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879181c3975d4836d21f7a8b3396fd4fdbcd48710e0754d2c110a7554baab332 | Text | 13,105 | 192 | <a name="topOfPage"></a>
# NetInf
NetInf is a collection of programs for blood flow simulations (forward modeling), Bayesian calibration (inverse modeling/parameter inference), and uncertainty quantification in microvascular networks.
Please see reference \[1\] (and its supplementary files) for further information on... |
9fdf3ba4d07e3b921f03f5b1272744111908b9834970962512b20468c2b1d41b | Text | 13,163 | 197 | [](https://zenodo.org/badge/latestdoi/248090643)
# 10xPilot_snRNAseq-human
## Study design
This project, led by [Matthew N. Tran](https://twitter.com/mattntran) and [Kristen R. Maynard](https://twitter.com/kr_maynard), describes a single nuclei RNA sequencing (snRNA-seq... |
635cfe1a76304034eafc5a309a29aead6cbcc2aa8e231a1b8e7db6fe113f4cd8 | Text | 13,296 | 218 | # PEKA
Positionally-enriched k-mer analysis (PEKA) is a software package for identifying enriched protein-RNA binding motifs from CLIP datasets. PEKA compares k-mer enrichment in proximity of high-confidence crosslink sites (tXn - thresholded crosslinks), located within crosslinking peaks and having a high cDNA count, ... |
59f6f0d6559c4c1414bc5f39f23a6b638a7d3e467bb030f86dabcaac549a94e9 | Text | 13,394 | 357 | <div align="center">
# 🙊 Detoxify
## Toxic Comment Classification with ⚡ Pytorch Lightning and 🤗 Transformers
[](https://badge.fury.io/py/detoxify)
)
- DEPP full genomes ([D2](h... |
10250e4d10659601304fab164f85ddfd457e5b11a6efb4bf08d9e57a3e868faa | Text | 13,551 | 258 | ProteinLigandBenchmarks
==============================
[//]: # (Badges)
[](https://github.com/openforcefield/protein-ligand-benchmark/actions/workflows/ci.yaml)
[: A benchmarking platform for molecular generation models
[](https://travis-ci.com/molecularsets/moses) [](https://badge.fury.io/py/molsets)
Deep generative models a... |
3f6993adab67e3ca96b3921da34af2e0bd54d0b3f7d197c91680d74e3574dfc2 | Text | 14,010 | 249 | SIMU simulates a GWAS based on real genotype data.
This tool is a extension of ``gcta --simu-qt`` and ``gcta --simu-cc`` functionality,
described [here](http://cnsgenomics.com/software/gcta/#GWASSimulation).
Additional features include
* simulation of two traits (``--num-traits 2``), with given genetic correlation (``-... |
27252f95f754920eeb37ce7291e27d8d5b0e93baa67f1f0397f858335a148cb0 | Text | 14,057 | 196 | # BioBERT
This repository provides the code for fine-tuning BioBERT, a biomedical language representation model designed for biomedical text mining tasks such as biomedical named entity recognition, relation extraction, question answering, etc.
Please refer to our paper [BioBERT: a pre-trained biomedical language repre... |
4cd133fd1eaf6b8476e2ded1bceee142f42de135b3628c912c7dddc974d83b1e | Text | 14,188 | 363 | .. image:: https://img.shields.io/pypi/v/hdbscan.svg
:target: https://pypi.python.org/pypi/hdbscan/
:alt: PyPI Version
.. image:: https://anaconda.org/conda-forge/hdbscan/badges/version.svg
:target: https://anaconda.org/conda-forge/hdbscan
:alt: Conda-forge Version
.. image:: https://anaconda.org/conda-... |
4d742c506d222a8c060db5a4193b7230c2e4f15561d551d71501d3d91195fd94 | Text | 14,373 | 199 | # Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients
This repository contains Python scripts to accompany our data descriptor paper titled "Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients". The code i... |
db46472426427f44d49a333ddf58d64a7e1ab3281e3180ef079417001e3f9cb7 | Text | 14,379 | 307 | AlphaRaw
================
[](https://github.com/MannLabs/alpharaw/actions/workflows/pip_installation.yml)
[ images and dissect the behavioral patterns of tumor cells.
## Overview
BEHAV3D... |
c1f8475ce5daf6346242d9860bd4c1a0208e4866a7edd197c98d5b17b1b9f931 | Text | 14,529 | 196 | # Paper-FallingAsleepBifurcation
The scripts and final data repository for paper: "Falling asleep follows a predictable bifurcation dynamic".
**Potential competing interests**:
JL and NG have a patent application filed (Patent Application No. 2509587.8) for the methods and computational framework here.
**Citations... |
53ec28ce11a517892ada621c910e5cb65476557392fe50887f183a530a59311f | Text | 14,633 | 485 | # 3D-RCAN
[](https://creativecommons.org/licenses/by-nc/4.0/)
3D-RCAN is the companion code to our paper:
[Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes](htt... |
259b33a838db825bdd3aac0ca665871c09459c06d32675ec2afee99582ff502d | Text | 14,821 | 283 | # DeepRetinotopy - A deep learning-based toolkit for retinotopic mapping
[](https://doi.org/10.5281/zenodo.21646928)
DeepRetinotopy is a toolkit that leverages a geometric deep learning model to predict retinotopic maps from brain shape. Our ... |
5833b409ff9e91e220fe3bcb851d68d410e327f3fe72e54aa05e3d81245c99b1 | Text | 15,021 | 138 | # CSPBenchmark: Benchmark of crystal structure prediction algorithms
Developed by Lai Wei and Dr. Jianjun Hu at <a href="http://mleg.cse.sc.edu" target="_blank">Machine Learning and Evolution Laboratory</a>.
University of South Carolina.
Citing our paper: Wei, Lai, Sadman Sadeed Omee, Rongzhi Dong, Nihang Fu, Yuqi S... |
84359f723dce618f487b632f981486866fbdfb8ccc6bf866cffb92b61f52e85b | Text | 15,057 | 299 | # spatial-calibration
Python code to check the calibration of displays
This will involve displaying a bunch of images on your display, taking
pictures of them with a high-quality camera (turning all the bells and
whistles off, ideally storing as RAW images), and then running them
through the included analysis to dete... |
e8ee1c2fc6d556d5015deaf156d88413d59a530cb9bb830b2e1b08107026b6a5 | Text | 15,098 | 284 | # ByteTrack
### Automatic Bite Detection in Children as a part of HomeBytes project
<div style="display: flex; align-items: center;">
<img src="assets/ByteTrack%20logo.jpg" alt="ByteTrack Logo" width="300" style="margin-right: 10px;"/>
<img src="assets/HomeBytes_logo.jpg" alt="HomeBytes Logo" width="300"/>
</di... |
24d04a748484c727bb99c288a5d632772f4efb0c9776c7624cf42f5fbc590de2 | Text | 15,248 | 309 | # LST-AI - Deep Learning Ensemble for Accurate MS Lesion Segmentation
> 2026-05-06: **LST-AI has received the [NeuroImage: Clinical Editors’ Choice Award 2025](https://www.sciencedirect.com/journal/neuroimage-clinical/about/editor-choice/neuroimage-clinical-editors-choice-award-2025-tun-wiltgen) 🏆**
Welcome to our c... |
ec3e7722428189c5a8a0cb495157717f5dfde3d319a2fc131c62a3af02727982 | Text | 15,329 | 225 | # PRS-CSx
**PRS-CSx** is a Python based command line tool that integrates GWAS summary statistics and external LD reference panels from multiple populations to improve cross-population polygenic prediction. Posterior SNP effect sizes are inferred under coupled continuous shrinkage (CS) priors across populations.
- T... |
81c575520b2ed49c043c32f5b1ca1803982b62d8b5b5373b3848c23eed92c081 | Text | 15,518 | 397 | +------------+-----------------------------------------------------------+
| **Info** | Official NI-DAQmx Python API |
+------------+-----------------------------------------------------------+
| **Author** | National Instruments |
+------------+------... |
6b14bfae176796e1bf09a899209e12fd80548a7e516c320043fe6dd9dca00fa7 | Text | 15,546 | 166 | # infer-subc
### A python-based image analysis tool to segment and quantify the morphology, interactions, and distribution of organelles.
## ** This repository has been archived, but development for this project continues in [SCohenLab/infer-subc](https://github.com/SCohenLab/infer-subc) **
<img src="infer_subc\asse... |
548528c62aefeb248e6eb6a212c4bbfabfb9186b236df23b40a94d0e2282f2db | Text | 15,550 | 249 | # popscle
`popscle` is a suite of population scale analysis tools for single-cell genomics data. The key software tools in this repository includes `demuxlet` (version 2) and `freemuxlet`, a genotyping-free method to deconvolute barcoded cells by their identities while detecting doublets.
### Quick Overview
With `po... |
b6697d9f3888c36d109f1b78c17689d83cf850805ea2ccec220b6db749b32d00 | Text | 16,093 | 200 | # Code for "Auditory sensory processing induces cortical and thalamic event-related desynchronization in the mouse"
This repository contains the full codebase used to preprocess, analyze, and visualize the electrophysiology and behavioral data in the publication:
> McGill, S. H., Xin, Q., Yadav, T., Zhao, C. W., ... |
a8c820061b58d04215cfcee46d61279f61e7c4bfd905a3001b030463e6f345e9 | Text | 16,288 | 291 | # Spectral Exponent
### a.k.a. spectral slope, power-law slope, PSD slope, slope of the aperiodic component, 1/f exponent
## UPDATE >>> PYTHON TRANSLATION NOW AVAILABLE<<<
this code allows to compute the spectral exponent of the resting EEG, based on the Power Spectral Density (PSD), over a given scaling region.
The... |
db60da3e5bb7ad9054c3507c6a9ae4cfed7cf7a343d202981099fd77eb466632 | Text | 16,553 | 276 | # Decoupling the Depth and Scope of Graph Neural Networks
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen
**Contact**: Hanqing Zeng (zengh@usc.edu)
[Latest version of the paper](https://arxiv.org/abs/2201.07858)
(Note: There is an [o... |
d656d3c8440c430d0cf72b2c39459c6e351c73ca620ddb754391b11ae8d05cba | Text | 16,575 | 161 | <img src="https://www.dropbox.com/scl/fi/brlupwxyu5eb4w42el6yj/header.png?rlkey=4wjx7rd6m8hjbc4b8xyb9hlfo&raw=1">
# BRAVEHEART: Open-Source Software for Automated Electrocardiographic and Vectorcardiographic Analysis
[](https://www.mathworks.com/products/m... |
b5903ac355079bc66489c260833b1a7a6b968c8e588d96e43c16afd624728b7a | Text | 16,739 | 218 | # Splicing Factor Activity Analysis
Estimate splicing factor activities from changes in exon inclusion or gene expression.
## Requirements
Install conda/mamba environment:
```shell
mamba env create -f environment.yaml
```
If you need to avoid accessing repo.anaconda.com, run thi... |
cac0247153ef3bbcaea17c1707d774a124359a98e9c9f1b4f2a8a7be4051fc7b | Text | 16,743 | 308 | <img src='imgs/horse2zebra.gif' align="right" width=384>
<br><br><br>
# CycleGAN and pix2pix in PyTorch
**Udpate in 2025**: we recently updated the code to support Python 3.11 and PyTorch 2.4. It also supports DDP for single-machine multiple-GPU training. (Please use `torchrun --nproc_per_node=4 train.py ...`)
**Ne... |
5bf8eeaaf15e68e0d5235c974213a3f6aa9a9f30f8149fe00e5ff890146a6e54 | Text | 16,996 | 255 | # The Quick, Draw! Dataset
The Quick Draw Dataset is a collection of 50 million drawings across [345 categories](categories.txt), contributed by players of the game [Quick, Draw!](https://quickdraw.withgoogle.com). The drawings were captured as timestamped vectors, tagged with metadata includin... |
3606e3eac1c55fdcdd5c471720b192e5cd31f7c7da70a1768d6ba0ee5a414195 | Text | 17,166 | 314 | # NeuroPyxels: loading, processing and plotting Neuropixels data in python
[](https://pypi.org/project/npyx/)
NeuroPyxels (npyx) is a python library built for electrophysiologists using Neuropixels electrodes. It features a suite of core utility functions for loa... |
a73193d56b89ed36365d65f1e266ff1a09a6c134ff902d7ff0caacdf2ccff617 | Text | 17,244 | 246 | # Data and code - Logarithmic coding leads to adaptive stabilization in the presence of sensorimotor delays
## Data:
The folder `data` contains all of the collected data that was analyzed in the paper, structured as follows.
The folder `behavior` contains the data acquired with the behavioral setup.
The folder `ima... |
84c2e273427c7a81f6338fbb786597f5b0d4e4e3be80580074eb5f3648e4334e | Text | 17,670 | 71 | ---
---
<h1 id="welcome-to-explore">Welcome to EXPLORE!</h1>
<p>EXPLORE stands for microElectrode eXperimental kit with Probe LOcation Rationalization in Electrophysiology. EXPLORE is an open-source collection of 3D-printed devices and protocols which aim to provide neuroscientists tools, methods, and guidelines to ... |
ce5fc8f60cc2fe24264936eb9cb9bbf2df7a795039f0363dbc8c22c07a0a706e | Text | 17,692 | 71 | ---
---
<h1 id="welcome-to-explore">Welcome to EXPLORE!</h1>
<p>EXPLORE stands for microElectrode eXperimental kit with Probe LOcation Rationalization in Electrophysiology. EXPLORE is an open-source collection of 3D-printed devices and protocols which aim to provide neuroscientists tools, methods, and guidelines to ... |
53d40f9d5771727d8d1b4436fac338cce3e7cc433e0eef1c750ce8a9e0044d5f | Text | 17,951 | 318 | [<picture><source media="(prefers-color-scheme: dark)" srcset="./doc/_static/logo-dark.svg"><source media="(prefers-color-scheme: light)" srcset="./doc/_static/logo.svg"><img alt="DeePMD-kit logo" src="./doc/_static/logo.svg"></picture>][logo-guide]
# DeePMD-kit
**Start from a pretrained Deep Potential model, fine-tu... |
8a9cce024b46d7e5f83dccb48d17ab8acea77610a998244350e06ad44329668a | Text | 17,979 | 261 | # Leveraging gene expression and genomic varation for cancer prediction using one-shot learning
The Cancer Genome Atlas (TCGA), a cancer genomics reference program, has molecularly characterized more than 20,000 primary cancer samples and paired normal samples covering 33 types of cancer.
This joint effort between the ... |
b5aaf822c6d3673ad2ce74cb5fa518d2b21c1637c9fac7877a2c15ee4beb5329 | Text | 18,112 | 371 | <table>
<tr>
<td valign="top">
<img src="Wiki/CellTracksColab_logo.png" width="800">
</td>
<td>
> In life sciences, tracking objects from movies is pivotal for quantifying behaviors of particles, organelles, bacteria, cells, and whole animals. **CellTracksColab** bridges the gap between tracking and analysis.
> **C... |
e970592230fb4ccf183f3378dc20b32629f8598887af4fccf2fa631d36abe194 | Text | 18,157 | 322 | # PyXOpto
PyXOpto is a collection of Python tools for performing Monte Carlo simulations
of light propagation in turbid media using massively parallel processing on a wide range of OpenCL-enabled devices. The tools allow steady-state and time-resolved simulations of light propagation, deposition and fluence simulation... |
4c7de7152c5c049dc76ba092ad909691a4d2954638ec93be7a3f0e1adffcfe25 | Text | 18,769 | 345 | # Molecular simulations for METL
This repository facilitates high-throughput Rosetta runs to compute energy terms for protein variants.
For more information, please see the [metl](https://github.com/gitter-lab/metl) repository and our manuscript:
**Biophysics-based protein language models for protein engineering**. ... |
78db0ec4bfc3e7c8ebcf136755900c7baadfc108d6e7c52129d3053e5f11c9e8 | Text | 18,771 | 320 | # cbig_network_correspondence
[](https://pypi.python.org/pypi/cbig_network_correspondence/)
[](https://pypi.python.org/pypi/c... |
aee3b8afebeb599b0e97b75c9321f2b26274c435d932978b1e8f6712181bbad6 | Text | 19,200 | 263 | # RESPAN: Restoration Enhanced SPine And Neuron Analysis.
[](https://github.com/lahammond/RESPAN/actions/workflows/tests.yml)
[](https://github.com/lahammond/RESPAN/rel... |
cc2342ce20236d8f0bde8aef4a715154692075b664860ab771bfbf489c4836b8 | Text | 19,410 | 359 | Mask DINO <img src="figures/dinosaur.png" width="30">
========
[](https://paperswithcode.com/sota/panoptic-segmentation-on-coco-minival?p=mask-dino-toward... |
4029448b7b515d2797eff36a07f1b0365012f23df5bb79ea4d62bd27b5947363 | Text | 19,462 | 222 | # Hierarchical encoding of natural sound mixtures
## Overview
This repository contains neuroimaging data obtained with functional ultrasound imaging (fUSI) in the auditory cortex of ferrets and humans passively listening to auditory stimuli. This data is analyzed in the following article:
Landemard A, Bimbard C, Bo... |
b446dbe517a2c3989c0c9a889062a3aa1e410dc83bd2a54fdbd6225e5e224160 | Text | 19,837 | 360 | # Molecular simulations for METL
[](https://zenodo.org/doi/10.5281/zenodo.10819523)
This repository facilitates high-throughput Rosetta runs to compute energy terms for protein variants.
For more information, please see the [metl](https://github.com/gitte... |
0596942cd8ae628deed2e78d3f9938e14fbccfed7293a6849a1063a5feb06c2b | Text | 22,226 | 242 | ## Connectome Mapper 3
This neuroimaging processing pipeline software is developed by the Connectomics Lab at the University Hospital of Lausanne (CHUV) for use within the [SNF Sinergia Project 170873](http://p3.snf.ch/project-170873), as well as for open-source software distribution.
[](https://pypi.org/project/gnnwr/)
[](https://pepy.tech/project/gnnwr)
A PyTor... |
59cd01fc1595a0254beea474011294b18633af606546bf871c50032722697d3c | Text | 25,152 | 608 | # spectral_connectivity
[](https://github.com/Eden-Kramer-Lab/spectral_connectivity/actions/workflows/release.yml)
[](https://zenodo.org/badge/lates... |
4cb43c8b35c1fd294d6ab926cf23ab67be08785e358af5b18c07f3ce6b4d7349 | Text | 25,674 | 395 | # Tonotopy is not preserved in a descending stage of auditory cortex
## Dataset Overview:
Previous studies based on layer specificity suggest that ascending signals from the thalamus to the sensory neocortex preserve spatially organized information, but it remains unknown whether sensory information descending from s... |
479b1dd71e803af3b526dc686e65e6c0f0d7c6a2c6bb6e273e1f1a97deddfa6a | Text | 25,766 | 584 | # factorial hidden Markov drift-diffusion model (FHMDDM)
The code for fitting the model and computing quantities to characterizing the model is in the programming language Julia, whereas the code for plotting the said quantities are in MATLAB.
# table of contents
* [tutorial](#tutorial)
* [installing the FHMDDM re... |
22c57fcc4c7101fa569cc9c2cf129e7baef51080ee2636e9904eacf1c39f4136 | Text | 26,374 | 270 |
# infer-subc
### A Python-based image analysis tool to segment and quantify the morphology, interactions, and distribution of organelles.
<img src="infer_subc\asse... |
aa7f307c6417b42c2a9996b3dd314dd61d1d1319adb8e28297edef162da3f024 | Text | 26,858 | 392 | # Triplanar U-Net ensemble network (TrUE-Net) model
## DL tool for white matter hyperintensities segmentation
## Contents
- [citation](#citation)
- [dependencies](#dependencies)
- [installation](#installation)
- [preprocessing](#preprocessing-and-preparing-data-for-truenet)
- [simple usage](#simple-usage)
- [ad... |
df4d92d3382cae9072ae7d201db8dcb2ef5b5e4864d91ea2f2a5ceea82d77592 | Text | 27,393 | 346 | # Histology-informed microstructural diffusion simulations for MRI cancer characterisation — the Histo-μSim framework
<div align="center">
<img src="https://github.com/radiomicsgroup/dMRIMC/blob/main/imgs/commbio25.png" alt="commbio" width="auto" height="auto">
</div>
If you use the code released in this repository... |
f071db0f5cd81703825c1be91df01a4d727db158cb84c66415023c5edfaad851 | Text | 27,479 | 381 | <p align="center">
<img style="width: 35%; height: 35%" src="cv_asl_svg.svg">
</p>
[](https://zenodo.org/badge/latestdoi/618300539)
[](https://pypi.python.org/pypi/cvasl/)
[](https://github.com/mljar/mljar-supervised/actions/workflows/run-tests.yml)
[](https://badge.fury.io/py/mlj... |
c8e107c2e35db94cf97183f97c3e9de5625658d88507777f76f93be8295483d1 | Text | 27,908 | 475 | # <img src="docs/imgs/logo-green.png" alt="icon" height="24" style="vertical-align:sub;"/> PFLlib: Personalized Federated Learning Library and Benchmark
🎯*We built a beginner-friendly federated learning (FL) library and benchmark: **master FL in 2 hours—run it on your PC!** [Contribute](#easy-to-extend) your algorith... |
1fbd4a2680e48302d7d8f9e7f35c8bc93abf0abdaf7e9f9842c96e8eaf653231 | Text | 27,982 | 343 | # Pretrained METL models
[](https://github.com/gitter-lab/metl-pretrained/actions/workflows/test.yml)
[](https://zenodo.org/doi/10.5281/zenodo.10819499)
T... |
6ddc0732b5212d29c522362f7325be14551fca9380dee87e75f6d31200d650ef | Text | 30,082 | 349 |
# LOTS-IM-GPU
LOTS-IM-GPU is a fast, fully-automatic, and unsupervised detection method to extract irregular textures of white matter hyperintensities (WMH) on brain FLAIR MRI. Unlike other recently proposed methods for doing WMH segmentation, LOTS-IM-GPU does not need any manual labelling of the WMH. Instead, LOTS-... |
a554364742e36de5e32148055aa5edfc88818a072ad30ecbe791108f64f41c83 | Text | 31,455 | 595 | [](https://pypi.org/project/npyx/)
[](https://doi.org/10.5281/zenodo.5509733)
[](https://github.com/m-beau/NeuroPyxels/blob/master/LICENSE)
[](https://github.com/neuroquery/pubget/actions/workflows/testing.yml)
[](https://codecov.io/gh/neuroquery/pubget)
[![pubget on GitHub](ht... |
fdf03b6b81996be8b19181e22e60f70f123488efc7a8199a5f2c7fedd2245d75 | Text | 34,352 | 442 | <div align="center">
<img src="images/ClairS-TO_icon.png" width="200" alt="ClairS-TO">
</div>
# ClairS-TO - a deep-learning method for long-read tumor-only somatic small variant calling
[](https://opensource.org/licenses/BSD-3-Clause)
Conta... |
fce8cf8e2befbd6218b89bd4fa63ee906132460a5b418aaa5a3e7aa3914f6ccf | Text | 35,210 | 610 | 
[](https://coveralls.io/github/NBISweden/AGAT)
[](https://agat.readthedoc... |
750fd3960b28d31aee1151202e85d5221c1858bfbcb9c74b978e438a233059f5 | Text | 35,302 | 268 | # APAeval
<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[](https://github.com/iRNA-COSI/APAeval/blob/main/LICENSE)
[](#co... |
e424bcff7055b201f97b87e6be4ab575d6d32b8b4e968078860170baac37e39b | Text | 35,872 | 635 | # pubget
[](https://github.com/neuroquery/pubget/actions/workflows/testing.yml)
[](https://codecov.io/gh/neuroquery/pubg... |
cfb777d4862f17dd7a236406b6089b4ab2ea36d4b6eb5299288134a078be8cc3 | Text | 37,714 | 469 | <div align="center">
<img src="images/ClairS-TO_icon.png" width="200" alt="ClairS-TO">
</div>
# ClairS-TO - a deep-learning method for long-read tumor-only somatic small variant calling
[](https://opensource.org/licenses/BSD-3-Clause) [
# Table of Contents
* [Diversity Statement and Code Notebook](https://github.com/dalejn/cleanBib#diversity-statement-and-code-notebook)
- [Diversity statement template](https://github.com/dalejn/cleanBib#diversity-statement-template)
+ [Template](http... |
e5c7c2d4b46c0e9b6aa6a496b1bebba9a0cb23cf0bdb1d4cbc0d209040f92209 | Text | 40,095 | 763 | <p align="center">
<a href="https://shawnrhoadsphd.com/pyEM/">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="assets/source/pyem-logo-horizontal-dark-editable.svg">
<img alt="pyEM" src="assets/source/pyem-logo-horizontal-editable.svg" width="420">
</picture>
</a>
</p>
<div align... |
bb565d2ae4a7cc11ec67b3b8d884efb5314ddb82888738640dfa0b350a266b16 | Text | 42,812 | 796 | [](https://github.com/py-why/EconML/actions/workflows/ci.yml)
[](https://pypi.org/project/econml/)
[](https://pypi.org/p... |
1bfb0a54c005618a723b7e5bd9332323a41a63e1bad2dad61eec7afb53e67db4 | Text | 46,283 | 808 | 
# Grounded-Segment-Anything
[](https://youtu.be/oEQYStnF2l8) [](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/automated-datase... |
414eb0947448a2fff72a4d2d247a995e0a2172028403088ff2640ac9094fa5bb | Text | 47,657 | 591 | # Harvey Lab Mouse VR
Developed by members of the Harvey Lab (http://harveylab.hms.harvard.edu/) and the HMS Research Instrumentation Core (http://instrumentation.hms.harvard.edu/). Community contributions to any aspects of the designs or documentation very welcome, please submit a pull request with a clear description... |
d9eae7c182e1cdef1d0351185e2a76e915947affa45016e450d03d970faf6813 | Text | 54,750 | 1,830 | 
NETCAL: An interactive platform for large-scale, NETwork and population dynamics analysis of CALcium imaging recordings
NETCAL is a MATLAB-built, dedicated software platform to record, manage and analyze high-speed high-resolution calcium imaging experiments. Its ease of use, inter... |
4797b6f3ec7867c3b931a596364f87e688c499dfd271b16d250e2445712da4e9 | Text | 61,652 | 780 | VAST-TOOLS
==========
Table of Contents:
- [Summary](#summary)
- [Requirements](#requirements)
- [Installation](#installation)
- [VAST-TOOLS](#vast-tools-1)
- [VASTDB Libraries](#vastdb-libraries)
- [Usage](#usage)
- [Help](#help)
- [Quick Usage](#quick-usage)
- [Alignment](#alignment)
- [Merging Outputs](#merg... |
3e1bc9e5f5bc1af692cb3fbae2ea127039257728d58e8ff9f9d39a7e8ba62a40 | Text | 65,689 | 1,704 | # Visual Cortex Speckle Imaging for Shape Recognition
This repository implements a deep learning approach for shape recognition using visual cortex speckle imaging patterns. The project uses PyTorch to build Convolutional LSTM (ConvLSTM) networks that classify geometric shapes based on speckle imaging data, following ... |
e25b348972d4e21ac6d4b241fa1326d1828b7e513259a0fdd2f3648fc4b8b928 | Text | 85,779 | 1,882 | # Scripts for Single-Cell Research Paper | 单细胞研究论文脚本集
download paper here:https://www.sciencedirect.com/science/article/pii/S200103702500399X
**English** | **中文**
This repository contains all scripts for the computational biology part of a single-cell research project, covering data ... |
cdfeaabfee0cdfa658fcf563407e9175591f1caba018e130f85e3524ed37dbd8 | Text | 99,574 | 416 | # OpenFF QCArchive Dataset Submission
## Dataset Lifecycle
All datasets submitted to QCArchive via this repository conform to the [Dataset Lifecycle](#the-lifecycle-of-a-dataset-submission).
See [STANDARDS.md](./STANDARDS.md) for submission standards.
Datasets must be submitted as pull requests.
## User Quickstart
... |
35e524f0ba899473c7e886efef24c92dee1a32a1727b8b676cc63201842bce98 | TypeScript | 36 | 2 | export * from "../layout/reducer";
|
a0d8cc9b52ca457b43dc511553279983898bf32f0af5686fc104cf1416d581ab | TypeScript | 64 | 2 | export { createStore } from './store';
export * from './layout'; |
ab05d299a2eff563d52e82f5853d7e75fb77d53d13c1da07a94ed57b1f026d3e | TypeScript | 74 | 4 | declare module '*.vue' {
import Vue from 'vue';
export default Vue;
}
|
3a7374b75830410e555ebdf1cde973392174a82a9485b54231b362873863e8d5 | TypeScript | 102 | 2 | export const PLUGIN_ID = 'workflowTrigger';
export const PLUGIN_NAME = 'dashboards-workflow-trigger';
|
ad7f44a241fd691bbdbb8baaa02510e9b395341353faa86c9b1ac11cc2ceea4d | TypeScript | 142 | 5 | declare module '@koumoul/vjsf' {
import { DefineComponent } from 'vue'
const Vjsf: DefineComponent<any, any, any>
export default Vjsf
}
|
657f38873a99f05ab3f4aefa21670c64e48bb5763e99e92b5891af42543d2eb8 | TypeScript | 159 | 7 | import { Action } from "redux";
export * from "./actions";
export * from "./layout";
export interface GeppettoAction extends Action<string> {
data?: any
} |
2594cb65b1b12c43e6d4a66b79dcca5df6a0c7fa55e683342f4492b8f3f8f3a6 | TypeScript | 162 | 6 | export default interface IDraggable {
/** @hidden @internal */
isEnableDrag(): boolean;
/** @hidden @internal */
getName(): string | undefined;
}
|
d2fd3204be08762ccd3168942664d8d31339e4b13736a939025bd8d87a61a573 | TypeScript | 172 | 9 | /// <reference types="vite/client" />
interface ImportMetaEnv {
readonly VITE_KAAPANA_BACKEND_ENDPOINT: string
}
interface ImportMeta {
readonly env: ImportMetaEnv
}
|
cb3a8a70a87f0b1872244cd0bbe5bb5ddd602d820b7766a4d515b04eab8c40c7 | TypeScript | 186 | 7 | /// <reference types="vite/client" />
declare module '*.vue' {
import type { DefineComponent } from 'vue'
const component: DefineComponent<{}, {}, any>
export default component
}
|
698aa83dbdc309eb0b95dd5b95e9b7608ad823e3a390db964e757962e070b848 | TypeScript | 190 | 7 | declare module "@kyvg/vue3-notification";
declare module '*.vue' {
import { DefineComponent } from 'vue'
const component: DefineComponent<{}, {}, any>
export default component
} |
cccd2428133b08e008e893345b90001bf62c7808dff7c72ada944705c1e04db0 | TypeScript | 190 | 9 | import Vue from 'vue'
import Axios from 'axios'
const axiosInstance = Axios.create({
baseURL: location.protocol + '//' + location.host,
timeout: 20000,
})
export default axiosInstance
|
e91bcd5a1f94406703f381c261bb241aa86b8cbb03d341ef340334530af40221 | TypeScript | 202 | 12 | export interface BorderNode {
type: string;
location: string;
children: BorderNode[];
config?: {
isMinimizedPanel?: boolean;
};
getId(): string;
getConfig(): any;
} |
0c74cb95db7f2849930900fb0a3d390980d2b810abbbbbe1f3a3d04d847e1e05 | TypeScript | 208 | 11 | class Action {
type: string;
data: Record<string, any>;
constructor(type: string, data: Record<string, any>) {
this.type = type;
this.data = data;
}
}
export default Action;
|
71cb1630dc47f4381aaed1dd6ccaa5e31ba7e241feb70df23583038891db792f | TypeScript | 215 | 5 | export const PURGE_AUTH = 'logOut'
export const SET_AUTH = 'setUser'
export const SET_ERROR = 'setError'
export const SET_AVAILABLE_WEBISTES = 'setAvailableWebsites'
export const SET_COMMON_DATA = 'set_common_data'
|
a3beacd3a97b85aa86cad5e470ce5e04824336cf8e7cf6f687bea7ef4f2b18d8 | TypeScript | 217 | 6 | export const CHECK_AUTH = 'checkAuth'
export const LOGIN = 'login'
export const LOGOUT = 'logout'
export const CHECK_AVAILABLE_WEBSITES = 'check_available_websites'
export const LOAD_COMMON_DATA = 'load_common_data'
|
91a351c70b847a1dc0a842120a328022069f74104b46f12ca12d3c8f62d0016d | TypeScript | 223 | 10 | /// <reference types="vite/client" />
interface ImportMetaEnv {
readonly VITE_KAAPANA_BACKEND_ENDPOINT: string
readonly VITE_NOTIFICATIONS_API_ENDPOINT: string
}
interface ImportMeta {
readonly env: ImportMetaEnv
}
|
097fe144eaee9725bc0ddba227c96f3f2b7b77caa030c7f83f6310429150a8d8 | TypeScript | 236 | 3 | // Separate entry: this imports @koumoul/vjsf, which only the workflow-triggering
// consumers install. Exposed as "@kaapana/base-ui/workflow-execution".
export { default as WorkflowExecution } from './components/WorkflowExecution.vue'
|
00ddcd2a25d7d0fa84f617277cef1b43c48b45d7814c67dcc49ccbdb181bd834 | TypeScript | 239 | 11 | import type { StorybookConfig } from '@storybook/vue3-vite'
const config: StorybookConfig = {
stories: ['../src/**/*.stories.ts'],
framework: '@storybook/vue3-vite',
core: {
disableTelemetry: true,
},
}
export default config
|
a0c235c23618d57717da22581a087a46e21b2deb0679481f2d88d33bc9120b38 | TypeScript | 248 | 7 | import type { LogLine } from '@/types/schemas'
export function logLinesToText(lines: LogLine[]): string {
return lines
.map((l: LogLine) => `${l.time.slice(0, 19).replace('T', ' ')} ${l.severity.padEnd(8)} ${l.message}`)
.join('\n')
}
|
89429234d07b77bc4a7f45378d61309b1a57d1fd01dd9aa12df742ab5843ec86 | TypeScript | 254 | 14 | import Vue from 'vue';
import Vuex from 'vuex';
import auth from './modules/auth.module'
import availableWebpages from './modules/commonData.module'
Vue.use(Vuex);
export default new Vuex.Store({
modules: {
auth,
availableWebpages,
},
});
|
72f3c03f3775e4965ff35c71a8953a47ddeb841955bd45ed03abc1bedc1f7016 | TypeScript | 259 | 11 | import type { Repository } from '@/shared/types/apiSchemas'
export interface RepositoryFormState {
name: string
description: string
repository_url: string
username: string
password: string
}
export type RepositoryDict = Record<string, Repository>
|
a74c66fa99f4e7c4ca72a9d9c5de4f5e4d72568e9fe3e09ca730250ed545530e | TypeScript | 261 | 7 | import http from '@/api/http'
import type { MenuResponse } from '@/types/menu'
export async function fetchMenu(fresh = false): Promise<MenuResponse> {
const res = await http.get<MenuResponse>(`/portal-api/menu${fresh ? '?fresh=1' : ''}`)
return res.data
}
|
e2d0664af31d2b89b9673311fccefa46b1e6fdde8a73f8334e2d4eeaf44bc5bf | TypeScript | 265 | 12 | /// <reference types="vite/client" />
interface ImportMetaEnv {
readonly VITE_APP_KAAPANA_BACKEND_ENDPOINT: string
readonly VITE_APP_NOTIFICATIONS_API_ENDPOINT: string
}
interface ImportMeta {
readonly env: ImportMetaEnv
}
declare module 'vue3-apexcharts'
|
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