Digital Libraries Papers

ALT The rapid growth of computer science (CS) papers increases the work needed to review research and find relevant studies. The growing use of AI tools in scientific writing makes it increasingly important to identify the authors behind this growth. Previous studies have tracked publication growth and followed researchers' careers, documenting the increasing number of authors and the many researchers who publish only briefly. However, annual averages of papers per author reflect different sets of researchers and may hide changes among the same authors. This study addresses this gap by linking individual changes to overall CS paper growth using arXiv, DBLP, and conference submission records for 2010-2025. With each paper divided equally among its authors, the total paper count equals the number of authors multiplied by their average papers per author. This relationship provides a decomposition of the two contributions of paper growth. This decomposition attributes nearly all growth in CS p

ALT Background: Despite the growth of bibliometric analyses, few studies offer guidance on how to report them, resulting in a lack of research transparency and completeness. To address this gap, we developed the Guidance List for the repOrting of Bibliometric AnaLyses (GLOBAL). Methods: An initial list of items for the GLOBAL was generated through a scoping review and refined through a modified two-round Delphi, as recommended by the EQUATOR Network. The guideline was finalized through a pilot test, where participants assessed their manuscripts using the checklist and Elaboration and Explanation (E&E) document, which outlines item expectations and provides examples. Results: The scoping review resulted in 32 items. In Round 1, 24 items reached consensus and thematic content analysis resulted in one additional item. This item, along with the eight items that did not reach consensus, were discussed in detail in Round 2. During the in-person meeting, one item was split into two, totalling ten

ALT The complexity of high-quality 3D digitised cultural heritage objects creates challenges for existing data management systems as they need to develop metadata management and processing capabilities to provide semantic insight into the interconnectivity of data that constitutes cultural heritage objects. To address these challenges, we propose a global federated authentication and authorisation mechanism, a data and metadata management system, and an integrated engine for designing and executing automated workflows that facilitate the processing of both data and metadata. The solution is evaluated with three distinct 3D digitised cultural objects and presents the complete process from data upload to cultural heritage object publication

ALT LitUbik is an open-source, self-hosted tool for conducting systematic reviews, from the registered protocol to the PRISMA 2020 diagram. LitUbik covers calibrated search equations, automated searching, deduplication, blinded multi-reviewer screening, full-text retrieval, snowballing, appraisal, extraction and reporting, on one server or across disconnected instances. At each stage, optional AI assistance from commercial or local language models offers proposals that a named reviewer accepts or rejects. Every AI call is logged, so the AI-use disclosure is written from a record. A systematic review of 31 papers on review tools, conducted in LitUbik, compares it with existing tools and illustrates its use. LitUbik is distributed under AGPLv3.

ALT Large language model (LLM) scores are being studied as information for scholarly evaluation. Interpreting these scores requires evidence about agreement with human judgments and its variation across evaluation conditions. Guided by an argument-based approach to validation, we compare four LLMs on 7,083 public ICLR 2024 submissions under a standard review condition and examine ten prompt conditions using DeepSeek-V3.2. We distinguish average score divergence, paper-level absolute error, and rank correlation, then examine domain differences and adjusted manuscript-feature associations. For DeepSeek-V3.2, Area Chair guidance reduces mean rating divergence from 1.460 to 0.199 points and mean absolute error from 1.577 to 0.942, while rank correlation changes from 0.377 to 0.387. Historical references combined with downward scoring instructions yield mean divergence of 0.052 and rank correlation of 0.321. Domain differences remain after global calibration. Longer titles have positive adjuste

ALT Official texts are corrected after publication: EU corrigenda, verified RFC errata, retractions. We measured whether these corrections are present in four public datasets used to train language models, and what models answer about the corrected passages. In EurlexResources, a legal training dataset, 1,989 of 2,082 testable single-language corrections to EU acts (95.5%, 94.6-96.3) remain in their erroneous form; in a random sample of 100, 46 change the meaning, a figure, a cross-reference, the competent authority or the scope (20 without scope). At least 124 corrected EU-law errors and at least 60 verified RFC errata (38 classified "Technical") are reproduced in their erroneous form in FineWeb-2 and in a sample of FineWeb. About 12,200 retracted papers (11,700-12,500) are present in the peS2o corpus, 79% with no mention of the retraction; separately, 53% of a random sample of retracted papers are still cited at least a year after retraction (OpenAlex). Asked 59 questions on serious corr

ALT Understanding who researchers collaborate with, when and how their research groups form, and how these evolve over time is a foundational prerequisite for constructing multidimensional, evidence-based researcher fingerprints – a requirement that has become increasingly urgent in the context of ongoing reforms in research evaluation advocated by initiatives such as DORA, the Leiden Manifesto, and CoARA. In this paper, we propose an egocentric approach for fine-grained research group detection, operating at the scale of individual researchers' ego networks, as extracted from Scientific Knowledge Graphs (SKGs), and requiring the ego node to be present in all identified research groups – a constraint that is not merely technical but reflects a commitment to researcher-centred analysis. Our approach builds on the Leiden algorithm as its computational core and is grounded in SKGs, which provide the heterogeneous, open scholarly metadata necessary to trace collaboration patterns at scale in a

ALT Tools that claim to detect emerging research topics are rarely judged on their own lists. Our main contribution is a protocol that makes such judgement possible. A bet freezes four things before its verdict can be known: the date of the photograph, the population of candidates, the verdict rule, and the comparison with chance drawn among the same candidates. The list has a fixed length, is read on two axes (champions and failures) against a computed ceiling, and is sealed by cryptographic fingerprint; sealing proves that a list existed at a date, not that it is right. A topic is a champion if its yearly volume of records triples within three years and reaches 100, a failure if it falls by 30%. We apply the protocol to one detector on PubMed/MEDLINE, in three separate blocks. On a retrospective benchmark (2017-2022), chance among the candidates yields 26.7 champions and 15.1 failures out of 120 slots; the detector yields 85 and 3, and under nine pre-registered variants of the verdict ru

ALT In library catalogues based on MARC21 bibliographic metadata schema there are about a dozen data elements that catalogers can utilize to record information about the different aspects of translations: whether or not the edition is a translation, source and target language, title of the original work, translator and other contributors, publication data of the original work, literary genre. Libraries however rarely explore the full arsenal, thus to find translation is not a trivial task. The paper describes these important MARC21 data elements, and shows a use case of finding translations in five catalogues (of the Norwegian, Croatian, Polish, Belgian and Dutch national libraries) explaining the differences among the practices, and the results.

ALT With the growth of scientific output, the peer review system is approaching a breaking point: reviewers are increasingly hard to secure, and turnaround times lengthen accordingly. LLM-assisted or LLM-generated peer review has been proposed as a remedy, but how such models would treat manuscripts already published in top-tier journals, the closest thing the current system has to a gold standard of scientific validity, is not known. Here we assembled a corpus of 200 articles, each released under a CC BY 4.0 license and published in 2026, from 20 general-science, life-science, and clinical-medicine journals including Nature, Science, and Cell, and asked four current LLMs (two proprietary, two open-weight) to produce independent peer-review reports of each one under a blinded protocol. Their decisions were overwhelmingly harsh and starkly inconsistent with each other: 605 of 800 reviews (75.6%) recommended major revision or rejection, but the rate at which any single model did so ranged fr

ALT Business students beginning a research project must learn how to identify relevant scholarly publications, distinguish disciplinary sources, construct database searches, and report the decisions behind their literature reviews. These tasks can be difficult because journal classification systems and bibliographic databases organize scholarly information differently. This paper introduces Journal Basket, a browser-based tool developed to help business students conduct journal-bounded literature searches and currently available in beta. Journal Basket enables users to construct journal collections from the Australian Business Deans Council (ABDC) Journal Quality List, choose from 42 business discipline presets, entire ABDC fields or the Financial Times FT50 list, apply journal rating thresholds, and generate search expressions for Scopus, Web of Science and Business Source. Users can also inspect and modify journal collections, download journal inventories, and generate methodological des

ALT Reviewer lottery error describes error due to a small set of peer reviews attending to only some of the issues with a paper that a larger pool of eligible reviewers would raise. We show this error can be reduced by surfacing a checklist of criticisms learned from a historical review corpus. Our approach generates a tree of checklists representing coarsenings of the atomic review claim space, then selects the coarsening that maximizes a theoretically-motivated measure of correlated agreement among reviewers. We realize this approach with an LLM-assisted pipeline and test it on NeurIPS 2021 and ICLR 2022 review data. In simulated review with LLM reviewers, the learned checklist reduces the mean squared error (MSE) of a three reviewer committee by 58% on NeurIPS and 21% on ICLR, and on both venues the checklist with the highest correlated agreement has the lowest error for committees of three or more. In a human study with 20 participants, the learned NeurIPS checklist lowers the MSE of a

ALT A knowledge graph needs a stable address for its evidence even where the literature offers competing classifications and agrees on none. Yet an address chosen for retrieval readily passes for the only classification available. This happens whenever records gathered under one name concern units that differ in boundaries, rank, or the criteria by which they are described or counted. In Sinitic dialectology the same localities recur in schemes whose units differ in membership and rank, and a unit may change its affiliation when the evidence for its diagnosis changes. Two tone counts, each quoted faithfully, may still count different things. The taxonomy of an entire field thus comes to rest on the unstable frame of a literature that contradicts itself. Neither verifying a quotation nor searching for conflicting values detects these differences. I distinguish the working address that gathers candidate evidence, the category that the source defines and the decision that admits a passage to

ALT Scientific progress depends on both developing established knowledge and disrupting it to make way for new ideas. Using the disruption index (D), our previous work showed that large teams tend to develop science, while small teams are more likely to disrupt it. Recent work challenges this finding, arguing that D mechanically depends on reference-list length. Using an alternative measure (oneD), it reaches the opposite conclusion: large teams are more disruptive. Combining Nobel Prize case studies, mathematical analysis, and large-scale publication data, we show that oneD misclassifies developmental contributions as disruptive and introduces the very sensitivity to reference-list length that it seeks to eliminate. The original measure, D, however, shows no evidence of the claimed systematic dependence on reference-list length. More fundamentally, even after controlling for reference-list length, larger teams remain more likely to produce strongly developmental work. Our findings reaffir

ALT Large Language Models (LLMs) such as ChatGPT can estimate research quality from article titles and abstracts, and decision makers may increasingly use LLM scores to support research assessment. In this context, it is a concern that LLM scores may be influenced by linguistic signals rather than the quality of the research described. This study investigates whether explicit novelty claims in research abstracts are associated with higher ChatGPT scores. Using 99,245 journal articles submitted to the UK Research Excellence Framework (REF) 2021, ChatGPT scores and percentile rank gains relative to REF departmental average scores (as a proxy for article-level scores) were compared between abstracts with and without any of 282 three-word phrases identified as indicating a novelty claim. Abstracts containing novelty claim phrases had higher mean ChatGPT quality scores in all four main REF research groupings (panels). Moreover, the score difference was higher for the originality dimension of re

ALT The increasing specialization of scientific research calls for fine-grained representations of publication content that can be applied at scale. SCALE extends the OpenAlex taxonomy with approximately 114,000 Scientific Concepts derived from author keywords, but assigning these Concepts to individual publications remains challenging. We introduce MATCH (Multi-stage Article Tagging to Concept Hierarchies), a framework that assigns SCALE Concepts using article titles, abstracts, and author keywords. MATCH combines multi-view embedding retrieval, deterministic taxonomy-aware filtering, and language-model reranking to select relevant Concepts while preserving their hierarchical paths. Its modular design supports both API-based and self-hosted inference. In a blind evaluation involving 22 domain experts across 11 fields and 110 articles, the self-hosted and API-based configurations achieved Precision of 81.5% and 83.3%, respectively, and Precision@5 of approximately 89% for both, with no sta

ALT Scientific document embeddings such as SPECTER2 are optimized for retrieval over large, heterogeneous corpora. On a small specialized corpus their similarity distributions can become highly compressed, which limits their usefulness for exploratory literature mapping. We study whether weakly supervised in-domain adaptation, using only metadata already available in open citation databases, changes this geometry. On ArtCon, a curated corpus of 704 publications on artificial and machine consciousness, we adapt the SPECTER2 proximity model with a lightweight LoRA adapter trained on positive pairs derived from bibliographic coupling in OpenAlex, using no manually annotated similarity labels. We compare the adapted embeddings with lexical (TF-IDF), citation-based (bibliographic coupling) and generic semantic (SPECTER2) representations under thresholded, fixed-density and symmetric k-nearest-neighbour graph constructions. Adaptation substantially decompresses the similarity distribution (mean

ALT This short conceptual paper recalls to the attention of the scientometrics community some long-standing open methodological problems and proposes new directions that might be taken in renewed engagements with them. These problems are the differential dependency of the global reference score on the included units' observed scores and the dilution of impact measurement accuracy due to collaboration, which causes a global convergence in country citation impact due to international collaborative research. Advantages and disadvantages of existing and new solutions are discussed.

ALT Two documents can be close because they cite the same works or because they write about the same things, and the two kinds of proximity often disagree. Science mapping reads them in separate maps or merges them, as hybrid clustering does; either way the disagreement is lost. We introduce multiplex coupling, a framework that couples the same documents by their references and by their terms, keeps the two layers separate and analyses their relation. Similarities are standardised against a null model, and the agreement between the layers is measured on random pairs of documents. Consensus clustering partitions each layer into roots, groups sharing an intellectual base, and themes, groups sharing a topic. A test on the roots × themes table identifies branching roots and convergent themes, and pairs of roots are followed over time. Every structure is reported with its persistence across numbers of neighbours, which on synthetic data identifies most false branching. Applications to Web of Sc

ALT Digital libraries and knowledge graphs need a way to organize very large collections so that related works are close and any work is quick to reach. The classifications libraries inherited for this, such as LCC and DDC, are maintained by editorial staff, are partly closed, and were not built against a stated, measurable objective. We propose the opposite: a general classification that is fitted, under the constraints that make a classification usable on a shelf or in a browse list, to the same quantity that a companion survey and benchmark (Awad, 2026) ranks existing schemes by. The objective is J = D + 0.005 N, where D is the normalized Dasgupta cost of the class tree on a fixed similarity graph (Dasgupta, 2016) and N is an expected navigation cost in bits with a per-level charge. Two constraints are hard: every class is a contiguous run of one fixed shelf order (the optimal-alphabetic-tree constraint of Hu and Tucker, 1971), and new works are filed by splitting leaf classes only, so