Server‐side workflow execution using data grid technology for reproducible analyses of data‐intensive hydrologic systems

Many geoscience disciplines utilize complex computational models for advancing understanding and sustainable management of Earth systems. Executing such models and their associated data preprocessing and postprocessing routines can be challenging for a number of reasons including (1) accessing and preprocessing the large volume and variety of data required by the model, (2) postprocessing large data collections generated by the model, and (3) orchestrating data processing tools, each with unique software dependencies, into workflows that can be easily reproduced and reused. To address these challenges, the work reported in this paper leverages the Workflow Structured Object functionality of the Integrated Rule‐Oriented Data System and demonstrates how it can be used to access distributed data, encapsulate hydrologic data processing as workflows, and federate with other community‐driven cyberinfrastructure systems. The approach is demonstrated for a study investigating the impact of drought on populations in the Carolinas region of the United States. The analysis leverages computational modeling along with data from the Terra Populus project and data management and publication services provided by the Sustainable Environment‐Actionable Data project. The work is part of a larger effort under the DataNet Federation Consortium project that aims to demonstrate data and computational interoperability across cyberinfrastructure developed independently by scientific communities.Plain Language SummaryExecuting computational workflows in the geosciences can be challenging, especially when dealing with large, distributed, and heterogeneous data sets and computational tools. We present a methodology for addressing this challenge using the Integrated Rule‐Oriented Data System (iRODS) Workflow Structured Object (WSO). We demonstrate the approach through an end‐to‐end application of data access, processing, and publication of digital assets for a scientific study analyzing drought in the Carolinas region of the United States.Key PointsReproducibility of data‐intensive analyses remains a significant challengeData grids are useful for reproducibility of workflows requiring large, distributed data setsData and computations should be co‐located on servers to create executable Web‐resourcesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137520/1/ess271_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137520/2/ess271.pd

Cultural and leadership predictors of corporate social responsibility values of top management: A GLOBE study of 15 countries.

This paper examines cultural and leadership variables associated with corporate social responsibility values that managers apply to their decision-making. In this longitudinal study, we analyze data from 561 firms located in 15 countries on five continents to illustrate how the cultural dimensions of institutional collectivism and power distance predict social responsibility values on the part of top management team members. CEO visionary leadership and integrity were also uniquely predictive of such values. Journal of International Business Studies (2006) 37, 823–837. doi:10.1057/palgrave.jibs.8400230

Dynamic genome evolution in a model fern

The large size and complexity of most fern genomes have hampered efforts to elucidate fundamental aspects of fern biology and land plant evolution through genome-enabled research. Here we present a chromosomal genome assembly and associated methylome, transcriptome and metabolome analyses for the model fern species Ceratopteris richardii. The assembly reveals a history of remarkably dynamic genome evolution including rapid changes in genome content and structure following the most recent whole-genome duplication approximately 60 million years ago. These changes include massive gene loss, rampant tandem duplications and multiple horizontal gene transfers from bacteria, contributing to the diversification of defence-related gene families. The insertion of transposable elements into introns has led to the large size of the Ceratopteris genome and to exceptionally long genes relative to other plants. Gene family analyses indicate that genes directing seed development were co-opted from those controlling the development of fern sporangia, providing insights into seed plant evolution. Our findings and annotated genome assembly extend the utility of Ceratopteris as a model for investigating and teaching plant biology

Reduction in Structural Disorder and Functional Complexity in the Thermal Adaptation of Prokaryotes

Genomic correlates of evolutionary adaptation to very low or very high optimal growth temperature (OGT) values have been the subject of many studies. Whereas these provided a protein-structural rationale of the activity and stability of globular proteins/enzymes, the point has been neglected that adaptation to extreme temperatures could also have resulted from an increased use of intrinsically disordered proteins (IDPs), which are resistant to these conditions in vitro. Contrary to these expectations, we found a conspicuously low level of structural disorder in bacteria of very high (and very low) OGT values. This paucity of disorder does not reflect phylogenetic relatedness, i.e. it is a result of genuine adaptation to extreme conditions. Because intrinsic disorder correlates with important regulatory functions, we asked how these bacteria could exist without IDPs by studying transcription factors, known to harbor a lot of function-related intrinsic disorder. Hyperthermophiles have much less transcription factors, which have reduced disorder compared to their mesophilic counterparts. On the other hand, we found by systematic categorization of proteins with long disordered regions that there are certain functions, such as translation and ribosome biogenesis that depend on structural disorder even in hyperthermophiles. In all, our observations suggest that adaptation to extreme conditions is achieved by a significant functional simplification, apparent at both the level of the genome and individual genes/proteins

Sensing and Analysis of Greenhouse Gas Emissions from Rice Fields to the Near Field Atmosphere

Greenhouse gas (GHG) emissions from rice fields have huge effects on climate change. Low-cost systems and management practices to quantify and reduce GHGs emission rates are needed to achieve a better climate. The typical GHGs estimation processes are expensive and mainly depend on high-cost laboratory equipment. This study introduces a low-cost sensor-based GHG sampling and estimation system for rice fields. For this, a fully automatic gas chamber with a sensor-integrated gas accumulator and quantifier unit was designed and implemented to study its performance in the estimation efficiency of greenhouse gases (CH4, N2O, and CO2) from rice fields for two crop seasons. For each crop season, three paddy plots were prepared at the experimental site and then subjected to different irrigation methods (continuous flooding (CF), intermittent flooding (IF), and controlled intermittent flooding (CIF)) and fertilizer treatments to study the production and emission rates of GHGs throughout the crop growing season at regular intervals. A weather station was installed on the site to record the seasonal temperature and rainfall events. The seasonal total CH4 emission was affected by the effects of irrigation treatments. The mean CH4 emission in the CIF field was smaller than in other treatments. CH4 and N2O emission peaks were high during the vegetative and reproductive phases of rice growth, respectively. The results indicated that CIF treatment is most suitable in terms of rice productivity and higher water use efficiency. The application of nitrogen fertilizers produced some peaks in N2O emissions. On the whole, the proposed low-cost GHGs estimation system performed well during both crop seasons and it was found that the adaption of CIF treatment in rice fields could significantly reduce GHG emissions and increase rice productivity. The research results also suggested some mitigation strategies that could reduce the production of GHGs from rice fields

Strategic choice and operational performance: a comparative study of commercial banks in Oman

The objective of this paper is to investigate various strategies adopted by banks in the Sultanate of Oman and to explore how these strategies may have helped these banks overcome different operational difficulties during periods of crisis. The empirical analysis in this study was done using binary logit regression technique with data from the Bank Scope database. Data was drawn from the balance sheets and income statements of commercial banks, and this data ranges from December 1999 to December 2017. Data from 1999 to 2009 was used for estimating the logit equations, and data from 2012 to 2017 was used for testing the predictive ability of the model. From a strategy point of view, our study of Omani banks focuses on the financial crises of 2000-2001 and 2008-2009, and the model predictions for years 2012 to 2017 brings forth the following conclusion: In order to ensure that a bank performs well (in terms of profit and asset growth), the bank’s management should focus on capital account management, interest spread management, good loan quality, and high loan-todeposit ratios. Finally, we also found that cost management and liquidity management are two areas of strategic choices that are not particularly important.El objetivo de este documento es investigar varias estrategias adoptadas por los bancos en el Sultanato de Omán y explorar cómo estas estrategias pueden haber ayudado a estos bancos a superar diferentes dificultades operativas durante los períodos de crisis. El análisis empírico en este estudio se realizó utilizando la técnica de regresión logit binaria con datos de la base de datos de Bank Scope. Los datos se obtuvieron de los balances y los estados de resultados de los bancos comerciales, y estos datos van de diciembre de 1999 a diciembre de 2017. Los datos de 1999 a 2009 se usaron para estimar las ecuaciones logit, y los datos de 2012 a 2017 se usaron para probar la predicción capacidad del modelo. Desde el punto de vista de la estrategia, nuestro estudio de los bancos omaníes se centra en las crisis financieras de 2000-2001 y 2008-2009, y las predicciones del modelo para los años 2012 a 2017 arrojan la siguiente conclusión: para garantizar que un banco tenga un buen desempeño (en términos de crecimiento de ganancias y activos), la administración del banco debe enfocarse en la administración de la cuenta de capital, la administración del diferencial de intereses, la buena calidad de los préstamos y las altas tasas de préstamos a depósitos. Finalmente, también encontramos que la administración de costos y la administración de liquidez son dos áreas de opciones estratégicas que no son particularmente importantes

Creating and Providing Data Management Services for the Biological and Ecological Sciences: Science Environment for Ecological Knowledge

The Science Environment for Ecological Knowledge (SEEK) [1] is an information technology project designed to address the many challenges associated with data accessibility and integration of large-scale biocomplexity data in the ecological sciences. The SEEK project is creating cyberinfrastructure encompassing three integrated systems: EcoGrid, a Semantic Mediation System (SMS) and an Analysis and Modeling System (AMS). SEEK enables ecologists to efficiently capture, organize and search for data and analytical processes (i.e., scientific workflows) from their desktop in a user friendly interface -- ultimately providing access to global data and analytical resources typically out of reach for many ecologists. The prototype application is ecological niche modeling