Sort

## Overview

Sort is a dataset for object detection tasks - it contains Sort annotations for 1,856 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).

Customer Order Delivery Dataset

This dataset contains customer orders from an e-commerce system with various timestamps for the full delivery process: - order purchase - approval time - shipping time - customer delivery time - estimated delivery

Columns

• order_id: Unique identifier for each order.
• customer_id: Customer identifier.
• order_status: Status (delivered, shipped, etc.)
• order_purchase_timestamp: Date and time of purchase.
• order_approved_at: When the order was approved.
• order_delivered_carrier_date: When the seller handed over to the carrier.
• order_delivered_customer_date: When the customer received the product.
• order_estimated_delivery_date: Estimated delivery date.

Possible Use Cases

• Delivery performance analysis
• Predictive modeling (e.g., delivery date prediction)
• Delay detection and logistics optimization

Law and Order TV Series Dataset

Law and Order TV Series Data

By Gove Allen [source]

About this dataset

The Law and Order Dataset is a comprehensive collection of data related to the popular television series Law and Order that aired from 1990 to 2010. This dataset, compiled by IMDB.com, provides detailed information about each episode of the show, including its title, summary, airdate, director, writer, guest stars, and IMDb rating.

With over 450 episodes spanning 20 seasons of the original series as well as its spin-offs like Law and Order: Special Victims Unit, this dataset offers a wealth of information for analyzing various facets of criminal justice and law enforcement portrayed in the show. Whether you are a student or researcher studying crime-related topics or simply an avid fan interested in exploring behind-the-scenes details about your favorite episodes or actors involved in them, this dataset can be a valuable resource.

By examining this extensive collection of data using SQL queries or other analytical techniques, one can gain insights into patterns such as common tropes used in different seasons or characters that appeared most frequently throughout the series. Additionally, researchers can investigate correlations between factors like episode directors/writers and their impact on viewer ratings.

This dataset allows users to dive deep into analyzing aspects like crime types covered within episodes (e.g., homicide cases versus white-collar crimes), how often certain guest stars made appearances (including famous actors who had early roles on the show), or which writers/directors contributed most consistently high-rated episodes. Such analyses provide opportunities for uncovering trends over time within Law and Order's narrative structure while also shedding light on societal issues addressed by the series.

By making this dataset available for educational purposes at collegiate levels specifically aimed at teaching SQL skills—a powerful tool widely used in data analysis—the intention is to empower students with real-world examples they can explore hands-on while honing their database querying abilities. The graphical representation accompanying this dataset further enhances understanding by providing visualizations that illustrate key relationships between different variables.

Whether you are a seasoned data analyst, a budding criminologist, or simply looking to understand the intricacies of one of the most successful crime dramas in television history, the Law and Order Dataset offers you a vast array of information ripe for exploration and analysis

How to use the dataset

Understanding the Columns

Before diving into analyzing the data, it's important to understand what each column represents. Here is an overview:

• Episode: The episode number within its respective season.
• Title: The title of each episode.
• Season: The season number in which each episode belongs.
• Year: The year in which each episode was released.
• Rating: IMDB rating for each episode (on a scale from 0-10).
• Votes: Number of votes received by each episode on IMDB.
• Description: Brief summary or description of each episode's plot.
• Director: Director(s) responsible for directing an episode.
• Writers: Writer(s) credited for writing an episode.
• Stars : Actor(s) who starred in an individual episode.

Exploring Episode Data

The dataset allows you to explore various aspects of individual episodes as well as broader trends throughout different seasons:

1. Analyzing Ratings:

- You can examine how ratings vary across seasons using aggregation functions like average (AVG), minimum (MIN), maximum (MAX), etc., depending on your analytical goals.
- Identify popular episodes by sorting based on highest ratings or most votes received.

2.Trends over Time:

- Investigate how ratings have changed over time by visualizing them using line charts or bar graphs based on release years or seasons.
- Examine if there are any significant fluctuations in ratings across different seasons or years.

3. Directors and Writers:

- Identify episodes directed by a specific director or written by particular writers by filtering the dataset based on their names.
- Analyze the impact of different directors or writers on episode ratings.

4. Popular Actors:

- Explore episodes featuring popular actors from the show such as Mariska Hargitay (Olivia Benson), Christopher Meloni (Elliot Stabler), etc.
- Investigate whether episodes with popular actors received higher ratings compared to ...

Phase Sort

## Overview

Phase Sort is a dataset for object detection tasks - it contains Enterocoelia annotations for 600 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).

Vegetables Sort Classifier

## Overview

Vegetables Sort Classifier is a dataset for object detection tasks - it contains Vegetables annotations for 1,000 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).

This dataset is about books. It has 1 row and is filtered where the book publisher is GreenProfile/Sort Of Books. It features 7 columns including author, publication date, language, and book publisher.

parzi-parzi/v0.8.3-patch-gen-dataset-topo-sort-extended dataset hosted on Hugging Face and contributed by the HF Datasets community

The San Francisco Controller's Office maintains a database of payments made to vendors from fiscal year 2007 forward. This data is presented on the Vendor Payments report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format, which represents summary data by purchase order. We have removed sensitive information from this data – this is intended to show payments made to entities providing goods and services to the City and County and to protect individuals. For example, we have removed payments to employees (reimbursements, garnishments) and jury members, revenue refunds, payments for judgments and claims, witnesses, relocation and rehousing, and a variety of human services payments. New data is added on a weekly basis. Supplier payments represent payments to City contractors and vendors that provide goods and/or services to the City. Certain other non-supplier payee payments, which are made to parties other than traditional City contractors and vendors, are also included in this dataset, These include payments made for tax and fee refunds, rebates, settlements, etc.

Dataset

This dataset was created by Divyansh Kushwaha

Released under MIT

Contents

This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v3.0", "robot_type": "wlkata_mirobot", "total_episodes": 1, "total_frames": 380, "total_tasks": 1, "chunks_size": 1000, "data_files_size_in_mb": 100, "video_files_size_in_mb": 500, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/AzuratiX/sort-gnr.

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015

Data Limitations:
Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal.

Data Collection Methodology:

The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database.

Secondary/Related Resources:

• State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx
• State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx
• Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx
• United Nations Standard Products and Services Code, http://www.unspsc.org/

Dataset

This dataset was created by Amogh N Rao

Contents

Dataset

This dataset was created by mr vynum

Contents

Order 3

## Overview

Order 3 is a dataset for object detection tasks - it contains 0 annotations for 1,999 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).

The provided data set includes key elements relevant to the order picking and rack scheduling problem (OPRSP) experiments involving different warehouse scales: small, medium, and large.

This data set includes names, designations, and discovery circumstances for the numbered asteroids, sorted in order of catalog number. A similar file sorted in alphabetic order by name is also available in the Small Bodies Node asteroid data archive.

Table of quantitative issues reported for each analytical grid (TRI, commune, district) and for each flood scenario.European Directive 2007/60/EC of 23 October 2007 on the assessment and management of flood risks (OJ 2007 L 288, 06-11-2007, p. 27) influences the flood prevention strategy in Europe. It requires the production of flood risk management plans aimed at reducing the negative consequences of flooding on human health, the environment, cultural heritage and economic activity.The objectives and requirements for implementation are set out in the Law of 12 July 2010 on a national commitment for the environment (LENE) and the decree of 2 March 2011. In this context, the primary objective of flood and flood risk mapping for IRRs is to contribute, by homogenising and objectivating knowledge of flood exposure, to the development of flood risk management plans (WRMs).This data set is used to produce maps of the issues exposed at an appropriate scale.

This data set includes names, designations, and discovery circumstances for the numbered asteroids, sorted in order of number. This data set supercedes past versions of both the ASTNAMES and DISCOVER asteroid data sets. It represents an extension of a file originally prepared by Pilcher (1979) [PILCHER1979] for the Tucson Revised Index of Asteroid Data (TRIAD) and updated in Pilcher (1989) [PILCHER1989] for the Asteroids II database. The data for asteroids numbered 4045 and greater were either provided by the Minor Planet Center or extracted from the Minor Planet Circulars, which are published by the Minor Planet Center on behalf of Commission 20 of the International Astronomical Union. In some cases, an institution or survey is credited with the discovery rather than an individual. No attempt has been made to correct the inconsistencies in the presentation of names (that is, some names are given with initials and some are not), or in the way locations are specified (for example, Pilcher distinguishes between the Lowell Observatory and Anderson Mesa locations in Flagstaff).

This is the dataset used for the study 'Compliance to guidelines in prescribing empirical antibiotics for individuals with uncomplicated urinary tract infection in a primary health facility of Ghana, 2019-2021' conducted at the Korle Bu Polyclinic/Family Medicine Department in Accra, Ghana

Table of flooding areas (area to be flooded in case of flooding of a certain type according to a certain scenario).Series of geographical data produced by the GIS High Flood Risk Directive (TRI) of... and mapped for reporting purposes for the European Flood Directive.European Directive 2007/60/EC of 23 October 2007 on the assessment and management of flood risks (OJ EU L 288, 06-11-2007, p. 27) influences the flood prevention strategy in Europe. It requires the production of flood risk management plans aimed at reducing the negative consequences of flooding on human health, the environment, cultural heritage and economic activity.The objectives and requirements for implementation are set out in the Law of 12 July 2010 on a national commitment for the environment (LENE) and the decree of 2 March 2011. In this context, the primary objective of flood and flood risk mapping for IRRs is to contribute, by homogenising and objectivating knowledge of flood exposure, to the development of flood risk management plans (PGRIs).This dataset is used to produce flood surface maps and flood risk maps, respectively, representing flood hazards and issues exposed on an appropriate scale. Their objective is to provide quantitative evidence to further assess the vulnerability of a territory for the three levels of probability of flooding (high, medium, low).