Comparison of Two VA Laboratory Data Repositories Indicates That Missing Data Vary Despite Originating From the Same Source

AbstractIn the last few decades, new developments in liver surgery have led to an expanded applicability and an improved safety. However, liver surgery is still associated with postoperative morbidity and mortality, especially in extended resections. We analyzed a large liver surgery database to investigate whether laboratory parameters like

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Missing data imputation in cardiometabolic risk assessment: A solution based on artificial neural networks

Computer Science and Information Systems ◽

10.2298/csis190710003v ◽

2020 ◽

Vol 17 (2) ◽

pp. 379-401

Author(s):

Dunja Vrbaski ◽

Aleksandar Kupusinac ◽

Rade Doroslovacki ◽

Edita Stokic ◽

Dragan Ivetic

Keyword(s):

Neural Networks ◽

Risk Assessment ◽

Missing Data ◽

Statistical Power ◽

Cardiometabolic Risk ◽

Laboratory Data ◽

Missing Data Imputation ◽

Common Solution ◽

Set Up ◽

Potential Tool

A common problem when working with medical records is that some measurements are missing. The simplest and the most common solution, especially in machine learning domain, is to exclude records with incomplete data. This approach produces datasets with reduced statistical power and can even lead to biased or erroneous final results. There are, however, many proposed imputing methods for missing data. Although some of them, such as multiple imputation, are mature and well researched, they can be prone to misuse and are not always suitable for building complex frameworks. This paper explores neural networks as a potential tool for imputing univariate missing laboratory data during cardiometabolic risk assessment, comparing it to other simple methods that could be easily set up and used further in building predictive models. We have found that neural networks outperform other algorithms for diverse fraction of missing data and different mechanisms causing their missingness.

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When Data Management Meets Project Management

Biodiversity Information Science and Standards ◽

10.3897/biss.3.37224 ◽

2019 ◽

Vol 3 ◽

Author(s):

Evgeniy Meyke

Keyword(s):

User Interfaces ◽

Web Application ◽

Laboratory Data ◽

Data Entry ◽

Data Types ◽

Laboratory Notebook ◽

Data Repositories ◽

Institutional Infrastructure ◽

Feature Request ◽

High Flexibility

Complex projects that collect, curate and analyse biodiversity data are often presented with the challenge of accommodating diverse data types, various curation and output workflows, and evolving project logistics that require rapid changes in the applications and data structures. At the same time, sustainability concerns and maintenance overheads pose a risk to the long term viability of such projects. We advocate the use of flexible, multiplatform tools that adapt to operational, day-to-day challenges while providing a robust, cost efficient, and maintainable framework that serves the needs data collectors, managers and users. EarthCape is a highly versatile platform for managing biodiversity research and collections data, associated molecular laboratory data (Fig. 1), multimedia, structured ecological surveys and monitoring schemes, and more. The platform includes a fully functional Windows client as well as a web application. The data are stored in the cloud or on-premises and can be accessed by users with various access and editing rights. Ease of customization (making changes to user interface and functionality) is critical for most environments that deal with operational research processes. For active researchers and curators, there is rarely time to wait for a cycle of development that follows a change or feature request. In EarthCape, most of the changes to the default setup can be implemented by the end users with minimum effort and require no programming skills. High flexibility and a range of customisation options is complemented with mapping to Darwin Core standard and integration with GBIF, Geolocate, Genbank, and Biodiversity Heritage Library APIs. The system is currently used daily for rapid data entry, digitization and sample tracking, by such organisations as Imperial College, University of Cambridge, University of Helsinki, University of Oxford. Being an operational data entry and retrieval tool, EarthCape sits at the bottom of Virtual Research Environments ecosystem. It is not a software or platform to build data repositories, but rather a very focused tool falling under "back office" software category. Routine label printing, laboratory notebook maintenance, rapid data entry set up, or any other of relatively loaded user interfaces make use of any industry standard relational database back end. This opens a wide scope for IT designers to implement desired integrations within their institutional infrastructure. APIs and developer access to core EarthCape libraries to build own applications and modules are under development. Basic data visualisation (charts, pivots, dashboards), mapping (full featured desktop GIS module), data outputs (report and label designer) are tailored not only to research analyses, but also for managing logistics and communication when working on (data) papers. The presentation will focus on the software platform featuring most prominent use cases from two areas: ecological research (managing complex network data digitization project) and museum collections management (herbarium and insect collections).

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How to publish your data with the EPOS Multi-scale Laboratories

10.5194/egusphere-egu21-7876 ◽

2021 ◽

Author(s):

Geertje ter Maat ◽

Otto Lange ◽

Martyn Drury ◽

Keyword(s):

Earth Sciences ◽

Solid Earth ◽

Earth Science ◽

Laboratory Data ◽

Supplementary Information ◽

Science Data ◽

Scientific Publications ◽

Data Repositories ◽

Multi Scale ◽

Metadata Standards

EPOS (the European Plate Observing System) is a pan-European e-infrastructure framework with the goal of improving and facilitating the access, use, and re-use of Solid Earth science data. The EPOS Thematic Core Service Multi-scale Laboratories (TCS MSL) represent a community of European Solid Earth sciences laboratories including high-temperature and high-pressure experimental facilities, electron microscopy, micro-beam analysis, analogue tectonic and geodynamic modelling, paleomagnetism, and analytical laboratories.&#160;Participants and collaborating laboratories from Belgium, Bulgaria, France, Germany, Italy, Norway, Portugal, Spain, Switzerland, The Netherlands, and the UK are already represented within the TCS MSL. Unaffiliated European Solid Earth sciences laboratories are welcome and encouraged to join the growing TCS MSL community.Laboratory facilities are an integral part of Earth science research. The diversity of methods employed in such infrastructures reflects the multi-scale nature of the Earth system and is essential for the understanding of its evolution, for the assessment of geo-hazards, and the sustainable exploitation of geo-resources.Although experimental data from these laboratories often provide the backbone for scientific publications, they are often only available as images, graphs or tables in the text or as supplementary information to research articles. As a result, much of the collected data remains unpublished, not searchable or even inaccessible, and often only preserved in the short term.The TCS MSL is committed to making Earth science laboratory data Findable, Accessible, Interoperable, and Reusable (FAIR). For this purpose, the TCS MSL encourages the community to share their data via DOI-referenced, citable data publications. To facilitate this and ensure the provision of rich metadata, we offer user-friendly tools, plus the necessary data management expertise, to support all aspects of data publishing for the benefit of individual lab researchers via partner repositories. Data published via TCS MSL are described with the use of sustainable metadata standards enriched with controlled vocabularies used in geosciences. The resulting data publications are also exposed through a designated TCS MSL online portal that brings together DOI-referenced data publications from partner research data repositories (https://epos-msl.uu.nl/). As such, efforts have already been made to interconnect new data (metadata exchange) with previous databases such as MagIC (paleomagnetic data in Earthref.org), and in the future, we expect to enlarge and improve this practice with other repositories.&#160;

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EPOS Multi-scale laboratories Data Services & Trans-national access program

10.5194/egusphere-egu2020-15358 ◽

2020 ◽

Author(s):

Richard Wessels ◽

Otto Lange ◽

Keyword(s):

Earth Sciences ◽

Solid Earth ◽

Earth Science ◽

Laboratory Data ◽

Remote Access ◽

Supplementary Information ◽

Scientific Publications ◽

Data Repositories ◽

Multi Scale ◽

Laboratory Facilities

EPOS (European Plate Observing System) is an ESFRI Landmark and European Research Infrastructure Consortium (ERIC). The EPOS Thematic Core Service Multi-scale laboratories (TCS MSL) represents a community of European solid Earth sciences laboratories including high temperature and pressure experimental facilities, electron microscopy, micro-beam analysis, analogue tectonic and geodynamic modelling, paleomagnetism, and analytical laboratories.Participants and collaborating laboratories from Belgium, Bulgaria, France, Germany, Italy, Norway, Portugal, Spain, Switzerland, The Netherlands, and the UK are already organized in the TCS MSL. Unaffiliated European solid Earth sciences laboratories are welcome and encouraged to join the growing TCS MSL community. Members of the TCS MSL are also represented in the EPOS Sustainability Phase (SP).Laboratory facilities are an integral part of Earth science research. The diversity of methods employed in such infrastructures reflects the multi-scale nature of the Earth system and is essential for the understanding of its evolution, for the assessment of geo-hazards, and for the sustainable exploitation of geo-resources.Although experimental data from these laboratories often provide the backbone for scientific publications, they are often only available as supplementary information to research articles. As a result, much of the collected data remains unpublished, inaccessible, and often not preserved for the long term.&#160;&#160;The TCS MSL is committed to make Earth science laboratory data Findable, Accessible, Interoperable, and Reusable (FAIR). For this purpose the TCS MSL has developed an online portal that brings together DOI-referenced data publications from research data repositories related to the TCS MSL context (https://epos-msl.uu.nl/).In addition, the TCS MSL has developed a Trans-national access (TNA) program that allows researchers and research teams to apply for physical or remote access to the participating EPOS MSL laboratories. Three pilot calls were launched in 2017, 2018, and 2019, with a fourth call scheduled for 2020. The pilot calls were used to develop and refine the EPOS wide TNA principles and to initialize an EPOS brokering service, where information on each facility offering access will be available for the user and where calls for proposals are advertised. Access to the participating laboratories is currently supported by national funding or in-kind contribution. Based on the EPOS Data policy & TNA General Principles, access to the laboratories is regulated by common rules and a transparent policy, including procedures and mechanisms for application, negotiation, proposal evaluation, user feedback, use of laboratory facilities and data curation.Access to EPOS Multi-scale laboratories is a unique opportunity to create new synergy, collaboration and innovation, in a framework of trans-national access rules.An example of such a successful collaboration is between MagIC and EPOS TCS MSL. This collaboration will allow paleomagnetic data and metadata to be exchanged between EPOS and the MagIC (https://www.earthref.org/MagIC) database. Such collaborations are beneficial to all parties involved and support the harmonization and integration of data at a global scale.

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Prediction of 72-hour mortality in patients with extremely high serum C-reactive protein levels using a novel weighted average of risk scores

PLoS ONE ◽

10.1371/journal.pone.0246259 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246259

Author(s):

Kai Saito ◽

Hitoshi Sugawara ◽

Kiyoshi Ichihara ◽

Tamami Watanabe ◽

Akira Ishii ◽

...

Keyword(s):

Missing Data ◽

Medical Center ◽

Laboratory Data ◽

Multivariate Logistic Regression Analysis ◽

Weighted Average ◽

High Serum ◽

Risk Scores ◽

C Reactive Protein ◽

Cutoff Value ◽

Reactive Protein

The risk factors associated with mortality in patients with extremely high serum C-reactive protein (CRP) levels are controversial. In this retrospective single-center cross-sectional study, the clinical and laboratory data of patients with CRP levels ≥40 mg/dL treated in Saitama Medical Center, Japan from 2004 to 2017 were retrieved from medical records. The primary outcome was defined as 72-hour mortality after the final CRP test. Forty-four mortal cases were identified from the 275 enrolled cases. Multivariate logistic regression analysis (MLRA) was performed to explore the parameters relevant for predicting mortality. As an alternative method of prediction, we devised a novel risk predictor, “weighted average of risk scores” (WARS). WARS features the following: (1) selection of candidate risk variables for 72-hour mortality by univariate analyses, (2) determination of C-statistics and cutoff value for each variable in predicting mortality, (3) 0–1 scoring of each risk variable at the cutoff value, and (4) calculation of WARS by weighted addition of the scores with weights assigned according to the C-statistic of each variable. MLRA revealed four risk variables associated with 72-hour mortality—age, albumin, inorganic phosphate, and cardiovascular disease—with a predictability of 0.829 in C-statistics. However, validation by repeated resampling of the 275 records showed that a set of predictive variables selected by MLRA fluctuated occasionally because of the presence of closely associated risk variables and missing data regarding some variables. WARS attained a comparable level of predictability (0.837) by combining the scores for 10 risk variables, including age, albumin, electrolytes, urea, lactate dehydrogenase, and fibrinogen. Several mutually related risk variables are relevant in predicting 72-hour mortality in patients with extremely high CRP levels. Compared to conventional MLRA, WARS exhibited a favorable performance with flexible coverage of many risk variables while allowing for missing data.

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Implementing the Sparrow laboratory data system in multiple subdomains of geochronology and geochemistry

10.5194/egusphere-egu21-13832 ◽

2021 ◽

Author(s):

Daven Quinn ◽

Benjamin Linzmeier ◽

Kurt Sundell ◽

George Gehrels ◽

Simon Goring ◽

...

Keyword(s):

Data Exchange ◽

Laboratory Data ◽

Geochemical Data ◽

Data Repositories ◽

Stratigraphic Model ◽

Enrich Sample ◽

Software Interfaces ◽

Cosmogenic Nuclide Dating ◽

Community Facilities ◽

Geological Context

Data sharing between laboratories is critical for building repeatable, comparable, and robust geochronology and geochemistry workflows. Meanwhile, in the broader geosciences, there is an increasing need for standardized access to aggregated geochemical data tied to basic geological context. Such data can be used to enrich sample and geochemical data repositories (e.g., EarthChem, Geochron.org, publisher archives), align geochemical context with other datasets that capture global change (e.g., Neotoma, the Paleobiology Database), and calibrate digital Earth models (e.g., Macrostrat) against geochronology-driven assessments of geologic time.A typical geochemical lab manages a large archive of interpreted data; standardizing and contributing data products to community-level archives entails significant manual work that is not usually undertaken. Furthermore, without widely accepted&#160; interchange formats, this effort must be repeated for each intended destination.Sparrow (https://sparrow-data.org), in development by a consortium of geochronology labs, is a standardized system designed to support labs&#8217; efforts to manage, contextualize, and share their geochemical data. The system augments existing analytical workflows with tools to manage metadata (e.g., projects, sample context, embargo status) and software interfaces for automated data exchange with community facilities. It is extensible for a wide variety of geochemical methods and analytical processes.In this update, we will report on the implementation of Sparrow in the Arizona Laserchron Center detrital zircon facility, and how that lab is using the system to capture geological context across its data archive. We will review similar integrations underway with U-Pb, 40Ar/39Ar, SIMS, optically stimulated luminescence, thermochronology, and cosmogenic nuclide dating. We will also discuss preliminary efforts to aggregate the output of multiple chronometers to refine age calibrations for the Macrostrat stratigraphic model.

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