The data scientist as a mainstay of the tumor board: global implications and opportunities for the global south by Myles Joshua Toledo Tan, Myles Joshua Toledo Tan, Myles Joshua Toledo Tan, Myles Joshua Toledo Tan, Myles Joshua Toledo Tan, Myles Joshua Toledo Tan, Myles Joshua Toledo Tan, Daniel Andrew Lichlyter, Nicholle Mae Amor Tan Maravilla, Weston John Schrock, Weston John Schrock, Frederic Ivan Leong Ting, Frederic Ivan Leong Ting, Frederic Ivan Leong Ting, Joanna Marie Choa-Go, Joanna Marie Choa-Go, Joanna Marie Choa-Go, Kishi Kobe Francisco, Mickael Cavanaugh Byers, Hezerul Abdul Karim, Nouar AlDahoul

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Health Care Professionals and Data Scientists’ Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study by Joana Seringa, Anna Hirata, Ana Rita Pedro, Rui Santana, Teresa Magalhães

“…ObjectiveThis study aimed to explore the perspectives of health care professionals and data scientists regarding the relevance, challenges, and potential benefits of using machine learning (ML) models to predict decompensation from patients with HF. …”
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Agent-based risk analysis model for road transportation of dangerous goods by Hassan Kanj, Ajla Kulaglic, Wael Hosny Fouad Aly, Mutaz A.B. Al-Tarawneh, Khaled Safi, Sawsan Kanj, Jean-Marie Flaus

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Boosting any learning algorithm with Statistically Enhanced Learning by Florian Felice, Christophe Ley, Stéphane P. A. Bordas, Andreas Groll

“…Abstract Feature engineering is of critical importance in the field of Data Science. While any data scientist knows the importance of rigorously preparing data to obtain good performing models, only scarce literature formalizes its benefits. …”
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Understanding game data work by Heikki Tyni, Olli Sotamaa, Taina Myöhänen

“…These new needs and functions have generated emerging forms of work, such as those of the data analyst, data engineer, and data scientist. Through in-depth interviews with 20 Finnish game industry professionals and an analysis of game industry job advertisements, this paper examines the work and identity of game industry data workers. …”
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Creating a Health Data Marketplace for the Digital Health Era by Imtiaz Khan, PhD, Mohamed A. Maher, Moderator: Anjum Khurshid, PhD

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ViT-DualAtt: An efficient pornographic image classification method based on Vision Transformer with dual attention by Zengyu Cai, Liusen Xu, Jianwei Zhang, Yuan Feng, Liang Zhu, Fangmei Liu

“…Experiments were conducted using the nsfw_data_scrapper dataset publicly available on GitHub by data scientist Alexander Kim. Our results demonstrated that ViT-DualAtt achieved a classification accuracy of 97.2% ± 0.1% in pornographic image classification tasks, outperforming the current state-of-the-art model (RepVGG-SimAM) by 2.7%. …”
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Genetic Polymorphisms in RNF138, ABCA1 and ESRRG-GPATCH2 Genes and their Role in Insulin Resistance Risk among Normal BMI Individuals in Indian Population: A Case-control Study by Sabitha Thummala, Sarah Fathima, Nithya Kruthi, Junaid Ahmed Khan Ghori, Katherine Saikia, Vivek Belde, Balamurali Andiyakkal Rajendran, Rahul Ranganathan

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More than data repositories: perceived information needs for the development of social sciences and humanities research infrastructures by Anna Sendra, Elina Late, Sanna Kumpulainen

“…Findings reveal that developing an infrastructure for conducting data-intensive research is a complicated task influenced by contrasting information needs between social sciences and humanities scholars and computer and data scientists, such as the demand for increased support of the former. …”
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Artificial intelligence in healthcare: A focus on the best practices by Haddiya Intissar, Ramdani Sara

“…Our presentation emphasizes the importance of establishing robust best practices within healthcare institutions and fostering collaboration among clinicians, data scientists, patients, and policymakers. Through careful consideration and ongoing refinement of AI technologies, we can leverage its potential to improve patient outcomes while upholding ethical standards and public health priorities.…”
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Refusing participation: hesitations about designing responsible patient engagement with artificial intelligence in healthcare by Flora Lysen, Sally Wyatt

“…We review the critiques of the critiques, themselves motivated by the wish to contribute, and not to leave the field solely to computer- and data scientists. In the final section, we express our doubts about the possibilities for developing positive, generative interventions, and explore ‘refusal’ and ‘hesitation’ as forms of critique and engagement. …”
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RADENN: A Domain-Specific Language for the Rapid Development of Neural Networks by Israel Pineda, Dustin Carrion-Ojeda, Rigoberto Fonseca-Delgado

“…All these features make RADENN an ideal tool for Data Scientists, Data Analysts, Big Data Engineers, Software Enginers, and anyone who needs a fast and efficient way to create prototypes and models without extensive programming or deep learning knowledge. …”
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TAPS Responsibility matrix: a tool for responsible data science by design by Visara Urovi, Remzi Celebi, Chang Sun, Parveen Kumar, Linda Rieswijk, Michael Erard, Arif Yilmaz, Kody Moodley, Michel Dumontier

“…When analyzing these data, data scientists implicitly agree to follow the rules governing these fields. …”
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A dataset of Uniswap daily transaction indices by network by Nir Chemaya, Lin William Cong, Emma Jorgensen, Dingyue Liu, Luyao Zhang

“…Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.…”
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‘We have opened a can of worms’: using collaborative ethnography to advance responsible artificial intelligence innovation by Andrés Domínguez Hernández, Richard Owen

“…We report on an interdisciplinary collaboration between science and technology studies scholars and data scientists developing an AI system to detect online misinformation. …”
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How data science and AI-based technologies impact genomics by Jing Lin, Kee Yuan Ngiam

“…However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. …”
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Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It by Yasir Hafeez, Khuhed Memon, Maged S. AL-Quraishi, Norashikin Yahya, Sami Elferik, Syed Saad Azhar Ali

“…<b>Background:</b> Artificial intelligence (AI) has recently made unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of designing and developing computer aided diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adoption and integration into the healthcare system still seems far-fetched. …”
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Using Gap Visualization to Navigate Multivariate Metabarcode Data, Select Primer Pairs, and Enhance Reference Data Quality by Xin‐Yi Chua, Louise Ord, Stephen J. Bent, David Lovell, Annette McGrath

“…We show how these visualization methods can enable amplicon survey study design and make fundamental molecular resources more accessible to a wider research audience beyond bioinformaticians and data scientists.…”
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pyAKI-An open source solution to automated acute kidney injury classification. by Christian Porschen, Jan Ernsting, Paul Brauckmann, Raphael Weiss, Till Würdemann, Hendrik Booke, Wida Amini, Ludwig Maidowski, Benjamin Risse, Tim Hahn, Thilo von Groote

“…It provides a standardized data model and a comprehensive solution for consistent AKI classification in research applications for clinicians and data scientists working with AKI data. The pipeline's high accuracy make it a valuable tool for clinical research and decision support systems.…”
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Measuring the Impact of the COVID-19 Pandemic on Diagnostic Delay in Rare Disease by Caitlin Hampson, William Evans, Lucy McKay, Lara Menzies

“…A cross-sector multi-stakeholder coalition was formed, Action for Rare Disease Empowerment (ARDEnt), with representation from patients with rare diseases and carers, patient advocacy groups, clinicians, academics, data scientists, and industry. A mixed methods approach was used to collect and collate information about the impact of the pandemic on diagnostic delay in rare disease. …”
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