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10,016 results for “Observation”
Ground-based observation data from the article " Elucidating the impacts of various atmospheric ventilation conditions on local and transboundary ozone pollution patterns: A case study of Beijing, China"
<p>Hourly ground-level O<sub>3</sub>, CO and NO<sub>2</sub> observational data were retrieved from the China Environmental Monitoring Station, ultimately comprising 714 stations in North China and 35 stations in Beijing after data quality control. The hourly data from meteorological stations in China were obtained from the China Meteorological Data Service Centre, including observational values of such weather elements as temperature, pressure, relative humidity, wind, total cloud cover, and precipitation.</p>
Variable Scintillation Arcs in Millisecond Pulsars observed with the Large European Array for Pulsars
<p>Dynamic spectra used in "Variable Scintillation Arcs in Millisecond Pulsars observed with the Large European Array for Pulsars". </p> <p>The dynamic spectra are simple ascii (the same output produced by psrflux), produced as described in the manuscript. A function to read them in python is included on github: https://github.com/ramain/scintillation/blob/master/scintillation/dynspectools/dynspectools.py, and they should all be compatible with the scintools package.</p>
Nebular spectra from Type Ia supernova explosion models compared to JWST observations of SN 2021aefx
<p>Hydrodynamical models and synthetic spectra from Blondin et al. (2023).</p>
Fig. 2. Observed 1H–1H in Cytotoxic xanthone derivatives from the twigs of Garcinia oligantha
Fig. 2. Observed 1H–1H COSY (blue lines) and Key HMBC (red arrows) correlations of 1–10. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output, Jan 2010 through Sep 2021)
<p>The dataset archived here includes an extended version of the analysis output shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide a single NetCDF file of the analysis output (9-km resolution EASEv2 grid, period Jan 2010 through Sep 2021, and between 45°N and 70°N, NE Asia excluded):<br> • daily_images.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20230830.pdf</p> <p>Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.</p> <p>Data usage statement:<br> This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/<br> If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks!</p> <p>References:</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM</p> <p>Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. <em>Remote Sensing of Environment</em>. https://doi.org/10.1016/j.rse.2020.111805</p> <p>Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(10), 3106–3130. https://doi.org/10.1029/2019MS001729</p>
Magmatic fingerprints of subduction initiation and mature subduction: Numerical modelling and observations from the Izu-Bonin-Mariana system (Supplementary Material)
<p>Video of the reference model as well as three end-member models described in the manuscript. Additionally, the source code that was used to run the models and the initial model setup for each model presented can be found. The numbering of the models is equivalent to the numbering used in the paper (Ritter et al., 2024 in Front. Earth Sci.)</p>
Observation data for pNARA v1.0
<p>This dataset includes the observations used to conduct and evaluate the prototype NOAA Aerosol Reanalysis version 1.0 (pNARA v1.0) for 2016. The MODIS NNR AOD data from Terra and Aqua are provided in 2016_MODIS_MOD04.tar and 2016_MODIS_MYD04.tar, respectively. The AOD and Almucantar retrievals from AERONET are provided in 2016_aeronet_alm.tar and 2016_aeronet_aod.tar. The PM2.5 from OpenAQ (2016_openaq.tar) and IMPROVE (2016_improve.tgz) are also included.</p>
VOCs observed in Nansha District of Guangzhou
<p>The measured data used for the study "Contribution of ship emission to volatile organic compounds based on one-year monitoring at a coastal site in the Pearl River Delta region"</p>
ASASSN-21qj astronomical observations
<p>Data from astronomical observatories looking at the star ASASSN-21qj.</p>
Association between a glucagon-like peptide 1 receptor genetic polymorphism and therapeutic response to sitagliptin in a sample of type 2 diabetic patients: an observational study
<p>Association between a glucagon-like peptide 1 receptor genetic polymorphism and therapeutic response to sitagliptin in a sample of type 2 diabetic patients: an observational study</p>
Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations (Data)
<p>README file for LBL-ERA5, LBL-GCM, and band datasets used in:<br> Raghuraman et al., 2023, Geophysical Research Letters,<br> "Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations "</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p> </p> <p>LBL-ERA5</p> <p>6 files (3 experiments with olr and olr_clr output separately):</p> <p>2003-2021.GFDL.all-2010-o3_*.nc - varying WMGHG,Ts,T,q,clouds, fixed o3</p> <p>2003-2021.GFDL.fo3_*.nc - varying WMGHG and fixed Ts,T,q,clouds,surface albedo,o3</p> <p>2003-2021.GFDL.ff_*.nc - fixed Ts,T,q,clouds,surface albedo, varying o3</p> <p> </p> <p>LBL-GCM (clear-sky only)</p> <p>4 AM4 files:</p> <p>AM4piclim-control.nc (for ERF)</p> <p>AM4piclim-4xCO2_1xCO2IRF.nc (for ERF)</p> <p>AM4piclim-control_STRAT.nc (for SARF)</p> <p>AM4piclim-4xCO2_1xCO2IRF_STRAT.nc (for SARF)</p> <p>4 CM3/AM3 files:</p> <p>CTLAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>EXPAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>CTLAM3CM3_P1_2003C.nc (for IRF)</p> <p>CTLAM3CM3_P1_2021C.nc (for IRF)</p> <p> </p> <p>Band-AM4/MERRA-Total: GFDL AM4 with prescribed SSTs and sea-ice (AMIP) and nudged with MERRA winds</p> <p>2 files:</p> <p>atmos.200001-202112.olr.nc - all-sky OLR</p> <p>atmos.200001-202112.olr_clr.nc - clear-sky OLR</p> <p> </p> <p>Observational and reanalysis files used in paper (AIRS, CERES EBAF and SSF, GISTEMP, ERA5 input data) can be downloaded from their respective websites (see paper's "Open Research" section).</p> <p> </p>
The Mode-2 Internal Solitary Wave Mooring Observations in Andaman Sea
<p>Observation Data of the Second Mode Internal Solitary Waves in Central Andaman Sea (10.17°N, 95.47°E).</p>
VISSS, PIP, and Parsivel snowfall observations from winter 2021/22 in Hyytiälä, Finland
<p>Dataset used in the first revision of the study </p> <blockquote> <p>Introducing the Video In Situ Snowfall Sensor (VISSS)<br> Maximilian Maahn, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe<br> submitted to Atmospheric Measurement Techniques, https://doi.org/10.5194/egusphere-2023-655</p> </blockquote> <p> </p>
Package for An Observational Study of ChatGPT Usage in Software Engineering Practice
<p>The package includes CSV files, and the exit form used by participants in our paper titled: "Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice".</p> <p>This package has been accepted for the FSE 2024 research track: https://2024.esec-fse.org/details/fse-2024-research-papers/43/Beyond-Code-Generation-An-Observational-Study-of-ChatGPT-Usage-in-Software-Engineeri</p>
In situ observational data for "Typhoon-associated short-term marine heat waves in the northern South China Sea coastal waters"
<p>The in situ observational data for "Typhoon-associated short-term marine heat waves in the northern South China Sea coastal waters", which will be submitted to the Journal of GRL</p>
Data for: Consistently heterogeneous structures observed at multiple spatial scales across fire-intact reference sites
<p>Geospatial polygons representing fire-suppressed control sites against which fire-intact reference sites were compared in Chamberlain et al. (2023). Control sites represent areas with 1) no record of fire history, 2) no record of late 20th century or early 21st century timber management, and 3) no "Fast Change" detected by the Landscape Change Monitoring System dataset. All sites are predominantly within the yellow pine and mixed-conifer zone of California's Sierra Nevada, USA. Polygon boundaries were defined using the NHDPlusV2 catchments, and were manually reshaped using aerial imagery to ensure that polygons were > 100 ha, represented primarily forested areas, and excluded major roads, infrastructure, and major rock outcrops.</p> <p>Detailed description of the methods used to produce this dataset provided in:<br> Chamberlain, C.P., Cova, G.R., Cansler, C.A., North, M.P., Meyer, M.D., Jeronimo, S.M.A., Kane, V.R., 2023. Consistently heterogeneous structures observed at multiple spatial scales across fire-intact reference sites. Forest Ecology and Management.</p>
Supplemental information data from: "Alkali Trace Elements Observed by MarSCoDe LIBS at Zhurong Landing Area on Mars: Quantitative Analysis and Its Geological Implications"
<p>This dataset includes the normalized peak areas of MarSCoDe scietific targets used in Table 3 of the corresponding article. The specific peaks are listed below.</p> <p>Data Set S1: Li 670.97 nm, Fe 260.04 nm, Al 309.37 nm, Ca 318.03 nm, Mg 518.15 nm, K 766.70 nm, and Na 818.55/819.70 nm.</p> <p>Data Set S2: Sr 421.67 nm, Fe 260.04 nm, Al 309.37 nm, Ca 318.03 nm, Mg 518.15 nm, K 766.70 nm, and Na 818.55/819.70 nm.</p> <p>Data Set S3: S 564 nm, Fe 260.04 nm, Al 309.37 nm, Ca 612.39 nm, Mg 448.24 nm, K 766.70 nm, and Na 589.16/589.76 nm.</p>
Jupiter's ground-based observations on 2018 May 24th-27th from the Very Large Telescope Imager and Spectrometer instrument
<p>The Very Large Telescope Imager and Spectrometer (VISIR) instrument on ESO's Very Large Telescope (VLT) has been used to support the NASA's Juno mission since 2016. The present dataset was collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programme 0101.C-0073(A)). The 2018 May 24th-27th dataset provides a comprehensive view of Jupiter's pole-to-pole thermal, chemical, and aerosol structure ; including the Great Red Spot; and the auroral-related heating in the southern polar stratosphere; after retrieval calculations.</p> <p>Here, we provide the destriped, cleaned, calibrated and projected data of 2018 May 24h to 27th dataset.</p> <p> </p> <p>For the 7.9µm filter, we have added the radial Doppler velocities files.</p>
Matching Registered Nurse Services With Changing Care Demands in Psychiatric Hospitals: A Multicenter Observational Study (MatchRN Psychiatry Study). Data from the Nurse Survey
<p>Match<sup>RN</sup> Psychiatry is a multicenter observational study of Swiss psychiatric hospitals. This study's purpose is to describe health care quality by exploring hospital structures such as nurse staffing and the work environment; processes such as the rationing of care; nurse outcomes, including job satisfaction and work-life balance. The sample for this study included approximately 1200 nurses from 115 units of 13 psychiatric hospitals in Switzerland's German-speaking region. The nurse survey covered three dimensions: work environment, patient safety climate, and the rationing of care. Data were collected between September 2019 and June 2021. The Match<sup>RN</sup> Psychiatry study is funded by the participating psychiatric hospitals and the Swiss Psychiatric Nursing Leaders Association (Vereinigung Pflegekader Psychiatrie Schweiz).</p>
ECMWF real-time forecast MJO RMMs and observations
<p>This data is downloaded from https://confluence.ecmwf.int/display/S2S/Tools#Tools-MJORMMsindices</p> <p> </p> <p>The data is only used for research purpose. If you have any question about this data, please contact the ECMWF directly.</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.