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.

Article Abstract To better allocate funds in the new EU research framework programme Horizon Europe, an assessment of current and past efforts is crucial. In this paper we develop and apply a multi-method qualitative and computational approach to provide a catalogue of climate crisis mitigation technologies on the EU level between 2014 and 2020. Using the approach, we observed no public EU-level funding for multiple technologies prioritised by the EU, such as low-carbon production and use of cement and chemicals, electric battery, and a number of industrial decarbonisation processes. We observed a rising trend in the funding of solar power and onshore wind, the adjacent to them power-to-X technology, as well as recycling. At the same time, the shares of funding into fuel cell, biofuel, demand-side energy management, microgrids, and waste management show a decline trend. With note of the exploratory character of the present paper, we propose that the EU Horizon 2020 funding of clean technologies only partially reflected the expectations of key institutionalised EU actors due to the existence of many non-funded prioritised technologies.

Abstract

This survey is the first detailed study on the phenomena of teacher absenteeism in Indonesia obtained from two unannounced visits to 147 sample schools in October 2002 and March 2003. The study was conducted by the SMERU Research Institute and the World Bank, affiliated with the Global Development Network (GDN). Similar surveys were carried out at the same time in seven other developing countries: Bangladesh, Ecuador, India, Papua New Guinea, Peru, Uganda, and Zambia.

This research focuses on primary school teacher absence rates and their relations to individual teacher characteristics, conditions of the community and its institutions, and the education policy at various levels of authority. A teacher was considered as absent if at the time of the visit the researcher could not find the sample teacher in the school.

This survey was conducted in randomly selected 10 districts/cities in four Indonesian regions: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara.

Geographic coverage

Java-Bali, Sumatera, Kalimantan-Sulawesi and Nusa Tenggara regions

Analysis unit

• Teachers
• Schools

Kind of data

Sample survey data [ssd]

Sampling procedure

Information from Indonesian Statistics Agency (BPS) and the Ministry of Education was used as a basis to build a sample frame. The data gathered included the amount of total population, a list of villages and primary school facilities in each district/city. Due to limited time and resources, this research only focused on primary schools. In Indonesia, there are two types of primary education facilities: primary schools and primary madrasah. Primary schools are regulated by the Ministry of National Education, using the general curriculum, while primary madrasah are regulated by the Ministry of Religious Affairs, using a mixed (general and Islamic) curriculum.

A sample of districts/cities and schools (consisting of primary schools and primary madrasah) were selected using the following steps. First, Indonesia was divided into several regions based on the number of total population: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara. Indonesian provinces that were suffering from various conflicts (such as Aceh, Central Sulawesi, Maluku, North Maluku, and Papua) were removed from the sample selection process. Then, from each region, a total of five districts and cities were randomly selected, taking into account the population of each district/city.

Second, 12 schools were selected in each district/city. Before choosing sampled schools, researchers randomly selected 10 villages in each district/city to be sampled, taking into account the location of these villages (in urban or rural areas). One of the 10 villages was a backup village to anticipate the possibility of a village that was too difficult to reach. In each village sampled, researchers asked residents about the location of primary schools/madrasah (both public and private) in these villages. They started visiting schools, giving priority to public primary schools/madrasahs. To meet the number of samples in each district/city, additional samples were selected from private schools.

Third, in each school sampled, the researcher would request a list of teachers. If a school visited was considered to be large, such as schools with more than 15 teachers, then the researcher would only interview 15 teachers chosen randomly to ensure that survey quality could be maintained despite the limited time and resources. Each school was visited twice, both on an unannounced date. From the 147 primary schools/madrasah in the sample, 1,441 teachers were selected in each visit (because this is a panel study, the teacher absence data that were used were taken only from teachers that could be interviewed or whose data were obtained from both visits). If there were teachers whose information was only obtained from one of the visits, then their data was not included in the dataset panel.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• Teacher Questionnaire, First Visit
• Teacher Questionnaire, Second Visit.

Cleaning operations

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

The STATA cleaning do-file and the data quality report on the dataset can also be found in external resources.

Quantitative survey along the small ruminant value chains in Ethiopia

Current set contains 12 Processors.

WARNING: Data cleaning is on going. We remind users that data downloadable from the portal is for analysis ONLY. Any cleaning happening to these files WILL NOT affect the database. Errors and inconsistencies MUST be reported to the project staff in charge of cleaning.

CIA World Factbook 2024–2025: Global Multi-Domain Metrics

💡 Dataset Overview: A Comprehensive Global Reference

This dataset is a comprehensive, multi-domain compilation derived from the CIA World Factbook 2024–2025.
It provides a unified and structured source for comparative analysis across seven critical dimensions covering 259 global entities including sovereign nations and dependent territories.

It serves as a foundational resource for projects in: - 🌍 Economics
- 🏛️ Political Science
- 🌱 Environmental Studies
- 📊 Data Visualization
- 🤖 Machine Learning

The dataset enables cross-domain insights into the factors driving global stability and development.

📚 Data Source and Provenance

• Official Source: CIA World Factbook (2024–2025)
• Authority: U.S. Central Intelligence Agency
• Coverage: Global (Sovereign nations + dependent territories)

The Factbook is a high-authority governmental reference providing detailed geographic, political, demographic, and socio-economic data.

🗃️ Dataset Structure: 7 Linked CSV Files

Each file is linked by a common key column - Country - enabling easy joins across domains.

⚙️ Data Integrity & Preprocessing Requirements

Raw values from the Factbook are human-readable and contain: - Unit suffixes (e.g., "sq km", "km", "m") - Percent signs ("%") - Thousands separators (e.g., "2,381,740")

Before quantitative analysis, perform data cleaning.

🔧 Required Cleaning Steps:

1. Remove unit labels, commas, and percentage signs.
2. Convert the cleaned columns to a numeric type (e.g., float).

🧹 Example Preprocessing Workflow (pandas)

# Cleaning a column with comma separators and unit suffix
df['Area_Total'] = (
  df['Area_Total']
   .str.replace(',', '', regex=True)
   .str.replace(' sq km', '', regex=True)
   .astype(float)
)

Uncovering Knowledge: A Clean Brain Tumor Dataset for Advanced Medical Research

Introduction:

• This dataset, available in RAR archive format, consists of four classes, including three tumor classes (Pituitary, Glioma and Meningioma) and one class representing normal brain MRI scans.
• The strength of this dataset in comparison with other releases across the Kaggle is the cleanness of data. In this regard, we subjected the initial dataset to a meticulous data cleaning pipeline. This pipeline involved several steps aimed at enhancing the dataset's integrity and usability.
• The initial data source for this dataset is the brain tumor classification MRI dataset, which can be accessed at this link.

Data Cleaning Process:

• Removal of Duplicate Samples: We employed an image vector comparison method to identify and remove duplicate samples, ensuring that each data point is unique.
• Correction of Mislabeled Images: Using our domain knowledge, we carefully inspected and corrected falsely labeled images, ensuring that they were appropriately categorized. This step greatly enhances the accuracy of the dataset.
• Image Resizing: All images in the dataset were resized to a memory-efficient yet academically accepted size of (224, 224), facilitating easier processing and analysis. Statistics: *Before the cleaning pipeline, the dataset contained the following number of samples for each class from the initial data source:
• Normal: 500
• Glioma: 926
• Meningioma: 937
• Pituitary: 901

After applying the data cleaning pipeline, the number of samples in each category decreased on average by approximately 3-9%. This reduction ensures the data integrity while maintaining a sufficient number of samples for comprehensive analysis.

Data Augmentation:

To enhance the diversity and robustness of the dataset, we employed various image augmentation techniques. These techniques were applied to the images without altering the labels. Here is a summary of the augmentation methods used: - Salt and Pepper Noise: Introducing random noise by setting pixels to white or black based on a specified intensity. - Histogram Equalization: Applying histogram equalization to enhance the contrast and details in the images. - Rotation: Rotating the images clockwise or counterclockwise by a specified angle. - Brightness Adjustment: Modifying the brightness of the images by adding or subtracting intensity values. - Horizontal and Vertical Flipping: Flipping the images horizontally or vertically to create mirror images.

Use Cases and Potential Investigations:

This dataset offers significant potential for various advanced medical research and analysis applications. Some interesting use cases and potential investigations using this dataset include: - Tumor Classification: Developing advanced machine learning models for accurate and automated brain tumor classification. - Treatment Planning: Analyzing the tumor characteristics to aid in treatment planning and decision-making processes. - Radiomics Analysis: Extracting quantitative features from the images for radiomics analysis to uncover valuable insights and patterns. - Comparative Studies: Conducting comparative studies among different tumor types to understand their unique characteristics and behaviors.

Acknowledgement

• We would like to express our sincere gratitude to the original dataset publisher, sartajbhuvaji, for their valuable contribution.
• This dataset is released under the CC0 license, making it open and accessible for everyone to use. While not mandatory, citing the dataset would be greatly appreciated.

Important Note

Those researchers who want to use this dataset for real world use cases, must consult with medical field experts (radiologists, ...) on the ground truth of the labels and their usability for their angle of research.

This portfolio provides a detailed analysis of the EUR/USD currency pair on a 15-minute timeframe, aiming to explore market patterns, volatility, and potential opportunities for developing algorithmic trading strategies.

Included in this work:

Data cleaning and preprocessing of historical records.

Exploratory analysis of prices, volumes, and movement ranges.

Pattern detection such as consecutive candles, trends, and reversals.

Quantitative metrics to assess risk and performance.

Dataset preparation for backtesting and predictive modeling.

This project is designed for traders, quantitative analysts, and data science enthusiasts interested in applying analytical methods to Forex markets, with a practical and replicable approach to generating financial insights.

The EU Horizon 2020 project DiverIMPACTS aims to promote the realisation of the full potential of crop diversification through rotation, multicropping and intercropping by demonstrating technical, economic and environmental benefits for famers, along the value chain and for society at large, and by providing innovations that can remove existing barriers and lock-ins of practical diffusion. DiverIMPACTS does so by combining findings from several participatory case studies with a set of field experiments across Europe, and translating these into strategies, recommendations and fit-for-purpose tools developed with and for farmers, advisors and other actors along the value chain. To first gain a good overview of the current situation, i.e. the existing success stories and challenges of crop diversification in Europe, Work Package 1 (WP 1) identified and analysed factors of success and failure associated with a variety of crop diversification experiences (CDEs) outside those already represented in the consortium (see Deliverable 1.1). WP 1 thus makes sure that the rich experience with crop diversification initiatives across Europe (e.g. from other Horizon 2020 projects) is taken into account for developing strategies, recommendations and tools. Deliverable 1.1 provided i) a list of key drivers (ex ante occurrence of market opportunities, environmental constraints, availability of enabling advisory services, land and workforce availability etc.) to be further considered in WP3, and WP5; and ii) a comprehensive and exhaustive description of the links between key factors and CDE types. This analysis is the basis for consolidating or updating the tentative typology of crop diversification situations used for setting up DiverIMPACTS (case studies), and was used for selecting experiences for more detailed investigations in T1.2. It also complements the identification and characterisation of lock-ins and barriers to crop diversification, and serves their overcoming. During the process of collecting, cleaning and analysing the survey data, a Database of European diversification experiences was created. All together 128 valid responses from 15 European countries – mainly from the project countries Belgium, France, Germany, Hungary, Italy, the Netherlands, Poland, Romania, Sweden, Switzerland, and UK, but also from Denmark, Finland, Luxemburg and Spain were received in T1.1, and were included in the database. The database is stored in original and back-up form in a tabular ='.csv'= format that can be opened in Excel on the Sharepoint system of the project and now on Zenodo, under restricted WP1 area. A further ='.csv'= file was created to store the metadata of the database. This file helps to have a better overview of the questions and sub-questions that were asked in the survey and the type of answer that could be provided to each of them (e.g. factor, Yes-No selection or character). Using the meta data and the database, a selection of personal data fields has been made (e.g. email addresses and names of people) that cannot be published with open access, and needs special attention and data handling. These variables were removed from the original database, and a public version of the database was created that can be shared with third parties. Links to the data files will be shared here after. Developing a Shiny(c) application in R was chosen as a solution to visualize the public data, and make it possible for Partners and all interested parties to interactively view the survey results. The Shiny application is shared as an R-package and are freely accessible on the internet. The users have the possibility to download application and public data in order to visualize them on their own computer. A remote solution, facilitating the consultation of the data, will be installed in CRA-W, where the open data analyses module will be hosted. A short user guide and tutorial is part of this deliverable for helping interested parties to use the Shiny interface. The chosen approach, linking R scripts, R packages and data files, will be useful in the future in order to continiously complete the data base and to update the application (new graphs, new functions regarding the demand of the main users). The release of the application will be shared using modern technologies of information and communication : project website, newsletter, blogs, twitter and other social networks. The main deliverable (D1.2) which is public, is available here : 10.5281/zenodo.3966852

Amazon (AMZN) Stock Price Time-Series Dataset: May 2012 - November 2012

Dataset Overview

This dataset provides a detailed, intraday view of Amazon's stock (AMZN) price movements from May 21, 2012, to November 14, 2012. Meticulously compiled, it offers a granular perspective on market dynamics, enabling robust quantitative analysis and modeling.

Content

The dataset encompasses the following key financial metrics for each trading day:

• Date: The specific date of the trading session.
• Open: The initial price at the commencement of trading.
• High: The maximum price attained during the trading day.
• Low: The minimum price recorded during the trading day.
• Close: The final trading price at the market's close.
• Adj Close: The closing price adjusted for corporate actions like dividends and stock splits, providing a true return on investment.
• Volume: The number of shares exchanged throughout the trading day, indicating market activity and liquidity.

Intended Use Cases

This dataset is tailored for sophisticated financial analysis, model development, and academic research. Potential applications include:

• Algorithmic Trading Strategy Development: Design and back-test trading algorithms using historical price movements and volume data.
• Volatility Modeling: Analyze and forecast stock price volatility using time-series analysis techniques (e.g., GARCH models).
• Financial Forecasting: Implement machine learning models to predict future stock prices based on historical patterns.
• Event Study Analysis: Examine the impact of specific events or news announcements on Amazon's stock price.
• Risk Management: Evaluate potential risks associated with investing in Amazon stock during this period.
• Academic Research: Conduct studies on market efficiency, price discovery, and the impact of market microstructure on stock behavior.

Data Considerations

• Time Zone: Data is timestamped with Eastern Time (ET).
• Data Cleaning: The dataset has been verified for accuracy, but users are encouraged to perform their own data quality checks.

Contect info:

You can contect me for more data sets if you want any type of data to scrape

-E_mail

-Linkdin

-Kaggle

-X

-Github

Abstract

The Consultative Group to Assist the Poor (CGAP) and Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) have conducted a baseline demand-side study of person-to-person (P2P) remittances in Jordan to gather insight into existing customers, non-customers and potential customers. This study informed the implementation of a larger project to improve access to remittances and other financial services through digital solutions for financially excluded groups. The focal population for this study was low-income Jordanians (defined as those with a monthly household income of under 400 Jordanian Dinars) and Syrian refugees who have been in Jordan for at least a year.

The study focused on remittance activity and awareness and access to technology, with market forecasting for a digital remittance product. Key findings elicited insights into potential barriers to a digital remittance product, as well as enabling factors, and revealed a small market opportunity.

Geographic coverage

West Amman, East Amman, Irbid, Zarqa, Mafraq, Karak, Ma’an, Azraq refugee camp, Zaatari refugee camp

Analysis unit

Individuals, households

Universe

Low-income Jordanians and Syrian refugees

Kind of data

Sample survey data [ssd]

Sampling procedure

The quantitative survey consisted of two independent samples:

• n= 1,091 low-income Jordanians (defined as those with less than 400JD of household income per month) • n= 1,041 Syrian refugees living in Jordan

Quotas were used for both groups so the sample better represented available univariate population data in terms of geographic distribution, age and gender. Refer to “Digittances Quantitative Data User Guide” for more information.

Mode of data collection

Face-to-face [f2f]

Research instrument

Refer to “Digittances Quantitative Data User Guide”

Cleaning operations

In addition to the quality control conducted during fieldwork, data cleaning was conducted after fieldwork was completed. This included checks for internal consistency, missing variables, blank variables, and outliers. Ipsos data storage is audited annually as part of our ISO 27001 and 20252 accreditations and is compatible with security accreditation.

Snapshot img

Hospital Cleaning Chemicals Market Size 2024-2028

The hospital cleaning chemicals market size is forecast to increase by USD 5.54 billion at a CAGR of 8.65% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for effective cleaning solutions to prevent Healthcare-Associated Infections (HAIs). HAIs pose a significant health risk to patients and add substantial costs to healthcare facilities. As a result, there is a heightened focus on implementing infection prevention measures, driving the market's expansion. Moreover, the emergence of online distribution channels is transforming the market landscape. These channels offer convenience, competitive pricing, and a wider reach, enabling smaller players to penetrate the market. However, the market is not without challenges. The adverse effects of cleaning chemicals on human health and the environment pose significant risks. Regulatory bodies are imposing stringent regulations on the use of chemicals, necessitating the development of eco-friendly alternatives. Companies that can innovate and offer sustainable, cost-effective solutions will have a competitive edge in this market. In , the market presents significant opportunities for growth, particularly for those that can address the challenges of regulatory compliance and sustainability while maintaining effectiveness against HAIs. Companies seeking to capitalize on these opportunities must stay informed of market trends and invest in research and development to meet evolving customer needs.

What will be the Size of the Hospital Cleaning Chemicals Market during the forecast period?

Request Free SampleThe market in the US is experiencing significant growth due to the increasing focus on hygiene and safety in healthcare facilities. Driving factors include rising healthcare spending, the emergence of infectious diseases, and stringent regulations set by the Healthcare Associated Infection (HAI) prevention initiatives. The market size is substantial, with key applications including the use of chlor alkali, solvents, phosphates, biocides, and other specialized cleaning agents. Recent developments in technology, such as the integration of medical devices and automated cleaning systems, are enhancing the market's potential. The future development prospects of the market are promising, with emerging economies and increasing demand for advanced cleaning solutions contributing to its growth. The market's gross margin is influenced by factors such as raw material costs, production expenses, and competitive pricing. The executive summary of a quantitative research report on this market would provide a comprehensive analysis of the market's dynamics, trends, and future growth prospects.

How is this Hospital Cleaning Chemicals Industry segmented?

The hospital cleaning chemicals industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeCleaning agentsDisinfecting and sterilizing agentsEnd-userState owned hospitalsPrivate hospitalsCommunity hospitalsGeographyNorth AmericaUSAPACChinaEuropeFranceGermanyUKSouth AmericaMiddle East and Africa

By Type Insights

The cleaning agents segment is estimated to witness significant growth during the forecast period.Hospital cleaning chemicals play a crucial role in maintaining a hygienic and safe environment in healthcare facilities. With an aging population and a rise in chronic diseases, disease diagnosis and healthcare spending are on the rise. Consequently, the development potential for hospital cleaning chemicals is significant. Medical technology advances and emerging economies also contribute to market growth. Key components of hospital cleaning chemicals include solvents, biocides, surfactants, and antimicrobial agents. The driving factors for the market are the need for comprehensive hygiene and safety measures, the increasing prevalence of infectious diseases, and healthcare expenditure. However, there are also restrictive factors, such as stringent regulations and sustainability concerns. The future development prospects for hospital cleaning chemicals are promising, with technological trends favoring ecofriendly cleaning solutions. Products by application include general-purpose cleaners, disinfectants, and sanitizers. The report scope covers market size, trends, and forecasts, as well as the impact of disease diagnosis and chronic diseases on market growth. In summary, hospital cleaning chemicals are essential for maintaining a clean and safe environment in healthcare facilities. The market is driven by factors such as the aging population, disease diagnosis, and healthcare spending, and is subject to regulatory requirements and sustainability concerns. The future development prospects include technological t

Abstract

Scientific abstract Background: Acute malnutrition in infants and children less than 5 years is persistent in the arid and semi-arid lands (ASALs) of East Africa and the Sahel region despite years of investment. In the ASALs of Kenya, the situation is exacerbated by deep-rooted poverty and unequal access to basic services, sustained community conflicts, migration, poor seasonal rainfall/drought and other shocks. Nutrition specific and nutrition sensitive national and county level programs have either not been developed or not implemented effectively.

Objectives: To understand and map immediate, underlying, basic and systemic drivers of acute malnutrition for the development of overarching as well as micro-solutions for the sustainable reduction of persistent acute malnutrition and inform pilot studies and Phase 2 (second phase of USAID Nawiri project implementation) activities in Turkana and Samburu counties.

Methods: This study is a longitudinal mixed-methods observational study of children less than 3 years and their mothers and/or caregivers in Samburu and Turkana counties. Both quantitative and qualitative methods were utilized in the data collection processes. Data collection commenced in January 2021. Data analysis, learning and adapting was also ongoing so that results could inform pilots, theory of change review and Phase 2 activities throughout the study.

Study outcomes: To develop new interventions, and to adapt and contextualize existing interventions to prevent global acute malnutrition (GAM); strengthen social and behavior change (SBC) strategies around maternal, infant and young child nutrition (MIYCN), water and sanitation (WASH), community health systems, gender dynamics, livelihoods and resilience, and to inform improvements of the current nutrition surveillance system.

Study duration: 24 months. Summary budget: Total budget is KSH 140,400,000.00. Lay summary: The nutritional status of mothers and young children in Kenya's ASALs are strongly affected by deep-rooted poverty and unequal access to basic services, sustained community conflict, migration, poor seasonal rainfall/drought and other shocks. Inadequate women empowerment and limited control over household resources, high workload, domestic violence, insufficient household food security, inadequate social support, inadequate health services and an unhealthy environment, as well as inadequate dietary intake and high disease burden, are among other factors that contribute to poor maternal infant and young child feeding practice in these areas. Consequently, more than one in ten reproductive age women and 2-3 in ten young children in Turkana and in Samburu are undernourished. As such, this study aims to provide evidence for the appropriate policy and program design to improve the nutritional status of children and their mothers living in the two counties.

Geographic coverage

ASAL Counties coverage ( Turkana and Samburu )

Analysis unit

The unit of analysis is the sampled households in Turkana and Samburu Counties

Universe

The survey covered households with children under 3 years and their mothers/caregivers

Sampling procedure

SAMBURU

The study sample was population-based, with stratification by sub-counties grouped into three survey zones (Central, North, and East) reflecting administrative sub-counties used in the Samburu Standardized Monitoring and Assessment of Relief and Transitions (SMART) Surveys. The study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 699 households. The qualitative data yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report.

The baseline data collection was carried out in June and July 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Wave 2 data collection was carried out in November-December 2021, Wave 3 in March-April 2022, Wave 4 in September-October 2022, Wave 5 in March-April 2023 and Wave 6 data collection in August-September 2023.

TURKANA

The study sample was population-based, with stratification by sub-counties grouped into four survey zones (Central, North, West, and South) reflecting administrative sub-counties used in the Turkana SMART Surveys.
The study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 1,211 households. The qualitative data yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report.

The baseline data collection was carried out in May and June 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Anthropometric data were collected from all under-5 children in the sampled households. Wave 2 data collection was carried out in October-November 2021, Wave 3 in March-April 2022, Wave 4 in September-October 2022, Wave 5 in March-April 2023 and Wave 6 data collection in August-September 2023.

Sampling deviation

None

Mode of data collection

Face-to-face [f2f]

Research instrument

In Wave 6, one questionnaire with three different sections (Household section, Mother/caregiver section and Child section was administered in each sampled household to the Mother/caregiver

The household section collected various information on Household democraphics, Household Food insecurity coping strategies, water,hygiene and sanitation(WASH), Household shocks experienced, Social safety nets and economic safety guards, Household food insecurity experience scale(FIES), Interventions and services received by households,

The mother/caregiver section included,Mothers/caregivers information,Pregnancy and antenatal care, Family planning, Women's minimum dietary diversity, Gender, women empowerment, violence and community conflict, Psychological wellbeing.

The child section includes Infant and young child feeding practices, Supplementation and consuption of iron-rich or iron-fortified foods, Caregiving practices, Food safety, hygiene and sanitation practices, Child immunization, health and health-seeking practices, Acute Malnutrition screening, Anthropometric measurements.

Cleaning operations

Data quality monitoring processes and checks were implemented throughout the data collection process, during the time of developing the data collection tools (through built-in quality control in the tablet-based platform), during training of fieldworkers, in real time during data collection (routine monitoring by the research team and periodic cross-checks against the protocols), and during the data cleaning process. During fieldwork, data quality was enhanced through regular spot checks and sit-ins by supervisors to verify the authenticity of data collected. Data were then reviewed and certified by the field coordinator before they were transferred to the server.

The quantitative data were collected using SurveyCTO, a survey platform for electronic data collection that has in-built skips and quality checks. Using this software increased efficiency and reduced the time needed for cleaning the data. In addition, the platform supported offline data capturing for regions with slow or no internet connectivity and data transmission when the internet became available. Fieldwork was conducted by trained fieldworkers using digital tablets with the questionnaire loaded in SurveyCTO. The questionnaire included the following modules: (1) identification and tracking, (2) demographics and household composition, (3) anthropometry of children <5 years and mothers, (4) socioeconomics, (5) household food security, (6) WASH, (7) health-seeking behavior, (8) MIYCN, (9) shock experience/exposure, and (10) shock preparedness and response. Data were uploaded from the tablets onto a secure African Population and Health Research Center (APHRC) server after each day of data collection. Data were synchronized automatically to a server when the tablet was in a location with network coverage. The uploaded data were then checked for quality daily by a data manager and a team dedicated to coordinate field procedures and at the APHRC head office in Nairobi.

Response rate

Turkana: 96.3% Samburu: 92.8%

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during the implementation of this longitudinal study to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically. If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the study sample is the result of a multi-stage stratified design and consequently needs to use more complex formulae. The Stata complex samples module was used to calculate sampling errors.

This dataset contains quantitative and qualitative data collected as part of a mixed-methods study examining emotion regulation and reintegration in non-clinically diagnosed UK military veterans. Data generation followed a concurrent mixed-methods design. The quantitative component consists of responses to the Emotion Regulation Questionnaire – Positive/Negative (ERQ-PN; De Jesús-Romero et al., 2024), administered via Qualtrics between April and June 2025. The survey included 16 Likert-scale items across four subscales (Negative Reappraisal, Negative Suppression, Positive Reappraisal, Positive Suppression) and demographic items covering age, gender, years of service, and years since leaving service. A total of 93 valid responses were included after listwise deletion for missing data, all 133 responses are recorded in the data. Columns represent individual items or demographic variables, and rows represent participants.The qualitative component comprises anonymised transcripts from 14 semi-structured interviews conducted via Microsoft Teams between May and July 2025. Interviews explored veterans’ experiences of post-service adjustment and their use of emotion regulation strategies in daily life. Transcripts were generated using Microsoft Teams’ automated transcription function, checked manually for accuracy, and anonymised to remove identifying information. Each transcript is stored as a separate text file in .docx.Data cleaning included removal of incomplete survey cases, standardisation of variable names, and anonymisation of qualitative materials. No device-based physiological data were collected. All data are in common file formats (.csv for survey data; .docx for transcripts), accessible with standard software packages.Missing data were minimal: 39 incomplete survey responses were excluded, leaving a final sample size of 93. No data errors were identified during cleaning. The dataset is intended for secondary use in research on military-to-civilian transition, emotion regulation, reintegration, or mixed-methods approaches to psychological study design.

BackgroundThe need for increased attention to surgical safety in low- and middle-income countries invited organizations worldwide to support improvements in surgical care. However, little is written about issues in instrument sterilization in low- and middle-income countries including Ethiopia.ObjectiveThe study aims to identify the impact of a sterile processing course, with a training-of-trainers component and workplace mentoring on surgical instrument cleaning and sterilization practices at 12 hospitals in Ethiopia.MethodA mixed-methods research design that incorporates both qualitative and quantitative research approaches to address issues in sterile processing was used for this study. The quantitative data (test results) were validated by qualitative data (hospital assessments, including observations and participant feedback). Twelve hospitals were involved in the training, including two university teaching hospitals from two regions of Ethiopia. In each of the two regions 30 sterile processing staff were invited to participate in a three-day course including theory and skills training; 12–15 of these individuals were invited to remain for a two-day training of trainers course. The collected quantitative data were analysed using a paired t-test by SPSS software, whereas comparative analysis was employed for the qualitative data.ResultsProcess, structural, and knowledge changes were identified following program implementation. Knowledge test results indicated an increase of greater than 20% in participant sterile processing knowledge. Changes in process included improved flow of instruments from dirty to clean, greater attention to detail during the cleaning and decontamination steps, more focused inspection of instruments and careful packaging, as well as changes to how instruments were stored. Those trained to be trainers had taught over 250 additional staff.ConclusionsIncreased attention to and knowledge in sterile processing practices and care of instruments with a short, one-week course provides evidence that a small amount of resources applied to a largely under-resourced area of healthcare can result in decreased risks to patients and staff. Providing education in sterile processing and ensuring staff have the ability to disseminate their learnings to other health care providers results in decreasing risks of hospital associated infections in patients.

This mixed-methods randomized controlled trial study, conducted in collaboration with three Dane County (Wisconsin) law enforcement agencies, compared the effects of an 8-week mindfulness training (MT) program relative to a waitlist control (WLC) group on biological, behavioral, and self-report measures of stress and stress-related health outcomes. Across a two-year data collection period, the research team randomly assigned 114 sworn law enforcement officers to MT or WLC groups. Across three timepoints (baseline, post-program, and 3-month follow-up), researchers assessed the impact of MT on perceived stress (Aim 1), physical and mental health outcomes including behaviorally assessed and self-reported sleep quality, cardiovascular risk factors, and symptoms of PTSD, anxiety, and depression (Aim 2), and stress-related biological and behavioral markers (Aim 3), including cortisol output and a behavioral assay of hippocampus function. Data collected as part of this study include quantitative measures obtained during laboratory visits and a week of field data collection, as well as optional semi-structured qualitative interview data. This collection currently contains the following file types available in zipped package format. Excluding changes made for confidentiality purposes, files have been released as they were received by ICPSR: Summary data: Master data file (nij_masterfile.csv) containing demographics, summed scores from self-report questionnaires, behavioral markers, biomarkers, and mindfulness practice logs; Fitbit activity, heart rate, and sleep data (nij_fitbitSummary.csv); saliva sample collection data (nij_salivaCollectionNotes.csv, nij_salivaQCSpreadsheet.xlsx, nij_salivaryCortCleaned.csv, nij_salivaryCortProcessed.csv, nij_salivaryCortRaw.csv); work event log data (nij_workEventsRaw.xlsx) Raw behavioral data files: for all timepoints, affective go/no-go task data (agnRaw) and mnemonic similarity task data (mstRaw) Summary behavioral data files (agnSummary): for all timepoints, affective go/no-go task data Raw Fitbit data files (fitbitRaw): activity/steps, heart rate, and sleep data for all timepoints Scripts: R, Python, and bash scripts, with readme files, that were used in biomarker and behavioral marker data cleaning/analysis Qualitative interview data and documentation are not available at this time.

The current body of research on the gender-energy nexus has largely concentrated on the effects of energy poverty within households, highlighting the impact on women in domestic settings. Nonetheless, women entrepreneurs engaged in productive activities are also pivotal in adopting new energy technologies. The second version was built on the first version of the dataset and incorporated significant updates, presenting raw and processed data from 40 face-to-face interviews conducted across multiple African countries, including Nigeria, which was previously excluded. The current Version 3 includes additional data cleaning, improved consistency checks, and is the most updated and reliable version for reference or analysis compared to previous versions.

The dataset focuses on micro and small-sized enterprises with at least one female owner, offering a unified and comprehensive sample to assess energy access among women entrepreneurs in Africa and explore the potential for renewable energy adoption.

The data collection through semi-structured, face-to-face interviews occurred between February and October 2024. The interviews followed a predetermined semi-structured questionnaire designed to collect quantitative and qualitative data. The notes section explains the main methods and references used in the dataset. Distinctions are also made between primary and secondary data for appliance power ratings, ensuring transparency in cases where secondary data supplements gaps. This version (as did Version 2) includes updated technical data, such as time-of-use information for appliances, enhancing the dataset's strength in providing technical insights.

Key components of the dataset include: - Socio-economic characteristics: Enterprise location, ISIC division and industry sector classification, main production goods, gender-based ownership structures, enterprise formality (based on registration), year of establishment or business start, enterprise size (number of employees), profit margins, and business challenges related to the owner's gender.
- Energy access and use: Type of energy carriers used, subapplications, energy supply shortages, energy consumption levels, type, number, power rating of appliances used, temperature requirements, time-of-use data, and energy expenditure.
- Potential for renewable energy adoption: Type and amount of process waste, perceived barriers and drivers for renewable energy adoption, willingness to invest in or pay for new technologies, and preferred financing methods for such technologies.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Oncolytics Biotech Inc. Forecast & Analysis

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Quantitative survey along the small ruminant value chains in Ethiopia Current set contains 7 credit providers. WARNING: Data cleaning is on going. We remind users that data downloadable from the portal is for analysis ONLY. Any cleaning happening to these files WILL NOT affect the database. Errors and inconsistencies MUST be reported to the project staff in charge of cleaning.

Abstract

The Programa Nasional Dezenvolvimentu Suku (PNDS) is Timor-Leste's nationwide community-driven development program that will provide annual grants of $50,000 - $75,000 to 442 villages from 2013 to 2022. Grants will be used to fund small-scale infrastructure projects identified, planned, constructed, managed, and maintained by local communities.

The PNDS Research and Evaluation Program (PNDS-REP) was designed by the World Bank in partnership with the Government of Timor-Leste and the Australian Department of Foreign Affairs and Trade. The Research and Evaluation Program uses field surveys, monitoring activities, and experiments to analyze factors constraining PNDS impacts and to develop impact-enhancing design modalities.

The PNDS-REP baseline data was collected prior to the implementation of PNDS in the sample villages and spans socio-economic conditions, local infrastructure, social services, and development projects; and the structure and function of local governance.

The PNDS-REP Baseline Survey incorporated both a Quantitative Baseline Survey (NBS) and a Qualitative Baseline Survey (LBS). The respective methodology and instruments were designed to complement each other. The NBS employed relatively short household and local leader surveys to collect data on economic, institutional, social and other factors across a relatively large sample. The LBS, on the other hand, employed semi-structured interviews and direct observation administered over a relatively long period within a relatively small sample to explore complex local governance and development processes.

The qualitative survey covered 16 suku (villages) and was administered between February and August 2014. The quantitative survey covered 102 suku and was administered between June and August 2014.

The follow-up surveys will cover the same villages as baseline surveys covered. The baseline surveys provide information that, when combined with data from follow-up surveys, may be used to construct before-and-after comparisons to indicatively assess the impacts of PNDS, the distribution of those impacts within and between villages, and analyze factors conditioning the level and distribution of impacts.

Geographic coverage

National

Analysis unit

• individuals,
• households,
• villages

Kind of data

Sample survey data [ssd]

Sampling procedure

1) The Quantitative Survey (NBS) was administered to a sample of 102 villages (suku) randomly selected from PNDS Phase-III villages, which in turn are a random sample of the 442 villages scheduled to be mobilized by PNDS but are the last of three batches of villages to be mobilized by PNDS. Within each of the 102 villages, two hamlets (aldeia) were randomly sampled and, in each of these two hamlets, eight households were randomly sampled.

In each sample village and hamlet, questionnaires with Village Chief (Xefe Suku) and Hamlet Chief (Xefe Aldeia) surveys were administered, respectively. Within each sample hamlet, 8 households were selected through random sampling. The sampling of households was conducted in the field with the village or hamlet head. The sampling procedures consisted of three steps: - Access List of Households: In many cases, a list of households in the hamlet was available in the office of the Hamlet Chief or on a board outside the office. When a list of households was not readily available, the field team constructed a list of households in conjunction with the Hamlet Chief. - Numbering Households: If the list of households in the hamlet was not already numbered, the field team assigned a number to each household sequentially. - Random Sampling: The total number of households (N) was entered into a random number generator application installed on the electronic tablets used by the field team for data collection. The random generator produced a random sample of 12 integers between 1 and N (total number of households). The first eight households formed the primary sample households, while the next four households served as reserves.

2) The Qualitative Survey (LBS) was administered to 16 villages sampled from among the 102 villages selected by the NBS. The 16 villages were sampled to provide balance across the following criteria: region, rural vs. peri-urban, intensity of conflict, veteran population and proximity to border.

Within sample villages, respondents were selected using three sampling methods: - Purposive Sampling. A list of key stakeholders (e.g., Village Chief, Hamlet Chief, Ritual Leader, and the local Priest) was prepared prior to the arrival in each field site. Such stakeholders regularly participate in local governance activities and oversee local public works projects. Key respondents were interviewed in the earlier stages of data collection, with additional stakeholders identified and interviewed throughout the field visit. - Snowball Sampling. Additional respondents were identified based on referrals from key stakeholders throughout field visits. - Convenience Sampling. Research teams purposively selected easily accessible respondents and interviewed them at their houses or during community events. This sampling methodology was utilized in the final stages of the data collection cycle primarily to access marginalized villagers.

Each method was executed at a different stage of a 12-day data collection cycle.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Quantitative Baseline Survey (NBS)

NBS consists of four different instruments:

Male Household Questionnaire (MHQ): The MHQ collects information on basic household characteristics; health; crops, irrigation, and income; consumption and markets; projects and community; decision-making and governance; cohesion; subjective well-being and satisfaction with public services; and information, social, and human capital. The questionnaire was designed to survey the male head of household or, in the absence of such, a working-age male between 30-59 years old. A total 947 MHQs were administered across the 100 sample villages.

Female Household Questionnaire (FHQ): The FHQ covers similar issues to the MHQ but also contains specific questions pertaining to maternal and child health. FHQs were administered to working age females between 30 and 59 years old and/or responsible for decisions regarding children in the household and/or day-to-day household activities. A total 1,114 FHQs were administered across the 100 sample villages.

Youth and Elderly Questionnaire (YHQ): The YHQ is a shorter version of the MHQ and contained questions on development projects and satisfaction with public services, local decision-making, and subjective well-being. The YHQ covers youth aged between 15-29 years and elderly aged above 55 years who reside in sample households and were not surveyed by the MHQ or FHQ. A total of 166 youth and 84 elderly respondents were surveyed across the 100 sample villages.

Village/Hamlet Chief Questionnaire (VC/HC-Q): The VC/HC-Q was administered to village and hamlet heads in the sample and ascertained information on village characteristics and the functions of chiefs. On average, one Village Chief and two Hamlet Chiefs were sampled in each village. However, one village contained only one hamlet and, in a number of villages, the village head was unavailable or refused to participate. In total, 198 Hamlet Chiefs and 95 Village Chiefs were interviewed.

Qualitative Baseline Survey (LBS)

LBS research instruments were developed to investigate the following narratives:

1) Social Cohesion: The research instrument was designed to investigate volume and quality of interactions between villagers and to further explore sub-themes such as: identity, conflict and conflict mediation, power and vulnerability, development needs and priorities, village and hamlet borders, historical context and reoccurring social problems.

2) Formal and Informal Local Institutions: The research instrument reviews defining processes occurring within villagelevel institutions, including local governance structures and community groups. The instrument further explored subthemes such as: leadership, power and decision making, financial management, collective action and communication strategies, and the creation and termination of village community groups.

3) Public Goods and Services: The research instrument mapped the life-cycle and quality of public goods and services within the village and explored sub-themes such as: project selection and decision making processes, project planning, project implementation, resource management, and development outcomes.

On average, research teams conducted 30 two-hour long semi-structured interviews per village.

Cleaning operations

Quantitative Baseline Survey

Field staff members were issued Google Nexus tablets that contained electronic surveys. Completed surveys were sent from the field using 3G connectivity to a remote online server after each survey had been scrutinized by the team leader. A member of the PNDS-REP team in Dili checked incoming surveys and, if logical inconsistencies or any other faults in data quality were detected, contacted the relevant teams for further clarification or correction. The programming of surveys, design of web interface, and maintenance and upkeep of the software and server was outsourced to a private firm, Catalpa International.

Qualitative Baseline Survey

After each field visit, LBS research teams transcribed field notes, reviewed audio recordings, and developed a village report that incorporated qualitative data corresponding to the forementioned research themes. The village report incorporated the tools of process tracing and thick description.

Overview

This dataset provides high-quality, clean, and comprehensive candlestick data for the BTC/USDT trading pair, spanning from September 1, 2017, to September 23, 2025. Sourced meticulously, this dataset is ideal for quantitative analysts, algorithmic traders, and machine learning enthusiasts looking for reliable, long-term financial data.

A key feature of this dataset is the inclusion of real, non-zero volume data, which is often missing or inaccurate in other publicly available datasets. With zero missing values and a generous MIT License, this dataset is perfect for both academic research and commercial applications.

Key Features

• 📈 Extended Timeframe: Over 8 years of continuous data, capturing multiple market cycles (bull runs, bear markets, and consolidation phases).
• 📊 Complete OHLCV Data: Contains Open, High, Low, Close, and actual Volume for each candle.
• 💯 100% Accurate & Clean: No missing values, no gaps. Ready for immediate use without extensive cleaning.
• 📄 MIT License: Use it freely for any project, personal or commercial.
• 🕒 Consistent Timestamps: Clean datetime column for easy time-series analysis.

Column Descriptions

• datetime: The UTC timestamp for the start of the candle.
• open: The opening price of BTC/USDT for the candle.
• high: The highest price reached during the candle.
• low: The lowest price reached during the candle.
• close: The closing price of BTC/USDT for the candle.
• volume: The total volume of BTC traded during the candle.

Potential Use Cases & Project Ideas

This dataset is a perfect starting point for a wide range of projects:

1. Backtesting Trading Strategies: Test and validate your trading algorithms (e.g., Moving Average Crossover, RSI, MACD, Order Blocks, etc.) on a long-term, reliable dataset.
2. Machine Learning Price Prediction: Train predictive models (like LSTM, ARIMA, or Random Forest) to forecast future price movements.
3. Market Analysis: Analyze market volatility, liquidity, and trading patterns across different years.
4. Data Visualization: Create insightful charts and dashboards to visualize Bitcoin's price history and market behavior.
5. Quantitative Research: Develop and test complex financial models and hypotheses.

We look forward to seeing the incredible projects you build with this data. Happy modeling!