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In our generation, there is an undoubted rise in the use of social media and specifically photo and video sharing platforms. These sites have proved their ability to yield rich data sets through the users' interaction which can be used to perform a data‐driven evaluation of capabilities. Nevertheless, this study reveals the lack of suitable data sets in photo and video sharing platforms and evaluation processes across them. In this way, our first contribution is the creation of one of the largest labelled data sets in Flickr with the multimodal data which has been open sourced as part of this contribution. It incorporates multimodal data, combining information from various sources such as user profiles, photo metadata, and crowdsourced features. Predicated on these data, we explored machine learning models and concluded that it is feasible to properly predict whether a user is a professional photographer or not based on self‐reported occupation labels and several feature representations out of the user, photo and crowdsourced sets. We also examined the relationship between the aesthetics and technical quality of a picture and the social activity of that picture. Finally, we depicted which characteristics differentiate professional photographers from non‐professionals. As far as we know, the results presented in this work represent an important novelty for identifying expertise in the domain of photography, which researchers from various domains can utilise for related applications. [ABSTRACT FROM AUTHOR]

Here we present a method for using the spatial x–y coordinate of an image cropped from the cylindrical surface of digital 3D drill core images and demonstrate how this spatial metadata can be used to improve unsupervised machine learning performance. This approach is applicable to any data set with known spatial context, however, here it is used to classify 400 m of drillcore imagery into 12 distinct classes reflecting the dominant rock types and alteration features in the core. We modified two unsupervised learning models to incorporate spatial metadata and an average improvement of 25% was achieved over equivalent models that did not utilize metadata. Our semi‐supervised workflow involves unsupervised network training followed by semi‐supervised clustering where a support vector machine uses a subset of M expert labeled images to assign a pseudolabel to the entire data set. Fine‐tuning of the best performing model showed an f1 (macro average) of 90%, and its classifications were used to estimate bulk fresh and altered rock abundance downhole. Validation against the same information gathered manually by experts when the core was recovered during the Oman Drilling Project revealed that our automatically generated data sets have a significant positive correlation (Pearson's r of 0.65–0.72) to the expert generated equivalent, demonstrating that valuable geological information can be generated automatically for 400 m of core with only ∼24 hr of domain expert effort. Plain Language Summary: This work presents a novel method for using the spatial context of digital core images to improve the descriptive accuracy of unsupervised machine learning algorithms. The addition of spatial metadata improves model performance by an average of 25%, with the best performing model in this study achieving an accuracy score of 90%. The output of this model was then used to estimate the amount of fresh and altered rock within a 400 m long drill core, which was shown to be of comparable quality to the same estimations made by geologists on the cores themselves. Key Points: Use of spatial metadata improves state‐of‐the‐art unsupervised feature extractionSemi‐supervised methods using spatial metadata outperform supervised methods for the same expert labeling effortNew methods described enable the standardization of core‐logging processes and improve cross‐dataset comparisons [ABSTRACT FROM AUTHOR]

Ovarian abnormality like polycystic ovarian disease (PCOD) is one of the most common diseases among women worldwide. PCOD not only has an impact on infertility but also hurts the psychological well‐being of women affecting their quality of life. In this study, a two‐class pattern learning problem is designed for the classification of PCOD. In total, 37 clinical parameters and abdominal ultrasound images of women are collected under the proper ethical protocol. Using only clinical data, an accuracy of 93.7% is obtained using Random Forest as the classifier which is further improved to 95.54% by using a Randomized Search CV during Random Forest classification. The ultrasound images are classified using the proposed Attention‐UNet architecture and a mean Dice score of 0.945 is obtained indicating more accurate segmentation. The segmented images are passed through the state‐of‐the‐art EfficientNet B6 for the classification of PCOS and non‐PCOS and recorded an accuracy of 95.47%. Using big data architecture Pyspark, the performance is further enhanced to 96.8% and 96.3% for clinical and ultrasound images respectively along with the reduced computational speed. The results of these classifiers are then used to create metadata and a customized Artificial Neural Network is applied for the final prediction of PCOD and non‐PCOD. From the results, it can be seen that the stacking model outperformed with an accuracy of 98.12% when compared to the single classifier. Our proposed method has very good performance with less computation, contributing a new architecture to evaluate PCOD and hence helping to improve the wellness of women. [ABSTRACT FROM AUTHOR]

Microorganisms have evolved diverse strategies to acquire the vital element nitrogen (N) from the environment. Ecological and physiological controls on the distribution of these strategies among microbes remain unclear. In this study, we examine the distribution of 10 major N acquisition strategies in taxonomically and metabolically diverse microbial genomes, including those from the Genomic Catalogue of Earth's Microbiomes dataset. We utilize a marker gene‐based approach to assess relationships between N acquisition strategy prevalence and microbial life history strategies. Our results underscore energetic costs of assimilation as a broad control on strategy distribution. The most prevalent strategies are the uptake of ammonium and simple amino acids, which have relatively low energetic costs, while energy‐intensive biological nitrogen fixation is the least common. Deviations from the energy‐based framework include the higher‐than‐expected prevalence of the assimilatory pathway for chitin, a large organic polymer. Energy availability is also important, with aerobic chemoorganotrophs and oxygenic phototrophs notably possessing ~2‐fold higher numbers of total strategies compared to anaerobic microbes. Environmental controls are evidenced by the enrichment of inorganic N assimilation strategies among free‐living taxa compared to host‐associated taxa. Physiological constraints such as pathway incompatibility add complexity to N acquisition strategy distributions. Finally, we discuss the necessity for microbially‐relevant spatiotemporal environmental metadata for improving mechanistic and prediction‐oriented analyses of genomic data. [ABSTRACT FROM AUTHOR]

Motivation: A well‐constructed nest is a key element of successful reproduction in most species of birds, and nest morphology varies widely across the class. Macroecological and macroevolutionary studies tend to group nest design into a small number of discrete categories, often based on taxonomic inference. In reality, however, many species display considerable intraspecific variation in their nest‐building behaviour, and broad‐level categories may include several functionally distinct nest types. To address this imprecision in the literature and facilitate future studies of broad‐scale variation in avian parental care, we here introduce a detailed, global comparative database of nest building in birds, together with preliminary correlations between these traits and species‐level environmental variables. Main types of variables contained: We present species‐level data for nest structure, location, height, material composition, sex of builder, building time and nest dimensions. Spatial location and grain: Global. Maps are presented at the 10 × 10 level. Time period and grain: Included species are generally extant, although we present some data for recently extinct taxa. The data were collected in 2017–2021 and was drawn from secondary sources published in 1992–2021. Major taxa and level of measurement: Partial or complete trait data is presented for 8601 species of birds, representing 36 of 36 orders and 239 of 243 families. Software format: Data have been uploaded as Supplementary Material in.csv format and are separated by species and source for all traits (Dataset S1, and Metadata) as well as summarized at the species level for the major structure and location variables (Dataset S2, and Metadata). [ABSTRACT FROM AUTHOR]

The complementary feeding period is a critical stage of child development when micronutrient needs are high and challenging to meet. Understanding if specific micronutrient gaps exist during this period is critical for effective programming. A Comprehensive Nutrient Gap Assessment (CONGA) was conducted in seven countries in Southeast Asia to estimate gaps in micronutrients commonly lacking in the diets of children aged 6–23 months and to establish the certainty of available evidence for each identified gap. Sixty‐eight evidence sources were identified during this analysis, and 310 micronutrient‐specific data points were identified across all seven countries. Data points varied in recency, representativeness and evidence type. The CONGA methodology enabled the estimation of a gap burden rating for each micronutrient in each country, as well as a rating of their evidence certainty. Micronutrient gaps were identified in vitamin D, zinc and iron and a potential gap was identified in calcium during the complementary feeding period in the region. Evidence relevant to intake and deficiency of folate, vitamin B12, thiamine, niacin, vitamin C and vitamin B6 was limited across the region. Proven strategies to address these gaps include increasing the availability and consumption of nutrient‐dense foods, micronutrient supplementation, large‐scale fortification of staple foods and condiments and point‐of‐use fortification through multiple micronutrient powders and fortified speciality foods. More recent data on micronutrient availability, intake and deficiency is urgently needed in Southeast Asia. Key messages: Older infants and young children aged 6–23 months experience micronutrient gaps during the complementary feeding period in Southeast Asia. Synthesis of evidence compiled across seven countries in the region identified gaps in vitamin D, zinc and iron and a potential gap in calcium during this critical period.National nutrition policies and programmes should consider multiple strategies to address these micronutrient gaps.Over half of the data points relevant to nutrient deficiency and intake compiled from the seven countries are over a decade old. More recent data on micronutrient availability, intake and deficiency is urgently needed in Southeast Asia. [ABSTRACT FROM AUTHOR]

The global crisis in antimicrobial resistance continues to grow. Estimating the risks of antibiotic resistance transmission across habitats is hindered by the lack of data on mobility and habitat‐specificity. Metagenomic samples of 6092 are analyzed to delineate the unique core resistomes from human feces and seven other habitats. This is found that most resistance genes (≈85%) are transmitted between external habitats and human feces. This suggests that human feces are broadly representative of the global resistome and are potentially a hub for accumulating and disseminating resistance genes. The analysis found that resistance genes with ancient horizontal gene transfer (HGT) events have a higher efficiency of transfer across habitats, suggesting that HGT may be the main driver for forming unique but partly shared resistomes in all habitats. Importantly, the human fecal resistome is historically different and influenced by HGT and age. The most important routes of cross‐transmission of resistance are from the atmosphere, buildings, and animals to humans. These habitats should receive more attention for future prevention of antimicrobial resistance. The study will disentangle transmission routes of resistance genes between humans and other habitats in a One Health framework and can identify strategies for controlling the ongoing dissemination and antibiotic resistance. [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]

Genetic diversity within species represents a fundamental yet underappreciated level of biodiversity. Because genetic diversity can indicate species resilience to changing climate, its measurement is relevant to many national and global conservation policy targets. Many studies produce large amounts of genome‐scale genetic diversity data for wild populations, but most (87%) do not include the associated spatial and temporal metadata necessary for them to be reused in monitoring programs or for acknowledging the sovereignty of nations or Indigenous peoples. We undertook a distributed datathon to quantify the availability of these missing metadata and to test the hypothesis that their availability decays with time. We also worked to remediate missing metadata by extracting them from associated published papers, online repositories, and direct communication with authors. Starting with 848 candidate genomic data sets (reduced representation and whole genome) from the International Nucleotide Sequence Database Collaboration, we determined that 561 contained mostly samples from wild populations. We successfully restored spatiotemporal metadata for 78% of these 561 data sets (n = 440 data sets with data on 45,105 individuals from 762 species in 17 phyla). Examining papers and online repositories was much more fruitful than contacting 351 authors, who replied to our email requests 45% of the time. Overall, 23% of our email queries to authors unearthed useful metadata. The probability of retrieving spatiotemporal metadata declined significantly as age of the data set increased. There was a 13.5% yearly decrease in metadata associated with published papers or online repositories and up to a 22% yearly decrease in metadata that were only available from authors. This rapid decay in metadata availability, mirrored in studies of other types of biological data, should motivate swift updates to data‐sharing policies and researcher practices to ensure that the valuable context provided by metadata is not lost to conservation science forever. [ABSTRACT FROM AUTHOR]

As more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed towards automating this process to save considerable time and effort. However, most approaches focus mainly on software bugs and employ models that do not sufficiently take into account the semantics and the non‐textual metadata of issues and/or produce models that may require manual tuning. A methodology that extracts both textual and non‐textual features from different types of issues is designed, providing a Jira dataset that involves not only bugs but also new features, issues related to documentation, patches, etc. Moreover, the semantics of issue text are effectively captured by employing a topic modelling technique that is optimised using the assignment result. Finally, this methodology aggregates probabilities from a set of individual models to provide the final assignment. Upon evaluating this approach in an automated issue assignment setting using a dataset of Jira issues, the authors conclude that it can be effective for automated issue assignment. [ABSTRACT FROM AUTHOR]

The Environmental Data Initiative (EDI) is a trustworthy, stable data repository, and data management support organization for the environmental scientist. In a bottom‐up community process, EDI was built with the premise that freely and easily available data are necessary to advance the understanding of complex environmental processes and change, to improve transparency of research results, and to democratize ecological research. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, we analyze metadata to provide a general description of EDI's collection of data and its data management philosophy and placement in the repository landscape. We discuss how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, we review measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise. [ABSTRACT FROM AUTHOR]

The Kakioka Magnetic Observatory (KMO) of the Japan Meteorological Agency (JMA) made atmospheric electric field observations at Kakioka and Memambetsu beginning in 1929 and 1949, respectively, and ending in February 2021 and December 2010, respectively. The digital numerical data are available on the KMO website, although not all observation periods are covered. In this article, we introduce the available datasets and summarize the metadata regarding the observations, such as measurement instruments, maintenance methods and data processing. [ABSTRACT FROM AUTHOR]

Historical materials contain descriptions of various events involved in natural processes and human societies (such as weather, earthquakes, volcanic eruptions, harvests, market prices, and famines). They have been used for studies in diverse fields, including natural sciences (such as seismology, climatology, and astronomy), social sciences, and humanities. Hence, researchers have made various efforts, such as identifying and obtaining the materials, reading handwritten characters and transcribing them into digital texts, standardizing formats, extracting descriptions of relevant phenomena, and constructing integrated datasets, to use documentary information. Because scientists, other than historians, find it quite challenging even to ascertain where to find appropriate research materials, the Rekiske Development Group has, therefore, developed a system called Rekiske to facilitate interdisciplinary sharing of information available from historical materials. Rekiske is designed for sharing bibliographic and scientific information of historical materials, along with researchers' experience and knowledge, through interdisciplinary collaboration. Rekiske can assist researchers in identifying and obtaining useful historical materials for research purposes. It also facilitates collaboration among experts to share their expertise and knowledge of historical information, materials, and recent studies. Using Rekiske, we register various types of information on historical materials regarding weather or earthquakes considered in geoscience research to utilize researchers' experiences and to produce new knowledge through interdisciplinary collaborations. The developed system is designed to be used as a metadata portal and pathfinder to promote and preserve historical data and facilitate an understanding of the spatiotemporal connection between nature and human society. We have applied Rekiske to historical climatic studies and discuss its usage, benefits, usability, and other remaining issues. [ABSTRACT FROM AUTHOR]

The Kakioka Magnetic Observatory (KMO) of the Japan Meteorological Agency (JMA) made geoelectric field observations at Kakioka, Memambetsu and Kanoya from 1932, 1949 and 1948, respectively, until February 2021, when observations at all three sites were terminated. The digital numerical data are available on the KMO website, although not all observation periods are covered. In this article, we introduce the available datasets and summarize the metadata regarding the observations, such as the electrodes, recording system, maintenance methods and data processing. [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 the countries. [ABSTRACT FROM AUTHOR]

Background: Despite the increasing ubiquity and accessibility of teledermatology applications, few studies have comprehensively surveyed their features and technical standards. Importantly, features implemented after the point of capture are often intended to augment image utilization, while technical standards affect interoperability with existing healthcare systems. We aim to comprehensively survey image utilization features and technical characteristics found within publicly discoverable digital skin imaging applications. Materials and Methods: Applications were identified and categorized as described in Part I. Included applications were then further assessed by three independent reviewers for post‐imaging content, tools, and functionality. Publicly available information was used to determine the presence or absence of relevant technology standards and/or data characteristics. Results: A total of 20 post‐image acquisition features were identified across three general categories: (1) metadata attachment, (2) functional tools (i.e., those that utilized images or in‐app content to perform a user‐directed function), and (3) image processing. Over 80% of all applications implemented metadata features, with nearly half having metadata features only. Individual feature occurred and feature richness varied significantly by primary audience (p < 0.0001) and function (p < 0.0001). On average, each application included under three features. Less than half of all applications requested consent for user‐uploaded photos and fewer than 10% provided clear data use and privacy policies. Conclusion: Post‐imaging functionality in skin imaging applications varies significantly by primary audience and intended function, though nearly all applications implemented metadata labeling. Technical standards are often not implemented or reported consistently. Gaps in the provision of clear consent, data privacy, and data use policies should be urgently addressed. [ABSTRACT FROM AUTHOR]

An integrated metadata schema was created to promote interoperability and to characterize historically collected geoscience data. The metadata standard describes the steps taken to develop a switching‐across methodology that allows geoscience metadata standards (ISO 19115, CSDGM, ANZLIC and INSPIRE) to communicate with one another. We also demonstrated a real example of one of the switching‐across that was created. A representative metadata schema was created using items from harmonization standards that might be optimally mapped. Semantic interoperability allows users to access equivalent classes across several metadata standards in a uniform and consistent rule. It also tries to provide fundamental aspects that might characterize acquisition information to enhance data reuse and current metadata standards for data preservation and distribution. [ABSTRACT FROM AUTHOR]

Data support knowledge development and theory advances in ecology and evolution. We are increasingly reusing data within our teams and projects and through the global, openly archived datasets of others. Metadata can be challenging to write and interpret, but it is always crucial for reuse. The value metadata cannot be overstated—even as a relatively independent research object because it describes the work that has been done in a structured format. We advance a new perspective and classify methods for metadata curation and development with tables. Tables with templates can be effectively used to capture all components of an experiment or project in a single, easy‐to‐read file familiar to most scientists. If coupled with the R programming language, metadata from tables can then be rapidly and reproducibly converted to publication formats including extensible markup language files suitable for data repositories. Tables can also be used to summarize existing metadata and store metadata across many datasets. A case study is provided and the added benefits of tables for metadata, a priori, are developed to ensure a more streamlined publishing process for many data repositories used in ecology, evolution, and the environmental sciences. In ecology and evolution, researchers are often highly tabular thinkers from experimental data collection in the lab and/or field, and representations of metadata as a table will provide novel research and reuse insights. [ABSTRACT FROM AUTHOR]

Lack of open access, seizure specific database has hindered the development of Automated Seizure Detection System (ASDS) along with state‐of‐the‐art feature selection and classification methods. Available databases contain noise, artefacts, different length & time EEG segments, associated comorbidities, clinical settings, and epilepsy/seizure types etc. Pre‐processing of such continuous EEG segments requires significant amount of time and may feed redundant information (with reference to a seizure event) to the classification model leading to inaccurate, real‐time seizure event detection systems. Hence, this paper proposes a metadata generation of large EEG databases (here, CHB‐MIT EEG scalp database v.1.0.0) for ASDS. We elucidate the need to generate seizure sensitive data through peri‐ictal and non‐seizure EEG segments. This paper performs multi‐variate analysis and two class (non‐seizure and seizure event) classification between these fixed length and time EEG segments from support vector machine (SVM) and k‐NN classifier. We thoroughly analysed the variation and dependence of different kernels, cost function, gamma, and degree for SVM based pipeline. The proposed pipeline has been compared with state‐of‐the‐art pipelines and has achieved good classification score. Such methods will help in development of generalised approach toward handling large EEG databases and machine learning applications for seizure detection and prediction. [ABSTRACT FROM AUTHOR]

The transition from conventional weather stations (CWSs) to automated weather stations (AWSs) of the Colombian coffee network has required testing their performance and adjusting the temperature measurements to ensure the continuity of the historical CWS series. In this study, the mean (Tmean), minimum (Tmin), and maximum temperature (Tmax) measurements of CWS and AWS operating in parallel at 36 locations between 2014 and 2019 were compared, and the biases of the daily temperature differences (CWS - AWS), the agreement index (d), and the percentage of data within the allowed range (PR05) were calculated. The most consistent method for calculating Tmean and Tmax for CWS was selected for use on the AWS data. With the standard normal homogeneity test and with the metadata, we found that the series of temperature differences between CWS and AWS was not homogeneous, instrument failures and sensor changes being the main causes of the lack of homogeneity. The statistical analyses indicated that the AWS data need to be adjusted to be continuous with the CWS series. To correct the temperature bias, two approaches were applied: quantile mapping and the additive constant. The results suggest that the quantile mapping adjustments improve the average bias at all stations but do not necessarily bring the percentage to within ±0.5°C. In Tmin and Tmean, 12 AWSs can give continuity with the historical series of the CWS, and for the rest of the stations and variables, the series of the AWSs are independent of the CWSs. [ABSTRACT FROM AUTHOR]

Reconnaissance teams collect perishable data after each disaster to learn about building performance. However, often these large image sets are not adequately curated, nor do they have sufficient metadata (e.g., GPS), hindering any chance to identify images from the same building when collected by different reconnaissance teams. In this study, Siamese convolutional neural networks (S‐CNN) are implemented and repurposed to establish a building search capability suitable for post‐disaster imagery. This method can automatically rank and retrieve corresponding building images in response to a single query using an image. In the demonstration, we utilize real‐world images collected from 174 reinforced‐concrete buildings affected by the 2016 Southern Taiwan and the 2017 Pohang (South Korea) earthquake events. A quantitative performance evaluation is conducted by examining two metrics introduced for this application: Similarity Score (SS) and Similarity Rank (SR). [ABSTRACT FROM AUTHOR]

The exponential growth of biomedical knowledge in computable formats challenges organizations to consider mobilizing artifacts in findable, accessible, interoperable, reusable, and trustable (FAIR+T) ways1. There is a growing need to apply biomedical knowledge artifacts to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations lack the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are insufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Fourth Annual MCBK public meeting, which was held virtually July 20 to July 21, 2021 and convened over 100 participants spanning diverse domains to frame and address important dimensions for mobilizing CBK. [ABSTRACT FROM AUTHOR]

Introduction: Computable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine‐interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T. Methods: We examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject‐predicate‐object triples to more clearly differentiate metadata categories. Results: We defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK‐to‐CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories. Conclusion: A wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders. [ABSTRACT FROM AUTHOR]

Data repositories are an essential component of data infrastructure. This article explores their functionalities with accompanying examples with a focus on open, quantitative data sets. Based on researcher needs, the principle of data friendliness is presented, which involves data context (data characteristics), research context (individual circumstances and research settings) and the context of reuse paths (conditions allowing access to, understanding of and use of data). An examination of data context suggests that metadata and data enrichment are required to provide successful data context. Enrichment is also required for orientation of the research context to provide greater insights across data sets. Finally the article considers the reuse paths, and the say that data paths intersect human interaction and how these are being addressed by different repositories. The review concludes that data repositories need to focus on how to present data that is fully interoperable and allows for reuse as well as access. [ABSTRACT FROM AUTHOR]

Zoos and aquaria, often regarded as preservation‐cum‐entertainment enterprises, are also actors in the effort to curb the biodiversity crisis: raising awareness, supporting conservation, and conducting research. We assessed trends in zoo and aquaria research topics and study organisms over time worldwide. For the zoos and aquaria registered in the Species360's Zoological Information Management System and the World Association of Zoos and Aquariums, we compiled metadata on their research published in the peer‐reviewed literature indexed in Scopus and carried out a keyword frequency analysis. The production of scientific papers by zoos increased at a much faster rate than the average accrual of scientific papers in the literature. Evolution of research themes ran parallel to that of biological sciences (e.g., development of molecular genetics or increased awareness about conservation). The focus of 48.5% of zoo‐led research was on vertebrates, of which mammal research was 33.7%. Whether zoos are effectively contributing to conservation may still be debatable, but our results highlight their institutional efforts to increase knowledge about the species in their care. Article impact statement: Research led by zoos has exploded over the last decade, particularly research on conservation and genetics. [ABSTRACT FROM AUTHOR]

Daiquiri is a web‐based user interface (UI) framework for control system monitoring and data acquisition designed for synchrotron beamlines. It provides simple, intuitive and responsive interfaces to control and monitor hardware, launch acquisition sequences and manage associated metadata. Daiquiri concerns itself only with the UI layer; it does not provide a scan engine or controls system but can be easily integrated with existing systems. [ABSTRACT FROM AUTHOR]

Summary: In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting‐edge, meaningful and integrated knowledge. Consideration of the below‐ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below‐ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below‐ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine‐root vs coarse‐root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I–VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers' views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning. [ABSTRACT FROM AUTHOR]

Repeatability is the cornerstone of science, and it is particularly important for systematic reviews. However, little is known on how researchers' choice of database, and search platform influence the repeatability of systematic reviews. Here, we aim to unveil how the computer environment and the location where the search was initiated from influence hit results.We present a comparative analysis of time‐synchronized searches at different institutional locations in the world and evaluate the consistency of hits obtained within each of the search terms using different search platforms.We revealed a large variation among search platforms and showed that PubMed and Scopus returned consistent results to identical search strings from different locations. Google Scholar and Web of Science's Core Collection varied substantially both in the number of returned hits and in the list of individual articles depending on the search location and computing environment. Inconsistency in Web of Science results has most likely emerged from the different licensing packages at different institutions.To maintain scientific integrity and consistency, especially in systematic reviews, action is needed from both the scientific community and scientific search platforms to increase search consistency. Researchers are encouraged to report the search location and the databases used for systematic reviews, and database providers should make search algorithms transparent and revise access rules to titles behind paywalls. Additional options for increasing the repeatability and transparency of systematic reviews are storing both search metadata and hit results in open repositories and using Application Programming Interfaces (APIs) to retrieve standardized, machine‐readable search metadata. [ABSTRACT FROM AUTHOR]

The world's most complex climate models are currently running a range of experiments as part of the Sixth Coupled Model Intercomparison Project (CMIP6). Added to the output from the Fifth Coupled Model Intercomparison Project (CMIP5), the total data volume will be in the order of 20PB. Here, we present a dataset of annual, monthly, global, hemispheric and land/ocean means derived from a selection of experiments of key interest to climate data analysts and reduced complexity climate modellers. The derived dataset is a key part of validating, calibrating and developing reduced complexity climate models against the behaviour of more physically complete models. In addition to its use for reduced complexity climate modellers, we aim to make our data accessible to other research communities. We facilitate this in a number of ways. Firstly, given the focus on annual, monthly, global, hemispheric and land/ocean mean quantities, our dataset is orders of magnitude smaller than the source data and hence does not require specialized ‘big data’ expertise. Secondly, again because of its smaller size, we are able to offer our dataset in a text-based format, greatly reducing the computational expertise required to work with CMIP output. Thirdly, we enable data provenance and integrity control by tracking all source metadata and providing tools which check whether a dataset has been retracted, that is identified as erroneous. The resulting dataset is updated as new CMIP6 results become available and we provide a stable access point to allow automated downloads. Along with our accompanying website (cmip6.science.unimelb.edu.au), we believe this dataset provides a unique community resource, as well as allowing non-specialists to access CMIP data in a new, user-friendly way. [ABSTRACT FROM AUTHOR]

Hemp (Cannabis sativa) is a burgeoning crop, but research‐based information about genetic and environmental effects of cannabinoid production is limited and will be essential for expanded cultivation. There are limited data available about the effect of environmental stressors on cannabinoid content, particularly for tetrahydrocannabinol (THC) in high‐cannabidiol (CBD) hemp. To address this, five stress treatments were applied in a replicated field trial with three high‐CBD hemp cultivars and cannabinoid content was assayed over a 3‐week time‐course spanning floral maturation. Cannabinoid production in terminal inflorescence shoot tip samples of three cultivars was measured under stress imposed by flooding, ethephon, powdery mildew, herbicide, and physical wounding in a split plot design. The treatments had limited effects on cannabinoid levels, with the exception of herbicide treatment which resulted in decreased cannabinoid content. Notably, there was no evidence that any of these stresses caused THC concentration or the ratio of THC to CBD to increase at harvest. [ABSTRACT FROM AUTHOR]

The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well‐documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using seven species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: 1) filtering the data to exclude collectors unlikely to collect the focal species and 2) incorporating collection covariates (collection type, time of collection and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as collector covariates. Month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta‐data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species. [ABSTRACT FROM AUTHOR]

Dominant diseases due to expanded CAG repeat tracts, such as spinocerebellar ataxia type 2 (SCA2), are prone to anticipation and worsening of clinical picture in subsequent generations. There is insufficient data about selective forces acting on the maintenance of these diseases in populations. We made a systematic review and meta‐analysis on the effect of the CAG length over age at onset, instability of transmissions, anticipation, de novo or sporadic cases, fitness, segregation of alleles, and ancestral haplotypes. The correlation between CAG expanded and age at onset was r2 = 0.577, and transmission of the mutant allele was associated with an increase of 2.42 CAG repeats in the next generation and an anticipation of 14.62 years per generation, on average. One de novo and 18 sporadic cases were detected. Affected SCA2 individuals seem to have more children than controls. The expanded allele was less segregated than the 22‐repeat allele in children of SCA2 subjects. Several ancestral SCA2 haplotypes were published. Data suggest that SCA2 lineages may tend to disappear eventually, due to strong anticipation phenomena. Whether or not the novel cases come from common haplotypes associated with a predisposition to further expansions is a question that needs to be addressed by future studies. [ABSTRACT FROM AUTHOR]

Introduction: The cesarean section rate around the world, currently estimated at 21.1%, continues to increase. Women who undergo a cesarean section sustain a seven- to ten-fold greater risk of infective morbidity compared with those who deliver vaginally.Material and Methods: We aimed to assess the impact of changing gloves intraoperatively on post-cesarean section infective morbidity (PROSPERO CRD42018110529). MEDLINE, Scopus, Web of Science, CINAHL, WHO Global Index Medicus, and Cochrane Central were searched for randomized controlled trials until June 2020. Published randomized controlled trials that evaluated the effects of glove changing during cesarean section on infective complications were considered eligible for the review. Two reviewers independently selected studies, assessed the risk of bias, and extracted data about interventions and adverse maternal outcomes. Dichotomous variables were presented and included in the meta-analyses as risk ratios (RR) with 95% confidence intervals (CI). The quality of evidence was assessed using the GRADE approach in alignment with the recommendations from the Cochrane Review Group.Results: We identified seven randomized controlled trials reporting data over 1948 women. Changing gloves during a cesarean section was associated with a statistically significantly lower incidence of wound infective complications (RR 0.41, 95% CI 0.26-0.65, p < 0.0001; GRADE moderate quality evidence). This intervention seemed to be effective only if performed after delivery of the placenta. No significant difference was seen in the incidence of endometritis (RR 0.96, 95% CI 0.78-1.20, p = 0.74; GRADE moderate quality evidence) and/or febrile morbidity (RR 0.73, 95% CI 0.30-1.81, p = 0.50; GRADE moderate quality evidence), regardless of the timing of the intervention.Conclusions: Changing gloves after delivery of the placenta during a cesarean section is associated with a significant reduction in the incidence of post-surgical wound complications compared with keeping the same gloves throughout the whole surgery. However, an adequately powered study to assess the limitations and cost-effectiveness of the intervention is needed before this recommendation can be translated into current clinical practice. [ABSTRACT FROM AUTHOR]

The amount of observational and specimen‐based biodiversity data available to researchers is increasing exponentially, yet the ability to manage and cite large, complex biodiversity datasets lags behind. This management and citation gap impedes reproducibility for data users and the ability for data publishers to track use and accumulate use citations, ultimately harming the longer‐term sustainability of the still‐emerging enterprise of research data‐sharing. Here we present an R package, occCite (v. 0.4.7), to aid researchers in querying large species occurrence data aggregators (specifically, the Global Biodiversity Information Facility, GBIF, and the Botanical Information and Ecology Network, BIEN), and store metadata such as primary data providers, database accession dates, DOIs, and the taxonomic source used for search terms. occCite also includes tools to summarize and visualize query results and generate citation lists of all data providers and software packages used during the query process. We provide examples of a basic occurrence search and citation workflow as well as an advanced workflow using features for custom optimized searches, visualization, and summary procedures. occCite improves upon existing R packages by uniting data from powerful API‐based query packages (rgbif and BIEN) into a unified object‐based framework, while maintaining metadata vital to best‐practice recommendations for documenting biodiversity analysis workflows. occCite aims to efficiently close the gap in the citation cycle between primary data providers and final research products, allowing researchers to meet dataset documentation standards without sacrificing time and resources to the demands of providing increasing levels of detail on their datasets. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are increasingly used in conservation and land‐use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10–20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model‐based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most. Article impact statement: Methodological decisions on how species distribution models (SDMs) are built can alter locations of priority areas up to 90%. [ABSTRACT FROM AUTHOR]

Data standardization combined with descriptive metadata facilitate data reuse, which is the ultimate goal of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Community data or metadata standards are increasingly created through an approach that emphasizes collaboration between various stakeholders. Such an approach requires platforms for collaboration on the development process that centers on sharing information and receiving feedback. Our objective in this study was to conduct a systematic review to identify data standards and reporting formats that use version control for developing data standards and to summarize common practices, particularly in earth and environmental sciences. Out of 108 data standards and reporting formats identified in our review, 32 used GitHub as the version control platform, and no other platforms were used. We found no universally accepted methodology for developing and publishing data standards. Many GitHub repositories did not use key features that could help developers to gather user feedback, or to create and revise standards that build on previous work. We provide guidance for community‐driven standard development and associated documentation on GitHub based on a systematic review of existing practices. Key Points: Developing data standards on Version Control System platforms like GitHub enables collaboration and transparencyMany standards do not use tools for collaboration: issue tracking, licensing, and automated website hosting (GitBook or GitHub Pages)We make recommendations and provide templates for creating descriptive version‐controlled data standard documentation on GitHub [ABSTRACT FROM AUTHOR]

1. Metadata plays an essential role in the long-term preservation, reuse, and interoperability of data. Nevertheless, creating useful metadata can be sufficiently difficult and weakly enough incentivized that many datasets may be accompanied by little or no metadata. One key challenge is, therefore, how to make metadata creation easier and more valuable. We present a solution that involves creating domain-specific metadata schemes that are as complex as necessary and as simple as possible. These goals are achieved by co-development between a metadata expert and the researchers (i.e., the data creators). The final product is a bespoke metadata scheme into which researchers can enter information (and validate it) via the simplest of interfaces: a web browser application and a spreadsheet. 2. We provide the R package dmdScheme (dmdScheme: An R package for working with domain specific MetaData schemes (Version v0.9.22), 2019) for creating a template domain-specific scheme. We describe how to create a domain-specific scheme from this template, including the iterative co-development process, and the simple methods for using the scheme, and simple methods for quality assessment, improvement, and validation. 3. The process of developing a metadata scheme following the outlined approach was successful, resulting in a metadata scheme which is used for the data generated in our research group. The validation quickly identifies forgotten metadata, as well as inconsistent metadata, therefore improving the quality of the metadata. Multiple output formats are available, including XML. 4. Making the provision of metadata easier while also ensuring high quality must be a priority for data curation initiatives. We show how both objectives are achieved by close collaboration between metadata experts and researchers to create domain-specific schemes. A near-future priority is to provide methods to interface domain-specific schemes with general metadata schemes, such as the Ecological Metadata Language, to increase interoperability. [ABSTRACT FROM AUTHOR]

1. Metadata plays an essential role in the long- term preservation, reuse, and interoperability of data. Nevertheless, creating useful metadata can be sufficiently difficult and weakly enough incentivized that many datasets may be accompanied by little or no metadata. One key challenge is, therefore, how to make metadata creation easier and more valuable. We present a solution that involves creating domain- specific metadata schemes that are as complex as necessary and as simple as possible. These goals are achieved by co- development between a metadata expert and the researchers (i.e., the data creators). The final product is a bespoke metadata scheme into which researchers can enter information (and validate it) via the simplest of interfaces: a web browser application and a spreadsheet. 2. We provide the R package dmdScheme (dmdScheme: An R package for working with domain specific MetaData schemes (Version v0.9.22), 2019) for creating a template domain- specific scheme. We describe how to create a domain- specific scheme from this template, including the iterative co- development process, and the simple methods for using the scheme, and simple methods for quality assessment, improvement, and validation. 3. The process of developing a metadata scheme following the outlined approach was successful, resulting in a metadata scheme which is used for the data generated in our research group. The validation quickly identifies forgotten metadata, as well as inconsistent metadata, therefore improving the quality of the metadata. Multiple output formats are available, including XML. 4. Making the provision of metadata easier while also ensuring high quality must be a priority for data curation initiatives. We show how both objectives are achieved by close collaboration between metadata experts and researchers to create domain- specific schemes. A near- future priority is to provide methods to interface domain- specific schemes with general metadata schemes, such as the Ecological Metadata Language, to increase interoperability. [ABSTRACT FROM AUTHOR]

Several recent papers have highlighted best practices, defined metadata standards and proposed checklists for reproducibility of species distribution models (SDMs). A new paper by Zurell et al. (2020) formalizes, extends and implements many of these ideas in a standardized protocol for reporting SDMs called ODMAP (Overview, Data, Model, Assessment, Prediction). ODMAP has the potential to revolutionize species distribution modeling, not through the development of new methods, but rather by providing a long overdue system to document, communicate and reproduce methods. ODMAP could lead to improved basic and applied science outcomes by increasing comprehension and transparency, and by guiding implementation of best practices. However, the impact that ODMAP has on SDMs depends in large part on the extent to which journals, reviewers and funding agencies encourage its use and whether mentors introduce it to the next generation of biodiversity scientists. Important next steps include formalizing mechanisms for the broader research community to participate in future development of ODMAP and related standards for best practices in biodiversity modeling. [ABSTRACT FROM AUTHOR]

The ACT‐America project is a NASA Earth Venture Suborbital‐2 mission designed to study the transport and fluxes of greenhouse gases. The open and freely available ACT‐America data sets provide airborne in situ measurements of atmospheric carbon dioxide, methane, trace gases, aerosols, clouds, and meteorological properties, airborne remote sensing measurements of aerosol backscatter, atmospheric boundary layer height and columnar content of atmospheric carbon dioxide, tower‐based measurements, and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over the Central and Eastern United States. We conducted 121 research flights during five campaigns in four seasons during 2016–2019 over three regions of the US (Mid‐Atlantic, Midwest and South) using two NASA research aircraft (B‐200 and C‐130). We performed three flight patterns (fair weather, frontal crossings, and OCO‐2 underflights) and collected more than 1,140 h of airborne measurements via level‐leg flights in the atmospheric boundary layer, lower, and upper free troposphere and vertical profiles spanning these altitudes. We also merged various airborne in situ measurements onto a common standard sampling interval, which brings coherence to the data, creates geolocated data products, and makes it much easier for the users to perform holistic analysis of the ACT‐America data products. Here, we report on detailed information of data sets collected, the workflow for data sets including storage and processing of the quality controlled and quality assured harmonized observations, and their archival and formatting for users. Finally, we provide some important information on the dissemination of data products including metadata and highlights of applications of ACT‐America data sets. Plain Language Summary: We describe the data collected and produced by the Atmospheric Carbon and Transport – America mission, including airborne and tower‐based measurements of greenhouse gases (e.g., carbon dioxide and methane) and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over North America. In this paper, we briefly describe the data collections and archival including the instruments and methodology used to generate, manage, and distribute the data, and the significance of these new measurements for the study of the North American carbon cycle. Key Points: Atmospheric Carbon and Transport – America (ACT‐America) provides a unique, weather‐oriented collection of atmospheric CO2, CH4, trace gases, and meteorological properties measurementsACT‐America data are free and open to the public from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)ACT‐America data are uniquely suited to improve the accuracy and precision of regional inverse greenhouse gas (GHG) flux estimates [ABSTRACT FROM AUTHOR]

The use of traits is growing in ecology and biodiversity informatics, with initiatives to collate trait data and integrate it into biodiversity databases. A need to develop better predictive capacity for how species respond to environmental change has in part motivated this focus. Functional traits are of most interest—those with a defined link to individual survival, development, growth and reproduction.Non‐trivial challenges arise immediately in deciding which functional traits to prioritise and how to characterise them. Here we discuss the advantages of a theoretical perspective for defining functional traits in the context of dynamical systems models of energy and mass exchange that link organisms to their environments. We argue that the theoretical frameworks upon which such models are built (biophysical ecology, metabolic theory) provide clear criteria to decide upon functional trait definitions, measurement requirements and associated metadata, via their mathematical connection to model parameters and state variables, and thus to system performance (survival, development, growth and reproduction).We distinguish 'descriptive' traits from 'functional' traits by dividing the latter into four classes—parameter, model, threshold and estimation—according to whether they are model parameters, define model structure, are threshold state variables or can be used to estimate model parameters.We develop a decision tree for this classification and illustrate it in the context of mammalian heat exchange but emphasise the scheme's generality to any kind of organism.We show how a theoretical perspective may change how we prioritise traits for collection and databasing in ways that are not necessarily more difficult to achieve, especially with new technologies, and provide clear guidance for requisite metadata. The use of theoretically driven criteria for prioritising the collection of functional trait data will maximise the generality, quality and consistency of trait databases for comparative analyses. Such databases will simultaneously facilitate the development of integrated predictive modelling frameworks across multiple organisational scales from individuals to ecosystems. ​ A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

Freshwater conservation is vital to the maintenance of global biodiversity. Ponds are a critical, yet often under‐recognized, part of this, contributing to overall ecosystem functioning and diversity. They provide habitats for a range of aquatic, terrestrial, and amphibious life, often including rare and declining species.Effective, rapid, and accessible survey methods are needed to enable evidence‐based conservation action, but freshwater taxa are often viewed as "difficult"—and few specialist surveyors are available. Datasets on ponds are therefore limited in their spatiotemporal coverage.With the advent of new recording technologies, acoustic survey methods are becoming increasingly available to researchers, citizen scientists, and conservation practitioners. They can be an effective and noninvasive approach for gathering data on target species, assemblages, and environmental variables. However, freshwater applications are lagging behind those in terrestrial and marine spheres, and as an emergent method, research studies have employed a multitude of different sampling protocols.We propose the Pond Acoustic Sampling Scheme (PASS), a simple protocol to allow a standardized minimal sample to be collected rapidly from small waterbodies, alongside environmental and methodological metadata. This sampling scheme can be incorporated into a variety of survey designs and is intended to allow access to a wide range of participants, without requiring complicated or prohibitively expensive equipment.Adoption of this sampling protocol would enable consistent sound recordings to be gathered by researchers and conservation organizations, and allow the development of landscape‐scale surveys, data sharing, and collaboration within an expanding freshwater ecoacoustic community—rather than individual approaches that produce incompatible datasets. The compilation of standardized data would improve the prospects for effective research into the soundscapes of small waterbodies and aid freshwater conservation efforts. [ABSTRACT FROM AUTHOR]

This article presents daily rainfall data and metadata for Ireland transcribed from historical manuscript and printed copies of rainfall registers located in Met Éireann's archives. To facilitate the transcription of rainfall observations from paper records, the historical manuscripts were scanned and integrated into Met Éireann's digital archives. The transcription from digital image to data format was undertaken in collaboration with students at Maynooth University as part of a novel crowdsourcing initiative to integrate data rescue activities into the classroom. In total, 3,616 station years of rainfall data (~1.32 million daily values) were transcribed. The data, which was double keyed, have undergone basic quality assurance to check for transcription errors and the resultant raw data and associated metadata are presented here. Ongoing work involves the application of further quality assurance and homogenization techniques to develop a long‐term, quality assured daily rainfall network for Ireland. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has infected millions around the world. Governments initially responded by requiring businesses to close and citizens to self‐isolate, as well as funding vaccine research and implementing a range of technologies to monitor and limit the spread of the disease. This article considers the use of smartphone metadata and Bluetooth applications for public health surveillance purposes in relation to COVID‐19. It undertakes ethical analysis of these measures, particularly in relation to collective moral responsibility, considering whether citizens ought, or should be compelled, to comply with government measures. [ABSTRACT FROM AUTHOR]

Background: Limited data exist about the use, efficacy, and prognostic factors influencing outcome when CyberKnife is used to treat dogs with intracranial neoplasia. Objectives: To determine the prognosis and associated prognostic factors for dogs that were imaged, determined to have primary intracranial tumors, and treated with CyberKnife radiotherapy. Animals Fifty‐nine dogs treated with CyberKnife radiotherapy for primary intracranial tumors. Methods: Retrospective medical record review of cases from January 2010 to June 2016. Data extracted from medical records included signalment, weight, seizure history, tumor location, tumor type (based on imaging), gross tumor volume, planned tumor volume, treatment dates, radiation dose, recurrence, date of death, and cause of death. Results: The median progression‐free interval (PFI) was 347 days (range 47 to 1529 days), and the median survival time (MST) was 738 days (range 4 to 2079 days). Tumor location was significantly associated with PFI when comparing cerebrum (median PFI 357 days; range 47‐1529 days) versus cerebellum (median PFI 97 days; range 97‐168 days) versus brainstem (median PFI 266 days; range 30‐1484 days), P =.03. Additionally, the presumed tumor type was significantly associated with MST (P <.001). Conclusions and Clinical Importance: Use of Cyberknife and SRT might improve MST, compared with RT, in dogs with intracranial neoplasia. [ABSTRACT FROM AUTHOR]

Data preservation is the mechanism of protecting and safeguarding the confidentiality and integrity of data. Data stored in huge databases may contain metadata, elements that may be imprecise and unstable, It may include sensitive data, personal profiles and so on, which is vulnerable to third parties such as hackers or attackers. They may misuse the data and as a consequence of this the confidentiality and privacy of the data gets lost. There is a need to conserve the data and make it available for reuse when needed. Hence, it needs a proficient method to maintain and protect individuals' data privacy regarding confidentiality and reliability. This paper intends to develop an advanced model for privacy preservation of huge data with the accomplishment of two stages, namely data sanitization and data restoration. Data sanitization process preserves the safety of sensitive data stored in huge databases, by means of hiding those sensitive data from unauthorized users. Data restoration is the process of recovering or restoring of data that is sanitized at the sender side. Concerning the secrecy, there is a need for an optimal key to hide the sensitive data at sender as well as receiver side. Subsequent to the data sanitization, it requires the same key to restore the sanitized data. Thus, the optimal key generation plays a vital role to maintain privacy preservation. In order to choose an optimal key, a modified Rider optimization Algorithm (ROA) named as Randomized ROA (RROA) model is implemented in this work. Furthermore, the efficiency of the proposed work is compared over the state‐of‐the‐arts models by concerning the sanitization as well as restoration efficiency. [ABSTRACT FROM AUTHOR]

Social media data are being increasingly used in conservation science to study human–nature interactions. User‐generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user‐generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well‐being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects' rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk‐based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online. Article impact statement: Collecting, storing, protecting, sharing, and managing social media data should be done in a manner that prevents potential risks to users. [ABSTRACT FROM AUTHOR]

The recent growth of online big data offers opportunities for rapid and inexpensive measurement of public interest. Conservation culturomics is an emerging research area that uses online data to study human–nature relationships for conservation. Methods for conservation culturomics, though promising, are still being developed and refined. We considered the potential of Wikipedia, the online encyclopedia, as a resource for conservation culturomics and outlined methods for using Wikipedia data in conservation. Wikipedia's large size, widespread use, underlying data structure, and open access to both its content and usage analytics make it well suited to conservation culturomics research. Limitations of Wikipedia data include the lack of location information associated with some metadata and limited information on the motivations of many users. Seven methodological steps to consider when using Wikipedia data in conservation include metadata selection, temporality, taxonomy, language representation, Wikipedia geography, physical and biological geography, and comparative metrics. Each of these methodological decisions can affect measures of online interest. As a case study, we explored these themes by analyzing 757 million Wikipedia page views associated with the Wikipedia pages for 10,099 species of birds across 251 Wikipedia language editions. We found that Wikipedia data have the potential to generate insight for conservation and are particularly useful for quantifying patterns of public interest at large scales. Article impact statement: Wikipedia is a valuable resource for conservation culturomics research. [ABSTRACT FROM AUTHOR]

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

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms, and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community was formed in 2016 to address these needs. This report summarizes the main outputs of the third annual MCBK public meeting, which was held virtually from June 30 to July 1, 2020 and brought together over 200 participants from various domains to frame and address important dimensions for mobilizing CBK. [ABSTRACT FROM AUTHOR]