Carolyn Lawrence-Dill

Last updated
Carolyn Lawrence-Dill
Carolyn Lawrence-Dill (cropped).jpg
Born (1974-05-18) May 18, 1974 (age 50)
El Paso, Texas, U.S.
Alma mater University of Georgia
Texas Tech University
Hendrix College
Known forPlant science data access and availability; gene function prediction tools and resources; making phenotype descriptions computable, research community-building
Scientific career
Fields Plant Biology
Bioinformatics
Institutions Agricultural Research Service, Iowa State University, Colorado State University
Doctoral advisor R. Kelly Dawe & Russell L. Malmberg

Carolyn Joy Lawrence-Dill (born May 18, 1974) is an American plant biologist and academic administrator. She develops computational systems and tools to help plant science researchers use plant genetics and genomics data for basic biology applications that advance plant breeding.

Contents

Early life and education

Carolyn Joy Lawrence-Dill, née Cogburn, was born in El Paso, Texas. She grew up in Throckmorton, then moved to Cleburne in 1989. She graduated in 1992 from Cleburne High School. Lawrence-Dill earned a B.A. degree in biology from Hendrix College in 1996. She received her M.S. degree in biology in 1997 from Texas Tech University where she worked on cotton physiology, and her Ph.D. degree in botany in 2003 from the University of Georgia. Her doctoral dissertation focused on integrating traditional and computational methods for inferring gene function in plants. [1]

Career

Following her formal education, Lawrence-Dill served for two years as a postdoctoral researcher under the direction of Volker Brendel [2] at Iowa State University.

In the summer of 2005, Lawrence-Dill began work as a research geneticist for the USDA-ARS. She served as the director of MaizeGDB, the maize model organism database through December 2013. In 2014 she joined the faculty of Iowa State University as an associate professor in the Departments of Genetics, Development and Cell Biology and Agronomy. In 2019 she was promoted to the rank of professor. In 2021, she was named Associate Dean for Research and Discovery for the Iowa State University College of Agriculture and Life Sciences and Associate Director of the Iowa Agriculture and Home Economics Experiment Station. In 2024, she joined Colorado State University as Dean of the College of Agricultural Sciences. [3]

Research

Lawrence-Dill's research focuses on mapping genomes and gene elements, [4] [5] [6] [7] predicting protein function, [8] [9] inventing new ways to link genes to phenotypic descriptions and images, [10] [11] developing ways to compute on phenotypic descriptions, [12] [13] [14] [15] organizing broad datasets for community access and use, [16] [17] [18] and developing computational tools that enable others to do all of these sorts of analyses directly. Although research and development projects are across the plant kingdom generally, much of her work focuses on maize.

Genomics

Lawrence-Dill has advanced plant scientists' ability to access plant genomics resources by sequencing and assembling genomes, annotating structural elements including genes, regulatory elements and CRISPR sites to genomes, and creating tools that enable researchers to analyze gene expression data.

Phenomics

Lawrence-Dill has advanced plant scientists' ability to compute on phenotype directly via connecting image-based phenotypes to genomics data, crowdsourcing for image-based machine learning, managing information for field and controlled environment high-throughput phenotyping, and computing on phenotypic descriptions.

Leadership and policy

Data sharing

Much of the work Lawrence-Dill has published seeks to advance data sharing to enable researchers to make use of others' findings, as some scientists harbor concerns about data sharing that those who generate materials and data will not derive prominence from downstream use and benefits derived from their own data. [19] However, generally limiting access to data prevents researchers from being able to test whether research results are reproducible. With respect to genomics data and materials, limiting access to digital sequence information (DSI) relevant to specific germplasm can keep researchers from being able to identify biological materials for novel research applications.

Climate and genetic engineering

Lawrence-Dill regularly addresses timely topics like climate change and genetic engineering, advising colleagues to engage in discussions on these topics with colleagues in other disciplines, with policymakers, and with the general public. Her guidance focuses on finding shared values, articulating social, environmental, and economic opportunities, and appealing to a better future rather than negative consequences.

In 2016, Lawrence-Dill and sociologist Shawn Dorius began work to better understand where negative public opinions on GMOs and climate change originate. While investigating how GMOs were portrayed in US news coverage, information on Russian interference in the 2016 United States elections emerged, with English language Russian state news from RT and Sputnik being ordered to register as foreign agents. This led the team to look into news reported by RT and Sputnik, where they found their portrayal of GMO topics to be very different from that in US media. Russian state news about GMOs was almost entirely negative, with seemingly intentional mis-associations linking GMOs with controversial, unrelated, and distasteful topics (e.g., topics on abortions of Zika-infected fetuses and the Trans-Pacific Partnership). The team hypothesized that this sort of activity could aim not only to stir up controversy in the US, but that to serve economic interests in Russia given that Russia’s number two industry is agriculture.

While their findings were under peer review, the Des Moines Register released a front-page article [20] describing their findings (February 25, 2018). The researchers released a preprint of the article via the SocArXiv [21] within a day to ensure that detailed materials, methods, and interpretations of the data were fully available. A media frenzy followed with coverage in more than 80 newspapers, online websites, and radio broadcasts, with audio coverage through National Public Radio’s Marketplace (February 28, 2018) and Iowa Public Radio’s River to River (March 2, 2018). There was even a political cartoon released by Greg Kearney, [22] and Bill Gates defended GMOs via a Reddit Ask Me Anything [23] discussion in the midst of the coverage. The peer-reviewed publication [24] was accepted March 11, 2018. Subsequent to media coverage of the GMO-Russia connections, reports of other seemingly unrelated hot topics showed signs of Russian influence with apparent intention to cause discord, [25] with demonstrations of influence campaigns emerging on wide-ranging topics from energy to human rights to international trade.

Scientific community building

Lawrence-Dill has brought together researchers across many communities to coordinate their work. This includes building consensus for standards and nomenclature, [26] [27] founding community organizations, [28] [29] and encouraging others through mentorship and training opportunities. [30]

Awards

Elected service

Related Research Articles

<span class="mw-page-title-main">Genetic engineering</span> Manipulation of an organisms genome

Genetic engineering, also called genetic modification or genetic manipulation, is the modification and manipulation of an organism's genes using technology. It is a set of technologies used to change the genetic makeup of cells, including the transfer of genes within and across species boundaries to produce improved or novel organisms.

Genomic imprinting is an epigenetic phenomenon that causes genes to be expressed or not, depending on whether they are inherited from the female or male parent. Genes can also be partially imprinted. Partial imprinting occurs when alleles from both parents are differently expressed rather than complete expression and complete suppression of one parent's allele. Forms of genomic imprinting have been demonstrated in fungi, plants and animals. In 2014, there were about 150 imprinted genes known in mice and about half that in humans. As of 2019, 260 imprinted genes have been reported in mice and 228 in humans.

<span class="mw-page-title-main">Phenotype</span> Composite of the organisms observable characteristics or traits

In genetics, the phenotype is the set of observable characteristics or traits of an organism. The term covers the organism's morphology, its developmental processes, its biochemical and physiological properties, its behavior, and the products of behavior. An organism's phenotype results from two basic factors: the expression of an organism's genetic code and the influence of environmental factors. Both factors may interact, further affecting the phenotype. When two or more clearly different phenotypes exist in the same population of a species, the species is called polymorphic. A well-documented example of polymorphism is Labrador Retriever coloring; while the coat color depends on many genes, it is clearly seen in the environment as yellow, black, and brown. Richard Dawkins in 1978 and then again in his 1982 book The Extended Phenotype suggested that one can regard bird nests and other built structures such as caddisfly larva cases and beaver dams as "extended phenotypes".

<span class="mw-page-title-main">Transposable element</span> Semiparasitic DNA sequence

A transposable element (TE), also transposon, or jumping gene, is a type of mobile genetic element, a nucleic acid sequence in DNA that can change its position within a genome, sometimes creating or reversing mutations and altering the cell's genetic identity and genome size.

<span class="mw-page-title-main">Genomics</span> Discipline in genetics

Genomics is an interdisciplinary field of molecular biology focusing on the structure, function, evolution, mapping, and editing of genomes. A genome is an organism's complete set of DNA, including all of its genes as well as its hierarchical, three-dimensional structural configuration. In contrast to genetics, which refers to the study of individual genes and their roles in inheritance, genomics aims at the collective characterization and quantification of all of an organism's genes, their interrelations and influence on the organism. Genes may direct the production of proteins with the assistance of enzymes and messenger molecules. In turn, proteins make up body structures such as organs and tissues as well as control chemical reactions and carry signals between cells. Genomics also involves the sequencing and analysis of genomes through uses of high throughput DNA sequencing and bioinformatics to assemble and analyze the function and structure of entire genomes. Advances in genomics have triggered a revolution in discovery-based research and systems biology to facilitate understanding of even the most complex biological systems such as the brain.

<span class="mw-page-title-main">Molecular genetics</span> Scientific study of genes at the molecular level

Molecular genetics is a branch of biology that addresses how differences in the structures or expression of DNA molecules manifests as variation among organisms. Molecular genetics often applies an "investigative approach" to determine the structure and/or function of genes in an organism's genome using genetic screens. 

<span class="mw-page-title-main">Comparative genomics</span> Field of biological research

Comparative genomics is a branch of biological research that examines genome sequences across a spectrum of species, spanning from humans and mice to a diverse array of organisms from bacteria to chimpanzees. This large-scale holistic approach compares two or more genomes to discover the similarities and differences between the genomes and to study the biology of the individual genomes. Comparison of whole genome sequences provides a highly detailed view of how organisms are related to each other at the gene level. By comparing whole genome sequences, researchers gain insights into genetic relationships between organisms and study evolutionary changes. The major principle of comparative genomics is that common features of two organisms will often be encoded within the DNA that is evolutionarily conserved between them. Therefore, Comparative genomics provides a powerful tool for studying evolutionary changes among organisms, helping to identify genes that are conserved or common among species, as well as genes that give unique characteristics of each organism. Moreover, these studies can be performed at different levels of the genomes to obtain multiple perspectives about the organisms.

<span class="mw-page-title-main">Paleopolyploidy</span> State of having undergone whole genome duplication in deep evolutionary time

Paleopolyploidy is the result of genome duplications which occurred at least several million years ago (MYA). Such an event could either double the genome of a single species (autopolyploidy) or combine those of two species (allopolyploidy). Because of functional redundancy, genes are rapidly silenced or lost from the duplicated genomes. Most paleopolyploids, through evolutionary time, have lost their polyploid status through a process called diploidization, and are currently considered diploids, e.g., baker's yeast, Arabidopsis thaliana, and perhaps humans.

Phenomics is the systematic study of traits that make up an organisms phenotype, which changes over time, due to development and aging or through metamorphosis such as when a caterpillar changes into a butterfly. The term phenomics was coined by UC Berkeley and LBNL scientist Steven A. Garan. As such, it is a transdisciplinary area of research that involves biology, data sciences, engineering and other fields. Phenomics is concerned with the measurement of the phenotype where a phenome is a set of traits that can be produced by a given organism over the course of development and in response to genetic mutation and environmental influences.

<span class="mw-page-title-main">Susan R. Wessler</span> American biologist

Susan Randi Wessler, ForMemRS, is an American plant molecular biologist and geneticist. She is distinguished professor of genetics at the University of California, Riverside (UCR).

Mouse Genome Informatics (MGI) is a free, online database and bioinformatics resource hosted by The Jackson Laboratory, with funding by the National Human Genome Research Institute (NHGRI), the National Cancer Institute (NCI), and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). MGI provides access to data on the genetics, genomics and biology of the laboratory mouse to facilitate the study of human health and disease. The database integrates multiple projects, with the two largest contributions coming from the Mouse Genome Database and Mouse Gene Expression Database (GXD). As of 2018, MGI contains data curated from over 230,000 publications.

<span class="mw-page-title-main">Gene targeting</span> Genetic technique that uses homologous recombination to change an endogenous gene

Gene targeting is a biotechnological tool used to change the DNA sequence of an organism. It is based on the natural DNA-repair mechanism of Homology Directed Repair (HDR), including Homologous Recombination. Gene targeting can be used to make a range of sizes of DNA edits, from larger DNA edits such as inserting entire new genes into an organism, through to much smaller changes to the existing DNA such as a single base-pair change. Gene targeting relies on the presence of a repair template to introduce the user-defined edits to the DNA. The user will design the repair template to contain the desired edit, flanked by DNA sequence corresponding (homologous) to the region of DNA that the user wants to edit; hence the edit is targeted to a particular genomic region. In this way Gene Targeting is distinct from natural homology-directed repair, during which the ‘natural’ DNA repair template of the sister chromatid is used to repair broken DNA. The alteration of DNA sequence in an organism can be useful in both a research context – for example to understand the biological role of a gene – and in biotechnology, for example to alter the traits of an organism.

Cognitive genomics is the sub-field of genomics pertaining to cognitive function in which the genes and non-coding sequences of an organism's genome related to the health and activity of the brain are studied. By applying comparative genomics, the genomes of multiple species are compared in order to identify genetic and phenotypical differences between species. Observed phenotypical characteristics related to the neurological function include behavior, personality, neuroanatomy, and neuropathology. The theory behind cognitive genomics is based on elements of genetics, evolutionary biology, molecular biology, cognitive psychology, behavioral psychology, and neurophysiology.

Molecular breeding is the application of molecular biology tools, often in plant breeding and animal breeding. In the broad sense, molecular breeding can be defined as the use of genetic manipulation performed at the level of DNA to improve traits of interest in plants and animals, and it may also include genetic engineering or gene manipulation, molecular marker-assisted selection, and genomic selection. More often, however, molecular breeding implies molecular marker-assisted breeding (MAB) and is defined as the application of molecular biotechnologies, specifically molecular markers, in combination with linkage maps and genomics, to alter and improve plant or animal traits on the basis of genotypic assays.

"Envirome" is a concept that relates the core of environmental conditions with the successful biological performance of living beings. This concept was created in genetic epidemiology, in which an envirome is defined as the total set of environmental factors, both present, and past, that affect the state, and in particular the disease state, of an organism. The study of the envirome and its effects is termed enviromics. The term was first coined in the field of psychiatric epidemiology by J.C. Anthony in 1995. More recently, use of the term has been extended to the cellular domain, where cell functional enviromics studies both the genome and envirome from a systems biology perspective. In plants, enviromics is directly related to complex ecophysiology, in which the wide environment of the plants, into an omics scale, can be dissected and understood as a mosaic of possible growing factors and the balance of diverse resources available. In ecology, this concept can be related to the Shelford's law of tolerance. The enviromics is conceived as a pillar of the Modern Plant Breeding, capable to connect the design and development of breeding goals concealing it with the agronomic targets for a climate-smart agriculture. It also has the ability to bridge the knowledge gaps between the different levels of systems biology and phenomics in the context of Gene–environment interaction.

Robert Anthony Martienssen is a British plant biologist, Howard Hughes Medical Institute–Gordon and Betty Moore Foundation investigator, and professor at Cold Spring Harbor Laboratory, US.

Model organism databases (MODs) are biological databases, or knowledgebases, dedicated to the provision of in-depth biological data for intensively studied model organisms. MODs allow researchers to easily find background information on large sets of genes, efficiently plan experiments, integrate their data with existing knowledge, and formulate new hypotheses. They allow users to analyse results and interpret datasets, and the data they generate are increasingly used to describe less well studied species. Where possible, MODs share common approaches to collect and represent biological information. For example, all MODs use the Gene Ontology (GO) to describe functions, processes and cellular locations of specific gene products. Projects also exist to enable software sharing for curation, visualization and querying between different MODs. Organismal diversity and varying user requirements however mean that MODs are often required to customize capture, display, and provision of data.

A plant genome assembly represents the complete genomic sequence of a plant species, which is assembled into chromosomes and other organelles by using DNA fragments that are obtained from different types of sequencing technology.

Robert J. Schmitz is an American plant biologist and epigenomicist at the University of Georgia where he studies the generation and phenotypic consequences of plant epialleles as well as developing new techniques to identify and study cis-regulatory sequences. He is an associate professor in the department of genetics and the UGA Foundation Endowed Pant Sciences Professor.

<span class="mw-page-title-main">James Schnable</span> American plant geneticist

James C. Schnable is a plant geneticist and the Nebraska Corn Checkoff Presidential Chair at the University of Nebraska – Lincoln where his research program focuses on developing new technologies for crop genetics and breeding.

References

  1. ↑ Dissertation Carolyn Joy Lawrence: A combined bioinformatic/molecular-based approach to understanding molecular motors in plants - website of the Library of the University of Georgia. Archived 2020-10-17 at the Wayback Machine
  2. ↑ Brendel, Volker. "Volker Brendel, Professor of Biology". Department of Biology, Indiana University.
  3. ↑ "Lawrence-Dill named dean of CSU College of Agricultural Sciences". The Fence Post.
  4. ↑ Andorf, Carson M.; Kopylov, Mykhailo; Dobbs, Drena; Koch, Karen E.; Stroupe, M. Elizabeth; Lawrence, Carolyn J.; Bass, Hank W. (December 2014). "G-Quadruplex (G4) Motifs in the Maize (Zea mays L.) Genome Are Enriched at Specific Locations in Thousands of Genes Coupled to Energy Status, Hypoxia, Low Sugar, and Nutrient Deprivation". Journal of Genetics and Genomics. 41 (12): 627–647. doi: 10.1016/j.jgg.2014.10.004 . PMID   25527104.
  5. ↑ Law, MeiYee; Childs, Kevin L.; Campbell, Michael S.; Stein, Joshua C.; Olson, Andrew J.; Holt, Carson; Panchy, Nicholas; Lei, Jikai; Jiao, Dian; Andorf, Carson M.; Lawrence, Carolyn J.; Ware, Doreen; Shiu, Shin-Han; Sun, Yanni; Jiang, Ning; Yandell, Mark (1 January 2015). "Automated Update, Revision, and Quality Control of the Maize Genome Annotations Using MAKER-P Improves the B73 RefGen_v3 Gene Models and Identifies New Genes". Plant Physiology. 167 (1): 25–39. doi:10.1104/pp.114.245027. PMC   4280997 . PMID   25384563.
  6. ↑ Campbell, Michael S.; Law, MeiYee; Holt, Carson; Stein, Joshua C.; Moghe, Gaurav D.; Hufnagel, David E.; Lei, Jikai; Achawanantakun, Rujira; Jiao, Dian; Lawrence, Carolyn J.; Ware, Doreen; Shiu, Shin-Han; Childs, Kevin L.; Sun, Yanni; Jiang, Ning; Yandell, Mark (1 February 2014). "MAKER-P: A Tool Kit for the Rapid Creation, Management, and Quality Control of Plant Genome Annotations". Plant Physiology. 164 (2): 513–524. doi:10.1104/pp.113.230144. PMC   3912085 . PMID   24306534. S2CID   11525262.
  7. ↑ Manchanda, Nancy; Portwood, John L.; Woodhouse, Margaret R.; Seetharam, Arun S.; Lawrence-Dill, Carolyn J.; Andorf, Carson M.; Hufford, Matthew B. (2 March 2020). "GenomeQC: a quality assessment tool for genome assemblies and gene structure annotations". BMC Genomics. 21 (1): 193. doi: 10.1186/s12864-020-6568-2 . PMC   7053122 . PMID   32122303.
  8. ↑ Wimalanathan, Kokulapalan; Lawrence-Dill, Carolyn J. (3 May 2021). "Gene Ontology Meta Annotator for Plants". bioRxiv   10.1101/809988 .
  9. ↑ Wimalanathan, Kokulapalan; Friedberg, Iddo; Andorf, Carson M.; Lawrence-Dill, Carolyn J. (April 2018). "Maize GO Annotation-Methods, Evaluation, and Review (maize-GAMER)". Plant Direct. 2 (4): e00052. doi:10.1002/pld3.52. PMC   6508527 . PMID   31245718. S2CID   90386060.
  10. ↑ Cho, Kyoung Tak; Portwood, John L.; Gardiner, Jack M.; Harper, Lisa C.; Lawrence-Dill, Carolyn J.; Friedberg, Iddo; Andorf, Carson M. (28 August 2019). "MaizeDIG: Maize Database of Images and Genomes". Frontiers in Plant Science. 10: 1050. doi: 10.3389/fpls.2019.01050 . PMC   6724615 . PMID   31555312.
  11. ↑ Zhou, Naihui; Siegel, Zachary D.; Zarecor, Scott; Lee, Nigel; Campbell, Darwin A.; Andorf, Carson M.; Nettleton, Dan; Lawrence-Dill, Carolyn J.; Ganapathysubramanian, Baskar; Kelly, Jonathan W.; Friedberg, Iddo (30 July 2018). "Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning". PLOS Computational Biology. 14 (7): e1006337. Bibcode:2018PLSCB..14E6337Z. doi: 10.1371/journal.pcbi.1006337 . PMC   6085066 . PMID   30059508. S2CID   51881800.
  12. ↑ Oellrich, Anika; Walls, Ramona L; Cannon, Ethalinda; Cannon, Steven B; Cooper, Laurel; Gardiner, Jack; Gkoutos, Georgios V; Harper, Lisa; He, Mingze; Hoehndorf, Robert; Jaiswal, Pankaj; Kalberer, Scott R; Lloyd, John P; Meinke, David; Menda, Naama; Moore, Laura; Nelson, Rex T; Pujar, Anuradha; Lawrence, Carolyn J; Huala, Eva (2015). "An ontology approach to comparative phenomics in plants". Plant Methods. 11 (1): 10. doi: 10.1186/s13007-015-0053-y . PMC   4359497 . PMID   25774204. S2CID   18382593.
  13. ↑ Braun, Ian; Lawrence-Dill, Carolyn (2018). "Computational Classification of Phenologs Across Biological Diversity" (PDF). Proceedings of the 9th International Conference on Biological Ontology: 2. Archived (PDF) from the original on 2019-01-14. Retrieved 2020-10-16.
  14. ↑ Braun, Ian R.; Lawrence-Dill, Carolyn J. (10 January 2020). "Automated Methods Enable Direct Computation on Phenotypic Descriptions for Novel Candidate Gene Prediction". Frontiers in Plant Science. 10: 1629. doi: 10.3389/fpls.2019.01629 . ISSN   1664-462X. PMC   6965352 . PMID   31998331. S2CID   210117404.
  15. ↑ Braun, Ian R.; Yanarella, Colleen F.; Lawrence-Dill, Carolyn J. (20 May 2020). "Computing on Phenotypic Descriptions for Candidate Gene Discovery and Crop Improvement". Plant Phenomics. 2020: 1–4. doi: 10.34133/2020/1963251 . PMC   7706311 . PMID   33313544.
  16. ↑ AlKhalifah, Naser; Campbell, Darwin A.; Falcon, Celeste M.; Gardiner, Jack M.; Miller, Nathan D.; Romay, Maria Cinta; Walls, Ramona; Walton, Renee; Yeh, Cheng-Ting; Bohn, Martin; Bubert, Jessica; Buckler, Edward S.; Ciampitti, Ignacio; Flint-Garcia, Sherry; Gore, Michael A.; Graham, Christopher; Hirsch, Candice; Holland, James B.; Hooker, David; Kaeppler, Shawn; Knoll, Joseph; Lauter, Nick; Lee, Elizabeth C.; Lorenz, Aaron; Lynch, Jonathan P.; Moose, Stephen P.; Murray, Seth C.; Nelson, Rebecca; Rocheford, Torbert; Rodriguez, Oscar; Schnable, James C.; Scully, Brian; Smith, Margaret; Springer, Nathan; Thomison, Peter; Tuinstra, Mitchell; Wisser, Randall J.; Xu, Wenwei; Ertl, David; Schnable, Patrick S.; De Leon, Natalia; Spalding, Edgar P.; Edwards, Jode; Lawrence-Dill, Carolyn J. (December 2018). "Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets". BMC Research Notes. 11 (1): 452. doi: 10.1186/s13104-018-3508-1 . PMC   6038255 . PMID   29986751.
  17. ↑ Lawrence, C. J. (1 January 2004). "MaizeGDB, the community database for maize genetics and genomics". Nucleic Acids Research. 32 (90001): 393D–397. doi:10.1093/nar/gkh011. PMC   308746 . PMID   14681441.
  18. ↑ Duvick, J.; Fu, A.; Muppirala, U.; Sabharwal, M.; Wilkerson, M. D.; Lawrence, C. J.; Lushbough, C.; Brendel, V. (23 December 2007). "PlantGDB: a resource for comparative plant genomics". Nucleic Acids Research. 36 (Database): D959 –D965. doi:10.1093/nar/gkm1041. PMC   2238959 . PMID   18063570. S2CID   10046054.
  19. ↑ Longo, Dan L.; Drazen, Jeffrey M. (20 January 2016). "Data Sharing". New England Journal of Medicine. 374 (3): 276–277. doi: 10.1056/NEJMe1516564 . PMID   26789876.
  20. ↑ Eller, Donnelle. "Anti-GMO articles tied to Russian sites, ISU research shows". Des Moines Register. Archived from the original on 17 October 2020. Retrieved 15 October 2020.
  21. ↑ Dorius, Shawn F; Lawrence-Dill, Carolyn J (27 February 2018). "Sowing the seeds of skepticism: Russian state news and anti-GMO sentiment". GM Crops & Food. 9 (2): 53–58. doi:10.1080/21645698.2018.1454192. PMC   6277062 . PMID   29561212.
  22. ↑ Kearney, G. M. "Drawing Attention". Archived from the original on 17 October 2020. Retrieved 15 October 2020.
  23. ↑ Gates, Bill. "r/IAmA - Comment by u/thisisbillgates on "I'm Bill Gates, co-chair of the Bill & Melinda Gates Foundation. Ask Me Anything."". reddit.
  24. ↑ Dorius, Shawn F.; Lawrence-Dill, Carolyn J. (3 April 2018). "Sowing the seeds of skepticism: Russian state news and anti-GMO sentiment". GM Crops & Food. 9 (2): 53–58. doi:10.1080/21645698.2018.1454192. PMC   6277062 . PMID   29561212. S2CID   4051135.
  25. ↑ Timberg, Craig; Romm, Tony. "These provocative images show Russian trolls sought to inflame debate over climate change, fracking and Dakota pipeline". Washington Post. Retrieved 20 October 2020.
  26. ↑ Gray, John; Bevan, Michael; Brutnell, Thomas; Buell, C. Robin; Cone, Karen; Hake, Sarah; Jackson, David; Kellogg, Elizabeth; Lawrence, Carolyn; McCouch, Susan; Mockler, Todd; Moose, Stephen; Paterson, Andrew; Peterson, Thomas; Rokshar, Daniel; Souza, Glaucia Mendes; Springer, Nathan; Stein, Nils; Timmermans, Marja; Wang, Guo-Liang; Grotewold, Erich (January 2009). "A Recommendation for Naming Transcription Factor Proteins in the Grasses". Plant Physiology. 149 (1): 4–6. doi:10.1104/pp.108.128504. PMC   2613739 . PMID   19126689.
  27. ↑ Lawrence, Carolyn J.; Dawe, R. Kelly; Christie, Karen R.; Cleveland, Don W.; Dawson, Scott C.; Endow, Sharyn A.; Goldstein, Lawrence S.B.; Goodson, Holly V.; Hirokawa, Nobutaka; Howard, Jonathon; Malmberg, Russell L.; McIntosh, J. Richard; Miki, Harukata; Mitchison, Timothy J.; Okada, Yasushi; Reddy, Anireddy S.N.; Saxton, William M.; Schliwa, Manfred; Scholey, Jonathan M.; Vale, Ronald D.; Walczak, Claire E.; Wordeman, Linda (11 October 2004). "A standardized kinesin nomenclature". Journal of Cell Biology. 167 (1): 19–22. doi:10.1083/jcb.200408113. PMC   2041940 . PMID   15479732. S2CID   30027277.
  28. ↑ Carroll, April Agee; Clarke, Jennifer; Fahlgren, Noah; Gehan, Malia A.; Lawrence-Dill, Carolyn J.; Lorence, Argelia (January 2019). "NAPPN: Who We Are, Where We Are Going, and Why You Should Join Us!". The Plant Phenome Journal. 2 (1): 1–4. doi: 10.2135/tppj2018.08.0006 . S2CID   186567373.
  29. ↑ Lawrence-Dill, Carolyn J.; Schnable, Patrick S.; Springer, Nathan M. (July 2019). "Idea Factory: the Maize Genomes to Fields Initiative". Crop Science. 59 (4): 1406–1410. doi: 10.2135/cropsci2019.02.0071 .
  30. ↑ Lawrence-Dill, Carolyn; Heindel, Theodore; Schnable, Patrick; Strong, Stephanie; Wittrock, Jill; Losch, Mary; Dickerson, Julie (16 May 2018). "Transdisciplinary Graduate Training in Predictive Plant Phenomics". Agronomy. 8 (5): 73. Bibcode:2018Agron...8...73L. doi: 10.3390/agronomy8050073 .
  31. ↑ "Maize Genetics Cooperation honors 2 faculty". Iowa State University. Retrieved 2023-09-20.
  32. ↑ "Women of Achievement". ywca. Archived from the original on 2020-10-17. Retrieved 2020-10-15.
  33. ↑ "IPPN Home". www.plant-phenotyping.org. Archived from the original on 2020-10-01. Retrieved 2020-10-15.
  34. ↑ "North American Plant Phenotyping Network". nappn.plant-phenotyping.org. Archived from the original on 2020-06-12. Retrieved 2020-10-15.
  35. ↑ "DivSeek International Network - A Global Community Driven Not-for-Profit Organization". DivSeek Intl. Archived from the original on 2020-10-17. Retrieved 2020-10-15.
  36. ↑ "Maize Genetics Executive Committee". maizegdb.org. Archived from the original on 2020-05-13. Retrieved 2020-10-15.

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