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Realistic-science, technology, engineering, and mathematics assisted by google classroom as a learning innovation in the new normal era to improve statistical thinking skill 7

Realistic-science, technology, engineering, and mathematics ...

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7th International Conference on Mathematics, Science, and Education 2020, ICMSE 2020

作者： Ardiansyah, A.S. Novita, N.A. Zhintya, H. Kurnianto, D. Mathematics Education Study Program Universitas Negeri Semarang Indonesia Applied Statistics and Computation Study Program Universitas Negeri Semarang Indonesia

Based on government policies regarding the implementation of learning from home, it is necessary the learning innovation to continue learning in the new normal era by utilizing existing technological advances. In this digital era, statistics thinking is important because it is needed to be able to interpret and understand and make good decisions from the statistical data obtained. From these problems, one of the innovations that can be done is by implementing Realistic Mathematics Education integrated Science, Technology, Engineering, and Mathematics assisted by Google Classroom (R-STEM-GC). This paper aims to determine the effect of R-STEM-GC learning on students' statistical thinking skills. This paper uses literature review method research that identifies, assesses, and interprets all findings on a research topic to answer existing research questions. It can concluded that realistic learning was more effective in terms of students' mathematical reasoning and communication than conventional learning;learning with the application of STEM can improve students' problem solving abilities compared to conventional learning;and the use of google classrooms that can improve communication skills. From the results of this analysis, the application of R-STEM-GC learning can be an innovative solution in improving students' statistical thinking skills in new normal era. © Published under licence by IOP Publishing Ltd.

关键词： Students

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A Hybrid Singly Ordered Correspondence with Correlation Approach to Analyzing the Relationship between Age Groups and Happiness Level in Indonesia

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IAENG International Journal of applied Mathematics 2023年第3期53卷 145页

作者： Sholihah, Fajriatus Ginanjar, Irlandia Hidayat, Yuyun Applied Statistics Study Program Padjadjaran University West Java 455363 Indonesia Statistics Department Padjadjaran University West Java 455363 Indonesia Statistics Department Padjadjaran University West Java 455363 Indonesia

When subpopulation objects are to be analyzed, qualitative data is frequently transformed into a contingency table. The categories of rows and columns in the contingency table are sequentially treated as objects and variables. In addition, the object frequently retrieves additional information with continuous variables. The novelty of this study was that it examined associations between discrete and continuous variables in two stages. The hybrid Singly Ordered Correspondence Analysis (SOCA) method was used to identify associations between nominal-scale objects and ordinal-scale discrete variables in the form of contingency tables. Correlation values were used to identify associations between objects and continuous variables. The SOCA method determined the principal coordinates of discrete variables. Concurrently, the correlation was used to determine the vector coordinates of the continuous variables. Confidence region and p-value calculations were performed to determine the contribution of a category to the structure of the association between two categorical variables with nominal and ordinal scales. The method was applied to population-based data on the level of happiness across age groups. The case study results indicated that all nominal and ordinal categories contributed significantly to the association between age group and province variables in Indonesia. The association results indicated that 27 provinces were positively associated with the age bracket of 17 to 41 years, with these provinces being associated with 13 indicators of the happiness index. The remaining seven provinces were linked to age groups of at least 41 years old, and six happiness index indicators were linked to these provinces. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Population statistics

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Four-Parameter Beta Mixed Models with Survey and Sentinel 2a Satellite Data for Predicting Paddy Productivity

SSRN

引用

SSRN 2024年

作者： Kusumaningrum, Dian Wijayanto, Hari Kurnia, Anang Notodiputro, Khairil Anwar Ardiansyah, Muhlis Parvez, Islam M.D. Business Mathematics Program Study School of Applied STEM Universitas Prasetiya Mulya BSD Campus BSD City Kav. Edutown I.1 Tangerang15339 Indonesia Department of Statistics IPB University Jl. Meranti Wing 22 Level 4 Dramaga Bogor16680 Indonesia BPS-Statistics of Kotawaringin Timur Jl Ahmad Yani no 5 Sampit 74322 Central Kalimantan Indonesia Department of Biomechanical Systems Faculty of Agriculture Ehime University 3-5-7 Tarumi Matsuyama790-8566 Japan

Ensuring food security, fostering agricultural sustainability, and driving economic development. However, existing prediction models often overlook the unique characteristics of paddy productivity distribution, which varies between areas, skewed, and bounded within a certain minimum and maximum range, following a four-parameter beta distribution. Consequently, these models yield less accurate and potentially misleading predictions. Additionally, most approaches fail to capture the complex interrelationships among variables that often occur when we incorporate satellite data alongside survey data that has been recognized as a key approach for improving prediction accuracy and optimizing farming practices. To address these shortcomings, this study introduces a four-parameter beta Generalized Linear Mixed Model (GLMM) augmented within a four-parameter beta Generalized Mixed Effect Tree (GMET). The four-parameter beta GMET, an extension of the four-parameter beta GLMM model integrated with a regression tree, offers enhanced flexibility in modeling complex relationships. Application of this methodology to an empirical study in Central Kalimantan reveals notable improvements over previous methods, as evidenced by substantially lower AIC and RRMSE values. Notably, the analysis identifies lagged values of band 4, band 8, and NDVI from Sentinel-2A satellite data as significant predictors of paddy productivity, overriding the importance of farmer survey variables. This underscores the potential of satellite data to be utilized in paddy productivity predictions, offering a more efficient and cost-effective alternative to farmer survey-based methods. By enhancing satellite technology, future efforts in paddy productivity prediction can achieve higher efficiency and accuracy, contributing to informed decision-making in agricultural management. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Realistic-science, technology, engineering, and mathematics assisted by google classroom as a learning innovation in the new normal era to improve statistical thinking skill

引用

Journal of Physics: Conference Series 2021年第4期1918卷

作者： A S Ardiansyah N A Novita H Zhintya D Kurnianto Mathematics Education Study Program Universitas Negeri Semarang Applied Statistics and Computation Study Program Universitas Negeri Semarang

Based on government policies regarding the implementation of learning from home, it is necessary the learning innovation to continue learning in the new normal era by utilizing existing technological advances. In this digital era, statistics thinking is important because it is needed to be able to interpret and understand and make good decisions from the statistical data obtained. From these problems, one of the innovations that can be done is by implementing Realistic Mathematics Education integrated Science, Technology, Engineering, and Mathematics assisted by Google Classroom (R-STEM-GC). This paper aims to determine the effect of R-STEM-GC learning on students' statistical thinking skills. This paper uses literature review method research that identifies, assesses, and interprets all findings on a research topic to answer existing research questions. It can concluded that realistic learning was more effective in terms of students' mathematical reasoning and communication than conventional learning; learning with the application of STEM can improve students' problem solving abilities compared to conventional learning; and the use of google classrooms that can improve communication skills. From the results of this analysis, the application of R-STEM-GC learning can be an innovative solution in improving students' statistical thinking skills in new normal era.

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Quantile smoothing splines regression on the air relative humidity toward the rubber production in South Sumatera

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AIP Conference Proceedings 2022年第1期2575卷

作者： Yollanda Nalita Yudhie Andriyana 1Magister Study Program of Applied Statistics Universitas Padjadjaran Jalan Dipati Ukur No.35 Bandung 40132 Indonesia 2Department of Statistics Universitas Padjadjaran Jalan Raya Bandung-Sumedang km 21 Jatinangor Sumedang 45363 Indonesia

Rubber is one of the Indonesian plantation commodities which has an important role in economic activity. Rubber can grow very well in the tropical area. One of the factors influencing the rubber production is the air relative humidity. This research is aimed finding out the influence of the air relative humidity and the rubber production in South Sumatera. The relationship pattern shows different shapes in some parts indicate a difficulty in specified parametrically, then a nonparametric approach was proposed. In this case we use smoothing splines technique. However, the pattern spreads asymmetrically and contains outliers. Hence, we also propose to use a quantile objective function called quantile smoothing splines. In this research, we divide the data distribution into five parts based on very low, low, moderate, high, and very high characteristics. The performances of quantile smoothing splines (with τ = 0.5, as median case) and smoothing splines (mean approach) are compared using Root Mean Square Error. The result has shown that the quantile smoothing splines regression gives smaller Root Mean Square Error compared to smoothing splines regression (mean approach).

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Steady Rayleigh-BénardRayleigh-Bénard convection between stress-free boundaries

arXiv

引用

arXiv 2020年

作者： Wen, Baole Goluskin, David LeDuc, Matthew Chini, Gregory P. Doering, Charles R. Department of Mathematics University of Michigan Ann ArborMI48109-1043 United States Department of Mathematics & Statistics University of Victoria VictoriaBCV8P 5C2 Canada Department of Physics University of Michigan Ann ArborMI48109-1040 United States Program in Integrated Applied Mathematics University of New Hampshire DurhamNH03824 United States Department of Mechanical Engineering University of New Hampshire DurhamNH03824 United States Center for the Study of Complex Systems University of Michigan Ann ArborMI48109-1042 United States

Steady two-dimensional Rayleigh-Bénard convection between stress-free isothermal boundaries is studied via numerical computations. We explore properties of steady convective rolls with aspect ratios π/5 ⩽ Γ ⩽ 4π, where Γ is the width-to-height ratio for a pair of counter-rotating rolls, over eight orders of magnitude in the Rayleigh number, 103 ⩽ Ra ⩽ 1011, and four orders of magnitude in the Prandtl number, 10−2 ⩽ Pr ⩽ 102. At large Ra where steady rolls are dynamically unstable, the computed rolls display Ra → ∞ asymptotic scaling. In this regime, the Nusselt number Nu that measures heat transport scales as Ra1/3 uniformly in Pr. The prefactor of this scaling depends on Γ and is largest at Γ ≈ 1.9. The Reynolds number Re for large-Ra rolls scales as Pr−1Ra2/3 with a prefactor that is largest at Γ ≈ 4.5. All of these large-Ra features agree quantitatively with the semi-analytical asymptotic solutions constructed by Chini & Cox (2009). Convergence of Nu and Re to their asymptotic scalings occurs more slowly when Pr is larger and when Γ is smaller. Copyright © 2020, The Authors. All rights reserved.

关键词： Prandtl number

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Comparing demographics of signatories to public letters on diversity in the mathematical sciences

arXiv

引用

arXiv 2019年

作者： Topaz, Chad M. Cart, James Eaton, Carrie Diaz Shrout, Anelise Hanson Higdon, Jude A. İnce, Kenan Katz, Brian Lewis, Drew Libertini, Jessica Smith, Christian Michael Department of Mathematics and Statistics Williams College WilliamstownMA United States Institute for the Quantitative Study of Inclusion Diversity and Equity WilliamstownMA United States Office of Institutional Research Williams College WilliamstownMA United States Digital and Computational Studies Program Bates College LewistonME United States Department of Mathematics Westminster College Salt Lake CityUT United States Department of Mathematics and Statistics Smith College NorthamptonMA United States Department of Mathematics and Statistics University of South Alabama MobileAL United States Department of Applied Mathematics Virginia Military Institute LexingtonVA United States Department of Sociology University of Wisconsin-Madison MadisonWI United States

In its December 2019 edition, the Notices of the American Mathematical Society published an essay critical of the use of diversity statements in academic hiring. The publication of this essay prompted many responses, including three public letters circulated within the mathematical sciences community. Each letter was signed by hundreds of people and was published online, also by the American Mathematical Society. We report on a study of the signatories’ demographics, which we infer using a crowdsourcing approach. Letter A highlights diversity and social justice. The pool of signatories contains relatively more individuals inferred to be women and/or members of underrepresented ethnic groups. Moreover, this pool is diverse with respect to the levels of professional security and types of academic institutions represented. Letter B does not comment on diversity, but rather, asks for discussion and debate. This letter was signed by a strong majority of individuals inferred to be white men in professionally secure positions at highly research intensive universities. Letter C speaks out specifically against diversity statements, calling them "a mistake," and claiming that their usage during early stages of faculty hiring "diminishes mathematical achievement." Individuals who signed both Letters B and C, that is, signatories who both privilege debate and oppose diversity statements, are overwhelmingly inferred to be tenured white men at highly research intensive universities. Our empirical results are consistent with theories of power drawn from the social sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Population statistics

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Commentaries on Viewpoint: Using V̇o2max as a marker of training status in athletes - can we do better?

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Journal of applied physiology (Bethesda, Md. : 1985) 2022年第1期133卷 148-164页

作者： Pedro L Valenzuela Manuel Mateo-March Xabier Muriel Mikel Zabala Alejandro Lucia David Barranco-Gil Grégoire P Millet Franck Brocherie Johannes Burtscher Martin Burtscher Benjamin J Ryan Rachel A Gioscia-Ryan Stephane Perrey Víctor Rodrigo-Carranza Fernando González-Mohíno José María González-Ravé Jordan Santos-Concejero Benedito S Denadai Camila C Greco Arturo Casado Carl Foster Raffaele Mazzolari Giulia Nicole Baldrighi Elisa Pastorio Davide Malatesta Aurélien Patoz Fabio Borrani Stephen J Ives Justin A DeBlauw Ricardo Dantas de Lucas Fernando Klitzke Borszcz Eduardo Marcel Fernandes Nascimento Luiz Guilherme Antonacci Guglielmo Tiago Turnes Richard T Jaspers Stephan van der Zwaard Romuald Lepers Julien Louis Anderson Meireles Hiago L. R. de Souza Géssyca T de Oliveira Marcelo P dos Santos Rhaí A Arriel Moacir Marocolo B Hunter S Meyler D Muniz-Pumares Renato M Ferreira Abigail S Sogard Stephen J Carter Timothy D Mickleborough Guilherme Pereira Saborosa Raphael Dinalli de Oliveira Freitas Paula Souza Alves dos Santos João Pedro de Souza Ferreira Francisco de Assis Manoel Sandro Fernandes da Silva Christoph Triska Bettina Karsten Dajo Sanders Elliot S Lipksi David J Spindler Matthijs K. C. Hesselink Rodrigo Zacca Márcio Fagundes Goethel David Bruce Pyne Brayden M Wood Peyton E Allen Jaden L Gabelhausen Alexandra M Keller Mast T Lige Alicia S Oumsang Greg L Smart Hunter L Paris Arthur H Dewolf Guillaume Toffoli Borja Martinez-Gonzalez Samuele M Marcora Daniela Terson de Paleville Ricardo J Fernandes Susana M Soares J. Arturo Abraldes Guilherme Matta Arthur Henrique Bossi D G McCarthy W Bostad J Gibala Mary Vagula Grupo de Investigación en Actividad física y Salud (PaHerg) Instituto de Investigación Hospital 12 de Octubre (imas12) Madrid Spain Faculty of Sport Sciences Universidad Europea de Madrid Madrid Spain Sport Science Department. Universidad Miguel Hernández Elche Spain Human Performance and Sports Science Laboratory Faculty of Sport Sciences University of Murcia Murcia Spain Department of Physical Education & Sport Faculty of Sport Sciences University of Granada Granada Spain Institute of Sport Sciences University of Lausanne Lausanne Switzerland Laboratory Sport Expertise and Performance (EA 7370) French Institute of Sport (INSEP) Paris France Department of Sport Science University of Innsbruck Innsbruck Austria Thermal and Mountain Medicine Division US Army Research Institute of Environmental Medicine Natick Massachusetts Department of Anesthesiology University of Michigan Ann Arbor Michigan EuroMov Digital Health in Motion University of Montpellier Montpellier France Sport Training Lab University of Castilla-La Mancha Toledo Spain Facultad de Ciencias de la Vida y de la Naturaleza Universidad Nebrija Madrid Spain Department of Physical Education and Sport University of the Basque Country UPV/EHU Vitoria-Gasteiz Spain Human Performance Laboratory São Paulo State University Rio Claro Brazil Center for Sport Studies Rey Juan Carlos University Madrid Spain University of Wisconsin-La Crosse La Crosse Wisconsin Department of Physical Education and Sport University of the Basque Country (UPV/EHU) Vitoria-Gasteiz Spain Department of Molecular Medicine University of Pavia Pavia Italy Department of Brain and Behavioural Sciences − Medical and Genomic Statistics Unit University of Pavia Pavia Italy Department of Biomedical Sciences for Health University of Milan Milan Italy Institute of Sport Sciences of University of Lausanne (ISSUL) University of Lausanne Lausanne Switzerland Health and Human Physiological Sciences Skidmore College Saratoga Springs New

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease study 2019

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The Lancet 2020年第10258期396卷 1204-1222页

作者： Abbafati, Cristiana Abbas, Kaja M. Abbasi, Mohammad Abbasifard, Mitra Abbasi-Kangevari, Mohsen Abbastabar, Hedayat Abd-Allah, Foad Abdelalim, Ahmed Abdollahi, Mohammad Abdollahpour, Ibrahim Abedi, Aidin Abedi, Parisa Abegaz, Kedir Hussein Abolhassani, Hassan Abosetugn, Akine Eshete Aboyans, Victor Abrams, Elissa M. Abreu, Lucas Guimarães Abrigo, Michael R.M. Abu Haimed, Abdulaziz Khalid Abualhasan, Ahmed Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Abushouk, Abdelrahman I. Acebedo, Alyssa Ackerman, Ilana N. Adabi, Maryam Adair, Tim Adamu, Abdu A. Adebayo, Oladimeji M. Adedeji, Isaac Akinkunmi Adekanmbi, Victor Adelson, Jaimie D. Adeoye, Abiodun Moshood Adetokunboh, Olatunji O. Adham, Davoud Advani, Shailesh M. Afarideh, Mohsen Afshari, Mahdi Afshin, Ashkan Agardh, Emilie E. Agarwal, Gina Agasthi, Pradyumna Agesa, Kareha M. Aghaali, Mohammad Aghamir, Seyed Mohammad Kazem Agrawal, Anurag Ahmad, Tauseef Ahmadi, Alireza Ahmadi, Keivan Ahmadi, Mehdi Ahmadieh, Hamid Ahmadpour, Ehsan Ahmed, Muktar Beshir Aji, Budi Akalu, Temesgen Yihunie Akinyemi, Rufus Olusola Akinyemiju, Tomi Akombi, Blessing Akunna, Chisom Joyqueenet Alahdab, Fares Al-Aly, Ziyad Alam, Khurshid Alam, Noore Alam, Samiah Alam, Tahiya Alanezi, Fahad Mashhour Alanzi, Turki M. Albertson, Samuel B. Alcalde-Rabanal, Jacqueline Elizabeth Alema, Niguse Meles Alemu, Biresaw Wassihun Alemu, Yihun Mulugeta Alhabib, Khalid F. Alhassan, Robert Kaba Ali, Muhammad Ali, Saqib Alicandro, Gianfranco Alijanzadeh, Mehran Alinia, Cyrus Alipour, Vahid Alizade, Hesam Aljunid, Syed Mohamed Alla, François Allebeck, Peter Almadi, Majid Abdulrahman Hamad Almasi, Ali Almasi-Hashiani, Amir Almasri, Nihad A. Al-Mekhlafi, Hesham M. Almulhim, Abdulaziz M. Alonso, Jordi Al-Raddadi, Rajaa M. Altirkawi, Khalid A. Alumran, Arwa Khalid Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Amare, Bekalu Amini, Saeed Amini-Rarani, Mostafa Aminorroaya, Arya Amiri, Fatemeh Amit, Arianna Maever L. Amugsi, Dickson A. Amul, Gianna Gayle Herrera Anbesu, Etsay Woldu Ancuceanu, Robert Anderl Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Infectious Disease Epidemiology London School of Hygiene and Tropical Medicine London United Kingdom Social Determinants of Health Research Center Tehran Iran Department of Neurology Cairo University Cairo Egypt School of Pharmacy Tehran Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran Iran Neuroscience Research Center Isfahan University of Medical Sciences Isfahan Iran Biostatistics Department Near East University Nicosia Cyprus Biostatistics and Health Informatics Madda Walabu University Bale Robe Ethiopia Research Center for Immunodeficiencies Tehran Iran Karolinska University Hospital Huddinge Sweden Department of Cardiology Dupuytren University Hospital Limoges France University of Limoges Limoges France Department of Pediatric Dentistry Federal University of Minas Gerais Belo Horizonte Brazil Department of Research Philippine Institute for Development Studies Quezon City Philippines Department of Healthcare Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Harvard Medical School Boston MA United States Department of Medicine Ain Shams University Cairo Egypt Hamadan University of Medical Sciences Hamadan Iran School of Medicine Cardiff University Cardiff United Kingdom School of Laboratory Medicine and Science University College Hospital Ibadan Ibadan Nigeria Institute of Cardiovascular Diseases University of Ibadan Ibadan Nigeria Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Cape Town South Africa Department of Global Health Stellenbosch University Cape Town South Africa School of Health Ardabil University of Medical Science Ardabil Iran Social Behavioral Research Branch National Institute of Health Bethesda MD United States Department of Oncology Georgetown University Washington DC DC United States Department of Health Metrics Sciences School of Medicine Seattle WA

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw value

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The mutational constraint spectrum quantified from variation in 141,456 humans (vol 581, pg 434, 2020)

引用

NATURE 2021年第7874期597卷 E3-E4页

作者： Gudmundsson, Sanna Karczewski, Konrad J. Francioli, Laurent C. Tiao, Grace Cummings, Beryl B. Alfoldi, Jessica Wang, Qingbo Collins, Ryan L. Laricchia, Kristen M. Ganna, Andrea Birnbaum, Daniel P. Gauthier, Laura D. Brand, Harrison Solomonson, Matthew Watts, Nicholas A. Rhodes, Daniel Singer-Berk, Moriel England, Eleina M. Seaby, Eleanor G. Kosmicki, Jack A. Walters, Raymond K. Tashman, Katherine Farjoun, Yossi Banks, Eric Poterba, Timothy Wang, Arcturus Seed, Cotton Whiffin, Nicola Chong, Jessica X. Samocha, Kaitlin E. Pierce-Hoffman, Emma Zappala, Zachary O'Donnell-Luria, Anne H. Minikel, Eric Vallabh Weisburd, Ben Lek, Monkol Ware, James S. Vittal, Christopher Armean, Irina M. Bergelson, Louis Cibulskis, Kristian Connolly, Kristen M. Covarrubias, Miguel Donnelly, Stacey Ferriera, Steven Gabriel, Stacey Gentry, Jeff Gupta, Namrata Jeandet, Thibault Kaplan, Diane Llanwarne, Christopher Munshi, Ruchi Novod, Sam Petrillo, Nikelle Roazen, David Ruano-Rubio, Valentin Saltzman, Andrea Schleicher, Molly Soto, Jose Tibbetts, Kathleen Tolonen, Charlotte Wade, Gordon Talkowski, Michael E. Neale, Benjamin M. Daly, Mark J. MacArthur, Daniel G. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Institute for Molecular Medicine Finland Helsinki Finland Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA National Heart & Lung Institute and MRC London Institute of Medical Sciences Imperial College London London UK Cardiovascular Research Centre Royal Brompton & Harefield Hospitals NHS Trust London UK Vertex Pharmaceuticals Inc Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA Department of Neurology Harvard Medical School Boston MA USA Cardiac Arrhythmia Service and Cardiovascular Research Center Massachusetts General Hospital Boston MA USA Estonian Genome Center Institute of Genomics University of Tartu Tartu Estonia Data Sciences Platform Broad Institute of MIT and Harvard Cambridge MA USA Diabetes Unit Massachusetts General Hospital Boston MA USA Program in Metabolism Broad Institute of MIT and Harvard Cambridge MA USA Department of Medicine Harvard Medical School Boston MA USA Division of General Internal Medicine Massachusetts General Hospital Boston MA USA Department of Public Health Faculty of Medicine University of Helsinki Helsinki Finland Cardiovascular Disease Initiative and Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Genomics Diabetes and Endocrinology Lund University Lund Sweden Folkhälsan Institute of Genetics Folkhälsan Research C

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