SCImago IBER Dataset (2024 Edition): Indicators on Research, Innovation, and Societal Impact
Description
The SCImago IBER Dataset (2024 Edition) provides a detailed overview of research, innovation, and societal impact indicators for institutions, regions, and countries within the Ibero-American region. Derived from SCImago’s annual rankings, this dataset contains data for 2024 extracted via web scraping. The dataset focuses on institutions that meet the inclusion criteria of at least one Scopus-indexed publication during the five-year period 2019–2023. This edition includes 16 indicators across three key dimensions: research, innovation, and societal impact. Notable updates for 2024 include three new societal impact indicators: contributions to the United Nations Sustainable Development Goals (SDGs), female participation in scientific output, and influence on public policy. The dataset also retains classic indicators such as scientific output, normalized impact, international collaboration, and technological impact. Data Highlights: • Coverage of institutions, regions, and countries in the Ibero-American region. • Three main dimensions: research, innovation, and societal impact. • 16 indicators, including: • Research: Scientific output, normalized impact, and leadership. • Innovation: Patents, technological impact, and innovative knowledge. • Societal Impact: SDG-related output, female participation, and policy influence. This dataset provides valuable insights for academics, policymakers, and institutional leaders aiming to benchmark performance, foster international collaboration, and evaluate the societal impact of research activities.
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Steps to reproduce
1. Data Source: The data was extracted from the SCImago IBER platform on December 20, 2024. SCImago uses sources like Scopus, Unpaywall, Patstat, PlumX, and Mendeley to compute indicators for institutions from 22 Ibero-American countries that published at least one Scopus-indexed document during 2019–2023. 2. SCImago Methodology: Indicators were calculated by SCImago using standardized processes, including: • Unique identification and classification of institutions by sector (e.g., universities, health) and hierarchy. • Attribution of publications and citations to institutions based on affiliations. • A five-year analysis window (2019–2023). • New 2024 indicators: SDG contributions, female participation in research, and public policy impact. 3. Data Extraction: • Web scraping collected institutional names, classifications, and metrics (e.g., normalized impact, patents, societal indicators) following ethical guidelines and SCImago policies. 4. Data Cleaning and Structuring: • Standardized institutional names to resolve ambiguities. • Grouped institutions by sector and hierarchy. • Organized indicators into a tabular format with research, innovation, and societal impact metrics. 5. Output: • The final dataset is stored in .xlsx format, with 16 indicators grouped across three dimensions: research, innovation, and societal impact. 6. Reproducibility: To replicate data extraction, use the following Python snippet: import requests from bs4 import BeautifulSoup import pandas as pd url = "https://www.scimagoiber.com/" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) soup = BeautifulSoup(response.content, 'html.parser') table = soup.find("table", {"id": "example-table-id"}) rows = table.find_all("tr") data = [[col.text.strip() for col in row.find_all("td")] for row in rows] columns = ["Institution", "Country", "Region", "Indicator_1", "Indicator_2", ...] df = pd.DataFrame(data, columns=columns) df.to_excel("SCImago_IBER_Dataset_2024.xlsx", index=False) • Adapt the script to SCImago’s table structures. • The methodology for calculating indicators follows SCImago’s standards.