Scientific and technical journal articles

The indicator 'Scientific and Technical Journal Articles' serves as a vital measure of a country's research output and intellectual vitality. Consistent publication in peer-reviewed journals reflects the strength of an country's research and innovation environment, indicating not only the quantity but also the quality of scientific inquiry being conducted. In 2020, the median value for scientific and technical journal articles globally was 427.34, suggesting a robust share of outputs across nations. This metric is not merely a reflection of numbers; it encapsulates the endeavors of various researchers, educators, and institutions striving for advancement in knowledge and technology.

The importance of this indicator is manifold. Firstly, scientific publications are critical for the dissemination of knowledge. They serve as a platform for researchers to share their findings with the global community, enabling further exploration and development. The peer-review process enhances the credibility of published research, ensuring that new discoveries are well-vetted before being communicated to the public. Moreover, scientific publications are often crucial for academic recognition, which can influence funding opportunities and career advancements for researchers.

This indicator has a symbiotic relationship with various other indicators of innovation and education. For instance, countries with higher rates of scientific publications typically exhibit strong performance in patent registrations, technological advancements, and higher education outputs. These linkages reinforce the idea that a thriving research ecosystem not only generates new knowledge but also translates it into practical applications. Furthermore, a well-funded research environment, marked by significant investment in education and innovation, often correlates with increased productivity in scientific publications.

Several factors influence the production of scientific and technical journal articles. One of the most prominent is government funding and investment in research. Countries that allocate substantial budgets to research institutions often yield higher publication rates. For example, the top five regions for journal articles in 2020 were dominated by nations such as China (669744.3 articles), and the United States (455855.57). This disparity highlights how investment in research capabilities and fostering an environment conducive to intellectual inquiry engenders high publication outputs.

Cultural factors play a significant role as well. Countries with a strong tradition of scholarly pursuit, such as Germany (109378.75) and the United Kingdom (105564.47), often display higher publication rates. This suggests that societal values emphasizing education and research can bolster academic productivity. Conversely, nations with less focus on research and development tend to publish fewer articles, as evidenced by the bottom five areas: Tuvalu (0.39), Nauru (0.9), and others, which have published less than a few articles in 2020. This disparity underscores the impact of local culture, policies, and resources on scientific output.

Strategies for enhancing the number of scientific and technical journal articles can address these disparities. Prioritizing funding for research institutions, increasing access to educational resources, and encouraging collaboration between academia and industry can all foster an environment conducive to scientific inquiry. Furthermore, nations can benefit from establishing partnerships with more advanced research institutions globally, allowing for knowledge transfer and capacity building. Establishing grants, scholarship opportunities, and incentives for publishing research can also engage the research community in higher outputs.

However, there are flaws in relying solely on the number of publications as a measure of research success. Quantity does not always equate to quality, and there are concerns about the phenomenon of ‘publish or perish’, where researchers might prioritize publishing to the detriment of meaningful scientific inquiry. Moreover, the focus on journal articles can overshadow other forms of knowledge dissemination, such as conference presentations, patents, and community engagement, which also contribute to the broader landscape of scientific communication.

From 1996 to 2020, the global values of scientific publications reflected a notable upward trajectory, indicating a growing commitment to research. In 1996, the total was recorded at 984812.17, gradually increasing to 2933010.7 by 2020. This upward trend underscores the escalating importance placed on research and development globally. The consistent growth reinforces how investment in education and innovation directly supports scientific advancement, suggesting a foundational need for continual improvement in these sectors to maintain progress.

In conclusion, the indicator of scientific and technical journal articles serves as a crucial barometer of national research output and can influence numerous aspects of societal development. Its relation to funding, cultural dynamics, and educational strategies underlines the organic nature of research ecosystems. While there may be some flaws and the need for a diversified approach to measuring research success, the upward trajectory in publication over decades points toward an increasingly invested global commitment to science and knowledge advancement.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'IP.JRN.ARTC.SC'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'IP.JRN.ARTC.SC'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))