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This data set provides detailed numerical data on the Life Cycle Inventory (LCI) analysis for assessing and comparing the environmental impact related to the treatment of sewage sludge (SS) in open windrow facilities for the production of compost and in an anaerobic digester for the production of biogas. The collection of the data is included in eleven tables, in which the machinery and duration for biogas and compost production procedures, as well as comparative data regarding the environmental impact category emissions for both scenarios are shown . Furthermore, electricity consumption and embodied energy for each scenario compared to other studies are given. The system boundary conditions were determined with the use of the Gabi software.

This dataset contains time series of reservoir releases (including any spills), evaporation loss, and rule curves for the Pong and Bhakra reservoirs, India. {"references": ["https://doi.org/10.3390/w11071413", "https://doi.org/10.1016/j.scitotenv.2019.06.021"]}

All temperature measurements in well T96-012 from September 1996 to December 2011. Data presented in this spreadsheet were digitized from temperature-time graphs presented in AMEC, Environmental and Infrastructure (2012) and Water Management Consultants, Inc. (2001) (references below) by Joseph Batir. The exact date of temperature data collection was not completely apparent and digitization may have introduced minor date errors, so instead all temperature measurements are associated with the month data collection occurred. The digitized date of temperature collection is included, but should be used with caution. The second tab includes all temperature data plotted as temperature versus depth plots. Raw data sources: AMEC Environment and Infrastructure. (2012) Long-Term Permafrost and Groundwater Monitoring Program for the Tailing Impoundment Five-Year Permafrost and Groundwater Data Analysis 2007-2011, 479 pp. Water Management Consultants, Inc. (2001) Red Dog Mine Long-Term Permafrost and Groundwater Monitoring Plan for the Tailing Impoundment, 24 pp.

The data contains vulnerability estimates (climate niche factor analysis) for 49 native fish species in the upper Danube River basin. The upper Danube River basin is mainly located in Germany and Austria. The time frame covered is 300 years from 1800 to 2100 including two Representative Concentration Pathways, RCP 4.5 and RCP 8.5. Vulnerability estimates are calculated for three time frames (1800-1830; 1900-1930and 2070-2100 (including two RCPs)) with the time frame 1970-2000 as the baseline. In all files the zone column gives the basin ID for the master basins layer. The mean column gives the mean vulnerability estimate for a sub-basin for a certain species. For the future scenarios the different predictions based on the different GCM-RCM combinations have to be combined using the median and the zone as unique identifier.

Siderophores are low molecular weight secondary metabolites produced by microorganisms under low iron stress as a specific iron chelator. In the present study, a rhizospheric bacterium was isolated from the rhizosphere of sesame plants from Salem district, Tamil Nadu, India and later identified as Bacillus subtilis LSBS2. It exhibited multiple plant-growth-promoting (PGP) traits such as hydrogen cyanide (HCN), ammonia, and indole acetic acid (IAA), and solubilized phosphate. The chrome azurol sulphonate (CAS) agar plate assay was used to screen the siderophore production of LSBS2 and quantitatively the isolate produced 296 mg/L of siderophores in succinic acid medium. Further characterization of the siderophore revealed that the isolate produced catecholate siderophore bacillibactin. A pot culture experiment was used to explore the effect of LSBS2 and its siderophore in promoting iron absorption and plant growth of Sesamum indicum L. Data from the present study revealed that the multifarious Bacillus sp. LSBS2 could be exploited as a potential bioinoculant for growth and yield improvement in S. indicum.

Zooplankton treatment: HZ: zooplankton added, LZ: Zooplankton removedCarbon treatment: 0C: No glucose addition, 0.5: 0.5 x Redfield. 1: 1xRedfield, 2: 2xRedfield, 3: 3XRedfieldpH Treatment:NpH: Normal pH, LpH: Low pH

The Social-Ecological Systems (SES) framework serves as a valuable framework to explore and understand social and ecological interactions, and pathways in water governance. Yet, it lacks a robust understanding of change. We argue an analytical and methodological approach to engaging global changes in SES is critical to strengthening the scope and relevance of the SES framework. Relying on SES and resilience thinking, we propose an institutional and cognitive model of change that institutions and natural resources systems co-evolve to provide a dynamic understanding of SES that stands on three causal mechanisms: institutional complexity trap, rigidity trap, and learning processes. We illustrate how Data Cube technology could overcome current limitations and offer reliable avenues to test hypothesis about the dynamics of social-ecological systems and water security by offering to combine spatial and time data with no major technical requirements for users.