Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission example data

These files contain the Confluence pipeline outputs, prior information (SOS) and Simulated SWOT shape files from the example in the "A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission" manuscript.

Ocean drifters from oil-on-water exercise in North Sea (Frigg oil field) June 2019

Ocean drifters from oil-on-water exercise in North Sea (Frigg oil field) June 2019. Described in more detail in Brekke, C., Espeseth, M. M., Dagestad, K.-F., Röhrs, J., Hole, L. R., & Reigber, A. (2021). Integrated analysis of multisensor datasets and oil drift simulations - a free-floating oil experiment in the open ocean. Journal of Geophysical Research: Oceans, 126, e2020JC016499. https://doi.org/10.1029/2020JC016499 Work is funded by grant no. 237906 (CIRFA) of the Norwegian Research Council.

An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona

The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets. How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a> This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions. <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is : VV_new_perNN_LSTWW where: <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65.

opencc-by-4.0May 2023View details →

New records of the Atlantic blue crab Callinectes sapidus Rathbun, 1896 in European waters

New records of the Atlantic blue crab Callinectes sapidus Rathbun, 1896 in European waters (between 2019 and 2022).

opencc-by-4.0Nov 2021View details →

The 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020)

Monitoring the water clarity of lakes is essential for the sustainable development of human society. However, existing water clarity assessments in China have mostly focused on lakes with areas > 1 km2, and the monitoring periods were mainly in the 21st century. In order to improve the understanding of spatiotemporal variations in lake clarity across China, based on the Google Earth Engine (GEE) cloud platform, a 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) was developed using Landsat series imagery and a robust water-color-parameter-based SD model. Noted, the LAWSD30 has been updated to 2021 using Landsat data from 2020 to 2022. The details are described in "30 m long-term LAke Water Secchi depth (SD) dataset (LAWSD30) of China (1985–2021)_Readme_V1.1.docx".

Data set for Water Affordability in the United States (Cardoso and Wichman, 2022)

Author Information  - Diego S. Cardoso, Department of Agricultural Economics - Purdue University  - Casey J. Wichman, School of Economics - Georgia Institute of Technology and Resources for the Future Corresponding Author: Casey J. Wichman: wichman@gatech.edu Period of data collection: 2017--2018 License: This data set can be used under terms of the <a href="https://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International license</a>.  Recommended citation for the research paper  Cardoso, D. S., & Wichman, C. J. (2022). Water affordability in the United States. Water Resources Research, 58, e2022WR032206. https://doi.org/10.1029/2022WR032206 Recommended citation for the data:  Cardoso, Diego S., & Wichman, Casey J. (2022). Data set for Water Affordability in the United States [Data set]. In Water Resources Research (v0.1, Vol. 58, Numbers e2022WR032206). Zenodo. https://doi.org/10.5281/zenodo.6991563     Please see the README.txt file for the data dictionary and additional information.

opencc-by-4.0Nov 2022View details →

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts. These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384242">https://doi.org/10.5281/zenodo.7384242</a> Classes: {0=other, 1=water} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ** Buscombe, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384242">https://doi.org/10.5281/zenodo.7384242</a>

opencc-by-4.0Jan 2022View details →

2707 water samples from Vallon de Nant analyzed for stable water isotopes (dD, d17O, d18O) and electric conductivity

Database gathering 2707 water samples from the Vallon de Nant (Westerm Swiss Alps) analyzed for stable isotopes of water (dD, d17O, d18O, d-excess, LC-excess, 17O-excess) and electric conductivity. The "VDN_database.csv" file includes the following columns: - time [dd/mm/yyyy HH:MM, UTC] - latitude [decimal degrees, WGS84] - longitude [decimal degrees, WGS84] - elevation [masl] - point: point name - dD [&permil;] - d17O [&permil;] - d18O [&permil;] - d-excess [&permil;] - LC-excess [&permil;] - 17O-excess [per meg] - conductivity [µS/cm] Water is sampled from: - 2 points of main stream (Avançon de Nant) at HyS1 and HyS2 - 5 different springs at GRAS, AUBG, ROCK, BRDG and ICEC - 3 piezometers at PZ1, PZ2 and PZ3 - 2 weather stations for rain at Auberge and Chalet - various locations (GPS points) for the Snowpack - various locations (GPS points) for the Glacier The points with regular sampling are shown on the map "VDN_sampling_points.png".

Primary data: Signal enhancement of hyperpolarized 15N sites in solution — increase in solid-state polarization at 3.35 T and prolongation of relaxation in deuterated water mixtures

Primary data for DOI: 10.1002/nbm.4787 NMR in Biomedicine. 2022;e4787 Title: Signal enhancement of hyperpolarized 15N sites in solution—increase in solid-state polarization at 3.35 T and prolongation of relaxation in deuterated water mixtures Authors: Ayelet Gamliel, David Shaul, J. Moshe Gomori, Rachel Katz-Brull Description: These primary datasets contain data presented in the above publication and consist of: 1. 15N-NMR spectra in solutions 2. 13C polarization buildup data in solid-state 3. 13C microwave profiles in solid state Please consult the Archive Guide.

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts. These Residual-UNet model data are based on NDWI images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263 </a> Classes: {0=other, 1=water} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263 </a>

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band MNDWI images of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 1-band MNDWI images of coasts. These Residual-UNet model data are based on MNDWI images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263 </a> Classes: {0=other, 1=water} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263 </a>

Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting

Dataset for 'Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting' article published at Cell Reports Physical Science, <a href="https://doi.org/10.1016/j.xcrp.2023.101252">https://doi.org/10.1016/j.xcrp.2023.101252</a>. Code used for data analysis, visualization and kinetics modelling can be found at <a href="https://github.com/AndreyBezrukov/Water_Sorption_Kinetics">AndreyBezrukov/Water_Sorption_Kinetics (github.com)</a>

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model

Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters.  An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: <a href="https://essd.copernicus.org/preprints/essd-2023-31/">https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023</a>  Dataset: A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution. "NH-SWE_dataset_MAP.png" shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters.  Files: The dataset is provided in two different formats: <ol> <li>Individual .csv files for each station in the NH-SWE dataset at "NH_SWE_dataset_vector_files.zip"</li> <li>Full-dataset .csv matrices with dates as rows and NH-SWE stations as columns at "NH_SWE_dataset_matrix_files.zip"</li> </ol> Metadata: "NH_SWE_METADATA.csv" Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the "README_fileformats.txt" file.  ΔSNOW model parameter regionalisation: The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in "DeltaSNOW_parameter_regionalisation.zip". The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the "README_regionalisation.txt" file.  Data use: Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See "README_datausage.txt" Version history: v1: Initial upload. The ΔSNOW model regionalisation was missing. v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code. Reported errors: The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY).  The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees). Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations.  Japan country code is "JA" in the metadata, although the official country code should be JP.

Thermodynamic properties of ammonia-water (NH3H2O mixture). In Esperanto

Thermodynamic data for the ammonia-water mixture are adapted from: Ibrahim, O. M. (1993). Thermodynamic properties of ammonia-water mixtures. In ASHRAE Transactions: Symposia (Vol. 93, p. 1495).

fish larvae abundance as a function of oceanographic variables in GoM deep waters

We describe the larval occurrence and abundance of six fish species with contrasting life histories and examine their relationship with oceanographic variables during two seasons in the deep-water region (>1000 m) of the southern Gulf of Mexico based on 12 cruises (2011-2018). Given that Caranx crysos adults are neritic, larval presence close to the continental shelf indicates offshore cross-shelf transport to oceanic waters, which likely leads to mortality. Generalized additive models indicated C. crysos abundance  was not related with oceanographic variables, while that of Auxis spp. (with neritic and oceanic adults) was related to wind speed, sea surface temperature and height and surface chlorophyll a. The mesopelagic Benthosema suborbitale, Notolychnus valdiviae and Bregmaceros atlanticus were more abundant and broadly distributed, and higher abundance was found in conditions indicative of higher nutrient availability and productivity, suggesting greater feeding success and survival. The distribution of the epi- and mesopelagic Cubiceps pauciradiatus extended through the southern Gulf of Mexico, and was related to wind speed, SST, stratification and chlorophyll a. Our results suggest that the abundance of the neritic species in oceanic waters could be mediated by regional cross-shelf transport, while that of oceanic species is linked with productivity.

A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?

Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.

opencc-by-4.0Oct 2022View details →

Data for: Two-dimensional infrared-Raman spectroscopy as a probe of water's tetrahedrality

Data for publication: T. Begusic and G. A. Blake, Two-dimensional infrared-Raman spectroscopy as a probe of water’s tetrahedrality (2022). Contains raw data, processed data, and processing and plotting scripts for the results presented in the manuscript. See README files enclosed in the dataset for details about the files and directories. Main results were produced with codes available at https://github.com/tbegusic/i-pi and https://github.com/tbegusic/encorr.

Water chemistry of LTER-Europe research site Lake Paione Superiore LTER_EU_IT_089 (1984-2013)

This dataset provides information about water chemical parameters for Lake Paione Superiore LTER_EU_IT_089: pH, Total alkalinity, conductivity, total nitrogen, major cations (calcium, magnesium, sodium, potassium), major anions (sulphate, nitrate, chloride) and silica for the period 1984-2013. Lake Paione Superiore (LPS) is a high altitude Alpine lake, located at 2269 m a.s.l. in the Bognanco Valley, Province of Verbania, Piedmont Region, Italy. It has a surface area of 0.68 ha and a maximum depth of 11.5 m. The Lake, together with Lake Paione Inferiore (LPI), is included in the monitoring sites of the UN-ECE Program ICP WATERS (International Cooperative Programme on Assessment and Monitoring of Acidification of Rivers and Lakes) for which the CNR Water Research Institute is the National Focal Centre for Italy. This dataset includes the following files: Metadata LTER_EU_IT_089.xls and per each parameter one xls file with data records. Detailed description of the site LPS is available at https://deims.org/7e5837a9-ee27-4e27-822a-f50e5217c313 Dataset for water chemistry of LPS for the  period 2014-2020 is available at https://doi.org/10.5281/zenodo.10519126

opencc-by-4.0Feb 2023View details →

Water displacement device, engines characterization

This dataset contains 44 formulations (rows), 23 features and 1 supervisor factors (24 columns). This data have been extracted from a water displacement device that measures the volume of submerged samples. The displaced water passes through a flow sensor that produces pulses. The features in the dataset are the times between the first 20 pulses (t0 - t19), the total pulse counting (pc), the total operation time (tt) and the DC (dc) component on each experiment (formulation). The supervising value is the volume of marbles with different diameters. The work consist on tuning the water displacement device so it yields accurate volumes from pulse patterns.