Detailed Core developers metrics and analytics for Term Structure, including historical data and trends.

As of July 2025, app store fees accounted for 22 percent of each dollar spent on the Roblox gaming platform. On average, Roblox pays developers and creators approximately 28 cents per dollar spent, with developer exchange payout accounting for 25 percent of the total payout.

This statistic shows the financing structure of real estate developers in China as of 2018. That year, 34 percent of real estate developers' funds were funds raised by companies themselves. In 2017, the real estate developer funding in China amounted to around 15.61 trillion yuan.

Unlock the power of ready-to-use data sourced from developer communities and repositories with Developer Community and Code Datasets.

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Developer Community and Code Datasets are a treasure trove of public data points gathered from tech communities and code repositories across the web.

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In this work, based on GitHub Archive project and repository mining tools, we process all available data into concise and structured format to generate GitHub developer behavior and repository evolution dataset. With the self-configurable interactive analysis tool provided by us, it will give us a macroscopic view of open source ecosystem evolution.

The Business Development Company (BDC) Data Sets provide the data extracted from disclosures filed by BDCs with the Commission in eXtensible Business Reporting Language (XBRL). The data is sourced from XBRL tagged periodic reports and other disclosures submitted by BDCs to the Commission. The BDC Data Sets provide a schedule of investments report, detailed financial data sets, and a summary non-financial data set.

RP-commenting-practices-multiple-sources

Replication package for the paper "What do Developers Discuss about Code Comments?"

Structure

Appendix.pdf
Tags-topics.md
Stack-exchange-query.md

RQ1/
  LDA_input/
    combined-so-quora-mallet-metadata.csv
    topic-input.mallet

  LDA_output/
    Mallet/
      output_csv/
        docs-in-topics.csv
        topic-words.csv
        topics-in-docs.csv
        topics-metadata.csv
      output_html/
        all_topics.html
        Docs/
        Topics/

RQ2/
  datasource_rawdata/
    quora.csv
    stackoverflow.csv
  manual_analysis_output/
    stackoverflow_quora_taxonomy.xlsx

Contents of the Replication Package

• Appendix.pdf- Appendix of the paper containing supplement tables

• Tags-topics.md tags selected from Stack overflow and topics selected from Quora for the study (RQ1 & RQ2)

• Stack-exchange-query.md the query interface used to extract the posts from stack exchnage explorer.

• RQ1/ - contains the data used to answer RQ1

  • LDA_input/ - input data used for LDA analysis
  • combined-so-quora-mallet-metadata.csv - Stack overflow and Quora questions used to perform LDA analysis
  • topic-input.mallet - input file to the mallet tool
  • LDA_output/
  • Mallet/ - contains the LDA output generated by MALLET tool
    • output_csv/
      • docs-in-topics.csv - documents per topic
      • topic-words.csv - most relevant topic words
      • topics-in-docs.csv - topic probability per document
      • topics-metadata.csv - metadata per document and topic probability
    • output_html/ - Browsable results of mallet output
      • all_topics.html
      • Docs/
      • Topics/

• RQ2/ - contains the data used to answer RQ2

  • datasource_rawdata/ - contains the raw data for each source
  • quora.csv - contains the processed dataset (like removing html tags). To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.
  • stackoverflow.csv - contains the processed stackoverflow dataset. To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.
  • manual_analysis_output/
  • stackoverflow_quora_taxonomy.xlsx - contains the classified dataset of stackoverflow and quora and description of taxonomy.
    • Taxonomy - contains the description of the first dimension and second dimension categories. Second dimension categories are further divided into levels, separated by | symbol.
    • stackoverflow-posts - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories.

      - quota-posts - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories.

Abstract

We have again updated the more popular data series from the Financial Structure database through 2008. Revised: April 2010. The revised dataset has some additional variables (two indicators of deposits in banks and in financial institutions relative to GDP added in 2007, and included in this latest update, some standard banking variables (ROE, ROA, cost-income ratio and z-score) as well as some measures of financial globalization: outstanding and net issues of international debt to GDP, loans from non-resident banks to GDP, off-shore deposits to domestic bank deposits, and remittance inflows to GDP.

We gratefully acknowledge the assistance of Pam Gill, Baybars Karacaovali and Edward Al-Hussainy with this update. Please note that most metrics have been recalculated for the entire time period to ensure consistency over time. The file contains a sheet with definitions and sources; for more detailed definitions and detailed description of the sources, please see the working paper attached as external resources.

This new database of indicators of financial development and structure across countries and over time is unique in that it unites a range of indicators that measure the size, activity, and efficiency of financial intermediaries and markets.

The compiled data permit the construction of financial structure indicators to measure whether, for example, a country's banks are larger, more active, and more efficient than its stock markets. These indicators can then be used to investigate the empirical link between the legal, regulatory, and policy environment and indicators of financial structure. They can also be used to analyze the implications of financial structure for economic growth.

Kind of data

Aggregate data [agg]

See Readme.md for more information.

Create your own initiative by combining existing applications with a custom site. Use this initiative to form teams around a problem and invite your community to participate.

We conducted semi-structured interviews with 23 individuals working in the broad field of sustainable and equitable urban development in the U.S.A. between August and September 2022. We used purposive and snowball sampling, targeting people with experience working in this field across multiple sectors, states, and types of organizations. Participants were informed and consented and interviews were audio-recorded and transcribed. Participants reported working in the following professions: Built environment, Transport & Mobility, Health and social care, Housing, Culture, Parks and Recreation, Water, Education, Energy, Workforce support, Sanitation, Waste, and Others. Participants' organization types included: local government, non-profit organizations, private sector, and others. Participants lived in various U.S. states., We conducted semi-structured interviews with 23 individuals working in the broad field of sustainable and equitable urban development in the U.S.A. between August and September 2022. There were 21 interviews, in which two participants attended two interviews. Participants were informed and consented (following research ethics approval from University College London's Bartlett School of Energy, Environment and Resources low-risk procedure) and interviews were audio-recorded and transcribed. Interviews were conducted virtually. Transcripts were generated through Zoom's computer-generated audio transcription (n=8) or using Rev.com (n=13). Transcripts were anonymized using best practices for sharing human subject data. We removed data that could allow a person to be identified, including direct and indirect identifiers (such as descriptions of their employer, geographical location, and other data that would be unique to a specific place). Generic terms were replaced with identifiable data u..., , # Semi-structured interviews about sustainable and equitable development in the USA

https://doi.org/10.5061/dryad.prr4xgxts

This dataset contains 21 transcripts of semi-structured interviews with 23 individuals working in the broad field of sustainable and equitable urban development in the U.S.A.

Description of the data and file structure

The interview transcripts are verbatim transcriptions of participants' audio, including pause fillers (such as 'um') and errors. The data do not include the researcher's observations, such as the participants' expressions or body language. Transcripts were generated through Zoom's computer-generated audio transcription (n=8) or using Rev.com (n=13). Transcripts were anonymized using best practices for sharing human subject data. We removed data that could allow a person to be identified, including direct and indirect identifiers (such as descriptions of their employer, geographical location, and other ...

Various concerns have been documented in the literature over the sharing of data, including the lack of incentives for sharing. To address this, several systems for recognition have been proposed (e.g. Data Authorship, CRediT, data citation…). This study was set up to discuss the past experiences with data sharing with persons involved with data sharing platforms to better understand the potential barriers and solutions. In total, 17 cohort holders and developers associated with three different data sharing platforms were recruited for a semi-structured interview. The goal of this study was to (1) document the views and opinions on different incentives for data sharing; (2) to explore experiences on data sharing and credit mechanisms within consortia; (3) to record views on the roles of different actors within academia to change the existing incentive structure for Open/FAIR Data; and (4) to investigate the interaction between data sharing practices and novel technologies.

Kafka with Spring Boot Course ## Requirements - Java 11 or greater is required - IntelliJ, Eclipse, or a similar IDE - Knowledge about Spring Boot - Experience writing tests using JUnit - Gradle or Maven knowledge is needed ## Description This course is structured to give you both theoretical and coding experience with Apache Kafka using Spring Boot. It is targeted at developers who want to build enterprise-standard Kafka client applications using Spring Boot. If you re looking to learn: - Use cases where Kafka fits really well - Internals of Kafka and how it works - Building enterprise-standard Kafka client applications (Producer/Consumer API) using Spring Boot - Writing unit/integration tests for Kafka client applications 👉 This is the right course for you. This is a pure hands-on oriented course — you ll learn concepts through code. By the end of this course, you will have a complete understanding of coding and implementing Kafka clients using Spring Boot with the

Basic properties of the developer collaboration network.

Require the developer of the system, system component, or system service to structure security-relevant hardware, software, and firmware to facilitate

Mexico External Public Debt: Structure by User: Development Banks data was reported at 11.617 USD bn in Feb 2019. This records a decrease from the previous number of 12.138 USD bn for Jan 2019. Mexico External Public Debt: Structure by User: Development Banks data is updated monthly, averaging 8.247 USD bn from Jan 1990 (Median) to Feb 2019, with 350 observations. The data reached an all-time high of 14.927 USD bn in Nov 1994 and a record low of 0.000 USD mn in Jun 1990. Mexico External Public Debt: Structure by User: Development Banks data remains active status in CEIC and is reported by Secretary of Finance and Public Credit. The data is categorized under Global Database’s Mexico – Table MX.F022: Domestic & External Public Debt.

This dataset contains grid files for subsurface maps created in GES interpretation software and exported as Zmap formated grid files. Depth values in SSTVD (subsea true vertical depth). The methods used for analysis and a detailed discussion of the results are presented in a paper by McCleery et al., (2018).

The ckanext-intro extension is designed as a hands-on tutorial for developers new to creating CKAN extensions. It offers an interactive learning experience, guiding users through the fundamental steps of extension development within the CKAN framework, as showcased during the OKCon 2013 workshop. This extension acts as a practical starting point, directing users towards more detailed official CKAN extension development tutorials for comprehensive learning. Key Features: Step-by-Step Learning: The extension presents a structured, commit-based approach, where each step of the extension-building process is contained within a distinct Git commit. Interactive Exploration: Developers can move between different development stages using Git commands, allowing them to easily review the changes and understand the incremental progress. Practical Introduction: Serves as a quick-start guide to CKAN extension development, enabling new developers to familiarize themselves with the core concepts efficiently. GitHub Integration: Enables users to browse each step of the extension's development directly on GitHub, facilitating easy code review and understanding. Technical Integration: The extension's installation involves the standard process for CKAN extensions, utilizing pip within the activated virtual environment. To activate the fully-built extension, the plugin needs to be added to the CKAN configuration file following the completion of interactive exercises. The Git-based structure provides a clear view on the structural changes required at each development step. Benefits & Impact: The primary benefit of using ckanext-intro is its role in onboarding new developers to the CKAN ecosystem. By providing a guided, hands-on experience, it reduces the initial learning curve associated with extension development. This approach ensures that developers gain practical knowledge and a solid foundation before diving into more complex and comprehensive documentation.

The settlement structure is made up of 3 component parts that are described as 'layers' namely:Layer 1: LetterkennyLayer 2: Strategic Towns, made up of 2 parts:2A: Strategic towns in the context of housing land supply and due to their 'Special Economic Function'; and2B: Strategic towns predominantly due to their 'Special Economic FunctionLayer 3: Rural towns and open countryside.The County Donegal Development Plan 2018-2024 can be found here!