Looking back over 2024, we wanted to reflect on where we are in meeting our goals, and report on the progress and plans that affect you - our community of 21,000 organisational members as well as the vast number of research initiatives and scientific bodies that rely on Crossref metadata.
In this post, we will give an update on our roadmap, including what is completed, underway, and up next, and a bit about what’s paused and why.
The Crossref2024 annual meeting gathered our community for a packed agenda of updates, demos, and lively discussions on advancing our shared goals. The day was filled with insights and energy, from practical demos of Crossrefâs latest API features to community reflections on the Research Nexus initiative and the Board elections.
Our Board elections are always the focal point of the Annual Meeting. We want to start reflecting on the day by congratulating our newly elected board members: Katharina Rieck from Austrian Science Fund (FWF), Lisa Schiff from California Digital Library, Aaron Wood from American Psychological Association, and Amanda Ward from Taylor and Francis, who will officially join (and re-join) in January 2025.
Background The Principles of Open Scholarly Infrastructure (POSI) provides a set of guidelines for operating open infrastructure in service to the scholarly community. It sets out 16 points to ensure that the infrastructure on which the scholarly and research communities rely is openly governed, sustainable, and replicable. Each POSI adopter regularly reviews progress, conducts periodic audits, and self-reports how theyâre working towards each of the principles.
In 2020, Crossrefâs board voted to adopt the Principles of Open Scholarly Infrastructure, and we completed our first self-audit.
In June 2022, we wrote a blog post âRethinking staff travel, meetings, and eventsâ outlining our new approach to staff travel, meetings, and events with the goal of not going back to ânormalâ after the pandemic. We took into account three key areas:
The environment and climate change Inclusion Work/life balance We are aware that many of our members are also interested in minimizing their impacts on the environment, and we are overdue for an update on meeting our own commitments, so here goes our summary for the year 2023!
In the scholarly communications environment, the evolution of a journal article can be traced by the relationships it has with its preprints. Those preprintâjournal article relationships are an important component of the research nexus. Some of those relationships are provided by Crossref members (including publishers, universities, research groups, funders, etc.) when they deposit metadata with Crossref, but we know that a significant number of them are missing. To fill this gap, we developed a new automated strategy for discovering relationships between preprints and journal articles and applied it to all the preprints in the Crossref database. We made the resulting dataset, containing both publisher-asserted and automatically discovered relationships, publicly available for anyone to analyse.
TL;DR
We have developed a new, heuristic-based strategy for matching journal articles to their preprints. It achieved the following results on the evaluation dataset: precision 0.99, recall 0.95, F0.5 0.98. The code is available here.
We applied the strategy to all the preprints in the Crossref database. It discovered 627K preprintâjournal article relationships.
We gathered all preprintâjournal article relationships deposited by Crossref members, merged them with those discovered by the new strategy, and made everything available as a dataset. There are 642K relationships in the dataset, including:
296K provided by the publisher and discovered by the strategy,
331K new relationships discovered by the strategy only,
15K provided by the publisher only.
In the future, we plan to replace our current matching strategy with the new one and make all discovered relationships available through the Crossref REST API.
Introduction
Relationships between preprints and journal articles link different versions of research outputs and allow one to follow the evolution of a publication over time. The Crossref deposit schema allows Crossref members to provide these relationships for new publications, either as a has-preprint relationship deposited with a journal article, or an is-preprint-of relationship deposited with a preprint.
To assist members who deposit preprints, we also try to connect deposited journal articles with preprints. The current method looks for an exact match between the title and first authors. We send possible matches as suggestions to the preprint server, which decides whether to update the metadata with the relationship.
At the time of writing, 137,837 journal articles in the Crossref database have a has-preprint relationship1, and 562,225 works of type posted-content (preprints belong to this type) have an is-preprint-of relationship2.
We suspected that many preprintâjournal article relationships are missing, as some members inevitably fail to deposit them, even after suggestions from the current matching strategy. Another factor is that the current strategy is fairly conservative, and probably misses a significant number of relationships. For these reasons, we decided to investigate whether we could improve on the current process. Doing so would allow us to infer missing relationships on a large scale, similar to how we automatically match bibliographic references to DOIs.
This preprint matching task can be defined in two directions:
We start with a journal article and we want to find all its preprints.
We start with a preprint and we want to find a subsequently published journal article.
On the one hand, matching from journal articles to preprints would allow us to enrich the database continually with new relationships, either periodically or every time new content is added. Since journal articles tend to appear in the database later than their preprints, it makes sense for a new journal article to trigger the matching and not the other way round. This way we can expect the potential matches to be already in the database at the time of matching.
On the other hand, matching from preprints to journal articles can be useful in a situation where we want to add relationships in an existing database retrospectively. In our case, the database contains many more journal articles than preprints, so for performance reasons it is better to start with preprints.
In both cases we are dealing with structured matching, meaning that we match a metadata record of a work (preprint or journal article), rather than unstructured text.
As a result of matching a single preprint or a single journal article, we should expect zero or more matched journal articles/preprints. Multiple matches occur when:
there are multiple versions of the matched preprint and/or
matched works have duplicates.
The image shows the result of matching a journal article to two versions of a preprint:
Matching strategy
Our matching strategy uses the following workflow:
Gathering a short list of candidates using the Crossref REST API.
Scoring the similarity between the input item and each candidate.
A final decision about which candidates, if any, should be returned as matches.
Gathering candidates is done using the Crossref REST API’s query.bibliographic parameter. The query is a concatenation of the title and authors’ last names of the input item. We filter the candidates based on their type, to leave only preprints or only journal articles, depending on the direction of the matching. In the future, instead of getting the candidates from the REST API, we will be using a dedicated search engine, optimised for preprint matching.
Scoring candidates is heuristic-based. Similarities between titles, authors, and years are scored independently, and the final score is their average. Titles are compared in a fuzzy way using the rapidfuzz library. Authors are compared pairwise using the ORCID ID, or first/last names if ORCID ID is not available. The similarity score between issued years is 1 if the article was published no earlier than one year before the preprint and no later than three years after the preprint, or 0 otherwise.
The final decision is made based on two parameters: minimum score and maximum score difference, both chosen based on a validation dataset. The following diagram depicts the results of applying these two parameters in all possible scenarios. First, any candidate scoring below the minimum score is rejected (grey area in the diagram). Second, the scores of the remaining candidates are compared with the score of the top candidate. If the score of a candidate is close enough to the score of the top candidate, it is returned as a match (blue area).
This process can result in the following scenarios:
Scenario A: there is no candidate above the minimum score. This means nothing matches sufficiently, so nothing is returned.
Scenario B: there is only one candidate above the minimum score. This means it is the best match and we don’t have much of a choice, so it is returned.
Scenario C: there are multiple candidates above the minimum score, and they all have similar scores. This means they all are similarly good matches, so all are returned.
Scenario D: there are multiple candidates above the minimum score, but their scores differ a lot. In this case, we don’t want to return all of them, but only those that are close to the top match. Intuitively, we don’t want to return less-than-great matches if we have really great ones. This is when the maximum score difference comes into play: we return the candidates with the âscore distanceâ to the top candidate lower than the maximum score difference.
We evaluated this strategy on a test set sampled from the Crossref metadata records. The test set contains 3,000 pairs (journal article, set of corresponding preprints). Half of the journal articles have known preprints and the other half don’t. The test set can be accessed here.
We used precision, recall, and F0.5 as evaluation metrics:
Precision measures the fraction of the matched relationships that are correct.
Recall measures the fraction of the true relationships that were matched.
F0.5 combines precision and recall in a way that favours precision.
The strategy achieved the following results: precision 0.9921, recall 0.9474, F0.5 0.9828. The average processing time was 0.96s.
We have investigated other approaches to making decisions about which candidates to return as matches (step 3 above), including using machine learning. At present none have outperformed the heuristic approach described above. The heuristic method is also preferred because of its fast performance.
Preprintâjournal article relationship dataset
We applied the strategy to the entire Crossref database:
We selected all preprints published until the end of August 2023. This included only works with type posted-content and subtype preprint, as reported by the REST API. There were 1,050,247 of them.
We ran the matching strategy (preprint -> journal article) on them. This resulted in 627,011 preprintâjournal article relationships.
The resulting relationships were combined with the relationships deposited by the Crossref members. We included relationships of types has-preprint or is-preprint-of, where both sides of the relationship exist in our database, were published until the end of August 2023, and are of proper types and subtypes (type=journal-article for the journal article and type=posted-content, subtype=preprint for the preprint).
The resulting dataset is a single CSV file with the following fields:
preprint DOI (string)
journal article DOI (string)
whether the publisher of the journal article deposited this relationship (boolean)
whether the publisher of the preprint deposited this relationship (boolean)
the confidence score returned by the strategy (float, empty if the strategy did not discover this relationship)
The dataset contains:
641,950 relationships in total, including 580,532 preprints and 565,129 journal articles,
14,939 of them were deposited by the Crossref members, but not discovered by the strategy,
330,826 of them were discovered by the strategy, but not provided by any Crossref member,
296,185 of them were both deposited by a Crossref member and discovered by the strategy.
Overall, based on the number of existing and newly discovered preprintâjournal article relationships, it seems that employing automated matching strategies would approximately double the number of these relationships in the Crossref database. In the future, we would like to match new journal articles on an ongoing basis. We also plan to make all discovered relationships available through the REST API.
In the meantime, we will be publishing the discovered relationships in the form of datasets, and we invite anyone interested to further analyse this data. And if you find out something interesting about preprints and their relationships, do let us know!
