Journal:Persistent identification of instruments

Full article title	Persistent identification of instruments
Journal	Data Science Journal
Author(s)	Stocker, Markus; Darroch, Louise; Krahl, Rolf; Habermann, Ted; Devaraju, Anusuriya; Schwardmann, Ulrich; D'Onofrio, Claudio; Häggström, Ingemar
Author affiliation(s)	TIB Leibniz Information Centre for Science and Technology, University of Bremen, British Oceanographic Data Centre, Helmholtz-Zentrum Berlin für Materialien und Energie, Metadata Game Changers, Gesellschaft für wissenschaftliche Datenverarbeitung Göttingen, Lund University, EISCAT Scientific Association
Primary contact	Email: markus dot stocker at tib dot eu
Year published	2020
Volume and issue	19(1)
Article #	18
DOI	10.5334/dsj-2020-018
ISSN	1683-1470
Distribution license	Creative Commons Attribution 4.0 International
Website	https://datascience.codata.org/articles/10.5334/dsj-2020-018/
Download	https://datascience.codata.org/articles/10.5334/dsj-2020-018/galley/962/download/ (PDF)

Abstract

Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent, and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments, which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures, and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and the BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyze adoption and consolidate the schema by addressing new stakeholder requirements.

Keywords: persistent identification, instruments, metadata, DOI, handle

Introduction

Between March 2018 and October 2019, the Research Data Alliance (RDA) Working Group (WG) Persistent Identification of Instruments (PIDINST) explored a community-driven solution for globally unambiguous and persistent identification of operational scientific measuring instruments. A "measuring instrument" is understood to be a “device used for making measurements, alone or in conjunction with one or more supplementary devices,” as defined by the Joint Committee for Guides in Metrology (JCGM). Hence, PIDINST chose to address the problem of persistently identifying the devices themselves (i.e., each unique device), the real-world assets with instantaneous capabilities and configurations, rather than the identification of material instrument designs (i.e., models).

Instruments are employed in numerous and diverse scientific disciplines. Instruments can be static (e.g., weather station, laboratory instrument) or mobile when mounted on moving platforms (e.g., remotely operated underwater vehicles, drones). They may be used in observation or experimentation research activities. They may be owned and operated by individual researchers, research groups, national, international, or global research infrastructures or other types of institutions. For instance, at the time of writing, the Integrated Carbon Observation System (ICOS) operates approximately 3,000 instruments at over 130 stations in 12 European countries. Astronomy is well known for their intense use of telescopes. Those working in the life sciences employ an array of instrument types, ranging from microscopes to sequencers. The engineering sciences, too, make heavy use of instruments.

Persistent identifiers (PIDs) have a long tradition for the globally unique identification of entities relevant to or involved in research. They were developed “to address challenges arising from the distributed and disorganised nature of the internet, which often resulted in URLs to internet endpoints becoming invalid,” (Klump and Huber, 2017) making it difficult to maintain a persistent record of science. Examples for well established persistent identifiers include:

• the digital object identifier (DOI), used to identify literature, data files, and other objects;
• the Open Researcher and Contributor ID (ORCID), a persistent identifier for identifying researchers;
• the International Geo Sample Number (IGSN), a persistent identifier for physical samples and sample collections;
• the Research Organization Registry (ROR), a persistent identifier for organizations; and
• the Research Resource Identifier (RRID), an identifier for physical resources, such as mice and antibodies, in the life sciences.

Borgman suggested that “to interpret a digital dataset, much must be known about the hardware used to generate the data, whether sensor networks or laboratory machines.” Borgmann also highlights that “when questions arise […] about calibration […], they sometimes have to locate the departed student or postdoctoral fellow most closely involved.” A persistent identifier for instruments would enable research data to be persistently associated with such crucial metadata, helping to set data into context. Moreover, discovering and retrieving an instrument’s metadata through resolvable identifiers aligns with the FAIR data management principles, a set of guiding principles for the management of research data and its metadata by making them findable, accessible, interoperable, and reusable. Buck et al. suggested that data provenance information is fundamental to a user’s trust in data and any data products generated. They also recommended persistent identifiers for instruments as one of the next levels of data interoperability required to better understand and evaluate our oceans. Thus, more broadly, FAIR metadata about instruments is critical in the scientific and research endeavors.

In addition to improving the FAIRness of instrument metadata, the persistent identification of instruments is also important for trusted cross-linking to valuable scientific objects, such as the research data they produce, which can be persistently identified themselves. A similar argument can be made for cross-links between instruments and literature since instruments (typically the instrument model) are generally mentioned in the literature as materials. Such cross-linking has received considerable attention in the community. The Scholix project and the corresponding RDA/WDS Scholarly Link Exchange (Scholix) WG have recently proposed and implemented a common schema to standardize the exchange of information about the links between literature and data. As a result, it is now easier for a data publisher that discovers a link between data and literature to share this information, and for the publisher of the article to benefit by establishing a cross-link from literature to data. With the PID Graph, the FREYA Project is now generalizing cross-linking literature and data to other entities, including people, organizations, funders, etc. Arguably it makes good sense to enrich these connections by adding instruments.

Currently, there is no globally implementable way to persistently identify measuring instruments. Addressing this challenge, the present article describes the results of the work conducted by PIDINST, an 18-month RDA Working Group project that aimed at establishing a cost-effective, operational solution based on existing PID infrastructures, combined with a robust metadata schema for accurate identification, retrieval, and automation into workflows. The solution was demonstrated with two institutional instrument providers.

Methodology

The PIDINST Case Statement specified the working group (WG) objectives and deliverables. The WG took an agile-type (empirical and iterative) approach, engaging with members and stakeholders through virtual and physical RDA Plenary meetings to ensure the results met with requirements. PIDINST operated following the methodology described in more detail in this section, summarized as follows:

1. Collect use cases.
2. Identify common metadata.
3. Develop and publish the schema, and implement community feedback to its versions.
4. Catalyze schema implementation by existing PID infrastructure.
5. Prototype adoption by existing institutional instrument providers.
6. Engage the wider community at RDA Plenaries.
7. Hold regular biweekly virtual meetings.

PIDINST began with collecting use cases describing how a particular stakeholder would benefit from persistent identification of instruments. Use case descriptions included an introduction to the domain and infrastructure, related work by the infrastructure (if applicable), and a table describing the required properties of instrument metadata associated with the persistent identifier. The metadata properties were described for their name, occurrence, definition, value datatype, and an indication whether properties should be in metadata held by the PID infrastructure or the institutional instrument provider, for instance on the landing page.

Building on the use cases—in particular, the table describing the required metadata properties—PIDINST identified, organized, and harmonized the metadata properties that were common across use cases. We tabulated metadata properties as reported in use cases, harmonized their names (e.g., "Identifier," "Instrument Identification," and "Persistent Identifier" were harmonized as "Persistent Identifier"), counted property occurrence, and grouped properties into 10 categories that emerged from the metadata analysis (i.e., were not predefined).

Given the identified common metadata, PIDINST iteratively developed a schema and obtained community feedback, particularly at RDA Plenaries. The first version was presented at the RDA 12th Plenary Meeting (Gaborone, November 2018). Following suggestions from that discussion, the properties ownerContact, ownerIdentifier, ownerIdentifierType, manufacturerIdentifier, manufacturerIdentifierType, and modelName were added to the schema. The revised version was presented at the RDA 13th Plenary Meeting (Philadelphia, April 2019) and finally at the RDA 14th Plenary Meeting (Helsinki, October 2019). Each revision took into account community feedback at RDA Plenaries, as well as issues posted on GitHub.

Having developed and published a metadata schema, PIDINST initiated discussions on schema implementation with existing PID infrastructures, in particular DataCite and ePIC. The discussions, held at RDA Plenaries and in virtual meetings, aimed to (1) create awareness among these infrastructures about PIDINST developments and (2) catalyse implementation. In addition to implementation by existing PID infrastructures, PIDINST also actively supported the adoption by existing institutional instrument providers through engaging institution representatives at RDA Plenaries and in virtual meetings. Several institutions have shown interest in implementing the proposed solution (discussed later in "Adoption"), and some have already taken concrete steps (next section).

PIDINST had its kick-off meeting at the RDA 11th Plenary Meeting (Berlin, March 2018) and had working sessions at each subsequent Plenary until the 14th Plenary Meeting (Helsinki, October 2019), where the group had its wrap-up session. The working sessions were generally well attended by a highly engaged audience. The wider community feedback informed and validated the developments. The work was conducted between Plenaries, and coordination, as well as discussion, was supported by biweekly open participation virtual meetings. PIDINST continues to maintain its deliverables and will be represented at future Plenaries.

Results

Between November 2017 and October 2018, the WG collected 14 use cases. An additional use case was submitted in February 2019, resulting in a total of 15, of which 14 included the table describing the required metadata and are thus considered complete. The majority of use cases are in the earth sciences (60%). Table 1 provides an overview of the collected use cases. All use cases for which we have obtained author permission to publish are available on GitHub.

Table 1. Overview of the use cases collected by RDA WG PIDINST. "Submission" is the month of first use case submission by authors to the WG. "Completed" is the month during which the use case was completed with the required metadata. In some instances, the metadata was provided later. For UC4, the authors didn’t provide the metadata; the use case was thus not completed (N/A). For UC1, UC5, UC8, and UC15 the metadata was provided after October 2018; these use cases were thus not considered in the metadata analysis. * = Use cases used in analysis on October 2018 Use case Title Domain Main author Submission Completed 1 GEOFON Global Seismic Network Earth sciences Quinteros, J. 11/2017 03/2019 2 * Helmholtz-Zentrum Berlin für Materialien und Energie Multidisciplinary Krahl, R. 11/2017 06/2018 3 * National Imaging Facility, Australia Multidisciplinary Tapat, V. 12/2017 06/2018 4 Institute for Electromagnetic Sensing of the Environment (CNR) Earth sciences Oggioni, A. 01/2018 N/A 5 Sensor Information System (AWI) Earth sciences Macario, A. 04/2018 12/2018 6 * Marine Sensor Web Enablement Working Group Earth sciences Huber, R. 05/2018 05/2018 7 * ORCID Publisher Demeranville, T. 05/2018 08/2018 8 Integrated Carbon Observation System Carbon Portal Earth sciences D'Onofrio, C. 06/2018 12/2018 9 * British Oceanographic Data Centre Earth sciences Darroch, L. 07/2018 07/2018 10 * European Southern Observatory Astronomy Bordelon, D. 08/2018 08/2018 11 * Forschungszentrum Jülich, Central Library (literature supplier for large-scale research facilities) Publisher Frick, C. 09/2018 09/2018 12 * PANGAEA, Data Publisher for Earth & Environmental Science Publisher Devaraju, A. 09/2018 09/2018 13 * Permanent Service for Mean Sea Level Earth sciences Darroch, L. 10/2018 10/2018 14 * LTER-Europe Earth sciences Oggioni, A. 10/2018 10/2018 15 UK Polar Data Centre Earth sciences Tate, A. 02/2019 02/2019

Performed in October 2018, we used the metadata of 10 then-completed use cases (marked with a ^* in Table 1, Column 1) in an analysis that identified, organized, and harmonized the common properties. We tabulated properties, harmonized their names, counted property occurrence, and grouped properties into the following 10 categories: Identification, Instrument, Model, Owner, Manufacturer, Date, Capability, Output, Related Instrument, and Publisher. Table 2 summarizes the analysis of metadata common to the use cases.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The original article lists references in alphabetical order; however, this version lists them in order of appearance, by design. All footnotes—which are simply URLs—from the original article were turned into either external links or full citations for this version.