{"slug":"company-rd-research-records","title":"Chem / Mat Science R&D records: hypotheses, experiments, results and failed work","summary":"R&D companies can license their dated research records, from early hypotheses and protocols to raw results, failed experiments and the decisions that followed.","category":"science","company_size":"","location":"","deal_min_cents":1000000,"deal_max_cents":30000000,"referral_base_cents":null,"boosters":[],"published_at":"2026-10-31T20:16:08.108106Z","referred_count":null,"status":"live","description":"We're looking for company R&D records that show how scientific work actually gets done: how teams form hypotheses, choose experiments, weigh evidence, catch errors and revise their conclusions. We want the reasoning behind the work, not just the scientific facts or the polished papers.\n\nThe most useful records are linked research episodes. Each one connects the question, the evidence available at the time, the competing explanations, the experiments or analyses chosen, the results and the decisions that followed. Records made at the time are preferred over looking back afterwards. Failed, null, inconclusive, replicated and successful work are all wanted, with outcome labels kept.\n\nWhat we collect:\n- Hypotheses and research decisions: research proposals, dated lab notebooks, experiment plans, decision logs and technical discussions covering alternatives, assumptions and reasons to proceed or stop\n- Experimental design and protocols: versioned protocols, controls, randomization or blinding, sample-size rationale, measurement definitions, equipment settings and protocol deviations\n- Raw observations: instrument outputs, images, assay results, sensor or time-series data and simulation outputs, linked to sample IDs, units, calibration, batches, uncertainty and excluded observations\n- Analysis and reproducibility: notebooks, code, dependencies, preprocessing, statistical models, inclusion and exclusion rules, sensitivity checks and version history\n- Failures and troubleshooting: failed or null experiments, anomalous results, investigation logs, abandoned approaches, corrective changes and linked reruns, including unresolved cases\n- Critique and replication: internal technical reviews, competing interpretations, replication attempts, validation results and before-and-after conclusions\n- Research-program history: project registries, initial forecasts, milestones, resource constraints, stop/go decisions and downstream validation across pursued and terminated projects\n\nPriority domains:\n- Materials, chemistry and batteries\n- Preclinical biology and biotech\n- Computational science and scientific ML\n- Industrial engineering and process R&D\n- Agriculture, environmental and climate R&D\n- Clinical research","history":"","industries":"Materials & chemistry, Batteries, Biotech, Contract research, Scientific software, Manufacturing & process development, Agritech, Environmental & climate, Clinical research","tools":[],"requirements":[],"nice_to_have":[],"included":[],"not_a_fit":[],"scoping_questions":[],"ideal_referrers":"","deal_note":"","intake_form":"general"}