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.
23
datasets available to search
ShareScore release 0.7.1
Dataset results
23 results for “Energy-efficient”
PTR-ToF-MS data from cooking experiments in Healthy Energy-efficient Urban Home Ventilation
<pre>The dataset contains high-resolution PTR-Tof MS data from preparing meals consisting of fried salmon and vegetables in SINTEFs ventilation laboratory. <br>The data are organized in csv files containing concatenated results of ppb-values. PTR-ToF-MS grouped by month, m/z-valuens in column names. Relatable to the list of experiments. See readme file for details and 10.1016/j.buildenv.2024.111743 for description</pre>
PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing
<p>The urban population continues to grow despite health risks associated with densely populated cities, such as traffic congestion and air pollution. At the same time cities are also further heating up due to climate change. Environmental monitoring is increasingly critical to react quickly to temporarily increased concentrations of, for example, carbon monoxide, nitrogen oxides, ozone, and particulate matter. <br>We introduce a significantly improved version of our PhytoNode, an energy-efficient sensor node designed for phytosensing, that is, using of plants as environmental sensors. We aim for a scalable and sustainable real-time monitoring solution following our vision of an `intelligent plant' as an inexpensive and accurate sensor node. <br>We measure electrical potentials and leaf temperatures of plants to assess their well-being and, in turn, environmental conditions. <br>The PhytoNode achieves long-term energy autonomy by harvesting energy via solar cells and shares data via Bluetooth Low Energy (BLE) communication. We process the gathered time series plant data onboard in real-time using methods of Machine Learning (ML) to analyze the plant's activity and to detect dangerous concentrations of gases. In a few showcasing experiments, we demonstrate the feasibility of both our hardware and software approach for continuous, long-term environmental monitoring based on phytosensing. By embedding engineered devices in living plants as a `plant wearable' that listens to plant responses, we hope to help pushing towards smarter future cities and healthier urban environments. </p> <p> </p> <p>Data repository for our paper "PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing", submitted to the 8th Future of Information and Communication Conference 2025 (FICC 2025). Please refer to the paper for more information.</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>
Raw data for High-speed shear mixing: a versatile energy-efficient ultra-fast strategy for solvent-free amine-functionalised solid CO2 adsorbents for direct air capture
<p><strong>Specification of affiliations:</strong></p> <ul> <li>Pavol Suly - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Abdulkadir Bozarslan - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Eva Domincova Bergerova - Centre of Polymer Systems</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
THE ROLE OF PROPERTY MANAGEMENT IN PROMOTING ENERGY-EFFICIENT SOLUTIONS FOR RENTALS
<p>Real estate management plays a key role in promoting energy efficient solutions when renting out properties. The purpose of the study is to analyze the impact of management companies on the introduction of energy-efficient technologies to increase the competitiveness of facilities and reduce operating costs. The methodology is based on the analysis of data on the application of modern energy-efficient solutions, including lighting, heating and automation systems in buildings in the Czech Republic. The results showed that the use of such technologies helps to reduce utility costs by 20-40% and increases the attractiveness of facilities for tenants. In conclusion, property management aimed at energy efficiency ensures the achievement of sustainable development and economic benefits for owners and tenants. These measures increase the market value of the properties and extend the lease terms, which strengthens the position in the real estate rental market.</p>
Sensor solutions for an energy-efficient and user-centered heating system
<p>Corresponding dataset for the article "Sensor solutions for an energy-efficient and user-centered heating system" published in the Journal of Sensors and Sensor Systems Special Issue "Sensors and Measurement Systems 2016".</p>
The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale
<p>Data and figures presented in the paper "The ESCAPE project: Energy-efficient scalable algorithms for weather prediction at exascale". The discussion paper is available at: https://doi.org/10.5194/gmd-2018-304</p>
HADES: An NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. Study dataset
<p>The publication and research associated with this dataset are currently under review in the Journal of Network and Computer Applications.</p> <p>In that research, we present HADES, an NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures. HADES is an OSM (Open Source MANO) extension that allows the configuration of virtual network functions and their subsequent resource allocation and deployment at the edge, minimizing energy consumption and ensuring the quality of service.</p> <p>This dataset contains the following:</p> <ul> <li>1 CSV file with execution time results of the iTAREA module for (60) different problem sizes and (3) execution environments.</li> <li>1 CSV file with energy consumption results of HADES deployments and other 5 assignment policies: First-fit, Random-fit, Fastest-fit, Best-fit, and kube-scheduler (the Kubernetes’ assignment policy).</li> </ul> <p>This work is supported by the European Union's H2020 research and innovation programme under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15 and IRIS PID2021-122812OB-I00 (MCI/AEI).</p>
Changes in key traits versus depth and latitude suggest energy-efficient locomotion, opportunistic feeding and light lead to adaptive morphologies of marine fishes.
1. Understanding patterns and processes governing biodiversity along broad-scale environmental gradients, such as depth or latitude, requires an assessment of not just taxonomic richness, but also morphological and functional traits of organisms. Studies of traits can help to identify major selective forces acting on morphology. Currently, little is known regarding patterns of variation in the traits of fishes at broad spatial scales. 2. The aims of this study were: (i) to identify a suite of key traits in marine fishes that would allow assessment of morphological variability across broad-scale depth (50 – 1200 m) and latitudinal (29.15 – 50.91°S) gradients; and (ii) to characterise patterns in these traits across depth and latitude for 144 species of ray-finned fishes in New Zealand waters. 3. Here, we describe three new morphological traits: namely, fin-base-to-perimeter ratio, jaw-length-to-mouth-width ratio, and pectoral-fin-base-to-body-depth ratio. Four other morphological traits essential for locomotion and food acquisition that are commonly measured in fishes were also included in the study. Spatial ecological distributions of individual fish species were characterised in response to a standardised replicated sampling design and morphological measurements were obtained for each species from preserved museum specimens. 4. With increasing depth, fishes, on average, became larger and more elongate, with higher fin-base-to-perimeter ratio and larger jaw-length-to-mouth-width ratio, all of which translates into a more eel-like anguilliform morphology. Variation in mean trait values along the depth gradient was stronger at lower latitudes for fin-base-to-perimeter ratio, elongation and total body length. Average eye size peaked at intermediate depths (500-700 m) and increased with increasing latitude at 700 m. 5. These findings suggest that, in increasingly extreme environments, fish morphology shifts towards a body shape that favours an energy-efficient undulatory swimming style and an increase in jaw-length versus mouth width for opportunistic feeding. Furthermore, increases in eye size with both depth and latitude indicate that changes in both the average ambient light conditions as well as seasonal variations in day-length can act to select ecomorphological adaptations in fishes. 10-Oct-2019
Understanding the Relation Between Performance and Energy-Efficiency in Object-Relational Mapping Frameworks
Open the record for dataset details and reuse information.
Source research data for the article titled "A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN)".
<p>The source research data set developed and utilized while working on the article "A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN)" for Sensors SI. The data set includes simulation results from OMNeT++ and the data to evaluate the parameters of the algorithms.</p>
Adaptive morphing of wing and tail for stable, resilient, and energy-efficient flight of avian-informed drones
<p>This repository contains the code and data collected during the experiments described in the paper "Adaptive morphing of wing and tail for stable, resilient, and energy-efficient flight of avian-informed drones".</p> <p>The zip file has the following structure</p> <ul> <li>code (with its own README file)</li> <li>data (flight data of each experiment)</li> <li>video (video of each experiment)</li> </ul>
Wyniki zawarte w Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey
<p>This resource contains the results included in Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey</p>
Rysunki dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains the figures for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
Wyniki symulacji dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains simulation results for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
Changes in key traits versus depth and latitude suggest energy-efficient locomotion, opportunistic feeding and light lead to adaptive morphologies of marine fishes.
Open the record for dataset details and reuse information.
Data from: Travelling Wave Pulse Coupled Oscillator (TWPCO) Using a Self-Organizing Scheme for Energy-efficient Wireless Sensor Networks
Recently, Pulse Coupled Oscillator (PCO)-based travelling waves have attracted substantial attention by researchers in wireless sensor network (WSN) synchronization. Because WSNs are generally artificial occurrences that mimic natural phenomena, the PCO utilizes firefly synchronization of attracting mating partners for modelling the WSN. However, given that sensor nodes are unable to receive messages while transmitting data packets (due to deafness), the PCO model may not be efficient for sensor network modelling. To overcome this limitation, the current study proposed a new scheme called the Travelling Wave Pulse Coupled Oscillator (TWPCO). For this, the study used a self-organizing scheme for energy-efficient WSNs that adopted travelling wave biologically inspired network systems based on phase locking of the PCO model to counteract deafness. From the simulation, it was found that the proposed TWPCO scheme attained a steady state after a number of cycles. It also showed superior performance compared to other mechanisms, with a reduction in the total energy consumption of 25 %. The results showed that the performance improved by 13 % in terms of data gathering. Based on the results, the proposed scheme avoids the deafness that occurs in the transmit state in WSNs and increases the data collection throughout the transmission states in WSNs.
"BUILDING THE FUTURE: ENERGY-EFFICIENT BUILDINGS AND LOW-CARBON TECHNOLOGIES"
Open the record for dataset details and reuse information.
Green AI in the Cloud: Energy-Efficient Architectural Tactics for ML-Enabled Systems on Public Platforms
Open the record for dataset details and reuse information.
Data from: Travelling Wave Pulse Coupled Oscillator (TWPCO) Using a Self-Organizing Scheme for Energy-efficient Wireless Sensor Networks
Open the record for dataset details and reuse information.
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.