Bibliographic data: e.g., DOI, ISSN
Author/researcher data: e.g., name, country, ORCID
Institutional affiliation data: e.g., Ringgold, ISNI, GRID, ROR
Research funding data: e.g., Open Funder ID, Grant ID
Subject/vocabulary data: e.g., MESH, Thema, SciBite
Research data metadata: e.g., supplementary material, DOI, additional taxonomies, credit IDsCRIS System providers:
Pure (Elsevier), Converis (Clarivate), DSpace-CRIS
PIDs:
ORCID, ROR, Ringgold, ISNI, RAiD
Some institutions don’t require researchers to use ORCID; records can be outdated if authors don’t consistently update; ORCID may not be accessible to authors in some geographies.
It’s hard to find content when it isn’t tagged properly with high-quality metadata. Additionally, researchers can have trouble authenticating to literature in the absence of standard IDs.
If authors can’t be identified with a standard ID, they may not be able to authenticate to content, get credited appropriately for their work, secure OA funding, or complete downstream processes without unnecessary manual effort. Costly manual effort is also required of publishers, institutions, and funders to disambiguate authors retrospectively.
Grant management systems:
Altum, GrantHub, Oracle PeopleSoft
PIDs:
ORCID, ROR, Ringgold, ISNI, RAiD, FundRef, grant IDs
Without disambiguated grant and funder details, grants may not be effectively utilized in later publication stages, leaving OA funding unclaimed and shifting coverage to research institutions. In an ecosystem that values a sustainable OA shift, this impacts everyone.
Hindered conflict of interest management among peer reviewers threatens research integrity, and low-quality data results in low accuracy of later-stage funding identification, tracking, and analysis of research output.
Lack of registered grant DOIs makes it difficult and costly to link funding to particular research outputs, resulting in missed OA opportunities as well as incomplete analysis to inform future funding investments.
Variability across grant application process/systems results in possible loss of metadata necessary to determine OA funding entitlements at a later stage, e.g., institutional affiliations.
Researchers depend on a variety of systems to do their work, but the systems don’t always work together. Missing integrations between the systems that researchers use (e.g., CRIS, grant management, curriculum management systems, etc.) often results in gaps in metadata and PID capture.
Low adoption of standardized PIDs (FundRef, RAiD, Ringgold, ISNI, ROR) due to limitations of legacy systems and/or lack of awareness.
Free text fields are great for gathering feedback; they’re not designed to capture granular data like an organizational identifier. Researchers often confuse proposal numbers with grant IDs later in the publication process—they need structure to improve the accuracy of data capture.
There may be points at which the data set is cut down for confidentiality purposes, and possibly lost unintentionally during the review and funding management process.
Valid research coming from under-represented researchers is hard to find due to lack of metadata, including DOIs.
Search and discovery are difficult due to inconsistency in identifying the user and enabling appropriate access to research.
Authors from under-represented areas may not have equitable access to search and discovery services or equitable opportunities for publication.
Though emerging PIDs will include output from conferences, no unique conference identifier exists, making tracking progress hard for funders and institutions.
Integrating with different citation tools using different PIDs makes managing references hard, and sometimes URLs are saved in place of DOIs.
Free-text fields prevail over PID pick-lists, and there’s inconsistent application of PIDs across research outputs e.g., data sets, equipment, setting(s), samples, software.
Researchers receive solicitations from publishers; but some are irrelevant. Better metadata associated with the researcher, their co-authors, the topic and the funding source(s) can help target the right offer to the right researcher, saving publishers time and effort and strenghtening relationships with authors.
Metadata lost upstream makes managing funding compliance onerous.
Global inequities hinder scientific progress.
Inability to easily find, verify, and reuse the data and artifacts underlying research, making it difficult to accurately interpret, cite and reproduce research findings.
Lack of available information about both corresponding author and all co-authors leads to manual input to identify funder and institutional mandates at best and missed funding requirements at worst.
Submission/Peer Review/Production:
ScholarOne, Editorial Manager/ProduXion Manager, Kriyadocs, River Valley, eJournalPress, BenchPress
Contributor roles:
CRediT
DOI registration:
Crossref, DataCite
Peer review tools:
PLOS Peer Review Center, StatReviewer, Publons
Plagiarism technology:
iThenticate, Turnitin
OA funding management:
CCC RightsLink, OA Switchboard, ChronosHub, Oable, SciPris
Digital content development & hosting:
HighWire, SilverChair, Ingenta, Research4Life, Atypon Literatum
Open Access Publishing:
F1000Research, eLife
PIDs:
DOI, ORCID, Ringgold, ISNI, ROR
Authors don’t know their affiliation ID or input secondary versus primary affiliation; too much manual data entry is error prone.
Under-utilization of metadata validation services.
If the researcher has submitted before, outdated information from their existing profile can be pulled into the submission.
Inconsistency between journal policies and metadata procedures.
Lack of funding information captured at submission and validated at acceptance.
Demand for increased interoperability between PIDs.
Without granular, accurate organizational affiliation identifiers for a manuscript, coupled with incomplete funding details, authors may miss the opportunity to get OA funding upon acceptance or miss the chance to opt into OA due to affordability concerns. OA initiatives/deals driven by institutions and funders may lack uptake as a result. Publishers are also unable to automate processes that reduce the cost of business model transformation. Manual effort is required to retrospectively cover the publication with proper funding sources, driving up the cost of publishing. No one benefits in this scenario.
Affiliation information and other metadata that is not consistently captured or validated within the submission system makes it difficult to identify conflicts of interest, monitor compliance with sanctions, etc.
There is an accepted metadata standard for transferring manuscripts, but the quality of data is inconsistent and elements are dropped.
Publishers and service providers are limited in providing systematic support to authors to comply with various funding mandates (e.g., attribution, license type) when funding IDs are missing from the publication workflow.
Affiliations are not static and can change at different points in the process e.g., at the time research was conducted versus the researcher’s current affiliation. This can impact whether the author retains publication rights, and which license governs reuse.
Publishers and institutions take on the time and expense of manually finding the papers that should have matched to an agreement and collaborating on a resolution.
If institution affiliation manually input by the author does not use a standardized name or PID (e.g., abbreviations, nicknames), this can interfere with matching to the correct OA funding source.
Using email address for affiliation identification can impact funding entitlements, especially if the email account is old, the researcher has multiple affiliations, or a personal account is used.
Funder and grant ids are frequently missing from metadata, impacting funding entitlements.
Poor affiliation disambiguation causes authors and institutions to pay one-off APCs that are otherwise eligible for pre-paid deals or discounts, adding unnecessary overhead to billing and reconciliation.
Complex institutional deals (e.g., carveouts) require granular metadata to accurately determine affiliation information for funding eligibility.
Lack of awareness or resources to upgrade JATSXML so there are often data elements dropped at the journal level during production due to incompatibility.
Grant IDs can change between submission and publication. If an extension is granted during the peer-review process, authors don’t always remember to update this information for both articles and data sets.
Limited adoption of standardized protocols and metadata during the production process results in inconsistent metadata and manual data enrichment.
Publishers are sometimes manually entering PIDs prior to registering DOIs for a more complete publication record.
Funder/grant affiliation is essential to later editorial and production workflows to support compliance, and when missing, puts an administrative burden on the author that diverts attention away from core research.
This is a laborious practice with high economic and opportunity costs that could be reduced with earlier, automated PID assertion and/or validation.
Open access / institutional repositories:
Figshare, Dryad, DIGITAL.CSIC, e-IEO, Zenodo, DataCite, CLOCKSS, protocols.io
Institutions are spending time manually curating data for archiving and reporting.
Many datasets don’t have DOIs, making them difficult to find, access, and reproduce.
Article level metrics:
Altmetrics, Panorama
Licensing services:
Creative Commons, CCC MarketPlace, CCC Rightslink, Publishers' Licensing Services
Difficult to track research/researcher impact due to lack of adoption of metadata standards.
Researcher rewards and recognition decisions, or future opportunities for funding, may be based on incomplete or inaccurate data, affecting reputation and career advancement.
Lack of consistent affiliation and funding data makes modeling, implementing, and tracking future agreements hard for institutions and publishers.
Data is not standardized across publisher platforms, creating unnecessary manual work to gather and normalize data for analysis.
The transition to modern models of OA publication is onerous and error-prone, prolonging a mixed-model landscape and the availability of open outputs to advance science.
Difficult to track funder impact due to lack of adoption of metadata standards.
Incomplete data makes it challenging to inform future future funding investments and to accurately report activities to the public.
Difficult to track research impact due to lack of adoption of metadata standards.
Lack of consistent affiliation and funding data makes modelling future agreements difficult for publishers and institutions.
The transition to OA is delayed, putting some publishers at risk of losing authors to funding mandates and losing revenue that is necessary to sustain operations.