Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

The Reference Data as a Service (RDaaS) API provides a list of codesets, classifications, and concordances that are used within Statistics Canada. These resources are shared to help harmonize data, enabling better interdepartmental data integration and analysis. This dataset provides an updated version of the StatCan RDaaS API specification, originally part of the Government of Canada’s GC API Store, which permanently closed on September 29th, 2023. The archived version of the original API specification can be accessed via the Wayback Machine . The specification has been updated to the OpenAPI 3.0 (Swagger 3) standard, enabling use of current tools and features for API exploration and integration. Key interactive features of the updated specification include: * Try-It-Out Functionality: Allows a user to interact with API endpoints directly from the documentation in their browser, submitting test requests and viewing live responses. * Interactive Parameter Input: Simplifies experimentation with filters and parameters to explore API behavior. * Schema Visualization: Provides clear representations of request and response structures.

API documentationhttps://www.ris.gov.tw/rs-opendata/api/Main/docs/v1API pathhttps://www.ris.gov.tw/rs-opendata/api/v1/datastore/ODRP003/YYYYMM (please specify year and month)

These datasets correspond to the daily statistics of the website data.gouv.fr cut out by year. The data comes from stats.data.gouv.fr and is compiled at the end of each year. Starting in 2020, the statistics of the site and the API are now separated. This dataset only applies to the site from 2020. Data before 2020 and from 2020 are not comparable. Documentation of the different columns is available here.

This dataset provides the whole set of OECD Quarterly National Accounts data and is recommended for users who wish to query a large amount of data. It is not designed for visualising results using the Table and Chart buttons. To access the ‘Developer API query builder’, click on the ‘Developer API’ button above.

The application programming interface (API), based on the SDMX standard, allows a developer to access the data using simple RESTful URL and HTTP header options for various choices of response formats including JSON. The query filter is generated according to the current data selection. To change the data selection, use the filters on the left.

To get started check the API documentation. For any question contact us

The mapping table between old OECD.Stat and new OECD Data Explorer codes is available here.

These indicators were presented in the previous dissemination system in the QNA dataset.
See User Guide on Quarterly National Accounts (QNA) in OECD Data Explorer: QNA User guide
See QNA Calendar for information on advance release dates: QNA Calendar
See QNA Changes for information on changes in methodology: QNA Changes
See QNA TIPS for a better use of QNA data: QNA TIPS
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

Database for statistics on higher education (DBH) collects information about the activity at Norwegian universities, university colleges and vocational schools. The database contains information about education, research, employees, finances, areas etc. and is managed by the Directorate for Higher Education and Competence (HK-dir). The information constitutes a statistical bank where data can be retrieved programmatically via API or reports via screenshots, as well as as a special order upon request. There is a client that is linked to API and can be used for testing or ad hoc queries: https://dbh.hkdir.no/dbhapiklient/ The StatBank is divided by subject and table. Within each table, the user can create their own query. The query is designed in JSON format and can be tested in the client. Data is provided as CSV or JSON. Transfer is done via HTTPS or via the client. Data can be retrieved as a sample via the query, or as a whole data set (bulk data). Table 1 in the client provides an overview of the content of the API. Documentation: https://dbh.hkdir.no/static/files/dokumenter/api/api_dokumentasjon.pdf

The API Documentation Management Software market is experiencing a significant transformation, driven by the escalating demand for seamless integration and communication between various software applications. As organizations increasingly rely on APIs to connect services and streamline operations, the need for compr

WONDER online databases include county-level Compressed Mortality (death certificates) since 1979; county-level Multiple Cause of Death (death certificates) since 1999; county-level Natality (birth certificates) since 1995; county-level Linked Birth / Death records (linked birth-death certificates) since 1995; state & large metro-level United States Cancer Statistics mortality (death certificates) since 1999; state & large metro-level United States Cancer Statistics incidence (cancer registry cases) since 1999; state and metro-level Online Tuberculosis Information System (TB case reports) since 1993; state-level Sexually Transmitted Disease Morbidity (case reports) since 1984; state-level Vaccine Adverse Event Reporting system (adverse reaction case reports) since 1990; county-level population estimates since 1970. The WONDER web server also hosts the Data2010 system with state-level data for compliance with Healthy People 2010 goals since 1998; the National Notifiable Disease Surveillance System weekly provisional case reports since 1996; the 122 Cities Mortality Reporting System weekly death reports since 1996; the Prevention Guidelines database (book in electronic format) published 1998; the Scientific Data Archives (public use data sets and documentation); and links to other online data sources on the "Topics" page.

This list contains the government API cases collected, cleaned and analysed in the APIs4DGov study "Web API landscape: relevant general purpose ICT standards, technical specifications and terms".

The list does not represent a complete list of all government cases in Europe, as it is built to support the goals of the study and is limited to the analysis and data gathered from the following sources:

• The EU open data portal

• The European data portal

• The INSPIRE catalogue

• JoinUp: The API cases collected from the European Commission JoinUp platform

• Literature-document review: the API cases gathered from the research activities of the study performed till the end of 2019

• ProgrammableWeb: the ProgrammableWeb API directory

• Smart 2015/0041: the database of 395 cases created by the study ‘The project Towards faster implementation and uptake of open government’ (SMART 2015/0041).

• Workshops/meetings/interviews: a list of API cases collected in the workshops, surveys and interviews organised within the APIs4DGov

Each API case is classified accordingly to the following rationale:

• Unique id: a unique key of each case, obtained by concatenating the following fields: (Country Code) + (Governmental level) + (Name Id) + (Type of API)

• API Country or type of provider: the country in which the API case has been published

• API provider: the specific provider that published and maintain the API case

• Name Id: an acronym of the name of the API case (it can be not unique)

• Short description

• Type of API: (i) API registry, a set, catalogue, registry or directory of APIs; (ii) API platform: a platform that supports the use of APIs; (iii) API tool: a tool used to manage APIs; (iv) API standard: a set of standards related to government APIs; (v) Data catalogue, an API published to access metadata of datasets, normally published by a data catalogue; (vi) Specific API, a unique (can have many endpoints) API built for a specific purpose

• Number of APIs: normally only one, in the case of API registry, the number of APIs published by the registry at the 31/12/2019

• Theme: list of domains related to the API case (controlled vocabulary)

• Governmental level: the geographical scope of the API (city, regional, national or international)

• Country code: the country two letters internal code

• Source: the source (among the ones listed in the previous) from where the API case has been gathered

Provide warning signs in the waters of Taoyuan City, information on the installation of lifebuoys, and the coordinates of their locations.Taoyuan City Government Open Data Platform OAS API standard documentation: https://data.tycg.gov.tw/v2/api-docsURL of the Swagger-generated API documentation page: https://data.tycg.gov.tw/opendata/api-docs

This dataset provides economic statistics in real prices in regions - using regional output producer index (ROPI) when available - is recommended for users who wish to query a large amount of data. It is not designed for visualising results using the Table and Chart buttons. To access the ‘Developer API query builder’, click on the ‘Developer API’ button above.

To get started check the API documentation

Dataflows covered

• Real gross domestic product (ROPI-adjusted for inflation) - Regions
• Real gross value added by main economic activity (ROPI-adjusted for inflation) - Regions
• Real labour productivity by main economic activity (ROPI-adjusted for inflation) - Regions

See method and detailed data sources in Regions and Cities at a Glance 2024, Annex.

Regions and territorial levels

Regions are subnational units below national boundaries and correspond to administrative divisions defined autonomously by countries according to different criteria. The OECD classifies regions into two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). This classification facilitates greater comparability of geographic units at the same territorial level.

The list and maps of OECD regions are presented in the OECD Territorial grid (pdf).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting. Correspondence between TL3 and metropolitan regions:(xlsx).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link).

Cite this dataset

OECD Regions, cities and local areas database (Economic statistics ROPI-adjusted for inflation - Regions (for 'Developer API'), http://oe.cd/geostats.

For any question or comment, please write to RegionStat@oecd.org

With the launch of the State of Hawaii's Open Data portal, the State of Hawaii has now begun providing residents, analysts, and civic developers with unparalleled access to State data for use in increasing transparency, driving civic innovation, and engaging participants in a more collaborative form of government. Visitors to the site will find over 150 datasets organized by six major topics, with more datasets continuing to be added to the site: Data on the portal has been optimized so that users of varying technical ability will find the site easy to navigate and use. Residents, journalists and analysts will find that the data can easily be contextualized for various purposes using intuitive features built directly within the State of Hawaii's Open Data portal. Videos detailing how to sort, filter, visualize data can be found within the video guide section of the site. Developers wishing to use the data for civic innovation will benefit from the CKAN Open Data API, a fully-documented, RESTful, Application Programming Interface (API). For more information about the API powering the State of Hawaii's Open Data Portal, please visit the developer's page. State-of-the-art social data features enable participants to create a more collaborative form of government by commenting, discussing, and sharing datasets with other participants on the platform or to publish them on other social networks like Twitter or Facebook. Users of the site are encouraged to participate in the development and future direction of the site by suggesting datasets to be added to the platform. Click the link below to view training materials for Citizens, staff and administrators. https://opendata.hawaii.gov/pages/training Click the link below to view documentation on the CKAN API. https://docs.ckan.org/en/2.9/api/index.html Click the link below to view Open Data site statistics. https://opendata.hawaii.gov/stats

Release Date: 2022-06-30.Release Schedule:.The data in this file were released on June 30, 2022...Key Table Information:.Beginning with reference year 2005, Nonemployer data are released using the Noise Infusion methodology to protect confidentiality. See Survey Methodology for complete information on the coverage and methodology of the Nonemployer Statistics data series...Data Items and Other Identifying Records:. This file contains data on the total number of firms and receipts. . Number of nonemployer establishments . Nonemployer Sales, value of shipments, or revenue ($1,000) . Noise range for nonemployer Sales, value of shipments, or revenue ..Geography Coverage:.The data are shown at the U.S. and State level for LFO and the U.S. level for Receipt Size Class. All other data is shown at the U.S., State, County, Combined Statistical Area, and Metropolitan/Micropolitan Statistical Areas...Industry Coverage:.The data are shown at the 2- through (where available) 6-digit NAICS code levels for all sectors with published data. Data for nonemployers generally are provided at broader levels of industry detail than data for employers. For specific exclusions and inclusions, see https://www.census.gov/programs-surveys/nonemployer-statistics/technical-documentation/methodology.html...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/nonemployer-statistics/data/2019/NS1900NONEMP.zip ..API Information:. Nonemployer Statistics data are housed in the Nonemployer Statistics API. For more information, see Census.gov: Developers: Available APIs: County Business Patterns and Nonemployer Statistics (1986-2019): Nonemployer Statistics APIs. ..Methodology:.The universe of this file is all firms with no paid employees or payroll with receipts of $1,000 or more (or $1 for the construction sector) and are subject to federal income tax. The universe is limited to industries in approximately 450 of the nearly 1,200 recognized North American Industry Classification System industries. The universe contains only those codes that are available through administrative records sources and are common to all three legal forms of organization applicable to nonemployer businesses. This is generally a broader level of detail than would typically be provided for employer data. For specific exclusions and inclusions, see https://www.census.gov/programs-surveys/nonemployer-statistics/technical-documentation/methodology.html...Nonemployer Statistics originate from tax return information of the Internal Revenue Service. The data are subject to nonsampling error such as errors of self-classification by industry on tax forms, as well as errors of response, nonreporting and coverage. Values provided by each firm are slightly modified to protect the respondent's confidentiality. For further information about methodology and data limitations, see Survey Methodology...Symbols:. G - Low noise; cell value was changed by less than 2 percent by the application of noise. H - Moderate noise; cell value was changed by 2 percent or more but less than 5 percent by the application of noise. J - High noise; cell value was changed by 5 percent or more by the application of noise. S - Withheld because estimate did not meet publication standards. N - Not available or not comparable. For a complete list of symbols, see Nonemployer Statistics (NES): About this Program: Nonemployer Glossary: Abbreviations and Symbols.. .Source:..U.S. Census Bureau, 2019 Nonemployer Statistics..For more information about Nonemployer Statistics, see Our Surveys and Programs: Nonemployer Statistics (NES)...Contact Information:..U.S. Census Bureau.Economy-Wide Statistics Division .Business Statistics Branch .(301) 763-2580 .ewd.nonemployer.statistics@census.gov

Release Date: 2020-05-21.Release Schedule:.The data in this file were released on May 21,2020...Key Table Information:.Beginning with reference year 2005, Nonemployer data are released using the Noise Infusion methodology to protect confidentiality. See Survey Methodology for complete information on the coverage and methodology of the Nonemployer Statistics data series...Data Items and Other Identifying Records:. This file contains data on the total number of firms and receipts. ..Geography Coverage:.The data are shown at the U.S. and State level for LFO and the US level for Receipt Size Class. All other data is shown at the U.S., State, County, Combined Statistical Area, and Metropolitan and Micropolitan Statistical Areas...Industry Coverage:.The data are shown at the 2- through 6-digit NAICS code levels for all sectors with published data...Footnotes:.Not applicable..FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/nonemployer-statistics/data/2018/NS1800NONEMP.zip ..API Information:. Nonemployer Statistics data are housed in the Nonemployer Statistics API. For more information, see Census.gov: Developers: Available APIs: County Business Patterns and Nonemployer Statistics (1986-2018): Nonemployer Statistics APIs. ..Methodology:.The universe of this file is all firms with no paid employees or payroll with receipts of $1,000 or more (or $1 for the construction sector) and are subject to federal income tax. The universe is limited to industries in approximately 450 of the nearly 1,200 recognized North American Industry Classification System industries. The universe contains only those codes that are available through administrative records sources and are common to all three legal forms of organization applicable to nonemployer businesses. This is generally a broader level of detail than would typically be provided for employer data. For specific exclusions and inclusions, see https://www.census.gov/programs-surveys/nonemployer-statistics/technical-documentation/methodology.html...Nonemployer Statistics originate from tax return information of the Internal Revenue Service. The data are subject to nonsampling error such as errors of self-classification by industry on tax forms, as well as errors of response, nonreporting and coverage. Values provided by each firm are slightly modified to protect the respondent's confidentiality. For further information about methodology and data limitations, see Survey Methodology...Symbols:. S - Withheld because estimate did not meet publication standards. N - Not available or not comparable. For a complete list of symbols, see Nonemployer Abbreviations and Symbols.. .Source:..U.S. Census Bureau, 2018 Nonemployer Statistics...Contact Information:.. U.S. Census Bureau. (301) 763-2580 . ewd.nonemployer.statistics@census.gov

🇩🇪 독일 English The dataset contains Co2 values of different locations in Constance and is divided into intervals of 2 minutes of data. The data was collected with "Smart Citizen Kits" and is available here in machine-readable format. The records may contain gaps due to device malfunctions, power and internet outages. API documentation: Summary – SmartCitizen API Reference Source: City of Konstanz, Department of Data Management and Statistics (Smart Citizen Kits)

The Location at Postcode house number API contains data about the location of addresses, building blocks, areas, cadastral object, cadastral subject, Public Space and zip code on City data. More about browsable interfaces, openAPI documentation and the APIs developed by the DataPunt program of Research, Information and Statistics can be found on this overview.

Analysis of ‘Top 10 NFT stats’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/outoftheloop/top-10-nft-stats on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

All-time top 10 NFT's

Content

Collection stats

Acknowledgements

https://docs.opensea.io/reference/api-overview

--- Original source retains full ownership of the source dataset ---

The dataset contains Co2 values of different locations in Constance and is divided into intervals of 2 minutes of data. The data was collected with "Smart Citizen Kits" and is available here in machine-readable format. The records may contain gaps due to device malfunctions, power and internet outages. API documentation: Summary – SmartCitizen API Reference Source: City of Konstanz, Department of Data Management and Statistics (Smart Citizen Kits)

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 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.

Methods

RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data were 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 from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were 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.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

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

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, data 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 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 data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

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.

The data in these CSV files 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.
country: The country from which the corresponding search originated.
device: The device used for the search.
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 index 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 follow the format 2018-01_RAMP_all.csv. Using this example, the file 2018-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2018.

Data Collection from August 19, 2018 Onward

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

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 2017. For a description of the data collection, processing, and output methods, please see the "methods" section below.

Methods RAMP Data Documentation – January 1, 2017 through August 18, 2018

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 from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were 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.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

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

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, data 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 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 data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

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.

The data in these CSV files 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.
country: The country from which the corresponding search originated.
device: The device used for the search.
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 index 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 follow the format 2017-01_RAMP_all.csv. Using this example, the file 2017-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2017.

References

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