Data

Annual articles published in scientific and technical journals

What you should know about this indicator

How is this data described by its producer?

Article counts refer to publications from a selection of conference proceedings and peer-reviewed journals from Scopus in science and engineering fields, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines.

Aggregation method:

Gap-filled total

Statistical concept and methodology:

Methodology: The Science and Engineering Indicators 2024 report “Publications Output: U.S. Trends and International Comparisons” uses a large database of publication records as a source of bibliometric data. Bibliometric data include each article’s title, author(s), authors’ institution(s), references, journal title, unique article-identifying information (journal volume, issue, and page numbers or digital object identifier), and year or date of publication. The PBS report uses Scopus, a bibliometric database owned by Elsevier and containing scientific literature with English titles and abstracts, to examine national and global scientific publication–related activity.?

Article counts refer to publications from a selection of conference proceedings and peer-reviewed journals in science and engineering fields from Scopus, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines: agricultural sciences, astronomy and astrophysics, biological and biomedical sciences, chemistry, computer and information sciences; engineering; geosciences, atmospheric sciences, and ocean sciences; health sciences; material sciences; mathematics and statistics; natural resources and conservation; physics; psychology; social sciences.

Statistical concept(s): The number of journal articles is presented using fractional counting: a method of counting science and engineering publications in which credit for coauthored publications is divided among the collaborating institutions or regions, countries, or economies based on the proportion of their participating authors. Fractional counting allocates the publication count based on the proportion of the coauthors named on the article with institutional addresses from each region, country, or economy. Fractional counting enables the counts to sum up to the number of total articles. (Source: https://ncses.nsf.gov/pubs/nsb202333/glossary)

Development relevance:

A scientific journal is a periodical publication intended to further the progress of science, usually by reporting new research. Most journals are highly specialized, although some of the oldest journals such as Nature publish articles and scientific papers across a wide range of scientific fields. Scientific journals contain articles that have been peer reviewed. When a scientific journal describes experiments or calculations, they must supply enough details that an independent researcher could repeat the experiment or calculation to verify the results. Each such journal article becomes part of the permanent scientific record.

Some journals, such as Nature, Science, Proceedings of the National Academy of Sciences of the United States of America (PNAS), and Physical Review Letters, have a reputation of publishing articles that mark a fundamental breakthrough in their respective fields.

Limitations and exceptions:

Because the bibliometric database is constantly updated, the National Center for Science and Engineering Statistics (NCSES) does not recommend comparing bibliometric data across different editions of the Science and Engineering Indicators publication. For each edition of Indicators, NCSES uses a fixed snapshot of the database. This means that although trends are comparable, the exact number of articles, citations, and other data will vary across editions. For more information about comparing fixed versus dynamic journal data sets, see Schneider et al. (2019). Data before 2003 is sourced from earlier editions of the Science and Engineering Indicators report and may not be strictly comparable with 2003-2022 data.

The Scopus database is constructed from articles and conference proceedings with an English-language title and abstract; therefore, the database contains an unmeasurable bias because not all science and engineering (S&E) articles and conference proceedings meet the English language requirement (Elsevier 2020). (Source: https://ncses.nsf.gov/pubs/nsb202333/technical-appendix)

Source
National Science Foundation Science and Engineering Indicators, via World Bank (2026)processed by Our World in Data
Last updated
February 27, 2026
Next expected update
February 2027
Date range
1996–2022

Sources and processing

National Science Foundation Science and Engineering Indicators, via World Bank – World Development Indicators

The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades.WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability.The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization.WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally.Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.

Retrieved on
February 27, 2026
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Science and Engineering Indicators, National Science Foundation (NSF), uri: https://ncses.nsf.gov/indicators. Indicator IP.JRN.ARTC.SC (https://data.worldbank.org/indicator/IP.JRN.ARTC.SC). World Development Indicators - World Bank (2026). Accessed on 2026-02-27.

The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades.WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability.The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization.WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally.Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.

Retrieved on
February 27, 2026
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Science and Engineering Indicators, National Science Foundation (NSF), uri: https://ncses.nsf.gov/indicators. Indicator IP.JRN.ARTC.SC (https://data.worldbank.org/indicator/IP.JRN.ARTC.SC). World Development Indicators - World Bank (2026). Accessed on 2026-02-27.

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Annual articles published in scientific and technical journals”. Our World in Data (2026). Data adapted from National Science Foundation Science and Engineering Indicators, via World Bank. Retrieved from https://archive.ourworldindata.org/20260512-185716/grapher/scientific-and-technical-journal-articles.html [online resource] (archived on May 12, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

National Science Foundation Science and Engineering Indicators, via World Bank (2026) – processed by Our World in Data

Full citation

National Science Foundation Science and Engineering Indicators, via World Bank (2026) – processed by Our World in Data. “Annual articles published in scientific and technical journals” [dataset]. National Science Foundation Science and Engineering Indicators, via World Bank, “World Development Indicators 125” [original data]. Retrieved May 12, 2026 from https://archive.ourworldindata.org/20260512-185716/grapher/scientific-and-technical-journal-articles.html (archived on May 12, 2026).

Quick download

Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/scientific-and-technical-journal-articles.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/scientific-and-technical-journal-articles.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/scientific-and-technical-journal-articles.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/scientific-and-technical-journal-articles.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/scientific-and-technical-journal-articles.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/scientific-and-technical-journal-articles.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/scientific-and-technical-journal-articles.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/scientific-and-technical-journal-articles.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear