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Computational models are complex scientific constructs that have become essential for us to better understand the world. Many models are valuable for peers within and beyond disciplinary boundaries. However, there are no widely agreed-upon standards for sharing models. This paper suggests 10 simple rules for you to both (i) ensure you share models in a way that is at least "good enough," and (ii) enable others to lead the change towards better model-sharing practices. [ABSTRACT FROM AUTHOR]

Effective communication is vital for academic project success, particularly in multidisciplinary teams with diverse backgrounds and disciplines. Misunderstandings can arise from differing interpretations of terms, which may go unnoticed. VisDict aims to bridge this gap by creating a visual dictionary within a science gateway to facilitate clear communication between workflow providers and domain researchers. This innovative approach translates computational science concepts into fields like physics and biology. This article delves into our method for building the visual dictionary, the insights gained from curating initial entries, and future plans for automated expansion and illustration of relevant terms. [ABSTRACT FROM AUTHOR]

Researchers and educators in humanities such as computational linguists, digital humanists, and those doing historical reconstructions are increasingly heavy users of computational and/or data resources. Many know about activities, working groups, and initiatives around the findable, accessible, interoperable, reusable (FAIR) principles and are a driving force for improving the sharing of data and software. However, it seems humanities researchers are less aware of the science gateways community and the end-to-end solutions that science gateways could provide, therefore lacking a driving force for adoption of this technology. This small-scale exploratory study will clarify some of the challenges and needs faced by computational researchers in the humanities that may explain their relatively low participation in the science gateways community. For this paper, we present the results of 19 interviews with seven women, 11 men, and one nonbinary individual from seven states and DC in the United States with a range of professional backgrounds and roles. [ABSTRACT FROM AUTHOR]

Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year—with 180,000 resulting deaths—mostly in sub-Saharan Africa. Surprisingly, little is known about the ecological niches occupied by C. neoformans in nature. To expand our understanding of the distribution and ecological associations of this pathogen we implement a Natural Language Processing approach to better describe the niche of C. neoformans. We use a Latent Dirichlet Allocation model to de novo topic model sets of metagenetic research articles written about varied subjects which either explicitly mention, inadvertently find, or fail to find C. neoformans. These articles are all linked to NCBI Sequence Read Archive datasets of 18S ribosomal RNA and/or Internal Transcribed Spacer gene-regions. The number of topics was determined based on the model coherence score, and articles were assigned to the created topics via a Machine Learning approach with a Random Forest algorithm. Our analysis provides support for a previously suggested linkage between C. neoformans and soils associated with decomposing wood. Our approach, using a search of single-locus metagenetic data, gathering papers connected to the datasets, de novo determination of topics, the number of topics, and assignment of articles to the topics, illustrates how such an analysis pipeline can harness large-scale datasets that are published/available but not necessarily fully analyzed, or whose metadata is not harmonized with other studies. Our approach can be applied to a variety of systems to assert potential evidence of environmental associations. Author summary: We expand the utility of Natural Language Processing (NLP), backtracking through metabarcodes, utilizing papers that may not mention our subject of interest, C. neoformans, in a departure from usual text analysis methods. We confirm that C. neoformans is associated with decomposing wood which is reinforced by the inferred literature studied here on C. neoformans and its close congeneric relatives. This work demonstrates the potential utility of pairing NLP with single-locus metagenetic data for the study of Neglected Tropical Diseases. While the results of this article are largely confirmatory, we present a novel method to study the ecological niches of rare pathogens that leverages the immense amount of data available to researchers in the NCBI Sequence Read Archive (SRA) combined with a text-mining analysis based on Natural Language Processing. We demonstrate that text processing, noun identification, and verb identification can play an important role in analyzing a large corpus of documents together with metagenetic data. Forging this connection requires access to all of the available ecological 18S ribosomal RNA and Internal Transcribed Spacer NCBI SRA datasets. These datasets use metabarcoding to query taxonomic diversity in eukaryotic organisms, and in the case of the Internal Transcribed Spacer, they specifically target Fungi. The presence of specific species is inferred when diagnostic 18S or ITS gene region sequences are found in the SRA data. We searched for C. neoformans in all 18S and ITS datasets available and gathered all associated journal articles that either cite the SRA data accessions or are cited in the SRA data accessions. Published metagenetic data often have associated metadata including: latitude and longitude, temperature, and other physical characteristics describing the conditions in which the metagenetic sample was collected. These metadata are not always presented in consistent formats, so harmonizing study methods may be needed to appropriately compare metagenetic data as commonly required in metanalysis studies. We present an analysis which takes as input articles associated with SRA datasets that were found to contain evidence of C. neoformans. We apply NLP methods to this corpus of articles to describe the niche of C. neoformans. Our results reinforce the current understanding of C. neoformans's niche, indicating the pertinence of employing an NLP analysis to identify the niche of an organism. This approach could further the description of virtually any other organism that routinely appears in metagenetic surveys, especially pathogens, whose ecological niches are unknown or poorly understood. [ABSTRACT FROM AUTHOR]

The article focuses on proliferation of institutional repositories (IRs) and mentions that disciplinary, professionals society and university depend on IRs for dissemination of scholarly research projects. Topics discussed include delivery under platform as service (PAAS) contracts from which repositories are evolving, encourage the use of cloud services and running application to render particular data formats.

Success in global surgery interventions cannot be claimed until consistent long-term follow up is achieved and corresponding outcomes are studied. However, post-operative outcomes remain inconsistently collected and analyzed in the setting of global surgery, with current efforts largely focused on the delivery of surgical care. Barriers in low- and middle-income countries include patient cost and distance, low surgical workforce capacity, poor patient health literacy, lack of affordable technology availability, inconsistent documentation, and structural deficiencies. Here, we suggest that future work can be focused on (1) enhancing systems to facilitate long-term follow up and care, (2) expanding availability and adoption of electronic medical record systems, and (3) collaboration with local surgeons in the development of international cross-organizational registries and standardized quality measures. Long-term collaborations between local healthcare administrators and providers, policymakers, international bodies, nonprofit organizations, patients, and the private sector are necessary to build and sustain processes to achieve reliable long-term follow up and rigorous data collection, with the goal of ultimately ensuring better patient outcomes. [ABSTRACT FROM AUTHOR]