Articles tagged with Data Sets
Researchers analyzed mortality data from 103 countries to track excess deaths during the COVID-19 pandemic. They found significant increases in deaths in some countries, while others saw a decrease due to social distancing measures.
A recent study published in eLife journal analyzed mortality data from 103 countries to determine the true rate of COVID-19 deaths. The researchers found significant underreporting of pandemic deaths, with some nations reporting 2-50 times higher excess deaths than official numbers.
Researchers applied supervised machine learning to lidar data from fishery surveys, automating the identification process in regions with a strong possibility of harboring fish. The technique decreased manual inspection in datasets from Yellowstone Lake and Gulf of Mexico studies by 61.14% and 26.8%, respectively.
A recent study found that meeting the Paris Agreement's 2°C target will increase net energy sector jobs globally by about 8 million by 2050. The majority of these new jobs will be in the solar and wind industries, while fossil fuel extraction jobs are expected to decline rapidly.
A recent study found that controversial posts on Reddit spread faster and reached more people than noncontroversial posts, with over 60,000 comments on the former. The researchers analyzed over 47,000 posts to understand why controversy can make online content more attention-grabbing.
A new analysis reveals that global toxic pollution and climate change disproportionately affect low-income countries, with the top-one-third of at-risk nations accounting for over two-thirds of the world's population. The study provides a list of top-ten countries most equipped to immediately begin risk reduction efforts.
A new neural model can detect 'inappropriateness' in chatbot messages, which are defined as content that may harm the reputation of the speaker's company without being toxic. The study provides a large collection of labeled datasets for further research and has been made publicly available.
A new portal, gEAR, enables researchers to easily utilize complex genetic data from sophisticated studies, advancing medical discoveries in hearing loss. The tool provides visualizations of datasets and allows inputting of own data for comparison, saving time and facilitating analysis.
Researchers found that subjective decisions play a critical role in shaping empirical results, emphasizing the need for open data, systematic robustness checks, and transparency in academic research. This study highlights the benefits of diverse analytical approaches and the importance of humility when communicating findings.
A decade's worth of data shows a significant decrease in mechanical ventilation use and an increase in non-invasive respiratory support, such as CPAP, for preterm infants. This shift is associated with improved health outcomes, including reduced lung injury and neurodevelopmental risks.
A new study maps spirituality and religiosity to a specific brain circuit centered in the periaqueductal gray (PAG), a brainstem region implicated in fear conditioning, pain modulation, altruistic behaviors, and unconditional love. The findings suggest that spiritual acceptance can be localized to this circuit.
Researchers develop AI system that uses cooperative strategies to improve human-AI collaboration in negotiations. The system, called CaSiNo, is trained on a dataset of negotiation dialogues and improves upon traditional menu-driven interfaces. It has real-world applications in business, education, and entrepreneurship.
A new study reveals similarities and differences in online conversation topics between Angelenos and New Yorkers. Online, Angelenos tend to discuss healthcare, jobs, and entertainment, while New Yorkers focus on art, politics, and nightlife.
Researchers analyzed visual complexity of written language in 47,000+ characters from 133 scripts, finding large scripts have more complex characters and complexity driven by linguistic units, not evolutionary change.
Researchers at UMSOM aim to expand genetic databases to include under-represented minority populations, improving polygenic risk score accuracy for clinical use. The goal is to identify best practices for translating scores into clinical practice across diverse populations.
A comprehensive study published in Nature Communications found that more than one million deaths worldwide were attributed to fossil fuel combustion in 2017. The research used a global dataset of air pollution emissions and modeling to identify specific sources of pollution, including coal, energy production, and cookstoves.
Researchers aim to identify when individuals are falling out of the flow state by detecting physiological cues with off-the-shelf sensors. AI will be used to introduce interactive stimuli that nudge subjects back into a higher cognitive state.
Researchers have developed a decision support system to identify the environmental impact of nanomaterials faster and more cost effectively. The library contains full physicochemical characterization of 69 nanomaterials, including calculated molecular descriptors.
Aalto University researchers have developed a machine learning-based method to produce synthetic data that preserves privacy and can be used for statistical analyses. The new method solves a major problem with existing methods, enabling researchers to share sensitive information without compromising individual identities.
Researchers at the University of Washington found that medical AI models rely on shortcuts, which can lead to diagnostic errors and misdiagnosis of COVID-19. These shortcuts ignore clinically significant indicators and rely on irrelevant factors, causing the model to make unexpected associations.
Researchers developed an AI-based tool called SCMER to identify rare groups of biologically important cells from single-cell datasets. The tool was applied to analyze several published datasets, revealing new genes and proteins involved in tumor development and drug resistance.
A new study finds that cultural collectivism is a strong predictor of mask usage in the US and globally, with high collectivistic cultures encouraging masking. The study analyzed datasets from the US and 29 countries, controlling for other factors to confirm the link between collectivism and mask use.
A recent study by GEOMAR scientists has provided a comprehensive understanding of the northern Chilean subduction zone, shedding light on the relationship between earthquakes and tsunamis. The data set, obtained through a unique deployment of ocean-bottom seismometers, revealed that aftershocks were located both beneath and above the p...
A study found that deep neural networks can accurately predict lung cancer type from CT scans, identifying new associations between genes and imaging features. This approach increases radiologists' confidence in assessing tumor types, informing individualized treatment planning.
A comprehensive study finds that many global groundwater wells are at risk of running dry due to declining water levels. The researchers analyzed construction records and monitoring well data from 40 countries, finding that 6-20% of wells may dry up if water levels continue to decline.
Researchers adapted an algorithm to identify similarities in escort ads, highlighting common parts and indicating potential human trafficking activity. The InfoShield algorithm outperformed other algorithms at identifying trafficking ads with 85% precision, flagging them without false positives.
A new algorithm developed by Joshua Welch and his team enables researchers to analyze large datasets using standard computer memory, greatly speeding up single-cell sequencing research. The technique allows for real-time analysis of millions of cells without reprocessing older data.
The expanded patient-level data will facilitate more efficient clinical trial design, enabling generation of novel drug development tools and solutions. The novel tools and solutions will enable better informed inclusion criteria, endpoint selection, and patient enrichment strategies.
Achuta Kadambi's article in Science explores how medical device physics can perpetuate bias across racial and gender lines. He suggests quantifying sample fairness and recalibrating performance metrics to address these issues.
Artificial neurons help decode cortical signals using a new algorithm that automates feature extraction and interpretation. The neural network architecture is automatically tuned to analyze signals from separate neural populations, providing physiologically meaningful results.
A new algorithm, C2FIV, uses facial motion to verify identities, providing an additional layer of security. With a success rate of over 90% accuracy in its preliminary study, the technology has broader applications beyond smartphone access, including workplace and online banking security.
A systematic review of 62 COVID-19 machine learning models found significant methodological flaws, biases, and lack of reproducibility. The models' training data was often poor quality, with issues such as biased datasets, small sample sizes, and a lack of clinical input.
Researchers at Penn State developed FairGNN, a novel framework that estimates sensitive attributes to reduce bias in graph neural networks. The model maintains high performance on node classification using limited user-supplied information while reducing bias.
A University at Buffalo researcher found that the brain's reading network is connected to cognitive domains beyond reading, including math. The study identified a 'connectivity fingerprint' suggesting that reading proficiency affects how we approach tasks and solve problems in other areas.
A graduate student used a class assignment to apply linear algebra techniques to analyze flow field data, identifying dominant modes that capture the most important characteristics of the flow. This technique, called proper orthogonal decomposition, allows for efficient study of unsteady processes with minimal noise and uncertainty.
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.