Fork this notebook to get started on accessing data in the BigQuery dataset by writing SQL queries using the BQhelper module.

Context

Google Patents Public Data, provided by IFI CLAIMS Patent Services, is a worldwide bibliographic and US full-text dataset of patent publications. Patent information accessibility is critical for examining new patents, informing public policy decisions, managing corporate investment in intellectual property, and promoting future scientific innovation. The growing number of available patent data sources means researchers often spend more time downloading, parsing, loading, syncing and managing local databases than conducting analysis. With these new datasets, researchers and companies can access the data they need from multiple sources in one place, thus spending more time on analysis than data preparation.

Content

The Google Patents Public Data dataset contains a collection of publicly accessible, connected database tables for empirical analysis of the international patent system.

Acknowledgements

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

For more info, see the documentation at https://developers.google.com/web/tools/chrome-user-experience-report/

“Google Patents Public Data” by IFI CLAIMS Patent Services and Google is licensed under a Creative Commons Attribution 4.0 International License.

Banner photo by Helloquence on Unsplash

The COKI Language Dataset contains predictions for 122 million academic publications. The dataset consists of DOI, title, ISO language code and the fastText language prediction probability score.

Methodology
A subset of the COKI Academic Observatory Dataset, which is produced by the Academic Observatory Workflows codebase [1], was extracted and converted to CSV with Bigquery and downloaded to a virtual machine. The subset consists of all publications with DOIs in our dataset, including each publication’s title and abstract from both Crossref Metadata and Microsoft Academic Graph. The CSV files were then processed with a Python script. The titles and abstracts for each record were pre-processed, concatenated together and analysed with fastText. The titles and abstracts from Crossref Metadata were used first, with the MAG titles and abstracts serving as a fallback when the Crossref Metadata information was empty. Language was predicted for each publication using the fastText lid.176.bin language identification model [2]. fastText was chosen because of its high accuracy and fast runtime speed [3]. The final output dataset consists of DOI, title, ISO language code and the fastText language prediction probability score.

Query or Download
The data is publicly accessible in BigQuery in the following two tables:

• coki-data-share.language.doi_language
• coki-data-share.language.iso_language

When you make queries on these tables, make sure that you are in your own Google Cloud project, otherwise the queries will fail.

See the COKI Language Detection README for instructions on how to download the data from Zenodo and load it into BigQuery.

Code
The code that generated this dataset, the BigQuery schemas and instructions for loading the data into BigQuery can be found here: https://github.com/The-Academic-Observatory/coki-language

License
COKI Language Dataset © 2022 by Curtin University is licenced under CC BY 4.0.

Attributions
This work contains information from:

• Microsoft Academic Graph which is made available under the ODC Attribution Licence.
• Crossref Metadata via the Metadata Plus program. Bibliographic metadata is made available without copyright restriction and Crossref generated data under a CC0 licence. See metadata licence information for more details.

References
[1] https://doi.org/10.5281/zenodo.6366695
[2] https://fasttext.cc/docs/en/language-identification.html
[3] https://modelpredict.com/language-identification-survey

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

Dados históricos de posição geográfica de veículos do BRT.

Dados completos disponíveis para consulta e download no data lake do data.rio. Os dados são capturados a cada minuto e tratados a cada hora. Dados sujeitos a alteração, como correções de buracos de captura e/ou ajustes de tratamento.

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.transporte_rodoviario_municipal.gps_brt`

 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.transporte_rodoviario_municipal.gps_brt` 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.transporte_rodoviario_municipal.gps_brt` LIMIT
 1000"
)

Cobertura temporal 24/11/2021 até o momento

Frequência de atualização Horária

Órgão gestor Secretaria Municipal de Transportes (SMTR)

Colunas

   Nome


   Descrição




   modo


   BRT – nesta tabela consta apenas este modo




   timestamp_gps


   Timestamp de emissão do sinal de GPS




   data


   Data do timestamp de emissão do sinal de GPS




   hora


   Hora do timestamp de emissão do sinal de GPS




   id_veiculo


   Código identificador do veículo (número de ordem).




   servico


   Serviço realizado pelo veículo.




   latitude


   Parte da coordenada geográfica (eixo y) em graus decimais (EPSG:4326 -
   WGS84)




   longitude


   Parte da coordenada geográfica (eixo x) em graus decimais (EPSG:4326 -
   WGS84)




   flag_em_movimento


   Veículos com 'velocidade' abaixo da 'velocidade_limiar_parado', são
   considerados como parado (false). Caso contrário, são considerados
   andando (true)




   tipo_parada


   Identifica veículos parados em terminais ou garagens.




   flag_linha_existe_sigmob


   Flag de verificação se a linha informada existe no SIGMOB.




   velocidade_instantanea


   Velocidade instantânea do veículo, conforme informado pelo GPS
   (km/h)




   velocidade_estimada_10_min


   Velocidade média nos últimos 10 minutos de operação (km/h)




   distancia


   Distância da última posição do GPS em relação à posição atual (m)




   versao


   Código de controle de versão do dado (SHA Github)

Dados do(a) publicador(a)

Nome:
Subsecretaria de Tecnologia em Transportes (SUBTT)
E-mail:
dados.smtr@prefeitura.rio

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