Articles tagged with Data Sets
Colorado State University researchers are part of a NASA team analyzing air quality and health data. The project aims to create a dataset identifying pollutant concentrations across the US from 2006 to present, facilitating studies connecting air quality to health outcomes.
Professor Sun's research focuses on deep learning and meta-learning for recognizing images and videos. Her team is working on a food app that uses AI to track nutrition and achieve a healthy diet, but faces challenges due to cultural diversity.
A new software tool called scfind allows researchers to quickly query datasets generated from single-cell sequencing, identifying which cell types any combination of genes are active in. This enables swift analysis of multiple datasets containing millions of cells by a wide range of users.
A new dataset from ATR Brain Information Communication Research Laboratory Group provides insights into Decoded Neurofeedback, a technique that uses AI and brain scanning to modify fear memories and boost confidence. The dataset includes five studies and could lead to new treatments for PTSD, phobias, and anxiety disorders.
Researchers used medium-resolution satellite images to generate a burned area dataset for sub-Saharan Africa, resulting in significantly higher estimates of burned areas compared to coarse-resolution images. The study estimated fire carbon emissions at 1.44 PgC, substantially surpassing previous estimates.
Researchers found that radar overestimates precipitation rates when partially frozen droplets are larger than their solid and liquid counterparts. The phenomenon, known as reflectivity maxima above freezing (RMAF), is more common on windward slopes of mountain ranges.
Researchers found that neuron structures differ between brain areas and individuals, with variations in shape and curvature affecting cognitive functions. The study used nanotomography to analyze brain tissue samples from 34 schizophrenia and 4 control cases.
Regional disparities exist in responses of secondary pollutants to emission reductions, especially fine particulate matter and ozone. Ozone shows rising signals in most areas, but declining signal exists in North America despite reduced nitrogen oxides.
A new study found that the revised concussion guidelines led to a significant reduction in symptom duration among young athletes. The adoption of active rest, recognizing pre-existing conditions, and educating athletes about recovery improved outcomes, with male athletes experiencing a median duration drop from 11 to 5 days and female ...
A newly merged global surface temperature dataset, including reconstructed land and marine measurements, reveals a consistent increased warming trend compared to previous estimations. The study provides evidence that the globe has warmed at a significantly faster rate than previously thought, with improved coverage of the Earth's surface.
Researchers used 'Neuropixels' probes to capture electrical signals from hundreds of neurons, revealing how visual information flows across the brain. The study found a hierarchy of neural activity, with lower areas representing simpler concepts and higher levels capturing complex ideas.
Computational materials scientists at Ames Laboratory have created an algorithm that uses a hybrid approach inspired by cuckoo birds' nesting habits to find novel high-entropy alloys. The new method significantly reduces the search time for these materials, which are highly sought after for their unique properties and applications.
Researchers at UCLA have developed Diffractive Deep Neural Networks (D2NNs) for all-optical object classification, achieving higher accuracy than individual constituent D2NNs and digital AI models. The success of the ensemble learning approach demonstrates the power of combining multiple predictions to obtain a more accurate prediction.
A new study by Dr Andrea Baronchelli and colleagues reveals a connection between cryptocurrency coding and market behavior. The researchers found that 4% of developers contribute to the code of two or more cryptocurrencies, questioning the transparency surrounding the coding process.
Researchers provide a 4D image of an active linkage zone between two major faults, offering insights into fault behavior and implications for seismic hazard assessment. The study highlights the importance of reevaluating 'silent' seismogenic sources in assessing earthquake risk.
A comprehensive study reveals that Germany's plant diversity has been declining steadily over the past decade, affecting both archaeophytes and neophytes. The decline in species diversity averages around two percent per decade, with many common species being affected.
A team of researchers has found evidence that UV radiation from sunlight significantly affects COVID-19 transmission. By analyzing daily COVID-19 cases and environmental conditions, they discovered a correlation between UV exposure and the growth rate of new cases.
A Purdue University research team uncovered flaws in a prominent dataset used to study brain activity and decode visual perception. The findings challenge previous conclusions on the possibility of reading minds through electric brain signals.
An international team of researchers has connected specific genetic signals with specific areas of the face, identifying 203 genomic regions that play a role in human facial development. The study sheds light on craniofacial malformations such as cleft lip and palate, as well as evolutionary differences between various populations.
A new method, nnU-Net, has been developed to configure self-learning algorithms for a large number of different imaging datasets, enabling the interpretation of three-dimensional imaging data and distinguishing between tumor and non-tumor tissue.
The Leipzig Catalogue of Vascular Plants (LCVP) is the largest and most comprehensive list of scientific names for all known plant species. With over 1.3 million entries, it vastly expands global knowledge of plant diversity and provides a reliable reference for researchers worldwide.
Researchers analyzed a massive dataset of seasonal events across the former Soviet Union and found that species' activities fail to keep up with their cues due to past evolution. Local adaptation patterns result in a massive imprint on nature's calendar, making geographic variation in seasonal timing more pronounced in spring and less ...
A new paper offers a framework for studying online hate and counter speech, analyzing millions of Twitter interactions. Researchers found that organized movements are more effective than individuals in countering hate speech.
A new USC study reveals that AI models lack common sense to generate plausible sentences, despite advances in natural language processing. The research challenges the effectiveness of current benchmark tests and finds that even the strongest models can make silly mistakes.
A new disaster database, DesignSafe, is changing how planners prepare for and respond to natural disasters. The database features over 293 published data sets, including insights on earthquake impact, hurricane damage, and more.
Georgia State University researchers have developed a software tool called COINSTAC, which allows for local data analysis and sharing of results, protecting patient privacy and anonymity. The platform is designed to be compatible with deep learning models, enabling the training of analyses on decentralized databases.
Researchers developed a new method to optimize materials exhibiting metal-insulator transitions (MIT) using Bayesian optimization and latent-variable Gaussian processes. The approach identified 12 previously unidentified MIT materials with optimal functionality and synthesizability.
Researchers establish objective standards for defining and identifying binary tropical cyclones (BTCs), which can bring extreme precipitation and cause serious disasters. The study analyzed two best-track datasets and provides a main standard for defining BTCs, based on separation distance and coexistence time.
A new open-source dataset has been created to provide county-level exposure numbers for tropical cyclones and human health. This tool will enable scientists to analyze multiple storms in different places and time periods, drilling down to see what happens in different health outcomes for people.
For the first time, astronomers have surveyed over 250 million stars in the Milky Way's bulge, measuring their chemical composition and gaining new insights into the galaxy's formation. The data will help scientists understand how the Milky Way formed its central bulge and gain a better understanding of other galaxies.
Recent research shows that chimpanzees, like humans, increasingly prioritize mutual and equitable friendships with others as they get older. Younger adults tend to form lopsided relationships, while older chimpanzees focus on maintaining smaller, fulfilling networks of close friends.
A new data set provides high-resolution daily temperatures from around the globe to study human health impacts from heat waves, risks to agriculture, droughts, and food insecurity. The CHIRTS-daily dataset offers accurate estimates of air temperatures for 1983-2016, supporting efforts to monitor, understand, and mitigate climate hazards.
Researchers have developed machine learning algorithms to predict which RNA-based toehold switches function well, enabling the identification and optimization of these tools. The algorithms analyzed a massive dataset of over 100,000 toehold switch sequences and predicted their behavior with high accuracy.
A novel method for automated pollen analysis has been developed by combining imaging flow cytometry with deep learning, allowing for accurate species identification and quantitative findings in just 20 minutes. The new tool was tested on 35 plant species and achieved an accuracy rate of 96%, outperforming traditional microscopy methods.
Researchers at Princeton University developed a tool to uncover potential biases in visual data sets, such as stereotypical images and underrepresentation. The tool, REVISE, uses statistical methods to inspect data sets for object-based, gender-based, and geography-based biases.
A global study maps areas with high carbon returns from natural forest regrowth, highlighting climate's role in carbon storage. Climate change mitigation strategy: restoring degraded woody vegetation could store substantial amounts of CO2.
Researchers at Mount Sinai Hospital developed a COVID-19 mortality prediction model based on patient's age, minimum oxygen saturation, and type of encounter. The model showed high accuracy (AUC=0·91) in predicting mortality among patients with COVID-19, offering potential for improved prognostication and management.
Regional differences in Russia's alcohol consumption patterns reveal a complex relationship between government measures and unlicensed alcohol sales. Studies have shown that regions with stricter policies exhibit reduced registered alcohol consumption, but unregistered alcohol remains a significant issue.
Researchers developed a machine learning model that analyzes molecular structure to predict enthalpy of formation with better accuracy than traditional approaches. The model's accuracy improves with more data, enabling the development of fully automated algorithms for predicting complex chemical phenomena.
A machine learning study conducted at Princeton University found that the meaning of words is shaped by culture, history, and geography. The researchers analyzed over 1,000 words in 41 languages and discovered that many everyday words have varying meanings across cultures.
Carnegie Mellon researchers create large dataset capturing interaction between sound, action, and vision to improve robotic perception. The study found that sounds can help robots differentiate between objects and predict physical properties of new objects.
A new study published in the Journal of Transport and Health found that walking to work can significantly improve a person's self-rated health compared to casual strolls. People who walked primarily for utilitarian purposes, such as commuting to work, reported better health than those who walked mostly for leisure.
A new study by Cornell researchers uses machine learning to assess the effectiveness of mathematical tools in predicting financial markets. The model can also predict future market movements, a task considered extraordinarily difficult due to markets' massive amounts of information and high volatility.
Experts have developed a platform for self-testing AI medical services, allowing for automated validation and improvement. The platform provides an opportunity to fine-tune algorithms with unlimited access to data instances, minimizing human factor manipulation.
A new study by scientists at the University of Cambridge's Autism Research Centre found that transgender and gender-diverse adults are three to six times more likely to be diagnosed as autistic. The research used data from over 600,000 adult individuals and confirmed previous smaller scale studies.
A new study from Stanford University's Immigration Policy Lab found that many refugees in the US move to different states soon after arrival, primarily seeking better job markets and social networks. Refugees are more likely to leave high-unemployment states and join those with booming economies.
Researchers at Texas A&M University are using advanced machine-learning analysis to reveal the evolution of cracks in rock and concrete. By combining data from multiple sources, including sound waves, electromagnetics, and pressure measurements, they aim to improve our understanding of crack damage and development.
Researchers developed a computational tool called PolyA-miner to analyze alternative polyadenylation (APA) sites in RNA strands. The tool precisely identifies novel APA sites that were not detected by traditional analytical approaches, revealing new insights into gene regulation.
Tong and collaborators are developing a model-ready emission dataset for CAM-CMAQ's wildfire forecasting system, aiming to improve accuracy and mitigate adverse fire effects. The dataset will also incorporate aerosol attenuation of photolysis in smoke plumes.
A team of researchers has quantified human activity across Antarctica, revealing that almost every area has been visited. Biodiversity is not well-represented within these areas, highlighting the need for swift action to declare new protected areas.
A major loss of grassland has occurred in Great Britain since 1990, with a net reduction of 7,668 km2 or 1.9 million acres, as woodland area increased by 5,236 km2 and urban areas expanded by 3,376 km2 between 1990 and 2015. The majority of this loss and increase occurred in Scotland and England respectively.
Researchers from Caltech and institutions like Northwestern University used deep learning and supercomputing to identify Nyx, a product of a long-ago galaxy merger. The discovery provides the first indication that a dwarf galaxy merged with the Milky Way disk.
Researchers at Cornell University used AI to investigate how reflection changes images, discovering clues like facial features and beards that can differentiate originals from reflections. The study has implications for training machine learning models and detecting faked images.
Researchers developed a new set of computational tools to identify cell-type specific methylation patterns, known as molecular barcodes, in complex cell mixtures. These new methods can distinguish between different cell types in tissues made up of multiple cell types.
A large-scale database of ground reaction force data from over 2,000 patients has been made publicly available for research purposes. The GAITREC database includes information on patients with joint transplants, fractures, and ligament injuries, providing a valuable resource for clinicians and researchers.
A team of researchers at Carnegie Mellon University has developed an automated method for making communications more polite, using a large dataset of labeled sentences. The system can restructure nonpolite directives or add words to make them more well-mannered, while maintaining the original meaning.
A team has devised deep-learning and other computational approaches that dramatically reduce image-analysis time by orders of magnitude. They report their results in Nature Biotechnology, accelerating image analysis in three major ways: deconvolution, 3D registration, and complex deconvolution.
By extending available data, researchers developed a method to estimate mass concentrations of particulate matter from humidity and visibility measurements. This approach has the potential to provide broader understanding of how particulate matter evolves and improve visibility in daily life.
Researchers have created a new map revealing the Earth's mantle at a depth of 3,000 kilometers by analyzing thousands of seismic waves collected over 30 years. The map shows hot and dense regions below Hawaii and French Polynesia.
Researchers at King Abdullah University of Science & Technology devised a new analytical tool to predict flood risk by adapting a classical statistical model for analyzing extreme rainfall in large datasets. The model demonstrated potential in capturing observed patterns in northeast America, promising improved prediction capabilities.