Scientometrics Using Open Data’s Post

📊 A recent study published by Amanda Heidt in Nature highlights the widespread issue of data distortion in bar charts within life-science papers, where nearly 30% of analyzed papers from 2023 containing common errors like non-zero y-axes or improper logarithmic scales. 💡These distortions, often unintentional, can lead to misinterpretation, especially by non-experts! The study emphasizes the need for improved data literacy and calls for clearer data visualization practices. 🌟 Solution? Researchers Markita Landry and Teng-Jui Lin propose guidelines, including clearer labeling and the use of error bars, to ensure accurate and comprehensible presentation of data for both the scientific community and the public. https://lnkd.in/erTZJq3i

The illusion of information adequacy A new PLOS One study (https://lnkd.in/epJ8wH5h) coined this term to describe people's strong tendency to believe they always have enough data to make an informed decision - regardless of what information they actually have. In the study, participants responded to a hypothetical scenario in which control participants were given full information and treatment participants were given about half the information (about a water issue involving a school). The study found that treatment participants believed they had comparably adequate information and were equally competent to make thoughtful decisions based on that information. In essence, the study shows that people assume they have enough information - even when they lack half of the relevant information. This can be extended to data science, where it is often assumed that the data at hand is sufficient to make decisions, even though assumptions fill in the gaps between data and models (implicitly or explicitly), leading to decisions. We briefly discuss this idea of data centricity at datacentricity.org (and more to come). Image courtesy of learningrabbithole.com. #datascience #decisionscience #datacentricity

I am excited to share a compelling new blog post that delves into the innovative dimensions of "PaCMAP: Large-Scale Dimension Reduction with Both Global and Local Structure". This research presents a novel approach to dimensionality reduction, addressing the complexities of maintaining both global and local structures in large-scale datasets. The findings illustrate the implications of PaCMAP in enhancing data visualization and analysis, making it a pivotal resource for researchers and practitioners in the field. I invite you to read the full post to explore the methodologies and results that can significantly impact how we understand and interpret complex data. You can access the blog post here: https://ift.tt/IzeOaM7.

***Research Data Management (RDM): A Key to Sustainable Research*** As academic researchers, we know that data is the backbone of our work. But what happens to that data after our projects are completed? Effective Research Data Management (RDM) isn't just about organization—it's about ensuring that data remains accessible, reproducible, and usable for future research. By adopting best practices in RDM, we not only enhance the integrity of our work but also contribute to the broader academic community. Let's ensure that our data tells its story long after our papers are published! #ResearchDataManagement #AcademicResearch #DataIntegrity #OpenScience #LibrariansInResearch

We could easily say that we have the #BestResearchers in our #Team who are fully equipped to complete a #PrimaryQuantitative data based #Research #Thesis or paper. Unlike many other research companies who are still relying on #SPSS for such #Analysis, #WriteAtSpot has gone way beyond by using #Amos and #SmartPLS for tackling much-complicated #RegressionModels. Our experts can not only collect data but, if needed, they can improvise it too with the help of our large pool of respondents all around the world. So from #Questionnaire preparation to #FinalAnalysis #Reporting, we will not trouble you at all. #ResearchExcellence #AdvancedAnalysis #GlobalData #InnovativeResearch #AcademicSupport #DataDriven #ResearchSolutions #QuantitativeResearch #SmartPLS #WriteAtSpot #DataScience #ResearchInnovation #AcademicResearch

Getting Insights Using #DataScience Skills and the Scientific Method | 1. Formulate question 2. Generate hypothesis 3. Gather/Generate data 4. Analyze data 5. Communicate results / Take action https://lnkd.in/gDfFAjWs

📊 Unpacking the Fuzzy C-Means (FCM) Algorithm: A Pillar of Fuzzy Clustering🌟 Recently, I revisited one of the most influential papers in the field of clustering: "FCM: The Fuzzy c-Means Clustering Algorithm" by James C. Bezdek, Robert Ehrlich, and William Full. This seminal work introduces the Fuzzy C-Means (FCM) algorithm, a key technique for grouping data when the boundaries between clusters are not clear. 🔑 Key Highlights of the Paper: - Fuzzy Clustering: FCM allows a data point to belong to multiple clusters with varying degrees of membership, better reflecting the complexity and uncertainty of real-world data. - Objective Function: The algorithm minimizes a function based on the distance between data points and centroids, weighted by membership degrees and a fuzzification parameter. - Iterative Process: Membership degrees and centroids are iteratively updated until a stable and optimal classification is achieved. ✨ **Why Is It Important? FCM is fundamental for data analysis in domains where the boundaries between groups are unclear, providing a powerful tool to handle uncertainty in data classification. This paper is a must-read for those interested in advanced clustering techniques and their application in complex data analysis problems. #Clustering #DataScience #FuzzyLogic #MachineLearning #FCM

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📣 Find our newly published article here! 📜 Application of Kirchhoff Migration from Two-Dimensional Fresnel Dataset by Converting Unavailable Data into a Constant 🔗 https://buff.ly/3YzPFzp #Kirchhoff_migration #Fresnel dataset #simulation_results #MSR_matrix #MDPIOpenAccess #ComSciMathMdpi #MathematicsMdpi

Getting Insights Using #DataScience Skills and the Scientific Method | 1. Formulate question 2. Generate hypothesis 3. Gather/Generate data 4. Analyze data 5. Communicate results / Take action https://lnkd.in/gDfFAjWs