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.
Aligned as much as possible with the COUNTER Code of Practice Release 5 glossary.
|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).|
|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:
● 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.|
Usage data for usage report generation should ensure that only intended usage is recorded and that all requests not intended by theare excluded.
Because the way usage records are generated can differ across platforms, it is impractical to describe all the possibleand techniques used to clean up the data. This Code of Practice therefore specifies only the requirements to be met by data used for building usage reports.
Return codes in this Code of Practice forUsage Metrics are not different from the specifications in the COUNTER Code of Practice Release 5. Successful and valid requests MUST be counted. Successful requests are those with specific HTTP status codes indicating successful retrieval of the content (200 and 304). HTTP status codes are defined and maintained by IETF (Fielding & Reschke, 2014).
The intent offiltering is to prevent over-counting which may occur when a clicks the same link multiple times in succession, e.g. when frustrated by a slow internet connection. Double-click filtering applies to all metric types. The filtering rule is as follows:
A “double-click” is defined as repeated access to a web accessible resource by the samewithin a session, within a time period. Double-clicks on a link by the same within a 30-second period MUST be counted as one action. For the purposes of the Code of Practice for Usage Metrics, the time window for a on any page is set at a maximum of 30 seconds between the first and second mouse clicks. For example, a click at 10.01.00 and a second click at 10.01.29 would be considered a double-click (one action); a click at 10.01.00 and a second click at 10.01.35 would count as two separate single clicks (two actions).
Amay be triggered by a mouse-click or by pressing a refresh or back button. When two actions are made for the same URL within 30 seconds the first MUST be removed and the second retained.
Any additional requests for the same URL within 30 seconds (between clicks) MUST be treated identically: always remove the first and retain the second.
There are different ways to track whether two requests for the same URL are from the sameand session. These options are listed in order of increasing reliability, with Option 4 being the most reliable.
Somecount the number of unique items that had a certain activity, such as a Unique_Dataset_Requests or Unique_Dataset_Investigations.
For the purpose of metrics, ais the typical unit of content being accessed by users. The MUST be identified using a unique identifier such as a DOI, regardless of format.
The rules for calculating the uniquecounts are as follows:
Multiple activities qualifying for the metric type in question representing the sameand occurring in the same user-sessions MUST be counted as only one “unique” activity for that dataset.
A “User Session” is defined as activity by ain a period of one hour. It may be identified in any of the following ways: by a logged ID + transaction date, by a logged ID (if users log in with personal accounts) + transaction date + hour of day (day is divided into 24 one-hour slices), by a logged cookie + transaction date + hour of day, or by a combination of IP address + user agent + transaction date + hour of day.
To allow for simplicity in calculatingSessions when a is not explicitly tracked, the day will be divided into 24 one-hour slices and a surrogate will be generated by combining the date + hour time slice + one of the following: user ID, cookie ID, or IP address + user agent. For example, consider the following transaction:
The above surrogatedoes not provide an exact analogy to a session. However, statistical studies show that the result of using such a surrogate results in unique counts are within 1– 2 % of unique counts generated with actual sessions.
Content providers that offer databases where a givenis included in multiple databases MUST attribute the Investigations and Requests metrics to just one database. They could use a consistent method of prioritizing databases or pick the database randomly.
The intent is to exclude web robots and spiders but include usage by humans accessing content through a scripting language or automated tool, whether interactively or standalone.
Web robots and crawlers intended forindexing and related applications SHOULD be excluded via the application of a blacklist of known agents for these robots. This blacklist MUST NOT include general purpose agents that are commonly used by researchers (e.g., python, curl, wget, and Java), and the blacklist will be maintained as a subset of the COUNTER Code of Practice Release 5 list of internet robots and crawlers (COUNTER-Robots, 2017). Generally, user agents reflecting programmatic access to specific datasets will not be included in the blacklist.
Usage counts by scripted and automated processes MUST NOT be excluded unless they can demonstrably be shown to originate from a blacklisted agent, such as anof a known agent. New or unknown agents SHOULD be counted unless there is demonstrable evidence that they represent solely a web indexing agent.
Many researchers access and analyze data using scripts or automated tools, especially large data sets, and excluding those uses would be inaccurate and bias the counts. The Access_Method of type Machine is used to distinguish this kind of access.
For the purpose of reporting usage according to the Code of Practice forUsage Metrics, machine access does not require prior permission and/or the use of specific endpoints or protocols. This is in contrast to the COUNTER Code of Practice Release 5.
The distinction between legitimate machine use and robot or webSection 7.5).traffic is made based on the agent (see