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10,016 results for “Observation”
Lagrangian trajectories tracking the origin and fate of a hydrothermal plume observed in the southeastern Pacific sector of the Southern Ocean
<p>A dataset of Lagrangian trajectories which were used to estimate the origin and fate of a hydrothermal plume in the southeastern Pacific sector of the Southern Ocean observed during the DY111 CUSTARD cruise. All variables have long names and units. These files have been used for the analysis in Birchill and Baker et al. ‘Pathways and timescales of Southern Ocean hydrothermal iron and manganese transport’ with further information about the methodology available in the paper. There are five forward-tracking experiments from suspected vent sites (CUSTARD_NEPR.nc, CUSTARD_SEPR.nc, CUSTARD_PAR.nc, CUSTARD_WCR.nc and CUSTARD_ECR.nc) included in the paper and one forward-tracking experiment that was not included in the paper (CUSTARD_EM.nc) as no trajectories crossed our sampling site. There was one backward-tracking experiment undertaken from the Ocean Observatories Initiative (OOI) station at which the hydrothermal signal was observed (CUSTARD_OOI_backward.nc).</p>
Table 8 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy."
<p><strong>Graphs of the data distributions, with the R<sup>2</sup> regression values of σFR and the corresponding functions.</strong></p> <p>In case of display problems or missing data, the file <a href="https://zenodo.org/api/files/305630bd-70bd-4347-99eb-bfd6fb3af726/Table%208_SFR_distributions_curves.xlsx">Table 8_SFR_distributions_curves.xlsx</a> on Drive available for consultation is this one: <a href="https://docs.google.com/spreadsheets/d/17UGHZlvZ2N-g6TOzgqpIe248_IitBmka8DbGWsv-lKQ/edit?usp=sharing">https://docs.google.com/spreadsheets/d/17UGHZlvZ2N-g6TOzgqpIe248_IitBmka8DbGWsv-lKQ/edit?usp=sharing</a> </p> <p>URL of the plots of the data distributions, with the <strong>R<sup>2</sup></strong> regression values of σFR at the time of the triggering of the 200 earthquakes analyzed in the paper <em>"Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy", paragraph </em><strong>3.02.</strong></p>
Table 4 for the Study: "Observation of correlation between earthquake triggering of M>4.3 and specific Sun-Moon-Planets positions in the Solar System, from 1600 in Italy."
<p><strong>Video explaining the determination of the Resulting Gravitational Force sigma for the 2009 L'Aquila earthquake.</strong></p> <p>For subtitles in English: <a href="https://youtu.be/-OVk2r8U8QA?t=563">https://youtu.be/-OVk2r8U8QA?t=563</a> --> URL of the video explaining the determination of the Resulting Gravitational Force sigma for the 2009 L'Aquila earthquake, for the article "Correlation observation between the triggering of M>4.3 earthquakes and specific Sun-Moon-Planet positions in the Solar System since 1600 in Italy" , SUBTITLES IN EN, paragraph 2.16.</p>
Monitoring moose in Montana using hunter observations to count moose by district
<p>During 2012–2016, we queried hunters of deer and elk for observations of groups of a non-target species, moose, across their statewide distribution in Montana. We used buffer analyses to filter data according to independent observations in space and time and then tallied observations of groups by hunting district. We then analyzed data in an abundance-detection framework with n-mixture models and evaluated the effects of covariates such as hunter effort, survey response totals, weekly session, and forest cover on detection probability before using models to predict moose abundance. Lastly, we converted model predictions of group abundnace to total abundance by multiplying number of groups by average group size per administrative region.</p>
Local observability of an augmented battery model
<p>This document represents a text file including a code for computing the local observability of an augmented battery model used for an OCV estimation method. This code is designed to be used in the computer algebra system (CAS) software Maxima.</p>
Level 2 base data in Lekima (2019) observed by Wenzhou S-band radar
<p>Level 2 base data in Lekima (2019) observed by Wenzhou S-band radar</p>
Excited state observation of active K-Ras reveals differential structural dynamics of wild-type versus oncogenic G12D and G12C mutants
<p>Despite the prominent role of the K-Ras protein in many different types of human cancer, major gaps in atomic-level information severely limit our understanding of K-Ras function in health and disease. Here, we report the quantitative backbone structural dynamics of K-Ras by solution NMR spectroscopy of the active state of wild-type K-Ras·GTP and two of its oncogenic P-loop mutants, G12D and G12C, using a novel nanoparticle-assisted spin relaxation method, relaxation dispersion and chemical exchange saturation transfer experiments covering the entire range of timescales from picosecond to milliseconds. Our combined experiments allow the detection and analysis of the functionally critical Switch I and Switch II regions that have previously remained largely unobservable by X-ray crystallography and NMR spectroscopy. Our data reveal cooperative transitions of K-Ras·GTP to a highly dynamic excited state that closely resembles the partially disordered K-Ras·GDP state. These results advance our understanding of differential GTPase activities and signaling properties of the WT versus mutants and may thus guide new strategies for the development of therapeutics.</p>
GLOBE Observer Summary and Measurement Data
<p>Data accessed from GLOBE Advanced Data Access Tool, used protocols "Land Cover" and "Mosquito Habitat Mapper" data from 01/01/1995-07/20/23 in Virginia, Maryland, North Carolina, and Delaware. As a part of a SEES2023 project investigating the effects of urbanization on mosquito population dynamics, this dataset was used in tandem with another to analyze how consistent GLOBE citizen science data is for inference/predictions about relationships between urbanization and larvae counts</p>
Observed properties of Starlink satellites
<p>Tabulated measurements of Starlink satellites observed by the Multi-site All-Sky CAmeRA (MASCARA) instrument. This was created by Peter Breslin during his master thesis at Leiden University. Thesis title: 'Mega-constellation satellites: Assessing their interference on ground-based astronomy'.</p>
Pan trap and plant-flower visitor observation data for: Multi-species crop mixtures increase insect biodiversity in an intercropping experiment
<ol> <li><span>Recent biodiversity declines require action across sectors such as agriculture. The situation is particularly acute for arthropods, a species-rich taxon providing important ecosystem services. To counteract negative consequences of agricultural intensification, creating a less hostile agricultural "matrix" through growing crop mixtures can reduce harm for arthropods without yield losses. </span></li> <li><span>While grassland biodiversity experiments showed positive plant biodiversity effects on arthropods, experiments manipulating crop diversity and agrochemical input use to study arthropods are lacking. </span></li> <li><span>Here, we experimentally manipulated crop diversity (1–3 species, fallows), crop species (wheat, faba bean, linseed, oilseed rape) and agrochemical input (high vs. low) and studied responses of arthropod biodiversity. We tested if arthropod responses were affected by crop diversity, mixtures and management. Additionally, we measured crop biomass.</span></li> <li><span>Crop biomass increased with crop diversity under high-input mangement, while under low management intensity, biomass was highest in two-species mixtures.</span></li> <li><span>Increasing crop diversity positively affected arthropod abundance and diversity, both under low- and high-input management. Crop mixtures containing faba bean, linseed or oilseed rape had particularly high arthropod diversity.</span></li> <li><span>Mass-flowering crops attracted more arthropods than legumes or cereals. Integrating intercropping into agricultural systems could increase flower visits by insects up to 15 million per hectare, thus likely also supporting pollination and pest-control ecosystem services.</span></li> <li><span>Flower-visitor network complexity increased in mixtures containing linseed and faba bean, and under low-input management.</span></li> <li><span>Intercropping can counteract insect declines in farmland by creating beneficial matrix habitat without compromising crop yield.</span></li> </ol>
Temperature measurements of full-scale wall element using Type K thermocouples to observe internal convection in loose-fill wood fiber insulation
<p>Internal convection of insulation materials is a phenomenon that occurs when a construction element is subjected to a temperature difference on either side of the element, as the temperature difference inside the insulation will facilitate an onset of air movement due to thermal buoyancy. This dataset represents the results of 11 unique experiments conducted at Aalborg University at the Department of the Built Environment, where a full-scale wall element insulated with loose-fill wood fiber insulation is investigated for internal convection. A large guarded hotbox is used to control the boundary conditions of either side of the wall element, to imitate a construction element subjected to external and internal boundary conditions, similar to a wall in a house. This dataset can be used to benchmark other insulation materials investigated at similar boundary conditions.</p> <p>The dataset is structured into steady-state experiments and dynamic experiments, where a total of 7 unique cases are conducted in steady-state conditions, and 4 unique cases are conducted in dynamic conditions. The dataset for the steady-state experiments is structured by the temperature difference that the full-scale wall element is exposed to, from the cold and hot side, while the dynamic experiments are structured by the amplitude of the temperature variation, along with if an artificial sun is used or not.</p> <p>The results for the internal convection of the loose-fill wood fiber insulation show similar results as other studies that have conducted experiments on other insulation materials.</p> <p>For more information, see doi: 10.54337/aau488363266</p>
Historical model forecast and observed visibility datasets
<p>yyyy_spot[00/12]_wrf61_prev18.csv: </p> <ul> <li>This dataset includes visibility observations, station latitude and longitude, time and model forecast variables from 2014 to 2020.</li> <li>The visibility observations were obtained from the Meteorological Information Comprehensive Analysis and Process System (MICAPS), which provided various weather measurements including temperature, pressure, humidity, precipitation, visibility, current weather, and other meteorological variables. Meteorological measurements were recorded at 3-hour intervals. Besides, the location and time of each record were provided.</li> <li>The model forecast variables are from the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM) provided by the US National Weather Service (NCEP). The WRF-NMM produces hourly predictions for a 60-hour forecast period, launching twice daily at 00:00 UTC and 12:00 UTC. Forecast variables incorporated in the model encompass temperature, relative humidity, cloud cover, wind speed, precipitation, and more. This dataset includes 26 variables, which are listed below.</li> </ul> <pre><code class="language-markdown">| Variables | Long Name | Description | | ---- | ---- | ---- | | DPT_GDS3_HTGL | Dew point | Height in 2 meters | | HGT_GDS3_0DEG | Geopotential | height Level of 0 deg (C) isotherm | | HGT_GDS3_CEIL | Geopotential | height Cloud ceiling | | HGT_GDS3_HTFL | Geopotential | height Highest tropospheric freezing level | | HGT_GDS3_SFC | Geopotential | height Ground/water surface | | T_CDC_GDS3_EATM | Total cloud cover | Entire atmosphere | | H_CDC_GDS3_HCY | High cloud cover | High cloud layer | | M_CDC_GDS3_MCY | Mid cloud cover | Mid cloud layer | | L_CDC_GDS3_MCY | Low cloud cover | Low cloud layer | | PLI_GDS3_SPDY | Parcel lifted index (to 500 hPa) | Layer between two levels at specified pressure difference from ground to level | | LFT_X_GDS3_ISBY | Surface lifted index | Layer between two isobaric levels | | PRES_GDS3_SFC | Pressure | Ground/water surface | | PRMSL_GDS3_MSL | Pressure reduced to Mean sea level | Mean sea level | | MSLET_GDS3_MSL | Mean sea level pressure | Mean sea level | | P_WAT_GDS3_EATM | Precipitable water | Entire atmosphere | | POP_GDS3_SFC | Probability of precipitation | Ground/water surface | | R_H_GDS3_HTGL | Relative humidity | Height in 2 meters | | R_H_GDS3_HYBL | Relative humidity | Hybrid level | | SPF_H_GDS3_HTGL | Specific humidity | Height in 2 meters | | SPF_H_GDS3_SPDY | Specific humidity | Layer between two levels at specified pressure difference from ground to level | | TMP_GDS3_HTGL | Temperature | Height in 2 meters | | TMP_GDS3_SFC | Temperature | Ground/water surface | | U_GRD_GDS3_ HTGL | U-component of wind | Height in 10 meters | | U_GRD_GDS3_SPDY | U-component of wind | Layer between two levels at specified pressure difference from ground to level | | V_GRD_GDS3_HTGL | V-component of wind | Height in 10 meters | | V_GRD_GDS3_SPDY | V-component of wind | Layer between two levels at specified pressure difference from ground to level |</code></pre> <ul> <li>To integrate the gridded model forecast data with sparse observation data, we have a two-step pre-processing process. Firstly, we synchronized the time zones of two datasets to UTC+00:00. Secondly, we used the Inverse Distance Weighted (IDW) approach for spatial matching. As a result, we generated a multi-variable time series dataset as follows:<br> <span class="math-tex">\(D=\{X_{N \times M \times T}, Y_{N \times T}\}\)</span> <br> where <span class="math-tex">\(X\)</span> is a three-dimensional tensor of size <span class="math-tex">\(N\times M\times T\)</span> (corresponding to <span class="math-tex">\(N\)</span> samples of <span class="math-tex">\(M\)</span> forecast variables of <span class="math-tex">\(T\)</span> hours), and <span class="math-tex">\(Y\)</span> is the observational data of size <span class="math-tex">\(N\times T\)</span> (corresponding to <span class="math-tex">\(N\)</span> samples of <span class="math-tex">\(T\)</span> hours). A sample from dataset D is represented as <span class="math-tex">\(s= \{ x_{M \times T}, y_T \} \in D\)</span>. In this datasets, <span class="math-tex">\(M=27\)</span>, and <span class="math-tex">\(T=60\)</span>. Besides, this dataset includes 18 hours of visibility observations before the model launching. It should be noted that some observations are missing for some reason.</li> </ul> <p><br> yyyy_spot[00/12]_wrf6_prev18.csv: </p> <ul> <li>This datasets are from yyyy_spot00/12_wrf61_prev18.csv and is a sliding-window extraction of 61 forecast moments, with each sliding-window consisting of 6 hours.</li> </ul>
Data for "Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)"
<p>This tar file contains the data produced for the manuscript "Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)". The contents include model data from the simulation presented in the paper, a four-year benchmark simulation using version 10.23.0 of GEOS with GOCART2G, spanning the period of 2016 through 2019. Aerosol mass budget terms including emissions, production, and deposition are included as well as the model data sampled according to the observational data sets for MODIS, AERONET, OMPS-LP, CALIOP, IMPROVE, and EMEP.</p>
Mountaintop gamma ray observations of three terrestrial gamma-ray flashes at the Säntis Tower, Switzerland with coincident radio waveforms
<p>We report on the mountain top observation of three terrestrial gamma-ray flashes (TGFs) that occurred during the summer storm season of 2021. To our knowledge, these are the first TGFs observed in a mountaintop environment and the first published European TGFs observed from the ground. A gamma-ray sensitive detector was located at the base of the Säntis Tower in Switzerland and observed three unique TGF events with coincident radio sferic data characteristic of TGFs seen from space. We will show an example of a 'slow pulse' radio signature, an -EIP, and a double peak TGF associated with an extraordinarily powerful and complicated positive-polarity sferic, where each TGF peak is possibly preceded by a short burst of stepped leader emission. </p>
Western Indian Ocean coral diversity observations from 1998–2022
<p><span>Coral reefs are threatened by climate change, thus effective policies and management require spatial prioritization for conservation investments. Our aim was to develop a spatially explicit ecological model to predict current (2020) and future (2050) numbers of coral taxa at moderate scales (i.e., ~6 km<sup>2</sup>). Our machine learning predictive models of coral community attributes in 7039 mapped reef cells in the western Indian Ocean were based on 35 spatially complete influential environmental proxies trained with ~1000 field surveys. Four models explored: influences of climate change, water quality, direct human-resource extraction, and variable selection processes on numbers of coral taxa. Two predictive models examined the predictions of all variables and compared them to a variable-restricted climate change (8 commonly used variables) and human influence model (9 variables). The most frequently selected temperature variables in all models were the median, skewness, excess heat, rate of temperature rise, and kurtosis. However, non-temperature variables of observer, depth, wave energy, dissolved oxygen, salinity, chlorophyll-a, calcite concentrations, sunlight, and net primary productivity were frequently as important or stronger. Human influences of national jurisdiction, distance to people, sediments, and nutrients were selected but less influential when compared to the climate or the full variable models. Comparing models indicated the importance of variable pre-selection processes and variable interactions in predicting climate change and human influences on coral diversity. Comparing climate scenarios in the moderate RCP2.6 and extreme RCP8.5 emission scenarios indicated fewer losses in coral taxa (RCP2.6 = 5.2%, RCP8.5 = 8.1% respectively) relative to cover (RCP2.6 = 14%, RCP8.5 = 34%) over the 30 years. Excess heat and rate of temperature rise variables used in the Intergovernmental Panel on Climate Change (IPCC) forecasts predict more negative effects on corals than our four models but shown here to have low to modest effects.</span></p>
Data for A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers
<p>This repository contains dataset for the paper entitled "A Physical Model for the Observed Inverse Energy Cascade in Typhoon Boundary Layers". The magnitude of inverse energy cascade flux is revised in version 2.0 according to Xia et al. (2009) (https://doi.org/10.1063/1.3275861). </p>
Data observed at Kawajima land subsidence observatory (Japan) and ensemble model
<p>This data set contains all data necessary to reproduce the results of the manuscript submitted by Akitaya & Aichi. The data set includes hydraulic head and land subsidence data observed at the Kawajima land subsidence observatory (Japan), and an ensemble model parameter set constructed using the evolutionary multimodal algorithm, ensemble simulation results, and predictive uncertainty analysis results obtained through ensemble model output statistics. Please refer to the "readme.txt" file provided in each individual folder for detailed instructions on how to read the data.</p>
ALMA and VLA observations of the Extended Green Object G19.01-0.03
<p>In this repository, we deposit final data products that underlie the following two articles:</p> <p>- ALMA observations of the Extended Green Object G19.01--0.03 - I. A Keplerian disc in a massive protostellar system, Williams et al. 2022, MNRAS, 509, 748</p> <p>- ALMA observations of the Extended Green Object G19.01--0.03 - II. A massive protostar with typical chemical abundances surrounded by four low-mass prestellar core candidates, Williams et al. 2023, MNRAS, 525, 6146</p> <p>The included README.md file describes the data files in detail.</p>
Dataset corresponding to the SIRs observed by the Wind spacecraft during 2007, 2008, 2018, and 2019
<p>Dataset corresponding to the Stream Interaction Regions (SIRs) observed by the Wind spacecraft during 2007, 2008, 2018, and 2019. The table consists of several columns detailing the solar wind (SW) properties and related parameters.</p> <p>In addition, we show the coronal holes (CHs) locations from which the high-speed streams originate using synoptic maps. CHs can be near the solar equator, at midlatitudes, or as low-latitude extensions of polar CH.</p>
Near-bottom Oxygen observations from Western Baltic Sea
<p>The provided dataset includes the near-bottom oxygen observations (NBO) from the western Baltic Sea (8.5-16°E and 53.67-57.5°N) and is presented in detail by Friedland, Vock, Piehl (2023, https://www.mdpi.com/2073-4441/15/18/3235).</p> <p>The provided data consists of a couple of individual dataset from several freely usable sources: IOW measurement database, ICES Dataset of Hydrography, Boknis Eck time series, EU Copernicus Marine Data Store and the databases of the Mecklenburg Western Pomerania state office for Environment, Conservation and Geology (LUNG-MV) and the Schleswig-Holstein state office for the Environment (LfU-SH) respectively. From the different databases, we extracted the measured dissolved oxygen concentrations (or the measured oxygen saturation, which was converted following Weiss (1970)), location (longitude, latitude, water depth) and depth of the measurement. Aiming to unify the data in the joint dataset, the different datasets were transformed to mg/l, duplicates were removed and measurements at the same time and location obtained by different methods were averaged. Due to many inconsistencies between the measurement depth and bathymetric depth as well as large variations in the measured bathymetric depth of the respective measuring stations, the measured bathymetric depth given in the datasets was replaced by the bathymetric depth retrieved from iowtopo and EMODnet Bathymetry. The datasets mentioned above, included measurements of dissolved oxygen from the whole water column. To sort out the NBO, the deepest measurement was used, if it was close enough to the bottom using as threshold: for water depths below 16m, measurements within the lower 25% of the water column and for water depths above 16m measurements from a maximum of 4 meters above the sea floor were selected.</p>
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.