Data to improve the visibility of demand and capacity across health services at all levels from system to regional to national.

Serbia Imports: Intermediate Goods: Parts & Accessories: Transportation Means data was reported at 1.248 USD bn in 2016. This records a decrease from the previous number of 1.368 USD bn for 2015. Serbia Imports: Intermediate Goods: Parts & Accessories: Transportation Means data is updated yearly, averaging 328.000 USD mn from Dec 2001 (Median) to 2016, with 16 observations. The data reached an all-time high of 2.046 USD bn in 2013 and a record low of 81.000 USD mn in 2001. Serbia Imports: Intermediate Goods: Parts & Accessories: Transportation Means data remains active status in CEIC and is reported by Statistical Office of the Republic of Serbia. The data is categorized under Global Database’s Serbia – Table RS.JA014: Imports: by Economic Destination: Annual.

Sewer Network Junction Collection contains layers that function as fittings, allowing for devices or lines to connect to another line at an intermediate vertex. See the Sewer Data Dictionary for complete descriptions and definitions of each Layer, Asset Group, and Asset Type. The dictionary also provides details of the Utility Network along with attribute field definitions, relationship definitions, tables and attribute domain values.

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at https://github.com/parallelworks/sl-archive-whondrs . A static copy of the GitHub repository is included in this data package as an archived version at the time of publishing this data package (March 2023). However, we recommend accessing these files via GitHub for full functionality. Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

Included here is the processed data illustrated in the figures of both the main text, and the supplemental material. Below is a description of each file's contents.

Figure2Dcode.m contains the MATLAB code that generates Figure 2D of the main text. It takes the band populations inferred from five iterations of measurements, and calculates the means and standard errors for data taken at each theta as defined in the main text.

Figure2Ddata.csv contains the data illustrated in Figure 2D of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/12 radians for each column that follows. Row 5 is the error for the n=1 population, row 6 is the error for the n=2 population, and row 7 is the error on the sum of the population in bands with index not equal to 1 or 2.

Figure3Bcode.ipynb contains the jupyter notebook that generates Figure 3B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be the intermediate quasi-momenta, and different colors are used to differentiate between different acceleration times.

Figure3Bdata.csv contains the data illustrated in Figure 3B of the main text. The five columns correspond to the five different trajectory evolution times (0.5, 0.9, 1.3, 1.7, 2.1 milliseconds) shown in the Figure 3B. The first nine rows correspond to the nine trajectory midpoint positions in the Brillouin zone, as showed in Figure 3A; the first row corresponds to a midpoint at K. The next nine rows are the errors on the measurements.

Figure4Ccode.m contains the MATLAB code that generates Figure 4C of the main text. It takes the band populations inferred from twelve iterations of measurements, each at a different theta as defined in the main text, and calculate the means and standard errors for data taken at each theta.

Figure4Cdata.csv contains the data illustrated in Figure 4C of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/6 radians for each column that follows. Row 11 is the error for the n=3 population, row 12 is the error for the n=4 population, and row 13 is the error on the sum of the population in bands with index not equal to 3 or 4.

FigureS3Bcode.m contains the MATLAB code that generates Figure S3B of the main text. It takes the band populations inferred from seven iterations of measurements, and calculates the means and standard errors for data taken for each hold time at quasi-momentum Q as defined in the main text. The result is then fitted to a sine with exponentially decaying envelope.

push_ramp.py (in Full Hamiltonian simulation.zip) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in basic_fcts.py. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data. A decay to account for coherence loss is added.

FigureS3data.csv contains the data illustrated in Figure 3 of the supplementary material. The first row is the data values, and the second row are the error bars.

FigureS4Bcode.m contains the MATLAB code that generates Figure S4B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be acceleration time, and different colors are used to differentiate between different intermediate points.

push_ramp.py (in Full Hamiltonian simulation.zip) starts with an initial state and evolves it according to the discretized schrödinger equation along the paths in q-space. The Hamiltonian is calculated in basic_fcts.py. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data.

FigureS4data.csv contains the data illustrated in Figure 4 of the supplementary material. The first five rows are the normalized ground band population for five different trajectory mid points on the K - M - K' line of the Brillouin zone.; the first row is for a midpoint at K, and the fifth row is for a midpoint at M. The columns give the trajectory traversal times; the first column corresponds to a traversal time of 0.1 ms and each column corresponds to a new traversal time incremented by 0.2 ms. Rows 6-10 are the error bars for the measurements.

FigureS5Bcode.zip contains the codes that generate Figure S5B of the main text. For each subplots in Fig.S5B, the corresponding MATLAB code in the zip file takes the band populations inferred from three iterations of measurements, and calculate the means and standard errors for data taken at each acceleration time.

FigureS5Bdata.csv contains the data illustrated in Figure 5B of the supplementary material. Rows 1-20 correspond to subpanel (iii) in the Figure S5 of the supplementary material. Rows 21-40 correspond to subpanel (ii) in the Figure S5 of the supplementary material. Rows 31-60 correspond to subpanel (i) in the Figure S5 of the supplementary material.

Rows 1-10 correspond to the band index and give the normalized band population; row 1 corresponds to band index n=1 and row 10 corresponds to band index n=10. Rows 21-30 correspond to the band index and give the normalized band population; row 21 corresponds to band index n=1 and row 30 corresponds to band index n=10. Rows 41-50 correspond to the band index and give the normalized band population; row 41 corresponds to band index n=1 and row 50 corresponds to band index n=10.

Rows 11-20 (31-40) [51-60] give the error in the band populations for measurements in panel iii (ii) [i].

FigureS5Cdata.csv contains the data illustrated in Figure 5C of the supplementary material. The first (second) column is the vertical (horizontal) axis. The fourth (third) column is the error in the points on the vertical (horizontal) axis.

push_ramp.py (in Full Hamiltonian simulation.zip) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in basic_fcts.py. In figure S6A, at each point in time shown the state is projected onto the instantaneous eigenbasis and the different band populations are extracted. In figure S6B and figure S6C, the whole experiment sequences corresponding to figure 2 and figure 4 in the main text are simulated, and the final population obtained is plotted, with the measurement results copied for reference.

FigureS7code.nb contains the mathematica notebook that generate Figure S7 of the main text. This code uses the two-band model described in the supplemental material to perform simulation.

Image_fitting.zip contains the MATLAB code and functions that were used to analyze the band mapping images. multiboxFit_v7_1.m is the main code that uses other MATLAB functions in the zip file. Overall, it takes absorption images as input, finds the position of each peak (BoxGenerator_v1_0.m), fit for the population in each peak in the images (createFit2D.m), assign the correct band number given the final quasi-momentum in the sequence (BoxesBandsThing_v2.m), and finally plot the inferred band populations, along with a visualization of the original images overlain with a Brillouin zone (PlotBZ_v2.m). The result of fits are saved in a separate file that are accessed by other analysis codes. Figure S2 and Figure S5C are also generated with this code.

Additional codes Q_path_BZ.py, group_velo.py & diffr_img.py are included in Full Hamiltonian simulation.zip to ensure the correct functionality of the codes included.

Intermediate acceptance criteria are the foundation for developing control strategies in process validation stage 1 in the pharmaceutical industry. At drug substance or product level such intermediate acceptance criteria for quality are available and referred to as specification limits. However, it often remains a challenge to define acceptance criteria for intermediate process steps. Available guidelines underpin the importance of intermediate acceptance criteria, because they are an integral part for setting up a control strategy for the manufacturing process. The guidelines recommend to base the definition of acceptance criteria on the entirety of process knowledge. Nevertheless, the guidelines remain unclear on how to derive such limits. Within this contribution we aim to present a sound data science methodology for the definition of intermediate acceptance criteria by putting the guidelines recommendations into practice (ICH Q6B, 1999). By using an integrated process model approach, we leverage manufacturing data and experimental data from small scale to derive intermediate acceptance criteria. The novelty of this approach is that the acceptance criteria are based on pre-defined out-of-specification probabilities, while also considering manufacturing variability in process parameters. In a case study we compare this methodology to a conventional +/- 3 standard deviations (3SD) approach and demonstrate that the presented methodology is superior to conventional approaches and provides a solid line of reasoning for justifying them in audits and regulatory submission.

China Total Business Enterprise R&D Personnel: Compound Annual Growth Rate data was reported at 11.961 % in 2022. This records an increase from the previous number of 9.937 % for 2021. China Total Business Enterprise R&D Personnel: Compound Annual Growth Rate data is updated yearly, averaging 10.675 % from Dec 1992 (Median) to 2022, with 29 observations. The data reached an all-time high of 26.734 % in 2005 and a record low of -14.226 % in 1998. China Total Business Enterprise R&D Personnel: 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 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.

Hyper-secretion and/or hyper-concentration of mucus is a defining feature of multiple obstructive lung diseases, including chronic obstructive pulmonary disease (COPD). Mucus itself is composed of a mixture of water, ions, salt and proteins, of which the gel-forming mucins, MUC5AC and MUC5B, are the most abundant. Recent studies have linked the concentrations of these proteins in sputum to COPD phenotypes, including chronic bronchitis (CB) and acute exacerbations (AE). We sought to determine whether common genetic variants influence sputum mucin concentrations and whether these variants are also associated with COPD phenotypes, specifically CB and AE. We performed a GWAS to identify quantitative trait loci for sputum mucin protein concentration (pQTL) in the Sub-Populations and InteRmediate Outcome Measures in COPD Study (SPIROMICS, n = 708 for total mucin, n = 215 for MUC5AC, MUC5B). Subsequently, we tested for associations of mucin pQTL with CB and AE using regression modeling (n = 822–1300). Replication analysis was conducted using data from COPDGene (n = 5740) and by examining results from the UK Biobank. We identified one genome-wide significant pQTL for MUC5AC (rs75401036) and two for MUC5B (rs140324259, rs10001928). The strongest association for MUC5B, with rs140324259 on chromosome 11, explained 14% of variation in sputum MUC5B. Despite being associated with lower MUC5B, the C allele of rs140324259 conferred increased risk of CB (odds ratio (OR) = 1.42; 95% confidence interval (CI): 1.10–1.80) as well as AE ascertained over three years of follow up (OR = 1.41; 95% CI: 1.02–1.94). Associations between rs140324259 and CB or AE did not replicate in COPDGene. However, in the UK Biobank, rs140324259 was associated with phenotypes that define CB, namely chronic mucus production and cough, again with the C allele conferring increased risk. We conclude that sputum MUC5AC and MUC5B concentrations are associated with common genetic variants, and the top locus for MUC5B may influence COPD phenotypes, in particular CB.

China Higher Education Total R&D Personnel: Full-Time Equivalent data was reported at 725,881.500 FTE in 2022. This records an increase from the previous number of 671,765.800 FTE for 2021. China Higher Education Total R&D Personnel: Full-Time Equivalent data is updated yearly, averaging 248,203.050 FTE from Dec 1991 (Median) to 2022, with 32 observations. The data reached an all-time high of 725,881.500 FTE in 2022 and a record low of 128,100.000 FTE in 1992. China Higher Education Total R&D Personnel: 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.

Protein-Protein, Genetic, and Chemical Interactions for INA (Mus musculus) curated by BioGRID (https://thebiogrid.org); DEFINITION: internexin neuronal intermediate filament protein, alpha

Protein-Protein, Genetic, and Chemical Interactions for IFFO2 (Mus musculus) curated by BioGRID (https://thebiogrid.org); DEFINITION: intermediate filament family orphan 2

Hyper-secretion and/or hyper-concentration of mucus is a defining feature of multiple obstructive lung diseases, including chronic obstructive pulmonary disease (COPD). Mucus itself is composed of a mixture of water, ions, salt and proteins, of which the gel-forming mucins, MUC5AC and MUC5B, are the most abundant. Recent studies have linked the concentrations of these proteins in sputum to COPD phenotypes, including chronic bronchitis (CB) and acute exacerbations (AE). We sought to determine whether common genetic variants influence sputum mucin concentrations and whether these variants are also associated with COPD phenotypes, specifically CB and AE. We performed a GWAS to identify quantitative trait loci for sputum mucin protein concentration (pQTL) in the Sub-Populations and InteRmediate Outcome Measures in COPD Study (SPIROMICS, n = 708 for total mucin, n = 215 for MUC5AC, MUC5B). Subsequently, we tested for associations of mucin pQTL with CB and AE using regression modeling (n = 822–1300). Replication analysis was conducted using data from COPDGene (n = 5740) and by examining results from the UK Biobank. We identified one genome-wide significant pQTL for MUC5AC (rs75401036) and two for MUC5B (rs140324259, rs10001928). The strongest association for MUC5B, with rs140324259 on chromosome 11, explained 14% of variation in sputum MUC5B. Despite being associated with lower MUC5B, the C allele of rs140324259 conferred increased risk of CB (odds ratio (OR) = 1.42; 95% confidence interval (CI): 1.10–1.80) as well as AE ascertained over three years of follow up (OR = 1.41; 95% CI: 1.02–1.94). Associations between rs140324259 and CB or AE did not replicate in COPDGene. However, in the UK Biobank, rs140324259 was associated with phenotypes that define CB, namely chronic mucus production and cough, again with the C allele conferring increased risk. We conclude that sputum MUC5AC and MUC5B concentrations are associated with common genetic variants, and the top locus for MUC5B may influence COPD phenotypes, in particular CB.

CN: Business Enterprise Researchers: % of National Total data was reported at 58.372 % in 2022. This records an increase from the previous number of 57.863 % for 2021. CN: Business Enterprise Researchers: % of National Total data is updated yearly, averaging 58.117 % from Dec 1991 (Median) to 2022, with 32 observations. The data reached an all-time high of 68.588 % in 2008 and a record low of 26.729 % in 1991. CN: Business Enterprise Researchers: % of National Total 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.

China GERD Performed: Higher Education Sector data was reported at 7.837 % in 2022. This records an increase from the previous number of 7.800 % for 2021. China GERD Performed: Higher Education Sector data is updated yearly, averaging 8.525 % from Dec 1991 (Median) to 2022, with 32 observations. The data reached an all-time high of 12.633 % in 1994 and a record low of 6.840 % in 2016. China GERD Performed: Higher Education Sector 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: Gross Domestic Expenditure 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.

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.

China Aerospace Industry: Trade Balance data was reported at -13.704 USD bn in 2021. This records a decrease from the previous number of -10.255 USD bn for 2020. China Aerospace Industry: Trade Balance data is updated yearly, averaging -9.486 USD bn from Dec 1992 (Median) to 2021, with 30 observations. The data reached an all-time high of -1.254 USD bn in 1995 and a record low of -28.690 USD bn in 2018. China Aerospace Industry: Trade Balance 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: Trade Statistics: 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.

China BERD: % of GDP data was reported at 1.982 % in 2022. This records an increase from the previous number of 1.871 % for 2021. China BERD: % of GDP data is updated yearly, averaging 0.983 % from Dec 1991 (Median) to 2022, with 32 observations. The data reached an all-time high of 1.982 % in 2022 and a record low of 0.244 % in 1996. China BERD: % of GDP 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: Business Enterprise Investment 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.

---- The bulletin collects SYNOP reports:FM 12 (SYNOP, Report of surface observation from a fixed land station).(Refer to WMO No.306 - Manual on Codes for the definition of WMO international codes)---- The SITC01 TTAAii Data Designators decode (2) as:T1 (S): Surface data.T2 (I): Intermediate synoptic hour.A1A2 (TC): Tristan da Cunha.(2: Refer to WMO No.386 - Manual on the GTS - Attachment II.5)