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3,206 results for “property (T)”
DNA structural properties of archaeal promoters
<p>Supplementary information used in a study regarding the characterization of promoter sequences of archaea.</p>
BC Ophiolitic UMR Mineralogy, Physical Properties, and ME Geochemistry
<p>This is dataset comprises: major-element geochemistry, physical properties (magnetic susceptibility, natural remanent magnetization, density, porosity), and mineralogy (XRD and TGA) for ophiolitic ultramafic rocks in British Columbia.</p>
Text-fig. 1. Geographic position of the beaver-bearing sites discussed in this paper. Red triangles – records of Castor, blue dots – records of Trogontherium. Bilz II – Bilzingsleben II, Ehr – Weimar-Ehringsdorf, Mosb 2 – Mosbach 2, Taub – Weimar-Taubach, Teg – Tegelen. (This map was created using ArcGIS® software by Esri. ArcGIS® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri). in Mortality Profiles Of Castor And Trogontherium (Mammalia: Rodentia, Castoridae), With Notes On The Site Formation Of The Mid-Pleistocene Hominin Locality Bilzingsleben Ii (Thuringia, Central Germany)
Text-fig. 1. Geographic position of the beaver-bearing sites discussed in this paper. Red triangles – records of Castor, blue dots – records of Trogontherium. Bilz II – Bilzingsleben II, Ehr – Weimar-Ehringsdorf, Mosb 2 – Mosbach 2, Taub – Weimar-Taubach, Teg – Tegelen. (This map was created using ArcGIS® software by Esri. ArcGIS® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri).
FIG. 7. — Dendrograms resulting from Q in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean
FIG. 7. — Dendrograms resulting from Q-mode cluster analysis and the assemblages identified in each section.
FIG. 5 in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean
FIG. 5. — Relative frequency curves of the planktonic foraminifera identified in Bo Section. Refer to Figure 2 for the explanation of the lithostratigraphical column.
FIG. 3 in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean
FIG. 3. — Relative frequency curves of the planktonic foraminifera identified in Ag. Giannis Section. Refer to Figure 2 for the explanation of the lithostratigraphical column.
FIG. 2 in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean
FIG. 2. — Lithostratigraphical columns of the Ag. Giannis, Potamos and Bo sections, Gavdos Island, Greece.
FIG. 4 in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean
FIG. 4. — Relative frequency curves of the planktonic foraminifera identified in Potamos Section. Refer to Figure 2 for the explanation of the lithostratigraphical column.
A Theoretical Window into the Wind Clumping Properties of Magnetic Hot Star Winds
<p>Winds from hot, massive OB stars are driven by scattering and absorption of the stellar radiation by spectral lines. The standard line-driven wind theory of CAK predicts a smooth, steady outflow but neglects a strong radiation instability, resulting in strong shocks and a highly structured, clumped wind. Treating clumping arising from this line-deshadowing instability (LDI) is of key importance in accurately interpreting observed spectral diagnostics of massive star winds. Indeed, if not correctly accounted for, such wind clumping may lead to quite dramatic errors in inferred mass-loss properties and to correspondingly large errors in massive-star evolution predictions. So far theory and observation of the LDI have only investigated wind clumping for non-magnetic OB stars. Meanwhile, quantitative wind clumping behaviour for magnetic massive stars has not been established. However, by now there is ample evidence from spectropolarimetric surveys that a subset of OB stars in our Galaxy possesses strong, global surface magnetic fields believed to be of primordial origin. This magnetic field leads to a quenching of mass loss and can significantly alter stellar evolution, with speculations that it may even lead to formation of high stellar mass black holes. Such mass-loss rates have up until now relied on smooth wind predictions, hence do not take into account the intrinsic clumpy structures. In this contribution I present the first results of 2D numerical simulations on magnetic LDI winds that self-consistently predict the wind clumping phenomenon. I show the possible pathways to structure formation and discuss this in light of our recently carried out analytical perturbation analysis. Finally, I discuss the resulting wind clumping properties and the possible effects on observational diagnostics.</p>
Structures and Properties of Known and Postulated Interstellar Cations
<p>Positive ions play a fundamental role in interstellar chemistry, especially in cold environments where chemistry is believed to be mainly ion driven. We have carried out new accurate quantum chemical calculations to identify the structures and energies of 262 cations with up to 14 atoms that are postulated to have a role in interstellar chemistry. Optimized structures and rotational constants were obtained at the M06-2X/cc-pVTZ level, while electric dipoles and total electronic energies were computed with CCSD(T)/aug-cc-pVTZ//M06-2X/cc-pVTZ single-point energy calculations.</p>
LIMITED PROPERTY RIGHTS TO PROPERTY OF ANOTHER
<p>Opportunities derived from the PROPERTY RIGHTS establish a person's legal capacity to OWN and USE property that does not belong to him/her regardless of the OWNER`S will.</p> <p>In this case, the OWNERSHIP over a thing belongs to ONE PERSON, while the OTHER PERSON also has a PROPERTY RIGHT TO THIS THING, but LIMITED IN CONTENT.</p> <p>This is the property right of a person who is not the owner of the property. This is a person's right to property that does not belong to him/her.</p> <p>This is a property right limiting the powers of the owner.</p> <p>These are the property rights of a non-owner with a limited scope of powers in relation to the property of another (possession and use only). Such property right is protected in the same way as the right of ownership, including from the owner.</p> <p>1. LIMITED PROPERTY RIGHTS TO PROPERTY OF ANOTHER are the following.</p> <p>1.1. POSSESSIVE RIGHTS - the right to own a thing of another</p> <p>Possession.</p> <p>Easement (right of use).</p> <p>Emphyteusis (right to use land for agricultural purposes).</p> <p>Superficies (right to build on a land plot).</p> <p>1.2. SECURITY RIGHTS (lien right) - encumbrance of other's things with own legal capabilities.</p> <p>1.2.1. Pledge</p> <p>1.2.2. Mortgage</p> <p>1.2.3. Lien – in case of non-fulfillment by the debtor of the obligation to pay for a thing or reimbursement to the creditor of related costs and other damages, the creditor`s right to withhold a thing until the debtor fulfills such obligation.</p> <p>1.3. PREEMPTIVE RIGHTS - the ability of a person to acquire the rights to property mainly before others, which does not depend on the will of the owner.</p> <p>Preemptive right to acquire the property of another.</p> <p>Inheritance of everyday household objects.</p> <p>Preemptive right to use the property of another.</p>
Mechanical properties of Nevado del Ruiz - St. Isabel volcanoes
<p><strong>Description and structure of the data </strong><br> Data of static and dynamic mechanical material properties organized in 6 columns. The first three columns contain the position in UTM coordinates (UTM zone 18N): easting, northing, and depth (positive numbers refer to points above the mean sea level and negative numbers to points below the sea level). The last three columns contain Poisson’s dynamic ratio, Young’s static modulus, and density. The first row of the file is dedicated to the following header: UTM_x (m), UTM_y (m), Z (m), Poisson_dy, Young_st (GPa), density (kg/m^3)</p> <p><strong>Static and dynamic mechanical elastic properties</strong></p> <p>The dynamic elastic properties are calculated from P-wave (Vp) and S-wave (Vs) tomographic velocities, derived from the seismicity recorded by the Colombian Geological Survey - Volcanological and Seismological Observatory of Manizales (OVSM) between January 1st, 2016 and February 19, 2019.</p> <p>Empirical relationships are used for the conversion into static values <a href="https://www.zotero.org/google-docs/?BltUnS">(Hautmann et al., 2013; Wang, 2000)</a>. The relationship between dynamic Young’s modulus (<span class="math-tex">\(E_{dy}\)</span>) and shear wave velocity, Vs, inferred from the tomography <a href="https://www.zotero.org/google-docs/?TLLWSb">(Telford et al., 1976)</a> is<strong> <strong><span class="math-tex">\(E_{dy} = 2\rho\left( 1+\nu_{dy} \right)V_{s}^2\)</span></strong></strong></p> <p>where ρ is density <a href="https://www.zotero.org/google-docs/?HcYYHH">(Jaeger, 2007)</a>, defined through the Nafe-Drake empirical curve <a href="https://www.zotero.org/google-docs/?hTnbN9">(Brocher, 2005)</a> that describes the density (g/cm3) as function of Vp between 1.5 km/sec and 8.5 km/sec:</p> <p><span class="math-tex">\(\varrho=1.6612V_{p}-0.4721{V}_{p}^{2}+0.0671{V}_{p}^{3}-0.0043{V}_{p}^{4}+0.000106{V}_{p}^{5}\)</span></p> <p>and the dynamic Poisson’s modulus, <span class="math-tex">\(\nu_{dy}\)</span>, is calculated from the relationship of Vp and Vs, as in the case of an isotropic medium for lack of better information (i.e. borehole tests), with the following formula <a href="https://www.zotero.org/google-docs/?VD2VWu">(Guéguen and Palciauskas, 1994; Heap et al., 2014)</a>:</p> <p><span class="math-tex">\({\nu}_{dy}=\frac{V_{p}^2-2V_{s}^2}{2(V_{p}^2-V_{s}^2)}\)</span></p> <p>The values of the dynamic Young’s modulus derived, <span class="math-tex">\(E_{dy}\)</span>, increase from 12 GPa to 135 GPa, while the range of dynamic Poisson’s ratio values is between 0.17 and 0.30.</p> <p>In order to convert the dynamic values of the Young modulus into static values, we apply a standard empirical relationship <a href="https://www.zotero.org/google-docs/?Zir7pl">(Wang, 2000)</a>: </p> <p><span class="math-tex">\(E_{st} = 0.415\times E_{dy} (GPa) - 1.056\)</span></p> <p><br> The resulting values for <span class="math-tex">\(E_{st}\)</span> of the upper crust is from 5 GPa to 56 GPa.</p> <p> </p> <p><strong>References</strong></p> <p><strong>Brocher</strong>, T.M., 2005. Empirical relations between elastic wavespeeds and density in the Earth’s crust. Bulletin of the Seismological Society of America 95, 2081–2092. https://doi.org/10.1785/0120050077<br> <strong>Guéguen</strong>, Y., Palciauskas, V., 1994. Introduction to the Physics of Rocks, Princeton University Press. ed. Princeton, New Jersey.<br> <strong>Hautmann</strong>, S., Hidayat, D., Fournier, N., Linde, A.T., Sacks, I.S., Williams, C.P., 2013. Pressure changes in the magmatic system during the December 2008/January 2009 extrusion event at Soufrière Hills Volcano, Montserrat (W.I.), derived from strain data analysis. Journal of Volcanology and Geothermal Research 250, 34–41. https://doi.org/10.1016/j.jvolgeores.2012.10.006<br> <strong>Heap</strong>, M.J., Baud, P., Meredith, P.G., Vinciguerra, S., Reuschlé, T., 2014. The permeability and elastic moduli of tuff from Campi Flegrei, Italy: implications for ground deformation modelling. Solid Earth 5, 25–44. https://doi.org/10.5194/se-5-25-2014<br> <strong>Telford</strong>, W.M., Geldart, L.P., Sheriff, R.E., Keys, D.A., 1976. Applied Geophysics. Cambridge University Press, Cambridge.<br> <strong>Wang</strong>, Z., 2000. Dynamic versus static elastic properties of reservoir rocks, in: Seismic and Acoustic Velocities in Reservoir Rocks. Soc. of Explor. Geophys., Tusla, Oklahoma, pp. 531–539.</p> <p> </p> <p>This dataset is one of the results of PICVOLC project. PICVOLC has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 793811.</p> <p> </p> <p> </p>
Incubation data, CO2 and CH4 flux data and soil properties of thaw slump soils on Kurungnakh, Lena Delta in July 2016 and July 2019
<p>CO2 and CH4 rates from incubations and potential fluxes: This dataset contains rates of CO2 and CH4 production and the potential CO2 and CH4 emission rates calculated from these incubation fluxes</p> <p>in situ CO2 and CH4 chamber fluxes: This dataset contains CO2 and CH4 fluxes measured with closed chambers from different sites on Kurungnakh in July 2016 and July 2019</p> <p>simulated soil temperature and modelled CO2 fluxes: This dataset contains daily mean soil temperature data simulated with JSBACH for 2016 and the annual CO2 fluxes simulated with a Q10 model and the Introductory Carbon Balance Model (ICBM)</p> <p>thaw depth, TOC in active layer, soil temperature 2016: This dataset contains the thaw depth, TOC pools in the active layer and the soil temperature during the measurement period in July 2016</p> <p>thaw depth, TOC in active layer, soil temperature 2019: This dataset contains the thaw depth, TOC pools in the active layer and the soil temperature during the measurement period in July 2019</p> <p> </p> <p> </p> <p> </p> <p> </p>
Phosphorus fractions and related properties in soils under Pinus sylvestris L. plantations in Spain
<p>This database presents information about the P fractions in soils determined following the method developed by Hedley et al. (1982) and modified by Tiessen and Moir (1993) and other soil chemical properties of soils under <em>Pinus sylvestris </em>L. plantations in Spain.</p> <p>Abbreviations of variables names and units are described below:</p> <p>pH: soil pH; EOC: easily oxidizable C (%); EA: exchangeable acidity (cmol<sub>(+)</sub>·kg<sup>-1</sup>); Ca: exchangeable Ca (cmol<sub>(+)</sub>·kg<sup>-1</sup>); Sat: base saturation of the exchangeable complex (%); Al<sub>A</sub>, Fe<sub>A</sub>: amorphous Al and Fe (mg kg<sup>-1</sup>); Al<sub>E</sub>: exchangeable Al (cmol<sub>(+)</sub>·kg<sup>-1</sup>); Al<sub>M</sub>, Fe<sub>M</sub>: organically bound Al and Fe (mg kg<sup>-1</sup>); SI: forest site index (m); Cmic: microbial biomass C (mg kg<sup>-1</sup>); Pmic: microbial biomass P (mg kg<sup>-1</sup>); Cmin: mineralizable C (mg·kg<sup>-1</sup>·week<sup>-1</sup>) ; AcPhos: acid phosphatase activity (µg·g<sup>-1</sup>·h<sup>-1</sup>); PAEM: available P (mg kg<sup>-1</sup>); PiNaHCO3, PoNaHCO3: inorganic and organic highly labile P (mg kg<sup>-1</sup>); PoNaOH; PiNaOH: inorganic and organic moderately labile P (mg kg<sup>-1</sup>); PHCl1M: primary P (mg kg<sup>-1</sup>); PHClconc: stable P (mg kg<sup>-1</sup>); PHClO4: residual P (mg kg<sup>-1</sup>); PTotal: addition of all previous P fractions analysed (mg kg<sup>-1</sup>).</p>
Parameters of dielectric properties and emissivity estimated for Earth arid areas.
<p>Dataset gives the parameters estimated from passive microwave observations of the dielectric properties and emissivities of arid areas at 10 to 89 GHz.</p>
Tailoring the optical and dynamic properties of iminothioindoxyl photoswitches through acidochromism
<p>Multi-responsive functional molecules are key for obtaining user-defined control of the properties and functions of chemical and biological systems. In this respect, pH-responsive photochromes, whose switching can be directed with light and acid–base equilibria, have emerged as highly attractive molecular units. The challenge in their design comes from the need to accommodate application-defined boundary conditions for both light- and protonation-responsivity. Here we combine time-resolved spectroscopic studies, on time scales ranging from femtoseconds to seconds, with density functional theory (DFT) calculations to elucidate and apply the acidochromism of a recently designed iminothioindoxyl (ITI) photoswitch. We show that protonation of the thermally stable <em>Z</em> isomer leads to a strong batochromically-shifted absorption band, allowing for fast isomerization to the metastable <em>E</em> isomer with light in the 500–600 nm region. Theoretical studies of the reaction mechanism reveal the crucial role of the acid–base equilibrium which controls the populations of the protonated and neutral forms of the <em>E</em> isomer. Since the former is thermally stable, while the latter re-isomerizes on a millisecond time scale, we are able to modulate the half-life of ITIs over three orders of magnitude by shifting this equilibrium. Finally, stable bidirectional switching of protonated ITI with green and red light is demonstrated with a half-life in the range of tens of seconds. Altogether, we designed a new type of multi-responsive molecular switch in which protonation red-shifts the activation wavelength by over 100 nm and enables efficient tuning of the half-life in the millisecond–second range.</p> <p> </p> <p>Article information: <a href="https://doi.org/10.1039/D0SC07000A">https://doi.org/10.1039/D0SC07000A</a></p>
A laboratory study of the photometric properties of Mars Global Soil Simulant MGS-1 and its variants
<p>Figures 2 and 5-16 of "A laboratory study of the photometric properties of Mars Global Soil Simulant MGS-1 and its variants" submitted to Planetary & Space Science.</p>
Database on soil properties of the CS1 Study case
<p>Soil properties dataset of the CS1 Study Case corresponding to the article "The combination of crop diversification and no tillage enhances key 1 soil quality parameters related to soil functioning without compromising crop yields in a low-input rainfed almond orchard under semiarid Mediterranean conditions" by Almagro et al.</p>
Centroid values of aerosol optical properties for 8 sub-types based in AERONET inversion data (1993–2018)
<p>In this project, we adapted our previously defined 5 aerosol optical typology scheme (Hamill et al. 2016) to result in a more discriminating 8 aerosol typology scheme (Giordano 2019). Previously we presented an aerosol classification based upon AERONET level 2.0 almucantar retrieval products from the period 1993 to 2012. In the initial phases of this research, we opto-physically identified five major types of <strong>Bulk Columnar Aerosol</strong> (BCA) based solely upon intensive optical properties of spectral Single Scattering Albedo (<strong>SSA</strong>), spectral Indices of Refraction (real – <strong>RRI</strong> and imaginary – <strong>IRI</strong>), and two Angstrom Exponents (extinction – <strong>EAE</strong> and absorption – <strong>AAE</strong>). These BCA were classified as Maritime Aerosol, Dust Aerosol, Urban Industrial Aerosol, Biomass Burning Aerosol, and Mixed Aerosol. The classification of a particular observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to the centroid of each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (200+), we kept the variable space to 5-D. To generate reference clusters, we only retained data points that were found to lie within 2 MD from the data centroid. Our typology is based on AERONET retrieved quantities, which do not include low optical depth values (AOD440nm < 0.4 as per AERONET criteria for almucantar scan inversion). </p> <p>The classifications obtained are made available to be used in interpreting aerosol retrievals from satellite-borne instruments and as input for regional climate models. A major result of this aerosol typology is a dataset describing the types of aerosol particles that are distinct from one another in optical properties and a geographic distribution of those aerosol types. We used the typology scheme upon the qualifying AERONET data archive and produced seasonal aerosol climatologies by aerosol type for each of the AERONET sites included in the study, regional aerosol climatology maps, and a time-integrated global aerosol climatology map based entirely upon ground-based photometric data (Giordano 2022). An internally hyperlinked compendium of the individual AERONET site aerosol climatologies was produced to contain the results of the first phase of this work [available at <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a>]. Each of these original five aerosol types (Hamill et al. 2016, Giordano 2019) was further discriminated into specific sub-types by this same scheme to achieve an 8-aerosol typology (Giordano 2019 Chapter 2). For example, optical discrimination into specific sub-types of Biomass Burning aerosol may provide insight into sources exhibiting spectrally distinct smoke properties. Here we segmented the Biomass Burning Aerosol type into the sub-types of <em>Flaming</em> (<strong>BMF</strong>) and <em>Smoldering</em> (<strong>BMS</strong>) using the centroid separation method and the MD criteria for in-class inclusion was adjusted to 1.5 MD. Similarly, we found great confidence in discriminating the MIXED aerosol type into two distinct regimes which we simply labeled as <em>MIXEDtype1</em> (<strong>MIXED1</strong>) and <em>MIXEDtype2</em> (<strong>MIXED2</strong>). These can be visually verified by examining any one of many possible renditions of 3-D optical spaces noting their 5-D centroids are separated by a distance of 3.47-3.85 MD [Giordano 2019 Chapter 2]. Likewise, the Urban Industrial Aerosol class was further discriminated into European Urban Industrial (<strong>EURO UI</strong>) and North American (<strong>NA UI</strong>), whose 5-D centroids are separated by a distance of 2.60–3.08 MD. We then used the previously employed mathematical strategies to sort the global AERONET data retrievals into the aerosol types classified against their reference standards. We believe the strategies regarding aerosol differentiation using polarization data (Hamill, Piedra and Giordano 2020) are an additional method useful for analysis of the newer AERONET version 3 data retrievals, and data collected from the deployment of newer CIMEL sun-photometers (with enhanced polarization measurement capabilities) to the network. The resulting AERONET-based 8-aerosol optical typology, in a 5-D basis is useful for applications in aerosol optics, including direct forward modeling of radiative transfer to determine the effects of aerosol absorption and/or scattering on vertical heating profiles and ground received irradiance quantities, for input into more complicated remote sensing algorithms, used as calibration/validation values for in-situ and laboratory experimental studies, and evaluating radiative forcing calculations in atmospheric models.</p> <p>[Work related to an 8-aerosol typology in 6-D, 8-D, 9-D and 10-D optical property bases, and their files, are to be published subsequently as a different database project in 2023.]</p>
Data set. Optical properties. J-aggregate:PVA polaritonic films.
<p>Optical properties (real and imaginary part of permittivity) of J-aggregate:PVA materials analysed in manuscript entitled "Bio-inspired building blocks for all-organic metamaterials from visible to near-infrared". </p> <p>arXiv preprint arXiv:2210.02315</p> <p> </p> <p> </p> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.