Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

Objectives: Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting: Data taken from employees at 3 different industrial sites in Australia. Participants: 7915 observations were included. Materials and methods: The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results: CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the numb...

This document, Innovating the Data Ecosystem: An Update of The Federal Big Data Research and Development Strategic Plan, updates the 2016 Federal Big Data Research and Development Strategic Plan. This plan updates the vision and strategies on the research and development needs for big data laid out in the 2016 Strategic Plan through the six strategies areas (enhance the reusability and integrity of data; enable innovative, user-driven data science; develop and enhance the robustness of the federated ecosystem; prioritize privacy, ethics, and security; develop necessary expertise and diverse talent; and enhance U.S. leadership in the international context) to enhance data value and reusability and responsiveness to federal policies on data sharing and management.

This is a hands-on workshop on the management of qualitative social science data, with a focus on data sharing and transparency. While the workshop addresses data management throughout the lifecycle – from data management plan to data sharing – its focus is on the particular challenges in sharing qualitative data and in making qualitative research transparent. One set of challenges concerns the ethical and legal concerns in sharing qualitative data. We will consider obtaining permissions for sharing qualitative data from human participants, strategies for (and limits of) de-identifying qualitative data, and options for restricting access to sensitive qualitative data. We will also briefly look at copyright and licensing and how they can inhibit the public sharing of qualitative data.

A second set of challenges concerns the lack of standardized guidelines for making qualitative research processes transparent. Following on some of the themes touched on in the talk, we will jointly explore some cutting edge approaches for making qualitative research transparent and discuss their potentials as well as shortcomings for different forms of research.

About this dataset

Context

The dataset tabulates the population of Parks by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Parks across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.97% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Parks is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Parks total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Parks Population by Gender. You can refer the same here

The statistical data for the applications and approvals of special research projects funded by the National Science and Technology Commission, including the number and amount of applications and approvals in the past 10 years by institution.

Research, Evaluation and Statistics account, Iteration 1, Fiscal year 2022

The gross domestic expenditure on R&D (GERD) in Finland saw no significant changes in 2022 in comparison to the previous year 2021 and remained at around 2.96 percent of GDP. In comparison to 2021, the gross domestic expenditure decreased not significantly by 0.03 percent of GDP (-1 percent). Find more key insights for the gross domestic expenditure on R&D (GERD) in countries like Denmark and Norway.

This table contains 9450 series, with data for years 2014 - 2015 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) North American Industry Classification System (NAICS) (75 items: Total all industries; Agriculture, forestry, fishing and hunting; Agriculture (except aquaculture) and support activities for crop production and animal production; Forestry, logging and support activities for forestry; ...) Country of control (3 items: Total country of control; Canada; Foreign) Field of research and development (42 items: Total in-house research and development expenditures in Canada by field of research and development; Natural sciences and engineering; Natural and formal sciences, computer sciences, and information technology and bioinformatics; Mathematics; ...).

Statistics on research output

The main goals of the National Post-acute and Long-term Care Study (NPALS) are to: (1) Estimate the supply of paid, regulated long-term care services providers; (2) Estimate key policy-relevant characteristics and practices of these providers; (3) Estimate the number of long-term care services users; (4) Estimate key policy-relevant characteristics of long-term care services users; (5) Produce national and state estimates where feasible within confidentiality and reliability standards; (6) Compare across provider sectors; and (7) Monitor trends over time.

NPALS used a two-stage probability-based sample design. In the first stage, a stratified random sample of providers were selected among adult day service centers (ADSCs); in the second stage, current services users (participants in ADSCs) were randomly selected.

The provider questionnaire included survey items on provider characteristics such as ownership, size, services offered, selected practices, and staffing; questions about aggregate user characteristics (age and race) were included. The services user datasets include user demographics, health conditions, limitations with activities of daily living, number of prescription medications, adverse events, and services used. This is the services user or participant level data file.

Introduction: During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. Objective: To study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Methodology: Qualitative and descriptive study applying nine attributes: (1) availability, (2) accessibility, (3) format, (4) licensing, (5) linkage, (6) funding, (7) editorial policy, (8) content and (9) statistics. Results: We analyzed 1340 papers, 1173 (87.5%) did not have research data. 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data and 5.2% share their data as supplementary material. Conclusions: There is a small percentage that shares their research data, however it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.

BackgroundThe 5-year survival rate of cancer patients is the most commonly used statistic to reflect improvements in the war against cancer. This idea, however, was refuted based on an analysis showing that changes in 5-year survival over time bear no relationship with changes in cancer mortality.MethodsHere we show that progress in the fight against cancer can be evaluated by analyzing the association between 5-year survival rates and mortality rates normalized by the incidence (mortality over incidence, MOI). Changes in mortality rates are caused by improved clinical management as well as changing incidence rates, and since the latter can mask the effects of the former, it can also mask the correlation between survival and mortality rates. However, MOI is a more robust quantity and reflects improvements in cancer outcomes by overcoming the masking effect of changing incidence rates. Using population-based statistics for the US and the European Nordic countries, we determined the association of changes in 5-year survival rates and MOI.ResultsWe observed a strong correlation between changes in 5-year survival rates of cancer patients and changes in the MOI for all the countries tested. This finding demonstrates that there is no reason to assume that the improvements in 5-year survival rates are artificial. We obtained consistent results when examining the subset of cancer types whose incidence did not increase, suggesting that over-diagnosis does not obscure the results.ConclusionsWe have demonstrated, via the negative correlation between changes in 5-year survival rates and changes in MOI, that increases in 5-year survival rates reflect real improvements over time made in the clinical management of cancer. Furthermore, we found that increases in 5-year survival rates are not predominantly artificial byproducts of lead-time bias, as implied in the literature. The survival measure alone can therefore be used for a rough approximation of the amount of progress in the clinical management of cancer, but should ideally be used with other measures.

Neutron scattering data from NCNR's thermal and cold neutron scattering instruments.

About this dataset

Context

The dataset tabulates the population of Gratis by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Gratis across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.0% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Gratis is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Gratis total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gratis Population by Race & Ethnicity. You can refer the same here

Financial overview and grant giving statistics of Research Foundation of Southern California

The Data Center Management Software market has emerged as a crucial component in the ever-evolving landscape of IT infrastructure management. With the increasing demand for efficient and reliable data centers, businesses across various sectors are leveraging this software to optimize operations, improve resource uti

About OPCRD

Optimum Patient Care Research Database (OPCRD) is a real-world, longitudinal, research database that provides anonymised data to support scientific, medical, public health and exploratory research. OPCRD is established, funded and maintained by Optimum Patient Care Limited (OPC) – which is a not-for-profit social enterprise that has been providing quality improvement programmes and research support services to general practices across the UK since 2005.

Key Features of OPCRD

OPCRD has been purposefully designed to facilitate real-world data collection and address the growing demand for observational and pragmatic medical research, both in the UK and internationally. Data held in OPCRD is representative of routine clinical care and thus enables the study of ‘real-world’ effectiveness and health care utilisation patterns for chronic health conditions.

OPCRD unique qualities which set it apart from other research data resources: • De-identified electronic medical records of more than 24.9 million patients • OPCRD covers all major UK primary care clinical systems • OPCRD covers approximately 35% of the UK population • One of the biggest primary care research networks in the world, with over 1,175 practices • Linked patient reported outcomes for over 68,000 patients including Covid-19 patient reported data • Linkage to secondary care data sources including Hospital Episode Statistics (HES)

Data Available in OPCRD

OPCRD has received data contributions from over 1,175 practices and currently holds de-identified research ready data for over 24.9 million patients or data subjects. This includes longitudinal primary care patient data and any data relevant to the management of patients in primary care, and thus covers all conditions. The data is derived from both electronic health records (EHR) data and patient reported data from patient questionnaires delivered as part of quality improvement. OPCRD currently holds over 68,000 patient reported questionnaire data on Covid-19, asthma, COPD and rare diseases.

Approvals and Governance

OPCRD has NHS research ethics committee (REC) approval to provide anonymised data for scientific and medical research since 2010, with its most recent approval in 2020 (NHS HRA REC ref: 20/EM/0148). OPCRD is governed by the Anonymised Data Ethics and Protocols Transparency committee (ADEPT). All research conducted using anonymised data from OPCRD must gain prior approval from ADEPT. Proceeds from OPCRD data access fees and detailed feasibility assessments are re-invested into OPC services for the continued free provision of patient quality improvement programmes for contributing practices and patients.

For more information on OPCRD please visit: https://opcrd.co.uk/

This dataset contains background data for a small study about how the recommendations for how to increase the FAIRness of research data are being adopted in scientific/scholarly communities. To get a rough indication of how large the group of Early Adopters of the FAIR Data Principles might be in Norway, I compared the number of unique authors of datasets published in 2019 with the number of unique authors of publications of research results in anthology chapters, articles and monographs (books) in the same year. As a use case, I chose my own university, UiT The Arctic University of Norway (UiT).

Financial overview and grant giving statistics of Washington Research Project Inc