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129 results for “Deep time”
Data from: Frog limbs in deep time: is jumping locomotion at the roots of the anuran Bauplan?
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Museum ‘dark data’ show variable impacts on deep-time biogeographic and evolutionary history
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Deep Reinforcement Learning for END-To-END Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
<p> </p> <p>Videos for the real flight tests and the simulation experiments </p>
Comprehensive phylogenomic time tree of bryophytes reveals deep relationships and uncovers gene incongruences in the last 500 million years of diversification
<p class="MsoNormal"><strong><span>Premise</span></strong></p> <p class="MsoNormal"><span>Bryophytes, land plants defined by a free-living gametophyte and an unbranched sporophyte, form a major component of terrestrial plant biomass, structuring ecological communities in all biomes. </span><span>Our understanding of the evolutionary history of hornworts, liverworts and mosses has been significantly reshaped by inferences from molecular data, highlighting extensive homoplasy in various traits and repeated bursts of diversification. However, the timing of key events in the phylogeny, and the degree to which the observed homoplasy represents error or biological processes, remain poorly resolved.</span></p> <p class="MsoNormal"><strong><span>Methods</span></strong></p> <p class="MsoNormal"><span>Using the GoFlag probe set, we sampled 405 exons representing 228 nuclear genes for 531 species from 51 of the 53 orders of bryophytes. We inferred the species phylogeny from gene tree analyses using concatenated and coalescence approaches, assessed gene conflict, and estimated the timing of divergences based on 29 fossil calibrations.</span></p> <p class="MsoNormal"><strong><span>Results</span></strong></p> <p class="MsoNormal"><span>The phylogeny resolves many relationships across the bryophytes, enabling us to resurrect five liverwort orders and recognize three more, and propose ten new orders of mosses. Most orders originated in the Jurassic or earlier and diversified in the Cretaceous or later. The phylogenomic data also highlight topological conflict in parts of the tree, suggesting complex processes of diversification that cannot be adequately captured in a single gene tree topology. </span></p> <p class="MsoNormal"><strong><span>Conclusions</span></strong></p> <p class="MsoNormal"><span>We sampled hundreds of homologous loci across a broad phylogenetic spectrum spanning at least 450 Ma of evolution, and these data resolved many of the critical nodes of the diversification of bryophytes. The data also highlight the need to explore the mechanisms underlying the phylogenetic ambiguity at specific nodes. The phylogenomic data provide an expandable framework toward reconstructing a comprehensive phylogeny of bryophytes and for investigating the transformations of traits in this important group of plants.</span></p>
Example data for the paper "A deep learning system for predicting time to progression of diabetic retinopathy"
<p>Example data for the paper "A deep learning system for predicting time to progression of diabetic retinopathy"</p>
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
The data for Time-dependent Stellar Flare Models of Deep Atmospheric Heating
<p>This repository contains model output (within two .tar.gz files) and a pdf document (analysis_tools_mdwarfradyngrid-v1.0.pdf) that explains the contents and use. The models are described in Kowalski, A.F., Allred, J.C., & Carlsson, M. <em>Time-dependent Stellar Flare Models of Deep Atmospheric Heating, </em>published 2024 July 5 in <em>The Astrophysical Journal</em> Volume 969, Number 2 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad4148">10.3847/1538-4357/ad4148</a>).</p> <p>The Jupyter notebook demo, radyn_xtools_Demo.ipynb is included in the PyPI package installation that is described in analysis_tools_mdwarfradyngrid-1.0.pdf.</p>
Deep-Time Marine Sedimentary Element Database
<p>The Deep-Time Marine Sedimentary Element Database (DM-SED), built upon the Sedimentary Geochemistry and Paleoenvironments Project (SGP; n = 28,753) database with an additional 433 studies and 34,874 data entries, totaling 63,627 entries. This database contains 2,522,255 discrete proxy values covering the distributions of major and trace elements and some isotopes in ancient marine sediments. It includes 9,207 entries from the Precambrian and 54,420 entries from the Phanerozoic, thus providing significant references for reconstructing deep-time Earth system evolution.</p>
Data for "Sea-level stability over geologic time owing to limited deep subduction of hydrated mantle"
<p>This repository contains data to reproduce the results displayed in the manuscript : "Sea-level stability over geologic time owing to limited deep subduction of hydrated mantle" by Cerpa, N. G., Arcay, D., & Padron-Navarta J. A.<br> The data includes the P-T paths and the slab-water retention for the 56 modeled subduction transects. </p>
Data supporting "Stems Matter: Xylem Physiological Limits Are an Accessible and Critical Improvement to Models of Plant Gas Exchange in Deep Time"
<p>Model outputs from from <em>Paleo</em>-BGC and <em>Paleo</em>-BGC+. Code detailing data structure and allowing reproduction of analysis can be found at github.com/wjmatthaeus</p>
Deep microbiome-based characterization of the alterations in resident bacterial communities of pasteurized bovine milk contaminated with Salmonella Typhimurium over time
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Simulations dataset and pre-trained models of "Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory" Ph.D. project
<p>Ph.D. project datasets and models release, <br><em>Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory.</em></p>
"Toy Data Set" referenced in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (https://doi.org/10.1101/2024.08.22.609110)
<p>This data set, referenced as "toy data set" in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (<a href="Lint-to-article">https://doi.org/10.1101/2024.08.22.609110</a>), mimics a high-throughput screening data set. To demonstrate the application of our analysis framework described in the main article this toy data set was generated. It contains in total 1536000 individual transient signals, splitted in 5 batches of each 200 plates in 1536-well plate format. Five distinct signal classes were used to resemble typical shapes encountered in biological experiments. Fequency of occurrences for each class is reported in the main article.</p>
Fig. 2 in Deep time extinction of largest insular ant predators and the first fossil Neoponera (Formicidae: Ponerinae) from Miocene age Dominican amber
Fig. 2 Artistic reconstruction of Neoponera vejestoria sp. nov. Artist: Minsoo Dong
Supplementary Files: "Improved baited remote underwater video (BRUV) for 24h re-al-time monitoring of surface and deep-sea marine species"
<p>In the supplementary material you will find: Figure S1: Photographs of the different parts of the innovative BRUV design; Video S1: Video footage of a bluntnose sixgill shark; Video S2: Types of markings in blue sharks bodies to identify individuals; Video S3: Other marine pelagic species.</p>
Data from: Decoding the drivers of deep-time wetland biodiversity: insights from an early Permian tropical lake ecosystem
<p><span>Wetlands are important to continental evolution, providing both the arenas and refugia for emerging and declining biotas, respectively. </span><span>Based on this significance and the high preservation potential, the resulting fossiliferous deposits play a key role in understanding past and future biodiversity. We reconstruct the trophic structure and age of the early Permian Manebach-Lake ecosystem, Germany, thriving in a wetland when the tropical biosphere faced profound upheaval in the peaking Late Palaeozoic Icehouse. Nine excavations, the high-resolution, spatiotemporal documentation of fossils and sediments, and the U-Pb radioisotopic dating of tuffs allow us to distinguish autogenic and allogenic factors that shaped the limnic biocoenosis. The Manebach Lake was an exorheic, stratified, perennial water body in the 10<sup>1</sup>–10<sup>2</sup> km<sup>2</sup> scale, integrated into the catchment draining much of the European Variscides. Lake formation paralleled an Asselian regional wet climatic interval and benefited from rising groundwater tables due to post-Variscan tectonics. Stromatolite-forming cyanobacteria, bivalves, several crustacean groups, amblypterids resembling <em>Paramblypterus duvernoyi</em> and xenacanthid sharks formed a differentiated biocoenosis. Digestive remains prove the rare presence of acanthodians, branchiosaurs, and large tetrapods. Anoxic events affected the mainly epilimnal community. The results indicate woody-debris-bearing lake littorals devoid of semi-aquatic and aquatic plants as places suitable for large stromatolites to grow, underpin the model of declining freshwater shark diversity in most Permian Variscan basins, demonstrate fish/amphibian ratios in limnic taphocoenoses to measure lake perenniality and reveal taphonomic biases in freshwater plant assemblages. Our outcomes highlight the need for more knowledge about the diversity, ecology, and fossilisation pathways of past limnic biotas, particularly microorganisms and actinopterygian fishes, to reconstruct deep-time continental ecosystems. </span></p>
Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector. Energy Policy (2023). Supplementary Information on Assumptions and Results
<p>This dataset supplements the article with the title "Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector", published in Energy Policy. </p> <p>The dataset contains the following:</p> <ul> <li>The Latin Hypercube Sample of the multipliers that are applied to the key input parameters of ETSAP-TIAM in order to generate 1000 different states of the world regarding economic and demographic growth, energy resources potentials, energy technology costs, climate sensitivity and radiative forcing, LULUCF CO<sub>2</sub> sink potential, CO<sub>2</sub> sequestration potential, and decoupling between energy consumption and economic development. The multipliers are sampled from the underlying probability distributions described in the article. </li> <li>The results (at the global scale) from four scenario families for each one of the 1000 wofld states. These scenario families are: <ul> <li>BASE_SSP2: Describes the development of the global energy system consistent with recent trends and policies.</li> <li>2C_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 2 °C as the maximum post-industrial temperature change from 2020 to 2100.</li> <li>2C_SSP2_DA30: Delayed climate action. The climate change mitigation policies of 2C_SSP2 start in 2030. </li> <li>1p5c_OS_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 1.5 °C as the maximum post-industrial temperature change from 2020 onwards to 2100</li> </ul> </li> </ul> <p>Key results included in the dataset are: Temperature change, Radiative Forcing, GHG concentrations, CO2 emissions, Marginal abatement cost, Electricity Supply Primary Energy Consumption, and Annual Total Global Energy System Cost.</p> <p>The dataset also includes sectoral results regarding energy consumption and use, such as shares of different electric uses, shares of hydrogen consumption in end-use sectors, hydrogen supply, Demand electrification by sector, Renewable energy consumption by sector, Alternative fuels consumption in transport, and Total final energy consumption by sector. </p>
Test-time training for deep MS/MS spectrum prediction improves peptide identification
<p>In bottom-up proteomics, peptide-spectrum matching is critical for peptide and protein identification. Recently, deep learning models have been used to predict tandem mass spectra of peptides, with the similarity scores of predicted and experimental spectra being integrated into peptide-spectrum matching. These models follow the supervised learning paradigm, which trains a general model using paired peptides and spectra from standard datasets and uses the model for prediction on experimental data. However, this approach can lead to inaccurate predictions due to differences between the training data and the experimental data, such as sample types, enzyme specificity, and instrument calibration. To address this issue, we proposed a Test-Time Training paradigm that adapts the pre-trained model to experimental data-specific models, namely PepT3. PepT3 results in a 10-40\% increase in peptide identification, depending on the distinctness of training and experimental data. Intriguingly, PepT3 improves the identification of tumor-specific neo-epitopes when applied to a complex patient-derived immunopeptidomic sample, with two-thirds of these neo-epitopes predicted to bind to the patient's human leukocyte antigen isoforms</p>
Comprehensive phylogenomic time tree of bryophytes reveals deep relationships and uncovers gene incongruences in the last 500 million years of diversification
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Data from: Decoding the drivers of deep-time wetland biodiversity: insights from an early Permian tropical lake ecosystem
Open the record for dataset details and reuse information.
ScienceDex guides
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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.