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10,016 results for “Observation”
On the Observability of Individual Population III Stars and Their Stellar-mass Black Hole Accretion Disks through Cluster Caustic Transits
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018ApJS..234...41W/abstract">On the Observability of Individual Population III Stars and Their Stellar-mass Black Hole Accretion Disks through Cluster Caustic Transits</a></p>
The observational signatures of convectively excited gravity modes in main-sequence stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2013MNRAS.430.1736S">The observational signatures of convectively excited gravity modes in main-sequence stars</a></p>
Pilot 1 user tracking and observations
<p>This dataset will contain all the data collected from observation and tracking location of the users participating in the experiments using the CROSSCULT Pilot 1app. It will include information about the users’ interaction with the NG collection information.</p> <p>The dataset will comprise anonymised information about the users who will participate in the NG visits, using the CROSSCULT app. Additionally, for users who give their permission, information can be automatically extracted/retrieved from their devices location. User agreement must be obtained before tracking and processing data about the user.</p> <p>The dataset generated from the observation will provide information on user behaviour. The exact make-up of the fields included in this dataset will be determined as part of the work carried out within CROSSCULT.</p>
Pilot 3 visitor interactions and staff observations
<p>This dataset will include the contributions of participants during the experiments. The information (linked to the corresponding profiles from the CROSSCULT-DS-pilot_3_Visitor_Profiles dataset) will comprise records of individual/collective interactions, plus observations from the guiding staff of the venues (e.g. about the mood of the participants, amount of idle time, reception of the micro-augmentations, etc.).</p> <p>The format of the data will be XML files structuring the different kind of contributions and contributors in every stage of the user experience.</p> <p>The information of this dataset will be provided by the participants in the proposed experiences. No reuse of additional information is foreseen, as this information cannot be enhanced by any external contribution.</p>
Replication data and theory code for: Observation of a Majorana zero mode in a topologically protected edge channel
<p>Replication Data for: Observation of a Majorana zero mode in a topologically protected edge channel</p>
Dataset for "Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations"
<p>This dataset provides wetland methane (CH<sub>4</sub>) emissions, their uncertainties and underlying CH<sub>4</sub> flux densities north from 45 N using three different wetland maps. The data products are derived using data from several eddy covariance CH<sub>4</sub> flux sites, random forest machine learning algorithms and three prescribed wetland maps. The data are at 0.5 by 0.5 deg or 1 by 1 deg resolution, depending on the wetland map used. The dataset covers years 2013 and 2014. CH<sub>4</sub> flux densities are provided only for grid cells with > 5 % wetland coverage.</p> <p>The three data products are provided in netCDF format files (.nc). Please see more details in the attributes saved in the netCDF files.</p> <p>RF-DYPTOP.nc<br> Upscaling based on DYPTOP dynamic wetland map. At 1 by 1 deg resolution.</p> <p>RF-GLWD.nc<br> Upscaling using GLWD static wetland map. At 0.5 by 0.5 deg resolution.</p> <p>RF-PEATMAP.nc<br> Upscaling using PEATMAP static wetland map. At 0.5 by 0.5 deg resolution.</p> <p> </p> <p>This dataset is related to Peltola et al. (2019) manuscript submitted to Earth System Science Data. Please cite this publication if you use this dataset in your work.</p> <p>Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D. F., Nilsson, M. B., Oechel, W. C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H. P., Sonnentag, O., Wille, C., Zona, D., and Aalto, T.: Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-28, in review, 2019.</p>
Observer Function Database - Asano (2015)
<p><strong>Source URL</strong>: <a href="https://www.rit.edu/cos/colorscience/re_AsanoObserverFunctions.php">https://www.rit.edu/cos/colorscience/re_AsanoObserverFunctions.php</a><br> <strong>Source DOI</strong>: <a href="https://doi.org/10.1371/journal.pone.0145671">10.1371/journal.pone.0145671</a><br> <br> <strong>Categorical observers</strong></p> <p>Categorical observers are observer functions that would represent color-normal populations. They are finite and discrete as opposed to observer functions generated from the individual colorimetric observer model. Thus, they would offer more convenient and practical approaches for the personalized color imaging workflow and color matching analyses. Categorical observers were derived in two steps. At the first step, 10,000 observer functions were generated from the individual colorimetric observer model using Monte Carlo simulation. At the second step, the cluster analysis, a modified k-medoids algorithm, was applied to the 10,000 observers minimizing the squared Euclidean distance in cone fundamentals space, and categorical observers were derived iteratively. Since the proposed categorical observers are defined by their physiological parameters and ages, their CMFs can be derived for any target field size.</p> <p>Categorical observers were ordered by the importance; the first categorical observer vas the average observer equivalent to CIEPO06 with 38 year-old for a given field size, followed by the second most important categorical observer, the third, and so on.</p> <p>The color matching analyses showed that ten categorical observers are good for general use and convenience to represent color normal populations. On average, the prediction error improvement was small after adding tenth categorical observers, and the prediction errors became one-third by introducing ten observers. Nevertheless, readers should be aware that the number of required categorical observers varies depending on an application (a pair of spectra viewed by observers). For example, the simulation revealed that as many as 50 categorical observers would be required to predict individual observers’ matches satisfactorily when a laser projector is viewed.</p> <p>Matlab code for the categorical observers and CMFs as well as model parameters for ten categorical observers are available for download below.</p> <p><strong>151 color-normal observers</strong></p> <p>CMFs of 151 color-normal observers were estimated by combining the individual colorimetric observer model and the color matching proposed in Asano’s PhD dissertation. The color matching consisted of five color matches aimed to highlight and detect inter-observer variability among color-normals. To obtain a set of CMFs for a given human observer, at first, the observer performed the five color matches with three repetitions. Then, his/her eight physiological parameters (used in the individual colorimetric observer model) were estimated from the color matching results by a non-linear optimization. The objective function was to optimize the eight physiological parameters such that the color differences between the human observer results and model predictions were minimized. Finally, the CMFs were reconstructed from the estimated physiological parameters and the observer's real age.</p> <p>The estimated CMFs for 151 color-normal human observers, the corresponding model parameters, and other information such as gender, experience in color-related subjective experiments, ethnic origin, color deficiency in family, diabetes, and intra-observer variability (Mean Color Difference from the Mean using CIEDE2000) for each of the 151 observers are available for download</p>
Simulations from: Formation of the first three gravitational-wave observations through isolated binary evolution
<p>The results of all simulations shown and discussed in "<a href="https://www.nature.com/articles/ncomms14906">Formation of the first three gravitational-wave observations through isolated binary evolution</a>", published in Nature Communications, 8, 14906 (<a href="https://arxiv.org/abs/1704.01352">arXiv</a>).</p> <p>Contents:</p> <p>README<br> Z0_002_LVT151012_1475624202.dat<br> Z0_001_GW150914_1394278271.dat<br> Z0_002_allMergers.dat<br> Z0_001_GW151226_294773844.dat<br> Z0_002_alphalambda0_01_allMergers.dat<br> Z0_001_LVT151012_784739229.dat<br> Z0_002_fLBV_10_allMergers.dat<br> Z0_001_allMergers.dat<br> Z0_005_GW151226_1432529107.dat<br> Z0_001_alphalambda0_01_allMergers.dat<br> Z0_005_LVT151012_1051180640.dat<br> Z0_001_fLBV_10_allMergers.dat<br> Z0_005_allMergers.dat<br> Z0_002_GW151226_997696601.dat</p> <p>All simulations made using <a href="http://compas.science/">COMPAS</a></p>
Eddy Covariance observations of three different sites from Estero El Soldado, Sonora, Mexico
<p>This database contains eddy covariance and micrometerogical measurements, it corresponds to three sites of study in Estero El Soldado (27.95413399, -110.9725342) Sonora, México. The flux tower was operating from June 12<sup>th</sup> 2018 to June 6<sup>st</sup> 2019. The dataset is divided in three sheets: site_1, site_2 and site_3 corresponding to the three sites where the tower was at each period. The three different scenarios consider a) bare soil and seawater column (site_1) from June 12th to October 12th 2018, n=134 days with 26 days of missing data, b) bare soil exposed some hours, seagrass present and shallow seawater column (site_2) from October 13th 2018 to February 24th 201, n= 135 days with 54 days of missing data and, 3) seagrass present and seawater column higher than previous case (site_3) from February 25th to June 6th 2019, with n=102 days with 20 days of missing data.</p> <p>A quality control filter was applied to the complete data set where outlier values were replaced with NaN. In addition to Mauder and Foken (2004) QC filter, we considered based on outliers the following cleaning : FCO2 values outside the range of -10 to 10 µmol m-2 s-1, as well as friction velocity (u*) >3 m s-1, shortwave incoming radiation >2000 W m-2, salinity <25 ppt and wind speed >25 m s-1.</p>
Observation of very short period atmospheric gravity waves in the lower Ionosphere using Very Low Frequency waves
<p>This is the VLF frequency data recorded at PARI station owned by Prof Morris Cohen, at Georgia Institute of Technology, Atlanta, USA.</p> <p>Data is for six transmitter signals, DHO, NAA, NAU, NLK, NPM, NML with both amplitude and Phase</p>
Data repository of the paper "The First Terrestrial Electron Beam Observed by The Atmosphere-Space Interactions Monitor" by D. Sarria et al.
<p>Data repository / Supporting information of the paper "The First Terrestrial Electron Beam Observed by The Atmosphere-Space Interactions Monitor" (2019) by D. Sarria et al.</p> <p>Access to the article: <a href="https://doi.org/10.1029/2019JA027071">https://doi.org/10.1029/2019JA027071</a></p>
Supporting data and code for "Nagaoka ferromagnetism observed in a quantum dot plaquette"
<p>This supplement contains the files needed to reproduce the all the figures in the main article. It contains the raw data from measurements and Python scripts to reproduce the figures made from the experimental data, as well as scripts that implement the extended Hubbard models used to make the simulated spectra and charge stability diagrams shown in the article figures.</p> <p>Refer to the readme.txt file for detailed information on each of the files.</p>
Support information for the paper: First ten months of TGF observations by ASIM
<p>23 files with data are uploaded, and description of data files can be found in </p> <p>AGUSupporting-Information_Ostgaard_asim_first_ten_months.docx (also uploaded)</p>
Fldigi file from WWV observation at 5MHz
<p>Festival of Frequency Measurement 1 October 2019</p> <p>Date and time of each measurement is included in the file.</p> <p>Radio is a Flexradio 5000A, which had been running continuously for 4 days in a temperature controlled room.</p> <p>Antenna is an inverted-V dipole cut for the amateur 60m band.</p> <p>Main difficulty was the weakness of the WWV signal for a period of about 4 hours in the early afternoon (1900z - 2300z).</p> <p>I hope the information is of use.</p>
Atmospheric observations of the water vapour continuum in the near-infrared windows - Data
<p>Dataset for our AMT paper (Atmospheric observations of the water vapour continuum in the near-infrared windows).</p> <p>Included is the best estimate of the continuum optical depth (18 September 2008). the ratio of this best estimate to the corresponding MT_CKD 3.2 optical depth from 18 September 2008, and the CAVIAR-field estimated continuum absorption coefficients using both MT_CKD and CAVIAR-lab. Other data are available from the corresponding author upon request.</p>
Data from "Comprehensive observations of the wire destruction and plasma channel reestablishment process during the initial stage of triggered lightning"
<p>The attached is the dataset assoicated with the paper titled "Comprehensive observations of the wire destruction and plasma channel reestablishment process during the initial stage of triggered lightning" which was submitted to <em>Geophysical Research Letters</em> . The data can be used freely for scientific purposes with appropriate citation.</p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Data for Observation of the fastest chemical processes in the radiolysis of water
<p>These are the data underlying the figures in the manuscript entitled "Observation of the fastest chemical processes in the radiolysis of water". </p>
Figure 1 in Observations on the mating behavior of the eastern lowland olingo Bassaricyon alleni (Carnivora: Procyonidae) in the Peruvian Amazon
Figure 1. The first observation of mating wild olingos. The female
Figure 1 in Observations on fragrance collection becaviour of euglossine bees (Hymenoptera* Apidae)
Figure 1. Male of Eulaema in tce 'covering pcase' of becaviour at Cacasecum discolor.
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.