ReviewNature reviews. Chemistry2022
The case for data science in experimental chemistry: examples and recommendations.
Review in Nature reviews. Chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Closed-Loop Solid-State Synthesis Planning for Materials Discovery With Large Language Models.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Data Management and Analysis of Metal-Organic Framework Synthesis Using Data Models.Journal of chemical information and modeling · 2026Article
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- A Perspective intoThe journal of physical chemistry letters · 2026Review
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models.Angewandte Chemie (International ed. in English) · 2025Article
- Neural Network Methods in the Development of MEMS Sensors.Micromachines · 2024Review
- Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation.The journal of physical chemistry. B · 2024Article
- COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems.Scientific data · 2024Article
- Accelerated chemical science with AI.Digital discovery · 2024Review
- Rapid planning and analysis of high-throughput experiment arrays for reaction discovery.Nature communications · 2023Article
- The Materials Provenance Store.Scientific data · 2023Article
- Data storage architectures to accelerate chemical discovery: data accessibility for individual laboratories and the community.Chemical science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.
Identifiers
37117931What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.