Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2012.15556

Bibliometric analysis of the world scientific production in Chemical Engineering during 2000-2011. Part 2: Analysis of the 1,000 most cited publications

Ruben Miranda·Esther Garcia-Carpintero
View original source ↗Read source document ↗Export metadata ↓

Abstract

A comprehensive bibliometric analysis of the scientific production of Chemical Engineering area has been carried out using the Web of Science database for the period 2000-2011 through three complementary studies. Part 2 demonstrated a displacement of the most cited publications to the Far East, especially due to China, however, this displacement is less important to that observed for total scientific production (Part 1). United States is still the country with the highest number of articles among the 1,000 most cited (31.5%), largely above what expected from their number of publications, followed by Germany (8.4%) and China (7.5%). The international collaboration, at least globally, seems not being an important issue for producing highly cited papers. In fact, only two from the top 25 most cited papers were international collaborations (8%). Furthermore, a large share of reviews among the 1,000 most cited papers (65%) has been observed. Although the number of institutions with more publications among the most cited in the area are from United States, the two institutions with the highest cited papers are CNRS (France) and CSIC (Spain). The most cited papers are highly concentrated in a few journals: around half of the most cited papers were published in five journals. Generally, the most cited papers are published in journals with high impact factors, however, there is also a significant number of highly cited papers published in journals with low or not having impact factor.

Explore related subjects

cs.DL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ruben Miranda, Esther Garcia-Carpintero. 2020-12-31. Bibliometric analysis of the world scientific production in Chemical Engineering during 2000-2011. Part 2: Analysis of the 1,000 most cited publications. https://arxiv.org/abs/2012.15556

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

From Code Archival to Knowledge Graph: Bridging Software Heritage, COAR Notify and Wikidata

Software is a first-class scientific object, yet validated links between source code and the scholarly record remain largely absent from the Linked Open Data (LOD) cloud, isolating archived artefacts from semantic discovery. This paper presents an end-to-end reconciliation pipeline that harvests, validates, and models publication-to-repository pairs from sources where the link between a paper and its source code is explicit and editorially verified: the software-centric journals JOSS, SoftwareX, and IPOL, together with the reproducibility reports of the SIGMOD Availability and Reproducibility Initiative (ARI). This yields a curated corpus of 4,397 $\langle$DOI, repository-URL$\rangle$ pairs. We design two distinct application profiles grounded in Wikidata classes (one for scholarly articles, one for software instances) aligned with the schema.org and CodeMeta vocabularies. This architectural separation enables rule-based reconciliation at two granularities: lightweight, inline publication references or standalone, first-class Wikidata software nodes equipped with SWHIDs, Software Heritage's content-addressed identifiers. A read-only lookup against Wikidata shows that only 82 of the harvested repositories were already modelled there; human-reviewed batches have since created 4{,}182 new software items cross-linked to their articles. We further show that payloads of the emerging COAR Notify protocol, an external effort we do not develop, map natively onto our input format, so the same backend could later serve a live enrichment stream. Our core contribution is a pair of application profiles that turn Wikidata into a connector between the scholarly record and archived source code; we openly release all code, application profiles, and harvested datasets.

cs.DL↗
arXiv2026

greCAPTCHA: Assessing Understanding as Evidence of Research Authorship Under Generative AI

Conferences, journals, funders, schools, and universities are struggling with a surge of potentially AI-generated submissions from ostensibly human authors, who may not have exercised sufficient human oversight for their manuscripts. In turn, institutions evaluating submissions can no longer reliably credit expertise based solely on authors' names on submitted work. To address this problem, we propose greCAPTCHA, a proctored assessment approach that measures authors' understanding of research manuscripts via the construct of capacity to verify, which we define as the knowledge and reasoning required to critically assess the contents underlying one's contributions to a manuscript. greCAPTCHA generates questions assessing multiple levels of understanding and provides an evaluative report based on authors' responses. Using a prototype implementation, we conduct a user study and semi-structured interviews with $31$ researchers to evaluate greCAPTCHA. Its automated scores predict which papers were or were not authored by study participants with an AUC of $0.90$. Participants reported positive overall experiences with the system and remarked on the appropriate construct validity for author understanding, while also suggesting important changes to be made before deployment. Our results provide initial evidence that greCAPTCHA can assess manuscript-specific understanding under proctored conditions.

cs.DL↗
arXiv2026

Geospatial Metadata Improves Discoverability by Connecting Datasets Across Scientific Disciplines

Research data repositories are essential infrastructure for scientific inquiry and for ensuring that datasets follow FAIR (Findable, Accessible, Interoperable, and Reusable) principles. However, repository reuse depends on the quality and completeness of geospatial and thematic metadata, which researchers generally provide voluntarily. Given limited curation resources, it is unsurprising that even Harvard Dataverse, the world's largest general-purpose research repository, contains many incomplete metadata records. Missing fields represent lost information and reduce interoperability. We find that datasets with more missing metadata receive fewer downstream citations and have fewer resolvable connections to other datasets. The implications are particularly important for geospatial datasets: only 0.3% of research datasets include a bounding box, and most represent archival points rather than complete geographic shapes. Our analysis shows that geospatial metadata helps connect concepts across disciplines. After embedding Harvard Dataverse datasets in a metadata knowledge graph, we find that datasets are twice as likely to connect across scientific disciplines through shared geospatial metadata as through keywords. This suggests that geographic metadata is a more reliable basis for cross-disciplinary interoperability than keyword vocabularies, which often remain discipline-specific. We train and fine-tune a small language model using datasets from Harvard Dataverse. Through geospatial metadata enrichment, we increase the share of datasets from different disciplines connected through metadata elements from 58.5% to 63.2%.

cs.DL↗