Scientific data articles within Nature Communications

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  • Article
    | Open Access

    Detection of radiation is important for environmental health and safety. Here the authors demonstrate a method for radiation detection and mapping in 2D using minimum number of detectors and inter-pixel padding to increase the contrast between pixels.

    • Ryotaro Okabe
    • , Shangjie Xue
    •  & Mingda Li

  • Article
    | Open Access

    Extracting scientific data from published research is a complex task required specialised tools. Here the authors present a scheme based on large language models to automatise the retrieval of information from text in a flexible and accessible manner.

    • John Dagdelen
    • , Alexander Dunn
    •  & Anubhav Jain

  • Article
    | Open Access

    Reduced-order models provide better understanding for complex spatio-temporal dynamics of fluid flows with high numbers of degrees of freedom and non-linear interactions. The authors propose a variational autoencoder and transformer framework for learning the temporal dynamics of the nonlinear reduced-order models relevant for fluid dynamics, weather forecasting, and biomedical engineering.

    • Alberto Solera-Rico
    • , Carlos Sanmiguel Vila
    •  & Ricardo Vinuesa

  • Article
    | Open Access

    Antibody Mediated Prevention (AMP) trials showed that the broadly neutralizing antibody VRC01 could prevent some HIV-1 acquisitions. Here the authors use VRC01 levels and the sensitivity of each acquired HIV virus to predict viral loads in the AMP studies and show that VRC01 influenced viral loads, though potency was lower in vivo than expected.

    • Daniel B. Reeves
    • , Bryan T. Mayer
    •  & Srilatha Edupuganti

  • Article
    | Open Access

    Networks with higher-order interactions provide better description of social and biological systems, however tools to analyze their function still need to be developed. The authors introduce here a decomposition of network in hyper-cores, that gives better understanding of spreading processes and can be applied to fingerprint real-world datasets.

    • Marco Mancastroppa
    • , Iacopo Iacopini
    •  & Alain Barrat

  • Review Article
    | Open Access

    In this Review article, the authors discuss emerging efforts to build ethical governance frameworks for data science health research in Africa and the opportunities to advance these through investments by African governments and institutions, international funding organizations and collaborations for research and capacity development.

    • Clement A. Adebamowo
    • , Shawneequa Callier
    •  & Sally N. Adebamowo

  • Article
    | Open Access

    Here, the reaction of the suicide inhibitor sulbactam with the M. tuberculosis β-lactamase (BlaC) is investigated with time-resolved crystallography. Singular Value Decomposition is implemented to extract kinetic information despite changes in unit cell parameters during the time-course of the reaction.

    • Tek Narsingh Malla
    • , Kara Zielinski
    •  & Marius Schmidt

  • Article
    | Open Access

    A major challenge in analyzing scRNA-seq data arises from challenges related to dimensionality and the prevalence of dropout events. Here the authors develop a deep graph learning method called scMGCA based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments, outperforming other state-of-the-art models across multiple platforms.

    • Zhuohan Yu
    • , Yanchi Su
    •  & Xiangtao Li

  • Article
    | Open Access

    In this Bayesian inference study, the authors aim to quantify the impact of the men’s 2020 UEFA Euro Football Championship on COVID-19 spread in twelve participating countries. They estimate that 0.84 million cases and 1,700 deaths were attributable to the championship, with most impacts in England and Scotland.

    • Jonas Dehning
    • , Sebastian B. Mohr
    •  & Viola Priesemann

  • Article
    | Open Access

    Recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, the authors introduce methodologies for creating condensed maps of the global dynamics of benchmark.

    • Simon Ott
    • , Adriano Barbosa-Silva
    •  & Matthias Samwald

  • Article
    | Open Access

    The digital transformation and Industry 4.0 technologies are rapidly shaping the future of manufacturing. Here, authors use reliable big data to quantitatively evaluate lubricants performance and select desirable candidates for application in target manufacturing processes.

    • Xiao Yang
    • , Heli Liu
    •  & Liliang Wang

  • Article
    | Open Access

    Accurate forecasts of lithium-ion battery performance will ease concerns about the reliability of electric vehicles. Here, the authors leverage electrochemical impedance spectroscopy and machine learning to show that future capacity can be predicted amid uneven use, with no historical data requirement.

    • Penelope K. Jones
    • , Ulrich Stimming
    •  & Alpha A. Lee

  • Article
    | Open Access

    Current data-driven modelling techniques perform reliably on linear systems or on those that can be linearized. Cenedese et al. develop a data-based reduced modeling method for non-linear, high-dimensional physical systems. Their models reconstruct and predict the dynamics of the full physical system.

    • Mattia Cenedese
    • , Joar Axås
    •  & George Haller

  • Article
    | Open Access

    The ejection sites of the martian meteorites are still unknown. Here, the authors build a database of 90 million craters and show that Tharsis region is the most likely source of depleted shergottites ejected 1.1 Ma ago, thus confirming that some portions of the mantle were recently anomalously hot.

    • A. Lagain
    • , G. K. Benedix
    •  & K. Miljković

  • Article
    | Open Access

    Recovery of underlying governing laws or equations describing the evolution of complex systems from data can be challenging if dataset is damaged or incomplete. The authors propose a learning approach which allows to discover governing partial differential equations from scarce and noisy data.

    • Zhao Chen
    • , Yang Liu
    •  & Hao Sun

  • Article
    | Open Access

    During geomagnetic substorms, the energy accumulated from solar wind is abruptly transported to ionosphere. Here, the authors show application of community detection on the time-varying networks constructed from all magnetometers collaborating with the SuperMAG initiative.

    • L. Orr
    • , S. C. Chapman
    •  & W. Guo

  • Article
    | Open Access

    The process of thin sheet crumpling is characterized by high complexity due to an infinite number of possible configurations. Andrejevic et al. show that ordered behavior can emerge in crumpled sheets, and uncover the correspondence between crumpling and fragmentation processes.

    • Jovana Andrejevic
    • , Lisa M. Lee
    •  & Chris H. Rycroft

  • Article
    | Open Access

    The Tafel slope in electrochemical catalysis is usually determined from experimental data and remains error-prone. Here, the authors develop a Bayesian approach for Tafel slope quantification, and apply it to study the prevalence of certain "cardinal" Tafel slopes in the electrochemical CO2 reduction literature.

    • Aditya M. Limaye
    • , Joy S. Zeng
    •  & Karthish Manthiram

  • Article
    | Open Access

    Accurate cell detection in dense bacterial biofilms is challenging. Here, the authors report an image analysis pipeline that is able to accurately segment and classify single bacterial cells in 3D fluorescence images: Bacterial Cell Morphometry 3D (BCM3D).

    • Mingxing Zhang
    • , Ji Zhang
    •  & Andreas Gahlmann

  • Article
    | Open Access

    The success of machine learning for scientific discovery normally depends on how well the inherent assumptions match the problem in hand. Here, Thiagarajan et al. alleviate this constraint by allowing the change of optimization criterion in a data-driven approach to emulate complex scientific processes.

    • Jayaraman J. Thiagarajan
    • , Bindya Venkatesh
    •  & Brian Spears

  • Article
    | Open Access

    Official data on the distribution of human population often ignores the changing spatio-temporal densities resulting from mobility. Here, authors apply an approach combining official statistics and geospatial data to assess intraday and monthly population variations at continental scale at 1 km2 resolution.

    • Filipe Batista e Silva
    • , Sérgio Freire
    •  & Carlo Lavalle

  • Article
    | Open Access

    The demands on transportation systems continue to grow while the methods for analyzing and forecasting traffic conditions remain limited. Here the authors show a parameter-independent approach for an accurate description, identification and forecasting of spatio-temporal traffic patterns directly from data.

    • A. M. Avila
    •  & I. Mezić

  • Article
    | Open Access

    The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.

    • Xavier Domingo-Almenara
    • , Carlos Guijas
    •  & Gary Siuzdak

  • Article
    | Open Access

    The incomplete nature and undefined structure of the existing catalysis research data has prevented comprehensive knowledge extraction. Here, the authors report a novel meta-analysis method that identifies correlations between a catalyst’s physico-chemical properties and its performance in a particular reaction.

    • Roman Schmack
    • , Alexandra Friedrich
    •  & Ralph Kraehnert

  • Article
    | Open Access

    Nanoparticle applications are limited by insufficient understanding of physiochemical properties on in vivo disposition. Here, the authors explore the influence of size, surface chemistry and administration on the biodisposition of mesoporous silica nanoparticles using image-based pharmacokinetics.

    • Prashant Dogra
    • , Natalie L. Adolphi
    •  & C. Jeffrey Brinker

  • Article
    | Open Access

    Systematic changes in stock market prices or in the migration behaviour of cancer cells may be hidden behind random fluctuations. Here, Mark et al. describe an empirical approach to identify when and how such real-world systems undergo systematic changes.

    • Christoph Mark
    • , Claus Metzner
    •  & Ben Fabry

  • Article
    | Open Access

    While automated reaction systems typically work for the synthesis of pre-defined molecules, automated systems to discover reactivity are more challenging. Here the authors report an autonomous organic reaction search engine that allows discovery of the most reactive pathways in a multi-reagent, multistep reaction system.

    • Vincenza Dragone
    • , Victor Sans
    •  & Leroy Cronin

  • Article
    | Open Access

    The huge amount of data generated in fields like neuroscience or finance calls for effective strategies that mine data to reveal underlying dynamics. Here Brunton et al.develop a data-driven technique to analyze chaotic systems and predict their dynamics in terms of a forced linear model.

    • Steven L. Brunton
    • , Bingni W. Brunton
    •  & J. Nathan Kutz

  • Article
    | Open Access

    Localisation microscopy enables nanometre-scale imaging of biological samples, but the method is too slow to use on dynamic systems. Here, the authors develop a mathematical model that optimises the number of frames required and estimates the maximum speed for super-resolution imaging.

    • Patrick Fox-Roberts
    • , Richard Marsh
    •  & Susan Cox

  • Article
    | Open Access

    Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.

    • Kristof T. Schütt
    • , Farhad Arbabzadah
    •  & Alexandre Tkatchenko

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