Data Discovery Research Articles

Perceptions of Librarians toward the Adoption of Linked Open Data; a Case of Selected University Libraries in Uganda

The study aimed at exploring the perception of librarians toward adopting Linked Open Data in Uganda, a case of selected university libraries. An exploratory survey approach was employed for this study using a mixed methods approach of data collection. The data was analyzed using SPSS and Ms Excel. The targeted population was 78 professional librarians in seven selected public universities. The study was guided by the UTAUT model using the performance expectancy construct. The findings revealed that the respondents perceived that Linked Data would assist the patrons in discovering relevant information and data through links to other data repositories. They also believed that Linked Data interlinks would be used to describe the relationship between two Uniform Resource Identifiers (URIs). The respondents were convinced that Linked Data will enrich data discovery and improve the users’ overall search experiences. The study found that respondents perceived that Linked Data will assist the patrons to discover relevant data and benefit research, enrich discovery of data, hence improving their overall searches. Linked Open Data is very important to enable data sharing through the Semantic Web.

Plant Reactome Knowledgebase: empowering plant pathway exploration and OMICS data analysis.

Plant Reactome (https://plantreactome.gramene.org) is a freely accessible, comprehensive plant pathway knowledgebase. It providescurated referencepathways from rice (Oryza sativa) andgene-orthology-based pathway projections to 129 additional species, spanning single-cell photoautotrophs, non-vascular plants, and higher plants, thus encompassing a wide-ranging taxonomic diversity. Currently, Plant Reactome houses a collection of 339 reference pathways, covering metabolic andtransport pathways, hormone signaling, genetic regulations ofdevelopmental processes, and intricate transcriptional networks that orchestrate a plant's response to abiotic and biotic stimuli. Beyond being a mere repository, Plant Reactome serves as a dynamic data discovery platform. Users can analyze andvisualize omics data, such as gene expression, gene-gene interaction, proteome,and metabolome data, all within the rich context of plant pathways. Plant Reactome is dedicated to fostering data interoperability, upholding global data standards, and embracing the tenets of the Findable, Accessible, Interoperableand Re-usable (FAIR) data policy.

Mining toxicogenomic data for dose-responsive pathways: implications in advancing next-generation risk assessment.

Introduction: While targeted investigation of key toxicity pathways has been instrumental for biomarker discovery, unbiased and holistic analysis of transcriptomic data provides a complementary systems-level perspective. However, in a systematic context, this approach has yet to receive comprehensive and methodical implementation. Methods: Here, we took an integrated bioinformatic approach by re-analyzing publicly available MCF7 cell TempO-seq data for 44 ToxCast chemicals using an alternative pipeline to demonstrate the power of this approach. The original study has focused on analyzing the gene signature approach and comparing them to in vitro biological pathway altering concentrations determined from ToxCast HTS assays. Our workflow, in comparison, involves sequential differential expression, gene set enrichment, benchmark dose modeling, and identification of commonly perturbed pathways by network visualization. Results: Using this approach, we identified dose-responsive molecular changes, biological pathways, and points of departure in an untargeted manner. Critically, benchmark dose modeling based on pathways recapitulated points of departure for apical endpoints, while also revealing additional perturbed mechanisms missed by single endpoint analyses. Discussion: This systems-toxicology approach provides multifaceted insights into the complex effects of chemical exposures. Our work highlights the importance of unbiased data-driven techniques, alongside targeted methods, for comprehensively evaluating molecular initiating events, dose-response relationships, and toxicity pathways. Overall, integrating omics assays with robust bioinformatics holds promise for improving chemical risk assessment and advancing new approach methodologies (NAMs).

The dark side of KIBS agency, the creative economy, and regional sustainability

This contribution develops critique from analysing forms of misconduct by knowledge-intensive business services (KIBS) firms, especially the largest, globally located and client-interactive on all five continents. Management consultancy infractions range from supplying spurious advice, to advising clients on fraudulent practice, to acting complicitly in corrupt government practices, including engaging in ‘state capture’ by channelling internal state revenue into private holding bank accounts. Because the litany of misconduct is too massive for encompassing in a single contribution, and in the spirit of this task, the spotlight is only on a few cases that represent typical ‘creative economy’ companies contracted to ‘Big 3′ P-KIBS (Professional KIBS) corporations. Of importance to open innovation’s critical edge, the contribution articulates the puzzle of why academic KIBS research has presented the field without comment on its ‘dark’ side, questioning ‘leadership’ and ‘agency’ ‘observer’ and ‘confirmation bias’. For interpretation of data discovery, we utilise evolutionary ‘pattern recognition’ methodology set within an evolutionary theoretical framework.

Dataset Discovery and Exploration: A Survey

Data scientists are tasked with obtaining insights from data. However, suitable data is often not immediately at hand, and there may be many potentially relevant datasets in a data lake or in open data repositories. As a result, data discovery and exploration are necessary, but often time consuming, steps in a data analysis workflow. Data discovery is the process of identifying datasets that may meet an information need. Data exploration is the process of understanding the properties of candidate datasets and the relationships between them. Data discovery and data exploration often go hand in hand and benefit from tool support. This article surveys research areas that can contribute to data discovery and exploration, particularly considering dataset search, data navigation, data annotation and schema inference. For each of these areas, we identify key dimensions that can be used to characterize approaches and the values they can hold, and apply the dimensions to describe and compare prominent results. In addition, by surveying several adjacent areas that are often considered in isolation, we identify recurring techniques and alternative approaches to related challenges, thereby placing results within a wider context than is generally considered.

InterMat: A Blockchain-Based Materials Data Discovery and Sharing Infrastructure

Material research and development driven by data analysis necessitates a substantial volume of data. However, conventional material data sharing platforms encounter challenges in sharing and integrating data across multiple platforms. This article proposes a blockchain-based materials data discovery and sharing infrastructure—InterMat, which is a material big data management and sharing framework model integrating cloud platforms and blockchain. It could support the full lifecycle of materials data sharing, including data generation, management, discovery, sharing, traceability, and valuation. The architecture of the InterMat, its unique method of constructing a consortium chain, and the protocol for data discovery are presented in this paper. Additionally, the method for materials data identifier and blockchain certification is established, which allows for a unified identifier on the blockchain and cloud-based data addressing from various organizations. InterMat has data discovery algorithms for various materials to achieve the discovery of similar materials data from different nodes. Furthermore, we have designed some blockchain smart contracts for InterMat to encourage data sharing across nodes. These contracts include a proof smart contract that records data sharing activities, ensuring transparency and traceability in the materials data flow. The other contract is a value-estimating contract to encourage high-quality data sharing. Finally, this article introduces the application case of InterMat, using steel materials as an example to demonstrate its applications in data management, data discovery, data valuation, etc. This study successfully addresses various challenges associated with the cross-platform sharing of materials data, such as issues related to data discovery, data rights and control, and willingness to share. InterMat can assist material researchers in discovering and accessing more data, which would create a new ecology for sharing data.

Discovery of Novel miRNAs in Colorectal Cancer: Potential Biological Roles and Clinical Utility.

Deregulated miRNAs are associated with colorectal cancer (CRC), with alterations depending on the tumor location. Novel tissue-specific miRNAs have been identified in different tumors and are associated with cancer. We used miRMaster to identify novel miRNAs in CRC from the TCGA and GEO data (discovery and validation groups). We used TCGA data from five tissues to analyze miRNA tissue specificity. miRDB was used to predict miRNA targets, and the UCSC Xena Browser was used to evaluate target expression. After successive analyses, we identified 15 novel miRNAs with the same expression patterns in CRC in both the discovery and validation groups. Four molecules (nov-miR-13844-5p, nov-miR-7154-5p, nov-miR-5035-3p, and nov-miR-590-5p) were differentially expressed in proximal and distal CRC. The nov-miR-3345-5p and nov-miR-13172-3p, which are upregulated in tumors, are only expressed in colorectal tissues. These molecules have been linked to a worse prognosis in right-sided colon and rectal carcinomas. An analysis revealed an association between eight novel miRNAs and 81 targets, mostly cancer-related genes, with varying expression based on tumor location. These findings provide new miRNAs with potential biological relevance, molecular biomarkers, and therapeutic targets for CRC treatment.

Thematic Analysis of Big Data in Financial Institutions Using NLP Techniques with a Cloud Computing Perspective: A Systematic Literature Review

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.

Exploring traditional Chinese medicine as a potential treatment for sarcopenia: A network pharmacology and data mining analysis of drug selection and efficacy.

Sarcopenia, as an increasingly pressing clinical issue, can be ameliorated through employment of traditional Chinese medicines. However, the current lack of specific pharmacological interventions for Sarcopenia necessitates further exploration of novel possibilities in traditional Chinese medicine for the treatment of this condition, utilizing advanced methodologies such as web pharmacology and data mining. Screening the essential targets of Sarcopenia, conducting matching between target and active molecules, as well as active molecules and herbs. Employing data mining techniques to analyze the screening outcomes, and molecular docking to compare the binding activities of active molecules with target proteins. The approach of using herbs for the treatment of Sarcopenia involves 13 targets, with 414 active compounds and 367 types of herbs. Data mining reveals that the herbs used in treating Sarcopenia are primarily characterized by their bitter taste, exerting their effects through dispelling dampness and promoting blood circulation. Moreover, 2 new formulas are postulated. Furthermore, molecular docking analysis indicates that the main active components of the herbs can be observed to tightly bind with the targets. Through network pharmacology and molecular docking, our findings reveal that herbs contain 15 key active components and 5 key targets, which correspond to 7 major herbs and 2 new formulas. Academically, these findings hold significant reference value for the development of novel drugs targeting Sarcopenia.

Self-Organizing Computational System for Network Anomaly Exploration using Learning Algorithms

The forum in the nation for reporting information security flaws had 14,871 reports by the end of 2021, a 46.6% increase from 2020. The total of 5,567 high risk vulnerabilities, an increase of nearly 1,400 over the previous year. Evidently, both the total number of vulnerabilities found annually, and the total number of high-risk vulnerabilities are rising. In order for data mining technology to play a wider part in the predictive investigation of network security models, it is advised that its capability have to be improved. This paper combines the concepts of data mining (DM) with machine learning (ML), which introduces similar technologies from DM technology and security establishing collection channel, thereby finally introduces the computer network security maintenance process based on data mining in order to improve the application effect of DM in the predictive analysis of network security models. In this paper, a self-organizing neural network technique that detects denial of service (DOS) in complicated networks quickly, effectively, and precisely is introduced. It also analyses a number of frequently employed computer data mining methods, including association, clustering, classification, neural networks, regression, and web data mining. Finally, it introduces a computer data mining method based on the self-organizing (SO) algorithm. In comparison to conventional techniques, the SO algorithm-based computer data mining technology is also used in defensive detection tests against Dos attacks. A detection average accuracy rate of more than 98.56% and a detection average efficiency gain of more than 20% are demonstrated by experimental data to demonstrate that tests based on the Data Mining connected SO algorithm have superior defensive detection effects than standard algorithms.

Feature Selection in the Data Stream Based on Incremental Markov Boundary Learning.

Recent years have witnessed the proliferation of techniques for streaming data mining to meet the demands of many real-time systems, where high-dimensional streaming data are generated at high speed, increasing the burden on both hardware and software. Some feature selection algorithms for streaming data are proposed to tackle this issue. However, these algorithms do not consider the distribution shift due to nonstationary scenarios, leading to performance degradation when the underlying distribution changes in the data stream. To solve this problem, this article investigates feature selection in streaming data through incremental Markov boundary (MB) learning and proposes a novel algorithm. Different from existing algorithms focusing on prediction performance on off-line data, the MB is learned by analyzing conditional dependence/independence in data, which uncovers the underlying mechanism and is naturally more robust against the distribution shift. To learn MB in the data stream, the proposal transforms the learned information in previous data blocks to prior knowledge and employs them to assist MB discovery in current data blocks, where the likelihood of distribution shift and reliability of conditional independence test are monitored to avoid the negative impact from invalid prior information. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of the proposed algorithm.

Data Paper's Functions in Scholarly Communication Ecosystem as Perceived by Natural Scientists

ABSTRACTData papers, a new class of scholarly publication emerging from the open‐science movement, foster data discovery and reuse by offering comprehensive descriptions of research data. Yet, despite their promising growth, the role of data papers in scholarly communication remains underexplored. This work therefore investigates the perceived contributions and functions of data papers to scholarly communication by interviewing 14 data‐paper authors operating in the field of natural science. Using conceptual frameworks adopted from Borgman (2007) and Van de Sompel et al. (2004), we identify four general functions of scholarly communication (i.e., legitimization; dissemination; access, preservation, and curation; and rewarding). Additionally, our data lead us to propose that verification is a distinct scholarly communication, underscoring the importance of data papers in validating research findings in the context of ensuring research transparency. By elucidating the crucial role that data papers now play within the scholarly communication ecosystem, this study seeks to raise the academic community's awareness of their fundamental position, as well as their co‐existence with other forms of data publication, in advancing scientific research.

Support vector machine in ultrahigh-dimensional feature space

Classification and feature selection play an important role in knowledge discovery in high-dimensional data. Although penalized Support Vector Machine (SVM) is among the most powerful methods for classification and automatic feature selection in high-dimensional feature space, it is not directly applicable to ultrahigh-dimensional cases, wherein the number of features far exceeds the sample size. In this paper, we suggest an efficient two-step method for simultaneous classification and identifying important features in the setting of ultrahigh-dimensional models. Specifically, we first develop an independence screening procedure to reduce the dimensionality of the feature space to a moderate scale, and then penalized support vector machine is applied to the dimension-reduced feature space to select important features further and estimate the coefficients, via a (penalized) model fit. Implementation of the suggested two-step method is not limited by the dimensionality of the models and entails much less computational cost. Numerical examples and a real data analysis are used to demonstrate the finite sample performance of our proposal.

Data Science Curriculum

With an increase in technology use and rapid generation of information and data, graduate-level programs with data science curricula have grown exponentially to match the demand for information professionals with data-related skills. However, a gap has formed between skills learned in school and skills information professionals need post-graduation. Previous research indicates a skills gap among data scientists who lack LIS training in areas like data curation, preservation, etc., which data science programs should address (Ortiz-Repiso et al., 2018). This study, as part of a broader project funded by IMLS (Grant # RE-250066-OLS-21), attempts to address current and future workforce demands in data librarianship and analytics, employer skill demand, and acquisition of skills.
 This poster reports on part of the larger project - content analysis of 294 distinct graduate program curricula involving significant data science coursework in 43 LIS schools and iSchools across North America. After collecting over 1,000 data science/librarianship-related courses from institutional websites, we coded them into seven categories using their titles and descriptions. Data analysis, curation, governance, and discovery were the most popular subjects, followed by data professions, miscellaneous courses, and data assessment. In conjunction with job ad analysis and surveys of stakeholders in data science and librarianship education by others, our study could serve as input for graduate programs/curricula design. Results from our study will increase understanding of the nature and structure of data science curricula in iSchools and LIS departments while giving students requisite skills to meet future needs as data science professionals/data librarians.

Systems Approach for the Adoption of New Technologies in Enterprises

There is a great challenge in the business sector to adopt new technologies that boost companies to break into Industry 4.0, especially to obtain the capacity to adopt and develop complex systems based on: artificial intelligence, Big Data, Data Mining, and Cyber Physical Systems. However, efforts tend to be more of an empirical process, rather than a prior analysis, that allows companies to identify the complexity of the situation and trigger a viable implementation. For this reason, this research carried out a systematic review to identify and analyze, from the Systems Science approach, the proposed and most used models to face these organizational problems. In total, 42 of the 3800 documents were filtered for discussion using a systems approach. In addition, one of the models was tested by interviews with Mexican managers to understand how it promotes the abstraction of complexity necessary for a viable system change. The findings at the end of the work were to determine the lack of systemic properties in the current proposals, especially in the efforts to adopt artificial intelligence and the need to have a suitable model for the context of technology.

Big Data Analytics for the ATLAS EventIndex Project with Apache Spark

The ATLAS EventIndex was designed to provide a global event catalogue and limited event-level metadata for ATLAS experiment of the Large Hadron Collider (LHC) and their analysis groups and users during Run 2 (2015-2018) and has been running in production since. The LHC Run 3, started in 2022, has seen increased data-taking and simulation production rates, with which the current infrastructure would still cope but may be stretched to its limits by the end of Run 3. A new core storage service is being developed in HBase/Phoenix, and there is work in progress to provide at least the same functionality as the current one for increased data ingestion and search rates and with increasing volumes of stored data. In addition, new tools are being developed for solving the needed access cases within the new storage. This paper describes a new tool using Spark and implemented in Scala for accessing the big data quantities of the EventIndex project stored in HBase/Phoenix. With this tool, we can offer data discovery capabilities at different granularities, providing Spark Dataframes that can be used or refined within the same framework. Data analytic cases of the EventIndex project are implemented, like the search for duplicates of events from the same or different datasets. An algorithm and implementation for the calculation of overlap matrices of events across different datasets are presented. Our approach can be used by other higher-level tools and users, to ease access to the data in a performant and standard way using Spark abstractions. The provided tools decouple data access from the actual data schema, which makes it convenient to hide complexity and possible changes on the backed storage.

Personalizing the user experience in Salesforce using AI technologies

Artificial intelligence (AI) technology has transformed the interactive marketing experience of clients. Despite a large number of studies investigating the use of AI in interactive marketing, customization as a significant concept remains unexplored in marketing using AI research and practice. authors investigate appropriate research as well as insights on Salesforce interactive advertising. The authors highlight practice challenges at various points of the customer experience and give critical managerial advice as a solution to such problems. Personalization may help your business in a variety of ways. For starters, it may enhance customer loyalty and engagement by making consumers feel appreciated and understood. Second, it may aid your company's revenue growth by encouraging clients to make more purchases based on personalized suggestions. Third, it can boost retention rates of clients by increasing trust and decreasing the possibility that consumers would migrate to a rival. Fourth, it may enhance overall consumer satisfaction through rendering it more easy, efficient, and pleasurable. Your company is well-positioned to provide uniquely customised experience to customers at scale by embracing AI-powered customization technology. AI can aid in the discovery of important consumer data, the generation of forecasts about consumer habits and preferences, and the optimization of content and product suggestions. One can guarantee that AI-driven personalisation is successful and satisfies your customers' expectations by doing so.

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data.

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

Effectiveness of molnupiravir vs nirmatrelvir-ritonavir in non-hospitalised and hospitalised patients with COVID-19: a target trial emulation study

Molnupiravir and nirmatrelvir-ritonavir have emerged as promising options for COVID-19 treatment, but direct comparisons of their effectiveness have been limited. This study aimed to compare the effectiveness of these two oral antiviral drugs in non-hospitalised and hospitalised patients with COVID-19. In this target trial emulation study, we used data from a territory-wide electronic health records database on eligible patients aged ≥18 years infected with COVID-19 who were prescribed either molnupiravir or nirmatrelvir-ritonavir within five days of infection between 16 March 2022 and 31 December 2022 in the non-hospitalised and hospitalised settings in Hong Kong. A sequence trial approach and 1:1 propensity score matching was applied based on age, sex, number of COVID-19 vaccine doses received, Charlson comorbidity index, comorbidities, and drug use within past 90 days. Cox regression adjusted with patients' characteristics was used to compare the risk of effectiveness outcomes (all-cause mortality, intensive care unit (ICU) admission or ventilatory support and hospitalisation) between groups. Subgroup analyses included age (<70; ≥70 years); sex, Charlson comorbidity index (<4; ≥4), and number of COVID-19 vaccine doses received (0-1; ≥2 doses). A total of 63,522 non-hospitalised (nirmatrelvir-ritonavir: 31,761; molnupiravir: 31,761) and 11,784 hospitalised (nirmatrelvir-ritonavir: 5892; molnupiravir: 5892) patients were included. In non-hospitalised setting, 336 events of all-cause mortality (nirmatrelvir-ritonavir: 71, 0.22%; molnupiravir: 265, 0.83%), 162 events of ICU admission or ventilatory support (nirmatrelvir-ritonavir: 71, 0.22%; molnupiravir: 91, 0.29%), and 4890 events of hospitalisation (nirmatrelvir-ritonavir: 1853, 5.83%; molnupiravir: 3037, 9.56%) were observed. Lower risks of all-cause mortality (absolute risk reduction (ARR) at 28 days: 0.61%, 95% CI: 0.50-0.72; HR: 0.43, 95% CI: 0.33-0.56) and hospital admission (ARR at 28 days: 3.73%, 95% CI: 3.31-4.14; HR: 0.72, 95% CI: 0.67-0.76) were observed in nirmatrelvir-ritonavir users compared to molnupiravir users. In hospitalised setting, 509 events of all-cause mortality (nirmatrelvir-ritonavir: 176, 2.99%; molnupiravir: 333, 5.65%), and 50 events of ICU admission or ventilatory support (nirmatrelvir-ritonavir: 26, 0.44%; molnupiravir: 24, 0.41%) were observed. Risk of all-cause mortality was lower for nirmatrelvir-ritonavir users than for molnupiravir users (ARR at 28 days: 2.66%, 95% CI: 1.93-3.40; HR: 0.59, 95% CI: 0.49-0.71). In both settings, there was no difference in the risk of intensive care unit admission or ventilatory support between groups. The findings were consistent across all subgroup's analyses. Our analyses suggest that nirmatrelvir-ritonavir was more effective than molnupiravir in reducing the risk of all-cause mortality in both non-hospitalised and hospitalised patients. When neither drug is contraindicated, nirmatrelvir-ritonavir may be considered the more effective option. HMRF Research on COVID-19, The Hong Kong Special Administrative Region (HKSAR) Government; Collaborative Research Fund, University Grants Committee, the HKSAR Government; and Research Grant from the Food and Health Bureau, the HKSAR Government; the Laboratory of Data Discovery for Health (D24H) funded by the AIR@InnoHK administered by Innovation and Technology Commission.

The Development of Data Warehouse and Data Mining System for Serious Mental Illness with High Risk to Violence (SMI-V) Psychiatric Patients: A Case Study of Thailand

This study aimed to develop the data warehouse and data mining system for serious mental illness with high risk to violence (SMI-V) psychiatric patients of Nakhon Ratchasima Rajanagarindra Psychiatric Hospital, and develop a classification model for SMI-V psychiatric patients. Star schema design and database management software package were used for development of data warehouse system. Moreover, data mining technique, i.e., feature selection by Wrapper method, along with various algorithms were applied for constructing the classification model to classify SMI-V patients from other psychiatric patients. Software programming were used to create web-based task with graphical user interface (GUI) that would allow users to work with the system via Internet. The results of data warehouse design and development showed that it consisted of one fact table surrounding with six dimensional tables. There were six classification algorithms analyzed in this study which were Random Forest, Random Tree, Decision Tree J48, ZeroR, OneR, and Multilayer Perceptron. The results of developing classification models revealed that the model using Decision Tree J48 algorithm achieved higher performance than other models in comparison. Keywords: Data Warehouse; Data Mining; Classification; Serious Mental Illness with High Risk to Violence; Psychiatric Patients