What we can learn from books in the digital age

Though books are often considered “old media” in the digital age, their production in this period in fact has been continually reimagined and redefined through new technologies of printing, especially paper and ink manufacturing. This paper explores how three specific recent printed books demonstrate this point in both form and content: David Brower’s Let the Mountains Talk, Let the Rivers Run (1995), William McDonough and Michael Braungart’s Cradle to Cradle (2002), and the 2008 Harper-Collins Green Bible. Brower’s book was printed on paper made from kenaf, a sustainable alternative to wood-based paper. Cradle to Cradle was printed on a synthetic polymer that could be endlessly remade into other products. The Green Bible was printed on recycled paper and used soy-based ink, and all verses with environmental content were printed in green. In each case, in form these printed books were meant to model innovative industrial information production while also through their content to motivate enhanced environmental consciousness

Drawing Samples for the Longitudinal Study of Entrepreneurial Groups from Process-Generated Data: A Proposal Based on the German Register of Companies

The growing interest in entrepreneurial groups as collective actors of entrepreneurship raises questions of how and with what kind of data this unit of analysis can be studied. While sampling and access to data on individual entrepreneurs (self-employed) or their business ventures (formal firms) rest upon established routines, a methodological discussion about identifying and sampling entrepreneurial groups is still in its infancy. In this article, we look at process-generated data as a potential linchpin to study entrepreneurial groups. More particularly, this article critically reflects upon the opportunities and challenges of the German Commercial Registry (CR) to function as a sampling frame and data source for an examination of entrepreneurial groups. This reflection includes a discussion about the key characteristics of entrepreneurial groups in order to derive minimal criteria that the data needs to provide, an evaluation of the CR following a data source study approach, and finally an assessment of the error proneness of this data and its consequences for the study of entrepreneurial groups. On this basis, we propose a sampling strategy of entrepreneurial groups with CR data. As such, this article contributes to a general methodological discussion of process-generated data, as it extends and practically applies the concept of a data source study. It also contributes to a methodological discussion about entrepreneurial groups as it offers a procedure to deal with varying group boundaries and the intertwinement of group and business activity typical for this social unit of analysis

2015 National Winter Canola Variety Trial

The objectives of the National Winter Canola Variety Trial (NWCVT) are to evaluate the performance of released and experimental varieties, determine where these varieties are best adapted, and increase the visibility of winter canola across the United States. Breeders, marketers, and producers use data collected from the trials to make informed variety selections. The NWCVT is planted at locations in the Great Plains, Midwest, northern U.S., and Southeast. Senior Authors: Michael Stamm and Scott Dooley, Deptartment of Agronomy, Kansas State University, Manhattan; Other Contributors: Sangu Angadi and Sultan Begna, New Mexico State University, Clovis; Brian Baldwin and Jesse Morrison, Mississippi State University, Starkville; Tracy Beedy, Oklahoma State University, Goodwell; Jourdan Bell, Texas AgriLife Research and Extension Service, Amarillo; Abdel Berrada, Colorado State University, Yellow Jacket; Harbans Bhardwaj, Virginia State University, Petersburg; Matthew Blair and Daniel Ambachew, Tennessee State University, Nashville; Indi Braden, Southeast Missouri State University, Cape Girardeau; Jack Brown, Jim Davis, and Megan Wingerson, University of Idaho, Moscow; Joshua Bushong, Oklahoma State University, Stillwater; Brian Caldbeck, Caldbeck Consulting, Philpot, Kentucky; Claire Caldbeck, Rubisco Seeds, Philpot, Kentucky; Ernst Cebert, Alabama A&M University, Normal; Gary Cramer, Kansas State University, Wichita; John Damicone and Tyler Pierson, Oklahoma State University, Stillwater; Heather Darby and Sara Ziegler, University of Vermont, St. Albans; Jason de Koff and Chris Robbins, Tennessee State University, Nashville; Dennis Delaney, Auburn University, Auburn, Alabama; Paul DeLaune, Texas AgriLife Research Service, Vernon; Dean Elvin, Marquette, Kansas; Eric Eriksmoen, North Dakota State University, Minot; Andrew Esser, Kansas State University, Belleville; John Gassett, Mitch Gilmer, H. Jordan, and Gary Ware, University of Georgia, Griffin; Todd Higgins, Lincoln University, Jefferson City, Missouri; Johnathon Holman and Scott Maxwell, Kansas State University, Garden City; Kimberly Hunter, USDA-ARS, Temple, Texas; Burton Johnson, North Dakota State University, Fargo; Jerry Johnson and Edward Asfeld, Colorado State University, Ft. Collins; Paul Lange, Conway Springs, Kansas; Kevin Larson, Colorado State University, Walsh; David Lee and Melvin Henninger, Rutgers University, Woodstown, New Jersey; Josh Lofton, Oklahoma State University, Stillwater; Charles Mansfield, Purdue University, Vincennes; Lloyd Murdock and John James, University of Kentucky, Lexington; Jerry Nachtman, University of Wyoming, Lingle; Clark Neely and Daniel Hathcoat, Texas A&M University, College Station; Calvin Pearson, Colorado State University, Fruita; Charlie Rife, High Plains Crop Development, Torrington, Wyoming; Brett Rushing, Mississippi State University, Newton; Dipak Santra, University of Nebraska-Lincoln, Scottsbluff; Robert Schrock, Kiowa, Kansas; Tyler Thomas, Fly Over States Ag Research, Troy, Kansas; Wade Thomason and Steve Gulick, Virginia Tech University, Blacksburg; Calvin Trostle and Jonathan Shockey, Texas AgriLife Extension Service, Lubbock; Dennis West, University of Tennessee, Knoxville

2006 national winter canola variety trial

Objectives of the National Winter Canola Variety Trial (NWCVT) are to evaluate the performance of released and experimental varieties, determine where these varieties are best adapted, and increase visibility of winter canola across the nation. Breeders, marketers, and producers use information collected from the trials. Over the past decade, the number of environments and entries tested have increased. The NWCVT is planted at locations in the Great Plains, Midwest, northern United States, and Southeast. The wide diversity of environments has improved our knowledge and understanding of winter canola variety performance

2014 National Winter Canola Variety Trial

The objectives of the National Winter Canola Variety Trial (NWCVT) are to evaluate the performance of released and experimental varieties, determine where these varieties are best adapted, and increase the visibility of winter canola across the United States. Breeders, marketers, and producers use data collected from the trials to make informed variety selections. The NWCVT is planted at locations in the Great Plains, Midwest, northern U.S., and Southeast. Senior Authors Michael Stamm, Dept. of Agronomy, Kansas State University, Manhattan Scott Dooley, Dept. of Agronomy, Kansas State University, Manhattan Other Contributors Sangu Angadi and Sultan Begna, New Mexico State University, Clovis Brian Baldwin, Mississippi State University, Starkville Abdel Berrada, Colorado State University, Yellow Jacket Harbans Bhardwaj, Virginia State University, Petersburg Indi Braden, Southeast Missouri State University, Cape Girardeau Joshua Bushong, Oklahoma State University, Stillwater Brian Caldbeck, Caldbeck Consulting, Philpot, Kentucky Claire Caldbeck, Rubisco Seeds, Philpot, Kentucky Ernst Cebert, Alabama A&M University, Normal Jeff Chandler, North Carolina State University, Mills River Gary Cramer, Kansas State University, Wichita John Damicone and Tyler Pierson, Oklahoma State University, Stillwater Heather Darby, University of Vermont, St. Albans Jeffery Davidson, Mike Bartolo, and Kevin Tanabe, Colorado State University, Rocky Ford Jim Davis and Megan Wingerson, University of Idaho, Moscow Dennis Delaney, Auburn University, Auburn, Alabama Paul DeLaune, Texas AgriLife Research Service, Vernon Eric Eriksmoen, North Dakota State University, Minot John Garner and Adam Heitman, North Carolina State University, Wallace John Gassett, Mitch Gilmer, H. Jordan, and Gary Ware, University of Georgia, Griffin Nicholas George, University of California-Davis Brent Gruenbacher and Mike Patry, Andale, Kansas Todd Higgins, Lincoln University, Jefferson City, Missouri Johnathon Holman, Kansas State University, Garden City Burton Johnson, North Dakota State University, Fargo Jerry Johnson, Colorado State University, Ft. Collins Rick Kochenower, Oklahoma State University, Goodwell Kevin Larson, Colorado State University, Walsh David Lee and Melvin Henninger, Rutgers University, Woodstown, New Jersey Charles Mansfield, Vincennes University, Vincennes Lloyd Murdock and John James, University of Kentucky, Lexington Jerry Nachtman, University of Wyoming, Lingle Clark Neely and Daniel Hathcoat, Texas A&M University, College Station Mick O’Neill and Curtis Owen, New Mexico State University, Farmington Calvin Pearson, Colorado State University, Fruita Charlie Rife, High Plains Crop Development, Torrington, Wyoming Dipak Santra, University of Nebraska-Lincoln, Scottsbluff Robert Schrock, Kiowa, Kansas Peter Sexton, South Dakota State University, Brookings Tyler Thomas, Fly Over States Ag Research, Troy, Kansas Wade Thomason and Steve Gulick, Virginia Tech University, Blacksburg Calvin Trostle and Jonathan Shockey, Texas AgriLife Extension Service, Lubbock Dennis West, University of Tennessee, Knoxville Amber Williams, USDA-ARS, Temple, Texa

2016 National Winter Canola Variety Trial

The objectives of the National Winter Canola Variety Trial (NWCVT) are to evaluate the performance of released and experimental varieties, determine where these varieties are best adapted, and increase the visibility of winter canola across the United States. Breeders, marketers, and producers use data collected from the trials to make informed variety selections. The NWCVT is planted at locations in the Great Plains, Midwest, northern U.S., and Southeast

Faster optimal univariate microgaggregation

Microaggregation is a method to coarsen a dataset, by optimally clustering data points in groups of at least $k$ points, thereby providing a $k$-anonymity type disclosure guarantee for each point in the dataset. Previous algorithms for univariate microaggregation had a $O(k n)$ time complexity. By rephrasing microaggregation as an instance of the concave least weight subsequence problem, in this work we provide improved algorithms that provide an optimal univariate microaggregation on sorted data in $O(n)$ time and space. We further show that our algorithms work not only for sum of squares cost functions, as typically considered, but seamlessly extend to many other cost functions used for univariate microaggregation tasks. In experiments we show that the presented algorithms lead to real world performance improvements

Neighborhood Structure Configuration Models

We develop a new method to efficiently sample synthetic networks that preserve the d-hop neighborhood structure of a given network for any given d. The proposed algorithm trades off the diversity in network samples against the depth of the neighborhood structure that is preserved. Our key innovation is to employ a colored Configuration Model with colors derived from iterations of the so-called Color Refinement algorithm. We prove that with increasing iterations the preserved structural information increases: the generated synthetic networks and the original network become more and more similar, and are eventually indistinguishable in terms of centrality measures such as PageRank, HITS, Katz centrality and eigenvector centrality. Our work enables to efficiently generate samples with a precisely controlled similarity to the original network, especially for large networks