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Institution

Texas A&M University

EducationCollege Station, Texas, United States
About: Texas A&M University is a education organization based out in College Station, Texas, United States. It is known for research contribution in the topics: Population & Finite element method. The organization has 72169 authors who have published 164372 publications receiving 5764236 citations.


Papers
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Journal ArticleDOI
TL;DR: Surprisingly, students learned just as effectively even when tutors were suppressed from giving explanations and feedback, and their learning in the interactive style of tutoring is attributed to construction from deeper and a greater amount of scaffolding episodes, as well as their greater effort to take control of their own learning by reading more.

854 citations

Journal ArticleDOI
Zhenqiang Su, Paweł P. Łabaj1, Sheng Li2, Jean Thierry-Mieg3  +161 moreInstitutions (54)
TL;DR: The complete SEQC data sets, comprising >100 billion reads, provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings, and measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling.
Abstract: We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

853 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the relationship of organizational learning culture, job satisfaction, and organizational outcome variables with a sample of information technology (IT) employees in the United States and found that learning organizational culture is associated with IT employee job satisfaction and motivation to transfer learning.
Abstract: Although organizational learning theory and practice have been clarified by practitioners and scholars over the past several years, there is much to be explored regarding interactions between organizational learning culture and employee learning and performance outcomes. This study examined the relationship of organizational learning culture, job satisfaction, and organizational outcome variables with a sample of information technology (IT) employees in the United States. It found that learning organizational culture is associated with IT employee job satisfaction and motivation to transfer learning. Turnover intention was found to be negatively influenced by organizational learning culture and job satisfaction. Suggestions for future study of learning organizational culture in association with job satisfaction and performance-related outcomes are discussed. With the current expansion of the global economy and the fast-changing evolution of technology and innovation, organizations are facing an ongoing need for employee learning and development. As knowledge increasingly becomes a key factor for productivity, it has also become a currency for competitive success. Understanding factors that contribute to organizational learning and the transfer of knowledge to the workplace environment are essential to

853 citations

Journal ArticleDOI
Patrick J. Keeling1, Patrick J. Keeling2, Fabien Burki1, Heather M. Wilcox3, Bassem Allam4, Eric E. Allen5, Linda A. Amaral-Zettler6, Linda A. Amaral-Zettler7, E. Virginia Armbrust8, John M. Archibald2, John M. Archibald9, Arvind K. Bharti10, Callum J. Bell10, Bank Beszteri11, Kay D. Bidle12, Connor Cameron10, Lisa Campbell13, David A. Caron14, Rose Ann Cattolico8, Jackie L. Collier4, Kathryn J. Coyne15, Simon K. Davy16, Phillipe Deschamps17, Sonya T. Dyhrman18, Bente Edvardsen19, Ruth D. Gates20, Christopher J. Gobler4, Spencer J. Greenwood21, Stephanie Guida10, Jennifer L. Jacobi10, Kjetill S. Jakobsen19, Erick R. James1, Bethany D. Jenkins22, Uwe John11, Matthew D. Johnson23, Andrew R. Juhl18, Anja Kamp24, Anja Kamp25, Laura A. Katz26, Ronald P. Kiene27, Alexander Kudryavtsev28, Alexander Kudryavtsev29, Brian S. Leander1, Senjie Lin30, Connie Lovejoy31, Denis H. Lynn32, Denis H. Lynn1, Adrian Marchetti33, George B. McManus30, Aurora M. Nedelcu34, Susanne Menden-Deuer22, Cristina Miceli35, Thomas Mock36, Marina Montresor37, Mary Ann Moran38, Shauna A. Murray39, Govind Nadathur40, Satoshi Nagai, Peter B. Ngam10, Brian Palenik5, Jan Pawlowski29, Giulio Petroni41, Gwenael Piganeau42, Matthew C. Posewitz43, Karin Rengefors44, Giovanna Romano37, Mary E. Rumpho30, Tatiana A. Rynearson22, Kelly B. Schilling10, Declan C. Schroeder, Alastair G. B. Simpson9, Alastair G. B. Simpson2, Claudio H. Slamovits9, Claudio H. Slamovits2, David Roy Smith45, G. Jason Smith46, Sarah R. Smith5, Heidi M. Sosik23, Peter Stief24, Edward C. Theriot47, Scott N. Twary48, Pooja E. Umale10, Daniel Vaulot49, Boris Wawrik50, Glen L. Wheeler51, William H. Wilson52, Yan Xu53, Adriana Zingone37, Alexandra Z. Worden2, Alexandra Z. Worden3 
University of British Columbia1, Canadian Institute for Advanced Research2, Monterey Bay Aquarium Research Institute3, Stony Brook University4, University of California, San Diego5, Marine Biological Laboratory6, Brown University7, University of Washington8, Dalhousie University9, National Center for Genome Resources10, Alfred Wegener Institute for Polar and Marine Research11, Rutgers University12, Texas A&M University13, University of Southern California14, University of Delaware15, Victoria University of Wellington16, University of Paris-Sud17, Columbia University18, University of Oslo19, University of Hawaii at Manoa20, University of Prince Edward Island21, University of Rhode Island22, Woods Hole Oceanographic Institution23, Max Planck Society24, Jacobs University Bremen25, Smith College26, University of South Alabama27, Saint Petersburg State University28, University of Geneva29, University of Connecticut30, Laval University31, University of Guelph32, University of North Carolina at Chapel Hill33, University of New Brunswick34, University of Camerino35, University of East Anglia36, Stazione Zoologica Anton Dohrn37, University of Georgia38, University of Technology, Sydney39, University of Puerto Rico40, University of Pisa41, Centre national de la recherche scientifique42, Colorado School of Mines43, Lund University44, University of Western Ontario45, California State University46, University of Texas at Austin47, Los Alamos National Laboratory48, Pierre-and-Marie-Curie University49, University of Oklahoma50, Plymouth Marine Laboratory51, Bigelow Laboratory For Ocean Sciences52, Princeton University53
TL;DR: In this paper, the authors describe a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans and their biology, evolution, and ecology.
Abstract: Current sampling of genomic sequence data from eukaryotes is relatively poor, biased, and inadequate to address important questions about their biology, evolution, and ecology; this Community Page describes a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans.

852 citations

Journal ArticleDOI
TL;DR: In this paper, the shape memory effect due to martensitic transformation and reorientation of polycrystalline shape memory alloy (SMA) materials is modeled using a free energy function and a dissipation potential.

848 citations


Authors

Showing all 72708 results

NameH-indexPapersCitations
Yi Chen2174342293080
Scott M. Grundy187841231821
Evan E. Eichler170567150409
Yang Yang1642704144071
Martin Karplus163831138492
Robert Stone1601756167901
Philip Cohen154555110856
Claude Bouchard1531076115307
Jongmin Lee1502257134772
Zhenwei Yang150956109344
Vivek Sharma1503030136228
Frede Blaabjerg1472161112017
Steven L. Salzberg147407231756
Mikhail D. Lukin14660681034
John F. Hartwig14571466472
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023211
2022938
20218,664
20208,925
20198,426