scientific-computing
115
spatial-de
Differential expression and marker discovery for spatial transcriptomics using Scanpy Wilcoxon / t-test or sample-aware pseudobulk PyDESeq2.
TianGzlab
research
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scientific-computing
108
sys-biological
DNA Methylation & Nociception (Pain Memory) systems.
8421bit
research
scientific-computing
106
neqsim-unisim-reader
Reads Honeywell UniSim Design / Aspen HYSYS .usc files via COM automation and converts them to NeqSim ProcessSystem / ProcessModule structures. USE WHEN: a user has UniSim/HYSYS simulation files and wants to recreate or compare the model in NeqSim. Covers COM API navigation, component mapping, EOS mapping, operation type mapping, topology reconstruction, sub-flowsheet handling, and result verification.
equinor
research
scientific-computing
106
neqsim-plant-data
Connecting NeqSim process simulations to plant historian data via tagreader. USE WHEN: reading data from OSIsoft PI or Aspen IP.21 historians, building tag mappings for process equipment, comparing simulated vs measured values, running digital twin loops, or integrating NeqSim models with operational data. Covers tagreader API, tag mapping patterns, data quality handling, mock data generation, and model-vs-plant comparison workflows.
equinor
research
scientific-computing
106
neqsim-notebook-patterns
Jupyter notebook patterns for NeqSim. USE WHEN: creating or reviewing Jupyter notebooks that use NeqSim for process simulation, thermodynamics, or PVT analysis. Covers dual-boot setup cell, class imports (devtools vs pip), notebook structure, visualization requirements, and results.json schema.
equinor
research
scientific-computing
106
neqsim-ccs-hydrogen
CO2 capture, transport, storage (CCS) and hydrogen systems patterns for NeqSim. USE WHEN: modeling CO2 pipelines, injection wells, impurity effects on phase behavior, CO2 dense phase transport, hydrogen blending, electrolysis, or any CCS/H2 value chain analysis. Covers CO2 phase behavior, impurity management, well integrity, and hydrogen systems.
equinor
research
scientific-computing
100
network-latency-testing
Network latency simulation and testing for measuring application behavior under various network conditions including high latency, packet loss, and bandwidth constraints.
PramodDutta
research
scientific-computing
100
stress-testing-patterns
Stress testing methodologies for finding breaking points, resource exhaustion thresholds, and degradation patterns under extreme load conditions.
PramodDutta
research
scientific-computing
97
drama-workflow
Coordinate plot point dramatic function analysis process, manage text preprocessing, parallel analysis, result integration. Suitable for plot point and dramatic function analysis of long texts, scenarios requiring structured analysis reports
GongLingRui
research
scientific-computing
95
genome-analyzer
Анализирует генетические данные пользователя из VCF файла. Используй когда пользователь спрашивает о своей генетике, наследственных признаках, предрасположенностях, метаболизме веществ (кофеин, алкоголь, лекарства), спортивных способностях, рисках заболеваний, питании на основе генов.
artwist-polyakov
research
scientific-computing
94
triage-malware
Triage a suspected malicious file hash. Use when investigating malware alerts or suspicious files. Analyzes GTI file report, behavioral indicators, identifies affected hosts, enriches network IOCs, and recommends containment actions.
dandye
research
scientific-computing
93
pymatgen
pymatgen (Python Materials Genomics) is a materials science Python library for structure analysis, thermodynamics, and electronic property calculation. Parse and create crystal structures (CIF, POSCAR, CIF), query the Materials Project database for DFT-computed properties, analyze phase diagrams and pourbaix diagrams, compute X-ray diffraction patterns, and generate DFT input files for VASP, Quantum ESPRESSO, and CP2K. Alternatives: ASE (Atomic Simulation Environment) for MD/geometry; AFLOW for high-throughput; OVITO for visualization.
jaechang-hits
research
scientific-computing
93
maxquant-proteomics
MaxQuant + Perseus proteomics pipeline: configure and run MaxQuant for label-free quantification (LFQ) and SILAC; parse proteinGroups.txt in Python; filter contaminants/reverse decoys; log2-transform and median-normalize LFQ intensities; impute MNAR missing values; t-test with FDR correction; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo instrument-native processing; FragPipe/MSFragger for GPU-accelerated database search.
jaechang-hits
research
scientific-computing
93
brenda-database
Query BRENDA Enzyme Database for kinetic parameters (Km, Vmax, kcat, Ki), enzyme classifications, substrate specificity, inhibitors, cofactors, and organism-specific enzyme data via SOAP/REST API. 80,000+ enzyme entries, 7M+ kinetic values. Requires free academic registration. For metabolic pathway modeling use cobrapy-metabolic-modeling; for metabolite structures use hmdb-database.
jaechang-hits
research
scientific-computing
93
cellpose-cell-segmentation
Deep learning cell and nucleus segmentation from fluorescence and brightfield microscopy images. Uses pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm that segments cells without requiring retraining on new image types. Outputs label masks for downstream morphology measurement and tracking. Use scikit-image watershed for rule-based segmentation; use Cellpose when deep learning generalization across staining conditions is needed.
jaechang-hits
research
scientific-computing
93
biopython-molecular-biology
Computational molecular biology toolkit for sequence manipulation, file I/O (FASTA/GenBank/PDB), NCBI database access (Entrez), BLAST automation, pairwise/multiple sequence alignment, protein structure analysis (Bio.PDB), and phylogenetic tree construction. Use for batch sequence processing, custom bioinformatics pipelines, format conversion, and programmatic PubMed/GenBank queries. For quick gene lookups use gget; for multi-service REST APIs use bioservices.
jaechang-hits
research
scientific-computing
93
attend
Route upstream epistemic deficits and evaluate execution-time risks during AI operations. Scans for unresolved upstream protocol needs, materializes intent into tasks, classifies each for risk signals, delegates low-risk tasks to executor, and surfaces elevated-risk findings for user judgment. Type: (ExecutionBlind, User, EVALUATE, ExecutionContext) → SituatedExecution. Alias: Prosoche(προσοχή).
jongwony
research
scientific-computing
93
mdanalysis-trajectory
Python library for analyzing molecular dynamics (MD) trajectories from GROMACS, AMBER, NAMD, CHARMM, and LAMMPS. Reads topology and trajectory files into Universe objects; supports RMSD, RMSF, radius of gyration, contact maps, hydrogen bond analysis, PCA, and custom distance/angle calculations across millions of frames. Use for structural analysis after MD simulations; use OpenMM or GROMACS directly for running simulations.
jaechang-hits
research
scientific-computing
93
datamol-cheminformatics
Pythonic wrapper around RDKit with simplified interface and sensible defaults for drug discovery cheminformatics. Use for SMILES parsing, molecular standardization, descriptor computation, fingerprints, similarity search, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformer generation, and molecular visualization. Returns native rdkit.Chem.Mol objects. Prefer datamol over raw RDKit for standard workflows; use RDKit directly for advanced control or custom parameters.
jaechang-hits
research
scientific-computing
93
pyopenms-mass-spectrometry
Mass spectrometry data processing with PyOpenMS. Use for LC-MS/MS proteomics and metabolomics workflows — mzML/mzXML file I/O, signal processing (smoothing, peak picking, centroiding), feature detection and linking across samples, peptide/protein identification with FDR control, untargeted metabolomics pipelines. For simple spectral matching and metabolite ID, use matchms instead.
jaechang-hits
research
scientific-computing
93
pathml
PathML is an open-source toolkit for computational pathology. Use it to process whole-slide images (WSIs): load slides, extract tiles, apply stain normalization and nuclear segmentation preprocessing, extract features, and train machine learning models. Supports H&E and multiplex imaging. Ideal for building end-to-end digital pathology pipelines from raw WSI files to quantitative outputs.
jaechang-hits
research
scientific-computing
93
neurokit2
NeuroKit2 is a Python toolkit for neurophysiological signal processing. Process ECG (heart rate, HRV, R-peak detection), EEG (complexity, power spectral density), EMG (muscle activation onset), EDA/GSR (skin conductance, SCR decomposition), PPG (photoplethysmography), and RSP (respiration) signals. Simulate synthetic signals for testing. Alternatives: BioSPPy (older, less maintained), MNE (EEG/MEG specialist), heartpy (ECG only), scipy.signal (raw DSP without biosignal abstraction).
jaechang-hits
research
scientific-computing
93
neuropixels-analysis
Pipeline for analyzing Neuropixels extracellular electrophysiology recordings. Covers probe geometry loading (ProbeInterface), spike sorting with Kilosort via SpikeInterface, quality metrics computation, unit curation (ISI violations, firing rate, signal-to-noise), and post-sort analysis (PSTH, tuning curves, population decoding) using pandas and matplotlib. Designed for acute and chronic Neuropixels 1.0/2.0/Ultra recordings from rodent and primate experiments.
jaechang-hits
research