scientific-brainstorming
Scientific Brainstorming
Overview
Scientific brainstorming is a structured ideation process for generating, connecting, and evaluating research ideas. Unlike casual brainstorming, scientific brainstorming applies formal methodologies (SCAMPER, TRIZ, Morphological Analysis, etc.) matched to the specific creative challenge. The process moves through divergent exploration, connection-making, critical evaluation, and synthesis to produce actionable research directions with testable hypotheses.
Key Concepts
Core Principles
Five principles guide effective scientific brainstorming sessions:
-
Collaborative: Brainstorming works best as dialogue, not monologue. Build on each other's ideas rather than presenting finished thoughts. Use "Yes, and..." framing to extend ideas before evaluating them. In AI-assisted sessions, the scientist contributes domain expertise and the AI contributes breadth and pattern-matching across disciplines.
-
Curious: Approach the problem space with genuine curiosity. Ask "what if" and "why not" before "why." Suspend expertise-driven assumptions temporarily to allow unexpected connections. Experts often dismiss novel directions because they conflict with established mental models -- curiosity counteracts this.
-
Domain-Aware: Ground brainstorming in real scientific constraints. Ideas must eventually connect to testable hypotheses, available methods, and feasible experiments. Domain knowledge channels creativity productively. Pure creativity without domain grounding produces ideas that cannot be tested; pure domain expertise without creativity produces incremental work.
-
Structured: Use formal ideation methods rather than unguided free association. Structure prevents cognitive fixation (repeatedly returning to the same idea space) and ensures systematic coverage of the possibility space. Unstructured brainstorming sessions typically explore less than 20% of the available idea space.
More from jaechang-hits/sciagent-skills
gene-database
Query NCBI Gene via E-utilities for curated gene records across 1M+ taxa. Retrieve official gene symbols, aliases, RefSeq accessions, summary descriptions, genomic coordinates, GO annotations, and interaction data. Use for gene ID resolution, cross-species queries, and gene function summaries. For sequence retrieval use Ensembl; for expression data use geo-database.
10snakemake-workflow-engine
Python-based workflow management system for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake automatically determines the execution order and parallelism. Supports local, SLURM, LSF, AWS, and Google Cloud execution via profiles; per-rule conda/Singularity environments. Use for bioinformatics NGS pipelines, ML training workflows, and any multi-step file-processing analysis. Use Nextflow instead for Groovy-based dataflow pipelines or when nf-core ecosystem integration is required.
10esm-protein-language-model
Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and protein embeddings. Use when designing novel proteins, extracting sequence representations for downstream ML, or predicting structure from sequence. Local GPU or EvolutionaryScale Forge cloud API. For traditional structure prediction use AlphaFold; for small-molecule cheminformatics use RDKit.
10matchms-spectral-matching
Mass spectrometry spectral matching and metabolite identification with matchms. Use for importing spectra (mzML, MGF, MSP, JSON), filtering/normalizing peaks, computing spectral similarity (cosine, modified cosine, fingerprint), building reproducible processing pipelines, and identifying unknown metabolites from spectral libraries. For full LC-MS/MS proteomics pipelines, use pyopenms instead.
10chembl-database-bioactivity
Query ChEMBL via Python SDK. Search compounds by structure/properties, retrieve bioactivity (IC50, Ki, EC50), find target inhibitors, run SAR, access drug mechanism/indication data.
10biopython-sequence-analysis
Biopython sequence analysis: parse FASTA/FASTQ/GenBank/GFF (SeqIO), NCBI Entrez (esearch/efetch/elink), remote/local BLAST, pairwise/MSA alignment (PairwiseAligner, MUSCLE/ClustalW), phylogenetic trees (Phylo). Use for gene family studies, phylogenomics, comparative genomics, NCBI pipelines. For PCR/restriction/cloning use biopython-molecular-biology; for SAM/BAM use pysam.
10