The documents contained in this dataset reflect NASA's comprehensive IT policy in compliance with Federal Government laws and regulations.

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.

Sensitive Regulated Data: Permitted and Restricted UsesPurposeScope and AuthorityStandardViolation of the Standard - Misuse of InformationDefinitionsReferencesAppendix A: Personally Identifiable Information (PII)Appendix B: Security of Personally Owned Devices that Access or Maintain Sensitive Restricted DataAppendix C: Sensitive Security Information (SSI)

Despite the increase in the number of journals issuing data policies requiring authors to make data underlying reporting findings publicly available, authors do not always do so, and when they do, the data do not always meet standards of quality that allow others to verify or extend published results. This phenomenon suggests the need to consider the effectiveness of journal data policies to present and articulate transparency requirements, and how well they facilitate (or hinder) authors’ ability to produce and provide access to data, code, and associated materials that meet quality standards for computational reproducibility. This article describes the results of a research study that examined the ability of journal-based data policies to: 1) effectively communicate transparency requirements to authors, and 2) enable authors to successfully meet policy requirements. To do this, we conducted a mixed-methods study that examined individual data policies alongside editors’ and authors’ interpretation of policy requirements to answer the following research questions. Survey responses from authors and editors along with results from a content analysis of data policies found discrepancies among editors’ assertion of data policy requirements, authors’ understanding of policy requirements, and the requirements stated in the policy language as written. We offer explanations for these discrepancies and offer recommendations for improving authors’ understanding of policies and increasing the likelihood of policy compliance.

This data corresponds to the article "Do You Have an Institutional Data Policy? A Review of the Current Landscape of Library Data Services and Institutional Data Policies" and covers 206 US universities classified as Carnegie "High" and "Very High" research levels. The dataset includes the following information for each school: basic demographics, basic information on library data services, existence of university data policy by type, and general information on policy contents where policy exists. Please see the data dictionary and the article for a full description of the dataset and its use.

Following April 7, 2014 Executive Order from Mayor Walsh, an Open and Protected Data Policy was drafted to guide the City in defining, protecting, and ultimately making Open Data available and useful to the public. The policy provides working definitions for Open Data, along with information on how it is to be published, reviewed, and licensed.

The data consists in crawled privacy policies from European privacy policies. They were split into paragraphs and annotated as containing or not personal data.

The question that was asked to annotators was "Does this paragraph contain the explicit mention of specific personal data (e.g. name, phone number, social security, …) being collected?".

A full description of the dataset can be found in D3.4 of the SMOOTH project

Use this guide to find information on Tempe data policy and standards.Open Data PolicyEthical Artificial Intelligence (AI) PolicyEvaluation PolicyExpedited Data Sharing PolicyData Sharing Agreement (General)Data Sharing Agreement (GIS)Data Quality Standard and ChecklistDisaggregated Data StandardsData and Analytics Service Standard

As of December 2019, ** percent of adults in the United States do not want political campaigns to be able to micro-target them through digital ads. Respondents to a survey of U.S. adults reported that internet companies should make no information about its users available to political campaigns in order to target certain voters with online advertisements. Additionally, * percent of U.S. adults say that any information should be made available for a campaign's use.

Calls in favour of Open Data in research are becoming overwhelming. They are at national [@RCKUOpen] and international levels [@Moedas2015, @RSOpen, @ams2016]. I will set out a working definition of Open Data and will discuss the key challenges preventing the publication of Open Data becoming standard practice. I will attempt to draw some general solutions to those challenges from field specific examples.

By encouraging and requiring that authors share their data in order to publish articles, scholarly journals have become an important actor in the movement to improve the openness of data and the reproducibility of research. But how many social science journals encourage or mandate that authors share the data supporting their research findings? How does the share of journal data policies vary by discipline? What influences these journals’ decisions to adopt such policies and instructions? And what do those policies and instructions look like? We discuss the results of our analysis of the instructions and policies of 291 highly-ranked journals publishing social science research, where we studied the contents of journal data policies and instructions across 14 variables, such as when and how authors are asked to share their data, and what role journal ranking and age play in the existence and quality of data policies and instructions. We also attempt to compare our results to the results of other studies that have analyzed the policies of social science journals, although differences in the journals chosen and how each study defines what constitutes a data policy limit this comparison. We conclude that a little more than half of the journals in our study have data policies. A greater share of the economics journals have data policies and mandate sharing, followed by political science/international relations and psychology journals. Finally, we use our findings to make several recommendations: Policies should include the terms “data”, “dataset” or more specific terms that make it clear what to make available; policies should include the benefits of data sharing; journals, publishers, and associations need to collaborate more to clarify data policies; and policies should explicitly ask for qualitative data.

A new database of 6124 policies at the intersection of agriculture and the environment. Implemented between 1960 and 2022 in over 200 countries. Comprises a wide range of types of policies (e.g., ranging from legislative changes to payments for ecosystem services), as well as a wide range of goals (e.g., from pesticide regulations to forest conservation). It allows to e.g. count such policies per country, filter to select specific policies, and to create policy indices, e.g. weighting countries' policies with contextual factors that enhance to hinder policy performance (e.g. policy budgets, enforcement, stringency, corruption).

The main database comes in the formats CSV, EXCEL, and DTA, country averages are provided in CSV and DTA, the dataset for the soil erosion policy analysis is provided in DTA and the code for the analysis is a DO-file.

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.

Data supporting a paper titled "Do data management policies become more open over time?"The data includes three matrices created as part of a framework analysis. The Reasons and Adjectives data show a binary coding of whether a policy contains the term or idea. The Policies matrix contains values from 0 - 4 with 0 representing a very closed policy type and 4 representing a very open policy type. The details of these policies can be found in ...[update when preprint is posted]Associated code can be found at: 10.25418/crick.28788704

Survey period: 08 April - 08 May, 2014 Top 10 Impact Factor journals in each of 22 categories

Figures https://doi.org/10.6084/m9.figshare.6857273.v1

Article https://doi.org/10.20651/jslis.62.1_20 https://doi.org/10.15068/00158168

Fundamentally approved policies

This collection contains survey data collected at the end of October 2004 from the 49 state law enforcement agencies in the United States that had traffic patrol responsibility. Information was gathered about their policies for recording race and ethnicity data for persons in traffic stops, including the circumstances under which demographic data should be collected for traffic-related stops and whether such information should be stored in an electronically accessible format. The survey was not designed to obtain available agency databases containing traffic stop records.

In December 2016 the City issued an Executive Order and CAO Data Policy No. 135 (https://nola.gov/chief-administrative-office/policies/policies/no-135-data-policy/) which mandates an annual report of the progress of the implementation of the policy and progress towards its goals.

In October 2021, STM commissioned a research on funder data policies. The top 100 funders based on number of Crossref records were selected, and analyzed for the availability of data polices. These data policies were analyzed according to the elements of the journal data policy framework as developed by Hrynaszkiewicz et al. (https://datascience.codata.org/article/10.5334/dsj-2020-005/). This research will be used as input for more alignment between funder and data policies.

The QoG Institute is an independent research institute within the Department of Political Science at the University of Gothenburg. Overall 30 researchers conduct and promote research on the causes, consequences and nature of Good Governance and the Quality of Government - that is, trustworthy, reliable, impartial, uncorrupted and competent government institutions. The main objective of our research is to address the theoretical and empirical problem of how political institutions of high quality can be created and maintained. A second objective is to study the effects of Quality of Government on a number of policy areas, such as health, the environment, social policy, and poverty. The dataset was created as part of a research project titled “Quality of Government and the Conditions for Sustainable Social Policy”. The aim of the dataset is to promote cross-national comparative research on social policy output and its correlates, with a special focus on the connection between social policy and Quality of Government (QoG). The data comes in three versions: one cross-sectional dataset, and two cross-sectional time-series datasets for a selection of countries. The two combined datasets are called “long” (year 1946-2009) and “wide” (year 1970-2005). The data contains six types of variables, each provided under its own heading in the codebook: Social policy variables, Tax system variables, Social Conditions, Public opinion data, Political indicators, Quality of government variables. QoG Social Policy Dataset can be downloaded from the Data Archive of the QoG Institute at http://qog.pol.gu.se/data/datadownloads/data-archive Its variables are now included in QoG Standard. Purpose: The primary aim of QoG is to conduct and promote research on corruption. One aim of the QoG Institute is to make publicly available cross-national comparative data on QoG and its correlates. The aim of the QoG Social Policy Dataset is to promote cross-national comparative research on social policy output and its correlates, with a special focus on the connection between social policy and Quality of Government (QoG). The dataset combining cross-sectional data and time-series data for a selection of 40 countries. The dataset is specifically tailored for the analysis of public opinion data over time, instead uses country as its unit of observation, and one variable for every 5th year from 1970-2005 (or, one per module of each public opinion data source). Samanni, Marcus. Jan Teorell, Staffan Kumlin, Stefan Dahlberg, Bo Rothstein, Sören Holmberg & Richard Svensson. 2012. The QoG Social Policy Dataset, version 4Apr12. University of Gothenburg:The Quality of Government Institute. http://www.qog.pol.gu.se