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141 results for “installation”
Daily indices of heat at select U.S. military installations (1990 - 2019)
<p>R data files for annual indices of heat of 30 Continental U.S. (CONUS) U.S. military installations from 1990-2019.</p> <p>Daily indices were derived from hourly meteorological estimates from the North American Land Data Assimilation System 2 (NLDAS-2) forcing dataset served as the primary source of weather and atmospheric data. We extracted parameters from NLDAS grid cells containing the centroid of each installation based on shapefiles from the Department of Defense (DoD) Military Installations, Ranges, and Training Areas (MIRTA) Dataset. We calculated relative humidity from specific humidity, temperature, and atmospheric pressure; heat index (HI) from temperature and relative humidity based on a US National Weather Service algorithm; and outdoor WBGT from air temperature, relative humidity, solar irradiance, barometric pressure, and wind speed using the method of Liljegren <em>et al.</em></p> <p>`daily_indices.Rmd` (328402 observations of 18 variables) contains indices for daily maximum, mean, minimum, and 95th percentile values of temperature (deg F), heat index (deg F), and WBGT (deg C) with urban and non-urban inputs to the Liljegren model.</p> <p>`morning_indices.Rmd` (328364 observations of 5 variables) contains indices for daily 0600 hour (local time) values of temperature (deg F), heat index (deg F), and WBGT (deg C, urban model). </p>
Square Street Dataset in Unreal with AirSim installed
<p>Thanks to Unreal Engine (UE) Editor, we were able to create custom scenes to test our approach. We created a street in the shape of a square, having 47 buildings spread over 0.16 square kilometers. The environment is feature-rich and, therefore, can be easily used for benchmarking monocular vision-based perception and planning pipelines. The whole environment is modular and can be easily expanded or configured to meet custom requirements.</p> <p> </p> <p>How to use:</p> <p>1. Extract the zip</p> <p>2. Open .uproject file in unreal</p> <p>3. Open SquareStreet Map</p> <p>4. Press Play to start AirSim</p> <p> </p> <p>Unreal Version: 4.25</p> <p>OS: Linux</p>
Annual indices of heat and humidity, U.S. Army installations, 1990-2018
<p>R data files for annual indices of heat of 25 Continental U.S. (CONUS) U.S. Army installations from 1990-2018 in list and long formats.</p> <p>Annual indices were derived from hourly meteorological estimates from the North American Land Data Assimilation System 2 (NLDAS-2) forcing dataset served as the primary source of weather and atmospheric data. We selected NLDAS grid cells containing the centroid of each installation based on shapefiles from the Department of Defense (DoD) Military Installations, Ranges, and Training Areas (MIRTA) Dataset. We calculated relative humidity from specific humidity, temperature, and atmospheric pressure; heat index (HI) from temperature and relative humidity based on a US National Weather Service algorithm; and outdoor WBGT from air temperature, relative humidity, solar irradiance, barometric pressure, and wind speed using the method of Liljegren <em>et al.</em></p>
Full inventory of ten permanent plots installed in pockets of different tree functional types along the Moni River transects (Yangambi, Democratic Republic of Congo)
<p>Most of the tropical forests of Central Africa are characterised by a remarkable abundance of light-demanding canopy species. A popular hypothesis is that these forests are still recovering from the intense slash-and-burn farming activities that ended abruptly in the 19th century with the arrival of the colonists. Today, it is assumed that the zones occupied by crop fields until the 19th century are covered by forests dominated by light-demanding species. However, this hypothesis of human disturbance has not yet been sufficiently tested using spatial distribution. So, using the 'Kernel Density Estimation' (KDE) tool in the SAGA GIS software, we mapped the density distribution of light-demanding species, subdivided into 3 tree functional types, along transects in the Moni river catchment. We also produced a similar map for a particular shade-tolerant species, 'Gilbertiodendron dewevrei'. The species were then divided into the following groups, known as 'functional tree types': LLP=Long-Lived Pioneer, NPLD=Non-Pioneer Light Demanding, SLP=Short-Lived Pioneer, and STS=Shade-Tolerant Species. At the end of this analysis, a density distribution map of the species of each tree functional type was produced. This map highlights the pockets (zones with a high density relative to the study site average) of tree functional types. For each type of pocket, we selected the pockets with a high density of trees of the group concerned and which were not on the edge between the forest and village crops or fallow land. This is how the location of the permanent plots was determined. Next, we installed a total of ten full forest inventory plots (1 ha each) inside and outside the pockets located by the KDE analysis along the Moni River transects. More specifically, we installed one plot in a pocket of short-lived pioneers (SLP-01), three plots in pockets of long-lived pioneers (LLP-01 to -03), two plots in pockets of NPLD (NPLD-01 and -02), two plots in pockets of the shade-tolerant species Gilbertiodendron dewevrei (GIL-01 and -02) and finally two plots were located in a mixed old-growth forest outside the pockets (MIX-01 and -02). These plots were established (1) for long-term monitoring of biodiversity and forest dynamics; and (2) to see if there is a difference in terms of species composition and abundance of light demanders between the forest inside the pockets and that outside the pockets.</p>
Sverm-Resonans installation at Ultima 2017
<p>Resonating guitars controlled by standstill. An interactive art installation shown at the Ultima Contemporary Music Festival 2017, at Sentralen, Oslo, Norway.</p> <p>Sverm-Resonans is focused on the meeting point between digital and acoustic sound making. Each of the guitars is equipped with a Bela micro-computer, which produces electronic sound through an actuator placed on the back of the guitars. There are no external speakers, all the sound generation is coming the vibration of the acoustic guitar. Each of the guitars produce a slowly pulsing sound - based on an additive synthesis with a slight randomness on the sine tones - that breathes and gives life to the soundscape. The guitars are also equipped with an infrared sensor that detects the presence of a person standing in front of the guitar, and which inversely controls the amplitude of a pulsating noise signal. That is, the longer you stand still, the more sound you will get.</p> <p>Sverm-Resonans is an installation by Alexander Refsum Jensenius, Kari Anne Vadstensvik Bjerkestrand, Victoria Johnson, Victor Gonzalez Sanchez, Agata Zelechowska, and Charles Martin.</p> <p>The installation is the result of the ongoing art/science research projects Sverm, MICRO and AAAI, three projects which in different ways explore human micromotion and musical microsound. Supported by University of Oslo, Research Council of Norway, Arts Council Norway, The Fund for Performing Artists, The Audio and Visual Fund, and The Nordic Culture Fund.</p> <p>Filming: Audun Bjerknes and David Buverud<br> Editing: Audun Bjerknes</p>
NGSAP-VC : Genomic Variant Calling as an Installable GALAXY Workflow Using NGS data.
<p>Implementation of genomic variants calling as an installable GALAXY workflows using NGS data. Repository contains two separate sets of simulated ebola test data. One for SNPs and INDELs calling and another for Structural Variants calling.</p>
Comparison of mass specra obtained with and Atom Probe installed in a TEM (JEOL F 200, designated as SATMET) at Groupe de Physique des Matériaux UMR 6634 (Saint Etienne du Rouvray, France) and in a LEAP 5000 XS
<p>Comparison of mass specra obtained with an Atom Probe installed in a TEM (JEOL F 200, designated as SATMET) at Groupe de Physique des Matériaux UMR 6634 (Saint Etienne du Rouvray, France) and in a LEAP 5000 XS </p> <p>Conditions of analyses</p> <p>Material: Fe-51.4Cr (at%) alloy</p> <p>Temperature of APT analyses: 78K<br>Pulse repetition rate in LEAP 5000 XS: 25 kHz<br>Pulse repetition rate in SATMET: 20 kHz<br>Number of events collected in LEAP 5000 XS: 807 784<br>Number of events collected in SATMET: 472 423</p>
CiROCCO Video - Sensor Installation Points in Dali Municipality
<p>This is a video about the Sensor Installation Points in Dali Municipality, as part of the Cypriot pilot on Desert Dust Storm event forecasting. The video was first published on the social media pages of UCY, and the reposted on CiROCCO's YouTube page in July 2024. Link here https://www.youtube.com/watch?v=6XEgXDYGd50 </p>
Marine stepping-stones: Connectivity of Mytilus edulis populations between offshore energy installations
Recent papers postulate that epifaunal organisms use artificial structures as stepping-stones to spread to areas that are too distant to reach in a single generation. With thousands of artificial structures present in the North Sea, we test the hypothesis that these structures are connected by water currents and act as an interconnected reef. Population genetic structure of the Blue mussel, Mytilus edulis was expected to follow a pattern predicted by particle tracking models (PTM). Correlation between population genetic differentiation, based on microsatellite markers, and particle exchange was tested. Specimens of M. edulis were found at each location, although the PTM indicated that locations >85 km offshore were isolated from coastal sub-populations. Fixation coefficient FST correlated with the number of arrivals in the PTM. However, the number of effective migrants per generation as inferred from coalescent simulations did not show a strong correlation with the arriving particles. Isolation by distance analysis showed no increase in isolation with increasing distance and we did not find clear structure among the populations. The marine stepping-stone effect is obviously important for the distribution of M. edulis in the North Sea and it may influence ecologically comparable species in a similar way. In the absence of artificial shallow hard substrates, M. edulis would be unlikely to survive in offshore North Sea waters. Although we found an indication that FST was lower between connected locations, isolation by distance analysis showed no increase in isolation with increasing distance. Finally, we did not find clear structure among the populations.
Dataset supporting publication: "The Problem of Geothermal Power Installation on Buildings: Structural Building Monitoring and Assessment During Drilling Activities"
<p>Dataset supporting publication: “The Problem of Geothermal Power Installation on Buildings: Structural Building Monitoring and Assessment During Drilling Activities” (publication available in <a href="https://zenodo.org/record/7266386#.Y1_e1XbMJPY">GEOFIT Zenodo</a>)</p> <p>Datasets resultant from structural health monitoring activities (accelerometer data).</p> <p>The current European Union (EU) policy aims to increase the use of “green” energies, and within this strategy the exploitation of the geothermal energy is a well promising approach. The European Horizon2020 project GEOFIT (Deployment of novel GEOthermal systems, technologies and tools for energy efficient building retroFITting) aims among the others to deploy and to integrate advanced methods of worksite inspection, ground research, and building structural monitoring, drilling and worksite characterization into advanced geothermal based retrofitting methods.</p> <p>When dealing with “plants of power production”, one needs to develop a Life Cycle Analysis and to apply a Life Cycle Assessment (LCA) for evaluating any environmental aspects and potential influences throughout the whole life cycle of a product or process or service. The paper first provides a preliminary discussion on this aspect. Then it focuses attention on a pilot site made available within the GEOFIT Consortium. The results from a structural monitoring campaign in this pilot site before and during the drilling operations associated to the implementation of the geothermal power system are presented discussed.</p>
Dataset supporting publication: "Geothermal Power: Monitoring the Building Response During Installation"
<p>Dataset supporting publication: “Geothermal Power: Monitoring the Building Response During Installation” (publication available for download: <a href="https://zenodo.org/record/4244246#.X6KWq1CCHIU">GEOFIT Zenodo</a>)</p> <p>The European project GEOFIT (Deployment of novel GEOthermal systems, technologies and tools for energy efficient building retrofitting) is gathering more than 20 partners from all around Europe. Its main objective is to deploy and to integrate advanced methods of worksite inspection, ground research, and building structural monitoring, drilling and worksite characterization into advanced geothermal based retrofitting methods. This contribution reports the experimental results to be achieved within GEOFIT at the location of specific case studies. In particular standard accelerometric measurements will be collected and compared with the information collected by the geo-radar system made available by one of the partners. This paper focuses on the structural monitoring of a two-story masonry building. The results of a preliminary data collection in the absence of drilling are also reported.</p>
Predicting the Install, Compile, and Test Attributes of a Library
<p>The task of selecting a third-party library is a common one, but it can be challenging due to the many factors that a developer has to consider. Previous research has mainly focused on specific attributes. In this paper, we take a comprehensive approach by identifying the most desirable attributes and examining how they are related to one another. Our work is split into three parts. First, we conduct a developer survey to rank their preferences on a set of 30 attributes from existing work, grouped into four categories: build, documentation, source code, and contribution. To confirm our results, in the second part of the study, we then mined 104,364 NPM libraries to find that the build (i.e., installation, compiling, and testing) of a library is important. Finally, in the third part of the study, we find that static source code attributes (i.e., number of files, repository size, and directories) correlate and predict the success of installing, building, and testing a library. Our work lays the groundwork on what attributes of a library are useful for predicting a successful build of a library.</p>
Machine Learning Techniques Application for Installation Torque Prediction of Helical Piles
<p>This database contains torque observations in the installation of helical piles used as a foundation in an infrastructure construction for power transmission towers. In this way, this database trains ML models for torque prediction.</p>
Timelapse of Indoor Monitoring of 3D printed Panels installed in Living Prototypes Exhibition in Aedes Forum, Berlin
<p>The presented timelapse is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy - CITA in Copenhagen, Denmark. The timelapse is part of Indoor Monitoring Framework developed for Living Prototype Exhibition at Aedes Forum in Berlin. </p>
Dataset of avian samples collected and analyzed in: Genetic identification of avian samples recovered from solar energy installations
<p class="MsoNormal">Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still remain, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or "feather spots" – known to be of avian origin but not readily assignable to species via morphology – may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.</p>
Balancing Authority Hourly Generation Of Installed Plant Capacities in CONUS
<p>This dataset contains the hourly generation time series for each Balancing Authority representing the installed capacity in each month from 2007 through 2020. The time series is aggregated from plant-level generation of plants that exist in the Energy Information Administration (EIA) database as of 2020. The time series are simulated <strong>actual generation</strong> meaning that the installed capacities have been applied to the hourly capacity factor profiles in <a href="https://doi.org/10.5281/zenodo.7901614">Bracken et al. 2023</a>. These historical generation time series reflect the actual monthly installed capacity at each plant; the EIA 860 and EIA 860m databases were mined to identify months of first operation, retirement, and extended periods of maintenance or non-operation. These databases were also used to create time series of Balancing Authority (BA) level hourly generation to reflect the actual monthly installed capacity. The BA-level time series tracks the BA membership of each power plant in each month; the time series reflects the monthly inventory and hourly production in each BA.</p> <p>For more information please refer to Campbell et al. 2023, Dynamically Downscaled Power Production for All EIA Wind and Solar Power Plants, in prep, and to the code repository at <a href="https://github.com/GODEEEP/godeeep-eia-power">https://github.com/GODEEEP/godeeep-eia-power</a>.</p> <p>The dataset contains three types of files:</p> <p>Monthly Plant-Level Inventory - The monthly plant-level inventory (<code>all_years_860m.csv</code>) contains the identification codes for each generator (including the <code>plant_code_unique</code> identifier to link with the solar and wind profiles in Bracken et al. 2023), the capacity in that month, the balancing authority that plant operated for in that month, the resource type (<code>solar</code> or <code>wind</code>), the month and year, and whether the nameplate capacity in that month needs to be scale to account for aggregation of very large wind power plants (i.e., more than 300 turbines).</p> <p>BA-level Hourly Generation - The BA-level hourly generation files (<code>solar_BA_generation.csv</code> and <code>wind_BA_generation.csv</code>) contain hourly generation at the BA level for each BA in the 2020 EIA 860 database. The first column contains a time stamp and each column header is the name of the BA as it exists in the EIA 860 database (e.g., ISO New England is ISNE, CAISO is CISO). The time series spans 2007 through 2020. The time series begin in 2007, as this aligns with the first year of publication of the EIA 923 monthly plant-level generation dataset and with the first year of available BA-level self-reported generation. The purpose of the temporal baseline alignment is for validation of the time series, discussed in Campbell et al. 2023. Validation metrics in this paper are provided at the BA-level.</p> <p>Plant-level Hourly Generation - The plant-level hourly generation files (<code>solar_plant_generation.csv</code> and <code>wind_plant_generation.csv</code>) contain hourly generation at the generator level for each power plant that exists in the EIA 860 database in 2020. The files are organized with an hourly timestamp for each row and a unique generator id for each column. The generator id is a concatenation of the EIA Plant ID and the EIA Generator ID with an underscore separating the strings. <strong>The plant-level hourly generation time series are intended to be aggregated to the BA-level.</strong> These plant-level time series are provided to the user to allow for re-aggregation for bespoke regional analyses.</p> <p> </p> <p><strong>Known Issues</strong></p> <ul> <li>The following wind power plants (identifier <code>plant_code_unique</code>) have a cf greater than 1 and were scaled to 0.885 <ul> <li>['2024', '2024_1', '2024_3', '2024_4', '7855', '7855_1', '7927', '7927_1', '7927_2', '7965', '7965_1', '7974', '7974_1', '52162', '52163', '54300', '54793', '54793_2', '55741', '55944', '55995_1', '56577', '57214', '57257', '57258', '57258_1', '57594', '57721', '57721_1', '58105', '58112', '58113', '58113_1', '59328', '59329', '59330', '59331', '61677', '61677_1', '61677_2', '62442', '64130']</li> </ul> </li> </ul> <p> </p> <p><strong>Changelog</strong></p> <ul> <li>v1.1.0 - Updates the basis for plant-level inventory from the EIA860 monthly reports (considered preliminary) to the EIA860 annual reports (considered complete and final).</li> </ul> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Marine stepping-stones: Connectivity of Mytilus edulis populations between offshore energy installations
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Dataset of avian samples collected and analyzed in: Genetic identification of avian samples recovered from solar energy installations
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Full inventory of ten permanent plots installed in pockets of different tree functional types along the Moni River transects (Yangambi, Democratic Republic of Congo)
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Full inventory data statistics of the 10 permanent inventory plots installed in the Moni river transects
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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)
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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.