Dataset

This dataset was created by Srihari K G

Released under MIT

Contents

This dataset is about book series. It has 1 row and is filtered where the books is Delete. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

(Includes MeSH 2023 and 2024 changes) The MeSH 2025 Update - Delete Report lists Descriptors and Supplementary Concept Records (SCRs) that have been removed from MeSH. This report includes MeSH changes from previous years, starting from 2023.

Context

To make this a seamless process, I cleaned the data and delete many variables that I thought were not important to our dataset. I then uploaded all of those files to Kaggle for each of you to download. The rideshare_data has both lyft and uber but it is still a cleaned version from the dataset we downloaded from Kaggle.

Use of Data Files

You can easily subset the data into the car types that you will be modeling by first loading the csv into R, here is the code for how you do this:

This loads the file into R

df<-read.csv('uber.csv')

The next codes is to subset the data into specific car types. The example below only has Uber 'Black' car types.

df_black<-subset(uber_df, uber_df$name == 'Black')

This next portion of code will be to load it into R. First, we must write this dataframe into a csv file on our computer in order to load it into R.

write.csv(df_black, "nameofthefileyouwanttosaveas.csv")

The file will appear in you working directory. If you are not familiar with your working directory. Run this code:

getwd()

The output will be the file path to your working directory. You will find the file you just created in that folder.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Link to block layer: Connecticut Broadband Availability Data by Block 2024

The REMOTE Panel is a four wave longitudinal dataset collected from over 3500 Norwegian employees over the course of 2022 to capture their experiences with remote and hybrid work arrangements.

The dataset is licensed under CC BY-NC-ND 4.0 for non-commercial use only. Note that the data will not be shared as long as the REMOTE project is ongoing, and all use of the data until the project is completed must be through REMOTE with one or more of the project members involved.

Users are asked to agree to the following terms and conditions when requesting the dataset: 1. I will not share the dataset with anybody outside the project that has requested the dataset. 2. I will require anyone in my team who uses this data to comply with the terms mentioned on this page. 3. Upon request from the authors at any time, I am obliged to delete any copy of the data I have. 4. By filling the request form, I will allow the authors to keep the information I provide (which is used by the authors to simply keep a record of who requested the dataset). Also, I will allow the authors to use this information to contact me. 5. I understand that the terms of use mentioned on this page are subject to change. I will be informed about such changes and comply with them. 6. If I violate any of the terms above, I will immediately delete the data and will not retain any portion of it. 7. By sending a request to access the dataset, I agree to all the above-mentioned terms.

huggingface-projects/DELETE-bot-fight-data dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset was created using LeRobot.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.1", "robot_type": "bi_piper", "total_episodes": 2, "total_frames": 1216, "total_tasks": 1, "total_videos": 8, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/cortexairobot/delete-episodes-from-dataset-1.

This data contains Create definer, trigger, after delete on, for each row begin, insert into values, end.

This dataset contains annotated interictal epileptiform discharge (IED) from 84 patients (Peking Union Medical College Hospital, China), each contributing 20 minutes of continuous raw EEG recordings, using MAT format. The IEDs are categorized into five types based on occurrence regions. The states of consciousness (wake/sleep) are annotated.

Note on version 2: The dataset has been updated. Specifically, a total of 37 annotations (31 deletions and 6 updates) were adjusted to ensure accuracy and consistency for analysis. These annotations were modified due to their atypical characteristics, differing from conventional interictal epileptiform discharges (IEDs). This refined dataset represents the final version used for training and validation in our associated paper entitled “An EEG dataset for interictal epileptiform discharges with spatial distribution information”.

The specific changes of annotations are listed as follow: MAT_Files: DA00103C.mat Delete:['305.846', '0', '!'] DA00100Z.mat Delete:['142.686', '0', '!'] DA00102T.mat Delete:['213.124', '0', '!'], ['388.27', '0', '!'], Update:['213.78', '0', '!end'] -> ['211.78', '0', '!end'] DA00102W.mat Delete:['48.274', '0', '!'], ['438.406', '0', '!'], ['516.94', '0', '!'], ['576.554', '0', '!'] DA00102Y.mat Delete:['605.436', '0', '!'] DA00103B.mat Delete:['1173.344', '0', '!'] DA00103I.mat Update:['1128.746', '0', '!end'] -> ['1127.746', '0', '!end'] DA00103K.mat Delete:['485.026', '0', '!'] DA00103M.mat Delete:['45.006', '0', '!'], ['76.166', '0', '!'], ['108.226', '0', '!'], ['189.608', '0', '!'], ['537.642', '0', '!'] DA00103N.mat Update:['1196.27', '0', '!end'] -> ['1195.27', '0', '!end'] DA00103Q.mat Delete:['696.692', '0', '!'], ['1213.206', '0', '!'], Update:['632.1', '0', '!'] -> ['632.2', '0', '!'] DA00103U.mat Delete:['1076.12', '0', '!'], ['1208.146', '0', '!'], ['1210.474', '0', '!'], ['1211.242', '0', '!'] DA00100S.mat Delete:['1204.8', '0', '!'] DA00103O.mat Delete:['12.542', '0', '!'] DA00103S.mat Delete:['1173.22', '0', '!'] DA001010.mat Delete:['1185.496', '0', '!'] DA001031.mat Delete:['0.684', '0', '!'], ['222.106', '0', '!'] DA00103E.mat Delete:['862.716', '0', '!'] DA00100V.mat Delete:['768.154', '0', '!']，['768.532', '0', '!'] DA00102R.mat Update:['552.244', '0', '!end'] →['551.244', '0', '!end']，['704.172', '0', '!end'] →['703.172', '0', '!end']

The changes in MAT_Files result in alterations in the npy_files: DA00103C_152000_154000_500_5.npy(Occipital-IED) -> DA00103C_152000_154000_500_0.npy(Non-IED) DA00100Z_70000_72000_500_2.npy(Frontal-IED) -> DA00100Z_70000_72000_500_0.npy (Non-IED) DA00102W_24000_26000_500_3.npy(Temporal-IED) -> DA00102W_24000_26000_500_0.npy (Non-IED) DA00102W_218000_220000_500_3.npy(Temporal-IED) -> DA00102W_218000_220000_500_0.npy (Non-IED) DA00102W_258000_260000_500_3.npy(Temporal-IED) -> DA00102W_258000_260000_500_0.npy(Non-IED) DA00102Y_302000_304000_500_4.npy(Centro-Parietal-IED) -> DA00102Y_302000_304000_500_0.npy(Non-IED) DA00103M_22000_24000_500_2.npy(Frontal-IED) -> DA00103M_22000_24000_500_0.npy(Non-IED) DA00103M_94000_96000_500_2.npy(Frontal-IED) -> DA00103M_94000_96000_500_0.npy(Non-IED) DA00103M_268000_270000_500_2.npy(Frontal-IED) -> DA00103M_268000_270000_500_0.npy(Non-IED) DA00103U_604000_606000_500_3.npy(Temporal-IED) -> DA00103U_604000_606000_500_0.npy(Non-IED) DA00103C_170000_172000_500_0.npy(Non-IED) -> DA00103C_170000_172000_500_5.npy(Occipital-IED) DA00100Z_0_2000_500_0.npy(Non-IED) -> DA00100Z_0_2000_500_2.npy(Frontal-IED) DA00102W_124000_126000_500_0.npy(Non-IED) -> DA00102W_124000_126000_500_3.npy(Temporal-IED) DA00102W_242000_244000_500_0.npy(Non-IED) -> DA00102W_242000_244000_500_3.npy(Temporal-IED) DA00102W_268000_270000_500_0.npy(Non-IED) -> DA00102W_268000_270000_500_3.npy(Temporal-IED) DA00102Y_612000_614000_500_0.npy(Non-IED) -> DA00102Y_612000_614000_500_4.npy(Centro-Parietal-IED) DA00103M_30000_32000_500_0.npy(Non-IED) -> DA00103M_30000_32000_500_2.npy(Frontal-IED) DA00103M_96000_98000_500_0.npy(Non-IED) -> DA00103M_96000_98000_500_2.npy(Frontal-IED) DA00103M_568000_570000_500_0.npy(Non-IED) -> DA00103M_568000_570000_500_2.npy(Frontal-IED) DA00103U_606000_607500_500_0.npy(Non-IED) -> DA00103U_606000_607500_500_3.npy(Temporal-IED)

To be deleted very soon

This dataset tracks the updates made on the dataset "MeSH 2023 Update - Delete Report" as a repository for previous versions of the data and metadata.

URL: https://geoscience.data.qld.gov.au/dataset/cr147788

• DELETE AFTER 05/07/2024 0900 HOURS - 2P2C OVERNIGHT TEST PETROLEUM PRODUCTION REPORT FOR PERIOD ENDING 04 JANUARY 2014

Bahamas number dataset provides contact information from trusted sources. We clarify this data by collecting phone numbers that come from reliable sources only. To ensure clearness, we provide source URLs. This shows where the data is gathered from. In addition, we offer 24/7 support. If you have any questions or need help, our team is always here. With List to Data, you can find phone numbers from different countries. However, we care about accuracy, so we collect the Bahamas number dataset carefully from trusted sources. So, you can rely on this data for business or personal use. With customer support, you never have to wait for help or more information. We also use opt-in data to respect privacy. This ensures you contact people who want to hear from you. Bahamas phone data gives you access to contacts in Bahamas. Also, you can filter the information by gender, age, and relationship status. However, this makes it easy to find exactly the people you want to connect with. We define this data by ensuring it follows all GDPR rules to keep it safe and legal. Our team works hard to remove invalid data. This way, you only get correct, useful numbers. In addition, our Bahamas phone data is perfect for businesses looking to target specific groups. Hence, you can easily filter your list to focus on certain types of customers. Besides, we remove invalid data regularly, so you will not have to deal with useless numbers. With regular updates, your phone data will always be ready when you need it. Bahamas phone number list is a collection of phone numbers from people in the Bahamas. We define this list by providing 95% correct and valid phone numbers that are ready to use. Also, we offer a replacement guarantee if you ever receive an invalid number. As a result, you will always have accurate data. We collect the phone numbers we provide based on customer permission. Moreover, we work hard to provide the best Bahamas phone number list for businesses and personal use. Also, we focus on gathering data correctly, so you don’t have to worry about getting incorrect information. Our replacement guarantee gives you peace of mind, knowing that you will always have valid numbers.

A. SUMMARY This dataset contains a version of the current San Francisco Board of Supervisor district boundaries that have been trimmed to exclude non-populated City and County territory that is outside of the contiguous land mass of the City and County. This excludes for example City and County land in the Farallon Islands, Angel Island, and Alameda County,

The underlying official current Supervisor District boundaries are available in the Current Supervisor Districts dataset.

B. HOW THE DATASET IS CREATED This dataset will be updated to reflect the most current Supervisor District boundaries available. It currently reflects the same boundaries found in Supervisor Districts (2022). More information on how this was created can be found in the datasets by year: Supervisor Districts (2022) and Supervisor Districts (2012)

C. UPDATE PROCESS Supervisor District boundaries are updated every 10 years following the federal decennial census. The Supervisor District boundaries reflected in this dataset will be manually updated after the next decennial census in 2030.

The dataset is also manually updated as new members of the Board of Supervisors take office. The most recent manual update date is reflected in the 'data_as_of' field.

D. HOW TO USE THIS DATASET This dataset can be joined to other datasets for analysis and reporting at the Supervisor District level. It is meant to facilitate visualization and mapping focussed on the contiguous land mass of the City & County and Treasure Island.

If you are building an automated reporting pipeline using Socrata API access, we recommend using this dataset if you would like the boundaries to automatically update after each decennial census to reflect the most recent Supervisor District boundaries. If you'd like your boundaries to remain static, see the Supervisor Districts (2022) dataset.

E. RELATED DATASETS Current Supervisor Districts Supervisor Districts (2022) Supervisor Districts (2012)

Japan number dataset allows you to filter phone numbers based on different criteria. You can pick contacts by gender, age, and whether they are single or taken. This feature makes it easy for you to find the right contacts for your needs. We define this title so you can access the most relevant information. Additionally, we regularly remove invalid data to keep the list accurate and reliable. Also, using the Japan number dataset makes your search much simpler. You can easily find contacts that fit your specific needs. Following GDPR rules helps us respect everyone’s privacy while providing useful information. Moreover, we always remove invalid data to keep the list correct. This way, you get the most reliable contact numbers. Japan Phone Data contains contact numbers collected from trusted sources. We define this title to make sure you have reliable and correct information. You can check the source URLs to see where we got the data. Moreover, we provide support 24/7 to help you with any questions. We are always available to support you. Additionally, we only collect opt-in data. This means that everyone on the list has agreed to share their contact details. With Japan Phone Data, you can feel confident that you have the right information. We gather data from trusted sources to ensure every number is correct. If you have any questions, you can reach out for help anytime. We want to help you connect with others easily. The List to Data helps you to find contact information for businesses. Japan phone number list helps you find the right phone numbers easily. You can filter this list by gender, age, and relationship status. This feature helps narrow your search and find exactly what you need. We define this list to provide the best data. Additionally, we remove invalid data regularly to keep the list fresh. Using the Japan phone number list is simple and quick. You can find contacts that match your needs without any hassle. Furthermore, we work hard to remove invalid data so you only see valid numbers. This effort helps keep your searches accurate and efficient. Overall, this list is a great tool for connecting with people in Japan while respecting their privacy.

Netflix is a popular streaming service that offers a vast catalog of movies, TV shows, and original contents. This dataset is a cleaned version of the original version which can be found here. The data consist of contents added to Netflix from 2008 to 2021. The oldest content is as old as 1925 and the newest as 2021. This dataset will be cleaned with PostgreSQL and visualized with Tableau. The purpose of this dataset is to test my data cleaning and visualization skills. The cleaned data can be found below and the Tableau dashboard can be found here .

Data Cleaning

We are going to: 1. Treat the Nulls 2. Treat the duplicates 3. Populate missing rows 4. Drop unneeded columns 5. Split columns Extra steps and more explanation on the process will be explained through the code comments

--View dataset

SELECT * 
FROM netflix;

--The show_id column is the unique id for the dataset, therefore we are going to check for duplicates
                                  
SELECT show_id, COUNT(*)                                                                                      
FROM netflix 
GROUP BY show_id                                                                                              
ORDER BY show_id DESC;

--No duplicates

--Check null values across columns

SELECT COUNT(*) FILTER (WHERE show_id IS NULL) AS showid_nulls,
    COUNT(*) FILTER (WHERE type IS NULL) AS type_nulls,
    COUNT(*) FILTER (WHERE title IS NULL) AS title_nulls,
    COUNT(*) FILTER (WHERE director IS NULL) AS director_nulls,
    COUNT(*) FILTER (WHERE movie_cast IS NULL) AS movie_cast_nulls,
    COUNT(*) FILTER (WHERE country IS NULL) AS country_nulls,
    COUNT(*) FILTER (WHERE date_added IS NULL) AS date_addes_nulls,
    COUNT(*) FILTER (WHERE release_year IS NULL) AS release_year_nulls,
    COUNT(*) FILTER (WHERE rating IS NULL) AS rating_nulls,
    COUNT(*) FILTER (WHERE duration IS NULL) AS duration_nulls,
    COUNT(*) FILTER (WHERE listed_in IS NULL) AS listed_in_nulls,
    COUNT(*) FILTER (WHERE description IS NULL) AS description_nulls
FROM netflix;

We can see that there are NULLS. 
director_nulls = 2634
movie_cast_nulls = 825
country_nulls = 831
date_added_nulls = 10
rating_nulls = 4
duration_nulls = 3 

The director column nulls is about 30% of the whole column, therefore I will not delete them. I will rather find another column to populate it. To populate the director column, we want to find out if there is relationship between movie_cast column and director column

-- Below, we find out if some directors are likely to work with particular cast

WITH cte AS
(
SELECT title, CONCAT(director, '---', movie_cast) AS director_cast 
FROM netflix
)

SELECT director_cast, COUNT(*) AS count
FROM cte
GROUP BY director_cast
HAVING COUNT(*) > 1
ORDER BY COUNT(*) DESC;

With this, we can now populate NULL rows in directors 
using their record with movie_cast 

UPDATE netflix 
SET director = 'Alastair Fothergill'
WHERE movie_cast = 'David Attenborough'
AND director IS NULL ;

--Repeat this step to populate the rest of the director nulls
--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET director = 'Not Given'
WHERE director IS NULL;

--When I was doing this, I found a less complex and faster way to populate a column which I will use next

Just like the director column, I will not delete the nulls in country. Since the country column is related to director and movie, we are going to populate the country column with the director column

--Populate the country using the director column

SELECT COALESCE(nt.country,nt2.country) 
FROM netflix AS nt
JOIN netflix AS nt2 
ON nt.director = nt2.director 
AND nt.show_id <> nt2.show_id
WHERE nt.country IS NULL;
UPDATE netflix
SET country = nt2.country
FROM netflix AS nt2
WHERE netflix.director = nt2.director and netflix.show_id <> nt2.show_id 
AND netflix.country IS NULL;


--To confirm if there are still directors linked to country that refuse to update

SELECT director, country, date_added
FROM netflix
WHERE country IS NULL;

--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET country = 'Not Given'
WHERE country IS NULL;

The date_added rows nulls is just 10 out of over 8000 rows, deleting them cannot affect our analysis or visualization

--Show date_added nulls

SELECT show_id, date_added
FROM netflix_clean
WHERE date_added IS NULL;

--DELETE nulls

DELETE F...

URL: https://geoscience.data.qld.gov.au/dataset/cr147787

• DELETE AFTER 05/07/2024 0900 HOURS - 2P2C OVERNIGHT TEST PETROLEUM RESOURCES AND RESERVES REPORT FOR PERIOD ENDING 04 JANUARY 2014

Bangladesh number dataset provides contact information from trusted sources. We only collect phone numbers from reliable sources and define this information. To ensure transparency, we also provide the source URL to show where the information was collected from. In addition, we offer 24/7 support. If you have a question or need help, we’re always here. However, we care about accuracy, so we carefully collect the Bangladesh number dataset from trusted sources. You may rely on this data for business or personal use. With customer support, you’ll never have to wait when you need help or more information. We use opt-in data to respect privacy. This way, we contact only people who want to hear from you. Bangladesh phone data gives you access to contacts in Bangladesh. Here you can filter information by gender, age, and relationship status. This makes it easy to find exactly the people you want to connect with. We define this data by ensuring it follows all GDPR rules to keep it safe and legal. Our system works hard to remove any invalid data so you get only accurate and valid numbers. List to Data is a helpful website for finding important phone numbers quickly. Also, our Bangladesh phone data is suitable for doing business targeting specific groups. You can easily filter your list to focus on specific types of customers. Since we remove invalid data regularly, you don’t have to deal with old or useless numbers. We assure you that all data follows strict GDPR rules, so you can use it without any problems. Bangladesh phone number list is a collection of phone numbers from people in Bangladesh. We define this list by providing 100% correct and valid phone numbers that are ready to use. Also, we offer a replacement guarantee if you ever receive an invalid number. This means you will always have accurate data. We collect phone numbers that we provide based on customer’s permission. Moreover, we work hard to provide the best Bangladesh phone number list for businesses and personal use. We gather data correctly, so you won’t have to worry about getting outdated or incorrect information. Our replacement guarantee means you’ll always have valid numbers, so you can relax and feel confident.

Final Data Book

## Overview

Final Data Book is a dataset for object detection tasks - it contains Delete Image Formula Paragraph annotations for 1,574 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).