The Health Statistics and Health Research Database is Estonian largest set of health-related statistics and survey results administrated by National Institute for Health Development. Use of the database is free of charge.

The database consists of eight main areas divided into sub-areas. The data tables included in the sub-areas are assigned unique codes. The data tables presented in the database can be both viewed in the Internet environment, and downloaded using different file formats (.px, .xlsx, .csv, .json). You can download the detailed database user manual here (.pdf).

The database is constantly updated with new data. Dates of updating the existing data tables and adding new data are provided in the release calendar. The date of the last update to each table is provided after the title of the table in the list of data tables.

A contact person for each sub-area is provided under the "Definitions and Methodology" link of each sub-area, so you can ask additional information about the data published in the database. Contact this person for any further questions and data requests.

Read more about publication of health statistics by National Institute for Health Development in Health Statistics Dissemination Principles.

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.

Data created during Computer Statistics assignment

Context and methodology

• This is used for the project in the context of the "Introduction to Research Data Management" course, 2024 winter semester. Originally it was made for a homework assignment in the "Computer Statistics" course, 2023 winter semester.
• The dataset consists of the following: code (and comment) written in the R markdown language that is to be compiled and executed in order to generate the 2 datasets created in the project; .pdf file generated from compiling and executing the aforementioned R code using RStudio; .txt file generated as part of one of the exercises in the assignment, also by compiling and executing the R code.
• The code was written by Vseslav Levchenko in R, using RStudio.

Technical details

• The code was written in RStudio and it is recommended to use it when working with R, however it is not strictly necessary. However, it is required to install the R language itself. For the other files, standard software like Microsoft Excel and any PDF reader are all that is needed.
• The code also contains necessary comments, and a .pdf file with the assignment's tasks is provided separately.

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.

Learning to Like Market Research Questions - Small Group Exercise - Report/Discussion on Group Exercise

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

Measures, analysis, and research into the digital economy key.

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.

Requests taken and satisfied by Archives and Records Management. Gives details for each request including time to service the request and demonstrates efforts to provide public and Boston municipal government with access to public records.

Data created during Computer Statistics assignment

Context and methodology

• This is used for the project in the context of the "Introduction to Research Data Management" course, 2024 winter semester. Originally it was made for a homework assignment in the "Computer Statistics" course, 2023 winter semester.
• The dataset consists of the following: code (and comment) written in the R markdown language that is to be compiled and executed in order to generate the 2 datasets created in the project; .pdf file generated from compiling and executing the aforementioned R code using RStudio; .txt file generated as part of one of the exercises in the assignment, also by compiling and executing the R code.
• The code was written by Vseslav Levchenko in R, using RStudio.

Technical details

• The code was written in RStudio and it is recommended to use it when working with R, however it is not strictly necessary. However, it is required to install the R language itself. For the other files, standard software like Microsoft Excel and any PDF reader are all that is needed.
• The code also contains necessary comments, and a .pdf file with the assignment's tasks is provided separately.

Overview of Market Research Questions, including who is the user base, what are patrons typically looking for, and fee-based data resources.

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.

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.

Sourced password security data and statistics compiled from industry reports, academic research, and government publications for 2026.

App Data Statistics Tool market valued at $2.75B in 2025, projected to reach $7.13B by 2034, growing at 11.2% CAGR. Comprehensive analysis of Software, Services, and deployment modes.

This dataset contains solutions to the Transit Network Design Problem over various problem instances.

Solutions for Mandl and Mumford problem instances. A description of the data is contained in the file “Data Information.pdf”.

Usage metrics for all types of scholarly output are one of the measures to assess Open Access impact and are a value added service of Open Access repositories. OpenAIRE has a succesful record in providing usage metrics for for a large number of repositories from around the world. OpenAIRE's Usage Counts service collects usage activity from OpenAIRE content providers, like Insitutional Repositories or national aggreegators like IRUS-UK, or LaReferencia, for usage events related to research products of the OpenAIRE graph, like publications. It subsequently creates and deploys aggregated statistics for these products and delivers standardized activity reports following the COUNTER CoP, about research usage and uptake. It complements existing citation mechanisms and assists institutional repository managers, research communities, research organizations, funders and policy makers track and evaluate research from an early stage. Following its successful record in publications, OpenAIRE's Usage Counts service is ready to be applied to another product of the OpenAIRE reasearch graph, i.e., the research data. The service will monitor and analyze usage activity for research data repositories, as well as usage reports from aggregators like Datacite. This usage will not only be aggregated but also combined with usage activity from publications, by exploiting other OpenAIRE services like the OpenAIRE ScholeXplorer. In this manner OpenAIRE Usage Counts Service will operate as a hub of usage statistics, linking together all kinds of scholarly output, offering a value added service for Open Access and realize the Open Analytics Framework and Infrastructure required for scientific reward in European Open Science Cloud. From the technical perspective, usage data will be collected in two ways: (1) by collecting usage events directly from data repositories and (2) from research data statistics aggregators exposing consolidated statistics via SUSHI-Lite. The final outcome is an OpenAIRE service for tracking, collection, cleaning, analysis, evaluation and COUNTER-compliant reporting of research data combined with other products from OpenAIRE research graph. The poster will describe two aspects: 1) The potential of the OpenAIRE Usage Counts service to explore a number of multidimensional scholarly performance indicators. 2) Contributing as a Usage Counts Hub to services aggregating OpenAIRE Research Graph product-level metrics.

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

Research dataset and analysis for Hair Color including statistics, forecasts, and market insights

In 2024, the United States ranked first by revenue in the data center market among the 5 countries presented in the ranking. United States' revenue amounted to 123.16 billion U.S. dollars, while China and Japan, the second and third countries, had records amounting to 94.40 billion U.S. dollars and 20.60 billion U.S. dollars, respectively.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Data Center.