I think this is an interesting dataset that I generated from ChatGPT, but I am not sure how to generate visuals for it. Does anyone have any suggestions?

Height (Wealth Level) Percentage of Population Rough Number of People Rough Wealth Range 1 inch (Poverty Level) 10.5% ~34.8 million $0 – $10,000 5 feet (Median Wealth) 50% ~165.5 million $10,000 – $100,000 6 feet (Affluent) 25% ~82.75 million $100,000 – $1,000,000 10 feet (Wealthy) 10% ~33.1 million $1,000,000 – $10,000,000 100 feet (Ultra-Wealthy) 1% ~3.31 million $10,000,000 – $1,000,000,000 1000 feet (Billionaire Class) 0.0002% ~660 individuals $1,000,000,000 and above

Looking to put together a searchable website where users can search a product and see the price / quantity deferential over the past 2-3 years. Any help pointing me in the right direction would be greatly appreciated.

Anyone have another source to download the REDD dataset? The original source http://redd.csail.mit.edu/, is not working and cant access. We badly need the full dataset. Thank you!

Hello, everyone! Does anyone here has data set for mushroom yield production that includes temperature and humidity data? We need at least 1,500 data for our simulation as part of our capstone project. Thank you.

I would really appreciate feedback on a version control for tabular datasets I am building, the Data Manager.

Main characteristics:

Like DVC and Git LFS, integrates with Git itself. Like DVC and Git LFS, can store large files on AWS S3 and link them in Git via an identifier. Unlike DVC and Git LFS, calculates and commits diffs only, at row, column, and cell level. For append scenarios, the commit will include new data only; for edits and deletes, a small diff is committed accordingly. With DVC and Git LFS, the entire dataset is committed again, instead: committing 1 MB of new data 1000 times to a 1 GB dataset yields more than 1 TB in DVC (a dataset that increases linearly in size between 1 GB and 2 GB, committed 1000 times, results in a repository of ~1.5 TB), whereas it sums to 2 GB (1 GB original dataset, plus 1000 times 1 MB changes) with the Data Manager. Unlike DVC and Git LFS, the diffs for each commit remain visible directly in Git. Unlike DVC and Git LFS, the Data Manager allows committing changes to datasets without full checkouts on localhost. You check out kilobytes and can append data to a dataset in a repository of hundreds of gigabytes. The changes on a no-full-checkout branch will need to be merged into another branch (on a machine that does operate with full checkouts, instead) to be validated, e.g., against adding a primary key that already exists. Since the repositories will contain diff histories, snapshots of the datasets at a certain commit have to be recreated to be deployable. These can be automatically uploaded to S3 and labeled after the commit hash, via the Data Manager.

Links:

https://news.ycombinator.com/item?id=35930895 [no full checkout] https://youtu.be/BxvVdB4-Aqc https://news.ycombinator.com/item?id=35806843 [general intro] https://youtu.be/J0L8-uUVayM

This paradigm enables hibernating or cleaning up history on S3 for old datasets, if these are deleted in Git and snapshots of earlier commits are no longer needed. Individual data entries can also be removed for GDPR compliance using versioning on S3 objects, orthogonal to git.

I built the Data Manager for a pain point I was experiencing: it was impossible to (1) uniquely identify and (2) make available behind an API multiple versions of a collection of datasets and config parameters, (3) without overburdening HDDs due to small, but frequent changes to any of the datasets in the repo and (4) while being able to see the diffs in git for each commit in order to enable collaborative discussions and reverting or further editing if necessary.

Some background: I am building natural language AI algorithms (a) easily retrainable on editable training datasets, meaning changes or deletions in the training data are reflected fast, without traces of past training and without retraining the entire language model (sounds impossible), and (b) that explain decisions back to individual training data.

I look forward to constructive feedback and suggestions!

I am creating Career Recommender for my Computer Science Project and I am looking for a dataset that contains information about all the different Careers/Jobs and stats about them like salary, field, and Education needed for it. Where can I find a dataset which would include these are at least some?

I am working on a project to collect data from Different sources (distributors, retail stores, etc.) thru different approaches (ftp, api, scrapping, excel, etc.). I would like to consolidate all the information and create dynamic reports. I would like to add all the offers and discounts suggested by these various vendors.

How do I get all this data? Is there a data provider who can provide the data? I would like to start with IT hardware and IT Electronic Consumers goods.

Any help is highly appreciated. TIA

https://docs.google.com/forms/d/e/1FAIpQLSdcIXGYOzN6HPDxz4cFlcJ-YNUmybVGF0urslilmuvehl_FLw/viewform?usp=sf_link

i need a small dataset of vehicles detection have at least 6 classes contain ambulance class for my project “smart traffic control” , I am use YOLO v8

Hey everyone!

We are a group of design students currently conducting academic research on an intriguing topic: the democratization of data and its potential of data to benefits the public. We believe that data can play a vital role in improving people’s lives outside the realm of business, and we would love to hear your thoughts and experiences on this subject.

If you have a moment, we kindly invite you to answer one or more of the following questions either privately or as a comment:

– Please share your most recent experience using datasets for self– worth or public value (non-business purposes). For example, a project that makes data accessible or extracts insights that can help the general public?

– Working on the project, what worked and what didn’t work? Were there barriers and challenges that you can share?

– Are there any insights or tips you would like to share following the project?

– Do you have any insights or thoughts regarding the use or accessibility of data for the public good?

Your contribution can be as brief or as detailed as you like. We greatly appreciate any answers, thoughts, or perspectives you are willing to share.

Thank you all!

Any data on hate speech of about 3000 to 5000 records such as: on inflation in pakistan, power outage in pakistan etc is required

:

import os import cv2 import numpy as np

Data organization

dataset_root = ‘path_to_dataset_root_directory’ categories = [‘healthy’, ‘diseased’]

for category in categories: category_dir = os.path.join(dataset_root, category) images = os.listdir(category_dir) for image_name in images: image_path = os.path.join(category_dir, image_name) # Perform further processing on each image

Preprocessing and disease detection

def preprocess_and_detect_disease(image_path): # Load the image image = cv2.imread(image_path)

# Preprocess the image gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0) # Apply image processing techniques (e.g., thresholding) _, thresholded_image = cv2.threshold(blurred_image, 100, 255, cv2.THRESH_BINARY) # Find contours in the image contours, _ = cv2.findContours(thresholded_image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # Loop over the contours and detect fruit disease for contour in contours: # Calculate the area of the contour area = cv2.contourArea(contour) # Set a threshold for disease detection threshold_area = 5000 if area > threshold_area: # Draw a bounding box around the detected fruit x, y, w, h = cv2.boundingRect(contour) cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2) # Display the output image cv2.imshow(‘Fruit Disease Detection’, image) cv2.waitKey(0) cv2.destroyAllWindows()

Example usage

image_path = ‘path_to_your_image.jpg’ preprocess_and_detect_disease(image_path)

i need a dataset of hardware componnent like mouse ,ram,pc,laptop etc…. for a school project

Your value to Reddit is your free posts and comments so remember to delete your posts before deleting your account!

I used this Reddit API script to delete 10 years of my comments and posts: https://codepen.io/j0be/full/WMBWOW/

Bye Reddit! It has been fun!

I’ve heard the entire reddit post and comment dataset is released monthly somewhere but can’t find it.
Does anyone know where?

​

https://forms.microsoft.com/e/5gBkjDhYKN

I will make this public after some time, and I really appreciate it for anyone who complete it.

Lots of subs are going to go dark/private because reddit will raise the price of api calls to them.

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/r/datasets is more pro cheap/free data than most subs. What do you think of the idea of going dark? Example explanation from another sub.
https://old.reddit.com/r/redditisfun/comments/144gmfq/rif_will_shut_down_on_june_30_2023_in_response_to/