The Nationwide Emergency Department Sample (NEDS) was created to enable analyses of emergency department (ED) utilization patterns and support public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The NEDS can be weighted to produce national estimates. The NEDS is the largest all-payer ED database in the United States. It was constructed using records from both the HCUP State Emergency Department Databases (SEDD) and the State Inpatient Databases (SID), both also described in healthdata.gov. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). The SID contain information on patients initially seen in the emergency room and then admitted to the same hospital. The NEDS contains 25-30 million (unweighted) records for ED visits for over 950 hospitals and approximates a 20-percent stratified sample of U.S. hospital-based EDs. The NEDS contains information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 75% of patients, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) is the largest all-payer emergency department (ED) database in the United States. yielding national estimates of hospital-owned ED visits. Unweighted, it contains data from over 30 million ED visits each year. Weighted, it estimates roughly 145 million ED visits nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

Sampled from the HCUP State Inpatient Databases (SID) and State Emergency Department Databases (SEDD), the HCUP NEDS can be used to create national and regional estimates of ED care. The SID contain information on patients initially seen in the ED and subsequently admitted to the same hospital. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

The NEDS contain information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 85% of patients, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.Restricted access data files are available with a data use agreement and brief online security training.

The Nationwide Emergency Department Sample (NEDS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). NEDS is the largest all-payer emergency department (ED) database in the United States, yielding national estimates of hospital-based ED visits. One of the most distinctive features of the NEDS is its large sample size, which allows for analysis across hospital types and the study of relatively uncommon disorders and procedures.

Maps of health needs are documents that define the health needs of individual regions and the entire country. We collected demographic and epidemiological data, data on provided services, as well as the utilisation of human resources and equipment. On their basis, we have prepared a forecast of future healthcare needs for individual provinces and the entire country.

From 2019, maps no longer appear in the form of multi-page documents. Subsequent updates of the analyses will only be published on the freely available digital platform.

Open the Data Resource: https://star.chesapeakebay.net/ The Chesapeake Bay Program's Strategic Science and Research Framework (SSRF) was developed to identify and assess the partnership's short- and long-term science needs. These science needs are captured and tracked in this continually updated database. The science needs that are captured in this database were:

Identified as necessary to make progress toward a Chesapeake Bay Watershed Agreement goal or outcome, Expressed through the Chesapeake Bay Program's Strategy Review System process, and/or Listed as a recommendation within a Scientific and Technical Advisory Committee workshop report.

The Chesapeake Bay Program uses this database to engage stakeholders, identify opportunities to better align or evolve resources, update activities and workgroups to address needs, and inform STAC of its research priorities. This database can also be used by science providers to identify projects or collaborations of interest on which to engage the program. Science providers can represent a wide range of entities including, but not limited to, academic institutions, federal and state agencies, local entities, non-profit organizations and citizen science programs.

FEMA’s Coordinated Needs Management Strategy (CNMS) uses a geospatial database to identify and track flood hazard study lifecycle and mapping needs within the flood hazard mapping program. CNMS supports community officials and FEMA personnel in analyzing and depicting the validity of flood studies to enhance the understanding of flood hazard risk and make informed decisions on community planning and flood mitigation.

Abstract

The Department of Education commissioned the Human Sciences Research Council (HSRC) to conduct a national survey on the locality and other information linked to all schools in South Africa. The 2000 version was intended to update the 1996 version of the register of needs database, include 3000 institutions that were previously excluded, provide accurate data on geolocality of schools, school conditions and the availability of resources, and measure progress and trends betwee 1996 and 2000.

Geographic coverage

The survey had national coverage

Analysis unit

Institutions

Universe

The universe of the survey was all schools in South Africa

Kind of data

Administrative records

Data appraisal

This version was adapted from a cleaned dataset prepared by the Developmental Policy Research Unit (DPRU), which was obtained by DataFirst at an unknown date between the original release and February 2012. This version replaces numerous string valued variables with integer valued ones, which is particularly useful as case sensitivity has artificially separated semantically identical observations into different categories for some variables. These integer valued string variables are labelled and the codelist relating those labels is provided as an accompanying document. The original version contained a number of anomalies, errors and misspecifications that make it intractable. This version partially remedies these issues and represents a cleaner version of the data than the original (which can be requested from DataFirst).

Abstract

The 2010 NEDS is similar to the 2004 Nigeria DHS EdData Survey (NDES) in that it was designed to provide information on education for children age 4–16, focusing on factors influencing household decisions about children’s schooling. The survey gathers information on adult educational attainment, children’s characteristics and rates of school attendance, absenteeism among primary school pupils and secondary school students, household expenditures on schooling and other contributions to schooling, and parents’/guardians’ perceptions of schooling, among other topics.The 2010 NEDS was linked to the 2008 Nigeria Demographic and Health Survey (NDHS) in order to collect additional education data on a subset of the households (those with children age 2–14) surveyed in the 2008 Nigeria DHS survey. The 2008 NDHS, for which data collection was carried out from June to October 2008, was the fourth DHS conducted in Nigeria (previous surveys were implemented in 1990, 1999, and 2003).

The goal of the 2010 NEDS was to follow up with a subset of approximately 30,000 households from the 2008 NDHS survey. However, the 2008 NDHS sample shows that of the 34,070 households interviewed, only 20,823 had eligible children age 2–14. To make statistically significant observations at the State level, 1,700 children per State and the Federal Capital Territory (FCT) were needed. It was estimated that an additional 7,300 households would be required to meet the total number of eligible children needed. To bring the sample size up to the required target, additional households were screened and added to the overall sample. However, these households did not have the NDHS questionnaire administered. Thus, the two surveys were statistically linked to create some data used to produce the results presented in this report, but for some households, data were imputed or not included.

Geographic coverage

National

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The eligible households for the 2010 NEDS are the same as those households in the 2008 NDHS sample for which interviews were completed and in which there is at least one child age 2-14, inclusive. In the 2008 NDHS, 34,070 households were successfully interviewed, and the goal here was to perform a follow-up NEDS on a subset of approximately 30,000 households. However, records from the 2008 NDHS sample showed that only 20,823 had children age 4-16. Therefore, to bring the sample size up to the required number of children, additional households were screened from the NDHS clusters.

The first step was to use the NDHS data to determine eligibility based on the presence of a child age 2-14. Second, based on a series of precision and power calculations, RTI determined that the final sample size should yield approximately 790 households per State to allow statistical significance for reporting at the State level, resulting in a total completed sample size of 790 × 37 = 29,230. This calculation was driven by desired estimates of precision, analytic goals, and available resources. To achieve the target number of households with completed interviews, we increased the final number of desired interviews to accommodate expected attrition factors such as unlocatable addresses, eligibility issues, and non-response or refusal. Third, to reach the target sample size, we selected additional samples from households that had been listed by NDHS but had not been sampled and visited for interviews. The final number of households with completed interviews was 26,934 slightly lower than the original target, but sufficient to yield interview data for 71,567 children, well above the targeted number of 1,700 children per State.

Mode of data collection

Face-to-face [f2f]

Research instrument

The four questionnaires used in the 2004 Nigeria DHS EdData Survey (NDES)— 1. Household Questionnaire 2. Parent/Guardian Questionnaire 3. Eligible Child Questionnaire 4. Independent Child Questionnaire—formed the basis for the 2010 NEDS questionnaires. These are all available in Appendix D of the survey report available under External Resources.

More than 90 percent of the questionnaires remained the same; for cases where there was a clear justification or a need for a change in item formulation or a specific requirement for additional items, these were updated accordingly. A one day workshop was convened with the NEDS Implementation Team and the NDES Advisory Committee to review the instruments and identify any needed revisions, additions, or deletions. Efforts were made to collect data to ease integration of the 2010 NEDS data into the FMOE’s national education management information system. Instrument issues that were identified as being problematic in the 2004 NDES as well as items identified as potentially confusing or difficult were proposed for revision. Issues that USAID, DFID, FMOE, and other stakeholders identified as being essential but not included in the 2004 NDES questionnaires were proposed for incorporation into the 2010 NEDS instruments, with USAID serving as the final arbiter regarding questionnaire revisions and content.

General revisions accepted into the questionnaires included the following: - A separation of all questions related to secondary education into junior secondary and senior secondary to reflect the UBE policy - Administration of school-based questions for children identified as attending pre-school - Inclusion of questions on disabilities of children and parents - Additional questions on Islamic schooling - Revision to the literacy question administration to assess English literacy for children attending school - Some additional questions on delivery of UBE under the financial questions section

Upon completion of revisions to the English-language questionnaires, the instruments were translated and adapted by local translators into three languages—Hausa, Igbo, and Yoruba—and then back-translated into English to ensure accuracy of the translation. After the questionnaires were finalized, training materials used in the 2004 NDES and developed by Macro International, which included training guides, data collection manuals, and field observation materials, were reviewed. The materials were updated to reflect changes in the questionnaires. In addition, the procedures as described in the manuals and guides were carefully reviewed. Adjustments were made, where needed, based on experience on large-scale survey and lessons learned from the 2004 NDES and the 2008 NDHS, to ensure the highest quality data capture.

Cleaning operations

Data processing for the 2010 NEDS occurred concurrently with data collection. Completed questionnaires were retrieved by the field coordinators/trainers and delivered to NPC in standard envelops, labeled with the sample identification, team, and State name. The shipment also contained a written summary of any issues detected during the data collection process. The questionnaire administrators logged the receipt of the questionnaires, acknowledged the list of issues, and acted upon them if required. The editors performed an initial check on the questionnaires, performed any coding of open-ended questions (with possible assistance from the data entry operators), and left them available to be assigned to the data entry operators. The data entry operators entered the data into the system, with the support of the editors for erroneous or unclear data.

Experienced data entry personnel were recruited from those who have performed data entry activities for NPC on previous studies. The data entry teams composed a data entry coordinator, supervisor and operators. Data entry coordinators oversaw the entire data entry process from programming and training to final data cleaning, made assignments, tracked progress, and ensured the quality and timeliness of the data entry process. Data entry supervisors were on hand at all times to ensure that proper procedures were followed and to help editors resolve any uncovered inconsistencies. The supervisors controlled incoming questionnaires, assigned batches of questionnaires to the data entry operators, and managed their progress. Approximately 30 clerks were recruited and trained as data entry operators to enter all completed questionnaires and to perform the secondary entry for data verification. Editors worked with the data entry operators to review information flagged as “erroneous” or “dubious” in the data entry process and provided follow up and resolution for those anomalies.

The data entry program developed for the 2004 NDES was revised to reflect the revisions in the 2010 NEDS questionnaire. The electronic data entry and reporting system ensured internal consistency and inconsistency checks.

Response rate

A very high overall response rate of 97.9 percent was achieved with interviews completed in 26,934 households out of a total of 27,512 occupied households from the original sample of 28,624 households. The response rates did not vary significantly by urban–rural (98.5 percent versus 97.6 percent, respectively). The response rates for parent/guardians and children were even higher, and the rate for independent children was slightly lower than the overall sample rate, 97.4 percent. In all these cases, the urban/rural differences were negligible.

Sampling error estimates

Estimates derived from a sample survey are affected by two types of errors: (1) non-sampling errors and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

Abstract

The main purpose of this assessment is to present an overview of the situation and the priority needs of older persons on the move in Latin America, with a focus on some countries in the Andean region and the northern part of Central America. The assessment also includes the impact and worsening of older persons access to and exercise of their rights and services, under the current situation caused by the COVID-19 pandemic. To this end, this assessment will provide data and evidence for decision-making, public-policy design, and the implementation of programmes that promote the rights of older persons on the move throughout the region and during the entire displacement cycle.

Geographic coverage

National coverage (Colombia, Ecuador, El Salvador, Honduras, Peru)

Analysis unit

Individual

Universe

Older people (over 60 years old)

Kind of data

Sample survey data [ssd]

Sampling procedure

The purpose of this assessment is to establish a consolidated body of knowledge on the guarantee of rights, needs, and services that cover older persons (over 60 years old) on the move in Latin America, particularly in Honduras, El Salvador, Colombia, Peru and Ecuador.

Given the existing limitations in determining the universe of older persons in the human mobility flows of interest, this assessment was not based on a statistically representative sample design. Nonetheless, it is considered that the breadth and quality of the information obtained serves to reflect the main trends in terms of the guarantee of rights and provision of services to older persons on the move.

Data was gathered via telephone and online surveys. The telephone surveys took place between 26 October and 27 November 2020. Among the contacts provided by UNCHR, associates and field partners, an initial database of 2,876 older persons in the five countries was consolidated; of these, the survey enumerators called 1,325 persons, of whom 772 accepted to participate. After debugging, 725 surveys were included in the analysis, i.e., those that met the criteria for completing the survey and had the respondent's consent on the use of the information.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire contains the following sections: - Survey data - Characteristics - Seeking Asylum, Returning, Internal Displacement and Migration - Access to income - Care and Support - Access to security - Access to Health Services - Access to Training Services - Access to Work - Access to Food Security - Access to Social Security - Access to Accommodation or Housing and Health Services - Community Participation and Integration - Non-Discrimination and Equality before the law - Access to Freedom of Expression, Opinion and Information - Accessibility and Mobility - Access to financial products

Success.ai delivers comprehensive access to Small Business Contact Data, tailored to connect you with North American entrepreneurs and small business leaders. Our extensive database includes verified profiles of over 170 million professionals, ensuring direct access to decision-makers in various industries. With AI-validated accuracy, continuously updated datasets, and a focus on compliance, Success.ai empowers businesses to enhance their marketing, sales, and recruitment efforts while staying ahead in a competitive market.

Key Features of Success.ai's Small Business Contact Data:

Extensive Coverage: Access profiles for small business owners and entrepreneurs across the United States, Canada, and Mexico. Our database spans multiple industries, from retail to technology, providing diverse business insights.

Verified Contact Details: Each profile includes work emails, phone numbers, and firmographic data, enabling precise and effective outreach.

Industry-Specific Data: Target key sectors such as e-commerce, professional services, healthcare, manufacturing, and more, with tailored datasets designed to meet your specific business needs.

Real-Time Updates: Continuously updated to maintain a 99% accuracy rate, our data ensures that your campaigns are always backed by the most current information.

Ethical and Compliant: Fully compliant with GDPR and other global data protection regulations, ensuring ethical use of all contact data.

Why Choose Success.ai for Small Business Contact Data?

Best Price Guarantee: Enjoy the most competitive pricing in the market, delivering exceptional value for comprehensive and verified contact data.

AI-Validated Accuracy: Our advanced AI systems meticulously validate every data point to deliver unmatched reliability and precision.

Customizable Data Solutions: From hyper-targeted regional datasets to comprehensive industry-wide insights, we tailor our offerings to meet your exact requirements.

Scalable Access: Whether you're a startup or an enterprise, our solutions are designed to scale with your business needs.

Comprehensive Use Cases for Small Business Contact Data:

1. Targeted Marketing Campaigns:

Refine your marketing strategy by leveraging verified contact details for small business owners. Execute highly personalized email, phone, and multi-channel campaigns with precision.

1. Sales Prospecting:

Identify and connect with decision-makers in key industries. Use detailed profiles to enhance your sales outreach, close deals faster, and build long-term client relationships.

1. Recruitment and Talent Acquisition:

Discover small business leaders and key players in specific industries to strengthen your recruitment pipeline. Access up-to-date profiles for sourcing top talent.

1. Market Research:

Gain insights into small business trends, operational challenges, and industry benchmarks. Leverage this data for competitive analysis and market positioning.

1. Local Business Engagement:

Foster partnerships with small businesses by identifying community leaders and entrepreneurial influencers in your target regions.

APIs to Enhance Your Campaigns:

Enrichment API: Integrate real-time updates into your CRM and marketing systems to maintain accurate and actionable contact data. Perfect for businesses looking to improve lead quality.

Lead Generation API: Maximize your lead generation efforts with access to verified contact details, including emails and phone numbers. Tailored for precise targeting of small business decision-makers.

Tailored Solutions for Diverse Needs:

Marketing Agencies: Create targeted campaigns with verified data for small business owners across diverse sectors.

Sales Teams: Drive revenue growth with detailed profiles and direct access to decision-makers.

Recruiters: Build a talent pipeline with current and verified data on small business leaders and professionals.

Consultants: Provide data-driven recommendations to clients by leveraging detailed small business insights.

What Sets Success.ai Apart?

170M+ Profiles: Access a vast and detailed database of small business owners and entrepreneurs.

Global Standards Compliance: Rest assured knowing all data is ethically sourced and compliant with global privacy regulations.

Flexible Integration: Seamlessly integrate data into your existing workflows with customizable delivery options.

Dedicated Support: Our team of experts is always available to ensure you maximize the value of our solutions.

Empower Your Outreach with Success.ai:

Success.ai’s Small Business Contact Data is your gateway to building meaningful connections with North American entrepreneurs. Whether you're driving targeted marketing campaigns, enhancing sales prospecting, or conducting in-depth market research, our verified datasets provide the tools you need to succeed.

Get started with Success.ai today and unlock the potential of verified Small Business ...

Project Overview This study used a community-based participatory approach to identify and investigate the needs of people experiencing homelessness in Dublin, Ireland. The project had several stages: A systematic review on health disparities amongst people experiencing homelessness in the Republic of Ireland; Observation and interviews with homeless attendees of a community health clinic; and Interviews with community experts (CEs) conducted from September 2022 to March 2023 on ongoing work and gaps in the research/health service response. This data deposit stems from stage 3, the community expert interview aspect of this project. Stage 1 of the project has been published (Ingram et al., 2023.) and associated data are available here. De-identified field note data from stage 2 of the project are planned for sharing upon completion of analysis, in January 2024. Data and Data Collection Overview A purposive, criterion-i sampling strategy (Palinkas et al., 2015) – where selected interviewees meet a predetermined criterion of importance – was used to identify professionals working in homeless health and/or addiction services in Dublin, stratified by occupation type. Potential CEs were identified through an internet search of homeless health and addiction services in Dublin. Interviewed CEs were invited to recommend colleagues they felt would have relevant perspectives on community health needs, expanding the sample via snowball strategy. Interview questions were based on World Health Organization Community Health Needs Assessment guidelines (Rowe at al., 2001). Semi-structured interviews were conducted between September 2022 and March 2023 utilising ZOOM™, the phone, or in person according to participant preference. Carolyn Ingram, who has formal qualitative research training, served as the interviewer. CEs were presented with an information sheet and gave audio recorded, informed oral consent – considered appropriate for remote research conducted with non-vulnerable adult participants – in the full knowledge that interviews would be audio recorded, transcribed, and de-identified, as approved by the researchers’ institutional Human Research Ethics Committee (LS-E-125-Ingram-Perrotta-Exemption). Interviewees also gave permission for de-identified transcripts to be shared in a qualitative data archive. Shared Data Organization 16 de-identified transcripts from the CE interviews are being published. Three participants from the total sample (N=19) did not consent to data archival. The transcript from each interviewee is named based on the type of work the interviewee performs, with individuals in the same type of work being differentiated by numbers. The full set of professional categories is as follows: Addiction Services Government Homeless Health Services Hospital Psychotherapist Researcher Social Care Any changes or removal of words or phrases for de-identification purposes are flagged by including [brackets] and italics. The documentation files included in this data project are the consent form and the interview guide used for the study, this data narrative and an administrative README file. References Ingram C, Buggy C, Elabbasy D, Perrotta C. (2023) “Homelessness and health-related outcomes in the Republic of Ireland: a systematic review, meta-analysis and evidence map.” Journal of Public Health (Berl). https://doi.org/10.1007/s10389-023-01934-0 Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. (2015) “Purposeful sampling for qualitative data collection and analysis in mixed method implementation research.” Administration and Policy in Mental Health. Sep;42(5):533–44. https://doi.org/10.1007/s10488-013-0528-y Rowe A, McClelland A, Billingham K, Carey L. (2001) “Community health needs assessment: an introductory guide for the family health nurse in Europe” [Internet]. World Health Organization. Regional Office for Europe. Available at: https://apps.who.int/iris/handle/10665/108440

In August 2021, the South Carolina Center for Rural and Primary Healthcare partnered with the University of North Carolina at Greensboro to produce South Carolina Public Libraries & Health: Needs and Opportunities, as part of its broader Rural Libraries and Health Cooperative Agreement program, funded by the the South Carolina Department of Health and Environmental via the Centers for Disease Control & Prevention National Initiative to Address COVID-19 Health Disparities Among Populations at High-Risk and Underserved, Including Racial and Ethnic Minority Population and Rural Communities, a non-research grant funded through the Coronavirus Response and Relief Supplemental Appropriations Act, 2021. The study documented a range of ways that South Carolina public libraries support health. It also assessed what needs public libraries have as they seek to support health in their communities. Based on that analysis, a model for continuing education to support the alignment of public libraries and health was developed. As an exploratory study, South Carolina Public Libraries & Health: Needs and Opportunities highlights implications for a variety of stakeholder groups including those working in the health sector at both local and state levels, as well as library workers and administrators, funders and policy makers, and researchers. Using snowball sampling techniques, 123 library workers from across the state completed a survey in September 2021 about their health partnerships and health-related continuing education needs; an additional 19 completed a portion of the survey. (2022-05-02)

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

BackgroundDespite the incentives and provisions created for hospitals by the US Affordable Care Act related to value-based payment and community health needs assessments, concerns remain regarding the adequacy and distribution of hospital efforts to address SDOH. This scoping review of the peer-reviewed literature identifies the key characteristics of hospital/health system initiatives to address SDOH in the US, to gain insight into the progress and gaps.MethodsPRISMA-ScR criteria were used to inform a scoping review of the literature. The article search was guided by an integrated framework of Healthy People SDOH domains and industry recommended SDOH types for hospitals. Three academic databases were searched for eligible articles from 1 January 2018 to 30 June 2023. Database searches yielded 3,027 articles, of which 70 peer-reviewed articles met the eligibility criteria for the review.ResultsMost articles (73%) were published during or after 2020 and 37% were based in Northeast US. More initiatives were undertaken by academic health centers (34%) compared to safety-net facilities (16%). Most (79%) were research initiatives, including clinical trials (40%). Only 34% of all initiatives used the EHR to collect SDOH data. Most initiatives (73%) addressed two or more types of SDOH, e.g., food and housing. A majority (74%) were downstream initiatives to address individual health-related social needs (HRSNs). Only 9% were upstream efforts to address community-level structural SDOH, e.g., housing investments. Most initiatives (74%) involved hot spotting to target HRSNs of high-risk patients, while 26% relied on screening and referral. Most initiatives (60%) relied on internal capacity vs. community partnerships (4%). Health disparities received limited attention (11%). Challenges included implementation issues and limited evidence on the systemic impact and cost savings from interventions.ConclusionHospital/health system initiatives have predominantly taken the form of downstream initiatives to address HRSNs through hot-spotting or screening-and-referral. The emphasis on clinical trials coupled with lower use of EHR to collect SDOH data, limits transferability to safety-net facilities. Policymakers must create incentives for hospitals to invest in integrating SDOH data into EHR systems and harnessing community partnerships to address SDOH. Future research is needed on the systemic impact of hospital initiatives to address SDOH.

The Department of City Planning aggregates information about 30,000+ facilities and program sites that are owned, operated, funded, licensed, or certified by a City, State, or Federal agency in the City of New York into a central database called the City Planning Facilities Database (FacDB). These facilities generally help to shape quality of life in the city's neighborhoods, and this dataset is the basis for a series of planning activities. This public data resource allows all New Yorkers to understand the breadth of government resources in their neighborhoods. The data is also complemented with a new interactive web map that enables users to easily filter the data for their needs. Users are strongly encouraged to read the database documentation, particularly with regard to analytical limitations.

Questions about this database can be directed to dcpopendata@planning.nyc.gov All previously released versions of this data are available at BYTES of the BIG APPLE Archive

1/3 arc-second (10m) raster topographical data from the USGS National Elevation Dataset (NED) for the Baltimore MSA. Nine separate 1/3 arc-second NED tiles were moasiced and reprojected to form this composite topographical dataset. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1/3 arc second data are: Horizontal datum of NAD83, Vertical datum of NAVD88. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

(Link to Metadata) This dataset is derived from the multi-resolution National Elevation Dataset (NED), at resolutions of both 1/3 arc-second (approx. 10 meters) and in limited areas, 1/9 arc-second (approx. 3 meters). Contours derived from this data may appear to have ?shifted? when compared to the 7.5 minute USGS Quad Maps, i.e., Digital Raster Graphics, for a variety of reasons: 1) The NED is a multi-resolution dataset, e.g., areas with LiDAR source data have superseded the original "quad" contours; 2) A result of the original contour vectors undergoing a NAD27 to NAD83 conversion, then the contour vector-to-raster resampling that produced the initial grid, followed by a resampling of that initial 10m grid to the 1/3 arc-second NED (~10.29m) and finally the raster-to-vector conversion yielding the current contours. VCGI extracted the Vermont portion of the NED and re-projected into Vermont State Plane Meters NAD83 (vertical units in feet). Production artifacts were filtered out of this source data prior to acquisition resulting in a much-improved base of elevation data for calculating contours, slope and hydrologic derivatives. The NED is the primary elevation data product produced and distributed by the USGS. The NED provides the best available public domain raster elevation data of the conterminous United States, Alaska, Hawaii, and territorial islands in a seamless format. The NED is derived from diverse source data, processed to a common coordinate system and unit of vertical measure. The source data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83). The source elevation values are provided in units of meters, and are referenced to the North American Vertical Datum of 1988 (NAVD 88) over the conterminous United States. The NED is updated on a nominal two month cycle to integrate newly available, improved elevation source data.

This tile of the National Elevation Dataset (NED) is 1/9 arc-second resolution. The National Elevation Dataset (NED) serves the elevation layer of The National Map, and provides basic elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use NED data for global change research, hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The NED is an elevation dataset that consists of seamless layers and a high resolution layer. Each of these layers are composed of the best available raster elevation data of the conterminous United States, Alaska, Hawaii, territorial islands, Mexico and Canada. The NED is updated continually as new data become available. All NED data are in the public domain. The NED are derived from diverse source data that are processed to a common coordinate system and unit of vertical measure. These data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83). All elevation values are in meters and, over the continental United States, are referenced to the North American Vertical Datum of 1988 (NAVD 88). The vertical reference will vary in other areas. NED data are available nationally (except for Alaska) at resolutions of 1 arc-second (approx. 30 meters) and 1/3 arc-second (approx. 10 meters), and in limited areas at 1/9 arc-second (approx. 3 meters). In most of Alaska, only lower resolution source data are available. As a result, most NED data for Alaska are at 2-arc-second (approx. 60 meters) grid spacing. Part of Alaska is available at the 1- and 1/3-arc-second resolution from IFSAR collections starting in 2010. Plans are in place for collection of statewide IFSAR in Alaska through 2016.

To record details of equines resident in the United Kingdom.