Articles tagged with Data Sets
Researchers developed a computational tool that combines cellphone records with satellite and geographic information systems to create timely and incredibly detailed poverty maps. The study focused on Senegal, creating maps detailing the poverty levels of 552 communities.
A new study by researchers at LMU Munich confirms that sponges are the oldest animal group, contradicting previous findings that suggested comb jellies were the first. The team used a refined method to analyze genetic data and found that sponges' simple multicellular structure is more primitive than initially thought.
Recent genomic analyses have flipped between whether sponges or comb jellies are the oldest lineage of living animals. New research by Professor Davide Pisani reveals that sponges are the most ancient lineage, with models that describe the data poorly favouring comb jellies and those that better describe the data favouring sponges.
A new study finds that in-store product sampling has both immediate and sustained effects on sales, with smaller stores benefiting more. The model also reveals that repeated sampling for a single product increases returns and expands product categories.
Researchers efficiently used Stampede2's 1024 Skylake processors to complete a 100-epoch ImageNet training with AlexNet in 11 minutes, setting the fastest time recorded to date. The Layer-Wise Adaptive Rate Scaling (LARS) algorithm enabled this breakthrough, allowing for larger-than-ever batch sizes and adaptive learning rate adjustments.
The Illinois Waterway has successfully recovered following the passage of the 1972 Clean Water Act, according to a study published in BioScience. The authors used a robust data set to demonstrate a strong linkage between water quality and the rebound of fish populations.
Scientists have now shown that individual neurons in the mouse brain often spool out spindly fibers nearly half a meter long. The team's dataset and analysis tool, MouseLight NeuronBrowser, offer new insights into how elaborately-branching neurons route information in the brain.
The Energy efficiency Data Portal & Protocol (EeDaPP) Initiative aims to create a standardized reporting template for loan-by-loan energy efficient mortgage assets. This will enable large-scale tracking of performance and facilitate earmarking for energy efficient bond issuance.
Scientists have unveiled a dataset mapping diverse migrant communities in the EU, providing insights for policymakers to develop targeted integration policies. The maps reveal residential patterns of migrants by nationality or country of origin, highlighting concentration, diversity, and segregation within cities.
Researchers developed a method to extract Higgs boson signal from noise data using quantum-compatible machine learning techniques, outperforming standard counterparts even with small datasets. The new approach is expected to be useful for problems beyond high-energy physics.
The Critical Assessment of Metagenome Interpretation (CAMI) Challenge evaluated computational tools for metagenomes, assessing assemblers, binners, and taxonomic profilers. The benchmarking results provide performance overviews for developers and applied scientists, informing the selection of suitable software for research questions.
The university's researchers compiled a large solar dataset from NASA's Solar Dynamics Observatory mission, making several hundred thousand solar events available to the public. The dataset has improved the quality of the data and will accelerate computer vision research on these solar images.
Researchers found a lower density in Mars' crust, indicating possible porosity, which could impact the planet's formation and evolution. The new gravity map reveals variations in crust thickness beneath volcanoes.
A recent study published in BMC Ecology found that grey mouse lemurs with high hair cortisol levels had a lower chance of survival. The researchers discovered that low cortisol levels were associated with a 13.9% higher chance of survival compared to those with high levels.
The study examined three types of distracting elements: sensorimotor, cognitive, and emotional. Researchers found that texting led to more dangerous driving, while a 'sixth sense' protected drivers from emotional upset or absent-mindedness.
Researchers from NIST and Michigan State University have developed an algorithm that automates the key decision point in fingerprint analysis, reducing human subjectivity and improving reliability. The new system uses machine learning to score latent prints based on their quality, allowing for more efficient processing of evidence.
The study found that brain activity in regions called the default mode network represent a kind of higher-level processing, building up representations of what's happening in the world around us. Participants who had previously watched the 'Sherlock' episode could anticipate events and recall them in the same order, suggesting that chu...
Researchers investigated hydro-edaphic conditions in two savanna areas, sampling 20 permanent plots and classifying 128 plant species into 34 families. The study highlights the importance of conservation in this ecosystem, which has been rapidly modified by agribusiness.
A new phylogenetic tree resolves key relationships among vertebrates, including the identification of lungfishes as closest living relatives of land vertebrates. The study uses a novel set of analyses for building large-scale genomic datasets.
Researchers from MIT's CSAIL developed an AI system called Pic2Recipe that can analyze photos of food and predict ingredients and suggest similar recipes. The system was trained on a database of over 1 million recipes and showed impressive results, particularly with desserts like cookies or muffins.
A new dataset provides improved representation of solar variability, enabling more accurate climate model simulations and distinguishing natural from anthropogenic processes. The dataset includes enhanced estimates of solar forcing and particle effects, which may counteract rising Earth temperatures.
A new study analyzing multiple ocean datasets reveals that the oceans are robustly warming, regardless of data used. The heat redistribution among global oceans experienced a significant shift over several decades.
A new tool called Wide-Open identifies overdue scientific datasets and encourages their public release. The system uses text mining to detect dataset references in published articles and enforces open data policies.
Computer scientists and social scientists collaborate to create Collaborative Interaction Corpus (CIC) dataset, featuring 135 participants' online activity and discussion data. The dataset can be used to address various computer-science questions, including predicting user behavior and machine learning.
Researchers designed UltraTracer to work with existing algorithms, turbo-charging them for faster processing and larger datasets. The software can compare tens of thousands of neuron shapes to better understand cell types.
Scientists at Diamond Light Source developed a new Pan-DDAA method to extract clear detail from X-ray diffraction data. The approach identifies noise sources and removes them, exploiting the ability of Diamond's beamline to repeat measurements quickly.
A new AI-based image detection method has shown promise in screening for cervical cancer, detecting abnormalities with higher sensitivity and specificity than traditional methods. The technique, developed by Lehigh University researchers, could be used to improve early detection and treatment of the disease in resource-poor regions.
A recent article showcases long-term international collaboration in atmospheric sciences through annotated group photographs from two workshops, separated by over 95 years. This highlights the importance of human connection in scientific research and the value of visual datasets.
A new study published in the European Journal of Political Research reveals that women's civil rights are essential for countries to become fully democratic. The research indicates that the failure to foster women's rights compromised any attempt at democratic governance in the Arab Spring countries.
Researchers at TGen have created the largest dataset to date of extracellular small RNAs, potential biomarkers for diagnosing medical conditions like concussions. The dataset was amassed from ASU student-athletes' biofluids and helmet sensor data, aiming to develop new diagnostic and therapeutic tools.
Researchers at Umeå University have created detailed 3D maps of the insulin-producing cells in the pancreas. The data includes information on cell volume and coordinates, providing a valuable resource for studying diabetes.
A new RAND Corporation study analyzes over 200 real-world zero-day software vulnerabilities, establishing initial baseline metrics that can augment other studies. The research finds that zero-day vulnerabilities have an average life expectancy of 6.9 years, making public disclosure a moderate level of protection.
The Hyper Suprime-Cam Subaru Strategic Program has released its first public dataset, containing almost 100 million galaxies and stars. This dataset will enable scientists to explore the nature of dark matter and dark energy, as well as study the formation and evolution of galaxies.
A new study reveals that plants domesticated by pre-Columbian peoples are more likely to dominate Amazonian forests, with 85 species showing significant impacts from past human influence. The research suggests a lasting impact of human activities on plant distribution and could aid in uncovering unidentified areas of past civilization.
The HSC-SSP survey has released its first public dataset, containing 70 million galaxies and stars. The data reveals the statistical properties of dark matter, building on previous discoveries of nine clumps of dark matter in 2015.
Researchers found that resistant soybean varieties yield more than susceptible ones even at low SCN infestation levels, with a yield advantage seen in environments with no SCN infestation. Despite mounting pressure from nematodes, varieties with PI 88788-resistance still provide moderate resistance and produce good yields.
Researchers improve methods to study Earth's history by addressing bias in data from old rocks. New technique reduces errors, providing more accurate paleointensity measurements.
Researchers discovered a strong behavioural rule, the rule of random attraction, that explains how complex patterns of collective movement emerge in zebrafish as they develop from larvae to adults. Younger fish spend less time applying this rule, resulting in fewer schools, while adults do more, leading to group formation.
A team of scientists has released the largest collection of observations made with radial velocity to be used for hunting exoplanets. The dataset includes almost 61,000 measurements of more than 1,600 nearby stars and has detected over 100 potential exoplanets, including one orbiting GJ 411.
Researchers at Tel Aviv University analyzed 15,000 bat vocalizations to identify concrete evidence of socially sophisticated species that learns communication. They found that bat calls contain information about the identity of the caller and addressee, as well as specific aggressive context and possible outcome of conversation.
A statistical approach called tree bootstrapping can accurately assess uncertainty in RDS studies, enabling researchers to draw firm conclusions about vulnerable groups. This method has been applied to existing and future RDS studies, providing a basis for policy responses.
The KAT tool analyzes K-mer datasets to identify error levels, biases, and contamination in sequencing data. It also checks genome assemblies for completeness and accuracy without external reference data.
Researchers at MIT's CSAIL develop a sound-recognition system that outperforms predecessors without requiring expensive hand-annotated data. The system is trained on video and achieves high accuracy rates, with applications in improving mobile device context sensitivity and situational awareness of autonomous robots.
A new recommendation algorithm allows individuals and items to belong to multiple overlapping groups, making it more realistic than existing models. The algorithm's predicted ratings proved more accurate than those from existing systems on five large datasets.
A study published in JAMA evaluated an algorithm using deep machine learning to detect referable diabetic retinopathy, achieving high sensitivities and specificities. The algorithm was validated on two separate data sets, showing promise for increasing efficiency and coverage of screening programs.
Researchers at URV develop collaborative filtering model with scalable algorithm for predicting individual preferences and group overlaps. The new approach provides more accurate predictions than existing algorithms by considering individual differences, making it ideal for large datasets.
The need for a bridging regulatory mechanism is highlighted in e-cigarette innovations; reducing the burden on regulators while maintaining product development. A reduced data set for initial products and focused tests for variants can support this process.
A new study by Indiana University finds that bisexual individuals are paid less than their heterosexual counterparts for doing the same jobs. The research suggests that workplace discrimination may be a factor contributing to this wage gap, particularly for bisexual men and women.
Researchers at Binghamton University developed a new multilevel input layer artificial neural network to predict flight delays. The model outperformed traditional networks in terms of accuracy and training time, predicting delay lengths with about 20% more accuracy than traditional models.
Researchers at Columbia University developed a new machine-learning algorithm called TeraStructure to analyze massive genetic data sets. The algorithm can estimate population structure more accurately and twice as fast as current state-of-the-art algorithms, making it potentially useful for identifying disease-causing genetic mutations.
A UK injury data collection pilot has found a significant association between deprivation levels and an increasing incidence rate of unintentional injuries. The study suggests that collecting injury data could help reduce A&E attendances by understanding patterns in communities.
Researchers found that patients with lower severity trauma have peaks in mortality probability around 14 and 21 days after admission. The study used the largest trauma database in Europe to analyze 165,559 trauma cases, including 19,289 with unknown outcomes.
A recent study demonstrates the potential of deep reinforcement learning in optimizing traffic signal timing, reducing average delay by 14% and vehicle waiting time by 13 seconds.
Research by marketing experts found that excessive positive online customer reviews can lead to higher return rates and increased costs for retailers. This is particularly true for products with low prices or from new customers, as it raises expectations and may result in disappointment upon delivery.
Researchers have created a comprehensive dataset of non-native English sentences, providing a valuable resource for linguistic insights and practical applications. The dataset, consisting of 5,124 sentences, includes annotated errors and can help improve computers' handling of non-native English speakers.
Geographers from UC Santa Barbara's Climate Hazards Group will help African scientists predict food deficits using remote sensing tools and climate data. The new project aims to provide early warning systems for severe hunger, giving policymakers four months of advanced notice to allocate resources.
Researchers at EMBL-EBI develop an algorithm to cluster peptide mass spectra, identifying 9 million consistently unidentified spectra. This breakthrough simplifies the detection of post-translational modifications and variants, paving the way for more efficient exploitation of proteomics data.
Researchers tested facial recognition algorithms on a dataset of one million images from around the world and found that accuracy rates dropped significantly when confronted with more distractions. Google's FaceNet performed strongest, but other algorithms struggled to maintain high accuracy rates at scale.
A UCLA-led study predicts the Sierra Nevada snowpack will take years to recover from drought, even with above-average precipitation. The research provides unprecedented detail and precision, offering insights into water availability in other mountain ranges.
Researchers developed a publication strategy that allows authors to document data workflow, enabling users to reproduce simulations from scratch. This increases the reliability of simulation results and enhances model utility.