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Fig. 7 in Stable isotope markers differentiate between mass-reared and wild Lepidoptera in sterile insect technique programs
Fig. 7. Isotope signatures of the European grapevine moth, Lobesia botrana; error bars are 2 standard deviations of the mean.
Fig. 1 in Stable isotope markers differentiate between mass-reared and wild Lepidoptera in sterile insect technique programs
Fig. 1. Percentage of sucrose based on dry weight (DW) in popular artificial diets for mass rearing various moth species, as described by Dyck (2010).
Fig. 3 in Stable isotope markers differentiate between mass-reared and wild Lepidoptera in sterile insect technique programs
Fig. 3. Isotope signatures of the African sugarcane borer, Eldana saccharina. Wild moths developed on sugarcane at Eston, and at Tinely Manor, a 3rd group developed on papyrus at Eston and a 4th group was mass reared on an artificial diet. Error bars are 3 standard deviations of the mean.
Fig. 2 in Stable isotope markers differentiate between mass-reared and wild Lepidoptera in sterile insect technique programs
Fig. 2. Isotope signatures of cactus, a laboratory formulated diet and of cactus moths, Cactoblastis cactorum, reared on these 2 substrates; error bars are 2 standard deviations of the mean.
Fig. 4 in Stable isotope markers differentiate between mass-reared and wild Lepidoptera in sterile insect technique programs
Fig. 4. Isotope signature of the light brown apple moth, Epiphyas postvittana (LBAM); error bars are 2 standard deviations of the mean.
Fig. 2 in Enhanced understanding of ectoparasite-host trophic linkages on coral reefs through stable isotope analysis
Fig. 2. Stable carbon and nitrogen isotope data for Haemulon flavolineatum blood vs. P3 (A and B), blood vs. adult gnathiids (C and D), and P3 vs. adult gnathiids (E and F). Solid triangles represent males and P3s, open triangles represent females. Error bars represent 1 SE of N = 5 individual P3 gnathiids analysed from each fish. Dashed line represents 1:1 linear relationship.
Fig. 1 in Enhanced understanding of ectoparasite-host trophic linkages on coral reefs through stable isotope analysis
Fig. 1. Mean δ13C and δ15N (±1 Standard Error) values of gnathiids (open squares), (A) Haemulon flavolineatum heart (grey diamond), blood (white diamond), and muscle (black diamond), Pederson shrimp (Ancylomenes pedersoni, open triangle), and Anilocra isopods (cross), (B) Stegastes diencaeus heart (grey diamond), and muscle (black diamond), and Pederson shrimp (open triangle), and (c) Holocentrus adscenscionis heart (grey diamond), and muscle (black diamond), and Anilocra isopods (cross).
Fig. 3 a in A study on benthic molluscs and stable isotopes from Kutch, western India reveals early Eocene hyperthermals and pronounced transgression during ETM2 and H2 events
Fig. 3 a Amber, plant fossil and glauconite pocket within shale indicated by yellow, blue and green arrows, respectively. b Granular amber within black shale. c Pyroclastics of unit 1. d Grey shale with amber (yellow arrow) and plant fossils (blue arrow) in unit 2. e Black shale with amber (yellow arrow) and plant fossils (blue arrow) in unit 2. f Grey shale with glauconite pocket (green oval) in unit 2. g Grey shale with pyrite (yellow oval) and plant matter (blue arrow) in unit 2. h Green shale in unit 3, glauconite indicated by green arrow; white dots are fragments of fossil molluscs. i Grey shale with pockets of glauconite and mollusc fragments in unit 3. j Green shale with pyrite (yellow arrow) in unit 3. k Shell limestone with mollusc fossils in unit 3; yellow and green arrows indicate Caestocorbula sp. and Claibornicardia sp., respectively. l Mollusc concentration in upper part of unit 3. m Black shale with plant fossil (blue arrow) in unit 4. n Lignite in unit 4. o Ferricrete in unit 4. Scale bars 10 mm
Fig. 2 in Parasites in Myodes glareolus and their association with diet assessed by stable isotope analysis
Fig. 2. The relationship between parasite intensity and δ 15N values for the mite Listrophorus brevipes in September (S1) and October (S2) samples of Myodes glareolus. Note the log-scale for parasite abundance.
Fig. 3 in Parasites in Myodes glareolus and their association with diet assessed by stable isotope analysis
Fig. 3. The average Pearsons Correlation Coefficient (error bars: SE) between abundance and δ 13C and δ 15N for the two parasite groups: ecto and endoparasite for females (left) and males (right). The data includes 13 parasites occurring on 5 or more individuals and indicate consistent correlations for endoparasites and δ 15 N, while this is not the case for δ 13C.
Fig. 1 in Parasites in Myodes glareolus and their association with diet assessed by stable isotope analysis
Fig. 1. The relationship between δ 13C and δ 15N values for the 21 Myodes glareolus sampled from Kongelunden Denmark in September (S1) and October (S2). The rodents present great variation in isotope values.
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 4. Statistical Metrics distribution of model on RIO as response of Terasort application
<p>Figure 4 shows the statistical metrics distribution of regression model on read rate as the response of Terasort application. The filled triangle point-up indicates the minimum stable sampling time for statistical metrics. The top-half of figure 4 shows the residual standard error (RSE) distribution as training data size increase. The remaining half is for the distribution of R2.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 3. Estimate coefficients distribution of model on read rate as response of Terasort application
<p>Figure 3 shows estimate coefficients distribution of model on read rate as the response of Terasort application. The filled triangle point-up indicates the position of the minimum stable sampling time for the corresponding estimate coefficients as well as the number above it shows the exact position value. The dashed line represents the average estimated coefficient of the regression model.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 1. ACF and PACF plot of TeraSort application
<p>In Figure 1, the autocorrelation plots of all resource usage parameters reveal non- randomness because of the corresponding autocorrelations, denoted by the circle, violate the dashed lines (95% confidence boundary) and are statistically significant for lags up to 100. The filled triangle point-up in partial autocorrelation plots marks the largest partial autocorrelation of usage parameters as well as the corresponding lag number.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 6. RSE and R2 of regression models of MapReduce applications
<p>Figure 6 shows the fit quality of regression models. It is following.The left panel and the right panel of figure 6 show the residual standard error (RSE) distribution and R2 distribution of each application. The good fit quality corresponds to a taller R2 bar and a shorter RSE bar. The R2 almost 1 and small RSE show the best fit quality of the regression models on memory usage as the response. The overall higher RSE and lower R2 of regression models on CPU as the response show the worse quality of fitting goodness. The regression models on read rate as the response also show a moderate fitting quality. For the regression models on write rate as the response, Terasort application exhibits the best quality and Teragen application as well. Others show the worse fitting quality. The results show that the regression models on intensive usage parameters as response exhibit the good fitting quality.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 2. ACF plot of Residuals of Regression Model of Terasort
<p>, are uncorrelated. If the error term is uncorrelated, it proves that there exists strong randomness in residuals of the model and provides the evidence for the unbiased estimate for the true standard error. The autocorrelation plot is used to check this assumption. Figure 2 shows the autocorrelation plot of residuals of regression models of TeraSort application. In Figure 2, the horizontal axis represents lag time and the vertical axis indicates the autocorrelation between residual at time t and residual at other lag time. At lag 0, autocorrelation is always equal to 1 and represents time series itself. Most of the autocorrelation at other lag time fall into the 95% confidence interval, only few of them violate the dashed line. Such a shape of ACF plot proves that residuals are uncorrelated and respects to the independent assumption of the linear regression model residuals.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 8. Minimum sample time of statistical metrics of MapReduce applications
<p>Figure 8 presents the minimum sampling time distribution of statistic metrics which ensures the stable modeling. Overall, the minimum sampling time of statistic metrics is smaller than sampling time of estimated coefficients. For different applications, a time-consuming application like Terasort needs the largest sampling time to tend to be stable. The Pi application shows the smallest minimum sampling time to reach stability.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 7. Minimum sampling time of MapReduce applications on various response variable
<p>Pi application needs the smallest sampling time. The remaining applications need the similar minimum sampling time. Overall, the stable regression models on memory usage as response show the least need for sampling time. The results show that various applications have different minimum sampling time to get stable. The application which performs more read/write operations shows larger sampling time need.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary
<p>In Figure 5, the positive dependency of different strength between each resource usage parameter and the corresponding previous usage parameter is exhibited for all MapReduce applications. It indicates that all current resource usage parameters are positively dependent on the previous values to some extent degree. Except for these common dependencies, there exist some special dependencies for different applications. On the top-left panel of Figure 5, CPU usage of Pi application shows the strongest positive dependency to lagged CPU usage, the Teragen application had the weakest positive dependency, and others exhibit the moderate positive dependency. </p>
Stable Water Isotope Surveys - Muskoka River Watershed ON CA 2015-16
<p>This dataset includes river stable water isotope surveys from the Muskoka River watershed ON CA, conducted from April 2015 through November 2016. Ten river survey sites and one headwater stream site are included with listed site ID, sample date and ratios of oxygen and hydrogen isotope ratios. Data includes location of river survey sites and additional source water sampling sites (precipitation, snowmelt, groundwater). Daily average streamflow records for the 2015-2016 period of surveys originally sourced from the Water Survey of Canada website are included here (but can also be downloaded independently) for four of the six gauging stations referenced with the river survey isotope dataset. Water Survey of Canada gauging station locations are also provided.</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)
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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.