This document presents the standard starting point language to use when drafting a formal data sharing agreement between a City entity and either another City entity or an outside party when two parties seek to share non-public data with one another. The document outlines the following major concerns:Parties to the agreementPurpose of the data sharing effort Period of the agreementDescription of the data to be sharedTiming and frequency of updates to the shared dataPoint(s) of contactCustodial responsibilitiesMethod of data transferPublication ReviewOther City terms and conditions This version 1.1 makes minor corrections of language originally formalized by the City's Data Governance Committee in June of 2020. Note that a data sharing agreement is not final or authorized without appropriate signatures from all parties represented by the agreement.

This Data Access Agreement (DAA) is freely available to use and is intended for use where data is accessed within a Trusted Research Environment (TRE) for the purposes of research and development for the public good. The DAA has been developed by the TRE Legal Toolkit Action Force of the Pan UK Data Governance Steering Group. The Pan UK Data Governance Steering Group is a working Group of the UK Health Data Research Alliance representing data custodians and policymakers across the four nations. The Steering Group is focused on simplifying and streamlining data access governance processes. The DAA terms and conditions should not be modified. The annexes are customisable to allow for differences between TREs. New with Version 6.0 we are providing a Personal Data and a Non-Personal Data template The latter intended for use only where the data accessed in the TRE and the Data Output are both not considered Personal Data or Confidential Patient Information (CPI) from the perspective of the Approved User and their Approved User Organisation who are accessing the data on behalf of the Sponsor Organisation. If research will involve access to Data that is Personal Data or CPI, or the creation of Data Outputs that are likely to become Personal Data or CPI in the hands of the intended recipients, the Personal Data version must be used. We wish to encourage widespread adoption of this template and it is freely available to use. If you do plan to adopt this template or would like to discuss any queries please get in touch with: Rachel Brophy, Head of Information and Research Governance, HDR UK (rachel.brophy@hdruk.ac.uk) cc: informationgovernance@hdruk.ac.uk Please see version control document for details of changes.

The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) is the largest publicly available all-payer inpatient care database in the United States. The NIS is designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. Starting with the 2012 data year, the NIS is a sample of discharges from all hospitals participating in HCUP, covering more than 97 percent of the U.S. population. For prior years, the NIS was a sample of hospitals. The NIS allows for weighted national estimates to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The NIS's large sample size enables analyses of rare conditions, such as congenital anomalies; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured. NIS data are available since 1988, allowing analysis of trends over time. The NIS inpatient data include clinical and resource use information typically available from discharge abstracts with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

The following apply to all three datasets:

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

The largest all-payer ambulatory surgery database in the United States, the Healthcare Cost and Utilization Project (HCUP) Nationwide Ambulatory Surgery Sample (NASS) produces national estimates of major ambulatory surgery encounters in hospital-owned facilities. Major ambulatory surgeries are defined as selected major therapeutic procedures that require the use of an operating room, penetrate or break the skin, and involve regional anesthesia, general anesthesia, or sedation to control pain (i.e., surgeries flagged as "narrow" in the HCUP Surgery Flag Software). Unweighted, the NASS contains approximately 9.0 million ambulatory surgery encounters each year and approximately 11.8 million ambulatory surgery procedures. Weighted, it estimates approximately 11.9 million ambulatory surgery encounters and 15.7 million ambulatory surgery procedures. Sampled from the HCUP State Ambulatory Surgery and Services Databases (SASD) and State Emergency Department Databases (SEDD) in order to capture both planned and emergent major ambulatory surgeries, the NASS can be used to examine selected ambulatory surgery utilization patterns. 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 NASS contains clinical and resource-use information that is included in a typical hospital-owned facility record, including patient characteristics, clinical diagnostic and surgical procedure codes, disposition of patients, total charges, facility characteristics, and expected source of payment, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NASS excludes data elements that could directly or indirectly identify individuals, hospitals, or states. The NASS is limited to encounters with at least one in-scope major ambulatory surgery on the record, performed at hospital-owned facilities. Procedures intended primarily for diagnostic purposes are not considered in-scope. Restricted access data files are available with a data use agreement and brief online security training.

This dataset collects the slides that were presented at the Data Collaborations Across Boundaries session in SciDataCon 2022, part of the International Data Week.

The following session proposal was prepared by Tyng-Ruey Chuang and submitted to SciDataCon 2022 organizers for consideration on 2022-02-28. The proposal was accepted on 2022-03-28. Six abstracts were submitted and accepted to this session. Five presentations were delivered online in a virtual session on 2022-06-21.

Data Collaborations Across Boundaries

There are many good stories about data collaborations across boundaries. We need more. We also need to share the lessons each of us has learned from collaborating with parties and communities not in our familiar circles.

By boundaries, we mean not just the regulatory borders in between the nation states about data sharing but the various barriers, readily conceivable or not, that hinder collaboration in aggregating, sharing, and reusing data for social good. These barriers to collaboration exist between the academic disciplines, between the economic players, and between the many user communities, just to name a few. There are also cross-domain barriers, for example those that lay among data practitioners, public administrators, and policy makers when they are articulating the why, what, and how of "open data" and debating its economic significance and fair distribution. This session aims to bring together experiences and thoughts on good data practices in facilitating collaborations across boundaries and domains.

The success of Wikipedia proves that collaborative content production and service, by ways of copyleft licenses, can be sustainable when coordinated by a non-profit and funded by the general public. Collaborative code repositories like GitHub and GitLab demonstrate the enormous value and mass scale of systems-facilitated integration of user contributions that run across multiple programming languages and developer communities. Research data aggregators and repositories such as GBIF, GISAID, and Zenodo have served numerous researchers across academic disciplines. Citizen science projects and platforms, for instance eBird, Galaxy Zoo, and Taiwan Roadkill Observation Network (TaiRON), not only collect data from diverse communities but also manage and release datasets for research use and public benefit (e.g. TaiRON datasets being used to improve road design and reduce animal mortality). At the same time large scale data collaborations depend on standards, protocols, and tools for building registries (e.g. Archival Resource Key), ontologies (e.g. Wikidata and schema.org), repositories (e.g. CKAN and Omeka), and computing services (e.g. Jupyter Notebook). There are many types of data collaborations. The above lists only a few.

This session proposal calls for contributions to bring forward lessons learned from collaborative data projects and platforms, especially about those that involve multiple communities and/or across organizational boundaries. Presentations focusing on the following (non-exclusive) topics are sought after:

1. Support mechanisms and governance structures for data collaborations across organizations/communities.

2. Data policies --- such as data sharing agreements, memorandum of understanding, terms of use, privacy policies, etc. --- for facilitating collaborations across organizations/communities.

3. Traditional and non-traditional funding sources for data collaborations across multiple parties; sustainability of data collaboration projects, platforms, and communities.

4. Data workflows --- collection, processing, aggregation, archiving, and publishing, etc. --- designed with considerations of (external) collaboration.

5. Collaborative web platforms for data acquisition, curation, analysis, visualization, and education.

6. Examples and insights from data trusts, data coops, as well as other formal and informal forms of data stewardship.

7. Debates on the pros and cons of centralized, distributed, and/or federated data services.

8. Practical lessons learned from data collaboration stories: failure, success, incidence, unexpected turn of event, aftermath, etc. (no story is too small!).

This dataset represents a group of paper records (a "series") within the Harvard School of Public Health Longitudinal Studies of Child Health and Developent records, 1918-2015 (inclusive), 1930-1989 (bulk), which can be accessed on-site at the Center for the History of Medicine at the Francis A. Countway Library of Medicine in Boston, Massachusetts. The series consists of research administrative records generated and compiled by the Harvard School of Public Health Longitudinal Studies of Child Health and Development to track and regulate data sharing and usage. Records include: research records inventories and location lists; research data sharing policies; data use forms; and correspondence with various researchers regarding data usage requests. Attached to the "Data Use Policy Records and Correspondence, 1963-1989" dataset is a file, digitized from its original paper copy, that serves as an example of the records that may be found in the series. Additional data and associated records are accessible onsite at the Center for the History of Medicine per the conditions governing access described below. Conditions Governing Access to Original Collection Materials: The series represented by this dataset includes student information that is restricted for 80 years from the date of record creation, longitudinal patient information that is restricted for 80 years from the most recently dated records in the collection, and Harvard University records that are restricted for 50 years from the date of record creation. Researchers should contact Public Services for more information. The Harvard School of Public Health Longitudinal Studies of Child Health and Development records were processed with grant funding from the Andrew W. Mellon Foundation, as awarded and administered by the Council on Library and Information Resources (CLIR) in 2016. An online finding aid to the collection may be accessed here: http://nrs.harvard.edu/urn-3:HMS.Count:med00211

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

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.

Abstract (en): The National Longitudinal Study of Adolescent Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-1995 school year. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. The additional files contained in this component of the Add Health project are from the Adolescent Health and Academic Achievement (AHAA) study and provide an opportunity to examine the effects of education on adolescent behavior, academic achievement, and cognitive and psychosocial development in the 1990s. The AHAA study contributes to Add Health by providing the high school transcripts of Add Health Wave III sample members. The AHAA data provides indicators of (1) educational achievement, (2) course taking patterns, (3) curricular exposure, and (4) educational contexts within and between schools, all of which can be linked to the Add Health survey data. The Adolescent Health and Academic Achievement (AHAA) study provides an opportunity to examine the effects of education on adolescent behavior, academic achievement, and cognitive and psychosocial development in the 1990s. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Adolescents in grades 7-12 and their families. Wave I, Stage 1 School sample: stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school, a school that sent graduates to the high school and that included a 7th grade, was also recruited from the community. Wave I, Stage 2: An in-home sample of 20,745 adolescents consisting of a core sample from each community plus selected special oversamples was interviewed in 1995. Eligibility for the oversamples was determined by the adolescent's responses on the In-School Questionnaire. Adolescents could qualify for more than one sample. At Wave II, respondents who were in grades 7-11 at Wave I were re-interviewed. Wave III: The in-home Wave III sample consists of Wave I respondents who could be located and re-interviewed six years later. Wave III also collected High School Transcript Release Forms to be used for the AHAA study. At Wave IV, 15,701 Wave I respondents were re-interviewed in 2008. 2012-09-10 The following three pages have been added to the Restricted Data Use Agreement for this study: a "General Information and Checklists" page, a "Using the Add Health Transcript Data in the ICPSR-DSDR Secure Data Enclave" page, and a page titled "ATTACHMENT A: Output Disclosure Risk Checks." Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. Eunice Kennedy Shriver National Institute of Child Health and Human Development (P01-HD31921). United States Department of Health and Human Services. National Institutes of Health. National Cancer Institute. United States Department of Health and Human Services. National Institutes of Health. National Institute on Alcohol Abuse and Alcoholism. United States Department of Health and Human Services. National Institutes of Health. National Institute on Deafness and Other Communication Disorders. United States Department of Health and Human Services. National Institutes of Health. National Institute on Drug Abuse. United States Department of Health and Human Services. National Institutes of Health. National Institute of General Medical Sciences. United States Department of Health and Human Services. National Institutes of Health. National Institute of Mental Health. United States Department of Health and Human Services. National Institutes of Health. National Institute of Nursing Research. United States Department of Health and Human Services. National Institutes of Health. Office of AIDS Research. United States Department of Health and Human Services. National Institutes of Health. Office of Behavioral and Social Sciences Research. United ...

States report information from two reporting populations: (1) The Served Population which is information on all youth receiving at least one independent living services paid or provided by the Chafee Program agency, and (2) Youth completing the NYTD Survey. States survey youth regarding six outcomes: financial self-sufficiency, experience with homelessness, educational attainment, positive connections with adults, high-risk behaviors, and access to health insurance. States collect outcomes information by conducting a survey of youth in foster care on or around their 17th birthday, also referred to as the baseline population. States will track these youth as they age and conduct a new outcome survey on or around the youth's 19th birthday; and again on or around the youth's 21st birthday, also referred to as the follow-up population. States will collect outcomes information on these older youth at ages 19 or 21 regardless of their foster care status or whether they are still receiving independent living services from the State. Depending on the size of the State's foster care youth population, some States may conduct a random sample of the baseline population of the 17-year-olds that participate in the outcomes survey so that they can follow a smaller group of youth as they age. All States will collect and report outcome information on a new baseline population cohort every three years. Units of Response: Current and former youth in foster care Type of Data: Administrative Tribal Data: No Periodicity: Annual Demographic Indicators: Ethnicity;Race;Sex SORN: Not Applicable Data Use Agreement: https://www.ndacan.acf.hhs.gov/datasets/request-dataset.cfm Data Use Agreement Location: https://www.ndacan.acf.hhs.gov/datasets/order_forms/termsofuseagreement.pdf Granularity: Individual Spatial: United States Geocoding: FIPS Code

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance publicly available dataset has 33 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors. This dataset requires a registration process and a data use agreement.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

Requesting Access to the COVID-19 Case Surveillance Restricted Access Detailed Data Please review the following documents to determine your interest in accessing the COVID-19 Case Surveillance Restricted Access Detailed Data file: 1) CDC COVID-19 Case Surveillance Restricted Access Detailed Data: Summary, Guidance, Limitations Information, and Restricted Access Data Use Agreement Information 2) Data Dictionary for the COVID-19 Case Surveillance Restricted Access Detailed Data The next step is to complete the Registration Information and Data Use Restrictions Agreement (RIDURA). Once complete, CDC will review your agreement. After access is granted, Ask SRRG (eocevent394@cdc.gov) will email you information about how to access the data through GitHub. If you have questions about obtaining access, email eocevent394@cdc.gov.

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

COVID-19 case surveillance data are collected by jurisdictions and are shared voluntarily with CDC. For more information, visit: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.

The deidentified data in the restricted access dataset include demographic characteristics, state and county of residence, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and comorbidities.

All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 case reports have been routinely submitted using standardized case reporting forms.

On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification. All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for lab-confirmed or probable cases.

On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.

Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question "Was the individual hospitalized?" where the possible answer choices include "Yes," "No," or "Unknown," the blank value is recoded to "Missing" because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race, ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<11 COVID-19 case records with a given values). Suppression includes low frequency combinations of case month, geographic characteristics (county and state of residence), and demographic characteristics (sex, age group, race, and ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These and other COVID-19 data are available from multiple public locations:

The 2012 National Survey of Early Care and Education (NSECE) is a set of four integrated, nationally representative surveys conducted in 2012. These were surveys of (1) households with children under 13, (2) home-based providers, (3) center-based providers, and (4) the center-based provider workforce. The 2012 NSECE documents the nation's current utilization and availability of early care and education (including school-age care), in order to deepen the understanding of the extent to which families' needs and preferences coordinate well with providers' offerings and constraints. The experiences of low-income families are of special interest as they are the focus of a significant component of early care and education and school-age child care (ECE/SACC) public policy. The 2012 NSECE calls for nationally-representative samples including interviews in all 50 states and Washington, DC. The study is funded by the Office of Planning, Research and Evaluation (OPRE) in the Administration for Children and Families (ACF), United States Department of Health and Human Services. The project team is led by the National Opinion Research Center (NORC) at the University of Chicago, in partnership with Chapin Hall at the University of Chicago and Child Trends. The Quick Tabulation and Public-Use Files are currently available via this site. Restricted-Use Files are also available at three different access levels; to determine which level of file access will best meet your needs, please see the NSECE Data Files Overview for more information. Level 1 Restricted-Use Files are available via the Child and Family Data Archive. To obtain the Level 1 files, researchers must agree to the terms and conditions of the Restricted Data Use Agreement and complete an application via ICPSR's online Data Access Request System. Level 2 and 3 Restricted-Use Files are available via the National Opinion Research Center (NORC). For more information, please see the access instructions for NSECE Levels 2/3 Restricted-Use Data. For additional information about this study, please see: NSECE project page on the OPRE website NSECE study page on NORC's website NSECE Research Methods Blog For more information, tutorials, and reports related to the National Survey of Early Care and Education, please visit the Child and Family Data Archive's Data Training Resources from the NSECE page.

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

The COVID-19 Trends and Impact Survey (CTIS) was conducted by the Delphi Group at Carnegie Mellon University (CMU) in the United States (US) and by the University of Maryland (UMD) Social Data Science Center (SoDa) globally, in partnership with Meta. CTIS was a daily repeated cross-sectional survey that ran continuously starting April 6, 2020 in the US and starting April 23, 2020 globally. Both surveys concluded data collection on June 25, 2022. CTIS collected data in 200+ countries and territories, including 114 where Meta provided survey weights. The sampling frame was Facebook users aged 18 years or older who have been active on the platform in the last month. Sampled Facebook users saw the invitation at the top of their Feed, but the surveys were collected by the universities using Qualtrics. Meta neither collected nor received survey responses. The sample was stratified by subnational regions. Respondents were sampled as frequently as every month and as infrequently as every six months, depending on the population density of the subnational region in which they lived. Due to the minimum sampling frequency, pooled analyses should not combine more than a month of data. There were 12 versions of the survey questionnaires. The Delphi US CTIS was translated into 8 languages. The UMD Global CTIS was translated into 66 languages. This collection is comprised of three categories of data: a. Individual-level microdata files, which will be available to eligible academic and nonprofit researchers with fully executed Data Use Agreements (DUAs). b. Daily aggregate estimates at the country and subnational region levels disseminated via public APIs at CMU and UMD. c. Weekly and monthly aggregate estimates broken out by respondent characteristics (e.g., age, gender, vaccination status) at the country and subnational administrative level-1 region-level disseminated via publicly available CSV-formatted contingency tables. This collection currently only contains the aggregate data, contingency tables and associated documentation. The microdata are forthcoming.

The primary objective of this study was to describe current market rate survey methods, practices, and policies in all 50 states, the District of Columbia, five territories, and the 28 Native American tribes that conduct their own market rate survey. A market rate survey is a tool to collect up-to-date information on what facilities, within given geographic areas, charge parents for various types of child care. A second objective was to identify the validity issues that emerge from this comparison of current market rate survey practices.

Variables are organized under six specific functions representing the market rate survey process. These were: (1) administration/organization of the market rate survey, (2) facility population and sample, (3) data collection, (4) data analysis, (5) dissemination of the results, and (6) rate setting policy.

Units of Response: Program

Type of Data: Survey

Tribal Data: Yes

Periodicity: One-time

Demographic Indicators: Geographic Areas;Military

SORN: Not Applicable

Data Use Agreement: Yes

Data Use Agreement Location: https://www.icpsr.umich.edu/rpxlogin

Granularity: Childcare Providers;Individual;State;Tribe

Spatial: United States

Geocoding: Unavailable

Note: BI means before intervention, and AI means after intervention.Basic characteristics of the sample.

Abstract copyright UK Data Service and data collection copyright owner.

The 2001 Census: Special Licence Household Sample of Anonymised Records (SL-HSAR) dataset comprises Sample of Anonymised Records (SARs) data that relate to 29 April 2001. They were created by the Office for National Statistics (ONS) as part of the 2001 Census of Population. All households were asked to complete a form giving information about the household and all individuals living in the household. Completion of the form was compulsory for the entire population. The Census schedule includes questions on housing and tenure, and demographic and socio-economic information for all household members.

The dataset comprises SARs data for 1% of households in England and Wales, including imputed values for households which were not enumerated during the Census. Individual data for households larger than 11 residents have been suppressed. To protect confidentiality, age data have been grouped into 2-year bands and there is no geographical breakdown available. A small amount of perturbation has been applied to the data to protect confidentiality. As with the Individual Licensed SAR (see under SNs 7210 and 7211), separate variables indicate whether or not imputation or perturbation has been applied to any given variable for each case in the sample. Documentation, training and user support for these data is undertaken by the SARs team at the Cathie Marsh Centre for Census and Survey Research (CCSR). A further release of data, which contains additional derived variables, will be made available at a later date.

The Secure Access version replaces the previous Special Licence version that was held under SN 5278, which is no longer available. Prospective users of the Secure Access data will need to fulfil additional requirements, including completion of face-to-face training and agreement to Secure Access' User Agreement and Breaches Penalties Policy, in order to obtain permission to use that version (see 'Access' section below).

Detailed SARs data:
A more detailed version of these data, containing geographical information at the level of Local Authority, is available as a Controlled Access Microdata Sample (CAMS). These can be accessed at all ONS sites. Applications to use these data should be made to ONS; further details can be found on their CAMS web page. The CAMS file includes data for Scotland and Northern Ireland as well as England and Wales.


Main Topics:

A full range of census topics are included in the dataset, for example household structure, ethnicity and religion, housing, transport, employment, education and health and caring.

This dataset contains templates of policies and MoU's on data sharing. You can download the Word-templates and adapt the documents to your national context.

Further to the original Enterprise Application request, the contract below has expired. Please provide the current status. Finance Capita CRM Trustmarque Solutions Ltd I'd like to apologise for the length of this request, and how tedious it may be to handle. That being said, please make an effort to provide all of this information. The information I'm requesting is regarding the software contracts that the organisation uses, for the following fields.Enterprise Resource Planning Software Solution (ERP): Primary Customer Relationship Management Solution (CRM): For example, Salesforce, Lagan CRM, Microsoft Dynamics; software of this nature. Primary Human Resources (HR) and Payroll Software Solution: For example, iTrent, ResourceLink, HealthRoster; software of this nature. The organisation’s primary corporate Finance Software Solution: For example, Agresso, Integra, Sapphire Systems; software of this nature. Name of Supplier: Can you please provide me with the software provider for each contract? The brand of the software: Can you please provide me with the actual name of the software. Please do not provide me with the supplier name again please provide me with the actual software name. Description of the contract: Can you please provide me with detailed information about this contract and please state if upgrade, maintenance and support is included. Please also list the software modules included in these contracts. Number of Users/Licenses: What is the total number of user/licenses for this contract? Annual Spend: What is the annual average spend for each contract? Contract Duration: What is the duration of the contract please include any available extensions within the contract. Contract Start Date: What is the start date of this contract? Please include month and year of the contract. DD-MM-YY or MM-YY. Contract Expiry: What is the expiry date of this contract? Please include month and year of the contract. DD-MM-YY or MM-YY.