Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Biosensor Dataset: Supporting research on biosensor technology for overall water quality monitoring

Dataset regarding vision-based and MEMS-based biosensor technology validation within ASTRAL IMTA labs.

Table 4 in Water quality, yield and cost-benefit analysis of rain water ponds of Cuttack district: A comparison between Indian major carp and GIFT Tilapia

Table 4: Growth and production of IMC poly-culture & GIFT mono-sex tilapia in T1 & T2 (2018-19)<table><tbody><tr><th>Parameters</th><th>Growth-production data T1 T2</th></tr></tbody><tbody><tr><th>Initial avg. weight (g)</th><td>19</td><td>8</td></tr><tr><th>Final avg. weight (g)</th><td>816</td><td>1333</td></tr><tr><th>Survival rate (%)</th><td>91</td><td>93.75</td></tr><tr><th>Production (kg/pond/9 months)</th><td>3383</td><td>8000</td></tr><tr><th>Total production (kg/ha/9 months)</th><td>8457.5</td><td>20000</td></tr></tbody></table>

Table 2 in Water quality, yield and cost-benefit analysis of rain water ponds of Cuttack district: A comparison between Indian major carp and GIFT Tilapia

Table 2: Fish stocking & production data-IMC poly-culture & GIFT Tilapia mono-sex culture in T1 & T2 (2018-19)<table><tbody><tr><th>Tank Pond Area Stocking Date of No. (ha) (no) Stocking</th><th>Types of fish Stocking</th><th>Growth estimate during stocking (August-2018)</th><th>Growth estimate during final Total FCR harvest (May-June-2020) production (kg)</th></tr></tbody><tbody><tr><th></th><td></td><td></td><td></td><td>Length in (cm) Weight (g) Length in (cm)</td><td>Weight (g)</td><td>Weight in (kg)</td><td></td></tr><tr><th>T1</th><td></td><td>4000</td><td></td><td>Catla</td><td>9-12 18-23</td><td>28.3-39.6</td><td>980-1070</td><td>1682</td><td>1.7</td></tr><tr><td>0.4</td><td>05.09.2020 Rohu</td><td>10-12 18-24</td><td>28.0-41.3</td><td>700-750</td><td>880</td></tr><tr><td></td><td></td><td>Mrigala</td><td>8-11 15-19</td><td>29.4-37.6</td><td>680-720</td><td>821</td></tr><tr><th>T2</th><td>0.4</td><td>6400</td><td>GIFT tilapia 15.09.2020 (O. niloticus)</td><td>6-7 8-9</td><td>28.3-32.1</td><td>1333</td><td>8000</td><td>1.2</td></tr></tbody></table>

Table 3 in Water quality, yield and cost-benefit analysis of rain water ponds of Cuttack district: A comparison between Indian major carp and GIFT Tilapia

Table 3: Comparision of operational cost, production and economic profit of IMC polyculture & GIFT mono-sex tilapia culture during 2018-19 at Jodamu village of district Ciuttack, Odisha<table><tbody><tr><th>Parameters</th><th>T1-IMC culture (area-.4 ha)</th><th>T2-GIFT Tilapia culture (area-.4 ha)</th></tr></tbody><tbody><tr><th>Operational Cost</th><td>Expenditure Expenditure (Rs/0.4ha/yr) (Rs/ha/yr)</td><td>Expenditure (Rs/0.4ha/yr)</td><td>Expenditure (Rs/ha/yr)</td></tr><tr><th>I. Expenditure</th><td></td><td></td><td></td><td></td></tr><tr><th>Watering/de-watering charges</th><td>3,000</td><td>7,500</td><td>3,000</td><td>7,500</td></tr><tr><th>Bleaching Powder 50kg@Rs30/kg</th><td>1,500</td><td>3,750</td><td>1,500</td><td>3,750</td></tr><tr><th>Organic Manure 1000kg@Rs 0.5/kg</th><td>500</td><td>1,250</td><td>500</td><td>1,250</td></tr><tr><th>DAP fertilizer 20kg@ 20/kg</th><td>400</td><td>1,000</td><td>400</td><td>1,000</td></tr><tr><th>Lime-800kg (IMC), 1000kg (GIFT tilapia) @ Rs 10/kg</th><td>8,000</td><td>20,000</td><td>10,000</td><td>25,000</td></tr><tr><th>GNOC-25kg (IMC), 42kg (GIFT tilapia) @ Rs 22/kg</th><td>550</td><td>1325</td><td>924</td><td>2310</td></tr><tr><th>Soyabin 21kg @Rs 30/kg</th><td>630</td><td>1,575</td><td>630</td><td>1,575</td></tr><tr><th>Curd 120 kg @ 40/kg</th><td>4800</td><td>12000</td><td>4800</td><td>12000</td></tr><tr><th>Yeast 5kg @ Rs 200/kg</th><td>1000</td><td>2500</td><td>1000</td><td>2500</td></tr><tr><th>Ricebran 80kg @ Rs 15.5/kg</th><td>1,240</td><td>3,100</td><td>1,240</td><td>3,100</td></tr><tr><th>Joggery-100kg (IMC), 208kg (GIFT tilapia) @ Rs 23/kg</th><td>2,300</td><td>5,750</td><td>4,784</td><td>11,960</td></tr><tr><th>IMC seed4000pc @ Rs 5/pc and GIFT seed cost 6400pc @ Rs 2/pc</th><td>20,000</td><td>50,000</td><td>12,800</td><td>32,000</td></tr><tr><th>IMC-F. Feed 5500kg @ Rs 40/kg and GIFT tilapia F. Feed 9800kg @ Rs 40/kg</th><td>2,20,000</td><td>5.50,000</td><td>3,92,000</td><td>9,80,000</td></tr><tr><th>Transport @ Rs10000/time</th><td>20,000</td><td>50,000</td><td>30,000</td><td>75,000</td></tr><tr><th>Man power for pond preparation, bio-security installation, Management, Feeding, 10,000 netting, watch and ward, marketing etc. @ 200/man day (IMC & GIFT)</th><td>25,000</td><td>30,000</td><td>75,000</td></tr><tr><th>Miscellaneous expenditure (medicine, aeration, transaction and coordination)</th><td>5,000</td><td>12,500</td><td>10,000</td><td>25,000</td></tr><tr><th>Total expenditure</th><td>2,98,920</td><td>7,47,300</td><td>5,03,578</td><td>15,22,070</td></tr><tr><th>IMC poly-culture & GIFT mono-sex tilapia Production and economic profit (2018-19)</th></tr><tr><th>II. Gross Income from GIFT tilapia</th><td>0.4 ha/yr</td><td>ha/yr</td><td>0.4 ha/yr</td><td>ha/yr</td></tr><tr><th>Total production (kg/yr)</th><td>3383</td><td>8457.5</td><td>8000</td><td>20000</td></tr><tr><th>IMC & GIFT Cost of fish @ Rs 160 & 140/kg (Rs)</th><td>5,41,280</td><td>13,53,300</td><td>11,20,000</td><td>28,00,000</td></tr><tr><th>Net income from fish (Gross income-expenditure) (Rs)</th><td>2,42,360</td><td>6,05,900</td><td>6,31,172</td><td>15,77,930</td></tr><tr><th>Return on expenditure (%)</th><td>81.07</td><td>0.81</td><td>125.33</td><td>103.67</td></tr><tr><th>Cost benefit ratio (C:B)</th><td>0.810</td><td>0.81</td><td>1.25</td><td>1.25</td></tr></tbody></table>

Table 1 in Water quality, yield and cost-benefit analysis of rain water ponds of Cuttack district: A comparison between Indian major carp and GIFT Tilapia

Table 1: Ranges and mean values (± SD) of water parameters in T1 &T2<table><tbody><tr><th>Parameters</th><th>T1-IMC poly-culture</th><th>T2-GIFT tilapia mono-sex</th></tr></tbody><tbody><tr><th></th><td>Min</td><td>Max</td><td>Mean ± SD</td><td>Min</td><td>Max</td><td>Mean ± SD</td></tr><tr><th>Temp (°C)</th><td>21.1</td><td>33</td><td>26.3±4.56</td><td>21.2</td><td>34.1</td><td>27.7±6.5</td></tr><tr><th>Transparency (cm)</th><td>24.33</td><td>35.67</td><td>27.71± 0.86</td><td>25.00</td><td>36.00</td><td>29.29± 0.81</td></tr><tr><th>DO (ppm)</th><td>4.3</td><td>7.5</td><td>6.07±1.35</td><td>4.5-</td><td>6.0</td><td>5.3±0.51</td></tr><tr><th>pH</th><td>7.0</td><td>8.4</td><td>7.5±0.55</td><td>6.0</td><td>8.0</td><td>7.5±0.35</td></tr><tr><th>Alkalinity mg/l</th><td>86.7</td><td>114.7</td><td>100.4±12.8</td><td>80.1</td><td>114.0</td><td>98.3±14.2</td></tr><tr><th>Ammonia mg/l</th><td>0.51</td><td>0.61</td><td>0.55±0.05</td><td>0.55</td><td>0.64</td><td>0.58±0.04</td></tr></tbody></table>

opencc-by-4.0Jul 2021View details →

Survey response of the H2020 Water-ForCE expert meeting on In situ calibration and validation of satellite products of water quality and hydrology

These slides provide an overview of the expert survey held in advance of the H2020 Water-ForCE workshop: In situ calibration and validation of satellite products of water quality and hydrology which was held as three virtual meeting sessions on 17, 18 and 20th May 2021: <ol> <li>Data availability, Accessibility and quality gaps</li> <li>Emerging technologies to address current gaps</li> <li>Data harmonization and sharing</li> </ol> Source data have been removed from the graphs included in these slides but may be requested from the authors. The workshop and outcomes are part of the H2020 funded project Water-ForCE: Water Scenarios For Copernicus Exploitation. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004186

opencc-by-4.0Jul 2021View details →

Groundwater and surface water quantity and quality measurement results for groundwater dependent ecosystem in Kazu leja, Latvia in 2019 and 2020

Groundwater and surface water quantity and quality measurement results and methodology of the investigations of the groundwater dependant ecosystem in Kazu leja, Latvia in 2019 and 2020 is presented. The data comprises: <ol> <li>Methodology</li> <li>Measurement site data [2_observation_sites.csv]</li> <li>Field and laboratory measurement results [3_field_and_lab_results.csv]</li> <li>Measurement uncertainty [4_measurement_uncertainity.csv]</li> <li>Automated water level, temperature, and electrical conductivity measurements [5_level_temperature_electricConductivity_loggers.csv]</li> </ol>

opencc-by-4.0Aug 2021View details →

Daily water quality data sets of the nine U.S. watersheds

US_daily_WQ_datasets_2.xlsx contains daily water quality (WQ) and discharge data from the nine watersheds in the United States. The data value of -999 means missing observation. The names (USGS station numbers) of nine WQ monitoring sites are Blanchard River (04189000), Cuyahoga River (0428000), Great Miami River (03271500), Honey Creek (04197100), Maumee River (04193500), Muskingum River (03150000), Portage River (04195500), Rock Creek (04197170), and Tiffin River (04185000). Daily WQ data were composed by following the procedure described in the Appendix S1 in Hirsch (2014). The all WQ data river were retrieved from the Heidelberg University's National Center for Water Quality Research site (https://ncwqr.org/monitoring/data) and discharge data were downloaded from the USGS National Water Information System (<a href="http://waterdata.usgs.gov/nwis/">http://waterdata.usgs.gov/nwis/</a> or https://doi.org/10.5066/F7P55KJN) in 2020. All the daily discharge data were acquired via the USGS National Water Information System. These data were used to evaluate the performance of the unbiased load estimates and confidence intervals of river loads based on the rating curve method using rejection sampling in the listed article below. To maintain the traceability of the proposed load estimation method and replicability of the results in the article, the authors of the article upload the data used in this repository.

Tourists' perceptions of water quality in Finland, dataset, related to article "Browning of Boreal Lakes: Do Public Perceptions and Governance Meet the Biological Foundations?"

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). Article abstract: "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."

opencc-zeroJan 2023View details →

Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters

Damming a stream inserts a lentic system (an impoundment or reservoir) into a lotic system, changing downstream hydrological, biogeochemical, and ecological processes. One such ecological effect of damming is to create a resource subsidy of easily captured and consumed zooplankton, which are preyed upon by filter-feeders and visual predators. The data included here were used to predict the density of lentic zooplankton subsidizing downstream habitats with water quality parameters as an alternative to microscopy. We also used this data to detect three different water quality regimes (high conductivity, high-CDOM, and a remainder) that are associated with differences in the density of zooplankton. This dataset is contained in two parts, both of which are focused on zooplankton density in the effluent of a series of tributary-impoundment reservoirs: 1) zooplankton density for a single summer season with water quality parameters and 2) zooplankton density for a series of three summers without water quality parameters.

opencc-by-4.0Apr 2023View details →

A laboratory-scale simulation framework for analyzing wildfire hydrologic and water quality effects

Datasets containing information on experimental conditions for each tested soil sample, measured hydrologic and water quality responses, and calculated response metrics.

opencc-zeroApr 2023View details →

Fencing farm dams to exclude livestock halves methane emissions and improves water quality

Agricultural practices have created tens of millions of small artificial water bodies ("farm dams" or "agricultural ponds") to provide water for domestic livestock worldwide. Among freshwater ecosystems, farm dams have some of the highest greenhouse gas (GHG) emissions per m2 due to fertilizer and manure run-off boosting methane production – an extremely potent GHG. However, management strategies to mitigate the substantial emissions from millions of farm dams remain unexplored. We tested the hypothesis that installing fences to exclude livestock could reduce nutrients, improve water quality, and lower aquatic GHG emissions. We established a large-scale experiment spanning 400 km across south-eastern Australia where we compared unfenced (N = 33) and fenced farm dams (N = 31) within 17 livestock farms. Fenced farm dams recorded 32% less dissolved nitrogen, 39% less dissolved phosphorus, 22% more dissolved oxygen, and produced 56% less diffusive methane emissions than unfenced dams. We found no effect of farm dam management on diffusive carbon dioxide emissions and on the organic carbon in the soil. Dissolved oxygen was the most important variable explaining changes in carbon fluxes across dams, whereby doubling dissolved oxygen from 5 to 10 mg L-1 led to a 74% decrease in methane fluxes, a 124% decrease in carbon dioxide fluxes, and a 96% decrease in CO2-eq (CH4 + CO2) fluxes. Dams with very high dissolved oxygen (>10 mg L-1) showed a switch from positive to negative CO2-eq. (CO2 + CH4) fluxes (i.e., negative radiative balance), indicating a positive contribution to reducing atmospheric warming. Our results demonstrate that simple management actions can dramatically improve water quality and decrease methane emissions while contributing to more productive and sustainable farming.

A physical perspective of recurrent water quality degradation: a case study in the Jiangsu coastal waters, China

This dataset is created for manuscript entitle of A physical perspective of recurrent water quality degradation: a case study in the Jiangsu coastal waters, China submitted to Journal of Geophysical Research: Oceans. This dataset should not be used without agreement from authors before the paper published.

opencc-by-4.0Apr 2023View details →

Stormwater filter water quality and quantity data - treatment of road runoff in the city of Vantaa, Finland

The water quantity and quality dataset is from road runoff filters in the city of Vantaa, Finland. Data are from 6 campaigns in 2017 and 2019. The data is documented in: Koivusalo, H., Dubovik, M., Wendling, L., Assmuth, E., Sillanpää, N., Kokkonen, T. 2023. Performance of sand and mixed sand-biochar filters for treatment of road runoff quantity and quality. Accepted to Water.

opencc-by-4.0Jun 2022View details →

Figure 8 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring

Figure 8 The final composite, sieved water mite sample ready for the picking process.

opencc-by-4.0Jun 2023View details →

Data for "Upward migration of calanoid copepods is driven by high food quality in surface waters in an alpine lake"

In this study, we explored why zooplankton migrated to surface waters at night from the perspective of their physiological characteristics and adaptability to the environment. The calanoid Arctodiaptomus sp. accumulated large amounts of polyunsaturated fatty acids (PUFAs) and astaxanthin, which relieved oxidative stress to fatty acids. The concentrations of lutein, a precursor of astaxanthin synthesis, were highest in surface water, indicating the enhancement of ultraviolet radiation (UVR) to precursor synthesis, which was confirmed by our indoor experiment. The calanoids migrated to surface water at night to obtain high concentrations of lutein and PUFAs from their diets. Relevant data for this study include: the vertical distribution of Arctodiaptomus sp. during the day and at night; concentrations of total astaxanthin, free astaxanthin, astaxanthin esters in Arctodiaptomus sp. at night and during the day; fatty acid concentration and the ratio of SAFAs (saturated fatty acids), MUFAs (monounsaturated fatty acids), and PUFAs in Arctodiaptomus sp. during the day and at night; the carotenoid concentrations  in seston at different depth of Lake Heihai during the day and at night; the lutein concentration in seston under UVR and dark treatment; main characteristics of Lake Heihai; fatty acid concentrations and the ratio of SAFAs , MUFAs, and PUFAs of seston in Lake Heihai; the relative abundance of Chlorophytes with the size of greater than 5 μm and 0.2-5 μm in different layers of Lake Heihai; fatty acid concentrations of the calanoids in Fuxian Lake.

opencc-zeroJul 2023View details →

Data for: Water depth and transparency drive the quantity and quality of organic matter in sediments of Alpine lakes on the Tibetan Plateau

The primary objectives of the study were to: (i) determine OM quantity and quality in the sediments of alpine lakes on the Tibetan Plateau, (ii) identify the primary environmental regulators of OM quantity and quality, and (iii) reveal OM transformations in the water column and sediments. Firstly, we collected sediments and lake water from 20 lakes with diverse morphology and sizes across the entire Tibetan Plateau. Secondly, We analyzed the bulk sedimentary OM and two leachable pools of the sedimentary OM, i.e., water-soluble OM and alkaline-extracted OM combining elemental and stable isotopic analysis, optical measurements (i.e., absorbance and fluorescence spectroscopy), and ultrahigh-resolution molecular techniques (i.e., electrospray ionization-assisted Fourier transform-ion cyclotron resonance mass spectrometry, ESI FT-ICR MS). We also measured the lake water physicochemical characteristics (i.e., depth, transparency, salinity, turbidity, DO, pH, TN, TP and Chl a), as well as the composition of dissolved OM in water column via optical measurements. Thirdly, we performed statistical analysis to determine the primary environmental control and predictors of the spatial variability in sedimentary OM on the Tibetan Plateau. The statistical analysis included non-parametric Kruskal-Wallis with Dunn post hoc test, redundancy analysis, and linear regression models. Main results of the study are that (i) sedimentary water-soluble OM and alkaline-extracted OM were both dominated by low-molecular-weight, low-aromaticity compounds with low contributions of terrestrial humic substances, suggesting that sedimentary leachable OM was primarily regulated by in-lake sources and processes; (ii) water depth, water transparency, and total phosphorus concentration in water column explained ~50% variance of sedimentary bulk and leachable OM, substantiating the importance of autochthonous sources and primary productivity in regulating the quantity and quality of sedimentary OM; (iii) in comparison to lake surface water DOM, water-soluble OM and alkaline-extracted OM from sediments had higher proportions of terrestrial humic-like substances, suggesting preferential preservation of allochthonous materials in the sediments.

opencc-zeroAug 2023View details →

Eutrophication, water quality, and fisheries: a wicked management problem with insights from a century of change in Lake Erie

The datasets here were used to examine relationships between the overall productivity of Lake Erie and the commercial harvest of lake whitefish (Coregonus clupeaformis), walleye (Sander vitreus), and yellow perch (Perca flavescens) during 1915–2011. Here, we provide the two datasets used in the paper by Sinclair et al. titled "Eutrophication, water quality, and fisheries: a wicked management problem with insights from a century of change in Lake Erie". Each dataset is provided as a separate tab in a single Excel worksheet. The first dataset ("Productivity") provides the annual values of the five metrics used to develop the index of overall Lake Erie productivity. The second dataset ("Commercial harvest") provides the total annual commercial harvest (kg) of the three fish species, which were obtained from the Great Lakes Fishery Commission (<a href="http://www.glfc.org/great-lakes-databases.php">http://www.glfc.org/great-lakes-databases.php</a>). A summary and explanation of each variable is provided in the "Info" tab. Further information on how values were calculated (and transformed if necessary) is provided in either the info tab or the methods and supporting information of the associated article.

opencc-by-4.0Aug 2023View details →

Time series of flow measurement and water quality monitoring at a large combined sewer overflow in Berlin

The table contains 3 years CSO monitoring time series, already split in 22 separated CSO events. All details about the monitoring set up in the papers and reports indicated below. Fields: <ul> <li>evtID: event ID</li> <li>myDateTime; date/time</li> <li>v: m/s velocity</li> <li>Q: m³/s flow</li> <li>TSS: mg/l TSS concentration</li> <li>COD: mg/l COD concentration</li> <li>CODf: mg/l dissolved COD concentration</li> <li>EC: µS/cm electric conductivity</li> <li>NH4_N: mg/l NH4_N concentration</li> </ul> TSS and COD have been measured with a spectrometer using a linear local calibration as presented in Lepot et al., 2016. ore information about the monitoring set up in  Sandoval, S., Torres, A., Pawlowsky-Reusing, E., Riechel, M., Caradot, N. (2013): The evaluation of rainfall influence on CSO characteristics: the Berlin case study. Water Science & Technology Vol. 68 (12): 2683-2690 10.2166/wst.2013.524 Caradot, N. (2012): Continuous Monitoring of Combined Sewer Overflows in the Sewer and the Receiving River: Return on Experience. Kompetenzzentrum Wasser Berlin gGmbH; MIA-CSO Project Report Riechel, M., Matzinger, A., Pawlowsky-Reusing, E., Sonnenberg, H., Uldack, M., Heinzmann, B., Caradot, N., von Seggern, D., Rouault, P. (2016): Impacts of combined sewer overflows on a large urban river - Understanding the effect of different management strategies. Water Research 105: 264-273 10.1016/j.watres.2016.08.017 Caradot, N., Sonnenberg, H., Riechel, M., Matzinger, A., Rouault, P. (2013): The influence of local calibration on the quality of UV-VIS spectrometer measurements in urban stormwater monitoring. Water Practice & Technology Vol 8 (No 3-4): 417-425 10.2166/wpt.2013.042 Lepot, M., Torres, A., Hofer, T., Caradot, N., Gruber, G., Aubin, J.-B., Bertrand-Krajewski, J.-L. (2016): Calibration of UV/Vis spectrophotometers: A review and comparison of different methods to estimate TSS and total and dissolved COD concentrations in sewers, WWTPs and rivers. Water Research 101 (15 September 2016): 519-534 10.1016/j.watres.2016.05.070