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205 results for “Wastewater”
Dataset of paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater"
<p>Dataset of operation of a bioelectrochemically-improved anaerobic digester (AD-BES), treating real fishery processing wastewater.<br>This dataset was used to publish the paper "Bioelectrochemically-improved anaerobic digestion of fishery processing industrial wastewater" in Journal of Water Process Engineering (DOI: 10.1016/j.jwpe.2024.105848).</p>
MetFrag Local CSV: CompTox (7 March 2019 release) Wastewater MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</p> <p>The Wastewater MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>Suspect Lists (1=presence, 0=absence):</p> <p>- ITNANTIBIOTIC, STOFFIDENT, REACH2017, ZINC15PHARMA and PFASMASTER</p> <p>Suspect Lists with scores from KEMI (see details on <a href="https://www.norman-network.com/nds/SLE/">NORMAN-SLE</a> and hyperlinks below):</p> <p>- <a href="https://zenodo.org/record/2628787">KEMIMARKET_EXPO</a>, <a href="https://zenodo.org/record/2628787">KEMIMARKET_HAZ</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_WDUIndex</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_StpSE</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_SEHitsOverDL</a></p>
PIE LTER, geographic information for the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA.
A description of the transects that were set up to study the impacts on the salt marsh vegetation of nutrient enrichment from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich, MA, USA. The marsh around Clubhead Creek, Rolwey, MA, USA was used as a reference.
Presence or absence of marsh plant species along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh, Plum Island Ecosystems LTER.
Presence or absence of marsh plant species along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh. Nutrient enrichment comes from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich. The marsh around Clubhead Creek, Rowley, MA was used as a reference.
Supplementary dataset for "Potential of Wastewater Reuse to Alleviate Water Scarcity under Future Warming Scenarios"
<p>The folder contains water gap data relative to the paper:<br>Kahn, M., Sangiorgio, M., and Rosa, L. (2025) Potential of wastewater reuse to alleviate water scarcity under future warming scenarios. Environmental Research Letters, 20, 034012<br>https://doi.org/10.1088/1748-9326/adb31d</p> <p>All water gaps data are in km3/yr.</p> <p><br>Gridded data(NetCDF at 0.5°)</p> <ul> <li>baseline (2001-2010) <ul> <li>Water_gap_baseline_no_wastewater_reuse: Water gap under baseline climate scenario with no wastewater reuse.</li> <li>Water_gap_baseline_treated_wastewater_reuse: Water gap under baseline climate scenario with treated wastewater reuse.</li> <li>Water_gap_baseline_full_wastewater_reuse: Water gap under baseline climate scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>1.5°C warming <ul> <li>Water_gap_15_no_wastewater_reuse: Water gap under 1.5°C warming scenario with no wastewater reuse.</li> <li>Water_gap_15_treated_wastewater_reuse: Water gap under 1.5°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_15_full_wastewater_reuse: Water gap under 1.5°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <ul> <li>3°C warming (5 models + average) <ul> <li>Water_gap_3_no_wastewater_reuse: Water gap under 3°C warming scenario with no wastewater reuse.</li> <li>Water_gap_3_treated_wastewater_reuse: Water gap under 3°C warming scenario with treated wastewater reuse.</li> <li>Water_gap_3_full_wastewater_reuse: Water gap under 3°C warming scenario with full wastewater reuse.</li> </ul> </li> </ul> <p> </p> <p>Aggregated data (.xlsx)</p> <ul> <li>country_level_water_gaps.xlsx: Water gap aggregated by country for all the considered scenarios.</li> <li>city_water_gaps.xlsx: 0.5° pixels with populations greater than 5,000,000 and non-zero water gaps corresponding to urban center.</li> <li>seasonal_variations.xlsx: Monthly water gaps of the 5 most water scarce countries.</li> </ul> <p><br>Note: the global water gap obtained by summing all the countries is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country.</p>
SARS-CoV-2 wastewater surveillance data and metadata in the Open Data Model format. Part 1: Québec City
<p>SARS-CoV-2 wastewater surveillance data and metadata in the Open Data Model format. Part 1: Québec City Authors</p> <ul> <li>Therrien, J-D<sup>1</sup></li> <li>Maere, T.<sup>1</sup></li> <li>Sanchez-Quete, F.<sup>2</sup></li> <li>Tsitouras, A.<sup>2</sup></li> <li>Goitom, E.<sup>3</sup></li> <li>Cloutier, F.<sup>4</sup></li> <li>Dufour, D.<sup>4</sup></li> <li>Proulx, F. <sup>4</sup></li> <li>Nicolaï, N.<sup>1</sup></li> <li>Philippe, R.<sup>1</sup></li> <li>Tohidi, M.<sup>1</sup></li> <li>Dorner, S.<sup>3</sup></li> <li>Frigon, D.<sup>2</sup></li> <li>Vanrolleghem, P.A.<sup>1</sup></li> </ul> <p>Affiliations</p> <ul> <li><sup>1</sup> model<em>EAU</em>, Département de génie civil et de génie des eaux, Université Laval</li> <li><sup>2</sup> Microbial Community Engineering Lab (MiCEL), Department of Civil Engineering, McGill University</li> <li><sup>3</sup> Polytechnique Montréal</li> <li><sup>4</sup> Ville de Québec</li> </ul> <p>General Remarks</p> <p>Wastewater-based surveillance of SARS-CoV-2 virus can detect between 1 and 30 infected individuals per 100,000 (including asymptomatic ones) by analyzing the population's sewage. As such, this method is very attractive since it costs only a fraction of clinical testing (as low as 1%). Human faeces may contain the virus a few days before a person becomes ill. Thus, this approach allows for detection of outbreaks 2-7 days before the increase in reported cases stemming from clinical screening tests (Bibby et al., 2021). Wastewater-based surveillance complements clinical testing by geolocating outbreaks, which may help targeting intensive screening programs. Moreover, it provides a quick indication of whether new public health measures (e.g., masks, social distancing, confinement, and curfew) are effective.</p> <p>Sampling</p> <p>The reported dataset contains open data collected in the province of Québec as part of the SARS-CoV-2 wastewater-based surveillance program <a href="https://www.centreau.ulaval.ca/en/covid/">CentrEau</a>-COVID. Four of the largest cities in the province (Montréal, Laval, Québec City, and Trois-Rivières), as well as the municipalities of four rural regions (Mauricie, Centre-du-Québec, Bas-St-Laurent, and Gaspésie) participated in the program. The entire dataset includes 31 sampling sites covering approximately half the population of the province of Québec (population size of 8.5 million). The timeframe covered by the dataset varies for each site. The earliest surveillance program was launched in March 2020, others followed soon after. Samples were collected using various methods, such as 24h composite samples, grab samples, and passive sampling using variations on the Moore swab method (Schang et al., 2020)</p> <p>Analysis</p> <p>Prior to the analysis of the samples for SARS-CoV-2, physiochemical parameters such as total suspended solids (TSS), turbidity, conductivity, ammonium concentration, and pH were measured. The samples were subsequently concentred by filtration using a MEC filter (0.45 um), followed by total RNA extraction using the Qiagen AllPrep PowerViral DNA/RNA Kit (Qiagen, USA) with some modifications (beta-mercaptoethanol concentration raised to 10% and lysis performed at 55 °C for 30 minutes) (Ahmed et al., 2020). SARS-CoV-2 viral RNA was detected by a one-step RT-qPCR. To assess the RNA recovery rate of the procedure, samples were spiked before extraction with a known concentration of Bovine Respiratory Syncytial Virus (BRSV) using the Zoetis INFORCE 3 vaccine (Zoetis, USA). In addition to SARS-CoV-2, samples were assessed for Pepper Mild Mottle Virus (PMMoV), the daily load of which is hypothesized to represent the fecal load contributions to the samples at a given site and time. PCR conditions and primer used to collect viral data are described in the files <code>primers.md</code> and <code>PCR conditions.md</code>.</p> <p>Compilation</p> <p>The measurements on wastewater samples carried out by the participating laboratories of this study are found in the <code>WWMeasure</code> table. The values provided by municipalities come from laboratories accredited by the Centre d'expertise en analyse environnementale du Québec (CEAEQ), in compliance with the latter's quality assurance protocols. The COVID-19-related public health data found in the <code>CPHD</code> table were collected from the Institut National de Santé Publique du Québec (INSPQ)'s public reports. Wastewater data taken in-situ at the sampling sites (e.g., the flow at pumping stations or water resource recovery facilities (WRRFs)) are found in the <code>SiteMeasure</code> table and were taken by the institutions responsible for managing the sites. All of the data, stemming from multiple sources, were combined into the <a href="https://github.com/Big-Life-Lab/ODM">Open Data Model (ODM)</a> standard format using the <a href="https://github.com/modelEAU/ODM-Import">ODM-Import python package</a> (see also Structure).</p> <p>Validation</p> <p>Wastewater and sample data were manually assessed for quality by our research collaborators. Data points for which the quality appeared to be uncertain were tagged with the value <code>True</code> in the <code>qualityFlag</code> column. Conversely, data deemed of good quality have a quality flag of <code>False</code>. Data that were not checked have a quality flag of <code>NA</code>. Textual comments describing the issues with the data points in more detail are also included in the dataset using the <code>notes</code> column of the relevant tables. Note that data validation was carried out by the data custodians responsible for each city in the dataset according to available resources. As the project continues and data validation is undertaken on more sections of the dataset, data may be re-analyzed, flagged, or commented as needed. Revisions to the dataset will be reported to the best of our ability.</p> <p>Structure</p> <p>The data contained in this dataset has been structured according to the <a href="https://github.com/Big-Life-Lab/ODM">Open Data Model (ODM) for Wastewater-Based Surveillance</a>. This model provides a standardized dictionary to collect and share data and metadata stemming from wastewater-based surveillance programs. By convention, it splits all data into 10+ thematic tables with each record representing a unique measurement, i.e., long format. For convenience, the <code>wide</code> folder presents the data found in all the other tables in a wide format, i.e., multiple measurements are aligned by <code>timestamp</code>, with each column representing a different parameter.</p> <p>Acknowledgements</p> <p>The authors would like to acknowledge that this dataset was collected thanks to the financial support of the Fonds de Recherche du Québec, the Molson Foundation, the Trottier Family Foundation, CentrEau and NSERC. The authors would also like to acknowledge the efforts of Douglas Manuel (Ottawa Hospital) and Howard Swerdfeger (Public Health Agency of Canada) for their original idea for the Open Data Model and continued development.</p> <p>References</p> <ol> <li> <p>Ahmed, W., Bertsch, P.M., Bivins, A., Bibby, K., Farkas, K., Gathercole, A., Haramoto, E., Gyawali, P., Korajkic, A., McMinn, B.R., Mueller, J.F., Simpson, S.L., Smith, W.J.M., Symonds, E.M., Thomas, K. v., Verhagen, R., Kitajima, M., 2020. Comparison of virus concentration methods for the RT-qPCR-based recovery of murine hepatitis virus, a surrogate for SARS-CoV-2 from untreated wastewater. Science of the Total Environment 739. <a href="https://doi.org/10.1016/j.scitotenv.2020.139960">https://doi.org/10.1016/j.scitotenv.2020.139960</a></p> </li> <li> <p>Bibby, K., Bivins, A., Wu, Z., North, D., 2021. Making waves: Plausible lead time for wastewater based epidemiology as an early warning system for COVID-19. Water Research 202, 117438. <a href="https://doi.org/10.1016/j.watres.2021.117438">https://doi.org/10.1016/j.watres.2021.117438</a></p> </li> <li> <p>Schang, C., Crosbie, N., Nolan, M., Poon, R., Wang, M., Jex, A., Scales, P., Schmidt, J., Thorley, B.R., Henry, R., Kolotelo, P., Langeveld, J., Schilperoort, R., Shi, B., Einsiedel, S., Thomas, M., Black, J., Wilson, S., McCarthy, D.T., 2020. Passive sampling of viruses for wastewater-based epidemiology: a case-study of SARS-CoV-2 [WWW Document]. URL <a href="https://www.researchgate.net/publication/347103410\_Passive\_sampling\_of\_viruses\_for\_wastewater-based\_epidemiology\_a\_case-study\_of\_SARS-CoV-2?channel=doi&linkId=5fd800f392851c13fe892393&showFulltext=true">https://www.researchgate.net/publication/347103410\_Passive\_sampling\_of\_viruses\_for\_wastewater-based\_epidemiology\_a\_case-study\_of\_SARS-CoV-2?channel=doi&linkId=5fd800f392851c13fe892393&showFulltext=true</a> (accessed 1.18.21).</p> </li> </ol>
SARS-CoV-2 RNA levels in Scotland's wastewater
<p>Nationwide, wastewater-based monitoring was newly established in Scotland to track the levels of SARS-CoV-2 viral RNA shed into the sewage network, during the COVID-19 pandemic. We present a curated, reference data set produced by this national programme, from May 2020 to February 2022.</p> <p>Viral levels were analysed by RT-qPCR assays of the N1 gene, on RNA extracted from wastewater sampled at 122 locations. Locations were sampled up to four times per week, typically once or twice per week, and in response to local needs.</p> <p>These wastewater data are contributing to estimates of disease prevalence and the viral reproduction number (R) in Scotland and in the UK.</p> <p>We report sampling site locations with geographical coordinates, the total population in the catchment for each site, and the information necessary for data normalisation, such as the incoming wastewater flow values and ammonia concentration, when these were available. The methodology for viral quantification and data analysis is briefly described, with links to detailed protocols online. Check the README for details and the project <a href="https://biordm.github.io/COVID-Wastewater-Scotland/">COVID-WW Website</a></p>
Wastewater Treatment Plant Inflow Rate, Network Water Levels and Rain Rate for Koeln Weiden, Germany
<p>wtp_network_rain.csv contains time series data of measured wastewater treatment plant inflow rates, water levels from multiple locations within the wastewater sewer network and rain rate measured from a rain recorder located at the wastewater treatment plant.</p>
S39 | KEMIWWSUS | Wastewater Suspect List based on Swedish Product Data
<p>This is the collection associated with list S39 KEMIWWSUS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S39</p> <p>KEMIWWSUS</p> <p><strong>Wastewater Suspect List based on Swedish Product Data</strong></p> <p>Wastewater Suspect List <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/Suspects_WasteWater_Sweden_KEMI20190212.xlsx">Original File with Mapped DTXSIDs</a> (12/02/2019)</p> <p>KEMIWWSUS <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/KEMIWWSUS_InChIKeys_12022019.txt">InChIKeys</a> (12/02/2019)</p> <p>A prioritized list of 1,123 substances relevant for wastewater based on Swedish product registry data, including scores. Provided by Stellan Fischer, KEMI.</p> <p>Update 14/11/2019: added CSV version and resulting updated XLSX.</p> <p> </p>
UV-Vis spectral dataset of distillation wastewaters from the production of essential oils of lavender cultivars and other aromatic plant species
<pre>The present database provides a set of 16 ultraviolet-visible (UV-Vis) spectra characterizing the residual by-products (distillation wastewaters) of the production of essential oils from lavender (<em>Lavandula angustifolia</em> Mill.) and other aromatic plant species, including interspecific hybrids and cultivars.</pre>
Dataset of the manuscript: Efficient removal of nanoplastics from industrial wastewater through synergetic electrophoretic deposition and particle-stabilized foam formation
<p>This dataset is based on the data underlying the figures shown in the manuscript titled Efficient removal of nanoplastics from industrial wastewater through synergetic electrophoretic deposition and particle-stabilized foam formation. A readme file is uploaded to decribe the content of all data folders. All data are sorted according to their appearance in the figures of the main manuscript.</p>
Data for: Evaluation of a full-scale wastewater treatment plant with ozonation and different post-treatments using a broad range of in vitro and in vivo bioassays
<p>This repository contains research data linked to the following publication: Kienle, C., Werner, I., Fischer, S., Lüthi, C., Schifferli, A., Besselink, H., Langer, M., McArdell, C.S. and Vermeirssen, E.L.M. 2022. Evaluation of a full-scale wastewater treatment plant with ozonation and different post-treatments using a broad range of <em>in vitro</em> and <em>in vivo</em> bioassays. Water Research, 118084. https://doi.org/10.1016/j.watres.2022.118084</p> <p>Abstract: Micropollutants present in the effluent of wastewater treatment plants (WWTPs) after biological treatment are largely eliminated by effective advanced technologies such as ozonation. Discharge of contaminants into freshwater ecosystems can thus be minimized, while simultaneously protecting drinking water resources. However, ozonation can lead to reactive and potentially toxic transformation products. To remove these, the Swiss Federal Office for the Environment recommends additional "post-treatment" of ozonated WWTP effluent using sand filtration, but other treatments may be similarly effective. In this study, 48 h composite wastewater samples were collected before and after full-scale ozonation, and after post-treatments (full-scale sand filtration, pilot-scale fresh and pre-loaded granular activated carbon, and fixed and moving beds). Ecotoxicological tests were performed to quantify the changes in water quality following different treatment steps. These included standard <em>in vitro</em> bioassays for the detection of endocrine, genotoxic and mutagenic effects, as well as toxicity to green algae and bacteria, and flow-through <em>in vivo</em> bioassays using oligochaetes and early life stages of rainbow trout.</p> <p>Results show that ozonation reduced a number of ecotoxicological effects of biologically treated wastewater by 66 - 93 %: It improved growth and photosynthesis of green algae, decreased toxicity to luminescent bacteria, reduced concentrations of hormonally active contaminants and significantly changed expression of biomarker genes in rainbow trout liver. Bioassay results showed that ozonation did not produce problematic levels of reaction products overall. Small increases in toxicity observed in a few samples were reduced or eliminated by post-treatments. However, only relatively fresh granular activated carbon (analyzed at 13,000 - 20,000 bed volumes) significantly reduced effects additionally (by up to 66 %) compared to ozonation alone. Inhibition of algal photosynthesis, rainbow trout liver histopathology and biomarker gene expression proved to be sufficiently sensitive endpoints to detect the change in water quality achieved by post-treatment.</p>
Life cycle assessment of struvite recovery and wastewater sludge end-use: A Flemish illustration
<p>This work was supported by the European Union's Horizon 2020 project Nutri2Cycle (Grant agreement No. 773682) and Interreg North-West Europe's ReNu2Farm (Grant: NEW601). These datasets are supplementary information to the manuscript titled <a href="https://www.sciencedirect.com/science/article/pii/S0921344922001732?via%3Dihub#!">"Life cycle assessment of struvite recovery and wastewater sludge end-use: A Flemish illustration"</a></p> <p> </p> <p> </p> <p> </p>
"Who Cares?": The Acceptance of Decentralized Wastewater Systems in Regions without Water Problems
<p>Transcripction of focus groups for the paper “Who Cares?”: The Acceptance of Decentralized Wastewater Systems in Regions without Water Problems. <a href="https://doi.org/10.3390/ijerph17239060">https://doi.org/10.3390/ijerph17239060</a> <em>Int. J. Environ. Res. Public Health</em> <strong>2020</strong>, <em>17</em>(23), 9060</p> <p><a href="https://zenodo.org/api/files/0039e4e3-1794-4be8-bb9c-63abf3512695/TRANSCRIPCI%C3%93N%20FOCUS%20GROUP%20ARQUITECTOS.docx">TRANSCRIPCIÓN FOCUS GROUP ARQUITECTOS</a></p> <p><a href="https://zenodo.org/api/files/0039e4e3-1794-4be8-bb9c-63abf3512695/TRANSCRIPCI%C3%93N%20FOCUS%20GROUP%20ECOLOGISTAS.docx">TRANSCRIPCIÓN FOCUS GROUP ECOLOGISTAS.docx </a></p> <p><a href="https://zenodo.org/api/files/0039e4e3-1794-4be8-bb9c-63abf3512695/TRANSCRIPCI%C3%93N%20FOCUS%20GROUP%20POBLACI%C3%93N%20GENERAL%201.docx">TRANSCRIPCIÓN FOCUS GROUP POBLACIÓN GENERAL 1.docx </a><br> <br> <a href="https://zenodo.org/api/files/0039e4e3-1794-4be8-bb9c-63abf3512695/TRANSCRIPCI%C3%93N%20FOCUS%20GROUP%20POBLACI%C3%93N%20GENERAL%202.docx">TRANSCRIPCIÓN FOCUS GROUP POBLACIÓN GENERAL 2.docx </a><br> </p>
DATASET: Environmental analysis of servicing centralised and decentralised wastewater treatment for population living in neighbourhoods
<p>DATASET:</p> <p>Article: Environmental analysis of servicing centralised and decentralised wastewater treatment for population living in neighbourhoods</p> <p>Journal of Water Process Engineering, Volume 37, October 2020, 101469</p> <p>https://doi.org/10.1016/j.jwpe.2020.101469</p>
Dataset: Development and optimization of a bioelectrochemical system for ammonium recovery from wastewater as fertilizer
<p>Dataset of Development and optimization of a bioelectrochemical system for ammonium recovery from wastewater as fertilizer</p> <p><a href="https://doi.org/10.1016/j.clet.2021.100142">https://doi.org/10.1016/j.clet.2021.100142</a></p> <p><a href="https://www.sciencedirect.com/journal/cleaner-engineering-and-technology">Cleaner Engineering and Technology</a> <a href="https://www.sciencedirect.com/journal/cleaner-engineering-and-technology/vol/4/suppl/C">Volume 4</a>, October 2021, 100142</p>
Analysis of nitrous oxide reductase diversity from wastewater: a SINTAX database
<p>This study explores the genetic landscape of nitrous oxide (N<sub>2</sub>O) reduction in wastewater treatment plants (WWTPs) by profiling 1083 high-quality metagenome-assembled genomes (HQ MAGs) derived from 23 Danish full-scale WWTPs. The analysis focuses on the distribution and diversity of nitrous oxide reductase (<em>nosZ</em>) genes, key players in N<sub>2</sub>O reduction, and their connection to other nitrogen metabolism pathways. A custom pipeline for clade-specific <em>nosZ</em> gene identification outperformed existing methods, revealing the presence of 503 nosZ sequences in 489 MAGs. Notably, 48.7% of the MAGs harboured <em>nosZ</em> genes, with clade II dominating (92.3%).</p> <p>Taxonomic profiling reveals the distribution of <em>nosZ</em> clade I and clade II-containing MAGs, emphasizing the dominance of <em>Bacteroidota</em> and <em>Pseudomonadota</em>. Notably, <em>Chloroflexota </em>exhibits unexpected affiliations with nosZ clade I. The taxonomic diversity of non-denitrifying N<sub>2</sub>O-reducers is also explored, highlighting the presence of these organisms in <em>Bacteroidota</em>, <em>Chloroflexota</em>, and other phyla.</p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset2
<p>This dataset contains the data underlying the following publication: Li Qimeng, Wang Cheng, Hua Ming, Shuang Chendong, Li Aimin, Gao Canzhu. (2017). High-efficient removal of phthalate esters from aqueous solution with an easily regenerative magnetic resin: Hydrolytic degradation and simultaneous adsorption. <em>Journal of Cleaner Production</em><em>. </em> <a href="https://doi.org/10.1016/j.jclepro.2017.11.121">https://doi.org/10.1016/j.jclepro.2017.11.121</a></p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset1
<p>This dataset contains the data underlying the following publication: Mouna Mahjoubi, Simone Cappello, Yasmine Souissi, Atef Jaouani and Ameur Cherif (February 7th 2018). Microbial Bioremediation of Petroleum Hydrocarbon– Contaminated Marine Environments, Recent Insights in Petroleum Science and Engineering Mansoor Zoveidavianpoor, IntechOpen, DOI: 10.5772/intechopen.72207</p> <p> </p>
Building Public Confidence in Constructed Wetlands for Wastewater Treatment and Reuse: Survey Data and Focus Group Transcripts
<p>Constructed wetlands have been proposed as a cost-effective wastewater treatment, storage, and reuse solution for communities that are considering alternative water supply options to meet essential demands. In 2016, we began exploring the idea of wastewater reuse and the construction of an experimental wetland in Sewanee, located in the southern U.S. state of Tennessee. As a major barrier to water reuse is often public resistance, we conducted a survey and focus groups to determine strategies to develop and initiate a community engagement campaign, aiming to empower residents to form reasoned opinions about local water supply options.</p> <p>This data set includes the survey that was distributed to Sewanee community members between November 2015 and February 2016, as well as protocols for three focus groups that were conducted with K12 teachers and community leaders on February 11 and 12, 2016. The survey results are summarized in a Microsoft Excel file. The three focus groups were transcribed, these transcripts are included here as PDF documents.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.