Articles tagged with Data Sets
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
Researchers studied mesoscale eddy observations to estimate theoretical predictability limits of eddy trajectories. Long-lived eddies have higher predictability limits, while short-lived ones are less predictable. The study also introduced a complexity index to elucidate OME track complexity.
A new study by Newcastle University shows that citizen scientists can accurately identify slug species with proper training and support. The research found that participants improved their identification skills throughout the project, with accuracy rates ranging from 47-70%.
Researchers found the median peak age for Olympic track-and-field athletes to be 27 years old. After 27, the probability of a peak performance drops significantly, highlighting the importance of training regimens in optimizing results.
The A4 study, a large clinical trial of pre-symptomatic Alzheimer’s disease, has yielded key insights into the condition. Researchers have collected extensive data on brain scans, blood samples, genetic information and cognitive tests from over 7,500 participants.
A new study published in Aging explores the potential of three large DNA methylation datasets to identify biological age signals in dogs. The researchers found that biological age methylation clocks are affected by population stratification and require heavy parameterization to achieve effective predictions.
Researchers developed a deep learning-based model to estimate rainfall intensity from surveillance audio, achieving a root mean absolute error of 0.88 mm h-1 and a coefficient of correlation of 0.765. This approach offers a new method for high-resolution hydrological sensing, contributing to environmental resilience and urban sensing.
Research by University of East Anglia and University of Texas found that stricter data privacy laws significantly reduced breaches, but negatively affected firms' market value. Companies compliant with GDPR invested more in data protection and were less likely to experience data breaches.
Scientists have discovered that planted mangroves can store a significant amount of carbon, reaching levels comparable to those in intact stands after just 20 years. The study used logistic models compiled from over 700 planted mangrove stands worldwide and found that the trees' carbon stock reached 71-73% of that found in intact stands.
Researchers developed a machine learning approach to identify potential subtypes in diseases, significantly enhancing disease classification and treatment strategies. The model uncovered 515 previously unannotated disease subtypes.
Researchers re-analyzed historical data from a classic displacement experiment and found that young starlings migrated independently, using their own direction. Adult starlings adjusted their migratory orientation to reach their normal wintering areas, while local conspecifics influenced the route of relocated young starlings.
The e-COL+ project aims to capture and reconstruct France's natural history collections in 3D, covering nearly 6% of the world's total natural specimens. The project will provide modern equipment, create a comprehensive dataset of 3D models, and build AI tools to improve model reconstruction.
A team of researchers developed EMOKINE software to measure the objective kinematic features of movements that express emotions. The software provides movement parameters from data sets at the touch of a button, allowing scientists to analyze emotional expression and intentions.
The new interactive atlas provides a library of case studies for adapting to drought in the US Southwest. The atlas offers geospatial solutions to water scarcity, helping communities implement effective adaptation strategies.
Research by Lancaster University found that people in higher social grades, including corporate world and education sectors, are adopting each other's speech patterns to be more inclusive. This 'resonance' has increased over the past 20 years, particularly among those with high social status.
Researchers developed FairDeDup, a cost-effective method to reduce social biases in AI systems by removing redundant data and incorporating controllable diversity dimensions. The approach enables accurate and fair AI training with fewer resources.
A new method, PURPLE, estimates how often underreported health conditions occur in different demographic groups, providing a more accurate picture of intimate partner violence. The algorithm shows that women from lower-income brackets are more likely to experience violence, consistent with previous literature.
The Kids First DRC has introduced an upgraded data portal to streamline big data search and analysis, improving collaborative pediatric research outcomes. The new portal integrates diverse datasets, including genomic information from the Children's Brain Tumor Network, to foster cross-disciplinary research.
A comprehensive, user-friendly repository has been created to help study Alzheimer's disease. The ssREAD database encompasses 277 integrated datasets from 67 scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells, and includes interactive visualizations for comprehensive analysis interpretations.
The study uses the Dermatological Vision Dataset and compares vision transformers with traditional CNNs, achieving an impressive 97.8% accuracy on validation sets. Integrating ViTs into dermatology represents a promising step toward more accurate diagnostics.
Researchers developed a reliable iris recognition method by applying statistical limits to the spatial domain zero crossing technique, reducing errors to 0.022%. The algorithm uses a neural network to recognize unique features of each person's iris, achieving over 99.78% accuracy.
Portland State University has secured a nearly $1 million grant from the National Science Foundation's Campus Cyberinfrastructure program to establish the Oregon Regional Computing Accelerator (Orca) cluster. The cluster will provide free-of-cost computing resources and cyberinfrastructure to colleges in rural, regional, and minority-s...
A novel approach to training AI systems uses information about spatial position to identify objects and navigate surroundings, inspired by children's visual development. The method improves contrastive learning models' effectiveness by incorporating simulated spatial context information, outperforming base models in various tasks.
A new dataset integrates global Health AI research, providing a structured resource for researchers, policymakers, and practitioners. The dataset includes 96,332 Health AI documents, covering publications, open research datasets, patents, grants, and clinical trials.
A new study from the University of Notre Dame shows that repeated trips to the grocery store can be a reliable indicator of creditworthiness. The research found that buying healthier groceries and consistent shopping habits are linked to on-time credit card payments.
A new transit station in Japan significantly reduced average healthcare expenditures per capita over four years, with savings of approximately $929.99. The study used a causal impact algorithm and time series data to analyze the medical expenditure data gathered from a suburban city on the West Japan Railway line.
A recent study by Kyoto University reveals that expatriates' boundary-spanning activities can lead to role stress, emotional exhaustion, and a sense of being an outsider among local employees. The study highlights the need for careful management to mitigate these negative impacts.
A team at the University of Münster developed an improved method for explaining machine predictions of chemical reactions, using mechanisms such as reproduction, mutation and selection. The algorithm creates customised molecular fingerprints that predict chemical reactions with surprising accuracy, suitable for predicting quantum chemi...
Research suggests that four proposed AI search engines for automating search and retrieval of digital histopathology slides have inadequate performance for routine clinical care. The algorithms showed less than 50% accuracy in some cases, highlighting the need for rigorous external validations and standardization before clinical adoption.
A study by Washington State University found ChatGPT's generative AI system provided inconsistent heart risk assessments for patients with chest pain. The AI failed to match traditional methods used by physicians and returned different results for the same patient data, highlighting its limitations in high-stakes clinical situations.
The new 'scLENS' tool overcomes challenges in single-cell transcriptomics by automatically differentiating signals from noise using Random Matrix Theory and Signal robustness test. This innovation significantly improves analysis accuracy and efficiency, enabling researchers to extract biological signals conveniently and automatically.
A new AI model generates realistic images of single cells, which are used as synthetic data to train an AI model for better cell segmentation. The researchers found that providing a more diverse dataset during training improves performance.
DynGAN detects and resolves mode collapse by establishing thresholds on discriminator outputs and training dynamic conditional generative models. This improves the diversity of generated samples, surpassing existing GANs and their variants.
Contraceptive services experienced downward trends from an initial increase in the month after Dobbs v. Jackson, indicating growing challenges for access. The study found decreasing workforce numbers providing contraception methods.
Mayo Clinic researchers have invented a new class of artificial intelligence (AI) algorithms called hypothesis-driven AI, which can help discover the complex causes of diseases like cancer and improve treatment strategies. This emerging class of AI offers an innovative way to use massive datasets to guide individualized medicine.
A new study found that non-medical cannabis use is significantly associated with a 96% decrease in odds of subjective cognitive decline. However, medical and dual-use were not significant in reducing the risk of SCD. The study highlights the potential protective effects of cannabis on cognition, but more longitudinal research is needed...
A new dataset has been released that combines molecular information about the poplar tree microbiome with ecosystem-level processes. The dataset provides detailed information on 27 genetically distinct variants of Populus trichocarpa, a bioenergy crop, and includes data on gene expression, soil chemistry, and microbial diversity.
A new satellite dataset provides unprecedented insights into global plant growth, derived from TROPOMI satellite observations. The Comprehensive Mechanistic Light Response (CMLR) gross primary production (GPP) dataset offers a more accurate measurement of plant productivity on a global scale.
Researchers found that doctors can confidently skip 50% of biopsies by combining MRI-based prostate imaging reporting and data system scores with prostate-specific antigen density testing. The study suggests that this approach can decrease patient harm and healthcare costs associated with unnecessary biopsies.
Researchers from the University of Washington created an AI algorithm to analyze infant poses using limited training data. By leveraging generative AI, they were able to produce high-quality results, enabling parents to monitor their babies' daily activities and detect potential health issues early.
Researchers investigate how different VAE model architectures, latent space configurations, and training datasets impact the performance of generative music models with explainable features. They find that measureVAE has higher reconstruction accuracy but lower musical attribute independence.
A recent study found that most Americans support democratic norms, with 17.2% of Democrats and 21.6% of Republicans supporting at least one norm violation. However, the researchers also discovered a divide between everyday citizens and elected officials who are pushing against democratic governance.
A study in São Paulo's central area found that poor wood condition, sidewalk root constriction, and drastic pruning are major predictors of urban tree failure. The researchers propose guidelines for stakeholders to reduce the number of failures, which average 2,000 per year.
Researchers introduced a novel method to augment in situ root datasets through an improved CycleGAN generator, achieving significant enhancements in speed, accuracy, and stability. The approach also boosts dataset versatility by including diverse culture mediums.
A minimal metadata set (MNMS) is established to enable in vivo data reuse and improve the sharing and reproducibility of research data. The MNMS is designed to contribute to making data from living animals compliant with the FAIR data concept, which emphasizes Easy-to-Find, Accessible, Interoperable, and Reusable data.
Researchers developed a machine-learning model to assess prostate cancer biopsy samples, overcoming limitations of traditional methods. The new model, nnU-Net, provides accurate 3D segmentation of glandular tissue structures, leading to better prognostic analyses and potential improvements in patient outcomes.
DomAda-FruitDet is a domain-adaptive anchor-free fruit detection model that achieves impressive average precision scores of up to 94.0% across various fruit datasets. The model effectively bridges the foreground and background domain gaps, enabling accurate and efficient auto-labeling in smart orchards.
A new study warns that rural pupils in England are facing underachievement in education due to socioeconomic factors. Despite better exam results, rural pupils from disadvantaged homes perform worse than urban pupils from similar backgrounds.
A new study refutes previous research suggesting Spinosaurus was an aquatic pursuit predator, highlighting methodological flaws and low accuracy of phylogenetic flexible discriminant analysis. The researchers provide guidelines for future studies to use with caution when applying this methodology to limited datasets.
A team of Rice University researchers has developed a platform for integrating DNA and RNA data from single-cell sequencing with greater speed and precision. The method, MaCroDNA, relies on a classical algorithm to identify matching pairs of data and outperformed state-of-the-art technologies in accuracy measurements.
A new study finds that biodiversity is becoming more similar through time, but an almost equal number of communities are becoming more distinct. The analysis of 527 datasets shows a balanced trend between homogenisation and differentiation across landscapes.
Researchers developed a novel machine learning-based approach to analyze diffuse reflectance spectroscopy data, achieving higher accuracies and speeds than existing methods. The 'wavelength-independent regressor' model overcomes use-error limitations by incorporating diverse datasets, making it suitable for clinical settings.
Researchers optimized polygenic risk scores using ancestrally diverse genomic data to improve accuracy across diverse populations. The recalibrated tests provided a more accurate assessment of disease risk for individuals with varied ancestral backgrounds.
Researchers found that widely used machine learning tools produce biased results for immunotherapy research, as they rely heavily on datasets from higher-income communities. This can lead to ineffective treatments for lower-income populations. The study highlights the need for accurate and unbiased data in machine learning models.
A new study found that 17% of young mothers in Wales experienced at least one child being taken into care between 2014 and 2021. The study also revealed that many care-experienced mothers experience early motherhood and placement instability, leading to concerns about family separation.
Researchers use machine learning to combine mismatched datasets and reduce variation by over 95%, retaining meaningful differences. The approach has potential to provide deeper understanding of normal metabolism and identify biomarkers for disease.
The study found a 4-fold increase in ASD diagnoses among 2-3 year-old children and a 2-fold increase in older ages. The research highlights the need for education and healthcare services to keep up with the growing demand, and emphasizes the importance of early intervention to improve communication and social skills.
The study found that different instrumental systems in the Global Ocean Observing System (GOOS) complement each other and are essential for accurately monitoring ocean warming. The GOOS, which includes platforms such as free drifting instrumented floats, mooring buoys, and autonomous pinniped data, provides a comprehensive view of ocea...
Researchers developed a predictive model to detect users and content related to Islamic State extremists on social media, identifying potential propaganda messages and their characteristics. The study's findings can help social media companies and law enforcement agencies track and prevent the spread of extremist propaganda.
A new study from the Max Planck Institute of Geoanthropology highlights the importance of ancient cities in understanding contemporary urban challenges. The research provides numerical datasets on road lengths, building types, population sizes, economic output, and environmental impacts to inform urban planning and policy.
Researchers have identified over 700 plant genera named for women, representing a significant increase in the number of genera linked to women. The dataset provides valuable information on the contributions of women to botany and can be used by other researchers to build upon this work.