These datasets contain peer-to-peer trades from various recommendation platforms.

Metadata includes

• peer-to-peer trades

• have and want lists

• image data (tradesy)

A collection of 22 data set of 50+ requirements each, expressed as user stories.

The dataset has been created by gathering data from web sources and we are not aware of license agreements or intellectual property rights on the requirements / user stories. The curator took utmost diligence in minimizing the risks of copyright infringement by using non-recent data that is less likely to be critical, by sampling a subset of the original requirements collection, and by qualitatively analyzing the requirements. In case of copyright infringement, please contact the dataset curator (Fabiano Dalpiaz, f.dalpiaz@uu.nl) to discuss the possibility of removal of that dataset [see Zenodo's policies]

The data sets have been originally used to conduct experiments about ambiguity detection with the REVV-Light tool: https://github.com/RELabUU/revv-light

This collection has been originally published in Mendeley data: https://data.mendeley.com/datasets/7zbk8zsd8y/1

Overview of the datasets [data and links added in December 2024]

The following text provides a description of the datasets, including links to the systems and websites, when available. The datasets are organized by macro-category and then by identifier.

Public administration and transparency

g02-federalspending.txt (2018) originates from early data in the Federal Spending Transparency project, which pertain to the website that is used to share publicly the spending data for the U.S. government. The website was created because of the Digital Accountability and Transparency Act of 2014 (DATA Act). The specific dataset pertains a system called DAIMS or Data Broker, which stands for DATA Act Information Model Schema. The sample that was gathered refers to a sub-project related to allowing the government to act as a data broker, thereby providing data to third parties. The data for the Data Broker project is currently not available online, although the backend seems to be hosted in GitHub under a CC0 1.0 Universal license. Current and recent snapshots of federal spending related websites, including many more projects than the one described in the shared collection, can be found here.

g03-loudoun.txt (2018) is a set of extracted requirements from a document, by the Loudoun County Virginia, that describes the to-be user stories and use cases about a system for land management readiness assessment called Loudoun County LandMARC. The source document can be found here and it is part of the Electronic Land Management System and EPlan Review Project - RFP RFQ issued in March 2018. More information about the overall LandMARC system and services can be found here.

g04-recycling.txt(2017) concerns a web application where recycling and waste disposal facilities can be searched and located. The application operates through the visualization of a map that the user can interact with. The dataset has obtained from a GitHub website and it is at the basis of a students' project on web site design; the code is available (no license).

g05-openspending.txt (2018) is about the OpenSpending project (www), a project of the Open Knowledge foundation which aims at transparency about how local governments spend money. At the time of the collection, the data was retrieved from a Trello board that is currently unavailable. The sample focuses on publishing, importing and editing datasets, and how the data should be presented. Currently, OpenSpending is managed via a GitHub repository which contains multiple sub-projects with unknown license.

g11-nsf.txt (2018) refers to a collection of user stories referring to the NSF Site Redesign & Content Discovery project, which originates from a publicly accessible GitHub repository (GPL 2.0 license). In particular, the user stories refer to an early version of the NSF's website. The user stories can be found as closed Issues.

(Research) data and meta-data management

g08-frictionless.txt (2016) regards the Frictionless Data project, which offers an open source dataset for building data infrastructures, to be used by researchers, data scientists, and data engineers. Links to the many projects within the Frictionless Data project are on GitHub (with a mix of Unlicense and MIT license) and web. The specific set of user stories has been collected in 2016 by GitHub user @danfowler and are stored in a Trello board.

g14-datahub.txt (2013) concerns the open source project DataHub, which is currently developed via a GitHub repository (the code has Apache License 2.0). DataHub is a data discovery platform which has been developed over multiple years. The specific data set is an initial set of user stories, which we can date back to 2013 thanks to a comment therein.

g16-mis.txt (2015) is a collection of user stories that pertains a repository for researchers and archivists. The source of the dataset is a public Trello repository. Although the user stories do not have explicit links to projects, it can be inferred that the stories originate from some project related to the library of Duke University.

g17-cask.txt (2016) refers to the Cask Data Application Platform (CDAP). CDAP is an open source application platform (GitHub, under Apache License 2.0) that can be used to develop applications within the Apache Hadoop ecosystem, an open-source framework which can be used for distributed processing of large datasets. The user stories are extracted from a document that includes requirements regarding dataset management for Cask 4.0, which includes the scenarios, user stories and a design for the implementation of these user stories. The raw data is available in the following environment.

g18-neurohub.txt (2012) is concerned with the NeuroHub platform, a neuroscience data management, analysis and collaboration platform for researchers in neuroscience to collect, store, and share data with colleagues or with the research community. The user stories were collected at a time NeuroHub was still a research project sponsored by the UK Joint Information Systems Committee (JISC). For information about the research project from which the requirements were collected, see the following record.

g22-rdadmp.txt (2018) is a collection of user stories from the Research Data Alliance's working group on DMP Common Standards. Their GitHub repository contains a collection of user stories that were created by asking the community to suggest functionality that should part of a website that manages data management plans. Each user story is stored as an issue on the GitHub's page.

g23-archivesspace.txt (2012-2013) refers to ArchivesSpace: an open source, web application for managing archives information. The application is designed to support core functions in archives administration such as accessioning; description and arrangement of processed materials including analog, hybrid, and
born digital content; management of authorities and rights; and reference service. The application supports collection management through collection management records, tracking of events, and a growing number of administrative reports. ArchivesSpace is open source and its

This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

Metadata includes

• product IDs

• bounding boxes

Basic Statistics:

• Scenes: 47,739

• Products: 38,111

• Scene-Product Pairs: 93,274

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

The Exhibit of Datasets was an experimental project with the aim of providing concise introductions to research datasets in the humanities and social sciences deposited in a trusted repository and thus made accessible for the long term. The Exhibit consists of so-called 'showcases', short webpages summarizing and supplementing the corresponding data papers, published in the Research Data Journal for the Humanities and Social Sciences. The showcase is a quick introduction to such a dataset, a bit longer than an abstract, with illustrations, interactive graphs and other multimedia (if available). As a rule it also offers the option to get acquainted with the data itself, through an interactive online spreadsheet, a data sample or link to the online database of a research project. Usually, access to these datasets requires several time consuming actions, such as downloading data, installing the appropriate software and correctly uploading the data into these programs. This makes it difficult for interested parties to quickly assess the possibilities for reuse in other projects.

The Exhibit aimed to help visitors of the website to get the right information at a glance by: - Attracting attention to (recently) acquired deposits: showing why data are interesting. - Providing a concise overview of the dataset's scope and research background; more details are to be found, for example, in the associated data paper in the Research Data Journal (RDJ). - Bringing together references to the location of the dataset and to more detailed information elsewhere, such as the project website of the data producers. - Allowing visitors to explore (a sample of) the data without downloading and installing associated software at first (see below). - Publishing related multimedia content, such as videos, animated maps, slideshows etc., which are currently difficult to include in online journals as RDJ. - Making it easier to review the dataset. The Exhibit would also have been the right place to publish these reviews in the same way as a webshop publishes consumer reviews of a product, but this could not yet be achieved within the limited duration of the project.

Note (1) The text of the showcase is a summary of the corresponding data paper in RDJ, and as such a compilation made by the Exhibit editor. In some cases a section 'Quick start in Reusing Data' is added, whose text is written entirely by the editor. (2) Various hyperlinks such as those to pages within the Exhibit website will no longer work. The interactive Zoho spreadsheets are also no longer available because this facility has been discontinued.

Project Title: Add title here

Project Team: Add contact information for research project team members

Summary: Provide a descriptive summary of the nature of your research project and its aims/focal research questions.

Relevant publications/outputs: When available, add links to the related publications/outputs from this data.

Data availability statement: If your data is not linked on figshare directly, provide links to where it is being hosted here (i.e., Open Science Framework, Github, etc.). If your data is not going to be made publicly available, please provide details here as to the conditions under which interested individuals could gain access to the data and how to go about doing so.

Data collection details: 1. When was your data collected? 2. How were your participants sampled/recruited?

Sample information: How many and who are your participants? Demographic summaries are helpful additions to this section.

Research Project Materials: What materials are necessary to fully reproduce your the contents of your dataset? Include a list of all relevant materials (e.g., surveys, interview questions) with a brief description of what is included in each file that should be uploaded alongside your datasets.

List of relevant datafile(s): If your project produces data that cannot be contained in a single file, list the names of each of the files here with a brief description of what parts of your research project each file is related to.

Data codebook: What is in each column of your dataset? Provide variable names as they are encoded in your data files, verbatim question associated with each response, response options, details of any post-collection coding that has been done on the raw-response (and whether that's encoded in a separate column).

Examples available at: https://www.thearda.com/data-archive?fid=PEWMU17 https://www.thearda.com/data-archive?fid=RELLAND14

Sample data for exercises in Further Adventures in Data Cleaning.

AutoTrain Dataset for project: sample

  Dataset Description

This dataset has been automatically processed by AutoTrain for project sample.

  Languages

The BCP-47 code for the dataset's language is unk.

  Dataset Structure







  Data Instances

A sample from this dataset looks as follows: [ { "image": "<500x375 RGB PIL image>", "target": 1 }, { "image": "<378x274 RGB PIL image>", "target": 0 } ]… See the full description on the dataset page: https://huggingface.co/datasets/MaulikMadhavi/autotrain-data-sample.

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

Harmonized Data: - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets. - The harmonization process started with cleaning all raw data files received from the Statistical Office. - Cleaned data files were then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process was run on the data. - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format.

This zip file contains: - 3 .zip files = projects to be imported into SmartPLS 3

DLOQ-A model with 7 dimensions DLOQ-A model with second-order latent variable ECSI model (Tenenhaus et al., 2005) to exemplify direct, indirect and total effects, as well as importance-performance map and moderation with continuous variables. ECSI Model (Sanches, 2013) to exemplify MGA (multi-group analysis)

• 5 files (csv, txt) with data to run 7 examples in SmartPLS 3

Note: - DLOQ-A = new dataset (ours) - ECSI-Tenenhaus et al. [model for mediation and moderation] = available at: http://www.smartpls.com > Resources > SmartPLS Project Examples - ECSI-Sanches [dataset for MGA] = available in the software R > library(plspm) > data(satisfaction)

Context

Cannabis is a genus of flowering plants in the family Cannabaceae.

Source: https://en.wikipedia.org/wiki/Cannabis

Content

In October 2016, Phylos Bioscience released a genomic open dataset of approximately 850 strains of Cannabis via the Open Cannabis Project. In combination with other genomics datasets made available by Courtagen Life Sciences, Michigan State University, NCBI, Sunrise Medicinal, University of Calgary, University of Toronto, and Yunnan Academy of Agricultural Sciences, the total amount of publicly available data exceeds 1,000 samples taken from nearly as many unique strains.

https://medium.com/google-cloud/dna-sequencing-of-1000-cannabis-strains-publicly-available-in-google-bigquery-a33430d63998

These data were retrieved from the National Center for Biotechnology Information’s Sequence Read Archive (NCBI SRA), processed using the BWA aligner and FreeBayes variant caller, indexed with the Google Genomics API, and exported to BigQuery for analysis. Data are available directly from Google Cloud Storage at gs://gcs-public-data--genomics/cannabis, as well as via the Google Genomics API as dataset ID 918853309083001239, and an additional duplicated subset of only transcriptome data as dataset ID 94241232795910911, as well as in the BigQuery dataset bigquery-public-data:genomics_cannabis.

All tables in the Cannabis Genomes Project dataset have a suffix like _201703. The suffix is referred to as [BUILD_DATE] in the descriptions below. The dataset is updated frequently as new releases become available.

The following tables are included in the Cannabis Genomes Project dataset:

Sample_info contains fields extracted for each SRA sample, including the SRA sample ID and other data that give indications about the type of sample. Sample types include: strain, library prep methods, and sequencing technology. See SRP008673 for an example of upstream sample data. SRP008673 is the University of Toronto sequencing of Cannabis Sativa subspecies Purple Kush.

MNPR01_reference_[BUILD_DATE] contains reference sequence names and lengths for the draft assembly of Cannabis Sativa subspecies Cannatonic produced by Phylos Bioscience. This table contains contig identifiers and their lengths.

MNPR01_[BUILD_DATE] contains variant calls for all included samples and types (genomic, transcriptomic) aligned to the MNPR01_reference_[BUILD_DATE] table. Samples can be found in the sample_info table. The MNPR01_[BUILD_DATE] table is exported using the Google Genomics BigQuery variants schema. This table is useful for general analysis of the Cannabis genome.

MNPR01_transcriptome_[BUILD_DATE] is similar to the MNPR01_[BUILD_DATE] table, but it includes only the subset transcriptomic samples. This table is useful for transcribed gene-level analysis of the Cannabis genome.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: http://opencannabisproject.org/ Category: Genomics Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://www.ncbi.nlm.nih.gov/home/about/policies.shtml - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. Update frequency: As additional data are released to GenBank View in BigQuery: https://bigquery.cloud.google.com/dataset/bigquery-public-data:genomics_cannabis View in Google Cloud Storage: gs://gcs-public-data--genomics/cannabis

Banner Photo by Rick Proctor from Unplash.

Inspiration

Which Cannabis samples are included in the variants table?

Which contigs in the MNPR01_reference_[BUILD_DATE] table have the highest density of variants?

How many variants does each sample have at the THC Synthase gene (THCA1) locus?

This dataset contains a small collection of 6 randomly selected CVs (Curriculum Vitae), representing various professional backgrounds. The dataset is intended to serve as a resource for research in fields such as Human Resources (HR), data analysis, natural language processing (NLP), and machine learning. It can be used for tasks like resume parsing, skill extraction, job matching, and analyzing trends in professional qualifications and experiences. Potential Use Cases: This dataset can be used for various research and development purposes, including but not limited to:

Resume Parsing: Developing algorithms to automatically extract and categorize information from resumes. Skill Extraction: Identifying key skills and competencies from text data within the CVs. Job Matching: Creating models to match candidates to job descriptions based on their qualifications and experience. NLP Research: Analyzing language patterns, sentence structure, and terminology used in professional resumes. HR Analytics: Studying trends in career paths, education, and skill development across different professions. Training Data for Machine Learning Models: Using the dataset as a sample for training and testing machine learning models in HR-related applications. Dataset Format: The dataset is available in a compressed file (ZIP) containing the 6 CVs in both PDF and DOCX formats. This allows for flexibility in how the data is processed and analyzed.

Licensing: This dataset is shared under the CC BY-NC-SA 4.0 license. This means that you are free to:

Share: Copy and redistribute the material in any medium or format. Adapt: Remix, transform, and build upon the material. Under the following terms:

Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. NonCommercial: You may not use the material for commercial purposes. ShareAlike: If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. Citation: If you use this dataset in your research or projects, please cite it as follows:

"Sample CVs Dataset for Analysis, Mushtaq et al., Kaggle, 2024."

Limitations and Considerations: Sample Size: The dataset contains only 6 CVs, which is a very small sample size. It is intended for educational and prototyping purposes rather than large-scale analysis. Anonymization: Personal details such as names, contact information, and specific locations may be anonymized or altered to protect privacy. Bias: The dataset is not representative of the entire population and may contain biases related to profession, education, and experience. This dataset is a useful starting point for developing models or conducting small-scale experiments in HR-related fields. However, users should be aware of its limitations and consider supplementing it with additional data for more robust analysis.

The purpose of data mining analysis is always to find patterns of the data using certain kind of techiques such as classification or regression. It is not always feasible to apply classification algorithms directly to dataset. Before doing any work on the data, the data has to be pre-processed and this process normally involves feature selection and dimensionality reduction. We tried to use clustering as a way to reduce the dimension of the data and create new features. Based on our project, after using clustering prior to classification, the performance has not improved much. The reason why it has not improved could be the features we selected to perform clustering are not well suited for it. Because of the nature of the data, classification tasks are going to provide more information to work with in terms of improving knowledge and overall performance metrics. From the dimensionality reduction perspective: It is different from Principle Component Analysis which guarantees finding the best linear transformation that reduces the number of dimensions with a minimum loss of information. Using clusters as a technique of reducing the data dimension will lose a lot of information since clustering techniques are based a metric of 'distance'. At high dimensions euclidean distance loses pretty much all meaning. Therefore using clustering as a "Reducing" dimensionality by mapping data points to cluster numbers is not always good since you may lose almost all the information. From the creating new features perspective: Clustering analysis creates labels based on the patterns of the data, it brings uncertainties into the data. By using clustering prior to classification, the decision on the number of clusters will highly affect the performance of the clustering, then affect the performance of classification. If the part of features we use clustering techniques on is very suited for it, it might increase the overall performance on classification. For example, if the features we use k-means on are numerical and the dimension is small, the overall classification performance may be better. We did not lock in the clustering outputs using a random_state in the effort to see if they were stable. Our assumption was that if the results vary highly from run to run which they definitely did, maybe the data just does not cluster well with the methods selected at all. Basically, the ramification we saw was that our results are not much better than random when applying clustering to the data preprocessing. Finally, it is important to ensure a feedback loop is in place to continuously collect the same data in the same format from which the models were created. This feedback loop can be used to measure the model real world effectiveness and also to continue to revise the models from time to time as things change.

This database includes simulated data showing the accuracy of estimated probability distributions of project durations when limited data are available for the project activities. The base project networks are taken from PSPLIB. Then, various stochastic project networks are synthesized by changing the variability and skewness of project activity durations. Number of variables: 20 Number of cases/rows: 114240 Variable List: • Experiment ID: The ID of the experiment • Experiment for network: The ID of the experiment for each of the synthesized networks • Network ID: ID of the synthesized network • #Activities: Number of activities in the network, including start and finish activities • Variability: Variance of the activities in the network (this value can be either high, low, medium or rand, where rand shows a random combination of low, high and medium variance in the network activities.) • Skewness: Skewness of the activities in the network (Skewness can be either right, left, None or rand, where rand shows a random combination of right, left, and none skewed in the network activities)
• Fitted distribution type: Distribution type used to fit on sampled data • Sample size: Number of sampled data used for the experiment resembling limited data condition • Benchmark 10th percentile: 10th percentile of project duration in the benchmark stochastic project network • Benchmark 50th percentile: 50th project duration in the benchmark stochastic project network • Benchmark 90th percentile: 90th project duration in the benchmark stochastic project network • Benchmark mean: Mean project duration in the benchmark stochastic project network • Benchmark variance: Variance project duration in the benchmark stochastic project network • Experiment 10th percentile: 10th percentile of project duration distribution for the experiment • Experiment 50th percentile: 50th percentile of project duration distribution for the experiment • Experiment 90th percentile: 90th percentile of project duration distribution for the experiment • Experiment mean: Mean of project duration distribution for the experiment • Experiment variance: Variance of project duration distribution for the experiment • K-S: Kolmogorov–Smirnov test comparing benchmark distribution and project duration • distribution of the experiment • P_value: the P-value based on the distance calculated in the K-S test

Hypothesis: The reliability can be adopted to quantitatively measure the sustainability of mega-projects.

Presentation: This dataset shows two scenario based examples to establish an initial reliability assessment of megaproject sustainability. Data were gathered from the author’s assumption with regard to assumed differences between scenarios A and B. There are two sheets in this Microsoft Excel file, including a comparison between two scenarios by using a Fault Tree Analysis model, and a correlation analysis between reliability and unavailability.

Notable findings: It has been found from this exploratory experiment that the reliability can be used to quantitatively measure megaproject sustainability, and there is a negative correlation between reliability and unavailability among 11 related events in association with sustainability goals in the life-cycle of megaproject.

Interpretation: Results from data analysis by using the two sheets can be useful to inform decision making on megaproject sustainability. For example, the reliability to achieve sustainability goals can be enhanced by decrease the unavailability or the failure at individual work stages in megaproject delivery.

Implication: This dataset file can be used to perform reliability analysis in other experiment to access megaproject sustainability.

Dataset Card for Dataset Name

This dataset consists of 925 sentences in English paired with a broad topic descriptor for use as example data in product demonstrations or student projects.

Curated by: billingsmoore Language(s) (NLP): English License: Apache License 2.0

  Direct Use

This data can be loaded using the following Python code. from datasets import load_dataset

ds = load_dataset('billingsmoore/text-clustering-example-data')

It can then be clustered using the… See the full description on the dataset page: https://huggingface.co/datasets/billingsmoore/text-clustering-example-data.

This data set includes results for hormone and pharmaceutical compounds analyzed from 2012 through 2016 in laboratory quality-control samples that are associated with environmental samples collected by the National Water-Quality Assessment (NAWQA) Project during 2013 through 2015 for a study of groundwater resources used for drinking-water supply across the United States. Hormone and pharmaceutical results are provided for laboratory set blanks and reagent spikes analyzed during a time period that encompasses laboratory analysis of the environmental samples collected by NAWQA. This data release includes: Table 1. Hormone results for laboratory set blanks, December 18, 2012 through March 7, 2016. Table 2. Pharmaceutical results for laboratory set blanks, December 14, 2012 through March 4, 2016. Table 3. Hormone results for laboratory reagent spikes, June 17, 2013 through December 11, 2015. Table 4. Pharmaceutical results for laboratory reagent spikes, June 18, 2013 through October 1, 2015.

KL3M Data Project

Note: This page provides general information about the KL3M Data Project. Additional details specific to this dataset will be added in future updates. For complete information, please visit the GitHub repository or refer to the KL3M Data Project paper.

  Description

This dataset is part of the ALEA Institute's KL3M Data Project, which provides copyright-clean training resources for large language models.

  Dataset Details

Format: Parquet… See the full description on the dataset page: https://huggingface.co/datasets/alea-institute/kl3m-data-ecfr-sample.

Urine Samples

## Overview

Urine Samples is a dataset for object detection tasks - it contains Cells annotations for 368 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).