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3,673 results for “Temporal”
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 2: All axial slices of sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients.
<p>Results from the sLORETA imaging indicates decreased neuronal activation within the Left<br> Fusiform Gyrus located in the Temporal lobe and Parrahippocampal gyrus, which is located in the<br> Limbic Lobe (Table 1). This correlates with other fMRI findings where patients with early onset<br> AN have exhibited reduced unilateral blood flow in the temporal lobe. The Parahippocampal and<br> Fusiform Gyri are centers that process emotions. Previous studies in which these regions have<br> shown activation involve women that have distorted perceptions of their bodies from a cognitive<br> perspective (Santel et al., 2006). It is apparent from our findings that the Fusiform Gyrus may play<br> a vital role in the processing of visual appearance of the human body. There is also a correlation<br> with the somatosensory limbic pathway in the limbic lobe, due to the similarity of function.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 1: sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients relative to Control participants
<p>The findings of the sLORETA analysis indicated that, the difference is statistically<br> significant (p=0.03) using a one-tailed t-test: Anorexia > Controls. The brains of the patients with<br> Anorexia Nervosa illustrated decreased neuronal activity in the Left Fusiform Gyrus and the Left<br> Parahippocampal Gyrus (p=0.03) in the resting-state brains when Anorexia Patients were sitting for<br> 3min, as compared to the Controls sitting for 3minutes.</p>
The role of temporal cues in voluntary stream segregation for cochlear implant users
<p>Data from "The role of temporal cues in voluntary stream segregation for cochlear implant users" (DOI: 10.1177/2331216518773226)</p> <p>List of variables:</p> <ul> <li>Subject: Listener's ID</li> <li>Electrode: Stimulation electrode.</li> <li>Rate: Stimulation pulse rate of the distractor stream. The target stream was always presented with a pulse rate of 300 pps.</li> <li>ABpairs: Number of AB duplets in the sequence.</li> <li>Hrate: Hit rate</li> <li>FArate: False alarm rate</li> <li>dprime: d' score</li> <li>d_se: Standard error of the d' score</li> <li>IOmodel: 1 for ideal observer model estimates and 0 for listener's d' scores</li> <li>control: 1 for the control (i.e. no distractor) condition</li> </ul> <p><strong>Note: In figure 3 from the paper, there is an error in the listeners' ID. Starting from the top panel, the correct IDs are: L1, L4, L5, L10, L6, L8 and L9. The IDs provided in the Data.txt file are correct.</strong></p>
Spontaneous Low-Voltage Fast Onset Seizures in Human Mesial Temporal Lobe Epilepsy Are Caused by Specific Inhibitory/Excitatory Imbalance
<p>Local field potential recordings during low voltage fast seizures recorded from microelectrodes in patients with medically refractory temporal lobe epilepsy</p>
New Ideas for Brain Modelling 4-Figure 2. One level of linking in a temporal model defines a particular ensemble mix.
<p> The image processing of section 0 has already been tried in de Campos, Babu and Varma (2009), where they tested the full dataset. Their results were better overall, with maybe 55% accuracy and over a larger dataset. As stated however, the tests here are only initial results and it would be expected that some improvement would be possible, especially if the images can be scaled. The recently found paper Kowalski (1972) looks significant and the general architecture (Greer, 2016, figure 2, for example)) could have analogies with bi-directional searches in the and-or with theorem-proving graphs architecture of that paper. As suggested, and-or could work from goals to axioms (the neural network in the general model and theorem-proving from axioms to goals (the concept trees in the general model). The paper Sukanya and Gayathri (2013) models at a higher behaviour level, but it is interesting that the behaviours are considered to be unique (time or sequence-based) sets of events and these event patterns are then clustered, rather than each individual event. The idea of using unique sets of nodes to cluster with has also been used for the symbolic neural network (Greer, 2011).</p>
Where and how our brain represents the temporal structure of observed action
<p>Dataset includes resting state function and structural scans used in the paper with the same title.</p>
New software protocols for enabling laboratory based temporal CT
<p>These are the accompanying datasets from the paper "<strong>New software protocols for enabling </strong><strong>laboratory based</strong><strong> temporal CT</strong>". There are two datasets: the germination of a mung bean captured using 54 uninterrupted time-lapse tomograms, and the precipitation of barite in a porous media with continuous golden-ratio projections.</p> <p>The data was collected using software extensions written for Nikon Metrology CT systems using IPC. These software extensions implemented the protocols highlighted in the paper, and can be found in the following GitHub repositories:</p> <p>Uninterrupted Time-Lapse CT: <a href="https://github.com/parmeshgajjar/TemporalCT-TimeLapse.Uninterrupted.git">https://github.com/parmeshgajjar/TemporalCT-TimeLapse.Uninterrupted.git</a></p> <p>Continuous Golden Ratio Acquisition CT: <a href="https://github.com/parmeshgajjar/TemporalCT-ContinuousGR.Acqusition">https://github.com/parmeshgajjar/TemporalCT-ContinuousGR.Acqusition</a></p> <p>The reconstructions were performed using the ASTRA toolbox with MATLAB. The code for this can be found at the following GitHub repository: <a href="https://github.com/jakobsj/TemporalCT-ContinuousGR.Reconstruction">https://github.com/jakobsj/TemporalCT-ContinuousGR.Reconstruction</a></p>
Community-Based Event Detection in Temporal Networks
<p>This dataset contains daily tweets ids during April 2013. This is provided to facilitate reproducibility of results presented in the following paper:</p> <p>Pablo Moriano, Jorge Finke, and Yong-Yeol Ahn. "Community-Based Event Detection in Temporal Networks." Scientific Reports 9, 4358, 2019. DOI: <a href="https://doi.org/10.1038/s41598-019-40137-0">https://doi.org/10.1038/s41598-019-40137-0</a></p> <p>These data are provided for non-commercial purposes only. If you use this dataset for research, please be sure to cite the above paper.</p>
Data for "Temporally Variable Stream Width and Surface Area Distributions in a Headwater Catchment" by Barefoot et al.
<p>This repository holds data necessary for reproducing results from Barefoot et al. (in prep). The data is a record of high-resolution stream width measurements in Stony Creek, a headwater stream in North Carolina, USA. Included are:</p> <ul> <li>Width measurements for 13 surveys collected from 2015 to 2016. </li> <li>High-resolution discharge measurements of discharge collected over the same period.</li> <li>Data summarizing the topology of the drainage network during each survey.</li> <li>A 5-year record of discharge from the closest USGS gauge for historical flow characterization. </li> <li>Data for estimating measurement error. </li> <li>A summary table for drainage density during each survey as well as other summary statistics. </li> <li>Geographic data detailing the mapped stream locations. </li> </ul> <p>The records span a range of discharge conditions measured from summer 2015 to spring 2016. Other hydrological data for this catchment for a longer range of time is available by request from Dr. Margaret Zimmer and Dr. Brian McGlynn. </p> <p>Code for analyzing and reproducing these results can be found on Github at [insert link to repo here.]</p>
Two multi-temporal datasets to track the enhanced landsliding after the 2008 Wenchuan earthquake
<p>We release two datasets that track the enhanced landsliding induced by the 2008 M<sub>w</sub> 7.9 Wenchuan earthquake over a portion of the Longmen mountains, at the eastern margin of the Tibetan plateau (Sichuan, China). The first dataset is a geo-referenced multi-temporal polygon-based inventory of pre- and coseismic landslides, post-seismic remobilisations of coseismic landslide debris, and post-seismic landslides (new failures). It covers 471 km<sup>2</sup> in the earthquake’s epicentral area, from 2005 to 2018. The second dataset records the debris flows that occurred from 2008 to 2017 in a larger area (~17,000 km<sup>2</sup>), together with information on their triggering rainfall as recorded by a network of rain gauges. For some well-monitored events, we provide more detailed data on rainfall, discharge, flow depth and density. The datasets can be used to analyse, at various scales, the patterns of landsliding caused by the earthquake. They can be compared to inventories relative to past or new earthquakes or other triggers to reveal common or distinctive controlling factors. To our knowledge, no other inventories that track the temporal evolution of earthquake-induced mass wasting have been made freely available thus far. Our datasets can be accessed from <a href="https://doi.org/10.5281/zenodo.1405490">https://doi.org/10.5281/zenodo.1405490</a>. We also encourage other researchers to share their datasets to facilitate research on post-seismic geological hazards.</p>
Datasets used to train and test the Cortical Spectro-Temporal Model (CSTM).
<p>ZIP files of folders containing all the datasets (audio file corpora) employed in our research to train the Encoder Layer (EL) and the SVMs and to test the complete CSTM. This folder includes a set of 840 corpora which are distributed in 2 corpora for each configuration organized by 2 sets of synthesized voices, 3 syllabic conditions (i.e. mono-, di- and tri-syllabic English words) and 10 completely different vocabularies all distributed in 6 acoustic variants, beyond the original version of the corpora.</p> <p>The 6 acoustic variants corresponds to: two levels of white noise (19.8 dB and 13.8 dB Signal to Noise Ratio (SNR) average Root Mean Square (RMS) power rate), two levels of reverberation (Reveberation-Time 60 dB (RT-60) value of 0.61 seconds and 1.78 seconds) and variations of pitch on both directions (from E to G and from E to C).</p>
Dataset and replication information for Temporal Discounting in Technical Debt: How do Software Practitioners Discount the Future?
<p>Dataset and replication information for the paper Temporal Discounting in Technical Debt: How do Software Practitioners Discount the Future? (Becker, C., Fagerholm, F., Mohanani, R., Chatzigeorgiou, A., 2009). The dataset consists of answers to a questionnaire on temporal discounting in a technical debt context. The respondents are from two companies. The raw data from the two companies is given separately. Information showing how to replicate the calculations required for analysis is given for each company. The dataset and replication information are given in both Excel (xlsx) and Open Document (ods) formats.</p>
Data from: Defensive fruit metabolites obstruct seed dispersal by altering bat behavior and physiology at multiple temporal scales.
<p>These data support the publication "Defensive fruit metabolites obstruct seed dispersal by altering bat behavior and physiology at multiple temporal scales".</p>
Code for 'Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'
<p>Code to reproduce the analyses in the study '<strong>Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'</strong></p> <p>The analysis requires two R scripts available here, plus original 2013-4 NHANES data files, downloadable from the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2013).</p> <p>The first R script, 'merging script.r' takes the original NHANES files, extracts the variables required for the study, merges them into a single data frame, and saves this in .csv format. The NHANES files it requires are:</p> <p># Demographics, food insecurity and BMI<br> DEMO_H.XPT<br> FSQ_H.XPT<br> BMX_H.XPT</p> <p># Summary files of food recalls<br> DR1TOT_H.XPT<br> DR2TOT_H.XPT</p> <p># Individual foods files from food recalls<br> DR1FF_H.XPT<br> DR2FF_H.XPT</p> <p>The second R script takes the .csv file output by the merging script, and reproduces the analyses and figures described in the paper.</p> <p>Initially uploaded by Daniel Nettle, April 23rd 2019. Slightly revised versions uploaded August 6th 2019 by Daniel Nettle.</p>
EXPLANATION OF PLATE I Left lateral aspect of skull of Stephanosaurus marginatus; one- fifth the natural size. Abbreviations.-D, lateral temporal fossa; DN, dentary; J, jugal; L, lachrymal; MX, maxilla; N, nasal; NO, nasal opening; OR, orbit; PD, predentary; PF, prefrontal; PM, premaxilla; Q, quadrate; QJ, quadrato-jugal; S, squamosal; SA, surangular. in On a new genus and species of carnivorous dinosaur from the Belly River Formation of Alberta, with a description of the skull of Stephanosaurus marginatus from the same horizon
EXPLANATION OF PLATE I Left lateral aspect of skull of Stephanosaurus marginatus; one- fifth the natural size. Abbreviations.-D, lateral temporal fossa; DN, dentary; J, jugal; L, lachrymal; MX, maxilla; N, nasal; NO, nasal opening; OR, orbit; PD, predentary; PF, prefrontal; PM, premaxilla; Q, quadrate; QJ, quadrato-jugal; S, squamosal; SA, surangular.
Data and model scripts for "Temporal shifts in iso/anisohydry revealed from daily observations of plant water potential in a dominant desert shrub"
<p>Model code and data as used for the first revision submitted to New Phytologist, Sept. 2019. </p> <p>Models are coded in JAGS and run in R via the package "rjags." Two model versions are presented:</p> <p>1) "mod_SAM.R" and "script_SAM.R" run the full, time-varying SAM model, utilizing both the plant water potential ("res.Rdata") and the environmental covariate data ("cov2.Rdata") and associated initial values ("initsSAM.Rdata")</p> <p>2) "mod_SIMPLE.R" and "script_SIMPLE.R" run the simple, time-invariant model, utilizing only the plant water potential data ("res.Rdata") and associated initial values ("initsSIMPLE.Rdata")</p>
Dataset _ Bombus pauloensis telemetry: Spatio-temporal use of the environment and floral resources.
<p>These data includes the GPS points collected from the tracked bees (<em>Bombus pauloensis</em> queens) that were used in the analysis of the home ranges and kernal density, data were usesd in the the MCP and the LUC analysis and pollen collected from the queens.</p>
example stimuli of "Behavioral effects of rhythm, carrier frequency and temporal cueing on the perception of sound sequences"
<p>Exemplary subset of stimuli accompanying the manuscript "Behavioral effects of rhythm, carrier frequency and temporal cueing on the perception of sound sequences"</p>
Figure 4 in Diversity and temporal variation of brown lacewings (Neuroptera, Hemerobiidae) from Atlantic rainforest areas in southeastern Brazil
Figure 4 Habitus of collected species of Megalomus Rambur, 1842 (Neuroptera, Hemerobiidae) and their geographical distribution in Neotropics; red circles = previous records, red stars = new records. A-B, M. impudicus (Gerstaecker, 1888). C-D, M. rafaeli Penny & Monserrat, 1985. E-F, M. ricoi Monserrat, 1997.
Figure 10 in Diversity and temporal variation of brown lacewings (Neuroptera, Hemerobiidae) from Atlantic rainforest areas in southeastern Brazil
Figure 10 Species distributions of Hemerobiidae (Neuroptera) along altitudinal gradient in five areas in the Atlantic rainforest of São Paulo state, Brazil, collected between October 2009 and December 2011. asl = above sea level, PEI = Parque Estadual Intervales, PEMD = Parque Estadual Morro do Diabo, PESM/NSV = Parque Estadual da Serra do Mar, Núcleo Santa Virgínia, PESM/NP = Parque Estadual da Serra do Mar, Núcleo Picinguaba and EEJI = Estação Ecológica Juréia-Itatins.
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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.