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103 results for “resource management”
Applying innovative cloud computing technology for the effective management of Groundwater resources to promote SUStainable food security within the Sokoto Basin, Nigeria (AGSUS)
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Figure 6 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 6: Percentage of maturity level of female swimming crabs
Figure 5 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 5. Percentage of spawning females Phase
Figure 4 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 4: Sex ratio distribution of swimming crab
Figure 1 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 1: Maturity level composition of swimming crab based on carapace width
Figure 3 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 3: Average weight of swimming crab
Figure 2 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 2: Average Carapace Width of Swimming Crab
Resource Guide for State DOT's Maintenance Equipment Fleet Management Decisions
<p>This research created a guide for state Departments of Transportation fleet management on utilizing equipment fleet management system data to make informed fleet management decisions. The research team took the historical equipment fleet data from the Oklahoma Department of Transportation and developed workflows, algorithms, and examples to demonstrate the use of historical equipment data for equipment decisions. Specifically, the research team demonstrated 1) the use of life cycle analysis and dynamic programming models for equipment replacement decisions; 2) developed algorithms to calculate equipment rental rates that can be used by the Department to forecast and allocate equipment operational budget among field districts and central office, 3) developed a procedure to make own-rent/lease decisions based on historical equipment management data. Using the two class codes of equipment (Class Code 5355 – 2 Yd. front-end loaders and Class Code 5385 – 1/2-ton fleetside pickup trucks) as examples, the research team presented the result of the equipment replacement models using both life cycle analysis and dynamic programming approaches and discussed the difference of those two methods. In addition, the impact of model input parameters (specifically depreciation cost estimation using both straight line and double declining balance depreciation calculation methods) on equipment replacement outcomes is discussed. The framework for deciding between on renting or owning for the two class codes of equipment was developed. Also, the equipment rental rates for the most frequently used equipment by ODOT were updated per class code.</p>
Resources for BMF CP72: The effectiveness of knowledge management systems in motivation and satisfaction in Vietnamese higher education institutions
<p>Code and data for reproducing the results in "BMF CP72: The effectiveness of knowledge management systems in motivation and satisfaction in Vietnamese higher education institutions" are available here.</p>
Financial, supplies, and human resource preparedness in management of COVID-19 pandemic among dental facilities in Nairobi County, Kenya
<p>The COVID-19 pandemic put a strain on healthcare facilities across the globe. Dental facilities pose the highest COVID-19 transmission risk categories due to the aerosol-generating procedures involved in dental practice. This study aimed to determine financial, supplies, and human resource preparedness in managing the COVID-19 pandemic among Nairobi County, Kenya dental facilities. A cross-sectional study was conducted using a mixed-methods approach among 183 dental facilities in Nairobi County. Data was collected using a KoboCollect questionnaire and analyzed using MS Excel and SPSS version 26. Dental facilities' readiness was assessed using the ReadyScore Criteria. Qualitative data was collected through one-on-one interviews with key informants and analyzed thematically. Readyscore Criteria analysis showed that 39 (21.3%) of the evaluated dental facilities were considered "ready," while 133 (72.7%) and 11 (6%) were considered to have "work to do" and "not ready" for the pandemic. Bivariate analysis showed that the level of facility (p<0.001), presence of other departments (p<0.001), funds sufficiency for COVID-19 emergency response (p=0.001), and clients attended per month (p=0.017) were statistically significant factors associated with pandemic preparedness scores. Regression analysis revealed that the presence of other departments among the dental facilities was a significant predictor of readiness, with a 4.5 times higher likelihood of being ready for a pandemic (aOR 4.591; 1.471–14.327, p=0.009) compared to other facilities. Support from healthcare authorities and capacity-building initiatives are recommended to enhance preparedness and resilience among dental facilities in the face of the COVID-19 pandemic.</p>
An in-silico analysis of information sharing systems for adaptable resources management: a case study of oyster farmers
<p>Model and data outcomes --> We developed an agent-based models involving oyster farmers sharing information to adapt to an ill-understood virus. Various scenarios of heterogeneity and information sharing (through social networks and centralized information system) are simulated.</p>
Data from: Direct and indirect effects of landscape and field management intensity on carabids through trophic resources and weeds
<p>Carabids are important biological control agents of weeds and other pests in agricultural fields. The carabid community is built upon direct and indirect ecological effects of landscape complexity, field management intensity and biotic components that in interaction make any prediction of community size and composition challenging.</p> <p>We analyse a large-scale sample of 60 European cereal fields using Structural Equation Modelling to quantify the direct effects of field management intensity and the surrounding landscape, and their indirect effect via biotic components, on carabid diversity.</p> <p>Our results highlight that direct and indirect effects of increasing landscape complexity, mediated by trophic resources, mainly affect carabids positively. Field management intensity only ever affects carabids through indirect effects that are generally negative, by suppressing standing weeds and weed seeds.</p> <p>Indirect effects on granivore carabid species depended on weed seed availability whereas omnivores depended on the availability of both weed seeds and animal prey.</p> <p><i>Synthesis and applications</i><span>: A consideration of both the direct and indirect effects of landscape and field management is necessary for predicting carabid communities and with interactions. These effects, mediated via trophic resources, supports the diversity and abundance of carabid communities and their provision of ecosystem services. Our results show that promoting crop diversity and connectivity to semi-natural habitats will directly enhance carabid communities in farmland by manipulating their migration from source habitats. A reduction in field management intensity will preserve local standing weeds and weed seeds, and indirectly support carabid communities. These local and landscape modifications could contribute to improve the natural regulation of pests and weeds by carabids.</span></p>
Data for: A new GTSeq resource to facilitate multijurisdictional research and management of walleye Sander vitreus
<p>Conservation and management professionals often work across jurisdictional boundaries to identify broad ecological patterns. These collaborations help to protect populations whose distributions span political borders. One common limitation to multijurisdictional collaboration is consistency in data recording and reporting. This limitation can impact genetic research which relies on data about specific markers in an organism's genome. Incomplete overlap of markers between separate studies can prevent direct comparisons of results. Standardized marker panels can reduce the impact of this issue and provide a common starting place for new research. Genotyping-in-thousands (GTSeq) is one approach used to create standardized marker panels for non-model organisms. Here we describe the development, optimization, and early assessments of a new GTSeq panel for use with walleye (<em>Sander vitreus</em>) from the Great Lakes region of North America. High genome-coverage sequencing conducted using RAD-capture provided genotypes for thousands of single nucleotide polymorphisms (SNPs). From these markers, SNP and microhaplotype markers were chosen that were informative for genetic stock identification (GSI) and kinship analysis. The final GTSeq panel contained 500 markers, including 197 microhaplotypes and 303 SNPs. Leave-one-out GSI simulations indicated that GSI accuracy should be greater than 80% in most jurisdictions. The false positive rates of parent-offspring and full-sibling kinship identification was found to be low. Finally, genotypes could be consistently scored among separate sequencing runs >94% of the time. Results indicate that the GTSeq panel that we developed should perform well for multijurisdictional walleye research throughout the Great Lakes region.</p>
PAReTT: a Python package for the Automated Retrieval and management of divergence time data from the TimeTree resource for downstream analyses (Dataset)
<p>Dataset for article by the same title submitted the the <em>Journal of Molecular Evolution</em>.</p>
BIG DATA ANALYTICS IN DIGITAL HUMAN RESOURCES MANAGEMENT: IMPACT ON THE RECRUITMENT PROCESS
<p>This study investigates how HR employees experience the big data phenomenon in the recruitment function of HRM and how their perceptions of the phenomenon have evolved. This study also examines how BD will affect organizational and HRM and how it can be improved in other functions of HR. In this exploratory study, which comprehensively addresses the BD phenomenon in HRM, the phenomenological design approach, one of the qualitative research methods, was applied to test the research questions and a semi-structured interview form was used for research data. Using the snowball sampling method, in-depth interviews were conducted with 10 HR employees working in large and semi-structured organizations in Turkey and the interviews were analyzed with MAXQDA 20. The findings show that HRM employees are aware of BD. On the other hand, it is understood that BD technologies provide easy accessibility in recruitment, offer a strategic competitive advantage, and enable more effective management of information management, which saves HR employees' work in a facilitating way. They benefit from technology as a decision support assistant. Finally, the research results provide theoretical and practical implications for future researchers and practitioners for the development and effective use of BD technology in the field of HRM.</p>
Tailored Diabetes Self-Management Resources
ClinicalTrials.gov study NCT02024750. IPD Sharing: NO. Countries: 1. Publications: 2.
Learning Crisis Resource Management: Practicing Versus Observational Role in Simulation Training
ClinicalTrials.gov study NCT01653704. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Resource quantity and heterogeneity drive successional plant diversity in managed and unmanaged boreal forests
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Data from: Direct and indirect effects of landscape and field management intensity on carabids through trophic resources and weeds
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Financial, supplies, and human resource preparedness in management of COVID-19 pandemic among dental facilities in Nairobi County, Kenya
Open the record for dataset details and reuse information.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.