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773 results for “bounds”
Experimental microcosm incubations assessing the effect of hypoxia on aqueous iron and organic carbon, pH, sediment organic carbon, and sediment iron-bound organic carbon
To assess the effect of changing oxygen concentrations on coupled carbon and iron cycling in freshwater ecosystems, we performed 6-week microcosm incubations. Incubations were inoculated with sediment and water from Falling Creek Reservoir, Vinton, VA, USA. We started the experiment with 102 microcosms split evenly into oxic and hypoxic treatments. After two weeks, we switched the treatment of approximately half of the remaining microcosms, generating a total of four oxygen regimes: hypoxic, oxic, hypoxic to oxic, and oxic to hypoxic. We sampled the microcosms destructively approximately twice per week, collecting aqueous samples for total and dissolved carbon and iron, as well as sediment samples for organic carbon and iron-bound organic carbon analysis. Iron-bound organic carbon was determined using citrate-bicarbonate-dithionite extractions.
Total organic carbon, total nitrogen, and iron-bound organic carbon in surficial sediment and settling particulate material from Falling Creek and Beaverdam Reservoirs in 2019 and 2021
This dataset includes measurements of sediment properties (total organic carbon, total nitrogen, and iron-bound organic carbon) in surficial sediment and sedimenting material from two reservoirs: Falling Creek and Beaverdam Reservoirs, both located in Vinton, VA, USA. To measure surficial sediment properties, sediment cores were collected at the deepest site in each reservoir using a gravity corer, and the top 1 cm was frozen then lyophilized. Sediment cores were collected approximately once per month in both reservoirs throughout the stratified period (May–November) in 2019 and 2021, though sampling frequency and duration varied by reservoir and year. Sedimenting material was sampled using sediment traps suspended approximately 1 m above the sediment in both reservoirs. Iron-bound organic carbon was measured using the citrate-bicarbonate-dithionite method, and we used a CN analyzer (Elementar VarioMax, Ronkonkoma, NY, USA) to determine the amount of OC per unit mass of sediment.
Raw diffraction data (CBF) for a structure of SARS-CoV-2 Main Protease bound to 2-Methyl-1-tetralone
<p>Data collected at beamline P11/PETRAIII Deutsches Elektronen Synchrotron DESY</p> <p>Info:</p> <p>run type: regular<br> run name: l6p17_09_001<br> start angle: 0.000000deg<br> frames: 1000<br> degrees/frame: 0.200000deg<br> exposure time: 40.000000ms<br> energy: 11.999832keV<br> wavelength: 1.033214A<br> detector distance: 200.000000mm<br> resolution: 1.304257A<br> aperture: 100um<br> filter transmission: 71.798748%<br> filter thickness: 75um<br> ring current: 119.222664mA</p> <p>Crystal-info:</p> <p>Co-crystallization of Sars-CoV-2 MPro with the compound was achieved by equlibrating a 6.25 mg/ml protein solution in 20 mM HEPES buffer (pH 7.8) containing 1 mM DTT, 1mM EDTA, and 150 mM NaCl against a reservoir solution of 100 mM MIB buffer (2:3:3 molar ratio of malonic acid, imidazole, and boric acid), pH 7.5, containing 25% v/v PEG 1500 and 5% v/v DMSO. Prior to crystallization compound solutions in DMSO were dried onto the wells of SwissCI 96-well plates. To achieve reproducible crystal growth seeding was used. Crystals appeared within a few hours and reached their final size after 2 -3 days. Crystals were manually harvested and flash cooled in liquid nitrogen for subsequent X-ray diffraction data collection.</p>
Supplementary Data: Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments (arXiv:2009.03287)
<p><strong>Supplementary Data</strong></p> <p><em>Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments (arXiv:2009.03287)</em></p> <p>The files in this record contain data from the scans of the models considered in the <a href="http://gambit.hepforge.org">GAMBIT</a> paper on neutrino masses.</p> <p>The files consist of</p> <ul> <li>21 <code>.yaml</code> files corresponding to different models, sampling parameters and/or priors</li> <li>11 final <code>.hdf5</code> files, containing the results of running GAMBIT with each yaml file</li> <li>10 <code>.margestats</code> files containing 1D credible regions for parameters and observables, obtained by running <a href="https://github.com/cmbant/getdist">getdist</a> on the hdf5 files</li> <li>An example file 3-NHB_Neff2_PC500_pp.pip file for plotting the results of a single hdf5 file with <a href="github.com/patscott/pippi">pippi</a></li> <li>A tarball including all files in this record except the hdf5 files.</li> </ul> <p>The different yaml, hdf5 and margestats files corresponding to different models, priors or setttings follow the naming scheme <code>[scan index]-[hierarchy][m_nu0 prior]_[Neff prior]_[scanner]_[extra]_[step].[extension]</code>, where</p> <ul> <li>scan index = <code>1</code>-<code>11</code></li> <li>hierarchy = <code>NH</code> (normal hierarchy), <code>IH</code> (inverted hierarchy)</li> <li>m_nu0 pior = <code>A</code> (linear-log prior on m_nu0), <code>B</code> (linear prior on m_nu0)</li> <li>Neff prior = <code>0</code> (Delta N_eff = 0), <code>1</code> (Delta N_eff > 0), <code>2</code> (Delta N_eff free)</li> <li>scanner = <code>PC500</code> (Polychord with 500 live points), <code>DIV10k</code> (Diver with NP=1e4)</li> <li>extra = blank (standard likelihood combination), <code>Lyalpha</code> (likelihood also includes eBOSS DR14 Lyman-alpha BAO scale measurements)</li> <li>step = blank (main scan), <code>pp</code> (postprocessing of outputs of main scan).</li> <li>extension = <code>yaml</code>, <code>margestats</code>, <code>hdf5/hdf5.tar.gz</code></li> </ul> <p>A few caveats to keep in mind:</p> <ol> <li> <p>The YAML files are designed to work with the tagged release of GAMBIT 1.5.0, and the pip file is tested with pippi 2.1. They may or may not work with later versions of either software (but you can of course always obtain the version that they do work with via the git history).</p> </li> <li> <p>The pip file is an example only. Users wishing to reproduce the more advanced plots in any of the GAMBIT papers should contact us for tips or scripts, or experiment for themselves. Many of these scripts are in multiple parts and require undocumented manual interventions and steps in order to implement various plot-specific customisations, so please do not expect the same level of polish as for files provided here or in the GAMBIT repo.</p> </li> </ol>
Data: "Solute transport in bounded porous media (...)" by Sole-Mari et al. (2020)
<p>Data corresponding to the results of the Monte Carlo set of simulations described in: "Solute transport in bounded porous media characterized by Generalized Sub-Gaussian log-conductivity distributions". Please send questions to guillem.sole.mari@outlook.com.</p>
All-atom Molecular Dynamics Simulations of SARS-CoV-2 Spike Receptor-binding Domain bound with ACE2
<p>Data includes all of the trajectories (1000) of classical all-atom molecular dynamics (MD) simulations of of SARS-CoV2 Spike Protein/ACE2 complex (PDB ID: 6M0J). In order to decrease the size of the file only protein rajectories were provided. Simulation has been performed with Desmond. Protein was placed in the cubic boxes with explicit TIP3P water models that have 10.0 Å thickness from surfaces of protein. The system is neutralized by adding counter ions, and salt solution of 0.15M NaCl was also used to adjust the concentration of the systems. The long-range electrostatic interactions were calculated by the particle mesh Ewald method. A cutoff radius of 9.0 Å was used for both van der Waals and Coulombic interactions. The temperature was set as 310K initially, and Nose–Hoover thermostat was used for adjustment. Martyna–Tobias–Klein protocol was employed to control the pressure, which was set at 1.01325 bar. The time-step was assigned as 2.0 fs. The default values were used for minimization and equilibration steps, and finally 100 ns production run was performed for the simulation.</p>
A crowdsourced sentence-bound chemical-induced disease relationship corpus
<p>A set of 3000 abstracts from PubMed were annotated for sentence-bound chemical-induced disease relationships in order to train a machine learning algorithm for the BioCreative V challenge.</p>
The Quadratic Zeeman effect used for state-radius determination in neutral donors and donor bound excitons in Si:P.
<p>Raw experimental data of Photoluminescence as a function of magnetic field for phosphorus impurity in silicon at 4.2K. First column is energy in meV, the other columns are the photo-luminescence intensities in arbitrary units measured at different magnetic fields. The first row indicates the values of the magnetic fields presented in each column. The photo-luminescence measured at 10T (and presented in this dataset as column 11) is shown in the paper as Fig.2. </p>
X-ray diffraction images for 5-aminolevulinic acid dehydratase with a putative reaction intermediate resembling the product porphobilinogen bound.
<p>X-ray diffraction images for yeast 5-aminolevulinic acid dehydratase co-crystallised with the substrate 5-aminolevulinic acid. The structure demonstrated a putative product-like intermediate bound covalently to Lys 263 with an amino side chain ligated to the active-site zinc ion in a position normally occupied by a catalytic hydroxide ion. The data were collected in two passes using the ESRF beamline ID29 in Feb 2002 and extend to approximately 1.6 Å resolution. </p>
Error bounds for kernel-based approximations of the Koopman operator
<p>This repository contains python scripts and data to re-create the result shown in</p><p>`Error bounds for kernel-based approximations of the Koopman operator, arxiv:2301.08637`</p><p>See README for detailed instructions on how to re-create these data.</p>
Pockmark Bounding Box Detection and Segmentation Labels
<p>This dataset contains 256x256 pixel jpeg images of gridded depth values as well as binary masks for pixels containing pockmarks (these jpegs are merged together with the depth image on the left and the mask on the right, making a 512x256 image). These are contained within the subdirectory 'PockmarkMaskAnnotations'.</p> <p>Additionally, this dataset contains a csv containing bounding box annotations (label, bounding box coordinates in terms of image pixels, and a unique integer for the label) of pockmarks in each image. These are contained within the subdirectory 'PockmarkBoxAnnotations'.</p> <p>Together, these annotations can be used to construct either a bounding box object detector, a bounding box and mask object detector, or a semantic segmentation model.</p> <p>These were the labels used for the experiments described in Lundine et al., 2023. See this reference to find original data sources to the collected bathymetry data.</p> <p>Lundine, M., Brothers, L., Trembanis, A., Deep learning-based pockmark detection: implications for quantitative seafloor characterization, Geomorphology, 2023, Volume 421, 108524, <a href="https://doi.org/10.1016/j.geomorph.2022.108524">https://doi.org/10.1016/j.geomorph.2022.108524</a>.</p> <p> </p>
Data from: Sorting states of environmental DNA: Effects of isolation method and water matrix on recovery of membrane-bound, dissolved, and adsorbed states of eDNA
<p>Environmental DNA (eDNA) once shed can exist in numerous states with varying behaviors including degradation rates and transport potential. In this study we consider three states of eDNA: 1) a membrane-bound state referring to DNA enveloped in a cellular or organellar membrane, 2) a dissolved state defined as the extracellular DNA molecule in the environment without any interaction with other particles, and 3) an adsorbed state defined as extracellular DNA adsorbed to a particle surface in the environment. Capturing, isolating, and analyzing a target state of eDNA provides utility for better interpretation of eDNA degradation rates and transport potential. While methods for separating different states of DNA have been developed, they remain poorly evaluated due to the lack of state-controlled experimentation. We evaluated the methods for separating states of eDNA from a single sample by spiking DNA from three different species to represent the three states of eDNA as state-specific controls. We used chicken DNA to represent the dissolved state, cultured mouse cells for the membrane-bound state, and salmon DNA adsorbed to clay particles as the adsorbed state. We performed the separation in three water matrices, two environmental and one synthetic, spiked with the three eDNA states. The membrane-bound state was the only state that was isolated with minimal contamination from non-target states. The membrane-bound state also had the highest recovery (54.11 ± 19.24 %), followed by the adsorbed state (5.08 ± 2.28 %), and the dissolved state had the lowest total recovery (2.21 ± 2.36 %). This study highlights the potential to sort the states of eDNA from a single sample and independently analyze them for more informed biodiversity assessments. However, further method development is needed to improve recovery and reduce cross-contamination.</p>
bioRxiv 10k figure bounding boxes
<p>This dataset contains figure bounding boxes corresponding to the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>.</p> <p>It provides annotations in two formats:</p> <ul> <li><a href="https://cocodataset.org/#format-data">COCO format (JSON)</a></li> <li>JATS XML with <a href="https://github.com/kermitt2/grobid/blob/0.7.0/doc/Coordinates-in-PDF.md">GROBID's "coords" attribute</a></li> </ul> <p>The COCO format contains bounding boxes in rendered pixel units, as well as PDF user units. The latter uses field names with the "pt_" prefix.</p> <p>The "coords" attribute uses the PDF user units.</p> <p>The dataset was generated by using an algorithm to find the figure images within the rendered PDF pages. The main algorithm used for that purpose is <a href="https://en.wikipedia.org/wiki/Scale-invariant_feature_transform">SIFT</a>. As a fallback, <a href="https://docs.opencv.org/4.5.3/d4/dc6/tutorial_py_template_matching.html">OpenCV's Template Matching</a> (with multi scaling) was used. There may be some error cases in the document. Very few documents were excluded, were neither algorithm was able to find any match for one of the figure images (six documents in the train subset, two documents in the test subset).</p> <p>Figure images may appear next to a figure description, but they may also appear as "attachments". The latter usually appears at the end of the document (but not always) and often on pages with dimensions different to the regular page size (but not always).</p> <p>This dataset itself doesn't contain any images. The PDF to render pages can be found in the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>.</p> <p>The dataset is intended for training or evaluation purposes of the semantic Figure extraction. The evaluation score would be calculated by comparing the extracted bounding boxes with the one from this purpose. (example implementation <a href="https://github.com/elifesciences/sciencebeam-judge">ScienceBeam Judge</a>)</p> <p>The dataset was created as part of <a href="https://elifesciences.org/">eLife</a>'s <a href="https://github.com/elifesciences/sciencebeam">ScienceBeam</a> project.</p>
Optimized Coefficients for the Generalized Karagiannidis–Lioumpas Approximations and Bounds to the Gaussian Q-Function
<p>This is a supplementary dataset for the publication:</p> <p>I. M. Tanash and T. Riihonen, "Generalized Karagiannidis–Lioumpas Approximations and Bounds to the Gaussian Q-Function with Optimized Coefficients," in<em> IEEE Communications Letters</em>, in press.</p> <p>The dataset contains the sets of the optimized coefficients for the novel GKL minimax approximations and bounds of the Gaussian Q-function, and the optimized coefficients for the GKL approximations in terms of the total error. The corresponding optimized coefficients are found up to 10 terms (N=10) for the two variations of the absolute error and for the relative error in terms of the minimax and the total errors.</p> <p>The Matlab function (func_extract_coef.m) extracts the required set of optimal coefficients from the provided dataset according to the selected optimization_criterion, error_type, number of terms, the bound or approximation type, and the variation. See help func_extract_coef for more information.</p> <p>A Matlab script (Example.m) is also provided as an example to illustrate the use of the provided Matlab function in extracting the required coefficients from the dataset, to calculate and plot the corresponding minimax absolute error function which is shown by figure Example.jpg. Another example is given in the same script to extract the coefficients of the total relative error.<br> </p>
Used portable batteries with bounding boxes
<p>This dataset contains images and labels of used portable batteries. Battery types are:</p> <ol> <li>Lithium-ion (LIION, class 0) </li> <li>Lithium polymer (LIPO, class 1) </li> <li>Lead-acid (PB, class 2)</li> <li>Nickel-cadmium (NICD, class 3)</li> <li>Nickel-metal hydride (NIMH, class 4)</li> </ol> <p>Each image contains multiple batteries of the same type. The file name defines the type (e.g. LIPO_IMG_4920.JPG is an image with multiple lithium polymer batteries). Each image file is accompanied by a label file (*.txt) with identical name except the file name extension. Each row in the label file contains a class label (0 to 4) followed by bounding box coordinates in YOLO format.</p> <p>This dataset was produced in Horizon 2020 funded project <a href="https://trinityrobotics.eu">TRINITY</a>.</p>
Sampled ΔH/Δλ and ΔH data from ABFE calculations of 19 ligands bound to MCL-1
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Included are the ABFE raw free energy samples for multiple replicates (labelled by `run` number) of 19 ligands to bound MCL-1. These ligands are originally detailed by Friberg et al. (https://doi.org/10.1021/jm301448p).</p> <p>All samples are provided as a set of `.xvg` files as generated by GROMACS 2021 (https://doi.org/10.5281/zenodo.5849961). The `.xvg` files are labelled as dhdl.N.xvg where N represents the λ state the free energy values were sampled from. The `.xvg` files contain both ΔH/Δλ and ΔH values, please see the header of each files for more information.</p> <p>Samples detailing the partial decoupling of the ligand from the protein-ligand complex are contained within the `complex` folder. These consist of an orientational restraint addition step (found within the `restraints-xvg` folders), charge annihilation step (found within the `coul-xvg` folders), and Van der Waals decoupling step (found within the `vdw-xvg` folders).</p> <p>Samples detailing the partial decoupling of the ligand from solvent are contained within the `ligand` folder and consist of a charge annihilation step (found within the individual `coul-xvg` folders) and a Van der Waals decoupling step (found within the individual `vdw-xvg` folders).</p>
Sampled ΔH/Δλ and ΔH data from ABFE calculations (using standard atomic masses) of 10 ligands bound to Cyclophilin D
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Included are the ABFE raw free energy samples for multiple replicates (labelled by `run` number) of 10 ligands to bound Cyclophilin D. These ligands are originally detailed by Grädler et al. (https://doi.org/10.1016/j.bmcl.2019.126717). Unlike other datasets in this work, which employed hydrogen mass repartitioning, the ligands here were calculated using standard atomic masses.</p> <p>All samples are provided as a set of `.xvg` files as generated by GROMACS 2021 (https://doi.org/10.5281/zenodo.5849961). The `.xvg` files are labelled as dhdl.N.xvg where N represents the λ state the free energy values were sampled from. The `.xvg` files contain both ΔH/Δλ and ΔH values, please see the header of each files for more information.</p> <p>Samples detailing the partial decoupling of the ligand from the protein-ligand complex are contained within the `complex` folder. These consist of an orientational restraint addition step (found within the `restraints-xvg` folders), charge annihilation step (found within the `coul-xvg` folders), and Van der Waals decoupling step (found within the `vdw-xvg` folders).</p> <p>Samples detailing the partial decoupling of the ligand from solvent are contained within the `ligand` folder and consist of a charge annihilation step (found within the individual `coul-xvg` folders) and a Van der Waals decoupling step (found within the individual `vdw-xvg` folders).</p>
Sampled ΔH/Δλ and ΔH data from ABFE calculations of 12 ligands bound to PWWP1
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Included are the ABFE raw free energy samples for multiple replicates (labelled by `run` number) of 12 ligands to bound PWWP1. These ligands are originally detailed by Böttcher et al. (https://doi.org/10.1038/s41589-019-0310-x).</p> <p>All samples are provided as a set of `.xvg` files as generated by GROMACS 2021 (https://doi.org/10.5281/zenodo.5849961). The `.xvg` files are labelled as dhdl.N.xvg where N represents the λ state the free energy values were sampled from. The `.xvg` files contain both ΔH/Δλ and ΔH values, please see the header of each files for more information.</p> <p>Samples detailing the partial decoupling of the ligand from the protein-ligand complex are contained within the `complex` folder. These consist of an orientational restraint addition step (found within the `restraints-xvg` folders), charge annihilation step (found within the `coul-xvg` folders), and Van der Waals decoupling step (found within the `vdw-xvg` folders).</p> <p>Samples detailing the partial decoupling of the ligand from solvent are contained within the `ligand` folder and consist of a charge annihilation step (found within the individual `coul-xvg` folders) and a Van der Waals decoupling step (found within the individual `vdw-xvg` folders).</p>
Sampled ΔH/Δλ and ΔH data from ABFE calculations of 10 ligands bound to Cyclophilin D
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Included are the ABFE raw free energy samples for multiple replicates (labelled by `run` number) of 10 ligands bound to Cyclophilin D. These ligands are originally detailed by Grädler et al. (https://doi.org/10.1016/j.bmcl.2019.126717). Unlike the other Cyclophilin D<br> dataset provided in this work, simulations for these ABFE calculations were carried out using hydrogen mass repartitioning (HMR). The MCL-1, HSP90, and PWWP1 also used HMR.</p> <p>All samples are provided as a set of `.xvg` files as generated by GROMACS 2021 (https://doi.org/10.5281/zenodo.5849961). The `.xvg` files are labelled as dhdl.N.xvg where N represents the λ state the free energy values were sampled from. The `.xvg` files contain both ΔH/Δλ and ΔH values, please see the header of each files for more information.</p> <p>Samples detailing the partial decoupling of the ligand from the protein-ligand complex are contained within the `complex` folder. These consist of an orientational restraint addition step (found within the `restraints-xvg` folders), charge annihilation step (found within the `coul-xvg` folders), and Van der Waals decoupling step (found within the `vdw-xvg` folders).</p> <p>Samples detailing the partial decoupling of the ligand from solvent are contained within the `ligand` folder and consist of a charge annihilation step (found within the individual `coul-xvg` folders) and a Van der Waals decoupling step (found within the individual `vdw-xvg` folders).<br> </p>
Sampled ΔH/Δλ and ΔH data from ABFE calculations of 18 ligands bound to HSP90
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Included are the ABFE raw free energy samples for multiple replicates (labelled by `run` number) of 18 ligands bound to HSP90. These ligands are originally detailed by Murray et al. (https://doi.org/10.1021/jm100059d).</p> <p>All samples are provided as a set of `.xvg` files as generated by GROMACS 2021 (https://doi.org/10.5281/zenodo.5849961). The `.xvg` files are labelled as dhdl.N.xvg where N represents the λ state the free energy values were sampled from. The `.xvg` files contain both ΔH/Δλ and ΔH values, please see the header of each files for more information.</p> <p>Samples detailing the partial decoupling of the ligand from the protein-ligand complex are contained within the `complex` folder. These consist of an orientational restraint addition step (found within the `restraints-xvg` folders), charge annihilation step (found within the `coul-xvg` folders), and Van der Waals decoupling step (found within the `vdw-xvg` folders).</p> <p>Samples detailing the partial decoupling of the ligand from solvent are contained within the `ligand` folder and consist of a charge annihilation step (found within the individual `coul-xvg` folders) and a Van der Waals decoupling step (found within the individual `vdw-xvg` folders).</p>
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.