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13,064 results for “predictions”
Dataset for: Indirect nitrous oxide emission factors of fluvial networks can be predicted by dissolved organic carbon and nitrate from local to global scales
<p>Streams and rivers are important sources of nitrous oxide (N<sub>2</sub>O), a powerful greenhouse gas. Estimating global riverine N<sub>2</sub>O emissions is critical for the assessment of anthropogenic N<sub>2</sub>O emission inventories. The indirect N<sub>2</sub>O emission factor (EF<sub>5r</sub>) model, one of the bottom-up approaches, adopts a fixed EF<sub>5r</sub> value to estimate riverine N<sub>2</sub>O emissions based on IPCC methodology. However, the estimates have considerable uncertainty due to the large spatiotemporal variations in EF<sub>5r</sub> values. Factors regulating EF<sub>5r</sub> are poorly understood at the global scale. Here, we combine 4-year in situ observations across rivers of different land use types in China, with a global meta-analysis over six continents, to explore the spatiotemporal variations and controls on EF<sub>5r</sub> values. Our results show that the EF<sub>5r</sub> values in China and other regions with high N loads are lower than those for regions with lower N loads. Although the global mean EF<sub>5r</sub> value is comparable to the IPCC default value, the global EF<sub>5r</sub> values are highly skewed with large variations, indicating that adopting region-specific EF<sub>5r</sub> values rather than revising the fixed default value is more appropriate for the estimation of regional and global riverine N<sub>2</sub>O emissions. The ratio of dissolved organic carbon to nitrate (DOC/NO<sub>3</sub><sup>-</sup>) and NO<sub>3</sub><sup>-</sup> concentration are identified as the dominant predictors of region-specific EF<sub>5r</sub> values at both regional and global scales because stoichiometry and nutrients strictly regulate denitrification and N<sub>2</sub>O production efficiency in rivers. A multiple linear regression model using DOC/NO<sub>3</sub><sup>-</sup> and NO<sub>3</sub><sup>-</sup> is proposed to predict region-specific EF<sub>5r</sub> values. The good fit of the model associated with easily obtained water quality variables allows its widespread application. This study fills a key knowledge gap in predicting region-specific EF<sub>5r</sub> values at the global scale and provides a pathway to estimate global riverine N<sub>2</sub>O emissions more accurately based on IPCC methodology.</p> <p>This dataset is a global integrated N<sub>2</sub>O dataset including data from 4-year (2017-2020) in situ measurements of six large rivers in China, 3-year (2018-2020) in situ measurements of urban river networks in Beijing of China, and 825 measurements from 70 published papers over six continents. The data includes dissolved N<sub>2</sub>O concentration, biogeochemical (DOC, NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup>, temperature, and DO), climatological (climate zones), and geographic (region, location, and land cover) information.</p>
Speos: An ensemble graph representation framework to predict core genes for complex diseases (Datasets)
<p>the "data.tar.gz" tarball contains the unprocessed or minimally processed data used by Speos. If you intend to use the framework or want to inspect the data, download this part of the dataset.</p> <p>The "final_datasets.tar.gz" tarball contains tsv-formatted, processed data matrices which are directly used as input features for the ensemble models. </p> <p>There are two tsv-files per disease, one labeled "normal", which contains the p input features alongside the gene identifiers and a column which indicates if the gene is labeled as Mendelian or not, and another file labeled "with_n2v_vectors", which also contains the 100-dimensional vectors produced by Node2Vec so the N2V+MLP method can be reproduced with the exact same parameters. </p> <p>All files contain a header row which describes the column and no index column.</p> <p>The "model_parameters.tar.gz" tarball contains the model parameters for all ensemble models used to produce the candidate genes.</p>
Source Data for: Temperature, species identity and morphological traits predict carbonate excretion and mineralogy in tropical reef fishes
<p>Source Data underlying figures of the paper "Temperature, species identity and morphological traits predict carbonate excretion and mineralogy in tropical reef fishes" by Mattia Ghilardi, Michael A. Salter, Valeriano Parravicini, Sebastian C. A. Ferse, Tim Rixen, Christian Wild, Matthias Birkicht, Chris T. Perry, Alex Berry, Rod W. Wilson, David Mouillot, Sonia Bejarano</p>
Analysis and Prediction of Athlete's Anxiety State based on Artificial Intelligence
<p>This is the data set used for the implementation of Project Analysis and Prediction of Athlete's Anxiety State based on Artificial Intelligence</p>
Assignment & prediction of 2-propanimine (CH3)2C=NH
<p>The measure frequency line list, the prediction, and the XIAM and ERHAM fit input and output file of 2-propanimine [(CH3)_2C=NH] of the ground state. File description and format see ReadMe.</p>
Familiar object benefit more from transsaccadic feature predictions
<p>The transsaccadic feature prediction mechanism associates peripheral and foveal information<br> belonging to the same object to make predictions about how an object seen in the periphery<br> would appear in the fovea or vice versa. It is unclear if such transsaccadic predictions require<br> experience with the object such that only familiar objects benefit from this mechanism by virtue<br> of having peripheral-foveal associations. In two experiments, we tested whether familiar<br> objects have an advantage over novel objects in peripheral-foveal matching and transsaccadic<br> change detection tasks.<br> In both experiments, observers were unknowingly familiarized with a small set of stimuli by<br> completing a sham orientation change detection task. In the first experiment, observers<br> subsequently performed a peripheral-foveal matching task, where they needed to pick the<br> foveal test object that matched a briefly presented peripheral target. In the second experiment,<br> observers subsequently performed a transsaccadic object change detection task where a<br> peripheral target was exchanged or not exchanged with another target after the saccade, either<br> immediately or after a 300 ms blank period.<br> We found an advantage of familiar objects over novel objects in both experiments. While<br> foveal-peripheral associations explained the familiarity effect in the matching task of the first<br> experiment, the second experiment provided evidence for the advantage of peripheral-foveal<br> associations in transsaccadic object change detection. Introducing a postsaccadic blank<br> improved the change detection performance in general but more for familiar than for novel<br> objects. We conclude that familiar objects benefit from additional object-specific predictions.<br> Keywords: Saccades, Transsaccadic prediction, perceptual learning, object recognition,<br> visual stability.</p>
Real-time Alignments for Predicting Decision-Making Time for Diagnostics over NGS Cycles
<p>Input datasets for an interpretable learning approach to predict decision-making time for diagnostics over NGS cycles. The dataset contains alignment files of two clinical sputum samples sequenced with an Illumina MiSeq sequencing device, and following a real-time sequencing protocol.</p> <p>The datasets consist of real-time alignment files for specific sequencing cycle intervals. The alignment has been performed to a database of respiratory microbes. Data preprocessing, including the removal of human host DNA, is integrated into the real-time alignment approach that has been applied to generate the alignments.</p>
CASP15 RNA target predictions with RoseTTAFold-NA
<p>Structure predictions of CASP-15 RNA targets made using RoseTTAFold-NA on December 9, 2022.</p>
Predicting the Install, Compile, and Test Attributes of a Library
<p>The task of selecting a third-party library is a common one, but it can be challenging due to the many factors that a developer has to consider. Previous research has mainly focused on specific attributes. In this paper, we take a comprehensive approach by identifying the most desirable attributes and examining how they are related to one another. Our work is split into three parts. First, we conduct a developer survey to rank their preferences on a set of 30 attributes from existing work, grouped into four categories: build, documentation, source code, and contribution. To confirm our results, in the second part of the study, we then mined 104,364 NPM libraries to find that the build (i.e., installation, compiling, and testing) of a library is important. Finally, in the third part of the study, we find that static source code attributes (i.e., number of files, repository size, and directories) correlate and predict the success of installing, building, and testing a library. Our work lays the groundwork on what attributes of a library are useful for predicting a successful build of a library.</p>
A big data–model integration approach for predicting epizootics and population recovery in a keystone species
<p>Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex non-linear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a non-parametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, <em>Cynomys ludovicianus</em>) and sylvatic plague as an example. We synthesized colony data between 2001–2020 from eight USDA Forest Service National Grasslands across the range of BTPD in central North America. We then modeled extinctions due to plague and colony recovery of BTPD in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summer/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally > 0.80). Thus, these spatially-explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially-explicit forecasting of disease-induced population fluctuations, for use in natural resource management decision-making.</p>
Annotated missense variants (hg38) for LoGoFunc prediction
<p>Annotated missense variants (hg38) for LoGoFunc prediction. Academic use only. For commercial use contact Yuval Itan</p>
Imputed data from for predicting skeletal stature using ancient DNA
<p>These are the three imputed genotype datasets from the following publication. Please consult the paper for details of the imputation approach, metadata for the samples, and the original data sources. These data are freely available but you should cite the original sources, as well as our paper. </p> <p>Predicting skeletal stature using ancient DNA; Cox S, Moots HM, Stock JT, Shbat A, Bitarello B, Nicklisch N, Alt K, Haak W, Rosenstock E, Ruff CB, Mathieson I; American Journal of Biological Anthropology, Jan 2022. <a href="https://doi.org/10.1002/ajpa.24426">https://doi.org/10.1002/ajpa.24426</a></p> <p> </p> <p> </p>
Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters
<p>Damming a stream inserts a lentic system (an impoundment or reservoir) into a lotic system, changing downstream hydrological, biogeochemical, and ecological processes. One such ecological effect of damming is to create a resource subsidy of easily captured and consumed zooplankton, which are preyed upon by filter-feeders and visual predators. The data included here were used to predict the density of lentic zooplankton subsidizing downstream habitats with water quality parameters as an alternative to microscopy. We also used this data to detect three different water quality regimes (high conductivity, high-CDOM, and a remainder) that are associated with differences in the density of zooplankton. This dataset is contained in two parts, both of which are focused on zooplankton density in the effluent of a series of tributary-impoundment reservoirs: 1) zooplankton density for a single summer season with water quality parameters and 2) zooplankton density for a series of three summers without water quality parameters.</p>
Thicker eggshells are not predicted by host egg ejection behaviour in four species of Australian cuckoo
<p>Defences of hosts against brood parasitic cuckoos include detection and ejection of cuckoo eggs from the nest. Ejection behaviour often involves puncturing the cuckoo egg, which is predicted to drive the evolution of thicker eggshells in cuckoos that parasitise such hosts. Here we test this prediction in four Australian cuckoo species and their hosts, using Hall-effect magnetic-inference to directly estimate eggshell thickness in parasitised clutches. In Australia, hosts that build cup-shaped nests are generally adept at ejecting cuckoo eggs, whereas hosts that build dome-shaped nests mostly accept foreign eggs. We analysed two datasets: a small sample of hosts with known egg ejection rates and a broader sample of hosts where egg ejection behaviour was inferred based on nest type (dome or cup). Contrary to predictions, cuckoos that exploit dome-nesting hosts (acceptor hosts) had significantly thicker eggshells relative to their hosts than cuckoos that exploit cup-nesting hosts (ejector hosts). No difference in eggshell thicknesses was observed in the smaller sample of hosts with known egg ejection rates, probably due to lack of power. Overall cuckoo eggshell thickness did not deviate from the expected avian relationship between eggshell thickness and egg length estimated from 74 bird species. Our results do not support the hypothesis that thicker eggshells have evolved in response to host ejection behaviour in Australian cuckoos, but are consistent with the hypothesis that thicker eggshells have evolved to reduce the risk of breakage when eggs are dropped into dome nests.</p>
Data Set for Predicting the Performance of ATL Model Transformations
<p>Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained.</p> <p>Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models that were transformed. We provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Additionally, we calculated the mean absolute percentage error in % and the 95th percentile of the absolute percentage error in % for each experiment and provide these results. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models, the Java projects used to measure the execution time of the transformations, and other supporting scripts, e.g. for the analysis of the results.</p> <p>A short introduction with a quick start guide can be found in README.md and a detailed documentation in documentaion.pdf.</p>
A crowd clustering prediction and captioning technique for public health emergencies
<p>Data set used to implement the project <strong>A crowd clustering prediction and captioning technique for public health emergencies crowd clustering prediction and captioning technique for public health emergencies</strong></p>
Data for: Prey resources are equally important as climatic conditions for predicting the distribution of a broad-ranged apex predator
<p>Aim: A current biogeographic paradigm states that climate regulates species distributions at continental scales and that biotic interactions are undetectable at coarse-grain extents. However, advances in spatial modelling show that incorporating food resource distributions are important for improving model predictions at large distribution scales. This is particularly relevant to understand the factors limiting distribution of widespread apex predators whose diets are likely to vary across their range.</p> <p>Location: Neotropical Central and South America</p> <p>Methods: The harpy eagle (<em>Harpia harpyja</em>) is a large raptor, whose diet is largely comprised of arboreal mammals, all with broad distributions across Neotropical lowland forest. Here, we used a hierarchical modelling approach to determine the relative importance of abiotic factors and prey resource distribution on harpy eagle range limits. Our hierarchical approach consisted of the following modelling sequence of explanatory variables: (a) abiotic covariates, (b) prey resource distributions predicted by an equivalent modelling for each prey, (c) the combination of (a) and (b), and (d) as in (c) but with prey resources considered as a single prediction equivalent to prey species richness.</p> <p>Results: Incorporating prey distributions improved model predictions but using solely biotic covariates still resulted in a high performing model. In the Abiotic model, Climatic Moisture Index (CMI) was the most important predictor, contributing 76 % to model prediction. Three-toed sloth (<em>Bradypus</em> spp.) was the most important prey resource, contributing 64 % in a combined Abiotic-Biotic model, followed by CMI contributing 30 %. Harpy eagle distribution had high environmental overlap across all individual prey distributions, with highest coincidence through Central America, eastern Colombia, and across the Guiana Shield into northern Amazonia. </p> <p>Main conclusions: With strong reliance on prey distributions across its range, harpy eagle conservation programs must therefore consider its most important food resources as a key element in the protection of this threatened raptor.</p>
Raw Data for the article: Mortality after transjugular intrahepatic portosystemic shunt in older adult patients with cirrhosis: A validated prediction model
<p><strong>Background and aims: </strong>Implantation of a transjugular intrahepatic portosystemic shunt (TIPS) improves survival in patients with cirrhosis with refractory ascites and portal hypertensive bleeding. However, the indication for TIPS in older adult patients (greater than or equal to 70 years) is debated, and a specific prediction model developed in this particular setting is lacking. The aim of this study was to develop and validate a multivariable model for an accurate prediction of mortality in older adults.</p> <p><strong>Approach and results: </strong>We prospectively enrolled 411 consecutive patients observed at four referral centers with de novo TIPS implantation for refractory ascites or secondary prophylaxis of variceal bleeding (derivation cohort) and an external cohort of 415 patients with similar indications for TIPS (validation cohort). Older adult patients in the two cohorts were 99 and 76, respectively. A cause-specific Cox competing risks model was used to predict liver-related mortality, with orthotopic liver transplant and death for extrahepatic causes as competing events. Age, alcoholic etiology, creatinine levels, and international normalized ratio in the overall cohort, and creatinine and sodium levels in older adults were independent risk factors for liver-related death by multivariable analysis.</p> <p><strong>Conclusions: </strong>After TIPS implantation, mortality is increased by aging, but TIPS placement should not be precluded in patients older than 70 years. In older adults, creatinine and sodium levels are useful predictors for decision making. Further efforts to update the prediction model with larger sample size are warranted.</p>
Data from: Effective population size in a partially clonal plant is not predicted by the number of genetic individuals
<p>Estimating effective population size (<em>N</em><sub>e</sub>) is important for theoretical and practical applications in evolutionary biology and conservation. Nevertheless, estimates of <em>N</em><sub>e</sub> in organisms with complex life-history traits remain scarce because of the challenges associated with estimation methods. Partially clonal plants capable of both vegetative (clonal) growth and sexual reproduction are a common group of organisms for which the discrepancy between the apparent number of individuals (ramets) and the number of genetic individuals (genets) can be striking, and it is unclear how this discrepancy relates to <em>N</em><sub>e</sub>.</p> <p>In this study, we analysed two populations of the orchid <em>Cypripedium calceolus</em> to understand how the rate of clonal vs. sexual reproduction affected <em>N</em><sub>e</sub>. We genotyped >1,000 ramets at microsatellite and SNP loci, and estimated contemporary <em>N</em><sub>e</sub> with the linkage disequilibrium method, starting from the theoretical expectation that variance in reproductive success among individuals caused by clonal reproduction and by constraints on sexual reproduction would lower <em>N</em><sub>e</sub>. We considered factors potentially affecting our estimates, including different marker types and sampling strategies, and the influence of pseudoreplication in genomic datasets on <em>N</em><sub>e</sub> confidence intervals. The magnitude of <em>N</em><sub>e</sub>/<em>N</em><sub>ramets </sub>and <em>N</em><sub>e</sub>/<em>N</em><sub>genets</sub> ratios we provide may be used as reference points for other species with similar life-history traits. Our findings demonstrate that <em>N</em><sub>e</sub> in partially clonal plants cannot be predicted based on the number of genets generated by sexual reproduction, because demographic changes over time can strongly influence <em>N</em><sub>e</sub>. This is especially relevant in species of conservation concern, in which population declines may not be detected by only ascertaining the number of genets.</p>
Data for: Endogenous DAF-16 spatiotemporal activity quantitatively predicts lifespan extension induced by dietary restriction
<p>In many organisms, dietary restriction (DR) leads to lifespan extension through the activation of cell protection and pro-longevity gene expression programs. In the nematode <em>C. elegans</em>, the DAF-16 transcription factor is a key aging regulator that governs the Insulin/IGF-1 signaling pathway and undergoes translocation from the cytoplasm to the nucleus of cells when animals are exposed to food limitation. However, how large is the influence of DR on DAF-16 activity, and its subsequent impact on lifespan has not been quantitatively determined. In this work, we assess the endogenous activity of DAF-16 under various DR regimes by coupling CRISPR/Cas9-enabled fluorescent tagging of DAF-16 with quantitative image analysis and machine learning. Our results indicate that DR regimes induce strong endogenous DAF-16 activity, although DAF-16 is less responsive in aged individuals. DAF-16 activity is in turn a robust predictor of mean lifespan in <em>C. elegans</em>, accounting for 78% of its variability under DR. Analysis of tissue-specific expression aided by a machine learning tissue classifier reveals that, under DR, the DAF-16 influence on lifespan extension mainly originates from the intestine and neurons. DR also drives DAF-16 activity in unexpected locations such as the germline and intestinal nucleoli.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.