A. SUMMARY It is the policy of the San Francisco Department of Public Health to comply with patient/client/resident rights regarding Protected Health Information (PHI) as set forth in the Health Insurance Portability and Accountability Act of 1996 (HIPAA). These guidelines exists to provide guidance only as it relates to the public release of COVID-19 data through the tracker webpages, so that public reporting of de-identified information of residents’ health status, demographic and other characteristics, and geographical information reflect consistent reporting practices and meaningful differences in health outcomes, conditions that impact health, and delivery of services while safeguarding patient/client/resident rights regarding PHI.

COVID-19 related data will be released routinely in a variety of data products related to the tracker, including datasets through SF OpenData. Some data products may include data by county or smaller analysis unit such as ZIP code, neighborhood, or census tract.

Download the attached PDF for the policy.

These datasets are part of the California Health and Human Services Agency (CalHHS) Data De-Identification Guidelines (DDG) in the "State and County Population Projections" Appendix. The DDG assists CalHHS departments in evaluating data for public release while ensuring the privacy of individuals represented in the data. California population estimates serve as a foundation for the population-based scoring assessments outlined in the DDG.

Background It is not known whether there are differences in the quality and recommendations between evidence-based (EB) and consensus-based (CB) guidelines. We used breast cancer guidelines as a case study to assess for these differences.

   Methods
   Five different instruments to evaluate the quality of guidelines were identified by a literature search. We also searched MEDLINE and the Internet to locate 8 breast cancer guidelines. These guidelines were classified in three categories: evidence based, consensus based and consensus based with no explicit consideration of evidence (CB-EB). Each guideline was evaluated by three of the authors using each of the instruments. For each guideline we assessed the agreement among 14 decision points which were selected from the NCCN (National Cancer Comprehensive Network) guidelines algorithm. For each decision point we recorded the level of the quality of the information used to support it. A regression analysis was performed to assess if the percentage of high quality evidence used in the guidelines development was related to the overall quality of the guidelines.


   Results
   Three guidelines were classified as EB, three as CB-EB and two as CB. The EB guidelines scored better than CB, with the CB-EB scoring in the middle among all instruments for guidelines quality assessment. No major disagreement in recommendations was detected among the guidelines regardless of the method used for development, but the EB guidelines had a better agreement with the benchmark guideline for any decision point. When the source of evidence used to support decision were of high quality, we found a higher level of full agreement among the guidelines' recommendations. Up to 94% of variation in the quality score among guidelines could be explained by the quality of evidence used for guidelines development.


   Conclusion
   EB guidelines have a better quality than CB guidelines and CB-EB guidelines. Explicit use of high quality evidence can lead to a better agreement among recommendations. However, no major disagreement among guidelines was noted regardless of the method for their development.

This document provides instructions for editing and submitting unit process or product system models to the USDA LCA Commons life cycle inventory (LCI) database. The LCA Commons LCI database uses the openLCA life cycle modeling tool's database schema. Therefore, this document describes how to import and edit data in openLCA and name and classify flows such that they properly import into and operate in the database. This document also describes metadata or documentation requirements for posting models to the LCA Commons. This document is an evolving standard for LCA Commons data. As USDA-NAL continues to gain experience in managing a general purpose LCI database and global conventions continue to evolve, so too will the LCA Commons Submission Guidelines. Resources in this dataset:Resource Title: LCA Commons Submission Guidelines_12/09/2015. File Name: lcaCommonsSubmissionGuidelines_Final_2015-12-09.pdf

Background Practice guidelines have been developed in response to the observation that variations exist in clinical medicine that are not related to variations in the clinical presentation and severity of the disease. Despite their widespread use, however, practice guideline evaluation lacks a rigorous scientific methodology to support its development and application. Discussion Firstly, we review the major epidemiological foundations of practice guideline development. Secondly, we propose a chronic disease epidemiological model in which practice patterns are viewed as the exposure and outcomes of interest such as quality or cost are viewed as the disease. Sources of selection, information, confounding and temporal trend bias are identified and discussed. Summary The proposed methodological framework for outcomes research to evaluate practice guidelines reflects the selection, information and confounding biases inherent in its observational nature which must be accounted for in both the design and the analysis phases of any outcomes research study.

To promote the work related to information security and personal data protection, establish a secure and trustworthy teaching, research, and administrative environment, and establish the University's Information Security and Personal Data Protection Management Committee in accordance with the Personal Data Protection Act, the Computer-Processed Personal Data Protection Act, and the Ministry of Education's regulations for information security and personal data management in the education system.

Background Guidelines published in major medical journals are very influential in determining clinical practice. It would be essential to evaluate whether conflicts of interests are disclosed in these publications. We evaluated the reporting of conflicts of interest and the factors that may affect such disclosure in a sample of 191 guidelines on therapeutic and/or preventive measures published in 6 major clinical journals (Annals of Internal Medicine, BMJ, JAMA, Lancet, New England Journal of Medicine, Pediatrics) in 1979, 1984, 1989, 1994 and 1999.

   Results
   Only 7 guidelines (3.7%) mentioned conflicts of interest and all were published in 1999 (17.5% (7/40) of guidelines published in 1999 alone). Reporting of conflicts of interest differed significantly by journal (p=0.026), availability of disclosure policy by the journal (p=0.043), source of funding (p < 0.001) and number of authors (p=0.004). In the entire database of 191 guidelines, a mere 18 authors disclosed a total of 24 potential conflicts of interest and most pertained to minor issues.


   Conclusions
   Despite some recent improvement, reporting of conflicts of interest in clinical guidelines published in influential journals is largely neglected.

Persons achieving National Physical Activity Guidelines Details Download JSON-STAT Persons achieving National Physical Activity Guidelines Preview Download PX Persons achieving National Physical Activity Guidelines Details Download XLSX Persons achieving National Physical Activity Guidelines

The NNC published a Research Protocol and Media Ethics as a guideline for researchers and journalists who are interested in learning more about the Marshall Islands Nuclear Legacy.

Background Numerous agencies have developed clinical practice guidelines for the management of postmenopausal osteoporosis. The study objective was to conduct a systematic assessment of the quality of osteoporosis guidelines produced since 1998. Methods Guidelines were identified by searching MEDLINE (1998+), the world wide web, known guideline developer websites, bibliographies of retrieved guidelines, and through consultation with content experts. Each guideline was then assessed by three independent appraisers using the 'Appraisal Instrument for Clinical Guidelines' (version 1) by Cluzeau. Results We identified 26 unique guidelines from 1998–2001 and 21 met our inclusion criteria. Of the 21 guidelines reviewed, 8 were developed by medical societies, 6 by national groups, 6 by government agencies, and 1 by an international group. Twelve of the guidelines were published, 7 were organizational reports, and 2 were accessible only from the web. Half or more of the 20 items assessing the rigor of guideline development were met by 15% (median quality score 23%, range 5–80%, (95% CI 16.5, 34.7)), 81% met at least half of the 12 items assessing guideline content and context (median score 58%, range 17–83%, (95% CI 50.8, 65.5)), and none met half or more of the items assessing guideline application (median score 0%, range 0–47%, (95% CI -0.5 to 12.6)). Eight guidelines described the method used to assess the strength of evidence, and in 6 there was an explicit link between recommendations and the supporting evidence. Ten guidelines were judged not suitable for use in practice, 10 were acceptable with modification, and one was acceptable for use without modification. Conclusion The methodological quality of current osteoporosis guidelines is low, although their scores for clinical content were higher. Virtually no guidelines covered dissemination issues. Few guidelines were judged as acceptable for use in their current format.

The Indigenous Data Guidelines set out the minimum requirements for Indigenous-designed data approaches and standards, inclusive of Indigenous rights to data governance and decision-making within the planning and design of Indigenous data collection and sharing. The Indigenous Data Guidelines also highlight the inadequacy of personal and individual consent and data privacy protections. For Indigenous Peoples, collective consent and data privacy protections, supported via community-controlled data infrastructure, are essential to ethical Indigenous data practices. These Indigenous Data Guidelines apply across all sections of the RDA COVID 19 Guidelines and Recommendations.

A collection of 22 data set of 50+ requirements each, expressed as user stories.

The dataset has been created by gathering data from web sources and we are not aware of license agreements or intellectual property rights on the requirements / user stories. The curator took utmost diligence in minimizing the risks of copyright infringement by using non-recent data that is less likely to be critical, by sampling a subset of the original requirements collection, and by qualitatively analyzing the requirements. In case of copyright infringement, please contact the dataset curator (Fabiano Dalpiaz, f.dalpiaz@uu.nl) to discuss the possibility of removal of that dataset [see Zenodo's policies]

The data sets have been originally used to conduct experiments about ambiguity detection with the REVV-Light tool: https://github.com/RELabUU/revv-light

This collection has been originally published in Mendeley data: https://data.mendeley.com/datasets/7zbk8zsd8y/1

Overview of the datasets [data and links added in December 2024]

The following text provides a description of the datasets, including links to the systems and websites, when available. The datasets are organized by macro-category and then by identifier.

Public administration and transparency

g02-federalspending.txt (2018) originates from early data in the Federal Spending Transparency project, which pertain to the website that is used to share publicly the spending data for the U.S. government. The website was created because of the Digital Accountability and Transparency Act of 2014 (DATA Act). The specific dataset pertains a system called DAIMS or Data Broker, which stands for DATA Act Information Model Schema. The sample that was gathered refers to a sub-project related to allowing the government to act as a data broker, thereby providing data to third parties. The data for the Data Broker project is currently not available online, although the backend seems to be hosted in GitHub under a CC0 1.0 Universal license. Current and recent snapshots of federal spending related websites, including many more projects than the one described in the shared collection, can be found here.

g03-loudoun.txt (2018) is a set of extracted requirements from a document, by the Loudoun County Virginia, that describes the to-be user stories and use cases about a system for land management readiness assessment called Loudoun County LandMARC. The source document can be found here and it is part of the Electronic Land Management System and EPlan Review Project - RFP RFQ issued in March 2018. More information about the overall LandMARC system and services can be found here.

g04-recycling.txt(2017) concerns a web application where recycling and waste disposal facilities can be searched and located. The application operates through the visualization of a map that the user can interact with. The dataset has obtained from a GitHub website and it is at the basis of a students' project on web site design; the code is available (no license).

g05-openspending.txt (2018) is about the OpenSpending project (www), a project of the Open Knowledge foundation which aims at transparency about how local governments spend money. At the time of the collection, the data was retrieved from a Trello board that is currently unavailable. The sample focuses on publishing, importing and editing datasets, and how the data should be presented. Currently, OpenSpending is managed via a GitHub repository which contains multiple sub-projects with unknown license.

g11-nsf.txt (2018) refers to a collection of user stories referring to the NSF Site Redesign & Content Discovery project, which originates from a publicly accessible GitHub repository (GPL 2.0 license). In particular, the user stories refer to an early version of the NSF's website. The user stories can be found as closed Issues.

(Research) data and meta-data management

g08-frictionless.txt (2016) regards the Frictionless Data project, which offers an open source dataset for building data infrastructures, to be used by researchers, data scientists, and data engineers. Links to the many projects within the Frictionless Data project are on GitHub (with a mix of Unlicense and MIT license) and web. The specific set of user stories has been collected in 2016 by GitHub user @danfowler and are stored in a Trello board.

g14-datahub.txt (2013) concerns the open source project DataHub, which is currently developed via a GitHub repository (the code has Apache License 2.0). DataHub is a data discovery platform which has been developed over multiple years. The specific data set is an initial set of user stories, which we can date back to 2013 thanks to a comment therein.

g16-mis.txt (2015) is a collection of user stories that pertains a repository for researchers and archivists. The source of the dataset is a public Trello repository. Although the user stories do not have explicit links to projects, it can be inferred that the stories originate from some project related to the library of Duke University.

g17-cask.txt (2016) refers to the Cask Data Application Platform (CDAP). CDAP is an open source application platform (GitHub, under Apache License 2.0) that can be used to develop applications within the Apache Hadoop ecosystem, an open-source framework which can be used for distributed processing of large datasets. The user stories are extracted from a document that includes requirements regarding dataset management for Cask 4.0, which includes the scenarios, user stories and a design for the implementation of these user stories. The raw data is available in the following environment.

g18-neurohub.txt (2012) is concerned with the NeuroHub platform, a neuroscience data management, analysis and collaboration platform for researchers in neuroscience to collect, store, and share data with colleagues or with the research community. The user stories were collected at a time NeuroHub was still a research project sponsored by the UK Joint Information Systems Committee (JISC). For information about the research project from which the requirements were collected, see the following record.

g22-rdadmp.txt (2018) is a collection of user stories from the Research Data Alliance's working group on DMP Common Standards. Their GitHub repository contains a collection of user stories that were created by asking the community to suggest functionality that should part of a website that manages data management plans. Each user story is stored as an issue on the GitHub's page.

g23-archivesspace.txt (2012-2013) refers to ArchivesSpace: an open source, web application for managing archives information. The application is designed to support core functions in archives administration such as accessioning; description and arrangement of processed materials including analog, hybrid, and
born digital content; management of authorities and rights; and reference service. The application supports collection management through collection management records, tracking of events, and a growing number of administrative reports. ArchivesSpace is open source and its

This data package contains summaries of evidence-based clinical practice guidelines which are intended to optimize patient care that is informed by a systematic review of evidence and an assessment of the benefits and harms of alternative care options.

The specifications and guidelines in this Data Management Plan will improve data consistency and availability of information. It will ensure that all levels of government and the public have access to the most up-to-date information; reduce or eliminate overlapping data requests and redundant data maintenance; ensure metadata is consistently created; and ensure that data services can be displayed by the consumer with the output of its choice.

A set of guidelines to help us all at the GLA understand the basic principles of data visualisation, provide some examples of good practice, working processes and links to tools we can all use. See blog for more details https://data.london.gov.uk/blog/city-intelligence-data-design-guidelines/

Data collected from major Canadian and international research data repositories cover data storage, preservation, metadata, interchange, data file types, and other standard features used in the retention and sharing of research data. The outputs of this project primarily aim to assist in the establishment of recommended minimum requirements for a Canadian research data infrastructure. The committee also aims to further develop guidelines and criteria for the assessment and selection o f repositories for deposit of Canadian research data by researchers, data managers, librarians, archivists etc.

The Pennsylvania Commission on Sentencing is a legislative agency of the Commonwealth of Pennsylvania. The Commission develops sentencing guidelines for judges to use when sentencing felony and misdemeanor offenses. The judges report sentences to the Commission on a Guideline Sentence Form. This data collection reflects all felonies and misdemeanors reported to the Commission that were sentenced during calendar year 1996. The data are contained in two files. Part 1, Records Data, provides information on each offender, including rudimentary demographic characteristics and prior offense history. Part 2, Offense Data, contains information on each offense, including the statutory citation for the offense, the Offense Gravity Score assigned by the Commission, the offender's Prior Record Score, and the sentence given the offender.

This Program Instruction (PI) provides state and tribal title IV-E agencies (agencies) guidance and describes the documentation requirements an agency must complete to maintain compliance with CCWIS requirements while evaluating new child welfare business processes, tools, or information technologies through pilots.

Metadata-only record linking to the original dataset. Open original dataset below.

This synthetic dataset has been carefully crafted to simulate policy compliance scenarios in organizations. It contains features relevant to evaluating adherence to regulations, such as compliance status, risk indicators, and operational attributes. The data is intended for research, experimentation, and machine learning applications, particularly in the fields of classification, predictive analytics, and risk assessment.

The dataset is fully synthetic, ensuring privacy and data security while maintaining realistic patterns and relationships. It provides an excellent starting point for researchers and data scientists exploring policy compliance modeling and related challenges.

Features:

The dataset contains 4,000 rows and 12 columns. Below is a detailed description of each feature:

1. Employee_ID - Unique identifier for each employee.
2. Name - The name of the employee.
3. Working_Days - The number of days the employee worked in a given month.
4. Target_Sales - The sales target assigned to the employee for the month.
5. Actual_Sales - The actual sales achieved by the employee for the month.
6. Customer_Satisfaction_Score - A numerical score representing customer satisfaction, ranging from 1 to 5.
7. Policy_Compliance - Indicates whether the employee complied with company policies. Possible values: Yes, No.
8. Low_Working_Days - Boolean flag indicating if the employee worked fewer than the required number of days.
9. Target_Not_Met - Boolean flag indicating if the employee failed to meet their sales target.
10. Low_Customer_Satisfaction - Boolean flag indicating if the employee's customer satisfaction score was below a threshold.
11. Non_Compliance_Reason - A text field explaining the reason for non-compliance, if applicable.
12. Month - The month corresponding to the record

Purpose:

This dataset is designed for: * Training machine learning models to predict policy compliance. * Exploring relationships between operational attributes and compliance outcomes. * Generating insights for decision-making and policy optimization.

Licensing:

The dataset is released under the CC BY 4.0 license, allowing free use with proper attribution.

This data package contains the results of a large-scale analysis of author guidelines from several publishers and journals active in chemistry research, showing how well the publishing landscape supports different criteria.