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135 results for “Sample size”
Text-fig. 1. SDQ vs stratigraphic time in samples of Mimomys savini and Arvicola from various Italian and German localities (in brackets: sample size), showing a parallel trend starting from ca. 200 ka. From Maul et al. (1998b: fig. 4), modified. in Independent Water Vole (Mimomys Savini, Arvicola: Rodentia, Mammalia) Lineages In Italy And Central Europe
Text-fig. 1. SDQ vs stratigraphic time in samples of Mimomys savini and Arvicola from various Italian and German localities (in brackets: sample size), showing a parallel trend starting from ca. 200 ka. From Maul et al. (1998b: fig. 4), modified.
Performance analysis of micro-expression recognition over different sample image sizes.
<p>Performance of micro-expression recognition accuracy analyzed using different sample sizes with motion and geometric features. The sample sizes analyzed are: 140x170, 280x340, 560x680 and 1120x1360 using SMIC, CASMEII, CAS(ME)^2 and SAMM. The experiments were conducted using three different feature extraction setups: (i) optimized BiWOOF, (ii) full-face graph, and (iii) full-face graph with amplitude-based emotion magnification method (A-EMM). The results were compared with the results of the original BiWOOF presented in [1] as the baseline study. These files are published under CC0 license.</p> <p> </p> <p>References</p> <p>[1] Liong, S. T., See, J., Wong, K., & Phan, R. C. W. (2018). Less is more: Micro-expression recognition from video using apex frame. <em>Signal Processing: Image Communication</em>, <em>62</em>, 82-92.</p>
Text-fig. 6. Scanning electron micrographs of multicarpellate and apocarpous floral structures from the Early Cretaceous Puddledock locality, Virginia, USA (a, b: PP43701, Puddledock sample 001; c: PP43000x, Puddledock sample 073). a) Anacostia? sp., strongly compressed, elongated receptacle with spirally arranged carpels (red dots; not all shown); note larger size compared to the other floral structures; b) Numerous Anacostia type pollen grains in proximal view from the base of floral structure in (a); note graded reticulum over the proximal pole of the pollen grains; c) Elongated receptacle with numerous carpels in a spiral arrangement, possibly representing an earlier developmental stage of Anacostia? sp. Scale bars = 1 mm (a, c), 10 µm (b). in Multiparted, Apocarpous Flowers From The Early Cretaceous Of Eastern North America And Portugal
Text-fig. 6. Scanning electron micrographs of multicarpellate and apocarpous floral structures from the Early Cretaceous Puddledock locality, Virginia, USA (a, b: PP43701, Puddledock sample 001; c: PP43000x, Puddledock sample 073). a) Anacostia? sp., strongly compressed, elongated receptacle with spirally arranged carpels (red dots; not all shown); note larger size compared to the other floral structures; b) Numerous Anacostia type pollen grains in proximal view from the base of floral structure in (a); note graded reticulum over the proximal pole of the pollen grains; c) Elongated receptacle with numerous carpels in a spiral arrangement, possibly representing an earlier developmental stage of Anacostia? sp. Scale bars = 1 mm (a, c), 10 µm (b).
Fig. 5 in Size composition, monthly condition factor and morphometrics for fishery-dependent samples of Rioraja agassizi (Chondrichthyes: Rajidae), off Santos, Southeast Brazil
Fig. 5. Relationships between total (black symbols) and gutted weight (g) (grey symbols) for male (above) and females (below) of Rioraja agassizi.
Fig. 2 in Size composition, monthly condition factor and morphometrics for fishery-dependent samples of Rioraja agassizi (Chondrichthyes: Rajidae), off Santos, Southeast Brazil
Fig. 2. Maps of Southeast Brazil, showing the fishing hauls by month, conducted by a total of eight fishing vessels. Symbols represent the fishing hauls from where samples of Rioraja agassizi were collected (dotted circles), or when this information was not available, the total hauls recorded by the skippers (crosses).
Fig. 6 in Size composition, monthly condition factor and morphometrics for fishery-dependent samples of Rioraja agassizi (Chondrichthyes: Rajidae), off Santos, Southeast Brazil
Fig. 6. Median condition factor by month (from March, 2005 to March, 2006) considering total (black line) and gutted weight (g) (grey line) for males (above) and females (below) of Rioraja agassizi. Vertical bars represent the data interval.
Fig. 4 in Size composition, monthly condition factor and morphometrics for fishery-dependent samples of Rioraja agassizi (Chondrichthyes: Rajidae), off Santos, Southeast Brazil
Fig. 4. Relationship of disc width (cm) as a dependent variable of total length (cm) for males (black symbols) and females (grey symbols) of Rioraja agassizi.
Fig. 1 in Size composition, monthly condition factor and morphometrics for fishery-dependent samples of Rioraja agassizi (Chondrichthyes: Rajidae), off Santos, Southeast Brazil
Fig. 1. Map of the study area, southeast Brazil, south-western Atlantic Ocean. Symbols represent all the fishing hauls from where samples of Rioraja agassizi were collected.
Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
Open the record for dataset details and reuse information.
Female fiddler crabs, Austruca lactea (Decapoda: Ocypodidae), adjust their rate of mate sampling based on remaining days until oviposition under a size-dependent temperature constraint
<p>The rate of mate sampling is one of the critical components associated with the sampling costs in female mate choice. In ectotherms, environmental temperature generally constrains locomotion performance. In addition, females will adjust the mate sampling rate depending on their breeding schedule because of the risk of remaining unfertilized eggs or a loss of benefits related to mating, if they lost the opportunity to copulate. This study investigated how these effects influence the rate of female mate sampling in the temperate fiddler crab (<em>Austruca lactea</em>) in the field. The number of sampled males per searching duration formed a convex curve against environmental temperature. The optimal environmental temperature increased with the female body size. These results suggest that the mate sampling rate would be under a size-dependent temperature constraint, and sampling costs would be lower for larger females than smaller individuals under high-temperature conditions. Furthermore, when there were fewer remaining days, the mate sampling rate increased. Females would hasten the sampling rate to ensure a suitable burrow for breeding. Mate sampling rate in female A. lactea is therefore associated with environmental temperature, female body size, and remaining days until oviposition.</p>
Taxonomic similarity does not predict necessary sample size for ex situ conservation: a comparison among five genera
<p>Effectively conserving biodiversity with limited resources requires scientifically informed and efficient strategies. Guidance is particularly needed on how many living plants are necessary to conserve a threshold level of genetic diversity in ex situ collections. We investigated this question for 11 taxa across five genera. In this first study analyzing and optimizing ex situ genetic diversity across multiple genera, we found that the percentage of extant genetic diversity currently conserved varies among taxa, from 40 to 95%. Most taxa are well below genetic conservation targets. Resampling datasets showed that ideal collection sizes vary widely even within a genus: one taxon typically required at least 50% more individuals than another (though Quercus was an exception). Still, across taxa, the minimum collection size to achieve genetic conservation goals is within one order of magnitude. Current collections are also suboptimal: they could remain the same size yet capture twice the genetic diversity with improved sampling design. We term this deficiency the "genetic conservation gap." Lastly, we show that minimum collection sizes are influenced by collection priorities regarding the genetic diversity target. In summary, current collections are insufficient (not reaching targets) and suboptimal (not efficiently designed), and we show how improvements can be made.</p>
Data from: Overcoming the challenge of small effective sample sizes in home-range estimation
Technological advances have steadily increased the detail of animal tracking datasets, yet fundamental data limitations exist for many species that cause substantial biases in home‐range estimation. Specifically, the effective sample size of a range estimate is proportional to the number of observed range crossings, not the number of sampled locations. Currently, the most accurate home‐range estimators condition on an autocorrelation model, for which the standard estimation frame‐works are based on likelihood functions, even though these methods are known to underestimate variance—and therefore ranging area—when effective sample sizes are small. Residual maximum likelihood (REML) is a widely used method for reducing bias in maximum‐likelihood (ML) variance estimation at small sample sizes. Unfortunately, we find that REML is too unstable for practical application to continuous‐time movement models. When the effective sample size N is decreased to N ≤ urn:x-wiley:2041210X:media:mee313270:mee313270-math-0001(10), which is common in tracking applications, REML undergoes a sudden divergence in variance estimation. To avoid this issue, while retaining REML's first‐order bias correction, we derive a family of estimators that leverage REML to make a perturbative correction to ML. We also derive AIC values for REML and our estimators, including cases where model structures differ, which is not generally understood to be possible. Using both simulated data and GPS data from lowland tapir (Tapirus terrestris), we show how our perturbative estimators are more accurate than traditional ML and REML methods. Specifically, when urn:x-wiley:2041210X:media:mee313270:mee313270-math-0002(5) home‐range crossings are observed, REML is unreliable by orders of magnitude, ML home ranges are ~30% underestimated, and our perturbative estimators yield home ranges that are only ~10% underestimated. A parametric bootstrap can then reduce the ML and perturbative home‐range underestimation to ~10% and ~3%, respectively. Home‐range estimation is one of the primary reasons for collecting animal tracking data, and small effective sample sizes are a more common problem than is currently realized. The methods introduced here allow for more accurate movement‐model and home‐range estimation at small effective sample sizes, and thus fill an important role for animal movement analysis. Given REML's widespread use, our methods may also be useful in other contexts where effective sample sizes are small.
Table S1 The grain size, TOC and elemental composition of sediment samples in the northern South China Sea
<p><span>Grain size measurements of the sediments were carried out on a Mastersizer 3000 laser particle size analyzer</span><span>. Prior to analysis organic matter was removed by leaching the sediment with 15% H<sub>2</sub>O<sub>2</sub>. Total organic carbon (TOC) of the sediments was measured in </span><span>Thermo EA-IsoLink elemental analyzer</span><span>. Prior to analysis carbon bound to carbonate minerals was removed by leaching the sediment with 10% HCl. Total element concentrations (Al, Ca, Ti, Fe, Mn) of the bulk sediments were measured after acid digestion (HF, HNO<sub>3</sub> and HCl) by </span><span>Thermo iCAP 7400 ICP-OES</span><span>. Solid phase iron speciation data were measured following sequential Fe extraction (Fe<sub>carb</sub>, Fe<sub>ox</sub>, Fe<sub>mag</sub>, Fe<sub>py</sub>) by T</span><span>hermo iCAP 7400 ICP-OES</span><span>.</span></p>
Grain size distribution of Amazon river sediment samples collected over the period 2005-2008; and ADCP water velocity profiles collected on the major tributaries of the Amazon in Bolivia and Peru, 2007-2008
<p>This dataset contains two items:</p> <p>- The grain size distribution of river sediment samples collected along the Amazon River and its tributaries during four sampling campaigns performed in June 2005 (lower Amazon, Brazil), March 2006 (lower Amazon, Brazil), May 2007 (Upper Madeira, Bolivia), and April 2008 (Upper Solimões-Amazonas, Peru). [spreadsheet "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_data.xlsx"].</p> <p>- River water velocity profiles derived from Acoustic Doppler Current Profiler (ADCP) measurements performed on the major tributaries of the Amazon in May 2007 (Upper Madeira, Bolivia) and April 2008 (Upper Solimões-Amazonas, Peru) [folder "ADCP_dataset_Amazon_2007-2008_Bouchez_data"].</p> <p>The dataset description and the relevant references are provided in the text files "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_description.docx" and "ADCP_dataset_Amazon_2007-2008_Bouchez_description.docx" .</p> <p>These data were acquired thanks to the support of the French National Service for Observation "HYBAM" ("Hydrogeochemistry of the Amazon Basin"), part of the CNRS National Infrastructure "OZCAR" ("Critical Zone Observatories: Applications and Research").</p>
A test of Bergmann's rule in the Early Triassic: Latitude, body size, and sampling in Lystrosaurus
<p>The ecogeographical rule known as Bergmann's rule suggests that there is a positive relationship between body size and latitude when comparing closely related taxa. The underlying mechanism, or mechanisms, to explain this pattern vary as widely as the taxa that seem to follow it, which has led to skepticism over whether Bergmann's rule should be considered a rule at all. Despite this, Bergmann's rule is widespread among modern birds, mammals, beetles, and some amphibians, but far fewer extinct taxa have been subjected to tests of Bergmann's rule. To test whether Bergmann's rule is detected in extinct taxa, we compared body size proxies in <em>Lystrosaurus</em> recovered from Early Triassic-aged strata in Antarctica, South Africa, India, and China. Our results reveal that average body size is largest at mid-northern paleolatitudes (~45°N) instead of the highest southern paleolatitudes (~70°S). Additionally, maximum body size is consistent across northern and southern hemispheres, indicating that Bergmann's rule did not apply for <em>Lystrosaurus</em> during the Early Triassic. To test potential sample size biases in our results, we used rarefaction and subsampling to show that only the Karoo Basin is well sampled, and that large individuals are exceedingly rare except in the Turpan-Junggar Basin of Xinjiang, China. Taken together, our results suggest that <em>Lystrosaurus</em> had the potential to reach large body sizes in each of the latitudinally widespread tectonic basins studied here, but that local conditions may have allowed individuals at mid-northern paleolatitudes a greater chance of reaching large size compared to southern congeners that suffered increased mortality at young/small sizes.</p>
Replication Data for: Magnetic measurements on micron-size samples under high pressure using designed NV centers
<pre><em>Data description.pdf </em>describes the uploaded data. <em>Figure Data.xlsx</em> contains the data represented in the figures of the main text and supplementary information. <em>Iron.zip and MgB2.zip </em>include the raw experimental data. <em>PlotIronData.m, PlotMgB2Data.m, SimulatedIronMagneticField.m</em>, <em>SphereField.m</em>, <em>CarttoPol.m</em>, <em>PoltoCart.m </em>are Matlab codes used to process and fit the data. </pre>
Figure. Study area (the Sea of Marmara, Türkiye) and the sampling stations. in The length-weight relationship and condition factors of coastal small-sized adult and juvenile fish species following dense mucilage in the Sea of Marmara, Türkiye
Figure. Study area (the Sea of Marmara, Türkiye) and the sampling stations.
Figure 1 in A new method for analyzing microplastic particle size distribution in marine environmental samples
Figure 1. Sampling sites in Sevastopol Bay, the Black Sea.
Evolution of analytical variability with sample size
<p>Final results obtained during the reproduction of pipelines used by teams during NARPS. Results were obtained by running the pipelines with 4 different sub-datasets of the original NARPS dataset with n=20, 40, 60 and 108 participants. </p> <p>Check the <a href="https://gitlab.inria.fr/egermani/analytic_variability_fmri">Gitlab repository</a> for more informations. </p>
melian009/Mispark: Preliminary analysis mutualistic networks in space, rarefaction, sierra size and sampling individuals, species, and traits
<p>Mutualistic networks in space, morphological traits and colors, or how to put all together to understand rare and common species in rapidly changing landscapes</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.