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3,018 results for “aire”
Civilian Casualties of WW2 air-raids on Kiel
<p>These data are the contents of the document 55177 in the City Archives of Kiel "Todesopfer durch Luftangriffe". The document lists civilian casualties of the bombing on Kiel during WW2 .</p> <p>The document was brought in electronic form by volunteers using the data-entry system (DES) of the Verein für Computergenealogie (=society for computer Genealogy) . As part of a master's thesis at the Institute of computer science of the University of Kiel coordinates were determined for addresses.</p> <p>The following fields are included in the data:</p> <ul> <li>page</li> <li>family name</li> <li>given name(s) </li> <li>day of death </li> <li>remarks </li> <li>day of birth</li> <li>occupation </li> <li>place of death</li> <li>place of residence </li> </ul> <p>The following details are not in the source. They were either calculated (age), entered manually (gender) or are the result of geocoding (coordinates) and subsequent matching with district boundaries.</p> <ul> <li>age </li> <li>gender </li> <li>place of residence longitude </li> <li>place of residence latitude </li> <li>place of death longitude</li> <li>place of death latitude </li> <li>place of residence district </li> <li>place of death district </li> </ul>
Passivity-Based Control for a Micro Air Vehicle using Unit Quaternions
<p>In this paper the development and practical implementation of a Passivity-Based<br> Control (PBC) algorithm to stabilize an Unmanned Aerial Vehicle (UAV) described with unit<br> quaternions are presented. First, a mathematical model based on Euler-Lagrange formulation<br> using a logarithmic mapping in the quaternion space is introduced. Then, a new methodology:<br> a quaternion-passivity-based control is derived, which does not compute excessive and complex Partial Differential Equations (PDEs) for synthesizing the control law, making a significant advantage in comparison with other methodologies. Therefore, the control design to a system as the quad-rotor is easily solved by the proposed methodology. Another advantage is the possibility to stabilize quad-rotor full dynamics which may not be possible with classical PBC techniques. Experimental results and numerical simulations to validate our proposed scheme are presented.</p>
Oxidation of thin films at the air-water interface of atmospheric aerosol
<p>X-ray reflectivity data (reflectivity vs Q) for the oxidation of insoluble organic material at the air-water interface. The organic material was extracted from atmospheric aerosol and sea water samples. The organic material was reacted with gas-phase ozone and aqueous phase hydroxyl radicals. The reflectivity data was collected at the Diamond Light source on I07 in May 2013 (SI8744) and April 2014 (SI9632) funded by STFC and NERC. The data supports a publication in Atmospheric Environment entitled “Are organic films from atmospheric aerosol and sea water inert to oxidation by ozone at the air-water interface?"</p>
Deep Learning with Satellite Images Enables High-Resolution Income Estimation: a Case Study of Buenos Aires
<p>This repository contains the datasets required for replicating the results in Abbate et al (forthcoming). The datasets also include per capita income estimates at a 50x50 meter resolution for the years 2013, 2018, and 2022, using satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census+survey data. The model, based on the EfficientnetV2 architecture, achieved high accuracy in predicting household incomes (R2=0.878), surpassing existing methods in spatial resolution and performance. </p> <p>Inside the Replication Package folder, the user can replicate the main results from the paper. This includes:</p> <ol> <li> <p><strong>Small Area Estimation (SAE) Replication:</strong></p> <ul> <li> <p><strong>Argentina Household Survey Data (EPH):</strong> Processed microdata for 2010, 2013, 2018, and 2022 (ARG_*_EPHC-S2_*.dta).</p> </li> <li> <p><strong>Argentina Census Microdata:</strong> Raw 2010 census microdata (censo2010_fullraw_p.dta).</p> </li> <li> <p><strong>Census Tract Map:</strong> Shapefile of 2010 census tracts (radios_eph_with_link.shp).</p> </li> <li> <p><strong>SAE Output:</strong> The final small_area_estimates.parquet file containing census tract-level population and estimated income, which serves as labels for the CNN model.</p> </li> </ul> </li> <li> <p><strong>CNN-based Income Prediction Replication (Paper Results):</strong></p> <ul> <li> <p><strong>CNN Model Income Predictions:</strong> Gridded 50x50m income estimates for Buenos Aires for 2013, 2018, and 2022 (income_estimates_*.shp).</p> </li> <li> <p><strong>Normalization Scalars:</strong> A CSV file (scalars_ln_pred_inc_mean_trimTrue.csv) to convert the model's log-scale outputs into real income values (2010 PPP-adjusted Argentinian pesos).</p> </li> <li> <p><strong>World Settlement Footprint (WSF):</strong> Satellite-based data (WSF2015_v2_-60_-36.tif) used to mask predictions in uninhabited areas.</p> </li> </ul> </li> </ol> <p>Key prediction datasets are published in shapefile format, while input data for SAE and other auxiliary files are in formats like .dta, .parquet, .csv, and .tif.</p> <p>Results can be replicated by connecting these datasets with the scripts available at the GitHub repo linked below.</p> <p>For researchers who wish to replicate the full analysis pipeline starting from the original source imagery, the data must be acquired commercially. The proprietary Pleiades and Pleiades NEO satellite imagery is owned by Airbus and can be purchased through their data portal: https://space-solutions.airbus.com/imagery/. To facilitate this process, we provide the unique product identifiers for each scene used in this study. These identifiers can be used to query the Airbus archive and purchase the exact scenes.</p> <ul> <li><strong>Pléiades</strong>: for 2013 imagery the IDs are DS_PHR1A_201302051411520_FR1_PX_W059S35_0807_03124, DS_PHR1A_201302071357305_FR1_PX_W059S35_0410_06105 and DS_PHR1A_201302071357509_FR1_PX_W059S35_0609_05426, and for 2018, DS_PHR1A_201803251356358_FR1_PX_W059S35_0909_03875, DS_PHR1A_201808021356574_FR1_PX_W059S35_0509_06938 and DS_PHR1A_201808021357186_FR1_PX_W059S35_0706_06104.</li> <li><strong>Pleiades NEO</strong>: for 2022 imagery the IDs used are 000047717_1_22_STD_A, 000047717_1_24_STD_A, 000047717_1_25_STD_A, 000047717_1_26_STD_A, 000058605_1_3_STD_A, 000058605_1_4_STD_A, 000058605_1_7_STD_A, and 000058608_1_2_STD_A.</li> </ul> <p><strong>Important Usage Note:</strong> Since the predictions for each 50x50m cell individually present some random variation, we recommend that the results are used by averaging out the estimations for each area of interest (e.g., municipalities, neighborhoods, sections, or census tracts) and not at an individual cell level. As detailed throughout the paper, the aggregated results, even in small areas such as census tracts, predict household incomes with precision.</p> <p>Furthermore, inside this repository, it is possible to access and use the model’s trained parameters to make predictions about different satellite images.</p> <p>Data can be visualized by accessing: <a href="https://ingresoamba.netlify.app">https://ingresoamba.netlify.app</a></p> <p> </p> <p> </p>
Modeled and observed river water temperature and discharge in the paper "Riverine heat waves on the rise, outpacing air heat waves"
<div>The observed and modeled data – mean daily water temperature (WT, °C) and mean daily discharge (Q,<em> </em>ft<sup>3</sup>/s) modeled by an LSTM model (averaged over 5 model runs) – for 1471 sites over 1980-2022 can be found here. Out of these 1471 sites, 1276 sites had good model performance and were used to identify and analyse air and riverine heat waves (RHW) in the paper "Riverine heat waves on the rise, outpacing air heat waves". </div> <div> <div> </div> </div>
Contrasting Activation Characteristics of Biomass Burning and Fossil Fuel Combustion Aerosols in Fogs and Clouds: Implications for Regional Air Quality and Climate
<p>The key 'jul' in data use 2021-01-01 as the referece day, for example, 2021-01-02 12:00:00 corresponding to jul of 2.5. </p>
Surface Ozone, NO2, and PM2.5 Concentrations Estimated by the Deep Learning model (Air Transformer) based on Satellite data.
<p>Surface ozone, NO2, and PM2.5 concentrations Estimated by the deep learning model (Air Transformer) based on massive ground-level monitoring, satellite observations, meteorological conditions, dynamic industrial emissions, and other ancillary data from May 2018 to June 2021.</p>
Characterizing ambient air quality and oil and gas air pollution emissions in Broomfield County, CO
<p>Unconventional oil and natural gas development (UOGD) has expanded rapidly across the United States in recent decades and raised concerns about associated air quality impacts. While significant effort has been made to quantify methane emissions, relatively few observations have been made of Volatile Organic Compounds (VOCs), especially during drilling and completion of new wells. Extensive air monitoring during development of several large, multi-well pads in Broomfield, Colorado, in the Denver-Julesburg Basin, provides a novel opportunity to examine changes in local air toxics and other VOC concentrations during well drilling and completions and production.</p>
Experimental Data of Air Heat Engine Characteristics
<p>Air heat engine characteristic experiment is an experimental method used to study the working principle, performance, and efficiency of air heat engines. An air heat engine is a device that uses thermal energy to compress and expand gases, thereby driving mechanical motion. During the experimental process, a small piston air compressor is usually used as the experimental object.</p>
Air accidents
<p>Air accidents simulated data</p>
Application of artificial neural network to forecast indoor air temperature in a building with artificial ventilation: impact of early stopping.
<p>Indoor air temperature prediction can facilitate energy-saving actions without compromising the indoor thermal comfort of occupants. The aim of this study was to analyse the performance of various artificial neural networks with a view to proposing an optimal approach for predicting the indoor temperature of a tertiary building with artificial ventilation. The MLP, CNN, LSTM models and the CNN-LSTM combination (long short-term memory network) were used and coupled with the optimisation algorithms (Adam, SGD) and the independent hyper-parameters early stopping and dropout. The parameters used are outdoor ambient temperature, outdoor relative humidity, indoor relative humidity, wet bulb temperature, black globe temperature and mean radiant temperature. The data is collected in an artificially ventilated building in Yaoundé, Cameroon. A numerical code was developed in Python to run the simulations. In order to study the impact of the parameters on the prediction, two scenarios were distinguished in this work: (1) all the parameters are input to the network, (2) only the parameters whose absolute value of the correlation coefficient was greater than or equal to 0.5 were used. The impact of early stopping is assessed by distinguishing two case studies: the first without early stopping, the second with early stopping. The results showed that without early stopping, the MLP, CNN, LSTM and CNN-LSTM networks are adequate for predicting the temperature with the second scenario, mainly with both the SGD and Adam algorithms, and CNN-LSTM is the most appropriate model because the MSE and MAE values obtained in this case were closer to 0. With early stopping, the learning time is reduced and the learning curves are improved; the models optimised better with the SGD algorithm in general, but the best neural network model was obtained with the Adam algorithm and the LSTM network for the performances MSE=0.0005, MAE=0.0130 with the second scenario.</p><p><strong>Keywords: </strong>prediction, indoor temperature, artificial neural network, early stopping, artificially ventilated building.</p>
The winter subset of the Saildrone 2021-2022 Mission to the Gulf Stream used for the publication "The importance of contemporaneous measurements for regional air-sea CO2 flux estimates"
<p>Data from the Saildrone 2021-2022 observational mission to the Gulf Stream. These data are published to accompany the publication "The importance of contemporaneous measurements for regional air-sea CO<sub>2</sub> flux estimates." Included in this dataset are the primary and processed variables used throughout the paper. The data associated with each saildrone is named by the drone number. Additionally, included in the structure for each drone are the gas transfer velocities for each scenario, MBL atmospheric CO2 interpolated to the time and location of the drone, and ERA-5 wind speed, sea level pressure, significant wave height, and drag coefficient interpolated to the time and location of the drone. These variables are used to calculate CO<sub>2</sub> fluxes for each scenario and are named as follows: "F" + gas transfer velocity equation used (DM18 or W14) + drone ID + scenario. Scenario A-D correspond to those outlined in the paper. Scenarios E and F correspond to the calculation of air-sea fluxes using all saildrone observed variables except for atmospheric CO<sub>2</sub> (from MBL product) and significant wave height (from ERA-5), respectively. </p>
Air-sea CO2 equibration timescales for Bach et al. 'Testing the climate intervention potential of ocean afforestation using the Great Atlantic Sargassum Belt''
<p>Data for the timescales of CO2 equilibration in the Great Atlantic <i>Sargassum</i> Belt from Fig. 3a and 3c of Bach et al. 2021 'Testing the climate intervention potential of ocean afforestation using the Great Atlantic <i>Sargassum</i> Belt'. </p><p>The data includes two files. The first file, tco2tres.nc, contains the 2-dimensional mapped 1 degree latitude x l degree longitude annual mean air-sea CO2 equilibration timescale<i> (T</i>co2<i>) </i>in months (from Figure 3a) and the annual mean ratio of the CO2 equilibration timescale to the surface residence time (<i>T</i>co2<i>/T</i>res) (from Figure 3c). The second file, tco2_seasonal.nc, contains the seasonal mean CO2 equilibration timescales, from Supplementary Figure 5.</p>
Interacting impacts of hydrological changes and air temperature warming on lake temperatures highlight the potential for adaptive management: Model output
<p>This archive includes the output from the General Ocean Turbulence Model for a series of simulations that used differing model drivers (inflow discharge, Q, and air temperature, T). The Experiment_output.zip contains text files generated from the GOTM workflow containing modelled water temperatures and the Mod_z.txt are the corresponding depths for these temperatures. </p><p>The file name corresponds to the change made to the driving data from baseline (unchanged conditions), a combination of air temperature <i>increase </i>and flow percentage change e.g. Mod_temp_T_2_Q_1.5 refers to an air temperature increase of 2 degrees Celsius and a flow increase of 50% and a Mod_temp_T_3.5_Q_0.7 refers to an air temperature increase of 3.5 degrees Celsius and a flow decrease of 30%.</p>
Air phyto-cleaning by an urban meadow – Filling the winter gap - DATA
<p>Database of article: Air phyto-cleaning by an urban meadow – Filling the winter gap.</p>
Open-air museum ''Vēveri'' pt3
"VĒVERI" RURAL EXHIBITION OF THE ETHNOGRAPHIC OPEN AIR MUSEUM OF LATVIA IN VECPIEBALGA The history of "Vēveri" in Vecpiebalga dates as far back as 400 years. It is mentioned for the first time in Swedish accountant documents in 1601 as one household and at the end of the 17th century it was marked on the map. http://brivdabasmuzejs.lv/en/museum/museum-veveri/ Source: Objaverse 1.0 / Sketchfab
Monmouth Victorian air brick
This is a 3D scan of a late Victorian air brick in the walls of St Mary's Priory Church in Monmouth, South Wales. It's one of many in the walls, which appear to be carved from a soft stone that has suffered quite a lot over the years. While the scanned air brick was also quite eroded, it was chosen as one that had suffered the least and so can be considered more representative of the original design. If you like this model please read more about it on the blog page below: http://parth3d.co.uk/monmouth-victorian-air-brick Please note that this model is provided without any license for commercial use. It is intended simply as a historical model you can have the fun of printing yourself. And of course it is intended to be a motivation for you to visit Monmouth yourself. Source: Objaverse 1.0 / Sketchfab
Air-Filtration unit from a German bunker WWII
Many German bunkers from WWII had these anti-gas air filtration systems installed that had to manually be operated to pump outside air through an air filter in to the bunker. The round pressure-valves in the walls can be found in most bunkers. These are called "uberdruckventil" and that basically means over-pressure-valve and would automatically let air out in case of overpressure inside the bunker. Source: Objaverse 1.0 / Sketchfab
De l'aire a l'aire. Museu d'Art Jaume Morera
**CAT** Escultura d'alumini i metall sobre base de fusta de l'escultor Leandre Cristòfol, creada el 1933. Adquirida per l'Ajuntament de Lleida el 1982, va integrar-se a les col·leccions del Museu d'Art Jaume Morera a partir del 2005. **ES** Escultura de aluminio y metal sobre base madera del escultor Leandre Cristòfol, creada en 1933. Adquirida por el Ayuntamiento de Lleida en 1982, se integró en las colecciones del Museo de Arte Jaume Morera a partir de 2005. **EN** Aluminium and metal sculpture on a wooden base created by sculptor Leandre Cristòfol in 1933. Acquired by the Lleida City Council in 1982, it was added to the collection of the Jaume Morera Art Museum in 2005. Número de registre: MALL 1967. [Adreça web >](https://mmorera.paeria.cat) Model: Aleix Barberà. Text: Àlex Rebollo. Source: Objaverse 1.0 / Sketchfab
Polish Air Force in the West officers's cap
ID no.: MLP 755/1 Museum: Polish Aviation Museum http://muzea.malopolska.pl/en/obiekty/-/a/26873/1124080 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
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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.