Share your research data

Raw and processed physiological data for figures. Supplemental Information on response characteristics of ventricular and subarachnoid recording electrodes.

Encuesta realizada a 181 docentes de instituciones educativas públicas, privadas, urbanas y rurales dentro del Distrito 13D02

Paper title: The economic value of variable selection methods: Evidence from volatility forecasting Authors: Zhiyuan Pan (Institute of Chinese Financial Studies, Southwestern University of Finance and Economics) Yue Zhou (Institute of Chinese Financial Studies, Southwestern University of Finance and Economics) Chuwen Huang (Institute of Chinese Financial Studies, Southwestern University of Finance and Economics) Overview This replication package provides all the cleaned data, code, and instructions required to reproduce all the empirical results in this paper, including the Tables and Figures.

These are the Excel databases and the R script that we have used.

This dataset contains coded information on news items and information sources in local digital journalism from the Coquimbo Region, Chile. The corpus includes 7,044 valid online news items published between 1 September and 31 December 2021 by five digital outlets: Diario El Día, Diario La Región de Coquimbo, Mi Radio, Radio Guayacán, and El Observatodo. The dataset includes a news-level table and a source-level long-format table with 12,354 coded source records. Variables describe media outlet, publication date, headline, topic, geographic scope, website section, recorded number of sources, hyperlinks, comments, URLs, source position, source name or role, and source category. The dataset can be reused for research on source diversity, media pluralism, local journalism, institutional visibility, thematic coverage, and comparative digital journalism in Chile and Latin America.

Data on intention to adoption autonomous delivery vehicles

Replication Package: "Optimal Bailouts in Banking and Sovereign Crises" by Sewon Hur, Zeynep Yom, and Cesar Sosa-Padilla Forthcoming in the Journal of International Economics

source codes and result files

This dataset contains the raw simulation data (Excel files) regarding the disturbance estimation and control allocation performance presented in the associated research paper.

This compilation of historical daily stock market price data relates to the Kenyan Nairobi Securities Exchange (NSE) for the full year 2025. This data is valuable for any machine learning algorithm that needs data (training, validation, testing). This compilation builds on an earlier dataset (2008-2012) that was initially compiled as part of a research project to predict the next day's stock price, based on the previous five days, using Artificial Neural Networks (ANN). This initial research [1],[2] tested 6 stocks[3] using ANN of configuration 5:21:21:1. The data was then enhanced as a new compilation of all stocks for the period 2007-2012[4]. This new dataset augments the NSE dataset from 2007-2012[4], 2013-2020[5], 2021[6], 2022[7], and 2023-2024[8]. The data was scraped from a publicly accessible website[9] licensed by NSE by exporting raw web data to spreadsheets, then cleaning it up to final CSV. Just like the previous compilations, each stock data row has 13 columns (1)Date (2)Stock Code (3)Stock Name (4)12month LowPrice (5)12month HighPrice (6)Day LowPrice (7)Day HighPrice (8)Day FinalPrice (9)Previous traded price (10)Change in price value (11)Change in price% (12)Volume traded (13)Adjusted price. One additional CSV file is also provided to show the stock market sector, with 3 columns (1)Market sector (2)Stock Code (3)Stock Name. This additional dataset provides researchers with an even larger dataset (2007-2025) of stock market data, including market sector information, for machine learning research. List of data files on this dataset: NSE_data_all_stocks_2025.csv NSE_data_stock_market_sectors_2025.csv References: [1] Wanjawa, B. W. (2014). A Neural Network Model for Predicting Stock Market Prices at the Nairobi Securities Exchange (Dissertation, University of Nairobi), https://erepository.uonbi.ac.ke/handle/11295/71466 [2] Wanjawa, B. W., & Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. arXiv preprint arXiv:1502.06434. [3] Wanjawa, Barack (2020), “Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks”, Mendeley Data, v3, http://dx.doi.org/10.17632/95fb84nzcd.3 [4] Wanjawa, Barack (2020), “Nairobi Securities Exchange All Stocks Prices 2007-2012”, Mendeley Data, v1, http://dx.doi.org/10.17632/5hk4zw32f5.1 [5] Wanjawa, Barack (2021), “Nairobi Securities Exchange (NSE) All Stocks Prices 2013-2020”, Mendeley Data, V2, doi: 10.17632/73rb78pmzw.2 [6] Wanjawa, Barack (2022), “Nairobi Securities Exchange (NSE) Kenya - All Stocks Prices 2021”, Mendeley Data, V5, doi: 10.17632/97hkwn5y3x.5 [7] Wanjawa, Barack (2024), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2022”, Mendeley Data, V2, doi: 10.17632/jmcdmnyh2s.2 [8] Wanjawa, Barack (2025), “Kenya Nairobi Securities Exchange (NSE) All Stocks Prices 2023-2024”, Mendeley Data, V2, doi: 10.17632/ss5pfw8xnk.2 [9] Synergy Systems Ltd. (2023). MyStocks. Retrieved Nov. 15, 2025, from https://live.mystocks.co.ke/