Philippines PH: Domestic Private Health Expenditure Per Capita: Current Price data was reported at 0.000 USD mn in 2015. This records an increase from the previous number of 0.000 USD mn for 2014. Philippines PH: Domestic Private Health Expenditure Per Capita: Current Price data is updated yearly, averaging 0.000 USD mn from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 0.000 USD mn in 2013 and a record low of 0.000 USD mn in 2001. Philippines PH: Domestic Private Health Expenditure Per Capita: Current Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank: Health Statistics. Current private expenditures on health per capita expressed in current US dollars. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

Philippines Hyperscale Data Center Market is Segmented by Data Center Type (Hyperscale Self-Build, Hyperscale Colocation), Component (IT Infrastructure, Electrical Infrastructure, and More), Tier Standard (Tier III, Tier IV), End-User Industry (Cloud and IT Services, Telecom, and More), Data Center Size (Large ≤25 MW, and More). The Market Size and Forecasts are Provided in Terms of Value (USD).

A boosted regression tree (BRT) model was developed to predict pH conditions in three-dimensions throughout the glacial aquifer system (GLAC) of the contiguous United States using pH measurements in samples from 18,258 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and when coupled with long flow paths, results in the most alkaline conditions. Conversely, in areas where glacial sediments are thin and carbonate-poor, pH conditions remain acidic. At depths typical of drinking-water supplies, predicted pH > 7.5 – which is associated with arsenic mobilization – occurs more frequently than predicted pH < 6 – which is associated with water corrosivity and the mobilization of other trace elements. A novel aspect of this model was the inclusion of numerically based estimates of groundwater flow characteristics (age and flow path length) as predictor variables. The sensitivity of pH predictions to these variables was consistent with hydrologic understanding of groundwater flow systems and the geochemical evolution of groundwater quality. The model was not developed to provide precise estimates of pH at any given location. Rather, it can be used to more generally identify areas where contaminants may be mobilized into groundwater and where corrosivity issues may be of concern to set priorities among areas for future groundwater monitoring. Data are provided in 2 tables and 3 compressed files that contain various files associated with the BRT model. The 2 tables include: 1) pH_Predictions_GLAC_GeochMod_Dataset.csv (GM dataset): This table is generally a subset of the pH dataset (the measured pH data for well sites that were separated into the training and testing dataset files “trnData.txt” and “testData.txt” included in model_archive.7z) that was used to model pH conditions but includes more complete geochemical data and also includes some additional wells from Wilson and others (2019). The table includes pH, general chemical characteristics, and concentrations of major and trace elements, calculated parameters, and mineral saturation indices (SI) computed with PHREEQC (Parkhurst and Appelo, 2013) for 9,655 groundwater samples from wells in the GLAC. 2) pH_Predictions_GLAC_Variable_Descriptions.txt: A table listing all variables (short abbreviation and long description) used in the BRT model, including the importance rank of the variable, units, and reference. The 3 compressed files include: 1) model_archive.7z: contains 15 files associated with the BRT model 2) rstack_dom.7z: rstack_dom.txt 3) rstack_pub.7z : rstack_pub.txt Refer to the README.txt file in model_archive.7z for information about the files in the archive and how to use them to run the BRT model. "The "rstack" files represent raster stacks which are a collection of raster layer objects with the same spatial extent and resolution and which are vertically aligned. Rstack.dom consists of raster layer objects at the depth typically used for domestic supplies and rstack.pub, those at the depth typically used for public supplies.

Context

As an educator, I wanted to collate the collected data by the Republic of the Philippines Department of Education to check the current state of education in the country.

Content

So far, this dataset contains the total number of enrollees per year level (Kindergarten-Grade 12) as well as the total number of teachers per region in the Philippines from AY 2010-2011 to AY 2020-2021.

Notes:

1. Enrollees are not sorted by sex
2. Enrollees for each strand in senior high school is accounted for
3. Since the implementation of the senior high school program started in 2016, values prior to said year is 0

This will be updated depending on the availability of data from DepEd.

Acknowledgement

When used, please acknowledge the Republic of the Philippines Department of Education as the source of the data.

Calcium and pH data collected at lake, reservoir, impoundment and stream site types in the Columbia River Basin. These data were downloaded from the National Water Quality Portal on 03/31/2022 and cleaned and filtered. We limited calcium data to just those values that described dissolved calcium and total calcium concentrations. We included total calcium to maximize the amount of data available for this risk assessment; dissolved and total calcium are strongly correlated since dissolved calcium is a constituent part of total calcium. We then cleaned these data by filtering to just those entries associated with water (‘ActivityMediaName’ = Water); dropping rows with ‘NA’ values; removing white spaces; removing duplicated entries; removing negative controls (e.g., field and lab blanks); removing sites associating with mining activity or mining reclamation; standardizing units (mg/L for calcium), and; removing values outside of a sensical range (e.g., 0-14 for pH). These filtering an ...

Data used to model and map pH and redox conditions in groundwater in the Northern Atlantic Coastal Plain aquifer system, eastern USA, are documented in this data release. The models use as input data measurements of pH and dissolved oxygen concentrations at about 3000 to 5000 wells, which were compiled primarily from U.S. Geological Survey and U.S. Environmental Protection Agency databases. The boosted regression trees machine learning method was used to build the models. Explanatory variables (predictors) describe geology, hydrology, chemistry, physical characteristics, anthropogenic influence, metrics from a groundwater flow model, and groundwater residence times in the aquifer system. Data for four models are documented--one model for pH and one model each for the probability of dissolved oxygen less than three threshold values (0.5, 1, and 2 milligrams per liter). The data are provided in data tables and raster files, organized as follows. There is one data table for the well data used to develop all four models (well data). There is one zipped group of 10 files (one for each aquifer) for explanatory input data used to make predictions at grid points (prediction input). There are 9 zipped groups of files for model output; these include 1 zip file of predictions at grid points for each of the 4 models (prediction output), 1 zip file for combined pH and dissolved oxygen predictions (combined prediction output); and 4 zip files of uncertainty intervals for predictions for each of the 4 models (uncertainty output). Filenames for prediction input and for model output are distinguished by codes abbreviating the aquifer name and position in the vertical stack of 19 regional aquifers and confining units, as follows: Surficial aquifer, 1surf; Upper Chesapeake aquifer, 3upch; Lower Chesapeake aquifer, 5loch; Piney Point aquifer, 7pipt; Aquia aquifer, 9aqia; Monmouth - Mt. Laurel Aquifer, 11moml; Matawan aquifer, 13mtwn; Magothy Aquifer, 15mgty; Potomac-Patapsco aquifer, 17popt; Potomac-Patuxent aquifer, 19popx. The data release also contains a tif-format raster file of the prediction grid and two data tables that separately describe the explanatory variables (predictors) and their sources.

This dataset consists of a 100 meter resolution raster of depth and area weighted averages for soil pH for each map unit key (MUKEY) in the U.S. Department of Agriculture, Natural Resources Conservation Service's (NRCS) State Soil Geographical (STATSGO2) database (NRCS, 2016). This raster was developed from selected criteria of soil parameters from the STATSGO2 database and mapped to MUKEYs.

The ascii grids associated with this data release are predicted distributions of continuous pH at the drinking water depth zones in the groundwater of Central Valley, California. The two prediction grids produced in this work represent predicted pH at the domestic supply and public supply drinking water depths, respectively and are bound by the alluvial boundary that defines the Central Valley. A depth of 46 m was used to stratify wells into the shallow and deep aquifer and were derived from depth percentiles associated with domestic and public supply in previous work by Burow et al. (2013). In this work, the median well depth categorized as domestic supply was 30 meters below land surface and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically Boosted Regression Trees (BRT) with a gaussian error distribution within a statistical learning framework within R's computing framework (http://www.r-project.org/). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1337 wells, and was compiled from two sources: US Geological Survey (USGS) National Water Information System (NWIS) Database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993-2014 was used. A total of 1003 wells (training dataset) were used to train the BRT model and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70 and the RMSE in standard pH units was were 0.26. The holdout r-squared was 0.43 and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources (see metadata) were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt et al. 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-m buffer. Results of the predictor variable influence as defined by Friedman (2001) for variables used in the final BRT model used for mapping can be downloaded from this landing page (see file named PredictorVariableInfluence_CentralValley_pH_BRT.csv).

Philippines PH: Control of Corruption: Estimate data was reported at -0.475 NA in 2017. This records an increase from the previous number of -0.488 NA for 2016. Philippines PH: Control of Corruption: Estimate data is updated yearly, averaging -0.563 NA from Dec 1996 (Median) to 2017, with 19 observations. The data reached an all-time high of -0.295 NA in 1998 and a record low of -0.830 NA in 2006. Philippines PH: Control of Corruption: Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WGI: Country Governance Indicators. Control of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as 'capture' of the state by elites and private interests. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.

Near-surface soil acidification is becoming prevalent in dryland cropping systems throughout the northern Great Plains. To ensure management recommendations are optimized for crop production, soil sampling guidelines are needed that account for depth stratification of soil pH in surface horizons. Soil pH data from two long-term dryland cropping system experiments were evaluated to document outcomes from three depth increments: 0-7.6 cm, 0-15.2 cm, and 0-30.5 cm. The experiments were established in 1984 and 1993 on the Area IV Soil Conservation Districts Cooperative Research Farm near Mandan, North Dakota USA. Soil cores were collected from the surface 30.5-cm depth near the middle of each experimental plot using a hydraulic probe. Collected soil cores were carefully split into 0-7.6, 7.6-15.2, and 15.2-30.5-cm increments and composited by depth. Samples were dried, mechanically ground, and analyzed within 6 wk of collection. Soil pH was measured in a 1:1 soil/water mixture (by mass) with an ion-selective glass electrode. From the sampled depths, weighted averages were used to calculate soil pH at 0-15.2 and 0-30.5 cm. Data may be used to better understand depth effects on soil pH under dryland cropping systems within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.

Philippines Whois Database, discover comprehensive ownership details, registration dates, and more for domains registered in Philippines with Whois Data Center.

This dataset presents modelled estimates of soil pH at 1km2 resolution across Great Britain. A Generalized Additive Model approach was used with Countryside Survey soil pH data from 2007 and including climate, atmospheric deposition, habitat, soil and spatial predictors. The model is based on soil pH data from 2446 locations across Great Britain and is representative of 0-15 cm soil depth. Soil pH was measured using 10g of field moist soil with 25ml de-ionised water giving a ratio of soil to water of 1:2.5 by weight. The Countryside Survey looks at a range of physical, chemical and biological properties of the topsoil from a representative sample of habitats across the UK. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

Lake Powell retains most of the phosphorus that it receives, leading to downstream phosphorus limitation. These data were compiled to examine controls on phosphorus cycling below Lake Powell in the Colorado River and from storm inputs from the Paria River. Objectives of our study were to determine how several forms of phosphorus, both organic and inorganic, were cycled under varying dissolved oxygen concentrations and pH, reflecting the range of values observed in the river over the years. These data represent nitrogen, phosphorus, calcium, and carbon concentrations, water quality parameters (pH, dissolved oxygen, temperature), sediment composition, total protein, and extracellular enzyme activity (alkaline phosphatase). Additionally, these data contain some previously unpublished longer term continuous pH data from the Colorado River. These data were primarily collected in the summer of 2021, before, during, and immediately following incubations of three different sediment types with Colorado river water. Sediment and overlying water for incubations were collected at one time point from three sites: the Paria River near the confluence with the Colorado River, the Colorado River approximately 23 river kilometers below Glen Canyon Dam, and the Colorado River near its inflow to Lake Mead at the Pearce Ferry boat ramp. Data were collected by the Southwest Biological Science Center-Grand Canyon Monitoring and Research Center researchers. These ambient river water and sediment data can be used to describe chemical and biological conditions in the river and data from bottle incubations can be used to examine how changing laboratory conditions affect nutrient availability and cycling.

Soil pH x 10 in H2O at 6 standard depths (to convert to pH values divide by 10). Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.

This data release contains groundwater-quality data for three parameters of interest (arsenic, manganese, and pH) and well information for sample sites for aquifers in the conterminous U.S. Water-quality data and well information were derived from a dataset compiled from three sources: the U.S. Geological Survey (USGS) National Water Information System (NWIS), the U.S. Environmental Protection Agency (USEPA) Safe Drinking Water Information System (SDWIS), and numerous agencies and organizations at the state, regional, and local level. The data compilation of the National Water Quality Program’s groundwater assessment team is an internal dataset informally referred to as the National Groundwater Aggregation (NGA). The current study of groundwater quality in the conterminous U.S. augments data compiled by others globally. Only geochemical parameters of interest (arsenic, manganese, pH) from wells in the national groundwater aggregation are presented—data from springs were not used. ...

Climatological mean monthly distributions of pH in the total H+ scale, total CO2 concentration (TCO2), and the degree of CaCO3 saturation for the global surface ocean waters (excluding coastal areas) are calculated using a data set for pCO2, alkalinity and nutrient concentrations in surface waters (depths less than 50 m), which is built upon the GLODAP, CARINA and LDEO database. The mutual consistency among these measured parameters is demonstrated using the inorganic carbon chemistry model with the dissociation constants for carbonic acid by Lueker et al. (2000) and for boric acid by Dickson (1990). The global ocean is divided into 24 regions, and the linear potential alkalinity (total alkalinity + nitrate) versus salinity relationships are established for each region. The mean monthly distributions of pH and carbon chemistry parameters for the reference year 2005 are computed using the climatological mean monthly pCO2 data adjusted to a reference year 2005 and the alkalinity estimated from the potential alkalinity versus salinity relationships. The climatological monthly mean values of pCO2 over the global ocean are compiled for a 4° x 5° grid for the reference year 2005, and the gridded data for each of 12 months are included in this database. This is updated version of Takahashi et al. (2009) for the reference year 2000 representing non-El Niño years using a database of about 6.5 million pCO2 data (less coastal areas of North and South America) observed in 1957-2012 (Takahashi et al., 2013). The equatorial zone (4°N-4°S) of the Pacific is excluded from the analysis because of the large interannual changes associated with the El Niño-Southern Oscillation events. The pH thus calculated ranges from 7.9 to 8.2. Lower values are located in the upwelling regions in the tropical Pacific and in the Arabian and Bering Seas; and higher values are found in the subpolar and polar waters during the spring-summer months of intense photosynthetic production. The vast areas of subtropical oceans have seasonally varying pH values ranging from 8.05 during warmer months to 8.15 during colder months. The warm tropical and subtropical waters are supersaturated by a factor of as much as 4.2 with respect to aragonite and 6.3 for calcite, whereas the cold subpolar and polar waters are less supersaturated only by 1.2 for aragonite and 2 for calcite because of the lower pH values resulting from greater TCO2 concentrations. In the western Arctic Ocean, aragonite undersaturation is observed.

pH data was compiled from data provided by different agencies around the Gulf of Mexico, research projects and cruises. Data source: National Water Quality Monitoring Council (NWQMC), Environmental Protection Agency (EPA), United States Geological Survey (USGS), National Estuarine Research System (NERRS), Texas Commission on Environmental Quality (TCEQ), Florida Keys National Marine Sanctuary (FKNMS), National Park Water Services (NPWS), Louisiana Department of Environmental Quality (LDEQ), Louisiana Universities Marine Consortium (LUMCON), Mississippi Department of Environmental Quality (MDEQ), Alabama Department of Environmental Management (ADEM), Florida Department of Environmental Protection (FDEP) and Texas A&M University (TAMU).

This dataset contains data collected within limestone cedar glades at Stones River National Battlefield (STRI) near Murfreesboro, Tennessee. This dataset contains measurements of soil pH at certain quadrat locations (points) within 12 selected cedar glades. These measurements were obtained according to the following protocol: (1) for each quadrat location (point), one soil sample was obtained under sterile conditions, using a trowel wiped with methanol and rinsed with distilled water, and was placed into an autoclaved jar with a tight-fitting lid and placed on ice, (2) soil samples were transported to lab facilities on ice and immediately refrigerated, (3) soil samples were transferred to clean aluminum trays and dried at 35 degrees Celcius for 48 hours to produce air-dry soil, (4) air-dry soils were then passed through a number 10 sieve to remove rocks and root fragments, (5) 10 grams of sieved, air-dried soil was added to 10 millileters distilled water in glass jars, (6) capped ...

SV3 data for future usage. Texas A&M University, Geochemical and Environmental Research Group data from a local source.

DOH SUSPENDED ITS NCOVTRACKER as of April 9, 2020 . No updates until its restored.

Context

The number of corona cases is starting to rapidly reaching more than a hundred cases yesterday. Following what is happening with other countries, we expect the confirmed cases to rise exponentially. I believe that gathering granular data for analysis will help researchers better prepare for pandemics like this in the future.

I have not yet seen a publicly available and easy to access dataset for the Phillipines yet, so I decided to generate my own using data from the Philippine Department of Health (DOH) uses for their Ncov Dashboard https://ncovtracker.doh.gov.ph/

I plan to query the dashboard to see the evolution of cases over time as well as adding supplementary information over time.

Content

The DOH uses a public arcgis backend for their NCOV dashboard - which allowed me to query the API for information about the cases. You can see the script I used in the attached github repo's main.py.

Right now the dataset only has the known cases in the Philippines cases_ph.csv. It includes the following information 1. the case number 2. gender 3. age 4. nationality 5. residence (city in PH) 6. Hospital Facility & GPS coordinates of hospital 7. Status & symptoms (which may change over time) 8. When the data was queried and which version of the data did it come from.

The Dataset is supplemented by the Daily PDF release of the DOH which inculdes 1. Date of onset of Symptops 2. Date of Admission 3. Detailed Travel & Exposure History

This is parsed by Camelot PDF reader and combined with the API data to generate the final csv. These pdfs are copied in the PDF subfolder of the Github Repository.

Acknowledgements

Philippine Department of Health for Compiling the data from around the Philippines Banner Photo by Fusion Medical Animation on Unsplash

Inspiration

Inspired by the datasets users have compiled for other countries.