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10,016 results for “Observation”
The groundwater-level anomalies observed by 446 monitoring wells in the North China Plain during 2003-2017
<p>The groundwater level anomalies observed by 446 monitoring wells in the North China Plain (NCP) during 2003-2017. The monthly GWL data were obtained from the Ministry of Water Resources of China and the groundwater yearbooks. The wells were relatively evenly distributed in NCP region . The GWL anomalies were calculated by subtracting the mean GWL values during 2003-2017 from the monthly time series values of each well. Unit: meter</p>
Observations for intensive stations in the Finnish Archipelago Sea 2006-2015
<p>Calibration data for the FICOS water quality simulator. To use them with the ficosUQ package, download the intensiveStationsData.xlsx file and place it in the ficosUQ/data folder.</p> <p>First sheet contains this description of the data, while sheets 2 to 4 contain data from stations in Brändö, Seili and Utö.</p> <p>Curated by Elina Miettunen, Finnish Environment Institute, 2.3.2023</p> <p>Description translated by Karel Kaurila, University of Helsinki, 2024</p> <p>This data set contains medians for the observations collected from intensive stations in Brändö, Seili and Utö between 2006-2015 for the surface and deep layers. The data set was curated such that its observations can be meaningfully compared to the predictions from the FICOS model in the water formation resolution. Data from each station is on its own sheet. The columns describing the time and place of the obsevations as follows:</p> <p>- Date : day the of the observation, dd/mm/yyyy<br>- VPD_name : official code for the water formation<br>- Block : internal code for the water formation in FICOS<br>- Lat, Lon : latitude and longitude</p> <p>In each table, the columns use the same names as the results.hdf5 file produced by FICOS and all observations are in the same units as the FICOS predictions. The column names ending in 0 correspond to observations on the surface layer (0-10 m), while the ones ending in 1 correspond to the deep layer (20-40 m). The observations used in calibration are the following:</p> <p>- cDIN_0(1) : Dissolved inorganic nitrogen (mg/m3)<br>- cDIP_0(1) : Dissolved inorganic phosphorus (mg/m3)<br>- Chla : Chlorophyll a (mg/m3)<br>- cC : Cyanobacteria biomass (mg N/m3)<br>- cA : Biomass for other algae (mg N/m3)</p> <p>Note that FICOS does not predict Chla directly, but instead as a combination of cA and cC.<br>The data are picked from a data set processed by Elina in December 2019 that contained all of the observation stations determined by Finnish Environment Institute to be representative of their water formations. This data was obtained from Hertta (Finnish Environment Institute’s open database, available in Finnish only) as follows:</p> <p>- Observations less than 3 m from the bottom of the sea were not included. Obvious outliers, such as very high nutrient concentrations that cannot be meaningfully compared to FICOS predictions, were also not included.</p> <p>- Observations from depths 0-10 m correspond to the FICOS model’s surface layer, while observations from depths 20-40 m correspond to FICOS model’s deep layer.</p>
Observing and controlling electrocyclic ring-opening reaction of cyclohexadiene at conical intersection
<div> <div> <div> <p>The photoinduced cyclohexadiene/hexatriene ring-opening reaction has been modeled via explicit wave-packet propagation (Quantum dynamics-QD) on a two-dimensional (2D) ab-initio Hamiltonian (H) parametrized with multiconfigurational perturbative wavefunction theory (XMS-CASPT2). Electronic coherences and omptimal control pulses to enhance the ring-opening quantum yield have been modeled. </p> </div> </div> </div>
Observation of vortices in a dipolar supersolid
<p>Supersolids are states of matter that spontaneously break two continuous symmetries: translational invariance<br>due to the appearance of a crystal structure and phase invariance due to phase locking of single-particle wave<br>functions, responsible for superfluid phenomena. While originally predicted to be present in solid helium,<br>ultracold quantum gases provided a first platform to observe supersolids, with particular success coming<br>from dipolar atoms. Phase locking in dipolar supersolids has been probed through e.g. measurements of<br>the phase coherence and gapless Goldstone modes, but quantized vortices, a hydrodynamic fingerprint of<br>superfluidity, have not yet been observed. Here, with the prerequisite pieces at our disposal, namely a method to<br>generate vortices in dipolar gases and supersolids with two-dimensional crystalline order, we report on<br>the theoretical investigation and experimental observation of vortices in the supersolid phase. Our work reveals<br>a fundamental difference in vortex seeding dynamics between unmodulated and modulated quantum fluids. This<br>opens the door to study the hydrodynamic properties of exotic quantum systems with multiple spontaneously<br>broken symmetries, in disparate domains such as quantum crystals and neutron stars.</p>
Observed soil water retention and soil hydraulic conductivity data and fits to those data by RIAfitter and KRIAfitter
<p>Weber soils SWRC.7z: zipped file with the Windows folders that hold all input and output data related to fitting the RIA parameterization (de Rooij, 2022, 2024a) to soil water retention data of 13 soils.</p> <p>Weber soils UHCC.7z: the zipped folder structure with fits of several models (de Rooij, 2024b, c) for the unsaturated hydraulic conductivity for the same 13 soils as the other file. The fitting code (KRIAfitter) used the fitted parameters of the other file plus soil hydraulic conductivity data to produce the fits.</p> <p>Both files contain Excel files that process the input and output of all fits.</p> <p>The soils are taken from Weber et al. (2019). The support of T.K.D. Weber in providing the data is gratefully acknowledged,</p> <p>N.B. The output format may differ somewhat from that reported in the User Manual of de Rooij (2024c) because the fits were used to improve details of the code and the way information is provided in the output files. These variations do not affect the parameter fitting process.</p> <p>References</p> <p>de Rooij, G. H.: Technical note: A sigmoidal soil water retention curve without asymptote that is robust when dry-range data are unreliable, Hydrol. Earth Syst. Sci., 26, 5849–5858, doi: 10.5194/hess-26-5849-2022, 2022.</p> <p>de Rooij, G.: Fitting the parameters of the RIA parameterization of the soil water retention curve (2.0), Zenodo [code], doi: 10.5281/zenodo.6491978, 2024a.</p> <p><br>de Rooij, G. H.: Averaging or adding domain conductivities to calculate the unsaturated soil hydraulic conductivity, Vadose Zone J., e20329, doi: 10.1002/vzj2.20329, 2024b.</p> <p><br>de Rooij, G.: Fitting the junction model and other models for the unsaturated hydraulic conductivity curve: KRIAfitter, Zenodo [code], doi: 10.5281/zenodo.14047942, 2024c.</p> <p><br>Weber, T. K. D., Durner, W., Streck, T., and Diamantopoulos, E.: A modular framework for modeling unsaturated soil hydraulic properties over the full moisture range, Water Resour. Res., 55, 4994-5011, doi: 10.1029/2018WR024584, 2019.</p> <p> </p>
Data from: Sporadic sampling not climatic forcing drives observed early hominin diversity
The role of climate change in the origin and diversification of early hominins is hotly debated. Most accounts of early hominin evolution link observed fluctuations in species diversity to directional shifts in climate or periods of intense climatic instability. None of these hypotheses, however, have tested whether observed diversity patterns are distorted by variation in the quality of the hominin fossil record. Here, we present a detailed examination of early hominin diversity dynamics, including both taxic and phylogenetically corrected diversity estimates. Unlike past studies, we compare these estimates to sampling metrics for rock availability (hominin-, primate-, and mammal-bearing formations) and collection effort, in order to assess the geological and anthropogenic controls on the sampling of the early hominin fossil record. Taxic diversity, primate-bearing formations, and collection effort show strong positive correlations, demonstrating that observed patterns of early hominin taxic diversity can be explained by temporal heterogeneity in fossil sampling rather than genuine evolutionary processes. Peak taxic diversity at 1.9 million years ago (Ma) is a sampling artefact, reflecting merely maximal rock availability and collection effort. In contrast, phylogenetic diversity estimates imply peak diversity at 2.4 Ma and show little relation to sampling metrics. We find that apparent relationships between early hominin diversity and indicators of climatic instability are, in fact, driven largely by variation in suitable rock exposure and collection effort. Our results suggest that significant improvements in the quality of the fossil record are required before the role of climate in hominin evolution can be reliably determined.
MAVEN Observations of H– Ions at Mars (Dataset)
<p>Datasets used in the analysis of Mars Atmosphere and Volatile EvoutioN (MAVEN)'s observations of H<sup>–</sup> ions at Mars, by Jones et al.</p> <p>Density data: Contains the orbit number, upstream solar wind energy (eV), upstream solar wind density (cm<sup>-3</sup>), H<sup>–</sup> ion density (cm<sup>-3</sup>), and H<sup>+</sup> ion density (cm<sup>-3</sup>) for observations made by MAVEN's Solar Wind Ion Analyzer (SWIA) and Solar Wind Electron Analyzer (SWEA).</p> <p>Solar longitude data: Contains the average Martian solar longitude (degrees) corresponding to each MAVEN orbit number.</p> <p>Sunward flux data: Contains differential energy fluxes (eV / (cm<sup>2</sup> sec sr eV)) for the sunward velocity direction, as measured at 64 detector energies (first row of dataset). Each row corresponds to a different MAVEN orbit.</p> <p>Anti–sunward flux data: Contains differential energy fluxes (eV / (cm<sup>2</sup> sec sr eV)) for the anti–sunward velocity direction, as measured at 64 detector energies (first row of dataset). Each row corresponds to a different MAVEN orbit.</p> <p>Orbit number data: Contains the MAVEN orbit numbers used in the analysis.</p>
Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations: Preprocessed Satellite and In-situ observation datasets
<p>This record includes all of the prepared data used in the manuscript, "Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations" (citation information forthcoming). As a part of this manuscript, we analyzed the ability for machine learning models to extract sea surface information (from salinity, temperature, sea height anomaly) to predict mixed layer depth. In this manuscript there are two experimental datasets: (1) info derived from CESM POP2 ocean model dataset (1989-1998), and (2) info derived from a combination of satellite sources and MLD from Argo profiles. More details below. </p> <p>All of these data files are preprocessed and organized to be used with the ml-ocean-bl github code found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/.</p> <ul> <li><strong>CESM POP2 Ocean model dataset</strong></li> </ul> <p>Preprocessed sea surface salinity (SSS), temperature (SST), sea surface height anomalies (SSH), and ocean mixed layer depth (MLD, or HMXL) derived from the CESM POP2 Ocean model. Specifically, CESM POP2 model in a hindcast forced by JRA55do atmospheric reanalysis from 1958 to present and initialized with an oceanic climatology as in e.g. <a href="https://journals.ametsoc.org/view/journals/phoc/aop/JPO-D-20-0217.1/JPO-D-20-0217.1.xml">Deppenmeier et al. (2021)</a>. The model outputs include the ocean mixed layer depth (MLD), sea surface salinity (SSS), sea surface temperature (SST), and sea height anomaly (SSH) at a temporal frequency of 5-days and an approximate latitude and longitude resolution of 0.1 degrees.</p> <p>Relevant files:</p> <ol> <li>full_EPO.nc, full_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, MLD for the equatorial Pacific Ocean (EPO) and southern Indian Ocean (SIO) (see manuscript for details). Data is regridded onto a 1/2 degree lat/lon 5 day grid to correspond with data used for Argo datasets (see below).</li> </ul> </li> <li>clim_EPO.nc, clim_SIO.nc, clim_std_EPO.nc, std_clim_EPO.nc, std_clim_SIO.nc <ul> <li>NetCDF4 containing mean and standard deviation climatologies of SSS, SST, SSH, and MLD for EPO and SIO.</li> </ul> </li> <li>std_anomalies_EPO.nc, std_anomalies_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, and MLD standardized anomalies for EPO and SIO. This is the dataset directly used for training in aforementioned manuscript. Use with ml-ocean-bl/ml-ocean-test/data. </li> </ul> </li> </ol> <ul> <li><strong>Satellite and Argo datasets</strong></li> </ul> <p>Preprocessed satellite sea surface salinity (SSS), temperature (SST), and sea surface height anomalies (SSH) and Argo-based mixed layer depth (MLD) profiles. Original data can be found at:</p> <p>(SST): Remote Sensing Systems. 2017. MW optimum interpolated SST data set. Ver. 5.0. PO.DAAC, CA, USA. Further information available at at <a href="https://doi.org/10.5067/GHMWO-4FR05">https://doi.org/10.5067/GHMWO-4FR05</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/REMSS/mw_OI/v5.0/.</p> <p>(SSS): Oleg Melnichenko. 2018. Aquarius L4 Optimally Interpolated Sea Surface Salinity. Ver. 5.0. PO.DAAC, CA, USA. Further information at <a href="https://doi.org/10.5067/AQR50-4U7CS">https://doi.org/10.5067/AQR50-4U7CS</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SalinityDensity/aquarius/L4/IPRC/v5/7day. </p> <p>(SSH): Zlotnicki, Victor; Qu, Zheng; Willis, Joshua. 2019. SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609. Ver. 1812. PO.DAAC, CA, USA. Information available at <a href="https://doi.org/10.5067/SLREF-CDRV2">https://doi.org/10.5067/SLREF-CDRV2</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SeaSurfaceTopography/merged_alt/L4/cdr_grid</p> <p>(MLD) Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019) processed dataset available at https://doi.org/10.5281/zenodo.4291175.</p> <p>Relevant files:</p> <ol> <li>https://github.com/NCAR/ml-ocean-bl/mloceanbl/preprocess_mld.py and .../preprocess_sss_sst_ssh.py. <ul> <li>Preprocessing code</li> </ul> </li> <li>sss_sst_ssh_anomalies.nc. <ul> <li>Regridded and resampled SSS, SST, SSH onto a 1/2 degree lat/lon 7day grid. Contains preprocessed seasonal data along with anomalies.</li> </ul> </li> <li> mldb_climatology_climatologystd_binned.nc <ul> <li>Smoothed argo-based mixed layer depths are used to calculate climatologies and standardized climatologies. 4 degree lat/lon gridded climatologies.</li> </ul> </li> <li>mldb_full_anomalies_stdanomalies_climatology_stdclimatology.nc <ul> <li>Contains the Argo profile-derived MLD, anomalies, standard anomalies, climatologies, and standardized climatologies with corresponding argo locations, times, and corresponding weeks. </li> </ul> </li> <li>equatorial_pacific_model_oi_re.nc, southern_indian_model_oi_re.nc <ul> <li>Model outputs for the Equatorial Pacific Ocean and Southern Indian Ocean. These gridded files contain the model outputs (vlcnn, vlcnn variance, OI, OI variance, reanalysis, and reanalysis variance - see manuscript for nomenclature details) at each of the 200 weeks available. It should be noted that, in the equatorial Pacific Ocean, the lat/lon location of (-138.75, -9.75) is masked during the training and filled with a NaN in the .nc files. </li> </ul> </li> </ol> <p> </p> <p> </p> <p>Contact D. Foster with any questions.</p> <p> </p>
APHIS Asian Longhorned Tick Observation Data 2010-2020
<p>NVSL tick observation data from APHIS (NVSL, 2020). NVSL is a national dataset of passive surveillance observations of tick species that impact animal health. NVSL samples are submitted when a suspected ALT is observed by regulatory field veterinarians, state and university diagnostic laboratories, and Food Safety and Inspection Service veterinarians. Data was collected from host animals, including humans, and environmental sources such as tick drags, carbon dioxide (CO2) traps, and animal inspections. Ticks identified on animals that were recently imported into the U.S. are not included. The first observation of ALT occurred in August 2010 and data is current as of November 2020. Data includes tick species, date, state, source (environment vs host) and number of ALT by life stage.</p>
Data for "Observation of a Superradiant Quantum Phase Transition in an Intracavity Degenerate Fermi Gas"
<p>This dataset is corresponding to the article "Observation of Superradiant Quantum Phase Transition in an Intracavity Degenerate Fermi Gas".</p>
WACCMX+DART output files associated with the publication "The Impact of Assimilating COSMIC-2 Observations of Electron Density in WACCMX"
<p>WACCMX+DART output files that are associated with the publication "The Impact of Assimilating COSMIC-2 Observations of Electron Density in WACCMX". Data files include DART diagnostics for the COSMIC-2 and Control experiments, as well as the vertical plasma drift velocities.</p>
Fig. 7 in (Alismataceae) de Guadeloupe (Petites Antilles). Observations palynologiques, cytogénétiques et chimiques
Fig. 7. — Formule chimique du β-D-glucoside de β-sitostérol.
Fig. 6. — Chromatogramme des extraits d in (Alismataceae) de Guadeloupe (Petites Antilles). Observations palynologiques, cytogénétiques et chimiques
Fig. 6. — Chromatogramme des extraits d'Echinodorus tenellus (1 et 3) et d'E. zombiensis (2 et 4).
Dataset: Comparative analysis of the coordinated motion of Hsp70s from different organelles observed by single molecule three-color FRET
<p>Dataset: Comparative analysis of the coordinated motion of Hsp70s from different organelles observed by single molecule three-color FRET</p>
Figure 3 in Two new species of Bulbamphiascus Lang (Copepoda: Harpacticoida: Diosaccidae) from Scotland and the Isles of Scilly, with additional observations on B. denticulatus (Thompson)
Figure 3. Bulbamphiascus incus sp. nov. ♀: (A) P1; (B) P3. ": (C) P3 exopod 3 anterior.
Figure 6 in The life-cycle of Hemigalichus chrotogale sp. nov. (Acari: Listrophoridae), with comparative observations on listrophorid morphology
Figure 6. Hemigalichus chrotogale sp. nov., heteromorphic male in dorsal view.
Data for the research article: "Detecting Seismo-ionospheric Anomalies Possibly Associated with the 2019 Ridgecrest (California) Earthquakes by GNSS, CSES and Swarm Observations"
<p>The zip archives contain 10 .mat files (MATLAB readable). Each .mat file can be loaded into the MATLAB workspace using the <em>load</em> command.</p>
Figure 9 in Abundance, reproduction, and feeding of three species of British terrestrial planarians: Observations over 4 years
Figure 9. Microplana terrestris. The number of hatchlings emerging from 13 viable cocoons.
Figure 2 in Abundance, reproduction, and feeding of three species of British terrestrial planarians: Observations over 4 years
Figure 2. The vegetable plots. Planks are placed around the perimeter.
Figure 1. Macrobiotus ciprianoi n in Observations on the ''tenuis group'' (Eutardigrada, Macrobiotidae) and description of a new Macrobiotus species
Figure 1. Macrobiotus ciprianoi n. sp. Habitus of the holotype. Scale bar: 60 mm.
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
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.