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BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 5.The chart of run time in emotional/unemotional detection in two databases.
<p>The processing time of our proposed method is of 0.420468 fps and in regular data is of 5.27 fps. As the results show, the EVM method has good effect in processing. Of course, the creation time of the magnification image sequence was not considered in the result. In other words, the processing time on a magnified images sequence is less than the non-magnification state. Figure 5 shows the running time in emotional/unemotional detection. </p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 18. Drawing of the time series for males and females of primary stage students and its prediction)
<p>Note that the Tabulated value equals 3.841 while the Q value is less than Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 17. Drawing of the time series for females of primary stage students and its prediction
<p>It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 16. Drawing of the time series for males of primary stage students and its prediction
<p>It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for male females primary stage, in which the residues value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 11. Drawing the time series for males and females primary stage after the First difference
<p>We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 9. Drawing the time series for males and females primary stage students
<p>The Unit Radix Dickey-Fuller Test is used to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.736458 , Statistic Test = 0.380545 , P-Value = 0.794 We notice from the values above P-Value = 0.794on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis ( The Nonexistence of a Radix Unit) implies that the time series is stable. Figure (9) represents the time series of females and males in the primary stage students.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 7. Drawing the time chain for Females primary stage students after the first difference
<p>We use the Unit Radix Dickey-Fuller Test to assert the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.47242, Statistic Test = 1.22797, P-Value = 0.9445 We notice from the values above P-Value =0.9445 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, it is noticed that the stability of the time series has been accomplished. See Figure 7.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 5. Drawing the time series for primary stage female students
<p>We use the Unit Radix Dickey-Fuller Test to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.233403, Statistic Test = 0.125769, P-Value = 0.6405 The values above P-Value = 0.6405 is noted on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis (The Nonexistence of a Radix Unit) implies that the time series is stable. Figure 5 represents the Time series of Female Primary Stage Students.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 3. Drawing the time series for Males primary stage students after getting the first difference
<p>The Unit Radix Dickey-Fuller Test is used to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.276151, Statistic Test = 0.87796, P-Value = 0.8984 We get to notice from the values above P-Value = 0.8984 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, it is observed that the stability of the Time Series has been accomplished . See figure 3.</p>
The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 1. Drawing the time series for males primary stage
<p>After collecting all the students’ dropout proportion for both males and females in the<br> primary stage, the first step of the Box-Jenkins is to draw the time chain data to understand the<br> chain's attitude.</p>
Hourly U.S. Building Electricity Use, Cost, and Emissions Baselines to Support Time-Sensitive Analyses of Energy Efficiency and Flexibility Measures
<p>These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (<a href="https://scout.energy.gov">scout.energy.gov</a>), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office.</p> <p>The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from <a href="https://www.eia.gov/outlooks/aeo/data/browser/%20/%20%7b%20/%20# \ }/?id=2-AEO2018 \ { \ & \ }cases=r ef2018 \ { \ & \ }sourcekey=0">EIA's Annual Energy Outlook (AEO) Reference Case projections</a> across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.</p> <p>The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below:</p> <ul> <li>'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from <a href="https://www.eia.gov/outlooks/aeo/data/browser/#/?id=2-AEO2018&sourcekey=0">AEO Summary Table A2</a>.</li> <li>'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each <a href="https://www.eia.gov/consumption/residential/maps.php">American Institute of Architects (AIA) climate zone</a>. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).</li> <li>'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.</li> </ul>
Impact of the COVID-19 pandemic on antidepressant use in eleven European regions: a comparative time series analysis 2018–2022
<p>Data and code supporting the article:</p> <p>Impact of the COVID-19 pandemic on antidepressant use in eleven European regions: a comparative time series analysis 2018–2022</p> <p>Prescription, prevalence and incidence data from January 2018 to December 2022 for Croatia, the Czech Republic, Finland, Germany, Slovenia, Sweden, and the United Kingdom (England, Northern Ireland, Scotland, and Wales).<br>Data include the numbers of dispensed defined daily doses (DDDs) and packs, aggregated by country and month, and prevalence and incidence of antidepressant dispensing.</p> <p>For more information, see the accompanying document ReadMe.md.</p>
Dataset: Use of bioresorbable fibers for short-wave infrared spectroscopy using time-domain diffuse optics
Open the record for dataset details and reuse information.
Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D
<p>This dataset contains experimental data related: "Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D", T.Craig, A. Kadu, K.J. Batenburg and S. Bals, <em><strong>Nanoscale</strong></em>, 2023,<strong>15</strong>, 5391-5402 (2023).</p>
Data from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario
<p><strong>Paper Abstract:</strong></p> <p>Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978-1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales. </p> <p> </p> <p><strong>Dataset details:</strong></p> <p>See paper. </p> <ul> <li>cc_72to20.tif: Yearly tree CC predictions (1972-2020)</li> <li>always_nonforest10_nowater.tif: continuous-non-canopy mask</li> <li>water.tif: water mask</li> <li>Yearly.zip: Annual predictors (including CC10) and asc outputs</li> </ul> <p> </p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020">ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)</a></p>
Fig. 4. Maximum clade credibility and divergence time estimations from BEAST reconstruction using 89 specimens and 20 in One in, one out: Generic circumscription within subtribe Manilkarinae (Sapotaceae)
Fig. 4. Maximum clade credibility and divergence time estimations from BEAST reconstruction using 89 specimens and 20 genes. Background colors represent the four Manilkarinae clades. Node labels are given as the mean of node age estimates for the main clades, including their 95% HPD and posterior probability (PP). The latter are only shown when PP <1. Epoch and ages in million years ago are represented at the bottom. Primary calibration points from fossils data are indicated with a red star, whereas clades constrained as monophyletic are labeled with an asterisk. RN: Réserves Naturelles; SF: Service Forestier.
FIG, 1. John William Daly (1933–2008) on the upper Río San Juan. This paper is dedicated to John Daly, our late friend and colleague, who helped collect three of the new species here described. In addition to his globally acclaimed discoveries in chemistry and pharmacology, John was an accomplished field herpetologist who contributed importantly to the systematics and natural history of dendrobatoid frogs (see Grant et al., 2006; Myers, 2009). This photograph shows John at age 37, with the upper Río San Juan behind him and branches overhead of a madroño tree (probably Garcinia magnifolia, syn. Rheedia chocoensis, Clusiaceae). When in South America, John was never far from a dendrobatid frog—this time, in the tree above his head, a tiny, undescribed semiarboreal species (also collected and later named "Dendrobates fuguritus" by our colleague Philip Silverstone). Other dendrobatids found nearby included Phyllobates aurotaenia (Boulenger, 1913), which was then being used for poisoning blowgun darts, and also the nontoxic species that we name Silverstoneia dalyi herein. (Photograph by C. W. Myers, 2 km above Playa de Oro, Chocó, February 16, 1971.) in Review of the Frog Genus Silverstoneia, with Descriptions of Five New Species from the Colombian Chocó (Dendrobatidae: Colostethinae)
FIG, 1. John William Daly (1933–2008) on the upper Río San Juan. This paper is dedicated to John Daly, our late friend and colleague, who helped collect three of the new species here described. In addition to his globally acclaimed discoveries in chemistry and pharmacology, John was an accomplished field herpetologist who contributed importantly to the systematics and natural history of dendrobatoid frogs (see Grant et al., 2006; Myers, 2009). This photograph shows John at age 37, with the upper Río San Juan behind him and branches overhead of a madroño tree (probably Garcinia magnifolia, syn. Rheedia chocoensis, Clusiaceae). When in South America, John was never far from a dendrobatid frog—this time, in the tree above his head, a tiny, undescribed semiarboreal species (also collected and later named "Dendrobates fuguritus" by our colleague Philip Silverstone). Other dendrobatids found nearby included Phyllobates aurotaenia (Boulenger, 1913), which was then being used for poisoning blowgun darts, and also the nontoxic species that we name Silverstoneia dalyi herein. (Photograph by C. W. Myers, 2 km above Playa de Oro, Chocó, February 16, 1971.)
Text-fig. 9. Scanning electron microscope (SEM) images of inaperturate Araucariacites sp. pollen from two fragmentary pollen sacs; Torres Vedras locality, Portugal. a) Fragmentary pollen sac; b) Granular inner surface of pollen sac (a); c) Orbicule showing finely striate surface; d) Group of pollen grains from pollen sac in (a) showing granular exine surface and numerous orbicules; note that the scale bar (12 Μm) is two times larger than that used for most other pollen grains illustrated in this paper (6 Μm). Specimens, TV44-S148025 (a, b, d), TV44-S148146 (c). Scale bars 150 Μm (a), 12 Μm (d), 3 Μm (b, c). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 9. Scanning electron microscope (SEM) images of inaperturate Araucariacites sp. pollen from two fragmentary pollen sacs; Torres Vedras locality, Portugal. a) Fragmentary pollen sac; b) Granular inner surface of pollen sac (a); c) Orbicule showing finely striate surface; d) Group of pollen grains from pollen sac in (a) showing granular exine surface and numerous orbicules; note that the scale bar (12 Μm) is two times larger than that used for most other pollen grains illustrated in this paper (6 Μm). Specimens, TV44-S148025 (a, b, d), TV44-S148146 (c). Scale bars 150 Μm (a), 12 Μm (d), 3 Μm (b, c).
Data from: Refining the evolutionary time machine: an assessment of whole genome amplification using single historical Daphnia eggs
<p>This dataset contains the original raw sequence files used in the associated publication "Refining the evolutionary time machine: an assessment of whole genome amplification using single historical <em>Daphnia</em> eggs" by O'Grady, Dhandapani, Colbourne & Frisch in Molecular Ecology Resources. DOI:10.1111/1755-0998.13524</p> <p> </p> <p>(filename > name used in associated publication)</p> <p>DF1_ATTACTC-GGCTCTG_L008_R1_001.fastq.gz -> DM1<br> DF2_TCCGGAG-GGCTCTG_L008_R1_001.fastq.gz -> DM2<br> DF3_CGCTCAT-GGCTCTG_L008_R1_001.fastq.gz -> DM3<br> DF4_GAGATTC-GGCTCTG_L008_R1_001.fastq.gz -> DM4<br> DF5_CTGAAGC-GGCTCTG_L008_R1_001.fastq.gz -> DM5<br> DF11_CTGAAGC-AGGCGAA_L008_R1_001.fastq.gz -> DM6<br> DF12_TAATGCG-AGGCGAA_L008_R1_001.fastq.gz -> DM7</p> <p>DF6_TAATGCG-GGCTCTG_L008_R1_001.fastq.gz -> DP1<br> DF7_ATTACTC-AGGCGAA_L008_R1_001.fastq.gz -> DP4<br> DF8_TCCGGAG-AGGCGAA_L008_R1_001.fastq.gz -> DP5<br> DF9_CGCTCAT-AGGCGAA_L008_R1_001.fastq.gz -> DP2<br> DF10_GAGATTC-AGGCGAA_L008_R1_001.fastq.gz -> DP3</p> <p>170426_E00397_0064_BHJ2JWALXX_2_TP-D7-004_1.fastq.gz -> DP6 (fw)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-004_2.fastq.gz -> DP6 (rv)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-010_1.fastq.gz -> DP7 (fw)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-010_2.fastq.gz -> DP7 (rv)</p> <p>170426_E00397_0064_BHJ2JWALXX_2_TP-D7-005_1.fastq.gz -> DP8 (fw)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-005_2.fastq.gz -> DP8 (rv)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-006_1.fastq.gz -> DP9 (fw)<br> 170426_E00397_0064_BHJ2JWALXX_2_TP-D7-006_2.fastq.gz -> DP9 (rv)</p>
The dataset for the submitted paper " Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model".
<p>These files are the data of the result in the submitted paper, titled "Time Series Analysis of Normal Mode Energetics for Rossby Wave Breaking and Saturation using a Simple Barotropic Model".</p> <ul> <li>File Description</li> </ul> <p>pv13.data : Exp. 1<br> pv17.data : Exp. 2</p> <p>The raw potential vorticity (PV) data for the Exp.1 and Exp.2, respectively, used in drawing the Fig.1, 2, and the supplemental movie 1 and 2.<br> These are the grid point value files, 72 levels for the zonal direction, 30 levels for meridional direction. More details are described in the next ctl files.</p> <p> </p> <p>pv13.ctl<br> pv17.ctl</p> <p>Description files for pv13.data and pv17.data. This will be called from grads_pv13.gs and grads_pv17.data, respectively.</p> <p>grads_pv13.gs<br> grads_pv17.gs</p> <p>GrADS script for mapping the PV.</p> <p> </p> <p>energy17.txt : Exp.2</p> <p>The time series table of energy values for exp.2.<br> One raw is identified by combination of the TIME in the experiment and zonal wave number N.</p>
ScienceDex guides
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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.