Global Government Open Data Management Platform Market size was valued at USD 1.75 Billion in 2024 and is projected to reach USD 3.38 Billion by 2032, growing at a CAGR of 8.54% from 2026 to 2032.

Global Government Open Data Management Platform Market Drivers

Increasing Demand for Transparency and Accountability: There is a growing public demand for transparency in government operations, which drives the adoption of open data initiatives. According to a survey by the World Bank, 85% of respondents in various countries indicated that transparency in government decisions is crucial for reducing corruption, prompting governments to implement open data platforms.

Technological Advancements: Rapid advancements in information and communication technology (ICT) facilitate the development and deployment of open data management platforms. The International Telecommunication Union (ITU) reported that global Internet penetration reached approximately 64% in 2023, enabling more citizens to access open data and engage with government services online.

Government Initiatives and Policies: Many governments are actively promoting open data through policies and initiatives. For instance, the U.S. government's Open Data Initiative, launched in 2013, has led to the publication of over 300,000 datasets on Data.gov. Additionally, the European Union's Open Data Directive, which aims to make public sector data available, is further encouraging governments to embrace open data practices.

This dataset contains data collected during a study "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries" conducted by Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

It is being made public both to act as supplementary data for "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries", Government Information Quarterly*, and in order for other researchers to use these data in their own work.

Methodology

The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems.

This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.

Description of the data in this data set

The file "OpenDataIndex_2013_2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:

1) Global Open Data Index (GODI) (4 editions)

2) Open Data Maturity Report (ODMR) (8 editions)

3) Open Data Inventory (ODIN) (6 editions)

4) Open Data Barometer (ODB) (5 editions)

5) Open, Useful and Re-usable data (OURdata) Index (3 editions)

6) Open Government Development Index (OGDI) (2 editions)

These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.

Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.

Format of the file.xls, .csv (for the first spreadsheet only)

Licenses or restrictionsCC-BY

For more info, see README.txt

Open government data (OGD) paradigm gained momentum in the recent years resulting in numerous OGD initiatives. These initiatives assured reliable and faster development of open data ecosystem on different administration levels. Diversity of government organizations dealing with different kinds of data, however, resulted in a variety of OGD initiatives. As a direct result, OGD portals developed through these initiatives show different functionalities, characteristics, and quality of provided data and services. This paper therefore aims to provide a better insight into the similarities and differences of data portals by analyzing their characteristics from thematical, semantical, functional, and technological perspective. The methodology used relies on the framework previously developed and implemented in Greece in 2015, consisting of multiple indicators assessing different characteristics of portals, in each of four perspectives. Results of the assessment show Croatian portals have well developed functionalities but also have limitations preventing data reuse. These limitations are mostly related to data discovery and absence of metadata and licenses by the publishing institutions.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

This dataset provides survey responses from 240 people surveyed as part of the "Investigating the Impact of Kenya’s Open Data Initiative on Marginalized Communities: Case Study of Urban Slums and Rural Settlements" project.

The data, collected in mid-2013 looks at issues of where citizens look for data, and how successful they have been in getting government information from different sources, as well as their awareness of the Kenya open data portal, and their interest in getting information through different digital channels in future.

Descriptive statistics have been analysed in the publication "Open Government Data for Effective Public Participation: Findings of a Case Study Research Investigating The Kenya's Open Data Initiative in Urban Slums and Rural Settlements", but no further analysis has yet been carried out.

Data descriptions

The Codebook.csv file lists variable names and the questions asked to elicit each response.

JHC-Data.csv contains the results from the questionnaires collected through structured in-person interview in the three locations. The questionnaires were administered at chiefs centres, community resource centres, constituency development fund office and religious centres). The questionnaires were filled in by every 2nd these centres.

More information

More information on the research project can be found at http://opendataresearch.org/project/2013/jhc

The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.

The Michigan Public Policy Survey (MPPS) is a program of state-wide surveys of local government leaders in Michigan. The MPPS is designed to fill an important information gap in the policymaking process. While there are ongoing surveys of the business community and of the citizens of Michigan, before the MPPS there were no ongoing surveys of local government officials that were representative of all general purpose local governments in the state. Therefore, while we knew the policy priorities and views of the state's businesses and citizens, we knew very little about the views of the local officials who are so important to the economies and community life throughout Michigan. The MPPS was launched in 2009 by the Center for Local, State, and Urban Policy (CLOSUP) at the University of Michigan and is conducted in partnership with the Michigan Association of Counties, Michigan Municipal League, and Michigan Townships Association. The associations provide CLOSUP with contact information for the survey's respondents, and consult on survey topics. CLOSUP makes all decisions on survey design, data analysis, and reporting, and receives no funding support from the associations. The surveys investigate local officials' opinions and perspectives on a variety of important public policy issues and solicit factual information about their localities relevant to policymaking. Over time, the program has covered issues such as fiscal, budgetary and operational policy, fiscal health, public sector compensation, workforce development, local-state governmental relations, intergovernmental collaboration, economic development strategies and initiatives such as placemaking and economic gardening, the role of local government in environmental sustainability, energy topics such as hydraulic fracturing ("fracking") and wind power, trust in government, views on state policymaker performance, opinions on the impacts of the Federal Stimulus Program (ARRA), and more. The program will investigate many other issues relevant to local and state policy in the future. A searchable database of every question the MPPS has asked is available on CLOSUP's website. Results of MPPS surveys are currently available as reports, and via online data tables. Out of a commitment to promoting public knowledge of Michigan local governance, the Center for Local, State, and Urban Policy is releasing public use datasets. In order to protect respondent confidentiality, CLOSUP has divided the data collected in each wave of the survey into separate datasets focused on different topics that were covered in the survey. Each dataset contains only variables relevant to that subject, and the datasets cannot be linked together. Variables have also been omitted or recoded to further protect respondent confidentiality. For researchers looking for a more extensive release of the MPPS data, restricted datasets are available through openICPSR's Virtual Data Enclave. Please note: additional waves of MPPS public use datasets are being prepared, and will be available as part of this project as soon as they are completed. For information on accessing MPPS public use and restricted datasets, please visit the MPPS data access page: http://closup.umich.edu/mpps-download-datasets

An understanding of the similar and divergent metrics and methodologies underlying open government data benchmarks can reduce the risks of the potential misinterpretation and misuse of benchmarking outcomes by policymakers, politicians, and researchers. Hence, this study aims to compare the metrics and methodologies used to measure, benchmark, and rank governments' progress in open government data initiatives. Using a critical meta-analysis approach, we compare nine benchmarks with reference to meta-data, meta-methods, and meta-theories. This study finds that both existing open government data benchmarks and academic open data progress models use a great variety of metrics and methodologies, although open data impact is not usually measured. While several benchmarks’ methods have changed over time, and variables measured have been adjusted, we did not identify a similar pattern for academic open data progress models. This study contributes to open data research in three ways: 1) it reveals the strengths and weaknesses of existing open government data benchmarks and academic open data progress models; 2) it reveals that the selected open data benchmarks employ relatively similar measures as the theoretical open data progress models; and 3) it provides an updated overview of the different approaches used to measure open government data initiatives’ progress. Finally, this study offers two practical contributions: 1) it provides the basis for combining the strengths of benchmarks to create more comprehensive approaches for measuring governments’ progress in open data initiatives; and 2) it explains why particular countries are ranked in a certain way. This information is essential for governments and researchers to identify and propose effective measures to improve their open data initiatives.

subject to appropriate attribution.

Data, lately, has received a lot of attention from various circles such as government officials, the community, businesses, law enforcement, and also civil society. The reason is actually very simple, because credible data is the key to the quality of development and good governance. Public policy, public services, law enforcement, government performance monitoring, and business opportunities all require credible data. Unfortunately, in practice, data is still often not managed seriously. There are still many cases where there are data that not only have various versions, but often also contradict each other. The One Data Initiative, or commonly called One Data Indonesia, is one of the Indonesian government's initiatives that tries to fix problems in the implementation and management of government data. The development of this initiative is also overseen by the Open Government Indonesia Action Plan. Along with welcoming International Open Data Day which falls on March 4, 2017, As an initiative that is being promoted by the central government regarding data governance reform within the Indonesian government, One Data Indonesia is an initiative that is expected to help the integration of planning, implementation, monitoring, evaluation, and control of development between the central government and the regions, and at the next level is the disclosure of government data that can be used by the community. In addition, the implementation of One Data is also expected to accelerate the implementation of the Electronic-Based Government System (SPBE/E-government) which is being prepared, both in terms of regulations and operational stages, by a number of related agencies involving the Presidential Staff Office, the Ministry of National Development Planning/Bappenas, the Ministry of PAN & RB, the Ministry of Communication and Informatics, and the State Administration Institution. In principle, Satu Data Indonesia strives to encourage better government data governance. Good data governance is highly dependent on quality and consistency in data management. For this reason, Satu Data Indonesia defines good data governance into three main principles. The three main principles that will be encouraged through the One Data Indonesia policy are: (i) a single standard data standard, (ii) one standard metadata, and (iii) data interoperability. With the application of these principles, it is hoped that the One Data policy will be able to realize an accountable, accurate, integrated, up-to-date, and open data management system. These three principles will be implemented through the Presidential Regulation on One Data that we are currently compiling. Currently, the initiation of One Data has reached the finalization stage of the preparation of the Presidential Regulation on One Data Indonesia. If it has been passed, the Presidential Regulation on One Data Indonesia is expected to stimulate efforts to improve government data governance in Indonesia. This means that the use of data will be more structured and will improve the quality of policies and public services in Indonesia as well. In addition, to ensure the smooth implementation later, pilot activities are also being carried out in seven ministries, institutions and also seven local governments. At the central government level, the One Data Initiative is currently being pilotedn in several Ministries, including the Ministry of National Development Planning/Bappenas and the Ministry of Maritime Affairs and Fisheries. At the local government level, the implementation of One Data Indonesia has been piloted in several pilot areas, including DKI Jakarta Province, Demak Regency, Bojonegoro Regency, Semarang City, Banda Aceh City, Mojokerto City, and Pontianak City. There are actually several priorities that can be resolved in order to facilitate the implementation of One Data Indonesia. First, harmonization between the role of Presidential Regulations (Perpres) and Regional Regulations (Perda). Second, finding a clear business process for the implementation of One Data Indonesia both in the Ministries/Institutions of a region. Third, the integration of other Ministries/Institutions data portals in one portal, to facilitate access and use of data by the public. There are several things that are still challenges for the implementation of One Data in Indonesia. One of them is the process of ratifying the Presidential Regulation on One Data Indonesia which requires coordination with various stakeholders which takes a long time. In addition, there are several policies related to statistics, which need to be adjusted to the context of data governance reform within the current government. One of them is Law No. 16 of 1997 concerning Statistics which still defines data only in the form of numbers, so that other forms of data such as spatial data are not included in it. In addition, the reluctance of many parties to integrate cross-sectoral data management within the government is also an obstacle. Where there are various types of data in each sector, but without any integration with other sectors. Finally, one of the things that hinders the implementation of this initiative according to the One Data Indonesia team is the existence of non-tax state revenue (PNPB) collected from data requests. This severely limits access to open data that should be easily and free of charge accessible to the public.

Respondents multi-selected between 6 choices to identify the main motivation for starting a data transparency initiative. Results can be grouped by country, type of government org and role of respondent. Choices included "no initiative" to account for those who did not have one.

The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

Respondents who said, "yes, we have an open government mandate" were then asked if it was funded. Responses are tabulated by type of government showing the % of respondents in each group who selected, Yes, No, or Unsure.

Population Health (BRFSS: HRQOL)

Examining Trends, Disparities and Determinants of Health in the US Population

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) offers an expansive collection of data on the health-related quality of life (HRQOL) from 1993 to 2010. Over this time period, the Health-Related Quality of Life dataset consists of a comprehensive survey reflecting the health and well-being of non-institutionalized US adults aged 18 years or older. The data collected can help track and identify unmet population health needs, recognize trends, identify disparities in healthcare, determine determinants of public health, inform decision making and policy development, as well as evaluate programs within public healthcare services.

The HRQOL surveillance system has developed a compact set of HRQOL measures such as a summary measure indicating unhealthy days which have been validated for population health surveillance purposes and have been widely implemented in practice since 1993. Within this study's dataset you will be able to access information such as year recorded, location abbreviations & descriptions, category & topic overviews, questions asked in surveys and much more detailed information including types & units regarding data values retrieved from respondents along with their sample sizes & geographical locations involved!

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How to use the dataset

This dataset tracks the Health-Related Quality of Life (HRQOL) from 1993 to 2010 using data from the Behavioral Risk Factor Surveillance System (BRFSS). This dataset includes information on the year, location abbreviation, location description, type and unit of data value, sample size, category and topic of survey questions.

Using this dataset on BRFSS: HRQOL data between 1993-2010 will allow for a variety of analyses related to population health needs. The compact set of HRQOL measures can be used to identify trends in population health needs as well as determine disparities among various locations. Additionally, responses to survey questions can be used to inform decision making and program and policy development in public health initiatives.

Research Ideas

• Analyzing trends in HRQOL over the years by location to identify disparities in health outcomes between different populations and develop targeted policy interventions.
• Developing new models for predicting HRQOL indicators at a regional level, and using this information to inform medical practice and public health implementation efforts.
• Using the data to understand differences between states in terms of their HRQOL scores and establish best practices for healthcare provision based on that understanding, including areas such as access to care, preventative care services availability, etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------| | Year | Year of survey. (Integer) | | LocationAbbr | Abbreviation of location. (String) | | LocationDesc | Description of location. (String) | | Category | Category of survey. (String) | | Topic | Topic of survey. (String) | | Question | Question asked in survey. (String) | | DataSource | Source of data. (String) | | Data_Value_Unit | Unit of data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Footnote symbol for data value. (String) | | Data_Value_Std_Err | Standard error of the data value. (Float) | | Sample_Size | Sample size used in sample. (Integer) | | Break_Out | Break out categories used. (String) | | Break_Out_Category | Type break out assessed. (String) | | **GeoLocation*...

Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive presence-only datasets. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using presence-absence and presence-only data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. Models’ predictions were associated to spatial estimates of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species-specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies. Methods Presence absence (PA) data PA data for each species were obtained from Coillte based on surveys performed in a fraction of the 6,000 properties they manage (Table 1) by asking property managers (who visit the forests they manage on a regular basis) whether deer were present and, if so, what species. Properties range in size from less than one to around 2,900 ha, and to assign the PA value to a specific location, we calculated the centroid of each property using the function st_centroid() from the package sf in R (Pebesma 2018). The survey was mainly performed in 2010 and 2013, in addition to further data collected between 2014 and 2016. Some properties were surveyed only once in the period 2010–2016, but for those that were surveyed more than once, the value for that location was considered “absence” if deer had never been detected in the property in any of the surveys, and “presence” in all other cases. In addition to these surveys, Coillte commissioned density surveys based on faecal pellet sampling in a subset of their properties between the years 2007 and 2020. Any non-zero densities in these data were considered “presences”, and all zeros were considered “absences”. These data were also summarised across years when a property had been repeatedly sampled and counted as presence if deer had been detected in any of the sampling years. PA data for NI were obtained from a survey carried out by the British Deer Society in 2016. The survey divided the British territory into 100 km2 grid cells, and deer presence or absence was assessed based on public contributions, which were then reviewed and collated by BDS experts. Since 100 km2 grid cells are quite large, we did not, as with the Coillte properties, calculate the centroid of each cell and assign the PA value of the cell to it. Instead, we randomly simulated positions within each cell and assigned the presence or absence value of the cell to each of them. We performed a sensitivity analysis to calculate an optimal number of positions that would capture the environmental variability within each cell, which was set to 5 random positions per grid cell. After processing, we obtained a total of 920 PA data across NI. 2.2.2 Presence-only (PO) data PO data were collected from various sources, mainly (but not only) from citizen science initiatives. The National Biodiversity Data Centre (NBDC) is an Irish initiative that collates biodiversity data coming from different sources, from published studies to citizen contributions. From this repository, we obtained all contributions on the three species, a total of 1,430 records. To this, we added the 164 records of deer in Ireland downloaded from the iNaturalist site, another citizen-contributed database that collects the same type of data. From the resulting dataset, we (1) removed all observations with a spatial resolution lower than 1 km2; (2) did a visual inspection of the data and comments and removed all observations that were obviously incorrect (i.e. at sea or that the comment specified it was a different species); (3) filtered out all the fallow deer reported in Dublin’s enclosed city park (Phoenix Park) since the population there was introduced and is artificially maintained and disconnected from the rest of populations in Ireland; and (4) filtered duplicate observations by retaining only one observation per user, location, and day. The Centre for Environmental Data and Recording (CEDaR) is a data repository for Northern Ireland (NI) that operates in the same way as the NBDC. They provided 872 records of deer in NI, coming from different survey, scientific, and citizen science initiatives, from which we removed all records provided with a spatial resolution lower than 1 km2. The location and species of 469 deer culled between 2019 and 2021 in NI were obtained from the British Agri-Food and Biosciences Institute. For the observations that did not have specific coordinates, we derived them from the location name or postcode if provided. As part of a nationally funded initiative to improve deer monitoring in Ireland (SMARTDEER), we developed a bespoke online tool to facilitate the reporting of deer observations by the general public and all relevant stakeholders e.g. hunters, farmers, or foresters. Observations were reported in 2021 and 2022 by clicking on a map to indicate a 1 km2 area where deer have been observed. For each user and session, we calculated the area of the surface covered in squares, simulated a number of positions proportional to the size of the polygon, and distributed them within it to generate a number of exact positions equivalent to the area where the user had indicated an observation. In total, the SMARTDEER tool allowed us to collect 4,078 presences across Ireland and NI. 2.3.2 Covariate selection Raster environmental covariates used in the models were obtained from the Copernicus Land Monitoring Service (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency EEA), whereas the vector layers (roads, paths) were obtained from the Open Street Map service (OpenStreetMap contributors, 2017. Planet dump [Data file from January 2022]. https://planet.openstreetmap.org). Vector layers were transformed into distance layers (distance to roads, distance to paths) using the distance() function from the package raster, and into density layers (density of roads, paths) using the rasterize() function of the same package (Hijmans 2021). All raster layers were resampled to the lowest resolution available in the used covariates, resulting in a 1 km2 resolution. A full description of the process of covariate selection (including screening for collinearity) can be found in the supplementary material. The covariates eventually used in the model were elevation (m), slope (degrees), tree cover (%), small woody feature density (%), distances to forest edge (m, positive distances indicate a location outside a forest, negative distances indicate a location within a forest), and human footprint index (Venter et al. 2016, 2018). All covariates were scaled by subtracting the mean and dividing by the standard deviation before entering the model (function scale() from the raster package).

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This light-duty vehicle inventory dataset provides information on vehicle registrations by vehicle type (car vs. truck), fuel type, and model year showing the changes in adoption trends over time and average fuel economies.

This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

This dataset is a fusion of three data types (operations and maintenance tickets, weather data, and production data) that was used to support machine learning analysis and evaluation of drivers for low performance at photovoltaic (PV) sites during compound, extreme weather events. After being processed with machine learning, the data was used in the "Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States" manuscript. Additional details are captured in the associated manuscript.

The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data.

This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

The platform, Open Budgets India (OBI), has resulted from collective efforts by many organisations and individuals, led by Centre for Budget and Governance Accountability (CBGA). CBGA is an independent, not-for-profit policy research organisation working towards enhancing transparency and accountability and fostering people's participation in governance by demystifying government budgets. Increasingly, people across the country are keen to understand and participate meaningfully in discussions on government budgets. However, the gaps in availability of relevant and accessible information on budgets in India at different levels have created a hindrance in this regard. Accessibility to comprehensive, relevant and easy to use data on budgets becomes a challenge as we move from the level of the Union Government to the States and then further below to the local levels. The improvements over the last decade with regard to the availability and quality of fiscal data in the country have been uneven - across States and across different types of schemes. In this context, our endeavour is to strengthen the discourse and demand for availability of all budget information in the public domain in a timely and accessible manner, at all levels of government in the country. As part of the efforts in this sphere, we have developed the portal - Open Budgets India (OBI), which is meant to be a comprehensive and user-friendly open data portal that can facilitate free, easy and timely access to relevant data on government budgets in India. OBI is an on-going initiative, and its evolution so far can be divided broadly into two phases. In the first phase, which was over the years 2015 to 2019, OBI was conceptualised and developed through the collective efforts of many individuals and organisations led by the research team at CBGA. In this first phase of development of OBI, the support and technical inputs provided by organisations like Macromoney Research Initiatives Private Limited, DataKind Bangalore, Centre for Internet and Society, and DataMeet played an important role. The second phase of development of the OBI, which started in early 2020, has been steered by Public Finance researchers at CBGA and multidisciplinary team at CivicDataLab (CDL). CDL has been the lead technology partner of the OBI in this phase, with CBGA leading the research and development work relating to fiscal information and data. This second phase of OBI has focused on vertically deepening our efforts towards making relevant fiscal information available in public domain, not only at the level of the Union Government and the States, but also for districts and below. The central idea behind the second phase has been to present a range of analytics, in addition to the raw fiscal data and budget documents, which could be relevant for facilitating public engagement with fiscal governance issues. The analytics have been presented in the form of a number of new dashboards on OBI and are centred around data on flagship Central Schemes in the country as well as select State Schemes for a number of States. Additionally, we are also making concerted efforts to enable the non-technical users to comprehend the technicalities around government budgets in the country and the fiscal data and analytics. The new dashboards and resources integrated on OBI in this second phase include: Schemes Dashboard containing the fiscal data and relevant analytics for 30 Central Schemes and nearly 75 State Schemes from 20 States; Sectors Dashboard comprising fiscal data on more than 10 social and economic sectors for all States; District Dashboard comprising district-wise fiscal data for all districts in six selected States for 12 Central Schemes; State Budget Explorers for three States that present the State Budget data in machine-readable formats; Budget Basics microsite which provides easy to comprehend explanations of fundamental concepts, terminologies and processes relating to government budgets; Short Videos meant to facilitate better understanding of some of the important developments and strategies in budgeting in India; and A discussion forum on budgets named the Budget Forum, which is meant to be a hub for discussions on public finance related topics and serve as a platform for sharing of relevant resources on budgets by different users. Access to Constituency-wise data can enable the elected representatives to engage a lot more effectively with the processes of policy design, expenditure priority setting and monitoring of implementation. It can also strengthen public oversight and participation in governance. This is particularly relevant for strengthening public financial management in the socio-economic sectors, where there is a need for improving allocative efficiency, utilisation of public resources, quality of services delivered and the development outcomes. Against such a backdrop, CBGA, in collaboration with its technology partner CivicDataLab, has also carried out an in-depth analytical exercise to map fiscal data for a number of development schemes to the Assembly Constituencies (ACs) and Parliamentary Constituencies (PCs) in six selected States. The overarching objective of this initiative is to explore how fiscal information available to the citizens and their elected representatives can be made more relevant locally in order to strengthen their oversight and participation in public financial management. Guided by such a vision, the initiative has: Mapped the administrative boundaries (Gram Panchayats and Urban Local Bodies) to the boundaries of the ACs and PCs (covering all the ACs and PCs) in six States, viz, Bihar, Chhattisgarh, Jharkhand, Maharashtra, Odisha and Uttar Pradesh; Collected, organised and mapped disaggregated fiscal information on major development schemes into ACs and PCs in the selected States (covering MGNREGS, SBM-G, SBM-U, PMAY-G, NSAP, PMFBY, PM-KISAN, SmSA, MDM, NHM, ICDS and PMMVY) for three to four financial years; and Developed analytics and visualizations with the AC-wise and PC-wise fiscal data to facilitate its uptake by different actors in the governance landscape. The Constituency-wise mapping of fiscal information for selected schemes for the selected States has been presented on a dashboard -- the Constituency Dashboard -- on the OBI portal. This 'open data' dashboard provides: The methodology developed for mapping the administrative units of a State into Constituencies; Complete and up-to-date Geo-files for the six selected States (i.e. the files that have mapped the administrative units into ACs and PCs); The methodologies for mapping fiscal data on 12 schemes into ACs and PCs; and The Constituency-wise mapped fiscal data for 12 schemes for the six selected States for three to four financial years (2018-19 to 2021-22). This initiative, over the two phases of development, has received financial support and guidance of a number of institutions, which include: Bill and Melinda Gates Foundation (BMGF) Omidyar Network (ON) International Development Research Centre - Think Tank Initiative (IDRC-TTI) National Foundation for India (NFI) Last updated on 25th February 2023.