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59 results for “soil quality”
The Jefferson Project 2018 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at <https://jeffersonproject.rpi.edu/> In 2018, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2019 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2020 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
2017 hydrologic, water quality, and soil quality data from The Jefferson Projects 8 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake’s food web and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had eight tributary monitoring stations around the lake collecting data on water quality, soil quality and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_ShelvingRock and TS_West. The stations have a sensor payload that may include some or all of the following sensors: EXO2 Multi-parameter sonde, CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter with five 3.0 MHz transducers, Argonaut-SL Doppler current meter, WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and down sampling to an hourly frequency.
Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)
<p><strong>Version update: Coordinates were not correct in previsous version and have been corrected now in version 2</strong></p> <p> </p> <p>Dataset to the manuscript: Schiedung et al. (2022, Catena) Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada ( <a href="https://doi.org/10.1016/j.catena.2022.106194">https://doi.org/10.1016/j.catena.2022.106194</a> )</p> <p>Data files, variables and parameter are described in <em>Var_names_dd_all.csv</em> for all data on each sample and <em>Var_names_dd_composites.csv </em>for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in <em>Schiedung_CATENA_DRIFT_v1.1.zip.</em></p> <p> </p> <p><strong> </strong></p>
Dataset to manuscript: Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India
<p>Raw data to the manuscript entitled "Soil organic carbon stocks and quality in small-scale tropical, sub-humid and semi-arid watersheds under shrubland and dry deciduous forest in southwestern India" by Severin-Luca Bellè, Jean Riotte, Muddu Sekhar, Laurent Ruiz, Marcus Schiedung and Samuel Abiven.</p> <p>Data files include all raw data of soil cores (20211111_Raw_data.zip), data measured on composited samples (20211111_Composite_data.zip) and DRIFT spectra (20211111_DRIFT_data.zip).</p> <p>Files ending with var_names are the README files.</p>
Air quality, soil moisture, green roof moisture and weather data from Meetjestad
<p>Soil moisture sensors were developed by citizen science collective Meet je Stad (Measure your City). Measure your City was started in 2015 by inhabitants of the City of Amersfoort, with the goal of measuring climate related indicators. To be able to do so, collaboration was sought with the City of Amersfoort (COA), the local Water Authority and the University of Applied Sciences of Amsterdam. For the first three years the initiative focused on measuring temperature and humidity. Importantly, citizens develop their own research questions, analyze the data together with professionals and discuss potential implications. By doing so, the collective uses citizen science to spread knowledge on both technology and climate change in the most grass-roots manner possible. Within the SCOREwater project, Measure your City was asked to expand measurements with soil moisture measurements and additional temperature and humidity sensors.</p> <p>An important note here is that Measure your City develops their own sensors, has developed their own data platform and uses its own gateways purchased from the Things Network. As a result, much effort is put into constructing sensors that are reliable, low-maintenance and accurate. The latter is important for the City of Amersfoort as well, which intends to not only work on shared knowledge and understanding, but also use the data for policy making. To do so the data has to be reliable. By deploying both these sensors and purchasing company-built sensors, we can compare the data to assess how reliable the Measure your City sensors are.</p> <p>The Measure your City can also be deployed on green roofs to measure soil moisture. Whereas the soil moisture sensor measures soil moisture on two depths (10 centimeter and 40 centimeter), the sensor on a roof only measures soil moisture on one depth. In addition to soil moisture, Measure your City also measures air temperature and relative humidity. Some sensors also measure air quality (particle matter).</p>
The Jefferson Project 2021 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2022 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2022, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
Hourly LC impacts - Soil Quality Index - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Soil Quality Index, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Size-dependent effects of oxo-degradable plastic contamination on soil quality and Zea mays L. performance
<p><span>Agricultural plastic mulch films represent a significant source of microplastic contamination in soils, raising concerns about soil health and food security. Oxo-degradable plastics (ODPs) have emerged as a potentially more eco-friendly alternative to conventional plastic mulch films, however, uncertainty remains around the degradation rate of ODPs in soil and their impacts on soil quality and crop health. Using a controlled mesocosm experiment, we evaluated the behaviour and impact of different concentrations of oxo-biodegradable macro- (>10 mm) or micro-plastic (<5 mm) (0.01, 0.1, 1, and 10% w/w) on the performance of <em>Zea mays</em> L. grown in an agricultural soil over a 6-week period. Contrary to expectation, no major fragmentation or degradation of ODP was observed during the experimental period, however, FTIR revealed the formation of carbonyl groups indicative of oxidation. Overall, our results showed that typical levels of plastic contamination (0.01% w/w) had very little effect on soil physicochemical properties, microbial activity or plant performance. However, higher levels of plastic contamination resulted in significant changes in soil pH, EC, NO<sub>3</sub><sup>-</sup>, bulk density, and soil moisture. At extreme plastic loading rates (10% Can you write w/w), both micro- and macro-sized ODPs caused significant reductions in plant height and foliar chlorophyll content, with microplastic treatments showing consistently greater effects than macroplastics. None of the plastic loading rates had an effect on shoot and root biomass or soil NH<sub>4</sub><sup>+</sup> and P concentrations or microbial community structure in comparison to the unamended controls. Our findings indicate that at realistic field concentrations, ODPs are likely to have little effect on agroecosystem functioning but that they may persist in soil for long periods of time leading to their progressive accumulation in agricultural soils if used over repeated cropping cycles. Further research is needed to evaluate the longer-term impacts of ODPs on soil quality and crop health under field conditions.</span></p>
Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States: Data
<p>The data in this repository are associated with the manuscript from Huber et al. (2024) titled "Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States" in the Journal of Geophysical Research: Atmospheres. Additional information regarding these data can be found in the attached readme file.</p>
Soilcare data agricultural soil quality improvement
<p>Dataset that contains survey data on Spanish and UK respondents on agricultural soil quality protection and improvement</p>
Figure 1 in Soil quality, leaf litter quality, and microbial biomass interactively drive soil respiration in a microcosm experiment
Figure 1. Principal components analysis of (A) soil quality and (B) leaf litter quality across the experimental treatments (Table S1-2). Soil quality was quantified as a combination of soil pH, C, N, and C:N; leaf litter quality was quantified as a combination of leaf Ca, C, lignin, Mg, N, P, C:N, C:P, and N:P. Soil and leaf litter were collected from Hainich National Park, Germany.
Figure 2 in Determination of Soil Quality in Maykop Based on the Content Analysis of Soil Algae and Cyanobacteria
Figure 2. Total number of cyanobacteria and algae in the soil of Maykop city districts: 1 – «Pulp and Paper Plant»; 2 – «Station»; 3 – «Cheremushki»; 4 – «Sunrise»; 5 – «Central Market»; 6 – Total number of species.
Figure 1 in Determination of Soil Quality in Maykop Based on the Content Analysis of Soil Algae and Cyanobacteria
Figure 1. Soil sampling sites in Maykop: 1 – «Cheremushki»; 2 – «Pulp and Paper Plant»; 3 – «Station»; 4 – «Central Market»; 5 – «Sunrise».
Figure 1 in Colony site choice of blue-tailed bee-eaters: influences of soil, vegetation, and water quality
Figure 1. Distribution of blue-tailed bee-eater colony and soil sampling sites on Kinmen Island 2000–2002. Population estimates are given in parentheses for colonies active in 2002.
Figure 2 in Colony site choice of blue-tailed bee-eaters: influences of soil, vegetation, and water quality
Figure 2. Vegetation height profile comparisons between used (solid lines) and abandoned (dashed lines) bluetailed bee-eater nest cavities within colony (X) and between colonies (L and M).
Soil physical quality of a tropical sandy Ferralsol
<p>Dados de pesquisa da dissertação de mestrado do primeiro autor, vinculado à Universidade Estadual de Maringá, Maringá, BR.</p>
Data from: Effects of pesticides on soil bacterial, fungal and protist communities, soil functions and crop quality in vineyards
<p>Pesticides can have unintentional effects on non-target organisms and change biotic communities. Such changes might be particularly important in soil microbial communities which drive many ecosystem functions and may affect crop quality. Here, we investigated, in a 3-year study, how vegetation control (by herbicide application) and soil copper content (from long-term copper-based fungicide application), affect biodiversity and the community structure of soil bacteria, fungi and protists and associated soil functions (respiration, decomposition) in Swiss vineyards. Furthermore, we determined the effects of these two management practices on grape quality as the most direct ecosystem service to farmers. Across all study years, the community composition of microorganisms was affected by herbicide application, however, a significant loss of operational taxonomic units (OTUs) was only observed in fungi and protists. Soil copper content reduced OTU richness of bacteria and protists in some years but had no significant effect on fungal richness. Copper changed the community composition in all three groups of soil microorganisms. While we found no effect of copper on soil functions, herbicide application reduced microbial respiration and biomass by about 39% and 45% respectively. However, decomposition rates remained virtually unchanged by any pesticide. Yeast assimilable nitrogen (YAN) levels in grape must were below the critical threshold of 140 mg/L in 40% of the vineyards without herbicide application and the variety Chasselas , whereas in vineyards with herbicide application it was only 20%. Synthesis and applications: Application of pesticides led to changes in richness and composition of soil microbial communities and directly reduced some soil functions (microbial biomass and respiration), but not all (decomposition). Some grape quality parameters can be indirectly enhanced by pesticide application, highlighting the trade-off between the interests of nature conservation and the interests of the farmer. Balancing these two diverging interests requires the establishment of alternative vineyard management allowing reduced pesticide application.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.
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