Articles tagged with Data Sets
Researchers found that partial peace agreements play an important role in building trust and strengthening relationships between negotiators. These agreements offer strategic advantages, including consolidating incremental progress, serving as metrics, testing measures, and signaling a larger commitment to the peace process.
A University of Cambridge report argues that menstrual tracking app data is a valuable resource for advertisers but poses significant privacy and safety risks to users. The report calls for better governance of the industry to protect user data and urges public health bodies to launch alternative apps.
Scientists from University of Leicester collaborate with traditional Amazonian communities to monitor biodiversity using artificial intelligence, enabling them to maintain control over their resources and territories. The project aims to reshape conservation approaches in the world's largest tropical rainforest.
Scientists at MIT have identified new potential targets for treating Alzheimer's disease, including a pathway involved in DNA damage repair. The study suggests that a combination of treatments targeting different cellular pathways may be more effective in blocking disease progression.
A new study by University of Cincinnati researcher Edson Chipalo found that children who experienced adverse childhood experiences are at a higher risk of homelessness during their childhood. The study highlights the need for prevention and early support to reduce long-term risks associated with childhood trauma.
A centralized and accessible database aims to integrate molecular simulation data, ensuring findability, accessibility, interoperability, and reusability. This will amplify the impact of these data and avoid duplication.
Researchers developed a new model called React-OT that can predict the transition state of chemical reactions in under a second with high accuracy. The model uses linear interpolation to generate better initial guesses, reducing the number of steps and computation time needed.
A novel method generates binary class labels in highly imbalanced datasets, addressing the challenge of labeling severely imbalanced data. The approach outperformed traditional methods, providing a more efficient way to identify fraud while minimizing false positives.
MIT researchers have found that a computational model of the ventral stream, which processes object recognition, also performs well on spatial tasks such as determining an object's location and orientation. This challenges the dominant perspective that the ventral stream is optimized for object recognition.
Researchers developed a new framework, PAC Privacy, to maintain AI model accuracy and ensure sensitive data remains safe from attackers. The new variant of PAC Privacy estimates anisotropic noise, reducing computational cost and boosting accuracy.
A recent study from UTSA researchers reveals that large language models (LLMs) can pose a serious threat to programmers who use them to help write code. The study found that up to 97% of software developers incorporate generative AI into their workflow, and 30% of code written today is AI-generated.
Researchers found that sleep duration returns to baseline within two days, but changes in sleep timing and structure can take more than a week, with eastward travel causing the most disruption. The study analyzed 1.5 million nights of data from wearables like Oura Ring.
Researchers will use sensors and software to predict AM part lifespan, enabling cost savings and extending part life. The project aims to improve Darwin software to provide detailed insights into manufacturing processes.
The C-Path-led initiative brought together clinical trial sponsors, eCOA technology providers, and regulators to identify and address eCOA implementation issues. Key resources developed during the initiative include guidance on COA data collection and a more open, collaborative environment.
Researchers highlight the underuse of Japanese bird banding data in avian movement research, revealing gaps in our understanding of East Asian migratory birds. The study proposes improving data accessibility and integrating traditional methods with modern tracking technologies to advance conservation efforts.
The new DESI results show that the impact of dark energy may be weakening over time, suggesting a fundamental shift in how we think about dark energy. The collaboration's findings are consistent with other measurements, including supernovae and the light left over from the dawn of the universe.
The Euclid data release unlocks a treasure trove of information for scientists to study the universe's cosmic history and invisible forces. With its exceptional field of view, Euclid captures an area 240 times larger than the Hubble Telescope, delivering outstanding image quality in both visible and infrared light spectrum.
A new study from Aalto University found that global population datasets may underestimate rural populations by as much as 53% to 84%, revealing a systematic and profound extent of the bias. The findings have far-reaching consequences, suggesting that the needs of people living in rural areas have been under-represented in decision-maki...
A new study shows that climate change affects short-term precipitation events lasting hours by significantly increasing their frequency, while longer-term rainfall events lasting days are influenced by global weather phenomena like El Niéo. This distinction is crucial for assessing the risk of flooding in different regions.
Researchers have developed a novel technique to overcome the spurious correlations problem in AI by eliminating a small portion of the training data that contains hard-to-understand features. This approach improves performance even when conventional techniques are ineffective.
A recent USC study found that Black immigrants in the United States have a 9% higher likelihood of being uninsured compared to U.S.-born white adults, despite having a 70.2% employment rate. The disparity is attributed to wage penalties and systemic barriers related to race and immigration status.
FAIR² Data Management leverages AI-assisted curation to structure research data for publication, enhancing research efficiency and reproducibility. The platform transforms data into a tool for discovery, tackling pressing challenges in global health, sustainability, and scientific innovation.
Researchers at Trinity College Dublin have discovered a biomarker panel that can accurately identify patients at risk of developing pancreatic cancer, offering new hope for early detection and improved treatment. The study uses a combination of blood and fluid analysis to distinguish between low- and high-risk patients.
A new standard dataset defines 16 clinical and patient-reported outcome measures for adults with heart valve disease. The International Consortium for Health Outcomes Measurement (ICHOM) developed the globally inclusive standard dataset.
The release of a unique Type Ia Supernovae dataset has significant implications for cosmologists measuring the universe's expansion history. The dataset, comprising 3628 supernovae, provides unprecedented precision and accuracy in exploring the properties of these events.
Researchers generated a dataset of water flow beneath the entire Antarctic Ice Sheet, improving projections of sea level rise. The model predicts higher degrees of glacier melt and mass loss by the end of the century.
A new method developed by Osaka Metropolitan University accurately predicts housing prices in Osaka City, with neighborhood perception being a key factor. The approach achieves nearly 75% accuracy by combining existing property data with machine-learning-processed street view images.
A new study found that hotter and colder regions on a star's surface can distort our interpretations of planets, particularly when looking at dips in starlight. This distortion can lead to misinterpretation of features such as planet size, temperature, and atmospheric composition.
InsectNet uses machine learning to identify over 2,500 insect species at 96% accuracy, providing critical information for farmers and researchers. The app can be fine-tuned for specific regions, making it useful for agricultural challenges worldwide.
The International Society for Health and Lung Transplantation (ISHLT) has re-launched the Global IMACS Registry to collect data on patients with durable mechanical circulatory support devices. The registry aims to advance MCS therapy and improve patient outcomes globally.
Two comprehensive datasets from the Gabriella Miller Kids First Pediatric Research Program explore childhood cancers and congenital disorders. The new datasets aim to identify genetic causes and links between these diseases in children, ultimately supporting the development of improved treatments.
A new computational tool, PsiPartition, simplifies genetic data analysis for evolutionary biology, allowing researchers to efficiently study species relationships. The novel method improves both computational efficiency and accuracy of phylogenetic trees.
Researchers at University of Birmingham have discovered three new protein biomarkers TFF3, LCN2, and CEACAM5 that show strong predictive potential for colorectal cancer. These biomarkers are linked to cell adhesion and inflammation, processes closely associated with cancer development.
A new 6D pose dataset has been introduced, providing high-quality RGB and depth images with annotated 6D pose data. This dataset achieves state-of-the-art accuracy rates of 97.05% and 98.09% for robotic grasping and automation applications.
A team led by the University of Tokyo has created a nearly 20-year-long dataset of the entire atmosphere, enabling new research on previously difficult-to-study regions. The dataset spans multiple levels of the atmosphere from ground level to the lower edge of space and could improve climate modeling and seasonal weather forecasting.
The UK Biobank has launched a groundbreaking study to measure over 5,400 proteins in 600,000 samples, offering unprecedented insights into disease development and progression. This massive dataset will enable researchers to explore how changes in protein levels influence diseases and develop personalized treatments.
Researchers at the University of New Hampshire developed an AI-powered algorithm to categorize over 706 million aurora images from NASA's THEMIS data set. This labeled database can help scientists better understand and forecast geomagnetic storms that disrupt vital communications and security infrastructure.
A large UK-wide study has identified specific clusters of long-term health conditions associated with lower quality of life, particularly those linked to chronic pain, depression, and cardiovascular disease. The findings highlight the need for tailored treatment approaches for patients with multiple long-term health conditions.
Researchers reveal that contradictory findings on territorial self-governance and ethnic civil war risk are primarily due to variations in measurement definitions. The study highlights the importance of considering the nuances of data choice in understanding conflict resolution.
A new method called Annotatability helps identify mismatches in cell annotations and better characterizes biological data structures. This approach enables more precise downstream analysis of biological signals, capturing cellular communities associated with target signals.
A new book analyzes data from 48 US states from 2008 to 2016, finding that election costs vary significantly across states and regions. The authors also found that trust in election spending is associated with how much is spent, and that a one-size-fits-all approach to legislation does not apply to election administration.
Researchers from Florida Atlantic University developed an AI system that accurately detects and tracks American Sign Language alphabet gestures using computer vision. The model achieved a detection process with high accuracy, recognizing complex hand gestures with an F1 score of 99% and mAP of 98%.
Researchers at Tokyo University of Science have developed a new method called black-box forgetting, which enables selective removal of unnecessary information from large pre-trained AI models. This approach enhances model efficiency and improves privacy by reducing computational resources and information leakage.
Insilico Medicine has successfully integrated cutting-edge generative AI models into its platform, achieving significant increases in accuracy and speed. The company's AI-powered solutions have shown promising results in preclinical candidates and Phase II clinical trials.
The partnership integrates Answer ALS’s Neuromine Data Portal with the AD Data Initiative's AD Workbench, providing a unified platform for data sharing and analysis. This collaboration enables integrative analysis of ALS and Alzheimer’s data, streamlining efforts and enhancing research impact.
Researchers from NUS and A*STAR have discovered a connection between the regulation of alternative splicing in different cell types and the predisposition to autoimmune diseases. The study used a population-scale single-cell gene expression profiling dataset to analyze splicing events specific to particular cell types, revealing ancest...
The latest report in the State of Open Data series provides quantitative analysis on the growth of open data sharing globally. Key findings show a strong growth in papers linking to data from universities, and policies for open data sharing are now more consistent globally.
The study discovered significant alterations in the region's state of stress and deformation following the 1975 Kalapana earthquake. The researchers found that Kīlauea's south flank experienced greater displacement prior to the earthquake, pointing to changes in mechanical properties influencing seismic activity.
The Polymathic AI team has released two massive datasets for training artificial intelligence models to find and exploit transferable knowledge between seemingly disparate fields. The datasets include data from dozens of sources, covering astrophysics, biology, acoustics, chemistry, fluid dynamics, and more.
A novel framework for retrieving concise entailing legal articles has been proposed, achieving state-of-the-art results across two datasets. The Retrieve–Revise–Refine framework combines small and large language models to improve precision while limiting recall loss.
The University of Rhode Island-based report card gives failing grades to over half the world's countries, with a median score of 52. Democratic countries tend to have better human rights records, but exceptions exist. The US scored a 62.5, ranking 66th, despite being a wealthy democratic country.
The National Center for Supercomputing Applications (NCSA) has received the Readers' Choice Award: Best HPC Collaboration and Editors' Choice: Best Use of HPC in Physical Sciences. This is the 14th consecutive year NCSA has been honored with an HPCwire award.
Researchers found that membership inference attacks on large language models (LLMs) are not effective in measuring information exposure risks. The common method used to test LLM leaks suffers from ambiguity due to the fluidity of language, making it difficult to define a representative set of non-member candidates.
A panel of bioethicists and experts emphasize that human accountability is crucial for healthcare decisions made by AI. The importance of diverse data sets was also stressed to avoid biases in AI-enabled medical technologies. Experts stress the need for collective liability among developers, programmers, and data scientists.
A recent machine learning study suggests that the association between gut bacteria and disease may be overstated. Instead, changes in microbial load were found to be a key factor in the presence of disease-associated microbial species. This discovery challenges current understanding of the gut microbiome's role in disease etiology.
Researchers at Lehigh University are using advanced algorithms and cross-domain data to help cities predict human movement patterns, enabling better planning and preparedness for events and emergencies. The model will account for variations in data streams from different sources, such as cell towers, GPS, and financial transactions.
The University of Washington School of Medicine has released a flagship AI-ready dataset for a type 2 diabetes study, featuring a diverse range of participants and novel measures. The dataset includes environmental sensor data, survey responses, eye-imaging scans, and traditional biologic variables.
Researchers developed a multimodal dataset, TimelyTale, to gather passenger-specific sensor data for context-relevant explanations. The approach effectively identified the timing and frequency of passenger demands for explanations, enabling the creation of a machine-learning model to predict the best time for providing an explanation.
Researchers at the University of Liverpool developed AI-driven mobile robots that can perform exploratory chemistry research tasks faster and more efficiently than humans. The robots use AI logic to make decisions, processing analytical datasets in real-time to determine the next steps in chemical synthesis.
Researchers at Radboud University Medical Center found that an extra year of education does not protect against brain aging and has no effect on brain structure. Despite positive correlations between education and cognitive benefits, the study suggests caution in assigning causation when only correlation is observed.