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4,276 results for “Estimation”
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
<p>1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. </p> <p>2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. </p> <p>3. The R package <em><span>aniMotum</span></em> eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. </p> <p>4. We highlight <em>aniMotum</em>'s<em> </em>capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use <em>aniMotum</em> for their own analyses. </p>
Code and data from: A hierarchical approach for estimating state-specific mortality and state transition in dispersing animals with incomplete death records
<p>Unbiased mortality estimates are fundamental for testing ecological and evolutionary theory as well as for developing effective conservation actions. However, mortality estimates are often confounded by dispersal, especially in studies where dead-recovery is not possible. In such instances, missing individuals (i.e. individuals with unobserved time of death) may have died or permanently emigrated from a study area, making inferences about their fate difficult. Mortality before and during dispersal, as well as the decision to disperse, usually depend on a suite of individual, social, and environmental covariates, which in turn can be used to draw conclusions about the fate of missing individuals.<br>Here, we propose a Bayesian hierarchical model that takes into account time-varying covariates to estimate transitions between life-history states and mortality in each state using mark-resighting data with missing individuals. Specifically, our framework estimates mortality rates in two states (resident and dispersing state) by treating the fate of missing individuals as a latent (i.e. unobserved) variable that is statistically inferred based on information from individuals with a known fate and given the individual, social, and environmental conditions at the time of disappearance. Our model also estimates rates of state transition (i.e. emigration) to assess whether a missing individual was more likely to have died or survived due to unobserved emigration from the study area. <br>We used simulations to check the validity of our model and assessed its performance with data of varying degrees of uncertainty. Our modeling framework provided accurate mortality and emigration estimates for simulated data of different sample sizes, proportions of missing individuals, and resighting intervals. Variation in sample size appeared to affect the precision of estimated parameters the most.<br>Our approach offers a solution to estimating unbiased mortality of both resident and dispersing individuals as well as the probability of emigration using mark-resighting data with incomplete death records. Conditional on the availability of data on known-fate individuals and relevant time-varying covariates, our model can reconstruct the fate (death or emigration) of missing individuals. The modularity of our framework allows mortality analyses to be tailored to a variety of species-specific life histories.</p>
Dataset for "Root Length Estimation: Automated Minirhizotron Image Analysis with Convolutional Networks without Segmentation"
<p>This data contains 4015 root images, splitted into 4 datasets, acquired using two minirhizotron (MR) system types - manual (Dataset 1 & Dataset 4) and automated (Dataset 2 & Dataset 3). It includes four crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. The data was acquired by researchers from Ben-Gurion University of the Negev, Beer Sheva, Israel, and used for research of automated TRL estimation with Convolutional Neural Networks.</p> <p>The annotations were conducted manually using the Rootfly software (Wells and Birchfield, Clemson University, South Carolina, USA), and data were transformed as CSV formats. In this software, the annotator must draw a root by marking points along the selected root. These points usually correspond to the coordinates at the start and the end of the root, and curving points along the root. These points are then connected in a line, the length of which reflects the real length of the selected root. The annotations has been done for all roots within an image, and for all images in the provided dataset.</p> <p>The provided annotations include the total root length (TRL) per image (mm) and the coordinates of annotated points.</p> <p>The annotations are given in two types of files:</p> <p>"TRL.csv" files: contain the image names and corresponding TRL values (mm).</p> <p>"pointsOutput.csv" files: contain the annotated image names and the coordinates of the points of the roots in the image (if the image contains roots) in the form of x1, y1, x2, y2, x3, y3, etc. It the image doesn't have roots, the file contains only its name.</p>
Age estimation of the Southern Ocean squid Moroteuthopsis longimana using beaks collected from predators' diet: number and increment widths in the rostrum sagittal section of the lower beak
<p>In this work we used upper and lower beaks of <em>Moroteuthopsis longimana </em>collected from the diet of Antarctic toothfish <em>Dissostichus mawsoni </em>from the Pacific (Ross and Amundsen Seas) and Atlantic (South Sandwich Islands) sectors of the Southern Ocean to study the feasibility of using beaks collected from predators’ stomachs to study the age of Southern Ocean oceanic squid and to estimate the age and growth patterns of <em>M. longimana. </em>We analysed the presence of micro-increments in the rostrum sagittal section (RSS) and the lateral wall of both upper and lower beaks. Lower beak’s RSS was the only section where it is possible to observe and count a readable sequence of increments that allowed to estimate the age of oceanic squid using beaks collected from predators’ stomachs.</p> <p>This dataset contains the number and width of increments found in the RSS of the 10 <em>M. longimana</em>’s lower beaks used in this study. Further information related with the studied beaks such as beak measurements, origin and sample date can be found in the published article.</p> <p>This dataset is associated with the article: Queirós JP, Bartolomé A, Piatkowski U, Xavier JC, Perales-Raya C (2023) Age and growth estimation of Southern Ocean squid <em>Moroteuthopsis longimana</em>: can we use beaks collected from predators’ stomachs? Marine Biology 170, 10. DOI 10.1007/s00227-022-04156-2</p>
Forest density estimates in the vicinity of the Ilirney lake system region, Chukotka, Russia
<p>Forest density estimates visually determined by describing the amount of present trees satellite imagery from Esri basemap (Esri) at an area of ~30x30 m qualitatively for 6515 stratified sampled locations at an equal number of locations based on elevation, aspect and slope angle. The density was categorized ranging from 1: single trees to 4: dense tree stands present, and contains a 0: no trees present.</p> <p>Format: ESRI shapefile, points; projection UTM58N; extent: 642085.1, 654775.1, 7462263, 7492833 m (xmin, xmax, ymin, ymax)</p>
Estimating interaction strengths for diverse horizontal systems using performance data
<p>1. Network theory allows us to understand complex systems by evaluating how their constituent elements interact with one another. Such networks are built from matrices that describe the effect of each element on all others. Quantifying the strength of these interactions from empirical data can be difficult, however, because the number of potential interactions increases non-linearly as more elements are included in the system, and not all interactions may be empirically observable when some elements are rare.</p> <p>2. We present a novel modelling framework that uses measures of species performance in the presence of varying densities of their potential interaction partners to estimate the strength of pairwise interactions in diverse horizontal systems.</p> <p>3. Our method allows us to directly estimate pairwise effects when they are statistically identifiable and to approximate pairwise effects when they would otherwise be statistically unidentifiable. The resulting interaction matrices can include positive and negative effects, the effect of a species on itself, and allows for non-symmetrical interactions.</p> <p>4. We show how to link the parameters inferred by our framework to a population dynamics model to make inferences about the effect of interactions on community dynamics and diversity.</p> <p>5. The advantages of these features are illustrated with a case study on an annual wildflower community of 22 focal and 52 neighbouring species, and a discussion of potential applications of this framework extending well beyond plant community ecology.</p>
Missing data in amortized simulation-based neural posterior estimation
<p>Supplementary Data to the publication "Missing data in amortized simulation-based neural posterior estimation", Wang et al. 2023.</p>
Poses of People in Art: A Data Set for Human Pose Estimation in Digital Art History
<p>Throughout the history of art, the pose—as the holistic abstraction of the human body's expression—has proven to be a constant in numerous studies. However, due to the enormous amount of data that so far had to be processed by hand, its crucial role to the formulaic recapitulation of art-historical motifs since antiquity could only be highlighted selectively. This is true even for the now automated estimation of human poses, as domain-specific, sufficiently large data sets required for training computational models are either not publicly available or not indexed at a fine enough granularity. With the <em>Poses of People in Art</em> data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators. It consists of 2,454 images from 22 art-historical depiction styles, including those that have increasingly turned away from lifelike representations of the body since the 19<sup>th</sup> century. A total of 10,749 human figures are precisely enclosed by rectangular bounding boxes, with a maximum of four per image labeled by up to 17 keypoints; among these are mainly joints such as elbows and knees. For machine learning purposes, the data set is divided into three subsets—training, validation, and testing—, that follow the established JSON-based Microsoft COCO format, respectively. Each image annotation, in addition to mandatory fields, provides metadata from the art-historical online encyclopedia WikiArt.</p>
DTM files from wildlife–vehicle collisions using kernel density estimation (KDE)
<p>21 CSV files that contain the Digital Terrain Model (DTM) from wildlife–vehicle collisions (WVC) hotspots using kernel density estimation (KDE) in Spain between 2016 and 2021. Data source of each WVC record is the Spanish General Directorate of Traffic (DGT).</p> <p>The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).</p> <p>This dataset is the output of the KDE analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
Data for: Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: A case study of two sympatric macaw species
<p><span></span></p> <p>Passive acoustic monitoring (PAM) offers the potential to dramatically increase the scale and robustness of species monitoring in rainforest ecosystems. PAM generates large volumes of data that require automated methods of target species detection. Species-specific recognisers, which often use supervised machine learning, can achieve this goal. However, they require a large training dataset of both target and non-target signals, which is time-consuming and challenging to create. Unfortunately, very little information about creating training datasets for supervised machine learning recognisers is available, especially for tropical ecosystems. Here we show an iterative approach to creating a training dataset that improved recogniser precision from 0.12 to 0.55. By sampling background noise using an initial small recogniser, we addressed one of the significant challenges of training dataset creation in acoustically diverse environments. Our work demonstrates that recognisers will likely fail in real-world settings unless the training dataset size is large enough and sufficiently representative of the ambient soundscape. We outline a simple workflow that can provide users with an accessible way to create a species-specific PAM recogniser that addresses these issues for tropical rainforest environments. Our work provides important lessons for PAM practitioners wanting to develop species-specific recognisers for acoustically diverse ecosystems.</p>
A multi-lab experimental assessment reveals that replicability can be improved by using empirical estimates of genotype-by-lab interaction
<p>Raw data sets, curated data sets and R code underlying the paper "A multi-lab experimental assessment reveals that replicability can be improved by using empirical estimates of genotype-by-lab interaction" </p> <p>https://doi.org/10.1101/2021.12.05.471264</p>
Data from: LoRaD: Marginal likelihood estimation with haste (but no waste)
<p>The Lowest Radial Distance (LoRaD) method is a modification of the recently-introduced Partition-Weighted Kernel method for estimating the marginal likelihood of a model, a quantity important for Bayesian model selection. For analyses involving a fixed tree topology, LoRaD improves upon the Steppingstone or Thermodynamic Integration (Path Sampling) approaches now in common use in phylogenetics because it requires sampling only from the posterior distribution, avoiding the need to sample from a series of <em>ad hoc</em> power posterior distributions, and yet is more accurate than other fast methods such as the Generalized Harmonic Mean (GHM) method. We show that the method performs well in comparison to the Generalized Steppingstone method on an empirical fixed-topology example from molecular phylogenetics involving 180 parameters. The LoRaD method can also be used to obtain the marginal likelihood in the variable-topology case if at least one tree topology occurs with sufficient frequency in the posterior sample to allow accurate estimation of the marginal likelihood conditional on that topology.</p>
An archive of net community production estimates derived from autonomous profiler observations at Ocean Station Papa
<p>This dataset contains an archive of marine net community production estimates for Ocean Station Papa, derived using autonomous profilers, including BGC-Argo floats and sea gliders. The archive contains data from the following original publications:</p> <ol> <li>Haskell, W. Z., Fassbender, A. J., Long, J. S., and Plant, J. N.: Annual Net Community Production of Particulate and Dissolved Organic Carbon From a Decade of Biogeochemical Profiling Float Observations in the Northeast Pacific, Global Biogeochemical Cycles, 34, 1–22, <a href="https://doi.org/10.1029/2020GB006599">https://doi.org/10.1029/2020GB006599</a>, 2020.</li> <li>Huang, Y., Fassbender, A. J., Long, J. S., Johannessen, S., and Bernardi Bif, M.: Partitioning the Export of Distinct Biogenic Carbon Pools in the Northeast Pacific Ocean Using a Biogeochemical Profiling Float, Global Biogeochemical Cycles, 36, <a href="https://doi.org/10.1029/2021gb007178">https://doi.org/10.1029/2021gb007178</a>, 2022.</li> <li>Pelland, N. A., Eriksen, C. C., Emerson, S. R., and Cronin, M. F.: Seaglider Surveys at Ocean Station Papa: Oxygen Kinematics and Upper-Ocean Metabolism, Journal of Geophysical Research: Oceans, 123, 6408–6427, <a href="https://doi.org/10.1029/2018JC014091">https://doi.org/10.1029/2018JC014091</a>, 2018.</li> <li>Plant, J. N., Johnson, K. S., Sakamoto, C. M., Jannasch, H. W., Coletti, L. J., Riser, S. C., and Swift, D. D.: Net community production at Ocean Station Papa observed with nitrate and oxygen sensors on profiling floats, Global Biogeochemical Cycles, 30, 859–879, <a href="https://doi.org/10.1002/2015GB005349">https://doi.org/10.1002/2015GB005349</a>, 2016.</li> <li>Yang, B., Emerson, S. R., and Bushinsky, S. M.: Annual net community production in the subtropical Pacific Ocean from in-situ oxygen measurements on profiling floats, Global Biogeochemical Cycles, 31, 728–744, <a href="https://doi.org/10.1002/2016GB005545">https://doi.org/10.1002/2016GB005545</a>, 2017.</li> </ol> <p> </p> <p><strong>Dataset description:</strong></p> <p>Separate Matlab (.mat) and NetCDF (.nc) files are provided for each original publication dataset. At minimum, each contains time (recorded as absolute days since 01 Jan 2000, 00:00:00), and time-explicit depth-integrated NCP. Additional fields include depth-resolved NCP, and water column depth (Plant et al., 2016; Pelland et al., 2018).</p> <p>Note that the Haskell et al. (2020) dataset (haskell20.mat, haskell20.nc) contains climatological NCP estimates. Accordingly, the time field represents the Year-day only. </p> <p>All NCP units are recorded in units of mmol O2/m2/d (depth-integrated) or mmol O2/m3/d (depth-resolved).</p> <p>Additional information on each dataset is contained within the data files. Additional processing information is contained in Izett et al. (under review).</p> <p> </p> <p><strong>The data were compiled in: </strong></p> <p>Izett, R. W., Fennel, K., Stoer, A. C., and Nicholson, D. P. Reviews and syntheses: Expanding the global coverage of gross primary production and net community production measurements using BGC-Argo floats, <em>Submitted to Biogeosciences. 2023</em>.</p> <p> </p> <p><strong>Please cite as:</strong></p> <p>Izett, R., Haskell, W., Huang, Y., Pelland, N., Plant, J., and Yang, B. 2023. An archive of net community production estimates derived from autonomous profiler observations at Ocean Station Papa. Database. Zenodo. doi: 10.5281/zenodo.7667521. Accessed on <em>DATE</em>. </p>
Long term mean Potential Evapotranspiration (PET) and Actual Evapotranspiration (EAT) estimates using World-Wide HYPE and different PET-formula
<p>Data of the article "<strong>Which Potential Evapotranspiration Formula to Use in Hydrological Modelling World-wide? </strong>" (Pimentel et al. 2023, <em>Water Resources Research, </em><a href="https://doi.org/10.1029/2022WR033447">https://doi.org/10.1029/2022WR033447</a>)</p>
Supporting data set for: On the challenge of obtaining an accurate solvation energy estimate in simulations of electrocatalysis
<p>The data set generated for the article: "On the challenge of obtaining an accurate solvation energy estimate in<br> simulations of electrocatalysis".</p> <p>Consists of subfolders for various sets of calculations. The data analysis procedure is shown in detail on <a href="https://bjk24.gitlab.io/bg-solvation/intro.html">this website</a>. If you want to peform the data analysis yourself, follow the instructions on the <a href="https://bjk24.gitlab.io/bg-solvation/docs/setup.html">setup page</a> of the website to download the repository, insert this data set into it, and run the Jupyter book.</p>
Counting animals in aerial images with a density map estimation model
<p>Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual post-processing has been used extensively, however, volumes of such data are increasing, necessitating some level of automation, either for complete counting or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as <em>Pygoscelis</em> penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations, and demonstrably improve monitoring efforts from aerial imagery. </p>
Estimating durations of disruptions to freight transport due to climate-related hazards
<p>A table of hazard intensity thresholds and notes on how to estimate the duration of disruption to freight transport (road and rail) due to climate-related hazards (extreme heat, flooding and tropical cyclone winds).</p>
Estimation of Freight Trip Produced and Attracted in NYC.
<p>This dataset is part of a project under the US DOT (No. 69A3551747124) titled "Quantifying and visualizing city truck route network efficiency using a virtual test bed."</p> <p>The file <em>fta_equi_gateways_3_dig_naics.csv </em>yields the number of freight trips attracted at the equity zone level (gateways included) and at 3-digit NAICS disaggregation for the city of New York.</p> <p>The file <em>ftp_equi_gateways_3_dig_naics.csv </em>yields the number of freight trips produced at the equity zone level (gateways included) and at 3-digit NAICS disaggregation for the city of New York.</p> <p>The file <em>ct2010_ct2020_equi_zone.csv</em> gives the equivalencies between the census tracts 2010, the census tracts 2020, and the New York City equitable zoning (more information available <a href="https://zenodo.org/record/7699609#.ZAaKaHbMKUk">here</a>).</p>
HoloSet - A Dataset for Visual-Inertial Pose Estimation in Extended Reality
<p>HoloSet was published at DATA'22 workshop at ACM SenSys'22. <br> <br> Overview: There is a lack of datasets for visual-inertial odometry applications in Mixed Reality (MR). To the best of our knowledge, there is no dataset available that is captured from an MR headset with a human as a carrier. To bridge this gap, we present a novel pose estimation dataset — called HoloSet — collected using Microsoft Hololens 2, which is a state-of-the-art head mounted device for XR. Potential applications for HoloSet include visual-inertial odometry, simultaneous localization and mapping (SLAM), and additional applications in XR that leverage visual-inertial data.</p> <p>HoloSet captures both macro and micro movements. For macro movements, the dataset consists of more than 66,000 samples of visual, inertial, and depth camera data in a variety of environments<br> (indoor, outdoor) and scene setups (trails, suburbs, downtown) under multiple user action scenarios (walk, jog). For micro movements, the dataset consists of more than 12,000 samples of additional articulated hand depth camera images while a user plays games that exercise fine motor skills and hand-eye coordination. We present basic visualizations and high-level statistics of the data and outline the potential research use cases for HoloSet.<br> <br> Please find the relevant publication at https://dl.acm.org/doi/abs/10.1145/3560905.3567763. </p>
Estimation of the Optical Feedback Factor C
<p>The dataset contains the estimated Optical Feedback factor value C for different experimental setups (i.e. laser-target distances, laser diode driving currents) as well as the estimated amplitude of the optical feedback interference signals. The calculations of C were performed using the non-uniform sampling approach [1].</p> <p>The raw data is provided in text format that is readable with Matlab for instance. The excel files summarize the estimated C and amplitude values for each set.</p> <p>[1] Bernal, O. D., Zabit, U., Jayat, F., & Bosch, T. (2021). <a href="https://www.mdpi.com/1424-8220/21/10/3528">Toward an Estimation of the Optical Feedback Factor C on the Fly for Displacement Sensing</a>. <em>Sensors</em>, <em>21</em>(10), 3528.</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.