Find research datasets worth reusing

opencc-by-4.0Aug 2018View details →

FIG. 11 in Monstrosities from the Sea. Taxonomy and tradition in Conrad Gessner's (1516-1565) discussion of cetaceans and sea-monsters

FIG. 11. — Monstrum marinus, sea-monster. Conrad Gessner (1558: 522). Bayerische Staatsbibliothek München, Rar. 2234.

FIG. 11 in Entre tradition classique et imaginaire germano-celtique: les monstres anthropomorphes des mers septentrionales, au Moyen Âge et au début de l'époque moderne

FIG. 11. — Sirène. Chapiteau de la cathédrale de Bâle, avant-choeur, fin XIIe siècle (cliché P. Heman, Bâle).

FIG. 9 in Entre tradition classique et imaginaire germano-celtique: les monstres anthropomorphes des mers septentrionales, au Moyen Âge et au début de l'époque moderne

FIG. 9. — Ondine? Sirène? Psautier dit de Charlemagne, Saint-Riquier, 800. (Paris, BnF, lat. 13159,f. 13v). © Bibliothèque nationale de France, Paris.

FIG. 7 in Entre tradition classique et imaginaire germano-celtique: les monstres anthropomorphes des mers septentrionales, au Moyen Âge et au début de l'époque moderne

FIG. 7. — Monachus maris. Thomas de Cantimpré, De natura rerum. Nord de la France, 1275-1290 (Valenciennes, B.M. 320, f. 117) © Bibliothèque municipale de Valenciennes.

FIG. 4 in Entre tradition classique et imaginaire germano-celtique: les monstres anthropomorphes des mers septentrionales, au Moyen Âge et au début de l'époque moderne

FIG. 4. — Zytiron. Hortus sanitatis, Mayence, 1491 (Bruxelles, KBR, Inc. B. 229). © Bibliothèque royale de Belgique, Bruxelles.

FIG. 2 in Entre tradition classique et imaginaire germano-celtique: les monstres anthropomorphes des mers septentrionales, au Moyen Âge et au début de l'époque moderne

FIG. 2. — Gladius. Westminster Abbey Bestiary, York,c. 1270-1290. (Abbaye de Westminster, Bibl., 22, f. 44). © Dean and Chapter of Westminster.

FIG. 1 in Entre tradition classique et imaginaire germano-celtique: les monstres anthropomorphes des mers septentrionales, au Moyen Âge et au début de l'époque moderne

FIG. 1. — Serra (au-dessus) et Gladius (en-dessous). Brunet Latin, Le livre du Trésor. France,c. 1310-1320 (Saint Petersbourg,B.N., fr. f.v.III,4, f. 45v). D'après Heck & Cordonnier 2011: 536.

FIG. 8 in Entre tradition classique et imaginaire germano-celtique: les monstres anthropomorphes des mers septentrionales, au Moyen Âge et au début de l'époque moderne

FIG. 8. — Episcopus marinus. Sluperius, Omnium fere gentium, nostraeque aetatis nationum, habitus et effigies, et in eosdem epigrammata. Anvers, 1572: 47, 48. (cliché Rémy Cordonnier, BASO, Saint-Omer).

opencc-by-4.0Oct 2020View details →

Supplementary Materials for Genomic insight into Pediococcus acidilactici HN9, a potential probiotic strain isolated from the traditional Thai-style fermented beef Nhang

Figure S1: Subsystem information of the HN9 based on the RAST annotation server,  Figure S2: Phylogenetic analysis of Pediococcus acidilactici HN9 and other strains at the species level, Figure S3: Sequence alignment of Enterolysin A from Pediococcus pentosaceus ATCC 25745 with Enterolysin A identified in the HN9,  Figure S4: Identification of bacteriocin-encoding genes from all bacterial strains in the genus Pediococcus,  Table S1: The information of strain used in this study,  Table S2: Metadata of all strains used in this study.

opencc-by-nc-4.0Sep 2024View details →

Traditional Chinese Medicine Multidimensional Knowledge Graph

<h3>Overview of the Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG)</h3> The Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG) is a comprehensive, open-source data platform developed by Jingqi Zeng in November 2024. This platform aims to integrate and standardize a vast array of data from multiple sources, encompassing both traditional Chinese medicine (TCM) and modern biomedical sciences. By organizing and linking this diverse information, TCM-MKG acts as a bridge that connects the ancient wisdom of TCM with contemporary medical research and applications. <h3>Key Features and Objectives:</h3> <ul> <li> Multi-source Data Integration: TCM-MKG consolidates data from over 30 authoritative resources, covering a broad spectrum of topics, including TCM terminology, Chinese patent medicines (CPM), Chinese herbal pieces (CHP), natural products (NP), chemical components, disease targets, and more. These data sources are carefully curated and interlinked, ensuring a rich, multi-dimensional view of TCM in relation to modern biomedical research. The platform incorporates data from reputable databases such as DrugBank, BioGRID, DisGeNET, STRING, and many others, ensuring that the TCM knowledge is not only expansive but also scientifically robust and cross-referenced with global biomedical standards. </li> <li> Standardized Design for Global Interoperability: TCM-MKG adheres to international data standards and integrates with widely-used global medical classification systems such as ICD-11, UMLS, MeSH, and DOID. This ensures that the platform’s data is globally comparable and facilitates easy integration with international research efforts, promoting collaboration and knowledge exchange across the fields of TCM and modern medicine. </li> <li> Open Source and Collaborative: In line with its mission to enhance transparency and accessibility, TCM-MKG is open-sourced in a structured tabular format. This allows researchers worldwide to freely access, contribute to, and expand upon the data, fostering interdisciplinary collaboration and accelerating innovation in both TCM research and modern medicine. </li> <li> Advanced Analytical Capabilities: By leveraging the power of knowledge graph technology and graph-based intelligence algorithms, TCM-MKG supports deep data mining and relational reasoning. Researchers can uncover hidden associations between TCM components, diseases, and targets, providing insights into the mechanisms of herbal interactions and offering new pathways for drug discovery and therapeutic research. </li> </ul> <h3>Personal Research Application:</h3> Using the TCM-MKG platform, I conducted a study titled "Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicine." This research applied advanced graph intelligence algorithms to quantitatively assess the compatibility mechanisms of Chinese herbal formulas (CHF). The study provides fresh insights into the underlying principles of TCM herbal combinations. This research has been published: Zeng, J., & Jia, X. (2025). Quantifying compatibility mechanisms in traditional Chinese medicine with interpretable graph neural networks. Journal of Pharmaceutical Analysis, 101342. <a href="https://doi.org/10.1016/j.jpha.2025.101342">https://doi.org/10.1016/j.jpha.2025.101342</a> The code and methodology for this research have been open-sourced and are available on <a href="https://github.com/ZENGJingqi/GraphAI-for-TCM" target="_new" rel="noopener">GitHub</a>. <h3>Acknowledgments:</h3> This work benefited from the integration of data from numerous open-access and authoritative databases. We acknowledge the valuable contributions of resources such as DrugBank, BindingDB, BioGRID, DisGeNET, and many others. These datasets provided essential insights into TCM, modern drug chemistry, genetics, diseases, and related fields, forming the foundation for the traditional Chinese medicine multi-dimensional knowledge graph (TCM-MKG) used in this study. Furthermore, we utilized the PSICHIC model (https://github. com/huankoh/PSICHIC) to analyze the binding interactions between components and targets. Full citations for these resources are included. <h3>Contact Information:</h3> For further inquiries or more detailed information, please feel free to contact: Email: <a rel="noopener">zjingqi@163.com</a>

opencc-by-4.0Sep 2024View details →

Structured Literature Review on Traditional Approaches in Current Natural Lanugage Processing

These files contain the foundation for the structured literature review (SLR) on traditional models, approaches and techniques in Natural Language Processing. In order to reproduce the results obtained in this SLR, we have uploaded two files: <ul> <li>slr_traditionalmodelsNLP.xlsx: The search strings as well as applied filters used in the SLR can be found in this attached Excel-file. Furthermore, the results of the SLR are also located in this Excel file.</li> <li>urls.txt Additional URLs for a quantitative analysis are located in this text-file.</li> </ul>

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Traditional medicinal use is linked with apparency, not specialized metabolite profiles in the order Caryophyllales

A better understanding of the relationship between plant specialized metabolism and traditional medicine has the potential to aid in bioprospecting and untangling of cross-cultural use patterns. However, given the limited information available for metabolites in most plant species, understanding medicinal use-metabolite relationships can be difficult. The order Caryophyllales has a unique pattern of lineages of tyrosine- or phenylalanine-dominated specialized metabolism, represented by mutually exclusive anthocyanin and betalain pigments, making Caryophyllales a compelling system to explore the relationship between medicine and metabolites by using pigment as a proxy for tyrosine- vs phenylalanine-dominant metabolism. We compiled a list of medicinal species in select tyrosine- or phenylalanine-dominant families of Caryophyllales (Nepenthaceae, Polygonaceae, Simmondsiaceae, Microteaceae, Caryophyllaceae, Amaranthaceae, Limeaceae, Molluginaceae, Portulacaceae, Cactaceae, and Nyctaginaceae) by searching scientific literature until no new medicinal uses were recovered. We then tested for phylogenetic clustering of uses using a "hot nodes" approach. To test potential non-metabolite drivers of medicinal use, like how often humans encounter a species (apparency), we repeated the analysis using only North American species across the entire order and performed phylogenetic generalized least squares regression (PGLS) with occurrence data from the Global Biodiversity Information Facility (GBIF). We hypothesized families with tyrosine-enriched metabolism would show clustering of different types of medicinal use compared to phenylalanine-enriched metabolism. Instead, wide-ranging, apparent clades in Polygonaceae and Amaranthaceae are overrepresented across nearly all types of medicinal use. Our results suggest that apparency is a better predictor of medicinal use than metabolite profile, although metabolism type may still be a contributing factor.

opencc-zeroJan 2024View details →

F I G U R E 3 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches

F I G U R E 3 Comparison of trait-based metrics expressed in relative number of individuals computed from eDNA () and traditional electro-fishing (TEF;) samples in the five river stretches (RS), A, B, C, D and E. Trait categories: BEN, benthic; EUR, eurytopic; INS, insectivorous; OMN, omnivorous; PHY, phytophilic; POT, potamodromous; RHE, rheophilic; TOL, tolerant; PEL pelagic. Significance of the differences between eDNA and TEF metrics are shown: ns, not significant (P> 0.05)

F I G U R E 4 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches

F I G U R E 4 Boxplots (, median value;, interquartile range;, full range;, outliers) showing the variability in the eDNAadapted fish index (six metrics) computed at three sites (Brangues, Rhins and Usses) where 10 eDNA water samples were collected once. At each site, the eDNA-based six-metric fish index was computed for each of the 45 possible pairs of samples

F I G U R E 2 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches

F I G U R E 2 Comparison of trait-based metrics expressed in number of species computed from eDNA () and traditional electrofishing (TEF;) samples in the five river stretches (RS), A, B, C, D and E. Trait categories: BEN, benthic; EUR, eurytopic; INS, insectivorous; OMN, omnivorous; PHY, phytophilic; POT, potamodromous; RHE, rheophilic; TOL, tolerant; PEL pelagic. Significance of the differences between eDNA and TEF metrics are shown: P <0.05; P <0.01; ns, not significant (P> 0.05)

F I G U R E 1 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches

F I G U R E 1 Sampling locations along river stretches (RS) A to E () of the main channel of the Rhône River, France, using both traditional electro-fishing (TEF) and eDNA., Sites sampled every 2 months (September 2015– August 2016);, sites where ten eDNA water samples (filtration capsules) were collected once;, sites located on the tributaries or the Rhône River itself sampled once for eDNA. The 10 metric fish index and the adapted six-metric fish index were computed at all sites with a black filled symbol within natural water bodies

opencc-by-4.0Feb 2016View details →

Fig. 2. Species accumulation curves for 45 in Mammal inventories in Seasonal Neotropical Forests: traditional approaches still compensate drawbacks of modern technologies

Fig. 2. Species accumulation curves for 45 sampling days for each method used to sample mammals in Serra do Japi Biological Reserve, JundiaÍ, State of SÃo Paulo, Brazil in July and August 2009 and January and February 2010.

opencc-by-4.0Feb 2016View details →

Fig. 1 in Mammal inventories in Seasonal Neotropical Forests: traditional approaches still compensate drawbacks of modern technologies

Fig. 1. Serra do Japi Biological Reserve location (SÃo Paulo, Brazil), and camera trap (white circles) and transect placement within the reserve limits. Thin line: limits of REBIO Serra do Japi. Satellite image from Google Earth®.