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

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

<p>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.</p><p>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  interchange formats, this effort must be repeated for each intended destination.</p><p>Sparrow (https://sparrow-data.org), in development by a consortium of geochronology labs, is a standardized system designed to support labs’ 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.</p><p>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, <sup>40</sup>Ar/<sup>39</sup>Ar, 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.</p>

scholarly journals The origin of upper Precambrian diamictites, northern Norway: a case study applicable to diamictites in general

Geologos ◽  
2017 ◽  
Vol 23 (3) ◽  
pp. 163-181 ◽  
Author(s):  
Mats O. Molén
Keyword(s):  
Quartz Sand ◽  
Laboratory Data ◽  
Gravity Flow ◽  
Geological Data ◽  
Submarine Fan ◽  
Northern Norway ◽  
Gravity Flows ◽  
Material Evidence ◽  
Geological Context

AbstractUpper Precambrian diamictites in Varangerfjorden (northern Norway) have been examined for evidence of origin, whether glaciogenic, gravity flow or polygenetic. Studies of geomorphology, sedimentology and surface microtextures on quartz sand grains are integrated to provide multiple pieces of evidence for the geological agents responsible for the origin of the diamictites. The documented sedimentary and erosional structures, formerly interpreted in a glaciogenic context (e.g., diamict structure, pavements and striations) have been reanalysed. Field and laboratory data demonstrate that, contrary to conclusions reached in many earlier studies, the diamictites and adjacent deposits did not originate from glaciogenic processes. Evidence from macrostructures may occasionally be equivocal or can be interpreted as representing reworked, glacially derived material. Evidence from surface microtextures, from outcrops which are believed to exhibit the most unequivocal signs for glaciation, display no imprint at all of glaciogenic processes, and a multicyclical origin of the deposits can be demonstrated. The geological context implies (and no geological data contradict this) an origin by gravity flows, possibly in a submarine fan environment. This reinterpretation of the diamictites in northern Norway may imply that the palaeoclimatological hypothesis of a deep frozen earth during parts of the Neoproterozoic has to be revised.

scholarly journals The State of Dissemination of Open Research Data in Ukraine and the World: Bibliometric Analysis

2021 ◽  
Vol 29 (4) ◽  
pp. 209-217
Author(s):  
Anton Boiko ◽  
Olha Kramarenko ◽  
Sardar Shabanov
Keyword(s):  
Bibliometric Analysis ◽  
Data Exchange ◽  
Open Science ◽  
Research Data ◽  
The State ◽  
Research Institutions ◽  
Data Repositories ◽  
Open Research ◽  
The World ◽  
Data Files

Purpose: To determine the current state of development of open science in the paradigm of open research data in Ukraine and the world, as well as to analyze the representation of Ukraine in the world research space, in terms of research data exchange. Design / Method / Research Approach: Methods of synthesis, logical and comparative analysis used to determine the dynamics of the number of research data journals and data files in the world, as well as to quantify the share of research data repositories in Ukraine and the world. Trend and bibliometric analysis were used to determine the share of publications with their open primary data; analysis of their thematic structures; identification of the main scientific clusters of such publications; research of geographic indicators and share of publications by research institutions. Findings: The study found a tendency to increase both the number of data logs and data files in Dryad (open data repository). The results of the analysis of the share of data repositories indexed in re3data (register of research data repositories) show that 51% of the total number are repositories of data from European countries, with Germany leading with 460 repositories, followed by the United Kingdom (302 repositories) and France (116 repositories). Ukraine has only 2 data repositories indexed in re3data. The trend of relevance of data exchange is confirmed by the increase of publications with datasets for the last 10 years (2011-2020) in 5 times. Research institutions and universities are the main sources of research data, which are mainly focused on the fields of knowledge in chemistry (23.3%); biochemistry, genetics and molecular biology (13.8%); medicine (12.9%). An analysis of the latest thematic groups formed on the basis of publications with datasets shows that there is a significant correlation between publications with open source data and COVID-19 studies. More than 50% of publications with datasets both in Ukraine and around the world are aimed at achieving the goal of SDG 3 Good Health. Theoretical Implications: It is substantiated that in Ukraine there is a need to implement specific tactical and strategic plans for open science and open access to research data. Practical Implications: The results of the study can be used to support decision-making in the management of research data at the macro and micro levels. Future Research: It should be noted that the righteous bibliometric analysis of the state of the dissemination of data underlying the research results did not include the assessment of quality indicators and compliance with the FAIR principles, because accessibility and reusability are fundamental components of open science, which may be an area for further research. Moreover, it is advisable to investigate the degree of influence of the disclosure of the data underlying the research result on economic indicators, as well as indicators of ratings of higher education, etc. Research Limitations: Since publications with datasets in Scopus-indexed journals became the information base of the analysis for our study, it can be assumed that the dataset did not include publications with datasets published in editions that the Scopus bibliographic database does not cover. Paper type: Theoretical

scholarly journals Development of data representation standards by the human proteome organization proteomics standards initiative

2015 ◽  
Vol 22 (3) ◽  
pp. 495-506 ◽  
Author(s):  
Eric W Deutsch ◽  
Juan Pablo Albar ◽  
Pierre-Alain Binz ◽  
Martin Eisenacher ◽  
Andrew R Jones ◽  
...  
Keyword(s):  
Mass Spectrometer ◽  
Data Exchange ◽  
Data Representation ◽  
Lessons Learned ◽  
Human Proteome ◽  
Data Types ◽  
Data Repositories ◽  
Controlled Vocabularies ◽  
Minimum Information ◽  
Genomics And Proteomics

Abstract Objective To describe the goals of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization, the methods that the PSI has employed to create data standards, the resulting output of the PSI, lessons learned from the PSI’s evolution, and future directions and synergies for the group. Materials and Methods The PSI has 5 categories of deliverables that have guided the group. These are minimum information guidelines, data formats, controlled vocabularies, resources and software tools, and dissemination activities. These deliverables are produced via the leadership and working group organization of the initiative, driven by frequent workshops and ongoing communication within the working groups. Official standards are subjected to a rigorous document process that includes several levels of peer review prior to release. Results We have produced and published minimum information guidelines describing what information should be provided when making data public, either via public repositories or other means. The PSI has produced a series of standard formats covering mass spectrometer input, mass spectrometer output, results of informatics analysis (both qualitative and quantitative analyses), reports of molecular interaction data, and gel electrophoresis analyses. We have produced controlled vocabularies that ensure that concepts are uniformly annotated in the formats and engaged in extensive software development and dissemination efforts so that the standards can efficiently be used by the community. Conclusion In its first dozen years of operation, the PSI has produced many standards that have accelerated the field of proteomics by facilitating data exchange and deposition to data repositories. We look to the future to continue developing standards for new proteomics technologies and workflows and mechanisms for integration with other omics data types. Our products facilitate the translation of genomics and proteomics findings to clinical and biological phenotypes. The PSI website can be accessed at http://www.psidev.info.

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

Medical Care ◽  
2009 ◽  
Vol 47 (1) ◽  
pp. 121-124 ◽  
Author(s):  
Kathleen A. McGinnis ◽  
Melissa Skanderson ◽  
Forrest L. Levin ◽  
Cynthia Brandt ◽  
Joseph Erdos ◽  
...  
Keyword(s):  
Missing Data ◽  
Laboratory Data ◽  
Data Repositories

scholarly journals Federated Learning used for predicting outcomes in SARS-COV-2 patients

2021 ◽  
Author(s):  
Mona Flores ◽  
Ittai Dayan ◽  
Holger Roth ◽  
Aoxiao Zhong ◽  
Ahmed Harouni ◽  
...  
Keyword(s):  
Data Exchange ◽  
Data Science ◽  
Laboratory Data ◽  
Vital Signs ◽  
Area Under The Curve ◽  
X Rays ◽  
Multiple Sources ◽  
Average Improvement ◽  
Science Collaboration ◽  
Oxygen Requirements

Abstract ‘Federated Learning’ (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the “EXAM” (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

scholarly journals The establishment of a Norwegian node in RDA

2019 ◽  
Author(s):  
Trond Kvamme ◽  
Philipp Conzett
Keyword(s):  
Data Management ◽  
Data Exchange ◽  
The United States ◽  
Open Science ◽  
Research Data ◽  
Sensitive Data ◽  
Data Repositories ◽  
Management Plans ◽  
Inclusive Approach ◽  
Processing And Storage

Norway has been selected as a new national node in RDA (Research Data Alliance). Until the end of the project in May 2020, the node will be engaging with research communities, supporting national agendas, and contributing to the EU Open Science Strategy to ensure capillary uptake of RDA principles and outputs. Moreover, they will be working to increase the participation in RDA nationally. The Norwegian RDA node (NO-RDA) will be run by a consortium of seven partners, each of them with specific roles in the activities around the node, and led by NSD - Norwegian Centre for Research Data. NO-RDA will focus on supporting the implementation of RDA outputs and recommendations and on areas of strategic importance for the Nordic region, such as Data Management Plans, FAIR Data Stewardship and management of sensitive data in research within the framework of current international and statutory regulations. In addition to NSD the node consists of NTNU, UiB, UiO, UiT, Unit og Uninett/Sigma2. The Research Data Alliance (RDA) was launched as a community-driven initiative in 2013 by the European Commission, the United States Government's National Science Foundation and National Institute of Standards and Technology, and the Australian Government’s Department of Innovation with the goal of building the social and technical infrastructure to enable open sharing and re-use of data. RDA has a grass-roots, inclusive approach covering all data lifecycle stages, engaging data producers, users and stewards, addressing data exchange, processing, and storage. It has succeeded in creating the neutral social platform where international research data experts meet to exchange views and to agree on topics including social hurdles on data sharing, education and training challenges, data management plans and certification of data repositories, disciplinary and interdisciplinary interoperability, as well as technological aspects.

scholarly journals When Data Management Meets Project Management

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).

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

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

<p>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. </p><p>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.</p><p>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.</p><p>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.</p><p>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. </p>

Laboratory Information Management Systems

2016 ◽  
pp. 313-326
Author(s):  
Patrizia Colangeli ◽  
Fabrizio De Massis ◽  
Francesca Cito ◽  
Maria Teresa Mercante ◽  
Lucilla Ricci
Keyword(s):  
Public Health ◽  
Information Management ◽  
Data Exchange ◽  
Laboratory Data ◽  
Information Management System ◽  
Veterinary Public Health ◽  
Data Encoding ◽  
Outbreak Investigations ◽  
Information Management Systems ◽  
Laboratory Information Management

The Laboratory Information Management System (LIMS) is recognized as a powerful tool to improve laboratory data management and to report human health as well as veterinary public health. LIMS plays an essential role in public health surveillance, outbreak investigations, and pandemic preparedness. The chapter aims is to provide an overview of LIMS use in veterinary fields as well as to report 20 years of experience of a Veterinary Public Institute in working with LIMS, illustrating the features of the LIMS currently in use in the institute and highlighting the different aspects that should be considered when evaluating, choosing, and implementing a LIMS. In depth, the chapter illustrates how LIMS simplifies the accreditation path according to ISO IEC 17025 and the role in the epidemiology and veterinary public health. For this aspect, it is very important to collect clear data, and for this reason, a LIMS has to activate formal checks and controls on business rules. To facilitate this issue, an interconnection between LIMS and other applications (internal or external to laboratory) could be improved to allow automatic data exchange. At the same time, the unique data encoding at national/international level should be used.

scholarly journals Electronic Integrated Disease Surveillance System and Pathogen Asset Control System

2012 ◽  
Vol 79 (2) ◽  
Author(s):  
Tom G. Wahl ◽  
Aleksey V. Burdakov ◽  
Andrey O. Oukharov ◽  
Azamat K. Zhilokov
Keyword(s):  
Control System ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Data Exchange ◽  
Laboratory Data ◽  
World Health ◽  
Open Architecture ◽  
Case Definitions ◽  
Disease Surveillance System

Electronic Integrated Disease Surveillance System (EIDSS) has been used to strengthen and support monitoring and prevention of dangerous diseases within One Health concept by integrating veterinary and human surveillance, passive and active approaches, case-based records including disease-specific clinical data based on standardised case definitions and aggregated data, laboratory data including sample tracking linked to each case and event with test results and epidemiological investigations. Information was collected and shared in secure way by different means: through the distributed nodes which are continuously synchronised amongst each other, through the web service, through the handheld devices. Electronic Integrated Disease Surveillance System provided near real time information flow that has been then disseminated to the appropriate organisations in a timely manner. It has been used for comprehensive analysis and visualisation capabilities including real time mapping of case events as these unfold enhancing decision making. Electronic Integrated Disease Surveillance System facilitated countries to comply with the IHR 2005 requirements through a data transfer module reporting diseases electronically to the World Health Organisation (WHO) data center as well as establish authorised data exchange with other electronic system using Open Architecture approach. Pathogen Asset Control System (PACS) has been used for accounting, management and control of biological agent stocks. Information on samples and strains of any kind throughout their entire lifecycle has been tracked in a comprehensive and flexible solution PACS.Both systems have been used in a combination and individually. Electronic Integrated Disease Surveillance System and PACS are currently deployed in the Republics of Kazakhstan, Georgia and Azerbaijan as a part of the Cooperative Biological Engagement Program (CBEP) sponsored by the US Defense Threat Reduction Agency (DTRA).

Sign in / Sign up

Export Citation Format

Share Document