Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

SMAP Daily Seamless Soil Moisture Products from 2015 to 2022 (Physics-constrained Gap-filling Method,PhyFill)

The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address this issue, this dataset presents a physics-constrained gap-filling method, shortly named PhyFill. The PhyFill method employs a partial convolutional neural network to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous daily soil moisture data on a global scale. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSE compared with the original SMAP soil moisture data. The PhyFill method can generate globally continuous, high-accuracy soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.

opencc-by-4.0Dec 2023View details →

Classification of obesity levels based on eating habits and physical condition

This dataset encompasses information intended for the assessment of obesity levels among individuals in the nations of Mexico, Peru, and Colombia.The main dataset is prepared by other authors in the article (https://doi.org/10.1016/j.dib.2019.104344) I have only used this dataset to perform my final project related to the Homework Assignment 6: Machine Learning Application in Project Dataset.  Here is some detaied explanation about the dataset:  The attributes related with eating habits are:<ol><li>Frequent consumption of high caloric food (FAVC)</li><li>Frequency of consumption of vegetables (FCVC)</li><li>Number of main meals (NCP)</li><li>Consumption of food between meals (CAEC)</li><li>Consumption of water daily (CH20)</li><li>Consumption of alcohol (CALC)</li></ol>The attributes related with the physical condition are:<ol><li>Calories consumption monitoring (SCC)</li><li>Physical activity frequency (FAF)</li><li>Time using technology devices (TUE)</li><li>Transportation used (MTRANS)</li></ol>other variables obtained were:<ol><li>Gender</li><li>Age</li><li>Height</li><li>Weight</li><li>family history with overweight</li><li>SMOKE activity</li></ol>Finally, all data was labeled and the class variable NObesity was created with the values of:a) Insufficient Weightb) Normal Weightc) Overweight Level Id) Overweight Level IIe) Obesity Type If) Obesity Type IIg) Obesity Type III

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Biological and physical controls on multidecadal acidification in a eutrophic estuary

Estuaries support many ecologically and economically important resources that are especially vulnerable to ocean acidification from rising anthropogenic CO2. However, complex local processes in estuaries complicate and may disguise long-term pH trends. For example, terrestrial nutrient runoff and marine coastal upwelling may exacerbate pH variability and declines. We investigated eutrophication impacts on acidification in a central California estuary, Elkhorn Slough, which receives high nutrient loads from intensive surrounding agriculture and upwelling of the California Current System. We examined drivers of acidification including nutrients, ecosystem metabolism, and upwelling by modelling pH trends over 20 years using a Generalized Additive Mixed Model at four sites from the National Estuarine Research Reserve Systemwide Monitoring Program and collected additional water samples to calculate aragonite saturation. Our models revealed acidification trends over two decades which were more pronounced near the marine inlet. Near the marine inlet, high nutrient levels and lower buffering are associated with the greatest rate of acidification in the estuary, which was four times greater than the trend from anthropogenic CO2 alone. However, tidally restricted areas experienced a different acidification pattern. A tidally restricted site recorded higher mean pH and aragonite saturation and increased pH levels associated with stronger upwelling conditions supplying marine-sourced nutrients. Therefore, variable ecosystem metabolism and tidal cycles are threats to acidity in this location. The effects of enhanced seasonal cycles or long-term trends in different zones of the estuary have implications for monitoring with a temporal frequency and scale to capture coastal acidification risks in estuaries.

opencc-zeroDec 2023View details →

opencc-by-4.0Mar 2024View details →

LRD Photometry and Physical Parameters in Blank JWST Fields (Kokorev+24)

Photometry and physical parameters of little red dots in an array of blank extragalactic fields as presented in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240109981K/abstract">Kokorev+24.</a>

opencc-by-4.0Nov 2023View details →

Data for a publication: "A new promising material for biological applications: multi-level physical modification of AgNPs-decorated PEEK"

The data set contains the data that were used within the article "A New Promising Material for Biological Applications: Multilevel Physical Modification of AgNP-Decorated PEEK" published in the journal Nanomaterials. Versions: V1&2: The dataset contained AFM and UV VIS data. V3: All the data used in the article are sorted in the file structure, which description is thoroughly described in the READ ME file. Additionally, the preprint DOI was added.

opencc-by-4.0Sep 2022View details →

An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015

This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

Physical characterization of agricultural plastic mulching films

The excel file regards the radiometric properties of the source material mulching films.  The radiometric tests were carried out at the University of Bari. The mulching films were used for the generation of microplastic test materials. The Italian mulching films were buried at the experimental field at the University of Bari.

opencc-zeroDec 2023View details →

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Data from: Evidence for reductions in physical and chemical plant defense traits in island flora

Reduced defense against large herbivores has been suggested to be part of the "island syndrome" in plants. However, empirical evidence for this pattern is mixed. In this paper, we present two studies that compare putative physical and chemical defense traits from plants on the California Channel Islands and nearby mainland based on sampling of both field and common garden plants. In the first study, we focus on five pairs of woody shrubs from three island and three mainland locations and find evidence for increased leaf area, decreased marginal leaf spines, and decreased concentrations of cyanogenic glycosides in island plants. We observed similar increases in leaf area and decreases in defense traits when comparing island and mainland genotypes grown together in botanic gardens, suggesting that trait differences are not solely driven by abiotic differences between island and mainland sites. In the second study, we conducted a common garden experiment with a perennial herb—Stachys bullata (Lamiaceae)—collected from two island and four mainland locations. Compared to their mainland relatives, island genotypes show highly reduced glandular trichomes and a nearly 100-fold reduction in mono- and sesquiterpene compounds from leaf surfaces. Island genotypes also had significantly higher specific leaf area, somewhat lower rates of gas exchange, and greater aboveground biomass than mainland genotypes across two years of study, potentially reflecting a broader shift in growth habit. Together, our results provide evidence for reduced expression of putative defense traits in island plants, though these results may reflect adaptation to both biotic (i.e., the historical absence of large herbivores) and climatic conditions on islands.

Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models.

Partially disordered proteins can contain both stable and unstable secondary structure segments and  are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated  methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters,  we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 17 compared to naive physics-based molecular docking.

Physical Exercise Measurements with Accelerometer and Gyroscope

The data represent records from an accelerometer and gyroscope during seven movement tasks that were standardized by a protocol. These tasks were performed consecutively, and each had to be completed as quickly as possible. Data are obtained from 81 children aged 9-11 years. Description of movement tasks (performed in following order): 4x10 m Run: The track is defined by two cones placed at a distance of 10 meters from each other. The cones must have a height of at least 20 cm. It is advisable to secure the cones to the floor with adhesive tape. It is also recommended to mark the correct placement of the cones on the floor with tape. After the start, an individual begins to run towards the opposite cone, circles it from the left, and continues back to the starting cone. This cone is circled from the right. The run continues back and forth with a touch of the opposite cone. On the final run, the individual must cross over the mark where they started and then accelerate, but more freely, to the start of the second section. Crawling: Immediately after starting the section, the individual lies down on the ground and crawls forward, with the entire torso needing to remain constantly on the mat. The total length of the course is 10 meters. The task ends at most 1 meter before the finish line (marked as No. 3 in the image). Crawling on all fours is not acceptable. Progressive Cone Shuttle Run: The track is marked by a series of five cones placed in a line one after the other. It is advisable to secure the cones to the ground with tape. It is also recommended to mark the correct placement of the cones on the floor with tape. The first cone (marked as 'A' – "return cone") is 1 meter away from the start, the second cone is 2 meters away. Each subsequent cone is always 1 meter away from the previous one (marked 'first' to 'fourth'). After the start, the participant runs to the first cone, touches it, and runs back to the return cone A. In this manner, from cone A, the participant runs progressively to cones two through four, returning to cone A each time. The participant must always touch each cone with one hand. This way, the length of the run progressively increases by 1 meter. After completing the run to the fourth cone, the participant circles outside all the cones and runs to the next station (marked as station 4). All runs are performed on the outside of the cones. Ring Collection and Placement Run: The track is marked by six equally sized sections of a Swedish ladder and one cone located 10 meters away from the start of the section. The individual sections are placed alternately on the left and right side of the line between the start and the end cone, each touching with one corner of the section. Inside each section, at its centre, rings are loosely placed. Immediately after the start, the participant runs and progressively moves through all the sections towards the cone, collecting rings. After circling the cone, the participant returns along the same path, placing the rings back into each section. It's important to emphasize that the rings must not be thrown but placed on the ground. From the last section, the participant runs directly to the next station, number 5. 10x Position Change Drill: The task is performed on a mat. The station is directly in the centre of the mat. Immediately after starting the section, the individual performs 10 cycles of position changes. One cycle consists of transitioning from standing upright to lying on the stomach, rolling over onto the back, and transitioning back to an upright standing position. When performing the cycles, it is necessary to ensure that each position is correctly maintained. After completing the last cycle, the individual immediately moves on to the next station, number 6. 10x Gymnastic Hoops Passing: Immediately after starting the section, the individual performs 10 movement cycles, where one cycle consists of crawling through a gymnastic hoop from a standing position, moving from the head to the feet. At the end of each cycle, the individual always lies on the ground, then steps out of it and stands upright. Only after this does the individual lean towards the hoop again and begin a new cycle. Immediately after the last cycle, there is a run to station number 7. 10x Bench Jumping (pushing off with one foot): Immediately after starting the section, the individual runs to the right edge (outer side) of the bench at its beginning and starts jumping over the bench back and forth. The jump is made from one foot, landing on the other. Between jumps, it is necessary to touch the ground with both feet. The jumps must be performed in a way that the individual continuously moves forward and does not jump in place. After completing the last jump, the individual immediately runs to the final finish mark, where a triple hit to the back is also performed. (identical to station number 2) Crawling: This section is carried out according to the same instructions and at the same location where the previous crawling (section 2) took place. The end of the section is at station number 3, which is also the finish line of the entire course.   The main data file is physical_exercise.csv. Two figures are attached describing the protocol: Figure 1: Protocol – Spatial delineation for movement task execution (Protocol_physical_activities_measurements.png) Figure 2: Protocol – Location and sequence of movement tasks (Protocol_physical_activities.png)   Definition of measurement conditions and settings: <table> <tbody> <tr> <td>The measurement device</td> <td>Axivity AX6 device (Axivity Ltd, United Kingdom)</td> </tr> <tr> <td>Recording frequency</td> <td>100 Hz with a range of ±16 G for accelerometer and ±2000 °/s for gyroscope</td> </tr> <tr> <td>Device orientation</td> <td>x, y, and z axes determine the mediolateral, craniocau- dal, and sagittal directions, respectively</td> </tr> <tr> <td>Device positioning on the body</td> <td>on the back between upper angles of the shoulder blades</td> </tr> </tbody> </table>

Physical and technical characterization of main agricultural plastics articles used for protected cultivation systems

The folder "Thermal and spectroscopic data on the characterization of mulch films.rar" includes differential scanning calorimetry (DSC), thermogravimetry (TG), Fourier-transform infrared spectroscopy (FTIR), and water contact angle (WCA) raw data and images relevant to the characterization of conventional and biodegradable mulch films. 7 mulching films consisting of 3 different conventional linear low-density polyethylene (LLDPE, coded as PE) films and 4 different biodegradable (PBAT-based, coded as BIO) films are analysed.In particular, data regarding M-BIOIT-15-black-0, M-PEIT-15-black-0, M-BIOEL-15-black-0, M-PEEL-15-black-0, M-PEEL-50-transparent-0, M-BIODE-30-black-0 and M-BIOFI-15-black-0 are included.

opencc-by-4.0Dec 2018View details →

Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop.

Physical features and other characteristics of smoke from cigarettes sold in Australia

Open the record for dataset details and reuse information.

Evaluation of high-resolution WRF simulation in urban areas - Effect of different physics schemes on simulation performance in the Rhine-Main-Neckar area

This dataset contains data sampled from a WRF sensitivity study saved in NetCDF format. The study was run over 4 months of the year 2020. The folders contain the following data: <table> <tbody> <tr> <td>File</td> <td>Datasets</td> <td>Description</td> </tr> <tr> <td>wrf_met_sample_full.nc</td> <td>all</td> <td>WRF meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>wrf_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>wrf_met_sample_full_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_met_sample_full_and_quant_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full_and_quant.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_pblh_sample_full.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain</td> </tr> <tr> <td>wrf_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_met_sample_full.nc</td> <td>all</td> <td>ERA5 meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>era5_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain, resampled onto the measured meteorology and with summary statistics</td> </tr> </tbody> </table> Each of these files contains the samples and statistics as NetCDF Variables. These Variables have multiple dimensions, which describe the individual datapoints. For the WRF samples, these dimensions are: <table> <tbody> <tr> <td>Dimension</td> <td>Description</td> </tr> <tr> <td>Time</td> <td>time since start of simulation</td> </tr> <tr> <td>station_id</td> <td>the ID of the station where the sample was taken (meteo - length 19, PBLH - length 2)</td> </tr> <tr> <td>pbl</td> <td>Planetary Boundary Layer scheme (Bou-Lac / MYJ / YSU)</td> </tr> <tr> <td>lsm</td> <td>Land Surface Model scheme (N / NMP)</td> </tr> <tr> <td>slm</td> <td>Surface Layer Model scheme (MM5 / MO)</td> </tr> <tr> <td>urb</td> <td>Urban Parametrization scheme (SLUCM / BEP)</td> </tr> </tbody> </table> Not all combinations between different simulation schemes exist, so some values in the NetCDF Variables are NaNs.

Cyber-Physical System power Consumption

<h1>Files</h1> This dataset is comprised of 5 CSV files contained in the data.zip archive. Each one represents a production machine from which various sensor data has been collected. The average cadence for collection was 5 measurements per second. The monitored devices where used for hydroforming. The collection period covered the period from 2023-06-01 until 2023-08-05. <h2>Data</h2> These files represent a complete data dump from the data available in the time-series database, InfluxDB, used for collection. Because of this some columns have no semantic value for detecting production cycles or any other analytics. Each file contains a total of 14 columns. Some of the columns are artefacts of the query used to extract the data from InfluxDB and can be discarded. These columns are: results, table _start, _stop <ul> <li>results - An artefact of the InfluxDB query, signifies postprocessing of results in this dataset. It is "mean".</li> <li>table - An artefact of the InfluxDB query, can be discarded.</li> <li>_start and _stop - Refers to ingestion related data, used in monitoring ingestion. </li> <li>_field - An artefact of the InfluxDB query, specifying what field to use for the query.</li> <li>_measurement - An artefact of the InfluxDB query, specifying what measurement to use for the query. Contains the same information as device_id.</li> <li>host - An artefact of the InfluxDB query, the unique name of the host used for the InfluxDB sink in Kubernetes.</li> <li>kafka_topic - Name of the Kafka topic used for collection.</li> </ul>   Pertinent columns are: <ul> <li>_time - Denotes the time at which a particular event has been measured, it is used as index when creating a dataframe.</li> <li>_time.1 - Duplicate of _time for sanity check and ease of analysis when _time is set as index</li> <li>_value - Represents the value measured by each sensor type.</li> <li>device_id - Unique identifier of the manufacturing device, should be the same as the file name, i.e. B827EB8D8E0C.</li> <li>ingestion_time - Timestamp when the data has been collected and ingested by influxDB.</li> <li>sid - Unique sensor ID; the power measurements can be found at sid 1.</li> </ul>   <h1>Annotations</h1> There are two additional files which contain annotation data:  <ul> <li>scamp_devices.csv - Contains mapping information between the dataset device ID (defined in column "DeviceIDMonitoring") and the ground truth file ID (defined in column "DeviceID")</li> <li>scamp_report_3m.csv - Contains the ground truth, which can be used for validation of cycle detection and analysis methods. The columns are as follows: <ul> <li>ReportID - Internal unique ID created during data collection. It can be discarded.</li> <li>JobID - Internal Scheduling Job unique ID.</li> <li>DeviceID - The unique ID of the devices used for manufacturing needs to be mapped using the scamp_device.csv data.</li> <li>StartTime - Start time of operations</li> <li>EndTime - End time of operations</li> <li>ProductID - Unique identifier of the product being manufactured.</li> <li>CycleTime - Average length of cycle in seconds, added manually by operators. It can be unreliable.</li> <li>QuantityProduced - Number of products manufactured during the timeframe given by StartTime and EndTime.</li> <li>QuantityScrap - Number of scraped/malformed products in the given timeframe. These are part of the QuantityProduced, not in addition to it.</li> <li>IntreruptionMinuted - Minutes of production halt.</li> </ul> </li> <li>scamp_patterns.csv - Contains the start and end timestamp for selected example production cycles. These where chosen based on expert users.</li> </ul> <h1>Jupyter Notebook</h1> We have provided a sample Jupyter notebook (verify_data.ipynb), which gives examples of how the dataset can be loaded and visualised as well as examples of how the sample patterns and ground truth can be addressed and visualised. <h2>Note</h2> The Jupyter Notebook contains an example of how the data can be loaded and visualised. Please note that both data should be filtered based on sid; the power measurements are collected by sid 1. See Notebook for example.

openmit-licenseNov 2024View details →

Apathy, motivation, and physical activity behavior: Material, data and R code

This new release includes updates to the code and additional material following the peer review process conducted by Communications in Kinesiology.

Dataset for "From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction"

This deposit contains the data related to the manuscript "From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction" submitted to the Astrophysical Journal. It includes the basic SDSS identifier, stellar mass, star formation rate, fractional formation time, and their errors. A detailed description can be found in Table 1 of the manuscript. The data is stored in a CSV file. It can be read with standard data analysis packages like <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">Pandas</a> in Python.  This deposit has also be updated to include a machine-readable table that follows the standards of the AAS Journals and Vizier (datafile1_ApJ57534.mrt). More information on this standard can be found in the <a href="https://journals.aas.org/mrt-overview/">AAS</a> or <a href="http://cds.u-strasbg.fr/doc/catstd.htx">CDS</a> documentation. This format can be read in Python with packages like <a href="https://docs.astropy.org/en/stable/api/astropy.io.ascii.Mrt.html">astropy</a>.

opencc-by-4.0Nov 2021View details →

Mental health, physical health, training load and subjective performance during the COVID-19 pandemic – a Swiss elite athletes' cohort study

Dataset of Swiss elite athletes (n=203) participating in a repeated online survey evaluating mental and physical health factors, as well as training and performance related metrics. After the first survey during the first lockdown between April and May 2020, there were monthly follow-up surveys over a 6-month period.

opencc-by-4.0Dec 2021View details →

Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"

Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript. The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review. Please consult the README file for information on the specifications of the files. These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.