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.

Israel IL: Researchers in R&D: per Million People data was reported at 8,255.404 Ratio in 2012. This records an increase from the previous number of 7,296.253 Ratio for 2011. Israel IL: Researchers in R&D: per Million People data is updated yearly, averaging 7,775.829 Ratio from Dec 2011 (Median) to 2012, with 2 observations. The data reached an all-time high of 8,255.404 Ratio in 2012 and a record low of 7,296.253 Ratio in 2011. Israel IL: Researchers in R&D: per Million People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Israel – Table IL.World Bank: Technology. The number of researchers engaged in Research &Development (R&D), expressed as per million. Researchers are professionals who conduct research and improve or develop concepts, theories, models techniques instrumentation, software of operational methods. R&D covers basic research, applied research, and experimental development.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Weighted average;

This dataset provides the data underlying the scientific article "Researchers’ willingness and ability to openly share their research data: a survey of COVID-19 pandemic-related factors". The abstract of the article is as follows: While previous studies show that the drivers and inhibitors for openly sharing research data are diverse and complex, there is a lack of studies empirically examining the influence of the COVID-19 pandemic on researchers’ open data sharing behavior. Using a questionnaire (n=135), this study investigates the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their research data. Fifty-one respondents (37.8%) stated that factors related to the COVID-19 pandemic increased their willingness and ability to openly share their research data, while 80 (59.3%) reported that various pandemic-related factors did not influence their willingness and ability in this way. As one of the possible influencing factors, this study finds a significant association between the COVID-19-relatedness of researchers’ research discipline and whether or not the COVID-19 pandemic led to a change in their willingness and ability to share their research data openly: χ2 (1) = 5.77, p < .05. Social influences on open data sharing behavior, institutional support for open data sharing, and the fear of potential negative consequences of open data sharing were nearly similar for the respondents who were and were not involved in COVID-19-related research. This study contributes scientifically by going beyond conceptual studies as it provides empirically-based insights concerning the influence of COVID-19 pandemic-related factors on researchers’ willingness and ability to openly share their data. As a practical contribution, this study discusses recommendations that policymakers can use to sustainably support open research data sharing in post-COVID-19 times.

Germany DE: Total R&D Personnel: Per Thousand Total Employment data was reported at 17.207 Per 1000 in 2022. This records an increase from the previous number of 16.760 Per 1000 for 2021. Germany DE: Total R&D Personnel: Per Thousand Total Employment data is updated yearly, averaging 13.326 Per 1000 from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 17.207 Per 1000 in 2022 and a record low of 11.921 Per 1000 in 1996. Germany DE: Total R&D Personnel: Per Thousand Total Employment data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

The data in this publication for Germany cover unified Germany from 1991 and western Germany only until 1990.

Between 1991 and 2021, the data for the PNP sector were included in the Government sector. In 2016, the method for calculating R&D coefficients was revised, introducing a break in series in the Higher Education sector. In particular, coefficients are thereafter based on time-use surveys.

From reference year 2014, the distribution of R&D personnel by occupation is requested in the government survey whereas it was previously estimated from data by qualification.

The method for calculating public-financed R&D in the business enterprise sector was reviewed, resulting in the revision of business enterprise R&D and the national total back to 1991.

In 1992 the methodology of the survey on resources devoted to R&D in the Government sector was changed.

For 1997, the methodology for allocating GBARD by socio-economic objective changed. For 1997 and from 2001 to 2015, the global budget reduction was not distributed proportionally across SEO by the Federal Ministry of Education and Research. Therefore, the sum of the breakdown for those years does not add to the total. From 2016 onwards the global reduction is distributed across SEO proportionally.

NO: Higher Education Researchers: Compound Annual Growth Rate data was reported at -0.239 % in 2022. This records a decrease from the previous number of 10.661 % for 2021. NO: Higher Education Researchers: Compound Annual Growth Rate data is updated yearly, averaging 3.964 % from Dec 1983 (Median) to 2022, with 30 observations. The data reached an all-time high of 10.661 % in 2021 and a record low of -4.344 % in 2020. NO: Higher Education Researchers: Compound Annual Growth Rate data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Norway – Table NO.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

In Norway, in 2007, a break in series occurs because of a change in compilation methods for health institutions. This affects both the Higher education sector (university hospitals) and Government sector (other hospitals).

In 1995, the survey sample was revised to improve coverage of small firms (10 to 50 employees) and non-manufacturing industries.

As of 1991, personnel in central administration units of higher education are not included, however the cost of such personnel is included in other current R&D expenditure (in line with the Frascati Manual).

In 1987, own funds from Public enterprises were reclassified from funds from Government to the funds from the Business Enterprise sector. As of 1989, R&D performed by PNP institutes has been included in the government sector.

The growth in resources devoted to R&D in 1984 is due to the expansion of the scope of the Business Enterprise sector survey.

Based on new information, the estimate for defence GBARD in 2019 was reduced by about 12% compared with the previous year. The decrease is due to updated information and not to changes in the appropriations. The method for compiling GBARD data changed in 1996. The series have been revised retrospectively to exclude contract research, state enterprises and payments to the European Commission.

The Public Health Research Database (PHRD) is a linked asset which currently includes Census 2011 data; Mortality Data; Hospital Episode Statistics (HES); GP Extraction Service (GPES) Data for Pandemic Planning and Research data. Researchers may apply for these datasets individually or any combination of the current 4 datasets.

The purpose of this dataset is to enable analysis of deaths involving COVID-19 by multiple factors such as ethnicity, religion, disability and known comorbidities as well as age, sex, socioeconomic and marital status at subnational levels. 2011 Census data for usual residents of England and Wales, who were not known to have died by 1 January 2020, linked to death registrations for deaths registered between 1 January 2020 and 8 March 2021 on NHS number. The data exclude individuals who entered the UK in the year before the Census took place (due to their high propensity to have left the UK prior to the study period), and those over 100 years of age at the time of the Census, even if their death was not linked. The dataset contains all individuals who died (any cause) during the study period, and a 5% simple random sample of those still alive at the end of the study period. For usual residents of England, the dataset also contains comorbidity flags derived from linked Hospital Episode Statistics data from April 2017 to December 2019 and GP Extraction Service Data from 2015-2019.

Researchers in R&D (per million people) in Italy was reported at 2796 in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Italy - Researchers in R&D (per million people) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

CZ: Number of Researchers: % of Total Researchers: Female data was reported at 27.687 % in 2022. This records an increase from the previous number of 27.101 % for 2021. CZ: Number of Researchers: % of Total Researchers: Female data is updated yearly, averaging 27.962 % from Dec 1999 (Median) to 2022, with 24 observations. The data reached an all-time high of 29.457 % in 2002 and a record low of 26.094 % in 1999. CZ: Number of Researchers: % of Total Researchers: Female data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

For Czechia: beginning in 2005, there is a change in methodology for the collection of R&D personnel data in FTE. Data are provided in FTE by the reporting units, and based on new, more precise guidelines. From 2005 onward, certain institutional units previously classified in the business sector have been reallocated to the Government sector to comply with the new System of National Accounts (SNA) 2008.

The data repository includes raw results for the paper and supporting information as well as post processing scripts to generate figures in the paper and supporting information. The repository also includes codes for generating the raw results. The data for this paper is stored on servers at Statistics Denmark. Due to security and privacy reasons, the data cannot be made publicly available and we as researcher do not have permission to extract or share micro data. Researchers who want to gain access to micro data can only be granted so by Statistics Denmark. Statistics Denmark has created detailed step-by-step descriptions of how such access is granted and a more general description of the Danish system for access to micro data. These descriptions, as of December 2018, are also uploaded to the Dataverse and available with Statistics Denmark: https://www.dst.dk/-/media/Kontorer/13-Forskning-og-Metode/Step-by-step-procedures-for-researchers-access-to-Microdata_082018.pdf?la=en https://www.dst.dk/ext/645846915/0/forskning/Access-to-micro-data-at-Statistics-Denmark_2014--pdf We welcome any inquiries with respect to data access.

China Total Researchers: Full-Time Equivalent data was reported at 2,637,193.100 FTE in 2022. This records an increase from the previous number of 2,405,509.400 FTE for 2021. China Total Researchers: Full-Time Equivalent data is updated yearly, averaging 1,181,575.900 FTE from Dec 1991 (Median) to 2022, with 32 observations. The data reached an all-time high of 2,637,193.100 FTE in 2022 and a record low of 471,400.000 FTE in 1991. China Total Researchers: Full-Time Equivalent data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.MSTI: Number of Researchers and Personnel on Research and Development: Non OECD Member: Annual.

The national breakdown by source of funds does not fully match with the classification defined in the Frascati Manual. The R&D financed by the government, business enterprises, and by the rest of the world can be retrieved but part of the expenditure has no specific source of financing, i.e. self-raised funding (in particular for independent research institutions), the funds from the higher education sector and left-over government grants from previous years.

The government and higher education sectors cover all fields of NSE and SSH while the business enterprise sector only covers the fields of NSE. There are only few organisations in the private non-profit sector, hence no R&D survey has been carried out in this sector and the data are not available.

From 2009, researcher data are collected according to the Frascati Manual definition of researcher. Beforehand, this was only the case for independent research institutions, while for the other sectors data were collected according to the UNESCO concept of “scientist and engineer”.

In 2009, the survey coverage in the business and the government sectors has been expanded.

Before 2000, all of the personnel data and 95% of the expenditure data in the business enterprise sector are for large and medium-sized enterprises only. Since 2000 however, the survey covers almost all industries and all enterprises above a certain threshold. In 2000 and 2004, a census of all enterprises was held, while in the intermediate years data for small enterprises are estimated.

Due to the reform of the S&T system some government institutions have become enterprises, and their R&D data have been reflected in the Business Enterprise sector since 2000.

Summary: This Plan is an important milestone in the Administrations Big Data Research and Development (R&D) Initiative

DECOVID, a multi-centre research consortium, was founded in March 2020 by two United Kingdom (UK) National Health Service (NHS) Foundation Trusts (comprising three acute care hospitals) and three research institutes/universities: University Hospitals Birmingham (UHB), University College London Hospitals (UCLH), University of Birmingham, University College London and The Alan Turing Institute. The original aim of DECOVID was to share harmonised electronic health record (EHR) data from UCLH and UHB to enable researchers affiliated with the DECOVID consortium to answer clinical questions to support the COVID-19 response.   ​​   ​​The DECOVID database has now been placed within the infrastructure of PIONEER, a Health Data Research (HDR) UK funded data hub that contains data from acute care providers, to make the DECOVID database accessible to external researchers not affiliated with the DECOVID consortium.  

This highly granular dataset contains 256,804 spells and 165,414 hospitalised patients. The data includes demographics, serial physiological measurements, laboratory test results, medications, procedures, drugs, mortality and readmission.

Geography: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UCLH provides first-class acute and specialist services in six hospitals in central London, seeing more than 1 million outpatient and 100,000 admissions per year. Both UHB and UCLH have fully electronic health records. Data has been harmonised using the OMOP data model. Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

The Check-In Dataset is the second public-use dataset in the Dunham's Data series, a unique data collection created by Kate Elswit (Royal Central School of Speech and Drama, University of London) and Harmony Bench (The Ohio State University) to explore questions and problems that make the analysis and visualization of data meaningful for dance history through the case study of choreographer Katherine Dunham. The Check-In Dataset accounts for the comings and goings of Dunham's nearly 200 dancers, drummers, and singers and discerns who among them were working in the studio and theatre together over the years from 1937 to 1962. As with the Everyday Itinerary Dataset, the first public-use dataset from Dunham's Data, data on check-ins come from scattered sources. Due to information available, it has a greater level of ambiguity as many dates are approximated in order to achieve accurate chronological sequence. By showing who shared time and space together, the Check-In Dataset can be used to trace potential lines of transmission of embodied knowledge within and beyond the Dunham Company. Dunham's Data: Digital Methods for Dance Historical Inquiry is funded by the United Kingdom Arts and Humanities Research Council (AHRC AH/R012989/1, 2018-2022) and is part of a larger suite of ongoing digital collaborations by Bench and Elswit, Movement on the Move. The Dunham's Data team also includes digital humanities postdoctoral research assistant Antonio Jiménez-Mavillard and dance history postdoctoral research assistants Takiyah Nur Amin and Tia-Monique Uzor. For more information about Dunham's Data, please see the Dunham's Data website. Also, visit the Dunham's Data research blog to view the interactive visualizations based on the Dunham's Data.

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Overview. The Post-Acute Care Supplement (PACS) files were developed from two administrative data sets that capture assessments at multiple points in post-acute care: the Outcome and Assessment Information Set (OASIS) Summary File (OASF): 2000-2010, which covers home health care, and the Long-Term Care Annual Summary File (LTCASF): 2000-2010, which covers nursing homes. Annual files were developed for the period 2000 to 2010 that summarize health conditions, functional status, and care patterns into one observation per beneficiary per year. Data Access. These data are not available from ICPSR. Because the data contain confidential CMS assessment data, researchers will need to secure a data use agreement from CMS. For researchers who secure a DUA, the files will be distributed to them through the Medicare/Medicaid Research Information Center (MedRIC) (medric@acumenllc.com).

IPLoRD – Intellectual Property Longitudinal Research Dataset – is a publicly available dataset that provides researchers with an applicant activity-centric view of Australian IP right filings. IPLoRD leverages IPGOD data to provide a history of an applicant’s filings over time broken down by financial year. Where available,\r the ABN of the applicant is provided to allow linkage with other government research datasets.IPLoRD makes it possible for economists and other researchers to examine relationships between the filer’s economic circumstances and their intellectual property portfolio. IPLoRD is a biennial release. This year’s version of IPLoRD provides coverage of filing activities from 1904 to the end of 2020.
\r \r To increase accessibility, IP Australia has provided IPLORD as a full-size single file, and as smaller files partitioned as the below time periods:
\r \r * 1903 to 1990\r * 1991 to 1995\r * 1996 to 2001\r * 2001 to 2005\r * 2006 to 2010\r * 2011 to 2016\r * 2016 to 2020\r

Argentina AR: Government Sector: Number of Researchers: % of Total Researchers: Female data was reported at 55.617 % in 2023. This records a decrease from the previous number of 55.732 % for 2022. Argentina AR: Government Sector: Number of Researchers: % of Total Researchers: Female data is updated yearly, averaging 51.473 % from Dec 1998 (Median) to 2023, with 26 observations. The data reached an all-time high of 55.732 % in 2022 and a record low of 41.398 % in 1999. Argentina AR: Government Sector: Number of Researchers: % of Total Researchers: Female data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.MSTI: Number of Researchers and Personnel on Research and Development: Non OECD Member: Annual.

In Argentina, the coverage of the business enterprises was expanded in 2015. BERD data are derived from a new survey from 2009. Since 1997, data for human resources relate to R&D. Before that, human resources data were expressed in terms of Science and Technology Activities (STA), involving R&D and diffusion activities of S&T (library services, training services, conferences, etc.). These have not been transferred to the OECD database. Since 2002, the source of funds data for private non-profit organisations, universities and S&T public organisations are requested for R&D. Before 2002, these sources of funds data were requested in terms of STA. These data were converted into R&D by means of a coefficient for each sector of performance. The main source of funds for science and technology activities in Argentina is the National Budget.

Collection of samples and data across the following diseases: Fit and well

Objective: The objective of this review is to identify what is known about Indigenous data analysis methods for research. Introduction: Understanding Indigenous data analyses methods for research is crucial in health research with Indigenous participants, to support culturally appropriate interpretation of research data, and culturally inclusive analyses in cross-cultural research teams. Inclusion Criteria: This review will consider primary research studies that report on Indigenous data analysis methods for research. Method: Medline (via Ovid SP), PsycINFO (via Ovid SP), Web of Science (Clarivate Analytics), Scopus (Elsevier), Cumulated Index to Nursing and Allied Health Literature CINAHL (EBSCOhost), ProQuest Central, ProQuest Social Sciences Premium (Clarivate) will be searched. ProQuest (Theses and Dissertations) will be searched for unpublished material. Studies published from inception onwards and written in English will be assessed for inclusion. Studies meeting the inclusion criteria will be assessed for methodological quality and data will be extracted.

Research Ship Knorr Underway Meteorological Data (delayed ~10 days for quality control) are from the Shipboard Automated Meteorological and Oceanographic System (SAMOS) program. IMPORTANT: ALWAYS USE THE QUALITY FLAG DATA! Each data variable's metadata includes a qcindex attribute which indicates a character number in the flag data. ALWAYS check the flag data for each row of data to see which data is good (flag='Z') and which data isn't. For example, to extract just data where time (qcindex=1), latitude (qcindex=2), longitude (qcindex=3), and airTemperature (qcindex=12) are 'good' data, include this constraint in your ERDDAP query: flag=~"ZZZ........Z." in your query. '=~' indicates this is a regular expression constraint. The 'Z's are literal characters. In this dataset, 'Z' indicates 'good' data. The '.'s say to match any character. The '' says to match the previous character 0 or more times. (Don't include backslashes in your query.) See the tutorial for regular expressions at https://www.vogella.com/tutorials/JavaRegularExpressions/article.html