Search

Research data sharing has become an expected component of scientific research and scholarly publishing practice over the last few decades, due in part to requirements for federally funded research. As part of a larger effort to better understand the workflows and costs of public access to research data, this project conducted a high-level analysis of where academic research data is most frequently shared. To do this, we leveraged the DataCite and Crossref application programming interfaces (APIs) in search of Publisher field elements demonstrating which data repositories were utilized by researchers from six academic research institutions between 2012–2022. In addition, we also ran a preliminary analysis of the quality of the metadata associated with these published datasets, comparing the extent to which information was missing from metadata fields deemed important for public access to research data. Results show that the top 10 publishers accounted for 89.0% to 99.8% of the datasets connected with the institutions in our study. Known data repositories, including institutional data repositories hosted by those institutions, were initially lacking from our sample due to varying metadata standards and practices. We conclude that the metadata quality landscape for published research datasets is uneven; key information, such as author affiliation, is often incomplete or missing from source data repositories and aggregators. To enhance the findability, interoperability, accessibility, and reusability (FAIRness) of research data, we provide a set of concrete recommendations that repositories and data authors can take to improve scholarly metadata associated with shared datasets. [ABSTRACT FROM AUTHOR]

Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them. This model combines demographic and easily quantifiable survey-based data and is tested on high resolution proximity data collected in two sites in South Africa. Given its simplicity and interpretability, we expect our method to be easily applied to other contexts as well and we identify relevant questions that need to be addressed during the data collection procedure. [ABSTRACT FROM AUTHOR]

TV drama, through synchronization with social phenomena, allows the audience to resonate with the characters and desire to watch the next episode. In particular, drama ratings can be the criterion for advertisers to invest in ad placement and a predictor of subsequent economic efficiency in the surrounding areas. To identify the dissemination patterns of social information about dramas, this study used machine learning to predict drama ratings and the contribution of various drama metadata, including broadcast year, broadcast season, TV stations, day of the week, broadcast time slot, genre, screenwriters, status as an original work or sequel, actors and facial features on posters. A total of 800 Japanese TV dramas broadcast during prime time between 2003 and 2020 were collected for analysis. Four machine learning classifiers, including naïve Bayes, artificial neural network, support vector machine, and random forest, were used to combine the metadata. With facial features, the accuracy of the random forest model increased from 75.80% to 77.10%, which shows that poster information can improve the accuracy of the overall predicted ratings. Using only posters to predict ratings with a convolutional neural network still obtained an accuracy rate of 71.70%. More insights about the correlations between drama metadata and social information dissemination patterns were explored. [ABSTRACT FROM AUTHOR]

Accessing and utilizing geospatial data from various sources is essential for developing scientific research to address complex scientific and societal challenges that require interdisciplinary knowledge. The traditional keyword-based geosearch approach is insufficient due to the uncertainty inherent within spatial information and how it is presented in the data-sharing platform. For instance, the Gulf of Mexico Coastal Ocean Observing System (GCOOS) data search platform stores geoinformation and metadata in a complex tabular. Users can search for data by entering keywords or selecting data from a drop-down manual from the user interface. However, the search results provide limited information about the data product, where detailed descriptions, potential use, and relationship with other data products are still missing. Language models (LMs) have demonstrated great potential in tasks like question answering, sentiment analysis, text classification, and machine translation. However, they struggle when dealing with metadata represented in tabular format. To overcome these challenges, we developed Meta Question Answering System (MetaQA), a novel spatial data search model. MetaQA integrates end-to-end AI models with a generative pre-trained transformer (GPT) to enhance geosearch services. Using GCOOS metadata as a case study, we tested the effectiveness of MetaQA. The results revealed that MetaQA outperforms state-of-the-art question-answering models in handling tabular metadata, underlining its potential for user-inspired geosearch services. [ABSTRACT FROM AUTHOR]

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

Many high-throughput sequencing datasets can be represented as objects with coordinates along a reference genome. Currently, biological investigations often involve a large number of such datasets, for example representing different cell types or epigenetic factors. Drawing overall conclusions from a large collection of results for individual datasets may be challenging and time-consuming. Meaningful interpretation often requires the results to be aggregated according to metadata that represents biological characteristics of interest. In this light, we here propose the hierarchical Genomic Suite HyperBrowser (hGSuite), an open-source extension to the GSuite HyperBrowser platform, which aims to provide a means for extracting key results from an aggregated collection of high-throughput DNA sequencing data. The hGSuite utilizes a metadata-informed data cube to calculate various statistics across the multiple dimensions of the datasets. With this work, we show that the hGSuite and its associated data cube methodology offers a quick and accessible way for exploratory analysis of large genomic datasets. The web-based toolkit named hGsuite Hyperbrowser is available at https://hyperbrowser.uio.no/hgsuite under a GPLv3 license. [ABSTRACT FROM AUTHOR]

Bibliographic references containing citation information of academic literature play an important role as a medium connecting earlier and recent studies. As references contain machine-readable metadata such as author name, title, or publication year, they have been widely used in the field of citation information services including search services for scholarly information and research trend analysis. Many institutions around the world manually extract and continuously accumulate reference metadata to provide various scholarly services. However, manually collection of reference metadata every year continues to be a burden because of the associated cost and time consumption. With the accumulation of a large volume of academic literature, several tools, including GROBID and CERMINE, that automatically extract reference metadata have been released. However, these tools have some limitations. For example, they are only applicable to references written in English, the types of extractable metadata are limited for each tool, and the performance of the tools is insufficient to replace the manual extraction of reference metadata. Therefore, in this study, we focused on constructing a high-quality corpus to automatically extract metadata from multilingual journal article references. Using our constructed corpus, we trained and evaluated a BERT-based transfer-learning model. Furthermore, we compared the performance of the BERT-based model with that of the existing model, GROBID. Currently, our corpus contains 3,815,987 multilingual references, mainly in English and Korean, with labels for 13 different metadata types. According to our experiment, the BERT-based model trained using our corpus showed excellent performance in extracting metadata not only from journal references written in English but also in other languages, particularly Korean. This corpus is available at http://doi.org/10.23057/47. [ABSTRACT FROM AUTHOR]

Objectives: To adopt the FAIR principles (Findable, Accessible, Interoperable, Reusable) to enhance data sharing, the Cure Sickle Cell Initiative (CureSCi) MetaData Catalog (MDC) was developed to make Sickle Cell Disease (SCD) study datasets more Findable by curating study metadata and making them available through an open-access web portal. Methods: Study metadata, including study protocol, data collection forms, and data dictionaries, describe information about study patient-level data. We curated key metadata of 16 SCD studies in a three-tiered conceptual framework of category, subcategory, and data element using ontologies and controlled vocabularies to organize the study variables. We developed the CureSCi MDC by indexing study metadata to enable effective browse and search capabilities at three levels: study, Patient-Reported Outcome (PRO) Measures, and data element levels. Results: The CureSCi MDC offers several browse and search tools to discover studies by study level, PRO Measures, and data elements. The "Browse Studies," "Browse Studies by PRO Measures," and "Browse Studies by Data Elements" tools allow users to identify studies through pre-defined conceptual categories. "Search by Keyword" and "Search Data Element by Concept Category" can be used separately or in combination to provide more granularity to refine the search results. This resource helps investigators find information about specific data elements across studies using public browsing/search tools, before going through data request procedures to access controlled datasets. The MDC makes SCD studies more Findable through browsing/searching study information, PRO Measures, and data elements, aiding in the reuse of existing SCD data. [ABSTRACT FROM AUTHOR]

Governments can use social media platforms such as Twitter to disseminate health information to the public, as evidenced during the COVID-19 pandemic [Pershad (2018)]. The purpose of this study is to gain a better understanding of Canadian government and public health officials' use of Twitter as a dissemination platform during the pandemic and to explore the public's engagement with and sentiment towards these messages. We examined the account data of 93 Canadian public health and government officials during the first wave of the pandemic in Canada (December 31, 2019 August 31, 2020). Our objectives were to: 1) determine the engagement rates of the public with Canadian federal and provincial/territorial governments and public health officials' Twitter posts; 2) conduct a hashtag trend analysis to explore the Canadian public's discourse related to the pandemic during this period; 3) provide insights on the public's reaction to Canadian authorities' tweets through sentiment analysis. To address these objectives, we extracted Twitter posts, replies, and associated metadata available during the study period in both English and French. Our results show that the public demonstrated increased engagement with federal officials' Twitter accounts as compared to provincial/territorial accounts. For the hashtag trends analysis of the public discourse during the first wave of the pandemic, we observed a topic shift in the Canadian public discourse over time between the period prior to the first wave and the first wave of the pandemic. Additionally, we identified 11 sentiments expressed by the public when reacting to Canadian authorities' tweets. This study illustrates the potential to leverage social media to understand public discourse during a pandemic. We suggest that routine analyses of such data by governments can provide governments and public health officials with real-time data on public sentiments during a public health emergency. These data can be used to better disseminate key messages to the public. [ABSTRACT FROM AUTHOR]

In this article, we present a methodology which takes as input a collection of retracted articles, gathers the entities citing them, characterizes such entities according to multiple dimensions (disciplines, year of publication, sentiment, etc.), and applies a quantitative and qualitative analysis on the collected values. The methodology is composed of four phases: (1) identifying, retrieving, and extracting basic metadata of the entities which have cited a retracted article, (2) extracting and labeling additional features based on the textual content of the citing entities, (3) building a descriptive statistical summary based on the collected data, and finally (4) running a topic modeling analysis. The goal of the methodology is to generate data and visualizations that help understanding possible behaviors related to retraction cases. We present the methodology in a structured step-by-step form following its four phases, discuss its limits and possible workarounds, and list the planned future improvements. [ABSTRACT FROM AUTHOR]

Common data models standardize the structures and semantics of health datasets, enabling reproducibility and large-scale studies that leverage the data from multiple locations and settings. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is one of the leading common data models. While there is a strong incentive to convert datasets to OMOP, the conversion is time and resource-intensive, leaving the research community in need of tools for mapping data to OMOP. We propose an extract, transform, load (ETL) framework that is metadata-driven and generic across source datasets. The ETL framework uses a new data manipulation language (DML) that organizes SQL snippets in YAML. Our framework includes a compiler that converts YAML files with mapping logic into an ETL script. Access to the ETL framework is available via a web application, allowing users to upload and edit YAML files via web editor and obtain an ETL SQL script for use in development environments. The structure of the DML maximizes readability, refactoring, and maintainability, while minimizing technical debt and standardizing the writing of ETL operations for mapping to OMOP. Our framework also supports transparency of the mapping process and reuse by different institutions. [ABSTRACT FROM AUTHOR]

Crowdfunding platforms allow entrepreneurs to publish projects and raise funds for realizing them. Hence, the question of what influences projects' fundraising success is very important. Previous studies examined various factors such as project goals and project duration that may influence the outcomes of fundraising campaigns. We present a novel model for predicting the success of crowdfunding projects in meeting their funding goals. Our model focuses on semantic features only, whose performance is comparable to that of previous models. In an additional model we developed, we examine both project metadata and project semantics, delivering a comprehensive study of factors influencing crowdfunding success. Further, we analyze a large dataset of crowdfunding project data, larger than reported in the art. Finally, we show that when combining semantics and metadata, we arrive at F1 score accuracy of 96.2%. We compare our model's accuracy to the accuracy of previous research models by applying their methods on our dataset, and demonstrate higher accuracy of our model. In addition to our scientific contribution, we provide practical recommendations that may increase project funding success chances. [ABSTRACT FROM AUTHOR]

Working with organs and extracted tissue blocks is an essential task in many medical surgery and anatomy environments. In order to prepare specimens from human donors for further analysis, wet-bench workers must properly dissect human tissue and collect metadata for downstream analysis, including information about the spatial origin of tissue. The Registration User Interface (RUI) was developed to allow stakeholders in the Human Biomolecular Atlas Program (HuBMAP) to register tissue blocks—i.e., to record the size, position, and orientation of human tissue data with regard to reference organs. The RUI has been used by tissue mapping centers across the HuBMAP consortium to register a total of 45 kidney, spleen, and colon tissue blocks, with planned support for 17 organs in the near future. In this paper, we compare three setups for registering one 3D tissue block object to another 3D reference organ (target) object. The first setup is a 2D Desktop implementation featuring a traditional screen, mouse, and keyboard interface. The remaining setups are both virtual reality (VR) versions of the RUI: VR Tabletop, where users sit at a physical desk which is replicated in virtual space; VR Standup, where users stand upright while performing their tasks. All three setups were implemented using the Unity game engine. We then ran a user study for these three setups involving 42 human subjects completing 14 increasingly difficult and then 30 identical tasks in sequence and reporting position accuracy, rotation accuracy, completion time, and satisfaction. All study materials were made available in support of future study replication, alongside videos documenting our setups. We found that while VR Tabletop and VR Standup users are about three times as fast and about a third more accurate in terms of rotation than 2D Desktop users (for the sequence of 30 identical tasks), there are no significant differences between the three setups for position accuracy when normalized by the height of the virtual kidney across setups. When extrapolating from the 2D Desktop setup with a 113-mm-tall kidney, the absolute performance values for the 2D Desktop version (22.6 seconds per task, 5.88 degrees rotation, and 1.32 mm position accuracy after 8.3 tasks in the series of 30 identical tasks) confirm that the 2D Desktop interface is well-suited for allowing users in HuBMAP to register tissue blocks at a speed and accuracy that meets the needs of experts performing tissue dissection. In addition, the 2D Desktop setup is cheaper, easier to learn, and more practical for wet-bench environments than the VR setups. [ABSTRACT FROM AUTHOR]

During a psychotherapy session, the counselor typically adopts techniques which are codified along specific dimensions (e.g., 'displays warmth and confidence', or 'attempts to set up collaboration') to facilitate the evaluation of the session. Those constructs, traditionally scored by trained human raters, reflect the complex nature of psychotherapy and highly depend on the context of the interaction. Recent advances in deep contextualized language models offer an avenue for accurate in-domain linguistic representations which can lead to robust recognition and scoring of such psychotherapy-relevant behavioral constructs, and support quality assurance and supervision. In this work, we propose a BERT-based model for automatic behavioral scoring of a specific type of psychotherapy, called Cognitive Behavioral Therapy (CBT), where prior work is limited to frequency-based language features and/or short text excerpts which do not capture the unique elements involved in a spontaneous long conversational interaction. The model focuses on the classification of therapy sessions with respect to the overall score achieved on the widely-used Cognitive Therapy Rating Scale (CTRS), but is trained in a multi-task manner in order to achieve higher interpretability. BERT-based representations are further augmented with available therapy metadata, providing relevant non-linguistic context and leading to consistent performance improvements. We train and evaluate our models on a set of 1,118 real-world therapy sessions, recorded and automatically transcribed. Our best model achieves an F1 score equal to 72.61% on the binary classification task of low vs. high total CTRS. [ABSTRACT FROM AUTHOR]

Background: Girls and women subjected to female genital cutting (FGC) risk experiencing obstetrical, gynecological, sexual, and psychological health problems. Therefore, Norway has established low-threshold specialized healthcare services where girls and women with FGC-related health problems can directly seek medical attention. Nevertheless, we lack data about access to these services, especially for non-maternity-related purposes. In this article, we explore experiences of seeking medical attention for health problems that are potentially FGC-related, aiming to identify factors that hinder or facilitate access to FGC-specialized services. Methods: We conducted a qualitative study in three Norwegian cities employing semi-structured repeat interviews with 26 girls and women subjected to FGC, participant observation, and three validation focus group discussions with 17 additional participants. We thematically analyzed the data and approached access as a dynamic process of interactions between individuals and the healthcare system that lasts from an initial perception of need until reception of healthcare appropriate to that need. Findings: We identified several barriers to healthcare, including 1) uncertainty about FGC as a cause of experienced health problems, 2) unfamiliarity with FGC-specialized services, 3) lack of assessment by general practitioners of FGC as a potential cause of health problems, and 4) negative interactions with healthcare providers. In contrast, factors facilitating healthcare included: 1) receiving information on FGC-related health problems and FGC-specialized services from a non-profit immigrant organization, 2) referral to gynecologists with good knowledge of FGC, and 3) positive interactions with healthcare providers. Conclusion: Assessing whether FGC is the cause for experienced health problems requires diagnostic competency and should not be left entirely to the patients. We recommend that Norwegian policymakers acknowledge the central role of GPs in the clinical management of patients with FGC-related health problems and provide them with comprehensive training on FGC. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this paper is to study under which circumstances wearable and health app users would accept a compensation payment, namely a digital dividend, to share their self-tracked health data. Methods: We conducted a discrete choice experiment alternative, a separated adaptive dual response. We chose this approach to reduce extreme response behavior, considering the emotionally-charged topic of health data sales, and to measure willingness to accept. Previous experiments in lab settings led to demands for high monetary compensation. After a first online survey and two pre-studies, we validated four attributes for the final online study: monthly bonus payment, stakeholder handling the data (e.g., health insurer, pharmaceutical or medical device companies, universities), type of data, and data sales to third parties. We used a random utility framework to evaluate individual choice preferences. To test the expected prices of the main study for robustness, we assigned respondents randomly to one of two identical questionnaires with varying price ranges. Results: Over a period of three weeks, 842 respondents participated in the main survey, and 272 respondents participated in the second survey. The participants considered transparency about data processing and no further data sales to third parties as very important to the decision to share data with different stakeholders, as well as adequate monetary compensation. Price expectations resulting from the experiment were high; pharmaceutical and medical device companies would have to pay an average digital dividend of 237.30€/month for patient generated health data of all types. We also observed an anchor effect, which means that people formed price expectations during the process and not ex ante. We found a bimodal distribution between relatively low price expectations and relatively high price expectations, which shows that personal data selling is a divisive societal issue. However, the results indicate that a digital dividend could be an accepted economic incentive system to gather large-scale, self-tracked data for research and development purposes. After the COVID-19 crisis, price expectations might change due to public sensitization to the need for big data research on patient generated health data. Conclusion: A continuing success of existing data donation models is highly unlikely. The health care sector needs to develop transparency and trust in data processing. An adequate digital dividend could be an effective long-term measure to convince a diverse and large group of people to share high-quality, continuous data for research purposes. [ABSTRACT FROM AUTHOR]

Biomedical research papers often combine disjoint concepts in novel ways, such as when describing a newly discovered relationship between an understudied gene with an important disease. These concepts are often explicitly encoded as metadata keywords, such as the author-provided terms included with many documents in the MEDLINE database. While substantial recent work has addressed the problem of text generation in a more general context, applications, such as scientific writing assistants, or hypothesis generation systems, could benefit from the capacity to select the specific set of concepts that underpin a generated biomedical text. We propose a conditional language model following the transformer architecture. This model uses the "encoder stack" to encode concepts that a user wishes to discuss in the generated text. The "decoder stack" then follows the masked self-attention pattern to perform text generation, using both prior tokens as well as the encoded condition. We demonstrate that this approach provides significant control, while still producing reasonable biomedical text. [ABSTRACT FROM AUTHOR]

Background: Informed consent (IC) is a healthcare standard emphasizing the meaning of human dignity as clarified in the Universal Declaration of Human Rights. Data about IC practices in Egypt is insufficient. This study aimed to assess the Egyptian patients'/guardians' experiences about IC and their expectations about its practices' purposes in general and according to the type of the healthcare facility. Methods: Self-administered questionnaire was carried out for 1092 participants who had undergone or were scheduled to a procedure requiring an IC at three studied types for Egyptian health care facilities. Ten statements were ranked twice by the participants to reflect their perception of IC purpose as per what is currently practiced and what they believe should be practiced. Results: IC implementation varies significantly (p<0.05) across the health care facilities in Egypt. The percentage of its implementation at the non-governmental facilities, governmental facilities, and university hospital was 85.9%, 77.8%, and 63.8 respectively. The first three ranked purposes of the current IC practices were: "Helping patient/guardian decide (64.9%)", "Documenting patient's/guardian's decision (59.3%)", and "Having shared decision (57.3%)". The perceived purposes of IC to be practiced were: "Informing the patient/guardian (68.4%)", "Making sure patient/guardian understand (65.3%)" and "Documenting patients/guardians decisions (65.1%)". "Being a meaningless routine" was reported by the majority to be ranked as a low purpose for IC current and preferred practices. Conclusion: The practice of IC is common within the Egyptian medical community. Participants believe that information disclosure "Making sure patients understand" has to help in IC decision making and its main purpose. However, unfortunately, this is not perceived as a current purpose of IC. There was consensus agreement that documenting the patient's/guardian's decision and informing the patient/guardian are perceived as both important current and preferred purposes for IC practices. [ABSTRACT FROM AUTHOR]

This study compared the results of data collected from a longitudinal query analysis of the MEDLINE database hosted on multiple platforms that include PubMed, EBSCOHost, Ovid, ProQuest, and Web of Science. The goal was to identify variations among the search results on the platforms after controlling for search query syntax. We devised twenty-nine cases of search queries comprised of five semantically equivalent queries per case to search against the five MEDLINE database platforms. We ran our queries monthly for a year and collected search result count data to observe changes. We found that search results varied considerably depending on MEDLINE platform. Reasons for variations were due to trends in scholarly publication such as publishing individual papers online first versus complete issues. Some other reasons were metadata differences in bibliographic records; differences in the levels of specificity of search fields provided by the platforms and large fluctuations in monthly search results based on the same query. Database integrity and currency issues were observed as each platform updated its MEDLINE data throughout the year. Specific biomedical bibliographic databases are used to inform clinical decision-making, create systematic reviews, and construct knowledge bases for clinical decision support systems. They serve as essential information retrieval and discovery tools to help identify and collect research data and are used in a broad range of fields and as the basis of multiple research designs. This study should help clinicians, researchers, librarians, informationists, and others understand how these platforms differ and inform future work in their standardization. [ABSTRACT FROM AUTHOR]

Objective: Fecal microbiota transplantation (FMT) is a highly effective treatment for Clostridioides difficile infection (CDI). However, the fecal transplant's causal components translating into clearance of the CDI are yet to be identified. The commensal bacteria Faecalibacterium prausnitzii may be of great interest in this context, since it is one of the most common species of the healthy gut microbiota and produces metabolites with anti-inflammatory properties. Although there is mounting evidence that F. prausnitzii is an important regulator of intestinal homeostasis, data about its role in CDI and FMT are relatively scarce. Methods: Stool samples from patients with recurrent CDI were collected to investigate the relative abundance of F. prausnitzii before and after FMT. Twenty-one patients provided fecal samples before the FMT procedure, at 2 weeks post-FMT, and at 2–4 months post-FMT. The relative abundance of F. prausnitzii was determined using quantitative polymerase chain reaction. Results: The abundance of F. prausnitzii was elevated in samples (N = 9) from donors compared to pre-FMT samples (N = 15) from patients (adjusted P<0.001). No significant difference in the abundance of F. prausnitzii between responders (N = 11) and non-responders (N = 4) was found before FMT (P = 0.85). In patients with CDI, the abundance of F. prausnitzii significantly increased in the 2 weeks post-FMT samples (N = 14) compared to the pre-FMT samples (N = 15, adjusted P<0.001). The increase persisted 2–4 months post-FMT (N = 15) compared to pre-FMT samples (N = 15) (adjusted P<0.001). Conclusions: FMT increases the relative abundance of F. prausnitzii in patients with recurrent CDI, and this microbial shift remains several months later. The baseline abundance of F. prausnitzii in donors or recipients was not associated with future treatment response, although a true predictive capacity cannot be excluded because of the limited sample size. Further studies are needed to discern whether F. prausnitzii plays an active role in the resolution of CDI. [ABSTRACT FROM AUTHOR]

Recognizing that microbial community composition within the human microbiome is associated with the physiological state of the host has sparked a large number of human microbiome association studies (HMAS). With the increasing size of publicly available HMAS data, the privacy risk is also increasing because HMAS metadata could contain sensitive private information. I demonstrate that a simple test statistic based on the taxonomic profiles of an individual's microbiome along with summary statistics of HMAS data can reveal the membership of the individual's microbiome in an HMAS sample. In particular, species-level taxonomic data obtained from small-scale HMAS can be highly vulnerable to privacy risk. Minimal guidelines for HMAS data privacy are suggested, and an assessment of HMAS privacy risk using the simulation method proposed is recommended at the time of study design. [ABSTRACT FROM AUTHOR]

The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. In particular, we focus on scholarly search interests and metadata, the primary source of data in a dataset retrieval system. We show that existing metadata currently poorly reflect information needs and therefore are the biggest obstacle in retrieving relevant data. Our findings indicate that for data seekers in the biodiversity domain environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important information categories. These interests are well covered in metadata elements of domain-specific standards. However, instead of utilizing these standards, large data repositories tend to use metadata standards with domain-independent metadata fields that cover search interests only to some extent. A second problem are arbitrary keywords utilized in descriptive fields such as title, description or subject. Keywords support scholars in a full text search only if the provided terms syntactically match or their semantic relationship to terms used in a user query is known. [ABSTRACT FROM AUTHOR]

Background and aims: Several studies reported a high incidence of pulmonary embolism (PE) among patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, but detailed data about clinical characteristics, risk factors of these patients and prognostic role of PE are still lacking. We aim to evaluate the occurrence of pulmonary embolism among patients with SARS-CoV-2 infection, and to describe their risk factors, clinical characteristics, and in-hospital clinical outcomes. Methods: This is a multicenter Italian study including 333 consecutive SARS-CoV-2 patients admitted to seven hospitals from February 22 to May 15, 2020. All the patients underwent computed tomography pulmonary angiography (CTPA) for PE detection. In particular, CTPA was performed in case of inadequate response to high-flow oxygen therapy (Fi02≥0.4 to maintain Sp02≥92%), elevated D-dimer (>0.5μg/mL), or echocardiographic signs of right ventricular dysfunction. Clinical, laboratory and radiological data were also analyzed. Results: Among 333 patients with laboratory confirmed SARS-CoV-2 pneumonia and undergoing CTPA, PE was detected in 109 (33%) cases. At CTPA, subsegmental, segmental, lobar and central thrombi were detected in 31 (29%), 50 (46%), 20 (18%) and 8 (7%) cases, respectively. In-hospital death occurred in 29 (27%) patients in the PE-group and in 47 (21%) patients in the non-PE group (p = 0.25). Patients in PE-group had a low rate of traditional risk factors and deep vein thrombosis was detected in 29% of patients undergoing compression ultrasonography. In 71% of cases with documented PE, the thrombotic lesions were located in the correspondence of parenchymal consolidation areas. Conclusions: Despite a low rate of risk factors for venous thromboembolism, PE is present in about 1 out 3 patients with SARS-CoV-2 pneumonia undergoing CTPA for inadequate response to oxygen therapy, elevated D-dimer level, or echocardiographic signs of right ventricular dysfunction. In most of the cases, the thromboses were located distally in the pulmonary tree and were mainly confined within pneumonia areas. [ABSTRACT FROM AUTHOR]

Background: Intravenous morphine (MO) decreases the effect of all oral platelet P2Y12 receptor inhibitors in vitro and observational reports suggest that its use may be associated with larger infarct size. Yet, there are limited data available about the impact of this interaction on clinical outcomes. We studied the effect of MO on mortality in ST-segment elevation myocardial infarction (STEMI) patients treated with primary PCI using a prospective registry. Methods: Of the 1255 patients who underwent primary PCI, 397 received MO based on physician's judgment. Clopidogrel was used as P2Y12 receptor antagonist in all cases. Median follow-up time was 7.5 years with 457 deaths. To adjust for confounding, two propensity score-based procedures were performed: 1 to 1 matching (PSM, 728 cases), and inverse probability of treatment weighting (IPTW) retaining data from all patients. Primary outcome measure was time to all-cause death, whereas predischarge left ventricular ejection fraction (LVEF) was used as secondary end point. Results: An adequate balance on baseline covariates was achieved by both methods. We found no difference in survival as the HR (MO/no MO) was 0.98 (95% confidence interval [CI]: 0.76–1.26), p = 0.86 using PSM and 1.01 (95% CI: 0.84–1.23), p = 0.88 with IPTW. Likewise, distributions of LVEFs were similar using either methods: with PSM, median LVEFs were 50.0% (interquartile range [IQR]: 43.0%–55.3%) vs 50.0% (IQR: 42.0%–55.0%) in the no MO and MO groups, respectively (p = 0.76), whereas using IPTW, they were 50.0% (IQR: 42.5%–55.0%) vs 50.0% (IQR: 41.0%–55.0%), respectively (p = 0.86). Conclusions: Our data suggest that morphine use may have no impact on long-term mortality and on predischarge ejection fraction in STEMI patients treated with primary PCI. [ABSTRACT FROM AUTHOR]

Scientific articles often contain relevant geographic information such as where field work was performed or where patients were treated. Most often, this information appears in the full-text article contents as a description in natural language including place names, with no accompanying machine-readable geographic metadata. Automatically extracting this geographic information could help conduct meta-analyses, find geographical research gaps, and retrieve articles using spatial search criteria. Research on this problem is still in its infancy, with many works manually processing corpora for locations and few cross-domain studies. In this paper, we develop a fully automatic pipeline to extract and represent relevant locations from scientific articles, applying it to two varied corpora. We obtain good performance, with full pipeline precision of 0.84 for an environmental corpus, and 0.78 for a biomedical corpus. Our results can be visualized as simple global maps, allowing human annotators to both explore corpus patterns in space and triage results for downstream analysis. Future work should not only focus on improving individual pipeline components, but also be informed by user needs derived from the potential spatial analysis and exploration of such corpora. [ABSTRACT FROM AUTHOR]

Postpartum depression is a worldwide public health concern. The prevalence of postpartum depression is reported to be greater in developing countries than in developed countries. However, to the best of our knowledge, no papers on postpartum depression in the Lao People's Democratic Republic have been published. In order to strengthen maternal and child health, the current situation of postpartum depression should be understood. This study aims to determine the prevalence of postpartum depression and identify factors associated with postpartum depression in Vientiane Capital, Lao People's Democratic Republic. Study participants were 428 women 6–8 weeks postpartum who visited four central hospitals in Vientiane Capital for postnatal care from July to August 2019. Structured questionnaires were used to collect socio-demographic, obstetrical and infant, and psychiatric data about the women and their partners. The Edinburgh Postnatal Depression Scale (EPDS) was used to identify suspected cases of postpartum depression with the cut-off score of 9/10. Multivariable logistic regression was used to examine independent factors that were associated with suspected postpartum depression (EPDS ≥10). The mean age of the 428 women was 28.1 years, and the prevalence of suspected postpartum depression was 31.8%. Multivariable logistic regression using variables that were statistically significant on bivariate analyses indicated that three variables were associated with suspected postpartum depression: unintended pregnancy (AOR = 1.66, 95% CI 1.00–2.73, P = 0.049), low birth satisfaction (AOR = 1.85, 95% CI 1.00–3.43, P = 0.049), and depression during pregnancy (AOR = 3.99, 95% CI 2.35–6.77, P <0.001). In this study, unintended pregnancy, low birth satisfaction, and depression during pregnancy were independent risk factors for postpartum depression. These results suggest that the mental health of pregnant women should be monitored, and that health care services, especially family planning and supportive birth care, should be strengthened to prevent postpartum depression. [ABSTRACT FROM AUTHOR]

Binary classification has numerous applications. For one, lie detection methods typically aim to classify each tested person either as "liar" or as "truthteller" based on the given test results. To infer practical implications, as well as to compare different methods, it is essential to assess the diagnostic efficiency, such as demonstrating the number of correctly classified persons. However, this is not always straightforward. In Concealed Information Tests (CITs), the key predictor value (probe-irrelevant difference) for "truthtellers" is always similar (zero on average), and "liars" are always distinguished by a larger value (i.e., a larger number resulting from the CIT test, as compared to the zero baseline). Thereby, in general, the larger predictor values a given CIT method obtains for "liars" on average, the better this method is assumed to be. This has indeed been assumed in countless studies, and therefore, when comparing the classification efficiencies of two different designs, the mean difference of "liar" predictor values in the two designs were simply compared to each other (hence not collecting "truthteller" data to spare resources). We show, based on the meta-data of 12 different experimental designs collected in response time-based CIT studies, that differences in dispersion (i.e., variance in the data, e.g. the extent of random deviations from the zero average in case of "truthtellers") can substantially influence classification efficiency–to the point that, in extreme cases, one design may even be superior in classification despite having a larger mean "liar" predictor value. However, we also introduce a computer simulation procedure to estimate classification efficiency in the absence of "truthteller" data, and validate this procedure via a meta-analysis comparing outcomes based on empirical data versus simulated data. [ABSTRACT FROM AUTHOR]

Empirical research on migration has historically been fraught with measurement challenges. Recently, the increasing ubiquity of digital trace data—from mobile phones, social media, and related sources of 'big data'—has created new opportunities for the quantitative analysis of migration. However, most existing work relies on relatively ad hoc methods for inferring migration. Here, we develop and validate a novel and general approach to detecting migration events in trace data. We benchmark this method using two different trace datasets: four years of mobile phone metadata from a single country's monopoly operator, and three years of geo-tagged Twitter data. The novel measures more accurately reflect human understanding and evaluation of migration events, and further provide more granular insight into migration spells and types than what are captured in standard survey instruments. [ABSTRACT FROM AUTHOR]

Many parts of Asia, including Taiwan, have suffered severely from freshwater fish invasions. However, few studies using an assemblage approach have been conducted in the region so far that would help to prioritize suitable preventive actions. In this study, we focused on the invasion process from the import stage to the establishment stage, and defined establishment success as the success during this predefined process. We used datasets of freshwater fish assemblages in Taiwan to (1) compare established versus non-established alien species to distinguish the determinants of establishment success, and (2) to use these determinants to test a life history hypothesis which predicts that the magnitudes of the determinants should be significantly different between established alien species and native species. We collated a dataset for freshwater fish species which were imported into Taiwan (n = 118) of which some successfully established (n = 26), and another dataset for freshwater fish species native to Taiwan (n = 77). For each imported species, we collected data for 17 variables, including two phylogenetic, two human-use, two invasion history, and 11 life history variables. We then used decision tree methods, which have advantages in analyzing datasets with many variables of mixed types without the need to make assumptions about data distributions and input data for missing values. Our results showed that aquaculture use and maximum body length were the most important determinants for predicting establishment success of alien freshwater fish in Taiwan. Comparing five important determinants between established alien versus native species showed that the established alien species were significantly more often used in aquaculture, were associated with a higher number of established countries, and had a larger body length and greater highest water temperature tolerance than the native species. We thus conclude that our results provided evidence to support our stated hypothesis. We suggest that aquaculture use, measures of body size, and the number of previously invaded countries may alert researchers and conservation managers to species with a high establishment potential, especially for countries with similar conditions as those in Taiwan. [ABSTRACT FROM AUTHOR]

Objective: Cash transfers are a common intervention to incentivize salutary behavior in resource-constrained settings. Many cash transfer studies do not, however, account for the effect of the size of the cash transfer in design or analysis. A randomized, controlled trial of a cash-transfer intervention is planned to incentivize appropriate surgical utilization in Guinea. The aim of the current study is to determine the size of that cash transfer so as to maximize compliance while minimizing cost. Methods: Data were collected from nine coastal Guinean hospitals on their surgical capabilities and the cost of receiving surgery. These data were combined with publicly available data about the general Guinean population to create an agent-based model predicting surgical utilization. The model was validated to the available literature on surgical utilization. Cash transfer sizes from 0 to 1,000,000 Guinean francs were evaluated, with surgical compliance as the primary outcome. Results: Compliance with scheduled surgery increases as the size of a cash transfer increases. This increase is asymptotic, with a leveling in utilization occurring when the cash transfer pays for all the costs associated with surgical care. Below that cash transfer size, no other optima are found. Once a cash transfer completely covers the costs of surgery, other barriers to care such as distance and hospital quality dominate Conclusion: Cash transfers to incentivize health-promoting behavior appear to be dose-dependent. Maximal impact is likely only to occur when full patient costs are eliminated. These findings should be incorporated in the design of future cash transfer studies. [ABSTRACT FROM AUTHOR]

To disseminate research, scholars once relied on university media services or journal press releases, but today any academic can turn to Twitter to share their published work with a broader audience. The possibility that scholars can push their research out, rather than hope that it is pulled in, holds the potential for scholars to draw wide attention to their research. In this manuscript, we examine whether there are systematic differences in the types of scholars who most benefit from this push model. Specifically, we investigate the extent to which there are gender differences in the dissemination of research via Twitter. We carry out our analyses by tracking tweet patterns for articles published in six journals across two fields (political science and communication), and we pair this Twitter data with demographic and educational data about the authors of the published articles, as well as article citation rates. We find considerable evidence that, overall, article citations are positively correlated with tweets about the article, and we find little evidence to suggest that author gender affects the transmission of research in this new media. [ABSTRACT FROM AUTHOR]

A citation is deemed as a potential parameter to determine linkage between research articles. The parameter has extensively been employed to form multifarious academic aspects like calculating the impact factor of journals, h-Index of researchers, allocate different research grants, find the latest research trends, etc. The current state-of-the-art contends that all citations are not of equal importance. Based on this argument, the current trend in citation classification community categorizes citations into important and non-important reasons. The community has proposed different approaches to extract important citations such as citation count, context-based, metadata, and textual based approaches. The contemporary state-of-the-art in citation classification community ignores significantly potential features that can play a vital role in citation classification. This research presents a novel approach for binary citation classification by exploiting section-wise in-text citation frequencies, similarity score, and overall citation count-based features. The study also introduces machine learning algorithms based novel approach for assigning appropriate weights to the logical sections of research papers. The weights are allocated to the citations with respect to their sections. To perform the classification, we used three classification techniques, Support Vector Machine, Kernel Linear Regression, and Random Forest. The experiment was performed on two annotated benchmark datasets that contain 465 and 311 citation pairs of research articles respectively. The results revealed that the proposed approach attained an improved value of precision (i.e., 0.84 vs 0.72) from contemporary state-of-the-art approach. [ABSTRACT FROM AUTHOR]

The Supplemental Nutrition Assistance Program (SNAP) is the second-largest and most contentious public assistance program administered by the United States government. The media forums where SNAP discourse occurs have changed with the advent of social and web-based media. We used machine learning techniques to characterize media coverage of SNAP over time (1990–2017), between outlets with national readership and those with narrower scopes, and, for a subset of web-based media, by the outlet's political leaning. We applied structural topic models, a machine learning methodology that categorizes and summarizes large bodies of text that have document-level covariates or metadata, to a corpus of print media retrieved via LexisNexis (n = 76,634). For comparison, we complied a separate corpus via web-scrape algorithm of the Google News API (2012–2017), and assigned political alignment metadata to a subset documents according to a recent study of partisanship on social media. A similar procedure was used on a subset of the print media documents that could be matched to the same alignment index. Using linear regression models, we found some, but not all, topics to vary significantly with time, between large and small media outlets, and by political leaning. Our findings offer insights into the polarized and partisan nature of a major social welfare program in the United States, and the possible effects of new media environments on the state of this discourse. [ABSTRACT FROM AUTHOR]

Research on regional linguistic variation typically involves data collection in the field. This process itself can take up several months if not years. In the present study we demonstrate how we can use web interactives in collaboration with media outlets for a fast gathering of regional, sociolinguistic data. In collaboration with SPIEGEL ONLINE and Tagesanzeiger, we developed a web interactive that predicts users' regional backgrounds from within German-speaking Europe. More than 1.9M people have participated in the interactive, more than 770K users have provided metadata. Said metadata allowed us to capture regional variation in language as of today, which we can compare to historical survey data–enabling us to track the evolution of German in Europe over the past 40 years. We report on regional levelling of lexical variants, a process which appears to be particularly prevalent in the northern parts of German-speaking Europe. We further found an effect of (former) national and regional borders on language use. This innovative paradigm allows us to collect sociolinguistic data of an unprecedented scale–at the same time it presents significant challenges, both of which–benefits and challenges–will be discussed in this contribution. [ABSTRACT FROM AUTHOR]

The manganese transport regulator MntR is a metal-ion activated transcriptional repressor of manganese transporter genes to maintain manganese ion homeostasis. MntR, a member of the diphtheria toxin repressor (DtxR) family of metalloregulators, selectively responds to Mn2+ and Cd2+ over Fe2+, Co2+ and Zn2+. The DtxR/MntR family members are well conserved transcriptional repressors that regulate the expression of metal ion uptake genes by sensing the metal ion concentration. MntR functions as a homo-dimer with one metal ion binding site per subunit. Each MntR subunit contains two domains: an N-terminal DNA binding domain, and a C-terminal dimerization domain. However, it lacks the C-terminal SH3-like domain of DtxR/IdeR. The metal ion binding site of MntR is located at the interface of the two domains, whereas the DtxR/IdeR subunit contains two metal ion binding sites, the primary and ancillary sites, separated by 9 Å. In this paper, we reported the crystal structures of the apo and Mn2+-bound forms of MntR from Bacillus halodurans, and analyze the structural basis of the metal ion binding site. The crystal structure of the Mn2+-bound form is almost identical to the apo form of MntR. In the Mn2+-bound structure, one subunit contains a binuclear cluster of manganese ions, the A and C sites, but the other subunit forms a mononuclear complex. Structural data about MntR from B. halodurans supports the previous hypothesizes about manganese-specific activation mechanism of MntR homologues. [ABSTRACT FROM AUTHOR]

Interception of potential invasive species at ports-of-entry is essential for effective biosecurity and biosurveillance programs. However, taxonomic assessment of the immature stages of most arthropods is challenging; characters for identification are often dependent on adult morphology and reproductive structures. This study aims to strengthen the identification of such specimens through DNA barcoding, with a focus on microlepidoptera. A sample of 241 primarily immature microlepidoptera specimens intercepted at U.S. ports-of-entry from 2007 to 2011 were selected for analysis. From this sample, 201 COI-5P sequences were generated and analyzed for concordance between morphology-based and DNA-based identifications. The retrospective analysis of the data over 10 years (2009 to 2019) using the Barcode of Life Data (BOLD) system demonstrates the importance of establishing and growing DNA barcode reference libraries for use in specimen identification. Additionally, analysis of specimen identification using public data (43.3% specimens identified) vs. non-public data (78.6% specimens identified) highlights the need to encourage researchers to make data publicly accessible. DNA barcoding surpassed morphological identification with 42.3% (public) and 66.7% (non-public) of the sampled specimens achieving a species-level identification, compared to 38.3% species-level identification by morphology. Whilst DNA barcoding was not able to identify all specimens in our dataset, its incorporation into border security programs as an adjunct to morphological identification can provide secondary lines of evidence and lower taxonomic resolution in many cases. Furthermore, with increased globalization, database records need to be clearly annotated for suspected specimen origin versus interception location. [ABSTRACT FROM AUTHOR]

Gene co-expression networks (GCNs) are constructed from Gene Expression Matrices (GEMs) in a bottom up approach where all gene pairs are tested for correlation within the context of the input sample set. This approach is computationally intensive for many current GEMs and may not be scalable to millions of samples. Further, traditional GCNs do not detect non-linear relationships missed by correlation tests and do not place genetic relationships in a gene expression intensity context. In this report, we propose EdgeScaping, which constructs and analyzes the pairwise gene intensity network in a holistic, top down approach where no edges are filtered. EdgeScaping uses a novel technique to convert traditional pairwise gene expression data to an image based format. This conversion not only performs feature compression, making our algorithm highly scalable, but it also allows for exploring non-linear relationships between genes by leveraging deep learning image analysis algorithms. Using the learned embedded feature space we implement a fast, efficient algorithm to cluster the entire space of gene expression relationships while retaining gene expression intensity. Since EdgeScaping does not eliminate conventionally noisy edges, it extends the identification of co-expression relationships beyond classically correlated edges to facilitate the discovery of novel or unusual expression patterns within the network. We applied EdgeScaping to a human tumor GEM to identify sets of genes that exhibit conventional and non-conventional interdependent non-linear behavior associated with brain specific tumor sub-types that would be eliminated in conventional bottom-up construction of GCNs. Edgescaping source code is available at under the MIT license. [ABSTRACT FROM AUTHOR]

Knowledge bases are largely developed and utilized in academic and industrial fields, such as DBPedia, VIAF, LoC, Getty, and which are published in the Linked Data format. However, if you want to view different resources on these knowledge bases, you have to switch between different web pages from these resources. Therefore, we proposed a decentralized data hub named SinoPedia, which consists of several linked data services and can re-publish these RDF data in one platform. Firstly, these different Linked Data services include: Linked Data Transformation Service (LDTS), Linked Data Query Service (LDQS), Linked Data Publishing Service (LDPS) and Linked Data Knowledge Service (LDKS). The resource URI is the basis and core of the linked data, thus we will focus on the resource forwarding mechanism in LDPS service which can rewrite resource URI using a global and standard format among knowledge bases. Some knowledge bases were configured in SinoPedia platform in this paper. In addition to the above services, Linked Data Reasoning Service (LDRS) and Linked Data Intelligence Service (LDIS) will be added to the platform in the future. In short, all of these Linked Data Services will form the core framework in order to providing a good linked data application ecosystem. [ABSTRACT FROM AUTHOR]

The aim of Jscatter is the processing of experimental data and physical models with the focus to enable the user to develop/modify their own models and use them within experimental data evaluation. The basic structures dataArray and dataList contain matrix-like data of different size including attributes to store corresponding metadata. The attributes are used in fit routines as parameters allowing multidimensional attribute dependent fitting. Several modules provide models mainly applied in neutron and X- ray scattering for small angle scattering (form factors and structure factors) and inelastic neutron scattering. The intention is to provide an environment with fit routines, data handling routines (based on NumPy arrays) and a model library to allow the user to focus onto user-written models for data analysis with the benefit of convenient documentation of scientific data evaluation in a scripting environment. [ABSTRACT FROM AUTHOR]

In 2004, the Alfred P. Sloan Foundation launched a new program focused on incubating a new field, “Microbiology of the Built Environment” (MoBE). By the end of 2017, the program had supported the publication of hundreds of scholarly works, but it was unclear to what extent it had stimulated the development of a new research community. We identified 307 works funded by the MoBE program, as well as a comparison set of 698 authors who published in the same journals during the same period of time but were not part of the Sloan Foundation-funded collaboration. Our analysis of collaboration networks for both groups of authors suggests that the Sloan Foundation’s program resulted in a more consolidated community of researchers, specifically in terms of number of components, diameter, density, and transitivity of the coauthor networks. In addition to highlighting the success of this particular program, our method could be applied to other fields to examine the impact of funding programs and other large-scale initiatives on the formation of research communities. [ABSTRACT FROM AUTHOR]

Climate change, mismanaged resource extraction, and pollution are reshaping global marine ecosystems with direct consequences on human societies. Sustainable ocean development requires knowledge and data across disciplines, scales and knowledge types. Although several disciplines are generating large amounts of data on marine socio-ecological systems, such information is often underutilized due to fragmentation across institutions or stakeholders, limited standardization across scale, time or disciplines, and the fact that information is often not searchable within existing databases. Compiling metadata, the information which describes existing sets of data, is an effective tool that can address these challenges, particularly when metadata corresponding to multiple datasets can be combined to integrate, organize and classify multidisciplinary data. Here, using Mexico as a case study, we describe the compilation and analysis of a metadatabase of ocean knowledge that aims to improve access to information, facilitate multidisciplinary data sharing and integration, and foster collaboration among stakeholders. We also evaluate the knowledge trends and gaps for informing ocean management. Analysis of the metadatabase highlights that past and current research in Mexico focuses strongly on ecology and fisheries, with biological data more consistent over time and space compared to data on human dimensions. Regional imbalances in available information were also evident, with most available information corresponding to the Gulf of California, Campeche Bank and Caribbean and less available for the central and south Pacific and the western Gulf of Mexico. Despite existing knowledge gaps in Mexico and elsewhere, we argue that systematic efforts such as this can often reveal an abundance of information for decision-makers to develop policies that meet key commitments on ocean sustainability. Surmounting current cross-scale social and ecological challenges for sustainability requires transdisciplinary approaches. Metadatabases are critical tools to make efficient use of existing data, highlight and address strengths and deficiencies, and develop scenarios to inform policies for managing complex marine social-ecological systems. [ABSTRACT FROM AUTHOR]

Introduction: The UK Biobank (UKB) is a resource that includes detailed health-related data on about 500,000 individuals and is available to the research community. However, several obstacles limit immediate analysis of the data: data files vary in format, may be very large, and have numerical codes for column names. Results: ukbtools removes all the upfront data wrangling required to get a single dataset for statistical analysis. All associated data files are merged into a single dataset with descriptive column names. The package also provides tools to assist in quality control by exploring the primary demographics of subsets of participants; query of disease diagnoses for one or more individuals, and estimating disease frequency relative to a reference variable; and to retrieve genetic metadata. Conclusion: Having a dataset with meaningful variable names, a set of UKB-specific exploratory data analysis tools, disease query functions, and a set of helper functions to explore and write genetic metadata to file, will rapidly enable UKB users to undertake their research. [ABSTRACT FROM AUTHOR]

Background: The exponential decrease in molecular sequencing cost generates unprecedented amounts of data. Hence, scalable methods to analyze these data are required. Phylogenetic (or Evolutionary) Placement methods identify the evolutionary provenance of anonymous sequences with respect to a given reference phylogeny. This increasingly popular method is deployed for scrutinizing metagenomic samples from environments such as water, soil, or the human gut. Novel methods: Here, we present novel and, more importantly, highly scalable methods for analyzing phylogenetic placements of metagenomic samples. More specifically, we introduce methods for (a) visualizing differences between samples and their correlation with associated meta-data on the reference phylogeny, (b) clustering similar samples using a variant of the k-means method, and (c) finding phylogenetic factors using an adaptation of the Phylofactorization method. These methods enable to interpret metagenomic data in a phylogenetic context, to find patterns in the data, and to identify branches of the phylogeny that are driving these patterns. Results: To demonstrate the scalability and utility of our methods, as well as to provide exemplary interpretations of our methods, we applied them to 3 publicly available datasets comprising 9782 samples with a total of approximately 168 million sequences. The results indicate that new biological insights can be attained via our methods. [ABSTRACT FROM AUTHOR]

MinION and GridION X5 from Oxford Nanopore Technologies are devices for real-time DNA and RNA sequencing. On the one hand, MinION is the only real-time, low cost and portable sequencing device and, thanks to its unique properties, is becoming more and more popular among biologists; on the other, GridION X5, mainly for its costs, is less widespread but highly suitable for researchers with large sequencing projects. Despite the fact that Oxford Nanopore Technologies’ devices have been increasingly used in the last few years, there is a lack of high-performing and user-friendly tools to handle the data outputted by both MinION and GridION X5 platforms. Here we present NanoR, a cross-platform R package designed with the purpose to simplify and improve nanopore data visualization. Indeed, NanoR is built on few functions but overcomes the capabilities of existing tools to extract meaningful informations from MinION sequencing data; in addition, as exclusive features, NanoR can deal with GridION X5 sequencing outputs and allows comparison of both MinION and GridION X5 sequencing data in one command. NanoR is released as free package for R at . [ABSTRACT FROM AUTHOR]

Three-dimensional electron microscopy techniques like electron tomography provide valuable insights into cellular structures, and present significant challenges for data storage and dissemination. Here we explored a novel method to publicly release more than 11,000 such datasets, more than 30 TB in total, collected by our group. Our method, based on a peer-to-peer file sharing network built around a blockchain ledger, offers a distributed solution to data storage. In addition, we offer a user-friendly browser-based interface, , for anyone interested to explore and download our data. We discuss the relative advantages and disadvantages of this system and provide tools for other groups to mine our data and/or use the same approach to share their own imaging datasets. [ABSTRACT FROM AUTHOR]

Background: The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze and interpret. Machine learning methods can be useful in overcoming this analytical challenge. However, different methods employ distinct strategies to handle complex datasets. It is unclear whether one method is more appropriate than others for modeling postmortem microbiomes and their ability to predict attributes of interest in death investigations, which require understanding of how the microbial communities change after death and may represent those of the once living host. Methods and findings: Postmortem microbiomes were collected by swabbing five anatomical areas during routine death investigation, sequenced and analyzed from 188 death cases. Three machine learning methods (boosted algorithms, random forests, and neural networks) were compared with respect to their abilities to predict case attributes: postmortem interval (PMI), location of death, and manner of death. Accuracy depended on the method used, the numbers of anatomical areas analyzed, and the predicted attribute of death. Conclusions: All algorithms performed well but with distinct features to their performance. Xgboost often produced the most accurate predictions but may also be more prone to overfitting. Random forest was the most stable across predictions that included more anatomic areas. Analysis of postmortem microbiota from more than three anatomic areas appears to yield limited returns on accuracy, with the eyes and rectum providing the most useful information correlating with circumstances of death in most cases for this dataset. [ABSTRACT FROM AUTHOR]

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility—thus ensuring that big data are not hard-to-(re)use data. We evaluate our approach via a user study, and show that 91% of participants were able to replicate a complex analysis involving considerable data volumes. [ABSTRACT FROM AUTHOR]

Information on species distribution is recognized as a crucial input for biodiversity conservation and management. To that end, considerable resources have been dedicated towards increasing the quantity and availability of species occurrence data, boosting their use in species distribution modeling and online platforms for their dissemination. Currently, those platforms face the challenge of bringing biology into modeling by making informed decisions that result in meaningful models, based on limited occurrence and ecological data. Here we describe BioModelos, a modeling approach supported by an online system and a core team of modelers, whereby a network of experts contributes to the development of species distribution models by assessing the quality of occurrence data, identifying potentially limiting environmental variables, establishing species’ accessible areas and validating modeling predictions qualitatively. Models developed through BioModelos become freely and publicly available once validated by experts, furthering their use in conservation applications. Our approach has been implemented in Colombia since 2013 and it currently consist of a network of nearly 500 experts that collaboratively contribute to enhance the knowledge on the distribution of a growing number of species and it has aided the development of several decision support products such as national risk assessments and biodiversity compensation manuals. BioModelos is an example of operationalization of an essential biodiversity variable at a national level through the implementation of a research infrastructure that enhances the value of open access species data. [ABSTRACT FROM AUTHOR]

Data are the foundation of science, and there is an increasing focus on how data can be reused and enhanced to drive scientific discoveries. However, most seemingly “open data” do not provide legal permissions for reuse and redistribution. The inability to integrate and redistribute our collective data resources blocks innovation and stymies the creation of life-improving diagnostic and drug selection tools. To help the biomedical research and research support communities (e.g. libraries, funders, repositories, etc.) understand and navigate the data licensing landscape, the (Re)usable Data Project (RDP) () assesses the licensing characteristics of data resources and how licensing behaviors impact reuse. We have created a ruleset to determine the reusability of data resources and have applied it to 56 scientific data resources (e.g. databases) to date. The results show significant reuse and interoperability barriers. Inspired by game-changing projects like Creative Commons, the Wikipedia Foundation, and the Free Software movement, we hope to engage the scientific community in the discussion regarding the legal use and reuse of scientific data, including the balance of openness and how to create sustainable data resources in an increasingly competitive environment. [ABSTRACT FROM AUTHOR]

The Google Books Ngram Viewer (Google Ngram) is a search engine that charts word frequencies from a large corpus of books and thereby allows for the examination of cultural change as it is reflected in books. While the tool's massive corpus of data (about 8 million books or 6% of all books ever published) has been used in various scientific studies, concerns about the accuracy of results have simultaneously emerged. This paper reviews the literature and serves as a guideline for improving Google Ngram studies by suggesting five methodological procedures suited to increase the reliability of results. In particular, we recommend the use of (I) different language corpora, (II) cross-checks on different corpora from the same language, (III) word inflections, (IV) synonyms, and (V) a standardization procedure that accounts for both the influx of data and unequal weights of word frequencies. Further, we outline how to combine these procedures and address the risk of potential biases arising from censorship and propaganda. As an example of the proposed procedures, we examine the cross-cultural expression of religion via religious terms for the years 1900 to 2000. Special emphasis is placed on the situation during World War II. In line with the strand of literature that emphasizes the decline of collectivistic values, our results suggest an overall decrease of religion's importance. However, religion re-gains importance during times of crisis such as World War II. By comparing the results obtained through the different methods, we illustrate that applying and particularly combining our suggested procedures increase the reliability of results and prevents authors from deriving wrong assumptions. [ABSTRACT FROM AUTHOR]