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54 results for “sampling efficiency”
Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials
<p>Archive with data supporting the paper "Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials" and the related PhD thesis by B. Pampel</p>
Figure 2 in Efficiency of colored modified box traps for sampling of tabanids
Figure 2. Location and position of modified box traps on the experimental field in Monjoroš Forest (first set of traps: 1 – black, 2 – brown, 3 – bordeaux, 4 – light violet, 5 – green, 6 – blue, 7 – red, 8 – yellow, 9 – orange, 10 – white; the second set was constructed by mirror symmetry along a line connecting traps 5 and 6).
Figure 5 in Efficiency of sampling methods for capturing soil-dwelling ants in three landscapes in southern Cameroon
Figure 5: Differentiation coefficient based on species richness (left) and Simpson's index (right) in three sampling methods (A) Bait, (B) Pitfall, (C) Quadrat.
Figure 4 in Efficiency of sampling methods for capturing soil-dwelling ants in three landscapes in southern Cameroon
Figure 4. Differentiation coefficient based on species richness (left) and simpson's index (right) in three habitats: (A) Upland, (B) Littoral, (C) Urban.
Data from: Fractal triads efficiently sample ecological diversity and processes across spatial scales
<p>The relative influence of ecological assembly processes, such as environmental filtering, competition, and dispersal, vary across spatial scales. Changes in phylogenetic and taxonomic diversity across environments provide insight into these processes, however, it is challenging to assess the effect of spatial scale on these metrics. Here, we outline a nested sampling design that fractally spaces sampling locations to concentrate statistical power across spatial scales in a study area. We test this design in northeast Utah, at a study site with distinct vegetation types (including sagebrush steppe and mixed conifer forest), that vary across environmental gradients. We demonstrate the power of this design to detect changes in community phylogenetic diversity across environmental gradients and assess the spatial scale at which the sampling design captures the most variation in empirical data. We find clear evidence of broad-scale changes in multiple features of phylogenetic and taxonomic diversity across aspect. At finer scales, we find additional variation in phylodiversity, highlighting the power of our fractal sampling design to efficiently detect patterns across multiple spatial scales. Thus, our fractal sampling design and analysis effectively identify important environmental gradients and spatial scales that drive community phylogenetic structure. We discuss the insights this gives us into the ecological assembly processes that differentiate plant communities found in northeast Utah.</p>
User preference optimization for control of ankle exoskeletons using sample efficient active learning
<p>A major challenge to the widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are ``tuned'' to optimize a physiological or biomechanical objective. However, these approaches are resource-intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is derived from many factors, including comfort and stability, among others. This work introduces an approach to conveniently tune four parameters of the exoskeleton controller that maximize user preference. We use an evolutionary algorithm to recommend potential parameters, which are ranked by a neural network that is pre-trained with previously collected preference data. The controller parameters that have the highest preference ranking are provided to the exoskeleton, and the wearer provides feedback as forced-choice comparisons. Our approach was able to converge on controller parameters preferred by the wearer compared to randomized parameters with an accuracy of 88% on average. The result indicates that the proposed algorithm was able to identify users' preferences while requiring less than 50 queries to users. This work demonstrates user preference can be used to tune high-dimensional controller spaces easily and accurately, which shows the potential of translating lower-limb wearable technologies into our daily lives.</p>
Data from: Fractal triads efficiently sample ecological diversity and processes across spatial scales
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User preference optimization for control of ankle exoskeletons using sample efficient active learning
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Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces
<p>CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Figure 3 in Efficiency of colored modified box traps for sampling of tabanids
Figure 3. Number of collected specimens of T. bromius in different colored traps.
Figure 5 in Efficiency of colored modified box traps for sampling of tabanids
Figure 5. Differences of reflectances of light-colored traps and green color.
Figure 1 in Efficiency of colored modified box traps for sampling of tabanids
Figure 1. Reflection spectra of the used colors.
Supporting Information for Automated and Efficient Sampling of Chemical Reaction Space
<p>These datasets include structures from normal mode sampling, reaction pathway sampling, and transition states for validation (originally from Grambow et al.), all computed using the ωB97X/6-31G(d) method. The corresponding energies and forces are compiled in the Atomic Simulation Environment database format. </p>
Combining sampling gear to optimally inventory species highlights the efficiency of eDNA metabarcoding
<p>Biodiversity surveys may require the use of multiple types of sampling gear to maximize the efficiency of species detections, yet few studies have investigated how to optimally distribute effort among gear. In this study, we conducted eDNA metabarcoding and capture-based sampling surveys (electrofishing, fyke netting, gillnetting, and seining) to sample fish species richness in a large northern temperate lake. We evaluated the success of the sampling methods individually and in combination to determine the allocation of effort and cost across sampling gear that provides the optimal approach for lake-wide species inventories. We found that eDNA metabarcoding detected more species than any other sampling method, including 11 species that were not detected with any capture-based approach. Optimal gear combination analyses revealed that detected species richness is maximized when most of the effort or budget is allocated to eDNA metabarcoding, with smaller allocations to seining and fyke netting. eDNA metabarcoding and capture sampling gear showed similar patterns of spatial heterogeneity in the fish community across habitat types, with pelagic samples forming a group that was distinct from nearshore samples. Our results indicate that eDNA metabarcoding is a rapid and cost-efficient tool for biodiversity monitoring and that assessing the complementarity of multiple sampling types can inform the development of optimal approaches for measuring fish species richness.</p>
Sample programs of an eco-redox model for the article: Microbial redox cycling enhances ecosystem thermodynamic efficiency and productivity
<p><span>Microbial life in low-energy ecosystems relies on individual energy conservation, optimizing </span><span>energy use in response to interspecific competition, and mutualistic interspecific syntrophy. Our study proposes a novel community-level strategy for increasing energy use efficiency. By</span> <span>utilizing a</span><span>n</span> <span>oxidation-reduction (redox) reaction network model that represents microbial redox metabolic interactions, we </span><span>investigated multiple species-level competition and cooperation within the network</span><span>. Our results suggest that microbial functional diversity allows for metabolic handoffs</span><span>, which in turn lead to increased energy use efficiency. Furthermore, the mutualistic division of labor and the resulting </span><span>complexity of redox pathways actively </span><span>drive material cycling, further promoting energy exploitation. Our findings reveal the potential of self-organized ecological interactions to develop efficient energy utilization strategies, with important implications for microbial ecosystem functioning and </span><span>co-</span><span>evolution of life and Earth.</span></p>
Combining sampling gear to optimally inventory species highlights the efficiency of eDNA metabarcoding
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Data from: James-Stein estimator improves accuracy and sample efficiency in human kinematic and metabolic data
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Sample programs of an eco-redox model for the article: Microbial redox cycling enhances ecosystem thermodynamic efficiency and productivity
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Data from: Empowering conservation practice with efficient and economical genotyping from poor quality samples
1. Moderate to high density genotyping (100+ SNPs) is widely used to determine and measure individual identity, relatedness, fitness, population structure and migration in wild populations. 2. However, these important tools are difficult to apply when high-quality genetic material is unavailable. Most genomic tools are developed for high quality DNA sources from lab or medical settings. As a result, most genetic data from market or field settings is limited to easily-amplified mitochondrial DNA or a few microsatellites. 3. To enable genotyping in conservation contexts, we used next-generation sequencing of multiplex PCR products from very low-quality DNA extracted from feces, hair, and cooked samples. We demonstrated utility and wide-ranging potential application in endangered wild tigers and tracking commercial trade in Caribbean queen conch. 4. We genotyped 100 SNPs from degraded tiger samples to identify individuals, discern close relatives, and detect population differentiation. Co-occurring carnivores do not amplify (e.g. Indian wild dog/Dhole) or are monomorphic (e.g. leopard). 62 SNPs from conch fritters and field-collected samples were used to test relatedness and detect population structure. 5. We provide proof-of-concept for a rapid, simple, cost-effective, and scalable method (for both samples and number of loci), a framework that can be applied to other conservation scenarios previously limited by low quality DNA samples. These approaches provide a critical advance for wildlife monitoring and forensics, open the door to field-ready testing, and will strengthen the use of science in policy decisions and wildlife trade.
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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)
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.