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13,064 results for “predictions”
Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
<p>Training and test datasets and scripts for training models.</p>
Predicting the secondary metabolic potential of microbiomes from marker genes using PSMPA
<p>Supplementary data for the paper <strong>Predicting the secondary metabolic potential of microbiomes </strong><strong>from marker genes using PSMPA</strong>.</p>
Supporting data for: Planted conifer seedling survival in a post-wildfire landscape in New Mexico: From experimental planting to predictive probabilistic surfaces
<p>Across the southwestern United States, high-severity wildfire is causing increasingly large patches of tree mortality, removing the seed sources required for the natural regeneration of these formerly conifer-dominated landscapes. Planting tree seedlings can accelerate succession and restore the ecosystem services that pre-wildfire forests provided, but in the semi-arid southwestern US, the survival of planted conifer seedlings is typically low. We present data on a seedling planting experiment within the footprint of the 2011 Las Conchas Fire in northern New Mexico, using microclimate variables and site-level topographic indices to determine which factors influence seedling survival of four conifer tree species. We then extrapolate these findings to the wider landscape for the most commonly planted species, producing a spatial projection of probabilistic seedling survival as a function of both site- and landscape-scale factors, using readily available topographic data. Both sets of analyses highlight the importance of landscape heterogeneity, quantifiable using topographic indices, in shaping microclimatic environments and affecting seedling survival in a predictable manner. Our results demonstrate that using these methods to guide future plantings may increase conifer tree seedling survival in high-severity post-wildfire landscapes.</p>
Evaluating deep learning for predicting epigenomic profiles
<p>Datasets and static codebase to replicate figures and results from the project.</p>
Fig. 5. Termite SDM predictions for species located within Southern Australia for A in Utilization of Community Science Data to Explore Habitat Suitability of Basal Termite Genera
Fig. 5. Termite SDM predictions for species located within Southern Australia for A. Porotermes adamsoni (Stolotermitidae)(left) and B. Stolotermes victoriensis (right), and C. Mastotermes darwiniensis (Mastotermitidae). Final model predictions were generated using our thinned occurrence dataset and final set of uncorrelated environmental variables for each species, with 10 bootstrap replicates with 'cloglog' outputs in which raw values are converted to a range of 0 - 1 to approximate a probability of occurrence (Cobos et al. 2018). Brighter colors indicate areas of higher suitability (higher probability of occurrence), while darker colors indicate areas of lower suitability (lower probability of occurrence)..
Fig. 4. Termite SDM predictions for species located within Eastern United States and Canada for A in Utilization of Community Science Data to Explore Habitat Suitability of Basal Termite Genera
Fig. 4. Termite SDM predictions for species located within Eastern United States and Canada for A. Zootermopsis nevadensis (Stolotermitidae)(left) B. Zootermopsis angusticollis (right), and C. Zootermopsis laticeps (bottom). Final model predictions were generated using our thinned occurrence dataset and final set of uncorrelated environmental variables for each species, with 10 bootstrap replicates with 'cloglog' outputs in which raw values are converted to a range of 0 - 1 to approximate a probability of occurrence (Cobos et al. 2019). Brighter colors indicate areas of higher suitability (higher probability of occurrence), while darker colors indicate areas of lower suitability (lower probability of occurrence).
Fig. 1 in Is Phylogeographic Congruence Predicted by Historical Habitat Stability, or Ecological Co-associations?
Fig. 1. Ecological niche models (ENMs) estimated for each of the five species from present-day to Last Glacial Maximum, and climatic stability based on the ENM time series. Color coding uses "warm" colors to indicate areas of highest probability of occurrence, or highest climatic stability. Species names are abbreviated as follows: C.p. (Cryptocercus punctulatus), R.f. (Reticulitermes flavipes), O.d. (Odontotaenius disjunctus), S.s. (Scolopocryptops sexspinosus), and N.a. (Narceus americanus).
Fig. 4 in Is Phylogeographic Congruence Predicted by Historical Habitat Stability, or Ecological Co-associations?
Fig. 4. Assessment of phylogeographic structure via comparison of FST (grey bars) versus Φ ST (black bars). All values represent mean differentiation across all pairs of BAPS clusters per species (i.e., "global" values). Species names are abbreviated as in Fig. 1.
Fig. 2 in Is Phylogeographic Congruence Predicted by Historical Habitat Stability, or Ecological Co-associations?
Fig. 2. Unrooted dendrograms representing two competing hypotheses about key drivers of phylogeographic congruence among five saproxylic invertebrates: abiotic factors related to historical climatic stability (left) versus biotic factors related to ecological co-associations (right). Scale bars represent either the inverse of a measure of habitat overlap (1 – Schoener's D; left), or the cumulative dissimilarity score for species interactions based on trophic guild, timing of colonization during succession, frequency of syntopy, and presumed interaction type (right). Numbers on nodes for the biotic drivers scenario indicate the number of jackknife replicates (out of 4) that supported a given predicted partition. Species names are abbreviated as in Fig. 1.
Fig. 3 in Is Phylogeographic Congruence Predicted by Historical Habitat Stability, or Ecological Co-associations?
Fig. 3. The number and distribution of spatial-genetic clusters, and phylogenetic relationships among mitochondrial DNA (mtDNA) haplotypes, for each of the five focal species. BAPS clusters were arbitrarily color-coded (alphabetic names are also shown), and spatial projections were based on membership of georeferenced individuals (Voronoї tessellations not shown). Grey shading identifies the "northern region" of the study area, referred to in the main text. Rooted phylogenetic trees (outgroup not shown) are simplified and color-coded corresponding to BAPS cluster membership of each mtDNA haplotype. Nodes with bootstrap support values>70% are marked by asterisks. Species names are abbreviated as in Fig. 1.
Using semiochemicals to predict biotic resistance and facilitation of invading phytophagous insects
<p>Invasive species are leading causes of biodiversity loss and economic damage. Allocating limited resources towards prevention and management of invasions requires risk assessments based on ecological knowledge for species of potential concern. Interactions of potentially invasive species with heterospecifics in a novel community will contribute to biotic resistance and facilitation experienced by the invader, and thus the likelihood of establishment. To experimentally predict heterospecific interactions, we conducted field experiments using synthetic aggregation-pheromone lures to measure the response of ecologically relevant species (possible predators, competitors, and facilitators) to the simulated presence of two potentially invasive spruce bark beetles, the North American <em>Dendroctonus rufipennis</em> (tested in Norway) and the European <em>Ips typographus</em> (tested in eastern Canada). The Canadian beetle community responded strongly to <em>I. typographus</em> lures, suggesting potential for considerable biotic resistance, whereas <em>D. rufipennis</em> lures prompted little response by the Norwegian beetle community. <em>D. rufipennis</em> was attracted to <em>I. typographus </em>lures, suggesting potential facilitation between these species through cooperative mass attack on trees. Our findings will inform invasive-species risk assessments for <em>I. typographus</em> and <em>D. rufipennis</em> and highlight useful methods for assessing interactions among other taxa that rely heavily on semiochemical communication.</p>
Trojan Detection Challenge - Analysis Track (Target Label Prediction) Dataset
<p>This contains the dataset for the target label prediction subtrack of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This contains the same files as the target_label_prediction folder in the full dataset Zenodo record, which can be found <a href="https://zenodo.org/record/6894041#.Yxl6x3XMJyM">here</a>, and it also contains the test networks. To download the test networks alone, the test set Zenodo record for the target label prediction track can be found <a href="https://zenodo.org/record/7217508">here</a>.</p>
Trojan Detection Challenge - Analysis Track (Target Label Prediction) Test Set
<p>This contains the test set for the target label prediction track of the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai/">competition website</a>.</p> <p>This is identical to the "test" folder in the full target label prediction track Zenodo record, which can be found <a href="https://zenodo.org/record/7213751">here</a>.</p>
Genetically predicted lipid traits, diabetes mellitus liability and carotid intima-media thickness in African ancestry individuals: a Mendelian randomization study
<p>Genetic variants used for the analysis of 'Genetically predicted lipid traits, diabetes mellitus liability and carotid intima-media thickness in African ancestry individuals: a Mendelian randomization study'.</p>
Supplementary material 4 from: Novoa A, Kumschick S, Richardson DM, Rouget M, Wilson JRU (2016) Native range size and growth form in Cactaceae predict invasiveness and impact. In: Daehler CC, van Kleunen M, Pyšek P, Richardson DM (Eds) Proceedings of 13th International EMAPi conference, Waikoloa, Hawaii. NeoBiota 30: 75–90. https://doi.org/10.3897/neobiota.30.7253
Results of the General Impact Scoring System applied to 70 cactus species (35 invasive and 35 non-invasive cactus species with a long history of introduction in South Africa) : Explanation note: The impact scores are expressed as the maximum impact over all the environmental and socioeconomic categories considered in this study.
Supplementary material 3 from: Novoa A, Kumschick S, Richardson DM, Rouget M, Wilson JRU (2016) Native range size and growth form in Cactaceae predict invasiveness and impact. In: Daehler CC, van Kleunen M, Pyšek P, Richardson DM (Eds) Proceedings of 13th International EMAPi conference, Waikoloa, Hawaii. NeoBiota 30: 75–90. https://doi.org/10.3897/neobiota.30.7253
Generic Impact Scoring System (GISS) : Explanation note: Detailed description of impact categories. An updated Excel version is available from the authors on request.
Operational sex ratio predicts the opportunity and direction of sexual selection across animals
<p>The operational sex ratio (OSR) has long been assumed to be a key ecological factor determining the opportunity and direction of sexual selection. However, recent theoretical work has challenged this view, arguing that a biased OSR does not necessarily result in greater monopolisation of mates and therefore stronger sexual selection in the mate-limited sex. Hence, the role of the OSR for shaping animal mating systems remains a conundrum in sexual selection research. Here we took a meta-analytic approach to test whether OSR explains interspecific variation in sexual selec- tion metrics across a broad range of animal taxa. Our results demonstrate that the OSR predicts the opportunity for sexual selection in males and the direction of sexual selection in terms of sex differences in both the opportunity for sexual selection and the Bateman gradient (i.e. the selection differential of mating success), as predicted by classic theory.</p>
Dataset for Bike Flow Prediction with Multi-Graph Convolutional Networks, Chai et al. (https://github.com/Di-Chai/GraphCNN-Bike/)
<p>This is contains the preprocessed, and raw data used to run the replication package from: <br>https://github.com/Di-Chai/GraphCNN-Bike/</p>
Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction
<p>Dataset relative to Molecular dynamics simulation performed for the work "Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction".<br><br>The dataset contains data of 42 alchemical simulations and is subdivided in 4 zip files.<br><br>Folders are named following the scheme: system_configuration_forcefield.<br>Zip file C1 contains .mdp files used for all the simulations.<br><br>Folders corresponding to simulations performed with the fit5_AC ff contains:<br>- topology files (topol.top, topol_RNA_chain_A.itp, topol_RNA_chain_B.itp)<br>- index files needed to reconstruct the demuxed trajectories (replica_index.xvg , replica_index.xvg)<br>- 16 folders, one for each replica (lam0 ... lam15), containing:<br> - final configuration (confout.gro)<br> - log file (md.log)</p> <p> - input file for md run (md.tpr)<br> - energies for the concatenated trajectories recomputed for the realtive replica hamiltonian (ener_trj_conc.edr)<br><br>Folders corresponding to simlations performed with fit_A parametrization only contains .edr files corresponding to energies for the concatenated trajectory computed for 14 set of DeQs drawn from a gaussian distribution, with the relative topologies.<br><br>Supplementary materials relative to simlations performed with fit_A parametrizationcan be found in: https://zenodo.org/records/6498021</p>
Supporting data and code for High Concentrations of Nanoparticles from Isoprene Nitrates Predicted in Convective Outflow Over the Amazon
<p>The Archive includes the CloudChem model code, which is used to simulate gas-phase chemical reactions and gas interactions with clouds. Additionally, it includes the simulated gas and particle concentration dataset used to produce figures for the paper "High Concentrations of Low Volatility Isoprene Nitrates Predicted in Convective Outflow Over the Amazon". For more information about the simulation setup and the results, please refer to the paper.</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.