A new open-source project published in the journal SoftwareX aims to change that. Developed by Yasiel Pérez Vera, the software presents an ecosystem of three interoperable plugins for Open Journal Systems, the widely used open-source platform that powers tens of thousands of scholarly journals worldwide. Rather than building yet another DOI workflow, the ecosystem implements two alternative persistent identifier schemes that carry no recurring registration costs: the Archival Resource Key, known as ARK, and the Persistent Uniform Resource Locator, or PURL. Both schemes offer decentralized, openly governed approaches to persistent identification. ARK identifiers support long-term stewardship through locally managed namespaces and resolver services, while PURLs provide stable redirection mechanisms that preserve identifier persistence independently of changes in the physical location of resources. The design philosophy is explicitly complementary rather than competitive: the ecosystem is not intended to replace DOI infrastructure, but to give journals that cannot sustain registration fees a flexible, sustainable alternative they can manage directly.

The software engineering story behind the project is as interesting as its economics. Conventional implementations of persistent identifiers in publishing platforms tend to tightly couple two very different responsibilities: generating identifiers and resolving them, meaning the act of translating an identifier into the current location of the resource it names. The new ecosystem deliberately separates these concerns. Identifier generation is handled by two plugins, one for ARKs and one for PURLs, each of which extends the native PubIdPlugin interface provided by Open Journal Systems. Identifier resolution, by contrast, is delegated to a third, independent component called the Resolver Gateway Plugin. This gateway receives incoming requests, identifies which identifier scheme is being used, retrieves the associated publication metadata from the platform’s database, and redirects the user to the appropriate resource. Because the gateway is generic, it operates independently of any specific identifier scheme, which means new identifier technologies can be added in the future simply by writing an additional publication identifier plugin that reuses the existing resolution infrastructure.

This architectural decision carries real practical consequences. Because all three components rely exclusively on the official plugin framework and extension mechanisms of Open Journal Systems, no modifications to the platform’s source code are required. Journals can install, update, or remove each component independently using the standard plugin management facilities, and the installation order of the plugins does not affect functionality. The ARK Plugin generates identifiers based on configurable Name Assigning Authority Numbers, object type prefixes, and local identifier patterns, storing them within the standard metadata model so they interact seamlessly with existing export, indexing, and publication services. Validation mechanisms prevent duplicate assignments and guarantee uniqueness within each journal namespace. The PURL Plugin follows the same principles, maintaining persistent identifiers separately from publication URLs so administrators can move content to new locations without invalidating existing citations. Crucially, both plugins can coexist within the same journal installation without conflict.

For journal administrators, the practical workflow is deliberately lightweight. To issue ARK identifiers, an institution first obtains a Name Assigning Authority Number from the ARK Alliance through an online registration form, a process that requires no fee. Once the namespace and resolver configuration are in place, the administrator configures the plugin through the standard settings interface, specifying the namespace, identifier pattern, target publication objects, and resolver URL. For PURLs, the administrator selects or operates a PURL resolution service and configures the corresponding namespace and generation policy. After that initial setup, everything happens automatically: whenever an article, issue, galley, or supplementary file is published through the normal editorial workflow, the appropriate plugin generates and validates an identifier, stores it in the publication metadata, and exposes it as an actionable link. Editors never need to touch identifier infrastructure again, and readers who click an identifier are transparently redirected to the current location of the content.

The validation of the software was conducted with unusual rigor for a publishing tool. The ecosystem was deployed on a dedicated server configured to replicate a typical institutional publishing infrastructure, hosting three scientific journals comprising approximately thirty published issues and 360 research articles. The functional validation protocol covered installation, configuration, automatic identifier generation, metadata persistence, and resolution through the gateway, with every test repeated thirty times under identical conditions. All twelve functional test cases passed consistently, and the validation processed 1,181 publication objects across every supported entity type, including journals, issues, articles, galleys, and supplementary files, without a single plugin failure or manual intervention. The tests also confirmed that all three plugins could operate simultaneously, demonstrating genuine interoperability rather than isolated functionality.

Performance measurements were equally encouraging. Using Apache JMeter 5.6.3 and the Linux time utility, the researchers measured the runtime overhead introduced by identifier generation and resolution across thirty independent executions of each operation. ARK generation took a mean of 15.8 milliseconds, PURL generation 14.7 milliseconds, identifier resolution 27.4 milliseconds, and complete HTTP redirections 31.2 milliseconds, with success rates of 100 percent for generation and 99.8 percent for resolution and redirection. These latencies are low enough that identifier management remains entirely transparent to editors and readers, an essential property for software that must sit inside a live editorial workflow without slowing it down. The deployment procedure itself was reproduced from clean installations of Open Journal Systems using only the official plugin installation mechanism, with no manual database changes, satisfying reproducibility expectations aligned with the FAIR principles for research software.

Perhaps the most striking evidence of the project’s relevance comes from its adoption. At the time of evaluation, 42 scientific journals across multiple countries had already deployed the ecosystem in production, with publicly verifiable, resolvable ARK and PURL identifiers confirmed through direct inspection of journal websites and live resolution requests. This is not a laboratory prototype but software supporting real editorial workflows on three continents. A comparison with conventional persistent identifier solutions for Open Journal Systems highlights the gap it fills: existing approaches typically depend on external registration agencies such as Crossref or DataCite, usually require recurring fees, offer limited support for multiple free identifier schemes, and lack an independent resolver component. The new ecosystem requires no external registration agency, imposes no recurring costs, supports self-hosted deployment, and remains fully extensible through the standard plugin framework, all while preserving compatibility with DOI-based solutions for journals that use both.

The broader significance extends beyond any single journal’s budget. Persistent identifiers are no longer valued merely for keeping citations alive; they increasingly serve as the connective tissue linking publications, datasets, software, researchers, institutions, and funders through interoperable, machine-actionable metadata, underpinning discovery, provenance tracking, reproducibility, and long-term preservation. When thousands of journals, particularly those in the Global South and the Diamond Open Access movement, operate outside the identifier ecosystem, the entire scholarly graph develops holes. By lowering the technical and economic barriers to participation, this open-source ecosystem, released under the GNU GPL v3 license with complete documentation and versioned releases in public repositories, offers a route toward a more complete and equitable identifier infrastructure. The author outlines future work including support for additional identifier schemes, improved interoperability with external scholarly infrastructures, automated testing and continuous integration pipelines, and validation across newer platform releases and larger publishing environments, positioning the software as a sustainable, community-extensible foundation for the next generation of scholarly communication.

Subject of Research: An open-source plugin ecosystem providing free ARK and PURL persistent identifiers for Open Journal Systems scholarly publishing

Article Title: An open-source ecosystem for implementing free persistent identifiers in open journal systems

Article References: Pérez Vera, Y. (2026). An open-source ecosystem for implementing free persistent identifiers in open journal systems. SoftwareX, 36, Article 103023. https://doi.org/10.1016/j.softx.2026.103023

Image Credits: AI Generated

DOI: Not provided

Keywords: persistent identifiers, Open Journal Systems, ARK identifiers, PURL, open access publishing, DOI alternatives, open-source software, scholarly communication, FAIR principles, resolver gateway, diamond open access, SoftwareX

The scientific foundation of DQPA rests on two pillars of international standardization. ISO 8000 establishes the principles of data quality management, while the ISO/IEC 33000 family provides the general mechanism for process capability assessment: a process reference model, process attributes rated on an ordinal scale, and a maturity model that aggregates those attributes into organizational levels. Although this mechanism has a long track record in domains such as software development, Green IT, and data quality certification, no international instantiation had ever covered data governance, data management, and data quality management jointly as integrated disciplines. The only ISO instantiation for the data domain, the ISO 8000-6x series, is confined to data quality management alone. The researchers built on a direct precedent, the MAMD model, and on the UNE 0077 through 0080 specifications—what they describe as the first standardization initiative to close that gap with normative status—enriched with the governance principles of ISO/IEC 38505 and the DAMA-DMBOK body of knowledge.

What distinguishes self-assessment from formal certification is its purpose. Under ISO/IEC 33000, assessment by an independent team enables certification with validity toward third parties, while self-assessment, performed by the organization itself, is an equally recognized application of the same method aimed at understanding one’s own situation recurrently and affordably as a basis for continuous improvement. Until now, no adequate instrument has existed for this second application, for two reasons. Independent assessment is too costly to repeat frequently, and its most expensive phase—evidence collection—depends heavily on tacit human knowledge that is difficult to automate. Moreover, translating normative processes into language that business profiles can act upon, one of the very purposes of data governance, rarely occurs in manual practice. Existing frameworks fall short in different ways: COBIT 2019 lacks an integrated data-domain maturity model; DCAM and CMMI-DMM support self-assessment but are not grounded in ISO/IEC 33000; and DAMA-DMBOK systematizes the disciplines without defining a maturity model of its own. None combines integrated coverage, an ISO/IEC 33000-based mechanism, automated scoring, automated recommendations, and open-source availability.

DQPA’s architecture is deliberately engineered around a strict separation between deterministic computation and generative artificial intelligence. The platform is a multilayer web application: a React single-page client, a Node.js and Express server exposing a REST API and hosting the deterministic assessment engine, a MongoDB document store accessed through Mongoose, and an external large language model service invoked only after results have been computed. Authentication is token-based with role-based access control, and both tiers deploy as independent containers. Crucially, the normative model itself—processes, questions, weightings, and improvement tasks—is maintained as configurable data through an administration module restricted to expert users, meaning the platform can adapt to revisions of the specifications or even to equivalent frameworks without touching the source code. This data-driven design is what makes the tool reusable and future-proof in a way that hard-coded assessment instruments cannot be.

The heart of the platform is its assessment engine, which algorithmically reproduces the measurement framework of ISO/IEC 33020. Question answers are first converted into weighted percentage scores at two granularities: one per process, from process-specific capability-level-1 questions, and one per capability level from 2 to 5, from cross-cutting questions shared across the scope. The four-category achievement scale maps onto these scores: Not implemented for 0 to 15 percent, Partially implemented for above 15 to 50 percent, Largely implemented for above 50 to 85 percent, and Fully implemented for above 85 to 100 percent. The staged aggregation rule then applies: a process reaches a given capability level when its process-specific score and all lower cross-cutting scores are Fully achieved and the score at that level is at least Largely achieved. The organizational maturity level, on a six-level scale from 0 to 5, is derived from the consolidated capability of the assessed processes in a staged manner. The entire computation is deterministic, traceable, and executed without any intervention from humans or the AI service—a design choice the authors argue is a property rather than a limitation, because transparency and auditability are explicit requirements of the ISO/IEC 33000 method itself.

Only after the numbers are settled does artificial intelligence enter the picture—and the boundaries are strict. The recommendation service neither trains nor fine-tunes any model. Instead, a pre-trained language model, Gemini 2.0 Flash Lite, selected after a structured comparison against alternatives including GPT-4o and GPT-4.1 nano for its large context window and high throughput, is conditioned at inference time by a purpose-built structured prompt. The model’s grounding is entirely deterministic: its input consists solely of the computed as-is state—levels, ratings, and gaps—and a catalogue of predefined improvement tasks curated by domain experts from an anonymized corpus of real projects, assessment reports, standards, and technical documentation. The prompt explicitly forbids the re-computation of levels and imposes prioritization criteria including impact on maturity, criticality of the gap, dependencies, feasibility, urgency, and normative alignment. If the model call fails, the service degrades gracefully to a template-based ordering of the curated catalogue, so a usable plan is always produced without the generative component.

The platform was validated in striking fashion against a real organization: a Spanish public river basin management body responsible for hydrological data acquisition and exploitation. Questionnaire responses were recorded in parallel through DQPA and through the manual procedure of an external expert assessment team, with each business process owner completing the instrument in roughly two and a half hours with support from assessors. The engine computed a largely achieved process-specific score, a partially achieved level-2 dimension, and an unattained level-3 dimension, yielding maturity level 1—precisely the level determined independently by the human experts. Verification went further: the engine’s logic was exhaustively checked against a reference spreadsheet used by the consulting team in professional practice, across all 1,024 possible rating combinations, with full agreement in every case, including boundary conditions between capability levels.

The quality of the AI-generated recommendations was then scrutinized by four expert evaluators—three of them external to the author team and blind to the study—who rated thirty recommendations on a five-point rubric covering consistency with the computed state, alignment with the specifications, actionability, and clarity for non-technical profiles. The overall mean was 4.35 out of 5, with no two evaluators differing by more than one point on any of the 480 ratings, and the restricted external-only mean of 4.23 confirmed the result does not depend on the internal rater. The platform was also applied to three further organizations—a local public administration, a port authority operating critical infrastructure, and a large private technology corporation—producing consistent operation across markedly different sectors, with resulting maturity levels ranging from 0 to 1. Perceived usability, measured with the System Usability Scale across four participants, averaged 80.6, comfortably above the scale’s commonly cited average of about 68. Performance testing showed the deterministic engine computing results in a median of 105 milliseconds and sustaining 150 concurrent users, while end-to-end report generation took a median of three seconds, dominated by the external model call.

The implications reach well beyond convenience. For public administrations and resource-constrained organizations facing obligations under the European Data Governance Regulation, DQPA substantially lowers the barrier to understanding and improving their data practices without depending on scarce certified assessors. For researchers, the platform’s elimination of inter-assessor variability in the computation phase opens the door to genuinely reproducible, longitudinal, and sector-level empirical study of data maturity—questions such as which processes systematically act as bottlenecks, or how maturity evolves after improvement plans are applied, that have been nearly impossible to address empirically until now. The authors are careful to delimit their claims: the platform does not verify declared evidence, cannot prevent deliberate misstatement, does not replace third-party certification, and its recommendations must be contextualized by each organization since the AI knows nothing of internal budgets or politics. Future work includes ablation studies contrasting catalogue-anchored with unconstrained generation, a self-hosted AI deployment for stricter data-residency control, sector-level benchmarking, and what-if simulation of improvement scenarios under explicit resource constraints. But the core message is already clear: the machinery once reserved for expensive consulting engagements has been reproduced as transparent, inspectable, open-source software that any organization can pick up and run.

Subject of Research: An open-source software platform for ISO/IEC 33000-based self-assessment of data governance, data management, and data quality maturity

Article Title: DQPA: A software platform for ISO/IEC 33000-based self-assessment of data governance, data management, and data quality maturity

Article References: Gualo, F., Ayuso, Y., Caballero, I., & Piattini, M. (2026). DQPA: A software platform for ISO/IEC 33000-based self-assessment of data governance, data management, and data quality maturity. SoftwareX, 35, Article 103012. https://doi.org/10.1016/j.softx.2026.103012

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103012

Keywords: data governance, data quality, data management, maturity models, ISO/IEC 33000, ISO 8000, self-assessment, process capability, large language models, open-source software, SoftwareX, UNE 0080