Scientific Data Research Articles

Biological Importance of Phellodendrine in Traditional and Modern Medicines: An Update on Therapeutic Potential in Medicine.

Herbal medicines have been used for the preparation of numerous pharmaceutical products for the treatment of human disorders. Plant-derived products have been used in medicine, nutraceuticals, perfumery, beverages, and cosmetics industries for different purposes. Herbal medicines are mainly derived from different parts of plant materials. Phellodendron bark has been widely known as one of the fundamental herbs of traditional Chinese medicine. Phellodendron bark contains phellodendrine as a main active phytochemical. Phellodendrine ((7S,13aS)-3,10-dimethoxy-7-methyl-6,8,13,13atetrahydro-5H-isoquinolino[2,1-b]isoquinolin-7-ium-2,11-diol), is a quaternary ammonium alkaloid. This present study aimed to investigate the biological potential and therapeutic effectiveness of phellodendrine in medicine through scientific data analysis of different research works on phellodendrine. The therapeutic value of phellodendrine was analyzed in the present work through scientific data available in Google, Google Scholar, ScienceDirect, and PubMed. All the scientific data on phellodendrine were collected from these databases using the terms herbal drugs and phellodendrine. Pharmacological and analytical data of phellodendrine were analyzed in the present work in order to know the medicinal importance of phellodendrine. Scientific data analysis of phellodendrine in the present work signified the biological importance of phellodendrine in medicine. Phellodendrine has numerous beneficial aspects in medicine due to its potential benefits in ulcerative colitis, inflammation, pancreatic cancer, nephritis, immune response, acetylcholinesterase activity, psoriasis, arthritis, atopic dermatitis, and oxidative stress. However, it also has significant effects on eicosanoid generation, neuraminidase-1, inflammasome generation, cytochrome p450, taste receptors, and hepatic gluconeogenesis. Furthermore, scientific data has indicated the presence of phellodendrine in different natural sources, including Phellodendri cortex. Analytical data on phellodendrines has signified their importance in the isolation and separation of pure phytochemicals in medicine. Pharmacokinetic parameters have highlighted the tissue distribution of phellodendrine in different tissue of human beings and higher animals. In the present work, scientific data analysis has indicated the biological importance, pharmacological activities, and analytical aspects of phellodendrine in medicine.

Biological Potential of Kakkalide in medicine for the Treatment of Human disorders: An Overview of Pharmacological aspects.

Pueraria lobata is an important herbal medicine of Fabaceae family that has been clinically used in Traditional Chinese Medicine to counteract human disorders and associated secondary complications. Kakkalide also called irisolidone 7-xylosylglucoside is an isoflavonoid of Puerariae flos, Pueraria lobata and Flos Puerariae. Moreover, Kakkalide has a wide range of bioactivities in medicine. Biological potential of kakkalide was investigated in the present work through scientific data analysis of different scientific research work on kakkalide in order to know its therapeutic potential in medicine. Scientific data on Pueraria lobata were collected and analyzed in the present work. All the scientific data were collected from Google, Google Scholar, Scopus, and Science Direct in the present work. Scientific data analysis of kakkalide revealed its biological importance and therapeutic potential in medicine. The present investigation signified kakkalide's effectiveness in inflammatory diseases, prostaglandin E2 production, liver complication, gastric injury, alcoholism, insulin-resistant endothelial dysfunction, aldose reductase enzyme, hyperlipidemia, estrogenic activity, and stroke. In addition, the bioavailability of kakkalide was also discussed in the present paper. Present work also revealed the significance of analytical techniques for the separation, isolation and identification of kakkalide in different biological and non-biological samples. Present paper signified the health-beneficial aspects of kakkalide in medicine.

Climate Justice and Climate Adaptation in California: Indigenous Community Climate Adaptation Leadership and Opportunities for Scientific Collaboration

Abstract Climate and weather-related disasters in California illustrate the need for immediate climate change action—both mitigation to reduce impacts and adaptation to protect our communities, relatives, and the ecosystems we depend upon. Indigenous frontline communities face even greater threats from climate impacts due to historical and political legacies of environmental injustice. Climate change adaptation actions have proven challenging to implement as communities struggle to access necessary climate data at appropriate scales, identify effective strategies that address community priorities, and obtain resources to act at a whole-community level. In this paper, we present three examples of Indigenous communities in California that have used a climate justice approach to climate change adaptation. These communities are drawing upon community knowledge and expertise to address the challenges of adaptation planning and taking actions that center community priorities. The three cases address emergency preparation and response, cultural burning and fire management, and community organizing and social cohesion. Across these spheres, they illustrate the ways in which a community-based and climate justice-focused approach to adaptation can be effective in addressing current threats while also addressing the legacy of imposed, socially constructed vulnerability and environmental injustices. Because we recognize the need for multiple knowledges and skills in adaptation actions, we include recommendations that have emerged based on what has been learned through these long-standing and engaged participatory research collaborations for climate scientists who wish to contribute to climate justice-focused adaptation efforts by using scientific data to support—not supplant—community efforts, target funding toward genuine community engagement and adaptation actions, and become aware of the historical and political legacies that created the climate vulnerabilities and injustices evident today.

Research and publication gaps on kratom and kratom products: a scoping review of current literature.

Kratom plant, products derived from the plant, and plant phytochemicals are of great interest among researchers, clinicians, and consumers. However, there is a paucity of rigorously collected scientific data on their risk/safety profile and public health impact. This scoping review discusses original research articles published between 2022 and 2023. It focuses on identifying publication gaps on topics related to epidemiology, public health, and risk/safety profiles comparing evidence collected by researchers from Southeast Asia and the West. Our review of the Scopus database identified a total of 55 publications, including clinical case reports and case series reports, surveys, studies enrolling human participants, and publications based on large-scale national surveys or large-scale national or international health system database records. Overall, there is dearth of reliable data on key epidemiological factors, including the prevalence rates, and on objective and reliable indices of the risk/safety profiles. Rigorous and systematic studies including improved epidemiological surveillance, human laboratory, and controlled clinical studies are urgently needed to advance our understanding of public health consequences of consuming kratom and kratom-derived products and to improve our understanding of their risk/safety profile and additional analytical studies to better inform development of needed regulatory oversight.

Studying K-12 and post-secondary teachers’ reflections on the value of a citizen science project for fostering learning: The case of a winter stream project

Citizen science programs offer the public opportunities to be involved in hands-on scientific data collection and study. Despite a history of incorporating citizen science in the K-12 classroom, little research has focused on projects that are appealing to both K-12 instructors and higher education faculty. Our study investigates initial barriers and motivations for participation in citizen science at multiple education levels using a winter, stream ecology citizen science project. Our findings indicate a growing interest in citizen science programs in winter, as they provide opportunities for collaboration, encourage flexible data collection, and offer tangible results for participants to reflect on their local ecological habitats.

BUNNI: Learning Repair Actions in Rule-driven Data Cleaning

In this work, we address the challenging and open problem of involving non-expert users in the data repairing problem as first-class citizens. Despite a large number of proposals that have been devoted to cleaning data from the point of view of expert users (IT staff and data scientists), there is a lack of studies from the perspective of non-expert ones. Given a set of available data quality rules, we exploit machine learning techniques to guide the user to identify the dirty values for each violation and repair them. We show that with a low user effort, it is possible to identify the values in tuples that can be trusted and the ones that are most likely errors. We show experimentally how this machine learning approach leads to a unique clean solution with high quality in scenarios where other approaches fail.

Using Grayscale Photos to Introduce High School Statistics Teachers to Reasoning with Digital Image Data

Statistics teaching at the high school level needs modernizing to include digital sources of data that students interact with every day. Algorithmic modeling approaches are recommended, as they can support the teaching of data science and computational thinking. Research is needed about the design of tasks that support high school statistics teachers to learn new statistical and computational approaches such as digital image analysis and classification models. Using our design framework, the construction of a task is described that introduces classification modeling using grayscale digital images. The task was implemented within a teaching experiment involving six high school statistics teachers. Our findings from this exploratory study indicated that the task design seemed to support statistical and computational thinking practices related to classification modeling and digital image data.

Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning

Multimodal monitoring (MMM) in the intensive care unit (ICU) has become increasingly sophisticated with the integration of neurophysical principles. However, the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes. This manuscript reviewed current neuromonitoring tools, focusing on intracranial pressure, cerebral electrical activity, metabolism, and invasive and noninvasive autoregulation monitoring. In addition, the integration of advanced machine learning and data science tools within the ICU were discussed. Invasive monitoring includes analysis of intracranial pressure waveforms, jugular venous oximetry, monitoring of brain tissue oxygenation, thermal diffusion flowmetry, electrocorticography, depth electroencephalography, and cerebral microdialysis. Noninvasive measures include transcranial Doppler, tympanic membrane displacement, near-infrared spectroscopy, optic nerve sheath diameter, positron emission tomography, and systemic hemodynamic monitoring including heart rate variability analysis. The neurophysical basis and clinical relevance of each method within the ICU setting were examined. Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools, helping clinicians make more accurate and timely decisions. These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies. MMM, grounded in neurophysics, offers a more nuanced understanding of cerebral physiology and disease in the ICU. Although each modality has its strengths and limitations, its integrated use, especially in combination with machine learning algorithms, can offer invaluable information for individualized patient care.

Navigating the confluence of econometrics and data science: Implications for economic analysis and policy

This paper explores the transformative integration of econometrics and data science, a synergy poised to redefine empirical research within economics. By merging traditional econometric methods with advanced data science techniques, such as machine learning algorithms and big data analytics, this interdisciplinary approach enables a deeper, more nuanced understanding of complex economic phenomena. We delve into the theoretical foundations underlying this integration, highlighting how machine learning algorithms like random forests and neural networks complement conventional regression analysis, thereby enhancing model complexity and predictive accuracy. The paper further discusses methodological advancements, including handling high-dimensional data, incorporating unstructured data through natural language processing, and the evolution of model selection processes empowered by machine learning. Practical applications are thoroughly examined across three pivotal areas: economic forecasting and policy analysis, financial markets and risk management, and social economic analysis and public policy, showcasing the significant contributions of this convergence to economic forecasting, policy formulation, and the assessment of public interventions. This comprehensive exploration underscores the potential of combining econometrics and data science to offer more precise and actionable insights for policymakers, researchers, and practitioners in the field of economics.

Fate and transport of viable Bacillus anthracis simulant spores in ambient air during a large outdoor decontamination field exercise

ABSTRACT The Wide Area Demonstration (WAD) was a field exercise conducted under the U.S. EPA’s Analysis of Coastal Operational Resiliency program, in conjunction with the U.S. Department of Homeland Security and the U.S. Coast Guard. The purpose of the WAD was to operationalize at field scale aspects of remediation activities that would occur following an outdoor release of Bacillus anthracis spores, including sampling and analysis, decontamination, data management, and waste management. The WAD was conducted in May 2022 at Fort Walker (formerly known as Fort A.P. Hill) and utilized Bacillus atrophaeus as a benign simulant for B. anthracis. B. atrophaeus spores were inoculated onto the study area at the beginning of the study, and air samples were collected daily during each of the different phases of the WAD using Dry Filter Units (DFUs). Ten DFU air samplers were placed at the perimeter of the study area to collect bioaerosols onto two parallel 47-mm diameter polyester felt filters, which were then subsequently analyzed in a microbiological laboratory for the quantification of B. atrophaeus. The study demonstrated the use of DFUs as a rugged and robust bioaerosol collection device. The results indicated that the highest B. atrophaeus spore air concentrations (up to ~ 5 colony forming units/m3) occurred at the beginning of the demonstration (e.g. during inoculation and characterization sampling phases) and generally downwind from the test site, suggesting transport of the spores was occurring from the study area. Very few B. atrophaeus spores were detected in the air after several weeks and following decontamination of exterior surfaces, thus providing an indication of the site decontamination procedures’ effectiveness. No B. atrophaeus spores were detected in any of the blank or background samples. Implications: Following an incident involving a release of Bacillus anthracis spores or other biological threat agent into the outdoor environment, understanding the factors that may affect the bioagent’s fate and transport can help predict viable contaminant spread via the ambient air. This paper provides scientific data for the first time on ambient air concentrations of bacterial spores over time and location during different phases of a field test in which Bacillus atrophaeus (surrogate for B. anthracis) spores were released outdoors as part of a full-scale study on sampling and decontamination in an urban environment. This study advances the knowledge related to the fate and transport of bacterial spores (such as those causing anthrax disease) as an aerosol in the outdoor environment over the course of three weeks in a mock urban environment and has exposure and health risk implications. The highest spore air concentrations occurred at the beginning of the study (e.g. during inoculation of surfaces and characterization sampling), and in the downwind direction, but diminished over time; few B. atrophaeus spores were detected in the air after several weeks and following decontamination. Therefore, in an actual incident, potential reaerosolization of the microorganism and subsequent transport in the air during surface sampling and remediation efforts should be considered for determining exclusion zone locations and estimating potential risk to neighboring communities. The data also provide evidence suggesting that the large-scale decontamination of outdoor surfaces may reduce air concentrations of the bioagent, which is important since exposure of B. anthracis via inhalation is a primary concern.

Internships in Clinical Data Science: A Brief Report

Field-based learning opportunities are considered to be an important and increasingly relevant part of professional learning in the biomedical sciences. The purpose of this study was to document internship opportunities in the combined fields of clinical data management and clinical data science among those attending the Society for Clinical Data Management 2022 annual conference. At the time of the survey there were 276 internship positions staffed across 47 organizations representing pharmaceutical, academic, clinical research, software development, and medical device companies, suggesting ample opportunities for those who wished to gain experience in such organizations. The greatest percentage of internships reported were in the United States, but with a wide range of opportunities worldwide.

SHEA position statement on pandemic preparedness for policymakers: pandemic data collection, maintenance, and release.

The Society for Healthcare Epidemiology in America (SHEA) strongly supports modernization of data collection processes and the creation of publicly available data repositories that include a wide variety of data elements and mechanisms for securely storing both cleaned and uncleaned data sets that can be curated as clinical and research needs arise. These elements can be used for clinical research and quality monitoring and to evaluate the impacts of different policies on different outcomes. Achieving these goals will require dedicated, sustained and long-term funding to support data science teams and the creation of central data repositories that include data sets that can be "linked" via a variety of different mechanisms and also data sets that include institutional and state and local policies and procedures. A team-based approach to data science is strongly encouraged and supported to achieve the goal of a sustainable, adaptable national shared data resource.

CHEOPS in-flight performance. A comprehensive look at the first 3.5 years of operations

Since the discovery of the first exoplanet almost three decades ago, the number of known exoplanets has increased dramatically. By beginning of the 2000s it was clear that dedicated facilities to advance our studies in this field were needed. The CHaracterising ExOPlanet Satellite ( is a space telescope specifically designed to monitor transiting exoplanets orbiting bright stars. In September 2023 completed its nominal mission duration of 3.5 years and remains in excellent operational conditions. As a testament to this, the mission has been extended until the end of 2026. Scientific and instrumental data have been collected throughout in-orbit commissioning and nominal operations, enabling a comprehensive analysis of the mission's performance. In this article, we present the results of this analysis with a twofold goal. First, we aim to inform the scientific community about the present status of the mission and what can be expected as the instrument ages. Secondly, we intend for this publication to serve as a legacy document for future missions, providing insights and lessons learned from the successful operation of CHEOPS. To evaluate the instrument performance in flight, we developed a comprehensive monitoring and characterisation (M C) programme. It consists of dedicated observations that allow us to characterise the instrument's response and continuously monitor its behaviour. In addition to the standard collection of nominal science and housekeeping data, these observations provide valuable input for detecting, modelling, and correcting instrument systematics, discovering and addressing anomalies, and comparing the instrument's actual performance with expectations. The precision of the measurements has enabled the mission objectives to be met and exceeded. The satellite's performance remains stable and reliable, ensuring accurate data collection throughout its operational life. Careful modelling of the instrumental systematics allows the data quality to be significantly improved during the light curve analysis phase, resulting in more precise scientific measurements. is compliant with the driving scientific requirements of the mission. Although visible, the ageing of the instrument has not affected the mission's performance. The satellite's capabilities remain robust, and we are confident that we will continue to acquire high-quality data during the mission extension.

Using Data Science Tools to Reveal and Understand Subtle Relationships of Inhibitor Structure in Frontal Ring-Opening Metathesis Polymerization.

The rate of frontal ring-opening metathesis polymerization (FROMP) using the Grubbs generation II catalyst is impacted by both the concentration and choice of monomers and inhibitors, usually organophosphorus derivatives. Herein we report a data-science-driven workflow to evaluate how these factors impact both the rate of FROMP and how long the formulation of the mixture is stable (pot life). Using this workflow, we built a classification model using a single-node decision tree to determine how a simple phosphine structural descriptor (Vbur-near) can bin long versus short pot life. Additionally, we applied a nonlinear kernel ridge regression model to predict how the inhibitor and selection/concentration of comonomers impact the FROMP rate. The analysis provides selection criteria for material network structures that span from highly cross-linked thermosets to non-cross-linked thermoplastics as well as degradable and nondegradable materials.

Fostering the Development of Earth Data Science Skills in a Diverse Community of Online Learners: A Case Study of the Earth Data Science Corps

Today’s data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world's most pressing environmental challenges. Despite the importance of these skills, Earth and Environmental Data Science (EDS) training is not equally accessible, contributing to a lack of diversity in the field. This creates a critical need for EDS training opportunities designed specifically for underrepresented groups. In response, we developed the Earth Data Science Corps (EDSC) which couples a paid internship for undergraduate students with faculty training to build capacity to teach and learn EDS using Python at smaller Minority Serving Institutions. EDSC faculty participants are further empowered to teach these skills at their home institutions which scales the program beyond the training lead by our team. Using a Rasch modeling approach, we found that participating in the EDSC program had a significant impact on undergraduate learners’ comfort and confidence with technical and non-technical data science skills, as well as their science identity and sense of belonging in science, two critical aspects of recruiting and retaining members of underrepresented groups in STEM.

Arabic Music Genre Identification

Music Information Retrieval (MIR) is one data science application crucial for different tasks such as recommendation systems, genre identification, fingerprinting, and novelty assessment. Different Machine Learning techniques are utilised to analyse digital music records, such as clustering, classification, similarity scoring, and identifying various properties for the different tasks. Music is represented digitally using diverse transformations and is clustered and classified successfully for Western Music. However, Eastern Music poses a challenge, and some techniques have achieved success in clustering and classifying Turkish and Persian Music. This research presents an evaluation of machine learning algorithms' performance on pre-labelled Arabic Music with their Arabic genre (Maqam). The study introduced new data representations of the Arabic music dataset and identified the most suitable machine-learning methods and future enhancements.

Understanding student emotions when completing assessment: technological, teacher and student perspectives

ABSTRACT Student emotions influence assessment task behaviour and performance but are difficult to study empirically. The study combined qualitative data from focus group interviews with 22 students and 4 teachers, with quantitative real-time learning analytics (facial expression, mouse click and keyboard strokes) to examine student emotional engagement in an online Data Science assessment task. Three patterns of engagement emerged from the interview data, namely whizz, worker and worrier. Related emotions for these were discerned in the real-time learning analytics data, informing interpretations of associations between emotional and other forms of engagement. Instead of displacing human insights, learning analytics used alongside student self-report and teacher professional insights augment our understanding of student emotions in a naturalistic school assessment setting.

BOOK REVIEW - Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

BOOK REVIEW - Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

Using summary tables to introduce principal component analysis in an elementary data science course

AbstractThis article presents a novel approach to introducing principal component analysis (PCA), using summary tables and descriptive statistics. Given its applicability across a variety of academic disciplines, this topic offers abundant opportunity for class discussion and activities. However, teaching PCA in an introductory class can be challenging due to the potential abstraction of multivariate datasets, and especially when students have a minimal background in statistics or data science. This method aims to help teachers bridge the gap between basic descriptive statistics and the more advanced concepts of PCA; this is done by disregarding mathematical optimization, while emphasizing the use of summary tables and the programming language R. The focus is on implementing this method in an introductory tertiary data science course; however, it may potentially be used in higher level courses, and across a variety of disciplines.

SAGES surgical data science task force: enhancing surgical innovation, education and quality improvement through data science.

SAGES surgical data science task force: enhancing surgical innovation, education and quality improvement through data science.