1,721,013 research outputs found
FAIRifizierung von Real World Data für die Gesundheitsforschung: Ein Petitum für modernes Record Linkage
No full textBACKGROUND: The provision of real-world data according to the FAIR principles is prerequisite for an efficient exploitation of the potential of health data for prevention and care. OBJECTIVES: To discuss the opportunities and limitations of reuse and record linkage of health data in Germany. MATERIALS AND METHODS: Initiatives to establish an improved research data infrastructure are presented and the limitations that hinder record linkage of personal health data are illustrated using an example. RESULTS: In general, health data in Germany do not meet the requirements of the FAIR principles. Their findability already fails because either no metadata are available or they are not posted in searchable repositories in a standardized way. Record linkage of personal health data is extremely limited by restrictive data protection regulations and the lack of a so-called unique identifier. Privacy-compliant solutions for linking health data, which are successfully practiced in neighboring European countries, could serve as a model here. CONCLUSIONS: The establishment of a National Research Data Infrastructure (NFDI), especially for personal health data (NFDI4Health), can only be realized with considerable efforts and legislative changes. Already existing structures and standards that have been for instance developed by the Medical Informatics Initiative and the Netzwerk Universitätsmedizin (English: University Medicine Network), and international initiatives such as the European Open Science Cloud should be taken into consideration
Toward a Domain-Overarching Metadata Schema for Making Health Research Studies FAIR (Findable, Accessible, Interoperable, and Reusable): Development of the NFDI4Health Metadata Schema
No full textBackground Despite wide acceptance in medical research, implementation of the FAIR (findability, accessibility, interoperability, and reusability) principles in certain health domains and interoperability across data sources remain a challenge. While clinical trial registries collect metadata about clinical studies, numerous epidemiological and public health studies remain unregistered or lack detailed information about relevant study documents. Making valuable data from these studies available to the research community could improve our understanding of various diseases and their risk factors. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) seeks to optimize data sharing among the clinical, epidemiological, and public health research communities while preserving privacy and ethical regulations. Objective We aimed to develop a tailored metadata schema (MDS) to support the standardized publication of health studies’ metadata in NFDI4Health services and beyond. This study describes the development, structure, and implementation of this MDS designed to improve the FAIRness of metadata from clinical, epidemiological, and public health research while maintaining compatibility with metadata models of other resources to ease interoperability. Methods Based on the models of DataCite, ClinicalTrials.gov, and other data models and international standards, the first MDS version was developed by the NFDI4Health Task Force COVID-19. It was later extended in a modular fashion, combining generic and NFDI4Health use case–specific metadata items relevant to domains of nutritional epidemiology, chronic diseases, and record linkage. Mappings to schemas of clinical trial registries and international and local initiatives were performed to enable interfacing with external resources. The MDS is represented in Microsoft Excel spreadsheets. A transformation into an improved and interactive machine-readable format was completed using the ART-DECOR (Advanced Requirement Tooling-Data Elements, Codes, OIDs, and Rules) tool to facilitate editing, maintenance, and versioning. Results The MDS is implemented in NFDI4Health services (eg, the German Central Health Study Hub and the Local Data Hub) to structure and exchange study-related metadata. Its current version (3.3) comprises 220 metadata items in 5 modules. The core and design modules cover generic metadata, including bibliographic information, study design details, and data access information. Domain-specific metadata are included in use case–specific modules, currently comprising nutritional epidemiology, chronic diseases, and record linkage. All modules incorporate mandatory, optional, and conditional items. Mappings to the schemas of clinical trial registries and other resources enable integrating their study metadata in the NFDI4Health services. The current MDS version is available in both Excel and ART-DECOR formats. Conclusions With its implementation in the German Central Health Study Hub and the Local Data Hub, the MDS improves the FAIRness of data from clinical, epidemiological, and public health research. Due to its generic nature and interoperability through mappings to other schemas, it is transferable to services from adjacent domains, making it useful for a broader user community
Dreistufige Verfahren zur Modellierung von Ernährungsdaten
No full textErnährung ist für viele chronische Krankheiten ein veränderbarer Risikofaktor und spielt somit bei deren Prävention eine wichtige Rolle. Allerdings stellt die Analyse dieser Assoziationen in epidemiologischen Studien aus zwei Gründen eine methodische Herausforderung dar: Erstens ist die Ernährung nur schwer akkurat zu erfassen, weswegen Ernährungsdaten häufig fehlerbehaftet sind, und zweitens ist die Ernährung ein komplexes Verhalten, das aufgrund der Multidimensionalität nur schwer zu operationalisieren ist. Diese Probleme führen dazu, dass der Einfluss der Ernährung auf die Entstehung von Krankheiten häufig verzerrt geschätzt wird. Sie treten selbst bei Ernährungsdaten auf, die mit einem modernen Instrument wie dem webbasierten 24-Stunden-Erinnerungsprotokoll erfasst werden. Mit diesem Instrument wurden in der IDEFICS/I.Family-Kohorte u.a. Ernährungsdaten von ca. 8.000 europäischen Kindern und ihren Eltern erfasst. Zudem wurden weitere Gesundheitsdaten erhoben. Diese Daten dienen in dieser Arbeit als Ausgangspunkt für die Entwicklung eines dreistufigen Verfahrens, das Messfehler und die Multidimensionalität der Ernährungsdaten berücksichtigt und mit dem die Verzerrung bei der Schätzung des Zusammenhangs zwischen Ernährungsmustern und gesundheitlichen Endpunkten reduziert werden kann. Dazu werden Messfehlerkorrektur- und Clusteranalysemethoden zu unterschiedlichen Korrekturalgorithmen kombiniert. Diese Algorithmen basieren auf Regressionskalibrierung, Simulationsextrapolation (SIMEX) und multipler Imputation. Der SIMEX-Ansatz baut auf dem in dieser Arbeit entwickelten SIMEX-Algorithmus für Box-Cox-transformierte Daten auf, der sich insbesondere zur Fehlerkorrektur bei univariaten stetigen Ernährungsexpositionen eignet. Es konnte in einer Simulationsstudie gezeigt werden, dass der Korrekturansatz basierend auf multipler Imputation gegenüber den anderen Ansätzen zu weniger verzerrten Effektschätzungen führte, weswegen dieser Ansatz für die Anwendung empfohlen wird
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Correcting for bias due to categorisation based on cluster analysis using multiple continuous error-prone exposures
Full text linkThe association between multidimensional exposure patterns and outcomes is
commonly investigated by first applying cluster analysis algorithms to derive
patterns and then estimating the associations. However, errors in the
underlying continuous, possibly skewed, exposure variables lead to
misclassified exposure patterns and therefore to biased effect estimates. This
is often the case for lifestyle exposures in epidemiology, e.g. for dietary
variables measured on daily basis. We introduce three new algorithms for
correcting the biased effect estimates, which are based on regression
calibration (RC), simulation extrapolation (SIMEX) and multiple imputation
(MI). In addition, the naive method ignoring the measurement error structure is
considered for comparison. These methods are combined with the k-means cluster
algorithm and the Gaussian mixture model to derive exposure patterns. The
performance of the correction methods is compared in a simulation study
regarding absolute, maximum and relative bias. The simulated data mimic a
typical situation in nutritional epidemiology when diet is assessed using
repeated 24-hour dietary recalls. Continuous and binary outcomes are
considered. Simulation results show, that the correction method based on RC and
MI perform better than the naive and the SIMEX-based method. Furthermore, the
MI-based approach, which can use outcome information in the error model, is
superior to the RC-based approach in most scenarios. Therefore, we recommend
using the MI-based approach.Comment: 25 pages, 2 figures; supplementary material attache
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