Articles tagged with Data Sets
The project aims to address rising healthcare costs, health disparities, and expands digital health through advances in AI. It also trains students from different disciplines to develop and apply data science techniques.
A new MIT study suggests that pedestrians choose routes that point most directly toward their destination, even if those routes are longer. This strategy, known as vector-based navigation, may have evolved to allow the brain to devote more power to other tasks.
The StEER Network's post-event reconnaissance helped assess building damage from Hurricane Michael, revealing widespread wind- and surge-induced damage. The dataset has been used to develop data-driven fragilities, train machine learning applications, and inform policy and practice improvements for coastal communities.
Researchers have developed a federated analytics system, FAMHE, that enables healthcare providers to collaborate on statistical analyses and machine learning models without exchanging underlying datasets. The system has been proven mathematically secure and accurately reproduced published studies in multi-centric settings.
A Texas A&M team led by Dr. Byung-Jun Yoon has received $2.4 million to develop new computational techniques for reducing the size of large scientific data sets. The goal is to preserve quantities of interest while minimizing data storage and processing needs.
Researchers at GlaxoSmithKline and CCDC combined proprietary and published datasets to train machine learning models for predicting stable polymorphs in new drug candidates. The approach leverages the large volume and variety of data in the Cambridge Structural Database, resulting in more confident predictions and improved model accuracy.
Researchers analyzed over 120 million English-language tweets to find a decrease in negative posts about COVID-19, particularly in countries with high vaccination rates. The study suggests that increased vaccination may have contributed to the drop in negativity, but further analysis is needed to fully understand this phenomenon.
A team of Harvard researchers created an integrated pipeline, STAMPScreen, to help genetic engineers identify target genes and perform screening studies. The protocol combines computational tools with lab experiments to quickly and efficiently test gene function in living cells.
Researchers used extensive 2D and 3D broadband seismic reflection data to visualize and understand the subsurface structures of the Orphan Basin. The study provides context for future assessments of source rock, reservoir, and seal strata in oil and gas exploration.
The COVID-19 pandemic has triggered unprecedented life expectancy losses across 29 countries, with a decline of over half a year observed in most nations. The largest declines were seen among males, particularly in the US, where a 2.2-year loss was reported.
Researchers at PSU and UT Arlington will assess how crowdsourced data can help establish pedestrian activity on streets. The study aims to provide a more accurate alternative to traditional counting methods, enabling safer studies and better urban planning.
Researchers at Lawrence Livermore National Laboratory calculated moment tensors for 130 underground nuclear and 10 chemical tests to aid explosion monitoring. The study's database of carefully documented explosions will be useful for researchers, allowing them to distinguish explosions from earthquakes and estimate yield.
Researchers analyzed 1,785 ancient human genomes to determine parental relatedness, revealing that cousin marriages occurred only 3% of the time. The new method allowed for more efficient screening of ancient DNA, also providing insights into population dynamics and demographic impact of agriculture.
Scientists compiled a 71-year potential risk index dataset to estimate TC impact severity on the Chinese mainland, showing increased damage over time. Recent upward trends in precipitation and wind values contribute to larger TC impacts along coastal China.
Researchers analyzed 1.4 million field observations and 73,000 museum records to find a strong correlation between species abundance in nature and their presence in museum collections. This method enables scientists to study species decline and estimate past abundances, providing insights into conservation efforts.
A European project developed standard solutions for energy refurbishment, considering various climate zones. These packages include prefabricated façades, decentralized ventilation systems, and smart ceiling fans to reduce energy consumption and improve comfort. Pilot sites showed significant savings and improvements in tenant well-being.
The world's largest repository of raw genomic sequences is missing critical data necessary for monitoring and preserving biodiversity. Only 14% of archived datasets contain information about when and where organisms were sampled, highlighting the need for community standards to preserve metadata.
A new study reveals that climate change will affect Antarctic seals, such as crabeater and Weddell seals, in distinct ways. Crabeater seals are more vulnerable due to their specialized diet and breeding on unstable pack-ice, while Weddell seals are less affected.
A new analytics platform, RDCA-DAP, will be launched to accelerate rare disease treatment innovation by hosting and standardizing rare diseases data. The platform is expected to empower patients and families to drive innovation in the field.
A new dataset combines demographic and financial information to identify communities at risk of climate-related gentrification. The Socio-Economic Physical Housing Eviction Risk (SEPHER) dataset provides insights into the impact of climate hazards on vulnerable populations, including increased evictions and rising housing costs.
A new study showcases the COVIDome Explorer, a public online portal for real-time COVID-19 data analysis, visualization and sharing. The platform enables rapid hypotheses testing, hypothesis generation and discoveries by experts and non-experts.
A new study found that larger cities produce more income inequality, with the poor experiencing a decline in quality of life. The researchers analyzed data from municipal areas across the US and found that the top 10% of earners gain an increasingly large portion of wealth as cities grow.
Earthquakes generated by controlled fluid injection at the Rangely oil field were caused by destabilizing fault pore pressure changes, according to a new mechanistic model. The study revisits data from the decades-old project in light of increased seismicity due to fluid injection.
A database of 3,000 lead isotope analyses has been compiled for the Iberian Peninsula, providing a comprehensive resource for geological and archaeological research. The IBERLID database includes standardized data on minerals, rocks, and metallic objects, facilitating comparison and analysis.
Researchers developed a novel evidence-based material recommender system that predicts high entropy alloy formation without data descriptors, overcomes data bias and poor availability. The method recommends an FeMnCoNi alloy as the most probable HEA and successfully synthesizes it, confirming its validity.
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 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.
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 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.
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.
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.