The Sexually Transmitted Disease (STD) Morbidity online databases on CDC WONDER contain case reports reported from the 50 United States and D.C., Puerto Rico, Virgin Islands and Guam. The online databases report the number of cases and disease incidence rates by year, state, disease, age, sex of patient, type of STD, and area of report. Data are produced by the U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention (CDC), National Center for HIV/AIDS, viral Hepatitis, STD and TB Prevention (NCHHSTP).

These data contain case counts and rates for sexually transmitted diseases (chlamydia, gonorrhea, and early syphilis which includes primary, secondary, and early latent syphilis) reported for California residents, by disease, county, year, and sex.

Data were extracted on cases with an estimated diagnosis date from 2001 through the last year indicated, from California Confidential Morbidity Reports and/or Laboratory Reports that were submitted to CDPH by July of the current year and which met the surveillance case definition for that disease. Because of inherent delays in case reporting and depending on the length of follow-up of clinical, laboratory and epidemiologic investigation, cases with eligible diagnosis dates may be added or rescinded after the date of this report.

Illinois 2000- 2016 STD Chlamydia counts by county by sex (where sex is known) by five year age groups. See attachment for metadata and censoring details under the "About" link. Null values in dataset reflect censored data. Cases reported with unknown sex have been excluded. Data Source: Illinois Department of Public Health STD Program.

Illinois 2000-2016 STD counts by county by sex (where sex is known). See attachment for metadata and censoring details under the "About" link. Null values in dataset reflect censored data. Cases reported with unknown sex have been excluded. Data Source: Illinois Department of Public Health STD Program.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

Small island states present a significant challenge in terms of sustainable tourism development. On a small island there are limited resources, economic and social activities tend to be concentrated on the coastal zone, and the interconnectivity between economic, environmental, social, cultural and political spheres is strong and pervasive. Consequently the sustainable development of tourism is more a practical necessity than an optional extra. This paper investigates the question of how to monitor sustainable tourism development (STD) in Samoa, an independent small island state in the South Pacific. It describes some of the methodological considerations and processes involved in the development of STD indicators and particularly highlights the importance of formulating clear objectives before trying to identify indicators, the value of establishing a multi-disciplinary advisory panel, and the necessity of designing an effective and flexible implementation framework for converting indicator results into management action. Available online and also kept in vertical file collection Call Number: VF 6920 [EL] Physical Description: 24 p. ; 29cm

This view of the Prevention Agenda Partner Contact Information: 2013 dataset contains the partners working on the prevention agenda priority area ,"Prevent HIV, STDs, Vaccine Preventable Diseases and Healthcare Associated Infections." The dataset is organized by county, priority area and focus area. Each partner's address, phone number and in many cases e-mail contact are provided. The Prevention Agenda 2013-17 is New York State’s health improvement plan for 2013 through 2017. This plan involves a unique mix of organizations including local health departments, health care providers, health plans, community based organizations, advocacy groups, academia, employers as well as state agencies, schools, and businesses whose activities can influence the health of individuals and communities and address health disparities. This unprecedented collaboration is designed to demonstrate how communities across the state can work together to improve the health and quality of life for all New Yorkers.The purpose of the dataset is to provide the public, health providers and tentative DOH partners with some basic information about who in NYS is working on prevention agenda related items. For more information check out http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/. The "About" tab contains additional details concerning this dataset.

The National Oceanographic Data Center processed data from sensors and oceanographic stations into a standard ocean data format known as the C022 Low-resolution data format.

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the standard deviation of SST (degrees Celsius) of the weekly time series from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The standard deviation of the long-term mean SST was calculated by taking the standard deviation over all weekly data from 2000-2013 for each pixel.

The NUIG_EyeGaze01(Labelled eye gaze dataset) is a rich and diverse gaze dataset, built using eye gaze data from experiments done under a wide range of operating conditions from three user platforms (desktop, laptop, tablet) . Gaze data is collected under one condition at a time.

The dataset includes gaze (fixation) data collected under 17 different head poses, 4 user distances, 6 platform poses and 3 display screen size and resolutions. Each gaze data file is labelled with the operating condition under which it was collected and has the name format: USERNUMBER_CONDITION_PLATFORM.CSV

CONDITION: RP- Roll plus in degree PP- Pitch plus in degree YP- Yaw plus in degree

RM- Roll minus in degree PM-Pitch minus in degree YM- Yaw minus in degree

50, 60, 70, 80: User distances

PLATFORM: desk- Desktop, lap- Laptop, tab- Tablet

Desktop display: 22 inch, 1680 x1050 pixels Laptop display: 14 inch, 1366x 768 pixels Tablet display: 10.1 inch 1920 x 800, pixels

Eye tracker accuracy: 0.5 degrees (for neutral head and tracker position)

The dataset has 3 folders called “Desktop”, “Laptop”, “Tablet” containing gaze data from respective platforms. The Desktop folder has 2 sub-folders: user_distance and head_pose. These have data for different user distances and head poses (neutral, roll, pitch, yaw )measured with desktop setup. The Tablet folder has 2 sub-folders: user_distance and tablet_pose,. These have data for different user distances and tablet+tracker poses (neutral, roll, pitch, yaw) measured with tablet setup . The Laptop folder has one sub-folder called user_distance which has data for different user distances, measured with laptop setup.

All data files are in CSV format. Each file contains the following data header fields:

("TIM REL","GTX", "GTY","XRAW", "YRAW","GT Xmm", "GT Ymm","Xmm", "Ymm","YAW GT", "YAW DATA","PITCH GT", "PITCH DATA","GAZE GT","GAZE ANG", "DIFF GZ", "AOI_IND","AOI_X","AOI_Y","MEAN_ERR","STD ERR")

The meanings of the header fields are as follows:

TIM REL: relative time stamp for each gaze data point (measured during data collection) "GTX", "GTY": Ground truth x, y positions in pixels "XRAW", "YRAW": Raw gaze data x, y coordinates in pixels "GT Xmm", "GT Ymm": Ground truth x, y positions in mm "Xmm", "Ymm": Gaze x, y positions in mm "YAW GT", "YAW DATA": Ground truth and estimated yaw angles "PITCH GT", "PITCH DATA": Ground truth and estimated pitch angles "GAZE GT","GAZE ANG": Ground truth and estimated gaze angles "DIFF GZ": Gaze angular accuracy "AOI_IND","AOI_X","AOI_Y": Index of the stimuli locations and their x, y coordinates "MEAN_ERR","STD ERR": Mean and standard deviation of error at the stimuli locations

For more details on the purpose of this dataset and data collection method, please consult the paper by authors of this dataset :

Anuradha Kar, Peter Corcoran: Performance Evaluation Strategies for Eye Gaze Estimation Systems with Quantitative Metrics and Visualizations. Sensors 18(9): 3151 (2018)

For the authoritative metadata record for the CAP-AU standard, see: http://www.bom.gov.au/metadata/19115/ANZCW0503900539. For further information about the CAP-AU standard, please contact the CAP-AU Custodian at cap-au@bom.gov.au or visit http://purl.org/cap-au/web/About.shtml. For the CAP-AU Specification itself, see http://purl.org/cap-au/web/Spec.shtml

CAP-AU is the Australian Profile of the Common Alerting Protocol (CAP). CAP is an open data standard that is developed and maintained by OASIS, a USA-based open standards organisation. CAP is an international xml encoding standard that facilitates the construction and exchange of all-hazard emergency alert and warning messages between various alerting technologies, systems and networks. CAP enables a single warning message to be prepared for dissemination simultaneously over a wide variety of warning systems that understand and can process CAP-formatted messages.

The standardised alerts produced using CAP can be exploited by a wide range of software applications and technology devices, each capable of processing the message and responding accordingly. Examples of the kinds of sensor and alerting technologies that can interoperate using CAP messages are data networks, landline and mobile phones, internet, fax, pagers, sirens, billboards and electronic road signs.

Early versions of CAP have been in use since 2009 in the various Australian jurisdictions and Commonwealth agencies that are responsible for distributing emergency warning messages to the Australian community. These existing implementations were used as the baseline to develop the common national approach used in this CAP-AU standard.

For further information about the CAP-AU standard please contact the CAP-AU Custodian at cap-au@bom.gov.au or visit http://purl.org/cap-au/web/About.shtml

Intellectual Property Statement / Copyright Notice - Use of the CAP-AU-STD documents shall be in accordance with the Intellectual Property Statements and Copyright Notices detailed within each CAP-AU-STD document downloaded from this collection

Clustering results on WebKB data set. (mean(± std)).

This dataset contains lines for all highways in the state of New Mexico. It is in a vector digital data structure digitized from a USGS 1:500,000 scale map of the state of New Mexico to which highways: Interstate, U.S., and State have been added. The source was ARC/INFO 5.0.1. and the conversion software was ARC/INFO 7.0.3. The size of the file is .36 Mb, compressed.

This layer represents boundaries for New Mexico tax district "OUT" categories and incorporated/municipal "IN" categories as identified on the "Certificate of Tax Rates" published for each of the State's thirty-three counties by the Department of Finance and Administration's Budget and Finance Bureau. Initial municipal boundaries acquired from RGIS and based on layers developed by the Earth Data Analysis Center (EDAC) at UNM. TRD revisions have been made by acquiring updated boundaries from data stewards at local jurisdictions. Data is a vector polygon digital data structure taken from the Census Bureau's TIGER/Line Files, 1994, for New Mexico. Known issues: This data layer may contain unintended inaccuracies and omissions. It is meant to serve as a baseline representation from which to make additions and improvements.