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1,840 results for “Representative”
raptr: Representative and Adequate Prioritization Toolkit in R
<p>1. An underlying aim in conservation planning is to maximize the long-term persistence of biodiversity. To fulfill this aim, the ecological and evolutionary processes that sustain biodiversity must be preserved. One way to conserve such processes at the feature level (eg. species, ecosystem) is to preserve a sample of the feature (eg. individuals, areas) that is representative of the intrinsic or extrinsic physical attributes that underpin the process of interest. For example, by conserving a sample of populations with local adaptations---physical attributes associated with adaptation---that is representative of the range of adaptations found in the species, protected areas can maintain adaptive processes by ensuring these adaptations are not lost. Despite this, current reserve selection methods overwhelmingly focus on securing an adequate amount of area or habitat for each feature. Little attention has been directed towards capturing a representative sample of the variation within each feature.</p> <p>2. To address this issue, we developed the <em>raptr R</em> package to help guide reserve selection. Users set "amount targets"---similar to conventional methods---to ensure that solutions secure a sufficient proportion of area or habitat for each feature. Additionally, users set "space targets" to secure a representative sample of variation in ecologically or evolutionarily relevant attributes (eg. environmental or genetic variation). We demonstrate the functionality of this package using simulations and two case studies. In these studies, we generated solutions using amount targets---similar to conventional methods---and compared them with solutions generated using amount and space targets.</p> <p>3. Our results demonstrate that markedly different solutions emerge when targeting a representative sample of each feature. We show that using these targets is important for features that have multimodal distributions in the process-related attributes (eg. species with multimodal niches). We also found that solutions could conserve a far more representative sample with only a slight increase in reserve system size.</p> <p>4. The <em>raptr R</em> package provides a toolkit for making prioritizations that secure an adequate and representative sample of variation within each feature. By using solutions that secure a representative sample of each feature, prioritizations may have a greater chance of achieving long-term biodiversity persistence.<br> </p>
Mitogenome alignment of 159 unique haplotypes representing 455 individual killer whales
<p><span>Genome sequences can reveal the extent of inbreeding in small populations. Here we present the first genomic characterization of type D killer whales, a distinctive eco/morphotype with a circumpolar, subantarctic distribution. Effective population size is the lowest estimated from any killer whale genome and indicates a severe population bottleneck. Consequently, type D genomes show among the highest level of inbreeding reported for any mammalian species (F<sub>ROH</sub> </span><span></span><span> 0.65). Detected recombination events of different haplotypes are up to an order of magnitude rarer than in other killer whale genomes studied to date. Comparison of genomic data from a museum specimen of a type D killer whale that stranded in New Zealand in 1955, with three modern genomes from the Cape Horn area, reveals high covariance and identity-by-state of alleles, suggesting these genomic characteristics and demographic history are shared among different social groups within this morphotype. Limitations to the insights gained in this study stem from the </span><span>non-independence of the three closely related modern genomes, the short coalescence time of most variation within the genomes, and the nonequilibrium population history which violates the assumptions of many model-based methods. Long-range linkage disequilibrium and extensive runs of homozygosity found in type D genomes provide the potential basis for coupling of genetic barriers to gene flow with other killer whale populations, and the distinctive morphology. </span></p>
Vertical structural complexity of plant communities represents the combined effects of resource acquisition and environmental stress on the Tibetan Plateau
<p><span>Knowledge of vertical structural complexity (VSC) is important, because the resulting spatial partitioning is closely linked to resource utilization and environmental adaptation. However, the spatial pattern of VSC </span><span>on </span><span>large scales and its underlying mechanisms are poorly understood. Here, we systematically investigated 2,013 plant communities through grid sampling on </span><span>the </span><span>Tibetan Plateau (TP). </span><span>VSC was quantified </span><span>as </span><span>the maximum plant height within a plot (Height-max), coefficient of variation of plant height</span><span> (Height-var)</span><span>, and Shannon evenness of plant height (</span><span>Height-even</span><span>)</span><span>. Precipitation dominated the spatial variation in VSC in forests and shrublands, supporting the classic physiological tolerance hypothesis (PTH). In contrast, for alpine meadows, steppes, and desert grasslands in extreme environments, non-resource limiting factors (e.g., wide diurnal temperature ranges and strong winds) dominate VSC variation. Generally, with the shifting of climate from favorable to extreme, the effect of resource availability gradually decreases, but the effect of non-resource limiting factors gradually increase</span><span>s</span><span>, and that the PTH only applicable in "favorable conditions". With </span><span>the help of</span><span> machine learning models, maps of </span><span>VSC</span><span> at 1-km resolution were produced for the TP for the first time</span><span>. Our</span><span> new findings and maps of VSC provide new insights into macroecological studies, especially for adaptation mechanisms and model optimization.</span></p>
Data and Software of "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids"
<h1><strong>Id: Data of following publication</strong></h1> <p>title = "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids",<br>journal = "<span>Composite Structures</span>",<br>volume =" 348",<br>pages = "118503",<br>year = "2025",<br>doi = "<a href="https://doi.org/10.1016/j.compstruct.2024.118503" target="_blank" rel="noopener">10.1016/j.compstruct.2024.118503</a>",<br>author = "José Rothkegel, Benjamin Renson, Michael Bruyneel, Ludovic Noels"</p> <p>Data doi on 10.5281/zenodo.10829042</p> <h1>pyRVE</h1> <h2><em>Python Code for Geometrical Generator for Braided Composites RVE</em></h2> <p>pyRVE is a code written in <em>Python</em> using the <em>GMSH API</em> that generates the Representative Unit Cell (RUC) of braided composites. It allows the generation of the RUC of triaxial braided for <em>Diamond</em> and <em>Regular</em> patterns.</p> <h2>Requirements</h2> <p>To run, it requires:</p> <ul> <li>The GMSH Python API, which must be built with OpenCascade support. <ul> <li>Choose a local installation directory; <code>CMAKE_INSTALL_PREFIX=$HOME/local/gmsh</code>, and <code>GMSHPY_INSTALL_DIRECTORY=$HOME/local/gmsh</code> e.g.;</li> <li>Make that directory part of your <code>export PYTHONPATH=$HOME/local/gmsh/lib:$PYTHONPATH</code>.</li> <li>After compiling use <code>make install</code>.</li> </ul> </li> <li>The CM3 app dG3D if the final RVE homogenized solution is needed (<a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a>).</li> <li>Make sure that the latest version of OpenCascade (OCCT) is used. Current used version in occt-V7.8.0.</li> </ul> <h2>Usage</h2> <h3>File Structure</h3> <p>A typical run case must have a file structure, where:</p> <ul> <li><code>brd</code>: the files <code>.brd</code> and <code>.brep</code> are located here. The <code>.brd</code> is a backup of the <code>braidClass</code> instance used in the model saved using <code>pickle</code>, the <code>.brep</code> is the Boundary Representation file that can be opened with <em>GMSH</em>.</li> <li><code>csv</code>: the <code>.csv</code> file saved here is the initial output of the code. It contains the actually used dimensions and the final cover factor of the braid.</li> <li><code>data</code>: It contains <code>.csv</code> files with the material properties and the dimensions of the tows. The original model dimensions are read from here.</li> <li><code>dir</code>: In the case of running the RVE homogenization, the directions of the tow fibers are stored here. They are saved for post processing.</li> <li><code>msh</code>: the mesh file <code>.msh</code> obtained after the geometry geneartion is stores here.</li> <li><code>png</code>: in the case of automatic post processing, png files are stored here.</li> <li><code>res</code>: this folder is used to store the homogenization results. They have to be moved here.</li> <li><code>stp</code>: if acitvated, a <code>.stp</code> file of the geometry is stored here</li> <li><code>svg</code>: the projection of the geometry on the <em>x-y</em> plane is stored here.</li> <li><code>vtk</code>: A copy of the mesh file without the matrix mesh is sotred here as a `.vtk`` file.</li> </ul> <h3>How to Run</h3> <p>We will consider the current file structure to run the example in 000_Base. To run the code, it can be called from the command prompt as</p> <div> <pre><code>python3 ../../source/mainRVE.py --name <i> --pattern <pattern></code></pre> </div> <p>In this case, the <code>--name</code> refers to the index that will be given to the model, where <code><i></code> must be changed to an integer and <code>--pattern</code> refers to the wanted pattern to be used, where <code><pattern></code> must be changed to either <code>dia</code> or <code>reg</code>.</p> <blockquote> <p>Note: <code><code>--name</code>cat</code> can also be used to reproduce the regular pattern benchmark of the paper. In that case, the volume fraction of fiber in the tows is hard coded as the provided value in the reference (i.e. 0.86). For other cases, the volume fraction is evaluated from the tow cross-sections.</p> <p>Note: <code>mainRVE.py</code> must be accesible from the directory where the case is being run. This example shows the usage of the current file structure.</p> </blockquote> <h3>All Command Line Options</h3> <p>The code can be run using further options that serve different purpouses, some serving pre processing needs and other serving run administration. The different command line options are:</p> <ul> <li>Required: <ul> <li><code>--name</code> : it gives a suffix to the run model. It is usually an integer.</li> <li><code>--pattern</code> : indicates the type of pattern to be used to build the geometry. The two current options are <code>dia</code> for diamond and <code>reg</code> for regular.</li> </ul> </li> <li>Optional <ul> <li><code>-dG3D</code>: it indicates that the homogenization of the generated RUC is to be perfomed.</li> <li><code>-GMSH</code> : it indicates that GMSH must be open upon competion of the generation of the mesh.</li> <li><code>-loadModel</code> : it will try to load a premade model. It will ignore <code>--pattern</code>.</li> <li><code>--rndPrm</code> : it will generate randomized geometrical parameters. It can be used to generate batches of results. It takes an argument that can be <code>2</code>, <code>4</code> or <code>6</code>. Currently, <code>2</code> gives a random value for <code>s_axial</code> and <code>theta</code>, <code>4</code> randomizes the same as <code>2</code> and adds <code>h_axial</code> and <code>h_bias</code>, and <code>6</code> randomizes the same as <code>4</code> and adds <code>w_axial</code> and <code>w_bias</code>.</li> </ul> </li> <li>Pre-Processing <ul> <li><code>-refCF</code>: it tells the code to generate a grid of values for <code>s_axial</code> and <code>theta</code> where only the cover factor is obtained. It is meant for posterior graphing purposes.</li> </ul> </li> </ul> <h3>Examples</h3> <p>Following the run options, a few examples are indicated</p> <ul> <li>A basic mesh generation run for the basic data, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg</code></pre> </div> <ul> <li>The generation of the cover factor data and export, considering a <strong>regular pattern</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --pattern reg -refCF</code></pre> </div> <ul> <li>A run for the modified basic data, where the <strong>2</strong> parameters are modified <em>randomly</em>, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg --rndPrm 2</code></pre> </div> <ul> <li>A run, where model <strong>2</strong> already exists in <code>brd</code> folder but not the <code>.msh</code> and <code>.vtk</code> files:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 -loadModel </code></pre> </div> <h2>Code Structure</h2> <p>The code is implemented into Python files, where <code>mainRVE.py</code> runs the whole code. The files are:</p> <ul> <li>Braid: <ul> <li><code>braidClass.py</code> :</li> <li><code>bzrPairClass.py</code> :</li> </ul> </li> <li>Geometry <ul> <li><code>bezrClass.py</code> :</li> <li><code>bilnClass.py</code> :</li> <li><code>patchClass.py</code> :</li> <li><code>pntSetClass.py</code> :</li> <li><code>pointClass.py</code> :</li> <li><code>sctnClass.py</code> :</li> <li><code>stripeClass.py</code> :</li> <li><code>surfClass.py</code> :</li> <li><code>surfOffClass.py</code> :</li> </ul> </li> <li>Material: <ul> <li><code>chamis.py</code> :</li> </ul> </li> <li>Tools: <ul> <li><code>dataIO.py</code> :</li> <li><code>postDirection.py</code> :</li> <li><code>tool.py</code> :</li> <li><code>toolData.py</code> :</li> </ul> </li> <li><code>curveClass.py</code> :*</li> </ul> <p> </p> <p> </p>
Cryo-EM and X-ray crystallography ligands represented as 3D voxel grids for training deep learning models
<p>Ligand datasets used to train and evaluate the models studied in <em>"Ligand Identification using Deep Learning</em><em>"</em> by Karolczak, J. <em>et al.</em></p> <p>The blobs_full.tar.gz and cryoem_blobs.zip files contain compressed 3D numpy arrays (*.npz) of all the ligand blobs extracted from X-ray and cryo-EM PDB deposits prior to quality filtering. The npz file names correspond to the PDB ID, chain, residue number, and ligand name of the extracted blob. The cmb_data.csv file contains the tabular data used to train the CheckMyBlob model. The X-ray data were later divided into training and testing subsets according to the xray_train.csv and xray_holdout.csv files, respectively. The ligand_mapping.csv file contains the mapping from ligand IDs to ligand group names. Finally, the cryoem_qscores.csv file contains Q-scores that were used to filter cryo-EM ligands.</p>
Needs-based approaches for representing personal transportation decision-making
<p>Data from a Utah State University research study, "Needs-based approaches for representing personal transportation decision-making". This version includes data from the focus group portion of the study. </p>
Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux
<p>This is dataset of global climate model simulation used in the paper "Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux" by Shimura et al. (2021)</p> <p>Followings are the explanation of data file.</p> <p>*** File naming rule ***<br> {data_group_name}_Exp{experiment_name}_TCnumber{tropical_cyclone_case_number}.nc</p> <p> data_group_name<br> - atm<br> - track</p> <p> experiment_name<br> - Wind<br> - Wave<br> - SlabO</p> <p> tropical_cyclone_case_number<br> - 001<br> - 002<br> ...<br> - 099<br> - 100</p> <p>*** Description on each data group ***<br> <br> atm: three dimentional atmospheric velocity data<br> - level: pressure levels for vertical atmospheric data<br> - longitude: Longitude<br> - latitude: Latitude<br> - velocity_u_component: averaged atmospheric eastward velocity</p> <p> track: data around tropical cyclone track<br> - time: UTC time (YYYYMMDDHH)<br> - longitude_center: Longitude of typhoon center<br> - latitude_center: Latitude of typhoon center<br> - central_pressure: typhoon central pressure<br> - maximum_surface_wind: typhoon maximum surface wind speed<br> - longitude_sfc: Longitude for surface data around typhoon center<br> - latitude_sfc: Latitude for surface data around typhoon center<br> - surface_wind_u_component: surface eastward wind around typhoon<br> - surface_wind_v_component: surface northward wind around typhoon<br> - sea_level_pressure: sea level pressure around typhoon<br> - latent_heat_flux: surface upward latent heat flux<br> - sensible_heat_flux: surface upward sensible heat flux<br> - time_atm: UTC time (YYYYMMDDHH) for atmospheric data<br> - level: pressure levels for atmospheric data<br> - longitude_atm: Longitude for atmospheric data around typhoon center<br> - latitude_atm: Latitude for atmospheric data around typhoon center<br> - velocity_u_component: 3d eastward velocity around typhoon<br> - velocity_v_component: 3d northward velocity around typhoon</p> <p> </p>
Representing surface heterogeneity in land-atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime - E3SM SCM data and code
<p>This dataset contains post-processed E3SM single-column model output and code used to produce the figures in the manuscript that we are targeting Geoscientific Model Development to submit. </p>
Replication package for: Subjective Models of the Macroeconomy: Evidence From Experts and Representative Samples
<p><strong>This repository contains the replication scripts and data of the analyses of the paper: </strong></p> <p>Andre, P., Pizzinelli, C., Roth, C., & Wohlfart, J. (2022). Subjective Models of the Macroeconomy: Evidence From Experts and Representative Samples. <em>Review of Economic Studies</em>.</p>
Field-comprehensive-data-from-a-representative-groundwater-depth-transect-in-Central-Asia
<p>Data and Code Availability for paper--Anthropogenic drought accelerates catastrophic regime shifts of groundwater-dependent ecosystems in drylands</p>
Conceptualizing relationships among hyporheic exchange, storage, and water age: data represented in published figures
<p>Hyporheic exchange is a key driver of ecosystem processes in streams, yet stream ecologists often fail to leverage detailed conceptual models developed by engineers and hydrologists describing the relationship between water storage, water balance, and water age (time elapsed since a conceptual parcel of water entered the hyporheic zone) in hyporheic zones. In a companion paper (G.C. Poole et al. <em>Hyporheic Hydr</em><em>au</em><em>lic Geometry: Conceptualizing relationships among hyporheic exchange, storage, and water age</em>, published in PLoS ONE; doi:<a href="https://doi.org/10.1371/journal.pone.0262080">10.1371/journal.pone.0262080</a>), we provide visualizations of these relationships in an effort to allow non-hydrologists to grasp four primary concepts along with associated research and management implications: 1) the rate of hyporheic exchange, size of the hyporheic zone, and hyporheic water age are inexorably linked; 2) such linkages can be leveraged to build understanding of hyporheic processes; 3) the age distribution of hyporheic water and hyporheic discharge is heavily skewed toward young water ages -- at any temporal scale of observation (minutes, hours, days, or months) older hyporheic water is rare relative to younger water; 4) the age distribution of water discharged from any hyporheic zone is not the same as the age distribution of water stored within that hyporheic zone. The data set presented here represents the numerical values represented by the figures published in the companion paper.</p>
Current flow files mapped by Omniscape representing the dispersal of saltwater crocodiles
<p>This folder contains raster data in asc format to be used with Omniscape to visualise predicted flow across a study area raster, using the core breeding habitat cells ('Saltwater crocodile core breeding habitat cells.asc') for saltwater crocodiles (<em>Crocodylus porosus</em>) in the Northern Territory, Australia, the optimized resistance surface ('Saltwater crocodile optimized resistance surface.asc'), and core habitat cells ('Saltwater crocodile core habitat cells.asc') as ground nodes, effectively representing locations where current could 'settle'.</p>
Fig. 1 in Tituboea purcharti sp. nov., the first representative of Clytrini from Socotra Island (Coleoptera: Chrysomelidae: Cryptocephalinae)
Fig. 1. Habitus of Tituboea purchati sp. nov. (holotype, male, 9.6 mm).
Dataset: Towards a representative reference for MRI-based human axon radius assessment using light microscopy
<p>Dataset for: Towards a representative reference for MRI-based human axon radius assessment using light microscopy</p>
COMPAS v02.27.05 output at representative metallicities
<p>This dataset contains the output data of rapid binary population synthesis produced using COMPAS v02.27.05. The details of the initial conditions and setup are described in the "COMPAS_settings_table.pdf" file. We simulate 5 million binaries, at representative metallicities, and store them in HDF5 files, the default format for COMPAS simulations. Each HDF5 file contains the following groups:</p> <ul> <li> ‘/BSE_Common_Envelopes’</li> <li> ‘/BSE_Double_Compact_Objects’</li> <li> ‘/BSE_Supernovae’</li> <li> ‘/BSE_System_Parameters’</li> <li> ‘/Run_Details’</li> </ul> <p>For details about the data, such as variable names and attributes, visit the <a href="https://compas.readthedocs.io/en/latest/">compas.readthedocs.io</a>.<br> The metallicities chosen for the simulations are:</p> <ol> <li>Z=0.0001: the lowest metallicity accounted for in our stellar evolution tracks;</li> <li>Z=0.0002: a proxy metallicity for I Zwicky 18;</li> <li>Z=0.001;</li> <li>Z=0.0021: a proxy metallicity for the Small Magellanic Cloud;</li> <li>Z=0.0047: a proxy metallicity for the Large Magellanic Cloud;</li> <li>Z=0.01;</li> <li>Z=0.0142: a metallicity representative of the Sun;</li> <li>Z=0.02: a super-solar metallicity, aka "old Solar";</li> <li>Z=0.03: the highest metallicity accounted for in our stellar evolution tracks;</li> <li>U[log<sub>10</sub>(0.0001), log<sub>10</sub>(0.03)]: log-uniformly distributed metallicities from the COMPAS metallicities.</li> </ol> <p>This results in 50 million binaries in total.</p> <p>In addition to the Table in the "COMPAS_settings_table.pdf" file, we include a "Run Details" text file, which contains the setup as specified via COMPAS, for a particular batch of the Z=0.02 simulation. This file contains a representative description of the global properties of all the 10 simulations, but some details, e.g. Clock-Time, vary from batch to batch.</p> <p>For questions, comments, or suggestions, contact compas-user@googlegroups.com or visit <a href="https://compas.science/">compas.science/</a>.<br> Visit <a href="https://github.com/TeamCOMPAS/COMPAS">Team COMPAS Github page</a> to get access to the source code.<br> Finally, if you use any of these data, we kindly request that, in recognition of this team effort, the paper is cited as:</p> <p>Team COMPAS: J. Riley et al., “Rapid Stellar and Binary Population Synthesis with COMPAS”, <em>The Astrophysical Journal Supplement Series</em>, vol. 258, no. 2, 2022. <a href="https://iopscience.iop.org/article/10.3847/1538-4365/ac416c/meta">doi:10.3847/1538-4365/ac416c</a>.</p>
The last representatives of the Superfamily Wellerelloidea (Brachiopoda, Rhynchonellida) in the westernmost Tethys (Iberian paleomargins) prior to their demise in the Early Toarcian Mass Extinction Event
<p><span>The last clade-level extinction episode affecting the Phylum Brachiopoda has been long-established in the Early Toarcian Mass Extinction Event (ETMEE) around the Pliensbachian-Toarcian transition, when several rhynchonellide groups became extinct and others underwent a notable renewal in the western Tethys. Among them, Wellerelloidea is a long-range superfamily severely affected by this environmental crisis, embodying the subfamily Cirpinae as the last wellerelloids worldwide, prior to their global extinction in the Pb-To transition. The profuse record of Lower Jurassic cirpines in the peri-Iberian paleomargins provides an opportunity to clarify the taxonomy of wellerelloid species in the pre-extinction interval. A new species (<em>Cirpa lucentina</em>) is erected and the revision of the Cirpinae taxa around the ETMEE is carried out. Morphometric analysis and the study of internal structures of the shells support the splitting between the genera <em>Cirpa</em> and <em>Salgirella</em>, adding new supplementary diagnostic criteria. The biogeographic distribution of this clade in the western Tethys and its evolutionary history in the Early Jurassic reveal </span><span>a <em>pervasive</em> colonization pattern of both epicontinental and epioceanic habitats. T</span><span>he Mediterranean origin of the last representatives of this group is ascertained, but while diversification of <em>Salgirella</em> took place in epioceanic habitats, speciation of <em>Cirpa</em> is unrelated to biochorema boundaries, even colonizing epicontinental seas until their extinction prior to the hyperwarming event that occurred in the basal Serpentinum Zone. A rhynchonellide morphogroup epitomized by cirpines was resilient to this event in the epicontinental seas. This morphogroup is also recorded after the extinction interval by means of the genus <em>Pseudogibbirhynchia</em>, thus postulating potential pre- and post-extinction phyletic relationships.</span></p>
A representative gel of ALAD following a restriction fragment length digestion
<p><span><strong>Background</strong>:</span><span> Rapid industrialization, urbanization, and population explosion in sub-Saharan Africa escalate environmental lead levels and subsequently blood lead levels in children. Its levels in one's environment account for his/her blood lead levels. One's susceptibility to lead toxicity is governed by his/her nutrition status, age and genetics. This study aimed at expounding susceptibility to lead toxicity by relating blood lead levels, delta-aminolevulinic acid dehydratase (ALAD) enzyme activity, and genetic variations of proteins that code for ALAD in urban children of Uganda.</span></p> <p><span><strong>Methods</strong>: </span><span>A total of 198 blood samples were spectrophotometrically analyzed for blood lead levels (BLL), hemoglobin (Hb) levels, and ALAD enzyme activity before DNA extraction, polymerase chain reaction, and restriction fragment length digestion for ALAD polymorphism. </span></p> <p><span><strong>Results</strong>: </span><span>Up to 99.5% of the total samples analysed coded for ALAD1 allele compared to 0.05% that coded for ALAD2. There was a significant relationship between BLL, Hb status and ALAD enzyme activity in the three isozymes (ALAD1-1, ALAD1-2 and ALAD2-2) in strength of ALAD1-1 (r = 0.42, p-value = 0.02) ˂ALAD1-2 (r = 0.62, p-value = ˂0.001) ˂ ALAD2-2 (r = 0.67, p-value = ˂0.001).</span></p> <p><span><strong>Conclusions</strong>: </span><span>The majority of children in Uganda code for the ALAD1 allele, which is important for blood lead ions hoarding during lead toxicity. Hoarding of blood lead not only delays exposure effects but also accumulates its levels in deposit tissues and this poses adverse effects later in their lives.</span></p>
Auxiliary Euro-Calliope datasets: Spatial data to represent a European energy system model at several spatial resolutions
<p>Main output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://doi.org/10.5281/zenodo.3246302">https://doi.org/10.5281/zenodo.3246302</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences:</p> <ol> <li>The spatial extent has been expanded to include Iceland.</li> <li>Two new spatial resolutions have been added: `ehighways` and `ehighways_disaggregated`.</li> </ol> <p>`ehighways` defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. `ehighways_disaggregated` provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the `ehighways` resolution. The mapping from statistical units to ehighways regions is defined in `./ehighways/statistical_units_to_ehighways_regions.csv`. `./ehighways/units.png` shows a map of the resulting 98 `ehighways` regions. The region colours are used to help differentiate regions and have no other meaning.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p>[1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)</p>
How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation? - Observed precipitation data
<p>The dataset contains the rain gauge hourly rainfall series used in the paper "How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation?". Each rain gauge series is saved in one Matlab variable, organized as a structure S with five fields:</p> <p>S.name: the identification name of the rain gauge station</p> <p>S.vals_mm: series of hourly rainfall in millimeter</p> <p>S.time_utc: time steps series, in UTC time</p> <p>S.elev_m: elevation of the station, in m a.s.l.</p> <p>S.xy_utm: station coordinates X and Y in meter in the Reference system WGS84/UTM zone 32N</p>
Figs 9–12 in New and little known representatives of the genus Chrysolina Motschulsky, 1860 (Coleoptera: Chrysomelidae) from China
Figs 9–12. Aedeagus in lateral and dorsal view. 9, 10 –Chrysolina (Arctolina) tshingilica
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.