Csv Exports And Imports Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

== Quick starts ==

Batch export podcast metadata to CSV files:

1) Export by search keyword: https://www.listennotes.com/podcast-datasets/keyword/

2) Export by category: https://www.listennotes.com/podcast-datasets/category/

== Quick facts ==

The most up-to-date and comprehensive podcast database available All languages & All countries Includes over 3,500,000 podcasts Features 35+ data fields , such as basic metadata, global rank, RSS feed (with audio URLs), Spotify links, and more Delivered in CSV format

== Data Attributes ==

See the full list of data attributes on this page: https://www.listennotes.com/podcast-datasets/fields/?filter=podcast_only

How to access podcast audio files: Our dataset includes RSS feed URLs for all podcasts. You can retrieve audio for over 170 million episodes directly from these feeds. With access to the raw audio, you’ll have high-quality podcast speech data ideal for AI training and related applications.

== Custom Offers ==

We can provide custom datasets based on your needs, such as language-specific data, daily/weekly/monthly update frequency, or one-time purchases.

We also provide a RESTful API at PodcastAPI.com

Contact us: hello@listennotes.com

== Need Help? ==

If you have any questions about our products, feel free to reach out hello@listennotes.com

== About Listen Notes, Inc. ==

Since 2017, Listen Notes, Inc. has provided the leading podcast search engine and podcast database.

Customs records of are available for CSV PLASTIC CO.,LTD. Learn about its Importer, supply capabilities and the countries to which it supplies goods

Abstract:

The following dataset is generated by the Brazilian Federal Government in the Portuguese language and finely presents all the data used to construct the Brazilian trade balance, as well as the data source from the Comex Stat system, detailed by NCM or by Municipalities of the exporter/importer (and SH4). The data is available in CSV with the column names in the first row. This database is detailed by NCM and is a CSV file with semicolon separator (;) detailed by year, month, NCM code, statistical unit code, country code of destination/origin of the product, code of the UF of origin/destination product, transport route code, loading/unloading URF code, statistical quantity, net kilogram, FOB dollar value (US$).

• Attention: The files should not be used in spreadsheet software, running the risk of losing lines and information.*

Export Dataset

Layout:

CO_YEAR; CO_MES; CO_NCM; CO_UNIT; CO_COUNTRY; SG_UF_NCM; CO_VIA; CO_URF; QT_ESTAT; KG_LIQUID; VL_FOB.

Description of columns and auxiliary tables:

CO_ANO: Year (1997 to 2021)

CO_MES: Month Codes (1:January 12:December)

CO_NCM: Mercosul Common Nomenclature Code - Used to control and identify goods sold in Brazil and other Mercosul countries (each NCM represents a different product) Mercosul countries: The Southern Common Market, commonly known by the Spanish abbreviation Mercosur, and Portuguese Mercosul, is a South American trade bloc established by the Treaty of Asunción in 1991 and the Protocol of Ouro Preto in 1994. Its full members are Argentina, Brazil, Paraguay, and Uruguay. Source: Wikipedia

CO_UNID: Statistical Measurement Unit Code which is a standard unit of measurement for each NCM, which can have values ​​such as kilogram, meter, liter, pairs, ton, and others.

CO_PAIS: Code of the name of the country with which the commercial operation was carried out (import or export)

SG_UF _NCM (acronym UF origin/destination of NCM): Code of the Federative Unit (state) of origin (export) or destination (import) of the merchandise.

CO_VIA: Code to identify the means of transport used (air, sea, road, rail, and others). In export, it is the method used to transport goods between the last place of shipment abroad. Upon import, it is configured through the means of access for goods at the first point of entry into the national territory.

CO_URF (Federal Revenue Unit): Code of the agency responsible for carrying out the procedures necessary for customs clearance of imported/exported merchandise

QT_ESTAT: In the breakdown by NCM, each product has its statistical unit. Most products have weight in kilograms as their statistical unit, but there are others: net kilogram, number (units), pairs, dozens, thousand, and ton. The complete table that relates each NCM to its statistical unit can be found in the “NCM _ UNIT” table. It is important to emphasize that statistical quantities of NCMs that contain different statistical units should not be added. [two]

KG_NET: Measure that expresses the net weight of the merchandise. Even products with statistical quantities other than kilograms also have a measurement in kilograms available, referring to the net weight of the merchandise, that is, merchandise disregarding packaging, boxes, or any other additional transport. It is worth remembering that this information, as well as other information provided in foreign trade operations, is free to fill out and is the sole responsibility of foreign trade operators. [two]

VL_FOB: The FOB value indicates the price of the merchandise in US Dollars (USD $) under Incoterm FOB (Free on Board), a modality in which the seller is responsible for shipping the merchandise while the buyer pays for freight, insurance, and other costs. post-shipment costs. [two]

Data source [1] Foreign Trade Statistics in Open Data. Available at: http://comexstat.mdic.gov.br/pt/home and https://www.gov.br/ [2] Manual for using statistical data on Brazilian foreign trade. Available at: https://balanca.economia.gov.br/balanca/manual/Manual.pdf

Customs records of are available for WENZHOU CSV INTL TRADE. Learn about its Importer, supply capabilities and the countries to which it supplies goods

Dataset

This dataset was created by Dhruv loves school Shah

Released under CC0: Public Domain

Contents

A subset of Tenders Electronic Daily (TED) data covering public procurement for the European Union and beyond from 2006-01-01 to 2023-12-31 in comma separated value (CSV) format. This data includes the most important fields from the contract notice and contract award notice standard forms, such as who bought what from whom, for how much, and which procedure and award criteria were used.

Generally, the data consists of tenders above the procurement thresholds. However, publishing below threshold tenders in TED is considered good practice, and thus a non-negligible number of below threshold tenders is present as well.

Please see the documentation below for important information on the data and its usage, including a version history of the export.

The European Commission is interested in the results of research on public procurement coming from the re-use of this data. Thus, we will be grateful to receive links to any papers, reports, or applications at GROW-G4@ec.europa.eu.

TED with broader coverage is also available in XML format at https://data.europa.eu/euodp/en/data/dataset/ted-1.

eForms

On 14 November 2022, the format of notices published in TED changed: the Publications Office displays both the current standard forms and eForms and makes them available for reuse. If you reuse TED data, your systems must be ready to process both types of notices. To help adapt your systems, you can find resources, models and schemas in the eForms Software Development Kit on GitHub (https://github.com/OP-TED/eForms-SDK/https://github.com/OP-TED/eForms-SDK/). Documentation is available on the Ted Developers Documentation site (https://docs.ted.europa.eu/), including eForms FAQs (https://docs.ted.europa.eu/home/FAQ/eforms.html).

Csv Active S R L Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

When you need to analyze crypto market history, batch processing often beats streaming APIs. That's why we built the Flat Files S3 API - giving analysts and researchers direct access to structured historical cryptocurrency data without the integration complexity of traditional APIs.

Pull comprehensive historical data across 800+ cryptocurrencies and their trading pairs, delivered in clean, ready-to-use CSV formats that drop straight into your analysis tools. Whether you're building backtest environments, training machine learning models, or running complex market studies, our flat file approach gives you the flexibility to work with massive datasets efficiently.

Why work with us?

Market Coverage & Data Types: - Comprehensive historical data since 2010 (for chosen assets) - Comprehensive order book snapshots and updates - Trade-by-trade data

Technical Excellence: - 99,9% uptime guarantee - Standardized data format across exchanges - Flexible Integration - Detailed documentation - Scalable Architecture

CoinAPI serves hundreds of institutions worldwide, from trading firms and hedge funds to research organizations and technology providers. Our S3 delivery method easily integrates with your existing workflows, offering familiar access patterns, reliable downloads, and straightforward automation for your data team. Our commitment to data quality and technical excellence, combined with accessible delivery options, makes us the trusted choice for institutions that demand both comprehensive historical data and real-time market intelligence

Batch-export podcast metadata to CSV, complete with 35+ detailed fields, in under 60 seconds. Our self-service platform delivers data instantly, and our expertly curated dataset guarantees top-tier quality by filtering out AI-generated and fake shows.

U.S. consumers demand variety, quality, and convenience in the foods they consume. As Americans have become wealthier and more ethnically diverse, the American food basket reflects a growing share of tropical products, spices, and imported gourmet products. Seasonal and climatic factors drive U.S. imports of popular types of fruits and vegetables and tropical products, such as cocoa and coffee. In addition, a growing share of U.S. imports can be attributed to intra-industry trade, whereby agricultural-processing industries based in the United States carry out certain processing steps offshore and import products at different levels of processing from their subsidiaries in foreign markets. This data set provides import values of edible products (food and beverages) entering U.S. ports and their origin of shipment. Data are from the U.S. Department of Commerce, U.S. Census Bureau. Food and beverage import values are compiled by calendar year into food groups corresponding to major commodities or level of processing. At least 10 years of annual data are included, enabling users to track long-term growth patterns.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Provide "Statistics of Import and Export Trade Volume of Each Park" to let the public understand the import and export and its growth trend of each park. In addition to updating this information every month, CSV file format is also provided for free download and use by the public.The dataset includes statistics on the import and export trade volume of parks such as Nanzih, Kaohsiung, Taichung, Zhonggang, Pingtung, and other parks (Lingguang, Chenggong, Gaoruan), with main fields including "Park, Import and Export (This Month, Year-to-Date)", "Export (This Month, Year-to-Date)", "Import (This Month, Year-to-Date)", and other important information.

Customs records of Sw are available for GRUPA CSV SP Z.O.O.. Learn about its Importer, supply capabilities and the countries to which it supplies goods

The Risk Management Agency (RMA) Cause of Loss Historical Files summarize participation information broken down by the causes of loss. Each link contains a ZIP file with compressed data containing CSV flat-files that can be imported into any standard spreadsheet and/or database for further analysis. Record description file located in each subfolder.

CoinAPI's Flat Files S3 API delivers historical crypto market data through downloadable CSV files. Access trades, quotes, and order book information in a user-friendly format. Our readable data files provide everything universities and analytics companies need.

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema 

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
  _id: 

Customs records of are available for CSV PHARMACEUTICALS INDIA PRIVATE I.. Learn about its Importer, supply capabilities and the countries to which it supplies goods

Version update: The originally uploaded versions of the CSV files in this dataset included an extra column, "Unnamed: 0," which is not RAMP data and was an artifact of the process used to export the data to CSV format. This column has been removed from the revised dataset. The data are otherwise the same as in the first version.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2020. For a description of the data collection, processing, and output methods, please see the "methods" section below.

Methods Data Collection

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above. Also as noted above, daily data are downloaded for each IR in two sets which cannot be combined. One dataset includes the URLs of items that appear in SERP. The second dataset is aggregated by combination of the country from which a search was conducted and the device used.

As a result, two CSV datasets are provided for each month of published data:

page-clicks:

The data in these CSV files correspond to the page-level data, and include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “page-clicks”. For example, the file named 2020-01_RAMP_all_page-clicks.csv contains page level click data for all RAMP participating IR for the month of January, 2020.

country-device-info:

The data in these CSV files correspond to the data aggregated by country from which a search was conducted and the device used. These include the following fields:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
index: The Elasticsearch index corresponding to country and device access information data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “country-device-info”. For example, the file named 2020-01_RAMP_all_country-device-info.csv contains country and device data for all participating IR for the month of January, 2020.

References

Google, Inc. (2021). Search Console APIs. Retrieved from https://developers.google.com/webmaster-tools/search-console-api-original.

The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency's Long Island Sound Study (https://longislandsoundstudy.net), characterized nitrogen export from forested watersheds and whether nitrogen loading has been increasing or decreasing to help inform Long Island Sound management strategies. The Weighted Regressions on Time, Discharge, and Season (WRTDS; Hirsch and others, 2010) method was used to estimate annual concentrations and fluxes of nitrogen species using long-term records (14 to 37 years in length) of stream total nitrogen, dissolved organic nitrogen, nitrate, and ammonium concentrations and daily discharge data from 17 watersheds located in the Long Island Sound basin or in nearby areas of Massachusetts, New Hampshire, or New York. This data release contains the input water-quality and discharge data, annual outputs (including concentrations, fluxes, yields, and confidence intervals about these estimates), statistical tests for trends between the periods of water years 1999-2000 and 2016-2018, and model diagnostic statistics. These datasets are organized into one zip file (WRTDSeLists.zip) and six comma-separated values (csv) data files (StationInformation.csv, AnnualResults.csv, TrendResults.csv, ModelStatistics.csv, InputWaterQuality.csv, and InputStreamflow.csv). The csv file (StationInformation.csv) contains information about the stations and input datasets. Finally, a short R script (SampleScript.R) is included to facilitate viewing the input and output data and to re-run the model. Reference: Hirsch, R.M., Moyer, D.L., and Archfield, S.A., 2010, Weighted Regressions on Time, Discharge, and Season (WRTDS), with an application to Chesapeake Bay River inputs: Journal of the American Water Resources Association, v. 46, no. 5, p. 857–880.