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19 results for “Water quality dataset”
LakeBeD-US: Ecology Edition - a benchmark dataset of lake water quality time series and vertical profiles
LakeBeD-US: Ecology Edition is a harmonized lake water quality dataset containing time series and vertical profiles of 21 lakes in the United States monitored by long-term monitoring institutions. These institutions include the North Temperate Lakes Long-Term Ecological Research program (NTL-LTER), Niwot Ridge Long-Term Ecological Research program (NWT-LTER), National Ecological Observatory Network (NEON), and the Carey Lab at Virginia Tech as part of the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) site in collaboration with the Western Virginia Water Authority. The data include depth-discrete observations of 17 water quality variables including temperature, dissolved oxygen, chemical properties, Secchi depth, and more. Observations are divided into data collected by automated sensors at a relatively high temporal frequency and manually sampled data at a relatively low temporal frequency. All data were collected in situ. The data are available as Apache Parquet files, and the included R scripts give guidance on how to utilize and query the dataset in R. LakeBeD-US: Ecology Edition is an ecological science-oriented companion to LakeBeD-US: Computer Science Edition. The Computer Science Edition is available on the Hugging Face Hub.
Dataset of water quality parameters (WP6 Cure4Aqua)
<p>Dataset of water quality parameters (alkalinity, ammonia, nitrates, nitrites, dissolved oxygen, pH, salinity, water temperature) in two flow-through (G1, G2) and two RAS (G3, G4) tanks </p>
Mohonk Preserve Amphibian and Water Quality Monitoring Dataset at 11 Vernal Pools from 1931-Present
"The Mohonk Preserve's Daniel Smiley Research Center has been monitoring species occupancy, reproductive success, and water quality of 11 vernal pools (Ski Loop, Bonticou, Terrace, Long Woodland Pool, Long Woodland Swamp, Oakwood, Sleepy Hollow, Hermits, North Mud Pond, Canaan, and Talus) on the Preserve each spring from April 1931 to May 2019 (present). This project aims to document changes in the reproductive behavior and phenology of amphibians and allow research access to historical, longitudinal records. The dataset is a paired record of amphibian occurence with environmental indicators spanning nearly 90 years of data collection. The dataset includes environmental conditions for the 730 sampling events associated with the species occurences with complete coverage air temperature and precipitation records and partial coverage for a variety of other weather and water quality measures. Species occurence data collection has included species identification and counts of live and dead adults, mated pairs, spermatophores, egg masses, juveniles, and tadpoles counts as well as a record of the level of frog calling. Weather conditions including precipitation, sky and wind codes; and water quality measurements including water temperature, pH, and depth. Collection of data was sporadic from 1931 - 1991 but has been collected consistently from 1991 to present. We also began monitoring dissolved oxygen, nitrate concentrations, and conductivity of the vernal pools using a YSI Sonde Professional Plus Instrument and turbidity using a turbidity tube in February 2018. The data collection is ongoing, as are digitization efforts, and the data package will receive periodic updates."
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
ROSSyndicate Cameron Peak Fire (CPF) reservoir water quality data: Latest Release: 2021- 11/2023 Dataset
<p><strong>Data Description:</strong> The majority of this dataset is water chemistry grab sample data collected post-Cameron Peak Fire in the Cache la Poudre Watershed between the years of 2021 and 2023. This dataset also includes historical data collected pre Cameron Peak Fire by the Rhoades lab at the US Forest Service Rocky Mountain Research Station. These data are focused on basic water quality parameters, as well as cations and anions. Data were collected at various reservoirs in the Cache la Poudre watershed as well as the mainstem of the Cache la Poudre River. This project is ongoing and additional data will be released as it is analyzed.</p> <p><strong>Background Information:</strong> The 2020 Cameron Peak wildfire (CPF) was the largest wildfire in Colorado history at over 200,000 acres. The CPF burned a large proportion of the Cache la Poudre watershed, in particular areas surrounding high elevation reservoirs. This work is funded to support ongoing source water protection programs by the City of Fort Collins, Greeley, Thornton and Northern Water. In collaboration with the Rocky Mountain Research Station (USFS, RMRS), we are sampling various reservoir, tributary, and mainstem sites of the Cache la Poudre watershed. This field campaign allows us to analyze trends in water quality focusing on nutrients and other key constituents mobilized post-fire. The goal of this project is to understand how these nutrients affect algal growth in reservoirs and how those changes are propagated downstream. The reservoirs studied are the following: Barnes Meadow Reservoir, Chambers Lake, Comanche Reservoir, Hourglass Reservoir, Joe Wright Reservoir, Long Draw Reservoir, and Peterson Lake. Historical data (prior to 2021) was collected by the Rhoades Lab at the USFS' Rocky Mountain Research Station.</p> <p><strong>The primary data file is data/cleaned/CPF_reservoir_chemistry_up_to_202301027.csv.</strong> Column definitions and units are defined in the file <em>metadata/Units_Cam_Peak.xlsx</em>. Methods used to collect these data are outline below or in <em>metadata/rmrs_procedures.png</em></p> <p>Location metadata file is <em>data/metadata/cpf_sites.csv</em>. A basic map showing all sampling locations is available at cpf_sites_map.html.</p> <p>Code is housed in the <em>scripts</em> folder and contains the following files:</p> <p>- <em>00_analysis_setup.R</em> provides loads packages and metadata files to be collated in <em>01_chem_prep.qmd</em>.</p> <p>- <em>01_chem_prep.qmd</em> adds metadata to most recent .csv of water chemistry data supplied by RMRS lab.</p> <p>- <em> distance_finder.R</em> uses NHDflowlines to calculate distances from furthest downstream site, PBD.</p> <p>- <em>cpf_sites_map.R</em> uses location metadata to create <em>cpf_sites_map.html</em></p> <p>- <em>demo.R</em> provides an example of how to download data from Zenodo directly in RStudio</p> <p><strong>Data are housed in the data folder and it contains the following:</strong></p> <p>- cleaned: This folder contains the most recently available dataset and has associated burn severity and location data added to the chemistry data. The addition of the metadata was accomplished using the `01_chem_prep.qmd` R script.</p> <p>- cleaned_archive: This folder contains an archive of previously cleaned data. <strong>Downstream users are encouraged to use the collated data file `CPF_reservoir_chemistry_up_to_20231027.csv`</strong> in the `cleaned` directory.</p> <p>- raw: These data were directly received by the ROSSyndicate from RMRS lab managers. Downstream users are encouraged to use the collated data file `CPF_reservoir_chemistry_up_to_20231027.csv` in the `cleaned` directory.</p> <p>- metadata: this contains location data, parameter/column name definitions, units, and methods used at the RMRS Lab. The `README` file in this folder explains burn severity classifications used in the files `sbs_watershed.csv`,`sbs_watershed.csv` and `cpf_sites.csv`</p> <p><strong>Sample Collection</strong></p> <p>Field measurements were taken using a Thermo Orion Star with RDO Optical and Conductivity probes. Time data, when present, are listed in MST. Samples were collected and processed using the Rocky Mountain Research Station's Biogeochemistry Lab, overseen by Timothy Fegel and Charles Rhoades, according to the methods described in rmrs_procedures.png</p> <p><strong>Version: v2023.12.13</strong></p>
Urban Riparian Wetland Water Quality Dataset_Stormwater Capture in Beaver-mediated Wetlands along Walnut Creek, Raleigh, North Carolina, USA
<p><span>This is the initial release of a </span><strong><span>water quality</span></strong><span> dataset pertaining to the <strong>riparian floodplain wetlands</strong> alongside Walnut Creek in Raleigh, North Carolina USA. Walnut Creek is the main drainage channel in an <strong>urbanized watershed</strong> (HUC-12: 030202011101) in central North Carolina. There are several riparian floodplain wetlands along the creek which are largely supplied by <strong>urban stormwater</strong> runoff including directed <strong>storm sewer flows</strong> and regular <strong>overbank flooding</strong> events. In many of these wetlands local water retention and residence time in the surface ponds is mediated by the damming activity of <strong>North American beavers (</strong><em><strong>Castor canadensis</strong></em><strong>)</strong>. This dataset contains data specific to the water quality values of <strong>Walnut Creek</strong>, its tributary <strong>Little Rock Creek</strong>, and the surface ponds and groundwater at the <strong>Walnut Creek Wetland Park</strong> which is actively influenced by resident beavers. The period of this dataset is from <strong>January </strong></span><strong><span>5</span><span>, 2023 through </span></strong><strong><span>October 28</span><span>, 2023</span></strong><span>. </span></p> <p><span>This dataset includes a variety of common <strong>water quality parameters</strong> measured in situ by use of a <strong>YSI Pro water quality meter</strong>, as well as <strong>dissolved nutrient values</strong> determined by <strong>laboratory analysis</strong> of collected water samples.<span> </span>YSI data was collected on a <strong>weekly</strong> basis and water samples were collected for laboratory analysis on a <strong>monthly</strong> basis. Additional measurements and collection took place during <strong>six large rainfall events</strong> to allow comparison between baseflow and stormflow conditions across the site.<span> </span>This dataset aims to provide a comprehensive look at the water quality of Walnut Creek in comparison with the surface ponds and groundwater in the Walnut Creek Wetland Park, which are all ultimately sourced from <strong>urban stormwater runoff</strong>. </span></p> <p><span>This water quality dataset is intended to accompany the <u>separate</u> <strong>hydrology dataset</strong> published on Zenodo at URL: <a href="https://doi.org/10.5281/zenodo.10709630">https://doi.org/10.5281/zenodo.10709630</a>. Together, these datasets are meant to support an improved understanding of the water availability and water quality found in connection with beaver-mediated stormwater capture in an urbanized watershed in the North Carolina Piedmont.</span></p> <p><span> </span><span>This dataset resulted from research supported with a Graduate Student Research Grant awarded by the <strong>North Carolina Water Resources Research Institute (WRRI)</strong>, under Project Number 23-10-W: "Stormwater Diversion, Storage, and Treatment by Beaver-enhanced Floodplain Wetlands in Piedmont Urban Watersheds". </span></p> <p><span>This material is based upon work supported by the <strong>National Science Foundation (NSF)</strong> Graduate Research Fellowship Program (GRFP) under Grant No. (DGE 2137100). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.</span></p> <p><span>Special thanks to <strong>Raleigh Parks</strong> and <strong>Walnut Creek Wetland Park</strong> for making this work possible.</span></p> <p><span>Laboratory analysis support for evaluation of dissolved nutrients (nitrate+nitrite, TKN, total phosphorus, and total organic carbon) was provided by the <strong>NC State Environmental and Agricultural Testing Services (EATS)</strong> laboratory, Department of Crop and Soil Sciences.</span></p> <p><span> </span><span>Additional laboratory analysis support for evaluation of dissolved nutrients (TKN and total phosphorus) was provided by the <strong>NC State Environmental Analysis Laboratory (EAL)</strong>, Department of Biological and Agricultural Engineering (BAE).</span></p> <p><span> </span><span>Usage of and technical support for the YSI Pro water quality meter used in this study was made possible by the <strong>Osburn Lab</strong>, Department of Marine, Earth and Atmospheric Sciences (MEAS), NC State University.</span></p>
Global surface water quality datasets under uncertain climate and socio-economic change, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<pre>Global ~10km (5 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 2005-2100, with annual and monthly temporal resolution. Simulations are made under three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and using five general circulation model (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0), following the ISIMIP3b protocol (<a href="https://protocol.isimip.org/#/ISIMIP3b">https://protocol.isimip.org/#/ISIMIP3b</a>). Output data are provided at annual and monthly temporal resolution over WorldClim time periods (2005-2020; 2021-2040; 2041-2060; 2061-2080; 2081-2100). Output data includes: - Discharge (m<sup>3</sup> s<sup>-1</sup>) - Water temperature (K)<br>- Total dissolved solids (TDS) load (g s<sup>-1</sup>)<br>- Biological oxygen demand (BOD) load (g s<sup>-1</sup>)<br>- Fecal coliform (FC) load (million cfu s<sup>-1</sup>) - Salinity; as indicated by TDS concentrations (mg l<sup>-1</sup>) - Organic pollution; as indicated by BOD concentrations (mg l<sup>-1</sup>) - Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml<sup>-1</sup>)<br><br>Note. A minimum discharge threshold of 0.1 m<sup>3</sup> s<sup>-1</sup> was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.<br><br>Full time series of these variables at 30 arcmin (0.5 degree) can be found at: <a href="https://zenodo.org/records/14677534">https://zenodo.org/records/14677534</a>.</pre>
Water quality dataset from stream water in the Khibiny massif, Kola Peninsula (Russia) in August 2017
<p>The dataset includes base chemistry, elemental concentrations, and sulfur isotope (δ34S_SO4) measurements from 11 stream water sampling locations.</p> <p>The measurements were taken in the Belaya and Vuonnemiok stream systems within the Khibiny massif (Kola Peninsula, Russia) during 25-30th of August 2017 and were analyzed at laboratories at Stockholm University, Sweden.</p> <p>The data was gathered to investigate potential pollution spreading in hydrological pathways from active apatite mining within the catchments, and it can be used for comparison with other mining-impacted sites in the Arctic.</p>
The compiled 8-year dataset (2012-2019) consisting of weekly river water quality indicators (CODMn, DO, NH3-N and PH ) in majors 10 sub-basin of Yangtze river based on imputation of machine learning
<p>Water quality is significantly affected by global climate change and human activities, with diverse critical factors shaping its state in rivers and lakes. In the study, we utilized four indicators to characterize water quality: the physical water quality parameters included dissolved oxygen (DO, mg/L) and PH, while the chemical water quality parameters encompassed chemical oxygen demand (CODMn, mg/L) and ammonia nitrogen (NH3-N, mg/L). This study establishes weekly water quality models for typical 10 sub-basins along the Yangtze River using machine learning methods, which incorporate the impacts of hydro-meteorological and anthropogenic factors.These 10 sub-basins represent the principal tributaries of the Yangtze River basin and include Dongting Lake, the upper Han River, the lower Han River, the Jialing River, the Jinsha River, the Li River, the Min River, Poyang Lake, the Xiang River, and the Yuan River. This data collection was performed by National Environmental Monitoring Centre (http://www.cnemc.cn/sssj/szzdjczb/index_1.shtml). The water quality indicators discussed in this study are assessed in accordance with the national standard GB 3838-2002. Please refer to the paper for details.</p>
Dataset for RANAS and water quality survey in Malawi
<p>Data outlines behaviour factors and water quality results used in the co-design and development of educational materials for Malawi WaterSPOUTT prototype</p>
Dataset for the paper submitted for peer-review with the title "Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model"
<p>The proposed dataset is related to the following article submitted for peer review:</p> <p>Hasanyar, M., Flipo, N., Romary, T., Wang, S. (2023), Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model, UNDER PEER-REVIEW</p> <p>It consists of command files for the prose-pa0.74 software available here: https://gitlab.com/prose-pa/prose-pa </p> <p>To run the model :</p> <p>1. Compile prose-pa0.74</p> <p>2. Copy the executable in the current directory</p> <p>3. In a terminal launch</p> <p>> ./prose-pa0.74 simulation.COMM test.log</p> <p>The “simulation.COMM” holds the settings for the ProSe-PA simulation related to the paper mentioned in the front head of the current file. </p> <p>The information on different parameters of “simulation.COMM” are included in “bathymetrie”, “Cmds”, “Inflows”, “layers”, “meteo”, “o2_obs”, “param_bio”, “Reaches” and “Singularities” folders.</p> <p>The “bathymetrie” folder holds the geometric information of several cross-sections along the river. </p> <p>The Cmds folder holds the “simulation.COMM” file. </p> <p>The “Inflows” folder the information about the boundary condition inflows to the river such as discharge, concentration of organic carbon, etc.</p> <p>The layer folder holds data of the initial conditions of the model (Table 2 in the article).</p> <p>The “meteo” folder holds the meteorological information.</p> <p>The “o2_obs” folder holds the observed oxygen data needed to do data assimilation. </p> <p>The “param_bio” folder holds information on the physiology of bacteria, phytoplankton, and other model species.</p> <p>The “Reaches” folder holds information about river reaches and their manning coefficient. </p> <p>The “param_range” file holds the variation range of model parameters considered in data assimilation together with their perturbation percentage.</p> <p>The output files are written in $HOME/Outputs folder. It is possible to change it directly in simulation.COMM, last entry “Output_folder”.</p>
Dataset for 'Water, Sanitation and Hygiene Infrastructure and Quality in Rural Healthcare Facilities in Rwanda'
<p>Dataset for 'Water, Sanitation and Hygiene Infrastructure and Quality in Rural Healthcare Facilities in Rwanda' BMC Public Health addressing: (i) general water and power characteristics of 17 rural healthcare facilities in Rwanda, assessments conducted in 2011 and 2012; (ii) functionality of water access points in 10 rural healthcare facilities in Rwanda, 2012, (iii) availability of soap and water for hand washing in 10 rural healthcare facilities in Rwanda, 2012, (iv) state of toilets and latrines in 10 rural healthcare facilities in Rwanda, 2012, (v) Total Coliform concentration (MPN/100 mL) in tap water samples and treated drinking water samples from rural health centers in Rwanda collected during rapid assessment in 2011 and baseline assessment in 2012, (vi) <em>E. coli</em> concentration (MPN/100 mL) in tap water samples and treated drinking water samples from rural health centers in Rwanda collected during rapid assessment in 2011 and baseline assessment in 2012, (vii) free chlorine residual (mg/L) in tap water samples and treated drinking water samples from rural healthcare facilities in Rwanda collected during rapid assessment in 2011 and baseline assessment in 2012.</p>
Biosensor Dataset: Supporting research on biosensor technology for overall water quality monitoring
<p>Dataset regarding vision-based and MEMS-based biosensor technology validation within ASTRAL IMTA labs.</p>
Tourists' perceptions of water quality in Finland, dataset, related to article "Browning of Boreal Lakes: Do Public Perceptions and Governance Meet the Biological Foundations?"
<p>Tourists' perceptions of water quality in Finland, dataset, related to article "Browning of Boreal Lakes: Do Public Perceptions and Governance Meet the Biological Foundations?" by Albrecht et al. (2022).<br> <br> <strong>Article abstract:</strong><br> "Surface water browning, also known as brownification, is an acute environmental issue concerning inland waters. We adopt an interdisciplinary perspective and combine limnology, ecology, environmental law, tourism, and economics to build a theoretical framework to understand the consequences and management needs concerning water browning, generate a research agenda, and pinpoint acute research needs. Using a systematic review approach, we identify gaps of knowledge and address the impacts of browning on lake ecosystem functioning. To support the biological information, we present primary survey data to define the recreational use and public perception of waters in Finland. We identified an urgent need for the development of indicators of brownification beyond EU’s Water Framework Directive (WFD) to be able to balance the costs of browning with terrestrial activities that increase browning. Despite a considerable body of research already exists, there is still a need for better understanding of the biogeochemical processes related to browning to progress towards ecosystem-based management of inland waters. Public perception of the quality of waterbodies in Finland was largely in agreement with the proportion of waterbodies classified to meet the good or excellent ecological status, but recreational fishers may value different aspects of water quality compared to the classification based on WFD. Consequently, to build an interdisciplinary understanding of how browning affects boreal lakes and their uses, we suggest improvements for environmental regulation concerning the impact assessment of terrestrial land use activities and ecosystem-based management of inland waters."</p>
Dataset for "Toward polarization-enhanced water quality remote sensing measurements from UAVs"
<p>Dataset used in publication "Toward polarization-enhanced water quality remote sensing measurements from UAVs," published and presented at the 2024 SPIE Future Sensing Technologies (FST) conference.</p>
Dataset and correlation matrix of topography, water quality and the number of species of macrophytes in the Akan Caldera lakes, Japan (2013–2014).
<p>Akan Caldera lakes_1: Dataset of topography, water quality, and the number of species of macrophytes in the Akan Caldera lakes.</p> <p>Akan Caldera lakes_2: Diagram illustrating the classification of the watershed variables.</p> <p>Akan Caldera lakes_3: Correlation matrix of topography, water quality, and the number of species of macrophytes in the Akan Caldera lakes.</p>
GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
Dataset used for the paper entitled: Large-scale water quality patterns across Portugal using a long-term nationwide dataset (1970-2024)
<p>This repository contains the dataset and plots generated for the paper described above, which are listed below</p> <ol> <li>Dataset: <ol> <li>Folder: dataset.zip</li> <li>File format: NPY (standard binary file format in NumPy/Python for NumPy arrays) </li> </ol> </li> <li>Plots: <ol> <li>Folder: HTML_plots.zip</li> <li>File format: HTML (for interactive exploring and broad accessibility)</li> </ol> </li> <li>Basins interception with Landuse: <ol> <li>Folder: basin_LU_intersect.zip</li> <li>File format: NPY (standard binary file format in NumPy/Python for NumPy arrays) </li> </ol> </li> </ol>
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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.