c8f0c53d02d0d1b12763a7e285120f | Text | 292 | 4 | # municipal-biogas-forecasting
Code used in original research article focused on biogas forecasting at the Muscatine, IA Water Resource Recovery Facility
The individual folders contain the datasets and associated Python code to reproduce the models described in the associated publication.
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a820a8e86b86dedb4b53783720098c986b7634afa4d22884880250e54d1dfde4 | Text | 294 | 5 | # VAN-R-scripts
This repository contains the R scripts that were used to analyze the RNA-seq dataset in the study *Plasma cell-free RNA profiling of Vietnamese Alzheimer’s patients reveals a linkage with chronic inflammation and apoptosis: a pilot study*
This study is pending peer review.
|
da273044d94dd153cba51eabd962a2acd4a154524649b51486b5cfa4e1887163 | Text | 294 | 18 | # Jia_2023
This repository stores all scripts used to do stats in Jia et al., 2023
Contact:
If you have any questions, please feel free to contact:
Xiao Chen {chenxiaophd@gmail.com}
Instructions:
These codes requires:
dpabi:
http://rfmri.org/DPABI
SPM:
https://www.fil.ion.ucl.ac.uk/spm/ |
46a4af627082392baa0bf598ee2a856ba8eee95346cf060db4bec3104b4f17a9 | Text | 298 | 7 | # NIRS-EEG
Please refer to
Maheen Siddiqui Paola PintiSabrina BrigadoiSarah Lloyd-FoxClare E ElwellMark H JohnsonIlias TachtsidisEmily JH Jones (2023) Using multi-modal neuroimaging to characterise social brain specialisation in infants eLife 12:e84122.
https://doi.org/10.7554/eLife.84122
|
b7b1b79456ab161edc6998747eb4514543c374740771e8a374b020f265ead3f3 | Text | 300 | 5 | To plot panels of Fig.4
1) Open Zenodo_Fig4_main.m
2) Run BinFirRateFig5 by uncommenting that line and run Zenodo_Fig4_main.m.
3) Run PlotBinFirRateBatch by uncommenting that line and run Zenodo_Fig4_main.m. This plots Fig4 A-D.
4) Run PlotPlotBinFirRateBatchResult.m separately for Fig4 E and F. |
785c5c876e85b61a649db10ebab957e5a4226529d536c3eddc41e7c7e351b528 | Text | 307 | 14 | # pyFiber
Object-oriented fiber photometry and behavioral data analysis toolkit.
## Installation
Available on [PyPi](https://pypi.org/project/pyfiber/).
```bash
pip install pyfiber
```
## Documentation
Detailed documentation and tutorials on [Read The Docs](https://pyfiber.readthedocs.io/en/latest/).
|
205d763401864a562b263c8eb3d54c54b5e6bdb9f86b4b3558e1ee1323791789 | Text | 322 | 10 | # Single-cell expression predicts neuron specific protein homeostasis networks
Open Biology <b>14</b>, 230386 (2024) \
https://royalsocietypublishing.org/doi/10.1098/rsob.230386
Biorxiv: https://doi.org/10.1101/2023.03.14.532571 \
Contact: Sebastian Pechmann\
Email: sebastian@pechmannlab.net\
Web: www.pechmannlab.ne... |
0fcf4c8d270fac950c5585c7c0a2194011317be7cd81c278f8a14cc154445c47 | Text | 327 | 6 | # Drug_Interactions
Predicting interactions between pairs of drugs using machine learning
Data and models can be found [here](https://drive.google.com/drive/folders/1tmYKOAoE4mYqRnHmaBucuPdYep6w0t5Z?usp=sharing)
Demo can be found [here](https://colab.research.google.com/drive/1s_6yQlYKC1DZzJSFq04BpmfxQtKtNa54?usp=sh... |
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