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4,276 results for “Estimation”
Fig. 2 in Estimating size at first maturity (L ) from Gonadossomatic Index (GSI) data
Fig. 2. Logistic curves of relative frequency of reproductive females as function of size class. (black losange) Logistic curve with an asymptote at one and L 50 of 17.3. (white square) Frequencies of reproductive females from the simulation of Fig. 1, considering animals captured in the three months of greatest reproductive activity. In this case, asymptote is at 0.75 (a maximum of 75% of animals could be identified as reproductive). (black triangle) Result from applying a regular logistic, with asymptote at one, to the data of the frequencies of reproductive females from the simulation of Fig. 1.
Fig. 1 in Estimating size at first maturity (L ) from Gonadossomatic Index (GSI) data
Fig. 1. Computer simulation of a growing age-group with temporal relative frequency at three different reproductive stages (maturing, spawning, post-spawning). For simplicity, each maturity stage has a standard deviation of one month and is one month apart. In this simulation L 50 is 17.3, attained at age 10, when most of the females are identified as reproductive.
Fig. 4 in Estimating size at first maturity (L ) from Gonadossomatic Index (GSI) data
Fig. 4. Frequency distribution of females with individual gonadossomatic index (GSI) as a percentage of the maximum GSI identified for each of the four species. Animals captured using seine and gill nets over 17 monthly samples (November/2002 to May/2004) from two sites (lagoa do Casamento and lagoa dos Gateados, Rio Grande do Sul State, Brazil).
Fig. 5. Estimated species richness E in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 5. Estimated species richness E(Sn) by strata at rio Negro (a-Sep, b-Nov 1997 and c-Feb 1998) and rio Branco (d-Sep
Fig. 3. Estimated species richness E in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 3. Estimated species richness E(Sn) by months of collection for (a) rio Negro and (b) rio Branco.
Dataset for "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets"
<p>This dataset is associated with a research article entitled "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets".</p>
Data from: Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease
<p>Age is an important ecological tool in wildlife conservation. However, it is difficult to estimate in most animals, including felines — most of whom are endangered. Here, we developed the first DNA methylation-based age-estimation technique — as an alternative to current age-estimation methods — for two feline species that share a relatively long genetic distance with each other: domestic cat (<i>Felis catus</i>; 79 blood samples) and an endangered <i>Panthera</i>, the snow leopard (<i>Panthera uncia</i>; 11 blood samples). We measured the methylation rates of two gene regions <span>using </span>methylation-sensitive high-resolution melting (MS-HRM). Domestic cat age was estimated with a mean absolute deviation (MAD) of 3.83 years. Health conditions influenced accuracy of the model. Specifically, the models built on cats with chronic kidney disease (CKD) had lower accuracy than those built on healthy cats. The snow leopard-specific model (i.e. the model that resets the model settings for snow leopards) had a better accuracy (MAD = 2.10 years) than that obtained on using the domestic cat model directly. This implies that our markers could be utilised across species, although changing the model settings when targeting different species could lead to better estimation accuracy. The snow leopard-specific model also successfully distinguished between sexually immature and mature individuals.</p>
Estimates of high tide flooding on roadways within urban areas along the United States Atlantic coast
<p>Estimates of high tide flooding (HTF) on roadways in urban areas along the US Atlantic Coast. These estimates were calculated using NOAA HTF areal extent estimates, OpenStreetMap roadway data, and 2010 census-designated urban area and census block data. See the corresponding manuscript (Gold et al., 2021 - link coming soon) and <a href="https://github.com/acgold/HTF-on-roads">GitHub repository</a> for additional information about these data.</p>
Wastewater Treatment Unit Processes Datasets: Pollutant removal efficiencies, evaluation criteria and cost estimations
<p>This dataset provides data on typical pollutant removal efficiencies, evaluation critera and cost estimation for 37 common wastewater treatment unit processes. The data is based on literature research, expert workshops and estimations.</p> <p>The following files are available:</p> <ul> <li>Pollutant removal efficiencies, evaluation criteria and cost estimations for wastewater treatment unit processes (dataset) - Microsoft Excel:<br> Database of pollutant removal efficiencies for 11 paramameters, cost estimation and evaluation criteria.</li> <li>Pollutant Removal Efficiencies for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Evaluation Criteria for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Cost Estimation for Wastewater Treatment Unit Processes (Dataset) - pdf</li> </ul> <p> </p> <p> </p>
Snow melt onset date estimates derived from CLARA-A2 SAL surface albedo dataset
<p>Snow melt onset date estimates for the Northern Hemisphere, 1982-2015. Derived from the CLARA-A2 SAL surface albedo dataset. Version for manuscript review.</p>
Training Deep Learning Models to Estimate SWAT Parameters using Streamflow Observations
<p>This folder provides the simulation and observational data</p> <p>Simulation data using SWAT (1000 realz)<br> Train, Val, and Test splits (80/10/10)<br> Observational data for ARW (WY2000-2016)</p>
Dataset for "Yield Estimation of the August 2020 Beirut Explosion by Using Physics-Based Propagation Simulations of Regional Infrasound"
<p>Dataset for “Yield Estimation of the August 2020 Beirut Explosion by Using<br> Physics-Based Propagation Simulations of Regional Infrasound”</p> <p>Authors: Keehoon Kim and Michael E. Pasyanos</p> <p>Lawrence Livermore National Laboratory, Livermore, CA, USA</p> <p>Description<br> This datset includes the infrasound waveform data recorded by the array at the<br> Mt. Meron (IMA) in Israel. A five-element array deployed by the National Data<br> Center of Israel (Fee et al., 2013), and all stations had Martec Tekelec MB2005<br> sensors which have a flat frequency response in the infrasound band (0.1–20Hz)<br> (Ponceau and Bosca, 2010). The infrasound data was provided by the National<br> Data Center of Israel, Soreq Nuclear Research Center, and the pressure<br> recordings only relevant to the 2020 Beirut explosion were uploaded to the<br> public repository (https://zenodo.org).</p> <p>File Description<br> IMA_array_infrasound_waveform.txt Pressure values in ASCII format for 5 stations</p> <p>Acknowledgments<br> This research was performed by the support from the U.S. Department of Energy,<br> National Nuclear Security Administration, Office of Defense Nuclear<br> Nonproliferation, Research and Development under the auspices of the U.S.<br> Department of Energy by the Lawrence Livermore National Laboratory under<br> Contract Number DE-AC52-07NA27344. This is LLNL Contribution LLNL-JRNL- 839419</p> <p>References<br> Fee, D., Waxler, R., Assink, J., Gitterman, Y., Given, J., Coyne, J., ... &<br> Grenard, P. (2013). Overview of the 2009 and 2011 Sayarim infrasound<br> calibration experiments. Journal of Geophysical Research Atmospheres, 118(12),<br> 6122-6143.</p> <p>Ponceau, D., & Bosca, L. (2010). Low-noise broadband microbarometers. In<br> Infrasound monitoring for atmospheric studies (pp. 119-140). Springer,<br> Dordrecht.</p>
Parameters of dielectric properties and emissivity estimated for Earth arid areas.
<p>Dataset gives the parameters estimated from passive microwave observations of the dielectric properties and emissivities of arid areas at 10 to 89 GHz.</p>
Using unoccupied aerial vehicles to estimate availability and group size error for aerial surveys of coastal dolphins
<p><span>Aerial surveys are frequently used to estimate the abundance of marine mammals, but their accuracy is dependent upon obtaining a measure of the availability of animals for visual detection. Existing methods for characterizing availability have limitations and do not necessarily reflect true availability. Here, we present a method of using small, vessel‐launched, multi‐rotor Unoccupied Aerial Vehicles (UAVs or drones) to collect video of dolphins to characterize availability and investigate errors surrounding group size estimates. We collected over 20 h of aerial video of dive‐surfacing behaviour across 32 encounters with the Australian humpback dolphin </span><em><span>Sousa sahulensis</span></em><span> off north‐western Australia. Mean surfacing and dive periods were 7.85 sec (</span><span>se</span><span> = 0.26) and 39.27 sec (</span><span>se</span><span> = 1.31) respectively. Dolphin encounters were split into 56 focal follows of consistent group composition to which example approaches to estimating availability were applied. Non‐instantaneous availability estimates, assuming a 7-sec observation window, ranged between 0.22 and 0.88, with a mean availability of 0.46 (CV = 0.34). Availability tended to increase with increasing group size. We found a downward bias in group size estimation, with true group size typically one individual more than would have been estimated by a human observer during a standard aerial survey. The variability of availability estimates between focal follows highlights the importance of sampling across a variety of group sizes, compositions, and environmental conditions. Through data re‐sampling exercises, we explored the influence of sample size on availability estimates and their precision, with results providing an indication of target sample sizes to minimize bias in future research. We show that UAVs can provide an effective and relatively inexpensive method of characterizing dolphin availability with several advantages over existing approaches. The example estimates obtained for humpback dolphins are within the range of values obtained for other shallow‐water, small cetaceans, and will directly inform a government‐run program of aerial surveys in the region.</span></p>
Data for: Effects of long-term ethanol storage of blood samples on the estimation of telomere length
<p>Telomeres, DNA structures located at the end of eukaryotic chromosomes, shorten with each cellular cycle. The shortening rate is affected by factors associated with stress, and, thus telomere length has been used as a biomarker of ageing, disease, and different life history trade-offs. Telomere research has received much attention in the last decades; however, there is still a wide variety of factors that may affect telomere measurements and to date no study has thoroughly evaluated the possible long-term effect of a storage medium on telomere measurements. In this study we evaluated the long-term effects of ethanol on relative telomere length (RTL) measured by qPCR, using blood samples of magpies collected over twelve years and stored in absolute ethanol at room temperature. We firstly tested whether storage time had an effect on RTL and secondly we modelled the effect of time of storage (from 1 to 12 years) in differences in RTL from DNA extracted twice in consecutive years from the same blood sample. We also tested whether individual amplification efficiencies were influenced by storage time, and whether this could affect our results. Our study provides evidence of an effect of storage time on telomere length measurements. Importantly, this effect shows a pattern of decreasing loss of telomere sequence with storage time that stops after approximate 4 years of storage, which suggests that telomeres may degrade in blood samples stored in ethanol. Our method to quantify the effect of storage time could be used to evaluate other storage buffers and methods. Our results highlight the need to evaluate the long-term effects of storage on telomere measurements, particularly in long-term studies.</p>
Gloss estimation of chocolate sprinkles with Hyperspectral Imaging
<p>Gloss is an important characteristic in the quality evaluation in chocolate production. However, standard glossing<br> measuring devices face several challenges when measuring food products, in particular those with curved surfaces and<br> small, such as chocolate sprinkles. Therefore, gloss evaluation of chocolate sprinkles is typically done by human visual<br> assessment. In this respect, hyperspectral imaging (HSI), combining spectroscopy and imaging, has gained attention as a<br> non-destructive and non-contact real-time detection tool for food quality analysis and control. This technique adds an<br> extra dimension to traditional machine vision techniques by providing images at a larger number of more narrow<br> wavebands. This can potentially increase the discrimination power.<br> <br> The main task of this dataset is to classify between 5 classes of chocolate production stages. The labels are indicated on the file names. The labels are: EXTRUDER, GLUCOSE, STAGE1, STAGE2, STAGE3.</p> <p> The .zip file contains two folders named:</p> <p>- envi_sprinkles_dataset_11_04_22 (Train Set)</p> <p>- envi_sprinkles_test_dataset_11_04_22 (Test Set)</p> <p>In envi_sprinkles_dataset_11_04_22 the file name convention is BATCH_CLASSNAME_INDEX.hdr</p> <p>In envi_sprinkles_test_dataset_11_04_22 the file name convention is CLASSNAME_INDEX.hdr</p> <p>Note that in the train set the batch information is present in the file name, if two samples belongs to the same batch means that they were produced at the same time.</p> <p>The samples format is ENVI. Consist of a header file and ENVI binary data file with file extensions <code>.hdr</code> and <code>.raw</code>, respectively. The function writes the wavelength and metadata information to the ENVI header file and the data cube containing the hyperspectral images to the ENVI binary data file.</p> <p>The image shape is [272x512x16] , [HEIGHT, WIDTH, BANDS], the pixel format is uint16.</p> <p> </p> <p> </p>
Stroke data from: Robust dynamic brain coactivation states estimated in individuals
<p><span>A confluence of evidence indicates that brain functional connectivity (FC) is not static but rather dynamic. </span><span>Capturing transient </span><span>network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an </span><span>individualized network-based dynamics analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task.</span> <span>Moreover, we evaluated the extent to which brain states showed hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics. </span><span>W</span><span>e discovered a right-lateralized brain state that occurred more frequently in men than in women, and more frequently in right-handed versus left-handed individuals. Lastly, we demonstrated longitudinal brain state changes in 42 patients with subcortical stroke over 6 months. Taken together, this approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.</span></p>
Online appendix and simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators
<p>The birth-death exposed-infectious (BDEI) phylodynamic model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters, from time-scaled phylogenetic trees.</p> <p>We implemented a highly parallelizable estimator for the BDEI model in a maximum likelihood framework (<a href="https://github.com/evolbioinfo/bdei">PyBDEI</a>) using a combination of numerical analysis methods for efficient equation resolution. This dataset contains the assessment of PyBDEI in comparison with a Bayesian implementation in <a href="http://www.beast2.org/">BEAST2</a> (mtbd package) and a deep learning estimator <a href="https://github.com/evolbioinfo/phylodeep">PhyloDeep</a>: the parameter values estimated by the 3 tools.<br><br>The PyBDEI and the theoretical findings behind it are described in A Zhukova, F Hecht, Y Maday, and O Gascuel. Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees Syst Biol 2023. This dataset contains the online Appendix (Fig S1-S3 and Table S1).</p>
IEEE 802.15.4 TSCH dataset for phase-based distance estimation
<p><strong>Introduction</strong></p> <p>This data set contains two collections of phase angle measurements created in two indoor and one outdoor environment that can be used for phase-based distance estimates. The measurements include phase samples created on two different frequency sets:</p> <ul> <li> <strong>TSCH standard frequencies</strong>: measurements are performed on default 16 channel frequencies {2405.0, 2410.0, 2415.0, 2420.0, 2425.0, 2430.0, 2435.0, 2440.0, 2445.0, 2450.0, 2455.0, 2460.0, 2465.0, 2470.0, 2474.0, 2480.0}MHz.</li> <li> <strong>Golomb ruler frequencies</strong>: the measurements are performed on 15 custom selected frequencies according to the Golomb ruler technique {2400.5, 2406.0, 2407.5, 2408.0, 2412.5, 2423.0, 2431.0, 2442.5, 2452.0, 2460.5, 2463.0, 2466.5, 2476.5, 2479.5, 2480.5}MHz.</li> </ul> <p><br> <strong>Measurement setup</strong></p> <p>Measurements were performed using AT86RF233 transceivers connected to the in-house <a href="https://log-a-tec.eu/hw-vesna.html">VESNA</a> platform. Two nodes were placed on a stand 1.6 m above the ground in three separate environments:</p> <ul> <li>in a 5x5m square office with no furniture</li> <li>in an indoor hallway with dimensions of 4x40m</li> <li>in a park without any nearby obstacles</li> </ul> <p>The actual distance between nodes was measured with a laser ranger with an accuracy of ±1.5 mm. There was no obstacle between the devices. Indoors, 17 WiFi access points were in operation during the measurement campaign.</p> <p><br> <strong>Phase measurement process</strong></p> <p>The devices involved first establish an IEEE 802.15.4 TSCH network. In it, they measure the phase difference on pre-selected frequencies. The phase measurement has been seamlessly integrated into a communication so that the devices obtain phase measurement with every packet sent.</p> <p><em>Why two collections?</em></p> <p>The set labelled "TSCH standard channels" contains phase measurements created at frequencies defined in the IEEE.802.15.4 standard for the 2.4 GHz band. The frequency step (<span class="math-tex">\(\Delta freq = freq_{i+1} - freq_{i}\)</span>) between two phase samples is equal to 5MHz, which results in a maximum distinguishable range of 30m for the distance estimation.</p> <p>To increase the range up to 300m, the frequency step must be reduced to 0.5 MHz. This requires 160 phase samples in the 2.4 GHz band used with a bandwidth of 80 MHz. However, measuring 160 phase samples on 160 frequencies would take a lot of time and therefore interfere with TSCH communications. One way to shorten the procedure is to use the Golomb ruler technique. This allows a large set of phase differences to be created from a small number of measured phases. This method was used in the creation of the set named "Golomb ruler frequencies". The data set also contains a Python example script that expands the set of 15 measured frequencies to a set of 160 samples.</p> <p><br> <strong>Folder structure</strong></p> <p>Each record collection is stored in a corresponding folder. Each folder contains .json files representing different environments. In addition to the data sets, the folders also contain figures and a sample Python script. The measurements are stored in JSON format. Each measured distance contains the number of measurements and the actual data. With each packet sent (identified by its Absolute Slot Number (ASN)), the phase difference between the devices is measured. The phase value is stored as an 8-bit value representing the range from 0 to 2 pi.</p>
Are trapping data suited for home-range estimation?
<p>Modern home-range estimation typically relies on data derived from expensive radio- or GPS-tracking. Although trapping represents a low-cost alternative to telemetry, there lacks an evaluation of the performance of home-range estimators on trap-derived data. Using simulated data, we evaluate three variables reflecting the key trade-offs ecologists face when designing a trapping study: 1) the number of observations obtained per individual, 2) the trap density, and 3) the proportion of the home range falling inside the trapping area. We compare the performance of five home-range estimators (MCP, LoCoH, KDE, AKDE, bicubic interpolation). We further explore the potential benefits of combining these estimators with asymptotic models, which leverage the saturating behavior of changes in the estimated home-range area as the number of observations increases to improve accuracy, as well as different data ordering procedures. We then quantified the bias in home-range size under the different scenarios investigated. The number of observations and the proportion of the home range within the trapping grid were the most important predictors of the accuracy and the precision of home-range estimates. The use of asymptotic models helped obtain accurate estimates at smaller sample sizes, while distance-ordering improved the precision and asymptotic consistency of estimates. While AKDE was the best-performing estimator under most conditions evaluated, bicubic interpolation was a viable alternative under common real-world conditions of low trap density and area covered. A case study using empirical data from white-tailed deer in Florida and another from jaguars in Belize demonstrated support for the findings of our simulation results. Although researchers with trap data often overlook home-range estimation, our results indicate that these data have the capacity to yield accurate estimates of home-range size. Trapping data can therefore lower the economic costs of home-range analysis, potentially enlarging the span of species, researchers and questions studied in ecology and conservation.</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.