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13,064 results for “predictions”
Predicting daily activity time through ecological niche modeling and microclimatic data
<p><span>1. </span><span>Climate temporality is a phenomenon that affects species' activity and distribution patterns across spatial and temporal scales. Despite the global availability of microclimatic data, their use to predict activity patterns and distributions remains scarce, particularly at fine temporal scales (e.g., < month). Predicting activity patterns based on climatic data may allow us to foresee some of the consequences of climate change, particularly for ectothermic vertebrates. </span></p> <p><span>2. </span><span>The Gila monster exhibits marked daily and seasonal activity patterns linked to physiology and reproduction. Here we evaluate if ecological niche models fitted using microclimate data can predict temporal activity patterns using the Gila monster (<em>Heloderma suspectum</em>) as a study system. Further, we identified if the activity patterns are related to physiological constraints.</span></p> <p><span>3. </span><span>We used dated occurrences from museum specimens and human observations to generate and test ecological niche models using minimum-volume ellipsoids. We generated hourly microclimatic data for each occurrence site for ten years using the NicheMapR package. For ecological niche modeling, we compared the traditional seasonal approach versus a daily activity pattern strategy for model construction. We tested both using the omission rate of independent observations (citizen science data). Finally, we tested if unimodal and bimodal activity patterns for each season could be recreated through ecological niche modeling and if these patterns followed known physiological constraints.</span></p> <p><span>4. </span><span>The unimodal and bimodal activity patterns previously reported directly from tracking individuals across the year were recovered by using niche modeling and microclimate across the species' geographical range. We found that upper thermal tolerances can explain the daily activity patterns of this species. </span></p> <p><span>5. </span><span>We conclude that ecological niche models trained with microclimatic data can be used to predict activity patterns at fine temporal scales, particularly on ectotherm species of arid zones coping with rapid climate modifications. Further, the use of fine temporal scale variables can lead to a better niche delimitation, enhancing the results of any research objective that uses correlative models.</span></p>
A Supervised Machine-Learning Prediction of Textile's Antimicrobial Capacity Coated with Nanomaterials
<p>The dataset contains P-Chem properties of NMs and experimental conditions for assessing the antimicrobial properties of inorganic and organic NMs using machine learning tools.</p>
50000 SNPs for genomic prediction of ash dieback susceptibility in European Ash
<p>This file is <a href="https://www.nature.com/articles/s41559-019-1036-6">Stocks et al (2019)</a> Supplementary Table 7j with major allele (MAA) and minor allele (MIA) identities added.<br> Estimated effect sizes (EES) from genomic prediction model trained on the pool-seq data using the top 50000 SNPs from the pool-seq GWAS<br> Contig = Contig in BATG0.5 assembly <br> Pos = SNP location in contig <br> EES.MIA = Estimated effect size of minor allele <br> EES.MIA.SE = Standard Error of Estimated effect size of minor allele<br> EES.MAA = Estimated effect size of major allele<br> EES.MAA.SE = Standard Error of Estimated effect size of major allele <br> MIA = identity of minor allele <br> MAA = identity of major allele</p>
MAGs and gapseq models for auxotrophy predictions in the human gut microbiome
<p>This dataset contains MAGs, their DNA sequence, genome statistics, quantification per sample, and their metabolic model reconstructions from two human population cohorts from northern Germany.</p>
PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS.
<p>This is the data for "PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS."</p>
DataSet-Neural signatures of linguistic predictions and listener's attention to speaker's communication intention
<p>Researchers can find the raw EEG data with scripts of EEG data analyses, false alarms data, and sentence materials related to he project entitled "Neural signatures of linguistic predictions and listener's attention to speaker's communication intention". Corrections due to the available article form: "I-" was replaced with "E-" and "I+" was replaced with "E+" in the online article form. In the same manner, "INTENTION" should be replaced with "PROSODIC EMPHASIS" in the scripts.</p>
RSW gun fault prediction benchmark data set (demo)
<p>The resistance spot welding (RSW) welding gun fault prediction benchmark data set has 72 multivariate time series in the training set and 8 in the testing set. Each time series length 604800 sampled at 1 Hz with missing values and has 20 dimensions (c1-c19 and the error code). We retain the missing value and the outliers of the welding gun time series for the potential of imputation research in the future.<br> This data set supports an academic paper named 'benchmark study for welding gun fault prediction'.</p> <p><strong>Feature name and explanation:</strong></p> <p>c1 : Electrode cap offset;</p> <p>c2 : Electrode force;</p> <p>c3 : Electrode position;</p> <p>c4 : Force build-up;</p> <p>c5 : Balance pressure;</p> <p>c6 : Friction;</p> <p>c7 : Maximum aperture;</p> <p>c8 : Maximum electrode force;</p> <p>c9 : Mtart friction;</p> <p>c10 : US2;</p> <p>c11 : Welding point count;</p> <p>c12 : Position count;</p> <p>c13: Setpoints of counterbalance pressure;</p> <p>c14: Setpoints of electrode force;</p> <p>c15 : Setpoints of electrode position;</p> <p>c16: Setpoints of sheet thickness;</p> <p>c17 : Setpoints of velocity;<br> c18: Setpoints of force build-up;<br> c19 : Offset value in robot.</p> <p><strong>Machine Learning Task:</strong><br> This dataset is suitable for a time series forecasting task, where machine learning models can be trained to predict future welding parameters based on the provided welding parameters time series in history. </p> <p><strong>Code for quick start:</strong></p> <p><a href="https://zenodo.org/record/7655025">https://zenodo.org/record/7655025</a></p> <p>If you want to have an overview of the data before downloading all of it, you can download only the files with the word "Damo" in the file name.</p> <p>For any question, please contact 1910633@stu.neu.edu.cn</p>
Fig. 5 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 5. Plots showing the correlation between the predicted distribution of Lucilia cuprina (A & C) and Lucilia sericata (B & D) to grease mass (kg) and human population density values (log values).
Fig. 6 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 6. Mean predicted distribution maps for Lucilia sericata (A) and Lucilia cuprina (B), produced using museum records, survey data and personal contact localities. The colour range indicates the likelihood of species distribution from dark blue (least likely) to red (most likely).
Fig. 3 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 3. Wool production of magisterial districts, estimated by total grease mass produced in 2011/2012 in South Africa.
Fig. 8 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 8. Mean distribution maps for Lucilia sericata (A & B) and Lucilia cuprina (C & D) from museum (A & C) and survey (B & D) data only.
Fig. 1 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa
Fig. 1. Map of South Africa, showing the occurrence records and collection trip routes for (A) Lucilia sericata and (B) Lucilia cuprina.
LiveDst predictions
<p>prediction_final_1.zip: 1-hr-ahead predictions for all selected storm events.<br> prediction_final_2.zip: 2-hr-ahead predictions for all selected storm events.<br> prediction_final_3.zip: 3-hr-ahead predictions for all selected storm events.<br> prediction_final_4.zip: 4-hr-ahead predictions for all selected storm events.<br> prediction_final_5.zip: 5-hr-ahead predictions for all selected storm events.<br> prediction_final_6.zip: 6-hr-ahead predictions for all selected storm events.<br> </p>
Shifts Marine Cargo Vessel Power Consumption Prediction Dataset
<p>This archive contains the data for the Shifts Benchmark on cargo vessel power consumption prediction. This dataset is provided by the Shifts Project to enable assessment of the robustness of models to distributional shift and the quality of their uncertainty estimates. A full description of the benchmark is available in https://arxiv.org/pdf/2206.15407. To find out more about the Shifts Project, please visit https://shifts.ai . </p> <p> </p> <p> </p>
Expanded distribution and predicted suitable habitat for the critically endangered yellow-tailed woolly monkey (Lagothrix flavicauda) in Peru
<p><span>The Tropical Andes Biodiversity Hotspot holds a remarkable number of species at risk of extinction due to anthropogenic habitat loss, hunting and climate change. One of these species, the Critically Endangered yellow-tailed woolly monkey (<em>Lagothrix flavicauda</em>), was recently sighted in Junín region, 206 kilometres south of its previously known distribution. The range extension, combined with continued habitat loss, calls for a re-evaluation of the species' distribution and available suitable habitat. Here, we present novel data from surveys at 53 sites in the regions of Junín, Cerro de Pasco, Ayacucho and Cusco. We encountered <em>L. flavicauda </em>at 9 sites, all in Junín, and the congeneric <em>L. l. tschudii</em> at 20 sites, but never in sympatry. Using these new localities along with all previous geographic localities for the species, we made predictive Species Distribution Models based on Ecological Niche Modelling using a generalized linear model and maximum entropy. Each model incorporated bioclimatic variables, forest cover, vegetation measurements, and elevation as predictor variables. Model evaluation showed >80% accuracy for all measures. Precipitation was the strongest predicter of species presence. Habitat suitability maps illustrate potential corridors for gene flow between the southern and northern populations, although much of this area is inhabited by <em>L. l. tschudii</em>. An analysis of the current protected area (PA) network showed ~47% of remaining suitable habitat is unprotected. With this, we suggest priority areas for new protected areas or expansions to existing reserves that would conserve potential corridors between <em>L. flavicauda</em> populations. Further surveys and characterization of the distribution in intermediate areas, combined with studies on genetic flow, are still needed to protect this species.</span></p>
Transgenerational effect on sexual reproduction in rotifer populations in relation to the environmental predictability of their habitats
<p>Understanding the processes that enable adaptation of organisms to time-varying environments is critically relevant in evolutionary ecology. A way to cope with environmental fluctuations where predictable conditions affect several generations of individuals is through non-genetic transgenerational effects. The phenotype of ancestors affects the phenotype of their descendants matching it with the expected environment of the latter. Facultatively sexual rotifers inhabiting water bodies that cover a wide gradient of environmental predictability in Eastern Spain are a good study model for this topic. In their life cycle sex is linked to diapausing-egg production that enables survival between growing seasons. In several rotifer species, sexual reproduction is inhibited in several generations after diapausing-egg hatching. We hypothesized that in ponds where the growing season length is more predictable, rotifer clones proliferate asexually longer, hence allowing a fuller exploitation of the growing season and therefore maximize diapausing-egg production by the end of the season. We tested this prediction by estimating the proportion of sexual females produced by eight clones of the rotifer <em>Brachionus plicatilis</em> inhabiting eight ponds (8x8= 64 clones) from our study system. Here, we present the raw data gathered from the experiment. </p>
Where should they come from? Where should they go? Several measures of seed source locality fail to predict plant establishment in early prairie restorations
<ol> <li>During the "decade on restoration," we must understand how to reliably re-establish native plant populations. When establishing populations through seed addition, practitioners prioritize obtaining seed from locations geographically near the restoration site (i.e., "local seed sourcing"). They are assumed to be under similar environmental conditions to the restoration site and should establish more robust plant populations and preserve local biotic interactions than seeds sourced from further away. However, this assumption remains virtually untested in realistic restoration settings and the importance of seed sourcing, relative to other factors such as seeding rate and management regimes, is unclear.</li> <li>To determine if seed source impacts plant establishment, abundance, and phenology, we developed a partnership between university researchers and a native seed producer that kept records on where their seed was sourced from and where it was planted. At each site, we recorded the abundance and phenological stage of five commonly used tallgrass prairie restoration species seeded at 24 sites undergoing restoration across Michigan. We considered two measures of seed source locality: geographic distance (seeds were sourced from locations 6–750km away from their respective restoration sites) and climate distance. We also obtained data on the seeding rate and post-seeding management efforts at each site.</li> <li>We found that no measure of seed source locality predicted the likelihood of plant establishment or abundance at restoration sites. However, sites sown with seed from further away, or from cooler and wetter climates, had a greater proportion of flowering individuals earlier in the season. Finally, sites with higher seeding rates had greater plant abundance, and post-seeding management of the restoration site increased the likelihood a species would establish by 36%.</li> <li>Overall, these results suggest that seed sourcing did not impact plant establishment or abundance in our system. However, using fewer local seed sources can alter flowering phenology.</li> <li>Our results suggest that tallgrass prairie restoration efforts should prioritize higher seeding rates, post-seeding management, and might expand the region seed sources are considered "local", though this could impact flowering phenology. Future research leveraging native seed producer records can help answer critical questions about restoration seed sourcing.</li> </ol>
Data for: Predicting age and mass at maturity from feeding behavior and diet in M. sexta: An empirical test of a life history model
<p>Feeding for most animals involves bouts of active ingestion alternating with bouts of no ingestion. In insects, the temporal patterning of bouts varies widely with resource quality and is known to affect growth, development time, and fitness. However, the precise impacts of resource quality and feeding behavior on insect life history traits is poorly understood. To explore and better understand the connections between feeding behavior, resource quality and insect life history traits, we combined laboratory experiments with a recently proposed mechanistic model of insect growth and development for a larval herbivore, <em>Manduca sexta</em>. We ran feeding trials for 4<sup>th</sup> and 5<sup>th</sup> instar larvae across different diet types (two hostplants and artificial diet) and used these data to parameterize a joint model of age and mass at maturity that incorporates both insect feeding behavior and hormonal activity. We found that the estimated durations of both feeding and non-feeding bouts were significantly shorter on low- than on high-quality diets. We then explored how well the fitted model predicted historical out-of-sample data on age and mass of <em>M</em>.<em> sexta</em>. We found that the model accurately described qualitative outcomes for the out-of-sample data, notably that a low-quality diet results in reduced mass and later age at maturity compared to high-quality diets. Our results clearly demonstrate the importance of diet quality on multiple components of insect feeding behavior (feeding and non-feeding), and partially validate a joint model of insect life history. We discuss the implications of these findings with respect to insect herbivory and discuss ways in which our model could be improved or extended to other systems.</p>
L4F - Training dataset and layers for biomass prediction
<p>Those data represent the Brazilian Amazon by 68,629,072 250-m pixels, and 141,032 pixels have LiDAR information and therefore had the AGB estimated, and converted to Mg ha-1 (file trainning_dataset.csv). To generate a wall-to-wall map of the Brazilian Amazon at 250-m resolution, we trained a Random Forest (RF) model 29 using the AGB estimated pixels and remote sensing layers formed by: MODIS vegetation indices, Shuttle Radar Topography Mission (SRTM) data, Tropical Rainfall Measuring Mission (TRMM), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) data, along with the central coordinates of each 250m pixel, stored in amazon_*.csv files.</p> <p>Derived from MODIS, we used the Vegetation Indices 16-Day L3 Global 250m temporal series (MOD13Q1) products from 2016, including the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), from MODIS. From TRMM we used the 3B43 V6 precipitation data, with each pixel value representing the monthly accumulated precipitation from 1998 to 2016 at a resolution of 0.25 degrees. From PALSAR, we used the L-band image in the HH and HV polarizations, acquired in 2015. When necessary, the remote sensing products were resampled by means to a 250 m grid.</p> <p>Random Forest models were tested using the H2O_Flow platform and produced the best model based on RMSE and R², containing NDVI q3, PALSAR HV, TRMM mean, X, SRTM, Y, PALSAR HH, EVI q1, EVI mean, NDVI mean, NDVI q1, EVI q3.</p>
Results from a National Survey of Canadian Perspectives on Predicting River Channel Migration and River Bank Erosion
<p>This dataset contains findings from a national survey of Canadian perspectives on predicting river channel migration and river bank erosion. The online survey was conducted by researchers from the University of Guelph from November 2, 2021 through January 31, 2022 and received more than 40 responses from regions across Canada.</p>
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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.