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10,013 results for “observation”
Sparse observations induce large biases in estimates of the global ocean CO2 sink: an ocean model subsampling experiment
<p>Dataset underlying the analysis in Hauck et al., 2023: Sparse observations induce large biases in estimates of the global ocean CO<sub>2</sub> sink - an ocean model subsampling experiment, Philosophical Transactions A</p> <p>Surface ocean partial pressure of CO<sub>2 </sub>(pCO<sub>2</sub>) and air-sea CO<sub>2</sub> flux reconstructions, using two mapping methods (MPI-SOM-FFN, CarboScope) three different sampling masks: SOCAT, SOCAT+SOCCOM, IDEAL (based on bgcArgo, Roemmich et al., 2019).</p> <p>Also, all FESOM-REcoM output fields that were used in the reconstructions are provided.</p> <p>We further provide the three masks that were used for subsampling: SOCAT, SOCAT+SOCCOM, IDEAL (bgcArgo).</p> <p> </p>
Global Meteor Network observations of Crew-5 Dragon trunk re-entry 2023-04-27
<p>This dataset contains video observations by some stations of the Global Meteor Network of the re-entry of the Crew-5 dragon trunk above Arizona on 2023-04-27 around 08:52 UTC.</p> <p>There are several types of files:</p> <ul> <li>FF files: these are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values.</li> <li>FR files: compressed video recordings of detected fireballs. These can be read with the RMS software.</li> <li>MP4 files: rendered movies of combined FF and FR files for one station (more can be made with FR_binviewer from RMS software).</li> <li>Platepar-files: these contain astrometry corresponding to the FITS files. These can be interpreted by the RMS software.</li> <li>ECSV files: these contain manually picked points (with SkyFit2.py from RMS) along the track of the reentry. For each point, time and apparent coordinates are recorded. These files can be interpreted by the WesternMeteorPyLib trajectory solver.</li> <li>trajectory-points.txt: solutions from the trajectory solver.</li> <li>reentry-map-v4.png: a rendered map of the trajectory (made in QGIS).</li> <li>compilation.png: rendered version of the FF-files of most stations.</li> </ul> <p>The files can be processed with the software in https://github.com/CroatianMeteorNetwork/RMS and https://github.com/wmpg/WesternMeteorPyLib.</p>
Wearable data and self reported fatigue scores from a remote observational study in Sjogren's disease, SLE and healthy participants
<p>Fatigue is a subjective, complex, and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. To date, there is no consensus about reliable quantitative assessments of fatigue.</p> <p>We collected observational data for a period of one month from 296 participants (healthy volunteers, Sjogren’s Syndrome, and Systemic Lupus Erythematosus patients) in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity, and sleep daily features, and app-based daily and weekly questions (e.g., pain, mood, general physical activity, and fatigue). When matching both sensor data and PROs, and excluding missing data, the dataset contains data from 183 subjects and 3950 recording days.</p> <p>The analysis of the association of digital data to self-reported fatigue was published at <em><strong>Rao C., et. al. (2023), Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease, Frontiers in Digital Health</strong></em>.</p> <p>Demographics, digital parameters, and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Details on the data files can be found under README.txt.</p>
LAGOS-US DEPTH v1.0: Data module of observed maximum and mean lake depths for a subset of lakes in the conterminous U.S.
The LAGOS-US LAKE DEPTH v1.0 module (hereafter, called DEPTH) contains in situ measurements of lake depth for a subset of all lakes (n = 17,675) in the conterminous U.S. > 1 ha (3.7% of 479,950) that are in the LAGOS-US LOCUS v1.0 data module (Smith et al. 2021). All 17,675 lakes in DEPTH have a maximum depth value and 6,137 lakes have a mean depth. DEPTH includes approximately 65 data sources obtained from community, government, and university monitoring programs, as well as academic reports and commercial websites. DEPTH includes lake identifiers, lake location, lake area, lake depth (both maximum and mean depth when available), source information, and data flags. The unique lake identifier (lagoslakeid) for all lakes is the same one used in LAGOS-US LOCUS v1.0.
Journey North - Oriole observations by volunteer community scientists across Central and North America (1997-2020)
This data package contains oriole migration data consisting of 14,476 total observational reports from 1997 - 2020 across North and Central America. These data were collected by 7,331 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Oriole Project is an ongoing study of oriole phenology conducted at broad spatial and temporal scales. Since 1997, community scientists have tracked first arrival dates and breeding and feeding behavior as well as the onset of fall migration and presence of oriole species throughout the winter months in the United States. Focal species are the Baltimore Oriole (Icterus galbula), Bullock’s Oriole (Icterus bullockii), and Orchard Oriole (Icterus spurius fuertesi). Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Oriole Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Journey North - Common Loon and Ice-Out observations by volunteer community scientists across North America (1997-2020)
This data package contains Common Loon migration and ice melt data consisting of 9,800 total observational reports from 1997 - 2020 across North America. These data were collected by 1,437 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Loon and Ice-Out Project is an ongoing study of loon and ice melt phenology conducted at broad spatial and temporal scales. Since 1997, community scientists have tracked first arrival dates of Common Loons (Gavia immer) and ice that has melted from bodies of water in the United States. Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Common Loon and Ice-Out Project Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Journey North - Gray Whale observations by volunteer community scientists across the Eastern Pacific Ocean (1997-2020)
This data package contains Gray Whale migration data consisting of 1,546 total observational reports from 1997 - 2020 across the Eastern Pacific Ocean. These data were collected by 163 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Gray Whale Project is a study of Gray Whale phenology conducted at broad spatial and temporal scales. Since 1997, community scientists have tracked the migration of Gray Whales (Eschrichtius robustus) through the Eastern Pacific Ocean. Observers also provide estimates of the number of whales sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Gray Whale Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
LTREB: Aboveground biomass, plant density, annual aboveground productivity, plant heights and snail observations in control and fertilized plots in a Spartina alterniflora-dominated salt marsh, North Inlet, Georgetown, SC: 1984-2025
Aboveground biomass and plant density were measured non-destructively as a component of a long-term project seeking to understand how salt marsh primary production and sediment chemistry respond to anthropogenic (e.g. eutrophication) and natural (e.g. sea-level rise) environmental change. Feedbacks between plants, sediments, nutrients and flooding were investigated with particular attention to mechanisms that keep marshes in equilibrium with sea level. Biomass was calculated from plant height measurements using allometric equations. Annual productivity was calculated from approximately-monthly biomass estimates. In addition to plant height measurements, observations of snails in sample plots were recorded. Other data collected as part of the project include marsh surface elevation and porewater nutrient concentrations. These data have been used to develop the Marsh Equilibrium Model, an important tool for coastal resource managers. Sampling occurred at Spartina alterniflora-dominated salt marsh sites in North Inlet, a relatively pristine estuary near Georgetown, SC on the SE coast of the United States. North Inlet is a tidally-dominated, bar-built estuary, with a semi-diurnal mixed tide and a tidal range of 1.4m. The 25-km2 estuary is comprised of about 20.5 km2 of intertidal salt marsh and mudflats, and 4.5 km2 of open water. Sampling began at one location in 1984, and at three additional locations in 1986. Sampling occurred approximately monthly through 2025. The study is on-going. There are four sampling locations at two sites. Two locations are in the low marsh; two locations are in the high marsh. One high marsh location had control sampling plots in addition to plots fertilized with nitrogen and phosphorus.
Metabolism dataset: one year of high-frequency temperature, dissolved oxygen, wind, photosynthetically active radiation observations and low-frequency nutrient data for 58 lakes in the Global Lake Ecological Observatory Network
Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We compiled data from 58 diverse and globally distributed and mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP and ER derived from those data. In addition, the data package includes median in-lake and stream concentrations of dissolved organic carbon and total phosphorus for a subset of 18 of those lakes.
Nearshore high-frequency temporal water quality observations and process-based modeling of aquatic ecosystem metabolism in Lake Tahoe completed by members of the Blaszczak Lab at the University of Nevada Reno, 2021-2023
The overarching goal of this project was to develop a process-based understanding of how watershed-to-lake connections drive nearshore productivity dynamics in a large oligotrophic mountain lake (Lake Tahoe). We addressed this goal through a combined approach of high-frequency sensor deployment and maintenance, ecosystem metabolism modeling, laboratory incubations, and routine monitoring of water chemistry and other parameters. The data we collected as part of this project and the ecosystem metabolism estimates we generated demonstrate how variable ecosystem productivity is in time and space in the nearshore of Lake Tahoe. Although maintenance of the sensor arrays during the exceptional winter of 2023 was challenging, we were able to capture the data necessary to estimate a complete time series of metabolic activity across two years with very different hydroclimatic conditions. Throughout this project we accomplished the following: 1. We generated over two years of daily estimates of ecosystem metabolism (gross primary productivity, ecosystem respiration, and net ecosystem productivity) from multiple locations on both the east and west shores of the lake and from areas in close proximity to and far away from stream water inflows. 2. We measured ammonium (NH4+) and nitrate (NO3-) concentrations in surface water samples from both Glenbrook and Blackwood creeks and the nearshore of Lake Tahoe for over two years. 3. We quantified rates of NH4+ and NO3- uptake in benthic samples of the dominant substrate type collected during peak streamflow, the receding limb, and baseflow conditions in 2023 from multiple locations in the nearshore using established laboratory incubation methods. 4. Finally, we used a combination of time series models and structural equation modeling to integrate our results and improve understanding of the direct and indirect effects of hydroclimatic variability on observed patterns in ecosystem metabolism in the nearshore. See this git code repository
ClimHyrdoDB Archive: Meteorologic and hydrologic observations from LTER and USFS sites, 2001-2020 - orignal database format
This dataset is an archive of the ClimHydroDB database, which was actively used from early 2001 to mid 2020. The database contained contributions from 62 contributors (primarily from the LTER Network and US Forest Service) and 672 research sites. Data records total approximately 16 million (raw) or 1.6 million (aggregated) for 22 meteorologic or hydrologic variables. This archive contains the 23 core tables of the ClimHydroDB database as text tables of comma separated values, plus the database entity relationship diagram (ERD), User Guide, database table descriptions (DDL, SQL script), and a zip file of related documents and presentations. Database design: At last upgrade, the database was implemented in Microsoft SQL Server 2008 (see DDL for more information). Database tables are primarily in a key-value pair arrangement, with controlled input for many fields, and extensive cross referencing. This design allows many types of descriptors to be assigned, e.g., for the types of activities taking place at research stations, or for physical parameters to describe a research area itself. The EML metadata for tables holding controlled vocabularies are described using the string “List of …”. Cross reference tables are described in metadata as such, including the parent table names. Database history: To facilitate intersite research within the LTER network, site data managers developed a system to provide climatic summaries dynamically, called ClimDB. Later funding from the U. S. Forest Service allowed the original database to be expanded to include hydrologic variables, and the combined database was renamed ClimHydroDB in 2003. The database also harvested real-time streamflow data from USGS gauging stations, using code developed by the Georgia Coastal Ecosystem LTER. As of 2021, the ClimHydroDB content is available as data packages from individual contributing sites, each containing identically formatted text tables in the ODM 1.1 format, for integration with CUAHSI tools
Tree Health Conditions (mortality, damage, disease, bark beetles) in Fuel Reduction Treatments Located Near Communities in Interior Alaska and the Cook Inlet Region of Alaska - Observations from July-August 2023
This dataset contains tree-, transect-, and site-level observations of forest stands at sites that received a fuel reduction treatment. Tree-level observations include species, diameter, living status, damage, disease, and bark beetle presence. Transect-level observations include level of coarse woody debris and bark beetle presence. Sites are categorized by region (recent/ongoing spruce beetle oubreak or endemic spruce beetle population levels) and treatment type (hand-thinned or mechanincally felled and masticated). These observations are from July-August 2023. Sites are located near communities in Interior Alaska and the Cook Inlet Region.
Variation in Landsat 8-estimated land surface temperature with elevation from Spartina alterniflora marsh cross sections in the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site and Virginia Coast Reserve (VCR) LTER sites for winter and summer observations spanning 2013-2018
We estimated land surface temperature from top of atmosphere brightness temperature provided by Landsat 8's band 10 (a thermal band). We collected these measurements first for Spartina alterniflora dominated marsh near the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) eddy covariance flux tower. Measurements were collected from pixels along three east-west cross sections that spanned a marsh edge to interior gradient. We extracted Landsat 8 data for all available cloud-free low tide dates during August, September, January and February during the years 2013 to 2018 and associated these with marsh elevation information from a 1 m^2 Digital Elevation Model (DEM), created by Haldik et al 2013, also available from the GCE data catalog (http://dx.doi.org/10.6073/pasta/4c5187ef603f70cd0a77ece24ef0fed9). We rescaled the DEM to the coarser spatial resolution of Landsat 8 (30 x 30 m) where the rescaled elevation was the mean of the constituent DEM values. Ultimately, we used generalized additive models to relate land surface temperature to elevation, while accounting for variation from spatial proximity, transect and sample date. These models revealed that land surface temperature was negatively related to marsh elevation on the marsh platform. We then confirmed the generality of this pattern by rederiving these same relationships for three cross sections of Spartina alterniflora marsh at Virginia Coast Reserve (VCR) LTER for winter sampling dates only (data also included here). DEM data for VCR LTER are available at https://www.vcrlter.virginia.edu/gisdata/LIDAR/USGS2015/. We used custom R functions that can convert Landsat 8 top of atmosphere brightness temperature or top of atmosphere radiance from band 10 data to land surface temperature, which are available at https://github.com/jloconnell/convert_top_of_atmosphere_thermal_to_land_surface_temperature. Currently, a provisional land surface temperature product is available on earthexplorer.usgs.gov, w
Graduated rain gauge (GRG) precipitation observations from 21 sites at the Jornada Basin LTER site, 1989-ongoing
This dataset contains long-term precipitation measurements from graduated rain gauges (GRGs) at 21 sites in the Jornada Basin of southern New Mexico, USA. Gauges are located on the Jornada Experimental Range (JER) and the Chihuahuan Desert Rangeland Research Center (CDRRC), and this set of gauges includes all 15 net primary production (NPP) study sites monitored by the Jornada Basin LTER program. At each site a 4 inch diameter cylindrical graduated rain gauge (11" x 0.01" capacity) is mounted on a 4x4 inch diameter redwood post or a wooden exclosure post next to gate at or near each site. For NPP sites, the primary collection is made on the day that monthly hydroprobe soil water content measurements are made. This enables correlation of precipitation with belowground soil water content. Additional data collections during the month may be made in coordination with other studies. Observations at each site come primarily from GRGs. However, at some sites in the NPP study, GRGs were not installed until later, and the nearest available rain gauge in the area has been used to gapfill the precipitation record prior to installation (details in methods section). Rain gauge identity and field measurement date is recorded with each observation in the data file. Other gauge types that may be listed are the Standard Can Gauge (DSRG or dipstick rain gauge), Belfort Weigh Bucket Rain Gauge (WBRG), and Qualimetrics Tipping Bucket Rain Gauge (TBRG). Data collection is ongoing for all 21 gauges in this dataset.
Systems Ecology Lab Bajada Site: Weekly phenology observations of shrub and grass species at a bajada site at Jornada Basin LTER from 2010-2019
This dataset contains field-observed plot level phenology data for three perennial shrubs and one perennial grass at a bajada site at the Jornada Basin LTER site from 2010 to present. Shrubs include creosote (Larrea tridentata), honey mesquite (Prosopis glandulosa), and tarbush (Fluorensia). The grass is bush muhly grass (Muhlenbergia porteri). The protocols and phenophase categories were developed by the US National Phenology Network (US-NPN) to detect the different life-cycles of the plant, from leaf development (breaking leaf buds, young unfolded leaves, percentage of leaves in canopy), to flower development (flower buds, open flowers, full flowering), and presence of fruit (ripe fruits, fruits from past growing season). Field data sheets were modified from the US-NPN to record the presence or absence of each phenophase recorded at each observation period for each tagged plant. This dataset is complete.
Red Knot observations on the Virginia Coast, 2007-2019
Red Knot observations on the Virginia Coast, 2007-2019 Understanding factors that influence a species' distribution and abundance across the annual cycle is required for range-wide conservation. Thousands of imperiled red knots (Calidris cantus rufa) stop on Virginia's barrier islands each year to replenish fat during spring migration. We investigated the variation in red knot presence and flock size, the effects of prey on this variation, and factors influencing prey abundance on Virginia's barrier islands. We counted red knots and collected potential prey samples at randomly selected sites from 2007 - 2018 during a two-week period during early and peak migration. Core samples contained crustaceans (Orders Amphipoda and Calanoida), blue mussels (Mytilus edulis), coquina clams (Donax variabilis), and miscellaneous prey (horseshoe crab eggs (Limulus polyphemus), angel wing clams (Cyrtopleura costata), and other organisms (e.g., insect larvae, snails, worms)). Estimated red knot numbers in Virginia during peak migration were highest in 2012 (11,959) and lowest in 2014 (2,857; 12-year peak migration x̄ = 7,175, SD = 2,869). Red knot and prey numbers varied across sampling periods and substrates (i.e., peat and sand). Red knots generally used sites with more prey. Miscellaneous prey (x̄ = 18.85/core sample, SE = 0.88) influenced red knot presence at a site early in migration, when we only sampled on peat banks. Coquina clams (x̄ = 11/core sample, SE = 0.30) and blue mussels (x̄ = 0.94/core sample, SE = 0.04) affected red knot presence at a site during peak migration, when we sampled both substrates. Few relationships between prey and red knot flock size existed, suggesting that other unmeasured factors determined red knot numbers at occupied sites. Tide and mean daily water temperature affected prey abundance. Maximizing the diversity, availability, and abundance of prey for red knots on barrier islands requires management that encourages the presence of both sand a
Supplementary Material for A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations
<p><strong>Description of the Supplementary Data</strong></p> <p>This record contains tabulated Bayesian and frequentist exclusion limits, profile likelihood maps and posterior probability maps for the publication S. Hoof, A. Geringer-Sameth, and R. Trotta, “<i>A Global Analysis of Dark Matter Signals from 27 Dwarf Spheroidal Galaxies using 11 Years of Fermi-LAT Observations</i>,” <a href="https://doi.org/10.1088/1475-7516/2020/02/012">JCAP 02 (2020) 012</a> (also available on the <a href="https://arxiv.org/abs/1812.06986">arXiv</a>). The dwarf spheroidal galaxies considered in this work are (in alphabetical order): Aquarius II, Boötes I, Canes Venatici I, Canes Venatici II, Carina, Carina II, Coma Berenices, Draco, Draco II, Fornax, Grus I, Hercules, Horologium I, Leo I, Leo II, Leo IV, Leo V, Pegasus III, Pisces II, Reticulum II, Sculptor, Segue 1, Sextans, Tucana II, Ursa Major I, Ursa Major II, and Ursa Minor.</p> <p>This record consists of the following files, which correspond to the limits presented Figures 9 and 10 of the paper. The files can be downloaded individually or obtained by downloading and unpacking the <code>record_2612268.zip</code>. In what follows,<code><strong>[CHANNEL]</strong></code> refers to the annihilation channel used, i.e. <i>e<sup>+</sup> e<sup>-</sup></i>, <i>μ<sup>+</sup> μ<sup>-</sup></i>, <i>τ<sup>+</sup> τ<sup>-</sup></i>, <i>b b̄</i>, <i>c c̄</i>, <i>t t̄</i>, <i>g g</i>, <i>W<sup>+</sup> W<sup>-</sup></i>, and <i>Z Z</i>. We also provide a simple plotting script for <code>Python</code>, named <code>plotting_script.py</code>, which provides basic plotting routines for all files.</p> <ul> <li>One-dimensional limits on <i><σ v></i>. The files <code>oneD_frequentist_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the frequentist limits (at 95% confidence level, 1 degree of freedom) given the value of the WIMP mass <i>m<sub>χ</sub></i> tabulated there. The files <code>oneD_Bayesian_limits_<strong>[CHANNEL]</strong>_channel.txt</code> contain the Bayesian limit (95% credibility conditioned on the mass <i>m<sub>χ</sub></i> tabulated there).</li> <li>Two-dimensional grid of profile likelihood values. The files <code>twoD_profile_likelihood_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain the natural logarithm of the profile likelihood w.r.t. the global best-fit likelihood value for that channel together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. Note that for obtaining the limits in Fig. 10, which are conditioned on the WIMP mass, one needs to rescale the profile likelihood values with the maximum profile likelihood for a given WIMP mass.</li> <li>Two-dimensional grid of posterior probabilities for each combination of <i>m<sub>χ</sub></i> and <i><σ v></i>. The files <code>twoD_posterior_probability_map_<strong>[CHANNEL]</strong>_channel.txt</code> contain probabilities (obtained using a log-uniform prior on <i><σ v></i>) together with the corresponding values of <i>m<sub>χ</sub></i> and <i><σ v></i>. The tabulated values of <i>m<sub>χ</sub></i> and <i><σ v></i> correspond to the centres of the respective bins in <i>m<sub>χ</sub></i> and <i><σ v></i> and the posterior probability contained in them (the total posterior probability sums to 1).</li> </ul> <p>Please contact the authors if you require different data or have any questions regarding this data set.</p>
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output)
<p>The datasets archived here include simulation results 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 netcdf files (9-km resolution EASEv2 grid, period Jan 2010 – Nov 2019, and between 45°N and 70°N, NE Asia excluded) of the four experiments of the manuscript: model-only (open-loop, OL) and data assimilation (DA) for each land model version, that is CLSM without and with the use of the PEATCLSM modules. The highest accuracy is provided by the DA product using PEATCLSM and Tb observations. When referring to the latter product use the name ‘PEATCLSM(Tb)’. We provide three types of netcdf files:<br> • daily_images_*.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack<br> • ObsFcstAna_images_*.nc: Brightness temperature observations, forecasts and analysis (Table 2), provided as netCDF image-chunked image stack<br> • incr_timeseries_*.nc: Data assimilation increments (Table 3), provided as netCDF timeseries-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20200505.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>
Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase (supplementary data)
<p>This a dataset of scanning transmission electron microscopy data showing Pt clusters nucleating in an ionic liquid. For each of the 4 movies there is the raw data (uncompressed .tif and compressed as .avi) and denoised versions (uncompressed .tif and compressed as .avi).</p> <p>This data is for the article "Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase" published in ChemNanoMat (2020), by Trond R. Henninen, Debora Keller and Rolf Erni. (https://onlinelibrary.wiley.com/doi/full/10.1002/cnma.202000503)</p> <p><strong>Movie 1:</strong> Homogeneous nucleations of two clusters in a suspended thin film of ionic liquid. </p> <p><strong>Movie 2: </strong>Heterogeneous nucleation of a ca 8-9 atom cluster near the edge of a nanodroplet supported on a carbon film.</p> <p><strong>Movie 3: </strong>Heterogeneous nucleation of multiple clusters in a nanodroplet. Shortly after nucleation, they coalesce to form disordered nanoclusters.</p> <p><strong>Movie 4:</strong> Heterogeneous nucleation and dissolution cycles of spherical particles in a nanodroplet.</p>
Field line resonances observed by EMMA
<p>This data set contains the fundamental Field Line Resonance (FLR) frequencies estimated by spectral analysis of magnetic signals detected by four station pairs (SUW-BEL, TAR-BRZ, OUJ-HAN, MUO-PEL) of the European quasi-Meridional Magnetometer Array (EMMA), during the periods:</p> <p>2012/09/22-2012/12/01<br> 2013/03/13-2013/03/27<br> 2013/05/25-2013/06/11<br> 2014/02/14-2014/03/09<br> 2015/03/13-2015/03/31<br> 2015/06/18-2015/06/27<br> 2017/05/26-2017/06/02</p> <p>The original geomagnetic field data are available from <a href="https://zenodo.org/record/3387216">https://zenodo.org/record/3387216</a>, and information on the method used to derive the FLR frequencies are described by <a href="https://www.annalsofgeophysics.eu/index.php/annals/article/view/7751">Del Corpo et al., (2019)</a>.</p> <p>The assumptions made to derive the FLR frequencies could be not valid during nighttime, so the estimated frequencies could be unreliable. They are included in this data set to stimulate further investigation and validation. To facilitate the individuation of trusted frequencies, information about the sunrise and sunset time of the midpoint between the station pair and of the conjugate point is provided. The data set includes also the cross-phase spectra from which the FLR frequencies are derived.</p> <p> </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.