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The repetitive use of ALS inhibitors for smallflower umbrella sedge (Cyperus difformis L.) control has selected for herbicide-resistant (R) populations that threaten the sustainability of rice (Oryza sativa L.) production and demand alternative control measures be developed. A better understanding of seedling recruitment patterns at the field level is required to optimize the timing and efficacy of control measures. Therefore, a population-based threshold model was developed for optimizing germination prediction in multiple acetolactate synthase (ALS)-R and ALS-susceptible (ALS-S) C. difformis biotypes and applied to field-level emergence predictions. Estimated base temperatures (Tb) ranged from 16.5 to 17.6 C with no clear pattern between biotypes; such values are higher than Tb values of other important rice weeds, as well as for rice. Germination rates increased linearly from 16 to 33.7 C. ALS-R seeds germinate faster due to smaller median thermal times to germination (θT(50)) while also displaying lower germination synchronicity across water potentials. Interestingly, ALS-R biotypes were capable of germinating under lower moisture availability, as indicated by their lower (more negative) base water potential values (Ψb(50)) for seed germination; Ψb(50) values ranged from −0.24 to −1.13 MPa. In-field soil germination measurements found thermal times to emergence varied across three water regimes (daily water, flooded, or saturated). Seedling emergence under the daily water treatment was fastest; however, total seedling density was lower than for the other water regimes. In order to optimize springtime C. difformis seedling emergence, soil moisture should be kept around field capacity, as germination is hindered at lower moisture contents. By predicting when most of the seed population germinates, the thermal-time model can address issues regarding the optimal timing for herbicide applications, thereby allowing for improved C. difformis management in rice fields.
How do scientists impact society in the twenty-first century? Many scientists are increasingly interested in the impact that their research will have on the public. Scientists likewise must answer the question above when applying for funding from government agencies, particularly as part of the 'Broader Impacts' criterion of proposals to the US National Science Foundation. This book equips scientists in all disciplines to do just that, by providing an overview of the origins, history, rationale, examples, and case studies of broader impacts, primarily drawn from the author's experiences over the past five decades. Beyond including theory and evidence, it serves as a 'how to' guide for best practices for scientists. Although this book primarily uses examples from the NSF, the themes and best practices are applicable to scientists and applications around the world where funding also requires impacts and activities that benefit society.
Communication and learning professionals understand the critical importance of knowing your audience. Professional societies such as the Visitor Studies Association (VSA) have been created to specifically promote audience research within museums and other informal education settings. They also publish the peer-reviewed journal Visitor Studies, Theory, Research, and Practice that contributes knowledge to the field (VSA, 2017). This is just one example of how the professional community promotes knowing your audience; there are many more.
Twenty years ago when we used the prefix “cyber” in Fossil Horses in Cyberspace (see the opening anecdote), it was an innovative use of the term. Today it is ubiquitous in our culture. Over the past several decades we have entered the computer era and this technology has revolutionized our lives. Of relevance here, it also has revolutionized the ways in which we can do Broader Impacts to reach out for societal impact. Many of the activities described in this book have been affected, or enabled, by computer technology.
The National Science Foundation’s (NSF) Broader Impacts merit review criterion was officially introduced in 1997 (Rothenberg, 2010). This coincided with the beginning of the widespread use of the internet by scientists. At that time many of us considered that an appropriate use of this new technology was to create a website about our research.
Some educators in the United States refer to formal education as K–16, which implies a seamless transition between grades 12 (high-school senior year) and 13 (college freshman year). For a variety of reasons, however, this transition is far less seamless than any other in this supposed K–16 continuum. In particular, this potentially rocky transition relates to the different cultures and expectations of K–12 teachers versus “grades” 13–16 professors, and how the students that they teach learn. It is for this reason that two separate chapters are presented on formal education.
Nowadays in academia mentoring is taken seriously and oftentimes is highly structured. This can include many steps along the pipeline, including at-risk students transitioning from high school, undergraduates, graduate students, postdocs, and early-career faculty working toward tenure. In reality, mentoring of one form or another, whether it is structured or informal, occurs throughout one’s academic career. Published studies from a variety of disciplines, ranging from STEM, to medicine (e.g., Detsky & Baerlocher, 2007), to the humanities (Pye et al., 2016), have highlighted the positive benefits of mentoring, including increased productivity, professional success, and career satisfaction. While mentoring or coaching has been practiced for millennia in academia, over the past several decades it has become more intentional.
During my Broader Impacts graduate seminar, students are assigned an exercise in which they video themselves doing elevator speeches. We then play these back and the entire class critiques what they have said. Listening to the groans beforehand, it is clear that most of the students do not enjoy this experience. (Although, in the course evaluations they later reflect that it was valuable for them to do the elevator speech.) In today’s world of sound bites and a few hundred characters (Twitter), the importance of focusing what you say into short dialog cannot be overemphasized. This style of communication is not restricted to your colleagues. It happens all the time, for example, at social events, in chance conversations in airports, to administrators and politicians working a crowd, to potential donors, and even a short conversation trying to “pitch” a research idea to an NSF program officer.
Informal science education traditionally has been overlooked relative to formal education. There are many reasons why this is the case. Society views formal education, particularly K–12, as a fundamental entitlement, and thus much of the emphasis (but typically not enough resources) has been placed on this kind of learning. Most education colleges in the United States focus on teacher training, counseling, assessment, and educational administration. With few exceptions, such as the Free-Choice Learning program at Oregon State University (OSU, 2018), the academic pursuit of informal science education or STEM learning research is typically relegated to a few professors, or done by professors assigned primarily to formal education. Another related issue is the matter of assessing outcomes.
The National Science Foundation (NSF) has developed a culture that by funding projects they are making investments in research and education. Implicit in this notion is that NSF is a partner in the project. Thus, there is an added expectation of resources provided by the host institutions receiving the funding, as well as other stakeholders. A corollary of NSF’s expectation is the notion of sustainability of projects – that is, that some components of the project will continue after NSF funding ends (Inset 17.1; Oxford Living Dictionaries, 2019). The noun “sustainability” and adjective “sustainable” have dramatically increased in frequency and public awareness over the past several decades (e.g., UNESCO, 2010). A decade ago the notion of sustainability, or sustainable projects, likewise became more commonplace at NSF, but initially within a different context, that of sustainable science.
A fundamental part of the National Science Foundation’s (NSF) mission is the commitment to diversity, equity, and inclusion (DEI; Box 9.1). This is not a new concept, but rather stems from the vision of Bush (1945). These core values have been codified in various NSF documents since that time (e.g., NSF, 2007), as well as strategic initiatives such as INCLUDES (NSF, 2017n). Any scientist wanting to submit competitive proposals to NSF is well advised to include an aspect of DEI within the framework of the projects. This component is best scaled for the size of the project; smaller projects can have these elements embedded within the proposal (e.g., recruitment of graduate students), whereas larger projects might benefit from major components and Broader Impacts initiatives involving DEI.
Several of the chapters here could be entire books in themselves, and this is one of them. The topics chosen – innovation, opportunity, and integration – are potentially diffuse and all-encompassing at the same time. These are intangible aspects to successful NSF projects that are hard to realize, predict, describe, or evaluate. Scientists should challenge themselves to look for these aspects from the beginning of their envisioning and development of NSF projects. The focus here will be on past experiences, representative examples, and interesting case studies.
Sooner or later, scientists with National Science Foundation support will experience some sort of evaluation, whether they like it or not. This is particularly true for larger projects with a greater emphasis on outcomes and impacts. The traditional notion in some principal investigators’ (PI) minds, that funds are awarded for researchers to “do good things” without eventual accountability, is unrealistic in today’s world. For a variety of reasons, the bar has been raised on being able to demonstrate the success of NSF projects, and in so doing, the value of the investment made using taxpayer funds. The problem with evaluation is that unless you are already involved in educational or psychological research, most STEM professionals do not understand what this process entails, how it is done, and how expensive it can be to do it right. There are also different kinds of evaluation procedures depending upon the project being analyzed. It also follows that, if done properly, evaluation is in itself a science with accepted protocols and best practices.
About 10 million people in the United States, or 5 percent of the working population, are employed in some aspect of STEM (Noonan, 2017). This number leaves more than a quarter of a billion other people in the United States who could potentially become engaged in the practice of science sometime during their lifetime. While it is unrealistic to expect full participation from the general public, any effort that increases engagement and broadens participation also promotes learning by doing, and thus science literacy. Public participation can also increase science identity for those who participate in the research enterprise. The general notion that STEM is primarily done by professionals – those who are paid to do so as their career – neglects the fact that few scientists were professionals two centuries ago.
A word cloud of about 30 words (Fig. 19.1) is a way to represent the content of this book. Along with the word cloud, some dominant themes emerge. These reappear in different contexts and chapters and thus provide the fabric that weaves together an understanding of Broader Impacts as envisioned here. Eight topics (Inset 19.1) are presented below because they rise to the top as the most important themes in the book.
Two decades ago, when the community of NSF proposers learned of the “new” Broader Impacts review criterion, many scrambled to figure out what they should say in their proposals. Professors said that they would infuse their research into the content of their courses. Others said that they would build a website about their research using the then-emerging internet.
A fundamental part of the process of research is collaboration and networking. The rationale and benefits of working with others are manifold. In an increasingly integrative pursuit, which at the same time has seen a growth of individual specialization, collaboration promotes the pooling of resources and sharing of diverse expertise. Many breakthroughs can come from these integrative or multidisciplinary collaborations. From a human perspective, the process of research has a social component in which scientists collaborate and make meaningful friendships, but if not careful these can also result in unpleasant consequences. Nevertheless, during my more than 40 years of research, I have largely benefitted from rewarding collaborations as well as forming lasting friendships with colleagues.
The Broader Impacts plan is fundamentally important to a successful NSF proposal. It can make or break the project. In the opening anecdote, there are at least four flaws in how the principal investigator (PI) went about developing his Broader Impacts plan; he: (1) had no clue about his audience; (2) waited until the last minute to start thinking about what he wanted to do; (3) left about one page for the Broader Impacts boilerplate text at the end of his Project Description; and (4) did not plan to budget any funds for these activities.
In this chapter we will first address these deficiencies and then discuss some other aspects of developing an effective Broader Impacts plan, one that will help with the goal of developing a successful NSF proposal. Although most PIs focus on the merits of the proposed research, in some cases an innovative, well-crafted, and integrated Broader Impacts plan can actually “carry” the project. An example of this is our Panama PIRE (2017) project.