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.
2,235
datasets available to search
ShareScore release 0.9.0
Dataset results
2,235 results for “engineering”
Data and R code from: Relics of beavers past: time and population density drive scale-dependent patterns of ecosystem engineering
<p><span>Like many ecological processes, natural disturbances exhibit scale-dependent dynamics that are largely a function of the magnitude, frequency, and scale at which they are assessed. Ecosystem engineers create patch-scale disturbances that affect ecological processes, yet we know little about how these effects scale across space or vary through time. Here, we investigate how patch disturbances by beavers (<i>Castor canadensis</i>), ecosystem engineers renowned for their pond-creation behavior, affect ecological processes across space and time. We evaluated how beaver population recovery influenced surface water dynamics in relation to population density over 70 years across multiple spatial scales (pond, watershed, and regional) in northern Minnesota. Surface water area was positively related to population density at the watershed scale; however, despite variation in beaver densities (and therefore surface water area) at the watershed scale, regional-scale surface water area was stable through time. This stability appears to have been driven by asynchronous beaver density fluctuations among watersheds, combined with the increasing importance of abandoned ponds. Beavers initially created and occupied larger ponds with greater surface water area, but through time shifted towards occupying smaller ponds. As ponds accumulated on the landscape proportionally more surface water was stored within abandoned ponds, which offset the smaller size of occupied ponds. Beaver engineering—driven by density-dependent mechanisms and the legacy effects from abandoned ponds—not only follows general patterns of patch disturbance dynamics by creating a spatial mosaic of patches, but the organism-created mosaic also appears to generate ecological stability at greater spatial scales. We suggest restoring beavers to landscapes is a viable method for increasing surface water storage and will ultimately help advance numerous conservation and rewilding objectives. Our study demonstrates that ecosystem engineering effects can be scale-dependent, indicating researchers should evaluate the ecological impact of engineers across diverse spatiotemporal scales to fully understand their functional roles in ecosystems.</span></p>
Determinant Factors in Systematic Review of Biomedical Engineering Topics
<p>Students and researchers around the world are increasingly turning to literature reviews. Review articles give you a broad picture of the field and help synthesize published research that is expanding at a rapid pace.</p> <p>Professionally crafted literature reviews, whether written by a student in class or by an experienced researcher for publication, should aim to add to the literature rather than detract from it. This is not an easy feat, but it is a necessary one.<br> Systematic reviews aim to identify, evaluate, and summarize the findings of all relevant individual studies on a related topic; which makes the available evidence more accessible to decision makers.</p> <p>Research Question<br> • Provide enough detail so that the audience can easily understand your purpose without the need for additional explanation.</p> <p>• It is not answered with a simple “yes” or “no”, but requires synthesis and analysis of ideas and sources before writing a response.</p> <p>Search strategy<br> • Organized structure of key terms used to search databases.</p> <p>• Combine key concepts to get accurate results.</p> <p>Inclusion and exclusion criteria<br> They are determined after the research question is established, usually before the search is performed, however, scoping searches may be necessary to determine the appropriate criteria</p> <p>Synthesis<br> • A review matrix and summary tables are convenient ways to quickly summarize and reorganize qualitative information and observations to achieve a preliminary outline.</p> <p>Systematic reviews serve several critical functions</p> <p>Provide synthesis of the state of knowledge in a field, from which future research priorities can be identified;</p> <p>Addressing questions that could not otherwise be answered by individual studies;</p> <p>Identify problems in primary research that should be rectified in future studies;Generate or evaluate theories about how or why phenomena occur.</p>
A 30-meter terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine
<p>This dataset contains the China terrace map at 30 m resolution in 2018. The map values and their corresponding classes are as follows:</p> <p><em>0: Non-terrace 1: Terrace 255: No data</em></p> <p>The 30 m China terrace map can also be viewed online at <a href="https://cbw.users.earthengine.app/view/chinaterracemap">https://cbw.users.earthengine.app/view/chinaterracemap</a></p> <p><strong>Citations:</strong></p> <p>When using this dataset, please cite both the dataset and the following data description article:</p> <p><em>Cao, B., Yu, L., Naipal, V., Ciais, P., Li, W., Zhao, Y., Wei, W., Chen, D., Liu, Z., and Gong, P.: A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine, Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, 2021.</em></p> <p> </p>
CFD and Testbench Measurements of the Deutz TCD12 V6 Diesel Engine
<p><strong>Motivation</strong><br> The data set was used to fit and validate an air path model of the TCD12 V6 diesel engine. </p> <p><strong>Research article</strong><br> Thiel, Michael, and Bernd Tibken. "A diesel engine air path model with a WG and an ETV for exhaust temperature controller design and embedded control." <em>Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering</em> (2022): 09544070211069216.</p> <p><strong>Description</strong></p> <ul> <li><strong>airpath_tcd12_true.pdf</strong>: Overview of the engine</li> <li><strong>airpath_tcd12.pdf: </strong>Overview of labels used</li> <li><strong>gt_doe.csv:</strong> Stationary data (CFD simulation)</li> <li><strong>gt_nrtc.csv:</strong> A section of NRTC test cycle (CFD simulation)</li> <li><strong>tb_nrtc.csv:</strong> A section of the NRTC (Egnine test bench)</li> </ul> <p> </p>
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility - Dataset
<p>This zip contains all input structures for paper the: Alchemical Free<br> Energy Estimators and Molecular Dynamics<br> Engines: Accuracy, Precision and Reproducibility</p> <p>Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney</p> <p>The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files</p> <p>The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q)</p> <p>There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop</p> <p>complex.pdb is structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file</p> <p>For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column.</p> <p>These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engiens that take AMBER input.</p>
EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) datasets
<p>This repository contains raw datasets gathered under the EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities by creating an evidence base on the usage of seven different energy interventions, and provide recommendations for improved design of future energy interventions to better meet the needs of people. Below is a brief description of the interventions.</p> <ol> <li>Stove-use monitoring systems (July 2019 to October 2019) - Stove-use monitoring systems (SUMs) were deployed on clay stoves in Kigeme camp, Rwanda in July 2019. The aim of the study was to evaluate stove usage patterns by measuring temperature profiles within stove enclosure and on the surface of stoves. The SUMs consisted of 2 sensors - a thermocouple to measure temperature within the stove and a Si7021 sensor to measure temperature outside the stove, connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in values exceeded a set threshold for either of the readings. The SUMs were powered by a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.<br> <br> The study was conducted in 2 phases. In phase 1 (02 July 2019 to 30 September 2019), data was collected from 15 SUMs and stored locally on SD cards as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (02 October 2019 to 17 October 2019), data was collected from 9 SUMs and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. The data from both phases of study is deposited here in SUM.zip. The cleaned dataset is available at <a href="https://doi.org/10.5281/zenodo.3946999">10.5281/zenodo.3946999</a>.<br> </li> <li>Mobile Lantern monitoring systems (July 2019 to December 2019) - Mobile lantern monitoring systems (LMSs) were deployed in Nyabiheke camp, Rwanda, in July 2019. The aim of the study was to evaluate lantern usage (static or mobile) and consumption (charge and discharge) patterns. The monitors consisted of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device was the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL was used to calculate step count of a user based on activity and freefall interrupts. Additionally, the voltage of lantern battery was measured using an in-house voltage monitor to understand the discharging and charging patterns. The step count and battery voltage data were stored only if a significant change in the step count was detected. The LMSs were powered through a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.<br> <br> The study was conducted in 2 phases. In phase 1 (03 July 2019 to 30 September 2019), data was collected from 60 lanterns and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (09 October 2019 to 18 December 2019), the design of lantern monitors was modified to circumvent these issues. The data was collected from 54 lanterns data and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. Additionally, an internal watchdog timer was used to reset the device in case of failures. While certain failures persisted, the data yield was considerably higher than phase 1 of the study. The data from both phases of study is deposited here in LMS.zip. The cleaned dataset is available at <a href="https://doi.org/10.5281/zenodo.4269809">10.5281/zenodo.4269809</a>.<br> </li> <li>Individual appliance monitors (December 2018 to January 2020) - Individual appliance monitors (IAMs) were deployed in Uttargaya settlement, Nepal, in December 2018. The IAMs were simple, cost-effective and unobtrusive devices to collect data on the energy usage of connected appliances. The aim of the study was to understand energy consumption and usage patterns of different household appliances in grid-connected sub-metered displaced communities. The monitoring system consisted of 2 types of devices – Energenie MiHome Smart Plugs MIHO005 (referred to as the IAM) to sense data relating to power and voltage drawn by the connected appliance, and gateway nodes to collect data from IAM. The main component of the gateway node was a Raspberry Pi fitted with an Energenie ENER314-RT (receiver-transmitter) add-on board to allow the Pi to communicate with the smart plugs. The data collected by the RPi gateway was stored locally in an SD card as well as sent to a remote server hosted at Coventry University.<br> <br> The study was conducted until January 2020. The raw data from the study is deposited here in IAM.zip. The cleaned dataset is available at <a href="https://doi.org/10.5281/zenodo.4271714">10.5281/zenodo.4271714</a>.<br> </li> <li>Footfall monitoring systems (December 2018 to January 2020) - Seven footfall monitoring systems (FMSs) were deployed alongside seven solar streetlights to measure step count of passers-by in the Uttargaya settlement, Nepal, in December 2018. The aim of the study was to understand the level of pedestrian movement in the area and evaluate the effect of streetlights on the level of activity. Therefore, the footfall monitors were deployed prior to commissioning of streetlights to gather baseline data. The footfall monitors consisted of a Raspberry Pi 3B, PiFace Real Time Clock and CAM008 70º night vision IR sensor to detect footfall. Upon detection, footfall count along with the direction of movement and the timestamp (measured from PiFace RTC) was stored onto an SD card and communicated to a remote server hosted at Coventry University. <br> <br> The study was conducted until January 2020. The raw data from the study is deposited here in FMS.zip. The cleaned dataset is available at <a href="https://doi.org/10.5281/zenodo.4271730">10.5281/zenodo.4271730</a>.<br> </li> <li>Standalone Solar System for a Community Hall (June 2019 to March 2021) - A standalone solar system was deployed in a Community Hall in Nyabiheke camp, Rwanda in June 2019. The aim of the study was to understand the energy consumption behavior within a set location, and create an evidence base on the value of energy and its benefits for growing cooperatives and learning communities. The standalone system comprised of 2kW of solar panels and 12.2 kWh GEL battery storage capacity. Additional components included a Victron 150/35 charge controller and a Venus GX and 48/3000 MultiPlus Inverter. The system powered four AC 2-pin sockets, a 30 W entrance light, and six 30 W indoor lights. Each light and socket were individually metered and controlled via a remote monitoring unit. This allowed for quotas, maximum draws and periods of use to be remotely controlled.<br> <br> The study was conducted until March 2021. The raw data from the study is deposited here in Hall.zip. The cleaned dataset until March 2020 is available at <a href="https://doi.org/10.5281/zenodo.3949776">10.5281/zenodo.3949776</a>.<br> </li> <li>PV-battery Microgrid (July 2019 to March 2021) - A PV-battery Microgrid was deployed in Kigeme camp, Rwanda in July 2019. The microgrid powered a playground and two nursery buildings. The aim of the study was to identify best practice in the construction, control and operation of a micro-grid as a shared resource, understand optimal design features for user interfaces that allow negotiation over energy priorities and needs and understand community priorities for energy in the context of early years education and the rate of growth in energy utilization. The micro-grid system comprised of a 2.5 kW of solar panels and 21.1 kWh GEL battery storage capacity. Additional components included a Victron 250/60 charge controller, Venus GX 48/1200 MultiPlus Inverter and BMV-700 series battery monitor. Each Nursery building had three classrooms (A, B and C) with separate entrances. Each classroom was fitted with an AC socket, five 10 Watt indoor lights and a 10 Watt outdoor entrance light. A spare socket was located in the first classroom of each nursery building (Classroom A). Two outdoor double sockets were installed at the playground, and fifteen 10 Watt lights were located in the roof structure. Three transmission line poles were fitted with three 10 Watt lights for safety and security purposes, which also enabled them to act as streetlights. Each light and socket was individually monitored and controlled via a programmable remote monitoring unit (RMU). Wireless AC smart meters were used to control and measure power consumption at the socket loads. These meters communicated with the RMU to receive commands and notified the RMU when a command had been received and to transmit usage data. Each light was connected to a CPE (customer-premises equipment) unit, with three lights per CPE, which communicated wirelessly with the RMU. The CPEs received information from the RMU on when to turn the lights on/off and set the brightness. The CPE also monitored the power consumption of the three lights. <br> <br> The study was conducted until March 2021. The raw data from the study is deposited here in Microgrid.zip. The cleaned dataset until March 2020 is available at <a href="https://doi.org/10.5281/zenodo.3949776">10.5281/zenodo.3949776</a>.<br> </li> <li>Standalone solar streetlights (Nepal - June 2019 to October 2020; Rwanda - July 2019 to March 2021) - Seven advanced streetlights were installed by HEED in Khalte, Nepal, in July 2019. Four advanced streetlights and eight normal solar streetlights have been installed in Gihembe, Rwanda, in July 2019. Each advanced streetlight consisted of a solar streetlight and an electrical socket box for excess energy use. The aim of the study was to pilot community co-designed solar streetlights with ground-level sockets to demonstrate alternative energy governance models using new technologies to build community resilience and capacity. The solar light comprised a 300 Watt solar panel, Victron charge controller, 2 kWh li-ion batteries, reprogrammable 60 W LED light, Victron Venus GX for data logging, Victron BMV 700 series battery monitor, ground-level sockets/USB ports and a footfall sensor (only in Nepal). A Victron Battery Protect and remote relay on the Venus GX is used to control access to the secondary load to ensure that there is always sufficient energy to power the light. <br> <br> The study was conducted until October 2020 in Nepal and March 2021 in Rwanda. The raw data from the study is deposited here in SL.zip. The cleaned dataset until March 2020 is available at <a href="https://doi.org/10.5281/zenodo.3947992">10.5281/zenodo.3947992</a>.</li> </ol>
Open access data from the International Design Engineering Annual (IDEA) Challenge 2021
<p>Open access dataset from the IDEA challenge 2021. </p> <p>The generation of this dataset has been undertaken as part of the ProtoTwin project (Improving the product development process through integrated revision control and twinning of digital-physical models during prototyping). The work was conducted at the University of Bristol in the Design and Manufacturing Futures Lab (<a href="http://www.dmf-lab.co.uk/">http://www.dmf-lab.co.uk</a>) and is funded by the Engineering and Physical Sciences Research Council (EPSRC), Grant reference <a href="https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/R032696/1">EP/R032696/1</a>. The dataset was generated in collaboration with the Norwegian Technical University (NTNU), University of Zagreb and University of Twente.</p> <p>For more information please contact Mark (mark.goudswaard @ bristol.ac.uk) or James ( james.gopsill @ bristol.ac.uk)</p>
Gamification in Software Engineering: The Mediating Role of Developer Engagement and Job Satisfaction
<p>Replication package with covariance matrices (instead of original dataset) and R script.</p>
Artifact: "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices
<p>Artifact for "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices</p>
Topological states in superlattices of HgTe class of materials for engineering three-dimensional flat bands
<p>In search of materials with three-dimensional flat band dispersions, using ab-initio computations we investigate how topological phases evolve as a function of hydrostatic pressure and uniaxial strain in two types of superlattices: HgTe/CdTe and HgTe/HgSe. In short-period HgTe/CdTe superlattices, our analysis unveils the presence of isoenergetic nodal lines, which could host strain-induced three-dimensional flat bands at the Fermi level without requiring doping, when fabricated, for instance, as core-shell nanowires. In contrast, HgTe/HgSe short-period superlattices are found to harbor a rich phase diagram with a plethora of topological phases. Notably, the unstrained superlattice realizes an ideal Weyl semimetal with Weyl points situated at the Fermi level. A small-gap topological insulator with multiple band inversions can be obtained by tuning the volume: under compressive uniaxial strain, the material transitions sequentially into a Dirac semimetal to a nodal-line semimetal, and finally into a topological insulator with a single band inversion.</p> <p>The provided repository contains data to reproduce the figures of the corresponding article.</p>
Avoiding high frequency thermoacoustic instabilities in cyrogenic rocket engines using Bayesian deep learning
<p>Destructive high-frequency thermoacoustic instabilities have afflicted liquid propellant rocket engine development for decades. The 90 MW cryogenic liquid oxygen/hydrogen multi-injector research combustor BKD operated by DLR Lampoldshausen is a platform that allows their study under realistic conditions. In this study, we use data from BKD experimental campaigns where the static chamber pressure and reactor-oxidizer ratio were varied such that the first tangential mode of the combustor is excited under some conditions. We train a Bayesian neural network to predict the occurence probability of thermoacoustic instabilities 500 ms in the future, given the power spectra of the most recent 300 ms sample of the dynamic pressure data and mass flowrate control signals as input. The Bayesian nature of our algorithms allow us to work in this "small data" setting where the size of our dataset is restricted by the effort and expense associated with each experimental run, without making overconfident extrapolations. We find that the network is able to accurately forecast the occurence probability of instabilities on unseen experimental runs. We envision that these algorithms will eventually be used online by rocket engine controllers to avoid regions of thermoacoustic instabilities.</p> <p> </p>
An Updated Inventory of Retrogressive Thaw Slumps Along the Vulnerable Qinghai-Tibet Engineering Corridor
<p>An inventory of 875 retrogressive thaw slumps over a landscape of 54000 km<sup>2</sup>, along the Qinghai-Tibet Engineering Corridor underlain by permafrost, was compiled using remote sensing and DeepLabv3+, a kind of deep learning model. The file in the format of Geopackage/GPKG contains the boundary of each retrogressive thaw slump as vectors in the Coordinate Reference System of EPSG:32646 - WGS 84. The associated attribute table includes probability, time of the satellite images, source of the satellite images, the near roads labels, year of initiation, longitude and latitude, area (units: m<sup>2</sup>), Deep Learning model. The corresponding names for the table fields are ‘Probability’, ‘Year-month’, ‘Source Image’, ‘Near roads’, ‘Initial year’, ‘Longitude’, ‘Latitude’, ‘Area’, ‘Deep Learning model’. The ‘Probability’, having values of ‘High’, ‘Medium’ and ‘Low’, measures how much we are sure about the mapped RTSs.</p>
Performance results of different scheduling algorithms used in the simulation of a modern game engine
<p><strong>Performance results of different scheduling algorithms used in the simulation of a modern game engine</strong></p> <p>These results are a companion to the paper entitled "<em>Exploring scheduling algorithms for parallel task graphs: a modern game engine case study</em>" by M. Regragui et al.</p> <p><strong>General information</strong></p> <p>This dataset contains raw outputs and scripts to visualize and analyze the scheduling results from our game engine simulator.<br> The result analysis can be directly reproduced using the script run_analysis.sh. A series of Jupyter Notebook files are also available to help visualize the results.</p> <p><strong>File information</strong></p> <p>- All Scenario*.ipynb files contain python scripts to visualize and analyze the simulation results.<br> - The Scenario*.py files contain python scripts that can be run directly with Jupyter Notebook.<br> - The requirements.txt file contains the names and versions of python packages necessary to reproduce the analysis.<br> - The run_analysis.sh file contains a bash script to install the required python packages and run the Scenario*.py scripts.</p> <p>The results are organized in five folders:</p> <p>1. Result_1 contains the results for Scenario 1 generated using file input_scenario_1.txt.<br> 2. Result_2 contains the results for Scenario 2 generated using file input_scenario_2.txt.<br> 3. Result_3 contains the results for Scenario 3 generated using file input_scenario_3.txt.<br> 4. Result_CP_1 contains the results for the critical path of Scenarios 1 and 2 generated using file input_CP_scenario_1.txt.<br> 5. Result_CP_3 contains the results for the critical path of Scenario 3 generated using file input_CP_scenario_3.txt.</p> <p>Each result file (e.g., HLF_NonSorted_Random_1_200_10.txt) contains 200 lines representing information of the 200 frames that were simulated. Each line contains four values: the frame number, the duration of the frame (in microseconds), a critical path estimation for the previous frame (in microseconds), and the load parameter (value between 0 and 1).</p> <p>The outputs of this analysis include some PDF files representing the figures in the paper (in order) and some CSV files representing the values shown in tables. The standard output shows the p-values computed in parts of the statistical analysis.</p> <p><strong>Software and hardware information</strong></p> <p>The simulation results were generated on an Intel Core i7-1185G7 processor, with 32 GB of LPDDR4 RAM (3200 MHz). The machine ran on Ubuntu 20.04.3 LTS (5.14.0-1034-oem), and g++ 9.4.0 was used for the simulator's compilation (-O3 flag).</p> <p>The results were analyzed using Python 3.8.10, pip 20.0.2 and jupyter-notebook 6.0.3. The following packages and their respective versions were used:</p> <p>- pandas 1.3.2<br> - numpy 1.21.2<br> - matplotlib 3.4.3<br> - seaborn 0.11.2<br> - scipy 1.7.1<br> - pytz 2019.3<br> - python-dateutil 2.7.3<br> - kiwisolver 1.3.2 <br> - pyparsing 2.4.7 <br> - cycler 0.10.0 <br> - Pillow 7.0.0<br> - six 1.14.0 </p> <p><strong>Simulation information</strong></p> <p>Simulation results were generated from 4 to 20 resources. Each configuration was run with 50 different RNG seeds (1 up to 50).</p> <p>Each simulation is composed of 200 frames. The load parameter (lag) starts at zero and increases by 0.01 with each frame up to a value equal to 100% in frame 101. After that, the load parameter starts to decrease in the same rhythm down to 0.01 in frame 200.</p> <p><strong>Algorithms abbreviation in presentation order</strong></p> <p>FIFO serves as the baseline for comparisons.</p> <p>1. FIFO: First In First Out.<br> 2. LPT: Longest Processing Time First.<br> 3. SPT: Shortest Processing Time First.<br> 4. SLPT: LPT at a subtask level.<br> 5. SSPT: SPT at a subtask level.<br> 6. HRRN: Highest Response Ratio Next. <br> 7. WT: Longest Waiting Time First.<br> 8. HLF: Hu's Level First with unitary processing time of each task.<br> 9. HLFET: HLF with estimated times.<br> 10. CG: Coffman-Graham's Algorithm.<br> 11. DCP: Dynamic Critical Path Priority.</p> <p><strong>Metrics</strong></p> <p>* SF: slowest frame (maximum frame execution time)<br> * DF: number of delayed frames (with 16.667 ms as the due date)<br> * CS: cumulative slowdown (with 16.667 ms as the due date)<br> </p>
Data for "Engineering Higgs dynamics by spectral singularities"
<p>These files contain the data for the article "Engineering Higgs dynamics by spectral singularities" (<a href="https://doi.org/10.48550/arXiv.2205.06826">https://doi.org/10.48550/arXiv.2205.06826</a>).</p> <p>In each .zip file, it is possible to find not only the relevant data, but also a script (.sh file extension) to generate each one of the 5 figures shown in the paper and Supplemental Information Material. The file "Phasediagram.zip" contains the data corresponding to the dynamical phase diagrams (Fig. 1 of the manuscript) for the model of flat and graphene-like density of states (DOS).</p> <p>The data for the representative dynamical phases and Fourier Transforms for the constant DOS and graphene-like DOS model can be found in the files "Dynamics_flatDOS.zip" and "Dynamics_grapheneDOS.zip" respectively. </p> <p>The Fourier Transforms of the x-component of pseudospins texture are stored in the files "FFT_x-componentofpseudospintexture_flatDOS.zip" and "FFT_x-componentofpseudospintexture_grapheneDOS.zip" for the flat DOS and graphene DOS case respectively.</p> <p>For all the .txt files inside the .zip files, we have added at the top of each column a brief description of the data recorded below. Some notations have been used and read as follows: "Delta" indicates the superconducting order parameter, and "\xi_k" means the quasiparticle energy.</p>
FABIAN: a daily product of Fractional Austral-summer Blue Ice over ANtarctica during 2000-2021 based on MODIS imagery using Google Earth Engine
<p>This is a supplementary data set for FABIAN: a daily product of Fractional Austral-summer Blue Ice over ANtarctica during<br> 2000{2021 based on MODIS imagery using Google Earth Engine</p> <p>The files include:</p> <p>(1) Snow spectra modelled by TARTES, a two-stream radiative transfer model for light in snow (Libois et al., 2013).</p> <p>(2) Spectra extracted from MODIS, using AUTO-EM.</p> <p>(3) Endmember selection results, including ESS, EMC, and AMUSES.</p> <p>The other hyperspectral data from field measurements can be acquired by contact the original authors.</p> <p>Libois, Q., Picard, G., France, J., Arnaud, L., Dumont, M., Carmagnola, C., King, M., 2013. Influence of grain shape on light penetration in snow. The Cryosphere 7, 1803-1818.</p>
Grey Literature in Software Engineering: A Critical Review
<p>Replication package of the study "Grey Literature in Software Engineering: A Critical Review" published in Information and Software Technology (IST).</p>
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
THE BATTERY MATERIALS SOURCING ENGINEER - Mark Huijben - University of Twente
<p>The need for radically different usage of our planet's resources has never been so high. There is an increasing demand for electricity in the years to come. We need innovative ways to store that energy to take it out whenever and wherever we need it. </p> <p>Professor in Nanomaterials for Energy Conversion and Storage, Mark Huijben elaborates on the development of next-level batteries. Not only having optimal performance but also being more sustainable. That begins with the design of a battery. Also, the materials being used make a huge difference in their recyclability. </p> <p>Gerwin Hoogsteen, a researcher on energy management for smart grids, surprises us with a creative perspective on energy usage and storage. He takes us to the year 2030 where energy is stored locally, in self-driving cars that drive to places with energy overload and take it to the place where you need it: your home. What hurdles do we need to take to make this a reality?</p>
Engineering Dust Coma Model (EDCM) for ESA's Comet Interceptor mission to a dynamically new comet
<p>This data-set contains all results from the Engineering Dust Coma Model (EDCM) for ESA's Comet Interceptor (CI) mission to a dynamically new comet.</p> <p>A full description of the model behind the data can be found in the peer-reviewed paper <strong>Marschall, Zakharov et al. (2022), <a href="https://doi.org/10.1051/0004-6361/202243648">https://doi.org/10.1051/0004-6361/202243648</a>.</strong> Please cite this data-set and the paper when using the data.</p> <p>Contemporary numerical models of dusty-gas coma are used to obtain spatial distribution of dust for a given set of parameters. By varying parameters within a range of possible values we obtain an ensemble of possible dust distributions. Then, this ensemble is statistically evaluated in order to define the most probable cases and hence reduce the dispersion. This ensemble can be used to estimate not only the likely dust abundance along e.g. a fly-by trajectory of a spacecraft but also quantify the associated uncertainty.</p> <p>The dust environment assessment for the case when the target comet is not known beforehand (or when its parameters are known with large uncertainty) is critical for spacecraft safety and planning. The EDCM provides an assessment of dust environment for the CI mission.</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.