Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,186
datasets available to search
ShareScore release 0.9.0
Dataset results
3,186 results for “efficiency”
Efficient genomics based 'end-to-end' selective tree breeding framework
<p>Since their initiation in the 1950s, worldwide selective tree breeding programs followed the recurrent selection scheme of repeated cycles of selection, breeding (mating), and testing phases and essentially remained unchanged to accelerate this process or address environmental contingences and concerns. Here, we introduce an "end-to-end" selective tree breeding framework that: 1) leverages strategically preselected GWAS-based sequence data capturing trait architecture information, 2) generates unprecedented resolution of genealogical relationships among tested individuals, and 3) leads to the elimination of the breeding phase through the utilization of readily available wind-pollinated (OP) families. Individuals' breeding values generated from multi-trait multi-site analysis were also used in an optimum contribution selection protocol to effectively manage genetic gain/co-ancestry trade-offs and traits' correlated response to selection. The proof-of-concept study involved a 40-year-old spruce OP testing population growing on three sites in British Columbia, Canada, clearly demonstrating our method's superiority in capturing most of the available genetic gains in a substantially reduced timeline relative to the traditional approach. The proposed framework is expected to increase the efficiency of existing selective breeding programs, accelerate the start of new programs for ecologically and environmentally important tree species, and address climate-change caused biotic and abiotic stress concerns more effectively.</p>
The Effect of Typing Efficiency and Suggestion Accuracy on Usage of Word Suggestions and Entry Speed
<p>Data collected during our experiments investigating the effect of suggestion accuracy and typing efficiency on usage of word suggestions, and entry speed</p>
Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics
<p>Considering the global intensification of aridity in tropical biomes due to climate change, we need to understand what shapes the distribution of drought sensitivity in tropical plants. We conducted a pantropical data synthesis representing 1117 species to test whether xylem-specific hydraulic conductivity (K<sub>S</sub>), water potential at leaf turgor loss (Ψ<sub>TLP</sub>), and water potential at 50% loss of K<sub>S</sub> (ΨP50) varied along climate gradients. The Ψ<sub>TLP</sub> and ΨP<sub>50</sub> increased with climatic moisture only for evergreen species, but K<sub>S</sub> did not. Species with high Ψ<sub>TLP</sub> and Ψ<sub>P50</sub> values were associated with both dry and wet environments. However, drought-deciduous species showed high Ψ<sub>TLP</sub> and ΨP<sub>50</sub> values regardless of water availability whereas evergreen species only in wet environments. All three traits showed a weak phylogenetic signal and a short half-life. These results suggest that environmental controls on trait variance, which in turn is modulated by leaf habit along climatic moisture gradients in the tropics.</p>
Dataset for article "Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"
<p>Dataset for article entitled "<strong>Performance and Efficiency Evaluation of Technology-Based Business Incubators: A Systematic Literature Review"</strong></p>
Figure 4 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 4. Scanning Electron Microscopy (SEM) images of the upper-surface of leaves sprayed with water (control), Silical®, Postar®, and Ultrafit® to visualize the diversity of trichome types (glandular: GT and non-glandular: NGT) and densities for tomato cultivars K-186 F1 and 023 F1.
Figure 2 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 2. Population of movable stages of Tetranychus urticae on tomato leaves over old 3–13 weeks after transplanting for two cultivars treated with commercial stimulants during the 2017 and 2018 summer seasons. Ultrafat * applied by adding to soil below the plants. Columns with the same letter represent means that are not significantly different according to Tukey's multiple range test (p <0.05). Vertical bars represent ± standard error of the mean (n = 36).
Figure 1 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 1. Population of movable stages of Tetranychus urticae on tomato leaves of several plant ages for cultivars K186 F1 and 023 F1 treated with some commercial stimulants during the 2017 (A) and 2018 (B) summer seasons. Population at three weeks after transplanting was immediately before treatment. Columns with the same letter represent means that are not significantly different according to Tukey's multiple range test (p <0.05). Vertical bars represent ± standard error of the mean (n = 21).
Figure 1. A in Evaluate the efficiency of releasing two predatory species at their optimal temperature for controlling Tetranychus urticae (Acari: Tetranychidae) in a croton greenhouse
Figure 1. A timeline representing the release of predatory species P. persimilis and S. punctillum to control the twospotted mite T. urticae in a croton greenhouse.
Figure 3 in Evaluate the efficiency of releasing two predatory species at their optimal temperature for controlling Tetranychus urticae (Acari: Tetranychidae) in a croton greenhouse
Figure 3. The comparison between the population of T. urticae in predators' greenhouse and control greenhouse.
Figure 3 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 3. Scanning Electron Microscopy (SEM) images of the lower-surface of leaves sprayed with water (control), Silical®, Postar®, and Ultrafit® to visualize the diversity of trichome types (glandular: GT and non-glandular: NGT) and densities for tomato cultivars K-186 F1 and 023 F1.
Figure 1 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks
Figure 1. Different amount of starting material – A. The whole individual of partially engorged tick; B. Anterior half of fully-engorged tick (above red line); C. Paired DNA extraction – mouthparts (1) and a part of scutum (2).
Figure 2 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks
Figure 2. Results of PCR amplification – A1. Efficient PCR with clear band of 250bp presented as successful amplification, determined by using a positive control; All amplified bands with sizes differing from the positive control were defined as non-specific alleles; Four types of different PCR results: A2. Non-specific alleles; A3. Poor amplification; A4. A combination of poor amplification and non-specific alleles; B. Unsuccessful amplification detected in paired DNA extraction, after using DNA obtained from mouthparts (B1), whereas target region was amplified for the same sample but using DNA obtained from a part of scutum (B2); a 50 bp DNA ladder (BioLabs, New England) (A, B).
Species‑specific influence of powdery mildew mycelium on the efficiency of PM accumulation by urban greenery - Data
<p>Dataset of article: Przybysz, A., Nawrocki, A., Mirzwa-Mróz, E. <em>et al.</em> Species-specific influence of powdery mildew mycelium on the efficiency of PM accumulation by urban greenery. <em>Environ Sci Pollut Res</em> (2023). https://doi.org/10.1007/s11356-023-28371-6</p>
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection'. </p> <p>The following files are included:</p> <ul> <li>best_algo.csv : labels for the classification and median performance of algorithms;</li> <li>ML_models.ipyb : jupyter notebook with the definition of the neural networks for both classifiers;</li> <li>pickle.zip : pickled models for the hardness classification and the algorithm selector;</li> <li>trajectories.zip : raw data files containing parts of the trajectories of each algorithm;</li> <li>Results.zip : results obtained using the approach on the stream of instances.</li> </ul>
Benchmark for energy efficient obstacle detection on head mounted wearable for the vision impaired
<p>Here we present a novel benchmark dataset with the associated challenge, that is to detect obstacles based on head-mounted sensors and lightweight wearable devices to assist Blind and Visually Impaired individuals (BVIs) navigate in indoor environments. The challenge encompasses three objectives: (1) as accurately as possible to detect the obstacles on the pathway that likely lead to a collision; (2) as durably as possible on a given amount of battery power for the detection algorithm or model to run; (3) as reliably as possible to compensate natural head turns so nearby objects would not trigger false alarms. The data provided in the benchmark are collected from the following head mounted sensors: (i) nine low-cost ultrasonic sensors; (ii) one high-end ultrasonic sensor with a larger detection range but higher power consumption; (iii) a 9-Degrees of Freedom (DOF) Inertial Measurement Unit (IMU). The resulting dataset consists of more than 188,000 unique sequences obtained from multiple subjects walking in three different indoor scenarios. This benchmark is to facilitate and encourage accurate yet fast obstacle detection solutions that can really benefit BVIs. </p>
Energy efficiency on Philips Lightings products for outdoor lighting
<p>The dataset is a compilation of specifications and performance metrics for different lighting products from Philips Lighting catalogs. It spans various products across different technology types and years, which suggests a focus on the evolution and comparison of lighting efficiency over time.</p> <p>Here are some key points about the dataset:</p> <p>- **Product Information**: Each entry in the `Nombre` column provides specific details about a Philips Lighting product, likely including the model and technical specifications.</p> <p>- **Technology Classification**: The `Tecno` column classifies each product according to its lighting technology, such as LED, CDM, SOX, etc. This allows for analysis across different types of lighting technologies.</p> <p>- **Energy Consumption and Efficiency**: The dataset includes data on energy consumption (`Consumo`) and efficiency (`Efi(lm/w)` and `Efi2`). These metrics are crucial for understanding the energy cost of running the lights and for analyzing improvements in energy efficiency over time. Efi is the calculated energy efficiency from the catalogue data and the Efi2 is the reported energy eficiency.</p> <p>- **Light Output and Quality**: The `Lumens` and `CCT` columns provide information on the brightness and color temperature of the lighting products. This is valuable for assessing the quality and suitability of the light for various applications.</p> <p>- **Economic Considerations**: The `Precio` column, while not filled in for all entries, would give insights into the economic aspect of the lighting products, potentially allowing for cost-benefit analysis.</p> <p>- **Temporal Trends**: The `Año` column indicates the year associated with the product, which can be used to track changes and advancements in lighting technology over time.</p> <p>- **Product Longevity**: The `Vida` column, although unspecified in the dataset preview, would generally relate to the lifespan of the lighting product, an important factor in both consumer choice and sustainability considerations.</p> <p>In summary, this dataset serves as a resource for analyzing Philips Lighting products' performance over time, understanding trends in lighting technology efficiency, and potentially assisting in strategic decisions related to product development, marketing, and sustainability efforts.</p>
Dataset of the manuscript: Efficient removal of nanoplastics from industrial wastewater through synergetic electrophoretic deposition and particle-stabilized foam formation
<p>This dataset is based on the data underlying the figures shown in the manuscript titled Efficient removal of nanoplastics from industrial wastewater through synergetic electrophoretic deposition and particle-stabilized foam formation. A readme file is uploaded to decribe the content of all data folders. All data are sorted according to their appearance in the figures of the main manuscript.</p>
Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm
<p>This publication provides data published in the article "Efficient rational approximation of optical response functions with the AAA algorithm" [1] in tabulated form along with the Matlab scripts that have been used to produce them. These scripts interface the finite element method solver JCMsuite [2,3]. The article presents rational approximations of optical response functions based on an extended version of the AAA algorithm [4] that allows to efficiently reconstruct sensitivty spectra and gives access to sensitivities of poles, residues, and zeros. Furthermore, the rational approximation of a scalar observalbe is used to construct solutions of the source free Maxwell's equation, i.e., a nonlinear eigenvalue problem. </p> <p><strong>The physical Structure</strong></p> <p>The example is based on the chiral metasurface introduced in [5]. For the sake of simplicity we added infinite layers of SiO\(_2\) to the top and the bottom of the structure. The original structure has a SiO\(_2\) substrate and a layer of PMMA polymethyl methacrylate (PMMA) deposited on top. PMMA can be modelled with the same refractive index of 1.45 as SiO\(_2\). Furthermore, our simulations include the 13 nm indium tin oxide (ITO) coating which drastically reduces the Q-factor as it is slightly absorbing. The accuracy of the discrete model is verified by assessing reflection, transmission, and absorption at 241 evenly spaced points within the specified range. Energy conservation requires that the discrepancy between their sum and the energy entering the system is zero. The numerical discretization is chosen such that the maximum relative error is less than \(3\times10^{−5}\).</p> <p><strong>Dispersion</strong></p> <p>Tabulated data for ITO has been taken from the <a href="https://refractiveindex.info/?shelf=other&book=In2O3-SnO2&page=Konig">refractiveindex.info</a> database (T. A. F. König et al., 2014, https://doi.org/10.1021/nn501601e) and the data for TiO2 was kindly provided the authors of [5]. The permittivity \(\varepsilon = (n+ik)^2\) is locally approximated as a rational function, i.e., only data in a vicinity of the frequency range of interest is considered. As we aim for a function with the symmetry \(f^\ast(\omega) = f(-\omega^\ast)\) we add the complex conjugated data at negative frequencies and enforce the symmetry in a second step. The partial fraction decomposition of the required function is of the form: \(\varepsilon(\omega) = \varepsilon_\infty + \sum_{j=1}^{4}a_j/(\omega-\omega_j) - a_j^\ast/(\omega+\omega_j^\ast)\) with the residues \(a_j\) and the poles \(\omega_j\). We expect 4 pairs of poles to sufficiently approximate the data within the range of interest (4 with positive and 4 with negative real parts).</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (at least 6.2.0)</li> <li>MATLAB (tested with version R2023b)</li> </ul> <p>In order to run the simulations with JCMsuite you must replace corresponding place holders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <p><strong>Usage</strong></p> <p>With the content of 'spectra.zip' you can reproduce results presented in the paper. Running the script 'plots.m' will not start any expensive simulation but use the provided data. With 'dispersion.m' the fits to the material data can be reproduced. Additionally, tabulated data is contained in 'data/ascii'. The archive 'eigenmodes.zip' must be extracted in the same directory as 'spectra.zip'.</p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger, Felix Binkowski: Efficient rational approximation of optical response functions<br>with the AAA algorithm, https://doi.org/10.48550/arXiv.2403.19404.</p> <p>[2] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192.</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763.</p> <p>[4] Y. Nakatsukasa, O. Sète, and L. N. Trefethen, The AAA Algorithm for Rational Approximation, SIAM Journal on Scientific Computing <strong>40</strong>, A1494 (2018), http://dx.doi.org/10.1137/16M1106122.</p> <p>[5] X. Zhang, Y. Liu, J. Han, Y. Kivshar, and Q. Song, Chiral emission from resonant metasurfaces, Science <strong>377</strong>, 1215 (2022), http://dx.doi.org/%2010.1126/science.abq7870.</p>
Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting
<p>Atomic structure data used in the research article entitled "Small Dataset Machine-Learning Approaches to Explore the Design Space of High-Entropy Alloys: Engineering ZnTe-based Multicomponent Alloys for the Photo-Splitting of Water"</p>
quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data
<p>This page contains the code and datasets used in "quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data".</p> <p>File <strong>test_datasets.zip</strong> containes three datasets:</p> <ul> <li><em>D1.RData</em>: scRNA-seq omics data derived from Salcher et. al (2022)</li> <li><em>D2.RData</em>: scRNA-seq omics data derived from Pineda et al. (2024)</li> <li><em>D3.RData</em>: in silico WGS SNP data.</li> </ul> <p>File <strong>test_scripts.zip</strong> containes the code to reproduce the results.</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.