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403 results for “physical data”
Soil Physical Data from the Shark River Slough, Everglades National Park (FCE), from November 2000 to January 2007
Soil pH, Eh and temperature readings are taken at SRS1b, SRS1c, SRS1d, SRS2 and SRS3. These measurements are taken only when the marsh is wet. Measurements are taken using an Orion model 250A meter, and the probes attached to the meter are the Orion Thermo pH probe and the Orion Eh probe. All readings are recorded in field notebooks, and then transferred into Microsoft Excel. The Eh reading is taken when the Eh probe is attached to the meter and the word "Ready" appears on the meter screen. The pH and temperature reading are taken when the pH probe is attached to the meter and the word "Ready" appears on the meter screen.
Soil Physical Data from the Taylor Slough, just outside Everglades National Park (FCE), from October 1998 to October 2006
Soil pH, Eh and temperature readings are taken at TS/Ph4 and TS/Ph5. These measurements are taken only when the marsh is wet. Measurements are taken using an Orion model 250A meter, and the probes attached to the meter are the Orion Thermo pH probe and the Orion Eh probe. All readings are recorded in field notebooks, and then transferred into Microsoft Excel. The Eh reading is taken when the Eh probe is attached to the meter and the word "Ready" appears on the meter screen. The pH and temperature reading are taken when the pH probe is attached to the meter and the word "Ready" appears on the meter screen.
Soil Physical Data from the Taylor Slough, within Everglades National Park (FCE), from September 1999 to November 2006
Soil pH, Eh and temperature readings are taken at TS/Ph1b,TS/Ph2,TS/Ph3 and TS/Ph6b. These measurements are taken only when the marsh is wet. Measurements are taken using an Orion model 250A meter, and the probes attached to the meter are the Orion Thermo pH probe and the Orion Eh probe. All readings are recorded in field notebooks, and then transferred into Microsoft Excel. The Eh reading is taken when the Eh probe is attached to the meter and the word "Ready" appears on the meter screen. The pH and temperature reading are taken when the pH probe is attached to the meter and the word "Ready" appears on the meter screen.
Cascade Project at North Temperate Lakes LTER Core Data Physical and Chemical Limnology 1984 - 2016
Physical and chemical variables are measured at one central station near the deepest point of each lake. In most cases these measurements are made in the morning (0800 to 0900). Vertical profiles are taken at varied depth intervals. Chemical measurements are sometimes made in a pooled mixed layer sample (PML); sometimes in the epilimnion, metalimnion, and hypolimnion; and sometimes in vertical profiles. In the latter case, depths for sampling usually correspond to the surface plus depths of 50percent, 25percent, 10percent, 5percent and 1percent of surface irradiance.
Physical and chemical data for various lakes near Toolik Research Station, Arctic LTER. Summer 1975 to 1989.
Decadal file describing the physical lake parameters recorded at various lakes near Toolik Research Station during summers from 1975 to 1989. Depth profiles at the sites of physical measures were collected in situ. Values measured included temperature, conductivity, pH, dissolved oxygen, Chlorophyll A, Secchi disk depth and PAR. Note that some sample depths also have additional parameters measured and available in separate files for water chemistry and primary production.
Physical and chemical data for various lakes near Toolik Research Station, Arctic LTER. Summer 2010 to 2021
Decadal file describing the physical/chemical values recorded at various lakes near Toolik Research Station. Sample site descriptors include site, date, time, depth. Depth profiles of physical measures collected in situ with Hydrolab Datasonde in the field include temperature, conductivity, pH, dissolved oxygen in both percent saturation and mg/l, SCUFA chlorophyll-a values in both volts and µg/l, and PAR.
Physical and biogeochemical oceanography data from Conductivity, Temperature, Depth (CTD) rosette deployments during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This data set contains measurements from various sensors mounted on the Conductivity, Temperature, Depth (CTD) rosette that was deployed in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE). 63 CTD casts were carried out during three legs in the period 21st December 2016 to 16th March 2017, including one test cast and one failed cast, for which no data is available. Data include temperature, salinity, pressure, dissolved oxygen, oxygen saturation, chlorophyll-a concentration, backscatter, and photosynthetically active radiation (PAR) and reported are also the computed variables density, depth, and sound velocity. All data has been quality controlled and post-cruise calibrated, except for the oxygen data. Data is provided at 1 dbar pressure intervals for the up- and down-casts separately and as a merged bottle file when Niskin bottles were closed. This circumpolar data set provides insights into the circumpolar hydrography and biogeochemistry of the Southern Ocean during one austral summer season.</p> <p><strong>Dataset contents</strong></p> <p>For transparency, the raw files and files produced at the intermediate stages of data processing have been provided, in addition to the final processed files.</p> <p><em>Raw data files: </em></p> <ul> <li>ace_ctd_raw_files.zip - includes raw files direct from instrument and XMLCON configuration files</li> </ul> <p><em>Intermediate files: </em></p> <ul> <li>files output at each stage of the SeaBird processing</li> </ul> <p><em>Processed data files: </em></p> <ul> <li>ace_ctd_CTD20200406CURRSGCMR - one final set of files for the complete sensor data;</li> <li>ace_ctd_BOTTLE20200406CURRSGCMR_hy1.csv - a merged bottle file extracted from the sensor data is also provided</li> </ul> <p><em>Metadata:</em></p> <ul> <li>range of files describing the CTD deployments, sensors, water sampling; quality-checking and processing of the files.</li> </ul> <p><strong>Dataset license</strong></p> <p>This physical and biogeochemical oceanography dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p><strong>Change log</strong></p> <p><strong>v1.1</strong><br> Quick summary of issues addressed in CTD DOI Update<br> - Resolved discrepancies between upcast and downcast MLD estimates<br> - ‘Bad’ datapoints in file dACE201601_002_ct1.csv which were not flagged with ‘4’ Bad measurement<br> - CTDFLUOR1, CTDFLUOR1Q, CTDFLUOR2, CTDFLUOR2Q ‘dark’ correction was not applied consistently in first processing and should have been applied to all fluorescence variables<br> - CTDFLUOR1Q, CTDFLUOR2Q quenching correction needed to be recalculated and reapplied after update to MLD and dark correction<br> - Limit the number of decimal places for fluorescence, PAR and backscattering variables according to the instrument sensitivity limits (which is 4 decimal places except for backscattering which is 6)<br> - Changed file names described in data_file_header.txt</p> <p>Additional details on ‘Issues’ and resolutions<br> Mixed layer depth estimates<br> - Large discrepancy in MLD estimates from upcast and downcasts at the same station was due to differences in the ‘reference’ depth i.e. depth other than 10 m was used when there were no datapoints at 10 m.<br> - Note: influence of time between casts was also checked and was not the driver of the discrepancies.<br> - Issue was resolved by setting the MLD for any cast where the reference depth was not 10 m to NaN.</p> <p>Bad data flagging<br> - 22 ‘bad’ datapoints for variables salinity, density, temperature and sound at the end of the downcast file dACE201601_002_ct1 were missed during the visual inspection of the first CTD processing and hence were not flagged as bad.<br> - The bad datapoints are now flagged as ‘4’ bad measurement</p> <p>Fluorescence<br> - In the first processing, dark correction was only applied to the files where quenching correction was needed, and only to the quenched corrected fluorescence variable, but should have been applied to all fluorescence variables in all files. This has been corrected<br> - In the first processing, the upcast MLD was used as the MLD estimate in quenching correction for both the upcast and downcast file. This has been changed so that the MLD from the same cast is used i.e. downcast estimate for the downcast file and upcast estimate for the upcasts file, unless the MLD estimate is NaN (because the reference depth was not 10 m), in that case the either the downcast or upcast estimate is used - whichever exists.</p> <p>Decimal places<br> - The number of decimal places for the fluorescence, PAR and backscattering variables far exceeded the sensitivity limits of the respective sensors - for the fluorescence and backscattering variables this was due to the additional calculations and corrections applied. For the PAR variable it was the output from the Seabird processing.</p> <p>Updated files list<br> The following files have been updated:<br> Folder: ace_bottle_BOTTLE20200406CURRSGCMR (all files within)<br> Folder: ace_ctd_CTD20200406CURRSGCMR (all files within)<br> ace_ctd_mld_CURRSSRGCMR20200405.csv<br> ace_ctd_visual_inspection_v2.csv<br> README.txt<br> data_file_header.txt<br> ace_physical_biogeochemical_oceanography_ctd_change_log.txt (new file)</p> <p><strong>v1.0</strong> - Initial release of physical and biogeochemical oceanography data set.</p>
Physical and biogeochemical oceanography data from underway measurements with an AquaLine Ferrybox during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This data set contains measurements from various sensors installed on the Aqualine Ferrybox system that was connected to the underway seawater supply in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE). Data was collected continuously except for periods when the pump of the underway system was switched off or the system was turned off. Data collection covers all three cruise legs in the period 24th December 2016 to 18th March 2017. Data collected with the CTG MiniPack CTD-F are temperature, salinity, pressure, and turbidity. Data collected by the Aanderaa oxygen optode include dissolved oxygen and oxygen saturation. An SBE 18 sensor measured pH. The CTG UniLux fluorometer measured chlorophyll-a concentration. All data has been quality controlled and post-cruise calibrated. Data is provided at 1-minute intervals along the cruise track. In addition, we provide satellite data (sea-surface temperature, sea-surface height, geostrophic velocity, sea-ice concentration) that was interpolated to the cruise-track and an estimate of frontal positions to supplement this underway data set where data was missing or for additional information. This circumpolar data set provides insights into the circumpolar surface ocean conditions and biogeochemistry of the Southern Ocean during one austral summer season.</p> <p>Note on version 1.0: The first version of this data set only contains temperature, salinity, pressure, and potential density in the post-processed file, since post-processing and quality control for turbidity, chlorophyll-a, dissolved oxygen, oxygen saturation, and pH have not been finalized. These variables will be added to the post-processed data file in a future release.</p> <p><strong>Dataset license</strong></p> <p>This dataset of physical and biogeochemical oceanography underway measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) October 2023 - May 2024
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from October 2023 up to May 2024 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: TIME in UTC [yyyy-MM-ddThh:mm:ssZ]; Latitude [deg]; Longitude [deg]; nominal depth [m]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature [°C]; Conductivity [mmS/cm]. Missing data are defined as NaN.</p>
Biological and Physical Monitoring Data of Restored Oyster Reef in Savannah River, Savannah, GA from May 2023 - February 2025
For the purposes of this study, we constructed two oyster reefs in Savannah, GA, USA using standard spat-on-shell restoration methodology. Reefs were constructed 1-2 meters from the marsh edge to reduce wave energy as it approached the shoreline, similar to a breakwater. We then conducted monitoring on the biological function of the reef, including live juvenile oyster coverage, size, and abundance for approximately 18 months. We also quantified the energy flux of waves offshore and onshore of the reef using water pressure measurements to determine the capability of these reefs at reducing wave energy. The oyster reefs in this study decreased wave energy by up to 40% compared to paired, non-reef control sites. Constructed oyster reefs also experienced healthy oyster population growth throughout the study, with live juvenile coverage of 17-40% almost 18 months post-deployment. This study took place in an erosion-prone area due to recreational and commercial boating traffic at the nearby Port of Savannah. Our results indicate that using restored oyster reefs as living shorelines is a technique with high potential for preventing shoreline loss in coastal areas vulnerable to anthropogenically-caused erosion. Restored Reef Site 1: 32.067957°, -80.985005° Control Site 1: 32.0675194°, -80.986369° Restored Reef Site 2: 32.062663°, -80.965147° Control Site 2: 32.063261°, -80.965889°
Consumer Stocks: Fish, Vegetation, and other Non-physical Data from Everglades National Park (FCE LTER), South Florida, USA from February 2000 to April 2005
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the numeric count data of fish, plants, and other fauna.
Consumer Stocks: Physical Data from Everglades National Park (FCE), South Florida from February 1996 to April 2008
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the physical data of the sampled plots.
Florida Bay Physical Data, Everglades National Park (FCE), South Florida from January 2001 to February 2002
Florida Bay physical data that includes surface temperature and salinity at Duck Key, Bob Allen Keys, and Sprigger Bank, Florida Bay in Everglades National Park, South Florida.
Florida Bay Physical Data, Everglades National Park (FCE LTER), Florida, USA, September 2000 - ongoing
Point measurements of Salinity, temperature and turbidity collected during visits to TS/Ph 7a, TS/Ph8, TS/Ph9, TS/Ph10, TS/Ph11, and Rabbit Key. Graphic representation of seagrass status and trends monitoring data and other related information can be located at http://serc.fiu.edu/seagrass/!CDreport/DataHome.htm
Benchmark Data for AI Safety for High Energy Physics
<p><strong>Datasets for the paper "AI Safety for High Energy Physics" by Ben Nachman and Chase Shimmin (<a href="https://arxiv.org/abs/1910.08606">arXiv:1910.08606</a>)</strong></p> <p>This record contains two files: particles_jj.npz and particles_yz.npz, which contain simulated events of dijet and Z+photon production, respectively, from proton-proton collisions at sqrt(s)=13 TeV.</p> <p>The parton-level events are generated with MadGraph5 aMC@NLO, which are then passed to Pythia 8 for parton showering and hardonization, and then finally to Delphes3 for ATLAS-like detector simulation. Reconstructed calorimeter towers are clustered using the anti-kT algorithm with radius parameter R=1.0. The highest-pT jet from each event is selected, and only events with jet pT > 300 GeV are saved.</p> <p>The Npz files contain three dictionary keys:</p> <ul> <li><strong>jets</strong><strong>:</strong> (N, 4)-shape array containing the pT, eta, phi, and mass of the leading R=1.0 jet for each event</li> <li><strong>constituents:</strong> (N, 128, 3)-shape array containing the pT, eta, phi of up to 128 highest-pT constituent momenta from the leading jet cluster. Jets with fewer than 128 constituents are padded with zero values.</li> <li><strong>photons:</strong> (N, 3)-shape array containing the pT, eta, phi of the leading reconstructed photon (if any) of the event. Events with no photon are filled with zeros.</li> </ul> <p>pT and mass values are stored in units of TeV.</p>
Data from: "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters"
<p>This dataset was used for the training of a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars. The light curves correspond to data obtained from The Optical Gravitational Lensing Experiment (<a href="https://ui.adsabs.harvard.edu/abs/1992AcA....42..253U/abstract">OGLE</a>), while ancillary information was obtained from the Gaia Data Release 2 (<a href="https://ui.adsabs.harvard.edu/link_gateway/2016A&A...595A...1G/doi:10.1051/0004-6361/201629272">GAIA DR2</a>). This repository contains the preprocessed OGLE light curves and the GAIA measurements corresponding to each cross-matched source. We also provided a subsample of cross-matched sources that were carefully validated following several steps described in the companion article (paper reference).</p> <p>This dataset is realized in tandem with the corresponding <a href="https://github.com/jorgemarpa/PELS-VAE">GitHub</a> and <a href="https://arxiv.org/abs/2005.07773">article</a>.</p> <p> </p> <p> </p>
Data used in "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean"
<div> <p>This repository contains the data used to generate the figures for the submitted manuscript "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean".</p> </div> <h3>Contents</h3> <div> <ul> <li> <p>Model input:</p> <ul> <li> <p>INPUTS: ocean model input/grid files</p> </li> <li> <p>PISCES_INPUTS: BGC input files</p> </li> <li> <p>OBC: open boundary forcing </p> </li> <li> <p>WEIGHTS: weight files for ERA interim forcing</p> </li> </ul> </li> </ul> </div> <div> <ul> <li> <p>Manuscript files:</p> <ul> <li> <p>data: files used to generate manuscript images</p> </li> <li> <p>config, src, notebooks: Python code and Jupyter notebooks used to generate images</p> </li> <li> <p>figures, supplementary: manuscript figures and supplementary figures</p> </li> </ul> </li> </ul> </div> <div> </div> <div><strong>Abstract: </strong>"We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12 degree. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass."</div>
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Terrasar measurement data of "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing"
<p>This data set was used to test of the method described in "Sar Super-Resolution Using Physics-Aware Adaptive Compressed Sensing". It consists of the related Terrasar data and a MATLAB file to import the data into MATLAB.</p>
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