This dataset contains two tables: creative_stats and removed_creative_stats. The creative_stats table contains information about advertisers that served ads in the European Economic Area or Turkey: their legal name, verification status, disclosed name, and location. It also includes ad specific information: impression ranges per region (including aggregate impressions for the European Economic Area), first shown and last shown dates, which criteria were used in audience selection, the format of the ad, the ad topic and whether the ad is funded by Google Ad Grants program. A link to the ad in the Google Ads Transparency Center is also provided. The removed_creative_stats table contains information about ads that served in the European Economic Area that Google removed: where and why they were removed and per-region information on when they served. The removed_creative_stats table also contains a link to the Google Ads Transparency Center for the removed ad. Data for both tables updates periodically and may be delayed from what appears on the Google Ads Transparency Center website. About BigQuery This data is hosted in Google BigQuery for users to easily query using SQL. Note that to use BigQuery, users must have a Google account and create a GCP project. This public dataset is included in BigQuery's 1TB/mo of free tier processing. Each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . Download Dataset This public dataset is also hosted in Google Cloud Storage here and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. We provide the raw data in JSON format, sharded across multiple files to support easier download of the large dataset. A README file which describes the data structure and our Terms of Service (also listed below) is included with the dataset. You can also download the results from a custom query. See here for options and instructions. Signed out users can download the full dataset by using the gCloud CLI. Follow the instructions here to download and install the gCloud CLI. To remove the login requirement, run "$ gcloud config set auth/disable_credentials True" To download the dataset, run "$ gcloud storage cp gs://ads-transparency-center/* . -R" This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Querying BigQuery tables You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME].

• Project: "bigquery-public-data"
• Table: "utility_us"

Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

If you're using Python, you can start with this code:

import pandas as pd
from bq_helper import BigQueryHelper
bq_assistant = BigQueryHelper("bigquery-public-data", "utility_us")

Process to Generate DuckDB Dataset

  1. Load Repository Metadata

Read repo_metadata.json from GitHub Public Repository Metadata Normalize JSON into three lists: Repositories → general metadata (stars, forks, license, etc.). Languages → repo-language mappings with size. Topics → repo-topic mappings.

Convert lists into Pandas DataFrames: df_repos, df_languages, df_topics.

  2. Enhance with BigQuery Data

Create a temporary BigQuery table (repo_list)… See the full description on the dataset page: https://huggingface.co/datasets/deepgit/github_meta.

Context

Stack Overflow is the largest online community for programmers to learn, share their knowledge, and advance their careers.

Content

Updated on a quarterly basis, this BigQuery dataset includes an archive of Stack Overflow content, including posts, votes, tags, and badges. This dataset is updated to mirror the Stack Overflow content on the Internet Archive, and is also available through the Stack Exchange Data Explorer.

Fork this kernel to get started with this dataset.

Acknowledgements

Dataset Source: https://archive.org/download/stackexchange

https://bigquery.cloud.google.com/dataset/bigquery-public-data:stackoverflow

https://cloud.google.com/bigquery/public-data/stackoverflow

Banner Photo by Caspar Rubin from Unplash.

Inspiration

What is the percentage of questions that have been answered over the years?

What is the reputation and badge count of users across different tenures on StackOverflow?

What are 10 of the “easier” gold badges to earn?

Which day of the week has most questions answered within an hour?

The Genome Aggregation Database (gnomAD) is maintained by an international coalition of investigators to aggregate and harmonize data from large-scale sequencing projects. These public datasets are available in VCF format in Google Cloud Storage and in Google BigQuery as integer range partitioned tables . Each dataset is sharded by chromosome meaning variants are distributed across 24 tables (indicated with “_chr*” suffix). Utilizing the sharded tables reduces query costs significantly. Variant Transforms was used to process these VCF files and import them to BigQuery. VEP annotations were parsed into separate columns for easier analysis using Variant Transforms’ annotation support . These public datasets are included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. Find out more in our blog post, Providing open access to gnomAD on Google Cloud . Questions? Contact gcp-life-sciences-discuss@googlegroups.com.

Context

Section 337, Tariff Act of 1930, Investigations of Unfair Practices in Import Trade. Under section 337, the USITC determines whether there is unfair competition in the importation of products into, or their subsequent sale in, the United States. Section 337 prohibits the importation into the US , or the sale of such articles by owners, importers or consignees, of articles which infringe a patent, copyright, trademark, or semiconductor mask work, or where unfair competition or unfair acts exist that can destroy or substantially injure a US industry or prevent one from developing, or restrain or monopolize trade in US commerce. These latter categories are very broad: unfair competition can involve counterfeit, mismarked or misbranded goods, where the sale of the goods are at unfairly low prices, where other antitrust violations take place such as price fixing, market division or the goods violate a standard applicable to such goods.

Content

US International Trade Commission 337Info Unfair Import Investigations Information System contains data on investigations done under Section 337. Section 337 declares the infringement of certain statutory intellectual property rights and other forms of unfair competition in import trade to be unlawful practices. Most Section 337 investigations involve allegations of patent or registered trademark infringement.

Fork this notebook to get started on accessing data in the BigQuery dataset using the BQhelper package to write SQL queries.

Acknowledgements

Data Origin: https://bigquery.cloud.google.com/dataset/patents-public-data:usitc_investigations

"US International Trade Commission 337Info Unfair Import Investigations Information System" by the USITC, for public use.

Banner photo by João Silas on Unsplash

About this notebookThis notebook was created using a helper script: Base.ipynb. This script has some helper functions that push output directly to Datawrapper to generate the graphs that have been included in the opnion piece. To run without the helper functions and bigquery alone use!pip install google-cloud-bigquerythen add:from google.cloud.bigquery import magicsproject_id = "your_project" # update as neededmagics.context.project = project_idbq_params = {}client = bigquery.Client(project=project_id)%load_ext google.cloud.bigqueryfinally, comment out the make_chart lines.### About dimensions-ai-integrity.ds_dp_pipeline_ripeta_staging.trust_markers_rawdimensions-ai-integrity.ds_dp_pipeline_ripeta_staging.trust_markers_raw is an internal table that is the result of runing a process over the text of publications in order to identify trustmarker segments including authors contributions.The process works as follows:The process aims to automatically segment research papers into their constituent sections. It operates by identifying headings within the text based on a pre-defined set of patterns and a rule-based system. The system first cleans and normalizes the input text. It then employs regular expressions to detect potential section headings. These potential headings are validated against a set of rules that consider factors such as capitalization, the context of surrounding words, and the typical order of sections within a research paper (e.g., certain sections not appearing after "References" or before "Abstract"). Specific rules also handle exceptions for particular heading types like "Keywords" or "Appendices." Once valid headings are identified, the system extracts the corresponding textual content for each section. The output is a structured representation of the paper, categorizing text segments under their respective heading types. Any text that doesn't fall under a recognized heading is also identified as unlabeled content. The overall process aims to provide a structured understanding of the document's organization for subsequent analysis.Author Contributions segments are identified using the following regex:"author_contributions": ["((credit|descript(ion(?:s)?|ive)| )*author(s|'s|ship|s')?( |contribution(?:s)?|statement(?:s)?|role(?:s)?){2,})","contribution(?:s)"]Access to dimensions-ai-integrity.ds_dp_pipeline_ripeta_staging.trust_markers_raw is available to peer reveiwers of the opinion piece.Datasets that allow external validation of the credit ontology process identification process have also been produced.

As the price of installing solar has gotten less expensive, more homeowners are turning to it as a possible option for decreasing their energy bill. We want to make installing solar panels easy and understandable for anyone. Project Sunroof puts Google's expansive data in mapping and computing resources to use, helping calculate the best solar plan for you. How does it work? When you enter your address, Project Sunroof looks up your home in Google Maps and combines that information with other databases to create your personalized roof analysis. Don’t worry, Project Sunroof doesn't give the address to anybody else. Learn more about Project Sunroof and see the tool at Project Sunroof’s site . Project Sunroof computes how much sunlight hits roofs in a year, based on shading calculations, typical meteorological data, and estimates of the size and shape of the roofs. You can see more details about how solar viability is determined by checking out methodology here. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The quality of our water is vital, and understanding the factors that impact it is crucial for both the environment and public health. A new collaboration between Google Cloud and Stream, a consortium of UK water companies with a collective vision to unlock water data, is putting the power of data and AI into the hands of communities, driving transparency and informed decision-making around water quality. This initiative leverages Stream's ever-growing catalogue of water sector data and combines it with Google Cloud's BigQuery and advanced Generative AI. The result? A revolutionary way to access, analyse, and understand complex water quality information.

Content

This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Individual reports can be parsed by export session ID (column A) or timestamp (column B). Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.

Main modifications

This is the list of manipulations performed on the original dataset, published by Möbius. All the cleaning process and rearrangements were performed in BigQuery, using SQL functions. 1) After I took a closer look at the source dataset, I realized that for my case study, I did not need some of the tables contained in the original archive. Therefore, I decided not to import - dailyCalories_merged.csv, - dailyIntensities_merged.csv, - dailySteps_merged.csv. as they proved redundant, their content could be found in the dailyActivity_merged.csv file. In addition, the files - minutesCaloriesWide_merged.csv, - minutesIntensitiesWide_merged.csv, - minuteStepsWide_merged.csv.
were not imported, as they presented the same data contained in other files in a wide format. Hence, only the files with long format containing the same data were imported in the BigQuery database.

2) To be able to compare and measure the correlation among different variables based on hourly records, I decided to create a new table based on LEFT JOIN function and columns Id and ActivityHour. I repeated the same JOIN on tables with minute records. Hence I obtained 2 new tables: - hourly_activity.csv, - minute_activity.csv.

3) To validate most of the columns containing DATE and DATETIME values that were imported as STRING data type, I used the PARSE_DATE() and PARSE_DATETIME() commands. While importing the - heartrate_seconds_merged.csv, - hourlyCalories_merged.csv, - hourlyIntensities_merged.csv, - hourlySteps_merged.csv, - minutesCaloriesNarrow_merged.csv, - minuteIntensitiesNarrow_merged.csv, - minuteMETsNarrow_merged.csv, - minuteSleep_merged.csv, - minuteSteps_merged.csv, - sleepDay_merge.csv, - weigthLog_Info_merged.csv files to BigQuery, it was necessary to import the DATETIME and DATE type columns as STRING, because the original syntax, used in the CSV files, couldn’t be recognized as a correct DATETIME data type, due to “AM” and “PM” text at the end of the expression.

Acknowlegement

1. Möbius' version of the data set can be found here.
2. Furberg, Robert; Brinton, Julia; Keating, Michael ; Ortiz, Alexa https://zenodo.org/record/53894#.YMoUpnVKiP9-

This dataset contains hourly cryptocurrency and stock market data collected from coingecko starting in March 2025. The collection pipeline was designed to demonstrate practical data management and automation skills: - Data Ingestion: A Python Script automatically scrapes fresh hourly data from coingecko and writes it into Google Sheets. - Data Offloading: To avoid Google Sheets’ row limitations, Python scripts periodically export data from Sheets into Google BigQuery. - Data Publishing: The data is shared to Kaggle via a scheduled notebook, ensuring the dataset is updated daily with the latest available records.

This setup provides a reliable, reproducible data stream that can be used for: - Practicing SQL queries for data extraction, filtering, and preparation before analysis - Exploratory data analysis of crypto and stock price movements - Building time-series forecasting models - Studying correlations between global assets - Demonstrating real-world ETL (Extract, Transform, Load) and data pipeline engineering

The dataset is continuously updated hourly, making it suitable both for live monitoring and historical trend analysis.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19062145%2F025ccf521f62db512b4a98edd0b3508a%2FKimia_Farma_Dashboard.jpg?generation=1748428094441761&alt=media" alt="">This project analyzes Kimia Farma's performance from 2020 to 2023 using Google Looker Studio. The analysis is based on a pre-processed dataset stored in BigQuery, which serves as the data source for the dashboard.

Project Scope

The dashboard is designed to provide insights into branch performance, sales trends, customer ratings, and profitability. The development is ongoing, with multiple pages planned for a more in-depth analysis.

Current Progress

✅ The first page of the dashboard is completed
✅ A sample dashboard file is available on Kaggle
🔄 Development will continue with additional pages

Dataset Overview

The dataset consists of transaction records from Kimia Farma branches across different cities and provinces. Below are the key columns used in the analysis: - transaction_id: Transaction ID code - date: Transaction date - branch_id: Kimia Farma branch ID code - branch_name: Kimia Farma branch name - kota: City of the Kimia Farma branch - provinsi: Province of the Kimia Farma branch - rating_cabang: Customer rating of the Kimia Farma branch - customer_name: Name of the customer who made the transaction - product_id: Product ID code - product_name: Name of the medicine - actual_price: Price of the medicine - discount_percentage: Discount percentage applied to the medicine - persentase_gross_laba: Gross profit percentage based on the following conditions:
Price ≤ Rp 50,000 → 10% profit
Price > Rp 50,000 - 100,000 → 15% profit
Price > Rp 100,000 - 300,000 → 20% profit
Price > Rp 300,000 - 500,000 → 25% profit
Price > Rp 500,000 → 30% profit
- nett_sales: Price after discount - nett_profit: Profit earned by Kimia Farma - rating_transaksi: Customer rating of the transaction

Files Provided

📌 kimia farma_query.txt – Contains SQL queries used for data analysis in Looker Studio
📌 kimia farma_analysis_table.csv – Preprocessed dataset ready for import and analysis

📢 Published on : My LinkedIn

Prelude

This dataset is a cleaned version of the Chicago Crime Dataset, which can be found here. All rights for the dataset go to the original owners. The purpose of this dataset is to display my skills in visualizations and creating dashboards. To be specific, I will attempt to create a dashboard that will allow users to see metrics for a specific crime within a given year using filters and metrics. Due to this, there will not be much of a focus on the analysis of the data, but there will be portions discussing the validity of the dataset, the steps I took to clean the data, and how I organized it. The cleaned datasets can be found below, the Query (which utilized BigQuery) can be found here and the Tableau dashboard can be found here.

About the Dataset

Important Facts

The dataset comes directly from the City of Chicago's website under the page "City Data Catalog." The data is gathered directly from the Chicago Police's CLEAR (Citizen Law Enforcement Analysis and Reporting) and is updated daily to present the information accurately. This means that a crime on a specific date may be changed to better display the case. The dataset represents crimes starting all the way from 2001 to seven days prior to today's date.

Reliability

Using the ROCCC method, we can see that: * The data has high reliability: The data covers the entirety of Chicago from a little over 2 decades. It covers all the wards within Chicago and even gives the street names. While we may not have an idea for how big the sample size is, I do believe that the dataset has high reliability since it geographically covers the entirety of Chicago. * The data has high originality: The dataset was gained directly from the Chicago Police Dept. using their database, so we can say this dataset is original. * The data is somewhat comprehensive: While we do have important information such as the types of crimes committed and their geographic location, I do not think this gives us proper insights as to why these crimes take place. We can pinpoint the location of the crime, but we are limited by the information we have. How hot was the day of the crime? Did the crime take place in a neighborhood with low-income? I believe that these key factors prevent us from getting proper insights as to why these crimes take place, so I would say that this dataset is subpar with how comprehensive it is. * The data is current: The dataset is updated frequently to display crimes that took place seven days prior to today's date and may even update past crimes as more information comes to light. Due to the frequent updates, I do believe the data is current. * The data is cited: As mentioned prior, the data is collected directly from the polices CLEAR system, so we can say that the data is cited.

Processing the Data

Cleaning the Dataset

The purpose of this step is to clean the dataset such that there are no outliers in the dashboard. To do this, we are going to do the following: * Check for any null values and determine whether we should remove them. * Update any values where there may be typos. * Check for outliers and determine if we should remove them.

The following steps will be explained in the code segments below. (I used BigQuery for this so the coding will follow BigQuery's syntax) ```

Examining the dataset

There are over 7.5 million rows of data

Putting a limit so it does not take a long time to run

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime LIMIT 1000;

Seeing which points are null

There are 85,000 null points so we can exclude them as it's not a significant amount since it is only ~1.3% of the dataset

Most of the null points are in the lat and long, which we will need later

Because we don't have the full address, we can't estimate the lat and long in SQL so we will have to delete the rows with Null Data

SELECT * FROM portfolioproject-350601.ChicagoCrime.Crime WHERE unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Deleting all null rows

DELETE FROM portfolioproject-350601.ChicagoCrime.Crime WHERE
unique_key IS NULL OR case_number IS NULL OR date IS NULL OR primary_type IS NULL OR location_description IS NULL OR arrest IS NULL OR longitude IS NULL OR latitude IS NULL;

Checking for any duplicates in the unique keys

None to be found

SELECT unique_key, COUNT(unique_key) FROM `portfolioproject-350601.ChicagoCrime....

Taxa de precipitação estimada de áreas do sudeste brasileiro. As estimativas são feitas de hora em hora, cada registro contendo dados desta estimativa. Cada área é um quadrado formado por 4km de lado. Dados coletados pelo satélite GOES-16.

  Como acessar


  Nessa página


  Aqui, você encontrará um botão para realizar o download dos dados em formato CSV e compactados com gzip. Ou, para mesmo resultado, pode clicar aqui.


  BigQuery




      SELECT


      *


      FROM


      `datario.meio_ambiente_clima.taxa_precipitacao_satelite`


      LIMIT


      1000




  Clique aqui
  para ir diretamente a essa tabela no BigQuery. Caso não tenha experiência com BigQuery,
  acesse nossa documentação para entender como acessar os dados.


  Python



    import
    basedosdados
    as
    bd


    # Para carregar o dado direto no pandas

    df
    =
    bd.read_sql
    (
    "SELECT * FROM `datario.meio_ambiente_clima.taxa_precipitacao_satelite` LIMIT 1000"
    ,
    billing_project_id
    =
    "<id_do_seu_projeto_gcp>"
    )




  R



    install.packages(
    "basedosdados"
    )

    library(
    "basedosdados"
    )


    # Defina o seu projeto no Google Cloud

    set_billing_id(
    "<id_do_seu_projeto_gcp>"
    )


    # Para carregar o dado direto no R

    tb <- read_sql(
    "SELECT * FROM `datario.meio_ambiente_clima.taxa_precipitacao_satelite` LIMIT 1000"
    )






  Cobertura temporal


  Desde 2020 até a data corrente




  Frequência de atualização


  Diário




  Órgão gestor


  Centro de Operações da Prefeitura do Rio (COR)




  Colunas



    Nome
    Descrição




        latitude
        Latitude do centro da área.



        longitude
        Longitude do centro da área.



        rrqpe
        Taxa de precipitação estimada, medidas em milímetros por hora.



        primary_key
        Chave primária criada a partir da concatenação da coluna data, horário, latitude e longitude. Serve para evitar dados duplicados.



        horario
        Horário no qual foi realizada a medição



        data_particao
        Data na qual foi realizada a medição







  Dados do publicador


  Nome: Patrícia Catandi
  E-mail: patriciabcatandi@gmail.com

Dados sobre as estações pluviométricas do alertario ( Sistema Alerta Rio da Prefeitura do Rio de Janeiro ) na cidade do Rio de Janeiro.

  Como acessar


  Nessa página


  Aqui, você encontrará um botão para realizar o download dos dados em formato CSV e compactados com gzip. Ou,
  para mesmo resultado, pode clicar aqui.


  BigQuery




      SELECT


      *


      FROM


      `datario.meio_ambiente_clima.estacoes_alertario`


      LIMIT


      1000




  Clique aqui
  para ir diretamente a essa tabela no BigQuery. Caso não tenha experiência com BigQuery,
  acesse nossa documentação para entender como acessar os dados.


  Python



    import
    basedosdados
    as
    bd


    # Para carregar o dado direto no pandas

    df
    =
    bd.read_sql
    (
    "SELECT * FROM `datario.meio_ambiente_clima.estacoes_alertario` LIMIT 1000"
    ,
    billing_project_id
    =
    "<id_do_seu_projeto_gcp>"
    )




  R



    install.packages(
    "basedosdados"
    )

    library(
    "basedosdados"
    )


    # Defina o seu projeto no Google Cloud

    set_billing_id(
    "<id_do_seu_projeto_gcp>"
    )


    # Para carregar o dado direto no R

    tb <- read_sql(
    "SELECT * FROM `datario.meio_ambiente_clima.estacoes_alertario` LIMIT 1000"
    )






  Cobertura temporal


  N/A




  Frequência de atualização


  Anual




  Órgão gestor


  COR




  Colunas



    Nome
    Descrição




      x
      X UTM (SAD69 Zona 23)



      longitude
      Longitude onde a estação se encontra.



      id_estacao
      ID da estação definido pelo AlertaRIO.



      estacao
      Nome da estação.



      latitude
      Latitude onde a estação se encontra.



      cota
      Altura em metros onde a estação se encontra.



      endereco
      Endereço completo da estação.



      situacao
      Indica se a estação está operante ou com falha.



      data_inicio_operacao
      Data em que a estação começou a operar.



      data_fim_operacao
      Data em que a estação parou de operar.



      data_atualizacao
      Última data em que os dados sobre a data de operação foram atualizados.



      y
      Y UTM (SAD69 Zona 23)







  Dados do publicador


  Nome: Patricia Catandi
  E-mail: patriciabcatandi@gmail.com

Overview

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries.

Content

Over 9000 stations' data are typically available.

The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches)

Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

This public dataset was created by the National Oceanic and Atmospheric Administration (NOAA) and includes global data obtained from the USAF Climatology Center. This dataset covers GSOD data between 1929 and present, collected from over 9000 stations. Dataset Source: NOAA

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — 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.

Photo by Allan Nygren on Unsplash

Procedimentos operacionais padrões (POP) existentes na PCRJ. Um POP é um procedimento que será usado para solucionar um evento. Um POP é composto de várias atividades. Um evento é uma ocorrência na cidade do Rio de Janeiro que exija um acompanhamento e na maioria das vezes uma ação da PCRJ, como por exemplo um buraco na rua. Acesse também através da API do Escritório de Dados: https://api.dados.rio/v1/

  Como acessar


  Nessa página


  Aqui, você encontrará um botão para realizar o download dos dados em formato CSV e compactados com gzip. Ou, para mesmo resultado, pode clicar aqui.


  BigQuery




      SELECT


      *


      FROM


      `datario.adm_cor_comando.procedimento_operacional_padrao`


      LIMIT


      1000




  Clique aqui
  para ir diretamente a essa tabela no BigQuery. Caso não tenha experiência com BigQuery,
  acesse nossa documentação para entender como acessar os dados.


  Python



    import
    basedosdados
    as
    bd


    # Para carregar o dado direto no pandas

    df
    =
    bd.read_sql
    (
    "SELECT * FROM `datario.adm_cor_comando.procedimento_operacional_padrao` LIMIT 1000"
    ,
    billing_project_id
    =
    "<id_do_seu_projeto_gcp>"
    )




  R



    install.packages(
    "basedosdados"
    )

    library(
    "basedosdados"
    )


    # Defina o seu projeto no Google Cloud

    set_billing_id(
    "<id_do_seu_projeto_gcp>"
    )


    # Para carregar o dado direto no R

    tb <- read_sql(
    "SELECT * FROM `datario.adm_cor_comando.procedimento_operacional_padrao` LIMIT 1000"
    )






  Cobertura temporal


  Não informado.




  Frequência de atualização


  Mensal




  Órgão gestor


  COR




  Colunas



    Nome
    Descrição




        id_pop
        Identificador do POP procedimento operacional padrão).



        pop_titulo
        Nome do procedimento operacional padrão.







  Dados do(a) publicador(a)


  Nome: Patrícia Catandi
  E-mail: patriciabcatandi@gmail.com

Chamados feitos ao 1746. São chamados desde março de 2011, quando começou o projeto 1746.

  Como acessar


  Nessa página


  Aqui, você encontrará um botão para realizar o download dos dados em formato CSV e compactados com gzip. Ou, para mesmo resultado, pode clicar aqui.


  BigQuery




      SELECT


      *


      FROM


      `datario.administracao_servicos_publicos.chamado_1746`


      LIMIT


      1000




  Clique aqui para ir diretamente a essa tabela no BigQuery. Caso não tenha experiência com BigQuery,
  acesse nossa documentação para entender como acessar os dados.


  Python



    import
    basedosdados
    as
    bd


    # Para carregar o dado direto no pandas

    df
    =
    bd.read_sql
    (
    "SELECT * FROM `datario.administracao_servicos_publicos.chamado_1746` LIMIT 1000"
    ,
    billing_project_id
    =
    "<id_do_seu_projeto_gcp>"
    )




  R



    install.packages(
    "basedosdados"
    )

    library(
    "basedosdados"
    )


    # Defina o seu projeto no Google Cloud

    set_billing_id(
    "<id_do_seu_projeto_gcp>"
    )


    # Para carregar o dado direto no R

    tb <- read_sql(
    "SELECT * FROM `datario.administracao_servicos_publicos.chamado_1746` LIMIT 1000"
    )






  Cobertura temporal


  Março de 2011




  Frequência de atualização


  Diário




  Órgão gestor


  SEGOVI




  Colunas



    Nome
    Descrição




        id_chamado
        Identificador único do chamado no banco de dados.



        data_inicio
        Data de abertura do chamado. Ocorre quando o operador registra o chamado.



        data_fim
        Data de fechamento do chamado. O chamado é fechado quando o pedido é atendido ou quando se percebe que o pedido não pode ser atendido.



        id_bairro
        Identificador único, no banco de dados, do bairro onde ocorreu o fato que gerou o chamado.



        id_territorialidade
        Identificador único, no banco de dados, da territorialidade onde ocorreu o fato que gerou o chamado. Territorialidade é uma região da cidade do Rio de Janeiro que tem com responsável um órgão especifico. Exemplo: CDURP, que é responsável pela região do porto do Rio de Janeiro.



        id_logradouro
        Identificador único, no banco de dados, do logradouro onde ocorreu o fato que gerou o chamado.



        numero_logradouro
        Número da porta onde ocorreu o fato que gerou o chamado.



        id_unidade_organizacional
        Identificador único, no banco de dados, do órgão que executa o chamado. Por exemplo: identificador da COMLURB quando o chamado é relativo a limpeza urbana.



        nome_unidade_organizacional
        Nome do órgão que executa a demanda. Por exemplo: COMLURB quando a demanda é relativa a limpeza urbana.



        unidade_organizadional_ouvidoria
        Booleano indicando se o chamado do cidadão foi feita Ouvidoria ou não. 1 caso sim, 0 caso não,



        categoria
        Categoria do chamado. Exemplo: Serviço, informação, sugestão, elogio, reclamação, crítica.



        id_tipo
        Identificador único, no banco de dados, do tipo do chamado. Ex: Iluminação pública.



        tipo
        Nome do tipo do chamado. Ex: Iluminação pública.



        id_subtipo
        Identificador único, no banco de dados, do subtipo do chamado. Ex: Reparo de lâmpada apagada.



        subtipo
        Nome do subtipo do chamado. Ex: Reparo de lâmpada apagada.



        status
        Status do chamado. Ex. Fechado com solução, aberto em andamento, pendente etc.



        longitude
        Longitude do lugar do evento que motivou o chamado.



        latitude
        Latitude do lugar do evento que motivou o chamado.



        data_alvo_finalizacao
        Data prevista para o atendimento do chamado. Caso prazo_tipo seja D fica em branco até o diagnóstico ser feito.



        data_alvo_diagnostico
        Data prevista para fazer o diagnóstico do serviço. Caso prazo_tipo seja F esta data fica em branco.



        data_real_diagnostico
        Data em que foi feito o diagnóstico do serviço. Caso prazo_tipo seja F esta data fica em branco.



        tempo_prazo
        Prazo para o serviço ser feito. Em dias ou horas após a abertura do chamado. Caso haja diagnóstico o prazo conta após se fazer o diagnóstico.



        prazo_unidade
        Unidade de tempo utilizada no prazo. Dias ou horas. D ou H.



        prazo_tipo
        Diagnóstico ou finalização. D ou F. Indica se a chamada precisa de diagnóstico ou não. Alguns serviços precisam de avaliação para serem feitos, neste caso é feito o diagnóstico. Por exemplo, pode de árvore. Há a necessidade de um engenheiro ambiental verificar a necessidade da poda ou não.



        id_unidade_organizacional_mae
        ID da unidade organizacional mãe do orgão que executa a demanda. Por exemplo: "CVA - Coordenação de Vigilância de Alimentos" é quem executa a demanda e obede a unidade organizacional mãe "IVISA-RIO - Instituto Municipal de Vigilância Sanitária, de Zoonoses e de Inspeção Agropecuária". A coluna se refere ao ID deste último.



        situacao
        Identifica se o chamado foi encerrado



        tipo_situacao
        Indica o status atual do chamado entre as categorias Atendido, Atendido parcialmente, Não atendido, Não constatado e Andamento



        dentro_prazo
        Indica se a data alvo de finalização do chamado ainda está dentro do prazo estipulado.



        justificativa_status
        Justificativa que os órgãos usam ao definir o status. Exemplo: SEM POSSIBILIDADE DE ATENDIMENTO - justificativa: Fora de área de atuação do municipio



        reclamacoes
        Quantidade de reclamações.







  Dados do(a) publicador(a)


  Nome: Patricia Catandi
  E-mail: patriciabcatandi@gmail.com

OpenAQ is an open-source project to surface live, real-time air quality data from around the world. Their “mission is to enable previously impossible science, impact policy and empower the public to fight air pollution.” The data includes air quality measurements from 5490 locations in 47 countries.

Scientists, researchers, developers, and citizens can use this data to understand the quality of air near them currently. The dataset only includes the most current measurement available for the location (no historical data).

Update Frequency: Weekly

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.openaq.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Dataset Source: openaq.org

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source and is provided "AS IS" without any warranty, express or implied.

Ocorrências disparadas pelo COR desde 2015. Uma ocorrência na cidade do Rio de Janeiro é um acontecimento que exije um acompanhamento e, na maioria das vezes, uma ação da PCRJ. Por exemplo, Buraco na pista, bolsão d'água, enguiço mecânico. Uma ocorrência aberta é uma ocorrência que ainda não foi solucionada. Acesse também através da API do Escritório de Dados: https://api.dados.rio/v1/

  Como acessar


  Nessa página


  Aqui, você encontrará um botão para realizar o download dos dados em formato CSV e compactados com gzip. Ou, para mesmo resultado, pode clicar aqui.


  BigQuery




      SELECT


      *


      FROM


      `datario.adm_cor_comando.ocorrencias`


      LIMIT


      1000




  Clique aqui
  para ir diretamente a essa tabela no BigQuery. Caso não tenha experiência com BigQuery,
  acesse nossa documentação para entender como acessar os dados.


  Python



    import
    basedosdados
    as
    bd


    # Para carregar o dado direto no pandas

    df
    =
    bd.read_sql
    (
    "SELECT * FROM `datario.adm_cor_comando.ocorrencias` LIMIT 1000"
    ,
    billing_project_id
    =
    "<id_do_seu_projeto_gcp>"
    )




  R



    install.packages(
    "basedosdados"
    )

    library(
    "basedosdados"
    )


    # Defina o seu projeto no Google Cloud

    set_billing_id(
    "<id_do_seu_projeto_gcp>"
    )


    # Para carregar o dado direto no R

    tb <- read_sql(
    "SELECT * FROM `datario.adm_cor_comando.ocorrencias` LIMIT 1000"
    )






  Cobertura temporal


  Não informado.




  Frequência de atualização


  Diário




  Órgão gestor


  COR




  Colunas



    Nome
    Descrição




        data_inicio
        Data e hora do registro do evento na PCRJ.



        data_fim
        Data e hora do encerramento do evento na PCRJ. O evento é encerrado quando é solucionado. Este atributo está vazio quanto o evento está aberto.



        bairro
        Bairro onde ocorreu o evento.



        id_pop
        Identificador do POP.



        status
        Status do evento (ABERTO, FECHADO).



        gravidade
        Gravidade do evento (BAIXO, MEDIO, ALTO, CRITICO).



        prazo
        Prazo esperado de solução do evento (CURTO, MEDIO(acima de 3 dias), LONGO( acima de 5 dias)).



        latitude
        Latitude em formato WGS-84 em que ocorreu o evento



        longitude
        Longitude em formato WGS-84 em que ocorreu o evento



        id_evento
        Identificador do evento.



        descricao
        Descrição do evento.



        tipo
        Tipo do evento (PRIMARIO, SECUNDARIO)







  Dados do(a) publicador(a)


  Nome: Patrícia Catandi
  E-mail: patriciabcatandi@gmail.com