Abstract Purpose Social media users share their ideas, thoughts, and emotions with other users. However, it is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications. Additionally, we investigate what features of the research articles help in such prediction. Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles. Design/methodology/approach Several tools are used for sentiment analysis, so we applied five sentiment analysis tools to check which are suitable for capturing a tweet's sentiment value and decided to use NLTK VADER and TextBlob. We segregated the sentiment value into negative, positive, and neutral. We measure the mean and median of tweets’ sentiment value for research articles with more than one tweet. We next built machine learning models to predict the sentiments of tweets related to scientific publications and investigated the essential features that controlled the prediction models. Findings We found that the most important feature in all the models was the sentiment of the research article title followed by the author count. We observed that the tree-based models performed better than other classification models, with Random Forest achieving 89% accuracy for binary classification and 73% accuracy for three-label classification. Research limitations In this research, we used state-of-the-art sentiment analysis libraries. However, these libraries might vary at times in their sentiment prediction behavior. Tweet sentiment may be influenced by a multitude of circumstances and is not always immediately tied to the paper's details. In the future, we intend to broaden the scope of our research by employing word2vec models. Practical implications Many studies have focused on understanding the impact of science on scientists or how science communicators can improve their outcomes. Research in this area has relied on fewer and more limited measures, such as citations and user studies with small datasets. There is currently a critical need to find novel methods to quantify and evaluate the broader impact of research. This study will help scientists better comprehend the emotional impact of their work. Additionally, the value of understanding the public's interest and reactions helps science communicators identify effective ways to engage with the public and build positive connections between scientific communities and the public. Originality/value This study will extend work on public engagement with science, sociology of science, and computational social science. It will enable researchers to identify areas in which there is a gap between public and expert understanding and provide strategies by which this gap can be bridged.
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Visualizing Life in the Deep: A Creative Pipeline for Data-Driven Animations to Facilitate Marine Mammal Research, Outreach, and Conservation
In this paper, we introduce a creative pipeline to incorporate physiological and behavioral data from contemporary marine mammal research into data-driven animations, leveraging functionality from industry tools and custom scripts to promote scientific insights, public awareness, and conservation outcomes. Our framework can flexibly transform data describing animals’ orientation, position, heart rate, and swimming stroke rate to control the position, rotation, and behavior of 3D models, to render animations, and to drive data sonification. Additionally, we explore the challenges of unifying disparate datasets gathered by an interdisciplinary team of researchers, and outline our design process for creating meaningful data visualization tools and animations. As part of our pipeline, we clean and process raw acceleration and electrophysiological signals to expedite complex multi-stream data analysis and the identification of critical foraging and escape behaviors. We provide details about four animation projects illustrating marine mammal datasets. These animations, commissioned by scientists to achieve outreach and conservation outcomes, have successfully increased the reach and engagement of the scientific projects they describe. These impactful visualizations help scientists identify behavioral responses to disturbance, increase public awareness of human-caused disturbance, and help build momentum for targeted conservation efforts backed by scientific evidence.
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- NSF-PAR ID:
- 10342952
- Date Published:
- Journal Name:
- 2021 IEEE VIS Arts Program (VISAP)
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary One of the activities of the Pacific Rim Applications and Grid Middleware Assembly (PRAGMA) is fostering Virtual Biodiversity Expeditions by bringing domain scientists and cyber infrastructure specialists together as a team. Over the past few years, PRAGMA members have been collaborating on virtualizing the Lifemapper software. Virtualization and cloud computing have introduced great flexibility and efficiency into IT projects. Virtualization refers to the technologies that provide a layer of abstraction between server hardware system and software that runs on it. This abstraction enables a logical view of computing resources and allows multiple servers to run on the same hardware. With this project, we are virtualizing Lifemapper by enabling its installation and configuration on a virtual cluster. Virtualization provides application scalability, maximizes resources utilization, and creates a more efficient, agile, and automated infrastructure. However, there are downsides to the complexity inherent in these environments, including the need for special techniques to deploy cluster hosts, dependence on virtual environments, and challenging application installation, management, and configuration. In this study, we report on progress of the Lifemapper virtualization framework focused on a reproducible and highly configurable infrastructure capable of fast deployment.
Lifemapper is a distributed software application developed by the Biodiversity Institute at The University of Kansas. Lifemapper creates and maintains a publicly accessible archive of species distribution maps calculated from public specimen data. Lifemapper software also provides a suite of tools for biodiversity researchers that calculate single and multispecies distribution predictions and macroecological analyses through application programming interfaces. Our goal is to create a viable solution that can be easily adopted and reused by scientists from multiple institutions or projects. This solution (1) allows fast deployment of ready‐made cluster images, (2) reproduces the complete Lifemapper processing pipeline on demand at multiple sites and in different hosting environments, and (3) enables scientists to perform Lifemapper‐facilitated data processing on restricted‐use data, very large datasets, or other unique data.
A key contribution of this work is describing the practical experience in taking a complex, clustered, domain‐specific, data analysis, and simulation system and enabling its operation on a variety of system configurations. Uses of this portability range from whole cluster replication to teaching and experimentation on a single laptop. System virtualization is used to practically define and make portable the full application stack, including all of its complex set of supporting software and allows Lifemapper deployment in a variety of environments.
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Background Even before the onset of the COVID-19 pandemic, children and adolescents were experiencing a mental health crisis, partly due to a lack of quality mental health services. The rate of suicide for Black youth has increased by 80%. By 2025, the health care system will be short of 225,000 therapists, further exacerbating the current crisis. Therefore, it is of utmost importance for providers, schools, youth mental health, and pediatric medical providers to integrate innovation in digital mental health to identify problems proactively and rapidly for effective collaboration with other health care providers. Such approaches can help identify robust, reproducible, and generalizable predictors and digital biomarkers of treatment response in psychiatry. Among the multitude of digital innovations to identify a biomarker for psychiatric diseases currently, as part of the macrolevel digital health transformation, speech stands out as an attractive candidate with features such as affordability, noninvasive, and nonintrusive. Objective The protocol aims to develop speech-emotion recognition algorithms leveraging artificial intelligence/machine learning, which can establish a link between trauma, stress, and voice types, including disrupting speech-based characteristics, and detect clinically relevant emotional distress and functional impairments in children and adolescents. Methods Informed by theoretical foundations (the Theory of Psychological Trauma Biomarkers and Archetypal Voice Categories), we developed our methodology to focus on 5 emotions: anger, happiness, fear, neutral, and sadness. Participants will be recruited from 2 local mental health centers that serve urban youths. Speech samples, along with responses to the Symptom and Functioning Severity Scale, Patient Health Questionnaire 9, and Adverse Childhood Experiences scales, will be collected using an Android mobile app. Our model development pipeline is informed by Gaussian mixture model (GMM), recurrent neural network, and long short-term memory. Results We tested our model with a public data set. The GMM with 128 clusters showed an evenly distributed accuracy across all 5 emotions. Using utterance-level features, GMM achieved an accuracy of 79.15% overall, while frame selection increased accuracy to 85.35%. This demonstrates that GMM is a robust model for emotion classification of all 5 emotions and that emotion frame selection enhances accuracy, which is significant for scientific evaluation. Recruitment and data collection for the study were initiated in August 2021 and are currently underway. The study results are likely to be available and published in 2024. Conclusions This study contributes to the literature as it addresses the need for speech-focused digital health tools to detect clinically relevant emotional distress and functional impairments in children and adolescents. The preliminary results show that our algorithm has the potential to improve outcomes. The findings will contribute to the broader digital health transformation. International Registered Report Identifier (IRRID) DERR1-10.2196/46970more » « less
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Abstract Large‐scale digitization projects such as
#ScanAllFishes andoVert are generating high‐resolution microCT scans of vertebrates by the thousands. Data from these projects are shared with the community using aggregate 3D specimen repositories like MorphoSource through various open licenses. We anticipate an explosion of quantitative research in organismal biology with the convergence of available data and the methodologies to analyse them.Though the data are available, the road from a series of images to analysis is fraught with challenges for most biologists. It involves tedious tasks of data format conversions, preserving spatial scale of the data accurately, 3D visualization and segmentations, and acquiring measurements and annotations. When scientists use commercial software with proprietary formats, a roadblock for data exchange, collaboration and reproducibility is erected that hurts the efforts of the scientific community to broaden participation in research.
We developed SlicerMorph as an extension of 3D Slicer, a biomedical visualization and analysis ecosystem with extensive visualization and segmentation capabilities built on proven python‐scriptable open‐source libraries such as Visualization Toolkit and Insight Toolkit. In addition to the core functionalities of Slicer, SlicerMorph provides users with modules to conveniently retrieve open‐access 3D models or import users own 3D volumes, to annotate 3D curve and patch‐based landmarks, generate landmark templates, conduct geometric morphometric analyses of 3D organismal form using both landmark‐driven and landmark‐free approaches, and create 3D animations from their results. We highlight how these individual modules can be tied together to establish complete workflow(s) from image sequence to morphospace. Our software development efforts were supplemented with short courses and workshops that cover the fundamentals of 3D imaging and morphometric analyses as it applies to study of organismal form and shape in evolutionary biology.
Our goal is to establish a community of organismal biologists centred around Slicer and SlicerMorph to facilitate easy exchange of data and results and collaborations using 3D specimens. Our proposition to our colleagues is that using a common open platform supported by a large user and developer community ensures the longevity and sustainability of the tools beyond the initial development effort.
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Abstract Community science projects offered in urban areas may be particularly effective at addressing environmental problems and engaging people in science, especially individuals whose identities have historically been underrepresented in the field. In this project, we worked with individuals from a racially diverse, low‐income community in San Diego, California to conduct community science to: 1) test a conceptual program model aimed at engaging diverse communities in science, and 2) contribute to scientific knowledge about the inputs and accumulations of trash in an urban watershed. While the program model did well at bolstering environmental stewardship, recruitment, and short‐term retention of community members as project participants, it was not as effective at building science understanding, interest in science, and awareness of doing science, indicating a need for a mindset approach. Despite this, the data collected by the community between 2014–2018 revealed in‐depth information about the spatial and temporal distributions of trash, including the identification of three main debris inputs: encampments, illegal dumping, and storm drain flows, as well as the validation of global trends of a predominance of plastics across waterways and through time. In a few instances, community stewards became community scientists—the quantity and quality of data collected improved, and community members presented results to authorities who responded with concordant management actions (e.g., help with cleanups, outreach to unhoused communities). Based on project outcomes, our revised community science program model includes a focus on strengthening a science mindset, in which even short‐term science interventions that improve the recognition of science, a sense of belonging, and access to mentorship may have meaningful long‐lasting effects on increased participation in science.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/VISAP52981.2021.00007
