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Summary: With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions. While data from large-scale consortia are useful as they allow studying the effects of different biological conditions, they can also contain unwanted biases from confounding experimental factors. Here, we introduce Metadata-guided Feature Disentanglement (MFD)—an approach that allows disentangling biologically relevant features from potential technical biases. MFD incorporates target metadata into model training, by conditioning weights of the model output layer on different experimental factors. It then separates the factors into disjoint groups and enforces independence of the corresponding feature subspaces with an adversarially learned penalty. We show that the metadata-driven disentanglement approach allows for better model introspection, by connecting latent features to experimental factors, without compromising, or even improving performance in downstream tasks, such as enhancer prediction, or genetic variant discovery. The code will be made available at https://github.com/HealthML/MFD. [ABSTRACT FROM AUTHOR]

Introduction Multimodal monitoring is the use of data from multiple physiological sensors combined in a way to provide individualized patient management. It is becoming commonplace in the civilian care of traumatic brain-injured patients. We hypothesized we could bring the technology to the battlefield using a noninvasive sensor suite and an artificial intelligence-based patient management guidance system. Methods Working with military medical personnel, we gathered requirements for a hand-held system that would adapt to the rapidly evolving field of neurocritical care. To select the optimal sensors, we developed a method to evaluate both the value of the sensor's measurement in managing brain injury and the burden to deploy that sensor in the battlefield. We called this the Value-Burden Analysis which resulted in a score weighted by the Role of Care. The Value was assessed using 7 criteria, 1 of which was the clinical value as assessed by a consensus of clinicians. The Burden was assessed using 16 factors such as size, weight, and ease of use. We evaluated and scored 17 sensors to test the assessment methodology. In addition, we developed a design for the guidance system, built a prototype, and tested the feasibility. Results The resulting architecture of the system was modular, requiring the development of an interoperable description of each component including sensors, guideline steps, medications, analytics, resources, and the context of care. A Knowledge Base was created to describe the interactions of the modules. A prototype test set-up demonstrated the feasibility of the system in that simulated physiological inputs would mimic the guidance provided by the current Clinical Practice Guidelines for Traumatic Brain Injury in Prolonged Care (CPG ID:63). The Value-Burden analysis yielded a ranking of sensors as well as sensor metadata useful in the Knowledge Base. Conclusion We developed a design and tested the feasibility of a system that would allow the use of physiological biomarkers as a management tool in forward care. A key feature is the modular design that allows the system to adapt to changes in sensors, resources, and context as well as to updates in guidelines as they are developed. Continued work consists of further validation of the concept with simulated scenarios. [ABSTRACT FROM AUTHOR]

Over the last decade, a plethora of training datasets have been compiled for use in language-based machine perception and in human-centered AI, alongside research regarding their compilation methods. From a primarily linguistic perspective, we add to these studies in two ways. First, we provide an overview of sixty-six training datasets used in automatic image, video, and audio captioning, examining their compilation methods with a metadata analysis. Second, we delve into the annotation process of crowdsourced datasets with an interest in understanding the linguistic factors that affect the form and content of the captions, such as contextualization and perspectivation. With a qualitative content analysis, we examine annotator instructions with a selection of eleven datasets. Drawing from various theoretical frameworks that help assess the effectiveness of the instructions, we discuss the visual and textual presentation of the instructions, as well as the perspective-guidance that is an essential part of the language instructions. While our analysis indicates that some standards in the formulation of instructions seem to have formed in the field, we also identified various reoccurring issues potentially hindering readability and comprehensibility of the instructions, and therefore, caption quality. To enhance readability, we emphasize the importance of text structure, organization of the information, consistent use of typographical cues, and clarity of language use. Last, engaging with previous research, we assess the compilation of both web-sourced and crowdsourced captioning datasets from various perspectives, discussing factors affecting the diversity of the datasets. [ABSTRACT FROM AUTHOR]

Motivation Software plays a crucial and growing role in research. Unfortunately, the computational component in Life Sciences research is often challenging to reproduce and verify. It could be undocumented, opaque, contain unknown errors that affect the outcome, or be directly unavailable and impossible to use for others. These issues are detrimental to the overall quality of scientific research. One step to address this problem is the formulation of principles that research software in the domain should meet to ensure its quality and sustainability, resembling the FAIR (findable, accessible, interoperable, and reusable) data principles. Results We present here a comprehensive series of quantitative indicators based on a pragmatic interpretation of the FAIR Principles and their implementation on OpenEBench, ELIXIR's open platform providing both support for scientific benchmarking and an active observatory of quality-related features for Life Sciences research software. The results serve to understand the current practices around research software quality-related features and provide objective indications for improving them. Availability and implementation Software metadata, from 11 different sources, collected, integrated, and analysed in the context of this manuscript are available at https://doi.org/10.5281/zenodo.7311067. Code used for software metadata retrieval and processing is available in the following repository: https://gitlab.bsc.es/inb/elixir/software-observatory/FAIRsoft_ETL. [ABSTRACT FROM AUTHOR]

This study evaluates the effectiveness of self-assessed exposure (SAE) data collection for characterization of hydrogen sulfide (H2S) risks in water and wastewater management, challenging the adequacy of traditional random or campaign sampling strategies. We compared 3 datasets derived from distinct strategies: expert data with activity metadata (A), SAE without metadata (B), and SAE with logbook metadata (C). The findings reveal that standard practices of random sampling (dataset A) fail to capture the sporadic nature of H2S exposure. Instead, SAE methods enhanced by logbook metadata and supported by reliable detection and calibration infrastructure (datasets B and C) are more effective. When assessing risk, particularly peak exposure risks, it is crucial to adopt measures that capture exposure variability, such as the range and standard deviations. This finer assessment is vital where high H2S peaks occur in confined spaces. Risk assessment should incorporate indices that account for peak exposure, utilizing variability measures like range and standard or geometric standard deviation to reflect the actual risk more accurately. For large datasets, a histogram is just as useful as statistical measures. This approach has revealed that not only wastewater workers but also water distribution network workers, can face unexpectedly high H2S levels when accessing confined underground spaces. Our research underscores the need for continuous monitoring with personal electrochemical gas detector alarm systems, particularly in environments with variable and potentially hazardous exposure levels. [ABSTRACT FROM AUTHOR]

Prokaryotes are ubiquitous in the biosphere, important for human health and drive diverse biological and environmental processes. Systematics of prokaryotes, whose origins can be traced to the discovery of microorganisms in the 17th century, has transitioned from a phenotype-based classification to a more comprehensive polyphasic taxonomy and eventually to the current genome-based taxonomic approach. This transition aligns with a foundational shift from studies focused on phenotypic traits that have limited comparative value to those using genome sequences. In this context, Bergey's Manual of Systematics of Archaea and Bacteria (BMSAB) and Bergey's International Society for Microbial Systematics (BISMiS) play a pivotal role in guiding prokaryotic systematics. This review focuses on the historical development of prokaryotic systematics with a focus on the roles of BMSAB and BISMiS. We also explore significant contributions and achievements by microbiologists, highlight the latest progress in the field and anticipate challenges and opportunities within prokaryotic systematics. Additionally, we outline five focal points of BISMiS that are aimed at addressing these challenges. In conclusion, our collaborative effort seeks to enhance ongoing advancements in prokaryotic systematics, ensuring its continued relevance and innovative characters in the contemporary landscape of genomics and bioinformatics. [ABSTRACT FROM AUTHOR]

Undercounting is a critical issue in migration statistics, resulting in bias. This typically arises from insufficient reporting requirements and problems with enforcing such requirements. The main sources of information on undercounting are the metadata accompanying official statistics and expert opinions. However, metadata and arbitrary expert opinions may be limited by overlooking important details in migration data shared by various countries. This includes potential oversight of changes in methodologies, definitions, or retrospective updates to the data following censuses. This work presents a methodological solution with three objectives to address undercounting in international migration data. First, we provide an overview of available metadata and expert opinions on undercounting in European migration flows. Secondly, we propose a novel data-driven approach that incorporates year-specific and duration-of-stay-adjusted classifications. The proposed methodological solution relies on comparisons of flows in the same direction reported by a given country with high-quality data reported by another set of countries. We use bilateral migration data provided by Eurostat, United Nations, and selected national statistical institutes. Duration-of-stay correction coefficients are derived through an optimization model or borrowed from the literature. Metadata and expert opinion scores can also be integrated to classify undercounting. Finally, we provide a dynamic classification of undercounting for thirty-two European countries (2002–21), accessible through an online Shiny application, offering flexibility and adaptability. The findings highlight significant undercounting in new European Union Member States, particularly Bulgaria, Latvia, and Romania. Interestingly, other European countries, including those presumed to maintain reliable population statistics, also exhibit notable periods of undercounting. [ABSTRACT FROM AUTHOR]

Cloud storage has been widely used in remote data management, although correct storage of the outsourced file is still challenging in practice. Proofs of Retrievability (PoRs), a storage-oriented cryptographic tool, support integrity checking and efficient retrieval of the file. However, due to the lack of a fully credible oversight mechanism or a serious dependence on a trusted third party, most PoRs are incapable of achieving essential and straightforward trust between participants (i.e. the client and server). While blockchain shows promise in solving this trust issue, existing blockchain-based storage systems are scenario-constrained as they require private/permissioned or special-construct blockchains. Consequently, none of these systems provide robust and decentralized trustworthiness. We propose a general Blockchain-based Automatic Audit (BAA) scheme for PoR without limitations based on specific blockchain types. Specifically, we present BAA via stitching together a carefully designed or chosen array of sub-components such as storage proofs and Turing-complete smart contracts. We also integrate BAA with specific PoR models to prove its strong generality and availability. To our best knowledge, our proposal is the first blockchain-based approach that enhances traditional PoR models with both automatic audit and fair payment. The final analysis and implemented prototype on Ethereum demonstrate the utility of BAA. [ABSTRACT FROM AUTHOR]

Distant viewing approaches have typically used image datasets close to the contemporary image data used to train machine learning models. To work with images from other historical periods requires expert annotated data, and the quality of labels is crucial for the quality of results. Especially when working with cultural heritage collections that contain myriad uncertainties, annotating data, or re-annotating, legacy data is an arduous task. In this paper, we describe working with two pre-annotated sets of medieval manuscript images that exhibit conflicting and overlapping metadata. Since a manual reconciliation of the two legacy ontologies would be very expensive, we aim (1) to create a more uniform set of descriptive labels to serve as a "bridge" in the combined dataset, and (2) to establish a high-quality hierarchical classification that can be used as a valuable input for subsequent supervised machine learning. To achieve these goals, we developed visualization and interaction mechanisms, enabling medievalists to combine, regularize and extend the vocabulary used to describe these, and other cognate, image datasets. The visual interfaces provide experts an overview of relationships in the data going beyond the sum total of the metadata. Word and image embeddings as well as co-occurrences of labels across the datasets enable batch re-annotation of images, recommendation of label candidates, and support composing a hierarchical classification of labels. [ABSTRACT FROM AUTHOR]

This article demonstrates a method using tools from the field of Natural Language Processing (NLP) to aid in analyzing theatrical texts and similar works. The method deploys pre-trained large language model neural networks to gather metadata for a text that is amenable to downstream statistical analyses surfacing patterns of interest in character dialogue. We specifically focus on Shakespeare's works, collecting metadata in the form of sentiment and emotion scores for each line of his plays. In addition to sentiment and emotion scores produced by NLP models, we also directly gather metadata such as genre, line length, and character gender. We show how these metadata may be used to illuminate a number of interesting patterns in Shakespearean character which may be difficult to detect from a direct reading of the texts. We use these metadata to expose statistically significant relationships in Shakespeare between character gender and the emotional content of that character's dialogue, controlling for genre. We also present here the publicly available dataset that we have compiled to perform these analyses. The data collects text from Shakespeare's plays along with a variety of metadata useful for this and other forms of analysis of Shakespeare's works. The methodology demonstrated here may be extended to other varieties of metadata provided by large NLP models. [ABSTRACT FROM AUTHOR]

Hollywood film remakes, as old as the cinema itself, have attracted much professional, critical, and academic attention. They have been viewed by art critics as products of cultural derivativity and imperialism and commended by financial experts as low-risk business investments, closely linked to other forms of brand extension, such as sequels and bestseller adaptations. In this article, we adopt a film-historical quantitative approach to Hollywood film remakes by analysing metadata obtained from the Internet Movie Database (IMDb) and verified against reliable print and web sources. We analyse 986 Hollywood remakes produced between 1915 and 2020 in terms of raw and relative frequencies of annual releases, genre (in)stability, and patterns of transnational reproduction. We contrast our findings with those outlined by Henderson (2014a) in his statistical survey of Hollywood sequels, series films, prequels, and spin-offs, presented in his monograph The Hollywood Sequel: History and Form, 1911–2010. Having completed his list with recent sequential productions released between 2011 and 2020, we investigate the potential parallels between Hollywood remaking and sequelization practices. Our findings demonstrate historical discrepancies in various 'content recycling' trends, which help better characterize the cultural and commercial significance of remakes and serial forms in the American film industry. [ABSTRACT FROM AUTHOR]

The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability and Technology. The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR's design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance. [ABSTRACT FROM AUTHOR]

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since late 2019 represented an unprecedented public health emergency, which included a need to fully understand coronavirus disease 2019 (COVID-19) across all ages and populations. In response, the US National Institute of Allergy and Infectious Diseases (NIAID) rapidly funded epidemiology studies that monitored COVID-19. However, the diversity and breadth of the populations studied in NIAID-funded COVID-19 observational cohorts were not easy to extrapolate because of siloed approaches to collect and report data within NIAID. Here, we describe the effort to develop a harmonized cohort-study reporting tool that includes common epidemiologic data elements as well as NIAID priorities. We report its implementation to analyze metadata from 58 COVID-19 cohort studies funded from February 2020 to June 2021, and we visualize key metadata, including geographic distribution, study duration, participant demographic characteristics, sample types collected, and scientific priorities addressed. A bibliographic analysis highlights the scientific publications and citations across these funded studies and demonstrates their enormous impact on the COVID-19 field. These analyses highlight how common data elements and reporting tools can assist funding agencies to capture the landscape and potential gaps during public health responses and how they can assist in decision making. [ABSTRACT FROM AUTHOR]

Why was the British Parliament so late in broadcasting its debates? Scholars have made recommendations on parliamentary communication, analysed its effects, and described the debates and arguments on broadcasting parliament. But who was making these arguments, and what role did parliamentarians' identities play in these debates? We show the crucial role that partisanship—but also the distinctions between government and opposition, senior and junior, and urban and rural MPs—played in Westminster's debates on broadcasting itself. We do so by applying our new method of 'structural collocation analysis' to all 3965 debate utterances on broadcasting parliament between 1935 and 2014—rather than merely the eleven official debates on broadcasting parliament studied thus far—comparing utterances by subgroups of MPs using metadata that we added to the digitised proceedings. We focus on issue ownership, discursive differences and MPs' reflections on broadcasting parliament. [ABSTRACT FROM AUTHOR]

Objectives Despite federally mandated collection of sex and gender demographics in the electronic health record (EHR), longitudinal assessments are lacking. We assessed sex and gender demographic field utilization using EHR metadata. Materials and methods Patients ≥18 years of age in the Mass General Brigham health system with a first Legal Sex entry (registration requirement) between January 8, 2018 and January 1, 2022 were included in this retrospective study. Metadata for all sex and gender fields (Legal Sex, Sex Assigned at Birth [SAAB], Gender Identity) were quantified by completion rates, user types, and longitudinal change. A nested qualitative study of providers from specialties with high and low field use identified themes related to utilization. Results 1 576 120 patients met inclusion criteria: 100% had a Legal Sex, 20% a Gender Identity, and 19% a SAAB; 321 185 patients had field changes other than initial Legal Sex entry. About 2% of patients had a subsequent Legal Sex change, and 25% of those had ≥2 changes; 20% of patients had ≥1 update to Gender Identity and 19% to SAAB. Excluding the first Legal Sex entry, administrators made most changes (67%) across all fields, followed by patients (25%), providers (7.2%), and automated Health Level-7 (HL7) interface messages (0.7%). Provider utilization varied by subspecialty; themes related to systems barriers and personal perceptions were identified. Discussion Sex and gender demographic fields are primarily used by administrators and raise concern about data accuracy; provider use is heterogenous and lacking. Provider awareness of field availability and variable workflows may impede use. Conclusion EHR metadata highlights areas for improvement of sex and gender field utilization. [ABSTRACT FROM AUTHOR]

Introduction Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication, and synthesis across studies. Recommendations In this perspective, we provide guidance to those working with EHR use metadata by describing 4 common types, how they are recorded, and how they can be aggregated into higher-level measures of EHR use. We also describe guidelines for reporting analyses of EHR use metadata—or measures of EHR use derived from them—to foster clarity, standardization, and reproducibility in this emerging and critical area of research. [ABSTRACT FROM AUTHOR]

This paper problematizes the increasing trend of metadata collection by law enforcement, in light of the 'going dark' debate, which was spurred by the widespread adoption of secure encryption standards. Focusing on Indian privacy law, which remains nascent as of writing, we examine and propose potential constitutional limitations on metadata collection, and provide substantive guidance on their application. These limitations are bifurcated into two stages: first, whether metadata collection infringes upon the right to privacy, and second, whether the infringement is justified. In determining whether the collection of metadata in a specific case infringes privacy, we conceive of a 'contextual approach', challenging the usual ontological subordination of 'metadata' in relation to 'content data'. At the second stage, we centre the standard of proportionality. We offer substantive guidance for Indian courts at each step of the test, including the development of a 'risk profile' of metadata collection practices. Such guidance is crucial, given the technically intricate nature of cases involving metadata processing. [ABSTRACT FROM AUTHOR]

Despite the fact that our physical observations can often be described by derived physical laws, such as the wave equation, in many cases, we observe data that do not match the laws or have not been described physically yet. Therefore recently, a branch of machine learning has been devoted to the discovery of physical laws from data. We test this approach for discovering the wave equation from the observed spatial-temporal wavefields. The algorithm first pre-trains a neural network (NN) in a supervised fashion to establish the mapping between the spatial-temporal locations (x, y, z, t) and the observation displacement wavefield function u (x, y, z, t). The trained NN serves to generate metadata and provide the time and spatial derivatives of the wavefield (e.g. utt and uxx) by automatic differentiation. Then, a preliminary library of potential terms for the wave equation is optimized from an overcomplete library by using a genetic algorithm. We, then, use a physics-informed information criterion to evaluate the precision and parsimony of potential equations in the preliminary library and determine the best structure of the wave equation. Finally, we train the 'physics-informed' neural network to identify the corresponding coefficients of each functional term. Examples in discovering the 2-D acoustic wave equation validate the feasibility and effectiveness of our implementation. We also verify the robustness of this method by testing it on noisy and sparsely acquired wavefield data. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance (AMR) is a multifaceted global health problem disproportionately affecting low- and middle-income countries (LMICs). The Capturing data on Antimicrobial resistance Patterns and Trends in Use in Regions of Asia (CAPTURA) project was tasked to expand the volume of AMR and antimicrobial use data in Asia. The CAPTURA project used 2 data-collection streams: facility data and project metadata. Project metadata constituted information collected to map out data sources and assess data quality, while facility data referred to the retrospective data collected from healthcare facilities. A down-selection process, labelled "the funnel approach" by the project, was adopted to use the project metadata in prioritizing and selecting laboratories for retrospective AMR data collection. Moreover, the metadata served as a guide for understanding the AMR data once they were collected. The findings from CAPTURA's metadata add to the current discourse on the limitation of AMR data in LMICs. There is generally a low volume of AMR data generated as there is a lack of microbiology laboratories with sufficient antimicrobial susceptibility testing capacity. Many laboratories in Asia are still capturing data on paper, resulting in scattered or unused data not readily accessible or shareable for analyses. There is also a lack of clinical and epidemiological data captured, impeding interpretation and in-depth understanding of the AMR data. CAPTURA's experience in Asia suggests that there is a wide spectrum of capacity and capability of microbiology laboratories within a country and region. As local AMR surveillance is a crucial instrument to inform context-specific measures to combat AMR, it is important to understand and assess current capacity-building needs while implementing activities to enhance surveillance systems. [ABSTRACT FROM AUTHOR]

Summary As demand for the automation of biological assays has increased over recent years, the range of measurement types implemented by multiwell plate readers has broadened and the list of published software packages that caters to their analysis has grown. However, most plate readers export data in esoteric formats with little or no metadata, while most analytical software packages are built to work with tidy data accompanied by associated metadata. 'Parser' functions are therefore required to prepare raw data for analysis. Such functions are instrument- and data type-specific, and to date, no generic tool exists that can parse data from multiple data types or multiple plate readers, despite the potential for such a tool to speed up access to analysed data and remove an important barrier for less confident coders. We have developed the interactive web application, Parsley, to bridge this gap. Unlike conventional programmatic parser functions, Parsley makes few assumptions about exported data, instead employing user inputs to identify and extract data from data files. In doing so, it is designed to enable any user to parse plate reader data and can handle a wide variety of instruments (10+) and data types (53+). Parsley is freely available via a web interface, enabling access to its unique plate reader data parsing functionality, without the need to install software or write code. Availability and implementation The Parsley web application can be accessed at: https://gbstan.shinyapps.io/parsleyapp/. The source code is available at: https://github.com/ec363/parsleyapp and is archived on Zenodo: https://zenodo.org/records/10011752. [ABSTRACT FROM AUTHOR]

Indian literary heritage is vast and is of great importance; to explore it, one has to devote them in studying dialects (Khariboli, Haryanavi, Brajbhasha, Awadhi, Bhojpuri, Marwari, etc.) especially when old Hindi is under the lens of observation. Chanda are poetic compositions that have well-defined structures. Dohā is a kind of chanda which, in our work, has been explored using Kabir's compositions as a case study. Kabir represents the cult of poets who relied on oral means for the propagation and consumption of poetry. The poems were communicated to the later generation through simple acts of recitation and hearing, leading to obvious mutation and multiple versions of the same compositions when documented later and hence needs restoration (at least metrically). Using the knowledge from the state-of-art models metadata generator , Text2Mātrā, and RPaGen and extending beyond them, this article is first of its kind to present Kabir's dohā within the scope of restoration, metrical computation, and statistics. Starting with the restoration process, the proposed algorithms generate data that are subjected to suitable statistical models. These models highlight the trends in the dataset, which is helpful in realizing abstract patterns inside the textual data. [ABSTRACT FROM AUTHOR]

With roots as a public health campaign in the United Kingdom, "Dry January" is a temporary alcohol abstinence initiative encouraging participants to abstain from alcohol use during the month of January. Dry January has become a cultural phenomenon, gaining increasing news media attention and social media engagement. Given the utility of capturing naturalistic discussions around health topics on social media, we examined Twitter chatter about Dry January and associated temporary abstinence experiences. Public tweets were collected containing the search terms "dry january" or "dryjanuary" posted between 15 December and 15 February across 3 years (2020–2). A random subsample stratified by year (n  = 3145) was pulled for manual content analysis by trained coders. Final codebook accounted for user sentiment toward Dry January, user account type, and themes related to Dry January participation. Engagement metadata (e.g. likes) were also collected. Though user sentiment was mixed, most tweets expressed positive or neutral sentiment toward Dry January (74.7%). Common themes included encouragement and support for Dry January participation (14.1%), experimentation with and promotion of nonalcoholic drinks (14.0%), and benefits derived from Dry January participation (10.4%). While there is promise in the movement to promote positive alcohol-related behavior change, increased efforts to deliver the campaign within a public health context are needed. Health communication campaigns designed to inform participants about evidence-based treatment and recovery support services proven to help people quit or cut down on their drinking are likely to maximize benefits. [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]

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]

Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible ("Cloud") data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community. [ABSTRACT FROM AUTHOR]

Wikidata has been widely used in Digital Humanities (DH) projects. However, a focused discussion regarding the current status, potential, and challenges of its application in the field is still lacking. A systematic review was conducted to identify and evaluate how DH projects perceive and utilize Wikidata, as well as its potential and challenges as demonstrated through use. This research concludes that: (1) Wikidata is understood in the DH projects as a content provider, a platform, and a technology stack; (2) it is commonly implemented for annotation and enrichment, metadata curation, knowledge modelling, and Named Entity Recognition (NER); (3) Most projects tend to consume data from Wikidata, whereas there is more potential to utilize it as a platform and a technology stack to publish data on Wikidata or to create an ecosystem of data exchange; and (4) Projects face two types of challenges: technical issues in the implementations and concerns with Wikidata's data quality. In the discussion, this article contributes to addressing three issues related to coping with the challenges in the specific context of the DH field based on the research findings: the relevance and authority of other available domain sources; domain communities and their practices; and workflow design that coordinates technical and labour resources from projects and Wikidata. [ABSTRACT FROM AUTHOR]

Much has been made in recent years of the transformative potential of digital resources and historical data for historical research. Historians seem to be flooded with retro-digitized and born-digital materials and tend to take these for granted, grateful for the opportunities they afford. In a research environment that increasingly privileges what is available online, the questions of why, where, and how we can access what we can access, and how it affects historical research have become ever more urgent. This article proposes a framework through which to contextualize the politics of (digital) heritage preservation, and a model to analyse its most important political dimensions, drawing upon literature from the digital humanities and history as well as archival, library, and information science. The first part will outline the global dimensions of the politics of digital cultural heritage, focusing on developments between and within the Global North and South, framed within the broader context of the politics of heritage and its preservation. The second part surveys the history and current state of digitization and offers a structured analysis of the process of digitization and its political dimensions. Choices and decisions about selection for digitization, how to catalogue, classify, and what metadata to add are all political in nature and have political consequences, and the same is true for access. The article concludes with several recommendations and a plea to acknowledge the importance of digital cataloguing in enabling access to the global human record. [ABSTRACT FROM AUTHOR]

Summary Supervised learning is widely used in biology for prediction, and ensemble learning, including stacking, is a promising technique for increasing and stabilizing the prediction accuracy. In this study, we developed an R package for stacking. This package depends on the R package caret and can handle models supported by caret. Stacking involves cross-validation of training data with multiple base learners, and the predicted values are used as explanatory variables for the meta-learner. In the prediction, the testing data were fed into the base models, and the returned values were averaged for each base learner. The averaged values were then fed into the meta-model, and the final predictions were returned. Using this package, the training and prediction procedures for stacking can be conducted using one-row scripts. Availability and implementation The R package stacking is available at the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/) and GitHub (https://github.com/Onogi/stacking). R scripts to reproduce the presented results are also reposited at GitHub. [ABSTRACT FROM AUTHOR]

Summary Whole slide images (WSIs) are multi-gigapixel images of tissue sections, which are used in digital and computational pathology workflows. WSI datasets are commonly heterogeneous collections of proprietary or niche specialized formats which are challenging to handle. This note describes an open-source Python application for efficiently converting between WSI formats, including common, open, and emerging cloud-friendly formats. WSIC is a software tool that can quickly convert WSI files across various formats. It has a high performance and maintains the resolution metadata of the original images. WSIC is ideal for pre-processing large-scale WSI datasets with different file types. Availability and implementation Source code is available on GitHub at https://github.com/John-P/wsic/ under a permissive licence. WSIC is also available as a package on PyPI at https://pypi.org/project/WSIC/. [ABSTRACT FROM AUTHOR]

Summary Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond. Availability and implementation Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology. [ABSTRACT FROM AUTHOR]

Motivation Increasingly complex omics datasets are being generated, along with associated diverse categories of metadata (environmental, clinical, etc.). Looking at the correlation between these variables can be critical to identify potential confounding factors and novel relationships. To date, some correlation globe software has been developed to aid investigations; however, they lack secure, dynamic visualization capability. Results GlobeCorr.ca is a web-based application designed to provide user-friendly, interactive visualization and analysis of correlation datasets. Users load tabular data listing pairwise variables and their correlation values, and GlobeCorr creates a dynamic visualization using ribbons to represent positive and negative correlations, optionally grouped by domain/category (such as microbiome taxa against other metadata). GlobeCorr runs securely (locally on a user's computer) and provides a simple method for users to visualize and summarize complex datasets. This tool is applicable to a wide range of disciplines and domains of interest, including the bioinformatics/microbiome and metadata examples provided within. Availability and Implementation See https://GlobeCorr.ca ; Code provided under an open source MIT license: https://github.com/brinkmanlab/globecorr. [ABSTRACT FROM AUTHOR]

Editormetrics analyses the role of editors of academic journals and their impact on the scientific publication system. Such analyses would best rely on open, structured, and machine-readable data about editors and editorial boards, which still remains rare. To address this shortcoming, the project Open Editors collects data about academic journal editors on a large scale and structures them into a single dataset. It does so by scraping the websites of 7,352 journals from 26 publishers (including predatory ones), thereby structuring publicly available information (names, affiliations, editorial roles, ORCID etc.) about 594,580 researchers. The dataset shows that journals and publishers are immensely heterogeneous in terms of editorial board sizes, regional diversity, and editorial role labels. All codes and data are made available at Zenodo , while the result is browsable at a dedicated website (https://openeditors.ooir.org). This dataset carries implications for both practical purposes of research evaluation and for meta-scientific investigations into the landscape of scholarly publications, and allows for critical inquiries regarding the representation of diversity and inclusivity across academia. [ABSTRACT FROM AUTHOR]

Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potentials, and evolutionary histories. However, even in the best-studied prokaryotic genomes, not all proteins can be characterized by classical in vivo, in vitro , and/or in silico methods—a challenge rapidly growing alongside the advent of next-generation sequencing technologies and their enormous extension of 'omics' data in public databases. These so-called hypothetical proteins (HPs) represent a huge knowledge gap and hidden potential for biotechnological applications. Opportunities for leveraging the available 'Big Data' have recently proliferated with the use of artificial intelligence (AI). Here, we review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research. [ABSTRACT FROM AUTHOR]

The presence and impact of variability in cells as the source material for genome engineering are important to consider for the design, execution and interpretation of outcomes of a genome-engineering process. Variability may be present at the genotype and phenotype level, yet the impact of these sources of variability on a genome-engineering experiment may not be regularly considered by researchers. In this perspective, we use clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein (Cas) genome editing of mammalian cells to provide examples of how variation within or across cell samples may mislead a researcher in their expectations about the cells they are engineering. Furthermore, we highlight the need for understanding the baseline cell genotype and phenotype to appropriately understand the starting cell material and interpret and attribute the impact of engineering on cells. We emphasize that heterogeneity within a cell pool and the inherent variability in the cellular materials used for genome engineering are complex, but of high value to characterize and account for where possible, to move toward the potential of generating desired and predictable engineered products. Provided is a framework cause-and-effect diagram for CRISPR/Cas9 genome editing toward identifying and mitigating potential sources of variability. We encourage researchers to consider the variability of source materials and undertake strategies, which may include those described here, for detecting, attributing and minimizing additional sources of variability where possible toward the aim of fostering greater reliability, confidence and reproducibility in genome-engineering studies. Graphical Abstract [ABSTRACT FROM AUTHOR]

The coronavirus SARS-CoV-2 is the most sequenced pathogen ever, with several million genome copies deposited in the GISAID database. This large amount of genomic information poses non-trivial bioinformatic challenges for those interested in studying the evolution of SARS-CoV-2. One common problem when studying the phylogeny of the coronavirus in its geographical context is to count with accurate information of the location of the samples. However, this information is filled by hand by research groups all over the world and sometimes typos and inconsistencies are introduced in the metadata when submitting the sequences to GISAID. Correcting these errors is laborious and time-consuming. Here, we provide a suite of Perl scripts designated to facilitate the curation of this vital information and perform a random sampling of genome sequences if necessary. The scripts provided here can be used to curate geographic information in the metadata and sample the sequences from any country of interest to ease the preparation of files for Nextstrain and Microreact, thus accelerating evolutionary studies of this important pathogen. CurSa scripts are accessible via: https://github.com/luisdelaye/CurSa/. [ABSTRACT FROM AUTHOR]

Scientists employing omics in life science studies face challenges such as the modeling of multiassay studies, recording of all relevant parameters, and managing many samples with their metadata. They must manage many large files that are the results of the assays or subsequent computation. Users with diverse backgrounds, ranging from computational scientists to wet-lab scientists, have dissimilar needs when it comes to data access, with programmatic interfaces being favored by the former and graphical ones by the latter. We introduce SODAR, the system for omics data access and retrieval. SODAR is a software package that addresses these challenges by providing a web-based graphical user interface for managing multiassay studies and describing them using the ISA (Investigation, Study, Assay) data model and the ISA-Tab file format. Data storage is handled using the iRODS data management system, which handles large quantities of files and substantial amounts of data. SODAR also offers programmable APIs and command-line access for metadata and file storage. SODAR supports complex omics integration studies and can be easily installed. The software is written in Python 3 and freely available at https://github.com/bihealth/sodar-server under the MIT license. [ABSTRACT FROM AUTHOR]

Background The life sciences are one of the biggest suppliers of scientific data. Reusing and connecting these data can uncover hidden insights and lead to new concepts. Efficient reuse of these datasets is strongly promoted when they are interlinked with a sufficient amount of machine-actionable metadata. While the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles have been accepted by all stakeholders, in practice, there are only a limited number of easy-to-adopt implementations available that fulfill the needs of data producers. Findings We developed the FAIR Data Station, a lightweight application written in Java, that aims to support researchers in managing research metadata according to the FAIR principles. It implements the ISA metadata framework and uses minimal information metadata standards to capture experiment metadata. The FAIR Data Station consists of 3 modules. Based on the minimal information model(s) selected by the user, the "form generation module" creates a metadata template Excel workbook with a header row of machine-actionable attribute names. The Excel workbook is subsequently used by the data producer(s) as a familiar environment for sample metadata registration. At any point during this process, the format of the recorded values can be checked using the "validation module." Finally, the "resource module" can be used to convert the set of metadata recorded in the Excel workbook in RDF format, enabling (cross-project) (meta)data searches and, for publishing of sequence data, in an European Nucleotide Archive–compatible XML metadata file. Conclusions Turning FAIR into reality requires the availability of easy-to-adopt data FAIRification workflows that are also of direct use for data producers. As such, the FAIR Data Station provides, in addition to the means to correctly FAIRify (omics) data, the means to build searchable metadata databases of similar projects and can assist in ENA metadata submission of sequence data. The FAIR Data Station is available at https://fairbydesign.nl. [ABSTRACT FROM AUTHOR]

Background Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination. Results We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles. Conclusion GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer. [ABSTRACT FROM AUTHOR]

Background Many open-source workflow systems have made bioinformatics data analysis procedures portable. Sharing these workflows provides researchers easy access to high-quality analysis methods without the requirement of computational expertise. However, published workflows are not always guaranteed to be reliably reusable. Therefore, a system is needed to lower the cost of sharing workflows in a reusable form. Results We introduce Yevis, a system to build a workflow registry that automatically validates and tests workflows to be published. The validation and test are based on the requirements we defined for a workflow being reusable with confidence. Yevis runs on GitHub and Zenodo and allows workflow hosting without the need of dedicated computing resources. A Yevis registry accepts workflow registration via a GitHub pull request, followed by an automatic validation and test process for the submitted workflow. As a proof of concept, we built a registry using Yevis to host workflows from a community to demonstrate how a workflow can be shared while fulfilling the defined requirements. Conclusions Yevis helps in the building of a workflow registry to share reusable workflows without requiring extensive human resources. By following Yevis's workflow-sharing procedure, one can operate a registry while satisfying the reusable workflow criteria. This system is particularly useful to individuals or communities that want to share workflows but lacks the specific technical expertise to build and maintain a workflow registry from scratch. [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 rise of consumer encryption has led to a fierce debate over whether the loss of potential evidence due to encryption will be offset by the increase in evidence available from electronic metadata. One major question raised by this debate is how jurors will interpret and value metadata as opposed to content information. Though there are plausible arguments in favor of the persuasive power of each type of evidence, to date no empirical study has examined how ordinary people, potential jurors, view each of these sorts of evidence. We address this issue through a series of survey experiments that present respondents with hypothetical criminal trials, randomly assigning them to descriptions featuring either metadata or content information. These studies show that the relative power of content and metadata information is highly contextual. Content information and metadata can be equally useful when conveying logically equivalent information. However, content information may be more persuasive where the defendant's state of mind is critical, while metadata can more convincingly establish a pattern of behavior. This suggests that the rise of encryption will have a heterogeneous effect on criminal cases, with the direction of the effect depending on the facts that the prosecution must prove. [ABSTRACT FROM AUTHOR]

The Digital Initiative for Classics: Epic Speeches (DICES) research group reports here on preliminary work to integrate research on Greek and Latin epic speeches into the larger ecosystem of linked open data (LOD) for classical scholarship. The ability to collate speech data from different researchers and to leverage external repositories of texts and characters opens up new possibilities for interrogation of the epic corpus. We briefly survey the current state of scholarship on epic speeches and of the digital infrastructure on which we propose to build. We outline a model for harmonizing speech data across studies and aligning it with existing LOD standards. Finally, we discuss some early proof-of-concept results and the larger implications of this approach for the field. The long-term aim of the DICES project is to build a database of metadata on direct speech in Greek and Latin epic, not only covering canonical texts such as Homer and Virgil, but also including the less-studied texts of the late antique period, which will benefit greatly both from the increased accessibility and also from the diachronic perspective afforded by a corpus-based approach. The envisioned database also has the potential to include diachronic data from additional genres and languages at a later stage. [ABSTRACT FROM AUTHOR]

Motivation The volume of public nucleotide sequence data has blossomed over the past two decades and is ripe for re- and meta-analyses to enable novel discoveries. However, reproducible re-use and management of sequence datasets and associated metadata remain critical challenges. We created the open source Python package q2-fondue to enable user-friendly acquisition, re-use and management of public sequence (meta)data while adhering to open data principles. Results q2-fondue allows fully provenance-tracked programmatic access to and management of data from the NCBI Sequence Read Archive (SRA). Unlike other packages allowing download of sequence data from the SRA, q2-fondue enables full data provenance tracking from data download to final visualization, integrates with the QIIME 2 ecosystem, prevents data loss upon space exhaustion and allows download of (meta)data given a publication library. To highlight its manifold capabilities, we present executable demonstrations using publicly available amplicon, whole genome and metagenome datasets. Availability and implementation q2-fondue is available as an open-source BSD-3-licensed Python package at https://github.com/bokulich-lab/q2-fondue. Usage tutorials are available in the same repository. All Jupyter notebooks used in this article are available under https://github.com/bokulich-lab/q2-fondue-examples. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Environmental DNA (eDNA), as a rapidly expanding research field, stands to benefit from shared resources including sampling protocols, study designs, discovered sequences, and taxonomic assignments to sequences. High-quality community shareable eDNA resources rely heavily on comprehensive metadata documentation that captures the complex workflows covering field sampling, molecular biology lab work, and bioinformatic analyses. There are limited sources that provide documentation of database development on comprehensive metadata for eDNA and these workflows and no open-source software. Results We present medna-metadata, an open-source, modular system that aligns with Findable, Accessible, Interoperable, and Reusable guiding principles that support scholarly data reuse and the database and application development of a standardized metadata collection structure that encapsulates critical aspects of field data collection, wet lab processing, and bioinformatic analysis. Medna-metadata is showcased with metabarcoding data from the Gulf of Maine (Polinski et al. , 2019). Availability and implementation The source code of the medna-metadata web application is hosted on GitHub (https://github.com/Maine-eDNA/medna-metadata). Medna-metadata is a docker-compose installable package. Documentation can be found at https://medna-metadata.readthedocs.io/en/latest/?badge=latest. The application is implemented in Python, PostgreSQL and PostGIS, RabbitMQ, and NGINX, with all major browsers supported. A demo can be found at https://demo.metadata.maine-edna.org/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary Biological data repositories are an invaluable source of publicly available research evidence. Unfortunately, the lack of convergence of the scientific community on a common metadata annotation strategy has resulted in large amounts of data with low FAIRness (Findable, Accessible, Interoperable and Reusable). The possibility of generating high-quality insights from their integration relies on data curation, which is typically an error-prone process while also being expensive in terms of time and human labour. Here, we present ESPERANTO, an innovative framework that enables a standardized semi-supervised harmonization and integration of toxicogenomics metadata and increases their FAIRness in a Good Laboratory Practice-compliant fashion. The harmonization across metadata is guaranteed with the definition of an ad hoc vocabulary. The tool interface is designed to support the user in metadata harmonization in a user-friendly manner, regardless of the background and the type of expertise. Availability and implementation ESPERANTO and its user manual are freely available for academic purposes at https://github.com/fhaive/esperanto. The input and the results showcased in Supplementary File S1 are available at the same link. [ABSTRACT FROM AUTHOR]

Motivation Microbiome datasets are often constrained by sequencing limitations. GenBank is the largest collection of publicly available DNA sequences, which is maintained by the National Center of Biotechnology Information (NCBI). The metadata of GenBank records are a largely understudied resource and may be uniquely leveraged to access the sum of prior studies focused on microbiome composition. Here, we developed a computational pipeline to analyze GenBank metadata, containing data on hosts, microorganisms and their place of origin. This work provides the first opportunity to leverage the totality of GenBank to shed light on compositional data practices that shape how microbiome datasets are formed as well as examine host–microbiome relationships. Results The collected dataset contains multiple kingdoms of microorganisms, consisting of bacteria, viruses, archaea, protozoa, fungi, and invertebrate parasites, and hosts of multiple taxonomical classes, including mammals, birds and fish. A human data subset of this dataset provides insights to gaps in current microbiome data collection, which is biased towards clinically relevant pathogens. Clustering and phylogenic analysis reveals the potential to use these data to model host taxonomy and evolution, revealing groupings formed by host diet, environment and coevolution. Availability and implementation GenBank Host-Microbiome Pipeline is available at https://github.com/bcbi/genbank%5fholobiome. The GenBank loader is available at https://github.com/bcbi/genbank%5floader. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Moderating terrorist live-streaming presents legal and technical trade-offs. The immediacy of live-streamed content requires a timely assessment of the content's compliance with community guidelines and an even more expeditious action of restriction or removal in case of portrayal of illegal content. Social media companies have heavily relied on technology to moderate content. The most frequently used tools to screen and filter content and ensure compliance with community standards and regulations include hashing technology, video-fingerprinting, natural language processing and metadata analysis. Terrorist content online presents nuances that make the task of removing it particularly challenging especially when companies must rely on machine learning content moderation. We identify three trade-offs in regulating content moderation of live-streamed content: technological neutrality v specificity, explainability v adversarial machine learning, and accuracy v time-efficiency. For each trade-off, we provide an analysis of the considerations lawmakers and regulators must take into account when balancing competing interests. [ABSTRACT FROM AUTHOR]

Motivation Technical advances have revolutionized the life sciences and researchers commonly face challenges associated with handling large amounts of heterogeneous digital data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles provide a framework to support effective data management. However, implementing this framework is beyond the means of most researchers in terms of resources and expertise, requiring awareness of metadata, policies, community agreements and other factors such as vocabularies and ontologies. Results We have developed the Globally Accessible Distributed Data Sharing (GADDS) platform to facilitate FAIR-like data-sharing in cross-disciplinary research collaborations. The platform consists of (i) a blockchain-based metadata quality control system, (ii) a private cloud-like storage system and (iii) a version control system. GADDS is built with containerized technologies, providing minimal hardware standards and easing scalability, and offers decentralized trust via transparency of metadata, facilitating data exchange and collaboration. As a use case, we provide an example implementation in engineered living material technology within the Hybrid Technology Hub at the University of Oslo. Availability and implementation Demo version available at https://github.com/pavelvazquez/GADDS. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

We introduce a kernel Lasso (kLasso) approach which is a type of sparse optimization that simultaneously accounts for spatial regularity and structural sparsity to reconstruct spatially embedded complex networks from time-series data about nodal states. Through the design of a spatial kernel function motivated by real-world network features, the proposed kLasso approach exploits spatial embedding distances to penalize overabundance of spatially long-distance connections. Examples of both random geometric graphs and real-world transportation networks show that the proposed method improves significantly upon existing network reconstruction techniques that mainly concern sparsity but not spatial regularity. Our results highlight the promise of data and information fusion in the reconstruction of complex networks, by utilizing both microscopic node-level dynamics (e.g. time series data) and macroscopic network-level information (metadata or other prior information). [ABSTRACT FROM AUTHOR]

Regulatory agencies rely upon rodent in vivo acute oral toxicity data to determine hazard categorization, require appropriate precautionary labeling, and perform quantitative risk assessments. As the field of toxicology moves toward animal-free new approach methodologies (NAMs), there is a pressing need to develop a reliable, robust reference data set to characterize the reproducibility and inherent variability in the in vivo acute oral toxicity test method, which would serve to contextualize results and set expectations regarding NAM performance. Such a data set is also needed for training and evaluating computational models. To meet these needs, rat acute oral LD50 data from multiple databases were compiled, curated, and analyzed to characterize variability and reproducibility of results across a set of up to 2441 chemicals with multiple independent study records. Conditional probability analyses reveal that replicate studies only result in the same hazard categorization on average at 60% likelihood. Although we did not have sufficient study metadata to evaluate the impact of specific protocol components (eg, strain, age, or sex of rat, feed used, treatment vehicle, etc.), studies were assumed to follow standard test guidelines. We investigated, but could not attribute, various chemical properties as the sources of variability (ie, chemical structure, physiochemical properties, functional use). Thus, we conclude that inherent biological or protocol variability likely underlies the variance in the results. Based on the observed variability, we were able to quantify a margin of uncertainty of ±0.24 log10 (mg/kg) associated with discrete in vivo rat acute oral LD50 values. [ABSTRACT FROM AUTHOR]