Search

Definition: In the context of open science, universities, research-performing and funding organizations and authorities worldwide are moving towards more responsible research assessment (RRA). In 2022, the European Coalition for Advancing Research Assessment (CoARA) published an agreement with ten commitments, including the recognition of the "diversity of contributions to, and careers in, research", the "focus on qualitative evaluation for which peer review is central, supported by responsible use of quantitative indicators", and the "abandon (of) inappropriate uses in research assessment of journal- and publication-based metrics". Research assessment (RA) is essential for research of the highest quality. The transformation of assessment indicators and procedures directly affects the underlying research information management infrastructures (also called current research information systems) which collect and store metadata on research activities and outputs. This entry investigates the impact of RRA on these systems, on their development and implementation, their data model and governance, including digital ethics. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the application of emojis to mood descriptions of fiction. The three goals are investigating whether Cho et al.'s model (2023) is a sound conceptual framework for implementing emojis and mood categories in information systems, mapping 30 mood categories to 115 face emojis and exploring and visualizing the relationships between mood categories based on emojis mapping. Design/methodology/approach: An online survey was distributed to a US public university to recruit adult fiction readers. In total, 64 participants completed the survey. Findings: The results show that the participants distinguished between the three families of fiction mood categories. The three families model is a promising option to improve mood descriptions for fiction. Through mapping emojis to 30 mood categories, the authors identified the most popular emojis for each category, analyzed the relationships between mood categories and examined participants' consensus on mapping. Originality/value: This study focuses on applying emojis to fiction reading. Emojis were mapped to mood categories by fiction readers. Emoji mapping contributes to the understanding of the relationships between mood categories. Emojis, as graphic mood descriptors, have the potential to complement textual descriptors and enrich mood metadata for fiction. [ABSTRACT FROM AUTHOR]

Interoperability between research management systems, especially digital libraries or repositories, has been a central theme in the community for the past years, with the discussion focused on means of enriching, linking, and disseminating outputs. This paper considers a frequently overlooked aspect, namely the migration of records across systems, by introducing the Stateful Library Analysis and Migration system (SLAM) and presenting practical experiences with migrating records from DSpace and Digital Commons repositories to Figshare. [ABSTRACT FROM AUTHOR]

Streamflow gauging stations not only track the pulse of rivers but also act as common reference points for hydrologic and other environmental analyses. As such, streamflow data and metadata on gauging stations – Geographic Information System (GIS) data on station locations, their upstream catchment boundaries and river flow networks – are critical for analyses. However, for India's river basins, the availability of such data is limited; when available, data are not in an analysis-ready format and can have substantial errors. Studies often use available information from India's water agencies as is, without checking its validity. This study addresses the above limitations by building a new dataset using existing metadata (from the Central Water Commission, CWC, and the Water Resources Information System, WRIS) and checking it against publicly available information from global data sources (e.g., World Wildlife Fund, Multi-Error-Removed Improved-Terrain Hydro and Copernicus) and online maps (e.g., Google Maps). The quality control process categorizes existing metadata based on their consistency with these sources; also, existing metadata are supplemented with additional information where needed. The new dataset developed here is called the "Geospatial dataset for Hydrologic analyses in India" (GHI) and uses Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales (HydroSHEDS) data as the underlying template. GHI has both geospatial and time series information. In this initial version of GHI, the spatial domain includes only the river basins of Peninsular India where daily streamflow data are publicly available. Following the quality control process, the CWC's 645 stations in Peninsular India were categorized into three groups: Group 1 (reliable metadata and adequate daily streamflow data; 213 stations), Group 2 (reliable metadata and inadequate or no daily streamflow data; 259 stations) and Group 3 (missing or unreliable metadata; 173 stations). For each of the 472 stations falling into groups 1 and 2, catchment-specific annual and monthly time series spanning 71 water years (1950–2020) of the following were compiled: observed precipitation from the Indian Meteorological Department (IMD); observed streamflow from WRIS; estimated precipitation, evapotranspiration (ET) and streamflow from ERA5-Land; and ET from the Global Land Evaporation Amsterdam Model (GLEAM). A preliminary analysis of catchment-scale time series of data indicates that, while the compiled data appear reasonable over most of the study domain, spurious runoff–precipitation ratios were observed in the hilly coastal regions of Western India. This adds to yet another data-related obstacle faced by the hydrologic community. In order to quantify historical changes and reconcile them with anticipated future changes, the community needs robust and reliable hydrographic and hydrometeorological datasets as well as unrestricted access to such datasets. The goal of this study is to highlight the limitations of existing datasets and pave the way for a community-led effort towards building the needed datasets. GHI serves as a placeholder until such datasets become available. Potential improvements to GHI are discussed. GHI is publicly available at 10.5281/zenodo.7563599. [ABSTRACT FROM AUTHOR]

The digital news preservation and management of low-resource languages are challenging tasks, especially in vast collections. Unique identification of individual digital objects is possible with well-defined attributes to assure efficient management, such as access, retrieval, preservation, usability, and transformability. The metadata element set is required to maximize the available attributes related to the digital objects. To create a comprehensive metadata set that contains all the necessary attributes and data about the digital news objects. It is more challenging and complicated when the archive contains articles from low-resourced and morphologically complex languages like Urdu and Arabic, which is difficult for machines to understand. The study presents challenges in low-resource languages (LRL) and research challenges. This metadata will help to link news articles based on similarity with other news articles stored in the digital news stories archive (DNSA) and ensures accessibility. In this study, we introduced 38 metadata elements set for the digital news stories preservation (DNSP) framework, of which 16 are explicit and 12 are implicit metadata elements. The paper presents how the digital news stories archive (DNSA) is enhanced to a multilingual archive and discusses the digital news stories extractor, which addresses major issues in implementing low-resource languages and facilitates normalized format migration. The extraction results are presented in detail for high-resource languages, that is, English, and low-resource languages (HRL), that is, Urdu and Arabic. The LRL encountered a high error rate during preservation compared to HRL, 10%, and 03%, respectively. The metadata extraction results show that HRL sources support all metadata elements as compared to LRL. The LRL has good support for explicit meta elements and many implicit meta elements with low extraction percentages. The LRL needs a more detailed study for accurate news content extraction and archiving for future access. [ABSTRACT FROM AUTHOR]

We present a draft Minimum Information About Geospatial Information System (MIAGIS) standard for facilitating public deposition of geospatial information system (GIS) datasets that follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The draft MIAGIS standard includes a deposition directory structure and a minimum javascript object notation (JSON) metadata formatted file that is designed to capture critical metadata describing GIS layers and maps as well as their sources of data and methods of generation. The associated miagis Python package facilitates the creation of this MIAGIS metadata file and directly supports metadata extraction from both Esri JSON and GEOJSON GIS data formats plus options for extraction from user-specified JSON formats. We also demonstrate their use in crafting two example depositions of ArcGIS generated maps. We hope this draft MIAGIS standard along with the supporting miagis Python package will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community as well as a future public repository for GIS datasets. [ABSTRACT FROM AUTHOR]

This research will analyze the adequacy of the representations of cartographic resources included in information retrieval systems to the search and access needs of the community specialized in Geography. Among the proposed objectives are to survey such systems at the public, governmental, and national levels; to identify the metadata standards or schemes used, and to study the ways of searching, accessing and using these resources. The work methodology to be implemented will be based on a descriptive-exploratory and quantitative-qualitative approach to be developed in three phases or work stages. The first phase will be devoted to the analysis of the representations based on the identification of the systems and the subsequent application of evaluation criteria and instruments to carry out the observation. The second phase will include a survey of the selected population to learn how users access the information contained in the cartographic resources, which will be complemented with semi-structured interviews based on a purposive sample of key subjects. In a third stage, the results of phase one and two will be analyzed comparatively in order to identify the relationships between the representations, the existing systems and the expectations and search needs of the users. [ABSTRACT FROM AUTHOR]

The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system. [ABSTRACT FROM AUTHOR]

Purpose: Readers articulate mood in deeply subjective ways, yet the underlying structure of users' understanding of the media they consume has important implications for retrieval and access. User articulations might at first seem too idiosyncratic, but organizing them meaningfully has considerable potential to provide a better searching experience for all involved. The current study develops mood categories inductively for fiction organization and retrieval in information systems. Design/methodology/approach: The authors developed and distributed an open-ended survey to 76 fiction readers to understand their preferences with regard to the affective elements in fiction. From the fiction reader responses, the research team identified 161 mood terms and used them for further categorization. Findings: The inductive approach resulted in 30 categories, including angry, cozy, dark and nostalgic. Results include three overlapping mood families: Emotion, Tone/Narrative, and Atmosphere/Setting, which in turn relate to structures that connect reader-generated data with conceptual frameworks in previous studies. Originality/value: The inherent complexity of "mood" should not dissuade researchers from carefully investigating users' preferences in this regard. Adding to the existing efforts of classifying moods conducted by experts, the current study presents mood terms provided by actual end-users when describing different moods in fiction. This study offers a useful roadmap for creating taxonomies for retrieval and description, as well as structures derived from user-provided terms that ultimately have the potential to improve user experience. [ABSTRACT FROM AUTHOR]

Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and ontologies explicitly describe metadata as well as application data objects and domains. The present paper proposes a novel approach to integrate FAIR digital objects and ontologies as metadata models in order to support data access for energy researchers, energy research applications, operational applications and energy information systems. As the first example domain to be modeled using an ontology and to get integrated with FAIR digital objects, a photovoltaic (PV) system model is selected. For the given purpose, a discussion of existing energy ontologies shows the necessity to develop a new PV ontology. By integration of FDOs, this new PV ontology is introduced in the present paper. Furthermore, the concept of FDOs is integrated with the PV ontology in such a way that it allows for generalization. By this, the present paper contributes to a sustainable data management for smart grid operation, especially for interoperability, by using ontologies and, hence, unambiguous semantics. An information system application that visualizes the PV system, its describing data and collected sensor data, is proposed. As a proof of concept the details of the use case implementation are presented. [ABSTRACT FROM AUTHOR]

Despite its advantages in improving access to information, metadata has the potential to cause harm, if used inappropriately. For instance, mass surveillance of phone calls can be used by intelligence agencies to target individuals. Language use can reinforce prejudices; poor language control undermines the efficiency of subject searches; and the opacity of discovery systems reduces the effectiveness of retrieval systems. There are also concerns about who owns the intellectual property associated with metadata creation. The talk concluded with a description of two proposed initiatives: by ISKO to investigate ways of improving metadata use in information discovery systems; and the recruitment for a fully-funded PhD studentship at Edinburgh Napier University to investigate the ethics of metadata. [ABSTRACT FROM AUTHOR]

Copyright of Information Library Magazine 'INFOLIB' is the property of National Library of Uzbekistan named after Alisher Navoi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Long-term digital preservation aims to ensure that the digital content of traditional socio-cultural institutions (libraries, museums, archives, etc.) remains accessible to user communities for a long period of time and for future generations. The article considers the problems and prospects of organizing long-term digital storage of electronic information resources. It is proposed to use for solving the problems of long-term digital preservation approaches focused on the reference model of the open archival information system (OAIS), described in the ISO 14721:2012 standard. To describe events/actions with a document during archival storage, it is proposed to use the PREMIS metadata model. [ABSTRACT FROM AUTHOR]

Background: Anomaly detection is an integral component of operating a clinical laboratory. It covers both the recognition of laboratory errors and the rapid reporting of clinically impactful results. Procedures for identifying laboratory errors and highlighting critical results can be improved by applying modern data-driven approaches. Content: This review will prepare the reader to appraise anomaly detection literature, identify common sources of anomalous results in the clinical laboratory, and offer potential solutions for common shortcomings in current laboratory practices. Summary: Laboratories should implement data-driven approaches to detect technical anomalies and keep them from entering the medical record, while also using the full array of clinical metadata available in the laboratory information system for context-dependent, patient-centered result interpretations. [ABSTRACT FROM AUTHOR]

The French Critical Zone research infrastructure, OZCAR-RI, gathers 20 observatories sampling various compartments of the critical zone, each having developed their own data management and distribution systems. A common information system (Theia/OZCAR IS) was built to make their in situ observation FAIR (findable, accessible, interoperable, reusable). The IS architecture was designed after consultation of the users, data producers and IT teams involved in data management. A common data model based on various metadata standards was defined to create information fluxes between observatories' ISs and the Theia/OZCAR IS. Controlled vocabularies were defined to develop a data discovery web portal offering a faceted search with various criteria, including variables names and categories that were harmonized in a thesaurus published on the web. This paper describes the IS architecture, the pivot data model and open-source solutions used to implement data discovery, and future steps to implement data downloading and interoperability services. [ABSTRACT FROM AUTHOR]

Despite large public investments in facilitating the secondary use of data, there is little information about the specific factors that predict data's reuse. Using data download logs from the Inter‐university Consortium for Political and Social Research (ICPSR), this study examines how data properties, curation decisions, and repository funding models relate to data reuse. We find that datasets deposited by institutions, subject to many curatorial tasks, and whose access and preservation is funded externally, are used more often. Our findings confirm that investments in data collection, curation, and preservation are associated with more data reuse. [ABSTRACT FROM AUTHOR]

Most research projects are data driven. However, many organizations lack proper information systems (IS) for managing data, that is, planning, collecting, analyzing, storing, archiving, and sharing for use and re-use. Many research institutions have disparate and fragmented data that make it difficult to uphold the FAIR (findable, accessible, interoperable, and reusable) data management principles. At the same time, there is minimal practice of open and reproducible science. To solve these challenges, we designed and implemented an IS architecture for research data management. Through it, we have a centralized platform for research data management. The IS has several software components that are configured and unified to communicate and share data. The software components are, namely, common ontology, data management plan, data collectors, and the data warehouse. Results show that the IS components have gained global traction, 56.3% of the total web hits came from news users, and 259 projects had metadata (and 17 of those also had data resources). Moreover, the IS aligned the institution's scientific data resources to universal standards such as the FAIR principles of data management and at the same time showcased open data, open science, and reproducible science. Ultimately, the architecture can be adopted by other organizations to manage research data. [ABSTRACT FROM AUTHOR]

To improve data usage in an interdisciplinary context, a clear understanding of the variables being measured is required for both humans and machines. In this paper, the I-ADOPT framework, which decomposes variable names into atomic elements, was tested within the context of continental surfaces and critical zone science, characterized by a large number and variety of observed environmental variables. We showed that the I-ADOPT framework can be used effectively to describe environmental variables with precision and that it was flexible enough to be used in the critical zone science context. Variable names can be documented in detail while allowing alignment with other ontologies or thesauri. We have identified difficulties in modeling complex variables, such as those monitoring fluxes between different environmental compartments and for variables monitoring ratios of physical quantities. We also showed that, for some variables, different decompositions were possible, which could make alignments with other ontologies and thesauri more difficult. The precision of variable names proved inadequate for data discovery services and a non-standard label (SimplifiedLabel) had to be defined for this purpose. In the context of open science and interdisciplinary research, the I-ADOPT framework has the potential to improve the interoperability of information systems and the use of data from various sources and disciplines. [ABSTRACT FROM AUTHOR]

Since many years libraries, archives and museums, the institutions entrusted with the dissemination and conservation of cultural heritage, contributed to metada standards definition, meta-dating methodologies and metadata representation in different syntaxes, by partecipating to national projects and international initiatives. Management systems and catalogs used in these contexts borrow from information and telecommunication technology tools, metodoligies and techniques to generate, organize, share and use various types of metadata. But the Information and Communication Technology (ICT) area is not only a supplier of technological tools and solutions as it also constitutes a rich basin in which metadata plays a fundamental role in designing architectures, modeling information systems and implementing services. Through an overview that includes national and European initiatives, especially in the digital transformation process of the public sector, this paper aims to offer a look to metadata beyond the traditional boundary of libraries and other cultural institutions and to underline some relevant aspects such as standardization, sharing, reuse and metadata quality assessment. This framework highlights the need to carry on with the path of cooperation between different functional domains and organizational contexts in order to consolidate and extend the (re)use of metadata schemes, ontologies and controlled vocabularies both in the redesign of digitized processes and in the implementation of services supporting them. By undertaking since design early stages a multidisciplinary approach based on metadata standards can ensure greater flexibility and higher interoperability. This vision requires the enhancement of intersectorial skills that meld metadata methodologies and syntaxes representation basic knowledge with the ability to model functional domains using metadata schemes and ontologies. [ABSTRACT FROM AUTHOR]

Information systems are developed by people with intent--they are designed to help creators and users tell specific stories with data. Within information systems, the often invisible structures of metadata profoundly impact the meaning that can be derived from that data. The Linked Infrastructure for Networked Cultural Scholarship project (LINCS) helps humanities researchers tell stories by using linked open data to convert humanities datasets into organized, interconnected, machine-processable resources. LINCS provides context for online cultural materials, interlinks them, and grounds them in sources to improve web resources for research. This article describes how the LINCS team is using the shared standards of linked data and especially ontologies--typically unseen yet powerful--to bring meaning mindfully to metadata through structure. The LINCS metadata--comprised of linked open data about cultural artifacts, people, and processes--and the structures that support it must represent multiple, diverse ways of knowing. It needs to enable various means of incorporating contextual data and of telling stories with nuance and context, situated and supported by data structures that reflect and make space for specificities and complexities. As it addresses specificity in each research dataset, LINCS is simultaneously working to balance interoperability, as achieved through a level of generalization, with contextual and domain-specific requirements. The LINCS team's approach to ontology adoption and use centers on intersectionality, multiplicity, and difference. The question of what meaning the structures being used will bring to the data is as important as what meaning is introduced as a result of linking data together, and the project has built this premise into its decision-making and implementation processes. To convey an understanding of categories and classification as contextually embedded--culturally produced, intersecting, and discursive--the LINCS team frames them not as fixed but as grounds for investigation and starting points for understanding. Metadata structures are as important as vocabularies for producing such meaning. [ABSTRACT FROM AUTHOR]

Development and implementation of Current Research Information Systems (CRIS) is one of the most transparent and practical approaches to curate research information on a national level. The process of building and implementing such systems is a complex and time consuming where successful results heavily depend on the established research information infrastructure of a country, the interoperability of the systems and the quality of the information which reside in them. The purpose of this paper is to analyse the existing Ukrainian Research Information Infrastructure and identify which databases could be reused and integrated with a national Ukrainian Current Research Information System (URIS). The analysis showed that there are functional databases and registries that collect data on research activities and could be used as a data sources for the URIS. In particular, the Unified State Electronic Database on Education is a potential data source on higher educational institutions, the National Repository of Academic Texts — on metadata on research output, internal database of the National Research Foundation of Ukraine and database on research projects maintained by Ukrainian Institute of Scientific Technical and Economic Information — on projects. Secondly, it was identified that Ukrainian research infrastructure lacks complete, up-to-date registry on researchers. Finally, we discussed the challenges and solutions for further steps in building national CRIS. [ABSTRACT FROM AUTHOR]

In this study, we introduce an open-source dataset holding power measurements of 175 residential photovoltaic (PV) systems that are distributed throughout the province of Utrecht, the Netherlands. The dataset features power measurements with a high temporal resolution, i.e., 1 min, for the period January 2014 until December 2017 (over 260 × 106 data points). Spatial information of the PV systems is mapped through latitude and longitude grids, with a resolution up to 150 × 150 m. In addition, we develop and publish a quality control routine that can be applied to validate and filter PV power measurements. Finally, we propose a method to estimate the rated DC capacity of a PV system based on the power measurements. We have deposited five files into the Zenodo repository [Visser et al. (2022). Zenodo, V. 0.0.1, Dataset https://doi.org/10.5281/zenodo.6906504], which are publicly available. Four numerical datasets are enclosed, holding unfiltered power measurements, filtered power measurements at two different stages and metadata. The latter includes information on the tilt angle, azimuth angle, the estimated DC and AC capacity, and location. Finally, a Python package featuring the quality control routine developed to validate and filter PV power measurements is published. [ABSTRACT FROM AUTHOR]

Increasing competition and loss of revenues force newsrooms to explore new digital solutions. The new solutions employ artificial intelligence and big data techniques such as machine learning and knowledge graphs to manage and support the knowledge work needed in all stages of news production. The result is an emerging type of intelligent information system we have called the Journalistic Knowledge Platform (JKP). In this paper, we analyse for the first time knowledge graph-based JKPs in research and practice. We focus on their current state, challenges, opportunities and future directions. Our analysis is based on 14 platforms reported in research carried out in collaboration with news organisations and industry partners and our experiences with developing knowledge graph-based JKPs along with an industry partner. We found that: (a) the most central contribution of JKPs so far is to automate metadata annotation and monitoring tasks; (b) they also increasingly contribute to improving background information and content analysis, speeding-up newsroom workflows and providing newsworthy insights; (c) future JKPs need better mechanisms to extract information from textual and multimedia news items; (d) JKPs can provide a digitalisation path towards reduced production costs and improved information quality while adapting the current workflows of newsrooms to new forms of journalism and readers' demands. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is beginning to transform traditional research practices in many areas. In this context, literature reviews stand out because they operate on large and rapidly growing volumes of documents, that is, partially structured (meta)data, and pervade almost every type of paper published in information systems research or related social science disciplines. To familiarize researchers with some of the recent trends in this area, we outline how AI can expedite individual steps of the literature review process. Considering that the use of AI in this context is in an early stage of development, we propose a comprehensive research agenda for AI-based literature reviews (AILRs) in our field. With this agenda, we would like to encourage design science research and a broader constructive discourse on shaping the future of AILRs in research. [ABSTRACT FROM AUTHOR]

Day by day, music streaming services grow the volume of data on the internet. To help the users to find songs that fit their interests, music recommender systems can be used to filter a large number of songs according to the preference of the user. However, the context in which the users listen to songs must be taken into account, which justifies the usage of context-aware recommender systems. Although there are some works about context-aware music recommender systems, there is a lack of automatic techniques for extracting contextual information for these systems. Thus, the goal of this work is to propose two methods to acquire contextual information (represented by embeddings) for each song, given the sequence of songs that each user has listened to. The first method, called Metadata-Aware, uses tags and genres to enrich the embeddings with additional information. The second method, called Dual Recurrent Neural Network, uses such a network to improve the embeddings generated from long sequences of songs. The embeddings generated by both methods were evaluated with four context-aware music recommender systems in two datasets. The results showed that the embeddings, obtained by our proposals, present better results than the state-of-the-art method proposed in the literature (in some cases with gains of more than 100 %). Finally, the experiments also showed that our second method provides better results than the first one. [ABSTRACT FROM AUTHOR]

Copyright of Iranian Journal of Information Processing & Management is the property of Iranian Information & Documentation Center (IRANDOC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent production line is the abbreviation of intelligent production line. Intelligent production line refers to a form of production organization that uses intelligent manufacturing technology to realize the production process of products. The actual manufacturing process includes different levels and links, and each step cooperates to create a high-efficiency production line. The intelligent production line includes 3 levels covering automation equipment, digital workshops, and intelligent factories and runs through 6 major links of intelligent manufacturing (intelligent management, intelligent monitoring, intelligent processing, intelligent assembly, intelligent inspection, and intelligent logistics). The emergence of the Internet of Things system has changed the way of information dissemination. The system combines radio frequency automatic identification and global positioning system technologies to achieve functions such as information exchange and processing, enabling information processing to be intelligent and improving resource utilization. Big data processing includes multiple data processing procedures, but data quality is the most important link in the entire process, and each data processing link will have an impact on the quality of big data. The big data processing process mainly includes data collection, data preprocessing, data storage, data processing and analysis, data display, data visualization, data application, and other links. This article aims to study the new progress of artificial intelligence algorithms for big data processing of IOT systems on intelligent production lines. It is hoped that through the development of intelligent production lines and big data processing technologies, ways to optimize artificial intelligence algorithms can be found. This study proposes a metadata replication method based on a separate replication strategy, which separates the replication process of the data operation log, each is independent, and shortens the data replication time. Combining the existing intelligent production line network platform in the laboratory and carrying out the research of the intelligent production line network state prediction system based on the neural network to design a network prediction system can prejudge the operation status of the intelligent production line network. The experimental results in this article show that when the Namenode mode is used to read data and when the number of clients reaches 8, the data processing basically remains unchanged. When the NCluster system reads data and when the number of clients is 6, the data is processed 1256. When the number of clients is 20, the data is processed 2100, the NCluster system will remain stable when the number of clients reaches 12, and compared with the Namenode system, it has obvious advantages. [ABSTRACT FROM AUTHOR]

Background: Research networks offer multidisciplinary expertise and promote information exchange between researchers across Europe. They are essential for the European Union's (EU) health information system as providers of health information and data. The aim of this mapping exercise was to identify and analyze EU research networks in terms of health data collection methods, quality assessment, availability and accessibility procedures. Methods: A web-based search was performed to identify EU research networks that are not part of international organizations (e.g., WHO-Europe, OECD) and are involved in collection of data for health monitoring or health system performance assessment. General characteristics of the research networks (e.g., data sources, representativeness), quality assessment procedures, availability and accessibility of health data were collected through an ad hoc extraction form. Results: Fifty-seven research networks, representative at national, international or regional level, were identified. In these networks, data are mainly collected through administrative sources, health surveys and cohort studies. Over 70% of networks provide information on quality assessment of their data collection procedures. Most networks share macrodata through articles and reports, while microdata are available from ten networks. A request for data access is required by 14 networks, of which three apply a financial charge. Few networks share data with other research networks (8/49) or specify the metadata-reporting standards used for data description (9/49). Conclusions: Improving health information and availability of high quality data is a priority in Europe. Research networks could play a major role in tackling health data and information inequalities by enhancing quality, availability, and accessibility of health data and data sharing across European networks. [ABSTRACT FROM AUTHOR]

There were analyzed 100 Latin American scientific journals of Legal Sciences with 19,826 articles published between 2005 and 2019. The objective is to offer a comprehensive perspective of the discipline based on the scientific output that circulates in non-commercial Open Access channels (Diamond OA), highlighting its publication and collaboration dynamics. The journals were identified from some of the main global Open Access platforms: DOAJ, Latindex, Redalyc and SciELO. Metadata was obtained based on Redalyc, OJS and SciELO. The analysis is based on automatic information retrieval and a complementary normalization strategy. A tendency to publish mainly at the national level and, secondly, at the institutional level is documented; as well as little presence of the Caribbean and Central America; and a growing trend of scientific collaboration, mainly driven by institutional networks. It is suggested that currently the communication of knowledge in the Latin American Legal Sciences takes place predominantly from a Diamond Open Access model and in this context, challenges of interoperability, retrieval and organization of bibliometric information are identified, in articulation with a growing legitimization of commercial publishing universes. [ABSTRACT FROM AUTHOR]

The Territorial Information System of Navarre - SITNA, after more than twenty years of experience, initiated in April 2020 a project, focused on restructuring, modernising and improving the range of services offered to the society. As a result, the project set out a new geoportal, capable of offering all the official geospatial information of the entities of Navarre: https://geoportal.navarra.es/ The main objective of the geoportal is to offer a single channel of communication that facilitates the continuous dissemination of relevant content for people of different profiles, both technical and with no prior knowledge of geospatial information. The mission of the geoportal is, therefore, to bring to the general public, all the geospatial information of Navarre through an easy and intuitive navigation, capable of attracting any type of web user. [ABSTRACT FROM AUTHOR]

Performing whole genome sequencing (WGS) for the surveillance of antimicrobial resistance offers the ability to determine not only the antimicrobials to which rates of resistance are increasing, but also the evolutionary mechanisms and transmission routes responsible for the increase at local, national, and global scales. To derive WGS-based outputs, a series of processes are required, beginning with sample and metadata collection, followed by nucleic acid extraction, library preparation, sequencing, and analysis. Throughout this pathway there are many data-related operations required (informatics) combined with more biologically focused procedures (bioinformatics). For a laboratory aiming to implement pathogen genomics, the informatics and bioinformatics activities can be a barrier to starting on the journey; for a laboratory that has already started, these activities may become overwhelming. Here we describe these data bottlenecks and how they have been addressed in laboratories in India, Colombia, Nigeria, and the Philippines, as part of the National Institute for Health Research Global Health Research Unit on Genomic Surveillance of Antimicrobial Resistance. The approaches taken include the use of reproducible data parsing pipelines and genome sequence analysis workflows, using technologies such as Data-flo, the Nextflow workflow manager, and containerization of software dependencies. By overcoming barriers to WGS implementation in countries where genome sampling for some species may be underrepresented, a body of evidence can be built to determine the concordance of antimicrobial sensitivity testing and genome-derived resistance, and novel high-risk clones and unknown mechanisms of resistance can be discovered. [ABSTRACT FROM AUTHOR]

Purpose: To determine whether computed tomography radiation dose data could be captured electronically across hospitals to derive regional diagnostic reference levels for quality improvement. Methods: Data on consecutive computed tomography examinations from 8 hospitals were collected automatically in a central database (Repository) from April 2017 to September 2017. The most frequently performed examinations were used to determine the standard protocols for each hospital. Diagnostic reference levels across hospitals were derived using statistical distribution for 2 radiation dose metrics. These values were compared between hospitals, within and between hospitals by scanner and against national Health Canada achievable doses and diagnostic reference levels. Results: Three master protocol groups, Head, Abdomen–Pelvis, and Chest–Abdomen–Pelvis, accounted for 43% of all valid studies (N = 40 277). For the Repository, 11 of 12 mean values and 75th percentile diagnostic reference levels were below the Health Canada mean and 75th percentile values, and one was the same as the Health Canada value. Mean radiation dose by protocol varied by as much as 97% between hospitals. There was no consistent pattern in the difference between mean doses between large and small hospitals. Conclusion: This electronic data acquisition process could be used to continually update achievable doses for frequently used computed tomography examinations in Ontario and eliminate the need for nationwide manual surveys. Results compared across institutions will allow hospitals to maintain achievable doses and lower patient exposure. [ABSTRACT FROM AUTHOR]

Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines. [ABSTRACT FROM AUTHOR]

Since bulk transfer bandwidth of the host is unstable, the universal serial bus (USB) 3.0 hyperspectral data transfer system can only achieve a data transfer rate of about 30 MBps which is less than one-fifteenth of USB 3.0 theoretical transfer rate of 5 Gbps. For aerial hyperspectral imager, data transfer system is required to meet different frame rates of detector for different speed-to-height ratios. In this paper, we propose a high-speed and adjustable synchronous transfer system. The USB 3.0 peripheral controller uses synchronous first in first out (FIFO) and automatic direct memory access (DMA) to achieve the highest data transfer bandwidth. The USB acquisition software collects a data block in every fixed time interval. The size in bytes of every data block must be an integer multiple of the maximum data packet payload size, which is a necessary condition for using automatic DMA and bulk transfers. The data transfer rate of the system could be adjusted by directly changing the data block size and acquisition time interval. The experimental results show that the synchronous transfer mechanism could facilitate the 100-MBps error-free and high data transfer bandwidth application on a hyperspectral data processing system. [ABSTRACT FROM AUTHOR]

Purpose: The rapid implementation of electronic health records (EHRs) resulted in a lack of data standardization and created considerable difficulty for secondary use of EHR documentation data within and between organizations. While EHRs contain documentation data (input), nurses and healthcare organizations rarely have useable documentation data (output). The purpose of this article is to describe a method of standardizing EHR flowsheet documentation data using information models (IMs) to support exchange, quality improvement, and big data research. As an exemplar, EHR flowsheet metadata (input) from multiple organizations was used to validate a fall prevention IM. Design: A consensus‐based, qualitative, descriptive approach was used to identify a minimum set of essential fall prevention data concepts documented by staff nurses in acute care. The goal was to increase generalizable and comparable nurse‐sensitive data on the prevention of falls across organizations for big data research. Methods: The research team conducted a retrospective, observational study using an iterative, consensus‐based approach to map, analyze, and evaluate nursing flowsheet metadata contributed by eight health systems. The team used FloMap software to aggregate flowsheet data across organizations for mapping and comparison of data to a reference IM. The FloMap analysis was refined with input from staff nurse subject matter experts, review of published evidence, current documentation standards, Magnet Recognition nursing standards, and informal fall prevention nursing use cases. Findings: Flowsheet metadata analyzed from the EHR systems represented 6.6 million patients, 27 million encounters, and 683 million observations. Compared to the original reference IM, five new IM classes were added, concepts were reduced by 14 (from 57 to 43), and 157 value set items were added. The final fall prevention IM incorporated 11 condition or age‐specific fall risk screening tools and a fall event details class with 14 concepts. Conclusion: The iterative, consensus‐based refinement and validation of the fall prevention IM from actual EHR fall prevention flowsheet documentation contributes to the ability to semantically exchange and compare fall prevention data across multiple health systems and organizations. This method and approach provides a process for standardizing flowsheet data as coded data for information exchange and use in big data research. Clinical Relevance: Opportunities exist to work with EHR vendors and the Office of the National Coordinator for Health Information Technology to implement standardized IMs within EHRs to expand interoperability of nurse‐sensitive data. [ABSTRACT FROM AUTHOR]

eLTER was a "Horizon 2020" project with the aim of advancing the development of long-term ecosystem research infrastructure in Europe. This paper describes how eLTER Information System infrastructure has been expanded by a climate service data product providing access to specifically pre-processed regional climate change scenario data from a state-of-the-art regional climate model ensemble of the Coordinated Regional Downscaling Experiment (CORDEX) for 702 registered ecological research sites across Europe. This tailored, expandable, easily accessible dataset follows FAIR principles and allows researchers to describe the climate at these sites, explore future projections for different climate change scenarios and make regional climate change assessments and impact studies. The data for each site are available for download from the EUDAT collaborative data infrastructure B2SHARE service and can be easily accessed and visualised through the Dynamic Ecological Information Management System – Site and Dataset Registry (DEIMS-SDR), a web-based information management system which shares detailed information and metadata on ecological research sites around the globe. This paper describes these data and how they can be accessed by users through the extended eLTER Information System architecture. The data and supporting information are available from B2SHARE. Each individual site (702 sites are available) dataset has its own DOI. To aid data discovery, a persistent B2SHARE lookup table has been created which matches the DOIs of the individual B2SHARE record with each DEIMS site ID. This lookup table is available at 10.23728/b2share.bf41278d91b445bda4505d5b1eaac26c (eLTER EURO-CORDEX Climate Service, 2020). [ABSTRACT FROM AUTHOR]

This article examines the impact of the digital humanities on information science and information scientists and how information scientists can contribute to digital collaborations in the digital humanities. This article uses three basic concepts—user-researcher, digital information system, and digital research object—as the framework for a description of the digital humanities shift and discusses the consequences of this shift for information science with reference to these concepts. The second part of the article investigates the two pillars of the digital humanities shift, acknowledgement of digital structure and the exploring mind, in more detail. In the case of correspondences, acknowledgement of digital structure involves respecting the 'thing-like' properties of letters that cannot be handled (searched, classified, etc.) unless they are translated into digitized metadata; this elevates metadata to genuine digital research objects. These theoretical issues are illustrated by the Prior archive, a collection of digitized letters, manuscript drafts, and other 'grey' material bequeathed from the New Zealand logician-philosopher Arthur Norman Prior. The concept of the 'exploring mind' is connected to a movement in information science, away from the needful user and information gap paradigm towards more open and exploratory information behaviour in the digital humanities. A concluding literature review examines selected works of information science that deals with non-standard, serendipitous information behaviour and identifies exploratory and serendipitous design features of information systems for the digital humanities. These features are represented in a taxonomy consisting of six design categories. [ABSTRACT FROM AUTHOR]

The past several years have seen a proliferation of software‐defined radio (SDR) data collection systems and processing platforms designed for or applicable to satellite navigation (satnav) applications. These systems necessarily produce datasets in a wide range of different formats. To correctly interpret this SDR data, essential information such as the packed sample format and sampling rate is needed. Communicating this metadata between creators and users has historically been an ad‐hoc, cumbersome, and error‐prone process. To address this issue, the satnav SDR community developed a metadata standard and normative software library to automate this process, thus simplifying the exchange of datasets and promoting the interoperability of satnav SDR systems. The standard was ratified and formally accepted as an Institute of Navigation Standard in January 2020. This article describes the ION GNSS SDR metadata standard and associated open‐source software project. All content associated with the standard is available on sdr.ion.org. [ABSTRACT FROM AUTHOR]

Background: In shale basins, petroleum systems are complex; they hold data sources in Big Data scales. The motivation of research lies with the facts of exploring effective inherent connectivity between unconventional petroleum systems. The connectivity between energy reservoir systems is ambiguous within a distinctive petroleum ecosystem. Heterogeneity and multidimensionality of unstructured data sources are additional challenges, precluding systematic modelling of diverse petroleum systems and their data integration process, including growing demand for storage systems. The research aims to establish the knowledge-based connectivity between petroleum systems through Information System (IS) articulations, visual analytics and data management. Method: We investigate the knowledge-based IS guided exploration and production systems to explore the connectivity between diverse unconventional petroleum systems and forecast the reservoir energy. We articulate Design Science Information System (DSIS), bring various IS artefacts, unify multiple domains of petroleum provinces and analyze the associativity between petroleum systems. In addition, use, reuse, effectiveness and interoperability are utility properties of IS artefacts that we evaluate. We implement IS solutions in the oil and gas industries to facilitate database management and reservoir energy exploration. Results: We simulate DSIS as an Unconventional Digital Petroleum Ecosystem (UDPE) as it allows us to investigate and ascertain the interplay between petroleum systems' elements and processes. Metadata cubes are computed for data views to visualize, interpret, and implement IS articulations in energy systems. We compute the structure and reservoir attribute views for interpreting energy-driven petroleum systems, prospect evaluation and business-knowledge management with a viable DSIS solution. Conclusions: The DSIS emerges as a knowledge-based digital ecosystem innovation, demonstrating how it can effectively interconnect geographically controlled petroleum systems. Its development, in the exploration of unconventional shale basins, is a knowledge-based reservoir-energy management solution. This research is beneficial to IS practitioners who wish to pursue energy research in reservoir ecosystem contexts. [ABSTRACT FROM AUTHOR]

Citations based relevant research paper recommendations can be generated primarily with the assistance of three citation models: (1) Bibliographic Coupling, (2) Co-Citation, and (3) Direct Citations. Millions of new scholarly articles are published every year. This flux of scientific information has made it a challenging task to devise techniques that could help researchers to find the most relevant research papers for the paper at hand. In this study, we have deployed an in-text citation analysis that extends the Direct Citation Model to discover the nature of the relationship degree-ofrelevancy among scientific papers. For this purpose, the relationship between citing and cited articles is categorized into three categories: weak, medium, and strong. As an experiment, around 5,000 research papers were crawled from the CiteSeerX. These research papers were parsed for the identification of in-text citation frequencies. Subsequently, 0.1 million references of those articles were extracted, and their in-text citation frequencies were computed. A comprehensive benchmark dataset was established based on the user study. Afterwards, the results were validated with the help of Least Square Approximation by Quadratic Polynomial method. It was found that degreeof- relevancy between scientific papers is a quadratic increasing/decreasing polynomial with respect to-increase/decrease in the in-text citation frequencies of a cited article. Furthermore, the results of the proposed model were compared with state-of-the-art techniques by utilizing a well-known measure, known as the normalized Discount Cumulative Gain (nDCG). The proposed method received an nDCG score of 0.89, whereas the state-of-the-art models such as the Content, Bibliographic-coupling, and Metadata-based Models were able to acquire the nDCG values of 0.65, 0.54, and 0.51 respectively. These results indicate that the proposed mechanism may be applied in future information retrieval systems for better results. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative framework to explore large audiovisual archives using Immersive Environments to place users inside a dataset and create an embodied experience. It starts by outlining the need for such a novel interface to meet the needs of archival scholars and the GLAM sector, and discusses issues in the current modes of access, mostly restrained to traditional information retrieval systems based on metadata. The paper presents the concept of "generous interfaces" as a preliminary approach to address these issues, and argues some of the key reasons why employing Immersive Visual Storytelling might benefit such frameworks. The theory of embodiment is leveraged to justify this claim, showing how a more embodied understanding of a collection can result in a stronger engagement for the public. By placing users as actors in the experience rather than mere spectators, the emergence of narrative is driven by their interactions, with benefits in terms of engagement with the public and understanding of the cultural component. The framework we propose is applied to two existing installations to analyze them in-depth and critique them, highlighting the key directions to pursue for further development. [ABSTRACT FROM AUTHOR]