Access OECD countries and selected non-member economies data through the OECD API.

Statistics on R&D Activities in the Business Sector: Indicators by OECD countries, resources allocated to R+D and period. National.

Statistics on R&D Activities in the Business Sector: C* Indicators by OECD countries, resources allocated to R+D and period. National.

The data have been collected via the official OECD Application Programming Interface (API) and includes the following indicators: EmpPlaRes - Employment at place of residence LfPartRa - Labour Force and Participation rate UnemReg - Unemployment in regions RegGdpTL2 - Regional Gross Domestic Product (Large regions TL2) GDPLT3 - Gross Domestic Product (Small regions TL3) RegEmIndu - Regional Employment by industry (ISIC rev 4) RegGVAWorker - Regional GVA per worker RegIncPC - Regional income per capita Source: https://data.oecd.org/api/ Data crawler: https://github.com/CUTLER-H2020/DataCrawlers/blob/master/Economic/OECD.R

Statistics on R&D Activities in the Business Sector: Indicators by OECD countries and resources allocated to R+D. National.

This paper utilizes quarterly panel data for 20 OECD countries over the period 1975:Q1-2014:Q2 to explore the importance of house prices and credit in affecting the likelihood of a financial crisis. Estimating a set of multivariate logit models, we find that booms in credit to both households and non-financial enterprises are important to account for when evaluating the stability of the financial system. In addition, we find that global housing market developments have predictive power for domestic financial stability. Finally, econometric measures of bubble-like behavior in housing and credit markets enter with positive and highly significant coefficients. Specifically, we find that the probability of a crisis increases markedly when bubble-like behavior in house prices coincides with high household leverage.

This Dataset is collected for this article, "Where Is Homeless? When Is Homeless?Chronotopic Analysis of OECD Definitions of the Homeless through Space, Time, and Body", written by Mohammad Abdalreza Zadeh, Carmela Cucuzzella, John R. Graham, and Ali Javedani. The article is under review process currently.

It is a collection of national defitions of homelessness among OECD countries. The hyper links to the governmental websites are available.

Expenditure, Enrolments, Entrants, Graduations, Personnel, Labour Market Status by Educational Attainment, Class Sizes, Demography.\r \r The data is owned by the joint international organisations: UNESCO-UIS, OECD and Eurostat.\r

The relevance of the indicators maintained by the "Building BioData.pt" project was assessed against the objectives of different organizations/initiatives: 1) Strategic objectives of BioData.pt; 2) Objectives of the Portuguese Roadmap for Research Infrastructures; 3) Objectives of ELIXIR; 4) Objectives of EOSC; 5) Sustainable Development Goals of the United Nations.

This is a subset of the OECD REGPAT database (February 2016) and the HAN database (September 2016). The complete data can be downloaded from http://www.oecd.org/sti/intellectual-property-statistics-and-analysis.htm#ipdata. This subset is used as an example for the analysis of regional innovator networks and covers selected tables on EPO applications with at least one inventor located in four German regions (Berlin, Hamburg, Munich, Stuttgart). The dataset also includes the R code for replication, OECD documentation files, and four text files used for (rough) cleaning of inventor names.

Here is the R code based on data from the OECD database to reproduce the results of the paper.

The data can be downloaded from the website of OECD: https://www.oecd.org/en/data/indicators.html.

Global Forum on Transparency and Exchange of Information for Tax Purposes - Regional and International Tax Initiatives (RITI) - DIFD Project number 300578-103

This table gives an overview of expenditure on regular education within the Netherlands.

Government finance schools, colleges and universities. It pays for research which is done by universities on its behalf. Furthermore it provides student grants and loans, allowances for school costs, provisions for students with a disability and child care allowances as well as subsidies to companies and non-profit organisations. The government reclaims unjustified payments for student grants and loans and allowances for school costs. It also receives interest and repayments on student loans as well as EU grants for education.

Parents and/or students have to pay tuition fees for schools, colleges and universities, parent contributions and contributions for school activities. They also have to purchase books and materials, pay for transport from home to school and back for students who are not eligible for subsidised transport, pay for private tutoring, pay interest and repayments on student loans, and repay wrongfully received student grants, loans and allowances for school costs. Parents and/or students receive child care allowances, provisions for students with a disability and an allowance for school costs as well as student grants and loans and scholarships of companies.

Companies and non-profit organisations incur costs for supervising trainees and apprentices who combine learning with work experience. They also contribute to the cost of work related education of their employees and spend money on research that is outsourced to colleges for higher professional education and universities. Furthermore they contribute to the childcare allowances given to households and provide scholarships to students. Companies receive subsidies and tax benefits for the creation of apprenticeship places and trainee placements and for providing transport for pupils.

Organisations abroad contract universities in the Netherlands to undertake research for them. The European Union provides funds and subsidies for education to schools, colleges and universities as well as to the Dutch government. Foreign governments contribute to international schools in the Netherlands that operate under their nationality.

The table also contains various indicators used nationally and internationally to compare expenditure on education and place it in a broader context. The indicators are compounded on the basis of definitions of Statistics Netherlands and/or the OECD (Organisation for Economic Cooperation and Development). All figures presented have been calculated according to the standardised definitions of the OECD.

In this table tertiary education includes research and development, except for the indicator Expenditure on education institutions per student, excluding R & D.

The statistic on education spending is compiled on a cash basis. This means that the education expenditure and revenues are allocated to the year in which they are paid out or received. However, the activity or transaction associated with the payment or receipt can take place in a different year.

Statistics Netherlands published the revised National Accounts in June 2018. Among other things, GDP and total government expenditures have been adjusted upwards as a result of the revision.

Data available from: 1995

Status of the figures: The figures from 1995 to 2020 are final. The 2021 figures are revised provisional, the 2022 figures are provisional.

Changes as of 7 December 2023: The revised provisional figures of 2021 and the provisional figures of 2022 have been added.

When will new figures be published? The final figures for 2021 will be published in the first quarter of 2024. The final figures for 2022 and the provisional figures for 2023 will be published in December 2024.

Net official development assistance, total and to least developed countries, as a proportion of the Organization for Economic Cooperation and Development (OECD) Development Assistance Committee donors’ gross national income (GNI).\r \r Data available in OECD Creditor Reporting System database.

This table contains estimates of generalised trust derived by applying a spatial microsimulation technique to estimates of generalised trust from the HILDA Wave 10 dataset. This dataset was …Show full descriptionThis table contains estimates of generalised trust derived by applying a spatial microsimulation technique to estimates of generalised trust from the HILDA Wave 10 dataset. This dataset was benchmarked to small area estimates from the 2011 Census. A full description of the method, benchmarks and validation is given in the User Guide. Due to the modelling process, these estimates are best used as ordinal values. Low levels of trust are represented by a 1, and high levels of trust are represented by a 7. This table forms part of the AURIN Social Indicators project. Copyright attribution: University of Canberra - National Centre for Social and Economic Modelling, (2011): ; accessed from AURIN on 12/3/2020. When using these data in published research, the authors need to include the following attribution in the text: 'These estimates are produced by NATSEM's Spatia Microsimulation model, which is described further in Tanton et al (2011)' and the following in the bibliography: Tanton, R., Vidyattama, Y., Nepal, B., & McNamara, J. (2011). Small area estimation using a reweighting algorithm. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(4), 931-951. DOI: 10.1111/j.1467-985X.2011.00721.x Licence type: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

This data is sourced from Organisation for the Economic Cooperation and Development (OECD), 2015. Environment at a Glance, http://www.keepeek.com/Digital-Asset-Management/oecd/environment/environment-at-a-glance-2015/co2-emission-intensities-per-capita-2013_9789264235199-graph7-en#page1. \r \r Data is Copyright OECD. All forms of use permitted with attribution. See terms and conditions at http://www.oecd.org/termsandconditions/. \r \r Data used to produce Figure ATM4 of the 2016 SoE. See https://soe.environment.gov.au/theme/climate/topic/2016/australias-emissions-context#climate-figure-ATM4\r

This data is sourced from Environment at a Glance 2015 © OECD 2015,http://www.oecd-ilibrary.org/environment/environment-at-a-glance-2015/co2-emission-intensities-per-gdp-2013_9789264235199-graph11-en. \r \r Data is Copyright OECD. All forms of use permitted with attribution. See terms and conditions at http://www.oecd.org/termsandconditions/. \r \r Dataset used to produce Figure ATM7 of the 2016 SoE. See https://soe.environment.gov.au/theme/climate/topic/2016/australias-emissions-context#climate-figure-ATM7\r

This repository contains the R-Code necessary to replicate the results of the multilevel regression in the article entitled: Improving Schooling through Effective Governance? The United States, Canada, South Korea, and Singapore in the Struggle for PISA Scores. In: Comparative Education. <\br> In addition, the repository includes Appendix D with the model diagnostics.

This table contains results from the national NAPLAN education tests for Years 3, 5, 7 and 9 for Reading, Writing and Numeracy for the AURIN Social Indicators project. Copyright attribution: …Show full descriptionThis table contains results from the national NAPLAN education tests for Years 3, 5, 7 and 9 for Reading, Writing and Numeracy for the AURIN Social Indicators project. Copyright attribution: University of Canberra - National Centre for Social and Economic Modelling, (2011): ; accessed from AURIN on 12/3/2020. When using these data in published research, the authors need to include the following attribution in the text: 'These estimates are produced by NATSEM's Spatia Microsimulation model, which is described further in Tanton et al (2011)' and the following in the bibliography: Tanton, R., Vidyattama, Y., Nepal, B., & McNamara, J. (2011). Small area estimation using a reweighting algorithm. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(4), 931-951. DOI: 10.1111/j.1467-985X.2011.00721.x Licence type: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

The need for careful assembly, training, and validation of quantitative structure–activity/property models (QSAR/QSPR) is more significant than ever as data sets become larger and sophisticated machine learning tools become increasingly ubiquitous and accessible to the scientific community. Regulatory agencies such as the United States Environmental Protection Agency must carefully scrutinize each aspect of a resulting QSAR/QSPR model to determine its potential use in environmental exposure and hazard assessment. Herein, we revisit the goals of the Organisation for Economic Cooperation and Development (OECD) in our application and discuss the validation principles for structure–activity models. We apply these principles to a model for predicting water solubility of organic compounds derived using random forest regression, a common machine learning approach in the QSA/PR literature. Using public sources, we carefully assembled and curated a data set consisting of 10,200 unique chemical structures with associated water solubility measurements. This data set was then used as a focal narrative to methodically consider the OECD’s QSA/PR principles and how they can be applied to random forests. Despite some expert, mechanistically informed supervision of descriptor selection to enhance model interpretability, we achieved a model of water solubility with comparable performance to previously published models (5-fold cross validated performance 0.81 R2 and 0.98 RMSE). We hope this work will catalyze a necessary conversation around the importance of cautiously modernizing and explicitly leveraging OECD principles while pursuing state-of-the-art machine learning approaches to derive QSA/PR models suitable for regulatory consideration.