The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Norway was last recorded at 4.25 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Canada was last recorded at 2.75 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

View data of the Effective Federal Funds Rate, or the interest rate depository institutions charge each other for overnight loans of funds.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, coastal buffers are removed, leaving the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal Buffers (this dataset)Place AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Cotton root rot is a century-old cotton disease that now can be effectively controlled with Topguard Terra fungicide. Because this disease tends to occur in the same general areas within fields in recurring years, site-specific application of the fungicide only to infested areas can be as effective as and considerably more economical than uniform application. The overall objective of this research was to demonstrate how site-specific fungicide application could be implemented based on historical remote sensing imagery and using variable-rate technology. Procedures were developed for creating binary prescription maps from historical airborne and high-resolution satellite imagery. Two different variable-rate liquid control systems were adapted to two existing cotton planters, respectively, for site-specific fungicide application at planting. One system was used for site-specific application on multiple fields in 2015 and 2016 near Edroy, Texas, and the other system was used on multiple fields in both years near San Angelo, Texas. Airborne multispectral imagery taken during the two growing seasons was used to monitor the performance of the site-specific treatments. Results based on prescription maps derived from historical airborne and satellite imagery of two fields in 2015 and one field in 2016 are reported in this article. Two years of field experiments showed that the prescription maps and the variable-rate systems performed well and that site-specific fungicide treatments effectively controlled cotton root rot. Reduction in fungicide use was 41%, 43%, and 63% for the three fields, respectively. The methodologies and results of this research will provide cotton growers, crop consultants, and agricultural dealers with practical guidelines for implementing site-specific fungicide application using historical imagery and variable-rate technology for effective management of cotton root rot. Resources in this dataset: Resource Title: A ground picture of cotton root rot File Name: IMG_0124.JPG Resource Description: A cotton root rot-infested area in a cotton field near Edroy, TX. Resource Title: An aerial image of a cotton field File Name: Color-infrared image of a field.jpg Resource Description: Aerial color-infrared (CIR) image of a cotton field infested with cotton root rot. Resource Title: As-applied fungicide application data File Name: Jim Ermis-Farm 1-Field 11 Fungicide Application.csv Resource Description: As-applied fungicide application rates for variable rate application of Topguard to a cotton field infested with cotton rot

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

General overview The following datasets are described by this metadata record, and are available for download from the provided URL.

• Raw log files, physical parameters raw log files
• Raw excel files, respiration/PAM chamber raw excel spreadsheets
• Processed and cleaned excel files, respiration chamber biomass data
• Raw rapid light curve excel files (this is duplicated from Raw log files), combined dataset pH, temperature, oxygen, salinity, velocity for experiment
• Associated R script file for pump cycles of respirations chambers

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Physical parameters raw log files

Raw log files 1) DATE= 2) Time= UTC+11 3) PROG=Automated program to control sensors and collect data 4) BAT=Amount of battery remaining 5) STEP=check aquation manual 6) SPIES=check aquation manual 7) PAR=Photoactive radiation 8) Levels=check aquation manual 9) Pumps= program for pumps 10) WQM=check aquation manual

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Respiration/PAM chamber raw excel spreadsheets

Abbreviations in headers of datasets Note: Two data sets are provided in different formats. Raw and cleaned (adj). These are the same data with the PAR column moved over to PAR.all for analysis. All headers are the same. The cleaned (adj) dataframe will work with the R syntax below, alternative add code to do cleaning in R.

Date: ISO 1986 - Check Time:UTC+11 unless otherwise stated DATETIME: UTC+11 unless otherwise stated ID (of instrument in respiration chambers) ID43=Pulse amplitude fluoresence measurement of control ID44=Pulse amplitude fluoresence measurement of acidified chamber ID=1 Dissolved oxygen ID=2 Dissolved oxygen ID3= PAR ID4= PAR PAR=Photo active radiation umols F0=minimal florescence from PAM Fm=Maximum fluorescence from PAM Yield=(F0 – Fm)/Fm rChl=an estimate of chlorophyll (Note this is uncalibrated and is an estimate only) Temp=Temperature degrees C PAR=Photo active radiation PAR2= Photo active radiation2 DO=Dissolved oxygen %Sat= Saturation of dissolved oxygen Notes=This is the program of the underwater submersible logger with the following abreviations: Notes-1) PAM= Notes-2) PAM=Gain level set (see aquation manual for more detail) Notes-3) Acclimatisation= Program of slowly introducing treatment water into chamber Notes-4) Shutter start up 2 sensors+sample…= Shutter PAMs automatic set up procedure (see aquation manual) Notes-5) Yield step 2=PAM yield measurement and calculation of control Notes-6) Yield step 5= PAM yield measurement and calculation of acidified Notes-7) Abatus respiration DO and PAR step 1= Program to measure dissolved oxygen and PAR (see aquation manual). Steps 1-4 are different stages of this program including pump cycles, DO and PAR measurements.

8) Rapid light curve data Pre LC: A yield measurement prior to the following measurement After 10.0 sec at 0.5% to 8%: Level of each of the 8 steps of the rapid light curve Odessey PAR (only in some deployments): An extra measure of PAR (umols) using an Odessey data logger Dataflow PAR: An extra measure of PAR (umols) using a Dataflow sensor. PAM PAR: This is copied from the PAR or PAR2 column PAR all: This is the complete PAR file and should be used Deployment: Identifying which deployment the data came from

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Respiration chamber biomass data

The data is chlorophyll a biomass from cores from the respiration chambers. The headers are: Depth (mm) Treat (Acidified or control) Chl a (pigment and indicator of biomass) Core (5 cores were collected from each chamber, three were analysed for chl a), these are psudoreplicates/subsamples from the chambers and should not be treated as replicates.

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Associated R script file for pump cycles of respirations chambers

Associated respiration chamber data to determine the times when respiration chamber pumps delivered treatment water to chambers. Determined from Aquation log files (see associated files). Use the chamber cut times to determine net production rates. Note: Users need to avoid the times when the respiration chambers are delivering water as this will give incorrect results. The headers that get used in the attached/associated R file are start regression and end regression. The remaining headers are not used unless called for in the associated R script. The last columns of these datasets (intercept, ElapsedTimeMincoef) are determined from the linear regressions described below.

To determine the rate of change of net production, coefficients of the regression of oxygen consumption in discrete 180 minute data blocks were determined. R squared values for fitted regressions of these coefficients were consistently high (greater than 0.9). We make two assumptions with calculation of net production rates: the first is that heterotrophic community members do not change their metabolism under OA; and the second is that the heterotrophic communities are similar between treatments.

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Combined dataset pH, temperature, oxygen, salinity, velocity for experiment

This data is rapid light curve data generated from a Shutter PAM fluorimeter. There are eight steps in each rapid light curve. Note: The software component of the Shutter PAM fluorimeter for sensor 44 appeared to be damaged and would not cycle through the PAR cycles. Therefore the rapid light curves and recovery curves should only be used for the control chambers (sensor ID43).

The headers are PAR: Photoactive radiation relETR: F0/Fm x PAR Notes: Stage/step of light curve Treatment: Acidified or control

The associated light treatments in each stage. Each actinic light intensity is held for 10 seconds, then a saturating pulse is taken (see PAM methods).

After 10.0 sec at 0.5% = 1 umols PAR After 10.0 sec at 0.7% = 1 umols PAR After 10.0 sec at 1.1% = 0.96 umols PAR After 10.0 sec at 1.6% = 4.32 umols PAR After 10.0 sec at 2.4% = 4.32 umols PAR After 10.0 sec at 3.6% = 8.31 umols PAR After 10.0 sec at 5.3% =15.78 umols PAR After 10.0 sec at 8.0% = 25.75 umols PAR

This dataset appears to be missing data, note D5 rows potentially not useable information

See the word document in the download file for more information.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR) from Q4 1967 to Q4 2024 about recession indicators, GDP, and USA.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The 2008 Sierra Leone Demographic and Health Survey (SLDHS) is the first DHS survey to be held in Sierra Leone. Teams visited 353 sample points across Sierra Leone and collected data from a nationally representative sample of 7,374 women age 15-49 and 3,280 men age 15-59. The primary purpose of the 2008 SLDHS is to provide policy-makers and planners with detailed information on Demography and health. This is the first Demographic and Health Survey conducted in Sierra Leone and was carried out by Statistics Sierra Leone (SSL) in collaboration with the Ministry of Health and Sanitation. The 2008 SLDHS was funded by the Sierra Leone government, UNFPA, UNDP, UNICEF, DFID, USAID, and The World Bank. WHO, WFP and UNHCR provided logistical support. ICF Macro, an ICF International Company, provided technical support for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators. The purpose of the SLDHS is to collect national- and regional-level data on fertility and contraceptive use, marriage and sexual activity, fertility preferences, breastfeeding practices, nutritional status of women and young children, childhood and adult mortality, maternal and child health, female genital cutting, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections, adult health, and other issues. The survey obtained detailed information on these topics from women of reproductive age and, for certain topics, from men as well. The 2008 SLDHS was carried out from late April 2008 to late June 2008, using a nationally representative sample of 7,758 households. The survey results are intended to assist policymakers and planners in assessing the current health and population programmes and in designing new strategies for improving reproductive health and health services in Sierra Leone. MAIN RESULTS FERTILITY Survey results indicate that there has been little or no decline in the total fertility rate over the past two decades, from 5.7 children per woman in 1980-85 to 5.1 children per woman for the three years preceding the 2008 SLDHS (approximately 2004-07). Fertility is lower in urban areas than in rural areas (3.8 and 5.8 children per woman, respectively). Regional variations in fertility are marked, ranging from 3.4 births per woman in the Western Region (where the capital, Freetown, is located) to almost six births per woman in the Northern and Eastern regions. Women with no education give birth to almost twice as many children as women who have been to secondary school (5.8 births, compared with 3.1 births). Fertility is also closely associated with household wealth, ranging from 3.2 births among women in the highest wealth quintile to 6.3 births among women in the lowest wealth quintile, a difference of more than three births. Research has demonstrated that children born too close to a previous birth are at increased risk of dying. In Sierra Leone, only 18 percent of births occur within 24 months of a previous birth. The interval between births is relatively long; the median interval is 36 months. FAMILY PLANNING The vast majority of Sierra Leonean women and men know of at least one method of contraception. Contraceptive pills and injectables are known to about 60 percent of currently married women and 49 percent of married men. Male condoms are known to 58 percent of married women and 80 percent of men. A higher proportion of respondents reported knowing a modern method of family planning than a traditional method. About one in five (21 percent) currently married women has used a contraceptive method at some time-19 percent have used a modern method and 6 percent have used a traditional method. However, only about one in twelve currently married women (8 percent) is currently using a contraceptive method. Modern methods account for almost all contraceptive use, with 7 percent of married women reporting use of a modern method, compared with only 1 percent using a traditional method. Injectables and the pill are the most widely used methods (3 and 2 percent of married women, respectively), followed by LAM and male condoms (less than 1 percent each). CHILD HEALTH Examination of levels of infant and child mortality is essential for assessing population and health policies and programmes. Infant and child mortality rates are also used as indices reflecting levels of poverty and deprivation in a population. The 2008 survey data show that over the past 15 years, infant and under-five mortality have decreased by 26 percent. Still, one in seven Sierra Leonean children dies before reaching age five. For the most recent five-year period before the survey (approximately calendar years 2003 to 2008), the infant mortality rate was 89 deaths per 1,000 live births and the under-five mortality rate was 140 deaths per 1,000 live births. The neonatal mortality rate was 36 deaths per 1,000 live births and the post-neonatal mortality rate was 53 deaths per 1,000 live births. The child mortality rate was 56 deaths per 1,000 children surviving to age one year. Mortality rates at all ages of childhood show a strong relationship with the length of the preceding birth interval. Under-five mortality is three times higher among children born less than two years after a preceding sibling (252 deaths per 1,000 births) than among children born four or more years after a previous child (deaths 81 per 1,000 births). MATERNAL HEALTH Almost nine in ten mothers (87 percent) in Sierra Leone receive antenatal care from a health professional (doctor, nurse, midwife, or MCH aid). Only 5 percent of mothers receive antenatal care from a traditional midwife or a community health worker; 7 percent of mothers do not receive any antenatal care. In Sierra Leone, over half of mothers have four or more antenatal care (ANC) visits, about 20 percent have one to three ANC visits, and only 7 percent have no antenatal care at all. The survey shows that not all women in Sierra Leone receive antenatal care services early in pregnancy. Only 30 percent of mothers obtain antenatal care in the first three months of pregnancy, 41 percent make their first visit in the fourth or fifth month, and 17 percent in have their first visit in the sixth or seventh month. Only 1 percent of women have their first ANC visit in their eighth month of pregnancy or later. BREASTFEEDING AND NUTRITION Poor nutritional status is one of the most important health and welfare problems facing Sierra Leone today and particularly afflicts women and children. The data show that 36 percent of children under five are stunted (too short for their age) and 10 percent of children under five are wasted (too thin for their height). Overall, 21 percent of children are underweight, which may reflect stunting, wasting, or both. For women, at the national level 11 percent of women are considered to be thin (body mass index

The benchmark interest rate in South Africa was last recorded at 7.25 percent. This dataset provides - South Africa Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Turkey was last recorded at 46 percent. This dataset provides the latest reported value for - Turkey Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Indonesia was last recorded at 5.50 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Australia was last recorded at 3.85 percent. This dataset provides - Australia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in South Korea was last recorded at 2.50 percent. This dataset provides - South Korea Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.