According to a survey conducted at the EmTech Digital conference in March 2019, U.S. business leaders shared their opinions on trust issues with regard to AI data quality and privacy. Nearly half of respondents reported a lack of trust in the quality of AI data in their companies, showing that there is still a long way to go to get quality AI data.

Survey of advanced technology, applications related to artificial intelligence technologies, by North American Industry Classification System (NAICS) and enterprise size for Canada and certain provinces, in 2022.

As of 2024, the industry of communication, media, and technology was the one with the largest share of organizations with fully operationalized data governance mitigation measures. ** percent of the respondents in this industry reported to have fully operationalized at least ** percent of the listed measures to mitigate artificial intelligence (AI) data governance-related risks. The industry was also the one with the highest overall adoption of AI-related data governance measures by the surveyed organizations, having an average of **** adopted measures.

The dataset collection in question is a compilation of related data tables sourced from the website of Tilastokeskus (Statistics Finland) in Finland. The data present in the collection is organized in a tabular format comprising of rows and columns, each holding related data. The collection includes several tables, each of which represents different years, providing a temporal view of the data. The description provided by the data source, Tilastokeskuksen palvelurajapinta (Statistics Finland's service interface), suggests that the data is likely to be statistical in nature and could be related to regional statistics, given the nature of the source. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

2011–2023. The tobacco disparities dashboard data utilized the Behavioral Risk Factor Surveillance System (BRFSS) data to measure cigarette smoking disparities by age, disability, education, employment, income, mental health status, race and ethnicity, sex, and urban-rural status. The disparity value is the relative difference in the cigarette smoking prevalence among adults 18 and older in a focus group divided by the cigarette smoking prevalence among adults 18 and older in a reference group. A disparity value above 1 indicates that adults in the focus group smoke cigarettes at a higher rate, as reflected by the disparity value, compared with the rate among adults in the reference group who smoke cigarettes. A disparity value below 1 indicates that adults in the focus group smoke cigarettes at a lower rate, as reflected by the disparity value, compared with the rate among adults in the reference group who smoke cigarettes. A disparity value of 1 means there is no relative difference in the rate of adults who smoke cigarettes for the two groups compared.

2018-2023 statistics related to communication and media Data and Resources 2018-2023 الاحصائيات المتعلقة بالاتصال والاعلامXLS 2018-2023 الاحصائيات المتعلقة بالاتصال والاعلام Explore Preview Download

Description: These are research indicators of doctorate holders in Europe that were compiled from the criteria and factors of the Eurostat. This dataset consists of data in five categories (i.e. Career Development of Doctorate Holders; Labour Market - Job Vacancy Statistics; Skill-related Statistics; European and International Co-patenting in EPO Applications and Ownership of Inventors in EPO Applications). The Eurostat Research Indicators consist of (1) Doctorate holders who have studied, worked or carried out research in another EU country (%); (2) Doctorate holders by activity status (%); (3) Doctorate holders by sex and age group; (4) Employed doctorate holders working as researchers by length of stay with the same employer (%); (5) Employed doctorate holders working as researchers by job mobility and sectors of performance over the last 10 years (%); (6) Employed doctorate holders by length of stay with the same employer and sectors of performance (%); (7) Employed doctorate holders by occupation (ISCO_88, %); (8) Employed doctorate holders by occupation (ISCO_08, %); (9) Employed doctorate holders in non-managerial and non-professional occupations by fields of science (%); (10) Level of dissatisfaction of employed doctorate holders by reason and sex (%); (11) National doctorate holders having lived or stayed abroad in the past 10 years by previous region of stay (%); (12) National doctorate holders having lived or stayed abroad in the past 10 years by reason for returning into the country (%); (13) Non-EU doctorate holders in total doctorate holders (%); (14) Unemployment rate of doctorate holders by fields of science; (15) Employment in Foreign Affiliates of Domestic Enterprises; (16) Employment in Foreign Controlled Enterprises; (17) Employment rate of non-EU nationals, age group 20-64; (18) Intra-mural Business Enterprise R&D Expenditures in Foreign Controlled Enterprises; (19) Job vacancy rate by NACE Rev. 2 activity - annual data (from 2001 onwards); (20) Job vacancy statistics by NACE Rev. 2 activity, occupation and NUTS 2 regions - quarterly data; (21) Job vacancy statistics by NACE Rev. 2 activity - quarterly data (from 2001 onwards); (22) Value Added in Foreign Controlled Enterprises; (23) Graduates at doctoral level by sex and age groups - per 1000 of population aged 25-34; (24) Graduates at doctoral level, in science, math., computing, engineering, manufacturing, construction, by sex - per 1000 of population aged 25-34; (25) Level of the best-known foreign language (self-reported) by degree of urbanisation; (26) Level of the best-known foreign language (self-reported) by educational attainment level; (27) Level of the best-known foreign language (self-reported) by labour status; (28) Level of the best-known foreign language (self-reported) by occupation; (29) Number of foreign languages known (self-reported) by educational attainment level; (30) Number of foreign languages known (self-reported) by degree of urbanisation; (31) Number of foreign languages known (self-reported) by labour status; (32) Number of foreign languages known (self-reported) by occupation; (33) Population by educational attainment level, sex, age and country of birth (%); (34) Co-patenting at the EPO according to applicants’/inventors’ country of residence - % in the total of each EU Member State patents; (35) Co-patenting at the EPO: crossing inventors and applicants; (36) Co-patenting at the EPO according to applicants’/inventors’ country of residence - number; (37) EU co-patenting at the EPO according to applicants’/ inventors’ country of residence by international patent classification (IPC) sections - number; (38) EU co-patenting at the EPO according to applicants’/inventors’ country of residence by international patent classification (IPC) sections - % in the total of all EU patents; (39) Domestic ownership of foreign inventions in patent applications to the EPO by priority year; (40) Foreign ownership of domestic inventions in patent applications to the EPO by priority year; and (41) Patent applications to the EPO with foreign co-inventors, by priority year.

This statistic shows the top AI related data priorities in U.S.-based organizations in 2019. Integrating AI and analytics systems was the top AI related data priority for 2019, with 58 percent of respondents indicating that it was a top priority of their company.

This data is compiled by the Cook County Department of Public Health using data from the Illinois Department of Public Health Vital Statistics. It includes the annual number of live births, and birth related outcomes and characteristics. Further analysis is available by birth mother's age group, race/ethnicity, and place/district of residence for all births in suburban Cook County. Also included is data related to infant mortality. Table of Contents and other information can be found at http://opendocs.cookcountyil.gov/docs/Birth_Table_Of_Contents_Data_Portal_fyn8-c3rk.pdf. Note: * Counts suppressed for events between 1 and 4, - Rates not calculated for events less than 20

Census REST files provide a way for users to request TIGER\Line information from Census GIS servers files through Representational State Transfer (REST)technology. Clients issue requests to the server through structured URLs. The server responds with map images, text-based geographic information, or other resources that satisfy the request. The 2016 Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas REST File contains Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas: Metropolitan NECTAs, Micropolitan NECTAs, Combined Statistical Areas, Metropolitan Statistical Areas, Micropolitan Statistical Areas; January 1, 2015 vintage; Generalized. This Rest service contains data as of January 1,2016.

The dataset contains information extracted from the website “Codewars” about different programming exercises created with the intention of fostering or developing abilities and competences. Said exercises are called “katas”. Our dataset collects information from recently solved katas and their statistics, applying the "Approved" filter. The total number of katas in that category amounts to 6.932 (18th March, 2021). Information about the date of execution is not available beyond the previously mentioned filter, but the date of publication of the kata is known. Taking this factor into account, the dataset contains katas published between March 2013 and March 2021.

The dataset for the Financial Statistics of the Government is provided in machine-readable CSV format. Please refer to the original PDF document for textual descriptions including footnotes. If there is any inconsistency between the PDF and CSV versions about the data concerned, the original PDF version shall prevail.

The dataset collection in question is a comprehensive assembly of related data tables sourced from Statistics Finland (Tilastokeskus). This collection includes several tables that contain related data, structured in a format that utilizes columns and rows for organization. The data within these tables is derived from the Statistics Finland's service interface (WFS). This collection provides a wealth of statistical information, potentially spanning various years, as suggested by the inclusion of 2013 and 2015 in some of the table names. Given the source, this dataset collection is likely to contain a wealth of valuable statistical data pertinent to Finland. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

The Statewide Planning and Research Cooperative System (SPARCS) Inpatient De-identified File contains discharge level detail on patient characteristics, diagnoses, treatments, services and charges. This data file contains basic record level detail for the discharge. The de-identified data file does not contain data that is protected health information (PHI) under HIPAA. The health information is not individually identifiable; all data elements considered identifiable have been redacted. For example, the direct identifiers regarding a date have the day and month portion of the date removed. A downloadable file of this dataset is available at: https://health.data.ny.gov/Health/Hospital-Inpatient-Discharges-SPARCS-De-Identified/mpue-vn67. For more information, including changes to the data from previous years, please visit http://www.health.ny.gov/statistics/sparcs/access/. The "About" tab contains additional details concerning this dataset.

Big Data Analytics In Healthcare Market size is estimated at USD 37.22 Billion in 2024 and is projected to reach USD 74.82 Billion by 2032, growing at a CAGR of 9.12% from 2026 to 2032.

Big Data Analytics In Healthcare Market: Definition/ Overview

Big Data Analytics in Healthcare, often referred to as health analytics, is the process of collecting, analyzing, and interpreting large volumes of complex health-related data to derive meaningful insights that can enhance healthcare delivery and decision-making. This field encompasses various data types, including electronic health records (EHRs), genomic data, and real-time patient information, allowing healthcare providers to identify patterns, predict outcomes, and improve patient care.

GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebookv2.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydatav2.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydatav2.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 95

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

Graph and download economic data for Producer Price Index by Industry: Data Processing, Hosting and Related Services (PCU518210518210) from Dec 2000 to Jun 2025 about information technology, processed, services, PPI, industry, inflation, price index, indexes, price, and USA.

Annual update of drug and alcohol related hospital discharges.

Previously published as two seperate publications. Alcohol related statistics has undergone National Statistics accreditation and awarded the National Statistics badge, while Drug related statistics assessment is pending.

Source agency: ISD Scotland (part of NHS National Services Scotland)

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Drug and Alcohol related hospital statistics

1. The competitive relationship index indicates the number of direct and indirect units that need to be purchased from each department when one unit of a product in that department needs to be increased. This model assumes that the ratio of import value to the total domestic need for that department is fixed, and its effect is similar to the domestic association table. 2. Purpose of collection: To demonstrate the industrial structure and interdependence between industry sectors. 3. Data collection method: Mainly refer to the industrial and commercial census conducted every five years and various relevant statistical data.

Data on the climate-related financial policy index (CRFPI) - comprising the global climate-related financial policies adopted globally and the bindingness of the policy - are provided for 74 countries from 2000 to 2020. The data include the index values from four statistical models used to calculate the composite index as described in D’Orazio and Thole 2022. The four alternative statistical approaches were designed to experiment with alternative weighting assumptions and illustrate how sensitive the proposed index is to changes in the steps followed to construct it. The index data shed light on countries’ engagement in climate-related financial planning and highlight policy gaps in relevant policy sectors.