23 papers · ranked by Valyu relevance
Lorraine Buis, Kara Burns, Tomer Sagi, Sylvia Cho + 3 more
Background There is a growing interest in using person-generated wearable device data for biomedical research, but there are also concerns regarding the quality of data such as missing or incorrect data. This emphasizes the importance of assessing data quality before conducting research. In order to perform data…
Lisa Ehrlinger, Wolfram Wöß
High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent tasks like data integration or data analytics. However, from a…
Flavia Serra, Verónika Peralta, Adriana Marotta, Patrick Marcel
The importance of context in data quality (DQ) was shown many years ago and nowadays is widely accepted. Early approaches and surveys defined DQ as fitness for use and showed the influence of context on DQ. This paper presents a Systematic Literature Review (SLR) for investigating how context is taken into account in…
Yuhan Zhou, Fengjiao Tu, Kewei Sha, Junhua Ding + 1 more
—Machine learning (ML) technologies have become substantial in practically all aspects of our society, and data quality (DQ) is critical for the performance, fairness, robustness, safety, and scalability of ML models. With the large and complex data in data-centric AI, traditional methods like exploratory data analysis…
Alramzana Nujum Navaz, Mohamed Adel Serhani, Hadeel T. El Kassabi, Ikbal Taleb + 3 more
'Ikbal Taleb' 'Flavio Bertini' 'Rahimeh Rouhi' 'Enrique Lopez Droguett'] Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the…
Daniel Schwabe, Katinka Becker, Martin Seyferth, Andreas Klaß + 1 more
'Tobias Schaeffter'] The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients’ lives. While trustworthiness concerns…
Sedir Mohammed, Hazar Harmouch, Felix Naumann, Divesh Srivastava
Data-oriented applications, their users, and even the law require data of high quality. Research has broken down the rather vague notion of data quality into various dimensions, such as accuracy, consistency, and reputation, to name but a few. To achieve the goal of high data quality, many tools and techniques exist to…
Minsik Lim, Doyeon An, Nayeong Son, Woongsang Sunwoo + 2 more
Background Data quality is the degree to which data are fit for their intended purpose and is described using quality dimensions. The increased use of medical data in clinical research and medical artificial intelligence development has rendered data quality assessment essential. Despite existing data quality…
Agate Jarmakovica
Healthcare data quality is a critical factor in clinical decision-making, diagnostic accuracy, and the overall efficacy of healthcare systems. This study addresses key challenges such as missing values and anomalies in healthcare datasets, which can result in misdiagnoses and inefficient resource use. The objective is…
Heidi Carolina Tamm, Anastasija Nikiforova
Rule Definition in Data Warehouses Authors: ['Heidi Carolina Tamm' 'Anastasija Nikiforova'] Abstract: In the contemporary data-driven landscape, ensuring data quality (DQ) is crucial for deriving actionable insights from vast data repositories. The objective of this study is to explore the potential for automating data…
Authors not listed
Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However…
Markus Matoni, Arno Kesper, Gabriele Taentzer
The digital transformation of our society is a constant challenge, as data is generated in almost every digital interaction. To use data effectively, it must be of high quality. This raises the question: what exactly is data quality? A systematic literature review of the existing literature shows that data quality is a…
Sijie Dong, Soror Sahri, Themis Palpanas
Data Science Systems Authors: ['Sijie Dong' 'Soror Sahri' 'Themis Palpanas'] Artificial intelligence (AI) has transformed various fields, significantly impacting our daily lives. A major factor in AI's success is high-quality data. In this paper, we present a comprehensive review of the evolution of data quality (DQ)…
Tom M Toner, Paul Miller, Thorsten Forster, Helen G Coleman + 1 more
Integration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships…
Thomas Naake, Johannes Rainer, Wolfgang Huber
Multiple factors can impact accuracy and reproducibility of mass spectrometry data. There is a need to integrate quality assessment and control into data analytic workflows. The MsQuality package calculates 40 low-level quality metrics based on the controlled mzQC vocabulary defined by the HUPO-PSI on a single mass…
Yu-Chieh Huang, Pierre Tremouilhac, Stefan Kuhn, Pei-Chi Huang + 6 more
A method for data review in chemical sciences with a focus on data for the characterization of synthetic molecules is described. As current procedures for data curation in chemistry rely almost exclusively on manual checking or peer reviewing, a (semi-)automatic procedure for the evaluation of data assigned to…
Jingcheng Yang, Yaqing Liu, Jun Shang, Qiaochu Chen + 13 more
The implementation of quality control for multiomic data requires the widespread use of well-characterized reference materials, reference datasets, and related resources. The Quartet Data Portal was built to facilitate community access to such rich resources established in the Quartet Project. A convenient platform is…
Yojana Gadiya, Tooba Abbassi-Daloii, Vassilios Ioannidis, Nick Juty + 5 more
While awareness of FAIR (Findable, Accessible, Interoperable, and Reusable) principles has expanded across diverse domains, there remains a notable absence of impactful narratives regarding the practical application of FAIR data. This gap is particularly evident in the context of in-vitro and in-vivo experimental…
Benedetta Banzi, Dario Righelli, Matteo Marchionni, Oriana Romano + 3 more
Quality control (QC) is a critical step in the analysis of imaging-based single-cell spatial omics data, yet standardized metrics tailored to these technologies are still lacking. Most existing QC approaches are adapted from single-cell sequencing workflows and rely on fixed thresholds, limiting their ability to…
Benjamin Neely, Yasset Perez-Riverol, Magnus Palmblad
The past decade has seen widespread advances in quality control (QC) materials and software tools focused specifically on mass spectrometry-based proteomics, yet the rate of adoption is inconsistent. Despite the fundamental importance of QC, it typically falls behind learning new techniques, instruments, or software.…
Shelley M. Fischer, Michael K. Joy, Wokje Abrahamse, Taciano L. Milfont + 1 more
Composite indices have been widely used to rank the environmental performance of nations. Such environmental indices can be useful in communicating complex information as a single value and have the potential to generate political and media awareness of environmental issues. However, indices that are poorly constructed…
Authors not listed
The precision of thermodynamic modeling for ionic liquid (IL)–solute systems is fundamentally reliant on the quality of experimental data. However, prevalent databases such as ILThermo frequently exhibit conflicting measurements for the same systems under identical temperature and pressure conditions. These disparities…
Amir Ali Moinfar, Fabian J. Theis
Deep generative models have become central to single-cell omics analysis, but their latent spaces remain difficult to interpret biologically. Linear factor models offer dimension-wise interpretability, but often lack the nonlinear flexibility, scalability, and integration quality required for large multi-batch atlases.…