The data are qualitative data consisting of notes recorded during meetings, workshops, and other interactions with case study participants. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The data cannot be accessed by anyone outside of the research team because of the potential to identify human participants. Format: The data are qualitative data contained in Microsoft Word documents. This dataset is associated with the following publication: Eisenhauer, E., K. Maxwell, B. Kiessling, S. Henson, M. Matsler, R. Nee, M. Shacklette, M. Fry, and S. Julius. Inclusive engagement for equitable resilience: community case study insights. Environmental Research Communications. IOP Publishing, BRISTOL, UK, 6: 125012, (2024).

This is being done as a capstone for the Google Data Analytics Certificate.

The data for Cyclistic (a fictional company) is located here: https://divvy-tripdata.s3.amazonaws.com/index.html

This data was made available by Motivate International Inc. under the following license: https://www.divvybikes.com/data-license-agreement.

This will focus on the data provided for 2022.

The Poverty Mapping Project: Poverty and Food Security Case Studies data set consists of small area estimates of poverty, inequality, food security and related measures for subnational administrative Units in Mexico, Ecuador, Kenya, Malawi, Bangladesh, Sri Lanka, Nigeria and Vietnam. These data come from country level cases studies that examine poverty and food security from a spatial analysis perspective. The data products include shapefiles (vector data) and tabular data sets (csv format). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) and Centro Internacional de Agricultura Tropical (CIAT). The data set was originally produced by CIAT, International Maize and Wheat Improvement Center (CIMMYT), International Livestock Research Institute (ILRI), International Food Policy Research Institute (IFPRI), International Rice Research Institute (IRRI), International Water Management Institute (IWMI), and International Institute for Tropical Agriculture (IITA).

The OCKO has developed over 50 case studies to enhance learning at workshops, training, retreats and conferences. Case studies make mission knowledge attractive and engaging by involving people in the decision making process.

Dataset

This dataset was created by Alan E. Geiman

Contents

Empirical research on democratization is dominated by case studies and small-N comparisons. This article is a first attempt to take stock of qualitative case-based research on democratization. It finds that most articles use methods implicitly rather than explicitly and are disconnected from the burgeoning literature on case-based methodology. This makes it difficult to summarize the substantive findings or to evaluate the contributions of the various approaches to our knowledge of democratic transition and consolidation. There is much to gain from a closer collaboration between methods experts and empirical researchers of democratization.

We describe two model-based diagnosis algo- rithms entered into the Third International Diag- nostic Competition. We focus on the first diag- nostic problem of the industrial track of the com- petition in which a diagnosis algorithm must de- tect, isolate, and identify faults in an electrical power distribution testbed in order to provide cor- rect abort recommendations. Both diagnosis al- gorithms are based on a qualitative event-based fault isolation framework augmented with model- based fault identification. Although based on a common framework, the fundamental difference between the two algorithms is that one is based on a global model for residual generation, fault iso- lation, and fault identification, whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe, compare, and contrast the two algorithms in terms of practical implementation and their diagnosis results.

The dataset contains the case studies findings in tables referenced and used in the ongoing research paper titled "Non-profit organisations’ capabilities to reduce open government data barriers: a conceptual framework", submitted to the EGOV-CeDEM-ePart conference, September 1-5, 2024, Ghent University and KU Leuven, Ghent/Leuven, Belgium.

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, clinical researchers struggle to find real-world examples of Open Data sharing. The aim of this 1 hr virtual workshop is to provide real-world examples of Open Data sharing for both qualitative and quantitative data. Specifically, participants will learn: 1. Primary challenges and successes when sharing quantitative and qualitative clinical research data. 2. Platforms available for open data sharing. 3. Ways to troubleshoot data sharing and publish from open data. Workshop Agenda: 1. “Data sharing during the COVID-19 pandemic” - Speaker: Srinivas Murthy, Clinical Associate Professor, Department of Pediatrics, Faculty of Medicine, University of British Columbia. Investigator, BC Children's Hospital 2. “Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project.” - Speaker: Maggie Woo Kinshella, Global Health Research Coordinator, Department of Obstetrics and Gynaecology, BC Children’s and Women’s Hospital and University of British Columbia This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Srinivas Murthy: Data sharing during the COVID-19 pandemic presentation and accompanying PowerPoint slides. Maggie Woo Kinshella: Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

Fictional but realistic data for electricity generation gas-fired units

The Camp Fire ignited on November 8, 2018 in the foothills of the Sierra Nevada in Butte County, California. The first 24 hours were characterized by a fast-moving fire with initial spread driven by high winds up to 22 m/s (50 mi/h) and long-range spotting up to 6.3 km (3.9 mi) into the community. The fire quickly impacted the communities of Concow, Paradise, and Magalia. The Camp Fire became the most destructive and deadly fire in California history, with over 18000 destroyed structures, 700 damaged structures, and 85 fatalities. After a preliminary reconnaissance, it was determined that abundant data was available to support an in-depth case study of this devastating wildland-urban interface (WUI) fire to increase our understanding of WUI fire spread, fire behavior, evacuation, and structure response.Over 2200 fire observation data points were documented, each assigned a geographic location and timestamp. Through extensive cross-referencing and quality control to reconcile inconsistencies, the data points were integrated to compile a timeline of the fire spread. Data attributes include the observation description, time, location, type of fire (i.e., a spot fire, vegetative, structural, or other type of fire), and information source.

this dataset is a cleaned datset obtained from Motivate International Inc. under this license. The data is for Q1-4 of 2019 and Q1 of 2020 of the hypothetical bike sharing company "Cyclistic" from Chicago. The dataset is a project for my Google Data analytics course.

Case study research is one of the most widely used research methods in Information Systems. In recent years, an increasing number of publications have used case studies with few sources of evidence, such as single interviews per case. While there is much methodological guidance on rigorously conducting multiple case studies, it remains unclear how researchers can achieve an acceptable level of rigour for this emerging type of multiple case study with few sources of evidence, i.e., multiple mini case studies. In this context, we synthesise methodological guidance for multiple case study research from a cross-disciplinary perspective to develop an analytical framework. Furthermore, we calibrate this analytical framework to multiple mini case studies by reviewing previous IS publications that use multiple mini case studies to provide guidelines to conduct multiple mini case studies rigorously. We also offer a conceptual definition of multiple mini case studies, distinguish them from other research approaches, and identify research situations for which the use of multiple mini case studies can be considered a pragmatic and rigorous approach (e.g., to research emerging and innovative phenomena).

This data set contains the spatial layers that were input into ecosystem services models (Maps.zip), including the A2 and B1 landuse change scenarios, and the polygon boundaries for the Pensacola watershed. This dataset also includes the future climate data (ClimateData.csv) used in scenarios. Model output for 20 stochastic runs of each A2 and B1 scenario is included in three files: 1) the calculated ES metrics that were used to calculate ES indicators (ESMetrics_CountyYearly.xlsx), 2) the scaled ES indicators used as input into HWBI regression models (ESIndicators_CountyYearly.xlsx), and 3) the calculated HWBI Domain and composite scores (HWBIDomains_CountyYearly.xlsx).

This dataset is associated with the following publication: Yee, S., E. Paulukonis, C. Simmons, M. Russell, R. Fulford, L. Harwell, and L. Smith. Projecting effects of land use change on human well-being through changes in ecosystem services. ECOLOGICAL MODELLING. Elsevier Science BV, Amsterdam, NETHERLANDS, 440(109358): 20, (2021).

Dataset

This dataset was created by Ankit Kumar Rana

Released under CC0: Public Domain

Contents

The two case studies used publicly available environmental data excerpted from the National Human Exposure Assessment Survey (NHEXAS) (https://github.com/USEPA/HEDS). These U.S. Environmental Protection Agency (EPA) Region 5 data were samples collected in 1995 to 1997 from the first visit, which removed temporal between-visit correlation from sites across Ohio, Michigan, Illinois, Indiana, Wisconsin, and Minnesota. The NHEXAS tap water sampling design distinguished original samples from quality control replicates, and all samples were analyzed in the same laboratory following EPA standard method 200.8 (version 4.4). The case studies analyze subsets of NHEXAS arsenic and chromium data that were selected to meet distributional assumptions and cannot be interpreted as NHEXAS analyses. This dataset is associated with the following publication: Furman, M., K. Thomas, and B. George. Separating Measurement Error and Signal in Environmental Data: Use of Replicates to Address Uncertainty. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 57(41): 15356-15365, (2023).

This dataset forms part of a wider 10-nation comparative study on the local and global public good role of higher education. The dataset here comprises transcripts of 82 interviews with university staff and policymakers or policy professionals in the four case study countries that the University of Oxford research team were responsible for, namely: Canada (n=19), England (n=35), Finland (n=20), and South Korea (n=8).

This document includes the underlying data of the case study conducted and presented in the confrence paper titled "Exploring Customer Journey Mining and RPA: Prediction of Customers’ Next Touchpoint".

Data Source: https://www.kaggle.com/datasets/kishlaya18/customer-purchase-journey-netherlands

An approach that relates biological condition of streams to landscape integrity data at the watershed, catchment and stream-reach scale in order to support stream management and decision-making. This dataset is associated with the following publication: Riato, L., S. Leibowitz, and M. Weber. The use of multiscale stressors in biological condition assessments: a framework to advance the assessment and management of streams. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 139699, (2020).

Cyclistic Case Study Dataset.

-- Credits to Coursera Team --

It's a dataset as part of case study for the Google Data Analytics Certification. Cyclistic is an imaginary company offering their cycle services in the Chicago region.

Details of Dataset

• This dataset contains details of ride taken by the users such as ride id, rideable type(Type of cycle), member casual(Type of member),start & end date times, names of starting & ending stations, latitude & longitude of the same.
• In Filtered data two extra columns have been added day of week & ride length and details of longitude & latitude have been removed.
• Lastly 6 months of data is stacked and stored as "all_tripStackedDataSet" resulting into "19,73,397" records

Acknowledgements

-- Credits to Coursera Team -- The whole dataset was provided by the Coursera Team for the purpose of analysis.

Inspiration

The analysis was done to understand the difference between two member types i.e. casual riders & annual members as company aimed to increase the annual membership by converting the casual riders as annual members.