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1,855 results for “Framework”
Bioplatforms Australia: Antibiotic Resistant Sepsis Pathogens Framework Initiative - Staphylococcus aureus BPH2986
GEO Series GSE152833. Staphylococcus aureus. 12 samples. Type: Expression profiling by high throughput sequencing.
DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data
GEO Series GSE255888. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Online companion - A New Cooperative Framework for a Fair and Cost-Optimal Allocation of Resources within a Low Voltage Electricity Community
<p>Simulation parameters used for the article "A New Cooperative Framework for a Fair and Cost-Optimal Allocation of Resources within a Low Voltage Electricity Community" submitted for review in IEEE Transactions on Smart grid</p>
Data Penggunaan Framework Pada Website JDIH Kementerian Indonesia
<p>Data identifikasi penggunaan framework yang digunakan pada website JDIH Kementerian Indonesia</p>
Aircraft measurement data obtained within the framework of the 3DO measurement campaign in summer 2018 over Berlin
<p>Based on airborne in-situ CO<sub>2</sub>, CH<sub>4</sub> and meteorological measurements from two flights on July 20<sup>th</sup> and 24<sup>th</sup> of 2018 we study the greenhouse gas emissions downwind of the German capital Berlin. Measurement flights were conducted within the Urban Climate Under Change [UC]2 programme (module B: Three-Dimensional Observation of Atmospheric Processes in Cities, 3DO). Results are published in T. Klausner, M. Mertens, H. Huntrieser, M. Galkowski, G. Kuhlmann, R. Baumann, A. Fiehn, P. Jöckel, M. Pühl, and A. Roiger: Urban greenhouse gas emissions from the Berlin area: A case study using airborne CO<sub>2</sub> and CH<sub>4</sub> in situ observations in summer 2018, Elementa: Science of the Anthropocene (Ref.: Ms. No. ELEMENTA-D-19-00074R1), 2019.</p>
Global Habitat Suitability for Framework-Forming Cold-Water Corals - Environmental Data
<p>Environmental data layers used in the production of global habitat suitability models for several species of Scleractinian corals from the publication:</p> <p><strong>Davies, A.J. & Guinotte, J.M. (2011) Global Habitat Suitability for Framework-Forming Cold-Water Corals. PLoS ONE 6(4): e18483. doi:10.1371/journal.pone.0018483</strong></p> <p>Files are ASCII raster layers, zipped using 7zip. Each raster has a WGS 1984 coordinate system.</p> <p>Brief methodology and outline of variables provided are presented below, for full details refer to the above manuscript in PLoS ONE:</p> <p><strong><em>Terrain variables</em></strong></p> <p>Several terrain attributes were extracted from the SRTM30 data (Table 1) following techniques and algorithms described in Wilson et al (2007). Individual approaches are described within the footnote of Table 1, however, briefly the extraction process and description of each variable is described here. Bathymetric position index (BPI) is an approach to determine topographical features based on their relative position within a neighbourhood, and can be calculated over fine or broad scales to capture smaller or larger terrain features respectively. This calculation has been developed into an ArcGIS tool by Wright et al. (2005). Slope was calculated using DEM Tools for ArcGIS developed by Jenness (2012), in particular the 4-cell method of calculating slope, which is accepted as the most accurate approach (Jones 1998). In this manuscript, slope is defined as the gradient in the direction of the maximum slope. Curvature attempts to describe terrain features and may provide an indication of how water would interact with the terrain. In this manuscript, plan and tangential curvature can describe how water would converge or diverge as it flows over relief, whilst profile curvature describes how water would accelerate or decelerate as it flows over relief (Jenness 2012). Aspect is defined as the direction of maximum slope and was converted to continuous radians following Wilson et al. (2007). Rugosity, terrain ruggedness index and roughness all generally describe the variability of the relief of the seafloor (Wilson et al. 2007). Rugosity is defined as the ratio of the surface area to the planar area across a neighbourhood of a central pixel (Jenness 2012). Terrain ruggedness index is defined as the mean difference between a central pixel and its surrounding cells and roughness which is the largest inter-cell difference of a central pixel and its surrounding cell (Wilson et al. 2007).</p> <p><strong><em>Environmental variables</em></strong></p> <p>Environmental variables were created using the variable up-scaling approach presented within (Davies & Guinotte 2011). This approach takes gridded layers of an environmental variable and drapes them over bathymetry to provide an indication of conditions near the seabed, it has been proven to work well over global and regional scales (Davies & Guinotte 2011, Guinotte & Davies 2012). In this manuscript, these environmental layers must be considered as representations of general conditions, as likely, the highly variable topography of the canyon will not yield a good prediction of environmental variables at such a small spatial scale. Limited CTD profiles were collected using a SeaBird 911+, collating data for turbidity (Seapoint, formazin turbidity units), dissolved oxygen (mg L<sup>-1</sup>), depth (m), conductivity (Siemens/m), temperature (°C), salinity, and pH. These casts were compared with the modelled layers (specifically dissolved oxygen, salinity and temperature) to determine their relative accuracy for certain areas of the seafloor</p> <p><strong>Variable name // Filename // Units // Reference (see original paper)</strong></p> <p>Terrain Variables</p> <p>Aspect // aspect // Degree // Jenness (2012)<br> Aspect – Eastness // eastness // // Wilson et al. (2007)<br> Aspect – Northness // northness // // Wilson et al. (2007)<br> Bathymetry // srtm30 // m // Becker et al. (2009)<br> Curvature – Profile // profilecurve // // Jenness (2012)<br> Curvature – Plan // plancurve // // Jenness (2012)<br> Curvature – Tangential // tangcurve // // Jenness (2012)<br> Roughness // roughness // // Wilson et al. (2007)<br> Rugosity // rugosity // // Jenness (2012)<br> Slope // slope // Degrees // Jenness (2012)<br> Terrain Ruggedness Index // tri // // Wilson et al. (2007)<br> Topographic Position Index // tpi // // Wilson et al. (2007) </p> <p>Environmental variables<br> Alkalinity // alk_stein // μmol l-1 // Steinacher et al. (2009)<br> Apparent oxygen utilisation // woaaoxu // mol O2 m-3 // Garcia et al. (2006b)<br> Chlorophyll a // modismin, modismean, modismax // mg m-3 // NASA Ocean Color<br> Dissolved inorganic carbon // dic_stein // μmol l-1 // Steinacher et al. (2009)<br> Dissolved oxygen // woadiso2 // ml l-1 // Garcia et al.(2006a)<br> Nitrate // woanit // μmol l-1 // Garcia et al. (2006b)<br> Omega aragonite // arag_stein // ΩARAG // Steinacher et al. (2009)<br> Omega aragonite // arag_orr // ΩARAG // Orr et al. (2005)<br> Omega calcite // calc_stein // ΩCALC // Steinacher et al. (2009)<br> Omega calcite // calc_orr // ΩCALC // Orr et al. (2005)<br> Percent oxygen saturation // woapoxs // % O2S // Garcia et al. (2006b)<br> Phosphate // woaphos // μmol l-1 // Garcia et al. (2006b)<br> Regional current velocity // regfl // m s-1 // Carton et al. (2005)<br> Salinity // woasal // pss // Boyer et al. (2005)<br> Silicate // woasil // μmol l-1 // Garcia et al. (2006b)<br> Seasonal variation index // lutzsvi // // Lutz et al. (2007)<br> Temperature // woatemp // °C // Boyer et al. (2005)<br> Particulate organic carbon // poc // g Corg m-2 yr-1 // Lutz et al. (2007)<br> Vertical current velocity // vertfl // m s-1 // Carton et al. (2005)<br> Vertically generalised productivity model // vgpmmin, vgpmmean, vgpmmax // mg C m-2 d-1 // Behrenfeld and Falkowski (1997)</p>
A framework for pore-scale simulation of broadband electrical conductivity and permittivity of porous media
<p>The data presented in a manuscript entitled "A framework for pore-scale simulation of electrical conductivity and permittivity of porous media in the frequency range from mHz to GHz" to be submitted to Journal of Geophysical Research: Solid Earth.</p>
Twinning in Zr-based Metal-organic Framework Crystals
<p>Cif files for three metal-organic framework compounds. </p>
Solvent Induced Enantioselectivity Reversal in a Chiral Metal Organic Framework
<p><strong>Abstract</strong></p> <p>Solvent induced enantioselectivity reversal is a rarely reported phenomenon in porous homochiral materials. Similar behavior has been studied in chiral HPLC, where minor modifications to the mobile phase can induce elution order reversal of two enantiomers on a chiral stationary phase column. We report the first instance of solvent-induced enantioselectivity reversal in a homochiral metal organic framework. Further, we highlight the complex enantioselectivity behavior of homochiral metal organic frameworks towards racemic mixtures in the presence of solvents through racemate-solvent enantioselectivity and loading experiments as well as enantiopure-solvent loading experiments. We hypothesize that this interesting selectivity reversal behavior is likely to be observed in other competitive adsorption, non-chiral selective processes involving a solvent.</p> <p> </p> <p>This dataset contains: PXRD data</p>
On the (Un-)Adoption of JavaScript Front-end Frameworks
<p>DBR</p>
Evaluating Frameworks Assemblies In Microservices-based Systems Using Imperfect Information (Video)
<p>This video presents a novel technique, called µAzimut, whose purpose is to generate, evaluate, and compare frameworks assemblies using potentially incomplete, imprecise, and changing descriptions of nonfunctional requirements and frameworks. The frameworks assemblies evaluation is based on a support score which allows modeling imperfect architectural knowledge.</p>
Data from: A unified framework for quantifying land carbon sequestration
<p>Land ecosystems offer an effective nature-based solution to climate change mitigation by absorbing approximately 30% of anthropically emitted carbon. This absorption is primarily based on constraints<span> from atmospheric and oceanic measurements while quantification from direct studies of the land carbon cycle itself displays great uncertainty. The latter hinders prediction of the future fate of the land carbon sink. </span>Here, we show a unified framework for quantifying land carbon sequestration. The framework unifies all carbon cycle processes under a theory of dynamic disequilibrium and all models with one matrix equation to represent a nonautonomous compartmental system. While carbon input and residence (transit) time have been used to quantify carbon storage capacity at dynamic equilibrium, we introduce the third quantity, carbon storage potential that integrates fluxes and timescales, to define dynamic disequilibrium of land carbon cycle under global change. The carbon storage potential is presently not directly measurable. Its estimation requires the integration of multiple data sets with models. We urge the research community to convert the existing models and develop new models in the matrix form before we can fully identify sources of uncertainty in model predictions, evaluate relative values of various data sets, and improve the accuracy of quantification of land carbon sequestration.</p>
Figure 1 from: Haak L, Greene S, Ratan K (2020) A New Research Economy: Socio-technical framework to open up lines of credit in the academic community. Research Ideas and Outcomes 6: e60477. https://doi.org/10.3897/rio.6.e60477
Figure 1 Activities and artifacts at each stage of the research process.
Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequenc-ing data analysis
<p>Allen institute 10X mouse data (<a href="https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-10x">https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-10x</a>) converted into h5 format including metadata, to be easily uploaded in Cytosplore-Transcriptomics.</p>
Data from: A framework for detecting natural selection on traits above the species level
To what extent can natural selection act on groupings above the species level? Despite extensive theoretical discussion and growing practical concerns over increased rates of global ecological turnover, the question has largely evaded empirical resolution. A flexible and robust hypothesis-testing framework for detecting the phenomenon could facilitate significant progress in resolving this issue. We introduce a permutation-based approach, implemented in the R package perspectev, which provides an explicit test of whether empirical patterns of correlation between upper level trait values and survivorship are reducible to correlations manifested at lower levels. The package is applicable to virtually any nested set of upper- and lower level groupings, a wide variety of upper level traits, and both historical and contemporary occurrence data. We apply this approach to five paleontological data sets that represent different magnitudes of extinction and differ in taxonomic breadth, geological timing and geographic extent. Using simulations, we demonstrate that this method is a robust means of detecting irreducibility in the relationship between upper level traits and survivorship, and outline circumstances in which the method is less effective. We also find evidence consistent with previous findings of selection above the species level for geographic range size in North American K-Pg molluscs and show that this phenomenon was evident for the same molluscan genera globally. Ultimately, we conclude that at certain points in history, some higher level taxonomic groups have survived differentially with respect to geographic range size in a manner that is not explained by the same trait at the species level, and we show that evidence for this phenomenon varies across taxa and extinction events. We release our method as a flexible and easy-to-use R package that will allow others to help determine the relative frequency of this macroevolutionary phenomenon, both in the fossil record and in estimates of contemporary extinction risk.
Data from: Demographic inferences using short-read genomic data in an Approximate Bayesian Computation framework: in silico evaluation of power, biases, and proof of concept in Atlantic walrus
Approximate Bayesian Computation (ABC) is a powerful tool for model-based inference of demographic population histories from large genetic data sets. For most organisms its implementation has been hampered by the lack of sufficient genetic data. Genotyping-by-sequencing (GBS) provides cheap genome-scale data to fill this gap, but its potential has not fully been exploited. Here, we explored power, precision and biases of a coalescent-based ABC approach where GBS data were modeled with either a population mutation parameter (θ) or with a fixed sites (FS) approach, allowing single or several segregating sites per locus. With simulated data ranging from 500 to 50,000 loci a variety of demographic models could be reliably inferred across a range of timescales and migration scenarios. Posterior estimates were informative with 1,000 loci for migration and split time in simple population divergence models. In more complex models posterior distributions were wide and almost reverted to the uninformative prior even with 50,000 loci. ABC parameter estimates, however, were generally more accurate than an alternative composite-likelihood method. Bottleneck scenarios proved particularly difficult and only recent bottlenecks without recovery could be reliably detected and dated. Notably, minor allele frequency filters – usual practice for GBS data – negatively affected nearly all estimates. With this in mind, we used a combination of FS and θ approaches on empirical GBS data generated from the Atlantic walrus (Odobenus rosmarus rosmarus), collectively providing support for a population split before the last glacial maximum followed by asymmetrical migration and a range-wide bottleneck. Overall, this study evaluates the potential and limitations of GBS data in an ABC-coalescence framework and proposes a best-practice approach.
Data from: Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework
The estimation of origination and extinction rates and their temporal variation is central to understanding diversity patterns and the evolutionary history of clades. The fossil record provides the only direct evidence of extinction and biodiversity changes through time and has long been used to infer the dynamics of diversity changes in deep time. The software PyRate implements a Bayesian framework to analyze fossil occurrence data to estimate the rates of preservation, origination and extinction while incorporating several sources of uncertainty. Building upon this framework, we present a suite of methodological advances including more complex and realistic models of preservation and the first likelihood-based test to compare the fit across different models. Further, we develop a new reversible jump Markov chain Monte Carlo algorithm to estimate origination and extinction rates and their temporal variation, which provides more reliable results and includes an explicit estimation of the number and temporal placement of statistically significant rate changes. Finally, we implement a new C++ library which speeds up the analyses by orders of magnitude, therefore facilitating the application of PyRate to large datasets. We demonstrate the new functionalities through extensive simulations and with the analysis of a large dataset of Cenozoic marine mammals. We compare our analytical framework against two alternative methods to infer origination and extinction rates revealing that PyRate decisively outperforms them across a range of simulated datasets. Our analyses indicate that explicit statistical model testing, which is often neglected in fossil-based macroevolutionary analyses, is crucial to obtain accurate and robust results.
Data from: An integrative framework of coexistence mechanisms in competitive metacommunities
Species distribution in a metacommunity varies according to their traits, the distribution of environmental conditions and connectivity among localities. These ingredients contribute to coexistence across spatial scales via species sorting, patch dynamics, mass effects and neutral dynamics. These mechanisms however seldom act in isolation and the impact of landscape configuration on their relative importance remains poorly understood. We present a new model of metacommunity dynamics that simultaneously considers these four possible mechanisms over spatially explicit landscapes and propose a statistical approach to partition their contribution to species distribution. We find that landscape configuration can induce dispersal limitations that have negative consequences for local species richness. This result was more pronounced with neutral dynamics and mass effect than with species sorting or patch dynamics. We also find that the relative importance of the four mechanisms varies not only among landscape configurations, but also among species, with some species being mostly constrained by dispersal and/or drift and others by sorting. Changes in landscape properties might lead to a shift in coexistence mechanisms and, by extension, to a change in community composition. This confirms the importance of considering landscape properties for conservation and management. Our results illustrate the idea that ecological communities are the results of multiple mechanisms acting at the same time and complete our understanding of spatial processes in competitive metacommunities.
Data from: Incorporating the disease triangle framework for testing the effect of soil-borne pathogens on tree species diversity
1. The enemies-induced Janzen-Connell (JC) effect, a classic model invoking conspecific negative density dependence (CNDD) and distance dependence, is a primary biodiversity maintenance hypothesis. Yet, conflicting evidence for the JC effect leads to disagreement about its role in maintaining forest diversity. 2. We focus this review on soil-borne pathogens, which are the primary agent inducing the JC effect in many forest ecosystems. Although the test of the pathogen-induced JC effect in ecology critically rests on the seedling mortality caused by soil pathogens, what has not been explicitly explored in the early literature but has increasingly received attention is the long-recognized fact that the environment can alter virulence of pathogens and host susceptibility (and thus pathogen-host interactions), as predicted by the classic disease triangle framework enlightened by pathology research in agricultural systems. 3. Here, following the disease triangle framework we review evidence on how the pathogen-induced JC effect may be contingent on context (e.g., environmental conditions, pathogen inoculum load, and genetic divergence in host and pathogen populations). The reviewed evidence reveals and clarifies the conditions where pathogens may or may not cause disease to hosts, thus contributing to reconciling the inconsistent results about the pathogen-induced JC effect in the literature. The context-dependence of the disease triangle predicts that the pathogen-induced JC effect would change under global change. 4. Gaining insights from evidence that the pathogen-induced JC effect is context-dependent, we suggest that future tests on the JC hypothesis be conducted under the framework of disease triangle, and we stress the necessity by controlling the effect of context factors on plant-pathogen interactions when testing for the JC effect. We conclude the review by proposing three lines of future research for testing the importance of the JC effect in maintaining global forest tree species diversity, with a particular emphasis on testing the effect of global warming on the strength of pathogen-host interactions for better predicting changes of forest biodiversity under climate change.
Online Companion: A Preference Informed Energy Sharing Framework for a Centralized Energy Community
<p>This manuscript serves as an electronic companion to [1]. We present the complementary input data, including prosumers target demand, PV power generation, and electricity market prices.</p>
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.