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Social research methodologists have postulated that the transparency of survey procedures and data processing is mandatory for assessing the Total Survey Error. Recent analyses of data from cross-national surveys have demonstrated an increase in the quality of documentation reports over time and significant differences in documentation quality between the projects. This research note replicates previous results with an extended set of documentation-related quality indicators describing the degree of completeness of information at the consecutive steps of the survey cycle. It also extends earlier findings by indicating no significant relationship between the quality of the survey documentation and the quality of the survey itself. We analysed a meta-data set of survey characteristics, studying all available up-to-date methodological reports of 1,145 national surveys from four large-scale multi-wave projects: the European Quality of Life Survey (2003–2016), European Social Survey (2002–2018), European Values Study (1981–2017), and International Social Survey Programme (1985–2018). [ABSTRACT FROM AUTHOR]

A need to manage and preserve indigenous knowledge is widely recognised. This is because it is understood to have the potential to promote sustainable development while also preserving indigenous peoples' rights, interests, and cultural identity. To understand the challenges of preserving and integrating digitized indigenous knowledge into open science platforms, a qualitative study was carried out. The study's theoretical underpinnings were based on the ideas that data must be discoverable, accessible, interoperable, and usable (FAIR) and that indigenous people have the power to control their data and the duty to disclose how it is used to advance and defend their rights and general well-being (CARE). The data were analysed thematically. The results demonstrate that indigenous knowledge is stored in separate databases at institutions in various countries. Additionally, there was no standardization in the organizations that were collecting the metadata. Because of a lack of metadata, the digital divide, and poor digital and indigenous librarian abilities, indigenous knowledge and data were practically inaccessible and unfindable. In the aftermath of the digital revolution and open research, this article brings a new viewpoint to the conversation about maintaining indigenous knowledge in sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Institutions that house Indigenous oral histories are often ignorant to the harm they perpetuate by treating these materials without rooting curatorial considerations in Indigenous approaches to knowledge. Issues of categorization, attribution, and access, among many others, often go unaddressed, causing serious harm to Indigenous peoples and their cultural resources. It is imperative that Western archives, libraries, and museums seek out alternatives to systems that perpetuate settler-colonialism, and work to place control of oral histories back in Indigenous hands. In this paper we present several considerations for working with Indigenous oral histories in Western archives, provide case studies that promote the ethical curation and sharing of Indigenous materials, and close with an overview of our current work to revitalize and redress a large collection of Native American oral histories presently housed at the University of Florida. [ABSTRACT FROM AUTHOR]

This paper introduces the open-source Urban Belonging (UB) toolkit, designed to study place attachments through a combined digital, visual and participatory methodology that foregrounds lived experience. The core of the toolkit is the photovoice UB App, which prompts participants to document urban experiences as digital data by taking pictures of the city, annotating them, and reacting to others' photos. The toolkit also includes an API interface and a set of scripts for converting data into visualizations and elicitation devices. The paper first describes how the app's design specifications were co-created in a process that brought in voices from different research fields, planners from Gehl Architects, six marginalized communities, and citizen engagement professionals. Their inputs shaped decisions about what data collection the app makes possible, and how it mitigates issues of privacy and visual and spatial literacy to make the app as inclusive as possible. We document how design criteria were translated into app features, and we demonstrate how this opens new empirical opportunities for community engagement through examples of its use in the Urban Belonging project in Copenhagen. While the focus on photo capture animates participants to document experiences in a personal and situated way, metadata such as location and sentiment invites for quali-quantitative analysis of both macro trends and local contexts of people's experiences. Further, the granularity of data makes both a demographic and post-demographic analysis possible, providing empirical ground for exploring what people have in common in what they photograph and where they walk. And, by inviting participants to react to others' photos, the app offers a heterogeneous empirical ground, showing us how people see the city differently. We end the paper by discussing remaining challenges in the tool and provide a short guide for using it. [ABSTRACT FROM AUTHOR]

A collection of approximately 12,000 late eighteenth- to mid-twentieth-century Spanish plays were purchased by the University of Illinois at Urbana-Champaign Library in the 1960s and bound into 669 volumes. These items have been technically accessible and browsable but practically undiscoverable. A project was undertaken to improve access and discoverability of this hidden collection. The volumes were disbound, stabilized, cataloged, digitized, and placed in storage with ideal preservation conditions. Metadata Maker, an open source cataloging tool, was a critical resource in making the project feasible and ensuring good enough records were actually good enough. [ABSTRACT FROM AUTHOR]

In 2020, the Program for Cooperative Cataloging (PCC) Task Group on Identity Management in NACO sponsored a 14-month PCC Wikidata Pilot, complete with learning objectives, for participants to experiment with Wikidata, an open linked data platform. UCLA Library joined the Pilot to create and edit Wikidata items related to UCLA Library's collections and UCLA Library entities. With the Pilot's conclusion, the UCLA Library Pilot team reflected on lessons learned. By assessing UCLA Library's experience against the Pilot's learning objectives, the authors hope to contribute on-the-ground insights that may be relevant to PCC's progress toward identity management, and the role Wikidata may play in this transition. [ABSTRACT FROM AUTHOR]

The rapid increase and use of educational technologies have a significant impact on students' privacy, personal information and metadata in higher education. The past two decades have also witnessed the process where information about students is stored and handled outside premises and control of learning institutions. The personal information about students and their activity while they interact with learning management systems (LMS) and online learning tools is increasing and is stored in cloud computing platforms, software‐as‐a‐service providers and other vendors. There are reported cases where students' personal data and information have been leaked, with possible privacy violations. Although this issue affects all students, it is important to understand the perspectives of culturally diverse students on the balancing of personal data/information and the use of technology integration in higher education. The purpose of this research was to investigate diverse students' perspectives on privacy, control of personal information, confidentiality and technology integration. The study also aimed to examine the challenges and experiences of loss of control over personal data and metadata encroachment, and protection of privacy in online education. Data were collected following a qualitative research design. Forty in‐depth and semi‐structured focus‐group interviews (26 female and 24 male students) from four academic disciplines (Education, Social Sciences, Business, and Health Sciences) in a public university in the United States were obtained and analysed following a constant comparative analysis approach. Findings show evidence of fear and anxiety about data encroachment, challenges from bureaucratic procedures and lack of educational awareness, privacy concerns about LMS and social media. These results contribute to a new understanding regarding diverse students' knowledge, challenges and experiences on how to balance privacy, confidentiality and technology integration in higher education. Practitioner notesWhat is already known about this topic Considering the increasing Internet world, where new technologies and mobile computing are interwoven in the use of digital technologies and access to information, balancing students' privacy and technology integration is essential in providing students assurance of their data security and privacy.Few studies have explored diverse students' perspectives on privacy and technology integration that include control of personal information, confidentiality and technology integration. Further, there is a dearth of studies on the challenges and experiences of diverse students regarding the loss of control over personal data and metadata encroachment, and protection of privacy in online education.Although rapid digitalization sparked studies about privacy and data usage, such as the perception of privacy breaches for the purpose of COVID‐19 pandemic control, few studies have also raised privacy issues related to student data usage in higher education institutions (HEIs) in the post‐COVID‐19 context. To realise justice technology integration and data usage in HEIs, students' perspectives on privacy should be examined.What this paper adds The results show the following: fear and anxiety about data encroachment, challenges from bureaucratic procedures and lack of educational awareness, privacy concerns about LMS and social media.The result from this work also illustrates that it would be hard for students to evaluate their privacy risk due to lack of educational opportunity to balance privacy and technologies usage, and the blurred boundaries between LMS and third parties including social media in technology integration.Implications for practice and/or policy The paper identifies how the rapid increase and use of educational technologies have, in many ways, influenced diverse students' personal information and metadata in higher education.The paper makes detail recommendations on how HEIs can address the issue of balancing privacy and technology integration as an essential strategy by providing students assurance of their data security and privacy. [ABSTRACT FROM AUTHOR]

This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasingly used to analyze and classify job postings. However, the effectiveness of these algorithms largely hinges on the quality and volume of the training data. In our study, we propose a multi-class classification methodology for job postings, drawing on AI models such as text-davinci-003 and the quantized versions of Falcon 7b (Falcon), Wizardlm 7B (Wizardlm), and Vicuna 7B (Vicuna) to generate synthetic datasets. These synthetic data are employed in two use-case scenarios: (a) exclusively as training datasets composed of synthetic job postings (situations where no real data is available) and (b) as an augmentation method to bolster underrepresented job title categories. To evaluate our proposed method, we relied on two well-established approaches: the feedforward neural network (FFNN) and the BERT model. Both the use cases and training methods were assessed against a genuine job posting dataset to gauge classification accuracy. Our experiments substantiated the benefits of using synthetic data to enhance job posting classification. In the first scenario, the models' performance matched, and occasionally exceeded, that of the real data. In the second scenario, the augmented classes consistently outperformed in most instances. This research confirms that AI-generated datasets can enhance the efficacy of NLP algorithms, especially in the domain of multi-class classification job postings. While data augmentation can boost model generalization, its impact varies. It is especially beneficial for simpler models like FNN. BERT, due to its context-aware architecture, also benefits from augmentation but sees limited improvement. Selecting the right type and amount of augmentation is essential. [ABSTRACT FROM AUTHOR]

Blockchain technology can address data falsification, single point of failure (SPOF), and DDoS attacks on centralized services. By utilizing IoT devices as blockchain nodes, it is possible to solve the problem that it is difficult to ensure the integrity of data generated by using current IoT devices. However, as the amount of data generated by IoT devices increases, scalability issues are inevitable. As a result, large amounts of data are managed on external cloud storage or distributed file storage. However, this has the disadvantage of being outside the blockchain network. This makes it difficult to ensure reliability and causes high latency during data download and upload. To address these limitations, we propose a method for managing large amounts of data in the local storage node of a blockchain network with improved latency and reliability. Each blockchain network node stores data, which is synchronized and recovered based on reaching a consensus between smart contracts in a cluster network. The cluster network consists of a service leader node that serves as a gateway for services and a cluster node that stores service data in storage. The blockchain network stores synchronization and recovery metadata created in the cluster network. In addition, we showed that the performance of smart contract execution, network transmission, and metadata generation, which are elements of the proposed consensus process, is not significantly affected. In addition, we built a service leader node and a cluster node by implementing the proposed structure. We compared the performance (latency) of IoT devices when they utilized the proposed architecture and existing external distributed storage. Our results show improvements up to 4 and 10 times reduction in data upload (store) and download latency, respectively. [ABSTRACT FROM AUTHOR]

Whereas the importance of information system (IS) certifications is increasing to prove compliance with regulatory and industry requirements, research reveals inconsistent findings concerning the effectiveness of IS certifications. Prior studies have concluded that such inconsistent findings stem partly from users' limited understanding of the nature and role of certifications due to ineffective certification presentations. We follow a three-phase, theory-driven design science approach to examine how to design effective certification presentations. First, we identified sources of users' limited understanding and formulated a revised certification presentation that facilitates users' peripheral information processing. We tested our design proposal in an online experiment with 300 participants. Second, we derived meta-requirements and design guidelines by validating a theory-driven model of certification presentations through an online survey with 352 participants. Third, we implemented three certification presentations complying with example guidelines and ran an online experiment with 400 participants to test whether these presentations are effective. We contribute to research and practice by proposing a design theory for certification presentations composed of peripheral cues inducing authority, social proof and likeability to increase users' trust perceptions. [ABSTRACT FROM AUTHOR]

This contribution presents the concept and implementation of a catalog (i.e., a structured collection) of dynamical system models, relevant to control theory. The key features of the catalog are: (a) a combination of human‐readable model documentation with executable code; (b) semantic metadata for each model plus a web platform to provide SPARQL search interface; and (c) model data and supporting software based on open source repositories and open for extension and improvement. [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]

Purpose: The purpose of this study was to understand the landscape of Indian research data repositories (RDRs) indexed in the re3data.org. The study analysed the metadata elements of Indian RDRs to identify their disciplinary orientations, typology, standards adopted, foreign collaborations, etc. The study ascertained the current status of the Indian RDRs by visiting their respective websites and tried to identify and map the exact disciplinary orientation of each RDR. Design/methodology/approach: The study used "content analysis" of the metadata elements extracted from re3data.org along with the information analysis of the respective websites of the registered RDRs. Findings: The study identified that only 80% of the Indian RDRs listed by the re3data.org is currently active. Most of the Indian RDRs are hosted by the central and state governments and are almost equally distributed among Life Sciences, Natural Sciences and Social Sciences domains. The data provided by the re3data.org for the Indian RDRs are not complete and up-to-date. Practical implications: The findings indicate the presence of a good number of inactive RDRs in the re3data.org. The study suggests using a revised version of the DFG subject classification scheme or considering a standard classification scheme for subject indexing. Originality/value: To the best of the authors' knowledge, this study is the first of its kind that critically analysed the metadata values extracted and moved further to identify the current status of Indian RDRs. [ABSTRACT FROM AUTHOR]

The Association for Library Collections & Technical Services (ALCTS) Board of Directors approved the Core Competencies for Cataloging and Metadata Professional Librarians, hereafter referred to as the "Core Competencies," in January 2017. The Core Competencies lists the skills required of professionals performing cataloging and metadata work in libraries of all types. In the six years since the document's release, the cataloging and metadata community has adopted new cataloging standards, experimented with new tools, and engaged in conversations and reparative efforts around inclusive metadata. In this paper, we, the authors of the Core Competencies, report the results of our survey research that assessed the current use of the document within the cataloging and metadata community and solicited comments on ways in which the document might be revised. We conclude with recommendations for immediate changes to the document, and for its future use and maintenance. [ABSTRACT FROM AUTHOR]

The International Conference on Cataloging Principles (Paris, 1961) led to wide acceptance of Seymour Lubetzky's distinction between books and works, where books denoted particular physical objects and works concerned conceptual abstractions associated with the creative labor of particular authors. Lubetzky's formulation of works is included in many of the world's cataloging frameworks, including the Functional Requirements for Bibliographic Records (FRBR). Several conceptual and practical difficulties arise from the widespread adoption of Lubetzkian works in practices associated with knowledge organization. However, FRBR and other knowledge organization frameworks that utilize works as central entities could be made more useable and useful if work, as an organizing principle, were de-emphasized and seen as one among many concepts used for aggregating sets and supersets of objects according to how likely they are to be useful to users of knowledge organization tools like catalogs. [ABSTRACT FROM AUTHOR]

Introduction: The establishment of an integrated and standard metadata system tailored to the local needs in health information management aims to improve the status quo, which in turn, leads to optimal data protection, increase accessibility, and improve information retrieval and user satisfaction. As such, this study endeavors to investigate the constituents of the HISO 10040.4 schema for organizing clinical information from the perspective of health information specialists. Methods: This applied study was conducted in the 2022 winter. To collect data, documentary study and survey were used. The population included 24 faculty members from Tehran University of Medical Sciences in the field of health information management. An online researcher-made questionnaire with 23 questions was used to collect data, and its face validity was confirmed by 3 experts. Finally, the results were reported using descriptive statistics. Results: To organize health information, 22 metadata elements of the HISO 10040.4 schema were scored and evaluated by the experts. Based on the results, the mean and standard deviation of the measured metadata elements were approved by the research community, indicating the extent of their agreement regarding the importance of the elements in describing data in health information centers worksheets. Conclusion: The proposed metadata elements are based on the standard schema of organizing health information and approved by the research community; therefore, they can meet the needs of health information centers. [ABSTRACT FROM AUTHOR]

Background Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats). [ABSTRACT FROM AUTHOR]

Box office is a challenging and crucial task for the movie distributors in decision making. In recent years, movie reviews are widely posted and shared on intelligent multimedia systems and everywhere. In this work, we employ both the metadata of the movie and the sentiment information of the users' reviews to establish an intelligent predicting model. In the sentiment polarity classification model, a co-attention network-based aspect-level sentiment analysis strategy is developed by using the specific word embedding representations from both the contexts and the aspect. Considering the movie success prediction, a Softmax Discriminant Classifier is used due to its capable of dealing with non-linear issues. The sentiments from review texts, together with the movie information are taken as input variables of the predictor. Experimental outcomes verify the working performance of the proposed method which indicates that our model can be further applied to the sentiment analysis and the predicting of movie success. [ABSTRACT FROM AUTHOR]

Transgender people face numerous obstacles to accessing adequate, affordable, and appropriate sexual and reproductive health (SRH) services as outlined by the UN's Sustainable Development Goal 3.7 target of achieving universal access to SRH services by 2030. However, transgender SRH sits as a poorly researched area within public health that makes it difficult to understand the current dilemmas facing transgender SRH. This article reports the findings of a study aimed at taking stock of global research in transgender SRH. A bibliometric analysis, used to gain insights from the retrieved literature's metadata, alongside a content analysis were utilized to examine the growth, impact, and content of retrieved articles. Nine hundred fourteen journal articles were retrieved, predominately in English (884; 96.7%). These involved 3653 authors from 46 affiliated countries. Most frequent keywords included HIV, PrEP, and gender identity; corresponding to the SRH issues studied, namely HIV/AIDs and gender reassignment. Top cited and overall articles originated heavily from US affiliated authors. Content analysis outlined the articles' inclusion of the transgender community to largely have a mixed focus with cisgender people in research, these articles largely disease-focused and conducted within cities in the United States. Growth in transgender SRH research was minimal until the early-2010s, after which a steep rise can be observed. Research retrieved has a disproportionate clinical and biomedical focus around HIV and related STI issues suggesting a failure to engage with reproductive justice and more comprehensive rights-based understanding of SRH. The sustained use of derogative language suggests a need for greater inclusion and awareness of trans identities within research and publishing. The dominance of the United States in authorship and as a site of research establishes the need for more geographically diverse research, trans, and LMIC-led research enquiry and creating greater opportunities for cross-cultural, comparative, and collaborative scholarly work. [ABSTRACT FROM AUTHOR]

This technical report is aimed at guiding the reporting of data on analytical test results, and related metadata, to EFSA in the context of the activities for the surveillance and monitoring of African Swine Fever. The objective is to explain in detail the individual data elements that are included in the EFSA Standard Sample Description version 2 (SSD2) data model. The guidance is intended to support the reporting countries in data transmission using eXtensible Markup Language (XML) data file transfer through the Data Collection Framework (DCF) according to the protocol described in the EFSA Guidance on Data Exchange version 2 (GDE2). The data elements are explained, including information about data type, list of allowed terms and associated business rules. Instructions about how to report common sampling schemes are also provided to ensure harmonised reporting among countries. [ABSTRACT FROM AUTHOR]

A web mapping XYZ Tile Layer Service, such as Google Earth (GE), provides an amazing resource for the visualization of spatial data against aerial and satellite imagery with global coverage, typically at a resolution finer than 5 m. However, the increasing requirement on spatial accuracy in farm information requires a greater appreciation of the issues involved in the use of such services. Position errors can be created in the georeferencing and orthorectification of images, transformation between reference frames (datums) in map projection, e.g., using a spheroid as compared to an ellipsoid earth model, and tectonic shifts. A review is provided of these issues, and a case study is provided of the horizontal positional accuracy of web map imagery for Australian mango orchards. Positional accuracies varied from 1.804 to 6.131 m across four farms using GE 2021 imagery, between 1.556 and 3.365 m in one farm for the most recent imagery available from each of four web map providers, and from 0.806 m (in 2016) to 10.634 m (in 2003) in one farm for the period of 2003 and 2021 using the historical GE imagery resource. A procedure involving the estimation of four transformation parameters was demonstrated for the alignment of GNSS data with GE imagery. However, as the scale factor was unity and the rotational value was near zero, the use of a simple horizontal mean shift vector was recommended. Further recommendations are provided on (i) the use of web mapping services, with a comparison of the use of UAV survey imagery, and (ii) the need for metadata, particularly the date of collection, on collected position data, in the context of use in farm management information systems. [ABSTRACT FROM AUTHOR]

Over the last 50 years, the landscape of marine data management has been transformed. Previously, each research project held its data privately and managed them as local files on disk; today, it is standard practice to share data collaboratively over the internet, often integrated with web tools that provide a global community of scientists with ready access to data analysis and visualization. NOAA Pacific Marine Environmental Laboratory (PMEL) developers and data managers have made and continue to make pivotal contributions toward this evolution. This article examines contributions that include a community-wide standard for metadata storage (e.g., climate and forecast [CF] metadata conventions), a widely used desktop computer tool (PyFerret), a pioneering web server providing visualization and analysis of distributed data (Live Access Server), tailor-made data management systems for uncrewed ocean platforms, and new developments in applications of machine learning to data quality control. We also describe the evolution of in-house PMEL data management, from PMEL developed tools to an open-science, interoperable data approach. [ABSTRACT FROM AUTHOR]

Libraries organize themselves into various departments reflecting their operations and the functional role of individual staff members. Traditionally, most libraries have defined forward facing services as public service. This understanding has often excluded those staff working within a technical services department (acquisitions, cataloging, eresource management), web design, digital services from viewing themselves as providing a public service. The authors analyze library functions in eresource management, licensing, metadata, and accessibility services to underscore the importance of understanding the shared nature of library work such that all staff in the library are seen to be working to promote the connection between patrons and resources, and thus have a connection to each patron who uses the library. By analyzing functional roles and sharing knowledge, libraries can enhance their organizational alignment to support patrons more holistically by broadening the definition of public services. The article emphasizes the necessity of ongoing communication, understanding, and the adoption of a values-based approach toward organizational culture and functioning. [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]

It is widely accepted that old cadastral maps have multiple uses, such as reestablishing cadastral parcel boundaries, municipality boundaries, and coastal limits, or conducting historical, economic, and social studies. In Spain, the Directorate General for Cadastre, and the National Geographic Institute, has numerous digitized old maps that are accessible to users. In the Comunidad Valenciana, the georeferencing of certain series of old cadastral maps is being carried out in phases, which is one of the subjects of this study. A metric analysis of two series of old cadastral maps from a municipality was conducted. One of the series was georeferenced by the Valencia Provincial Cadastre Office, while the other was georeferenced in this research. Additionally, a spatial data infrastructure (SDI) was created, providing WMS, catalog, and document download services. Metadata were also published, containing information about the source, digitalization process, georeferencing, and achieved accuracy, following the ISO 19115 standard for geographic metadata. Furthermore, through individual and group interviews, participatory social research was conducted, to assess the use of old cadastral maps and the created SDI services, aiming to understand the users' appreciation of the services. The results of the social research indicate that the SDI services created are highly valued, but certain conditions need to be met to ensure their effective use by the general public in order to avoid misuses and misinterpretations. [ABSTRACT FROM AUTHOR]

As the price of NAND-flash storage decreases, large-scale backend distributed file systems are being constructed as all-flash storage without HDDs. In fact, the performance of an SSD can sharply decrease due to the internal garbage collection overhead along with write amplification. Lustre distributed file system provides Data-on-MDT (DoM) feature, which stores small files directly in Metadata Server instead of Object Storage Server. Despite of its benefit on communication traffic, DoM fills Metadata Target (MDT) much faster, causing garbage collection with write amplification and drastically reduces the performance of MDT. Also, DoM I/O uses the I/O bandwidth causing I/O bandwidth starvation of other metadata I/O jobs on MDS. We therefore propose two types of I/O separation scheme: Data separation for write amplification, I/O bandwidth separation for bandwidth starvation. We separate the physical placement of DoM data, normal metadata, and journaling data using multi-stream SSD. We also virtually isolated I/O resource of DoM I/O and metadata I/O by limiting the bandwidth of DoM I/O using Linux cgroup. Our schemes enhance the I/O throughput of MDT by 70%, IOPS by 81% preventing write amplification and provide a stable performance of metadata I/O on MDS. [ABSTRACT FROM AUTHOR]

Despite the growth in digital text collections, the ability to retrieve words or phrases with specific attributes is limited, for example, to retrieve words with a specific meaning within a specific section of a text. Many systems work with coarse bibliographic metadata. To enable fine-grained retrieval, it is necessary to encode texts with granular metadata. Sample texts were encoded with granular metadata. Five categories of metadata that can be used to capture additional data about texts were used, namely, morphological, syntactic, semantic, functional and bibliographic. A prototype was developed to parse the encoded texts and store the information in a database. The prototype was used to test the extent to which words or phrases with specific attributes could be retrieved. Retrieval on a detailed level was possible through the prototype. Retrieval using all five categories of metadata was demonstrated, as well as advanced searches using metadata from different categories in a single search. This article demonstrates that when granular metadata is used to encode texts, retrieval is improved. Relevant information can be selected, and irrelevant information can be excluded, even within a text. [ABSTRACT FROM AUTHOR]

Automatic Keyphrase Extraction involves identifying essential phrases in a document. These keyphrases are crucial in various tasks, such as document classification, clustering, recommendation, indexing, searching, summarization, and text simplification. This paper introduces a platform that integrates keyphrase datasets and facilitates the evaluation of keyphrase extraction algorithms. The platform includes BibRank, an automatic keyphrase extraction algorithm that leverages a rich dataset obtained by parsing bibliographic data in BibTeX format. BibRank combines innovative weighting techniques with positional, statistical, and word co-occurrence information to extract keyphrases from documents. The platform proves valuable for researchers and developers seeking to enhance their keyphrase extraction algorithms and advance the field of natural language processing. [ABSTRACT FROM AUTHOR]

The Digital Public Library of America has created a process by which rights-free or openly licensed resources that have already been harvested can be copied over into Wikimedia Commons, thus creating a simple path for including those digital collections materials into Wikipedia articles. By meeting internet users where they already are, rather than relying on them to navigate to individual digital libraries, the access and usage of digital assets is dramatically increased, in particular to user groups that might otherwise not have a reason to interact with such digitized resources. [ABSTRACT FROM AUTHOR]

Digital preservation systems and practices are rooted in research and development efforts from the late 1990s and early 2000s when the cultural heritage sector started to tackle these challenges in isolation. Since then, the commercial sector has sought to solve similar challenges, using different technical strategies such as software defined storage and function-as-a-service. While commercial sector solutions are not necessarily created with long-term preservation in mind, they are well aligned with the digital preservation use case. The cultural heritage sector can benefit from adapting these modern approaches to increase sustainability and leverage technological advancements widely in use across Fortune 500 companies. [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]

Controlled vocabularies used in cultural heritage organizations (galleries, libraries, archives, and museums) are a helpful way to standardize terminology but can also result in misrepresentation or exclusion of systemically marginalized groups. Library of Congress Subject Headings (LCSH) is one example of a widely used yet problematic controlled vocabulary for subject headings. In some cases, systemically marginalized groups are creating controlled vocabularies that better reflect their terminology. When a widely used vocabulary like LCSH and a controlled vocabulary from a marginalized community are both available as linked data, it is possible to incorporate the terminology from the marginalized community as an overlay or replacement for outdated or absent terms from more widely used vocabularies. This paper provides a use case for examining how the Homosaurus, an LGBTQ+ linked data controlled vocabulary, can provide an augmented and updated search experience to mitigate bias within a system that only uses LCSH for subject headings. [ABSTRACT FROM AUTHOR]

For many machine learning applications in drug discovery, only limited amounts of training data are available. This typically applies to compound design and activity prediction and often restricts machine learning, especially deep learning. For low-data applications, specialized learning strategies can be considered to limit required training data. Among these is meta-learning that attempts to enable learning in low-data regimes by combining outputs of different models and utilizing meta-data from these predictions. However, in drug discovery settings, meta-learning is still in its infancy. In this study, we have explored meta-learning for the prediction of potent compounds via generative design using transformer models. For different activity classes, meta-learning models were derived to predict highly potent compounds from weakly potent templates in the presence of varying amounts of fine-tuning data and compared to other transformers developed for this task. Meta-learning consistently led to statistically significant improvements in model performance, in particular, when fine-tuning data were limited. Moreover, meta-learning models generated target compounds with higher potency and larger potency differences between templates and targets than other transformers, indicating their potential for low-data compound design. [ABSTRACT FROM AUTHOR]

Campaign contributions are a staple of congressional life. Yet, the search for tangible effects of congressional donations often focuses on the association between contributions and votes on congressional bills. We present an alternative approach by considering the relationship between money and legislators' speech. Floor speeches are an important component of congressional behavior, and reflect a legislator's policy priorities and positions in a way that voting cannot. Our research provides the first comprehensive analysis of the association between a legislator's campaign donors and the policy issues they prioritize with congressional speech. Ultimately, we find a robust relationship between donors and speech, indicating a more pervasive role of money in politics than previously assumed. We use a machine learning framework on a new dataset that brings together legislator metadata for all representatives in the US House between 1995 and 2018, including committee assignments, legislative speech, donation records, and information about Political Action Committees. We compare information about donations against other potential explanatory variables, such as party affiliation, home state, and committee assignments, and find that donors consistently have the strongest association with legislators' issue-attention. We further contribute a procedure for identifying speech and donation events that occur in close proximity to one another and share meaningful connections, identifying the proverbial needles in the haystack of speech and donation activity in Congress which may be cases of interest for investigative journalism. Taken together, our framework, data, and findings can help increase the transparency of the role of money in politics. [ABSTRACT FROM AUTHOR]

Open data has been improving both publishing platforms and the consumers-oriented process over the years, providing better openness policies and transparency. Although organizations have tried to open their data, the enrichment of their resources through the Web of Data has been decreasing. Linked data has been suffering from notable difficulties in different stages of its life cycle, becoming over the years less attractive to users. According to that, we decided to explore how the lack of some opening requirements affects the decline of the Web of Data. This paper presents the Web of Data radiography, analyzing the governmental domain as a case study. The results indicate that it is necessary to strengthen the data opening process to improve resource enrichment on the Web and have better datasets. These improvements describe that open data must be public, accessible (in machine-readable formats), described (use of robust, granular metadata), reusable (made available under an open license), complete (published in primary forms), and timely (preserve the value of the data). The implementation of these characteristics would enhance the availability and reuse of datasets. Besides, organizations must understand that opening and enriching their data require a completely new approach, and they have to pay special attention and control to this project, generally by putting money, the commitment by management at all levels, and lots of time. On the contrary, given the magnitude of availability and reuse problems identified in the opening and enrichment data process, it is believed that the Web of Data model would inevitably lose the interest it aroused at the beginning if not addressed immediately by data quality, openness, and enrichment issues. Besides, its use would be restricted to a few particular niches or would even disappear altogether. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to assess the estimability of the proposed model, referred to as the mixed semipara-metric switching meta-regression model. The model is expressed as a Generalized Additive Model (GAM), which accommodates a high- dimensional set of covariates not usually considered in metadata. Furthermore, the applicability of the mixed semiparametric switching meta-regression model in meta-analysis settings is established in this study. [ABSTRACT FROM AUTHOR]

Background Measuring the appropriateness of antibiotic use is crucial for antibiotic stewardship (ABS) programmes to identify targets for interventions. Objectives To assess the technical feasibility of converting electronic medical record (EMR) data into ABS indicators. Methods In this observational feasibility study covering a period of 2 years, the EMRs of patients hospitalized at a large non-university hospital network and receiving at least one dose of a systemic antibiotic were included. ABS indicators measuring steps in the process of antibiotic prescription proposed by the literature were collected and rephrased or defined more specifically to be calculable if needed. Algorithms were programmed in R to convert EMR data into ABS indicators. The indicators were visualized in an interactive dashboard and the plausibility of each output value was assessed. Results In total, data from 25 337 hospitalizations from 20 723 individual patients were analysed and visualized in an interactive dashboard. Algorithms could be programmed to compute 89% (25/28) of all pre-selected indicators assessing treatment decisions automatically out of EMR data, with good data quality for 46% (13/28) of these indicators. According to the data quality observed, the most important issues were (i) missing or meaningless information on indication (e.g. 'mild infection') and (ii) data processing issues such as insufficiently categorized metadata. Conclusions The calculation of indicators assessing treatment decisions from EMRs was feasible. However, better data structure and processing within EMR systems are crucial for improving the validity of the results. [ABSTRACT FROM AUTHOR]

Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health. [ABSTRACT FROM AUTHOR]

Biological imaging is one of the primary tools by which we understand living systems across scales from atoms to organisms. Rapid advances in imaging technology have increased both the spatial and temporal resolutions at which we examine those systems, as well as enabling visualisation of larger tissue volumes. These advances have huge potential but also generate ever increasing amounts of imaging data that must be stored and analysed. Public image repositories provide a critical scientific service through open data provision, supporting reproducibility of scientific results, access to reference imaging datasets and reuse of data for new scientific discovery and acceleration of image analysis methods development. The scale and scope of imaging data provides both challenges and opportunities for open sharing of image data. In this article, we provide a perspective influenced by decades of provision of open data resources for biological information, suggesting areas to focus on and a path towards global interoperability. [ABSTRACT FROM AUTHOR]

Bioimaging has now entered the era of big data with faster-than-ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis, and management practices, which are currently hampering the full potential of image data being realized. Here, we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimaging data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field. [ABSTRACT FROM AUTHOR]

The two volumes of this Special Issue explore the intersections of digital libraries, epigraphy and paleography. Digital libraries research, practices and infrastructures have transformed the study of ancient inscriptions by providing organizing principles for collections building, defining interoperability requirements and developing innovative user tools and services. Yet linking collections and their contents to support advanced scholarly work in epigraphy and paleography tests the limits of current digital libraries applications. This is due, in part, to the magnitude and heterogeneity of works created over a time period of more than five millennia. The remarkable diversity ranges from the types of artifacts to the methods used in their production to the singularity of individual marks contained within them. Conversion of analogue collections to digital repositories is well underway—but most often not in a way that meets the basic requirements needed to support scholarly workflows. This is beginning to change as collections and content are being described more fully with rich annotations and metadata conforming to established standards. New use of imaging technologies and computational approaches are remediating damaged works and revealing text that has, over time, become illegible or hidden. Transcription of handwritten text to machine-readable form is still primarily a manual process, but research into automated transcription is moving forward. Progress in digital libraries research and practices coupled with collections development of ancient writtten works suggests that epigraphy and paleography will gain new prominence in the Academy. [ABSTRACT FROM AUTHOR]

Recommendation systems help customers to find interesting and valuable resources in the internet services. Their priority is to create and examine users' individual profiles, which contain their preferences, and then update their profile content with additional features to finally increase the users' satisfaction. Specific characteristics or descriptions and reviews of the items to recommend also play a significant part in identifying the preferences. However, inferring the user's interest from his activities is a challenging task. Hence it is crucial to identify the interests of the user without the intervention of the user. This work elucidates the effectiveness of textual content together with metadata and explicit ratings in boosting collaborative techniques. In order to infer user's preferences, metadata content information is boosted with user-features and item-features extracted from the text reviews using sentiment analysis by Vader lexicon-based approach. Before doing sentiment analysis, ironic and sarcastic reviews are removed for better performance since those reviews inverse the polarity of sentiments. Amazon product dataset is used for the analysis. From the text reviews, we identified the reasons that would have led the user to the overall rating given by him, referred to as features of interest (FoI). FoI are formulated as multi-criteria and the ratings for multiple criteria are computed from the single rating given by the user. Multi-Criteria-based Content Boosted Hybrid Filtering techniques (MCCBHF) are devised to analyze the user preferences from their review texts and the ratings. This technique is used to enhance various collaborative filtering methods and the enhanced proposed MCKNN, MCEMF, MCTFM, MCFM techniques provide better personalized product recommendations to users. In the proposed MCCBHF algorithms, MCFM yields better results with the least RMSE value of 1.03 when compared to other algorithms. [ABSTRACT FROM AUTHOR]

This article analyzes the archival tools’ historical, scientific, and cultural dimensions and their underlying techniques. The aim is then to identify the ways in which it could be possible to go beyond the inevitable approximation of the information in the inventories, going toward research developed not only on metadata but also, and above all, on the data of individual documents. [ABSTRACT FROM AUTHOR]

Therapy discontinuation, a term used to describe clients ending treatment prematurely, is a common problem faced by mental healthcare professionals. This study considers how negative experiences in therapy impact the client's experience in therapy and how this contributes to premature discontinuation. Video posts that used the hashtag bad therapist (#badtherapist) were collected from the social media platform TikTok and analyzed using inductive qualitative content analysis procedures. Five themes emerged from our analysis including: invalidation, biases, boundary violations, including religious beliefs without client agreement, and denial of trauma. Oftentimes, clients who prematurely discontinue services or openly discuss negative therapy experiences in therapy are labeled as "resistant"; however, the data suggest that more careful consideration of therapist behavior and self-of-the-therapist issues is needed. Clinical implications and limitations to the study are discussed. [ABSTRACT FROM AUTHOR]

New daily minimum temperature (TN), maximum temperature (TX) and precipitation amount (PPT) database CADTEP (CAtalan Daily TEmperature and Precipitation data set) has been developed for Catalonia, Spain. The source dataset consists of the available climate records from Catalonia provided by the State Meteorology Agency of Spain (AEMET) and the Catalan Meteorological Service (SMC). We selected the long and fairly complete records and created further long series by merging the records of nearby stations. Finally, 26 TN, 26 TX and 72 PPT time series covering the period 1950–2021 have been accepted for CADTEP from the several hundreds of, but mostly much shorter time series. This database has sufficiently dense time series to reach accurate estimates of regional climate variability. CADTEP time series were subjected to thorough quality control and time series homogenization. In the quality control, many shorter series were still in use. Both in quality control and time series homogenization, several series surrounding Catalonia, but close to the borders, were also considered to achieve a higher data accuracy in CADTEP. The homogenization was performed with ACMANTv5.1. In this new version of ACMANT, metadata dates pointing on likely occurrences of nonclimatic changes (breaks) are contemplated in the homogenization as sure break positions. Finally, we examined the trend and variability of TN, TX and PPT over 1950–2021 using CADTEP. Differences between the use of homogenized and raw data, as well as between the inclusion and exclusion of metadata use in the homogenization procedure were controlled. The most important conclusions are related to the trend analysis, namely the speed of the warming of summer TX is faster than in other seasons, faster than the warming of TN in general, and the triple of the mean global warming speed. [ABSTRACT FROM AUTHOR]

As remote conferencing is becoming commonplace and the term "metaverse" is frequently appearing in the media, society's attention is increasingly focused on communication using video and audio. Although the role of video in such communication tends to gain greater interest, the role of audio--for example, in creating a sense of immersion--is also significant. Noboru Harada, a senior distinguished researcher at NTT Communication Science Laboratories, has been researching speech and acoustic communication for more than 20 years and is currently focusing on research and standardization of natural and highly functional speech and acoustic communication technology to achieve, for example, selective transmission of sounds you want to convey and sounds you want to hear. We interviewed him about his research accomplishments, standardization activities, and attitude as a researcher. [ABSTRACT FROM AUTHOR]

The history of the emerging area of tools for managing research resources and the data produced from them is summarised from the perspective of two decades of use in teaching and research at one institution. These tools are a portal or electronic laboratory notebook for computational chemistry interfaced in one direction to a high-performance computing resource and in the other direction to a modern research data repository. The essential features of both these tools are described over two generations of each, with examples of student work cited as examples using persistent identifiers or PIDs, better known as DOIs. Underpinning this is the metadata describing the data being processed. The article outlines the evolution of managing such metadata-rich data and its progress towards what can now be summarised by the acronym FAIR data, itself enabling future emerging areas such as knowledge graphs. [ABSTRACT FROM AUTHOR]