AcadWriting.sg : Discourse Structure Analysis

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Discourse Analysis and Information Extraction tools to construct scientific/scholarly knowledge graphs. The focus is on analyzing the information-argument structure of academic text, and on extracting and modeling cause-effect relationships reported in research papers.

What to do next: Paste text in the input boxes on the left, and select coding scheme or extraction type on the menu bar.

Example sociology research Abstract to paste in the input box:

Racial attachments are understood to be socially constructed and endogenous to gender , socioeconomic and religious identities . Yet we know surprisingly little about the effect of such identities on the particular racial labels that individuals self-select . In this article , I investigate how social identities shape the racial labels chosen by biracial individuals in the United States , a rapidly growing population who have multiple labeling options . Examining national surveys of more than 37,000 respondents of Latino-white , Asian-white and black-white parentage , I disentangle how gender , socioeconomic status , and religious identity influence racial labeling decisions . Across biracial subgroups and net of all other influences , economic affluence and Jewish identity predict whiter self-identification , whereas belonging to a religion more commonly associated with racial minorities is associated with a minority identification . Gender , however is the single best predictor of identification , with biracial women markedly more likely than biracial men to identify as multiracial . These findings help us better understand the contextual nature of racial identification and the processes via which social identities interact with racial meanings in the United States .

Example agriculture research Abstract to paste in the input box:

BACKGROUND: Seed-borne diseases have seriously affected the sustainability of sorghum cultivation in China as the demand for organic products in the winemaking industry has limited the use of chemical fungicides.
RESULTS: This study conducted a comprehensive analysis of fungal diversity in sorghum seeds from three major growing regions in Guizhou Province. Using a combination of traditional tissue separation and high-throughput sequencing, we identified Colletotrichum, Fusarium, Cladosporium, and Alternaria as dominant fungi. Pathogenicity tests revealed that strains GD202206, GD202219, and GD202242 were pathogenic and were identified as C. sublineola through morphological and multi-locus phylogeny analysis (ITS、CAPDH、ACT、CHS-1 and TUB2). 16 fungicides for seed priming experiments with sorghum seeds, the results indicated that priming with KHCO₃ significantly enhanced germination of the sorghum seeds, with both indoor and outdoor emergence rates notably higher. Analysis of the fungal changes before and after KHCO₃ priming revealed a significant reduction in the abundance of the Colletotrichum genus. Additionally, KHCO₃ altered the community structure of fungi within the sorghum seeds, reducing population richness. Inter-generic relationships were rebalanced, with antagonism decreased and synergy increased following KHCO₃ treatment. Non-target metabolomic analysis indicated that KHCO₃ enhances sorghum seed germination via the phenylalanine and flavonoid pathways and exhibits antifungal properties through the cyanoamino acid metabolic pathway.
CONCLUSION: This study identified C. sublineola as the primary pathogenic fungus carried by sorghum seeds. KHCO₃ treatment has a dual effect on sorghum seeds: on one hand, it suppresses pathogen transmission by reducing the abundance of the Colletotrichum genus; on the other hand, it promotes germination and seedling emergence, thereby enhancing both germination and emergence rates.

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