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10,812 results for “novel”
ThermoScenes: Multimodal Neural Radiance Fields for Thermal Novel View Synthesis
<p>Thermal+RGB dataset for ThermoNeRF</p>
A Novel Model Hierarchy Isolates the Limited Effect of Supercooled Liquid Cloud Optics on Infrared Radiation
<p>This dataset contains data used in and resulting from an upcoming paper. For further detail on methodology and experiments, see that paper.</p> <h2>Supercooled liquid water optics</h2> <h3>Complex refractive indices (CRIs)</h3> <ul> <li>Water_DW_300.txt</li> <li>water_RFN_240K.txt</li> <li>water_RFN_253K.txt</li> <li>water_RFN_263K.txt</li> <li>water_RFN_273K.txt</li> </ul> <p>Water_DW_300.txt is sourced from Downing & Williams 1975 (https://doi.org/10.1029/JC080i012p01656). water_RFN_240K.txt, water_RFN_253K.txt, water_RFN_263K.txt, and water_RFN_273K.txt are sourced from Rowe et al. 2020 (https://doi.org/10.1029/2020JD032624).</p> <h3>CESM lookup tables of liquid water optics</h3> <ul> <li>CESM_CRI_RFN_240K.nc</li> <li>CESM_CRI_RFN_253K.nc</li> <li>CESM_CRI_RFN_263K.nc</li> <li>CESM_CRI_RFN_273K.nc</li> </ul> <p>These optics sets were created from the corresponding Rowe et al. 2020 CRI.</p> <p> </p> <h2>SCAM output</h2> <p>History files for the four MPACE SCAM runs.</p> <ul> <li>Control: tutorial.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>240K optics: cri240K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>263K optics: cri263K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>273K optics: cri273K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980-2018 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980-2018 experiment. For the variable FLDS (downwelling longwave flux at the surface), each optics set has a mean, count (n), and standard deviation file. These statistics are calculated over the 39 years of the model run and across all 3 ensemble members. </p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>B1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the B1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850 data</h2> <p>Data used to create graphs shown in PAPER from the F1850 experiment. For each optics run there is a mean, count (n), and standard deviation file. These statistics are calculated over the 40 years of the model run for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.std.All_data.non_filtered.nc</li> </ul>
Assembly-based analysis of the infant gut microbiome reveals novel ubiquitous plasmids
<p>Assembly-based plasmids found in the gut microbiome of 12 infants born in Norway (BabyBiome project).</p>
Data from: Convergent rates of protein evolution identify novel targets of sexual selection in primates
<p>Sexual selection is the differential reproductive success of individuals, resulting from competition for mates, mate choice, or success in fertilization. In primates, this selective pressure often leads to the development of exaggerated traits which play a role in sexual competition and successful reproduction. In order to gain insight into the mechanisms driving the development of sexually selected traits, we used an unbiased genome-wide approach across 21 primate species to correlate individual rates of protein evolution to relative testes size and sexual dimorphism in body size, two anatomical hallmarks of sexual selection in mammals. Among species with presumed high levels of sperm competition, we detected strong conservation of testes-specific proteins responsible for spermatogenesis and ciliary form and function. In contrast, we identified accelerated evolution of female reproductive proteins expressed in the vagina, cervix, and fallopian tubes in these same species. Additionally, we found accelerated protein evolution in lymphoid tissue, indicating that adaptive immune functions may also be influenced by sexual selection. This study demonstrates the distinct complexity of sexual selection in primates revealing contrasting patterns of protein evolution between male and female reproductive tissues.</p>
A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning: Supplemental data
<p>This dataset includes the crater map and equatorial crater depths and diameters presented in the paper: A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, using Machine Learning.</p>
Dataset related to the manuscript entitled "Inhalation with Vitamin D3 Metabolites - A Novel Strategy to Restore Vitamin D3 Deficiencies in Lung Tissue"
<p><strong>The dataset contains the results presented in the manuscript entitled "Inhalation with Vitamin D3 Metabolites - A Novel Strategy to Restore Vitamin D3 Deficiencies in Lung Tissue" Appl. Sci. 2023, 13, 10672. </strong></p><p> </p><p>The presented dataset contains the results of the implementation of the following research project: </p><p><i>Project title</i><strong>:</strong> "Assessment of the possibility of using vitamin D3 in the prevention and treatment of pulmonary fibrosis in the course of hypersensitivity pneumonitis - <i>in vivo</i> studies"</p><p><i>Founder</i><strong>: </strong>National Scientific Center, Poland</p><p><i>Project number</i><strong>:</strong> UMO-2020/38/E/NZ7/00366</p><p><i>Principal Investigator</i>: Marta Kinga Lemieszek</p><p><i>Main investigators:</i><strong> </strong>Michał Chojnacki, Jakub Anisiewicz, Ilona Leśniowska</p><p><i>Place of project implementation</i><strong>:</strong> Department of Medical Biology, Institute of Rural Health, Jaczewskiego 2, 20-090 Lublin, Poland</p>
SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae
<p><strong>Parameters</strong></p><p>Force field: AMBER ff19SB</p><p>Water type: TIP3P</p><p>Ions: NaCl </p><p>Ligand topology force field: GAFF2</p><p>Temperature: 298k</p><p>Pressure: 1 bar</p><p>minimization step: 20000 on 5 nanoseconds</p><p>initial velocity is changed by changing "ntx" and "ig"</p><p>C2: ntx = 5 , ig = 8</p><p>C3: ntx = 2 , ig = 5</p><p> </p><p><strong>Uploads</strong>- </p><p>1. Zip file of all 3 main files</p><p>2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>3. Zip file of C1</p><p>4. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>5. Zip file of C2</p><p>6. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)</p><p>7. Zip file of C3</p><p>8. Zip and unzip file of <strong>Initial</strong> PDB of complex prior to MD simulation with <strong>Post</strong> MD PDB (C1, C2, C3)</p><p>9. Zip file of <strong>topology</strong> files for C1, C2, and C3</p>
Eurasian tree sparrows are more food neophobic and habituate to novel objects more slowly than house sparrows
<p>Introductions of non-native species throughout the world have had severe ecological consequences. However, most research has focused on environmental and ecological factors that allow for introduced species to succeed and become invasive, with fewer studies assessing the roles of behavioural and cognitive traits. To help fill this knowledge gap, we studied neophobia, an aversion towards novelty, in the non-native Eurasian tree sparrow (<em>Passer montanus</em>), and compared results to previous work in a more successful invasive congener, the house sparrow (<em>Passer domesticus</em>). We assessed the neophobia of wild-caught Eurasian tree sparrows by measuring their responses to novel objects and novel foods and their ability to habituate to initially novel objects. We predicted that Eurasian tree sparrows, as less successful invaders, would overall be more neophobic than house sparrows. Although we did not observe differences in neophobia towards novel objects in the two species, Eurasian tree sparrows were significantly less willing to try novel foods than house sparrows. Eurasian tree sparrows were also slower to habituate to repeated presentations of the same initially novel object compared to house sparrows. Multiple factors certainly influence invasion success, but our results suggest that neophobia might limit the success of an introduced species in novel environments.</p>
A Novel Laboratory Technique for Measuring Grain Size Specific Transport Characteristics of Bed Load Pulses
<p>We present a novel, time-efficient and non-destructive laboratory technique to investigate grain size specific transport characteristics of bed load pulses. The method consists of a through-water, high-resolution image acquisition followed by the application of a supervised color classification algorithm (Gaussian Maximum Likelihood Classification). Quality assessment based on a confusion matrix approach and basic random sampling showed a high classification performance. By statistically analyzing the temporal and spatial color distribution of the experimental reach, characteristic parameters to describe the propagation behavior were determined. The analyzed bed load pulse consisted of five different grain size classes of dyed quartz sand and gravel, each having a unique color. The initial experimental bed was uni-colored and contained the same size fractions as the augmented pulse.</p>
Targeted MAG recovery of novel Muirbacteria, Wallbacteria, Riflebacteria and Fusobacteria using SIngleM
<p>Metagenome-assembled genomes (MAGs) from four underrepresented phyla, recovered using targeted analysis of metagenomes included in the <a href="sandpiper.qut.edu.au/">Sandpiper </a>website, analysed using <a href="https://github.com/wwood/singlem">SingleM</a>.</p>
Complete genome analysis of a novel narnavirus in sweet viburnum (Viburnum odoratissimum)
<p>genome.fasta is Vo narna-like virus complete genomo file.</p><p>JPSH_1.fq.gz and JPSH_1.fq.gz are transcriptome sequencing raw data.</p><p>JPSH.fq.gz is siRNA sequencing raw data.</p><p>trinity.JPSH.Trinity.fasta is Trinity assembly result.</p><p>trinity.nr.JPSH is DIAMOND-BLASTX result</p><p> </p>
Environmental effects on genetic variance are likely to constrain adaptation in novel environments
<p>Adaptive plasticity allows populations to cope with environmental variation but is expected to fail as conditions become unfamiliar. In novel conditions, populations may instead rely on rapid adaptation to increase fitness and avoid extinction. Adaptation should be fastest when both plasticity and selection occur in directions of the multivariate phenotype that contain abundant genetic variation. However, tests of this prediction from field experiments are rare. Here, we quantify how additive genetic variance in a multivariate phenotype changes across an elevational gradient, and test whether plasticity and selection align with genetic variation. We do so using two closely related, but ecologically distinct, sister species of Sicilian daisy (Senecio, Asteraceae) adapted to high and low elevations on Mount Etna. Using a paternal half-sibling breeding design, we generated and then reciprocally planted c.19,000 seeds of both species, across an elevational gradient spanning each species' native elevation, and then quantified mortality and five leaf traits of emergent seedlings. We found that genetic variance in leaf traits changed more across elevations than between species. The high-elevation species at novel lower elevations showed changes in the distribution of genetic variance among the leaf traits, which reduced the amount of genetic variance in the directions of selection and the native phenotype. By contrast, the low-elevation species mainly showed changes in the amount of genetic variance at the novel high elevation, and genetic variance was concentrated in the direction of the native phenotype. For both species, leaf trait plasticity across elevations was in a direction of the multivariate phenotype that contained a moderate amount of genetic variance. Together, these data suggest that where plasticity is adaptive, selection on genetic variance for an initially plastic response could promote adaptation. However, large environmental effects on genetic variance are likely to reduce adaptive potential in novel environments.</p>
Supplementary data and scripts for the publication "Discovery of numerous novel Helitron-like elements in eukaryote genomes using HELIANO"
<p>##Information of directories. Each directory contains necessary data and scripts to do the corresponding analysis</p> <p>1. Benchmarking/: benchmarking analysis for heliano. (For Figure 3, Supplementary Table S2)<br>2. GeneCapture/: analysis for captured genes within HLEs. (For Figure 6, Supplementary Table S11)<br>3. HLE_model_species/: analysis for heliano results on three model species. (For Table 1, Figure 4, Supplementary Table S5-9, Supplementary Figure 4)<br>4. HLE_sampled_species/: analysis for heliano results on 404 sampled species. (For Figure 5, Supplementary Table S10, Supplementary Figure 5)<br>5. hmm_model/: build hmm model for Hel and Rep domains. (For RepHel.hmm that is important to detect and classify HLEs)<br>6. Phylogenetical_tree/: making phylogenetical trees. (For Figure 1, Supplementary Figure 2-3)<br>7. Sample_species/: about how to sample species from various genome assemblies. (Supplementary Table S3)<br>8. HLE_feature/: information about the position of RepHel domain and the length of HLE sequences. (Supplementary Figure 1)</p>
Novel biodegradable Zn alloys obtained by rapid solidification and high pressure torsion - NCN Poland Project - Dataset Feb.2024
<p>Dataset contains results obtained during the first year of the project No. 2021/40/C/ST5/00071 "Novel biodegradable Zn alloys obtained by rapid solidification and high pressure torsion" funded by NCN Poland. </p> <p>The dataset contains: SEM, TEM, nanoindentation, hardness and uniaxial tensile testing results. </p>
Quantitative results of the analysis of novel ossicle particles used in mandible bone regeneration
<p>Dataset corresponding to the results of the characterization analysis of novel holothurian ossicle biomaterials. These biomaterials were evaluated at three levels:</p> <p>1) Ex vivo analysis to determine thr potential cytotoxic effects of these biomaterials on human fibroblasts using LIVE/DEAD and quantification of DNA released to the medium.</p> <p>2) In vivo analysis to determine the potential systemic effects of these biomaterials grafted subcutaneously in laboratory rats.</p> <p>3) Histochemical and immunohistochemical analysis to determine the potential effects of these biomaterials on mandible bone regeneration.</p> <p>These results correspond to the publication entitled "<span>EVALUATION OF HOLOTHURIAN OSSICLES AS A BIOLOGICAL BIOMATERIAL FOR MANDIBULAR BONE REGENERATION</span>".</p>
Alignment and Tree file from: Two novel species of tropical Morchella (Ascomycota, Pezizales, Morchellaceae) discovered in the UNESCO Rinjani Lombok Biosphere Reserve, Indonesia
<p>Alignment and Tree file from: Two novel species of tropical Morchella (Ascomycota, Pezizales, Morchellaceae) discovered in the UNESCO Rinjani Lombok Biosphere Reserve, Indonesia</p>
Raw data for Figures 2-4 for journal article: "Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid""
<p>This is a data set containing the raw data for figures 2-4 from the journal article:</p> <p>"Experimental Evaluation of the Adhear, a Novel Transcutaneous Bone Conduction Hearing Aid"</p> <p>Original article DOI: 10.1055/a-1308-3888</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/33260222/</p> <p> </p> <p>The data is contained within MATLAB figure (.fig) files, all saved with MATLAB version R2020a.</p> <p> </p>
Fig. 12 in Updating the morphological phylogenetics of Nopinae (Araneae: Caponiidae): novel terminals and characters, with two new species
Fig. 12. Aamunops yiselae sp. nov., paratype, ♀ (CNAN-Ar 6945), spinnerets, posterior view. A. Spinning field. B. Left ALS and PMS. C. Right ALS and PMS. D. Right PMS. E. Detail of presumed minor ampullated gland spigot on PMS. F. Detail of aciniform gland spigot on AMS. Abbreviations: see Material and methods.
Fig. 11 in Updating the morphological phylogenetics of Nopinae (Araneae: Caponiidae): novel terminals and characters, with two new species
Fig. 11. Aamunops yiselae sp. nov., paratype, ♀ (CNAN-Ar 6945). A. Left pretarsus IV, prolateral view. B. Left pretarsus IV, anterior view. C. Presumed slit sensilla on left tarsus IV, dorsal view. D. Tarsal organ, dorsal view. E. Internal genitalia, ventral view. F. Same, posterior view. Abbreviations: see Material and methods.
Fig. 4 in Updating the morphological phylogenetics of Nopinae (Araneae: Caponiidae): novel terminals and characters, with two new species
Fig. 4. Aamunops yiselae sp. nov., paratype, ♀ (CNAN-Ar 6945). A. Habitus, dorsal view. B. Habitus, ventral view. C. Carapace, dorsal view. D. Sternum and mouth parts, ventral view. E. Left palp, prolateral view. F. Left palp, retrolateral view. G. External genital area, ventral view. H. Right tarsus and metatarsus IV, prolateral view, arrows show the adesmatic joints. I. Left tarsus and metatarsus IV, retrolateral view, arrows show the adesmatic joints. Abbreviations: see Material and methods. Scale bars: A‒B = 1 mm; C‒G = 0.5 mm; H‒I = 0.2 mm.
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.