Project description:Benchmarking systems are important features for the implementation of efficacy in basic and applied sciences. These systems are urgently needed for many fields of science since there is an imbalance present between funding policies and research evaluation. Here, a new approach is presented with an international study project that uses visualisation techniques for benchmarking processes. The project is entitled New Quality and Quantity Indices in Science (NewQIS). The juxtaposition of classical scientometric tools and novel visualisation techniques can be used to assess quality and quantity in science. In specific, the tools can be used to assess quality and quantity of research activity for distinct areas of science, for single institutions, for countries, for single time periods, or for single scientists. Also, NewQIS may be used to compare different fields, institutions, countries, or scientists for their scientific output. Thus, decision making for funding allocation can be made more transparent. Since governmental bodies that supervise funding policies and allocation processes are often not equipped with an in depth expertise in this area, special attention is given to data visualisation techniques that allow to visualize mapping of research activity and quality.
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.
Project description:Parasites are a major force in evolution, and understanding how host life history affects parasite pressure and investment in disease resistance is a general problem in evolutionary biology. The threat of disease may be especially strong in social animals, and ants have evolved the unique metapleural gland (MG), which in many taxa produce antimicrobial compounds that have been argued to have been a key to their ecological success. However, the importance of the MG in the disease resistance of individual ants across ant taxa has not been examined directly. We investigate experimentally the importance of the MG for disease resistance in the fungus-growing ants, a group in which there is interspecific variation in MG size and which has distinct transitions in life history. We find that more derived taxa rely more on the MG for disease resistance than more basal taxa and that there are a series of evolutionary transitions in the quality, quantity, and usage of the MG secretions, which correlate with transitions in life history. These shifts show how even small clades can exhibit substantial transitions in disease resistance investment, demonstrating that host-parasite relationships can be very dynamic and that targeted experimental, as well as large-scale, comparative studies can be valuable for identifying evolutionary transitions.
Project description:Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in, for example, tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users' trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.
Project description:ABSTRACT 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. This paper proposed a synergistic materials governance flow with incorporation of domain knowledge to provide a high-quality data foundation for accelerating materials design and discovery.
Project description:In cells, intra- and intermolecular interactions of proteins confer function, and the dynamic modulation of this interactome is critical to meet the changing needs required to support life. Cross-linking and mass spectrometry (XL-MS) enable the detection of both intra- and intermolecular protein interactions in organelles, cells, tissues, and organs. Quantitative XL-MS enables the detection of interactome changes in cells due to environmental, phenotypic, pharmacological, or genetic perturbations. We have developed new informatics capabilities, the first to enable 3D visualization of multiple quantitative interactome data sets, acquired over time or with varied perturbation levels, to reveal relevant dynamic interactome changes. These new tools are integrated within release 3.0 of our online cross-linked peptide database and analysis tool suite XLinkDB. With the recent rapid expansion in XL-MS for protein structural studies and the extension to quantitative XL-MS measurements, 3D interactome visualization tools are of critical need.
Project description:Diversity indices are commonly used to measure changes in marine benthic communities. However, the reliability (and therefore suitability) of these indices for detecting environmental change is often unclear because of small sample size and the inappropriate choice of communities for analysis. This study explored uncertainties in taxonomic density and two indices of community structure in our target region, Japan, and in two local areas within this region, and explored potential solutions. Our analysis of the Japanese regional dataset showed a decrease in family density and a dominance of a few species as sediment conditions become degraded. Local case studies showed that species density is affected by sediment degradation at sites where multiple communities coexist. However, two indices of community structure could become insensitive because of masking by community variability, and small sample size sometimes caused misleading or inaccurate estimates of these indices. We conclude that species density is a sensitive indicator of change in marine benthic communities, and emphasise that indices of community structure should only be used when the community structure of the target community is distinguishable from other coexisting communities and there is sufficient sample size.
Project description:In recent years, the number and scale of environmental citizen science programmes that involve lay people in scientific research have increased rapidly. Many of these initiatives are concerned with the recording and identification of species, processes which are increasingly mediated through digital interfaces. Here, we address the growing need to understand the particular role of digital identification tools, both in generating scientific data and in supporting learning by lay people engaged in citizen science activities pertaining to biological recording communities. Starting from two well-known identification tools, namely identification keys and field guides, this study focuses on the decision-making and quality of learning processes underlying species identification tasks, by comparing three digital interfaces designed to identify bumblebee species. The three interfaces varied with respect to whether species were directly compared or filtered by matching on visual features; and whether the order of filters was directed by the interface or a user-driven open choice. A concurrent mixed-methods approach was adopted to compare how these different interfaces affected the ability of participants to make correct and quick species identifications, and to better understand how participants learned through using these interfaces. We found that the accuracy of identification and quality of learning were dependent upon the interface type, the difficulty of the specimen on the image being identified and the interaction between interface type and 'image difficulty'. Specifically, interfaces based on filtering outperformed those based on direct visual comparison across all metrics, and an open choice of filters led to higher accuracy than the interface that directed the filtering. Our results have direct implications for the design of online identification technologies for biological recording, irrespective of whether the goal is to collect higher quality citizen science data, or to support user learning and engagement in these communities of practice.
Project description:BACKGROUND:Social media are as popular as ever, and concerns regarding the effects of social media use on adolescent well-being and mental health have sparked many scientific studies into use effects. Social media research is currently at an important crossroads: conflicting results on social media use's effects on well-being are abundant, and recent work in the field suggests that a new approach is required. The field is in need of an approach involving objective data regarding use where necessary and attention to different kinds of detail such as the why and how of social media use. OBJECTIVE:We present a novel paradigm implementing a principle from educational sciences called stimulated recall and demonstrate how it can be applied to social media use research. Our stimulated recall paradigm implements a number of elements that can fill the gaps currently present in social media and well-being research. METHODS:Objective data are collected regarding users' social media behaviors through video footage and in-phone data and used for a structured stimulated recall interview to facilitate detailed and context-sensitive processing of these objective data. In this interview, objective data are reviewed with the participant in an act of co-research, in which details such as the reasons for their use (eg, boredom) and processes surrounding their use (eg, with whom) are discussed and visualized in a stimulated recall chart. RESULTS:Our ongoing study (N=53) implementing this paradigm suggests this method is experienced as pleasant by participants in spite of its personal and intensive nature. CONCLUSIONS:The stimulated recall paradigm offers interesting and necessary avenues for approaching social media use research from new angles, addressing aspects of use that have thus far remained underexposed. The answers to questions such as "Why do adolescents use social media?" "In what ways exactly do they use social media?" and "How does social media use make them feel in the moment?" are now within reach, an important step forward in the field of social media use and well-being research.
Project description:UnlabelledInteractive examination of RNA multiple alignments for covariant mutations is a useful step in non-coding RNA sequence analysis. We present three parallel implementations of an RNA visualization metaphor: Colorstock, a command-line script using ANSI terminal color; SScolor, a Perl script that generates static HTML pages; and Ratón, an AJAX web application generating dynamic HTML. Each tool can be used to color RNA alignments by secondary structure and to visually highlight compensatory mutations in stems.AvailabilityAll source code is freely available under the GPL. The source code can be downloaded and a prototype of Ratón can be accessed at http://biowiki.org/RnaAlignmentViewers.