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Thinking process templates for constructing data stories with SCDNEY.

ABSTRACT: Background: Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches can assist in accelerating scientific discovery. Methods: We developed a series of living workshops for building data stories, using Single-cell data integrative analysis (scdney). scdney is a wrapper package with a collection of single-cell analysis R packages incorporating data integration, cell type annotation, higher order testing and more. Results: Here, we illustrate two specific workshops. The first workshop examines how to characterise the identity and/or state of cells and the relationship between them, known as phenotyping. The second workshop focuses on extracting higher-order features from cells to predict disease progression. Conclusions: Through these workshops, we not only showcase current solutions, but also highlight critical thinking points. In particular, we highlight the Thinking Process Template that provides a structured framework for the decision-making process behind such single-cell analyses. Furthermore, our workshop will incorporate dynamic contributions from the community in a collaborative learning approach, thus the term 'living'.

SUBMITTER: Cao Y

PROVIDER: S-EPMC10905113 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Thinking process templates for constructing data stories with SCDNEY.

Cao Yue Y Tran Andy A Kim Hani H Robertson Nick N Lin Yingxin Y Torkel Marni M Yang Pengyi P Patrick Ellis E Ghazanfar Shila S Yang Jean J

F1000Research 20231215

<h4>Background</h4>Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches ...[more]

PMID: 38434622

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Project description:IntroductionInvolving service users in health service design and delivery is considered important to improve the quality of healthcare because it ensures that the delivery of healthcare is adapted to the needs of the users. Co-production is a process used to involve service users, but multiple papers have highlighted the need for the mechanisms and values guiding co-production to be more clearly stated. The aim of this paper was to evaluate the mechanisms and values that guided the co-production approach of the Stories for Change project, which used Public Narrative as part of the co-design process to create change in National Health Service maternity services.MethodsThis study was conducted using a rapid feedback evaluation approach. Semistructured interviews (n = 16) were the main source of data, six of which were maternity service users, with observations (5 h) and documentary analysis also carried out in parallel. RREAL sheets were used for data analysis to organize data based on key topics of interest.ResultsThis study identified three broad mechanisms and values underpinning the co-production approach: creating an open and safe space to share ideas, learning how to tell stories using Public Narrative and having service providers who play a key role in strengthening the health system listen to stories compelling them to action. This study identified the main areas for improvement of the Stories for Change project related to recruitment, the inclusion of participants, the co-design process, the Skills Session and the Learning Event.ConclusionOur study provided a deeper understanding of the co-production approach that addresses the need to uncover the mechanism and values underlying co-production and co-design approaches. This study expands on the literature pertaining to the influence of storytelling in creating meaningful change in health care. We propose a co-design methodology that uses Public Narrative as a model for service user engagement to help inform future healthcare development processes.Patient or public contributionThe experiences and perceptions of maternity service users and health professionals informed this evaluation. The project organizers were involved in the manuscript preparation stage by providing feedback, and service users wrote a commentary on the project from the lived experience perspective.

Project description:BackgroundGillespie's stochastic simulation algorithm (SSA) for chemical reactions admits three kinds of elementary processes, namely, mass action reactions of 0th, 1st or 2nd order. All other types of reaction processes, for instance those containing non-integer kinetic orders or following other types of kinetic laws, are assumed to be convertible to one of the three elementary kinds, so that SSA can validly be applied. However, the conversion to elementary reactions is often difficult, if not impossible. Within deterministic contexts, a strategy of model reduction is often used. Such a reduction simplifies the actual system of reactions by merging or approximating intermediate steps and omitting reactants such as transient complexes. It would be valuable to adopt a similar reduction strategy to stochastic modelling. Indeed, efforts have been devoted to manipulating the chemical master equation (CME) in order to achieve a proper propensity function for a reduced stochastic system. However, manipulations of CME are almost always complicated, and successes have been limited to relative simple cases.ResultsWe propose a rather general strategy for converting a deterministic process model into a corresponding stochastic model and characterize the mathematical connections between the two. The deterministic framework is assumed to be a generalized mass action system and the stochastic analogue is in the format of the chemical master equation. The analysis identifies situations: where a direct conversion is valid; where internal noise affecting the system needs to be taken into account; and where the propensity function must be mathematically adjusted. The conversion from deterministic to stochastic models is illustrated with several representative examples, including reversible reactions with feedback controls, Michaelis-Menten enzyme kinetics, a genetic regulatory motif, and stochastic focusing.ConclusionsThe construction of a stochastic model for a biochemical network requires the utilization of information associated with an equation-based model. The conversion strategy proposed here guides a model design process that ensures a valid transition between deterministic and stochastic models.

Project description:Rationale: Stimulation of serotonin 2A (5-HT2A) receptors by lysergic acid diethylamide (LSD) and related compounds such as psilocybin has previously been shown to increase primary process thinking - an ontologically and evolutionary early, implicit, associative, and automatic mode of thinking which is typically occurring during altered states of consciousness such as dreaming. However, it is still largely unknown whether LSD induces primary process thinking under placebo-controlled, standardized experimental conditions and whether these effects are related to subjective experience and 5-HT2A receptor activation. Therefore, this study aimed to test the hypotheses that LSD increases primary process thinking and that primary process thinking depends on 5-HT2A receptor activation and is related to subjective drug effects. Methods: Twenty-five healthy subjects performed an audio-recorded mental imagery task 7 h after drug administration during three drug conditions: placebo, LSD (100 mcg orally) and LSD together with the 5-HT2A receptor antagonist ketanserin (40 mg orally). The main outcome variable in this study was primary index (PI), a formal measure of primary process thinking in the imagery reports. State of consciousness was evaluated using the Altered State of Consciousness (5D-ASC) rating scale. Results: LSD, compared with placebo, significantly increased primary index (p < 0.001, Bonferroni-corrected). The LSD-induced increase in primary index was positively correlated with LSD-induced disembodiment (p < 0.05, Bonferroni-corrected), and blissful state (p < 0.05, Bonferroni-corrected) on the 5D-ASC. Both LSD-induced increases in primary index and changes in state of consciousness were fully blocked by ketanserin. Conclusion: LSD induces primary process thinking via activation of 5-HT2A receptors and in relation to disembodiment and blissful state. Primary process thinking appears to crucially organize inner experiences during both dreams and psychedelic states of consciousness.

Project description:Background Environmental health and other researchers can benefit from automated or semi-automated summaries of data within published studies as summarizing study methods and results is time and resource intensive. Automated summaries can be designed to identify and extract details of interest pertaining to the study design, population, testing agent/intervention, or outcome (etc.). Much of the data reported across existing publications lack unified structure, standardization and machine-readable formats or may be presented in complex tables which serve as barriers that impede the development of automated data extraction methodologies. As full automation of data extraction seems unlikely soon, encouraging investigators to submit structured summaries of methods and results in standardized formats with meta-data tagging of content may be of value during the publication process. This would produce machine-readable content to facilitate automated data extraction, establish sharable data repositories, help make research data FAIR, and could improve reporting quality. Objectives A pilot study was conducted to assess the feasibility of asking participants to summarize study methods and results using a structured, web-based data extraction model as a potential workflow that could be implemented during the manuscript submission process. Methods Eight participants entered study details and data into the Health Assessment Workplace Collaborative (HAWC). Participants were surveyed after the extraction exercise to ascertain 1) whether this extraction exercise will impact their conducting and reporting of future research, 2) the ease of data extraction, including which fields were easiest and relatively more problematic to extract and 3) the amount of time taken to perform data extractions and other related tasks. Investigators then presented participants the potential benefits of providing structured data in the format they were extracting. After this, participants were surveyed about 1) their willingness to provide structured data during the publication process and 2) whether they felt the potential application of structured data entry approaches and their implementation during the journal submission process should continue to be further explored. Conclusions Routine provision of structured data that summarizes key information from research studies could reduce the amount of effort required for reusing that data in the future, such as in systematic reviews or agency scientific assessments. Our pilot study suggests that directly asking authors to provide that data, via structured templates, may be a viable approach to achieving this: participants were willing to do so, and the overall process was not prohibitively arduous. We also found some support for the hypothesis that use of study templates may have halo benefits in improving the conduct and completeness of reporting of future research. While limitations in the generalizability of our findings mean that the conditions of success of templates cannot be assumed, further research into how such templates might be designed and implemented does seem to have enough chance of success that it ought to be undertaken. Data extraction; Automated data extraction; Electronic data extraction manuscript submission; Structured data; Data sharing; Natural language processing (NLP); Semantic authoring; Author opinion; Author feedback; Author willingness; Data summary; Systematic review; Study quality; Study quality reporting; Risk of bias reporting; Study evaluation; Data extraction tool; Data extraction software; Standardized data; Data templates; Author submission requirements; Machine readable; Knowledge translation; Science translation.

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Thinking process templates for constructing data stories with SCDNEY.

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Thinking process templates for constructing data stories with SCDNEY.

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