In addition to Police Districts, every resident lives in a Police Service Area (PSA), and every PSA has a team of police officers and officials assigned to it. Residents should get to know their PSA team members and learn how to work with them to fight crime and disorder in their neighborhoods. Each police district has between seven and nine PSAs. There are a total of 56 PSAs in the District of Columbia.Printable PDF versions of each district map are available on the district pages. Residents and visitors may also access the PSA Finder to easily locate a PSA and other resources within a geographic area. Just enter an address or place name and click the magnifying glass to search, or just click on the map. The results will provide the geopolitical and public safety information for the address; it will also display a map of the nearest police station(s).Each Police Service Area generally holds meetings once a month. To learn more about the meeting time and location in your PSA, please contact your Community Outreach Coordinator. To reach a coordinator, choose your police district from the list below. The coordinators are included as part of each district's Roster.Visit https://mpdc.dc.gov for more information.

View metadata for key information about this dataset.The PSA boundaries replaced a much smaller boundary, Sectors in 2009. In several Districts, PSA's split Sector boundaries and therefore a historical comparison would not necessarily be accurate. These polygon features were created by the Crime Mapping & Analysis Unit and they represent an initiative by the Philadelphia Police Department that started in 2009. A Police District can have up to 4 PSA's and as few as 2. There is a Police Lieutenant assigned to each PSA and officers are limited to patrol their specific boundary with the goal of familiarity with the area residents and businesses.See also the related datasets:Police DistrictsPolice DivisionsFor questions about this dataset, contact publicsafetygis@phila.gov. For technical assistance, email maps@phila.gov.

This dataset contains 114 t2-weighted MRI images of the prostate with corresponding segmentations.The segmentations include two labels, 1 - Transition Zone, 2 - Peripherial Zone. Most of the images include corresponding PIRADS and PSA values, which are available in the file PSA_PIRADS.csv.

For more information concerning the images, see the following article.

Please cite the following articles, if you are using this dataset:

Gibala, S.; Obuchowicz, R.; Lasek, J.; Schneider, Z.; Piorkowski, A.; Pociask, E.; Nurzynska, K. Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels. J. Clin. Med. 2023, 12, 2836. https://doi.org/10.3390/jcm12082836

Gibała, S.; Obuchowicz, R.; Lasek, J.; Piórkowski, A.; Nurzynska, K. Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol. Appl. Sci. 2023, 13, 9871. https://doi.org/10.3390/app13179871

This proposal relates to the call of the PSA PSA COVID-19 Rapid Project 001 data collection effort in Mexico. The COVID-19 pandemic presents a critical need to identify best practices for communicating health information to the global public. It also provides an opportunity to test theories about risk communication. As part of a larger Psychological Science Accelerator COVID-19 Rapid Project, a global consortium of researchers will experimentally test competing hypotheses regarding the effects of framing messages in terms of losses versus gains. We will examine effects on three primary outcomes: intentions to adhere to policies designed to prevent the spread of COVID-19, opinions about such policies, and the likelihood that participants seek additional policy information. Whereas research on negativity bias and loss aversion predicts that loss-framing will have greater impact, research on encouraging the adoption of protective health behaviour suggests the opposite (i.e., gain-framing will be more persuasive). We will also assess effects on experienced anxiety. Given the potentially low cost and the scalable nature of framing interventions, results could be valuable to health organizations, policymakers, and news sources globally.

Austin Energy’s Power Supply Adjustment recovers fuel for generation, transportation, renewable purchase power agreements, purchase power to serve retail customers, ERCOT fees, hedging and the balance from the previous period. The adjustment is reviewed annually. Find more information at http://austinenergy.com/go/reports.

Abstract

The 2022 Philippines National Demographic and Health Survey (NDHS) was implemented by the Philippine Statistics Authority (PSA). Data collection took place from May 2 to June 22, 2022.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, fertility preferences, family planning practices, childhood mortality, maternal and child health, nutrition, knowledge and attitudes regarding HIV/AIDS, violence against women, child discipline, early childhood development, and other health issues.

The information collected through the NDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population. The 2022 NDHS also provides indicators anchored to the attainment of the Sustainable Development Goals (SDGs) and the new Philippine Development Plan for 2023 to 2028.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the 2022 NDHS was based on a two-stage stratified sample design using the Master Sample Frame (MSF) designed and compiled by the PSA. The MSF was constructed based on the listing of households from the 2010 Census of Population and Housing and updated based on the listing of households from the 2015 Census of Population. The first stage involved a systematic selection of 1,247 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 22 or 29 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the preselected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15–49 who were either usual residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on women’s safety.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were used for the 2022 NDHS: the Household Questionnaire and the Woman’s Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, academe, and international agencies. The survey protocol was reviewed by the ICF Institutional Review Board.

After all questionnaires were finalized in English, they were translated into six major languages: Tagalog, Cebuano, Ilocano, Bikol, Hiligaynon, and Waray. The Household and Woman’s Questionnaires were programmed into tablet computers to allow for computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the languages for each questionnaire.

Cleaning operations

Processing the 2022 NDHS data began almost as soon as fieldwork started, and data security procedures were in place in accordance with confidentiality of information as provided by Philippine laws. As data collection was completed in each PSU or cluster, all electronic data files were transferred securely via SyncCloud to a server maintained by the PSA Central Office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the area of assignment. Timely generation of field check tables allowed for effective monitoring of fieldwork, including tracking questionnaire completion rates. Only the field teams, project managers, and NDHS supervisors in the provincial, regional, and central offices were given access to the CAPI system and the SyncCloud server.

A team of secondary editors in the PSA Central Office carried out secondary editing, which involved resolving inconsistencies and recoding “other” responses; the former was conducted during data collection, and the latter was conducted following the completion of the fieldwork. Data editing was performed using the CSPro software package. The secondary editing of the data was completed in August 2022. The final cleaning of the data set was carried out by data processing specialists from The DHS Program in September 2022.

Response rate

A total of 35,470 households were selected for the 2022 NDHS sample, of which 30,621 were found to be occupied. Of the occupied households, 30,372 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 28,379 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 27,821 women, yielding a response rate of 98%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Philippines National Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 NDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Population pyramid
• Five-year mortality rates

See details of the data quality tables in Appendix C of the final report.

This proposal relates to the call of the PSA PSA COVID-19 Rapid Project 001 data collection effort in Russia. The COVID-19 pandemic presents a critical need to identify best practices for communicating health information to the global public. It also provides an opportunity to test theories about risk communication. As part of a larger Psychological Science Accelerator COVID-19 Rapid Project, a global consortium of researchers will experimentally test competing hypotheses regarding the effects of framing messages in terms of losses versus gains. We will examine effects on three primary outcomes: intentions to adhere to policies designed to prevent the spread of COVID-19, opinions about such policies, and the likelihood that participants seek additional policy information. Whereas research on negativity bias and loss aversion predicts that loss-framing will have greater impact, research on encouraging the adoption of protective health behaviour suggests the opposite (i.e., gain-framing will be more persuasive). We will also assess effects on experienced anxiety. Given the potentially low cost and the scalable nature of framing interventions, results could be valuable to health organizations, policymakers, and news sources globally.

A borehole is any hole drilled or dug into the ground. The material (soil and or rock) from the hole is collected and tested in a laboratory to find out the structure and type of the soil and or rock beneath the ground. A borehole record or log is a written description of the material that comes out of the ground as a result of drilling a borehole. Geotechnical boreholes are usually shallow (0-30m). They are drilled by engineering companies before building new structures. Before building starts site investigations are carried out to find out the quality of the ground (strength and depth of soil and to see if rock and or groundwater is present). Geotechnical boreholes drilled in Ireland that have been submitted to the GSI from engineering companies. This dataset has been extracted from the National Geotechnical Borehole Database with some additional reports donated by Transport Infrastructure Ireland (TII) and some PSA tests acquired in-house.Each zip contains a series of pdfs, an excel file and an information document . Each pdf file is recorded in the excel file attached with all the information related to the test.

Y.J.L. has received funding from EU Horizon 2020 MSCA-IF No. 841432. P.K was funded by the JSPS International Research Fellow program. J.P. acknowledges JAXA's International Top Young Fellowship (ITYF). VMC data are publicly available at ESA PSA (ftp://psa.esac.esa.int/pub/mirror/VENUS-EXPRESS/VMC/). UVI data are publicly available at the JAXA archive website, DARTS (http://darts.isas.jaxa.jp/), and the NASA archive website, PDS (https://pds.nasa.gov/). This is the derived data, presented in a publication entitled "Spatial and temporal variability of the 365-nm albedo of Venus observed by the camera on board Venus Express" (JGR:Planet, doi: 10.1029/2019JE006271). See the paper for details. See 'Readme.txt' for the file descriptions.

This data publication includes particle size distribution data of natural volcanic ash samples used as starting material for laboratory experiments simulating the aggregation/disaggregation of colliding volcanic ash particles. Full details of the experimental method can be found in Del Bello et. al. (2015) and in the data description file provided here.n Here we report raw particle size distribution data obtained through separation analysis. Two types of volcanic ash were analysed: i) andesitic ash from the Sakurajima volcano (Japan), collected from July 2013 deposits (named Sak sample); ii) phonolitic ash collected from the basal fallout layer of the ~10 ka old Pomici Principali eruptive unit [Di Vito et al., 1999]) of the Campi Flegrei (named Ppa). For both compositions, 3 different starting materials were obtained by hand sieving the natural samples into three main particle size classes: (i) <32 μm, (ii) 32–63 μm, and (iii) 63–90 μm. For the phonolitic composition Ppa two additional starting materials were obtained by mixing the <32 μm and the 32–63 μm classes in known proportions. n For each starting material, the grain size distribution of the sample was measured by a multiwavelength separation analyzer (LUMIReader®, https://www.lum-gmbh.com/lumireader_en.html). This device measures space and time resolved profiles of the transmitted light across the water-diluted sample (5% solid content) during sedimentation of particles. The cumulative volume-weighted particle size distribution is obtained from the extinction profiles using the multi-wavelength Particle size Analyser modulus (PSA). Details on the sample preparation procedure can be found in Detloff et al. (2006). n For each measurement performed (see Table 1), a pdf file and a excel file are provided. The pdf file lists the analysis summary, including a description of the analysis settings and conditions, materials used, and distribution model adopted for the fit. It also provides graphs of the obtained volume weighted cumulative grain size distribution, and of the measured transmission profiles for each wavelength (870 nm, 630 nm and 470 nm, respectively). The Excel (.xlsx format) file include 4 datasheets, listing the results (sheet name ending _R) and the fit data (sheet names ending _F01,_F02, *_F03) obtained for the different instrument wavelength. In each datasheet the following data are listed in the columns: particle grain size (x3 in µm), volume weighted distribution (Q3(x) in %), Martin diameter (x3m in µm), volume weighted density distribution (q3(x) in 1/µm). The fit datasheets also include information on the fit such as distribution model used and distribution parameters (quantiles, median, standard deviation, span, etc..).n A full list of the files included is given in List_of_files_DelBello et al 2017.pdf. Data e Risorse Questo dataset non ha dati vulcani

This proposal relates to the call of the PSA PSA COVID-19 Rapid Project 001 data collection effort in China. The COVID-19 pandemic presents a critical need to identify best practices for communicating health information to the global public. It also provides an opportunity to test theories about risk communication. As part of a larger Psychological Science Accelerator COVID-19 Rapid Project, a global consortium of researchers will experimentally test competing hypotheses regarding the effects of framing messages in terms of losses versus gains. We will examine effects on three primary outcomes: intentions to adhere to policies designed to prevent the spread of COVID-19, opinions about such policies, and the likelihood that participants seek additional policy information. Whereas research on negativity bias and loss aversion predicts that loss-framing will have greater impact, research on encouraging the adoption of protective health behaviour suggests the opposite (i.e., gain-framing will be more persuasive). We will also assess effects on experienced anxiety. Given the potentially low cost and the scalable nature of framing interventions, results could be valuable to health organizations, policymakers, and news sources globally.

ObjectivePsoriatic arthritis (PsA) and rheumatoid arthritis (RA) are the most common types of inflammatory musculoskeletal disorders that share overlapping clinical features and complications. The aim of this study was to identify shared marker genes and mechanistic similarities between PsA and RA.MethodsWe utilized datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. To identify the marker genes, we applied two machine learning algorithms: the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE). Subsequently, we assessed the diagnostic capacity of the identified marker genes using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). A transcription factor (TF) network was constructed using data from JASPAR, HumanTFDB, and GTRD. We then employed CIBERSORT to analyze the abundance of immune infiltrates in PsA and RA, assessing the relationship between marker genes and immune cells. Additionally, cellular subpopulations were identified by analyzing single-cell sequencing data from RA, with T cells examined for trajectory and cellular communication using Monocle and CellChat, thereby exploring their linkage to marker genes.ResultsA total of seven overlapping DEGs were identified between PsA and RA. Gene enrichment analysis revealed that these genes were associated with mitochondrial respiratory chain complex IV, Toll-like receptors, and NF-κB signaling pathways. Both machine learning algorithms identified Ribosomal Protein L22-like 1 (RPL22L1) and Lymphocyte Antigen 96 (LY96) as potential diagnostic markers for PsA and RA. These markers were validated using test sets and experimental approaches. Furthermore, GSEA analysis indicated that gap junctions may play a crucial role in the pathogenesis of both conditions. The TF network suggested a potential association between marker genes and core enrichment genes related to gap junctions. The application of CIBERSORT and single-cell RNA sequencing provided a comprehensive understanding of the role of marker genes in immune cell function. Our results indicated that RPL22L1 and LY96 are involved in T cell development and are associated with T cell communication with NK cells and monocytes. Notably, high expression of both RPL22L1 and LY96 was linked to enhanced VEGF signaling in T cells.ConclusionOur study identified RPL22L1 and LY96 as key biomarkers for PsA and RA. Further investigations demonstrated that these two marker genes are closely associated with gap junction function, T cell infiltration, differentiation, and VEGF signaling. Collectively, these findings provide new insights into the diagnosis and treatment of PsA and RA.

Unemployment Rate in Philippines decreased to 3.90 percent in May from 4.10 percent in April of 2025. This dataset provides - Philippines Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

CHARACTERISTICS Planet: Venus Radius: RV = 6051.8 km (volumetric mean planetary radius) Spacecraft: ESA/Venus Express Spacecraft coordinates system: Venus Solar Orbital (VSO) equivalent to Sun-State coordinate system:

+XVSO points towards the Sun from the planet’s centre,

+ZVSO towards Venus’ North pole and perpendicular to the orbital plane defined as the XVSO–YVSO plane passing through the centre of Venus,

YVSO completes the orthogonal system.

Time span: 01/04/2006 to 25/11/2014 Total number N of candidate bow shock crossings in the database: N = 4950 Number of quasi-parallel bow shock crossings: N|| = 844 Number of quasi-perpendicular bow shock crossings: N(\perp) = 4106

ORIGINAL DATASETS USED The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used.

METHOD To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in: Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, Journal of Geophysical Research, 127, e2021JA029942. https://doi.org/10.1029/2021JA029942

They consist of two consecutive steps:

Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane (\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right))performed on the datasets of Persson et al. (2023), Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, Zenodo (https://doi.org/10.5281/zenodo.7679677).

Corrector algorithm based on magnetic field measurements.

We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted θBn (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that:

45∘<θBn<135∘: quasi-perpendicular shock condition

θBn≤45∘ and θBn(\geq)135∘: quasi-parallel shock condition

Uncertainty on these angles is estimated to be ± 5º.

For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12.

VARIABLES DESCRIPTION

This database contains the following ASCII variables:

Bow shock times in Venus Express' database (1-s resolution): Tbs

Venus Solar Orbital coordinates of the shock, in units of Venus radius RV (RV = 6051.8 km): XVSO, YVSO, ZVSO and Euclidean distance (R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}) (in RV)

Solar Zenith angle in degrees: SZA = (\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}) (in º)

Angle between average B-field direction and shock normal assuming a smooth shock surface (\theta_{Bn}) (ThetaBn, in º, calculated with atan2(norm(cross(B,ñ),dot(B,ñ)), with B the magnetic field vector and ñ the vector normal to the shock surface):

45 < ThetaBn < 135 deg: quasi-(\perp) shock

ThetaBn (\leq) 45 deg & ThetaBn (\geq) 135 deg: quasi-|| shock

Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, Bx, By, Bz (in nT).

Flag for direction of crossing:

flag = 0: magnetosheath (\longrightarrow) solar wind (2447 events)

flag = 1: solar wind (\longrightarrow) magnetosheath (2503 events)

WARNING

This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of: Persson et al. (2023), Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, Zenodo (https://doi.org/10.5281/zenodo.7679677)

This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a first indicator of the shock location, and with caution: it CANNOT, and WILL NOT substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 RV (with RV = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.

Current formatting optimised for MATLAB.

ACKNOWLEDGEMENTS C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36.

LICENSE AND RIGHTS This database is shared under a Creative Commons CC-BY-4.0 license.

Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), Austrian Academy of Sciences, 2022-10-05 Contact email: cyril.simon.wedlund@gmail.com

INFOMAR Seabed Samples Particle Size Analysis represent locations where samples have been taken and particle size analysis (PSA) carried out on samples. PSA is applied to determine the range of sediment sizes contained in the sample. These size classes can then be grouped into mud, sand and gravel on the basis of their diameter with the boundary between mud and sand size grains at 63µm (0.063mm) and the boundary between sand and gravel size grains at 2mm. The relative proportion of the grains in the three categories is then used to classify the sediment present in the sample. The Folk Classification scheme has 15 classes to describe the sediment. Gradistat software was used to classify the PSA output for each sediment sample into a Folk sediment class and to provide information on the sorting and metrics for the mode and D50. None

PurposeThis service is intended to provide a high-level view of recently observed and forecasted fire danger for national and geographic area decision makers.Input dataStatic copy of Remote Automated Weather Station (RAWS) locations from National Interagency Fire Center (https://data-nifc.opendata.arcgis.com/datasets/nifc::public-view-interagency-remote-automatic-weather-stations-raws/about), station identifiers and names quality controlled by geographic area lead.Static copy of Predictive Service Area (PSA) boundaries from the National Interagency Fire Center (https://data-nifc.opendata.arcgis.com/datasets/nifc::national-predictive-service-areas-psa-boundaries/about).Tables of key RAWS associated with each PSA and the historical percentiles of Energy Release Component (ERC) and Burning Index (BI). Exact data sources and methods vary by geographic area but generally include 20 years of observations for the full calendar year. All fire danger rating calculations in this service are standardized around fuel model Y (also known as “2016 forest”).Daily observations from the Weather Information Management System (WIMS) accessed at 16:30 Pacific: 1) most recent 3 days of daily observed weather, derived variables, ERC, and BI; and 2) next 3 days of daily forecasted ERC and BI.AnalysisThe most recent day of observed and the next forecasted fire danger indices are converted to percentiles based on the historical percentile tables.Trend analysis categories determined by: 1) observed uses most recent daily observation compared to two days prior; 2) forecasted uses current day forecast compared to two days in the future; and 3) increase (>= +3), decrease (<= -3), or no change (< 3 diff) based on difference in absolute ERC or BI values, not percentiles.Aggregation to PSA: 1) non-reporting stations are ignored in calculations; 2) the PSA will be assigned a null value if it has no reporting stations, 3) simple means of RAWS percentiles; and 4) trends determined using simple means of index values from associated RAWS for equivalent time periods and same change thresholds (see above).

Philippines administrative level 0-4 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE: See COD-PS caveat about treatment of National Capital (Manila) data. OCHA acknowledges PSA and the National Mapping and Resource Information Authority (NAMRIA) as the sources. LMB is the source of official administrative boundaries of the Philippines. In the absence of available official administrative boundary, the IMTWG have agreed to clean and use the PSA administrative boundaries which are used to facilitate data collection of surveys and censuses. The dataset can only be considered as indicative boundaries and not official. Its updated to reflect the new areas within BARMM; It uses the new 10-digit pcode consistent with government PSGC as of 2023

This COD-AB was most recently reviewed for accuracy and necessary changes in April 2024. The COD-AB does not require any update.

Sourced from National Mapping and Resource Information Authority (NAMRIA), Philippines Statistics Authority (PSA)

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Philippines COD-PS.

As this is an island country, no edge-matched (COD-EM) version of this COD-AB is required.

Please see the COD Portal.

Administrative level 1 contains 17 feature(s). The normal administrative level 1 feature type is ""currently not known"".

Administrative level 2 contains 88 feature(s). The normal administrative level 2 feature type is ""currently not known"".

Administrative level 3 contains 1,642 feature(s). The normal administrative level 3 feature type is ""currently not known"".

Administrative level 4 contains 42,048 feature(s). The normal administrative level 4 feature type is ""currently not known"".

Recommended cartographic projection: Asia South Albers Equal Area Conic

This metadata was last updated on January 13, 2025.

The data underlying the public sector finances statistical bulletin are presented in the tables PSA 1 to 10.

This proposal relates to the call of the PSA PSA COVID-19 Rapid Project 001 data collection effort in Japan. The COVID-19 pandemic presents a critical need to identify best practices for communicating health information to the global public. It also provides an opportunity to test theories about risk communication. As part of a larger Psychological Science Accelerator COVID-19 Rapid Project, a global consortium of researchers will experimentally test competing hypotheses regarding the effects of framing messages in terms of losses versus gains. We will examine effects on three primary outcomes: intentions to adhere to policies designed to prevent the spread of COVID-19, opinions about such policies, and the likelihood that participants seek additional policy information. Whereas research on negativity bias and loss aversion predicts that loss-framing will have greater impact, research on encouraging the adoption of protective health behaviour suggests the opposite (i.e., gain-framing will be more persuasive). We will also assess effects on experienced anxiety. Given the potentially low cost and the scalable nature of framing interventions, results could be valuable to health organizations, policymakers, and news sources globally.

A grab sample is a sample of sediment taken from the seabed. This is a sediment sample database of samples collected to date by INSS, INFOMAR and related projects. They include ADFish, DCU, FEAS, GATEWAYS, IMAGIN, IMES, INIS_HYRDO, JIBS, MESH, SCALLOP, SEAI, UCC. Each sample point has some information recorded during collection including location, sample ID and preliminary description. Where available the shapefile also shows the results of particle size analysis (PSA) carried out on samples from 2004. PSA groups grains into mud, sand and gravel on the basis of their diameter. The relative proportion of the grains in the three categories is given as a percentage and used to classify the sample using the Folk Classification scheme. This analysis is also used to determine if the sample is well or poorly sorted. The database contains information (where available) on: YEAR, SURVEY, VESSEL, SAMPLE_ID, SAMPLER, DATE, TIME, LAT, LONG, DEPTH, RECOVERY, DSCRIPTION, COMMENT, REPORT, MUD, SAND, GRAVEL, PSA_DSCRPT, FOLK_CLASS, SOURCE, IMAGE. None