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3,630 results for “Forming”
Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms
<p>Dataset and script used for the publication <em>Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms, </em>doi (to be determined).</p>
Unveiling the potential of redox chemistry to form size tunable, high index silicon particles
<p>In the present work, the effect of changing the precursor ratio of silicon between sodium silicde and a hexacoordinated silicon complex to form various sizes of particles is studied. TEM images show the size difference between particles produced with different ratios. Particles produced with a 1:1 ratio are 45 nm in diameter and up to a 1:4 precursor ratio is used to make 230 nm particles. X-ray diffraction patterns confirm the presence of crystalline silicon for all sizes, while Raman spectroscopy shows how different degree of oxidation occurs thanks to different particle sizes, shifting the Raman peak. The surface chemistry is also studied to evidence the growth mechanism.</p>
Point-frame measurement of maximum canopy height for plant growth forms at the 2007 Anaktuvuk River Fire scar measured in 2019.
This file contains maximum plant heights from point frame measurements made in the southern section of the 2007 Anaktuvuk River fire scar, at a severely burned site and a nearby unburned site. Pin-vegetation contact was recorded using a 0.56 m2 frame with 41 evenly spaced sampling points. Data were collected during peak green in summer 2019. These data were used to examine the impact of post-fire changes in plant community composition and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaska moist acidic tussock tundra.
MCR LTER: Coral Reef: Growth-predation risk trade-offs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats; Data for Ladd et al., 2025, Scientific Reports.
This dataset is in support of the manuscript: Growth-predation risk tradeoffs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats. These data were collected to 1) document how Acropora pulchra is distributed around the island of Moorea, and 2) to better understand the ecological processes that shape that distribution. Data include 1) results from surveys around the island of Moorea documenting the presence and size distribution of Acropora pulchra thickets, 2) results from an experiment measuring the growth and survivorship of Acropora pulchra fragments in the presence and absence of fish predators at nearshore fringing reef sites and adjacent sites in the mid lagoon (n = 20 sites in total), and 3) ancillary data on nitrogen content and dN15 in the tissue of the macroalgae Turbinaria ornata, sediment accumulation, and corallivore biomass at the experimental sites. All data were collected in 2016 and 2017.
Data and R code for Tansley review New Phytologist 2021: "An integrated framework of plant form and function: The belowground perspective"
<p>The files in this archive are related to the paper of Weigelt, Mommer, Andraczek et al. (2021) An integrated framework of plant form and function: The belowground perspective. Tansley Review New Phytologist. The paper developed and tested a new conceptual framework of plant form and function linking above and belowground traits of 2510 species. We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth.</p> <p>This archives contains four files:</p> <ol> <li><strong>Weigelt et al.2021RCode.DataCleaning.txt</strong> - RCode for the complete data processing starting with the downloaded database files from the Plant Trait Database version 5.0 (TRY, Kattge et al. 2020), the Global Root Trait database (GRooT, Guerrero-Ramirez et al. 2020) and a small number of additional data files listed in Table S2 of the original paper. Additional information was later incorporated using FungalRoot Database (Soudzilovkaia et al. 2020), nodDB Database (Tedersoo et al. 2018) and a compiled dataset on rooting depth (Fan et al. 2017). The code processes, cleans and merges the data and produces a final table for PCA analysis of species specific mean traits. This final table is provided as a second file in this archive (Weigelt_et_al_2021_Main.PCA.Matrix.xlsx). A second part of the RCode.DataCleaning extracts species-specific individual trait data where root and shoot traits were measured on the same plant individual or plot. This data was compiled from 43 studies identified in Table S2 of the original publication. The final table for individual trait data is the third file in this archive (Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx).</li> <li><strong>Weigelt_et_al_2021_Main.PCA.Matrix.xlsx</strong> – Datafile with species-specific global mean trait data for 17 traits of 2510 species with at least one root and one shoot trait available. Meta-data is provided in the data file.</li> <li><strong>Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx</strong> – Datafile with species-specific trait data where root and shoot traits were measured on the same individual or plot for 6 traits of 455 species. Meta-data is provided in the data file.</li> <li><strong>Weigelt et al.2021RCode.Analysis.txt – </strong>RCode for all analyses and figures provided in the paper for both the species mean and individual based dataset. The Code is annotated to help reproducibility of the analysis.</li> </ol>
Longitudinal urban form dataset of Midtown Manhattan: Measuring urban form evolution via quantitative descriptions of plots, buildings and streets from 1890 to the present
<p>This dataset contains data described and used in the research article <strong>"The impact of urban form on physical change: A quantitative and diachronic analysis of urban form evolution in Midtown Manhattan"</strong>. </p> <p>The longitudinal dataset contains urban form data on nearly 17,000 individual plots (parcels) in Midtown Manhattan, documented through four subsequent time frames: 1890, 1920, 1956 and 2021. The data was compiled from historical cartographic resources and open-access geospatial datasets listed in the ReadMe file. </p> <p>The dataset includes an array of quantitative descriptions of plots, buildings and streets central to the field of urban morphology, and the binary information of physical change (1: change, 0: no change) identified via diachronic comparison of each time frame at the scale of plots.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The dataset presented in this repository has been generated as part of a PhD research conducted at the University of Melbourne, Faculty of Architecture, Building and Planning and funded by the University of Melbourne - Melbourne Research Scholarship: </p> <p><strong>Tümtürk, O</strong>. (2024). <strong>A data-driven investigation on urban form evolution: Methodological and empirical support for unravelling the relation between urban form and spatial dynamics</strong>. Unpublished PhD Thesis. The University of Melbourne, Australia. </p>
Wollestraat 29, Bruges (BE): high-resolution images of dry wood cores taken form a medieval floor joists, for tree-ring analysis
<ul><li>Dry-wood cores taken from historical timbers of a floor joists in the medieval building 'De Oude Steen', Wollestraat 29, Bruges (Belgium).</li><li><a href="https://id.erfgoed.net/erfgoedobjecten/29956 ">https://id.erfgoed.net/erfgoedobjecten/29956 </a></li><li>The cores were sampled at 22/02/2023 with a dry-wood borer (internal diameter 12 mm, external diameter 19 mm).</li><li>The cores were surfaced with increasingly finer sanding papers, from P60 up to P4000.</li><li>The cores were photograpphed with a Sony alpha7R IV full frame camera and FE 90 mm F/2.8G macro lens.</li><li>The<a href="https://www.wsl.ch/en/services-produkte/skippy/"> Skippy</a> system served as the image capturing platform.</li><li>The individual digital macro-photos were stitched with PTGui into a mosaic image (.tiff).</li><li>The mosaic images have a resolution of ~4 µm.</li></ul>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
Raw Data for "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction"
<p>This upload contains the raw data used for Fig. 3-5 in "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction". Experimental conditions and details about the datasets are given in a "ReadMe.txt" file.</p>
Hunting for vampires and other unlikely forms of parity violation at the Large Hadron Collider: lhe files
<p>Parton-level simulations in the PV-mSME model with various values of lambdaPV and for the standard model (lambdaPV=0).</p> <p>This dataset includes one example lhe file generated by MadGraph for each sample used in the <a href="https://arxiv.org/abs/2205.09876">paper</a>.<br> <br> The MadGraph version and the modifications we make to its generated code are included in the code sharing dataset on <a href="https://github.com/Rupt/paper-hunting-vampires">git</a> and <a href="https://doi.org/10.5281/zenodo.6827723">Zenodo</a>.<br> <br> Main files are named `liv_3j_4j_${lambdaPV}_0.lhe.gz`, where lambdaPV is a floating point number with "." replaced with "p".<br> <br> Other files named `liv_rot_${hour}_0.lhe.gz ` are from the rotated PV-mSME from the appendix that studies the effect of a rotating planet. Each rotation in radians is <em>hour * 2 pi / 24 </em>(for discrete rotations in a 24 hour day).<br> <br> The suffix "_0" encodes that each was generated with the first in our sequence of random seeds.</p> <p>`sm_3j_4j_0.lhe.gz` is simulated from the Standard Model, which is physically equivalent to lambdaPV = 0.</p>
Arquivos input-forms configurados para incorporação de acervos audiovisuais ao DSpace
<p>Arquivos <em>input-forms</em> em xml e txt configurados para incorporação de acervos audiovisuais ao software DSpace, fazendo parte de um dos resultados de pesquisa da dissertação intitulada Gestão de acervos audiovisuais em repositórios, sob orientação da Prof.ª Dra. Maria Giovanna Guedes Farias e coorientação do Prof. Dr. Luiz Tadeu Feitosa.</p>
The Archaeological Ceramics from Mahurjhari: Vessel Forms by Stratigraphic Layer
<p>This spreadsheet contains the quantities of each type of vessel form found in each stratigraphic layer of each excavated trench at Mahurjhari, India. The data is arranged according to the excavated trenches. For each trench, the combined MNI count and number of bases are presented.</p> <p> </p>
Archaeological Ceramics from Mahurjhari: the Vessel Forms
<p>A descriptive and illustrated list of the vessel forms identified and defined during the analysis of archaeological ceramics in the excavated assemblage from the site at Mahurjhari, excavated by the Deccan College, Pune 2000-2003.</p>
Trellis-forming stems of a tropical liana Condylocarpon guianense (Apocynaceae): a plant-made safety net constructed by simple "start-stop" development
<p>Data supporting article describing mechanical and structural organisation of a climin g plant trellis system sin the tropical rainforest of French Guiana</p> <p>Tropical vines and lianas have evolved mechanisms to avoid mechanical damage during their climbing life histories. We explore the mechanical properties and stem development of a tropical climber that develops trellises in tropical rain forest canopies. We measured the young stems of <em>Condylocarpon guianensis</em> (Apocynaceae) that construct complex trellises via self-supporting shoots, attached stems and unattached pendulous stems. The results suggest that in this species there is a size (stem diameter) and developmental threshold at which plant shoots will make the developmental transition from stiff young shoots to later flexible stem properties. Shoots that do not find a support remain stiff, becoming pendulous and retaining numerous leaves. The formation of a second TYPE II (lianoid) wood is triggered by attachment, guaranteeing increased flexibility of light-structured shoots that transition from self-supporting searchers to inter-connected net-like trellis components. The results suggest that this species shows a “hard-wired” development that limits self-supporting growth among the slender stems that make up a liana trellis. The strategy is linked to a stem-twining climbing mode and promotes a rapid transition to flexible trellis elements in cluttered densely branched tropical forest habitats. These are situations that are prone to mechanical perturbation via wind action, tree falls and branch movements. The findings suggest that some twining lianas are mechanically fine-tuned to produce trellises in specific habitats. Trellis building is carried out by young shoots that can perform very different functions via subtle development changes in order to ensure a safe space occupation of the liana canopy.</p>
EPOCHAL (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms): Nurse home visit form (English and German)
<p>This questionnaire was developed for the EPOCHAL study (Effects of Pollen on Cardiorespiratory Health and Allergic symptoms). The study was sponsored and led by Swiss TPH in Basel, Switzerland and approved by the local ethics committee (Ethikkomission Nordwest- und Zentralschweiz EKNZ, project ID 2021-00151). Written informed consent was obtained from every participant prior to study inclusion. </p> <p>This is the "nurse home visit form" which was administered 6 times for each participant during weekly home visits by our study nurses during the pollen season. It includes questions about the following topics:<br> 1) Potential for Covid-19 infection, changes in vaccination status<br> 2) Overall health status<br> 3) Allergic symptoms: nose, eyes, lungs<br> 4) Sleep, mood and quality of life<br> 5) Medication use<br> 6) Time spent outdoors<br> 7) Daily covariate information: coffee and alcohol intake, eating, smoking, vigorous exercise<br> 8) Blood pressure measurements<br> 9) Heart rate variability recording<br> 10) Exhaled nitric oxide measurements<br> 11) Pulmonary function testing (spirometry)<br> 12) Comments</p> <p>The questionnaire is also available in German under the same DOI.</p> <p>Please note that this questionnaire was administered electronically on a tablet, and contains:</p> <ul> <li>Form logic, which determines the relevance of some questions based on previous answers. Affected questions are typically shown in grey color.</li> <li>Instructions (in bold blue font) to the participant/study nurse to guide the process of data collection (e.g. “please hand over the tablet to the participant/nurse”).</li> <li>Warnings (in large red font) and directions (in large grey font) to warn nurses against performance of spirometry measurements if contraindications were present. For example, when the nurse entered high blood pressure in Topic #8, or when recent surgery was indicated in Topic #11. Warnings and directions also flag incidental findings (e.g., high blood pressure ≥160 mmHg (systolic) or ≥100 mmHg (diastolic) requiring urgent action.</li> </ul>
Water in the terrestrial planet-forming zone of the PDS 70 disk
<p>This release includes the portion of the JWST-MIRI MRS spectrum of the PDS 70 disk analysed in the paper by Perotti et al. (2023). The original observational data are part of the Guaranteed Time Observation (GTO) program 1282 (PI: Th. Henning) with observation number 66 and will become public on 2 August, 2023 on the MAST database (https://archive.stsci.edu/). This release contains:<br> <br> 1) the full rebinned (4.9-22.5 μm) JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023);<br> 2) the JWST-MIRI MRS spectrum of PDS 70 in the 6.78-7.36 μm region used for the water line analysis shown in Fig. 3 of Perotti et al. (2023);<br> 3) the Spitzer-IRS low-resolution spectrum observed as part of the Spitzer-IRS GTO program 40679 (PI: G. Rieke) shown in Fig.1 of Perotti et al. (2023). </p> <p>The first dataset consists of one .csv file (1_PDS70_fig2_MIRI_Perotti23.csv) which contains the the 4.9-22.5 μm JWST-MIRI MRS spectrum of PDS 70 presented in Fig. 2 of Perotti et al. (2023). The spectrum is rebinned by averaging 15 spectral points and assign errors σ to the rebinned spectral points assuming a normal error distribution with equal weights for each individual spectral element.<br> <br> The second dataset consists of one .dat file and one python script. One .dat file contains the JWST-MIRI spectrum of PDS 70 in the 6.78-7.36 μm region shown in Fig. 3 of Perotti et al. (2023) where the brightest water emission lines are observed (2_PDS70_fig3_MIRI_Perotti23.dat). The JWST-MIRI continuum-subtracted spectrum and the best-fit water LTE slab model are included. The Python script used to reproduce Figure 3 of Perotti et al. (2023) is also provided (2_script_plot_fig3.py). <br> <br> The third dataset consists of one .dat file (3_PDS70_fig1_IRS_Perotti23.dat) which represents the Spitzer-IRS low-resolution spectrum of PDS 70 shown in Figure 1 of Perotti et al. (2023).</p>
Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN
<p>This repository contains the data released in the paper 'Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN' <em>(DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>).</em></p> <p>We release a detailed catalogue of Giant Star-forming Clumps (GSFCs), detected for the full set of Galaxy Zoo: Clump Scout galaxies observed by SDSS using the Faster R-CNN architecture with the Zoobot classification-CNN as a feature extraction backbone.</p> <p>The final models and code are made publicly available via Github: <a href="https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout">https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout</a>.</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI: <a href="https://doi.org/10.1093/rasti/rzae013">10.1093/rasti/rzae013</a>) when using the data in this repository.</p> <p>The csv-file <em>FRCNN_Zoobot_SDSS_GZCS_detections.csv</em> has the following columns. Alternatively, the file <em>FRCNN_Zoobot_SDSS_GZCS_detections.gzip</em> contains the same data but stored as a parquet-file.</p> <table> <tbody><tr> <th>Column name</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>specobjid</td> <td>SDSS spec object ID</td> </tr> <tr> <td>dr7objid</td> <td>SDSS DR7 object ID</td> </tr> <tr> <td>clump_id</td> <td>Clump index</td> </tr> <tr> <td>clump_label_id</td> <td>Clump label ID (1 or 2)</td> </tr> <tr> <td>clump_label_name</td> <td>Clump label name</td> </tr> <tr> <td>clump_score</td> <td>Detection score for the clump</td> </tr> <tr> <td>clump_centre_ra</td> <td>Clump centroid RA in degrees</td> </tr> <tr> <td>clump_centre_dec</td> <td>Clump centroid dec in degrees</td> </tr> <tr> <td>clump_flux_u</td> <td>Clump u-band flux in Jy</td> </tr> <tr> <td>clump_flux_g</td> <td>Clump g-band flux in Jy</td> </tr> <tr> <td>clump_flux_r</td> <td>Clump r-band flux in Jy</td> </tr> <tr> <td>clump_flux_i</td> <td>Clump i-band flux in Jy</td> </tr> <tr> <td>clump_flux_z</td> <td>Clump z-band flux in Jy</td> </tr> <tr> <td>clump_flux_err_u</td> <td>Clump u-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_g</td> <td>Clump g-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_r</td> <td>Clump r-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_i</td> <td>Clump i-band flux error in Jy</td> </tr> <tr> <td>clump_flux_err_z</td> <td>Clump z-band flux error in Jy</td> </tr> <tr> <td>clump_mag_u</td> <td>Clump u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_g</td> <td>Clump g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_r</td> <td>Clump r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_i</td> <td>Clump i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_z</td> <td>Clump z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_ext_mag_u</td> <td>Clump u-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_g</td> <td>Clump g-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_r</td> <td>Clump r-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_i</td> <td>Clump i-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_ext_mag_z</td> <td>Clump z-band extinction (E(B-V), AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u</td> <td>Clump corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_g</td> <td>Clump corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_r</td> <td>Clump corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_i</td> <td>Clump corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_z</td> <td>Clump corrected z-band magnitude (AB-mag)</td> </tr> <tr> <td>clump_mag_corr_u_g</td> <td>Clump colour (u-g)</td> </tr> <tr> <td>clump_mag_corr_g_r</td> <td>Clump colour (g-r)</td> </tr> <tr> <td>clump_mag_corr_r_i</td> <td>Clump colour (r-i)</td> </tr> <tr> <td>clump_mag_corr_i_z</td> <td>Clump colour (i-z)</td> </tr> <tr> <td>clump_flux_ratio</td> <td>Est. clump/galaxy near-UV flux ratio (u-band)</td> </tr> <tr> <td>is_clump_3pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >3%</td> </tr> <tr> <td>is_clump_8pct</td> <td>Flag (True/False) if clump/galaxy flux ratio is >8%</td> </tr> <tr> <td>galaxy_ra</td> <td>Host galaxy RA in degrees</td> </tr> <tr> <td>galaxy_dec</td> <td>Host galaxy dec in degrees</td> </tr> <tr> <td>galaxy_z</td> <td>Host galaxy redshift</td> </tr> <tr> <td>galaxy_mag_u</td> <td>Host galaxy u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_g</td> <td>Host galaxy g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_r</td> <td>Host galaxy r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_i</td> <td>Host galaxy i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_z</td> <td>Host galaxy z-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_u</td> <td>Host galaxy u-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_g</td> <td>Host galaxy g-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_r</td> <td>Host galaxy r-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_i</td> <td>Host galaxy i-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_mag_err_z</td> <td>Host galaxy z-band magnitude error (AB-mag)</td> </tr> <tr> <td>galaxy_flux_u</td> <td>Host galaxy u-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_g</td> <td>Host galaxy g-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_r</td> <td>Host galaxy r-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_i</td> <td>Host galaxy i-band flux in Jy</td> </tr> <tr> <td>galaxy_flux_z</td> <td>Host galaxy z-band flux in Jy</td> </tr> <tr> <td>galaxy_expAB_r</td> <td>Host galaxy axis ratio from SDSS</td> </tr> <tr> <td>galaxy_expRad_r</td> <td>Host galaxy exponential fit scale radius from SDSS</td> </tr> <tr> <td>galaxy_lmass</td> <td>Host galaxy log mass in MSun</td> </tr> <tr> <td>galaxy_lssfr</td> <td>Host galaxy log specific SFR</td> </tr> <tr> <td>galaxy_mag_corr_u</td> <td>Host galaxy corrected u-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_g</td> <td>Host galaxy corrected g-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_r</td> <td>Host galaxy corrected r-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_i</td> <td>Host galaxy corrected i-band magnitude (AB-mag)</td> </tr> <tr> <td>galaxy_mag_corr_z</td> <td>Host galaxy corrected z-band magnitude (AB-mag)</td> </tr> </tbody> </table> <p> </p>
Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions - data
<p>Data set pertaining to the manuscript "Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions", accepted for publication in Nature Chemistry.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data') if applicable.<br> 2. As-measured data ('raw').</p> <p>Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme.</p> <p><br> The following files are provided:</p> <p>Photoemission data pertaining to ETMD measurements:<br> alcl3-K-etmd.h5 (ETMD after Al K-shell photoionization)<br> alcl3-L23-etmd.h5 (ETMD after Al L-shell photoionization)</p> <p>Calculated energies of the ETMD final states after 1s ionization. The energies were calculated at the CAS-CI/cc-pVDZ level. The states were shifted so that the lowest-energy state corresponds to the LC-ωPBE/aug-cc-pVTZ and aug-cc-pCVTZ value obtained in a polarizable continuum:<br> Dataset_ETMD_after_1s_ionization.csv<br> Dataset_ETMD_after_2p_ionization.csv</p> <p>Geometrical coordinates of the clusters that were used for energy calculation:<br> clusters.dat<br> clusters_small.dat</p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p> <p> </p> <p>Version history:</p> <p>v3 - Al L2,3 data: Orientation of the analyser hemisphere corrected. Direction of the linear polarization vector added. All other data unchanged.<br> v2 - cluster coordinates added, all other data unchanged.<br> v1 - initial upload.</p>
Workflow for detecting biomedical articles with openly available underlying datasets - Datasets and extraction forms
<p>The open data screening datasets contain both automatically detected (TRUE) Open Data statements by <a href="https://github.com/quest-bih/oddpub">ODDPub</a>, and its manual validation using <a href="https://github.com/bgcarlisle/Numbat">Numbat</a> extraction tool. Furthermore, extraction forms for both screenings – 2020 and 2021 – are included. The manually processed dataset for the calculation of the inter-rater reliability of manual validation can be also found here. </p> <p>(i) Data from articles published in 2020 (file ‘<em>charite_open_data_2020.csv</em>’) have been collected applying a slightly different sequence of questions in the extraction workflow than the articles published in 2021 (file ‘<em>charite_open_data_2021.csv</em>’). Both datasets were cleaned for any personal data or internal comments. Thus, they do not contain the default columns which in the raw export from Numbat contained commentaries regarding different question. Also, in another regard these files do not represent raw outputs of the Numbat extraction tool, but a processed version. This means that articles validated by more than two raters were first reconciled in Numbat, resulting in one final decision (output of extractions <strong>after reconciliation</strong>). Then from the output of extractions <strong>before reconciliation</strong> those articles validated by only 1 rater (and thus not part of the inter-rater reliability calculation) were selected, which were afterwards joined with the already reconciled dataset. </p> <p>The actual decision about Openness of validated dataset can be analysed in various ways: </p> <ol> <li>Column ‘<em>open_data_assessment</em>’/’<em>assessment</em>’ shows a binary decision between Open Data TRUE and FALSE. </li> <li>If that column indicates ‘<em>NULL</em>’, the dataset was classified into ‘non’-open category, and the result can be found on one of the following ways: <ul> <li>Column ‘<em>reference_to_data</em>’ as ‘<em>n_a</em>’ for excluded articles, e.g. not producing any data.</li> <li>Column ‘<em>data_access</em>’ as ‘<em>restricted</em>’. </li> <li>Column ‘<em>own_or_reuse_data</em>’ as ‘<em>open_data_reuse</em>’. </li> </ul> </li> </ol> <p>The original extraction form contains an option ‘unsure_open_data’ besides ‘<em>open_data</em>’/’<em>no_open_data</em>’ which was resolved either during reconciliation between multiple raters or by case-related consultation with a second rater in case of doubt, and is not included here. </p> <p>(ii) The inter-rater reliability calculation was made on randomly selected 100 articles for 2 raters. The third rater screened 20 articles sample, which is part of 100 sample. The tables provided here include both article-level data, and dataset-level data. </p> <p>(iii) The Numbat extarction forms used for the screenings in 2020 and 2021 are included in two formats - JSON and Markdown.</p> <p>(iv) ‘<em>data_dictionary_open_data.csv</em>’ table documents all variables of each data file containing here. </p>
Supplemental materials of the Castaño-Sánchez et. al. (2023) article (Agricultural Systems) containing the IFSM model input parameters not included in the main text, and the Criollo ranches survey form
CONTEXT: The southwestern United States is experiencing an increasingly warmer and drier climate that is affecting cattle production systems of the region. Adaptation strategies are needed that will not compromise environmental quality or profitability. Options include the use of desert-adapted beef cattle biotypes, such as Rarámuri Criollo cattle, and crossbreds of Criollo with more traditional British breeds. Currently, most calves raised in the Southwest are grain finished, often with irrigated crops produced in the hydrologically-threatened Ogallala Aquifer region. A viable alternative may be grass finishing with the rainfed forage of the arid and semi-arid rangeland of the Southwest or in the temperate grasslands of the Northern Plains. OBJECTIVE: Compare the environmental impacts and production costs of grain-finishing in Texas and grass-finishing in the Northern plains and the Southwest with traditional Angus cattle vs. Criollo and Criollo x Angus cattle. METHODS: Nine supply chain strategies were simulated using the Integrated Farm System Model to compare farm-gate life cycle intensities of greenhouse gas emissions (carbon footprint), fossil energy footprint, nitrogen footprint, blue water footprint and production costs using representative (appropriate soils, climate, and management) ranch and feedlot operations. RESULTS AND CONCLUSIONS: For both finishing options (grass, grain), Criollo x Angus cattle had the best environmental (3%-27% lower), and production cost (4-23% lower) outcomes followed by pure Criollo and then Angus cattle. Crossbred production combined the lower feed supplementation requirements of Criollo cows with heavier final carcasses of offspring from Angus genetics. Crossbred cattle with grass finishing in the Southwest or Northern Plains outperformed on most environmental variables as well as production costs, mostly due to reduced external input requirements (primarily feed). A downside for grass-finished crossbreds was greater carbon fo
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.