A Study of the Correlation Between Sixth Grade Students\u27 Attitudes Toward Reading and Their Performance on a Standardized Test

The purpose of this study was to determine if there is a statistically significant correlation between sixth grade students\u27 attitudes toward reading and their standardized test scores. More specifically, the area in question was whether a significant relationship existed between how well students scored on an exam and how much they enjoyed reading. The subjects consisted of 75 sixth graders who had not repeated the grade nor were in Special Education. All of the subjects took the DRP exam in May of 1998 and were given the Elementary Reading Attitude Survey in September of 1998. Four teachers and their classes participated in the study. Each teacher administered the Survey, however, the researcher collected and tabulated all results. A Pearson product moment coefficient of correlation was used to analyze the data. The results demonstrated that there was no statistically significant relationship between students\u27 attitudes and their DRP scores

Data science of the natural environment: a research roadmap

Data science is the science of extracting meaning from potentially complex data. This is a fast moving field, drawing principles and techniques from a number of different disciplinary areas including computer science, statistics and complexity science. Data science is having a profound impact on a number of areas including commerce, health, and smart cities. This paper argues that data science can have an equal if not greater impact in the area of earth and environmental sciences, offering a rich tapestry of new techniques to support both a deeper understanding of the natural environment in all its complexities, as well as the development of well-founded mitigation and adaptation strategies in the face of climate change. The paper argues that data science for the natural environment brings about new challenges for data science, particularly around complexity, spatial and temporal reasoning, and managing uncertainty. The paper also describes a case study in environmental data science which offers up insights into the promise of the area. The paper concludes with a research roadmap highlighting 10 top challenges of environmental data science and also an invitation to become part of an international community working collaboratively on these problems

The U.S. water data gap: A survey of state-level water data platforms to inform the development of a national water portal

Water data play a crucial role in the development and assessment of sustainable water management strategies. Water resource assessments are needed for the planning, management, and the evaluation of current practices. They require environmental, climatic, hydrologic, hydrogeologic, industrial, agricultural, energy, and socioeconomic data to assess and accurately project the supply of and demand for water services. Given this context, we provide a review of the current state of publicly available water data in the United States. While considerable progress has been made in data science and model development in recent years, data limitations continue to hamper analytics. A brief overview of the water data sets available at the federal level is used to highlight the gaps in readily accessible water data in the United States. Then, we present a systematic review of 275 websites that provide water information collected at the state level. Data platforms are evaluated based on content (ground and surface water, water quality, and water use information) along with the analytical and exploratory tools that are offered. Wev discuss the degree to which existing state-level data sets could enrich the data available from federal sources and review some recent technological developments and initiatives that may modernize water data. We argue that a national water data portal, more comprehensive than the U.S. Energy Information Administration, addressing the significant gaps and centralizing water data is critical. It would serve to quantify the risks emerging from growing water stress and aging infrastructure and to better inform water management and investment decisions

International Summit on Integrated Environmental Modeling

This report describes the International Summit on Integrated Environmental Modeling (IEM), held in Washington, DC on 7th-9th December 2010. The meeting brought together 57 scientists and managers from leading US and European government and non-governmental organizations, universities and companies together with international organizations. The Summit built on previous meetings which have been convened over a number of years, including: the US Environmental Protection Agency (US EPA) workshop on Collaborative Approaches to Integrated Modeling: Better Integration for Better Decision- Making (December, 2008); the AGU Fall Meeting, San Francisco (December 2009); and the International Congress on Environmental Modeling and Software (July 2010). From these meetings there is now recognition that many separate communities are involved in developing IEM. The aim of the Summit was to bring together two key groupings, the US and Europe, with the intention of creating a community open to all

An overview of the model integration process: From pre-integration assessment to testing

© 2016 Elsevier Ltd Integration of models requires linking models which can be developed using different tools, methodologies, and assumptions. We performed a literature review with the aim of improving our understanding of model integration process, and also presenting better strategies for building integrated modeling systems. We identified five different phases to characterize integration process: pre-integration assessment, preparation of models for integration, orchestration of models during simulation, data interoperability, and testing. Commonly, there is little reuse of existing frameworks beyond the development teams and not much sharing of science components across frameworks. We believe this must change to enable researchers and assessors to form complex workflows that leverage the current environmental science available. In this paper, we characterize the model integration process and compare integration practices of different groups. We highlight key strategies, features, standards, and practices that can be employed by developers to increase reuse and interoperability of science software components and systems

Integrated environmental modeling : a vision and roadmap for the future

Integrated environmental modeling (IEM) is inspired by modern environmental problems, decisions, and policies and enabled by transdisciplinary science and computer capabilities that allow the environment to be considered in a holistic way. The problems are characterized by the extent of the environmental system involved, dynamic and interdependent nature of stressors and their impacts, diversity of stakeholders, and integration of social, economic, and environmental considerations. IEM provides a science-based structure to develop and organize relevant knowledge and information and apply it to explain, explore, and predict the behavior of environmental systems in response to human and natural sources of stress. During the past several years a number of workshops were held that brought IEM practitioners together to share experiences and discuss future needs and directions. In this paper we organize and present the results of these discussions. IEM is presented as a landscape containing four interdependent elements: applications, science, technology, and community. The elements are described from the perspective of their role in the landscape, current practices, and challenges that must be addressed. Workshop participants envision a global scale IEM community that leverages modern technologies to streamline the movement of science-based knowledge from its sources in research, through its organization into databases and models, to its integration and application for problem solving purposes. Achieving this vision will require that the global community of IEM stakeholders transcend social, and organizational boundaries and pursue greater levels of collaboration. Among the highest priorities for community action are the development of standards for publishing IEM data and models in forms suitable for automated discovery, access, and integration; education of the next generation of environmental stakeholders, with a focus on transdisciplinary research, development, and decision making; and providing a web-based platform for community interactions (e.g., continuous virtual workshops)