The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Evaluation values of financial risk on quantitative indices.

This dataset provides foundational factor and portfolio return data used in empirical finance and asset pricing research. It contains: - Fama–French 3-Factor and 5-Factor models - Size (ME), Book-to-Market (B/M), Operating Profitability (OP), and Investment (Inv) portfolios - Bivariate portfolios (e.g., 2x3 Size-B/M sorts) - Industry portfolio returns All data originate from the Kenneth R. French Data Library and are based on CRSP and Compustat databases. Data are value-weighted and expressed in percentages.

Some files in this dataset contain header comments describing data sources and methodology (as shown below):

This file was created using the 202508 CRSP database.
The 1-month TBill rate data until 202405 are from Ibbotson Associates. 
Starting from 202406, the 1-month TBill rate is from ICE BofA US 1-Month Treasury Bill Index.

To correctly read such files in Python (pandas), use the comment parameter — it automatically ignores all lines starting with a specific symbol (e.g., none here, so you can skip manually):

Example 1 — Automatically detect header rows:

import pandas as pd

# Detect the first numeric line to find where data starts
file_path = "F-F_Research_Data_5_Factors_2x3.csv"

with open(file_path) as f:
  lines = f.readlines()

# Find where the header line (column names) appears
for i, line in enumerate(lines):
  if "Mkt-RF" in line:
    skip_rows = i
    break

df = pd.read_csv(file_path, skiprows=skip_rows, sep=r"\s+")
print(df.head())

Example 2 — Skip a known number of comment lines manually:

df = pd.read_csv("F-F_Research_Data_5_Factors_2x3.csv", skiprows=3, sep=r"\s+")

Example 3 — If comments are prefixed (e.g., with #):

df = pd.read_csv("F-F_Research_Data_5_Factors_2x3.csv", comment="#", sep=",")

File Structure Example

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

The Global Food Security Index (GFSI) considers the issues of food affordability, availability, quality and safety, and natural resources and resilience across a set of 113 countries. The index is a dynamic quantitative and qualitative benchmarking model constructed from 59 unique indicators that measure the drivers of food security across both developing and developed countries.

📊 About Dataset This dataset provides a comprehensive historical record of the S&P 500's constituent companies and their respective weights, spanning from September 30, 2000, to 2024.

The data is sourced from the annual reports of the SPDR S&P 500 ETF (SPY), the largest and oldest exchange-traded fund that tracks the S&P 500. Because the SPY ETF's investment strategy is to hold all 500 stocks in the index in direct proportion to their index weighting, its holdings report is a precise, real-world snapshot of the S&P 500's composition at that time.

This dataset offers a clean, point-in-time view of the market's structure, allowing for analysis that avoids "survivorship bias" (i.e., analyzing only the companies that are currently in the index). It is a valuable resource for quantitative analysts, financial researchers, and data scientists looking to study the evolution of the U.S. large-cap market.

Each file represents an annual snapshot, typically from the end of the fiscal year (September 30th), and contains a list of all companies held by the ETF and their percentage weight in the portfolio.

💡 Potential Use Cases This historical data is ideal for a wide range of financial analysis, quantitative modeling, and academic research.

Market & Sector Trend Analysis:

Visualize the evolution of the U.S. economy by tracking the rise and fall of sector weights over time. For example, you can map the decline of Industrials and Energy and the dramatic rise of the Information Technology sector since 2000.

Analyze the changing landscape of top-tier companies, observing how giants from 2000 (like GE, Exxon, and Cisco) were replaced by today's tech leaders (like Apple, Microsoft, and Nvidia).

Quantitative Strategy Backtesting:

Build and test investment strategies against a historically accurate benchmark. For example, you could test a "sector rotation" strategy based on economic indicators and see how it would have performed.

Simulate portfolio performance without survivorship bias, leading to more realistic and robust backtest results.

Market Concentration Analysis:

Track the concentration of the index in its top 10 holdings.

Analyze how today's market concentration compares to historical periods like the 2000 dot-com bubble, which can be an indicator of market risk.

Academic & Economic Research:

Study the relationship between sector performance and the business cycle. For instance, do certain sectors (like Consumer Staples) consistently outperform during recessions?

Analyze factor investing (e.g., Value, Growth, Momentum) by applying factor definitions to the index constituents at each point in time.

Portfolio Management & Benchmarking:

Use the historical data as a benchmark to evaluate the performance of an active investment portfolio.

Analyze a portfolio's "active share" and "tracking error" against the S&P 500's actual composition from any given year.

Quantitative index factors.

Baidu Search Index is a big data analytics tool developed by Baidu to track changes in keyword search popularity within its search engine. By analyzing trends in the Baidu Search Index for specific keywords, users can effectively monitor public interest in topics, companies, or brands.

As an ecosystem partner of Baidu Index, Datago has direct access to keyword search index data from Baidu's database, leveraging this information to build the BSIA-Consumer. This database encompasses popular brands that are actively searched by Chinese consumers, along with their commonly used names. By tracking Baidu Index search trends for these keywords, Datago precisely maps them to their corresponding publicly listed stocks.

The database covers over 1,100 consumer stocks and 3,000+ brand keywords across China, the United States, Europe, and Japan, with a particular focus on popular sectors like luxury goods and vehicles. Through its analysis of Chinese consumer search interest, this database offers investors a unique perspective on market sentiment, consumer preferences, and brand influence, including:

• Brand Influence Tracking – By leveraging Baidu Search Index data, investors can assess the level of consumer interest in various brands, helping to evaluate their influence and trends within the Chinese market.

• Consumer Stock Mapping – BSIA-consumer provides an accurate linkage between brand keywords and their associated consumer stocks, enabling investor analysis driven by consumer interest.

• Coverage of Popular Consumer Goods – BSIA-consumer focuses specifically on trending sectors like luxury goods and vehicles, offering valuable insights into these industries.

• Coverage: 1000+ consumer stocks

• History: 2016-01-01

• Update Frequency: Daily

Index Time Series for Alpha Architect U.S. Quantitative Momentum ETF. The frequency of the observation is daily. Moving average series are also typically included. Under normal circumstances,the fund will invest at least 80% of its net assets (plus any borrowings for investment purposes) in U.S.- listed companies that meet the Sub-Adviser"s definition of momentum ("Momentum Companies "). The Sub-Adviser employs a multi-step, quantitative, rules-based methodology to identify a portfolio of approximately 50 to 200 equity securities with the highest relative momentum.

Language is both a cause and a consequence of the social processes that lead to conflict or peace. “Hate speech” can mobilize violence and destruction. What are the characteristics of “peace speech” that reflect and support the social processes that maintain peace? This study used existing peace indices, machine learning, and on-line, news media sources to identify the words most associated with lower-peace versus higher-peace countries. As each peace index measures different social properties, they can have different values for the same country. There is however greater consensus with these indices for the countries that are at the extremes of lower-peace and higher-peace. Therefore, a data driven approach was used to find the words most important in distinguishing lower-peace and higher-peace countries. Rather than assuming a theoretical framework that predicts which words are more likely in lower-peace and higher-peace countries, and then searching for those words in news media, in this study, natural language processing and machine learning were used to identify the words that most accurately classified a country as lower-peace or higher-peace. Once the machine learning model was trained on the word frequencies from the extreme lower-peace and higher-peace countries, that model was also used to compute a quantitative peace index for these and other intermediate-peace countries. The model successfully yielded a quantitative peace index for intermediate-peace countries that was in between that of the lower-peace and higher-peace, even though they were not in the training set. This study demonstrates how natural language processing and machine learning can help to generate new quantitative measures of social systems, which in this study, were linguistic differences resulting in a quantitative index of peace for countries at different levels of peacefulness.

The dataset presents the SNI time series for the period 01/31/2023-01/31/2024. SNI is a quantitative index reflecting sanctions pressure on financial markets. In earlier studies, we have proven that SNI causes changes in domestic financial markets. Consequently, the growth of this index shows an increase in sanctions tension and is the reason for the volatility of financial assets.

SNI is justified in the article: Rychkov, V. (2023). Estimate of the Impact of Trade Sanctions on the Russian Financial Markets. In: Isaeva, E., Rocha, Á. (eds) Science and Global Challenges of the 21st Century – Innovations and Technologies in Interdisciplinary Applications. Perm Forum 2022. Lecture Notes in Networks and Systems, vol 622. Springer, Cham. https://doi.org/10.1007/978-3-031-28086-3_70

When using SNI, a link to the Dataset and article is required!

Computer-based chemogenomics approaches compare macromolecular drug targets based on their amino acid sequences or derived properties, by similarity of their ligands, or according to ligand-target interaction models. Here we present ARTS (Assay Related Target Similarity) as a quantitative index that estimates target similarity directly from measured affinities of a set of probe compounds. This approach reduces the risk of deducing artificial target relationships from mutually inactive compounds. ARTS implements a scoring scheme that matches intertarget similarity based on dose–response measurements. While all experimentally derived target similarities have a tendency to be data set-dependent, we demonstrate that ARTS depends less on the used data set than the commonly used Pearson correlation or Tanimoto index.

Cut-off point of quantitative indices according to the Youden index, and its corresponding sensitivity and specificity, to diagnosis interstitial lung disease in Sjögren’s syndrome.

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

Index Time Series for Alpha Architect U.S. Quantitative Value ETF. The frequency of the observation is daily. Moving average series are also typically included. The Sub-Adviser employs a multi-step, quantitative, rules-based methodology to identify a portfolio of approximately 50 to 200 undervalued U.S. equity securities with the potential for capital appreciation. A security is considered to be undervalued when it trades at a price below the price at which the Sub-Adviser believes it would trade if the market reflected all factors relating to the company"s worth.

Yearly citation counts for the publication titled "Diagnostic performance of the specific uptake size index for semi-quantitative analysis of I-123-FP-CIT SPECT: harmonized multi-center research setting versus typical clinical single-camera setting".

Correlations of quantitative indices and semiquantiative methods and lung function tests.

Abstract

This study examines various dimensions of primary health care delivery in Uganda, using a baseline survey of public and private dispensaries, the most common lower level health facilities in the country.

The survey was designed and implemented by the World Bank in collaboration with the Makerere Institute for Social Research and the Ugandan Ministries of Health and of Finance, Planning and Economic Development. It was carried out in October - December 2000 and covered 155 local health facilities and seven district administrations in ten districts. In addition, 1617 patients exiting health facilities were interviewed. Three types of dispensaries (both with and without maternity units) were included: those run by the government, by private for-profit providers, and by private nonprofit providers, mainly religious.

This research is a Quantitative Service Delivery Survey (QSDS). It collected microlevel data on service provision and analyzed health service delivery from a public expenditure perspective with a view to informing expenditure and budget decision-making, as well as sector policy.

Objectives of the study included: 1) Measuring and explaining the variation in cost-efficiency across health units in Uganda, with a focus on the flow and use of resources at the facility level; 2) Diagnosing problems with facility performance, including the extent of drug leakage, as well as staff performance and availability;
3) Providing information on pricing and user fee policies and assessing the types of service actually provided; 4) Shedding light on the quality of service across the three categories of service provider - government, for-profit, and nonprofit; 5) Examining the patterns of remuneration, pay structure, and oversight and monitoring and their effects on health unit performance; 6) Assessing the private-public partnership, particularly the program of financial aid to nonprofits.

Geographic coverage

The study districts were Mpigi, Mukono, and Masaka in the central region; Mbale, Iganga, and Soroti in the east; Arua and Apac in the north; and Mbarara and Bushenyi in the west.

Analysis unit

• local dispensary with or without maternity unit

Universe

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

The sample design was governed by three principles. First, to ensure a degree of homogeneity across sampled facilities, attention was restricted to dispensaries, with and without maternity units (that is, to the health center III level). Second, subject to security constraints, the sample was intended to capture regional differences. Finally, the sample had to include facilities in the main ownership categories: government, private for-profit, and private nonprofit (religious organizations and NGOs). The sample of government and nonprofit facilities was based on the Ministry of Health facility register for 1999. Since no nationwide census of for-profit facilities was available, these facilities were chosen by asking sampled government facilities to identify the closest private dispensary.

Of the 155 health facilities surveyed, 81 were government facilities, 30 were private for-profit facilities, and 44 were nonprofit facilities. An exit poll of clients covered 1,617 individuals.

The final sample consisted of 155 primary health care facilities drawn from ten districts in the central, eastern, northern, and western regions of the country. It included government, private for-profit, and private nonprofit facilities. The nonprofit sector includes facilities owned and operated by religious organizations and NGOs. Approximately one third of the surveyed facilities were dispensaries without maternity units; the rest provided maternity care. The facilities varied considerably in size, from units run by a single individual to facilities with as many as 19 staff members.

Ministry of Health facility register for 1999 was used to design the sampling frame. Ten districts were randomly selected. From the selected districts, a sample of government and private nonprofit facilities and a reserve list of replacement facilities were randomly drawn. Because of the unreliability of the register for private for-profit facilities, it was decided that for-profit facilities would be identified on the basis of information from the government facilities sampled. The administrative records for facilities in the original sample were first reviewed at the district headquarters, where some facilities that did not meet selection criteria and data collection requirements were dropped from the sample. These were replaced by facilities from the reserve list. Overall, 30 facilities were replaced.

The sample was designed in such a way that the proportion of facilities drawn from different regions and ownership categories broadly mirrors that of the universe of facilities. Because no nationwide census of for-profit health facilities is available, it is difficult to assess the extent to which the sample is representative of this category. A census of health care facilities in selected districts, carried out in the context of the Delivery of Improved Services for Health (DISH) project supported by the U.S. Agency for International Development (USAID), suggests that about 63 percent of all facilities operate on a for-profit basis, while government and nonprofit providers run 26 and 11 percent of facilities, respectively. This would suggest an undersampling of private providers in the survey. It is not clear, however, whether the DISH districts are representative of other districts in Uganda in terms of the market for health care.

For the exit poll, 10 interviews per facility were carried out in approximately 85 percent of the facilities. In the remaining facilities the target of 10 interviews was not met, as a result of low activity levels.

Sampling deviation

In the first stage in the sampling process, eight districts (out of 45) had to be dropped from the sample frame due to security concerns. These districts were Bundibugyo, Gulu, Kabarole, Kasese, Kibaale, Kitgum, Kotido, and Moroto.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• District Health Team Questionnaire;
• District Facility Data Sheets;
• Uganda Health Facility Survey Questionnaire;
• Facility Data Sheets;
• Facility Patient Exit Poll Questionnaire.

The survey collected data at three levels: district administration, health facility, and client. In this way it was possible to capture central elements of the relationships between the provider organization, the frontline facility, and the user. In addition, comparison of data from different levels (triangulation) permitted cross-validation of information.

At the district level, a District Health Team Questionnaire was administered to the district director of health services (DDHS), who was interviewed on the role of the DDHS office in health service delivery. Specifically, the questionnaire collected data on health infrastructure, staff training, support and supervision arrangements, and sources of financing.

The District Facility Data Sheet was used at the district level to collect more detailed information on the sampled health units for fiscal 1999-2000, including data on staffing and the related salary structures, vaccine supplies and immunization activity, and basic and supplementary supplies of drugs to the facilities. In addition, patient data, including monthly returns from facilities on total numbers of outpatients, inpatients, immunizations, and deliveries, were reviewed for the period April-June 2000.

At the facility level, the Uganda Health Facility Survey Questionnaire collected a broad range of information related to the facility and its activities. The questionnaire, which was administered to the in-charge, covered characteristics of the facility (location, type, level, ownership, catchment area, organization, and services); inputs (staff, drugs, vaccines, medical and nonmedical consumables, and capital inputs); outputs (facility utilization and referrals); financing (user charges, cost of services by category, expenditures, and financial and in-kind support); and institutional support (supervision, reporting, performance assessment, and procurement). Each health facility questionnaire was supplemented by a Facility Data Sheet (FDS). The FDS was designed to obtain data from the health unit records on staffing and the related salary structure; daily patient records for fiscal 1999-2000; the type of patients using the facility; vaccinations offered; and drug supply and use at the facility.

Finally, at the facility level, an exit poll was used to interview about 10 patients per facility on the cost of treatment, drugs received, perceived quality of services, and reasons for using that unit instead of alternative sources of health care.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

STATA cleaning do-files and the data quality reports on the datasets can also be found in external resources.

This dataset contains the quantitative measurement of the Digital Economy and Society Index (DESI), the ASEAN Digital Integration Index (ADII), and the Digital Intelligence Index (DII) in 2019.