Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

This dataset includes (1) a .txt file of processed time-series with four traffic congestion levels for the borough of Manhattan, NYC, averaged every 3 hours for the duration of 2020, and (2) an R script for completing analysis of the traffic time series to determine patterns in traffic over the year 2020, and to evaluate the impact of stay-at-home orders implemented in response to the COIVD-19 pandemic.

This data release presents the results of analyses of biota and water samples collected on multiple dates from 2007 to 2014 at 3 locations in Lake Mead National Recreation Area. Data are presented in 3 spreadsheets containing sample analyses for (1) stable isotopes in biota (2007-2014), (2) synthetic organic compounds in biota (2013-2014), and (3) synthetic organic compounds in water (2013-2014)

Analysis

## Overview

Analysis is a dataset for object detection tasks - it contains Wire annotations for 1,717 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).

For each sample, the number of reads and the mean coverage are indicated. Each sample metadata included the percentage of the genome covered by at least 1X (% > 1X), 5X (% > 5X), and 10X (% > 10X) sequencing depth. When available, the latitude and longitude are specified. NA: information not available. (XLSX)

Software for calculating multifractal partitions and moments of a time series.

Here is the updated list with web_events.csv included:

1. Orders Dataset:

   • Contains details of customer orders, including order dates, total order amounts (in USD), and associated customer IDs.
   • Helps analyze revenue trends, customer purchasing behavior, and seasonal patterns.

2. Accounts Dataset:

   • Represents customer account information such as account IDs, names, and sales representative assignments.
   • Useful for understanding customer demographics and their engagement with the company.

3. Regions Dataset:

   • Defines geographical regions managed by sales representatives, including region names and IDs.
   • Enables regional sales performance analysis and comparative insights across regions.

4. Sales Representatives Dataset:

   • Includes data on sales representatives, their IDs, names, and the regions they serve.
   • Helps link customer accounts to specific regions and analyze rep performance.

5. Web Events Dataset:

   • Logs customer interactions on the company's website, including timestamps, event types, and user IDs.
   • Helps track user activity, identify trends in website engagement, and optimize the online user experience.

These datasets collectively enable comprehensive insights into sales performance, customer behavior, website engagement, and regional trends, forming the backbone of the interactive dashboard.

Dashboard Link:

Click Here To Visit Dashboard 📊

This dataset is about books. It has 1 row and is filtered where the book publisher is ESRC Centre for Analysis of Risk and Regulation. It features 7 columns including author, publication date, language, and book publisher.

Reef core samples from select sites on Kauai, Oahu, and Molokai were taken to study reef accretion. Most field work occurred between 1995-2002. Parameters derived include Substrate Description, Calibrated Core Age, and Calibrated Age Range.

Dataset

This dataset was created by sikha njanaseelan

Contents

***Background ***I try to make dataset for analysis my school performance.

***Discussion ***I have collected students past and present records, that will help me analysis of school performance.

*** Acknowledgements ***We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Isothermal titration calorimetry (ITC) is the only technique able to determine both the enthalpy and entropy of noncovalent association in a single experiment. The standard data analysis method based on nonlinear regression, however, provides unrealistically small uncertainty estimates due to its neglect of dominant sources of error. Here, we present a Bayesian framework for sampling from the posterior distribution of all thermodynamic parameters and other quantities of interest from one or more ITC experiments, allowing uncertainties and correlations to be quantitatively assessed. For a series of ITC measurements on metal:chelator and protein:ligand systems, the Bayesian approach yields uncertainties which represent the variability from experiment to experiment more accurately than the standard data analysis. In some datasets, the median enthalpy of binding is shifted by as much as 1.5 kcal/mol. A Python implementation suitable for analysis of data generated by MicroCal instruments (and adaptable to other calorimeters) is freely available online.

This data release documents the data and models used to assess flows, concentrations, and loads of highway and urban runoff and of stormwater within receiving streams in southern New England. There are more than 48,000 locations in southern New England where roads cross streams and many more locations where runoff from developed areas may discharge to receiving streams; information about runoff discharges and the quantity and quality of stormflow upstream and downstream of discharge points is needed to inform resource-management decisions. This analysis was done with a version 1.1.1 of the Stochastic Empirical Loading and Dilution Model (SELDM) that was populated with regional statistics for southern New England. SELDM uses basin properties and hydrologic statistics to simulate runoff from a site of interest, which may be a highway site or another developed (urban) area, and concurrent stormflow from an upstream basin to calculate downstream values, which are the sum of contributi ...

This Data Set Contains Insights into Mobile App Usage Patterns, including Screen Time, notifications received, and app openings. But the data is a litle bit inconsistent this was intensional for aplications in studies about data Exploration and data Cleaning

Features: 1. Date: The date of the each day for each app. 2. App: The name of the mobile application, some names are wrong. 3. Usage (minutes): Total minutes spent using the app for each day. 4. Notifications: Number of notifications received from the app. 5. Times Opened: How many times the app was launched.

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

Opinion surveys often employ multiple items to measure the respondent's underlying value, belief, or attitude. To analyze such types of data, researchers have often followed a two-step approach by first constructing a composite measure and then using it in subsequent analysis. This paper presents a class of hierarchical item response models that help integrate measurement and analysis. In this approach, individual responses to multiple items stem from a latent preference, of which both the mean and variance may depend on observed covariates. Compared with the two-step approach, the hierarchical approach reduces bias, increases efficiency, and facilitates direct comparison across surveys covering different sets of items. Moreover, it enables us to investigate not only how preferences differ among groups, vary across regions, and evolve over time, but also levels, patterns, and trends of attitude polarization and ideological constraint. An open-source R package, hIRT, is available for fitting the proposed models.

The data analytic market size is projected to grow from USD 69.40 billion in the current year to USD 877.12 billion by 2035, representing a CAGR of 25.93%, during the forecast period till 2035.

The data and analysis of the surveys to study the users' opinion about the presence of an avatar during a learning experience in Mixed Reality. Also there are demographic data and the open questions collected. This data was used in the paper Evaluating the Effectiveness of Avatar-Based Collaboration in XR for Pump Station Training Scenarios for the GeCon 2024 Conference.

Rock Analysis

## Overview

Rock Analysis is a dataset for object detection tasks - it contains Rock annotations for 300 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).

Footfall Analysis

## Overview

Footfall Analysis is a dataset for object detection tasks - it contains Person annotations for 393 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 [MIT license](https://creativecommons.org/licenses/MIT).