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.
A research group led by NCKU professor I-Non Chiu conducted the first cosmological study on galaxy clusters identified by eROSITA, analyzing 550 galaxy clusters. The results suggest that Dark Energy occupies up to 76% of the total energy density in the Universe.
Researchers used a multiomics approach to analyze changes in transposable elements after influenza A virus infection, identifying transcription factors contributing to individual responses. The study provides insights into the variable severity of illness among individuals infected with the same virus.
Online radiologists choose studies based on financial attractiveness, leading to delays in high-priority cases. This study found that expedited priority class contained the highest percentage of delayed studies.
A new guide has been created to standardize fossil pollen datasets, enabling researchers to compile and analyze large-scale syntheses of palaeoecological data. The FOSSILPOL workflow and R-package provide a step-by-step process for handling data preparation, ensuring good data quality and minimizing erroneous interpretations.
Researchers found that machine-learning models trained with descriptive data label rule violations more harshly than humans, leading to potential serious implications in the real world. This study highlights the need for careful consideration of data labeling and training methods to ensure fairness and accuracy in AI decision-making.
Researchers used cancer proteomics data to identify gene candidates for therapeutic targeting, focusing on protein kinases in uterine endometrial cancer cells. Public molecular resources and multi-omics data analysis can prioritize genes of interest for future studies.
Researchers have developed a new method called EvoAug that uses artificial DNA sequences inspired by evolution to train deep neural networks for genome analysis. This approach enables the model to recognize regulatory motifs more accurately, leading to better performance and potential breakthroughs in understanding human health.
A deep learning model has been developed to classify cancer cells into distinct types, enabling accurate prediction of metastatic potential. The tool achieves high accuracy and is simple to use, making it a promising solution for medical practitioners.
A mobile application utilizing Python and a single-element ultrasound transducer has been developed for photoacoustic tomography (PAT) image reconstruction. The application successfully reconstructs high-quality images with signal-to-noise ratio values above 30 decibels, making it suitable for point-of-care diagnosis in low-resource se...
More than 3 million sq km of Asian elephant habitat has been lost in 300 years, with suitable habitats cut by nearly two-thirds. The study suggests that the remaining elephant populations may not have adequate habitat areas, setting up a high potential for conflicts with people living in those areas.
A team of researchers from Carnegie Mellon University has developed an AI-based system to help clinicians make decisions quickly and precisely in the ICU. The system, called the AI Clinician Explorer, provides recommendations for treating sepsis based on data from over 18,000 patients.
A study by Drexel University and Vanderbilt University analyzed 82 relevant conversations on Instagram direct messages where teens asked for help, revealing that most disclosures were about mental health concerns. Support was offered in most cases, but specific sets of circumstances led to denial.
A team of experts identified 29 sources of bias in AI/ML models for medical imaging, including data collection, preparation, and deployment. The study provides a comprehensive roadmap for mitigating these biases and ensuring fairness, equity, and trust in AI/ML models.
GPMeta accelerates pathogen detection in metagenomic sequencing (mNGS) tests, achieving higher accuracy while significantly reducing processing time. The approach uses a succinct hash index scheme and multi-GPU support to handle massive data sets.
A study found that high-quality labeling of images boosts perceptions of training data credibility, leading to increased trust in AI systems. However, biases in the data can reduce trust in certain aspects.
A recent study from Aarhus University found that music used for studying and sleeping share similar characteristics, such as slow tempo and repetitive patterns. The study suggests that these similarities can be attributed to the calming effects of the music on the brain, creating a conducive environment for both tasks.
A machine learning program can spot risky conversations on Instagram by analyzing metadata clues, such as conversation length and participant engagement. The system was 87% accurate in identifying risky chats using sparse and anonymous details from over 17,000 private chats.
A new deep learning-based model estimates breast density with high precision, correlating it to cancer risk. The model's performance is comparable to that of human experts, but it can be trained faster and on smaller datasets.
Max Planck scientists explore the possibilities of artificial intelligence in materials science, discussing how combining physics-based modeling with AI can unlock complex material designs. The research focuses on overcoming limitations of traditional methods and handling sparse, noisy data.
A team of researchers has developed a new human-in-the-loop system to improve the accuracy and interpretability of deep neural networks. The system uses an interactive one-click method for annotating images, reducing the co-occurrence bias inherent in training datasets.
The new dataset provides a 'ranking' of countries contributing most to global warming, with CO2 emissions driving the most warming. Countries like Brazil and Indonesia are rising in their contribution, while industrialised nations see slight declines.
Researchers developed a model to track COVID-19 data, predicting transmission and informing health surveillance systems. The model successfully predicted the spread of COVID-19 in Cali, Colombia, highlighting the importance of high-resolution data in understanding virus dynamics.
A national analysis of pediatric hospitalizations from 2009 to 2019 found a significant increase in mental health diagnoses, with attempted suicide being the leading cause. The study highlights the growing importance of addressing mental health concerns in children and adolescents.
Researchers have developed a non-invasive method to track human aging using retinal scans, which are less expensive and more accurate than other aging clocks. The study found that changes in the eye can provide an actionable evaluation of gero-protective therapeutics, offering a new tool for tracking aging.
Researchers developed an AI tool called SILIC to identify 169 species, including 137 birds, from bird sounds in Yushan National Park. The dataset provides detailed acoustic activity patterns of wildlife across short and long temporal scales.
A machine-learning model was trained on 10 million tweets to infer users' subjective wellbeing. The study found that New Year's Day is the most popular holiday, followed by Defender of the Fatherland Day and International Women's Day. The researchers also discovered gender differences in attitudes towards certain holidays.
Researchers developed AI models based on UNet and MobileNet architectures to analyze standardized abnormalities in CT images, accurately identifying object presence and confidence. These models achieved an absolute percentage error of less than 5 percent, comparable to human professionals.
Researchers found that COVID-19 infections are linked to an increased risk of developing liver problems, acute pancreatitis, and other GI disorders. The study analyzed over 14 million medical records and estimated that SARS-CoV-2 infections have contributed to over 6 million new cases of GI disorders in the US.
A novel AI architecture, relational reasoning network, accurately identifies anatomical landmarks in CT scans for orthodontic treatments. The model learns spatial relationships between landmarks without explicit image segmentation, achieving accuracy comparable to conventional methods.
Researchers seek to develop algorithms providing meaningful explanations for AI decision-making, enabling higher human trust and adoption in fields like science. The project focuses on symbolic reasoning and estimating explanation accuracy, addressing the need for transparent AI systems.
Researchers at MIT developed a technique to improve machine-learning models' reliability without requiring additional data or extensive computing resources. The method uses a simpler companion model to estimate uncertainty, enabling more effective uncertainty quantification.
Researchers tested three common techniques to make algorithms fairer and found that one approach didn't reduce social norm bias at all. They proposed a new technique: a formula to directly measure social norm bias in an algorithm so it can be corrected. This bias can persist even after overt discrimination is removed.
Researchers found that electric car adoption in California was associated with real-world reductions in air pollution and asthma-related emergency room visits. The study also highlighted an 'adoption gap' between low-resource zip codes, pointing to opportunities for environmental justice.
Researchers identified 7 key symptoms of long COVID, including heart issues and joint pain, in a study of 52,461 patients. The findings could help healthcare providers diagnose and treat the condition more effectively.
Dr. Nico Spiller to develop new analysis methods using machine learning to analyze complex brain data related to memory, decision making, and movement. The fellowship aims to provide insights into neurodegenerative diseases such as Alzheimer's and Parkinson's disease.
A new measure called c-value helps researchers choose between techniques based on the chance that a new method is more accurate for a specific dataset. The tool answers questions like whether to use alternative estimation methods despite potential costs and effort.
A recent study published in Nature has discovered several new disease genes and provided new insights into the effects of known genetic factors on disease. The study highlights an underappreciated complexity in dosage effects of genetic variants, challenging traditional Mendelian inheritance laws.
A new study used satellite data and public registry information to track the changing identities of commercial fishing vessels, revealing that nearly 20% of high seas fishing is carried out by unregulated or unauthorized vessels. The study found hotspots of potential IUU fishing in the Southwest Atlantic Ocean and western Indian Ocean.
A team of scientists reviewed the effectiveness of reanalysis data products for studying the West African climate. They found that ERA5 achieved considerable progress in reducing biases and improving representation compared to its predecessor, ERA-interim.
Researchers identified a common brain network underlying several psychiatric illnesses, including schizophrenia and depression. The transdiagnostic network shows gray matter decreases in specific brain regions across most studies.
Researchers at Drexel University used GPT-3 to spot early signs of Alzheimer's in spontaneous speech, achieving 80% accuracy. The program analyzed word-use, sentence structure and meaning from transcripts to identify characteristic profiles of Alzheimer's speech.
A recent study has identified common and unique cellular processes in six neurodegenerative diseases, providing new insights into the underlying causes of these conditions. The research used machine learning analysis to compare RNA markers in whole blood samples from patients with distinct diseases, revealing eight shared themes across...
A team of ecologists is calling for the designation of World Heritage Environmental Datasets to secure funding and ensure their long-term accessibility. These datasets, which include vital information on climate change adaptation, resource management, and environmental policy, are essential to understanding global change.
Researchers analyzed data from over 80,000 primary care physicians and found no clear connection between MIPS scores and clinical performance. Doctors with low MIPS scores performed worse on some process measures but better on others, suggesting that the system may prioritize paperwork over patient outcomes.
The Washington D.C. metro area's dashboard is being developed at PSU, allowing users to see all the data together in one place. The project aims to improve nonmotorized planning by providing clean, quality-checked biking and walking count data.
A new study maps the global landscape of antimicrobial resistance, revealing surprising transmissions in Sub-Saharan Africa and highlighting the need for tailored strategies to combat resistance. The research, which analyzed sewage samples from 243 cities in 101 countries, found that resistance genes are more frequently transmitted acr...
Scientists used AI-driven PandaOmics platform to analyze gene expression datasets from DNA repair diseases, identifying biomarkers associated with treatment response. The study focused on genes that stratify cancer patients by survival outcomes, providing potential targets for personalized therapies.
Researchers have discovered that oligodendrocyte precursor cells (OPCs) play a crucial role in synaptic pruning, cleaning up unwanted connections between neurons. By analyzing a massive dataset of 3D brain cell structures, the team found OPCs digesting parts of neighboring neurons.
A global dataset reveals widespread witchcraft beliefs varying substantially between countries and world regions. Higher education and economic security are associated with lower belief in witchcraft. Weak institutions, low social trust, and conformist culture also correlate with higher witchcraft prevalence.
Chung-Ang University researchers propose a new algorithm, MR-UCB and MR-APE, to tackle stochastic multi-armed bandit problems with heavy-tailed noise distributions. The methods guarantee minimal loss for worst-case scenarios with minimal prior information.
Researchers created synthetic knee x-ray images to complement real images in osteoarthritis classification. Medical experts were unable to distinguish between authentic and synthetic images, highlighting the potential of synthetic data for collaboration and testing.
Researchers modelled relationship between plant diversity and environmental conditions, capturing how diversity varies along environmental gradients. The models predict highest concentrations of plant diversity in environmentally heterogeneous tropical areas like Central America and the Amazonia.
The Julich Brain Atlas provides detailed maps of brain cells and receptors, enabling better understanding of brain connectivity and function. The atlas allows for correlation between brain network activity and underlying anatomy, aiding in the diagnosis of psychiatric disorders.
Researchers at KAUST develop a novel multivariate skew-elliptical link model to address the challenges of highly imbalanced health data. The new model provides a better fit to COVID-19 datasets and offers flexibility over existing models.
A new study by MIT researchers shows that mobile phones can collect useful structural integrity data while crossing bridges. The study found that information about bridge vibrations can be extracted from smartphone-collected accelerometer data, and that this method could add years to a road bridge's lifespan. By leveraging crowdsourced...
Researchers at University of Jyväskylä used machine learning to predict ACL injuries in elite athletes but found a low overall accuracy rate. The study analyzed the largest data set ever collected and provided valuable insights into the challenges of predicting injuries in individual athletes.
Researchers used machine learning to track turbulent structures in fusion reactors, gaining detailed information on their behavior and heat flows. The approach enables more accurate engineering requirements for reactor walls and could lead to improved energy efficiency.
The Global Jukebox, an online tool for exploring music and performing arts from around the world, has made its dataset and data available to the public. The database includes 5,776 recordings representing 1,026 societies, with detailed musical style categorization data and additional features such as breath management and instrumentation.
The US Department of Energy's Oak Ridge National Laboratory has developed a massive geographic dataset, USA Structures, using deep learning to forecast potential damage and accelerate emergency response. The dataset provides critical information on building outlines and attributes, enabling FEMA to prioritize response efforts.
A joint study by TAU and Hebrew University accurately dated 21 destruction layers at 17 archaeological sites in Israel, using geomagnetic field reconstruction. The new data verify Biblical accounts of Egyptian, Aramean, Assyrian, and Babylonian military campaigns against the Kingdoms of Israel and Judah.