mobile-menu mobile-menu-arrow Menu
 

The COUNTER Code of Practice for Research Data

The Code of Practice for Research Data Usage Metrics standardizes the generation and distribution of usage metrics for research data, enabling for the first time the consistent and credible reporting of research data usage.

COUNTER welcomes input and feedback from the community on this first iteration, so that it can be further developed and refined.

 

 
Code of Practice
Text size:   /
Glossary

Appendix A: Glossary of Terms for CoPRD

Aligned as much as possible with the COUNTER Code of Practice Release 5 glossary.

Abstract See Description.
Access_Method A COUNTER attribute indicating whether the usage related to investigations and requests was generated by a human user browsing and searching a website (Regular) or by a computer (Machine).
Author(s) See Creator
Collection A curated collection of metadata about content items.
Component A uniquely identifiable constituent part of a content item composed of more than one file (digital object).
Content item A generic term describing a unit of content accessed by a user of a content host. Typical content items include articles, books, chapters, datasets, multimedia, etc.
Content provider An organization whose function is to commission, create, collect, validate, host, distribute, and trade information in electronic form.
Creator(s) The person/people who wrote/created the datasets whose usage is being reported-
Data repository A content provider that provides access to research data.
Data type The field identifying type of content. The Code of Practice for Research Data Usage Metrics only recognizes the Data type Dataset.
Dataset An aggregation of data, published or curated by a single agent, and available for access or download in one or more formats, with accompanying metadata. Other term: data package.
Description A short description of a dataset. Accessing the description falls into the usage category of Investigations.
DOI (digital object identifier) The digital object identifier is a means of identifying a piece of intellectual property (a creation) on a digital network, irrespective of its current location (IDF).
Double-click A repeated click or repeated access to the same resource by the same user within a period of 30 seconds. COUNTER requires that double-clicks must be counted as a single click.
Host types A categorization of Content Providers used by COUNTER. The Code of Practice for Research Data Usage Metrics uses the following host types:

●      Repository

●      Data Repository

Internet robot, crawler, spider An identifiable, automated program or script that visits websites and systematically retrieves information from them, often to provide indexes for search engines rather than for research. Not all programs or scripts are classified as robots.
Investigation A category of COUNTER metric types that represent a user accessing information related to a dataset (i.e. a description or detailed descriptive metadata) or the content of the dataset itself.
Log file analysis A method of collecting usage data in which the web server records all of its transactions.
Machine A category of COUNTER Metric Types that represents a machine accessing content, e.g. a script written by a researcher. This does not include robots, crawlers and spiders.
Master reports Reports that contain additional filters and breakdowns beyond those included in the standard COUNTER reports.
Metadata A series of textual elements that describes a content item but does not include the item itself. For example, metadata for a dataset would typically include publisher, a list of names and affiliations of the creators, the title and description, and keywords or other subject classifications.
Metric types, Metric_Type An attribute of COUNTER usage that identifies the nature of the usage activity.
ORCID (Open Researcher and Contributor ID) An international standard identifier for individuals (i.e. authors) to use with their name as they engage in research, scholarship, and innovation activities.
Persistent Identifier (PID) Globally unique identifier and associated metadata for research data, or other entities (articles, researchers, scholarly institutions) relevant in scholarly communication.
Platform An interface from an aggregator, publisher, or other online service that delivers the content to the user and that counts and provides the COUNTER usage reports.
Provider ID A unique identifier for a Content Provider and used by discovery services and other content sites to track usage for content items provided by that provider.
Publication date, Publication_Date An optional field in COUNTER item reports and Provider Discovery Reports. The date of release by the publisher to customers of a content item.
Publisher An organization whose function is to commission, create, collect, validate, host, distribute and trade information online and/or in printed form.
Regular A COUNTER Access_Method. Indicates that usage was generated by a human user browsing/searching a website, rather than by a computer.
Reporting period, Reporting_Period The total time period covered in a usage report.
Request A category of COUNTER Metric Types that represents a user accessing the dataset content.
Session A successful request of an online service. A single user connects to the service or database and ends by terminating activity that is either explicit (by leaving the service through exit or logout) or implicit (timeout due to user inactivity). (NISO).
SUSHI An international standard (Z39-93) that describes a method for automating the harvesting of reports. Research Data SUSHI API Specification is an implementation of this standard for harvesting Code of Practice for Research Data Usage Metrics reports.
Total_Dataset_Investigations A COUNTER Metric_Type that represents the number of times users accessed the content of a dataset, or information describing that dataset (i.e. metadata).
Total_Dataset_Requests A COUNTER Metric_Type that represents the number of times users requested the content of a dataset. Requests may take the form of viewing, downloading, or emailing the dataset provided such actions can be tracked by the content provider’s server.
Transactions A usage event.
Unique_Dataset_Investigations A COUNTER Metric Type that represents the number of unique “Datasets” investigated in a user-session.
Unique_Dataset_Requests A COUNTER Metric Type that represents the number of unique datasets requested in a user-session.
User A person who accesses the online resource.
User agent An identifier that is part of the HTTP/S protocol that identifies the software (i.e. browser) being used to access the site. May be used by robots to identify themselves.
Version Multiple versions of a dataset are defined by significant changes to the content and/or metadata, associated with changes in one or more components.
Year of publication Calendar year in which a dataset is published.

 

 

1.0 Introduction

This is the first version of a Code of Practice for Research Data. The purpose of this report is to enable data repositories and platform An interface from a “Content Provider” that delivers the content to the user A person who accesses the online resourceand that counts and provides the COUNTER usage reports.providers to produce consistent, comparable, and credible usage metrics for research data. This first release of the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics has been kept intentionally narrow in scope to focus on the dataset See “Data_Type”.level and avoid creating unnecessary hurdles to adoption.

1.1. General Information

1.1.1 Purpose

The purpose of the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics is to facilitate the recording, exchange, and interpretation of online usage data by establishing open standards and protocols for the provision of content-provider-generated usage statistics that are consistent, comparable, and credible.

1.1.2 Scope

This Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics is aligned with the COUNTER Code of Practice Release 5 and provides a framework for recording and exchanging online usage statistics for research data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.at an international level. It covers the following areas: data elements to be measured; definitions of these data elements; content and format of usage reports; requirements for data processing; and guidelines to avoid duplicate counting.

1.1.3 Relationship to COUNTER Code of Practice Release 5

Developed by members from the research data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.management community (RDM) in close coordination with COUNTER, this Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.follows the COUNTER Code of Practice Release 5 (COUNTER Code of Practice Release 5, 2017) recommendations as much as possible (where relevant) and deviates from them only when necessary.

There are different use cases and practices between research data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.and the majority of scholarly resources. For example, research data does not need to be reported at the institutional level, but geographic aggregation may be important. Another significant difference is the need for aggregation of usage across components for all versions of a dataset. It is common practice for research data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.to be versioned, and we recommend reporting the usage data for each specific version and the combined usage for all versions.

The first release of the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics only describes reporting of usage at the dataset See “Data_Type”.level. For future releases, reporting usage statistics for dataset See “Data_Type”.components will be considered based on community feedback. Following the COUNTER Code of Practice Release 5, standard usage statistics are not reported by format distribution, e.g., no separate numbers for downloads in CSV and XLSX formats.

Download volume (i.e., file size) can be reported. There are widely varying practices in the research data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.community regarding the granularity and structure of datasets, components, and collections. Reporting download volume Journals:  Numbered collection A subset of the content of a service; a collection is a branded group of online information products from one or more content providers that can be subscribed to/licensed and searched as a complete group. For the COUNTER reporting is restricted to pre-set collections that are defined like “Databases”. See “Database”. Note a package or bundle provided by a publisher is not considered a “Database” or “Collection”.of a minimum of one journal issue; in printed form, volumes of more than one issue A collection A subset of the content of a service; a collection is a branded group of online information products from one or more content providers that can be subscribed to/licensed and searched as a complete group. For the COUNTER reporting is restricted to pre-set collections that are defined like “Databases”. See “Database”. Note a package or bundle provided by a publisher is not considered a “Database” or “Collection”.of journal articles associated with each other via allocation of a specific issue number and presented as an identifiable unit online and/or as a physically bound and covered set of numbered pages in printare not normally bound together by the publisher, but are frequently bound together in hardback by the purchasing library to aid preservation of the printed product. Books: Numbered collection A subset of the content of a service; a collection is a branded group of online information products from one or more content providers that can be subscribed to/licensed and searched as a complete group. For the COUNTER reporting is restricted to pre-set collections that are defined like “Databases”. See “Database”. Note a package or bundle provided by a publisher is not considered a “Database” or “Collection”.of articles, chapters, or entries that is part of a larger, multi-volume work, either published together or seriallymakes it easier to compare usage for research data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.packaged into datasets with different granularity.

Geolocation information and country are reported, but not IP addresses. For large countries (e.g. United States) reporting at the state or province level may be enabled. Reporting of geolocation information helps to better understand usage for the same datasets hosted in multiple locations, and for datasets where usage is dependent upon the location of the user, e.g., datasets describing research in a particular geolocation.

Usage metrics are reported for each specific version of a dataset, as well as the combined usage for all versions. Usage metrics are only reported for individual datasets. In this version of the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics there is no report format for reporting usage for collections of datasets, for example all datasets in a data repository.

1.1.4 Strategy

The Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics will evolve in response to the demands of the international library, data management, and content provider An organization whose function is to commission, create, collect, validate, host, distribute and trade information in electronic form and in doing so provides usage data. This includes organisations sometimes referred to as “Publishers”, “Vendors”, “Platforms” and/or “Intermediaries”.communities. The Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics is continually under review; feedback on its scope and application are actively sought from all interested parties.

1.1.5 Governance

The Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics is developed by the Make Data Count project (Make Data Count, 2017), in close collaboration with Counter Online Metrics (COUNTER) (Project COUNTER, 2002), a non-profit organization that maintains the COUNTER Code of Practice.

1.1.6 Definitions

This Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics provides definitions of data elements and other terms that are relevant not only to the usage reports specified in this document, but also to other reports that content providers may wish to generate. Every effort has been made to use existing COUNTER, ISO, NISO, etc. definitions where appropriate, and these sources are cited (see References and Appendix A). The following key definitions are used by the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics:

  • Dataset: An aggregation of data, published or curated by a single agent, and available for access or download in one or more formats, with accompanying metadata (Dekkers & Isaac, 2018). A dataset See “Data_Type”.is a subtype of a COUNTER content item. Synonymous term: data package.
  • Component: Part of the data available for a dataset See “Data_Type”.that can be accessed or downloaded individually. Aligns with a COUNTER component. Synonymous terms: data file, data granule.
  • Collection: A curated aggregation of datasets. Related terms: catalog, repository.
  • Version: Multiple versions of a dataset See “Data_Type”.are defined as significant changes to the content and/or metadata, associated with changes in one or more components, and that would result in changes to fixity attributes of the components.

1.1.7 Versions

The Code of Practice for Research Data Usage Metrics will be extended and upgraded as necessary, based on input from the communities it serves. Future versions might be integrated into the COUNTER Code of Practice. A continuous maintenance process will allow the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics to evolve over time minimizing the need for major version changes.

1.1.8 Auditing and Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics Compliance

No content provider An organization whose function is to commission, create, collect, validate, host, distribute and trade information in electronic form and in doing so provides usage data. This includes organisations sometimes referred to as “Publishers”, “Vendors”, “Platforms” and/or “Intermediaries”.following the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics has been audited at the time of this first release of the Code of Practice for Research Data Usage Metrics.

While we expect the auditing process for research data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.usage reporting to be similar to audits in the context of the COUNTER Code of Practice Release 5, it is not yet known which organizations are willing to perform audits according to the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics, and how these audits differ from COUNTER Code of Practice Release 5 audits. For these reasons audits for research data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.usage reporting according to the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics are not required at this point in time.

1.1.9 Privacy and User A person who accesses the online resourceConfidentiality

Statistical reports or data that reveal information about individual users will not be released or sold by content providers without the permission of that individual user, the consortium, and its member institutions (ICOLC Guidelines for Statistical Measures of Usage of Web-Based Information Resources (1998, revised 2001, 2006), 2006).

1.1.10 Relationship to other Standards, Protocols and Codes

The Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics builds on several existing industry initiatives and standards that address content provider-based online performance measures. In addition to the COUNTER Code of Practice this includes the Scholix Metadata A series of textual elements that describe a “Content Item” but does not include the “Item” itself.  For example, “Metadata” for a “Journal Article” would, typically include:  “Publisher”; “Journal” title, volume, issue and page numbers; copyright information; list of names and affiliations of the “Authors”; “Author” organization addresses; “Article title” and “Abstract” of the “Article”; and keywords or other subject classifications.Schema for the Exchange of Scholarly Communication Links (Burton et al., 2017) and the NISO Alternative Assessment Metrics Project (NISO RP-25-2016: Outputs of the NISO Alternative Assessment Metrics Project, 2016).

Where appropriate, definitions of data elements and other terms from these sources have been used in this Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics, and these are identified in Appendix A.

1.2 Changes from Previous Versions

This is the first release of the Code of Practice for Research Data Data that supports research findings and may include “Databases”, spreadsheets, tables, raw transaction logs, etc.Usage Metrics.

 

 


How to become Counter Compliant

All academic libraries across the world use and trust COUNTER usage reports to inform renewal and new purchasing decisions, to inform faculty about the value of the library and its resources and to understand user behaviour and improve the user experience.

Counter will help publishers and vendors to become compliant. The The Friendly Guide for Providers Release 5  and Technical Notes and will provide the information you will need to start the process. Content providers transitioning from Release 4 to Release 5 compliance will also find transition timeline useful in their planning.

Audit Process

To comply with the Code of Practice, publishers and vendors must be independently audited within six months of signing the Declaration of COUNTER Compliance, and annually thereafter.

There are three approved COUNTER auditors:

COUNTER will also accept an audit by any Chartered Accountant (UK), CPA (USA) or their equivalent elsewhere.

 
Release 5 Queries COP Register Members Guides Members

Gold and Silver Sponsors