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3,186 results for “efficiency”
Survey questionnaire and results on Structural Barriers to Investment in Energy Efficiency Policies in the Private Rented Sector
<p>The online survey was designed and conducted in the framework of the EU H2020 project ENPOR ("Actions to Mitigate Energy Poverty in the Private Rented Sector). The aim of the survey was to receive statistically sound insights on structural factors that affect the implementation of energy efficiency policies for the alleviation of energy poverty in the European Private Rented Sector. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “EUSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s, landlords and tenant associations, etc.</p>
Survey questionnaire and results on Pay-for-Performance (P4P) schemes for energy efficiency measures
<p>The online survey was designed and conducted in the framework of the EU H2020 SENSEI project. The aim of the survey was to identify stakeholders’ perceptions on how P4P programmes could be integrated into the existing EU regulatory and market framework. We developed a semi-quantitative, self-completion online questionnaire, using the online tool “Alchemer”. The online survey collects input from different experts from the field of academia, consultancies, policymaking, and the energy industry. </p> <p>The questionnaire is used by <em>Tzani</em> <em>et al </em>(2022) to investigate how policy developments and adjustments in the EU can facilitate the design of performance-based energy efficiency programmes. The study combines a Strengths, Weaknesses, Opportunities, and Threats framework with an Analytical Hierarchy Process method and a Threats, Opportunities, Weaknesses, and Strengths matrix for the analysis of different stakeholder perceptions and the formulation of policy strategies.</p> <p>If you use this questionnaire in an academic publication, please cite the corresponding article:</p> <p><em>Tzani, D., Exintaveloni, D.S., Stavrakas, V., Flamos, A. </em><em>Devising policy strategies for the deployment of energy efficiency Pay-for-Performance programmes in the European Union</em><em>. </em></p>
Sharing land via keystone structure: retaining naturally regenerated trees may efficiently benefit birds in plantations
<p class="MsoListParagraph"><span>Meeting food/wood demands with increasing human population and per-capita consumption is a pressing conservation issue, and is often framed as a choice between land sparing and land sharing. Although most empirical studies comparing the efficacy of land sparing and sharing supported land sparing, land sharing may be more efficient if its performance is tested by rigorous experimental design and habitat structures providing crucial resources for various species––keystone structures––are clearly involved. We launched a manipulative experiment to retain naturally regenerated broad-leaved trees when harvesting conifer plantations in central Hokkaido, northern Japan. We surveyed birds in harvested treatments, unharvested plantation controls and natural forest references one-year before the harvest and for three consecutive post-harvest years. We developed a hierarchical community model separating abundance and space-use (territorial proportion overlapping treatment plots) subject to imperfect detection to assess population consequences of retention harvesting. Application of the model to our data showed that retaining some broad-leaved trees increased total abundance of forest birds over the harvest rotation cycle. Specifically, pre-harvest survey showed that the amount of broad-leaved trees increased forest bird abundance in a concave manner (i.e., in a form of diminishing-return). After harvesting, a small amount of retained broad-leaved trees mitigated negative harvesting impacts on abundance though retention harvesting reduced the space-use. Nevertheless, positive retention effects on the post-harvest bird density as the product of abundance and space-use exhibited a concave form. Thus, small profit reductions were shown to yield large increases in forest bird abundance. The difference in bird abundance between clear-cutting and low amounts of broad-leaved tree retention increased slightly from the first to second post-harvesting years. We conclude that retaining a small amount of broad-leaved trees may be a cost-effective on-site conservation approach for the management of conifer plantations. Retention of 20-30 broad-leaved trees per ha may be sufficient to maintain higher forest bird abundance than clear-cutting over the rotation cycle. Retention approaches can be incorporated into management systems using certification schemes and best management practices. Developing an awareness of the roles and values of naturally regenerated trees is needed to diversify plantations.</span></p>
Data of "Efficient generation of entangled multi-photon graph states from a single atom"
<p>Data published in "<em>Efficient generation of entangled multi-photon graph states from a single atom</em>"</p>
PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System
<p>Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments we gather electrophysiological data with sensor nodes. We expose the plant <em>Zamioculcas zamiifolia</em> to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus.</p> <p> </p> <p>Data repository for our paper "PhytoNodes for Environmental Monitoring: Stimulus Classification based on<br> Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System", submitted to the GoodIT conference. Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>raw_data: </em>The datasets from our plant experiments for the stimuli wind, temperature, red light, blue light, and no stimulus.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. Find the training and testing datasets in the archives folder as well as the trained classifiers in the results folder.</li> <li><em>classification_results.ods: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time).</li> <li><em>TFLite_Models: </em>The trained classifiers in TensorFlow Lite Format.</li> <li><em>00_AI_BLE_MeasuringOnlyWind: </em>Source code for classification on STM-based PhytoNodes (using MCDCNN two-class classifier) and Bluetooth communication. The code is written for the STM32WB55 Nucleo board and can be transferred to the dongle.</li> <li><em>zavrsniProjekt_iOS: </em>Source code of the iOS app used to receive data from the STM-based PhytoNodes.</li> <li><em>Watchplant_application_documentation.pdf: </em>Instructions to build and use the iOS app.</li> </ul>
Data from: Experimental Investigation of Efficiency and Deposit Process Temperature during Multi-Layer Friction Surfacing
<p>This dataset contains the data for the publication "Experimental Investigation of Efficiency and Deposit Process Temperature during Multi-Layer Friction Surfacing"</p>
EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping
<p>EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping </p> <p>The dataset can be unzipped and includes an image dataset and a vector dataset, which includes nine land use/land cover categories (i.e., cropland, orchard, forestland, grassland, construction land, transportation land, water body, bare land, terrace) for object-oriented remote sensing image mapping using deep learning.</p>
Standardized Dataset of the Ecosystem's Water Use Efficiency, Gross Primary Productivity and the Evapotranspiration Deficit Index for 1982–2017 over the Middle East
<p>This data aimed to investigate the spatial-temporal variability of Standardized Actual Evapotranspiration (sAET), Gross Primary Productivity (sGPP) and Water Use Efficiency (WUE) anomalies series, and the Standardized Evapotranspiration Deficit Index (SEDI). The Middle East (ME), was selected as a case study to monitoring drought events as one of the major natural disasters for the ecosystem. To this end, the yearly gross primary production of GLASS, GIMMS, FloxCom, and VPM datasets for the study area spanning 1982–2017 was used to develop the sGPPR data. On the other hand, the Global Land Evaporation Amsterdam Model (GLEAM-version (v3.3a)), which estimated the several components of terrestrial evaporation (annual actual and potential evaporation (AET, PET)) was used for the same period this aimed to detect the variability of the SEDI.<br> This version of the yearly GLASS-sGPPR dataset (1982–2017) is available for the ME at 0.05° spatial resolution, as the original data of the GPP-GLASS products, While, sGPPR dataset of GIMMS, FloxCom, and VPM are also at annual temporal resolution, and at 0.5 degree spatial resolution spanning 1982–2016 for GIMMS, FloxCom, and 2000-2016 for VPM (Excel wrokbook .xlsx). The SEDI data are also available at 0.25 degree spatial resolution for 1980–2018 ( Raster files (TIFF)). For more details about Standardization of the GPP and evapotranspiration deficit data see: <strong>Alsafadi, K., Al-Ansari, N., Mokhtar, A., Mohammed, S., Elbeltagi, A., Sammen, S. S., & Bi, S. (2021). An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East. <em>Environmental Research Letters</em>. <a href="http://dx.doi.org/10.1088/1748-9326/ac4765">10.1088/1748-9326/ac4765</a></strong></p>
Data for manuscript "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data"
<p>Output files generated by rMATS-turbo for the two example datasets described in the manuscript titled "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data".</p> <table> <tbody> <tr> <td>File</td> <td>Description</td> <td>Cell lines</td> <td>BioProject</td> </tr> <tr> <td>PC3E-GS689.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 1 described in the manuscript</td> <td>PC3E and GS689 cell lines</td> <td>PRJNA438990</td> </tr> <tr> <td>CCLE.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 2 described in the manuscript</td> <td>1,019 CCLE human cancer cell lines</td> <td>PRJNA523380</td> </tr> </tbody> </table> <p>A detailed description of the output files is available in the manuscript and the rMATS-turbo software GitHub repository (https://github.com/Xinglab/rmats-turbo).</p>
Nesting Tasks Dataset for 2D-Nesting Efficiency Estimation
<p>Nesting efficiency dataset</p> <p>This is the raw dataset associated with the paper “Graph Neural Networks Comparison for 2D-Nesting Efficiency Estimation”, by C.Lallier, L. Vézard, B. Pinaud and G. Blin, 2022. Consisting of 100,000 nesting tasks.</p> <p><strong>Usage:</strong></p> <p>The files are: <em>tasks.gz, parts.gz, constraints.gz, </em>and<em> shapes.gz</em>. They are in PICKLE file format version 5 with a gzip compression. Example to load a file :</p> <pre><code class="language-python">import pandas as pd tasks = pd.read_pickle('tasks.gz')</code></pre> <p> </p> <p><strong>Description:</strong></p> <p><em>Tasks.gz</em> file contains nestings high-level descriptors. It is composed of the following columns:</p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <th scope="row">efficiency</th> <td>float</td> <td>The variable to predict (label). Given in % </td> </tr> <tr> <th scope="row">duration</th> <td>integer</td> <td>input data. The nesting algorithm convergence time. Given in s.</td> </tr> <tr> <th scope="row">sheet_width</th> <td>integer</td> <td>input data. The width of the nesting area. Given in m<sup>-4</sup></td> </tr> <tr> <th scope="row">sheet_length</th> <td>integer</td> <td>input data. Facultative. The height of the nesting area. Given in m<sup>-4</sup></td> </tr> <tr> <th scope="row">sheet_type</th> <td>integer</td> <td>input data. Kind of the nesting.</td> </tr> <tr> <th scope="row">tasks_index</th> <td>integer</td> <td>Generated data. Join key between tables.</td> </tr> <tr> <th scope="row">is_train, is_val, is_test</th> <td>boolean</td> <td>Generated data. Can be used as mask for the train, val and test subsets.</td> </tr> </tbody> </table> <p><em>Parts.gz</em> contains description of the parts to be nested : </p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><strong>tasks_index</strong></td> <td>integer</td> <td>Reference to the join key from the <em>Task</em> table. </td> </tr> <tr> <td><strong>parts_id</strong></td> <td>integer</td> <td>Generated part id.</td> </tr> <tr> <td><strong>shape_hash</strong></td> <td>integer</td> <td>Reference to the hash of the part's shape, join key from the <em>Shape </em>table.</td> </tr> </tbody> </table> <p><em>Shapes.gz</em> is the description of the shapes of the parts to be nested : </p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><strong>shape_hash</strong></td> <td>integer</td> <td>Generated data. Join key between tables.</td> </tr> <tr> <td><strong>raw</strong></td> <td>list of integers</td> <td>List of x, y tuples for each point. Unit is m<sup>-4</sup></td> </tr> <tr> <td><strong>sizes</strong></td> <td>list of integers</td> <td>List of sub-shapes sizes. </td> </tr> </tbody> </table> <p><em>Constraints.gz</em> describes constraints and their parameters:</p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><strong>type</strong></td> <td>string</td> <td>Generated constraint type.</td> </tr> <tr> <td><strong>tasks_index</strong></td> <td>integer</td> <td>Reference to the join key from the <em>Task</em> table.</td> </tr> <tr> <td><strong>parts_1, parts_2</strong></td> <td>list of integers</td> <td>References to the <em>parts_id</em> from the <em>Parts</em> table. </td> </tr> <tr> <td><strong>p1_x, p1_y and p2_x, p2_y</strong></td> <td>list of floats</td> <td>Input data. Origin position (x, y) of the constraint on parts. For each part of the constraint.</td> </tr> <tr> <td><strong>r1_start, r1_end, r1_flip_x</strong></td> <td>list of floats</td> <td>Input data. Rotation (start, end, and flip_x) parameters of the constraint. Multiple ranges accepted.</td> </tr> <tr> <td><strong>y_min, y_max</strong></td> <td>list of floats</td> <td>Input data. Range from (y_min, y_max). Multiple ranges accepted.</td> </tr> <tr> <td> <p><strong>x_offset, y_offset, motif_order, x_alignment_type, y_alignment_type, proximity_type, max_distance, groups_relative_orientation, is_frozen</strong></p> </td> <td>float</td> <td>Input data. Other constraint parameters.</td> </tr> </tbody> </table>
Data (corrected spectra per pixels) from "Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery"
<p>open access data related to the paper "<strong>Artificial reefs efficiency changes among types as revealed by underwater hyperspectral imagery</strong>" Riera E., Ungerman M., Pey A., Rigot G., Hubas C., Rossi F. (under revision to Restoration Ecology)</p> <ul> <li>pixels_corrected_spectra.npy: data matrix of the corrected spectra for each pixels</li> <li>wavelenghts.npy: the vector of the wavelengths </li> <li>pixels_metada.csv: metadata related the corrected spectra for each pixels</li> </ul> <p>To be processed on python for further statistical analyses. script available on github: <a href="https://github.com/ELI-RIERA/HYPER3D">https://github.com/ELI-RIERA/HYPER3D</a></p>
Measurement report: Radiative efficiencies of (CF3)2CFC, CF3OCFCF2, and CF3OCF2CF3
<p>Absorption cross-sections of emerging greenhouse gases (GHG) were measured to estimate the radiative efficiency using high-resolution Fourier transform infrared spectroscopy (HR-FTIR). For quantitative spectroscopy, the Beer–Lambert parameters of absorber pressure, temperature, and optical path length (OPL) were accurately determined to be traceable to the primary standards. The OPL of the multipass cell mounted on the HR-FTIR spectrometer was spectroscopically calibrated. A ratio of the averaged N<sub>2</sub>O absorptions was found to be in the range of 2217.4–2219.0 cm<sup>-1</sup>, with a spectral resolution of 0.026 cm<sup>-1</sup>, yielding a ratio of OPLs that falls between the multipass cell and reference cell. This cell-to-cell comparison method is free from the uncertainty in the referring line strength, which reduced the calibration uncertainty compared with the direct line-strength referring method. With the OPL-calibrated multipass cell (3.169 ± 0.079 m), the absorption cross-sections were measured at low absorber pressures with a spectral resolution of 2 cm<sup>-1</sup>, integrated at 10 cm<sup>-1</sup> intervals, and multiplied by the new narrow band model to yield the radiative efficiencies. The radiative efficiency values of CF<sub>4</sub>, SF<sub>6</sub>, and NF<sub>3</sub> were evaluated to be 0.085 ± 0.002, 0.573 ± 0.016, and 0.195 ± 0.008 W m<sup>-2</sup> ppb<sup>-1</sup>, respectively, which are consistent with previously reported values. For the emerging GHGs, the radiative efficiency values were determined to be 0.201 ± 0.008 Wm<sup>-2</sup>ppb<sup>-1</sup> for heptafluoroisobutyronitrile (CF<sub>3</sub>)<sub>2</sub>CFCN; commercially referred to as <em>Novec-4710</em>), 0.328 ± 0.013 Wm<sup>-2</sup>ppb<sup>-1</sup> for perfluoro methyl vinyl ether (CF<sub>3</sub>OCFCF<sub>2</sub>; PMVE), and 0.544 ± 0.022 Wm<sup>-2</sup>ppb<sup>-1</sup> for 1,1,1,2,2-pentafluoro-2-(trifluoromethoxy)ethane (CF<sub>3</sub>OCF<sub>2</sub>CF<sub>3</sub>; PFMEE).</p>
Vamos: VNTR annotation using efficient motif sets, 64-haplotype motifs
<p>This is the efficient motif set for 382,492 VNTR loci in 64 haplotype-resolved assemblies (10.1126/science.abf7117). The initial release is with compression parameter (delta) = 0.1</p>
STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale (validation data)
<p>Raw and refined data for the validation tests performed in the paper "STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale":<br> - rallpack 1, analytical, STEPS3, STEPS4, mesh scaling with STEPS4. It is so simple that I provide only the raw data. The space saving using refined data is minimal</p> <p>- rallpack 3, STEPS 3, STEPS 4. Both raw and refined data are provided.</p> <p>- caburst, STEPS 3, STEPS 4. Both raw and refined data are provided.</p>
Accurate modeling and efficient QoS analysis of scalable adaptive systems under bursty workload
<p>The datasets include the traces used for the research and experiments on modelling and analyzing systems that execute under bursty workload:</p> <ul> <li>numReq10secondsfrom360000to660000-Paris contains a summary of the requests traces published in <a href="http://ita.ee.lbl.gov/html/contrib/WorldCup.html">http://ita.ee.lbl.gov/html/contrib/WorldCup.html</a> , by grouping into a single count the number of requests that servers in Paris region received every 10 seconds .</li> <li>mawi10seconds contains a summary of the traces published in <a href="http://mawi.wide.ad.jp/mawi/ditl/ditl2009/">http://mawi.wide.ad.jp/mawi/ditl/ditl2009</a> , by grouping the number of packets every 10 seconds into a single count. </li> </ul>
Dataset for "Q-SCALE: Quantum Sensor Calibration for Advanced Learning and Efficiency"
<p>The dataset contains the data used in the article "Q-SCALE: Quantum Sensor Calibration for Advanced Learning and Efficiency".</p> <p>A low-cost monitoring system composed of 6 monitoring stations was positioned at the official monitoring station of Torino Rubino in the city of Turin (Italy). The official station is managed by the environmental agency ARPA Piemonte.</p> <p>Each low-cost station contains four low-cost light-scattering PM sensors (Honeywell HPMA115C0-003), one temperature and relative humidity sensor (DHT22), and one atmospheric pressure sensor (BME/BMP280).<br>The sampling time of the PM sensors was set to one second, while the other sensors generated measurements every 3-4 seconds.</p> <p>The official monitoring station uses both a gravimetric and a beta attenuation instrument for measuring PM.</p> <p>The data contained in this dataset was collected from November 2022 to June 2023. It contains the median of the PM2.5, relative humidity, temperature, and atmospheric pressure measurements of the low-cost sensors, after being aggregated to either one minute or one hour.</p> <p>The official measurements of the beta attenuation device are also provided.</p> <p>Measurements of low-cost sensors are expressed in UTC, while official measurements are expressed in UTC+1.</p> <p>Official PM measurements can be also found at https://aria.ambiente.piemonte.it/qualita-aria/dati.</p>
Fig. 9 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 9 Overall framework of proposed automated malaria diagnosis and species identification. CNN, Convolutional neural network; RBC, red blood cell; YOLO, You Only Look Once (model)
Fig. 8 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 8 Examples of false positive predictions by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)
Fig. 6 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 6 Comparison of detection performance by the original YOLOv4 model and the YOLOv4-RC3_4 model. Red arrows indicate cells not detected by the original YOLOv4 model, green arrows indicate the same cells detected by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)
Fig. 3 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 3 Network structure of YOLOv4. CSP, cross-spatial connection; SPP, spatial pyramid pooling layer; PANet, Path Aggregation Network; CBM, Convolutional, Batch Normalisation, and Activation; CBL, Convolutional, Batch normalisation, and Leaky-ReLU; Conv, convolutional; Concat, concatenation
ScienceDex guides
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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.