Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.

Other statistics published alongside the statistical first release. These are not National Statistics, but complement the information in the main release. FE trends FE trends provides an overview of adult (19+) government-funded further education and all age apprenticeships in England. It looks to provide trends between 2008/09 and 2013/14 and to give an overview of FE provision, characteristics of learners and outcomes over time. International Comparisons Supplementary Tables The Organisation for Economic Co-operation and Development (OECD) produces an annual publication, Education at a Glance, providing a variety of comparisons between OECD countries. The table provided here contains a summary of the relative ranking in education attainment of the 25-64 year old population in OECD countries in 2012. The OECD statistics use the International Standard Classification of Education. Within this, “at least upper secondary education” is equivalent to holding qualifications at Level 2 or above in the UK, and “tertiary education” is equivalent to holding qualifications at Level 4 or above in the UK. STEM This research is the result of a Department for Business, Innovation and Skills (BIS) funded, sector led project to gather and analyse data to inform the contribution that further education makes to STEM in England. This project was led by The Royal Academy of Engineering, and governance of the project was specifically designed to ensure that those with an interest in STEM were actively engaged and involved in directing and prioritising outputs. The November 2012 report builds on the FE and Skills STEM Data report published in July 2011 (below). It provides further analysis and interpretation of the existing data in a highly graphical format. It uses the same classified list of S,T, E and M qualifications as the 2011 report compiled through an analysis of the Register of Regulated Qualifications and the Learning Aim Database, updated with the most recent completions and achievements data taken from the Individualised Learner Record and the National Pupil Database.

In Horizon 2020 the Commission committed itself to running a flexible pilot on open research data (ORD Pilot). The ORD pilot aims to improve and maximise access to and re-use of research data generated by Horizon 2020 projects. It takes into account the need to balance openness and protection of scientific information, commercialisation and IPR, privacy concerns, security as well as data management and preservation questions.

This ORD pilot comprises various selected areas of Horizon 2020 ('core areas' ). Projects not covered by the scope of the pilot can participate on an individual and voluntary project-by-project basis ('opt-in'). Projects may also decide not to participate in the pilot ('opt-out') at any stage of the project lifecycle.

As of the Work Programme 2017 the ORD pilot scope is extended to cover all thematic areas of Horizon 2020 so as to make open research data the default, but retaining opt-out possibilities – however, this does not yet apply to the datasets analysed below.

The ORD pilot applies primarily to the data needed to validate the results presented in scientific publications. Other data can also be provided by the beneficiaries on a voluntary basis, as stated in their Data Management Plans (DMP). Costs associated with data management, including the creation of a data management plan, can be claimed as eligible costs in any Horizon 2020 grant.

It should be noted that the potential participation in the pilot is not part of the evaluation of proposals: in other words, proposals are not evaluated more favourably because they are part of the ORD pilot and are not penalised for opting out of the ORD pilot.

The legal requirements for projects participating in this pilot are contained in article 29.3 of the Model Grant Agreement.

This file does not contain research data generated by Horizon 2020 projects themselves. Rather it provides an overview of the take-up of the Commission's Open Research Data Pilot (ORD Pilot) It gives statistics by call about proposals: - Opting out of the Pilot on Open Access Research data in H2020 - Participating in the Pilot on Open Access Research data in H2020 on a voluntary bases (opt-in).

This overview encompasses two finalised datasets obtained from CORDA: 2014-2015 and 2015-2016. Data obtained from CORDA. the following instruments are excluded: SME instrument, cofund, and prizes. ERC grants are also not included for the 2015-2016 sample. These datasets have been cleaned in order to reduce overlap and replace previous datasets. In this period, 68% of the funded projects in the core areas (CA) participate in the ORD. Correspondingly, the average opt-out rate in signed grant agreements is 32%. Outside the core areas, 9% of projects make use of the voluntary opt-in possibility.

Sydney has a constantly updated bank of information for historians, researchers and demographers.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. We show experimentally that, in agreement with our predictions, receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell-types.

This session follows four themes. First, it will describe the RDC, the principal data it supports and the application process. Second, it will discuss the growth in administrative and linked administrative data files being made available by Statistics Canada. Third, it will highlight some of the pilot data, particularly business related, that the RDC hosts. The session concludes with a discussion on how the McMaster RDC and Data Services (DLI) has worked together to promote the use of data on campus to meet the needs of researchers.

Research data on family road trip patterns, recommended stop intervals for children by age, travel challenges, and planning behaviors in the United States.

Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records. This publication contains aggregated data for the paper. It also contains the evaluation data of all model/hyper-parameter training and test runs.

Data-driven research: How to Use Data/Statistics Hooks in Short-Form Video. Based on analysis of 13.5M clips.

This spreadsheet contains data relating to the number of published datasets on 4TU.ResearchData between 2009 and July 2020.4TU.ResearchData is a data repository for datasets in science, engineering and design. The four sets of data are:1. 4TU.ResearchData - Dataset Publication by Year2. 4TU.ResearchData - Individual Datasets deposited per year and subject3. 4TU.ResearchData - Annual Dataset Publication by Organisation

Research dataset and analysis for Diagnostics including statistics, forecasts, and market insights

Incorporate research data.........................

Introduction

Market Research Industry Statistics: The market research industry is a vital component of modern business strategy, serving as the "eyes and ears" of organizations by offering crucial insights into market dynamics, consumer behavior, and competition.

Its core functions encompass market analysis, consumer behavior analysis, competitor assessment, product development support, marketing optimization, and strategic planning.

Evolving with technology, it has transitioned from traditional data collection methods to online surveys, social media analysis, and AI-driven data analytics.

While facing challenges like data privacy and information overload, the industry also presents opportunities for specialization, ethical data practices, and advanced analytics, ensuring its continued importance in guiding businesses through a complex and data-driven marketplace.

Data are based on information from all death certificates filed in the 50 states and the District of Columbia and processed by the National Center for Health Statistics (NCHS). Restricted data available through the Research Data Center include geographical indicators, exact date of birth and death of decedent, among others.

Test for group differences across grant funding of health scholars on perceptions of challenges and issues in sharing research data.

Health scholars’ practices of sharing health research data.

The Drug-Involved Mortality (DIM) data are an enhancement to the National Vital Statistics System, Mortality data providing information on substances, as well as prescription (using generic names only – no trademarks) and illicit drugs mentioned on death certificates of residents of the United States and the District of Columbia. Data files were created by examining the literal text of three fields on the death certificate: Part I-Cause of Death, Part II –Significant Conditions Contributing to Death and Box 43, a verbatim description of how the injury occurred. The DIM data system does not support individual examination of “literal text” and the actual variables showing free-form text are not included in these files.

🔗 Related Datasets

• Publications Using SAMHSA DataDrug Abuse Warning Network, 2010: Area Profiles of Drug-related Mortality — HealthData.Gov
• Provisional drug overdose death counts for specific drugs — HealthData.Gov
• NCHS - Drug Poisoning Mortality by County: United States — HealthData.Gov

About this dataset

Context

The dataset tabulates the population of Malta by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Malta. The dataset can be utilized to understand the population distribution of Malta by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Malta. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Malta.

Key observations

Largest age group (population): Male # 35-39 years (52) | Female # 25-29 years (62). 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.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Malta population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Malta is shown in the following column.
• Population (Female): The female population in the Malta is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Malta for each age group.

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 Malta Population by Gender. You can refer the same here

Research dataset and analysis for Targeting Systems including statistics, forecasts, and market insights