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3,655 results for “Structural data”
Alaskan Peatland Experiment: Community structure and productivity data for 2007-2010 VII - BGNPP
This dataset contains belowground net primary productivity measurements from root-ingrowth bags deployed October 2009-September 2010 at all plots at the fen and bog within the Alaskan Peatland Experiment. Within the fen site a water table manipulation has been ongiong since 2005, with control, lowered and raised water table treatment plots. Samples at the bog were collected in a plot established within the lowland black spruce permafrost plateau (permafrost), and two plots established within collapse scars embedded within the plateau. One collapse scar formed ~ 45 years ago (old collapse) and the other formed ~ 25 years ago based upon aerial photography provided by the BCEF LTER. The data provided in this data set can be sorted by site and plot.
Alaskan Peatland Experiment: Community structure and productivity data for 2007-2010 VIII - VGA
This dataset contains calculated vascular green area (VGA) by species within each gas flux collar in each plot at both the bog and fen sites of the Alaskan Peatland Experiment. VGA calculations are present at the fen site from 2008-2010, while calculations at the bog include 2009-2010. Within the fen site a water table manipulation has been ongiong since 2005, with control, lowered and raised water table treatment plots. Samples at the bog were collected in a plot established within the lowland black spruce permafrost plateau (permafrost), and two plots established within collapse scars embedded within the plateau. One collapse scar formed ~ 45 years ago (old collapse) and the other formed ~ 25 years ago based upon aerial photography provided by the BCEF LTER. The data provided in this data set can be sorted by site and plot.
Alaskan Peatland Experiment: Community structure and productivity data for 2007-2010 IX - Stem Density
This dataset contains stem density counts for each species within sub-collar plots (5 per collar) with the gas flux collars at each plot and site of the Alaskan Peatland Experiment. Stem counts were started at the fen in 2008, while counts started at the bog in 2009. Within the fen site a water table manipulation has been ongiong since 2005, with control, lowered and raised water table treatment plots. Samples at the bog were collected in a plot established within the lowland black spruce permafrost plateau (permafrost), and two plots established within collapse scars embedded within the plateau. One collapse scar formed ~ 45 years ago (old collapse) and the other formed ~ 25 years ago based upon aerial photography provided by the BCEF LTER. The data provided in this data set can be sorted by site and plot.
Alaskan Peatland Experiment: Community structure and productivity data for 2007-2010 X - Leaf Area
This dataset contains surface area measurements for all vascular species present within a plot with an abundance greater than 5%. Surface area measurements at the fen include 2008-2010, while at the bog are for 2009-2010. Within the fen site a water table manipulation has been ongiong since 2005, with control, lowered and raised water table treatment plots. Samples at the bog were collected in a plot established within the lowland black spruce permafrost plateau (permafrost), and two plots established within collapse scars embedded within the plateau. One collapse scar formed ~ 45 years ago (old collapse) and the other formed ~ 25 years ago based upon aerial photography provided by the BCEF LTER. The data provided in this data set can be sorted by site and plot.
MCR LTER: Coral Reef: Community structure outdoor flume data in support of Edmunds 2019 Marine Biology
This dataset contains data in support of Edmunds, P.J., S.S. Doo, R.C. Carpenter, 'Changes in coral reef community structure in response to year-long incubations under contrasting pCO2 regimes', Marine Biology, 2019, doi:10.1007/s00227-019-3540-2. Here, the effects of ocean acidification (OA) on back reef communities from Mo'orea, French Polynesia (17.492S, 149.826W), were tested from 12 November 2015 to 16 November 2016 in outdoor flumes maintained at various mean pCO2 levels. Change in mass and percent cover were recorded monthly. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Data for: Brain structural connectivity predicts brain functional complexity
<p>Data used in analyses for "Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal"</p>
Data from "Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques"
<p>DATA FILES from the study below:</p> <p><strong><a href="https://www.biorxiv.org/content/10.1101/603530v1">Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques</a></strong></p> <p>Jérôme Sallet, MaryAnn P Noonan, Adam Thomas, Jill X O’Reilly, Jesper Anderson, Georgios KPapageorgiou, Franz X Neubert, Bashir Ahmed, Jackson Smith, Andrew H Bell, Mark J Buckley, LéaRoumazeilles, Steven Cuell, Mark E Walton, Kristine Krug, Rogier B Mars, Matthew FS Rushworth</p> <p>bioRxiv 603530; doi: <a href="https://doi.org/10.1101/603530">https://doi.org/10.1101/603530</a></p> <p>*.nii.gz files could be opened with FSLeyes -<a href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)">https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)</a></p> <p>Dara are also available from : https://www.jeromesallet.org/data-ofc-reversal-learning</p>
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>
SI data: A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al
<p>Journal: ACS Combinatorial Science<br> Title: A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al<br> Author(s): Joress, Howie; DeCost, Brian; sarker, suchismita; Braun, Trevor; Jilani, Sidra; Smith, Ryan; Ward, Logan; Laws, Kevin; Mehta, Apurva; Hattrick-Simpers, Jason</p>
Data for "Age effect on tree structure and biomass allocation in Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.)"
<p>VAPU dataset for tree biomass was collected from southern Finland in 1988-1990 by the Finnish Forest Research Institute (Metla, now Natural Resources Institute Finland, Luke) (VAPU data set).</p> <p>Those sample trees (162 Scots pine and 163 Norway spruce) are originated from the whole VAPU data set. The sheet 'Pine' and 'Spruce' data have been matched between 'sample branch measurements' and the 'biomass' information (by cluster X, Y, and plot, tree number).</p> <p>Biomass estimation for foliage and branches has been described here: https://doi.org/10.1016/j.ecolmodel.2004.04.024 and https://doi.org/10.1093/treephys/25.7.803<br> </p>
Data From: Inflorescence and flower development in Orchidantha chinensis T. L. Wu (Lowiaceae; Zingiberales): similarities to inflorescence structure in the Strelitziaceae
<p>The monotypic Lowiaceae remains the least known family in the plant order Zingiberales, yet it holds an important key to unraveling the phylogenetic placement of the families Musaceae, Heliconiaceae, Strelitziaceae, and Lowiaceae. After nine phylogenetic studies the (Lowiaceae, Strelitziaceae) clade is the only stable clade that has emerged in this half of the order. This study was undertaken to verify the unusual inflorescence and flower structure in Orchidantha, and to search for new characters that might be used in future phylogenetic analyses. We describe both inflorescence and flower development in a previously unstudied species, confirm inflorescence morphology in the genus, and compare the structure of the inflorescence in the Lowiaceae with that of the Strelitziaceae, its potential sister group. </p> <p>The inflorescence of Orchidantha is born at the end of a vegetative shoot and is composed of two lateral branches that each bear four bracts and a single flower, before aborting. The fourth bract and its associated flower form the highly reduced flower cluster (florescence) that characterizes this genus. In technical terms Orchidantha has a polytelic synflorescence that lacks a main florescence (it has a truncated polytelic synflorescence) and bears solitary flowers in coflorescences on determinate enriching branches. The enriching branches produce a fixed number of bracts before aborting (i.e., they are special paracladia). Many of these features are shared with the Strelitziaceae.</p> <p>Similarities between the Lowiaceae and Strelitziaceae include inflorescence structure, the presence of a long prolongation of the ovary, and a delay in the formation of the third sepal during flower development, a character that is also shared with the Musaceae. Inflorescence and flower structure is now well established in this small, but important family.</p>
Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation
<p>Scanning transmission electron microscopy data related to paper "Scanning transmission electron microscopy data related to paper "Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation", <a href="https://doi.org/10.1017/S1431927620024307">https://doi.org/10.1017/S1431927620024307</a>.</p>
Input Data for "Molecular Lignin Solubility and Structure in Organic Solvents"
<p>Input structures for a manuscript, along with selected output data and structures. This directory structure contains a cut-down copy of the directories used to generate the simulation data and the analysis. In order to make this fit into the 50GB Zenodo limit, it was constructed with the following tar command: `tar -zcvf ligninsolvationstudy.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*xtc" --exclude="*gro" --exclude="*log" --exclude="*[0-9].out" --exclude="*npz" --exclude="*pkl" --exclude="*npy" --exclude="*png" --exclude="*bmim*" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*xst" --exclude="*edr" --exclude="*txt" --exclude="*state_prev.cpt" LigninSolvation`, which intentionally excludes large files. The full dataset is available upon request.</p> <p><strong>Directory Descriptions</strong></p> <p><strong>BuildSolventBoxes</strong> contains the scripts and inputs needed to make the solvent boxes suitable for use with the VMD solvate plugin.<br> <strong>BuildSystems</strong> assembles the lignin polymers and solvates them into a complete simulation system. Depends on the outputs from [LigninBuilder](https://github.com/jvermaas/LigninBuilder).<br> <strong>Equilibrium</strong> has all the equilibrium trajectories and the scripts needed to set them up.<br> <strong>FEP</strong> has the free energy perturbation calculation key outputs (the fepout files) and the scripts needed to set up the calculation and analyze them.</p> <p>The scripts are <em>mostly</em> python scripts, but some are also in tcl, and have the appropriate file endings. GROMACS run input files (.tpr) and namd configuration files (.namd) may also be of general interest.</p>
Data for: Microclimate structures communities, predation and herbivory in the High Arctic
<p> </p> <p>In a warming world, changes in climate may result in species-level responses as well as changes in community structure through knock-on effects on ecological interactions such as predation and herbivory. Yet, the links between these responses at different levels are still inadequately understood. Assessing how microclimatic conditions affect each of them at local scales provides information essential for understanding the consequences of macroclimatic changes projected in the future. </p> <p>Focusing on the rapidly changing High Arctic, we examine how a community based on a common resource species (avens, <i>Dryas spp</i>.), a specialist insect herbivore (<i>Sympistis zetterstedtii</i>), and natural enemies of lepidopteran herbivores (parasitoids) varies along a multidimensional microclimatic gradient. We ask (1) how parasitoid community composition varies with local abiotic conditions, (2) how the community-level response of parasitoids is linked to species-specific traits (koino- or idiobiont life cycle strategy and phenology) and (3) whether the effects of varying abiotic conditions extend to interaction outcomes (parasitism rates on the focal herbivore and realized herbivory rates). </p> <p>We recorded the local communities of parasitoids, herbivory rates on <i>Dryas</i> flowers and parasitism rates in <i>Sympistis</i> larvae at 20 sites along a mountain slope. For linking community-level responses to microclimatic conditions with parasitoid traits, we used joint species distribution modelling. We then assessed whether the same abiotic variables also affect parasitism and herbivory rates, by applying generalized linear and additive mixed models.</p> <p>We find that parasitism strategy and phenology explain local variation in parasitoid community structure. Parasitoids with a koinobiont strategy preferred high-elevation sites with higher summer temperatures or sites with earlier snowmelt and lower humidity. Species of earlier phenology occurred with higher incidence at sites with cooler summer temperatures or later snowmelt. Microclimatic effects also extend to parasitism and herbivory, with an increase in the parasitism rates of the main herbivore <i>S. zetterstedtii</i> with higher temperature and lower humidity, and a matching increase in herbivory rates. </p> <p>Our results show that microclimatic variation is a strong driver of local community structure, species interactions and interaction outcomes in Arctic ecosystems. In view of ongoing climate change, these results predict that macroclimatic changes will profoundly affect arctic communities. </p> <p> </p>
Data, Sensitivity of 21st-century projected ocean new production changes to idealized biogeochemical model structure
<p>Data for reproducing figures in journal article submitted to Biogeosciences in December 2020.</p> <p>Data generated from global 1-degree simulations of the CESM in an ocean-ice configuration.</p> <p>NP model by Brett. See 10.5281/zenodo.4361705 for code for NP model and to use this dataset to recreate paper figures.</p>
Supplementary Data -A STUDY ON SOME STRUCTURAL FEATURES RESPONSIBLE FOR SARS-COV-2 INFECTION FATALITY
<p>A correlation between hydrodynamic properties like radius of gyration ( Rg ) vs Molecular weight of spike protein of SARS - COV-2 biopolymers .</p>
A set of generated Instagram Data Download Packages (DDPs) to investigate their structure and content
<p><strong>Instagram data-download example dataset</strong></p> <p>In this repository you can find a data-set consisting of 11 personal Instagram archives, or Data-Download Packages (DDPs).</p> <p> </p> <p><strong>How the data was generated</strong></p> <p>These Instagram accounts were all new and generated by a group of researchers who were interested to figure out in detail<br> the structure and variety in structure of these Instagram DDPs. The participants user the Instagram account extensively for approximately a week. The participants also intensively communicated with each other so that the data can be used as an example of a network. </p> <p>The data was primarily generated to evaluate the performance of de-identification software. Therefore, the text in the DDPs particularly contain many randomly chosen (Dutch) first names, phone numbers, e-mail addresses and URLS. In addition, the images in the DDPs contain many faces and text as well. The DDPs contain faces and text (usernames) of third parties. However, only content of so-called `professional accounts' are shared, such as accounts of famous individuals or institutions who self-consciously and actively seek publicity, and these sources are easily publicly available. Furthermore, the DDPs do not contain sensitive personal data of these individuals. </p> <p><br> <strong>Obtaining your Instagram DDP</strong></p> <p>After using the Instagram accounts intensively for approximately a week, the participants requested their personal Instagram DDPs by using the following steps. You can follow these steps yourself if you are interested in your personal Instagram DDP. </p> <p>1. Go to www.instagram.com and log in<br> 2. Click on your profile picture, go to *Settings* and *Privacy and Security*<br> 3. Scroll to *Data download* and click *Request download*<br> 4. Enter your email adress and click *Next*<br> 5. Enter your password and click *Request download*</p> <p>Instagram then delivered the data in a compressed zip folder with the format **username_YYYYMMDD.zip** (i.e., Instagram handle and date of download) to the participant, and the participants shared these DDPs with us.</p> <p> </p> <p><strong>Data cleaning</strong></p> <p>To comply with the Instagram user agreement, participants shared their full name, phone number and e-mail address. In addition, Instagram logged the i.p. addresses the participant used during their active period on Instagram. After colleting the DDPs, we manually replaced such information with random replacements such that the DDps shared here do not contain any personal data of the participants.</p> <p> </p> <p><strong>How this data-set can be used</strong></p> <p>This data-set was generated with the intention to evaluate the performance of the de-identification software. We invite other researchers to use this data-set for example to investigate what type of data can be found in Instagram DDPs or to investigate the structure of Instagram DDPs. The packages can also be used for example data-analyses, although no substantive research questions can be answered using this data as the data does not reflect how research subjects behave `in the wild'. </p> <p><br> <strong>Authors</strong></p> <p>The data collection is executed by Laura Boeschoten, Ruben van den Goorbergh and Daniel Oberski of Utrecht University. For questions, please contact l.boeschoten@uu.nl. </p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The researchers would like to thank everyone who participated in this data-generation project.</p>
Data from: Genomic data reveal deep genetic structure but no support for current taxonomic designation in a grasshopper species complex
<p>Taxonomy has traditionally relied on morphological and ecological traits to interpret and classify biological diversity. Over the last decade, technological advances and conceptual developments in the field of molecular ecology and systematics have eased the generation of genomic data and changed the paradigm of biodiversity analysis. Here we illustrate how traditional taxonomy has led to species designations that are supported neither by high throughput sequencing data nor by the quantitative integration of genomic information with other sources of evidence. Specifically, we focus on <em>Omocestus antigai </em>and<em> O. navasi</em>, two montane grasshoppers from the Pyrenean region that were originally described based on quantitative phenotypic differences and distinct habitat associations (alpine vs. Mediterranean-montane habitats). To validate current taxonomic designations, test species boundaries, and understand the factors that have contributed to genetic divergence, we obtained phenotypic (geometric morphometrics) and genome-wide SNP data (ddRADSeq) from populations covering the entire known distribution of the two taxa. Coalescent-based phylogenetic reconstructions, integrative Bayesian model-based species delimitation, and landscape genetic analyses revealed that populations assigned to the two taxa show a spatial distribution of genetic variation that do not match with current taxonomic designations and is incompatible with ecological/environmental speciation. Our results support little phenotypic variation among populations and a marked genetic structure that is mostly explained by geographic distances and limited population connectivity across the abrupt landscapes characterizing the study region. Overall, this study highlights the importance of integrative approaches to identify taxonomic units and elucidate the evolutionary history of species.</p>
Data for "Variation in Upper Plate Crustal and Lithospheric Mantle Structure in the Greater and Lesser Antilles from Ambient Noise Tomography"
<p>This is the phase velocity information and the shear wave model for the g-cubed paper:</p> <p>"Variation in Upper Plate Crustal and Lithospheric Mantle Structure in the Greater and Lesser Antilles from Ambient Noise Tomography"</p>
Data from: Different genetic structures revealed resident populations of a specialist parasitoid wasp in contrast to its migratory host
Genetic comparisons of parasitoids and their hosts are expected to reflect ecological and evolutionary processes that influence the interactions between species. The parasitoid wasp, Cotesia vestalis, and its host diamondback moth (DBM), Plutella xylostella, provide opportunities to test whether the specialist natural enemy migrates seasonally with its host or occurs as resident population. We genotyped 17 microsatellite loci and two mitochondrial genes for 158 female adults of C. vestalis collected from 12 geographical populations, as well as nine microsatellite loci for 127 DBM larvae from six separate sites. The samplings covered both the likely source (southern) and immigrant (northern) areas of DBM from China. Populations of C. vestalis fell into three groups, pointing to isolation in northwestern and southwestern China and strong genetic differentiation of these populations from others in central and eastern China. In contrast, DBM showed much weaker genetic differentiation and high rates of gene flow. TESS analysis identified the immigrant populations of DBM as showing admixture in northern China. Genetic disconnect between C. vestalis and its host suggests that the parasitoid did not migrate yearly with its host but likely consisted of resident populations in places where its host could not survive in winter.
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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.