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103 results for “drinking water”
Regional drinking water quality monitoring program: long-term monitoring of water quality in select canals, reservoirs, and treatment plants of the greater Phoenix, Arizona metropolitan area drinking water system, ongoing since 1998
Regional Drinking Water Quality Monitoring Program ================================================== Arizona Statue University (ASU) has been working with regional water providers (Salt River Project (SRP), Central Arizona Project (CAP)) and metropolitan Phoenix cities since 1998 on algae-related issues affecting drinking water supplies, treatment, and distribution. The results have improved the understanding of taste and odor (T&O) occurrence, control, and treatment, improved the understanding of dissolved organic and algae dynamics, and initiated a forum to discuss and address regional water quality issues. The monitoring benefits local Water Treatment Plants (WTPs) by optimizing ongoing operations (i.e., reducing operating costs), improving the quality of municipal water for consumers, facilitating long-term water quality planning, and providing information on potentially future-regulated compounds. ASU has been monitoring water quality in terminal reservoirs (Lake Pleasant, Saguaro Lake, and Bartlett Lake) continuously from 1998 to the present for algae-related constituents (taste and odors, and more recently metals from the upper reservoirs), nutrients, and disinfection by-product precursors (i.e., total and dissolved organic carbon and organic nitrogen). Additional monitoring has been conducted in the SRP and CAP canal systems and in water treatment plants in Phoenix, Tempe and Peoria. During this work the Valley has been in a prolonged drought and recently one above average wet year, and this data provides important baseline data for development of new or expanded WTPs and management of existing WTPs in the future. The current work has improved the understanding of T&O sources and treatment, but additional research and monitoring into the future is necessary. Reservoir monitoring is conducted once per month at Bartlett Lake, Saguaro Lake, and Lake Pleasant, and quarterly at Roosevelt, Apache, and Canyon Lakes. Samples are depth integrated in the epilimnion a
S5 | KWRSJERPS | KWR Drinking Water Suspect List
<p>This is the collection associated with list S5 KWRSJERPS 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>S5</p> <p>KWRSJERPS</p> <p><strong>KWR Drinking Water Suspect List</strong></p> <p>KWR Suspects <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/NormanTargetSuspects-KWR_withStruct_DTXSIDs.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/NormanTargetSuspects-KWR_withStruct_DTXSIDs.xlsx">XLSX</a> (3/10/2017)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/kwrsjerps">KWRSJERPS List</a></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/NormanTargetSuspects-KWR_InChIKeys.txt">KWR InChIKeys</a> (15/03/2016)</p> <p>Sjerps <em>et al</em>. 2016 Water Research 93: 254-264. <br> DOI: <a href="http://www.sciencedirect.com/science/article/pii/S0043135416300938">10.1016/j.watres.2016.02.034</a></p>
Water quality in a basin for drinking water in the North-West of Italy (2022-2023)
<p>Information about water quality in La Loggia basin was collected in different seasons in 2022 and 2023, both on the basin surface and at different depths.</p> <p>Samples were collected and analysed in lab, for the following parameters:</p> <ul> <li>Total chlorophyll</li> <li>Blue-green algae</li> <li>Diatoms</li> <li>Green algae</li> <li>Planktothrix</li> <li>Transparency</li> <li>Temperature</li> <li>Dissolved oxygen</li> <li>pH</li> <li>Conductivity</li> <li>Turbidity</li> <li>Bromide</li> <li>Bromate</li> <li>Chloride</li> <li>Chlorite</li> <li>Chlorate</li> <li>Fluoride</li> <li>Nitrite</li> <li>Nitrate</li> <li>Orthophosphate</li> <li>Sulfates</li> </ul> <p>Samples were collected in the same dates of Sentinel-2 passages, in order to be used for the of satellite derived water quality products.<br> The shared data are not representative of drinking water distributed to users, since a multi-step treatment is performed on raw water in order to assure water safety and law-compliant quality standards.</p> <p><br> The coordinates of the sampling points are also available in the dataset.</p>
Data from: Chronic Rapamycin administration via drinking water mitigates the pathological phenotype in a Krabbe disease mouse model through autophagy activation.
<p>ABSTRACT </p><p>Krabbe disease (KD) is a rare disorder caused by a deficiency of the lysosomal enzyme galactosylceramidase (GALC), resulting in the accumulation of the cytotoxic metabolite psychosine (PSY) in the nervous system. This accumulation triggers demyelination and neurodegeneration. Despite ongoing research, the underlying pathogenic mechanisms remain incompletely understood, and there is currently no cure available.</p><p>Previous studies from our lab revealed the presence of autophagy dysfunctions in KD pathogenesis, as evidenced by the presence of p62-tagged protein aggregates in the brains of KD mice and increased p62 levels in the KD sciatic nerve. We also demonstrated that the autophagy inducer Rapamycin (RAPA) can partially restore the wild-type (WT) phenotype in KD primary cells by reducing the number of p62 aggregates.</p><p>In this study, we tested RAPA in the Twitcher (TWI) mouse, a spontaneous KD mouse model. We administered the drug ad libitum via drinking water (15 mg/L) starting from post-natal day (PND) 21-23. We longitudinally monitored the motor performance of the mice through grip strength and rotarod tests, along with various biochemical parameters related to KD pathogenesis (i.e. autophagy markers expression, myelination, astrogliosis, and PSY accumulation).</p><p>Our findings demonstrate that RAPA significantly enhances motor functions at specific treatment time points and reduces astrogliosis in TWI brain, spinal cord, and sciatic nerves. Using western blot and immunohistochemistry, we observed a decrease in p62 aggregates in TWI nervous tissues, which corroborates our earlier in-vitro results. Furthermore, RAPA treatment partially reduces PSY levels in the spinal cord.</p><p>In conclusion, our results support the consideration of RAPA as a supportive therapy for KD. Importantly, as RAPA is already available in pharmaceutical formulations for clinical use, its potential for KD treatment can be promptly evaluated in clinical trials.</p>
Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome
<p>Demultiplexed fastq files used for sequencing analysis, processed taxonomy read data for all samples and controls, and relevant environmental metadata as described in "Not the silver bullet: assessing the effects of silver-containing antimicrobial showerheads on the drinking water microbiome".</p>
S64 | NATOXAQ | NaToxAq: Natural Toxins and Drinking Water Quality - From Source to Tap
<p>This is the collection associated with list S64 NaToxAq on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p><strong>NaToxAq: Natural Toxins and Drinking Water Quality - From Source to Tap</strong></p> <p>NaToxAq (<a href="https://natoxaq.ku.dk">https://natoxaq.ku.dk</a>) is a European Training Network (ETN) funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 722493, to produce knowledge about natural toxins in aquatic environments. This is a list of all NaToxAq chemicals registered in MassBank within the project, provided by Tobias Schulze, UFZ and hosted on the NORMAN Suspect List Exchange (<a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a>). DOI: 10.5281/zenodo.3695174</p> <p> </p>
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>
Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management
<p>The datasets and accompanying R script included in this upload are provided to complement the manuscript titled <em>"Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management."</em> These resources are intended to facilitate the replication and verification of the analyses presented in the paper.</p> <p> </p> <p> </p>
Data from: Do the health benefits of boiling drinking water outweigh the negative impacts of increased indoor air pollution exposure?
<p><strong>Background: </strong>Billions of the world's poorest households are faced with the lack of access to both safe drinking water and clean cooking. One solution to microbiologically contaminated water is boiling, often promoted without acknowledging the additional risks incurred from indoor air degradation from using solid fuels.</p> <p><strong>Objectives: </strong>This modeling study explores the tradeoff of increased air pollution from boiling drinking water under multiple contamination and fuel use scenarios typical of low-income settings.</p> <p><strong>Methods: </strong>We calculated the total change in disability-adjusted life years (DALYs) from indoor air pollution (IAP) and diarrhea from fecal contamination of drinking water for scenarios of different source water quality, boiling effectiveness, and stove type. We used Uganda and Vietnam, two countries with a high prevalence of water boiling and solid fuel use, as case studies. </p> <p><strong>Results: </strong>Boiling drinking water reduced the diarrhea disease burden by a mean of 1110 DALYs and 368 DALYs per 10,000 people for adults and children <5 years in Uganda, respectively, for high-risk water quality and the most efficient (lab-level) boiling scenario, with smaller reductions for less contaminated water and ineffective boiling. Similar results were found in Vietnam, apart from fewer avoided DALYs in children due to different demographics. In both countries, for households with high baseline IAP from existing solid fuel use, adding water boiling to cooking on a given stove was associated with a limited increase in IAP DALYs due to the log-linear dose-response curves. Boiling, even at low effectiveness, was associated with <em>net </em>DALY reductions for medium- and high-risk water, even if using unclean stoves/fuels. Replacing traditional stoves with improved stoves coupled with effective boiling practices significantly reduced total DALYs. </p> <p><strong>Discussion: </strong>Boiling water generally resulted in a net decrease in DALYs. Future efforts should empirically measure health outcomes from IAP vs. diarrhea associated with boiling drinking water using field studies with different boiling methods and stove types.</p>
Evaluation of point-of-use treatments and biochar to reduce 1,2,3-trichloropropane (TCP) contamination in drinking water
<p>From the 1940s to the 1980s, 1,2,3-trichloropropane (TCP) was widely present as an impurity in soil fumigants to eliminate plant parasitic nematodes. TCP also saw wide usage as a degreaser and solvent in industrial processes. In rural agricultural regions with a history of fumigant use, TCP is a common pollutant in groundwater. As a potent suspected carcinogen (California MCL 5 ng/L), TCP poses a risk to communities reliant on domestic wells. Lacking the populations needed for more centralized water treatment facilities, these communities often use point-of-use (POU) treatment technologies or buy bottled water. In this study, we tested commercially available water pitchers equipped with carbon filters for point-of-use TCP treatment efficacy. As a less costly carbon alternative, we also tested low-cost, locally sourced biochar made from almond shells. Biochar could serve as a sustainable alternative to the current coconut and coal-based carbon feedstocks. Pitcher point-of-use filters removed at or above 98% of TCP in tap water derived from untreated groundwater during their lifetime of use. In our batch studies, almond biochar did absorb TCP at a lower efficiency than commercially available granulated carbon. Ultimately, the study's findings could assist affected communities and households in identifying efficient and cost-effective treatment technologies at the domestic well and household levels.</p>
Dataset: Nutrient Removal in Roadside Stormwater Bioretention Cells Amended with Drinking Water Treatment Residuals
<p>This dataset contains storm volumes and nitrogen and phosphorus concentrations measured at the inflow and outflow of four roadside, lined bioretention cells in a field study conducted in Burlington, Vermont</p> <p>Phosphorus data are published in: Ament, M. R., Roy, E. D., Yuan, Y., & Hurley, S. E. (2022). Phosphorus removal, metals dynamics, and hydraulics in stormwater bioretention systems amended with drinking water treatment residuals. <em>Journal of Sustainable Water in the Built Environment</em>, <em>8</em>(3), 04022003.</p> <p>Nitrogen data are included in a manuscript currently under review.</p> <p> </p> <p> </p>
Distribution System Environmental and Sequencing Datasets for Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System
<p>The dataset of environmental parameters and sequence fastqs used to create figures and do analysis in the paper <strong>Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System </strong>submitted to Environmental Science & Technology</p>
Data from: Health trade-offs of boiling drinking water with solid fuels: A modeling study
Open the record for dataset details and reuse information.
Evaluation of point-of-use treatments and biochar to reduce 1,2,3-trichloropropane (TCP) contamination in drinking water
Open the record for dataset details and reuse information.
Annexes to the Update of the risk assessment of nickel in food and drinking water
<p><strong>Annex A – Benchmark dose analysis </strong></p> <p>The Annex is provided as a separate pdf file containing the detailed results of the benchmark dose analyses from which no reference point was selected. </p> <p><strong>Annex B – Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment </strong></p> <p>The Annex is provided as a separate Excel file containing the dietary surveys per country and age group.</p> <p><strong>Annex C – Occurrence data on nickel in food and drinking water</strong></p> <p>The Annex is provided as a separate Excel file containing summary statistics on occurrence data on nickel.</p> <p><strong>Annex D – Chronic and acute dietary exposure to nickel and the contribution of different food groups to the dietary exposure</strong></p> <p>The Annex is provided as a separate Excel file containing the chronic and acute dietary exposure to nickel per survey and the contribution of different food groups to the dietary exposure. </p>
Data Sets: An Assessment of Water Trusts: Drinking Water Quality and Provision in Six Low-income, Peri-urban Communities of Lusaka, Zambia
<p>Data set associated with the manuscript published in GeoHealth titled An Assessment of Water Trusts: Drinking Water Quality and Provision in Six Low-income, Peri-urban Communities of Lusaka, Zambia. This includes bacterial, nitrate, specific conductance, and Water Trust survey data collected from Lusaka, Zambia in 2013, 2014, 2016, an 2019.</p>
Cup for drinking mineral water no. 2
ID no.: MNS/MS/109 - H Museum: Nowy Sącz District Museum https://muzea.malopolska.pl/en/objects-list/2460 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Trace element composition of drinking water in Almaty
<p>Water is an important component of all life on Earth, and water pollution with heavy metals can lead to detrimental consequences for public health. The purpose of this study was to determine the health risks caused by trace elements present in the drinking water supply systems of Almaty City. As part of this research, the elemental composition of 78 drinking water samples taken in winter, summer, and autumn of 2023 in different areas of the city was studied.Based on the data obtained, drinking water contamination indices were calculated for heavy metal groups, and the degree of water suitability for drinking purposes was assessed for each sampling point.</p>
SWDA Inequalities Dataset: Safe Water Drinking Water Access Indicator and Inequalities Indexes of Brazilian Municipalities
<p>The SWDA Inequalities Dataset consist on results of Safe Drinking Water Access - SDWA indicators and inequalities indexes obtained by an adapted Lorenz Curve approach calculated for all Brazilian municipalities.The main datasource is the Brazillian 2010 Demographic Census, which is the last Population Census data publicaly available. The SDWA indicators are weighted by the SDWA values corresponding to census tracts considering permanent private household residents (Eq. 2). The safe drinking water sources are: (i) general water distribution network; (ii) well or spring located on the property; (iii) rainwater stored in a cistern, cement box, etc.; (iv) or well or spring outside the property, tank car, rainwater stored otherwise, river, dam, lake or stream, or another form of water supply. Considering that these drinking water sources differ in terms of accessibility, availability and water quality, the AHP Method was applied to obtain different weights for each form of water access (Eq. 1).</p> <p>The Gini Index (G_water), Concentration Coefficient (C_water) and Dissimilarity Index (D_water), were adapted to analyze inequalities in SDWA. The Safe Drinking Water Concentration Index is obtained from the Lorenz Curve, with the percentage of residents in permanent households accumulated and ordered by <em>per capita income</em> in the abscissa of the graph (Eq. 3). The <em>per capita income</em> is obtained by the ratio between the total nominal monthly income and the number of residents of the permanent households of each census tract. The Safe Drinking Water Gini Index was similarly adapted, with the residents accumulated and ordered by the safe drinking water rate in the abscissa of the graph (Eq. 4). The ratios between Upper and Lower Quintiles (R_8020) were obtained for each municipality, that is, the relationship between the rates of SDWA for permanent private households that represent 20% of the residents with the highest average <em>per capita</em> income (4th quintile represented by <em>Q<sub>4</sub></em>) and the most economically unfavorable 20% (1st quintile represented by <em>Q<sub>1</sub></em>) (Eq. 5). The Safe Drinking Water Dissimilarity Index is calculated by the weighted average of the absolute difference of the SDWA indicator of each quintile of the average <em>per capita income</em> and the average rate (Eq. 6). The inequalities indexes are also presented as normalized values (G_water_norm, C_water_norm, R_8020_norm, D_water_norm).</p> <p><span class="math-tex">\(t_{water} = 0.376*t_{piped} + 0.309*t_{well} + 0.274*t_{cistern} + 0.041*t_{others} \;\;\;\;\;\;\;\;\;\; (Eq. 1) \)</span></p> <p><span class="math-tex">\(0.041 \leq t_{water} \leq 0.368\)</span></p> <p><span class="math-tex">\(t_{piped} + t_{well} + t_{cistern} + t_{others} = 1\)</span></p> <p><em>t </em>is the relationship between residents in permanent private households with access (piped, well, cistern and/or other) and the total number of residents in permanent private households.</p> <p><span class="math-tex">\(SDWA = \frac{\sum_{i=1}^{n} (t_{water_{norm_i}}*p_i)}{\sum_{i=1}^{n}(p_i)} \;\;\;\;\;\;\;\;\;\; (Eq. 2)\)</span></p> <p>considering the percentage of residents in permanent private households of each sector (<em>p<sub>i</sub></em>) and the number of census tracts (<em>n</em>).</p> <p><span class="math-tex">\(C_{water} = (p_{r_i}*t_{water_{norm_{i+1}}} - p_{r_{i+1}}*t_{water_{norm_{i}}}) + ... + (p_{r_{i-1}}*t_{water_{norm_{i}}} - p_{r_i}*t_{water_{norm_{i-1}}}) \;\;\;\;\;\;\;\;\;\; (Eq. 3)\)</span></p> <p>where <span class="math-tex">\(p_{r_i}\)</span> stand for a percentage of residents in permanent private households ordered by average income <em>per capita</em>, with <em>C<sub>water</sub></em><sub> </sub>ranging between -1 and 1.</p> <p><span class="math-tex">\(G_{water} = 1 - {\sum_{i=1}^{n}(p_{w_i}-p_{w_{i-1}})*(t_{water_{norm_i}} + t_{water_{norm_{i-1}}}}) \;\;\;\;\;\;\;\;\;\; (Eq. 4)\)</span></p> <p>where <span class="math-tex">\(p_{w_i}\)</span> is the percentage of residents in permanent private households ordered by the weighted water access rate ( <span class="math-tex">\(t_{water_{norm}}\)</span>), and <em>G<sub>water</sub></em> ranging between 0 and 1.</p> <p><span class="math-tex">\(R_{80/20} = \frac{t_{water_{norm_{Q_4}}}}{t_{water_{norm_{Q_1}}}} \;\;\;\;\;\;\;\;\;\; (Eq. 5)\)</span></p> <p>with <em>R<sub>80/20</sub></em><sub> </sub>ranging between 0 and ∞.</p> <p><span class="math-tex">\(D_{water} = \frac{1}{2\overline t_{water_{norm}}} \times \sum_{i=1}^{n}\beta_i |t_{water_{norm_i}} - \overline t_{water_{norm}}| \{ \sum \beta_i = 1 \;\;\;\;\;\;\;\;\;\; (Eq. 6)\)</span></p> <p>with <em>β<sub>i</sub></em> standing for the proportion of group <em>i</em> in the sample, <span class="math-tex">\(\overline t_{water_{norm}}\)</span> the normalized mean safe drinking water access rate of the municipality, <span class="math-tex">\(t_{water_{norm_i}}\)</span> the normalized safe drinking water access rate of group <em>i</em>, with a value between 0 and 1; and <em>n</em> the number of groups (<em>n</em>= 5); with <em>D<sub>water</sub> </em>presenting values between 0 and 1.</p>
ScienceDex guides
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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.