Data Service Providers Research Articles

IDog: a multi-omics resource for canids study.

iDog (https://ngdc.cncb.ac.cn/idog/) is a comprehensive public resource for domestic dogs (Canis lupus familiaris) and wild canids, designed to integrate multi-omics data and provide data services for the worldwide canine research community. Notably, iDog 2.0 features a 15-fold increase in genomic samples, including 29.55 million single nucleotide polymorphisms (SNPs) and 16.54 million insertions/deletions (InDels) from 1929 modern samples and 29.09 million SNPs from 111 ancient Canis samples. Additionally, 43487 breed-specific SNPs and 530 disease/trait-associated variants have been identified and integrated. The platform also includes data from 141 BioProjects involving gene expression analyses and a single-cell transcriptome module containing data from 105057 Beagle hippocampus cells. iDog 2.0 also includes an epignome module that evaluates DNA methylation patterns across 547 samples and chromatin accessibility across 87 samples for the analysis of gene expression regulation. Additionally, it provies phenotypic data for 897 dog diseases, 3207 genotype-to-phenotype (G2P) pairs, and 349 dog disease-associated genes, along with two newly constructed ontologies for breed and disease standardization. Finally, 13 new analytical tools have been added. Given these enhancements, the updated iDog 2.0 is an invaluable resource for the global cannie research community.

Impact of big data and data analytics on the provision of data services in academic libraries

PurposeThe purpose of this study is to determine the level of awareness among library and information science (LIS) professionals regarding the perceived utility of big data (BD) and data analytics (DA) in academic libraries, as well as their influence on the provision of data services (DSs).Design/methodology/approachA cross-sectional survey was carried out to collect the data for this study. The population of this study comprised LIS professionals working in public sector university libraries. A four-factor measurement model estimating the influence of BD and DA on the provision of DSs in academic libraries was tested using the structural equation modelling.FindingsThe findings revealed that awareness (AW) (β = 0.141, CR = 2.534, p = 0.011) demonstrated a significant positive influence on the provision of DSs. The perceived utility of BD (β = 0.058, CR = 0.582, p = 0.561), and perceived utility of DA (β = 0.141, CR = 2.534, p = 0.905) exhibits a positive but statistically non-significant impact on the provision of DSs (β = 0.010, CR = 0.120, p = 0.905). The goodness of fit indices suggest a favourable fit for the model, as evidenced by the following values: χ2 = 1.400, DF = 164; p = 0.001; IFI = 0.954; TLI = 0.946; CFI = 0.953; GFI = 0.906; and RMSEA = 0.043.Originality/valueA new perspective on the use of BD and DA in academic libraries is presented in this study. It presents a four-factor measurement model on the influence of BD and DA on the provision of DSs in university libraries.

How Data is Documented: An Analysis of Metadata Schemas for Research Data

The importance of metadata has long been understood by those in the fields of librarianship and research data management. Currently, there are many research data service providers that allow deposit of research data or gather metadata for research data housed elsewhere, following various metadata schemas to describe their collections. These schemas were designed to make their datasets organized, discoverable, and reusable. As data curators initially recognize the need for and begin working on metadata schemas for research data, most previous studies in this field were conducted from their perspectives. However, few of these studies discuss a schema from the perspective of data (re)users. This study aims to take a small step towards filling this gap by evaluating four influential metadata schemas in light of researchers' information-seeking behaviors for research data. This study explores the factors that affect researchers’ efforts in seeking, understanding, and evaluating (re)usable data, and examines how these factors are represented across various fields in the current metadata schemas for research data. The methodology for this study consists of three parts: a meta-analysis of relevant research on researcher’s seeking behaviors for (re)usable data, a crosswalk of the metadata schemas, and a comparison of findings from the previous stages. The findings from this study will offer insights into the development of research data metadata schema in the future, which will enhance the infrastructure for data sharing, ultimately facilitating more effective (re)use of existing data.

Application Effect Evaluation of Intelligent Community Service Platform in Grassroots Governance

The purpose of this study is to design an intelligent community service platform applied in grassroots governance. First of all, this paper uses a distributed framework to build a service platform to provide data services for the intelligent community. Secondly, intelligent machine learning models are used to accelerate network training for community data classification tasks. At the same time, the high-precision association mechanism based on multi-source heterogeneous big data is introduced to store different types of topic data into the corresponding topic database, so as to realize the fusion processing of structured, semi-structured and unstructured data. Four kinds of account information are set up, including platform administrators, community administrators, community service units and community residents, to meet the diverse needs of community residents. The simulation test shows that the intelligent community service platform passes the function test, and the maximum change of response time is less than 1%, which proves the feasibility of applying the intelligent community service platform in grass-roots governance.

Researchers' perceptions of the trustworthiness, for reuse purposes, of government health data in Victoria, Australia: Implications for policy and practice.

In 2022 the Australian Data Availability and Transparency Act (DATA) commenced, enabling accredited "data users" to access data from "accredited data service providers." However, the DATA Scheme lacks guidance on "trustworthiness" of the data to be utilised for reuse purposes. Objectives: To determine: (i) Do researchers using government health datasets trust the data? (ii) What factors influence their perceptions of data trustworthiness? and (iii) What are the implications for government and data custodians? Method: Authors of published studies (2008-2020) that utilised Victorian government health datasets were surveyed via a case study approach. Twenty-eight trust constructs (identified via literature review) were grouped into data factors, management properties and provider factors. Results: Fifty experienced health researchers responded. Most (88%) believed that Victorian government health data were trustworthy. When grouped, data factors and management properties were more important than data provider factors in building trust. The most important individual trust constructs were: "compliant with ethical regulation" (100%) and "monitoring privacy and confidentiality" (98%). Constructs of least importance were knowledge of "participant consent" (56%) and "major focus of the data provider was research" (50%). Conclusion: Overall, the researchers trusted government health data, but data factors and data management properties were more important than data provider factors in building trust. Implications: Government should ensure the DATA Scheme incorporates mechanisms to validate those data utilised by accredited data users and data providers have sufficient quality (intrinsic and extrinsic) to meet the requirements of "trustworthiness," and that evidentiary documentation is provided to support these "accredited data."

Evaluating Experts’ Perspectives on Utilizing mHealth for Data Services in HIV Program Implementation in Cameroon

<i>Introduction</i>: The widespread adoption of mobile health (mHealth) technology has become a crucial aspect of healthcare delivery, particularly in regions with limited access to traditional healthcare systems. Cameroon, a Central African nation grappling with a high prevalence of HIV/AIDS, presents a suitable context for mHealth interventions. This study aims to assess expert opinions on the application of mHealth for data services in implementing HIV programs in Cameroon. <i>Methods</i>: Employing a mixed-methods approach, we utilized a quantitative cross-sectional design (via a survey questionnaire) and a qualitative design (through in-depth interviews). The purposive sample technique was employed to recruit health specialists from diverse organizations and healthcare facilities across the country. <i>Results</i>: Analysis of responses from mHealth experts revealed that 76% held a positive perspective on using mHealth for data services in HIV program implementation in Cameroon, while 24% expressed a negative opinion. Statistical analysis did not reveal a significant association (p = 0.265) between qualifications and perspectives on mHealth. Monitoring and Evaluation (M&E) experts exhibited a notable association (94.1%) with positive perspectives on mHealth utilization. In contrast, IT specialists and public health experts demonstrated slightly lower percentages (78.6% and 60%, respectively). <i>Conclusion</i>: Experts in mHealth conveyed optimism about using mHealth to provide data services for implementing HIV programs in Cameroon. The study discovered that mHealth users' perceptions were positively correlated with age group and work position. However, there was no discernible relationship between the attitudes of mHealth users and gender or educational attainment.

Research on Theory and Practice of Data Quality Management

Data quality management includes both theory and technology, as well as practice and management. Through data quality management, enterprises can obtain high-quality data, which is an important prerequisite for developing data products, providing data services, and realizing the value of data. It is also the core goal of enterprise data governance. This article first reviews and compares the relevant theories of data quality management; Then summarize the data quality practices carried out by typical case enterprises; Finally, based on the theory and practical cases of data quality management, valuable experiences for data quality management are proposed, and prospects for data quality management are presented.

Special Issue: 2023 Research Data Access and Preservation (RDAP) Summit

The 2023 Research Data Access and Preservation (RDAP) Summit, Building on Experience: Centering Communities in Data Creation and Access, focused on engagement with and building communities within data environments, including how data is being made more accessible for a wider range of communities. The 2023 RDAP Summit was a natural extension from the prior year’s theme of Envisioning an Inclusive Data Future, which highlighted the ways data service providers tailor their services to address specific needs. A selection of presentations from this year’s Summit were expanded into articles for this special issue covering topics on developing and maintaining communities that address aspects across the research data life cycle.

Anonymous and linkable ring signcryption scheme for location-based services in VANETs

In Location-Based Service (LBS), the vehicle user sends query messages, such as the nearest health centre, restaurant, etc., to the service provider for efficient and accurate service. The service provider gathers data regarding the received query, takes the service charge from the vehicle user, and provides these services efficiently and accurately. The service provider may reveal the vehicle user's privacy if the service provider is malicious. Similarly, if the vehicle user is malicious, he/she may also try to opt for the service from the service provider without paying the service charges. Therefore, vehicle users' queries and the service provider's data privacy are serious concerns in LBS. Some schemes, such as k-anonymity, cryptographic algorithms, etc., have been proposed to overcome these issues. The k-anonymity technique only offers vehicle users' query privacy. Cryptographic algorithms such as homomorphic encryption, oblivious transfer, etc., provide vehicle users' query and the service provider's data privacy at high communication and computation costs but reveal the vehicle user's identity. To overcome these problems, this work proposes an Anonymous and Linkable Ring Signcryption (ALRS) scheme for LBS in VANETs. It provides the service provider's data privacy and the vehicle user's query privacy, keeping the vehicle user's identity anonymous from the service provider. In addition, it maintains linkability (the number of times the same user requests a query) without revealing the identity of the vehicle user. Implementation and detailed results show that the ALRS scheme performed better than the current scheme regarding communication and computation costs and preserves all the privacy requirements.

Hierarchical Access Structure-based Scheme with Outsourcing and Revocation Mechanism for Cloud Environment

Ciphertext policy attribute-based encryption (CPABE) is one of the efficient implementations of encrypted access control scheme for cloud computing. Though multiple implementations of CPABE exist, there are some issues that need to be addressed, including efficient revocation approach, decryption time, storage cost etc. In this paper, an efficient scheme that incorporates a hierarchical access structure, outsourced decryption, as well as user and attribute revocation is presented. The hierarchical access structure is utilized to encrypt multiple data using one common access structure and makes the encryption process more efficient. The outsourcing server is used to perform partial decryption, so that all heavy computations are performed by this server and less overhead is incurred by the data user. The proposed framework also integrates the evaluation of trustworthiness of data users and service providers to ensure trusted and encrypted access control procedures. The paper also presents an analysis of the time required for performing different operations. Simulation results show that the proposed scheme outperforms the existing approaches.

Blockchain-based access control system for efficient and GDPR-compliant personal data management

New digital technologies generate large amounts of information. This data is processed by Service Providers in order to improve and develop new services and products, but also to fund themselves. However, processing personal data may result in the extraction of sensitive information, which, in turn, may lead to jeopardizing the users’ privacy. To mitigate this significant risk, the European Parliament and Council of the European Union elaborated the General Data Protection Regulation (GDPR). This regulation forces Service Providers to obtain Data Subjects’ explicit consent prior to collecting and processing their personal data. Nevertheless, the GDPR’s legislative text does not define how Service Providers must transparently demonstrate that they already have these consents. Moreover, most individuals do not know the rights they have over their personal data, neither does this regulation provide them with efficient methods to be aware of what third parties are doing with such data. In order to address this situation, we propose a lightweight blockchain-based GDPR-compliant personal data management platform. The new solution provides public access to immutable evidences that reflect the reached agreements between Data Subjects and Service Providers. In this way, Service Providers can effectively demonstrate that they are fulfilling the regulation, and Data Subjects are able to control and manage their personal data according to their legitimate rights. We have implemented the new system, and we have performed a detailed study which includes: GDPR-compliance, provided functionality, security and privacy issues, and the cost in terms of gas and US dollars of the different operations to be run on the blockchain.

Continuing Education and Data Training Initiatives are Needed to Positively Impact Academic Librarians Providing Data Services

A Review of: Fuhr, J. (2022). Developing data services skills in academic libraries. College & Research Libraries, 83(3), 474. https://doi.org/10.5860/crl.83.3.474 Objective – To measure the existing data services skills of academic librarians and gather information on the preferred training programs available to enhance those skill Design – Survey questionnaire. Setting – Libraries in Canada, the United States, the United Kingdom, and Australia. Subjects – One hundred and twenty respondents who self-identified as providing data services. Most (85%) worked in academic libraries with 7% in hospital libraries, 3% in government libraries and 5% in other types of libraries. Methods – Permission was received from the institution ethics board to administer an incentivized survey. All respondents received a 22-question survey which consisted of a mix of Likert-scale questions, multiple choice, open-ended, and short answer questions. The survey was open for two months, beginning on February 20, 2020. One hundred and twenty responses were collected from librarians. A regression analysis was run for the four-skill set categories: general data services, programming languages and software, library instruction, and soft skills. The four variables measured were: geographic region, percentage of time spent performing data management services, length of time served in the data services role, and overall length of time spent in the library science field. Main Results – The strongest data services skill sets were soft skills and instruction. The weakest skill set was programming languages and software. The more time a librarian spent providing data services, the higher their self-assessed score was for programming languages and software and general data services. Librarians from the United States rated themselves higher than Canadian librarians in data analysis software, data visualization, data mining, programming languages, text editors and project management. Preferred forms of professional development were learning by doing and self-directed learning. Biggest impediments to professional development were lack of time (34%), high cost (28%), and lack of support from administrators and supervisors (26%). Qualitative comments revealed challenges related to a lack of support, a lack of direction, and a lack of defined roles. Conclusion – The survey revealed that additional training and development skills initiatives are necessary for practitioners supporting data services in academic libraries. Academic data librarianship is an emerging field with vaguely articulated roles for the data practitioner in a broad range of settings. Furthermore, the skills and training needed are not clearly defined. The standardization of education, training and the core competencies needed for the mechanics of the roles are challenging to define because of diversity within the field. Libraries embarking on providing data management services need to explore what services their community of researchers needs and plan to equip their staff with appropriate skill sets.

Trust Model for Cloud Data Service Providers

Trust Model for Cloud Data Service Providers

Use of Social Media Among the Agricultural Students of Tertiary Institutions in Kwara State, Nigeria

The study examined the use of social media among the agricultural students of tertiary institutions in Kwara State, Nigeria. One hundred and twenty agricultural students was selected in four tertiary institutions in Kwara State, Nigeria. The data collected was analysed with the use of frequency count, Percentage, Mean and Standard deviation. The result revealed that 20.8 years was the average age of the respondents. About 64.2% were male and about 91.7% gets information through social media. The average monthly money used for buying data for subscription was 1922.00 Naira and the average hours spent on social media per day was 7.38 hours. About 77.5 % of the respondents uses smart phones to access social media platforms. Facebook (93.3%) was the most frequently used social media platform. Communication/calling (87.5%) was the main motive for using social media. Social media makes communication to be faster (=4.11) was the highest ranked perception statement. High cost of data ((=2.64) was the highest ranked factor affecting the use of social media. The study therefore recommends that data service providers should consider reducing the price of data and government should make policies that will be of benefits to data users.

Business Modeling Innovation Using Artificial Intelligence Technology

Startups embracing artificial intelligence (AI) as a component of their business models are quickly emerging right now. The application of AI technology in business has been going on for a while, even though recent research reveals that new or alternative business models are being implemented.It might be claimed that AI technology has been used to business models for a very long time, which puts into question the uniqueness of these business models. In order to better understand how AI companies' business models may differ from traditional IT business models, this study compares them to each other. The first step is to create a taxonomy of business models for AI businesses using a sample of 162 Worldwide Stratup, from which four archetypal business models are derived: Deep technology researcher, data analytics supplier, AI product and service provider, and facilitator of AI development The following are three main elements of startup business models for AI firms that are discussed based on this descriptive analysis: (1) new value propositions made possible by AI, (2) Various uses of data to create value and (3) How AI technology affects general business reasoning. By defining their key purposes, common instantiations, and distinguishing features, this research adds to our fundamental understanding of the AI start-up business model. This study suggests intriguing directions for further investigation in the field of entrepreneurship. À It is structured to foster entrepreneurial activity. Taxonomies and models are actually instrument.

Business Modeling Innovation Using Artificial Intelligence Technology

Startups embracing artificial intelligence (AI) as a component of their business models are quickly emerging right now. The application of AI technology in business has been going on for a while, even though recent research reveals that new or alternative business models are being implemented.It might be claimed that AI technology has been used to business models for a very long time, which puts into question the uniqueness of these business models. In order to better understand how AI companies' business models may differ from traditional IT business models, this study compares them to each other. The first step is to create a taxonomy of business models for AI businesses using a sample of 162 Worldwide Stratup, from which four archetypal business models are derived: Deep technology researcher, data analytics supplier, AI product and service provider, and facilitator of AI development The following are three main elements of startup business models for AI firms that are discussed based on this descriptive analysis: (1) new value propositions made possible by AI, (2) Various uses of data to create value and (3) How AI technology affects general business reasoning. By defining their key purposes, common instantiations, and distinguishing features, this research adds to our fundamental understanding of the AI start-up business model. This study suggests intriguing directions for further investigation in the field of entrepreneurship. À It is structured to foster entrepreneurial activity. Taxonomies and models are actually instrument.

D2D Group Key Agreement Scheme for Smart Devices in HANs

<p>The home area network (HAN) is one of the most widely researched areas in recent years. HANs integrate 5G/6G networks and artificial intelligence technology to provide data services for home users. The devices in HANs collect and transmit data relating to users’ daily activities for analysis by remote service providers. These data often contain a large number of users’ personal privacy. The disclosure of these data could have far-reaching consequences for the privacy of the individuals involved. Some researchers are dedicated to investigating the authentication of smart devices by the system. However, the increased frequency of interactions between devices and gateways, as well as between devices themselves, is a defining characteristic of HANs. In this paper, a device-to-gateway (D2G) authentication scheme is proposed. Based on the authentication result, a partial key is generated for smart devices and the gateway. Finally, a device-to-device (D2D) group key agreement scheme is presented. The security and efficiency of the proposed scheme are proved according to the analysis.</p> <p> </p>

A Multidimensional Data Utility Evaluation and Pricing Scheme in the Big Data Market

Big data as a derivative of information technology facilitates the birth of data trading. The technology surrounding the business value of big data has come into focus. However, most of the current research focuses on improving the performance of big data analytics algorithms. Data pricing is still one of the main issues in data trading. Therefore, we aim to tackle the problem of evaluating the utility of data in the big data trading market and the problem of maximizing the profits of the various roles involved in data trading. To this end, we propose a Multidimensional Data Utility Evaluation (MDDUE) method through three data quality dimensions, namely, data size, availability, and completeness. Next, we propose a big data trading market model including data providers, service providers, and service users. An optimal data-pricing scheme based on a three-party Stackelberg game is proposed to maximize the participants’ profits. Finally, a machine learning model is used to verify the rationality and validity of the MDDUE. The results show that MDDUE can evaluate the utility of data more accurately than previous work. The existence and uniqueness of the Nash equilibrium are demonstrated through numerical experiments.

Developing a Centralized Hub for Research Data Services: Trainings and Resources in Health Sciences Contexts.

In 2021, a new national center was funded by the National Library of Medicine (NLM) to build capacity for providing data services within health sciences libraries by coordinating, developing, and disseminating trainings, curricular resources, and curated pathways for learning. Research data services has been a growing service component for health sciences libraries for over a decade, and efforts have come from individuals, professional societies, task forces, and interest groups; however, there is still a great deal of unrealized potential in this area as well as growing needs driven by new requirements from funders and publishers and increasing demand from institutions for data science skills and support for data-related research needs. The National Center for Data Services (NCDS) was established in July 2021 and is the newest of the Network of the National Library of Medicine (NNLM)'s national offices and centers. NCDS works with the NNLM's seven regional medical libraries and national offices and centers, and draws upon extensive connections with national data and library organizations, all toward the aim of developing capacity for data services in the health information community while centering efforts toward equity, diversity, and accessibility throughout.

Pricing on‐demand services: Alternative ways of combining usage and access fees

Many firms are adopting an “everything or anything” as a service—“XaaS”—model to sell goods and value‐added services. Buyers for such goods are heterogeneous both in the number of units they desire and the valuations for each unit. Suppliers designing such revenue models can offer either a usage‐based per‐unit fee or an access‐based per‐period fee, often combining them both as a nonlinear two‐part tariff plan that imposes both types of fees on all buyers or let buyers self‐select from a menu of per‐unit and per‐period plans. We develop a theoretical model to analyze the economic implications of alternative designs for mixing per‐unit and per‐period fees. Our analysis produces the following practical insights on these plans' profitability and market coverage. First, the menu is generally a better way of combining access and usage fees for a firm selling digital goods with zero variable costs. Second, the preferred design switches to a two‐part tariff if the firm's production environment resells or runs on top of a back‐end infrastructure or data service provider that imposes nonnegligible variable costs. Third, we show that the revenue advantage of these two designs (which employ both fees) over a simple plan that employs only one fee (best of per unit and per period) is most significant when the rate of change in marginal valuation is relatively similar across buyers.