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2,015 results for “context”
CONTENT -- Multi-context genetic modeling TWAS and eAssociation summary statistics
<p>We provide the summary statistics of running CONTENT, the context-by-context approach, and UTMOST on over 22 phenotypes. The phenotypes are listed in the manuscript, and their respective studies and sample size can be found in a table under the supplementary section of the manuscript. All 3 methods were trained on GTEx v7 as well as CLUES, a single-cell RNA sequencing dataset of PBMCs. The data include the gene name, model, cross-validated R^2, prediction pvalue, TWAS p value, TWAS Z score, and a column titled "hFDR" indicating whether the association was statistically significant while employing hierarchical FDR. The benefits of employing such an approach for all methods can be found in the manuscript.</p> <p> </p> <p>We also include the eAssociations that we obtain by training prediction models on GTEx and CLUES alone. For the CxC and UTMOST approaches, these files contain the gene, context, pvalue and adjusted R^2. For CONTENT, these include the gene, context and pvalue and adjusted R^2 for each CONTENT model--the column names are described like a regression of y~x, rsq_y_x, so rsq_observed_full is the adjusted R^2 from regressing the observed expression onto the cross-validated full model predictions. In cases where the R^2 is higher from the specific or shared models, it's best to use either of those rather than the full model for out of sample prediction.</p>
Constraining Andean Propagation of Exhumation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context - Supporting Information
<p>Supporting information accompanying the publication "Constraining Andean Propagation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context" published in Tectonics. The dataset contains apatite and zircon (U-Th-Sm)/He and apatite fission track data from the Tilcara Range and San Lucas block, Jujuy, Argentina, as well as additional QTQt thermal models that are discussed in the paper.</p> <p>Table S1 contains full single-grain results from apatite fission track, apatite (AHe) (U-Th-Sm)/He and zircon (ZHe) (U-Th-Sm)/He analyses. Outliers are marked in grey and are not included in the weighted mean age. Figure S1 supports (U-Th-Sm)/He data graphically. Apatite fission track (AFT) data is supported by radial plots in Figure S2. Figure S3 shows QTQt thermal models using either AHe, AFT or ZHe single-grain ages. All of the models results are explained in the main text.</p>
Resource-Rational Lossy-Context Surprisal (Model Predictions)
<p>Resource-Rational Lossy-Context Surprisal is a computationally implemented model of how humans process language, predicting at what points in complex sentences they experience comprehension difficulty. It unifies the memory-based and expectation-based paradigms in psycholinguistics, and provides a more refined account of when hierarchical structure is difficult to comprehend for humans.</p> <p>This repository contains output of the model on a battery of test sentences exhibiting iterated recursive structure, described in associated publications on Resource-Rational Lossy-Context Surprisal. The filenames are referred to in the source code, to be published together with a forthcoming journal publication on the model.</p> <p>The model was first described in the following publication:</p> <p><em>Lexical Effects in Structural Forgetting: Evidence for Experience-Based Accounts and a Neural Network Model</em></p> <p>(Michael Hahn, Richard Futrell, Edward Gibson), 33rd Annual CUNY Human Sentence Processing Conference, 2020</p>
Self-medication for anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima-2021
<p><strong>Background:</strong> To determine the relationship between self-medication and anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima 2021.</p> <p><strong>Methods:</strong> The research method was deductive, basic and with a quantitative approach; the design used was non-experimental, descriptive, correlational, cross-sectional, and prospective. Spearman's Rho analysis was performed to validate the hypothesis.</p> <p><strong>Results:</strong> 384 users were evaluated, finding 93.5% aged 18-59 years, of whom 53.4% were female, 42.7% had completed high school, 57.8% were single and 51.6% presented physical symptoms, preferably muscular tension accompanied by pain, 60.7% presented behavioral symptoms, highlighting unusual sadness in the face of COVID-19 and 70.1% presented cognitive symptoms with greater frequency of concern about contracting COVID-19. In addition, the greater the symptoms of anxiety, the higher the self-medication increased from 9.0% to 21.1%, a similar case was evidenced in self-medication on their own initiative where the increase was from 7.5% to 33.3%; likewise, self-medication without medical prescription increased from 15.8% to 47.7%, the consumption of anxiolytics or antidepressants increased from 0.8% to 26.3% caused by the symptoms of anxiety.</p> <p><strong>Conclusion:</strong> It was determined that there is a moderate relationship between self-medication and anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima 2021.</p> <p><strong>Keywords:</strong> Self-medication, prescription, anxiety, depression, COVID-19.</p> <p><strong>Background:</strong> To determine the relationship between self-medication and anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima 2021.</p> <p><strong>Methods:</strong> The research method was deductive, basic and with a quantitative approach; the design used was non-experimental, descriptive, correlational, cross-sectional, and prospective. Spearman's Rho analysis was performed to validate the hypothesis.</p> <p><strong>Results:</strong> 384 users were evaluated, finding 93.5% aged 18-59 years, of whom 53.4% were female, 42.7% had completed high school, 57.8% were single and 51.6% presented physical symptoms, preferably muscular tension accompanied by pain, 60.7% presented behavioral symptoms, highlighting unusual sadness in the face of COVID-19 and 70.1% presented cognitive symptoms with greater frequency of concern about contracting COVID-19. In addition, the greater the symptoms of anxiety, the higher the self-medication increased from 9.0% to 21.1%, a similar case was evidenced in self-medication on their own initiative where the increase was from 7.5% to 33.3%; likewise, self-medication without medical prescription increased from 15.8% to 47.7%, the consumption of anxiolytics or antidepressants increased from 0.8% to 26.3% caused by the symptoms of anxiety.</p> <p><strong>Conclusion:</strong> It was determined that there is a moderate relationship between self-medication and anxiety symptoms in the context of COVID-19, in users who go to a drugstore in Los Olivos, Lima 2021</p> <p> </p>
See no evil in the voice-to-voice customer service context
<p>A sample of more than 28,000 front-line employee (FLE) - customer interactions, extrapolating from foundational framing, we pit conventional service approaches against one another to propose a dual-process model, situating customer frustration/satisfaction as mediators of the indirect relationships between resolution/relational tactics and call duration – a key customer service efficiency outcome.</p>
New insights into the decadal variability in glacier volume of a tropical ice-cap explained by the morpho-topographic and climatic context, Antisana, (0°29' S, 78°09' W)
<p>The dataset contains five periods of surface elevation change observed on the Antisana icecap in the inner tropical region. Data were obtained by geodetic observations of aerial photographs and high-resolution satellite images for the study periods: 1956-1965, 1965-1979, 1979-1997, 1997-2009, and 2009-2016.</p>
Text-fig. 1. Context and location of the Govone outcrop. a: Location of the Piedmont Basin at the northern margin of the Mediterranean Basin and distribution of Messinian evaporites. b: Simplified geological map of the Piedmont Basin showing the location of the Govone outcrop close to the town of Alba. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 1. Context and location of the Govone outcrop. a: Location of the Piedmont Basin at the northern margin of the Mediterranean Basin and distribution of Messinian evaporites. b: Simplified geological map of the Piedmont Basin showing the location of the Govone outcrop close to the town of Alba.
Mechanics of graphene in context
<p><strong>Mechanics of graphene in context</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>Graphene is a two-dimensional form of crystalline carbon, either a single layer of carbon atoms forming a honeycomb lattice or several coupled layers of this honeycomb structure. The word graphene, when used without specifying the form, usually refers to single-layer graphene. Graphene is a parent form of all graphitic structures of carbon: graphite, which is a three-dimensional crystal consisting of relatively weakly coupled graphene layers; nanotubes, which may be represented as scrolls of graphene; and buckyballs, spherical molecules made from graphene with some hexagonal rings replaced by pentagonal rings. The basic electronic structure of graphene and, as a consequence, its electric properties are very peculiar. By applying a gate voltage or using chemical doping by adsorbed atoms and molecules, one can create either electron or hole conductivity in graphene that is similar to the conductivity created in semiconductors. However, in most semiconductors there are certain energy levels where electrons and holes do not have allowed quantum states, and, because electrons and holes cannot occupy these levels, for certain gate voltages and types of chemical doping, the semiconductor acts as an insulator. Graphene, on the other hand, does not have an insulator state, and conductivity remains finite at any doping, including zero doping. Existence of this minimal conductivity for the undoped case is a striking difference between graphene and conventional semiconductors. Electron and hole states in graphene relevant for charge-carrier transport are similar to the states of ultra-relativistic quantum particles, that is, quantum particles moving at the speed of light. The honeycomb lattice of graphene actually consists of two sublattices, designated A and B, such that each atom in sublattice A is surrounded by three atoms of sublattice B and vice versa. This simple geometrical arrangement leads to the appearance that the electrons and holes in graphene have an unusual degree of internal freedom, usually called pseudospin. In fact, making the analogy more complete, pseudospin mimics the spin, or internal angular momentum, of subatomic particles. Within this analogy, electrons and holes in graphene play the same role as particles and antiparticles in quantum electrodynamics. Graphene provides a bridge between materials science and some areas of fundamental physics, such as relativistic quantum mechanics. There is another reason why graphene is of special interest to fundamental science: it is the first and simplest example of a two-dimensional crystal, that is, a solid material that contains just a single layer of atoms arranged in an ordered pattern. Two-dimensional systems are of huge interest not only for physics and chemistry but also for other natural sciences. In particular, due to very strong thermal fluctuations of atomic positions that remain correlated at large distances, long-range crystalline order cannot exist in two dimensions. Instead, only short-range order exists, and it does so only on some finite scale of characteristic length, a caveat that should be noted when graphene is called a two-dimensional crystal. For this reason, two-dimensional systems are inherently flexural, manifesting strong bending fluctuations, so that they cannot be flat and are always rippled or corrugated. Graphene, because of its relative simplicity, can be considered as a model system for studying two-dimensional physics and chemistry in general. Other two-dimensional crystals besides graphene can be derived by exfoliation from other multilayer crystals or by chemical modification of graphene. Modern electronics are basically two-dimensional in that they use mainly the surface of semiconducting materials. Therefore, graphene and other two-dimensional materials are considered very promising for many such applications.</p>
Mechanics of polymeric materials in context
<p><strong>Mechanics of polymeric materials in context</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>Mechanics is the science concerned with the motion of bodies under the action of forces, including the special case in which a body remains at rest. Of first concern in the problem of motion are the forces that bodies exert on one another. This leads to the study of such topics as gravity, electricity, and magnetism, according to the nature of the forces involved. Given the forces, one can seek the manner in which bodies move under the action of forces; this is the subject matter of mechanics proper. Mechanics may be divided into three branches: statics, which deals with forces acting on and in a body at rest; kinematics, which describes the possible motions of a body or system of bodies; and kinetics, which attempts to explain or predict the motion that will occur in a given situation. Alternatively, mechanics may be divided according to the kind of system studied. The simplest mechanical system is the particle, defined as a body so small that its shape and internal structure are of no consequence in the given problem. More complicated is the motion of a system of two or more particles that exert forces on one another and possibly undergo forces exerted by bodies outside of the system. The central concepts in classical mechanics are force, mass, and motion. Neither force nor mass is very clearly defined by Newton, and both have been the subject of much philosophical speculation since Newton. Both of them are best known by their effects. Mass is a measure of the tendency of a body to resist changes in its state of motion. Forces, on the other hand, accelerate bodies, which is to say, they change the state of motion of bodies to which they are applied. The interplay of these effects is the principal theme of classical mechanics. Although Newton's laws focus attention on force and mass, three other quantities take on special importance because their total amount never changes. These three quantities are energy, momentum, and angular momentum. Any one of these can be shifted from one body or system of bodies to another. In addition, energy may change form while associated with a single system, appearing as kinetic energy, the energy of motion; potential energy, the energy of position; heat, or internal energy, associated with the random motions of the atoms or molecules composing any real body; or any combination of the three. Nevertheless, energy, momentum, and angular momentum are conserved. These three conservation laws arise out of Newton's laws, but Newton himself did not express them. They had to be discovered later. It is a remarkable fact that, although Newton's laws are no longer considered to be fundamental, nor even exactly correct, the three conservation laws derived from Newton's laws, the conservation of energy, momentum, and angular momentum, remain exactly true even in quantum mechanics and relativity. In fact, in modern physics, force is no longer a central concept, and mass is only one of a number of attributes of matter. Energy, momentum, and angular momentum, however, still firmly hold center stage. The continuing importance of these ideas inherited from classical mechanics may help to explain why this subject retains such great importance in science today. Statics is the study of bodies and structures that are in equilibrium. For a body to be in equilibrium, there must be no net force acting on it. In addition, there must be no net torque acting on it. When a body has a net force and a net torque acting on it owing to a combination of forces, all the forces acting on the body may be replaced by a single force called the resultant, which acts at a single point on the body, producing the same net force and the same net torque. The body can be brought into equilibrium by applying to it a real force at the same point, equal and opposite to the resultant. This force is called the equilibrant.</p>
People in Social Context (PISC) Dataset
<p>The People in Social Context (PISC) dataset is a new dataset that focuses on social relationships. It consists of 22,670 images of 9 types of social relationships. We provide annotation of the bounding boxes of all people, as well as the social relationship between all pairs of people in the images. In addition, we provide occupation annotation. For more details on the collection process and statistics of the dataset, please see our paper.</p> <ul> <li><strong>annotation_image_info.json</strong> contains bounding box annotation and information about image source, image size, image id.</li> <li><strong>domain.json</strong> contains annotation of the 3 types of coarse relationships: {intimate, not intimate, no relation}.</li> <li><strong>relationship.json</strong> contains annotation of the 6 types of fine relationships: {friends, family, couple, professional, commercial, no relation}.</li> <li><strong>occupation.json</strong> contains annotation of the occupation.</li> <li><strong>domain_split</strong> and <strong>relation_split</strong> contain train/val/test split.</li> <li>after downloading all image parts (images-*), extract using: cat images-* | tar zx</li> </ul> <p>The dataset can be applied, but not limited to the following research areas:</p> <ul> <li>social relationship study</li> <li>people detection</li> <li>occupation recognition</li> </ul> <p>Please cite the following paper if you use the PISC dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>J. Li, Y. Wong, Q.Zhao, M. Kankanhalli<br> <strong>Dual-Glance Model for Deciphering Social Relationships</strong><br> <em>ICCV</em>, 2017.<br> http://doi.org/</li> </ul>
Data from: "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal"
<p>Processed datasets used for analysis in "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal" by Hooven et al. published in <em>Mammal Research</em>. R scripts used to process and analyze these data are available from: <a href="https://github.com/nhooven/elk-individual-habitat">https://github.com/nhooven/elk-individual-habitat</a></p> <p>WS_sampled.csv, SU_sampled.csv, UA_sampled.csv, AW_sampled.csv - Processed telemetry datasets (with relocation data removed), resultant files from script "01 - Pre-processing.R".</p> <p>WS_HRs.csv, SU_HRs.csv, UA_HRs.csv, AW_HRs.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by individual. </p> <p>WS_groups.csv, UA_groups.csv, AW_groups.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by groups. </p> <p>Note: Raw telemetry data and home range polygons are not available due to the sensitive nature of providing animal locations publicly. Please direct any questions or concerns to the corresponding author (nathan.d.hooven@gmail.com). </p>
Figure 6 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 6: Average IJ movement in each of the 3 species when corner placed, in both conspecific and heterospecific conditions. Sc = Steinernema carpocapsae, Sf = Steinernema feltiae, Sg = Steinernema glaseri. The solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range).
Figure 3 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 3: Index of Dispersion for each of the three species when applied alone in the center of dispersal boxes. Values of D> 1 indicate increasing aggregation. Solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range).
Figure 2 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 2: Pyrex experimental arenas to assess responses when nematodes were added to opposite corners. Arenas filled with approximately 1200 g of sand at 10% moisture. A: Heterospecific experiment arena, where corners have different species of IJs. B: Conspecific test arena, where a single species of IJ was placed at one corner. C: 5 x 5 sampling grid; samples were collected at each circle.
Figure 5 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 5: Aggregation shown by each of the 3 species when corner placed, in both conspecific and heterospecific conditions. Increasing values of D indicate increasing aggregation. Sc = Steinernema carpocapsae, Sf = Steinernema feltiae, Sg = Steinernema glaseri. The solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range).
Figure 1 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 1: Polypropylene experimental arenas to assess introduction at a common point. Arenas filled with approximately 1200 g of sand at 10% moisture. Image shows introduction point on 60mm filter paper and sample locations.
Figure 4 in Group Movement in Entomopathogenic Nematodes: Aggregation Levels Vary Based on Context
Figure 4: Aggregation shown by each of the three species when center placed, in both conspecific (alone) and heterospecific conditions. Increasing values of D indicate increasing aggregation. Sc = Steinernema carpocapsae, Sf = Steinernema feltiae, Sg = Steinernema glaseri. The solid line within each box indicates the median, black diamonds indicate the arithmetic mean, and black circles indicate outliers (observations with values> 1.5 * the interquartile range). NOTE that y-axis scale changes significantly across the three panels.
Fig. 2 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 2: Flowchart of steps and methods followed (AHP: Analytic Hierarchy Process, FM: Fuzzy Membership).
Fig. 7 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 7: Spatial representation of the Fishing pressure index from the small scale coastal fishery (FPc).
Characterisation and comparison of Mycoplasma bovis strain types from Irish and Scottish bovine isolates in a global context - code and datasets.
<p>The objectives of this paper were to firstly, characterise the strains and genetic diversity within isolates of Mycoplasma bovis collected from clinical samples of bovine respiratory disease in Ireland and Scotland, and secondly, to provide a global phylogenetic context to these isolates. </p> <p>This archive contains associated Jupyter notebooks and metadata used in the analysis for the study.</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.