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556 results for “Infrastructure”
Evaluation of stormwater urban ecological infrastructure in Phoenix, Arizona (USA): a case study of a small-scale bioretention basin system
In 2017, Arizona State University finished construction on a pedestrian mall central to its Tempe campus, which included a small-scale bioretention basin system for stormwater management. This study analyzed the flood control and water quality improvement performance of the small-scale bioretention basin system in the Phoenix Metropolitan Area, AZ USA. Flood control efficacy was quantified by calculating discharge from the basin system using water level loggers and measuring soil moisture levels using soil moisture probes. Stormwater runoff samples were collected for twenty-one storm events and analyzed for nitrogen and phosphorus constituent concentrations. Nutrient concentrations at the system inflow and outflow were used to determine percent change in concentration. Water quality improvement performance was compared to results from previous studies on bioretention basin system performance. These data were used to create relevant graphical figures. Results were obtained by performing statistical analysis calculations on the data measurements. The results indicated that the bioretention basin system performed adequately for flood control and water quality improvement, supporting the use of stormwater urban infrastructure systems in arid and semi-arid climates. Further research can reveal how these systems may perform during more severe storm events and offer improvements for future designs.
Pan‐Arctic Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw
<p>The datasets are issued from the combination of records of the ESA EO4PAC and Permafrost_cci and HORIZON 2020 Nunataryuk projects. The EO4PAC project aimed to develop a new generation of geospatial products for the observation of permafrost and associated changes from space with a special focus on the coastal Arctic. Four components were considered in the creation of the datasets:</p> <p>(1) Landsat-7/8 for the detection of coastline changes over the 2000-2020 period (Tanguy et al., 2024).</p> <p>(2) Sentinel-1/2 for the detection and mapping of coastal infrastructures (Bartsch et al. 2024), updating Wang et al. (2021).</p> <p>(3) Permafrost_cci timeseries for retrieval of trends of ground temperature and active layer thickness for the 2000-2020 period (Obu et al. 2021a,b), evaluated based on Martin et al (2023) and CALM et al. (2024).</p> <p>(4) Sea level rise by 2100 (Garner et al. 2022).</p> <p>The respective output provides a consistent mapping of settlements along arctic and permafrost-dominated coasts (2), and associated coastline and permafrost conditions changes during the last 20 years (1, 3). Combined together, an assessment of Arctic infrastructures at risk due to permafrost change (GT, ALT) and coastline erosion was possible, the latter with projections for the years 2030, 2050 and 2100.<a name="_heading=h.jkogrw14ymt"></a></p> <p>References</p> <p>Bartsch, Annett, Pointner, Georg, & Nitze, Ingmar. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (Version 2) [Data set]. Zenodo. https://zenodo.org/records/10160636.</p> <p>CALM, GTN-P, Wieczorek, M., Heim, B., Streletskiy, D., Bartsch, A., 2024, GTN-P CALM: 34 years of Active Layer Thickness (ALT) across latitudinal and elevational gradients in the Northern Hemisphere [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.972777</p> <p>Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., et al. (2022). IPCC AR6 sea level projections [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6382554">https://doi.org/10.5281/zenodo.6382554</a></p> <p>Martin, Julia; Boike, Julia; Chadburn, Sarah; Zwieback, Simon; Anselm, Norbert; Goldau, Maybrit; Hammar, Jennika; Abramova, Ekatarina N; Lisovski, Simeon; Coulombe, Stéphanie; Dakin, Brampton; Wilcox, Evan James; Giamberini, Mariasilvia; Rader, Fieke; Suominen, Otso; Rudd, Daniel Alexander; Mastepanov, Mikhail; Young, Amanda (2023): T-MOSAiC 2021 myThaw data set [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.956039, In: Boike, Julia; Hammar, Jennika; Goldau, Maybrit; Miesner, Frederieke; Anselm, Norbert (2024): Circumarctic seasonal measurements of permafrost parameters (thaw depth, snow depth, vegetation and tree height, water level and soil properties) [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.971787</p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Tanguy, R., Bartsch, A., Nitze, I., Irrgang, A., Petzold, P., Widhalm, B., von Baeckmann, C., Boike, J., Martin, J., Efimova, A., Vieira, G., Whalen, D., Heim, B., Wieszorek, M., Grosse, G.: Pan‐Arctic Assessment of Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw, Earth’s Future, 10.1029/2024EF005013.</p> <p>Wang, S., Ramage, J., Bartsch, A., & Efimova, A. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4529610" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.4529610</a></p>
Data for "Breaking the Paywall: The role of Open Journal System as key Open Science infrastructure"
<h3><strong>Context</strong></h3> <p>This research was conducted within the NSF-SEEKCommons Project, a research initiative dedicated to supporting Open Science and Open Access in disciplinary research. The project has a special interest in understanding the role that critical infrastructure has in supporting open initiatives. The Open Journal System (OJS) serves as a long-standing fundamental piece for Open Access throughout the globe. Hence, it provides valuable information about experiences developing, deploying, and maintaining open technologies. </p> <h3><strong>Methods<br></strong></h3> <div> <div>We used mixed methods for our research, triangulating repository data, installation data, interviews, and documentary analysis. We collected repository data using a report generator (Kopp [2018] 2024) that uses repository metadata to present general statistics about a Git project. The resulting information was manually curated, disambiguated, and annotated to have a homogeneous set of developers with information about their institutional affiliation and country. </div> <div> </div> <div>Names are normalized based on the information in qualitative interviews and by browsing the full-extent commits in the GitHub repository. Other sources for this were the institutional materials (available in current and archived versions of the PKP website), meeting minutes, the user forum, and further project documentation available online. GitHub handles are homologated to their most comprehensive version. For institutional and country affiliation, we resorted to GitHub profiles, PKP documentation and forums, institutional domains available in emails, and researchers' ORCID IDs. </div> </div> <h3><strong>Available files</strong></h3> <ol> <li><strong>Information about the codebase</strong> (number of files, lines of code, and timestamp) organized by <strong>month, quarter, and semester. </strong><br>See file: OJS_GitStats_04-24.csv</li> <li>Information about the historical evolution of the codebase (number of files, lines of code, and timestamp), including <strong>a description of the top committers for each month</strong>. Commiters are described by including their institutional affiliation and country of origin. <br>See file: OJS_DevStats_Institution-Country_1.tsv</li> <li>Information about the <strong>historical evolution of the codebase </strong>focusing on <strong>top committers</strong>, along with their institution and country. This file is formatted to map the co-occurrence of developers and attributes by month between 2004-2024.<br>See file: OJS_DevStats_Institution-Country_2.tsv</li> <li>Selected fields to describe<strong> working and regularly maintained plugins for OJS as of October 2024.</strong> Includes name of the plugin, homepage, description, maintainer, and institutional affiliation. <br>See file: OJS_Plugins_2024_Processed.tsv</li> <li>Details of the aggregated <strong>information</strong> included in <strong>Table</strong> <strong>5</strong> of the article.<br>See file: OJS_Plugins_2024_Table5.tsv</li> <li><strong>Snapshot</strong> to XML information of the <strong>plugin gallery of OJS </strong>(October 21) retrieved from PKP website (Smecher 2024)<br>See file: OJS_Plugins_2024.csv</li> </ol> <h3>Funding</h3> <p><span>The SEEKCommons Project is funded by the U.S. National Science Foundation (NSF), grant #2226425</span></p>
Microclimate data associated with green infrastructure in Lancaster Pennsylvania, 2022
Green stormwater infrastructure (GSI) is being increasingly implemented as a stormwater management practice. A key reason for its popularity is the potential for co-benefits, such as heat mitigation, in addition to stormwater management functions. This data is the result of field investigation of heat patterns around GSI in Lancaster, Pennsylvania, providing some of the first ever direct measurements of microclimate and thermal comfort near GSI. During summer 2022, we collected data along transects at 10 rain gardens. Microclimate variables were quantified using a Kestrel 5400 Heat Stress Tracker and were used to calculate metrics that represent heat stress experienced by a human such as wet bulb globe temperature (WBGT) and mean radiant temperature (MRT). Measurements were also made at nearby impervious surface and lawn reference sites.
Urban Ecological Infrastructure (UEI) in the greater Phoenix, Arizona metropolitan area and surrounding Sonoran desert region (2010-2017)
Urban ecological infrastructure (UEI) encompasses all infrastructure in a city that supports ecological structure and function, and by extension, provides ecosystem services to urban residents and is a broad, all-encompassing concept for "nature in cities". This idea includes commonly recognized forms of infrastructure, such as parks, residential yards, community gardens, lakes and rivers, and street trees. But UEI also includes less recognized forms, such as vacant lots, agricultural fields, canals, and water retention basins. Despite being widely recognized as important to urban landscapes, the wide variety, and various forms of urban ecological infrastructure are rarely documented in a single source. To address this, we consolidated various aquatic, terrestrial, and wetland UEI throughout the Phoenix Metropolitan area so researchers can incorporate this UEI into project designs and models. Since people’s perceptions of UEI differ not only by the three broad classifications but also by the individual characteristics of UEI, each feature is classified not only as aquatic, terrestrial, or wetlands but also given on of fifteen unique classifications. Incorporation of UEI into both planning and research design can promote practices that increase both biodiversity and human well-being while also possibly limiting negative landscape perceptions.
Models and Infrastructure used in "Deep Statistical Model Checking"
<p>This repository contains the models and all other infrastructure (learning procedure, NNs, Jani generator, maps, modes & mcsta binaries) used in the FORTE 2020 paper "Deep Statistical Model Checking".</p>
SMART Infrastructure Facility Building Data
<p><strong>SMART Building Data</strong></p> <p>Authors: J. Barthelemy, B. Arshard, N. Verstaevel, P. Perez<br> Contact: SMART Infrastructure Facility - smart-iot@uow.edu.au<br> Version: 08 July 2020</p> <p><strong>Description</strong></p> <p>The time series data has been generated by Droplet sensors installed in every room of the SMART Infrastructure Facility of the University of Wollongong. The sampling rate is set to one minute and the data is transmitted via a LoRaWAN network.</p> <p>Each device sense temperature, humidity, luminosity, pressure, movement and CO2 (*). In addition, they transmit their orientation (*), node id, room id and battery voltage. Each packet received by the LoRaWAN network is also characterized by a RSSI, an SNR and a checksum. The (*) CO2 and orientation data are not accurate.</p> <p><strong>Data dictionary</strong></p> <p>The dataset contains the following features:</p> <p>* info : the type of information for the current row. It can be: <br> - temp -> temperature (Celcius)<br> - humidity -> humidity (%)<br> - light -> luminosity (1024 levels, 0 being the darkest and 1024 the brightest)<br> - co2 -> CO2 (1024 levels, 0 being the lowest, 1024 the highest)<br> - pressure -> pressure (hPa)<br> - orient -> orientation of the sensor<br> - nodeId -> internal id of the sensor<br> - roomNum -> room id of the sensor<br> - voltage -> battery voltage of the sensor (V)<br> - movement -> motion detection (True/False)<br> - checksum -> checksum of the packet transmitted via LoRaWAN<br> * room_id : the room id in which the sensor is installed (see map of the building)<br> * date_time : the timestamp of the data<br> * bool_v : boolean value (true/false) for movement<br> * str_v : string value for nodeId, checksum, roomNum<br> * long_v : integer (long) value for light, orient, rssi, co2, humidity<br> * dbl_v : real (double) value for voltage, pressure, temp, snr</p> <p><strong>Notes</strong></p> <p>- The uncompressed dataset is a 40Gb CSV file.<br> - Droplet sensors specifications: https://nube-io.com/wp-content/uploads/Droplet-Specifications-V5.0-1.pdf.<br> - More information about the SMART Infrastructure Facility is available here: https://www.uow.edu.au/smart/.</p>
Oil and Gas Infrastructure Mapping (OGIM) database
<p>The Oil and Gas Infrastructure Mapping (OGIM) database is a global, spatially explicit, and granular dataset of oil and gas infrastructure. It is developed by Environmental Defense Fund (EDF) (<a href="https://www.edf.org/">www.edf.org</a>) and MethaneSAT, LLC (<a href="https://www.methanesat.org/">www.methanesat.org</a>), a wholly owned subsidiary of EDF. The OGIM database helps fill a crucial geospatial data need, by supporting the quantification and source characterization of oil and gas methane emissions. The database is developed via acquisition, analysis, curation, integration, and quality-assurance (performed at EDF) of publicly available geospatial data sources. These oil and gas facility datasets are reported by governments, industry, academics, and other non-government entities.</p> <p>OGIM is a collection of data tables within a GeoPackage. Each data table within the GeoPackage includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions, including: oil and gas production wells, offshore production platforms, natural gas compressor stations, oil and natural gas processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. OGIM v2.7 includes approximately 6.7 million features, including 4.5 million point locations of oil and gas wells and over 1.2 million kilometers of oil and gas pipelines.</p> <p>Please see the PDF document in the “Files” section of this page for more information about this version, including attribute column definitions, key changes since the previous version, and more. Full details on database development and related analytics can be found in the following Earth System Science Data (ESSD) journal paper. Please cite this paper when using any version of the database:</p> <p><span>Omara, M., Gautam, R., O'Brien, M., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D., Chulakadabba, A., Miller, C., Franklin, J., Wofsy, S., and Hamburg, S.: Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution, Earth Syst. Sci. Data Discuss., </span><a href="https://doi.org/10.5194/essd-15-3761-2023"><span>https://doi.org/10.5194/essd-15-3761-2023</span></a><span>, 2023.</span></p> <p>Important note: While the results section of this manuscript is specific to v1 of the OGIM, the methods described therein are the same methods used to develop and update v2.7. Additionally, while we describe our data sources in detail in the manuscript above, and include maps of all acquired datasets, this open-access version of the OGIM database does not include the locations of about 300 natural gas compressor stations in Russia. Future updates may include these locations when appropriate permissions to make them publicly accessible are obtained. </p> <p>OGIM v2.7 is based on public-domain datasets reported in February 2025 or prior. Each record in OGIM indicates a date (SRC_DATE) when the original source of the record was published or last updated. Some records may contain out-of-date information, for example, if a facility’s status has changed since we last visited a data source. We anticipate updating the OGIM database on a regular cadence and are continually including new public domain datasets as they become available.</p> <p>---</p> <p>Point of Contact at Environmental Defense Fund and MethaneSAT, LLC: Madeleine O’Brien (maobrien@methanesat.org) and Mark Omara (momara@edf.org).</p>
FolkArtiNet: Folk music groups: their artistic practice and infrastructural needs in the COVID-19 era and beyond - survey data
<p> The online survey was one of three methods used for collecting information about the infrastructural needs of the folk music groups. It included a series of questions about different areas of artistic activity, such as working on repertoire, collaboration among group members, storage and sharing of data, and organization of artistic events. The survey data includes all questions and answers in csv format.</p>
Raw data for Infrastructure and Awareness Landscape Analysis in Latin America
<p>Persistent Identifiers (PIDs), such as Digital Object Identifiers (DOIs), are foundational to connecting and enhancing the visibility of Latin American research within a global framework. Although the region is rich in diverse and impactful research, many repositories remain only partially integrated into international registries and aggregators, limiting their discoverability and reach. The adoption of PIDs across repositories in Latin America varies widely, underscoring the need for increased awareness about the role of open PIDs in advancing research accessibility and visibility.</p> <p>This dataset offers a comprehensive overview of the current landscape of repositories, publishing systems, and Open Science policies across Latin America, shedding light on the institutional and national efforts that support an open and inclusive research infrastructure. It highlights the importance of collaboration among researchers, institutions, funders, librarians, and government agencies in fostering Open Science practices and encouraging strategic PID adoption. By expanding these open practices and strengthening PID adoption, Latin American research can achieve greater integration and impact within the global research ecosystem.</p> <p>You can read the full report titled "Infrastructure and Awareness Landscape Analysis in Latin America" at <a href="https://doi.org/10.5281/zenodo.14010858" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14010858</a> </p>
Stocktaking GO FAIR Discovery IN - Use cases, infrastructure
<p>In order to build a better ecosystem for data discovery tools the Data Discovery Implementation Group of GO Fair (https://www.go-fair.org/implementation-networks/overview/discovery) collected use cases between 2019 and 2020 from a variety of sources. We also detail the ‘Actors’ for these use cases and the ‘Source’ providing links, whenever possible. Since we found over a hundred individual use cases, we decided to cluster them to provide a better overview. The clustering, as well as the results of a small survey among data infrastructure specialists to find how they rate the importance of the clusters are detailed in the documentation to this dataset, a draft of which can currently be found <a href="https://docs.google.com/document/d/1sq78eCFYgmWcMFYcbNonA2KrkUO1qdGr7d49tHuRcRM/edit?usp=sharing">here</a>. The code and data to produce the figures in the documentation are available as R code in the GO_FAIR_Discovery_Use_case-master.zip file. The use cases themselves are available as Excel sheet and csv. </p>
Data from a three-phase Delphi study used to investigate Knowledge Infrastructure for Research Data in Norway, KIRDN_Data; PhD project
<p>A modified three-phase Delphi study was used to explore the knowledge infrastructure for research data in Norway. The study includes different stakeholders involved in research data sharing. A Delphi study is characterised by the use of an expert panel to elicit opinions on a shared reality from different perspectives. Data collection is performed in several rounds with the intention of reaching consensus or solving an issue. </p> <p>A group of 24 experts took part in the study. The group consisted of policy-makers, representatives of national service providers, and researchers and research support staff from four Norwegian universities. The participants were invited based on their involvement in the development of policies, infrastructure or data-related research support. The research support staff were recruited to include representatives from different research support services at the universities, including libraries, research offices and IT departments. While the researchers were selected from based on their receival of EU funding with requirements of data management plans.</p> <p>Data were collected in three phases. The first phase, the ‘exploration phase’, was conducted using open interviews lasting approximately one hour in January/February 2018. The purpose of this phase was to obtain an initial overview of the panel members opinions’ on issues regarding research data management.</p> <p>In the second phase, the ‘evaluation phase’, conducted in August/September 2018, participants answered a survey containing nine questions on topics such as data stewardship, DMPs, ethical aspects of data sharing and core functions in a research data infrastructure. The survey was designed to further explore issues and tensions uncovered in the first interviews. Several of the questions were formulated as statements that the participants were asked to agree or disagree upon. </p> <p>The third, ‘concluding phase’ was conducted using interviews in March/April 2019. These interviews lasted approximately 30 minutes and were based on results from the questionnaire as well as the first interview. Participants were asked whether they had thoughts on the preliminary findings of the study. </p> <p>Based on requests from some of the participants, the questions were sent to all participants prior to the data collection, in all three phases. The participants were also sent the transcripts from the interviews and were asked for permission to share the complete material or parts of the data material to which they contributed. </p>
Gaussian Process Model and Sensor Placement for Detroit Green Infrastructure: Datasets and Code
<ol> <li><strong>code.zip: </strong>Zip folder containing a folder titled "code" which holds: <ol> <li>csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features;</li> <li>csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period;</li> <li>csv file titled "newGIsites_AllData.csv" which contain the other 130 GI sites in Detroit and their design and physiographic features;</li> <li>csv file titled "Detroit_Data_MeanDesignFeatures.csv" which contain the design and physiographic features for all of Detroit;</li> <li>Jupyter notebook titled "GI_GP_SensorPlacement.ipynb" which provides the code for training the GP models and displaying the sensor placement results;</li> <li>a folder titled "MATLAB" which contains the following: <ol> <li>folder titled "SFO" which contains the SFO toolbox for the sensor placement work</li> <li>file titled "sensor_placement.mlx" that contains the code for the sensor placement work</li> <li>several .mat files created in Python for importing into Matlab for the sensor placement work: "constants_sigma.mat", "constants_coords.mat", "GInew_sigma.mat", "GInew_coords.mat", and "R1_sensor.mat" through "R6_sensor.mat"</li> <li>several .mat files created in Matalb for importing into Python for visualizing the results: "MI_DETselectedGI.mat" and "DETselectedGI.mat"</li> </ol> </li> </ol> </li> </ol>
Data on public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki
<p>A public participatory GIS -survey dataset detailing public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki, Finland.</p>
Aerial Power Infrastructure Detection Dataset
<p><strong>Aerial Power Infrastructure Detection Dataset:</strong></p> <p>Autonomous inspection of power networks with Unmanned Aerial Vehicles (UAVs) has recently gained significant scientific attention mainly due to rapid advances in UAV technology. In this context, the UAV must autonomously navigate across the network acquiring high resolution data in a safe and fast manner, which poses challenges especially in cases where the location of infrastructure components, i.e. poles, is not known. The Aerial Power Infrastructure Detection Dataset is constructed aiming to create a repository available to the research community, which can be used for training online detection models, which in turn facilitate UAV localization across the network.</p> <p>Specifically, this dataset is used for implementing the “Pole Detection” process of ICARUS toolkit, which is a vision-based UAV monitoring platform for autonomous inspection of Medium Voltage (MV) power distribution network. Specifically, the UAV is supplied with the best-known coordinates of poles and navigates to the designated location searching for the pole. As soon as the pole is detected, using an one-class detection model, the UAV applies a control procedure to correct its position by aligning directly above the pole. For the training, we used samples containing the T-shaped bar of the pole with the insulators and the top of pole [1].</p> <p>The dataset consists of top-view images of MV poles from various locations across Cyprus. Images were captured across different seasons to account for a variety of background conditions, such as grass or ground, as well as at different heights to account for variations in the UAV’s height during inspection. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (t-bars):</p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>T-Bars</td> </tr> <tr> <td>Training</td> <td>10760</td> <td>10012</td> </tr> <tr> <td>Validation</td> <td>2587</td> <td>2370</td> </tr> <tr> <td>Testing</td> <td>1572</td> <td>1449</td> </tr> </tbody> </table> <p> </p> <p>Reference:</p> <p>[1] A. Savva et al., "ICARUS: Automatic Autonomous Power Infrastructure Inspection with UAVs," 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 918-926, doi: 10.1109/ICUAS51884.2021.9476742.</p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following :</strong></p> <blockquote> <p>Antonis Savva, Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2023). Aerial Power Infrastructure Detection Dataset (2.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7781388</p> </blockquote>
UKRI Digital Research Infrastructure Mapping Survey Dataset (for Net Zero Scoping Project)
<p>This dataset was generated as an output for the DRI Mapping exercise carried out during the UKRI Net Zero Digital Research Infrastructure (DRI) Scoping Project undertaken from 2021-2023. The "README.md" provides more information about the dataset and how to use it.</p> <p>The report associated with this dataset is available at:</p> <p>https://doi.org/10.5281/zenodo.7805987</p>
Public Transit Infrastructure and Heat Perceptions in Hot and Dry Climates (June-July, 2018; Phoenix, Arizona, USA)
Increasing the use of public transit is an important sustainability goal targeted by many cities worldwide. However, cities in hot and warming climates risk to compromise residents’ health and thermal comfort by incentivizing public transit use and, thus, subjecting them to prolonged heat exposure. This dataset contains data collected during a study on the relationships between public transit infrastructures, microclimate and heat perceptions in the hot and dry city of Phoenix, Arizona. A field campaign at six Phoenix bus stops was held between June 6 and July 27, 2018. Filed campaign consisted of surveying bus riders at bus stops and measuring microclimate variables at sun exposed and shaded locations at bus stops. Standard, advertising and art bus stop types along an arterial Phoenix road in South Mountain Village neighborhood were sampled. Standard and advertising bus stop shelters were metal with no landscaping, art stops had a larger polycarbonate canopy, integrated artwork, trees and landscaping features. Eighty-three participants filled out the survey, 241 microclimate measurements and 1003 surface temperatures at bus stops were taken. Data were collected at three intervals: 7:00-9:00am, 12:00-2:00pm, and 3:00-5:00pm. Differences between sun and shade, as well as heat perceptions were analyzed using statistical methods. The research team has found that certain infrastructure types are more effective in reducing particular microclimate variables, for instance, trees were most effective in reducing air temperature by as much as 1.3°C on average, and shade from vertical advertising sign was most effective in reducing mean radiant temperature by an average of 11°C. Many surface temperatures of sun exposed materials sampled at bus stops exceeded skin burn thresholds. Study participants perceived stops with improved infrastructure and landscaping as slightly cooler. Data collected in this study gives a glimpse of current microclimate conditions at Phoenix bus stops
The LEXIS Distributed Data Infrastructure: Demonstrator System
<p><strong>The LEXIS Distributed Data Infrastructure: Demonstrator System</strong><br> by: LEXIS Project and Work Package 3 Team</p> <p>Project Lead: IT4Innovations National Supercomputing Centre (Czech Republic)<br> Work Package 3 Lead: Leibniz Supercomputing Centre (LRZ, Garching b. M., Germany)</p> <p>The enormous amounts of data generated in modern industry, business and science pose a significant challenge to those extracting actionable intelligence from data, using various filtering and analysis techniques. In this "Big Data" setting, the LEXIS project (Large-scale EXecution for Industry & Society) provides a user-friendly portal and platform for optimised execution of mixed Cloud-HPC (HPC: High-Performance Computing) workflows. The system will rely on advanced, distributed orchestration solutions (Bull Ystia Orchestrator, based on TOSCA and Alien4Cloud technologies), the High-End Application Execution Middleware HEAppE, and new hardware capabilities for maximizing efficiency in data processing, analysis and transfer (e.g. Burst Buffers with GPU- and FPGA-based data reprocessing).</p> <p>LEXIS handles computation tasks and data from three Pilots, based on representative and demanding HPC/Cloud-Computing use cases in Industry and Science: i) compute-/data-intensive and time-consuming simulations of turbo-machinery and gearbox systems in Aeronautics, ii) Earthquake and Tsunami simulations which are accelerated to enable accurate real-time analysis, and iii) Weather and Climate HPC simulations where massive amounts of in situ data are assimilated to improve forecasts.</p> <p>Here, we introduce and show a demonstrator of the LEXIS Distributed Data Infrastructure (DDI), the core data back-end of the LEXIS project. The DDI provides a unified "File Space" for LEXIS, across the participating sites and computing centres. Based on iRODS (irods.org) and EUDAT-B2SAFE (eudat.eu), it will ensure reliable and efficient access to large datasets in the Terabyte range and beyond. We have prepared virtual machine templates for the LEXIS Cloud resources which implement a Demonstrator of the LEXIS DDI. The system, once instantiated, consists of two iRODS-iCAT (provider) servers, representing the LRZ and IT4I iRODS zones, and of two client machines for access to the distributed data management system. Thus, interested colleagues can explore the possibilities offered by this system on an "own" demonstrator instance. Please contact us at info[at]lexis-project.eu if you are interested.</p>
The commitment to global sea level rise over the next 500 years: exploring the threat of the Antarctic Ice Sheet to coastal infrastructure
<p>Within Australia alone, more than A$226 billion of coastal infrastructure is vulnerable to the anticipated rise in sea level by the end of the century. The IPCC Fifth Assessment Report concludes that the likely increase in global mean sea level during the 21st century ranges from 26-55 centimetres (under the low-end RCP2.6 climate scenario) to 45-82 centimetres (under the high-end RCP8.5 climate scenario). However, these projections do not take into account the potential for collapse of the marine-based sectors of the Antarctic Ice Sheet.</p> <p>Recent evidence has indicated that the IPCC projections may be under-estimates, with sea level increases of up to 2.5 metres possible by the end of the 21st century. Modelling studies have also demonstrated the potential for the Antarctic Ice Sheet to undergo irreversible collapse during the coming centuries, leading to dramatic increases in global sea level on time scales relevant to critical coastal infrastructure such as refineries and airports. The most extreme prediction is that Antarctica could contribute 15.65±2.00 metres to global sea level by the year 2500.</p> <p>Here, we combine climate modelling and ice sheet modelling to explore the evolution of the Antarctic Ice Sheet over the next 500 years under a range of climate scenarios. We run the models many times to take into account gaps in our understanding of ice sheet dynamics. This allows us to generate robust projections of the Antarctic contribution to global sea level from the present to the year 2500, complete with quantified confidence intervals. We conclude that the sea level contribution during the 21st century will be modest, consistent with the IPCC Fifth Assessment Report, but that melting of the Antarctic Ice Sheet will accelerate thereafter. By the year 2500, we predict that the Antarctic contribution to global sea level will be at least 5 metres.</p>
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </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.