Articles tagged with Data Sets
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.
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.
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.
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.
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.
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 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.
A multivariate analysis of sea level data reveals that barystatic and steric contributions drive most interannual variability in GMSL, with ENSO playing a significant role. The study highlights the magnitude of these contributions associated with ENSO events.
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.
Using air conditioners increases electricity bills on average by 42%, particularly affecting poorer households. Climate change amplifies this impact, especially in Europe and North America.
The gnomAD Consortium has published its first major studies of human genetic variation, revealing new insights into rare types of genetic variation. The research provides better tools for clinical geneticists to diagnose patients with rare genetic diseases and evaluate proposed drug targets.
A new methodology for assessing the credibility of historical global land use/cover datasets has been proposed, addressing temporal and spatial changes. The approach evaluates accuracy, rationality, and likelihood assessments through five case studies, providing a framework for improving data quality.
A study found significant gender imbalance in medical imaging datasets used to train classifiers, leading to biased AI-based diagnostic systems. The analysis showed that underrepresented groups were misclassified more frequently than the majority group.
A new soil moisture product has been developed for mainland China, utilizing a dual-pass data assimilation system that auto-calibrates model parameters. The product provides gridded soil moisture data with a spatial resolution of 0.25° over China, showing consistent patterns with precipitation and evaporation.
A Tel Aviv University-led study published in Nature found that complexity in analytical methods contributes to variability in research outcomes. Researchers analyzed the same dataset using different analysis methods, resulting in varying conclusions. The study highlights the need for improved methodology and data sharing to advance sci...
The Pofatu Database provides a comprehensive compilation of geochemical analyses and contextual information for archaeological sources and artefacts. It enables researchers to reconstruct ancient strategies of raw material and artefact procurement, facilitating comparability and reproducibility in provenance studies.
Researchers developed a deep-learning framework called Morpheus to perform pixel-level morphological classifications of objects in astronomical images. The tool can handle complex images and provide detailed classification results with confidence levels.
Researchers found that winning back lost customers can lead to increased profits, but requires a failure-tolerant organizational culture that encourages open discussion and accountability. Successful reacquisition management also involves establishing guidelines for employees to follow when addressing customer defections.
A new high-resolution 3D map of the mouse brain has been published, providing a reference atlas for the neuroscience community. The map enables whole-brain studies and improves research by allowing researchers to precisely co-register different types of data, enabling bigger-picture views and comparisons.
The TACC COVID-19 Twitter dataset enables researchers to analyze social media communications and identify trends in pandemic responses. The dataset, which contains over 40 million tweets, can be used for topic modeling, entity analysis, and event detection, facilitating discoveries about the spread of misinformation and racist messaging.
A new study found that men are over twice as likely to die from COVID-19, with older men and those with underlying conditions being particularly at risk. The research suggests that additional supportive care may be necessary for these groups.
Researchers propose a new methodology to alter survey datasets to protect consumer privacy, preserving reasonable accuracy. Survey data is vulnerable to breaches and unauthorized use, with some employees stealing sensitive data from their former companies.
A new study finds that teachers exhibit almost identical levels of pro-White racial bias as non-teachers, highlighting the need for additional support and training to mitigate implicit biases in schools. The research suggests that schools are microcosms of society, and teachers require help in combating their biases.
The new dataset, published in PLOS ONE, analyzed 15,506 instances of social scientists' testimony and found that economists testified more than four times as often as political scientists and over 10 times as often as sociologists. Economists also represented think tanks, while anthropologists had the lowest rate of testimony.
Researchers found that state-of-the-art ASR systems performed worse on black speakers than white speakers, with error rates of 0.35 and 0.19 words per hour respectively. The study attributes these disparities to limitations in the acoustic models' ability to capture African American Vernacular English pronunciation and prosody.
Researchers at University of Münster develop AI tool to predict reaction outcomes using molecular structures, enabling accurate predictions for yields and stereoselectivities. The model can be applied to diverse reactions and is expected to significantly change the approach to chemical syntheses.
Using DES data, researchers found more than 300 trans-Neptunian objects, including 245 discoveries made by DES. The method developed by Pedro Bernardinelli can also be used to search for TNOs in upcoming astronomy surveys.
Agriculture-Vision dataset enables farmers to analyze aerial images and gain actionable insights into crop performance. The dataset, developed by researchers at the University of Illinois Grainger College of Engineering and Intelinair, includes over 100,000 images from corn and soybean fields across the Midwest.
A team of researchers developed a standardized survey tool to collect data on rural households. The Rural Household Multi-Indicator Survey (RHoMIS) includes over 30,000 interviews from 33 countries and provides insights into smallholder farming practices, climate change, and social inclusion.
Researchers found that social banks produce smaller returns to their owners, remunerate depositors at below market interest rates, and grant loans below market interest rates. This supports the hypothesis that ownership explains the results, as stakeholder banks exhibit different interest rate behavior.
The World Karst Spring hydrograph dataset (WoKaS) offers a comprehensive collection of over 400 karst spring discharge data, providing valuable insights into the world's fastest-flowing groundwater. This database supports trend analyses, impact studies, and model evaluations for sustainable water management.
A system created by MIT researchers can pinpoint and replace specific information in Wikipedia sentences while retaining humanlike grammar and style. This technology has potential to automatically update factual inconsistencies in Wikipedia articles, reducing time and effort spent by human editors.
A study of over 760,000 restaurant reviews found that location bias affects 98% of restaurants, altering customer consideration by up to 16%. Improved ranking systems can lead to higher reviewer satisfaction (up to 12%) and more diversified top-restaurant recommendations (up to 24%).