Dataset

This dataset was created by Oscar NG

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Mark Dobres

Contents

Qualitative data gathered from interviews that were conducted with case organisations. The data is analysed using a qualitative data analysis tool (AtlasTi) to code and generate network diagrams. Software such as Atlas.ti 8 Windows will be a great advantage to use in order to view these results. Interviews were conducted with four case organisations. The details of the responses from the respondents from case organisations are captured. The data gathered during the interview sessions is captured in a tabular form and graphs were also created to identify trends. Also in this study is desktop review of the case organisations that formed part of the study. The desktop study was done using published annual reports over a period of more than seven years. The analysis was done given the scope of the project and its constructs.

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

Financial News Headlines. Visit https://dataone.org/datasets/sha256%3Ade01b1cf5318d53f0296b475ff28734d90acd6240a76f1eee1df39fefda07ef0 for complete metadata about this dataset.

Dataset

This dataset was created by Will Newt

Contents

Data Mining

## Overview

Data Mining is a dataset for object detection tasks - it contains Uangrupiah annotations for 692 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Data Mining Kel 11

## Overview

Data Mining Kel 11 is a dataset for classification tasks - it contains Beras annotations for 59,785 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Preventive healthcare is a crucial pillar of health as it contributes to staying healthy and having immediate treatment when needed. Mining knowledge from longitudinal studies has the potential to significantly contribute to the improvement of preventive healthcare. Unfortunately, data originated from such studies are characterized by high complexity, huge volume, and a plethora of missing values. Machine Learning, Data Mining and Data Imputation models are utilized a part of solving these challenges, respectively. Toward this direction, we focus on the development of a complete methodology for the ATHLOS Project – funded by the European Union’s Horizon 2020 Research and Innovation Program, which aims to achieve a better interpretation of the impact of aging on health. The inherent complexity of the provided dataset lies in the fact that the project includes 15 independent European and international longitudinal studies of aging. In this work, we mainly focus on the HealthStatus (HS) score, an index that estimates the human status of health, aiming to examine the effect of various data imputation models to the prediction power of classification and regression models. Our results are promising, indicating the critical importance of data imputation in enhancing preventive medicine’s crucial role.

Locations and numbers of past producing metal and coal mining projects in NW US and Canada. This dataset is associated with the following publication: Sergeant, C., E. Sexton, J. Moore, A. Westwood, S. Nagorski, J. Ebersole, D.M. Chambers, S.L. O'Neal, R.L. Malison, R. Hauer, D.C. Whited, J. Weitz, J. Caldwell, M. Capito, M. Connor, C.A. Frissell, G. Knox, E.D. Lowery, R. Macnair, V. Marlatt, J. McIntyre, M.V. McPhee, and N. Skuce. Risks of mining to salmonid-bearing watersheds. Science Advances. American Association for the Advancement of Science (AAAS), Washington, DC, USA, 8(26): eabn0929, (2022).

Dataset

This dataset was created by Yuxian Chen

Contents

This dataset contains the SQL tables of the training and test datasets used in our experimentation. These tables contain the preprocessed textual data (in a form of tokens) extracted from each training and test project. Besides the preprocessed textual data, this dataset also contains meta-data about the projects, GitHub topics, and GitHub collections. The GitHub projects are identified by the tuple “Owner” and “Name”. The descriptions of the table fields are attached to their respective data descriptions.

This area of the website provides information on three of the safety programs established by FMCSA to support this mission. The three programs covered by this area include reviews, roadside inspections of commercial vehicles and drivers, and traffic enforcement stops of CMVs operating in an unsafe manner. Each program is implemented in conjunction with the states and devoted to improving motor carrier safety by reducing the number and severity of crashes involving large trucks and buses.

Retrofitting is an essential element of any comprehensive strategy for improving residential energy efficiency. The residential retrofit market is still developing, and program managers must develop innovative strategies to increase uptake and promote economies of scale. Residential retrofitting remains a challenging proposition to sell to homeowners, because awareness levels are low and financial incentives are lacking. The U.S. Department of Energy's Building America research team, Alliance for Residential Building Innovation (ARBI), implemented a project to increase residential retrofits in Davis, California. The project used a neighborhood-focused strategy for implementation and a low-cost retrofit program that focused on upgraded attic insulation and duct sealing. ARBI worked with a community partner, the not-for-profit Cool Davis Initiative, as well as selected area contractors to implement a strategy that sought to capitalize on the strong local expertise of partners and the unique aspects of the Davis, California, community. Working with community partners also allowed ARBI to collect and analyze data about effective messaging tactics for community-based retrofit programs. ARBI expected this project, called Retrofit Your Attic, to achieve higher uptake than other retrofit projects, because it emphasized a low-cost, one-measure retrofit program. However, this was not the case. The program used a strategy that focused on attics-including air sealing, duct sealing, and attic insulation-as a low-cost entry for homeowners to complete home retrofits. The price was kept below $4,000 after incentives; both contractors in the program offered the same price. The program completed only five retrofits. Interestingly, none of those homeowners used the one-measure strategy. All five homeowners were concerned about cost, comfort, and energy savings and included additional measures in their retrofits. The low-cost, one-measure strategy did not increase the uptake among homeowners, even in a well-educated, affluent community such as Davis. This project has two primary components. One is to complete attic retrofits on a community scale in the hot-dry climate on Davis, CA. Sufficient data will be collected on these projects to include them in the BAFDR. Additionally, ARBI is working with contractors to obtain building and utility data from a large set of retrofit projects in CA (hot-dry). These projects are to be uploaded into the BAFDR.

Interview and workshop transcripts from EPSRC Digital Transformations Communities and Cultures Network + (http://www.communitiesandculture.org/) project Digital Data Analytics, Public Engagement and the Social Life of Methods (http://www.communitiesandculture.org/projects/digital-data-analysis/). Methodology described in papers available at the above link.

These are artificially made beginner data mining datasets for learning purposes.

Case study:

• FEELS LIKE HOME is an interior design company, which has about 100 000 registered customers and provide services for more than 200 000 clients annually.
• The range of the products can be divided in 5 major classes: Decor accessories, Furniture, Textiles, Lighting and Art with an option to purchase Limited Edition versions for an extra charge. These goods can be distributed by 3 channels: Physical stores, yearly catalogs and the companies’ website.
• FEELS LIKE HOME has been doing a great job during recent years, achieving decent profits and revenues, but the future remains volatile. In order to solve the problem of instability the company is planning to launch new marketing program, especially to improve the accuracy of marketing campaigns.

The aim of FeelsLikeHome_Campaign dataset is to create project is in which you build a predictive model (using a sample of 2500 clients’ data) forecasting the highest profit from the next marketing campaign, which will indicate the customers who will be the most likely to accept the offer.

The aim of FeelsLikeHome_Cluster dataset is to create project in which you split company’s customer base on homogenous clusters (using 5000 clients’ data) and propose draft marketing strategies for these groups based on customer behavior and information about their profile.

FeelsLikeHome_Score dataset can be used to calculate total profit from marketing campaign and for producing a list of sorted customers by the probability of the dependent variable in predictive model problem.

Configuration file for DrEdGE website

"The Africa Power–Mining Database 2014 shows ongoing and forthcoming mining projects in Africa categorized by the type of mineral, ore grade, size of the project. The database draws on basic mining data from Infomine surveys, the United States Geological Survey, annual reports, technical reports, feasibility studies, investor presentations, sustainability reports on property-owner websites or filed in public domains, and mining websites (Mining Weekly, Mining Journal, Mbendi, Mining-technology, and Miningmx). Comprising 455 projects in 28 SSA countries with each project’s ore reserve value assessed at more than $250 million, the database collates publicly available and proprietary information. It also provides a panoramic view of projects operating in 2000–12 and anticipated demand in 2020. The analysis is presented over three timeframes: pre-2000, 2001–12, and 2020 (each containing the projects from the previous period except for those closing during that previous period)."

This dataset comprises the raw data and R Script for the following published article: Schoderer, M., & Ott, M. (2022). Contested water-and miningscapes–Explaining the high intensity of water and mining conflicts in a meta-study. World Development, 154, 105888. The article seeks to better understand the dynamics of mining and water conflicts, specifically under which (combinations of) conditions environmental defenders step outside the legal framework in their contestation of mining projects, according to existing case study-based research. More information on the methodology is available in the paper.

The file Water and mining conflicts full dataset includes the qualitative information extracted from published articles, the scoring scheme and the normalized scores used in the R analysis. The R Script QCA_Preventive water and mining conflicts describes the fuzzy-set, two-step Qualitative Comparative Analysis conduct to understand under which conditions environmental defenders choose non-legal means in conflicts that occur in the planning or licensing stage of a mining project The CSV file Normalized scores_preventive is the raw data used in the R Script QCA_Preventive water and mining conflicts The R Script QCA_Reactive water and mining conflicts describes the fuzzy-set, two-step Qualitative Comparative Analysis conduct to understand under which conditions environmental defenders choose non-legal means in conflicts that occur when the mining project is already in operation The CSV file Normalized scores_reactive is the raw data used in the R Script QCA_Reactive water and mining conflicts

Geese Counting Project #2

## Overview

Geese Counting Project #2 is a dataset for object detection tasks - it contains Geese annotations for 647 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).