Is end user productivity an oxymoron?

The primary goal of end user computing is to increase the productivity of the end user. By using information technology the end users have changed the scope and depth of their job tasks, making productivity changes hard to measure

End users\u27 perceptions concerning computer applications implemented in broadcast stations

In an era of limited resources, organizations must scrutinize every purchase carefully. A critical and sometimes very expensive purchase is for computer hardware and software. The reasons for the purchase are generally given as productivity improvements or personnel reductions. However, assessment of why computer applications are really implemented from the end users\u27 perspective has not been reported. In order to answer questions to justify expenditures it would be helpful to understand how end users perceive the reasons for implementation of computer applications in their stations. To this end a questionnaire was sent to all broadcast radio and TV stations in the State of Wisconsin. The end users (station managers) were requested to identify reasons for present computer application implementations and areas of implementations for future use. The results of this study compare type of computer being used and station characteristics with a checklist of reasons for computer application implementations in the broadcast industry. This research begins the identification of other factors that are Justifiable reasons for computer application implementations. In doing this, end users can broaden the how, why, what, and when of their computer application purchases. We all believe that the computer is a tool to help us do our jobs better, but what does that mean and how can we document these feelings? Or as Jon Van (I99I) and G. P. Zachary (1991) have stated, are we really getting what we expected from the computer applications we have implemented? This research study does not pretend to completely answer these or other questions in this area. However, it is a beginning and valuable research that needs to be augmented

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Allied Academies International Conference page 105 STUDENTS ’ PERCEPTIONS OF ONLINE COURSES: WHAT DO WE WANT TO KNOW

One of the authors was given a chance to teach a course normally only taught on campus as an online course for the first time. In addition, this author attempted to have comparable learning situation in both courses. But since the delivery methods were different, the instructor wanted a way to assess the students ’ preferences and values of their learning experiences. This led to the development of a survey, which was given at the beginning and at the end of the course, in both classes to compare the students ’ experiences. The results, although limited, do suggest that surveying students about their learning preferences and values can be helpful to instructors. Also, this experience supports the need for continued and new research into student perceptions of online courses

LAGOS-NE : A multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. lakes

Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states. LAGOS-NE contains data for 51101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 datamodules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situmeasurements of lake water quality for a subset of the lakes fromthe past 3 decades for approximately 2600â\u80\u9312 000 lakes depending on the variable. The database contains approximately 150000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from87 lake water quality data sets fromfederal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest andmost comprehensive databases of its type because it includes both in situmeasurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scale