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4,276 results for “Estimation”
Fig. 4 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 4. Scatterplot showing total body length in mm versus estimated volume of blood and plasma extracted in Ml. The box-and-whisker plots are centered on the mean body length for each of the three juvenile stages. The box edges are placed at the 2nd and 3rd quartiles for volume estimates and the whiskers show extreme minimum and maximum volumes. The mean estimate of extracted volume by juvenile stage is shown as a labeled dashed-red horizontal line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 1 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 1. Traps used in the first study. (A) Small emergence trap, (B) fish-baited emergence trap, (C) fish-baited tripod, (D) open-mesh fish-baited trap and (E) lighted plankton trap. Note that the sample container holding a small French grunt fish for the fish-baited emergence trap (B) and the fish-baited tripod trap (C) are identical units other than the sealed floats attached to the top of the sample container when used with the fish-baited emergence trap.
Fig. 2 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 2. Traps used in the second study. The lighted plankton trap, in the left foreground, stands on short legs—four large emergence traps can be seen in the middleground to the right of the lighted plankton trap. A second lighted plankton trap in the background can be seen towards the center of the frame.
Fig. 3 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 3. Scatterplots of total body length in mm plotted against eye length in mm along the long axis. The upper plot shows measurements for zuphea and the lower plot for praniza. The body length cutoff values separating juvenile stages are shown as a dotted-green line. Gnathiids collected from emergence traps are seen as gold-filled squares and those collected from light traps are presented as purple-filled triangles. Differences in the ontological sampling bias of these two trap designs can be seen by comparing the two scatterplots. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 6 in Comparison of sampling methodologies and estimation of population parameters for a temporary fish ectoparasite
Fig. 6. Histograms of trap counts by sample day and juvenile stage. The upper histograms show counts from emergence traps and the lower histograms show counts from light traps. Mean count for each histogram is shown as a dashed horizontal line. See text for an explanation of the number of sampling days shown in each plot.
Fig. 2 in Qualitative and quantitative methods for estimating Spirorchiidiasis burden in sea turtles
Fig. 2. Scatter plot with trend line showing the correlation between eggs counts using the two quantitative methods (MH and CH) in the spleen.
Fig. 1 in Qualitative and quantitative methods for estimating Spirorchiidiasis burden in sea turtles
Fig. 1. Eggs of H. mistroides in the McMaster chamber for quantification of splenic egg burden (Scale bar: 200 μm).
AI4AGRI Soil roughness estimation dataset
<p>The dataset contains two sets of images, representing digital images of soil samples used for training ML models to estimate the soil roughness. For more details on the experiments please refer to (and cite): Ivanovici, M., Popa, S., Marandskiy, K., & Florea, C. (2024). Deep automatic soil roughness estimation from digital images. European Journal of Remote Sensing. DOI: https://doi.org/10.1080/22797254.2024.2342955 </p> <p>The zip archives containts a readme text file with the detailed description of the dataset.</p> <p>Funded by the European Union. The AI4AGRI project entitled “Romanian Excellence Center on Artificial Intelligence on Earth Observation Data for Agriculture” received funding from the European Union’s Horizon Europe research and innovation programme under the grant agreement no. 101079136. </p> <p>Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. </p>
Fig. 3 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 3. Current (A and F) and future distribution models (B-E, G-J) for Haemadipsa rjukjuana in Korea. Dark gray represents over 0.5 MaxEnt value (suitable habitat) and light gray represents below 0.5 (unsuitable habitat). A is projected to the current climate conditions (2020), and F was built with the restricted spatial area between Heuksando Island and Gageodo Island. B-E are projections of the Maxent model to SSP585 of GISS-E2-1 climate scenarios by NASA and G-J were SSP585 of INM-CM4-8 scenarios by The Institute of Numerical Mathematics. B-E and G-J are respectively 2040, 2060, 2080, and 2100.
Fig. 2 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 2. Projection of MaxEnt Haemadipsa rjukjuana distribution model from Heuksando Island and Gageodo Island to the current climate condition of South Korea. Red color (lower value) represents less suitable habitats and blue (higher value close to 1.0) represents suitable habitats for H. rjukjuana.
Fig. 1 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 1. The map of study sites (inset) and the Korean Peninsula. Haemadipsa rjukjuana was identified from the regions shaded in gray.
Estimated particle parameters of an aluminium matrix composite
<p>The dataset consists of section profile parameters in tabular form (assumed to come from prolate spheroids) of ellipses fitted to measured particles of an aluminium matrix composite from metallographic analysis. The dataset can be used to reconstruct the trivariate spatial spheroid (prolate form) distribution.</p>
A Site Atmospheric State Best Estimate of Temperature for Lauder, New Zealand (1997-2012)
<p>A Site Atmospheric State Best Estimate (SASBE) of the temperature profile above the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) site at Lauder, New Zealand, has been developed. Data from multiple sources are combined within the SASBE to generate a high temporal resolution data set that includes an estimate of the uncertainty on every value. The SASBE has been developed to enhance the value of measurements made at the distributed GRUAN site at Lauder and Invercargill (about 180 km apart), and to demonstrate a methodology which can be adapted to other distributed sites.</p> <p>Within GRUAN, a distributed site consists of a cluster of instruments at different locations.<br> The temperature SASBE combines measurements from radiosondes and automatic weather stations at Lauder and Invercargill, and ERA5 reanalysis, which is used to calculate a diurnal temperature cycle to which the SASBE converges in the absence of any measurements.<br> The SASBE provides hourly temperature profiles at 16 pressure levels between the surface and 10 hPa for the years 1997 to 2012. Every temperature value has an associated uncertainty which is calculated by propagating the measurement uncertainties, the ERA5 ensemble SDs, and the ERA5 representativeness uncertainty through the retrieval chain.</p> <p>This best-estimate temperature data product for Lauder is expected to be valuable for satellite and model validation as measurements of atmospheric essential climate variables are sparse in the Southern Hemisphere.</p> <p>A publication describing the data product is submitted to Earth System Science Data Discussions. The title of the publication is: Combining Data from the Distributed GRUAN Site<br> Lauder-Invercargill, New Zealand, to Provide a Site Atmospheric State Best Estimate of Temperature.</p> <p> </p> <p> </p>
Copepod diapause duration estimation code for manuscript: Lipid load triggers migration to diapause in Arctic Calanus copepods - insights from underwater imaging
<p>This is a script and data upload by Frederic Maps (Université Laval) which creates figure 10 as well as other exploratory plots for publication Schmid, Maps, Fortier 2018: Lipid load triggers migration to diapause in Arctic Calanus copepods - insights from underwater imaging, to be published in the journal of plankton research (JPR).</p>
LibriCount, a dataset for speaker count estimation
<p><strong>LibriCount10 0dB Dataset</strong></p> <p>This is the description to the LibriCount10 synthetic dataset for speaker count estimation.</p> <p>Therefore for each recording we provide the ground truth number of speakers within the file name, where `k` in, `k_uniquefile.wav` is the maximum number of concurrent speakers with the 5 seconds of recording.</p> <p>The dataset contains a simulated cocktail party environment of [0..10] speakers, mixed with 0dB SNR from random utterances of different speakers from the <a href="http://www.openslr.org/12/">LibriSpeech</a> `CleanTest` dataset.</p> <p>All recordings are of 5s durations, and all speakers are active for the most part of the recording.</p> <p>For each unique recording, we provide the audio wave file (16bits, 16kHz, mono) and an annotation `json` file with the same name as the recording.</p> <p><strong>Metadata</strong></p> <p>In the annotation file we provide information about the speakers sex, their unique speaker_id, and vocal activity within the mixture recording in samples. Note that these were automatically generated using a <a href="https://github.com/wiseman/py-webrtcvad">voice activity detection</a> system.</p> <p>In the following example the annotation shows a speaker count of 3 speakers as can be extracted from the number of elements in the list:</p> <pre><code class="language-json">[ { "sex": "F", "activity": [[0, 51076], [51396, 55400], [56681, 80000]], "speaker_id": 1221 }, { "sex": "F", "activity": [[0, 51877], [56201, 80000]], "speaker_id": 3570 }, { "sex": "M", "activity": [[0, 15681], [16161, 68213], [73498, 80000]], "speaker_id": 5105 } ]</code></pre> <p><br> </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>
China's transboundary water resources estimation using machine learning approaches
<p>China's transboundary water resources estimated using machine learning models (random forest, gradient boosting, and stacking).</p>
Estimated counts for simulated data (Homo sapiens) from Soneson et al (2016)
<p>Estimated counts for isoforms for the simulated data (Homo sapiens) using kallisto from Soneson et al (2016) "<em>Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage</em>" Genomy Biology 17(12).</p> <p>From the Methods:</p> <p>"The transcriptome catalogs used as the basis for the expression estimation and simulation were Ensembl GRCh37.71 for the human simulation (using only the chromosomes in the primary genome assembly)."</p> <p>From the Background:</p> <p>"Finally, we define the counting bins as the isoforms themselves and use kallisto (v0.42.1) to estimate the number of reads assigned to each isoform (<em>kallisto</em>)."</p> <p>These text files contain the estimated counts per isoform for the 6 samples.</p>
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation
<p>While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. This dataset consists of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object.</p> <p>If you use this dataset in your research, please cite the following publication:</p> <p>T. Grenzdörffer, M. Günther, and J. Hertzberg, “YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation,” in <em>2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31-June 4, 2020</em>. IEEE, 2020.</p> <pre><code>@InProceedings{Grenzdoerffer2020ycbm, title = {{YCB-M}: A Multi-Camera {RGB-D} Dataset for Object Recognition and {6DoF} Pose Estimation}, author = {Grenzd{\"{o}}rffer, Till and G{\"{u}}nther, Martin and Hertzberg, Joachim}, booktitle = {2020 {IEEE} International Conference on Robotics and Automation, {ICRA} 2020, Paris, France, May 31-June 4, 2020}, year = {2020}, publisher = {{IEEE}} }</code></pre> <p>This paper is also available on arXiv: <a href="https://arxiv.org/abs/2004.11657">https://arxiv.org/abs/2004.11657</a></p> <p> </p> <p>To visualize the dataset, follow these instructions (tested on Ubuntu Xenial 16.04):</p> <pre><code class="language-bash"># IMPORTANT: the ROS setup.bash must NOT be sourced, otherwise the following error occurs: # ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type # nvdu requires Python 3.5 or 3.6 sudo add-apt-repository -y ppa:deadsnakes/ppa # to get python3.6 on Ubuntu Xenial sudo apt-get update sudo apt-get install -y python3.6 libsm6 libxext6 libxrender1 python-virtualenv python-pip # create a new virtual environment virtualenv -p python3.6 venv_nvdu cd venv_nvdu/ source bin/activate # clone our fork of NVIDIA's Dataset Utilities that incorporates some essential fixes pip install -e 'git+https://github.com/mintar/Dataset_Utilities.git#egg=nvdu' # download and transform the meshes # (alternatively, unzip the meshes contained in the dataset # to <path to venv_nvdu>/lib/python3.6/site-packages/nvdu/data/ycb/aligned_cm) nvdu_ycb -s # run nvdu_viz to visualize the dataset cd <a subdirectory of the YCB-M dataset with some frames> nvdu_viz --name_filters '*.jpg' </code></pre> <p>For further details, see README.md.</p>
Yearly Estimation of Ecological Functional Attributes of vegetation from 2005-2010 over the Peneda Geres National Park
<p>Yearly Estimation of Ecological Functional Attributes of vegetation using MSAVI index based on Landsat data from 2005-2010 over the Peneda Geres National Park</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.