Institution
University of Minnesota
Education•Minneapolis, Minnesota, United States•
About: University of Minnesota is a education organization based out in Minneapolis, Minnesota, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 117432 authors who have published 257986 publications receiving 11944239 citations. The organization is also known as: University of Minnesota, Twin Cities & University of Minnesota-Twin Cities.
Topics: Population, Transplantation, Poison control, Health care, Gene
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the SPR Board provides recommendations for publishing data on electrodermal activity (EDA) and a short outline of principles for EDA measurement is given, and recommendations from an earlier report (Fowles et al., ) are incorporated.
Abstract: This committee was appointed by the SPR Board to provide recommendations for publishing data on electrodermal activity (EDA). They are intended to be a stand-alone source for newcomers and experienced users. A short outline of principles for electrodermal measurement is given, and recommendations from an earlier report (Fowles et al., ) are incorporated. Three fundamental techniques of EDA recording are described: (1) endosomatic recording without the application of an external current, (2) exosomatic recording with direct current (the most widely applied methodology), and (3) exosomatic recording with alternating current-to date infrequently used but a promising future methodology. In addition to EDA recording in laboratories, ambulatory recording has become an emerging technique. Specific problems that come with this recording of EDA in the field are discussed, as are those emerging from recording EDA within a magnetic field (e.g., fMRI). Recommendations for the details that should be mentioned in publications of EDA methods and results are provided.
1,609 citations
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TL;DR: The author challenges the traditional notion that changes to medical education are most appropriately made at the level of the curriculum, or the formal educational programs and instruction provided to students, and proposes that the medical school is best thought of as a “learning environment” and that reform initiatives must be undertaken with an eye to what students learn.
Abstract: Throughout this century there have been many efforts to reform the medical curriculum. These efforts have largely been unsuccessful in producing fundamental changes in the training of medical students. The author challenges the traditional notion that changes to medical education are most appropriately made at the level of the curriculum, or the formal educational programs and instruction provided to students. Instead, he proposes that the medical school is best thought of as a "learning environment" and that reform initiatives must be undertaken with an eye to what students learn instead of what they are taught. This alternative framework distinguishes among three interrelated components of medical training: the formal curriculum, the informal curriculum, and the hidden curriculum. The author gives basic definitions of these concepts, and proposes that the hidden curriculum needs particular exploration. To uncover their institution's hidden curricula, he suggests that educators and administrators examine four areas: institutional policies, evaluation activities, resource-allocation decisions, and institutional "slang." He also describes how accreditation standards and processes might be reformed. He concludes with three recommendations for moving beyond curriculum reform to reconstruct the overall learning environment of medical education, including how best to move forward with the Medical School Objectives Project sponsored by the AAMC.
1,608 citations
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02 May 2004TL;DR: WordNet::Similarity as mentioned in this paper is a Perl package that makes it possible to measure the semantic similarity and relatedness between a pair of concepts (or synsets) using WordNet.
Abstract: WordNet::Similarity is a freely available software package that makes it possible to measure the semantic similarity and relatedness between a pair of concepts (or synsets). It provides six measures of similarity, and three measures of relatedness, all of which are based on the lexical database WordNet. These measures are implemented as Perl modules which take as input two concepts, and return a numeric value that represents the degree to which they are similar or related.
1,608 citations
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TL;DR: The authors provide an extensive meta-analysis of personality-int intellectual ability correlations, and a review of interest-intellectual ability associations that provide evidence for communality across the domains of personality of J. L. Holland's (1959) model of vocational interests.
Abstract: The authors review the development of the modern paradigm for intelligence assessment and application and consider the differentiation between intelligence-as-maximal performance and intelligence-as-typical performance. They review theories of intelligence, personality, and interest as a means to establish potential overlap. Consideration of intelligence-as-typical performance provides a basis for evaluation of intelligence-personality and intelligence-interest relations. Evaluation of relations among personality constructs, vocational interests, and intellectual abilities provides evidence for communality across the domains of personality of J. L. Holland's (1959) model of vocational interests. The authors provide an extensive meta-analysis of personality-intellectual ability correlations, and a review of interest-intellectual ability associations. They identify 4 trait complexes: social, clerical/conventional, science/math, and intellectual/cultural.
1,606 citations
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Macquarie University1, University of Minnesota2, VU University Amsterdam3, University of Oslo4, Centre national de la recherche scientifique5, Tohoku University6, Curtin University7, Landcare Research8, Polish Academy of Sciences9, University of Tokyo10, Utrecht University11, University of Córdoba (Spain)12, University of New South Wales13
TL;DR: Global-scale quantification of relationships between plant traits gives insight into the evolution of the world's vegetation, and is crucial for parameterizing vegetation-climate models.
Abstract: Summary • Global-scale quantification of relationships between plant traits gives insight into the evolution of the world’s vegetation, and is crucial for parameterizing vegetation‐ climate models. • A database was compiled, comprising data for hundreds to thousands of species for the core ‘leaf economics’ traits leaf lifespan, leaf mass per area, photosynthetic capacity, dark respiration, and leaf nitrogen and phosphorus concentrations, as well as leaf potassium, photosynthetic N-use efficiency (PNUE), and leaf N : P ratio. • While mean trait values differed between plant functional types, the range found within groups was often larger than differences among them. Future vegetation‐ climate models could incorporate this knowledge. • The core leaf traits were intercorrelated, both globally and within plant functional types, forming a ‘leaf economics spectrum’. While these relationships are very general, they are not universal, as significant heterogeneity exists between relationships fitted to individual sites. Much, but not all, heterogeneity can be explained by variation in sample size alone. PNUE can also be considered as part of this trait spectrum, whereas leaf K and N : P ratios are only loosely related.
1,606 citations
Authors
Showing all 118112 results
Name | H-index | Papers | Citations |
---|---|---|---|
Walter C. Willett | 334 | 2399 | 413322 |
David J. Hunter | 213 | 1836 | 207050 |
David Miller | 203 | 2573 | 204840 |
Mark I. McCarthy | 200 | 1028 | 187898 |
Dennis W. Dickson | 191 | 1243 | 148488 |
David H. Weinberg | 183 | 700 | 171424 |
Eric Boerwinkle | 183 | 1321 | 170971 |
John C. Morris | 183 | 1441 | 168413 |
Aaron R. Folsom | 181 | 1118 | 134044 |
H. S. Chen | 179 | 2401 | 178529 |
Jie Zhang | 178 | 4857 | 221720 |
Jasvinder A. Singh | 176 | 2382 | 223370 |
Feng Zhang | 172 | 1278 | 181865 |
Gang Chen | 167 | 3372 | 149819 |
Hongfang Liu | 166 | 2356 | 156290 |