Quality and quantity indices in science: use of visualization tools.
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ABSTRACT:
SUBMITTER: Groneberg-Kloft B
PROVIDER: S-EPMC2726685 | biostudies-literature | 2009 Aug
REPOSITORIES: biostudies-literature
Publications
Quality and quantity indices in science: use of visualization tools.
Groneberg-Kloft Beatrix B Quarcoo David D Scutaru Cristian C
EMBO reports 20090801 8
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Project description:Online citizen science offers a low-cost way to strengthen the infrastructure for scientific research and engage members of the public in science. As the sustainability of online citizen science projects depends on volunteers who contribute their skills, time, and energy, the objective of this study is to investigate effects of motivational factors on the quantity and quality of citizen scientists' contribution. Building on the social movement participation model, findings from a longitudinal empirical study in three different citizen science projects reveal that quantity of contribution is determined by collective motives, norm-oriented motives, reputation, and intrinsic motives. Contribution quality, on the other hand, is positively affected only by collective motives and reputation. We discuss implications for research on the motivation for participation in technology-mediated social participation and for the practice of citizen science.
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Project description:BackgroundIdentifying key determinants is crucial for improving program implementation and achieving long-term sustainment within healthcare organizations. Organizational-level complexity and heterogeneity across multiple stakeholders can complicate our understanding of program implementation. We describe two data visualization methods used to operationalize implementation success and to consolidate and select implementation factors for further analysis.MethodsWe used a combination of process mapping and matrix heat mapping to systematically synthesize and visualize qualitative data from 66 stakeholder interviews across nine healthcare organizations, to characterize universal tumor screening programs of all newly diagnosed colorectal and endometrial cancers and understand the influence of contextual factors on implementation. We constructed visual representations of protocols to compare processes and score process optimization components. We also used color-coded matrices to systematically code, summarize, and consolidate contextual data using factors from the Consolidated Framework for Implementation Research (CFIR). Combined scores were visualized in a final data matrix heat map.ResultsNineteen process maps were created to visually represent each protocol. Process maps identified the following gaps and inefficiencies: inconsistent execution of the protocol, no routine reflex testing, inconsistent referrals after a positive screen, no evidence of data tracking, and a lack of quality assurance measures. These barriers in patient care helped us define five process optimization components and used these to quantify program optimization on a scale from 0 (no program) to 5 (optimized), representing the degree to which a program is implemented and optimally maintained. Combined scores within the final data matrix heat map revealed patterns of contextual factors across optimized programs, non-optimized programs, and organizations with no program.ConclusionsProcess mapping provided an efficient method to visually compare processes including patient flow, provider interactions, and process gaps and inefficiencies across sites, thereby measuring implementation success via optimization scores. Matrix heat mapping proved useful for data visualization and consolidation, resulting in a summary matrix for cross-site comparisons and selection of relevant CFIR factors. Combining these tools enabled a systematic and transparent approach to understanding complex organizational heterogeneity prior to formal coincidence analysis, introducing a stepwise approach to data consolidation and factor selection.
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Project description:Data-driven machine learning (ML) is widely employed in the analysis of materials structure-activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size (for traditional ML models) or the mismatch between model parameters and sample size (for deep-learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation and specific ML approaches, and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance. Following this, we propose a synergistic data quantity governance flow with the incorporation of materials domain knowledge. After summarizing the approaches to incorporating materials domain knowledge into the process of ML, we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications. The work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.
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Project description:Faba beans are considered one of the most important crops for animal feed. The genotype × environment interaction (GEI) has a considerable effect on faba bean seed production. The objectives of this study included assessing multiple locations and genotypes to understand how various ecosystems and faba bean genotypes relate to one another, and suggesting the ideal climatic conditions, crop management system, and genotypes so that they are carefully chosen for their stability. A 2-year experiment was conducted in order to define the stability across four environments based on stability indices for certain characteristics: moisture (%), ash content (%), crude protein content (%), crude fat (%), total starch (%), and crude fiber content (%). Statistically significant differences indicated that GEIs were present. The heritability was generally high for qualitative traits in comparison with quantitative traits. The crude protein content, plant height, and thousand-seed weight were all positively correlated with the seed yield; however, the other qualitative variables were adversely correlated. The crude protein content of the cultivar Tanagra displayed a high stability index, followed by Ste1. Under conventional management, Tanagra demonstrated high values for the seed yield in Giannitsa and Florina. Ste1 and Ste2 are particularly promising genetic materials that showed high values under low-input conditions. The best genotypes to use and the most favorable environments/types of cultivation were the Tanagra cultivar, followed by the Ste2 genotype, according to the additive main effects and multiplicative interaction (AMMI) and genotype plus genotype-by-environment (GGE) biplot models. Earliness showed significant heritability values and very high stability indices, again indicating qualitative behavior according to genetic parameters. With the exception of the number of pods per plant, which demonstrated low heritability while having excellent index values, traits like seed yield showed relatively low-stability-based heritability values. Global efforts aimed at improving the genetics of faba beans might benefit from genotypes that exhibit consistent yields in various conditions.
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Project description:IntroductionFindMyApps is a tablet-based eHealth intervention, designed to improve social health in people with mild dementia or mild cognitive impairment.MethodsFindMyApps has been subject to a randomized controlled trial (RCT), Netherlands Trial Register NL8157. Following UK Medical Research Council guidance, a mixed methods process evaluation was conducted. The goal was to investigate the quantity and quality of tablet use during the RCT, and which context, implementation, and mechanisms of impact (usability, learnability and adoption) factors might have influenced this. For the RCT, 150 community dwelling people with dementia and their caregivers were recruited in the Netherlands. For the process evaluation, tablet-use data were collected by proxy-report instrument from all participants' caregivers, FindMyApps app-use data were registered using analytics software among all experimental arm participants, and semi-structured interviews (SSIs) were conducted with a purposively selected sample of participant-caregiver dyads. Quantitative data were summarized and between group differences were analyzed, and qualitative data underwent thematic analysis.ResultsThere was a trend for experimental arm participants to download more apps, but there were no statistically significant differences between experimental and control arm participants regarding quantity of tablet use. Qualitative data revealed that experimental arm participants experienced the intervention as easier to use and learn, and more useful and fun than control arm participants. Adoption of tablet app use was lower than anticipated in both arms.ConclusionsA number of context, implementation and mechanism of impact factors were identified, which might explain these results and may inform interpretation of the pending RCT main effect results. FindMyApps seems to have had more impact on the quality than quantity of home tablet use.
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