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1,003 results for “darkness”
Data from: Observed and dark diversity dynamics over millennial time scales: Fast-life history traits linked to expansion lags of plants in northern Europe
<p>Global change drivers (e.g. climate and land use) affect the species and functional traits observed in a local site but also its dark diversity—the set of species and traits locally suitable but absent. Dark diversity links regional and local scales and, over time, reveals taxa under expansion lags by depicting the potential biodiversity that remains suitable but is absent locally. Since global change effects on biodiversity are both spatially and temporally scale dependent, examining long-term temporal dynamics in observed and dark diversity would be relevant to assessing and foreseeing biodiversity change. Here, we used sedimentary pollen data to examine how both taxonomic and functional observed and dark diversity changed over the past 14500 years in northern Europe. We found that taxonomic and functional observed and dark diversity increased over time, especially after the Late Glacial and during the Late Holocene. However, dark diversity dynamics revealed expansion lags related to species' functional characteristics (dispersal limitation and stress intolerance) and an extensive functional redundancy when compared to taxa in observed diversity. We highlight that assessing observed and dark diversity dynamics is a promising tool to examine biodiversity change across spatial scales, its possible causes, and functional consequences.</p>
Steady-state and time-resolved data for the article "Genetically encoded non-canonical amino acids reveal asynchronous dark reversion of chromophore, backbone and side-chains in EL222"
<p>1. Steady-state data</p> <p>Spectra were taken with/without continuous irradiation with a wavelength of 450 nm.</p> <p>1.1 CNF absorption region (2200-2260 cm<sup>-1</sup>)</p> <p>1.2. Amide absorption region (1500-1800 cm<sup>-1</sup>)</p> <p>1.3. UV/Visible absorption region (320-550 nm)</p> <p>Each spectral region has 3 datasets:</p> <p>- DARK</p> <p>- LIT (photostationary state)</p> <p>- DIFFERENCE (lit-minus-dark)</p> <p>The first row contains the variants.</p> <p>The first column contains the wavenumber (in cm<sup>-1</sup>) or wavelength (in nm).</p> <p>2. Time-resolved data.</p> <p>Dark recovery reaction after switching lights off (wavelength of 450 nm).</p> <p>2.1. EL222_WT_H2O</p> <p>2.2. EL222_WT_D2O</p> <p>2.3. EL222_W31CNF</p> <p>2.4. EL222_L35CNF</p> <p>2.5. EL222_N53CNF</p> <p>2.6. EL222_Y136CNF</p> <p>2.7. EL222_M151CNF</p> <p>2.8. EL222_L216CNF</p> <p>2.9. EL222_LOV_H2O</p> <p>2.10. EL222_LOV_D2O</p> <p>For each variant we have measured 3 spectral regions (except for 2.1, 2.2., 2.9 and 2.10, which lack CNF):</p> <p>- CNF: 2200-2270 cm<sup>-1</sup></p> <p>- AMIDE: 1500-1750 cm<sup>-1</sup></p> <p>- UVVIS: 320-550 nm</p> <p>For each probe we have 2 datasets:</p> <p>- DATA: the experimental lit-minus-dark absorbance difference spectra. </p> <p>The first row contains the time delays (in seconds).</p> <p>The first column contains the wavenumbers (in cm<sup>-1</sup>) or wavelength (in nm).</p> <p>- LDA: the lifetime density maps calculated from the corresponding DATA using the inverse Laplace transform and maximum entropy penalty.</p> <p>The first row contains the lifetimes (in seconds).</p> <p>The first column contains the wavenumbers (in cm<sup>-1</sup>) or wavelength (in nm).</p> <p>If you use these datasets please cite:</p> <p>Chaudhari, A.S.; Chatterjee, A.; Domingos, C.A.O.: Andrikopoulos, P. C.; Liu, Y.; Andersson, I.; Schneider, B.; Lórenz-Fonfría, V.A.; Fuertes, G. Genetically Encoded Non-canonical Amino Acids Reveal Asynchronous Dark Reversion of Chromophore, Backbone and Side-chains in EL222. <em>Protein Science</em>. 2023 <a href="https://doi.org/10.1002/pro.4590">https://doi.org/10.1002/pro.4590</a></p>
FIGURE 3 in New dark species of Cribraria (Myxomycetes) from wetland ecosystem in Tatarstan (Russia)
FIGURE 3. Habitat of Cribraria gothica. A. Macrohabitat. B. Sporocarps on microhabitat in mcc, Cg = C. gothica, Cm = C. microcarpa.
FIGURE 2 in New dark species of Cribraria (Myxomycetes) from wetland ecosystem in Tatarstan (Russia)
FIGURE 2. Cribraria gothica sp. nov. (SR900130). A–C. Sporangium in habit; D. Sporangium in SEM; E. Sporocyst in SEM; F. Spores and nodes in TL; G. Spores in SEM; H. Pattern of peridium net in SEM; I. Pattern of calycle in SEM. Scale bars: A–D = 500 µm, E = 100 µm, F, H, I = 10 µm, G = 5 µm.
FIGURE 1 in New dark species of Cribraria (Myxomycetes) from wetland ecosystem in Tatarstan (Russia)
FIGURE 1. Locality of sampling. a. Satellite image of natural region. b. Borders of "Russian-German Switzerland" and location (X) of sampling. Scale bars: a = 100 km, b = 1 km.
Dark Matter-Induced Stellar Oscillations
<p>Reproduction package for the paper "Dark Matter-Induced Stellar Oscillations".</p>
FIGURE 3. Trichacis tristis. A–B in Illuminating a Dark Taxon: Revision of European Trichacis Förster (Hymenoptera Platygastridae) reveals a glut of synonyms
FIGURE 3. Trichacis tristis. A–B. Female, SMNS_Hym_Pla_000804; A. Lateral habitus; B. Dorsal habitus; C. Female, ventral habitus, SMNS_Hym_Pla_000720; D–E. Male, OSUC 45132; D. Dorsal head and mesosoma; E. Lateral habitus.
FIGURE 4 in Illuminating a Dark Taxon: Revision of European Trichacis Förster (Hymenoptera Platygastridae) reveals a glut of synonyms
FIGURE 4. Selected type specimens of Trichacis; A. T. tristis, neotype female, anterior head; B. T. tristis, neotype female, lateral habitus; C. Platygaster didas, lectotype male, dorsal habitus; D. P. pisis, lectotype male, dorsal habitus; E. P. remulus, lectotype male, dorsal habitus; F. T. vitreus, holotype female, dorsal habitus; G. T. nosferatus, holotype female, lateral habitus; H. T. weiperti, holotype female, anterior head and antenna; I. T. striata Masner, holotype female, lateral habitus image by J. Mackey, CNCI.
FIGURE 2 in Illuminating a Dark Taxon: Revision of European Trichacis Förster (Hymenoptera Platygastridae) reveals a glut of synonyms
FIGURE 2. Antennae of Trichacis and Isocybus. A. T. tristis female, SMNS_Hym_Pla_000217; B. T. tristis male, SMNS_ Hym_Pla_000220; C. Isocybus female (apical antennomere missing), SMNS_Hym_Pla_000870.
FIGURE 1 in Illuminating a Dark Taxon: Revision of European Trichacis Förster (Hymenoptera Platygastridae) reveals a glut of synonyms
FIGURE 1. Genus-level characters of Trichacis and Isocybus. A–B. Trichacis tristis female, SMNS_Hym_Pla_000797; A. Dorsal head and mesosoma, showing broad aspect of clava; B. Dorsal head showing narrow aspect of clava; C. Isocybus female, dorsal habitus, SMNS_Hym_Pla_000880; D. Isocybus female, anterior head, SMNS_Hym_Pla_000851; E. Trichacis tristis female, anterior head, SMNS_Hym_Pla_000805.
FIGURE 5. Specimens from HNHM. A in Illuminating a Dark Taxon: Revision of European Trichacis Förster (Hymenoptera Platygastridae) reveals a glut of synonyms
FIGURE 5. Specimens from HNHM. A. Amblyaspis afurcata, holotype female, lateral habitus; B. Amblyaspis hungarica, holotype female, dorsal habitus; C. Amblyaspis pannonica, holotype female, dorsal habitus; D. Amblyaspis tatika, holotype female, dorsal habitus; E. T. tristis male, lateral habitus, from collecting event of T. hajduica; F. T. tristis male, lateral habitus, from collecting event of T. fusciala; G. T. tristis female, dorsal habitus, from collecting event of T. hajduica.
FIGURE 6 in Illuminating a Dark Taxon: Revision of European Trichacis Förster (Hymenoptera Platygastridae) reveals a glut of synonyms
FIGURE 6. Maximum likelihood tree of all Trichacis barcodes available from BOLD and GBOL (n=496). Only significant branch supports are shown (SH-aLRT> 80 / ultrafast bootstrap> 95).
Parametrising non-linear dark energy perturbations
<p>This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Parametrising non-linear dark energy perturbations" (<a href="https://arxiv.org/abs/1910.01105">https://arxiv.org/abs/1910.01105</a>).</p> <p><br> This paper has also been published JCAP and can be found in Volume 2020, April 2020, which can be accessed at this link: <a href="https://iopscience.iop.org/article/10.1088/1475-7516/2020/04/039">https://iopscience.iop.org/article/10.1088/1475-7516/2020/04/039</a>.</p> <p>Directories</p> <ul> <li><strong>codes</strong>: This directory contains k-evolution code used to generate and post-process the simulation data.</li> <li><strong>notebooks_data</strong>: This directory includes the data and jupyter notebooks to reproduce the figures presented in the paper.</li> <li><strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>notebooks_data</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "<strong>notebooks_data</strong>" directory to access the simulation data.</li> </ol> <p><br> If you have any feedback or request feel free to email farbod.hassani@gmail.com</p>
Clustering dark energy imprints on cosmological observables of the gravitational field
<p>This file contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clustering dark energy imprints on cosmological observables of the gravitational field" (<a href="https://arxiv.org/abs/2007.04968">https://arxiv.org/abs/2007.04968</a>).</p> <p><br> This paper has also been published in MNRAS which can be accessed at this link: <a href="https://doi.org/10.1093/mnras/staa3589">https://doi.org/10.1093/mnras/staa3589</a>.</p> <p>Directories</p> <ul> <li><strong>codes</strong>: This directory includes the codes to generate and post-process the simulation data.</li> <li><strong>data</strong>: This directory contains the data generated as part of the project.</li> <li><strong>jupyter_notebooks</strong>: This directory includes Jupyter notebooks to reproduce the figures presented in the paper.</li> <li><strong>supplementary_materials</strong>: This directory contains the supplementary materials associated with the project.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>jupyter_notebooks</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Navigate to the "<strong>data</strong>" directory to access the simulation data.</li> </ol> <p><br> If you have any feedback or request feel free to email farbod.hassani@gmail.com</p>
Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers using Constrained Spherical Deconvolution: Data
<p>The data was simulated in GATEv8.0. The macro's and other files for the simulation can be found in the folders: Sim_50_40, Sim80_60 and Sim90_70. Here, the different files to perform the simulation are given, as well as the output data, found in the folders Flats and Projs (flatfields and projections) in the form of a csv-file. The csv-file contains 5000 rows (one for each image), and each row contains a flattened 450x450 image.</p> <p>The files for visualization of the fiber directions are given in the folder MRtrix. This folder contains the reconstructed scatter magnitudes and their respective directions in a mat-file. On this set of directions the constrained spherical deconvolution is applied using the sf_dirs.mif, sf_mask.mif, sf_response.mif and data.mif files. This returns the orientation density function in each voxel as output (odf_csd_14.mif). An alternative approach is performing a Funk-Radon transform on the coefficients of the scatter function (data_coeffs_10.mif) to obtain odf_frt_10.mif.</p> <p>Using a peak finding algorithm the fiber orientations are extracted from these odf-files and written to peaks_x.mif files, with or without a threshold (10% of largest amplitude).</p>
Clusternets: A deep learning approach to probe clustering dark energy
<p>This directory contains all the necessary data, codes, and notebooks to reproduce the results of the paper titled "Clusternets: A deep learning approach to probe clustering dark energy"</p> <p>Directories</p> <ul> <li><strong>Codes</strong>: This directory contains different codes used to generate and post-process the simulation data, including k-evolution, gevolution, Pylians, CLASS, and LATfield2.</li> <li><strong>Final figures and data</strong>: This directory includes the data for the power spectra, simulation settings and Jupyter notebooks to produce the figures.</li> </ul> <p>How to Use</p> <ol> <li>Download the files to your local machine.</li> <li>Navigate to the directory where the files are saved.</li> <li>Install the necessary packages</li> <li>Navigate to the "<strong>Final figures and data</strong>" directory and open the Jupyter notebooks in your preferred environment.</li> <li>Run the cells in the notebooks to reproduce the figures.</li> <li>Navigate to the "<strong>Codes</strong>" directory and use the appropriate code to generate and post-process the simulation data.</li> <li>Use the simulation setting files in the "<strong>Final figures and data</strong>" directory to replicate the simulations.</li> </ol> <p>Note: Some of the simulations may require high computational resources and may take a significant amount of time to run.<br> <br> If you have any feedback or request feel free to email chgeniamirsbu@gmail.com and farbod.hassani@gmail.com</p>
FIGURES 40–45. 40. Face with dark median spot. Dictya bergi Valley. 41 in Overview of the Sciomyzidae (Diptera: Sciomyzoidea) of the Americas south of the United States
FIGURES 40–45. 40. Face with dark median spot. Dictya bergi Valley. 41. Body with many dark spots at base of seta and setulae. Dorsum of Dictya bergi Valley. 42. Habitus of Dictya. Dictya sp., female. Photo by Steve Marshall. 43. Ocellar setae absent (lower arrow points to location of ocellar setae, when present, in other genera of Sciomyzidae). Postocellar setae absent (upper arrow points to location of postocellar setae, when present, in other genera of Sciomyzidae). Thecomyia chrysacra Marinoni & Steyskal. 44. Ptilinal fissure absent (arrow points to location of ptilinal fissure, when present, in other genera of Sciomyzidae). Sepedon spinipes (Scopoli) (Palearctic). Photo by J. Ebeling. From Knutson & Vala (2011). 45. One pair of scutellar setae (arrows). Thecomyia chrysacra Marinoni & Steyskal.
Dark Pattern Detection Tool Capabilities and Limitations
<p>It concludes the details information on Dark pattern tools and their coverage of detecting dark patterns.</p>
Dark pattern Data Availability
<p>It concludes the details information on Dark pattern datasets and their coverage of dark patterns instances.</p>
Collider Datasets for Simplified Dark Matter models
<p>This is a set of datasets containing MonoJet and DiJet interpolation grids. When using the simplified dark matter models in gambit, the backend "DMsimp_data" must be made first, which will download these datasets to the correct folder.</p> <p>If using monojet data outside of GAMBIT, please use the files in the DMsimp_monojet_data folder, which have been formatted with appropriate headings. Vector DM data is not included in this additional folder due to additional unitarity considerations required to use the data.</p> <p>If using this data, please cite this zenodo record, along with the gambit simplified model study (https://arxiv.org/abs/2209.13266).</p>
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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.