Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,709
datasets available to search
ShareScore release 0.9.0
Dataset results
3,709 results for “urbanization.”
Interviewing users of services of selected urban areas (Vantaa)
<p>As part of the EU project SAFE (https://eusafe.fa.uni-lj.si/), Laurea Unversity of Applied Sciences investigated living conditions and infrastructure offers in Vantaa (Finland) in order to work out possible suggestions for improvement. Students of Laurea Unversity of Applied Sciences conducted a survey in Vantaa in November 2023. The dataset is a compilation of the raw data.</p>
Figure 8 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 8. Proportion of number of wetland plant species by hemerobia in studied territories.
Figure 5 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 5. Proportion of number of plant species within light ecogroups in studied territories.
Data for 'Global Assessment of Interannual Hazard Variability in Coastal Urban Areas and Ecosystems'
<p>This dataset supports Odériz et al. (2024). 'Global Assessment of Interannual Hazard Variability in Coastal Urban Areas and Ecosystems'</p>
Figure 1 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 1. Map of the sampling localities in Georgia.
Strategic Integration of Urban Excess Heat Sources in District Heating: A Spatio-Temporal Optimization Paradigm
<p>This data repository supports the techno-economic and geospatial optimization analysis of district heating systems (DHS) in Stockholm, with a particular focus on integrating Urban Excess Heat (UEH) sources. The dataset includes both input data and results, organized by the key analytical tools employed in the study: GIS, OSeMOSYS, and the Hotmaps Dispatch Model.</p> <p>The <strong>folder structure</strong> is organized as follows:</p> <p>/Data<br>│<br>├── Input_Data/<br>│ ├── GIS/<br>│ ├── OSeMOSYS/<br>│ ├── Hotmaps_Dispatch/<br>│<br>└── Results/<br> ├── GIS/<br> │ ├── Optimized DHS network extensions for UEH sources<br> │ ├── Long-term capacity and emissions projections for DHS<br> ├── Dispatch outputs for different electricity price scenarios<br><br></p> <h2>Dataset Details</h2> <p>1. Input Data Folder: This folder includes all data used as inputs across the three primary tools:<br> - GIS: Contains geospatial data on the DHS network paths, source locations, and spatial configurations for network optimization.<br> - OSeMOSYS: Provides techno-economic parameters for heat generation technologies, including capital and operational costs, fuel price projections, and emissions factors.<br> - Hotmaps Dispatch: Includes detailed demand profiles, projected electricity prices, and the supply temperature requirements essential for dispatch simulations.</p> <p>2. Results Folder: This folder contains the results generated by each tool after processing the input data:<br> - GIS: Outputs optimized paths for network expansion and geospatial mapping of UEH sources.<br> -OSeMOSYS: Contains projections for DHS technology adoption, emissions reduction, and cost optimization over a 23-year model period.<br> - Hotmaps Dispatch: Stores dispatch model results under various electricity pricing and temperature scenarios, capturing the optimal deployment of DHS resources.</p> <h2>Summary of Tools Used</h2> <p>- GIS (Geospatial Information System): Used to locate UEH sources and map their proximity to the existing DHS network, guiding the optimization of network expansions.<br>- OSeMOSYS (Open Source Energy Modelling System): Performs long-term optimization for the DHS, factoring in the addition of UEH sources, electricity price scenarios, and emissions constraints.<br>- Hotmaps Dispatch Model: Simulates operational dispatch of heat generation technologies in response to varying electricity prices and network temperatures, providing insights into real-time heat supply optimization.</p> <p>This repository thus provides a comprehensive dataset to support the techno-economic and spatial analysis of DHS, facilitating the integration of UEH sources in urban heating systems through scenario-based optimizations.</p> <p><br><br></p>
Urban Air Pollution and Child Neurodevelopmental Conditions: A Systematic Bibliometric Review
<p>The dataset for this research was compiled through an advanced PubMed search targeting publications from a one-year period, with keywords focused on air pollution, neurodevelopment, and associated disorders. From an initial pool of 450 publications, filtering based on the co-occurrence of relevant keywords reduced this to approximately 44 papers. The analysis was conducted using VOSviewer to generate a visual map of relationships between air pollution and child neurodevelopment. To ensure consistency, a thesaurus was applied to standardize terminology, refining the final network for a detailed examination of keyword clusters and their interactions. For those seeking to replicate this process, Appendix A provides an in-depth, step-by-step methodology for building the networks and utilizing the data files to recreate the visualizations.</p>
Quantitative assessment of the reservoir-induced and urbanization-induced impact on multivariate flood risk via a nonstationary vine Copula model
<p>Here we show the results of the characteristics of the floods at Huayuankou, Lanzhou and Toudaoguai gauges selected by AMS and POT mentod, respectively. Besides that, the inormation about the reservoirs and the imprevious layer in the control catchment of each station is also uploaded.</p>
Dietary diversification of an insect predator along an urban-rural gradient
<p>Here we provide the raw data and R scripts of our study "Dietary diversification of an insect predator along an urban-rural gradient"</p> <p><span>Abstract: Urbanisation generally leads to a loss of taxonomic and functional diversity in almost all animal taxa, yet a mosaic of highly variable habitats within the urban matrix could offer a diversity of insect prey to highly mobile predators such as bats. We therefore asked if insect-feeding bats change in trophic interactions along rural-urban gradients. </span><span>We predicted that the diet of common noctule bats (<em>Nyctalus noctula</em>) diversifies with increasing levels of urbanisation and that urban bats include more pest and nuisance insect species than rural conspecifics.</span><span>Using metabarcoding of faecal samples over three years of sampling, we observed a more diverse diet in urban compared with rural noctule bats. Furthermore, urban bats consumed more than twice as many agricultural pests and six times as many nuisance insects as their rural conspecifics. Finally, insect species richness in the diet decreased with increasing levels of sealed surface and vegetation cover at the sampling site.</span><span>We argue that a highly mobile bat species such as the noctule bat compensate for the lower abundance of insects in urban areas by foraging over relatively large spatial scales, including adjacent rural areas. A high proportion of pest and nuisance insects highlights the importance of urban bats for providing important ecosystem services to humans. Urban planning needs to consider maintaining and establishing dark flight corridors and a diversity of habitats to support urban bat populations. </span></p>
Dataset: Survey for Identifying User Groups of Active Transport Modes in Urban Centers
<p>This dataset was derived from an online questionnaire conducted between June and July 2022. The survey aimed to identify groups of individuals based on patterns revealed through clustering methods, considering the importance assigned to various indicators associated with the dimensions of comfort, safety and security, and time and distance. <span>CSV file structure details are available at 'csv_structure.xlsx'. </span></p> <p>The questionnaire also included a set of items designed to capture the respondents' sociodemographic context, specifically:</p> <ul> <li>Gender </li> <li>Parental Status</li> <li>Age Group</li> <li>Educational Level</li> <li>Country of Residence </li> <li>Employment Situation </li> </ul>
InRePlast: Dataset for micro- and macroplastic found in the urban drainage system
<p>In the BMBF funded project ‘Environmental political instruments to reduce the plastic pollution of inland waters via drainage systems - InRePlast' plastics and plastic compounds were collected and analysed in wastewater treatment plants (wwtp) and in road drains. The project focussed primarily on the size range of macroplastics with a particle size of 5 mm and above. Furthermore, the so-called large microplastics in the size range between 1 mm and 5 mm were included in the analyses. Inbetween the years 2019 and 2021 four wwtp and roads in the associated municipalities (Aachen, Stolberg, Simmerath and Roetgen (Germany)) were investigated for one year each. The data contains information on all 13,211 particles found in wwtp and all 35,253 particles from road drains.</p> <p>The final report on the <span>Sub-project C </span>can be found at <a href="https://doi.org/10.2314/KXP:1858873908">https://doi.org/10.2314/KXP:1858873908</a> (in German language).</p>
Outdoor NB-IoT and 5G coverage and channel information data in urban environments
<p>This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G).</p> <p>Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: <a href="https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements">https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements</a> </p> <p>The dataset includes a metadata file providing the following information for each campaign: </p> <ul> <li>date of collection;</li> <li>start time and end time of collection;</li> <li>length;</li> <li>type (walking/driving).</li> </ul> <p>Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization.</p> <p>The dataset contains the following data for NB-IoT:</p> <ul> <li>Raw data for each campaign, stored in two .csv files. For a generic campaign <X>, the files are: <ul> <li>NB-IoT_coverage_C<X>.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);</li> <li> NB-IoT_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file for each city: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <NPCI, Operator ID, eNodeB-ID> unique combination detected at the coordinates of the data point.</li> <li>Estimated positions of eNodeBs, stored in a csv file for each city;</li> <li>A matlab script and a function to extract and generate processed data from the raw data for each city.</li> </ul> <p>The dataset contains the following data for 5G:</p> <ul> <li>Raw data for each campaign, stored in two .xslx files. For a generic campaign <X>, the files are: <ul> <li>5G_coverage_C<X>.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);</li> <li> 5G_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file: data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <PCI, Beamforming Index, Operator ID> unique combination detected at the coordinates of the data point.</li> <li>A matlab script and a supporting function to extract and generate processed data from the raw data.</li> </ul> <p>In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches:</p> <ul> <li>A campaign-by-campaign linear interpolation (both NB-IoT and 5G);</li> <li>A bidimensional interpolation on all campaigns combined (NB-IoT only).</li> </ul> <p>A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in:</p> <p>L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. <span>DOI: </span><a href="https://doi.org/10.3390/s23094266" target="_blank" rel="noopener"><span>10.3390/s23094266</span></a>.</p> <p>The second interpolation approach is instead introduced and described in:</p> <p>L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation.</p> <p>Positioning using the 5G data was furthermore in investigated in: </p> <p>K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," <span>IEEE Communications Magazine, Volume 62, Issue 5, pp</span><span>. 44-49, May</span><span> 202</span><span>4</span><span>. DOI: </span><a href="https://doi.org/10.1109/MCOM.011.2200707" target="_blank" rel="noopener"><span>10.1109/MCOM.011.2200707</span></a><span>.</span></p> <p><span>G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: <a href="https://doi.org/10.1109/OJCOMS.2024.3499370" target="_blank" rel="noopener"><span>10.1109/OJCOMS.2024.3499370</span></a>.</span></p> <p>Please refer to the above publications when using and citing the dataset. </p>
Impact on energy and air quality of connected and autonomous vehicles in an urban context
<p>Despite numerous studies related to autonomous vehicles and connected vehicles (CAVs) and their impact on the economy or on traffic performance (eg, flow management, accidents), there are not many studies that relate these benefits to the environmental component. In this context, the objective of this work consisted in the integrated assessment of the impacts of CAVs on traffic performance, atmospheric emissions CO<sub>2</sub> and NO<sub>x, </sub>and air quality.</p> <p>To this end, a roundabout in the city of Aveiro was selected as a case study, and different scenarios were created: base scenario, considering the current typology of vehicles (conventional); scenario 2, considering defensive behavior CAVs; scenario 3, considering assertive behavior CAVs; and scenario 1, considering all types of vehicles mentioned above. To ensure a comprehensive analysis, all scenarios were evaluated for a period of 24 hours, corresponding to the period of the experimental campaign carried out, and a cascade of models was applied.</p> <p>First, the PTV VISSIM model was applied which allowed, configuring, calibrating and validating the network under study for an evaluation of the traffic performance. Second, the VSP model was applied to estimate atmospheric emissions, Finally, the CFD VADIS model was applied to air quality assessment.</p> <p>The results obtained allowed us to conclude that the introduction of CAVs, promotes longer travel times, especially during times of higher traffic, and an increase in emissions, mainly by the CAVs with defensive behavior. In terms of air quality, there were large differences in terms of NO<sub>2</sub> concentrations, with the CAVs promoting a degradation of air quality, especially during peak traffic hours.</p>
Dataset: Ambient BTEX concentrations during the COVID-19 lockdown in a peri-urban environment (Orléans, France)
<p>The dataset of the manuscript "Ambient BTEX concentrations during the COVID-19 lockdown in a peri-urban environment (Orléans, France)" are presented here</p>
The impact of urbanization on body size of Barn Swallows Hirundo rustica gutturalis
<p><span><span><span><span><span><span><span><span><span><span><span>Urbanization implies a dramatic impact on ecosystems, which may lead to drastic phenotypic differences between urban and non-urban individuals. For instance, urbanization is associated with increased metabolic costs, which may constrain body size, but urbanization also leads to habitat fragmentation, which may favour increases in body mass when for instance it correlates with dispersal capacity. However, this apparent contradiction has rarely been studied. This is particularly evident in China where the urbanization process is currently occurring at an unprecedented scale. Moreover, no study has addressed this issue across large geographical areas encompassing locations in different climates. In this regard, Barn Swallows (<i>Hirundo rustica</i>) are a suitable model to study the impact of urbanization on wild animals because they are a widely distributed species tightly associated with humans. Here, we collected body mass and wing length data for 359 breeding individuals of Barn Swallow (<i>H. r. gutturalis</i>) from 128 sites showing different levels of urbanization around the whole China. Using a set of linear mixed-effects models, we assessed how urbanization and geography influenced body size measured using body mass, wing length and their regression residuals. Interestingly, we found that the impact of urbanization was sex-dependent, negatively affecting males' body mass, its regression residuals, and females' wing length. We also found that northern and western individuals were larger, regarding both body mass and wing length, than southern and eastern individuals. Females were heavier than males, yet males had slightly longer wings than females. Overall, our results showed that body mass of males was particularly sensitive trait to urbanization, latitude and longitude, while it only showed a weak response to latitude in females. Conversely, while wing length showed a similar geographical pattern, it was only affected by urbanization in the case of females. Further research is needed to determine if these phenotypic differences are associated with negative effects of urbanization or potential selective advantages.</span></span></span></span></span></span></span></span></span></span></span></p>
Calculated parameters related to new particle formation events at an urban and a mountain station in Northern China
<p>Data sets included in publication: Measurement Report: New particle formation characteristics at an urban and a mountain station in Northern China</p> <p>https://acp.copernicus.org/preprints/acp-2021-266/ </p>
Data for: Serotonin transporter (SERT) polymorphisms, personality and problem-solving in urban great tits
<p class="CxSpFirst"><span><span><span><span><span><span><span><span><span><span><span>Understanding underlying genetic variation can elucidate how diversity in behavioral phenotypes evolves and is maintained. Genes in the serotonergic signaling pathway, including the serotonin transporter gene (<i>SERT)</i>, are candidates for affecting animal personality, cognition and fitness. In a model species, the great tit (<i>Parus major</i>), we reevaluated previous findings suggesting relationships between <i>SERT</i> polymorphisms, neophobia, exploratory behavior and fitness parameters, and performed a first test of the relationship between single nucleotide polymorphisms (SNPs) in SERT and problem-solving in birds. We found some evidence for associations between <i>SERT </i>SNPs and neophobia, exploratory behavior and laying date. Furthermore, several SNPs were associated with behavioral patterns and success rates during obstacle removal problem-solving tests performed at nest boxes. In females, minor allele homozygotes (AA) for nonsynonymous SNP226 in exon 1 made fewer incorrect attempts and were more likely to problem-solve. In both sexes, there was some evidence that minor allele homozygotes (CC) for SNP84 in exon 9 were more likely to problem-solve. Only one SNP-behavior relationship was statistically significant after correcting for multiple comparisons, but several were associated with substantial effect sizes. Our study provides a foundation for future research on the genetic basis of behavioral and cognitive variation in wild animal populations. </span></span></span></span></span></span></span></span></span></span></span></p>
Rome (ITALY) - Urban Agriculture spatial dataset (years 2007 and 2013)
<p><strong>Motivation</strong></p> <p>The data in this dataset is a spatial inventory of <strong>urban agriculture</strong> (UA) carried out in the city of Rome (Italy) (Grande Raccordo Anulare (GRA)). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts to validate the data, some information may be incorrect.</p> <p><strong>Description of the dataset</strong></p> <p><em><strong>Typologies of UA</strong></em></p> <ul> <li><strong>Residential garden: </strong>Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.</li> <li><strong>Community garden: </strong>A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.</li> <li><strong>Urban farm: </strong>Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus exclusively on horticulture, vineyard, olive groves and orchard.</li> <li><strong>Institutional garden: </strong>Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).</li> <li><strong>Illegal garden: </strong>Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.</li> </ul> <p><em><strong>Land use typologies</strong></em></p> <ul> <li><strong>Horticulture: </strong>annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).</li> <li><strong>Vineyard: </strong>grape vines grown in order to produce wine or table grape.</li> <li><strong>Olive groves: </strong>olive trees grown in order to produce olive oil or table olives.</li> <li><strong>Orchards: </strong>mixed trees such as orange, stone fruit, pome fruit, olive trees.</li> <li><strong>Mixed crops: </strong>an area grown with a mix of horticulture crops and fruit trees, not divisible.</li> </ul> <p><strong>Credit</strong></p> <p>Pulighe G., Lupia F. (2016) <em>Mapping spatial patterns of urban agriculture in Rome (Italy) using Google Earth and web-mapping services. </em><strong>Land Use Policy</strong> 59(2016) 49-58.</p> <p><a href="http://www.sciencedirect.com/science/article/pii/S0264837716300059"><em>www.sciencedirect.com/science/article/pii/S0264837716300059</em></a></p>
Data archive for paper "Machine Learning Emulation of Urban Land Surface Processes"
<p>This archive contains models, data* (Overview), as well as the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002744">Machine Learning Emulation of Urban Land Surface Processes</a>".</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/">Singularity</a> (tested with version 3.6.3-1.el8)</li> </ul> <p>Please note that all steps require <a href="https://sylabs.io/">Singularity</a> to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>.</p> <p><strong>Overview</strong></p> <p>A general overview of the repository structure is given below. Due to licensing restrictions analysis and forcing data (*) cannot be included and need to be requested separately (see Initialization). Data derivatives (**) from either analysis or forcing, as well as intermediary data (***), are not included as they can be generated by rerunning experiments (see Usage).</p> <pre><code>. ├── data │ ├── analysis* │ ├── forcing* │ ├── teb │ ├── utils │ ├── wps │ └── wrf ├── hpc ├── models │ ├── teb │ ├── unn │ ├── wps │ └── wrf-unn ├── notebooks ├── outputs │ ├── analysis** │ ├── benchmark*** │ ├── forcing** │ ├── kerastuner*** │ ├── notebooks │ ├── tabular │ ├── teb** │ ├── unn** │ ├── wps*** │ └── wrf ├── paper │ └── figures ├── singularity └── tools </code></pre> <p><strong>Initialization</strong></p> <p>Forcing and analysis data need to be requested separately. The following directories should map to their respective data archives:</p> <ul> <li><code>./data/analysis</code> -> <a href="http://doi.org/10.5281/zenodo.4678387">Grimmond et al. (2013)</a></li> <li><code>./data/forcing</code> -> <a href="http://doi.org/10.5281/zenodo.4679279">Grimmond et al. (2021)</a></li> </ul> <p><strong>Usage</strong></p> <p>To rerun all experiments and reproduce results, run <code>tools/run_all.sh</code> from your command prompt. After completion, all results are saved in the <code>outputs</code> directory. Note that WRF simulations require high CPU time and may take hours or days to complete.</p> <p>Alternatively, if <a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System (PBS)</a> is available on your system, the following helpers may be used instead:</p> <pre><code>qsub hpc/submit_init.pbs qsub hpc/submit_tuner.pbs qsub hpc/submit_unn.pbs qsub hpc/submit_find_median_unn.pbs qsub hpc/submit_wrf.pbs qsub hpc/submit_postprocess.pbs qsub hpc/submit_benchmark.pbs </code></pre> <p>Note that you may need to modify PBS helper scripts to suit your specific environment.</p> <p><strong>Development notes</strong></p> <p>See DEVELOP.md.</p> <p><strong>License</strong></p> <p>The source code developed for this work is licensed under MIT (<code>LICENSE_CODE.txt</code>). For licensing information of third-party software see licenses under the <code>models</code> directory. Data files in this archive, including the initial and boundary condition data from the European Centre for Medium-Range Weather Forecasts (<code>data/wps/ungrib</code>), are licensed under CC BY-NC 4.0 (<code>LICENSE_DATA.txt</code>).</p>
Urban Soundscapes in the Imaginaries of Native Digital Users: Guidelines for Soundscape Design
<p>Collages and narratives collected in Seoul and in Paris to illustrate the participant's experience with urban soundscapes, together with their full semantic analyses according to the analysis grid presented in the reference publication.</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.