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Yu H, Cao W, Fang T, Jin J, Pei G. EEG β oscillations in aberrant data perception under cognitive load modulation. Sci Rep 2024; 14:22995. [PMID: 39362975 PMCID: PMC11450174 DOI: 10.1038/s41598-024-74381-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 09/25/2024] [Indexed: 10/05/2024] Open
Abstract
Data-driven decision making (DDDM) is becoming an indispensable component of work across various fields, and the perception of aberrant data (PAD) has emerged as an essential skill. Nonetheless, the neural processing mechanisms underpinning PAD remain incompletely elucidated. Direct evidence linking neural oscillations to PAD is currently lacking, and the impact of cognitive load remains ambiguous. We address this issue using EEG time-frequency analysis. Data were collected from 21 healthy participants. The experiment employed a 2 (low vs. high cognitive load) × 2 [PAD+ (aberrant data accurately identified as aberrant) vs. PAD- (non-aberrant data correctly recognized as normal)] within-subject laboratory design. Results indicate that upper β band oscillations (26-30 Hz) were significantly enhanced in the PAD + condition compared to PAD-, with consistent activity observed in the frontal (p < 0.001, [Formula: see text] = 0.41) and parietal lobes (p = 0.028, [Formula: see text] = 0.22) within the 300-350 ms time window. Additionally, as cognitive load increased, the time window of β oscillations for distinguishing PAD+ from PAD- shifted earlier. This study enriches our understanding of the PAD neural basis by exploring the distribution of neural oscillation frequencies, decision-making neural circuits, and the windowing effect induced by cognitive load. These findings have significant implications for elucidating the pathological mechanisms of neurodegenerative disorders, as well as in the initial screening, intervention, and treatment of diseases.
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Affiliation(s)
- Haihong Yu
- Maritime School, Ningbo University, Ningbo, China
- School of Economics and Management, Ningbo University of Technology, Ningbo, China
| | - Wei Cao
- Maritime School, Ningbo University, Ningbo, China
| | - Tie Fang
- Maritime School, Ningbo University, Ningbo, China
| | - Jia Jin
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, 550# Dalian West Road, Shanghai, 200083, China.
| | - Guanxiong Pei
- Zhejiang Laboratory of Philosophy and Social Sciences - Laboratory of Intelligent Society and Governance, Zhejiang Lab, 1818# Wenyixi Road, Hangzhou, 311121, China.
- Development Strategy and Cooperation Center, Zhejiang Lab, Hangzhou, China.
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2
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Krauss A. Science of science: A multidisciplinary field studying science. Heliyon 2024; 10:e36066. [PMID: 39296115 PMCID: PMC11408022 DOI: 10.1016/j.heliyon.2024.e36066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 07/24/2024] [Accepted: 08/08/2024] [Indexed: 09/21/2024] Open
Abstract
Science and knowledge are studied by researchers across many disciplines, examining how they are developed, what their current boundaries are and how we can advance them. By integrating evidence across disparate disciplines, the holistic field of science of science can address these foundational questions. This field illustrates how science is shaped by many interconnected factors: the cognitive processes of scientists, the historical evolution of science, economic incentives, institutional influences, computational approaches, statistical, mathematical and instrumental foundations of scientific inference, scientometric measures, philosophical and ethical dimensions of scientific concepts, among other influences. Achieving a comprehensive overview of a multifaceted field like the science of science requires pulling together evidence from the many sub-fields studying science across the natural and social sciences and humanities. This enables developing an interdisciplinary perspective of scientific practice, a more holistic understanding of scientific processes and outcomes, and more nuanced perspectives to how scientific research is conducted, influenced and evolves. It enables leveraging the strengths of various disciplines to create a holistic view of the foundations of science. Different researchers study science from their own disciplinary perspective and use their own methods, and there is a large divide between quantitative and qualitative researchers as they commonly do not read or cite research using other methodological approaches. A broader, synthesizing paper employing a qualitative approach can however help provide a bridge between disciplines by pulling together aspects of science (economic, scientometric, psychological, philosophical etc.). Such an approach enables identifying, across the range of fields, the powerful role of our scientific methods and instruments in shaping most aspects of our knowledge and science, whereas economic, social and historical influences help shape what knowledge we pursue. A unifying theory is then outlined for science of science - the new-methods-drive-science theory.
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Affiliation(s)
- Alexander Krauss
- London School of Economics, London, UK
- Institute for Economic Analysis, Spanish National Research Council, Barcelona, Spain
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3
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2023 Beijing Health Data Science Summit. HEALTH DATA SCIENCE 2024; 4:0112. [PMID: 38854991 PMCID: PMC11157085 DOI: 10.34133/hds.0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/05/2023] [Indexed: 06/11/2024]
Abstract
The 5th annual Beijing Health Data Science Summit, organized by the National Institute of Health Data Science at Peking University, recently concluded with resounding success. This year, the summit aimed to foster collaboration among researchers, practitioners, and stakeholders in the field of health data science to advance the use of data for better health outcomes. One significant highlight of this year's summit was the introduction of the Abstract Competition, organized by Health Data Science, a Science Partner Journal, which focused on the use of cutting-edge data science methodologies, particularly the application of artificial intelligence in the healthcare scenarios. The competition provided a platform for researchers to showcase their groundbreaking work and innovations. In total, the summit received 61 abstract submissions. Following a rigorous evaluation process by the Abstract Review Committee, eight exceptional abstracts were selected to compete in the final round and give presentations in the Abstract Competition. The winners of the Abstract Competition are as follows:•First Prize: "Interpretable Machine Learning for Predicting Outcomes of Childhood Kawasaki Disease: Electronic Health Record Analysis" presented by researchers from the Chinese Academy of Medical Sciences, Peking Union Medical College, and Chongqing Medical University (presenter Yifan Duan).•Second Prize: "Survival Disparities among Mobility Patterns of Patients with Cancer: A Population-Based Study" presented by a team from Peking University (presenter Fengyu Wen).•Third Prize: "Deep Learning-Based Real-Time Predictive Model for the Development of Acute Stroke" presented by researchers from Beijing Tiantan Hospital (presenter Lan Lan). We extend our heartfelt gratitude to the esteemed panel of judges whose expertise and dedication ensured the fairness and quality of the competition. The judging panel included Jiebo Luo from the University of Rochester (chair), Shenda Hong from Peking University, Xiaozhong Liu from Worcester Polytechnic Institute, Liu Yang from Hong Kong Baptist University, Ma Jianzhu from Tsinghua University, Ting Ma from Harbin Institute of Technology, and Jian Tang from Mila-Quebec Artificial Intelligence Institute. We wish to convey our deep appreciation to Zixuan He and Haoyang Hong for their invaluable assistance in the meticulous planning and execution of the event. As the 2023 Beijing Health Data Science Summit comes to a close, we look forward to welcoming all participants to join us in 2024. Together, we will continue to advance the frontiers of health data science and work toward a healthier future for all.
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Danelakis A, Langseth H, Nachev P, Nelson A, Bjørk MH, Matharu MS, Tronvik E, May A, Stubberud A. What predicts citation counts and translational impact in headache research? A machine learning analysis. Cephalalgia 2024; 44:3331024241251488. [PMID: 38690640 DOI: 10.1177/03331024241251488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
BACKGROUND We aimed to develop the first machine learning models to predict citation counts and the translational impact, defined as inclusion in guidelines or policy documents, of headache research, and assess which factors are most predictive. METHODS Bibliometric data and the titles, abstracts, and keywords from 8600 publications in three headache-oriented journals from their inception to 31 December 2017 were used. A series of machine learning models were implemented to predict three classes of 5-year citation count intervals (0-5, 6-14 and, >14 citations); and the translational impact of a publication. Models were evaluated out-of-sample with area under the receiver operating characteristics curve (AUC). RESULTS The top performing gradient boosting model predicted correct citation count class with an out-of-sample AUC of 0.81. Bibliometric data such as page count, number of references, first and last author citation counts and h-index were among the most important predictors. Prediction of translational impact worked optimally when including both bibliometric data and information from the title, abstract and keywords, reaching an out-of-sample AUC of 0.71 for the top performing random forest model. CONCLUSION Citation counts are best predicted by bibliometric data, while models incorporating both bibliometric data and publication content identifies the translational impact of headache research.
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Affiliation(s)
- Antonios Danelakis
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Computer Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Helge Langseth
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Computer Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Parashkev Nachev
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Amy Nelson
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Marte-Helene Bjørk
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Manjit S Matharu
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Headache and Facial Pain Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
| | - Erling Tronvik
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Arne May
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Anker Stubberud
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
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5
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Wu T, Gao X, An F, Sun X, An H, Su Z, Gupta S, Gao J, Kurths J. Predicting multiple observations in complex systems through low-dimensional embeddings. Nat Commun 2024; 15:2242. [PMID: 38472208 PMCID: PMC10933326 DOI: 10.1038/s41467-024-46598-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 03/04/2024] [Indexed: 03/14/2024] Open
Abstract
Forecasting all components in complex systems is an open and challenging task, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework, namely, feature-and-reconstructed manifold mapping (FRMM), which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon, electroencephalogram (EEG) signals, foreign exchange market, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor, and thus has potential for applications in many other real-world systems.
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Affiliation(s)
- Tao Wu
- College of Management Science, Chengdu University of Technology, Chengdu, 610059, China
| | - Xiangyun Gao
- School of Economics and Management, China University of Geosciences, Beijing, 100083, China.
- Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resources, Beijing, 100083, China.
| | - Feng An
- School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China.
| | - Xiaotian Sun
- School of Economics and Management, China University of Geosciences, Beijing, 100083, China
| | - Haizhong An
- School of Economics and Management, China University of Geosciences, Beijing, 100083, China
- Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resources, Beijing, 100083, China
| | - Zhen Su
- Potsdam Institute for Climate Impact Research (PIK)-Member of the Leibniz Association, Potsdam, 14473, Germany
- Department of Computer Science, Humboldt University at Berlin, Berlin, 12489, Germany
| | - Shraddha Gupta
- Potsdam Institute for Climate Impact Research (PIK)-Member of the Leibniz Association, Potsdam, 14473, Germany
- Department of Physics, Humboldt University at Berlin, Berlin, 12489, Germany
| | - Jianxi Gao
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
- Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research (PIK)-Member of the Leibniz Association, Potsdam, 14473, Germany.
- Department of Physics, Humboldt University at Berlin, Berlin, 12489, Germany.
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6
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Jin J, Hu P, Song H, Li J, Wu J, Zeng Z, Li Q, Wang L, Lin X, Tan X. Highly sensitive and repeatable recording photopolymer for holographic data storage containing N-methylpyrrolidone. MATERIALS HORIZONS 2024; 11:930-938. [PMID: 38093700 DOI: 10.1039/d3mh01729j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The low photosensitivity of phenanthraquinone-doped poly(methyl methacrylate) (PQ/PMMA) severely limits its recording speed for holographic data storage. A high-performance holographic recording medium based on a unique combination of N-methylpyrrolidone (NMP) regulated PQ/PMMA has been developed. A NMP-PQ/PMMA photopolymer with high sensitivity, high diffraction efficiency and negligible volume shrinkage was successfully fabricated by tuning the composition of the PMMA matrix by varying the ratio of NMP to monomers. The photosensitivity is increased by 6.9 times (from 0.27 cm J-1 to 1.86 cm J-1), the diffraction efficiency is increased from 60% to > 80%, and volume shrinkage is decreased by a factor of 2 (from 0.4% to 0.2%). Further investigation revealed that the addition of NMP significantly reduced the molecular weight of PMMA and increased the amount of MMA residuals, while also improving the solubility of PQ molecules. More interestingly, for the first time, the NMP-PQ/PMMA material could record data information repeatedly at least 6 times. The present study elucidates that the introduction of NMP not only modulates the molecular weight of PMMA but also enables the residual monomer MMA to more easily combine with PQ to form a photoproduct for improved holographic performance.
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Affiliation(s)
- Junchao Jin
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
| | - Po Hu
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
- Henan Provincial Key Laboratory of Intelligent Lighting, Huanghuai University, Zhumadian 463000, China
| | - Haiyang Song
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
| | - Jinhong Li
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
| | - Junhui Wu
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
| | - Zeyi Zeng
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
| | - Qingdong Li
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
| | - Li Wang
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
| | - Xiao Lin
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
| | - Xiaodi Tan
- College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China.
- Information Photonics Research Center, Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350117, China.
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7
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Krauss A, Danús L, Sales-Pardo M. Early-career factors largely determine the future impact of prominent researchers: evidence across eight scientific fields. Sci Rep 2023; 13:18794. [PMID: 37914796 PMCID: PMC10620415 DOI: 10.1038/s41598-023-46050-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 10/26/2023] [Indexed: 11/03/2023] Open
Abstract
Can we help predict the future impact of researchers using early-career factors? We analyze early-career factors of the world's 100 most prominent researchers across 8 scientific fields and identify four key drivers in researchers' initial career: working at a top 25 ranked university, publishing a paper in a top 5 ranked journal, publishing most papers in top quartile (high-impact) journals and co-authoring with other prominent researchers in their field. We find that over 95% of prominent researchers across multiple fields had at least one of these four features in the first 5 years of their career. We find that the most prominent scientists who had an early career advantage in terms of citations and h-index are more likely to have had all four features, and that this advantage persists throughout their career after 10, 15 and 20 years. Our findings show that these few early-career factors help predict researchers' impact later in their careers. Our research thus points to the need to enhance fairness and career mobility among scientists who have not had a jump start early on.
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Affiliation(s)
- Alexander Krauss
- London School of Economics, London, UK.
- Institute for Economic Analysis, Spanish National Research Council, Barcelona, Spain.
| | - Lluís Danús
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain
| | - Marta Sales-Pardo
- Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Spain.
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8
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Santamaria-Garcia H, Moguilner S, Rodriguez-Villagra OA, Botero-Rodriguez F, Pina-Escudero SD, O'Donovan G, Albala C, Matallana D, Schulte M, Slachevsky A, Yokoyama JS, Possin K, Ndhlovu LC, Al-Rousan T, Corley MJ, Kosik KS, Muniz-Terrera G, Miranda JJ, Ibanez A. The impacts of social determinants of health and cardiometabolic factors on cognitive and functional aging in Colombian underserved populations. GeroScience 2023; 45:2405-2423. [PMID: 36849677 PMCID: PMC10651610 DOI: 10.1007/s11357-023-00755-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 02/14/2023] [Indexed: 03/01/2023] Open
Abstract
Global initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (N = 23,694; M = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.
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Affiliation(s)
- Hernando Santamaria-Garcia
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA.
- Pontificia Universidad Javeriana (Ph.D. Program in Neuroscience, Department of Psychiatry), Bogotá, Colombia.
- Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia.
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, and CONICET, Buenos Aires, Argentina
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Felipe Botero-Rodriguez
- Pontificia Universidad Javeriana (Ph.D. Program in Neuroscience, Department of Psychiatry), Bogotá, Colombia
| | - Stefanie Danielle Pina-Escudero
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Gary O'Donovan
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
- Facultad de Medicina, Universidad de los Andes, Bogotá, Colombia
| | - Cecilia Albala
- Instituto de Nutrición Y Tecnología de los Alimentos, Universidad de Chile, Avenida El Líbano 5524, Macul, Santiago, Chile
| | - Diana Matallana
- Pontificia Universidad Javeriana (Ph.D. Program in Neuroscience, Department of Psychiatry), Bogotá, Colombia
- Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Mental Health Department, Hospital Universitario Fundación Santa Fe de Bogotá, Memory Clinic, Bogotá, Colombia
| | - Michael Schulte
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Andrea Slachevsky
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopathology Department - Institute of Biomedical Sciences (ICBM), Neurocience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago de Chile, Chile
- Geroscience Center for Brain Health and Metabolism, (GERO), Santiago de Chile, Chile
- Memory and Neuropsychiatric Center (CMYN), Memory Unit - Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago de Chile, Chile
- Servicio de Neurología, Departamento de Medicina, Clínica Alemana-Universidad del Desarrollo, Santiago de Chile, Chile
| | - Jennifer S Yokoyama
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Katherine Possin
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Lishomwa C Ndhlovu
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Tala Al-Rousan
- Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA
| | - Michael J Corley
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York, NY, USA
| | - Kenneth S Kosik
- Neuroscience Research Institute. Department of Molecular Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, UK
- Department of Primary Care, Ohio University, Athens, USA
| | - J Jaime Miranda
- CRONICAS Center of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
- Department of Medicine, School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
- The George Institute for Global Health, UNSW, Sydney, Australia
| | - Agustin Ibanez
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA.
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile.
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, and CONICET, Buenos Aires, Argentina.
- Trinity College Dublin (TCD), Dublin, Ireland.
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9
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Brito ACM, Oliveira MCF, Oliveira ON, Silva FN, Amancio DR. Network Analysis and Natural Language Processing to Obtain a Landscape of the Scientific Literature on Materials Applications. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 37270838 DOI: 10.1021/acsami.3c01632] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Recent progress in natural language processing (NLP) enables mining the literature in various tasks akin to knowledge discovery. Obtaining an updated birds-eye view of key research topics and their evolution in a vast, dynamic field such as materials science is challenging even for experienced researchers. In this Perspective paper, we present a landscape of the area of applied materials in selected representative journals based on a combination of methods from network science and simple NLP strategies. We found a predominance of energy-related materials, e.g., for batteries and catalysis, organic electronics, which include flexible sensors and flexible electronics, and nanomedicine with various topics of materials used in diagnosis and therapy. As for the impact calculated through standard metrics of impact factor, energy-related materials and organic electronics are again top of the list across different journals, while work in nanomedicine has been found to have a lower impact in the journals analyzed. The adequacy of the approach to identify key research topics in materials applications was verified indirectly by comparing the topics identified in journals with diverse scopes, including journals that are not specific to materials. The approach can be employed to obtain a fast overview of a given field from the papers published in related scientific journals, which can be adapted or extended to any research area.
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Affiliation(s)
- Ana Caroline M Brito
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo 13560-970, Brazil
| | - Maria Cristina F Oliveira
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo 13560-970, Brazil
| | - Osvaldo N Oliveira
- São Carlos Institute of Physics, University of São Paulo, Sao Carlos, São Paulo 13560-970, Brazil
| | - Filipi N Silva
- Indiana University Network Science Institute, Bloomington, Indiana 47408, United States
| | - Diego R Amancio
- Institute of Mathematical Sciences and Computing, University of São Paulo, São Carlos, São Paulo 13566-590, Brazil
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10
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Lin Z, Yin Y, Liu L, Wang D. SciSciNet: A large-scale open data lake for the science of science research. Sci Data 2023; 10:315. [PMID: 37264014 DOI: 10.1038/s41597-023-02198-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 05/02/2023] [Indexed: 06/03/2023] Open
Abstract
The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field.
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Affiliation(s)
- Zihang Lin
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- School of Computer Science, Fudan University, Shanghai, China
| | - Yian Yin
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Lu Liu
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
| | - Dashun Wang
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA.
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA.
- Kellogg School of Management, Northwestern University, Evanston, IL, USA.
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
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11
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Liu L, Jones BF, Uzzi B, Wang D. Data, measurement and empirical methods in the science of science. Nat Hum Behav 2023:10.1038/s41562-023-01562-4. [PMID: 37264084 DOI: 10.1038/s41562-023-01562-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 02/17/2023] [Indexed: 06/03/2023]
Abstract
The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding 'science of science'. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field's diverse methodologies and expand researchers' toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.
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Affiliation(s)
- Lu Liu
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA
| | - Benjamin F Jones
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- National Bureau of Economic Research, Cambridge, MA, USA
- Brookings Institution, Washington, DC, USA
| | - Brian Uzzi
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
| | - Dashun Wang
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA.
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA.
- Kellogg School of Management, Northwestern University, Evanston, IL, USA.
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
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12
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Weinberger M, Zhitomirsky-Geffet M. Modeling a successful citation trajectory structure for scholar's impact evaluation in Israeli academia. Heliyon 2023; 9:e15673. [PMID: 37159699 PMCID: PMC10163662 DOI: 10.1016/j.heliyon.2023.e15673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/11/2023] Open
Abstract
One of the main concerns of researchers and institutions is how to assess the future performance of scholars and identify their potential to become successful scientists. In this study, we model scholarly success in terms of the probability of a scholar belonging to a group of highly impactful scholars as determined by their citation trajectory structures. To this end, we developed a new set of impact measures based on a scholar's citation trajectory structure (rather than on absolute citation or h-index rates), that show a stable trend and scale for highly impactful scholars, independent of their field of study, seniority and citation index. These measures were then incorporated as influence factors into the logistic regression models and used as features for probabilistic classifiers based on these models to identify the successful scholars in the heterogeneous corpus of 400 of most and least cited professors from two Israeli universities. From the practical point of view, the study may yield useful insights and serve as an aid in making promotion decisions by institutions, as well as a self-assessment tool for researchers who strive to increase their academic influence and become leaders in their field.
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13
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Shi F, Evans J. Surprising combinations of research contents and contexts are related to impact and emerge with scientific outsiders from distant disciplines. Nat Commun 2023; 14:1641. [PMID: 36964138 PMCID: PMC10039062 DOI: 10.1038/s41467-023-36741-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 02/15/2023] [Indexed: 03/26/2023] Open
Abstract
We investigate the degree to which impact in science and technology is associated with surprising breakthroughs, and how those breakthroughs arise. Identifying breakthroughs across science and technology requires models that distinguish surprising from expected advances at scale. Drawing on tens of millions of research papers and patents across the life sciences, physical sciences and patented inventions, and using a hypergraph model that predicts realized combinations of research contents (article keywords) and contexts (cited journals), here we show that surprise in terms of unexpected combinations of contents and contexts predicts outsized impact (within the top 10% of citations). These surprising advances emerge across, rather than within researchers or teams-most commonly when scientists from one field publish problem-solving results to an audience from a distant field. Our approach characterizes the frontier of science and technology as a complex hypergraph drawn from high-dimensional embeddings of research contents and contexts, and offers a measure of path-breaking surprise in science and technology.
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Affiliation(s)
- Feng Shi
- TigerGraph, 3 Twin Dolphin Dr, St. 225, Redwood City, CA, 94065, USA
- Knowledge Lab, University of Chicago, 1155 E. 60th Street #211, Chicago, IL, 60637, USA
| | - James Evans
- Knowledge Lab, University of Chicago, 1155 E. 60th Street #211, Chicago, IL, 60637, USA.
- Department of Sociology, University of Chicago, 1126 E. 59th St. #420, Chicago, IL, 60637, USA.
- Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, 87501, USA.
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14
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Impact of field of study (FoS) on authors’ citation trend. Scientometrics 2023. [DOI: 10.1007/s11192-023-04660-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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15
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Krenn M, Pollice R, Guo SY, Aldeghi M, Cervera-Lierta A, Friederich P, dos Passos Gomes G, Häse F, Jinich A, Nigam A, Yao Z, Aspuru-Guzik A. On scientific understanding with artificial intelligence. NATURE REVIEWS. PHYSICS 2022; 4:761-769. [PMID: 36247217 PMCID: PMC9552145 DOI: 10.1038/s42254-022-00518-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/30/2022] [Indexed: 05/27/2023]
Abstract
An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of 'scientific understanding' from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field.
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Affiliation(s)
- Mario Krenn
- Max Planck Institute for the Science of Light (MPL), Erlangen, Germany
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
| | - Robert Pollice
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Si Yue Guo
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
| | - Matteo Aldeghi
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
| | - Alba Cervera-Lierta
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Pascal Friederich
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Gabriel dos Passos Gomes
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Florian Häse
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA USA
| | - Adrian Jinich
- Division of Infectious Diseases, Weill Department of Medicine, Weill Cornell Medical College, New York, USA
| | - AkshatKumar Nigam
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
| | - Zhenpeng Yao
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Ontario Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario Canada
- Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow, Toronto, Ontario Canada
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16
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Zhu J, Chen M, Liang H, Li Q, Jiang X. Research strategies in click chemistry: Measuring its cognitive contents and knowledge flow. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.107936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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17
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Li Q, Wang X, Fu L, Wang J, Yao L, Gan X, Zhou C. Scientific X-ray: Scanning and quantifying the idea evolution of scientific publications. PLoS One 2022; 17:e0275192. [PMID: 36170296 PMCID: PMC9518912 DOI: 10.1371/journal.pone.0275192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 09/12/2022] [Indexed: 11/24/2022] Open
Abstract
The rapid development of modern science nowadays makes it rather challenging to pick out valuable ideas from massive scientific literature. Existing widely-adopted citation-based metrics are not adequate for measuring how well the idea presented by a single publication is developed and whether it is worth following. Here, inspired by traditional X-ray imaging, which returns internal structure imaging of real objects along with corresponding structure analysis, we propose Scientific X-ray, a framework that quantifies the development degree and development potential for any scientific idea through an assembly of 'X-ray' scanning, visualization and parsing operated on the citation network associated with a target publication. We pick all 71,431 scientific articles of citation counts over 1,000 as high-impact target publications among totally 204,664,199 publications that cover 16 disciplines spanning from 1800 to 2021. Our proposed Scientific X-ray reproduces how an idea evolves from the very original target publication all the way to the up to date status via an extracted 'idea tree' that attempts to preserve the most representative idea flow structure underneath each citation network. Interestingly, we observe that while the citation counts of publications may increase unlimitedly, the maximum valid idea inheritance of those target publications, i.e., the valid depth of the idea tree, cannot exceed a limit of six hops, and the idea evolution structure of any arbitrary publication unexceptionally falls into six fixed patterns. Combined with a development potential index that we further design based on the extracted idea tree, Scientific X-ray can vividly tell how further a given idea presented by a given publication can still go from any well-established starting point. Scientific X-ray successfully identifies 40 out of 49 topics of Nobel prize as high-potential topics by their prize-winning papers in an average of nine years before the prizes are released. Various trials on articles of diverse topics also confirm the power of Scientific X-ray in digging out influential/promising ideas. Scientific X-ray is user-friendly to researchers with any level of expertise, thus providing important basis for grasping research trends, helping scientific policy-making and even promoting social development.
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Affiliation(s)
- Qi Li
- Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xinbing Wang
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Luoyi Fu
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jianghao Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Ling Yao
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Xiaoying Gan
- Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chenghu Zhou
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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18
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Li X, Liu X, Deng X, Fan Y. Interplay between Artificial Intelligence and Biomechanics Modeling in the Cardiovascular Disease Prediction. Biomedicines 2022; 10:2157. [PMID: 36140258 PMCID: PMC9495955 DOI: 10.3390/biomedicines10092157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/26/2022] [Accepted: 08/28/2022] [Indexed: 11/16/2022] Open
Abstract
Cardiovascular disease (CVD) is the most common cause of morbidity and mortality worldwide, and early accurate diagnosis is the key point for improving and optimizing the prognosis of CVD. Recent progress in artificial intelligence (AI), especially machine learning (ML) technology, makes it possible to predict CVD. In this review, we first briefly introduced the overview development of artificial intelligence. Then we summarized some ML applications in cardiovascular diseases, including ML-based models to directly predict CVD based on risk factors or medical imaging findings and the ML-based hemodynamics with vascular geometries, equations, and methods for indirect assessment of CVD. We also discussed case studies where ML could be used as the surrogate for computational fluid dynamics in data-driven models and physics-driven models. ML models could be a surrogate for computational fluid dynamics, accelerate the process of disease prediction, and reduce manual intervention. Lastly, we briefly summarized the research difficulties and prospected the future development of AI technology in cardiovascular diseases.
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Affiliation(s)
- Xiaoyin Li
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Xiao Liu
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Xiaoyan Deng
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
| | - Yubo Fan
- Beijing Advanced Innovation Centre for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China
- School of Engineering Medicine, Beihang University, Beijing 100083, China
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19
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Li H, Wu M, Wang Y, Zeng A. Bibliographic coupling networks reveal the advantage of diversification in scientific projects. J Informetr 2022. [DOI: 10.1016/j.joi.2022.101321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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20
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Ram SK, Nandan S, Sornette D. Significant hot hand effect in the game of cricket. Sci Rep 2022; 12:11663. [PMID: 35803977 PMCID: PMC9270381 DOI: 10.1038/s41598-022-14980-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 05/16/2022] [Indexed: 11/09/2022] Open
Abstract
We investigate the predictability and persistence of individual and team performance (hot-hand effect) by analyzing the complete recorded history of international cricket. We introduce an original temporal representation of performance streaks, which is suitable to be modelled as a self-exciting point process. We confirm the presence of predictability and hot-hands across the individual performance and the absence of the same in team performance and game outcome. Thus, Cricket is a game of skill for individuals and a game of chance for the teams. Our study contributes to recent historiographical debates concerning the presence of persistence in individual and collective productivity and success. The introduction of several metrics and methods can be useful to test and exploit clustering of performance in the study of human behavior and design of algorithms for predicting success.
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Affiliation(s)
- Sumit Kumar Ram
- Connection Science, Massachusetts Institute of Technology, Cambridge, USA.
- Department of Management, Technology and Economics, ETH Zürich, Scheuchzerstrasse 7, 8092, Zurich, Switzerland.
| | - Shyam Nandan
- Swiss Seismological Service, ETH Zürich, Sonneggstrasse 5, 8092, Zurich, Switzerland
| | - Didier Sornette
- Department of Management, Technology and Economics, ETH Zürich, Scheuchzerstrasse 7, 8092, Zurich, Switzerland.
- Institute of Risk Analysis, Prediction and Management (Risks-X), Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology (SUSTech), Shenzhen, China.
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21
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Fu C, Yue X, Shen B, Yu S, Min Y. Patterns of interest change in stack overflow. Sci Rep 2022; 12:11466. [PMID: 35794248 PMCID: PMC9259656 DOI: 10.1038/s41598-022-15724-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 06/28/2022] [Indexed: 12/02/2022] Open
Abstract
Stack Overflow is currently the largest programming related question and answer community, containing multiple programming areas. The change of user's interest is the micro-representation of the intersection of macro-knowledge and has been widely studied in scientific fields, such as literature data sets. However, there is still very little research for the general public, such as the question and answer community. Therefore, we analyze the interest changes of 2,307,720 users in Stack Overflow in this work. Specifically, we classify the tag network in the community, vectorize the topic of questions to quantify the user's interest change patterns. Results show that the change pattern of user interest has the characteristic of a power-law distribution, which is different from the exponential distribution of scientists' interest change, but they are all affected by three features, heterogeneity, recency and proximity. Furthermore, the relationship between users' reputations and interest changes is negatively correlated, suggesting the importance of concentration, i.e., those who focus on specific areas are more likely to gain a higher reputation. In general, our work is a supplement to the public interest changes in science, and it can also help community managers better design recommendation algorithms and promote the healthy development of communities.
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Affiliation(s)
- Chenbo Fu
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, 310023, China.
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
| | - Xinchen Yue
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, 310023, China
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Bin Shen
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, 310023, China
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Shanqing Yu
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, 310023, China
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Yong Min
- Computational Communication Research Center, Beijing Normal University, Zhuhai, 519087, China
- School of Journalism and Communication, Beijing Normal University, Beijing, 100875, China
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22
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Hotness prediction of scientific topics based on a bibliographic knowledge graph. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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23
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Abstract
Scientific retraction has been on the rise recently. Retracted papers are frequently discussed online, enabling the broad dissemination of potentially flawed findings. Our analysis spans a nearly 10-y period and reveals that most papers exhaust their attention by the time they get retracted, meaning that retractions cannot curb the online spread of problematic papers. This is striking as we also find that retracted papers are pervasive across mediums, receiving more attention after publication than nonretracted papers even on curated platforms, such as news outlets and knowledge repositories. Interestingly, discussions on social media express more criticism toward subsequently retracted results and may thus contain early signals related to unreliable work. Retracted papers often circulate widely on social media, digital news, and other websites before their official retraction. The spread of potentially inaccurate or misleading results from retracted papers can harm the scientific community and the public. Here, we quantify the amount and type of attention 3,851 retracted papers received over time in different online platforms. Comparing with a set of nonretracted control papers from the same journals with similar publication year, number of coauthors, and author impact, we show that retracted papers receive more attention after publication not only on social media but also, on heavily curated platforms, such as news outlets and knowledge repositories, amplifying the negative impact on the public. At the same time, we find that posts on Twitter tend to express more criticism about retracted than about control papers, suggesting that criticism-expressing tweets could contain factual information about problematic papers. Most importantly, around the time they are retracted, papers generate discussions that are primarily about the retraction incident rather than about research findings, showing that by this point, papers have exhausted attention to their results and highlighting the limited effect of retractions. Our findings reveal the extent to which retracted papers are discussed on different online platforms and identify at scale audience criticism toward them. In this context, we show that retraction is not an effective tool to reduce online attention to problematic papers.
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24
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Co-Authorship Networks Analysis to Discover Collaboration Patterns among Italian Researchers. FUTURE INTERNET 2022. [DOI: 10.3390/fi14060187] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
The study of the behaviors of large community of researchers and what correlations exist between their environment, such as grouping rules by law or specific institution policies, and their performance is an important topic since it affects the metrics used to evaluate the quality of the research. Moreover, in several countries, such as Italy, these metrics are also used to define the recruitment and funding policies. To effectively study these topics, we created a procedure that allow us to craft a large dataset of Italian Academic researchers, having the most important performance indices together with co-authorships information, mixing data extracted from the official list of academic researchers provided by Italian Ministry of University and Research and the Elsevier’s Scopus database. In this paper, we discuss our approach to automate the process of correct association of profiles and the mapping of publications reducing the use of computational resources. We also present the characteristics of four datasets related to specific research fields defined by the Italian Ministry of University and Research used to group the Italian researchers. Then, we present several examples of how the information extracted from these datasets can help to achieve a better understanding of the dynamics influencing scientist performances.
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25
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Shi Z, Liu P, Liao X, Mao Z, Zhang J, Wang Q, Sun J, Ma H, Ma Y. Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing. BIODESIGN RESEARCH 2022; 2022:9898461. [PMID: 37850146 PMCID: PMC10521697 DOI: 10.34133/2022/9898461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/26/2022] [Indexed: 10/19/2023] Open
Abstract
Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.
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Affiliation(s)
- Zhenkun Shi
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Pi Liu
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Xiaoping Liao
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Zhitao Mao
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Jianqi Zhang
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Qinhong Wang
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Jibin Sun
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Hongwu Ma
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
| | - Yanhe Ma
- Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Technology Innovation Center of Synthetic Biology, Tianjin 300308China
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26
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Reframing evidence in evidence-based policy making and role of bibliometrics: toward transdisciplinary scientometric research. Scientometrics 2022. [DOI: 10.1007/s11192-022-04325-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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27
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Hu P, Li J, Jin J, Lin X, Tan X. Highly Sensitive Photopolymer for Holographic Data Storage Containing Methacryl Polyhedral Oligomeric Silsesquioxane. ACS APPLIED MATERIALS & INTERFACES 2022; 14:21544-21554. [PMID: 35486469 PMCID: PMC9100513 DOI: 10.1021/acsami.2c04011] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/22/2022] [Indexed: 06/14/2023]
Abstract
Herein, via introducing eight methacryl polyhedral oligomeric silsesquioxane (Ma-POSS), we dramatically enhance the holographic performance of phenanthraquinone-doped poly(methyl methacrylate) (PQ/PMMA) photopolymer with excellent characteristics of high sensitivity, high diffraction efficiency, and neglectable volume shrinkage for holographic data storage, the photosensitivity, diffraction efficiency, and volume shrinkage reaching 1.47 cm/J, ∼75%, and ∼0.09%, respectively. Ma-POSS here dramatically enhances the photosensitivity ∼5.5 times, diffraction efficiency more than 50%, and suppressed the volume shrinkage over 4 times. Further analysis reveals that Ma-POSS obviously increased the molecular weight by grafting PMMA to be a star-shaped macromolecule. And the residual C═C of POSS-PMMA dramatically increased the photosensitivity. Moreover, the star-shaped POSS-PMMA acting as a plasticizer dramatically enhances the mechanical properties and so reduces the photoinduced volume shrinkage of PQ/PMMA. Finally, by the use of the POSS-PMMA/PQ in a collinear holography system, it appeared to be promising for a fast but low bit error rate in holographic information storage. The current study thence has not only successfully synthesized photopolymer materials with potential for highly sensitive holographic storage applications but also investigated the microphysical mechanism of the impact of Ma-POSS on the holographic properties of PQ/PMMA photopolymer and clarified the thermal- and photoreaction processes of the POSS-PMMA/PQ photopolymer.
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Affiliation(s)
- Po Hu
- College
of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China
- Henan
Provincial Key Laboratory of intelligent lighting, Huanghuai University, Zhumadian 463000, China
| | - Jinhong Li
- College
of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China
| | - Junchao Jin
- College
of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China
| | - Xiao Lin
- College
of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350117, China
| | - Xiaodi Tan
- Information
Photonics Research Center, Key Laboratory of Optoelectronic Science
and for Medicine of Ministry of Education, Fujian Provincial Key Laboratory
of Photonics Technology, Fujian Provincial Engineering Technology
Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350117, China
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28
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Jo WS, Liu L, Wang D. See further upon the giants: Quantifying intellectual lineage in science. QUANTITATIVE SCIENCE STUDIES 2022. [DOI: 10.1162/qss_a_00186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Abstract
Newton’s centuries-old wisdom of standing on the shoulders of giants raises a crucial yet underexplored question: Out of all the prior works cited by a discovery, which one is its giant? Here, we develop a novel, discipline-independent method to identify the giant for any individual paper, allowing us to systematically examine the role and characteristics of giants in science. We find that across disciplines, about 95% of papers stand on the shoulders of giants, yet the weight of scientific progress rests on relatively few shoulders. Defining a new measure of giant index, we find that, while papers with high citations are more likely to be giants, for papers with the same citations, their giant index sharply predicts a paper’s future impact and prize-winning probabilities. Giants tend to originate from both small and large teams, being either highly disruptive or highly developmental. And papers that did not have a giant but later became a giant tend to be home-run papers that are highly disruptive to science. Given the crucial importance of citation-based measures in science, the developed concept of giants may offer a useful new dimension in assessing scientific impact that goes beyond sheer citation counts.
Peer Review
https://publons.com/publon/10.1162/qss_a_00186
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Affiliation(s)
- Woo Seong Jo
- Center for Science of Science & Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
| | - Lu Liu
- Center for Science of Science & Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA
| | - Dashun Wang
- Center for Science of Science & Innovation, Northwestern University, Evanston, IL, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, USA
- Kellogg School of Management, Northwestern University, Evanston, IL, USA
- McCormick School of Engineering, Northwestern University, Evanston, IL, USA
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29
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Identifying disruptive technologies by integrating multi-source data. Scientometrics 2022. [DOI: 10.1007/s11192-022-04283-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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30
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31
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Taheri S, Aliakbary S. Research trend prediction in computer science publications: a deep neural network approach. Scientometrics 2022. [DOI: 10.1007/s11192-021-04240-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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32
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Hou JW, Ma HF, He D, Sun J, Nie Q, Lin W. Harvesting random embedding for high-frequency change-point detection in temporal complex systems. Natl Sci Rev 2021; 9:nwab228. [PMID: 35571607 PMCID: PMC9097594 DOI: 10.1093/nsr/nwab228] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/21/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Recent investigations have revealed that dynamics of complex networks and systems are
crucially dependent on the temporal structures. Accurate detection of the time instant at
which a system changes its internal structures has become a tremendously significant
mission, beneficial to fully understanding the underlying mechanisms of evolving systems,
and adequately modeling and predicting the dynamics of the systems as well. In real-world
applications, due to a lack of prior knowledge on the explicit equations of evolving
systems, an open challenge is how to develop a practical and model-free
method to achieve the mission based merely on the time-series data recorded from
real-world systems. Here, we develop such a model-free approach, named temporal
change-point detection (TCD), and integrate both dynamical and statistical methods to
address this important challenge in a novel way. The proposed TCD approach, basing on
exploitation of spatial information of the observed time series of high dimensions, is
able not only to detect the separate change points of the concerned systems without
knowing, a priori, any information of the equations of the systems, but also to harvest
all the change points emergent in a relatively high-frequency manner, which cannot be
directly achieved by using the existing methods and techniques. Practical effectiveness is
comprehensively demonstrated using the data from the representative complex dynamics and
real-world systems from biology to geology and even to social science.
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Affiliation(s)
- Jia-Wen Hou
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai200433, China
- Centre for Computational Systems Biology, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai200433, China
| | - Huan-Fei Ma
- School of Mathematical Sciences, Soochow University, Suzhou215006, China
| | - Dake He
- Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai200092, China
| | - Jie Sun
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai200433, China
- School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai200433, China
| | - Qing Nie
- Department of Mathematics, Department of Developmental and Cell Biology, and NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA92697-3875, USA
| | - Wei Lin
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai200433, China
- Centre for Computational Systems Biology, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai200433, China
- School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai200433, China
- Shanghai Key Laboratory for Contemporary Applied Mathematics, LNMS (Fudan University), and LCNBI (Fudan University), Shanghai200433, China
- State Key Laboratory of Medical Neurobiology, and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai200032, China
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33
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Kane PB, Benjamin DM, Barker RA, Lang AE, Sherer T, Kimmelman J. Forecasts for the Attainment of Major Research Milestones in Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2021; 10:1047-1055. [PMID: 32333550 DOI: 10.3233/jpd-201933] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Projections about when research milestones will be attained are often of interest to patients and can help inform decisions about research funding and health system planning. OBJECTIVE To collect aggregated expert forecasts on the attainment of 11 major research milestones in Parkinson's disease (PD). METHODS Experts were asked to provide predictions about the attainment of 11 milestones in PD research in an online survey. PD experts were identified from: 1) The Michael J. Fox Foundation for Parkinson's Research data base, 2) doctors specializing in PD at top ranked neurology centers in the US and Canada, and 3) corresponding authors of articles on PD in top medical journals. Judgments were aggregated using coherence weighting. We tested the relationship between demographic variables and individual judgments using a linear regression. RESULTS 249 PD experts completed the survey. In the aggregate, experts believed that new treatments like gene therapy for monogenic PD, immunotherapy and cell therapy had 56.1%, 59.7%, and 66.6% probability, respectively of progressing in the clinical approval process within the next 10 years. Milestones involving existing management approaches, like the approval of a deep brain stimulation device or a body worn sensor had 78.4% and 82.2% probability of occurring within the next 10 years. Demographic factors were unable to explain deviations from the aggregate forecast (R2 = 0.029). CONCLUSIONS Aggregated expert opinion suggests that milestones for the advancement of new treatment options for PD are still many years away. However, other improvements in PD diagnosis and management are believed to be near at hand.
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Affiliation(s)
- Patrick Bodilly Kane
- Biomedical Ethics Unit, STREAM Research Group, McGill University, Montreal, QC, Canada
| | - Daniel M Benjamin
- University of Southern California, Information Sciences Institute, Marina del Rey, CA, USA
| | - Roger A Barker
- Department of Clinical Neuroscience, John van Geest Centre for Brain Repair, WT/MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Anthony E Lang
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON, Canada
| | - Todd Sherer
- The Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Jonathan Kimmelman
- Biomedical Ethics Unit, STREAM Research Group, McGill University, Montreal, QC, Canada
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34
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Sebastian Y, Chen C. The boundary-spanning mechanisms of Nobel Prize winning papers. PLoS One 2021; 16:e0254744. [PMID: 34379631 PMCID: PMC8357150 DOI: 10.1371/journal.pone.0254744] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 07/05/2021] [Indexed: 12/24/2022] Open
Abstract
The breakthrough potentials of research papers can be explained by their boundary-spanning qualities. Here, for the first time, we apply the structural variation analysis (SVA) model and its affiliated metrics to investigate the extent to which such qualities characterize a group of Nobel Prize winning papers. We find that these papers share remarkable boundary-spanning traits, marked by exceptional abilities to connect disparate and topically-diverse clusters of research papers. Further, their publications exert structural variations on a scale that significantly alters the betweenness centrality distributions in existing intellectual space. Overall, SVA not only provides a set of leading indicators for describing future Nobel Prize winning papers, but also broadens our understanding of similar prize-winning properties that may have been overlooked among other regular publications.
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Affiliation(s)
- Yakub Sebastian
- College of Engineering, IT & Environment, Charles Darwin University, Casuarina, Northern Territory, Australia
- * E-mail:
| | - Chaomei Chen
- College of Computing & Informatics, Drexel University, Philadelphia, PA, United States of America
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35
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Pfrieger FW. TeamTree analysis: A new approach to evaluate scientific production. PLoS One 2021; 16:e0253847. [PMID: 34288914 PMCID: PMC8294527 DOI: 10.1371/journal.pone.0253847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 06/14/2021] [Indexed: 11/18/2022] Open
Abstract
Advances in science and technology depend on the work of research teams and the publication of results through peer-reviewed articles representing a growing socio-economic resource. Current methods to mine the scientific literature regarding a field of interest focus on content, but the workforce credited by authorship remains largely unexplored. Notably, appropriate measures of scientific production are debated. Here, a new bibliometric approach named TeamTree analysis is introduced that visualizes the development and composition of the workforce driving a field. A new citation-independent measure that scales with the H index estimates impact based on publication record, genealogical ties and collaborative connections. This author-centered approach complements existing tools to mine the scientific literature and to evaluate research across disciplines.
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Affiliation(s)
- Frank W. Pfrieger
- Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives, Strasbourg, France
- * E-mail: ,
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36
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Predicting publication productivity for authors: Shallow or deep architecture? Scientometrics 2021. [DOI: 10.1007/s11192-021-04027-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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37
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Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:8810046. [PMID: 34234823 PMCID: PMC8216800 DOI: 10.1155/2021/8810046] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 05/26/2021] [Indexed: 11/18/2022]
Abstract
Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series.
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38
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A deep-learning based citation count prediction model with paper metadata semantic features. Scientometrics 2021. [DOI: 10.1007/s11192-021-04033-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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39
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Wang K, Shi W, Bai J, Zhao X, Zhang L. Prediction and application of article potential citations based on nonlinear citation-forecasting combined model. Scientometrics 2021. [DOI: 10.1007/s11192-021-04026-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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40
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An J, Chua CK, Mironov V. Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin. Int J Bioprint 2021; 7:342. [PMID: 33585718 PMCID: PMC7875058 DOI: 10.18063/ijb.v7i1.342] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 01/18/2021] [Indexed: 02/07/2023] Open
Abstract
The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future.
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Affiliation(s)
- Jia An
- Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
| | - Chee Kai Chua
- Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372
| | - Vladimir Mironov
- 3D Bioprinting Solutions, 68/2 Kashirskoe Highway, Moscow, Russian Federation 115409
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41
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Min C, Bu Y, Wu D, Ding Y, Zhang Y. Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process. Inf Process Manag 2021. [DOI: 10.1016/j.ipm.2020.102428] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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42
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Zingg C, Nanumyan V, Schweitzer F. Citations driven by social connections? A multi-layer representation of coauthorship networks. QUANTITATIVE SCIENCE STUDIES 2020. [DOI: 10.1162/qss_a_00092] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
To what extent is the citation rate of new papers influenced by the past social relations of their authors? To answer this question, we present a data-driven analysis of nine different physics journals. Our analysis is based on a two-layer network representation constructed from two large-scale data sets, INSPIREHEP and APS. The social layer contains authors as nodes and coauthorship relations as links. This allows us to quantify the social relations of each author, prior to the publication of a new paper. The publication layer contains papers as nodes and citations between papers as links. This layer allows us to quantify scientific attention as measured by the change of the citation rate over time. We particularly study how this change correlates with the social relations of their authors, prior to publication. We find that on average the maximum value of the citation rate is reached sooner for authors who have either published more papers or who have had more coauthors in previous papers. We also find that for these authors the decay in the citation rate is faster, meaning that their papers are forgotten sooner.
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Affiliation(s)
| | - Vahan Nanumyan
- Chair of Systems Design, ETH Zurich, Zurich, Switzerland
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43
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Zhao Y, Du J, Wu Y. The impact of J. D. Bernal’s thoughts in the science of science upon China: Implications for today’s quantitative studies of science. QUANTITATIVE SCIENCE STUDIES 2020. [DOI: 10.1162/qss_a_00064] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
John Desmond Bernal (1901–1970) was one of the most eminent scientists in molecular biology and is also regarded as the founding father of the science of science. His book The social function of science laid the theoretical foundations for the discipline. In this article, we summarize four chief characteristics of his ideas in the science of science: the sociohistorical perspective, theoretical models, qualitative and quantitative approaches, and studies of science planning and policy. China has constantly reformed its scientific and technological system based on research evidence of the science of science. Therefore, we analyze the impact of Bernal’s science-of-science thoughts on the development of China’s science of science, and discuss how they might be usefully taken still further in quantitative studies of science.
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Affiliation(s)
- Yong Zhao
- Information Research Center, China Agricultural University, Beijing, China
| | - Jian Du
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Yishan Wu
- Chinese Academy of Science and Technology for Development, Beijing, China
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44
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45
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Siudem G, Żogała-Siudem B, Cena A, Gagolewski M. Three dimensions of scientific impact. Proc Natl Acad Sci U S A 2020; 117:13896-13900. [PMID: 32513724 PMCID: PMC7322031 DOI: 10.1073/pnas.2001064117] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The growing popularity of bibliometric indexes (whose most famous example is the h index by J. E. Hirsch [J. E. Hirsch, Proc. Natl. Acad. Sci. U.S.A. 102, 16569-16572 (2005)]) is opposed by those claiming that one's scientific impact cannot be reduced to a single number. Some even believe that our complex reality fails to submit to any quantitative description. We argue that neither of the two controversial extremes is true. By assuming that some citations are distributed according to the rich get richer rule (success breeds success, preferential attachment) while some others are assigned totally at random (all in all, a paper needs a bibliography), we have crafted a model that accurately summarizes citation records with merely three easily interpretable parameters: productivity, total impact, and how lucky an author has been so far.
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Affiliation(s)
- Grzegorz Siudem
- Faculty of Physics, Warsaw University of Technology, 00-662 Warsaw, Poland;
| | | | - Anna Cena
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland
| | - Marek Gagolewski
- Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland
- Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland
- School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
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46
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47
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Janosov M, Musciotto F, Battiston F, Iñiguez G. Elites, communities and the limited benefits of mentorship in electronic music. Sci Rep 2020; 10:3136. [PMID: 32081912 PMCID: PMC7035280 DOI: 10.1038/s41598-020-60055-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 02/03/2020] [Indexed: 01/02/2023] Open
Abstract
While the emergence of success in creative professions, such as music, has been studied extensively, the link between individual success and collaboration is not yet fully uncovered. Here we aim to fill this gap by analyzing longitudinal data on the co-releasing and mentoring patterns of popular electronic music artists appearing in the annual Top 100 ranking of DJ Magazine. We find that while this ranking list of popularity publishes 100 names, only the top 20 is stable over time, showcasing a lock-in effect on the electronic music elite. Based on the temporal co-release network of top musicians, we extract a diverse community structure characterizing the electronic music industry. These groups of artists are temporally segregated, sequentially formed around leading musicians, and represent changes in musical genres. We show that a major driving force behind the formation of music communities is mentorship: around half of musicians entering the top 100 have been mentored by current leading figures before they entered the list. We also find that mentees are unlikely to break into the top 20, yet have much higher expected best ranks than those who were not mentored. This implies that mentorship helps rising talents, but becoming an all-time star requires more. Our results provide insights into the intertwined roles of success and collaboration in electronic music, highlighting the mechanisms shaping the formation and landscape of artistic elites in electronic music.
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Affiliation(s)
- Milán Janosov
- Department of Network and Data Science, Central European University, Budapest, 1051, Hungary.
| | - Federico Musciotto
- Department of Network and Data Science, Central European University, Budapest, 1051, Hungary
| | - Federico Battiston
- Department of Network and Data Science, Central European University, Budapest, 1051, Hungary
| | - Gerardo Iñiguez
- Department of Network and Data Science, Central European University, Budapest, 1051, Hungary. .,Department of Computer Science, Aalto University School of Science, Aalto, 00076, Finland. .,IIMAS, Universidad Nacional Autonóma de México, Ciudad de México, 01000, Mexico.
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48
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Gallotti R, De Domenico M. Effects of homophily and academic reputation in the nomination and selection of Nobel laureates. Sci Rep 2019; 9:17304. [PMID: 31754196 PMCID: PMC6872660 DOI: 10.1038/s41598-019-53657-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 10/12/2019] [Indexed: 11/21/2022] Open
Abstract
In collective decision-making, a group of independent experts propose individual choices to reach a common decision. This is the case of competitive events such as Olympics, international Prizes or grant evaluation, where groups of experts evaluate individual performances to assign resources, e.g. scores, recognitions, or funding. However, there are systems where evaluating individual's performance is difficult: in those cases, other factors play a relevant role, leading to unexpected emergent phenomena from micro-scale interactions. The Nobel assignment procedure, rooted on recommendations, is one of these systems. Here we unveil its network, reconstructed from official data and metadata about nominators, nominees and awardees between 1901 and 1965, consisting of almost 12,000 individuals and 17,000 nominations. We quantify the role of homophily, academic reputation of nominators and their prestige neighborhood, showing that nominees endorsed by central actors - who are part of the system's core because of their prestigious reputation - are more likely to become laureate within a finite time scale than nominees endorsed by nominators in the periphery of the network. We propose a mechanistic model which reproduces all the salient observations and allows to design possible countermeasures to mitigate observed effects.
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Affiliation(s)
- Riccardo Gallotti
- CoMuNe Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Povo, TN, Italy
| | - Manlio De Domenico
- CoMuNe Lab, Fondazione Bruno Kessler, Via Sommarive 18, 38123, Povo, TN, Italy.
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49
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Li W, Aste T, Caccioli F, Livan G. Early coauthorship with top scientists predicts success in academic careers. Nat Commun 2019; 10:5170. [PMID: 31729362 PMCID: PMC6858367 DOI: 10.1038/s41467-019-13130-4] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 10/22/2019] [Indexed: 11/18/2022] Open
Abstract
We examined the long-term impact of coauthorship with established, highly-cited scientists on the careers of junior researchers in four scientific disciplines. Here, using matched pair analysis, we find that junior researchers who coauthor work with top scientists enjoy a persistent competitive advantage throughout the rest of their careers, compared to peers with similar early career profiles but without top coauthors. Such early coauthorship predicts a higher probability of repeatedly coauthoring work with top-cited scientists, and, ultimately, a higher probability of becoming one. Junior researchers affiliated with less prestigious institutions show the most benefits from coauthorship with a top scientist. As a consequence, we argue that such institutions may hold vast amounts of untapped potential, which may be realised by improving access to top scientists. By examining publication records of scientists from four disciplines, the authors show that coauthoring a paper with a top-cited scientist early in one's career predicts lasting increases in career success, especially for researchers affiliated with less prestigious institutions.
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Affiliation(s)
- Weihua Li
- Department of Computer Science, University College London, London, WC1E 6EA, UK.,Systemic Risk Centre, London School of Economics and Political Sciences, London, WC2A 2AE, UK
| | - Tomaso Aste
- Department of Computer Science, University College London, London, WC1E 6EA, UK.,Systemic Risk Centre, London School of Economics and Political Sciences, London, WC2A 2AE, UK
| | - Fabio Caccioli
- Department of Computer Science, University College London, London, WC1E 6EA, UK.,Systemic Risk Centre, London School of Economics and Political Sciences, London, WC2A 2AE, UK.,London Mathematical Laboratory, 8 Margravine Gardens, London, WC 8RH, UK
| | - Giacomo Livan
- Department of Computer Science, University College London, London, WC1E 6EA, UK. .,Systemic Risk Centre, London School of Economics and Political Sciences, London, WC2A 2AE, UK.
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Abstract
Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features.
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