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722 results for “use case”
Data from: Using full-length metabarcoding and DNA barcoding to infer community assembly for speciose taxonomic groups: a case study
<p>How insect communities are assembled in nature remains largely unknown. In particular, whether habitat filtering or competition serves as the main mechanism in forming insect communities is rarely subject to an in-depth investigation. One bottleneck lies in the difficulty of species identification when dealing with a large number of diverse insects. However, High-Throughput Sequencing (HTS) technology coupled with classic DNA barcoding offers a great opportunity to infer community assembly for this speciose group. In this study, using 13,909 full-length barcodes obtained by Sanger sequencing or the SOAPBarcode metabarcoding method, we showed that competition was the main assembly mechanism for the moth communities studied in temperate forests of China. The two sequencing methods showed highly consistent results with regards to both diversity composition and community assembly mechanism. Significant phylogenetic signals and structure suggested that the focal moth communities were the result of the non-neutral assembly process, which was further confirmed by results of neutral assembly test that accounted for immigration and speciation rates. In conclusion, HTS coupled with a well-curated DNA barcode library can facilitate community assembly inferences, especially for speciose taxonomic groups.</p>
Effects of taphonomic deformation on geometric morphometric analysis of fossils: a case study using the dicynodont Diictodon feliceps (Therapsida, Anomodontia)
<p>Taphonomic deformation, the distortion of fossils as a result of geological processes, poses problems for the use of geometric morphometrics in addressing paleobiological questions. Signal from biological variation, such as ontogenetic trends and sexual dimorphism, may be lost if variation from deformation is too high. Here, we investigate the effects of taphonomic deformation on geometric morphometric analyses of the abundant, well known Permian therapsid <i>Diictodon feliceps</i>. Distorted <i>Diictodon </i>crania can be categorized into seven typical styles of deformation: lateral compression, dorsoventral compression, anteroposterior compression, 'saddle-shape' deformation (localized collapse at cranial mid-length), anterodorsal shear, anteroventral shear, and right/left shear. In simulated morphometric datasets incorporating known 'biological' signals and subjected to uniform shear, deformation was typically the main source of variance but accurate 'biological' information could be recovered in most cases. However, in empirical datasets, not only was deformation the dominant source of variance, but little structure associated with allometry and sexual dimorphism was apparent, suggesting that the more varied deformation styles suffered by actual fossils overprint biological variation. In a principal component analysis of all anomodont therapsids, deformed <i>Diictodon </i>specimens exhibit significant dispersion around the 'true' position of this taxon in morphospace based on undistorted specimens. The overall variance associated with deformation for Anomodontia as a whole is minor, and the major axes of variation in the study sample show a strong phylogenetic signal instead. Although extremely problematic for studying variation in fossil taxa at lower taxonomic levels, the cumulative effects of deformation in this study are shown to be random, and inclusion of deformed specimens in higher-level analyses of morphological disparity are warranted. Mean morphologies of distorted specimens are found to approximate the morphology of undistorted specimens, so we recommend use of species-level means in higher-level analyses when possible.</p>
Identifying diagnostic genetic markers for a cryptic invasive agricultural pest: a test case using the apple maggot fly, Rhagoletis pomonella (Diptera: Tephritidae)
Insect pests destroy ~15% of all USA crops, resulting in losses of $15 billion annually. Thus, developing cheap, quick and reliable methods for detecting harmful species is critical to curtail insect damage and lessen economic impact. The apple maggot fly, Rhagoletis pomonella (Diptera: Tephritidae), is a major invasive pest threatening the multibillion-dollar apple industry in the Pacific Northwest USA. The fly is also sympatric with a benign but morphologically similar and genetically closely related species, R. zephyria, which attacks non-commercial snowberry. Unambiguous species identification is essential due to a zero-infestation policy of apple maggot for fruit export. Mistaking R. zephyria for R. pomonella triggers unnecessary and costly quarantines, diverting valuable control resources. Here we develop and apply a relatively simple and cost-effective diagnostic approach using Illumina sequencing of double digest restriction-site associated DNA markers. We identified five informative single nucleotide polymorphisms (SNPs) and designed a diagnostic test based on agarose gel electrophoresis of restriction enzyme digested polymerase chain reaction amplification products (RFLPs) to distinguish fly species. We demonstrated the utility of this approach for immediate, one day species identification by scoring apple- and snowberry-infesting flies of known host plant identity, reared directly from 11 sites throughout Washington. However, if immediate diagnosis is not required, or hundreds to thousands of specimens must be assessed, then a direct Illumina-based sequencing strategy, similar to that used here for diagnostic SNP identification can be powerful and cost-effective. The genomic strategy we present is effective for R. pomonella and also transferable to many cryptic pests.
Data from: Development of a sustainability assessment algorithm and its validation using case studies on cryogenic machining
This work presents a comprehensive structure for evaluating the sustainability of machining processes. Industries can contribute towards developing a sustainable future by using algorithms that evaluate the sustainability of their processes. Inspired by the literature, the proposed model involves a set of metrics that are critical in evaluating the impact of a process on society, environment, and economy. The flexibility of this model allows decision-makers to use the available responses to identify the most favorable process. The entropy weight method was suggested for objectively calculating the weights of each indicator. A multi-criteria decision-making method i.e., Technique for Order Preference based on Similarity to Ideal Solution (TOPSIS), was used to rank processes in the decreasing order of their sustainability. The proposed algorithm was successfully validated with case studies from the published literature. A MATLAB code was also created so that industries may expeditiously apply this method to evaluate the sustainability of machining processes.
Lulu - a software simulator for P colonies. Use case scenarios and demonstration videos
<p>The videos show three different examples of using the Lulu P colony simulator.</p> <p>The Lulu P colony simulator is available under an open-source MIT license at https://github.com/andrei91ro/lulu_pcol_sim. All of the secondary applications, including Lulu_Kilobot are available (also under open-source licenses) at https://github.com/andrei91ro.</p> <p>The first two videos present the simulator running addition (+1) and subtraction (-1). In these two examples, the simulator is ran in a step by step mode in order to clearly visualize the results of running each simulation step. For this reason the total simulation time reported at the end of the simulation is in the order of minutes.</p> <p>The average (of five runs) simulation time for a normal (non-interactive) simulation is 0.0021050 seconds for the addition and 0.0047492 seconds for the subtraction examples.</p> <p>The third example (lulu_kilobot_30_steps) presents the simulator running a more complex P colony that controls a Kilobot robot simulated in V-REP. This P colony is based on the subtraction P colony in the sense that each move the robot makes is marked by the removal of an f object from the environment.</p> <p>The input file used in the addition example (lulu_sim_ag_increment):</p> <p>pi = {<br /> A = {l_p};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, l_p};<br /> B = {AG_1};<br /> AG_1 = ({e, e}; < e->f, e<->l_p >, < l_p->e, f<->e >);<br /> }</p> <p>The input file used in the subtraction example (lulu_sim_ag_decrement):</p> <p>pi = {<br /> A = {l_m, l_p, l_z};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, l_m};<br /> B = {AG_1};<br /> AG_1 = ({e, e};<br /> < e->e, e<->l_m >,<br /> < l_m->l_p, e<->f/e<->e >,<br /> < f->e, l_p<->e >,<br /> < l_p->l_z, e<->e >,<br /> < e->e, l_z<->e > );<br /> }</p> <p>The input file used in the Kilobot example (lulu_kilobot_30_steps):</p> <p>pi = {<br /> A = {l_m, m_0, m_S, m_L, m_R, c_R, c_G, c_B};<br /> e = e;<br /> f = f;<br /> n = 2;<br /> env = {f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, f, l_m};<br /> B = {AG_command, AG_motion};<br /> AG_command = ({e, e};<br /> < e->e, e<->l_m >,<br /> < l_m->m_S, e<->f/e<->e >,<br /> < f->e, m_S<->e >,<br /> < m_S->m_0, e<->e >,<br /> < e->e, m_0<->e > );</p> <p> AG_motion = ({e, e};<br /> < e->l_m, e<->m_S >,<br /> < m_S->e, l_m<->e/e->e ><br /> < e->e, e<->m_0 >,<br /> < m_0->e, e<->m_0/e->e >);<br /> }</p> <p> </p>
Data for "Assessment of using field-aligned currents to drive the Global Ionosphere Thermosphere Model: A case study for the 2013 St Patrick's Day geomagnetic storm"
GITM Simulation results for the paper "Assessment of using field-aligned currents to drive the Global Ionosphere Thermosphere Model: A case study for the 2013 St Patrick's Day geomagnetic storm"
Use Case 3 obtained datasets
<p>Data from the AGV (automated guided vehicle) and its onboard sensors.</p>
Data and code from Lamb et al., "Evaluating conservation units using network analysis: a sea duck case study"
<p>This file consists of data and code used to construct network models for scoters in North America and is associated with the manuscript "<strong>Evaluating conservation units using network analysis: a sea duck case study</strong>" published in Frontiers in Ecology and the Environment. </p><p> </p><p><strong>Continental scoter network mapping </strong>is the R script used to run analyses.</p><p> </p><p><strong>duck_nodes</strong> is the main datafile. Columns are organized as follows:</p><p>id - unique identifier</p><p>species - species from which the centroid was obtained (BLSC = black scoter, SUSC = surf scoter, WWSC = white-winged scoter)</p><p>stage - period of the annual cycle to which the centroid belongs (W = winter, B = breeding, S = spring staging, M = fall staging and molt, WM = winter migration, BM = breeding migration, MM = molt migration, SM = spring migration)</p><p>site - position of centroid within season (i.e., W1 = first site occupied during winter, W2 = second site occupied, etc.)</p><p>cycle - number of annual cycles following transmitter attachment (1 = first cycle after attachment, 2 = second cycle after attachment, etc.)</p><p>sex - sex of individual (M = male, F = female)</p><p>age - age of individual (HY = hatch year, SY = second year, TY = third year, ASY = after second year, ATY = after third year, AHY = after hatch year</p><p>capture_reg - general area where individual was captured</p><p>capture_subreg - specific region within capture region where individual was captured</p><p>lon - longitude of centroid</p><p>lat - latitude of centroid</p><p>duration - number of days spent at centroid</p><p>start - date of arrival at centroid</p><p>end - date of departure from centroid</p><p>jstart - Julian date of arrival at centroid</p><p>jend - Julian date of departure from centroid</p><p>season - season of annual cycle in which centroid occurred (W = winter, F = fall, B = breeding, S = spring)</p><p>year - calendar year in which centroid began</p><p>to - node in which centroid is grouped</p><p>from - node in which previous centroid is grouped (i.e., node in which indiviual was located before moving to present node)</p><p>to_sea - season of annual cycle in which centroid occurred</p><p>from_sea - season of annual cycle in which previous centroid occurred</p><p>type - movement type to centroid; the first letter represents the season (coded as in "season" column), and the second represents the nature of the movement (WD = dispersal within a season, M = migration among seasons)</p><p>type2 - same as "type", but with dispersal movements coded by the stage in which they occur (W = winter, B = breeding, SM = spring migration, WM = winter migration)</p><p>ew - capture location in eastern (east; Atlantic and Great Lakes) or western (west; Pacific) North America</p><p>count_ind_sp - total number of tracked individuals of the species represented by centroid</p><p>wt - base centroid weight (all centroids equal, deployments excluded)</p><p>wt_sp - species-adjusted centroid weight: for centroid <i>x</i> in species <i>s</i>, weight<i>x</i> = (<i>N </i>centroids) × (1 / (<i>n </i>centroids in <i>s</i>))</p><p>wt_dur - duration-adjusted centroid weight: for centroid <i>x</i>, weight<i>x</i> = (days at centroid location) × 365-1</p><p>wt_ind - individual-adjusted centroid weight: for centroid <i>x</i> in individual<i> j</i>, weight<i>x</i> = 1 / (<i>n </i>centroids in <i>j</i>)</p><p>wt_ind_sp - individual and species adjusted centroid weight: for centroid <i>x</i>, individual <i>j</i>, and species <i>s</i>, weight<i>x</i> = (<i>N </i>centroids / (<i>N </i>species * <i>n</i> individuals in <i>s</i>)) × (1 / (<i>n </i>centroids in <i>j</i>))</p><p>wt_cap - capture location adjusted centroid weight: for centroid <i>x</i> and capture location <i>c, </i>weight<i>x </i>= (<i>N </i>centroids / <i>N</i> capture locations) / <i>n</i> centroids in <i>c</i></p><p>wt_ew - east-west adjusted centroid weight: for for centroid <i>x</i> and region <i>r, </i>weight<i>x </i>= (<i>N </i>centroids / <i>N</i> regions) / <i>n</i> centroids in <i>r</i></p><p>wt_spew - species and east-west adjusted centroid weight: for centroid <i>x</i> species <i>s</i>, and region <i>r, </i>weight<i>x </i>= (<i>N </i>centroids / (<i>N</i> species × <i>N</i> regions)) / <i>n </i>centroids for species <i>s</i> in region <i>r</i></p><p>wt_indspew - individual, species, and east-west adjusted centroid weight: for centroid <i>x,</i> individual<i> j, </i>species <i>s</i>, and region <i>r, </i>weight<i>x </i>= <i>N </i>centroids / (<i>N</i> species × <i>N</i> regions × <i>n</i> centroids for individual <i>j </i>× <i>n</i> individuals for species <i>s</i> in region <i>r</i>)</p><p>wt_spewcap - species, east-west, and capture location adjusted centroid weight: for centroid <i>x,</i> species <i>s</i>, capture location <i>c, </i>and region <i>r, </i>weight<i>x </i>= <i>N </i>centroids / (<i>N</i> species × <i>N</i> regions × <i>n</i> centroids for species <i>s</i> in capture location <i>c </i>× <i>n</i> capture locations for species <i>s</i> in region <i>r</i>)</p>
Monitoring of large-scale archaeological excavations using photogrammetric techniques - Nea Paphos case study
<p>Photogrammetric data (images) for five contexts from a single archaeological trench from the Paphos Agora Project (Excavations conducted in Nea Paphos Archaeological Site by Jagiellonian Unversity in Krakow - under the direction of Prof. Ewdoksia Papuci-Władyka). Images for photogrammetric modelling of all contexts were taken in 2015 with the NIKON D7100 camera (24 Mpx resolution), with the number of images per model varying from 25 to 41. For photogrammetric processing, the Agisoft PhotoScan and Reality Capture were used. </p><p>The data (images) were uploaded together with project files from Agisoft Metashape and RealityCapture, exported results (3D models, orthoimages and DEMs) and reference data - point clouds from terrestrial laser scanner measurements are available as supplementary material for the article in Journal of Archaeological Science: Reports. Also, data for one layer below and above, which do not include reference point clouds, have been added.</p>
Data files for Sheehan et al. 2023 'City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona' DOI: https://doi.org/10.3390/rs15245709
<p>Data files for Sheehan et al. (2023) City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona. Remote Sensing. 15(24) DOI: <a href="https://doi.org/10.3390/rs15245709">https://doi.org/10.3390/rs15245709</a></p> <p>Description of contents: </p> <p>xView-YOLOv3_Model6_Barcelona_weights.pt</p> <p>This file contains the pre-trained weights for the xView-YOLOv3 model (model code available here: https://github.com/ultralytics/xview-yolov3). These weights were trained on a manually created training data set of vehicles present in WorldView 2/3 imagery covering the city of Barcelona. The weights relate to Model 6 set up: a single vehicle class (parked, static and moving), RGB imagery, Barcelona training data set derived anchor boxes, 1500 x 1500 pixel sized images and to 1000 epochs. </p> <p> </p>
Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: A case study of the MIMIC-IV emergency department module
<p><strong>Objective</strong> <br>To assess the use of Health Level Seven Fast Healthcare Interoperability Resources (FHIR<sup>®</sup>) for implementing the Findable, Accessible, Interoperable, and Reusable guiding principles for scientific data (FAIR). Additionally, present a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR. <br><br><strong>Material and Methods</strong> <br>A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department dataset (MIMIC-ED), a deidentified clinical dataset converted into FHIR. The FAIRness of this dataset was assessed using a set of common FAIR assessment indicators. <br><br><strong>Results</strong> <br>The FHIR distribution of MIMIC-ED, comprising an implementation guide and demo data, was more FAIR compared to the non-FHIR distribution. The FAIRness score increased from 60 to 82 out of 95 points, a relative improvement of 37%. The most notable improvements were observed in interoperability, with a score increase from 5 to 19 out of 19 points, and reusability, with a score increase from 8 to 14 out of 24 points. A total of 14 FAIR implementation choices were identified. <br><br><strong>Discussion</strong> <br>Our work examined how and to what extent the FHIR standard contributes to FAIR data. Challenges arose from interpreting the FAIR assessment indicators. This study stands out for providing a real-world example of a dataset that was made more FAIR using FHIR. <br><br><strong>Conclusion</strong> <br>To the best of our knowledge, this is the first study that formally assessed the conformance of a FHIR dataset to the FAIR principles. FHIR improved the accessibility, interoperability, and reusability of MIMIC-ED. Future research should focus on implementing FHIR in research data infrastructures. Keywords: FAIR Guiding Principles, HL7 FHIR, Reusable Data, MIMIC-IV</p>
INTELLIMAN_WP2_Application Requirements and Integration_T2.4_Fresh food handling use case analysis, integration and validation_Apple 6D pose estimation dataset_v0
<p>The dataset contains the data generated for the training of the 6D pose estimation neural network<br>DOPE related to the publication:<br>M. Costanzo, M. De Simone, S. Federico, C. Natale and S. Pirozzi, "Enhanced 6D Pose Estimation for<br>Robotic Fruit Picking," 2023 9th International Conference on Control, Decision and Information<br>Technologies (CoDIT), Rome, Italy, 2023, pp. 901-906, doi: 10.1109/CoDIT58514.2023.10284072.</p>
Prediction of COVID-19 case numbers using state-space modeling and wastewater virus datasets
Open the record for dataset details and reuse information.
Simulation cases of a lab-scale wet-operated stirred media mill using coupled CFD-DEM
<p>Simulation cases described in the article, "Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force", DOI: <a href="https://doi.org/10.1016/j.powtec.2024.120060" rel="nofollow">https://doi.org/10.1016/j.powtec.2024.120060</a></p> <p><strong>Pre-requisites:</strong> LIGGGHTS, OpenFOAM-6, cfdemCoupling, and their corresponding dependencies, Python (>3.6)</p> <p>*The versions of simulation softwares used in the simulation cases are taken from Institute for Particle Technology's (iPAT) GitLab repository: <a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a></p> <p>To run the simulations in this repository, one should first install the pre-requisites i.e., LIGGGHTS, OpenFOAM-6 and cfdemCoupling. The repositiries can be found at Institute for Particle Technology's GitLab (<a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a>) if not, they shall be requested.</p> <p>Running the simulations in the repositories includes, generation of the cases in "Base_Cases_Init", using the "generateCases.py" file (Python3), then run the "variables_Modify.py" file. Running of the "jobfile_Modify.py" and "jrun.py", sequentially, will submit the simulations to a HPC cluster. After the successful run of these simulations, the cases in the folders "Base_Cases_Stable" and "Base_Cases_Stable_Lubrication" can be launched in the same manner as described above, i.e., sequentially running "generateCases.py", "variables_Modify.py", "jobfile_Modify.py" and "jrun.py" (one needs to check if the corresponding restart files are existing in the Base_Cases_Stable*/Base_Case_Stable/Restart folder, which are generated from the "Base_Cases_Init" runs). Following this, the cases in "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" can be run using the same method as described above (one needs to check if the corresponding restart files are existing in the Base_Cases_Run*/Base_Case_Run/Restart folder, which are generated from the "Base_Cases_Stable" runs). After successfully running of the simulations the python file "generateAndRunPostFiles.py", in each of the corresponding "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" folders should be run.</p> <p> </p> <p> </p> <p> </p> <p><strong>Description:</strong> This repository provides the simulation cases to generate and run the simulation cases of the "stirred media mill" (MiniCeR). The simulations are setup to couple the CFD and DEM via two-way coupling and the corresponding files in the "Run" folder contain the post-processing scripts to extract the "collision/stress energies" and assemble them into a "collision/stress energy distribution". The simulations are setup in three stages, namely, "Init", "Stable", and "Run". The combinations of operating settings can be easily modified and the respective cases can be generated using the python scripts in the corresponding repositories. The scripts to run the simulations on the HPC-cluster systems are also added.</p> <p><strong><em>a. Init:</em></strong> This stage is to initialize the system with the particles. Three insertion faces are used to generate and insert the required number of particles (calculated according to their size and filling degree) into the system. The "base case" folder contains the necessary DEM scripts of the case setup and the required CAD (geometry) files. The python script "generateCases.py" generates the requested simulation cases according to the specified operating settings. It uses the help of "MakeCases.sh". The "variables_Modify.py" file modifies the variables in the generated folders of the simulation cases to alter the operation setting values. The "jobfile_Modify.py", and the "jrun.py" are used to modify the cluster job files and run the submit the simulation jobs onto the cluster, respectively.</p> <p><strong><em>b. Stable:</em></strong> This is the first stage couples the CFD and DEM. The restart files generated in the "Init" stage are used to start the coupling and run for a specified time. It follows the similar system as init, i.e., to generate the cases and modify the variables, but with additional generation and modifications in the CFD folder i.e., the mesh generation, etc. The simulations are launched in the same way as described above and the corresponding restart files are extracted.</p> <p><strong><em>c. Run:</em></strong> This second stage of the coupling of CFD and DEM launches the srabilized system and extracts the collision energies and stores them in ".txt" files which are postprocessed later to assemble the stress energy distribution. The post-processing to extract the stress energy distribution is done using the "Stress_Energy_Calculation.py" and "generateAndRunPostFiles.py", which generate corresponding folders of post-processing in each of the corresponding case folders.</p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
Training dataset for the Drivers use case
<p>Training dataset for the Drivers use case (C language) obtained from the "excavator" dataset <a href="https://zenodo.org/record/4383876">https://zenodo.org/record/4383876</a> (simplified version of the original linux code).</p>
Nordic comparative study on green space use during COVID-19 – Case Helsinki (NCS-Helsinki)
<p>This dataset includes the data used in the Helsinki case study of an original research paper “<em>Pandemic urban resilience in the Nordic context - a cross-city analysis on associations between outdoor recreation and green infrastructure</em>” (Fagerholm et al., upcoming).</p> <p>The data were collected in the PLAN-Health (PLAN-H) research project (<a href="https://participatorymapping.org/project/health-promoting-urban-environment-plan-h/">https://participatorymapping.org/project/health-promoting-urban-environment-plan-h/</a>) and include point data of respondent-mapped outdoor locations visited in leisure time.</p>
Dataset used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study."
<p>This dataset repository includes eight raster layers (Reference System EPSG:3035 - ETRS89-extended / LAEA Europe), used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study", and obtained by the adaptation of analyses carried out by previous studies (Morabito et al., 2021; Guerri et al., 2021; 2022).</p> <p>Further information regarding the source, study period, and horizontal resolution is available in the attached text file. </p> <p> </p> <p><strong><em>References</em></strong></p> <p>Guerri, G., Crisci, A., Congedo, L., Munafò, M., Morabito, M., <strong>2022</strong>. A functional seasonal thermal hot-spot classification: Focus on industrial sites. Science of The Total Environment 806, 151383.<a href="http://https://doi.org/10.1016/j.scitotenv.2021.151383"> https://doi.org/10.1016/j.scitotenv.2021.151383</a>.</p> <p>Guerri, G., Crisci, A., Messeri, A., Congedo, L., Munafò, M., Morabito, M., <strong>2021</strong>. Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers. Remote Sensing 13, 538. <a href="https://doi.org/10.3390/rs13030538">https://doi.org/10.3390/rs13030538</a>.</p> <p>Morabito, M., Crisci, A., Guerri, G., Messeri, A., Congedo, L., Munafò, M., <strong>2021</strong>. Surface Urban Heat Islands in Italian Metropolitan Cities: Tree Cover and Impervious Surface Influences. Science of The Total Environment 751, 142334. <a href="https://doi.org/10.1016/j.scitotenv.2020.142334">https://doi.org/10.1016/j.scitotenv.2020.142334</a>.</p>
H2020 Platone Italian Demonstrator Use Case 1-2 Topology and asset description
<p>Description of the database "areti_production_PV_customer_2021":</p> <p>Data of the PV plant installed at the users' place.</p> <p>Data referred to several customers related to the year 2021.</p> <p>In the database you will find:</p> <p>PoD: Point od Delivery of the users' place (PoD) identification code;<br> Rated Power (peak) [W]: photovoltaic plant rated power peak in W;<br> Rated Voltage [Vac]: photovoltaic plant rated voltage in V;<br> Supply Type (at PoD): supply voltage of plat where the PV plant is connected (1Ph: single-phase; 3Ph: three-phase).</p> <p>_________________________________<br> </p> <p>Description of the database "areti_storage_customer_2021":</p> <p>Data of storage system installed at the users' place.</p> <p>Data referred to several customers related to the year 2021.</p> <p>In the database you will find: </p> <p>PoD: Point od Delivery of the users' place (PoD) identification code;<br> Rated Power (charging) [kVA]: rated power in kVA of storage during charging phase;<br> Rated Power (discharging) [kVA]: rated power in kVA of storage during discharging phase;<br> Max Rated Power (charging) [kVA]: max rated power in kVA of storage during charging phase;<br> Max Rated Power (discharging) [kVA]: max rated power in kVA of storage during discharging phase;<br> Rated Voltage [V]: rated voltage in V of storage;<br> Supply Type: supply type (1Ph: single-phase);<br> Max capacity [kWh]: max capacity in kWh of storage;<br> DoD: Depth of Discharge of storage; <br> Setting Power Factor Range [p.u.]: range of settable power factor of storage;</p> <p>(Useful link to consult Italian UC:</p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a></p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a></p> <p><a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
REIP: a Reconfigurable Environmental Intelligence Platform and Software Framework for Fast Sensor Network Prototyping - use case dataset
<p>Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We show the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems.</p> <p>This dataset was created for the case study in Section 5 of the paper.</p>
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