This study investigates the extent to which data science projects follow code standards. In particular, which standards are followed, which are ignored, and how does this differ to traditional software projects? We compare a corpus of 1048 Open-Source Data Science projects to a reference group of 1099 non-Data Science projects with a similar level of quality and maturity.results.tar.gz: Extracted data for each project, including raw logs of all detected code violations.notebooks_out.tar.gz: Tables and figures generated by notebooks.source_code_anonymized.tar.gz: Anonymized source code (at time of publication) to identify, clone, and analyse the projects. Also includes Jupyter notebooks used to produce figures in the paper.The latest source code can be found at: https://github.com/a2i2/mining-data-science-repositoriesPublished in ESEM 2020: https://doi.org/10.1145/3382494.3410680Preprint: https://arxiv.org/abs/2007.08978

This dataset provides a rich snapshot of GitHub users from India, capturing various aspects of their public profiles. It's a valuable resource for analyzing trends in coding activity, repository management, and user engagement within the Indian developer community. Whether you're interested in exploring how developers grow their followers, examining language preferences, or identifying patterns in contributions and achievements, this dataset offers multiple points of analysis.

Key Features: - Username: GitHub usernames of the individuals. - Gender Pronoun: Preferred gender pronouns (if available). - Followings: Number of people each user follows. - Joining Year: The year they joined GitHub. - Contributions: Number of contributions made in the last year. - Achievements: Number of GitHub achievements unlocked by the user. - Stars: Total number of stars on their repositories. - Repositories: Number of repositories created. - Followers: Number of followers each user has. - Location: User location details, primarily from India. - Languages: Primary programming language used by the individual. - Social Links: Links to their other social platforms (LinkedIn, personal websites, etc.). - Sorting Type: Categorized based on followers, repositories, or recent joining.

This dataset can be used for: - Profiling the Indian developer community. - Tracking open-source contributions and achievements. - Analyzing programming language preferences and repository management. - Exploring the relationship between social followings and coding contributions.

Perfect for data science, social network analysis, and open-source research.

Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.
The major contributions have been materialized in the form of novel algorithms.
Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.
Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.
Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".

If you use the data, implementation, or any details of the paper, please cite!

BIBTEX:

_

@inproceedings{nedelkoski2020multi,
 title={Multi-source Distributed System Data for AI-Powered Analytics},
 author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej},
 booktitle={European Conference on Service-Oriented and Cloud Computing},
 pages={161--176},
 year={2020},
 organization={Springer}
}

_

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.

Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

The original contributions presented in the study are included in the article and online through the TAME Toolkit, available at: https://uncsrp.github.io/Data-Analysis-Training-Modules/, with underlying code and datasets available in the parent UNC-SRP GitHub website (https://github.com/UNCSRP). This dataset is associated with the following publication: Roell, K., L. Koval, R. Boyles, G. Patlewicz, C. Ring, C. Rider, C. Ward-Caviness, D. Reif, I. Jaspers, R. Fry, and J. Rager. Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 4: 893924, (2022).

The city of Austin has administered a community survey for the 2015, 2016, 2017, 2018 and 2019 years (https://data.austintexas.gov/City-Government/Community-Survey/s2py-ceb7), to “assess satisfaction with the delivery of the major City Services and to help determine priorities for the community as part of the City’s ongoing planning process.” To directly access this dataset from the city of Austin’s website, you can follow this link https://cutt.ly/VNqq5Kd. Although we downloaded the dataset analyzed in this study from the former link, given that the city of Austin is interested in continuing administering this survey, there is a chance that the data we used for this analysis and the data hosted in the city of Austin’s website may differ in the following years. Accordingly, to ensure the replication of our findings, we recommend researchers to download and analyze the dataset we employed in our analyses, which can be accessed at the following link https://github.com/democratizing-data-science/MDCOR/blob/main/Community_Survey.csv. Replication Features or Variables The community survey data has 10,684 rows and 251 columns. Of these columns, our analyses will rely on the following three indicators that are taken verbatim from the survey: “ID”, “Q25 - If there was one thing you could share with the Mayor regarding the City of Austin (any comment, suggestion, etc.), what would it be?", and “Do you own or rent your home?”

Scientific and related management challenges in the water domain require synthesis of data from multiple domains. Many data analysis tasks are difficult because datasets are large and complex; standard formats for data types are not always agreed upon nor mapped to an efficient structure for analysis; water scientists may lack training in methods needed to efficiently tackle large and complex datasets; and available tools can make it difficult to share, collaborate around, and reproduce scientific work. Overcoming these barriers to accessing, organizing, and preparing datasets for analyses will be an enabler for transforming scientific inquiries. Building on the HydroShare repository’s established cyberinfrastructure, we have advanced two packages for the Python language that make data loading, organization, and curation for analysis easier, reducing time spent in choosing appropriate data structures and writing code to ingest data. These packages enable automated retrieval of data from HydroShare and the USGS’s National Water Information System (NWIS), loading of data into performant structures keyed to specific scientific data types and that integrate with existing visualization, analysis, and data science capabilities available in Python, and then writing analysis results back to HydroShare for sharing and eventual publication. These capabilities reduce the technical burden for scientists associated with creating a computational environment for executing analyses by installing and maintaining the packages within CUAHSI’s HydroShare-linked JupyterHub server. HydroShare users can leverage these tools to build, share, and publish more reproducible scientific workflows. The HydroShare Python Client and USGS NWIS Data Retrieval packages can be installed within a Python environment on any computer running Microsoft Windows, Apple MacOS, or Linux from the Python Package Index using the PIP utility. They can also be used online via the CUAHSI JupyterHub server (https://jupyterhub.cuahsi.org/) or other Python notebook environments like Google Collaboratory (https://colab.research.google.com/). Source code, documentation, and examples for the software are freely available in GitHub at https://github.com/hydroshare/hsclient/ and https://github.com/USGS-python/dataretrieval.

This presentation was delivered as part of the Hawai'i Data Science Institute's regular seminar series: https://datascience.hawaii.edu/event/data-science-and-analytics-for-water/

This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the Publisher's Website. Data Generation The research project engages a story about perceptions of fairness in criminal justice decisions. The specific focus involves a debate between ProPublica, a news organization, and Northpointe, the owner of a popular risk tool called COMPAS. ProPublica wrote that COMPAS was racist against blacks, while Northpointe posted online a reply rejecting such a finding. These two documents were the obvious foci of the qualitative analysis because of the further media attention they attracted, the confusion their competing conclusions caused readers, and the power both companies wield in public circles. There were no barriers to retrieval as both documents have been publicly available on their corporate websites. This public access was one of the motivators for choosing them as it meant that they were also easily attainable by the general public, thus extending the documents’ reach and impact. Additional materials from ProPublica relating to the main debate were also freely downloadable from its website and a third party, open source platform. Access to secondary source materials comprising additional writings from Northpointe representatives that could assist in understanding Northpointe’s main document, though, was more limited. Because of a claim of trade secrets on its tool and the underlying algorithm, it was more difficult to reach Northpointe’s other reports. Nonetheless, largely because its clients are governmental bodies with transparency and accountability obligations, some of Northpointe-associated reports were retrievable from third parties who had obtained them, largely through Freedom of Information Act queries. Together, the primary and (retrievable) secondary sources allowed for a triangulation of themes, arguments, and conclusions. The quantitative component uses a dataset of over 7,000 individuals with information that was collected and compiled by ProPublica and made available to the public on github. ProPublica’s gathering the data directly from criminal justice officials via Freedom of Information Act requests rendered the dataset in the public domain, and thus no confidentiality issues are present. The dataset was loaded into SPSS v. 25 for data analysis. Data Analysis The qualitative enquiry used critical discourse analysis, which investigates ways in which parties in their communications attempt to create, legitimate, rationalize, and control mutual understandings of important issues. Each of the two main discourse documents was parsed on its own merit. Yet the project was also intertextual in studying how the discourses correspond with each other and to other relevant writings by the same authors. Several more specific types of discursive strategies were of interest in attracting further critical examination: Testing claims and rationalizations that appear to serve the speaker’s self-interest Examining conclusions and determining whether sufficient evidence supported them Revealing contradictions and/or inconsistencies within the same text and intertextually Assessing strategies underlying justifications and rationalizations used to promote a party’s assertions and arguments Noticing strategic deployment of lexical phrasings, syntax, and rhetoric Judging sincerity of voice and the objective consideration of alternative perspectives Of equal importance in a critical discourse analysis is consideration of what is not addressed, that is to uncover facts and/or topics missing from the communication. For this project, this included parsing issues that were either briefly mentioned and then neglected, asserted yet the significance left unstated, or not suggested at all. This task required understanding common practices in the algorithmic data science literature. The paper could have been completed with just the critical discourse analysis. However, because one of the salient findings from it highlighted that the discourses overlooked numerous definitions of algorithmic fairness, the call to fill this gap seemed obvious. Then, the availability of the same dataset used by the parties in conflict, made this opportunity more appealing. Calculating additional algorithmic equity equations would not thereby be troubled by irregularities because of diverse sample sets. New variables were created as relevant to calculate algorithmic fairness equations. In addition to using various SPSS Analyze functions (e.g., regression, crosstabs, means), online statistical calculators were useful to compute z-test comparisons of proportions and t-test comparisons of means. Logic of Annotation Annotations were employed to fulfil a variety of functions, including supplementing the main text with context, observations, counter-points, analysis, and source attributions. These fall under a few categories. Space considerations. Critical discourse analysis offers a rich method...

Measuring and ranking the free software developers in a particular geographical space is a wayof knowing the existing community and also allows assessing the impact of certain policies inthe dynamics of such a community. Besides, it is interesting to try and find out why there aredifferences from one place to the next and how these differences evolve with time. In this paper,our main interest is to measure and rank the community of free software developers in Spain andalso check its geographical distribution. This paper measures differences by province, providing aclassification of provinces according to the number and type of developers present in each place.

A COVID-19 misinformation / fake news / rumor / disinformation dataset collected from online social media and news websites. Usage note:Misinformation detection, classification, tracking, prediction.Misinformation sentiment analysis.Rumor veracity classification, comment stance classification.Rumor tracking, social network analysis.Data pre-processing and data analysis codes available at https://github.com/MickeysClubhouse/COVID-19-rumor-datasetPlease see full info in our GitHub link.Cite us:Cheng, Mingxi, et al. "A COVID-19 Rumor Dataset." Frontiers in Psychology 12 (2021): 1566.@article{cheng2021covid, title={A COVID-19 Rumor Dataset}, author={Cheng, Mingxi and Wang, Songli and Yan, Xiaofeng and Yang, Tianqi and Wang, Wenshuo and Huang, Zehao and Xiao, Xiongye and Nazarian, Shahin and Bogdan, Paul}, journal={Frontiers in Psychology}, volume={12}, pages={1566}, year={2021}, publisher={Frontiers} }

Description

Over 8 million GitHub issue titles and descriptions from 2017. Prepared from instructions at How To Create Data Products That Are Magical Using Sequence-to-Sequence Models.

Original Source

The data was adapted from GitHub data accessible from GitHub Archive. The constructocat image is from https://octodex.github.com/constructocat-v2.

License

MIT License

Copyright (c) 2018 David Shinn

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Overview

This replication package contains data and scripts used in our study. The package is structured into four main components.

Folder Structure

1. Data

• ProcessedData: Contains refined datasets that guide our research questions.

• RawData: Contains raw scraped data about dependents from GitHub, selected for analysis.

2. RepoClonerDataAnalyser

• The starting repository for the study.

• Select the top 10 libraries and their dependents.

• Clones repositories and analyzes all research questions.

• Implemented in Python.

3. methodTypeResolutionJavaParser

• A Java project used for method resolution.

• This tool is used for parsing and resolving method types after cloning repositories and filtering potential Java files using the RepoClonerDataAnalyzer project.

4. JacocoCoverageReporter

• Converts raw JaCoCo HTML coverage reports into CSV format.

• Implemented in Python

5. Survey Forms

• Contains pdf version of survey forms that was shared to maintainers.

Usage Instructions

Each project within this package has its own README file with detailed setup and execution instructions. Below is a high-level guide:

1. Data Collection: Use RepoClonerDataAnalyser to select, clone, and filter dependents.

2. Method Resolution: Run methodTypeResolutionJavaParser on filtered Java files.

3. Coverage Analysis: Use JacocoCoverageReporter to convert JaCoCo HTML reports into CSV format and then Use RepoClonerDataAnalyser for further analysis.

4. Data Analysis: Utilize the processed data in the Data folder for research insights.

Requirements

• Python 3.x

• Java 8+

• Required dependencies (listed in individual project README files. In version 1, you might notice a random GitHub repository URL provided in individual README. It is intended solely for context and clarity. It does not lead to an accessible resource and results in a 404 error. We have removed it in version 2 to avoid any confusion.)

Replication materials for "A Review of Best Practice Recommendations for Text-Analysis in R (and a User Friendly App)". You can also find these materials on GitHub repo (https://github.com/wesslen/text-analysis-org-science) as well as the Shiny app in the GitHub repo (https://github.com/wesslen/topicApp).

Here we deposit the datasets we have extracted for ten states in the US. In each zip file, we include each state's accident records, road networks, and network features. For further information about using the dataset and how we extracted the data, check out our GitHub repository for instructions.

Data package for "Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection", published in ICSE 2024, with updates from Artifact Evaluation.Paper link: https://www.computer.org/csdl/proceedings-article/icse/2024/021700a166/1RLIWqviwEMSee Github repo for updates: https://github.com/ISU-PAAL/DeepDFAData dictionary:before.zip: CFGs of Big-Vul dataset, generated by Joern.preprocessed_data.zip: preprocessed data from Big-Vul for running DeepDFA, including preprocessed Joern CFGs and abstract dataflow embeddings.DeepDFA-code.zip: most recent version of the code as of the publication of this artifact, see Github repo for updates: https://github.com/ISU-PAAL/DeepDFAMSR_data_cleaned.csv: original Big-Vul dataset, see original source: https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_DatasetMSR_LineVul: LineVul's preprocessed version of the Big-Vul dataset, see original source: https://github.com/awsm-research/LineVulChangelog:v1 2023-09-20: original data package and Github repo published.v2 2024-01-04: added full instructions and bug fixes for Artifact Evaluation.v3 2024-01-10: integrated feedback from Artifact Evaluation.

Data Update Notice 数据更新通知

We are pleased to announce that the GlobPOP dataset for the years 2021-2022 has undergone a comprehensive quality check and has now been updated accordingly. Following the established methodology that ensures the high precision and reliability, these latest updates allow for even more comprehensive time-series analysis. The updated GlobPOP dataset remains available in GeoTIFF format for easy integration into your existing workflows.

2021-2022 年的 GlobPOP 数据集经过全面的质量检查,现已进行相应更新。 遵循确保高精度和可靠性的原有方法,本次更新允许进行更全面的时间序列分析。 更新后的 GlobPOP 数据集仍以 GeoTIFF 格式提供,以便轻松集成到您现有的工作流中。

To reflect these updates, our interactive web application has also been refreshed. Users can now explore the updated national population time-series curves from 1990 to 2022. This can be accessed via the same link: https://globpop.shinyapps.io/GlobPOP/. Thank you for your continued support of the GlobPOP, and we hope that the updated data will further enhance your research and policy analysis endeavors.

交互式网页反映了人口最新动态,用户现在可以探索感兴趣的国家1990 年至 2022 年人口时间序列曲线,并将其与人口普查数据进行比较。感谢您对 GlobPOP 的支持,我们希望更新的数据将进一步加强您的研究和政策分析工作。

If you encounter any issues, please contact us via email at lulingliu@mail.bnu.edu.cn.

如果您遇到任何问题,请通过电子邮件联系我们。

Introduction

Continuously monitoring global population spatial dynamics is essential for implementing effective policies related to sustainable development, such as epidemiology, urban planning, and global inequality. 持续监测全球人口空间动态对于实施与可持续发展相关的有效政策至关重要,例如流行病学、城市规划和全球不平等。

Here, we present GlobPOP, a new continuous global gridded population product with a high-precision spatial resolution of 30 arcseconds from 1990 to 2022. Our data-fusion framework is based on cluster analysis and statistical learning approaches, which intends to fuse the existing five products(Global Human Settlements Layer Population (GHS-POP), Global Rural Urban Mapping Project (GRUMP), Gridded Population of the World Version 4 (GPWv4), LandScan Population datasets and WorldPop datasets to a new continuous global gridded population (GlobPOP). The temporal and spatial validation results demonstrate that the GlobPOP dataset is highly accurate. GlobPOP是一套新的连续全球网格人口产品,时间跨度为从 1990 年到 2022 年,空间分辨率为 30 弧秒。数据生产融合框架基于聚类分析和统计学习方法,旨在融合现有的五个 产品(GHS-POP、GRUMP、GPWv4、LandScan和WorldPop)。时空验证结果表明GlobPOP 数据集高度准确。

With the availability of GlobPOP dataset in both population count and population density formats, researchers and policymakers can leverage our dataset to conduct time-series analysis of population and explore the spatial patterns of population development at various scales, ranging from national to city level. 通过人口计数和人口密度格式的 GlobPOP 数据集,研究人员和政策制定者可以利用该数据集对人口进行时间序列分析,并探索不同尺度的人口发展时空模式。

Data description

The product is produced in 30 arc-seconds resolution(approximately 1km in equator) and is made available in GeoTIFF format. There are two population formats, one is the 'Count'(Population count per grid) and another is the 'Density'(Population count per square kilometer each grid)

Each GeoTIFF filename has 5 fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below with the example filename:

GlobPOP_Count_30arc_1990_I32

Field 1: GlobPOP(Global gridded population)
Field 2: Pixel unit is population "Count" or population "Density"
Field 3: Spatial resolution is 30 arc seconds
Field 4: Year "1990"
Field 5: Data type is I32(Int 32) or F32(Float32)

More information

本数据相关论文已发表在Scientific Data,代码可在GitHub获取。

Please refer to the paper for detailed information:

Liu, L., Cao, X., Li, S. et al. A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning. Sci Data 11, 124 (2024). https://doi.org/10.1038/s41597-024-02913-0.

The fully reproducible codes are publicly available at GitHub: https://github.com/lulingliu/GlobPOP.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data. Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set. Exceptions to this are: Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence. Data from Gateway to Research is made available under an Open Government Licence (Version 3.0). Contents Survey data & analysis: esrc_data-survey-analysis-data.zip Other data: esrc_data-other-data.zip Transcripts: esrc_data-transcripts.zip Data Management Plan: esrc_data-dmp.zip Survey data & analysis The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data. The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled. The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly. A pdf copy of the survey questions is available on GitHub. The survey data has been decoupled into: survey-results-key.csv - maps a question number and the responses to the actual question values. q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16). q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23). q17-institutions.csv - the institution/location of the respondent (Q17). q18-funding.csv - funding sources within the last 5 years (Q18). Please note the code that has been used to do the analysis will not run with the decoupled survey data. Other data files included CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered. DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021. projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence. locations.csv - latitude and longitude for the institutions in the cleaned locations. subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot. topics.csv - topic classification for the ESRC projects for the 24th February data snapshot. Interview transcripts The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts: 1269794877.md 1578450175.md 1792505583.md 2964377624.md 3270614512.md 40983347262.md 4288358080.md 4561769548.md 4938919540.md 5037840428.md 5766299900.md 5996360861.md 6422621713.md 6776362537.md 7183719943.md 7227322280.md 7336263536.md 75909371872.md 7869268779.md 8031500357.md 9253010492.md Data Management Plan The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.

The Community-Driven Model Service Platform market is experiencing robust growth, projected to reach $35.14 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 10.1% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of machine learning and artificial intelligence across diverse sectors, coupled with the need for readily accessible and collaboratively improved models, is driving significant demand. The open-source nature of many platforms fosters innovation and reduces barriers to entry for both developers and businesses. Furthermore, the rise of cloud-based solutions offers scalability and cost-effectiveness, contributing to market expansion. The platform's segmentation into adult and children's applications reflects diverse use cases, ranging from sophisticated research projects to educational tools, further broadening its appeal. The presence of established players like Kaggle, GitHub, and Hugging Face indicates a maturing market with strong community engagement, while the existence of on-premises options caters to businesses with stringent data security requirements. Geographical expansion is also a significant contributor to growth, with North America and Europe currently leading the market, while Asia-Pacific is poised for significant future expansion driven by increasing digitalization and technological advancements. The market's continued growth is anticipated to be driven by advancements in model training techniques, the development of more user-friendly interfaces, and the increasing integration of these platforms with other data science tools and workflows. Challenges remain, however, such as ensuring data quality and addressing potential biases in community-contributed models. Furthermore, regulatory concerns around data privacy and model transparency will need to be carefully addressed to maintain sustainable growth. The competitive landscape is expected to remain dynamic, with ongoing innovation and consolidation among existing players and the emergence of new entrants. The strategic focus on improving model accessibility, enhancing community engagement, and expanding into new geographical markets will be key determinants of success in this rapidly evolving sector.

Analysis of ‘Austin's data portal activity metrics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/data-portal-activity-metricse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Austin's open data portal provides lots of public data about the City of Austin. It also provides portal administrators with behind-the-scenes information about how the portal is used... but that data is mysterious, hard to handle in a spreadsheet, and not located all in one place.

Until now! Authorized city staff used admin credentials to grab this usage data and share it the public. The City of Austin wants to use this data to inform the development of its open data initiative and manage the open data portal more effectively.

This project contains related datasets for anyone to explore. These include site-level metrics, dataset-level metrics, and department information for context. A detailed detailed description of how the files were prepared (along with code) can be found on github here.

Example questions to answer about the data portal

1. What parts of the open data portal do people seem to value most?
2. What can we tell about who our users are?
3. How are our data publishers doing?
4. How much data is published programmatically vs manually?
5. How data is super fresh? Super stale?
6. Whatever you think we should know...

About the files

all_views_20161003.csv

There is a resource available to portal administrators called "Dataset of datasets". This is the export of that resource, and it was captured on Oct 3, 2016. It contains a summary of the assets available on the data portal. While this file contains over 1400 resources (such as views, charts, and binary files), only 363 are actual tabular datasets.

table_metrics_ytd.csv

This file contains information about the 363 tabular datasets on the portal. Activity metrics for an individual dataset can be accessed by calling Socrata's views/metrics API and passing along the dataset's unique ID, a time frame, and admin credentials. The process of obtaining the 363 identifiers, calling the API, and staging the information can be reviewed in the python notebook here.

site_metrics.csv

This file is the export of site-level stats that Socrata generates using a given time frame and grouping preference. This file contains records about site usage each month from Nov 2011 through Sept 2016. By the way, it contains 285 columns... and we don't know what many of them mean. But we are determined to find out!! For a preliminary exploration of the columns and what portal-related business processes to which they might relate, check out the notes in this python notebook here

city_departments_in_current_budget.csv

This file contains a list of all City of Austin departments according to how they're identified in the most recently approved budget documents. Could be helpful for getting to know more about who the publishers are.

crosswalk_to_budget_dept.csv

The City is in the process of standardizing how departments identify themselves on the data portal. In the meantime, here's a crosswalk from the department values observed in all_views_20161003.csv to the department names that appear in the City's budget

This dataset was created by Hailey Pate and contains around 100 samples along with Di Sync Success, Browser Firefox 19, technical information and other features such as: - Browser Firefox 33 - Di Sync Failed - and more.

How to use this dataset

• Analyze Sf Query Error User in relation to Js Page View Admin
• Study the influence of Browser Firefox 37 on Datasets Created
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Hailey Pate

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Student Engagement

Predicting Engagement and Exam Performance

By [source]

About this dataset

This dataset contains information on student engagement with Tableau, including quizzes, exams, and lessons. The data includes the course title, the rating of the course, the date the course was rated, the exam category, the exam duration, whether the answer was correct or not, the number of quizzes completed, the number of exams completed, the number of lessons completed, the date engaged, the exam result, and more

How to use the dataset

The 'Student Engagement with Tableau' dataset offers insights into student engagement with the Tableau software. The data includes information on courses, exams, quizzes, and student learning.

This dataset can be used to examine how students use Tableau, what kind of engagement leads to better learning outcomes, and whether certain course or exam characteristics are associated with student engagement

Research Ideas

• Creating a heat map of student engagement by course and location
• Determining which courses are most popular among students from different countries
• Identifying patterns in students' exam results

Acknowledgements

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 365_course_info.csv | Column name | Description | |:-----------------|:----------------------------------| | course_title | The title of the course. (String) |

File: 365_course_ratings.csv | Column name | Description | |:------------------|:---------------------------------------------------------| | course_rating | The rating given to the course by the student. (Numeric) | | date_rated | The date on which the course was rated. (Date) |

File: 365_exam_info.csv | Column name | Description | |:------------------|:-------------------------------------------------| | exam_category | The category of the exam. (Categorical) | | exam_duration | The duration of the exam in minutes. (Numerical) |

File: 365_quiz_info.csv | Column name | Description | |:-------------------|:----------------------------------------------------------------------| | answer_correct | Whether or not the student answered the question correctly. (Boolean) |

File: 365_student_engagement.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | engagement_quizzes | The number of times a student has engaged with quizzes. (Numeric) | | engagement_exams | The number of times a student has engaged with exams. (Numeric) | | engagement_lessons | The number of times a student has engaged with lessons. (Numeric) | | date_engaged | The date of the student's engagement. (Date) |

File: 365_student_exams.csv | Column name | Description | |:-------------------------|:---------------------------------------------------| | exam_result | The result of the exam. (Categorical) | | exam_completion_time | The time it took to complete the exam. (Numerical) | | date_exam_completed | The date the exam was completed. (Date) |

File: 365_student_hub_questions.csv | Column name | Description | |:------------------------|:----------------------------------------| | date_question_asked | The date the question was asked. (Date) |

File: 365_student_info.csv | Column name | Description | |:--------------------|:-------------------------------------------------------| | student_country | The country of the student. (Categorical) | | date_registered | The date the student registered for the course. (Date) |

File: 365_student_learning.csv | Column name | Description | |:--------------------|:------------------------------...