Search

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery of Findable, Accessible, Interoperable, and Reusable (FAIR) data to users worldwide. ModelCIF describes the specific set of attributes and metadata associated with macromolecular structures modeled by solely computational methods and provides an extensible data representation for deposition, archiving, and public dissemination of predicted three-dimensional (3D) models of macromolecules. It is an extension of the Protein Data Bank Exchange / macromolecular Crystallographic Information Framework (PDBx/mmCIF), which is the global data standard for representing experimentally-determined 3D structures of macromolecules and associated metadata. The PDBx/mmCIF framework and its extensions (e.g., ModelCIF) are managed by the Worldwide Protein Data Bank partnership (wwPDB, wwpdb.org) in collaboration with relevant community stakeholders such as the wwPDB ModelCIF Working Group (wwpdb.org/task/modelcif). This semantically rich and extensible data framework for representing computed structure models (CSMs) accelerates the pace of scientific discovery. Herein, we describe the architecture, contents, and governance of ModelCIF, and tools and processes for maintaining and extending the data standard. Community tools and software libraries that support ModelCIF are also described.

Data governance capability is fundamental to coal mine intelligence construction. It could accelerate the intelligent construction of coal mines and promote the digital transformation of the coal industry so as to building a modern energy system in digital China. This paper therefore discusses the theory and methods of improving data governance capability for intelligent coal mines. Specifically, this study analyzes its strategic demands of intelligent coal mine construction on data governance and offers a working definition for data governance capability for intelligent coal mines. Then an evaluative system is proposed for the evaluation of data governance capability for intelligent coal mines. Further, by drawing on capability maturity theory, this study establishes a maturity model that specifies the improvement path of data governance capability for intelligent coal mines. Finally, based on the PDCA cycle theory, this paper puts forward strategies for improving data governance capability for intelligent coal mines, in order to 1)supplement existing theories of data governance capability enhancement, and 2)provide references for future practices of data governance.

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.

Copyright of Records Management: Journal of the Records Management Society of Japan is the property of Records Management Society of Japan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent coal mine data governance is the key bottleneck to achieving the high-level development goal of coal mine intelligent construction. It is of great significance to ensure data operation compliance, ensure data quality, prevent and control data risks, and improve data value. However, there is a lack of perfect methodology to guide the theoretical research and technical practice of intelligent coal mine data governance. In order to solve this problem, an intelligent coal mine data governance element-mechanism-hierarchy-process reference model is constructed from four dimensions of data governance elements, governance mechanism, governance hierarchy and governance process. It provides a multi-dimensional fusion methodology perspective and theoretical analysis logic to achieve an understanding of key issues. It is concluded that complex system theory, data strategic management theory, collaborative innovation theory, digital continuity theory, public governance theory, information life cycle theory, and PDCA cycle theory are the important theoretical basis of intelligent coal mine data governance. Under the guidance of the reference model of intelligent coal mine data governance, and based on the relevant theoretical basis, an intelligent coal mine data governance system framework is constructed. It includes five major components: data governance environment, driving and supporting factors, top-level design, data governance domain, and data governance process and capability. Benchmarking the framework of intelligent coal mine data governance system, it is concluded that intelligent coal mine data governance still needs to further break through the five key issues. The issues are data value movement law disclosure, metadata and data dictionary construction, data quality and data security system management rule design, complex giant system data coupling model development and digital intelligence generation law modeling.

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Field observations of the soil-to-atmosphere CO2 flux—soil respiration, RS—are a prime example of ‘long tail’ data that historically have had neither centralized databases nor an agreed-upon reporting format. This has hindered scientific transparency, analytical reproducibility, and syntheses with respect to this globally-important component of the carbon cycle. Here we propose a new data and metadata reporting format for RS data, based on engagement with a wide range of researchers in the earth and ecological sciences as well as expert advisory panels. Our goal was a reporting format that would be relevant and useful for synthesis activities, optimizing data discoverability and usability while not placing an undue burden on data contributors. We describe previous RS data collection efforts, lessons learned from related databases and data-oriented networks (e.g., FLUXNET) in earth and ecological sciences, and the process of community consultation. The proposed reporting format focuses on chamber-level data and metadata, specifying measurement conditions and, for a given measurement period defined by beginning and ending timestamps, a mean RS flux (or CO2 concentration) and associated ancillary measurements. With input from the research community, we have also developed research data and metadata templates to support data collection adhering to the reporting format. Fundamentally, this format aims to enable findable, accessible, interoperable, and reusable data, while providing ‘future-proofing’ capabilities to support reanalyses using as yet unknown algorithms or approaches. This proposed RS reporting format is openly available online and is intended to be a dynamic document, subject to further community feedback and/or change.

Field observations of the soil-to-atmosphere CO2 flux—soil respiration, RS—are a prime example of ‘long tail’ data that historically have had neither centralized databases nor an agreed-upon reporting format. This has hindered scientific transparency, analytical reproducibility, and syntheses with respect to this globally-important component of the carbon cycle. Here we propose a new data and metadata reporting format for RS data, based on engagement with a wide range of researchers in the earth and ecological sciences as well as expert advisory panels. Our goal was a reporting format that would be relevant and useful for synthesis activities, optimizing data discoverability and usability while not placing an undue burden on data contributors. We describe previous RS data collection efforts, lessons learned from related databases and data-oriented networks (e.g., FLUXNET) in earth and ecological sciences, and the process of community consultation. The proposed reporting format focuses on chamber-level data and metadata, specifying measurement conditions and, for a given measurement period defined by beginning and ending timestamps, a mean RS flux (or CO2 concentration) and associated ancillary measurements. With input from the research community, we have also developed research data and metadata templates to support data collection adhering to the reporting format. Fundamentally, this format aims to enable findable, accessible, interoperable, and reusable data, while providing ‘future-proofing’ capabilities to support reanalyses using as yet unknown algorithms or approaches. This proposed RS reporting format is openly available online and is intended to be a dynamic document, subject to further community feedback and/or change.

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system.

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system.

This paper compares and discusses the data standards of materials that form the basis of MLA collaboration, i.e., the collaboration between Museums, Libraries, and Archives, and mentions the possibilities and issues of MLA collaboration from the viewpoint of the data standards of materials and Linked Open Data (LOD) [ABSTRACT FROM AUTHOR]

This paper examines the concept of open-source data interoperability in the United States' K-12 education domain, specifically addressing the implications of interoperability for data justice. The term 'data justice' is a relatively recent coinage; the framing and meaning of this term are still evolving, and it has not yet been applied in the educational domain. Building on the nascent research and theorizing in the fields of both data justice and interoperability in educational contexts, this paper provides an overview of the current state of this intersection. Additionally, the authors draw on their direct experiences implementing interoperability initiatives in several U.S. states in order to build a foundational understanding of the risks and opportunities for data justice in the realm of interoperability and data standards in education. The paper concludes with a call for more research to be completed on this complex sociotechnical topic. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data is the core resource to promote the development of intelligent coal mine technology, innovation of application mode and improvement of market competitiveness. Data governance is the key bottleneck for the development of smart mine from primary stage to advanced stage. However, at present, there is no consensus on the concept connotation, development goal and key technologies of data governance for smart mine. In order to solve the above problems, this paper analyzes the important position of data governance in the construction of smart mines, and defines the concept and connotation of data governance for smart mine. Data governance for smart mine is guided by the governance concept of 'precipitation, reuse, sharing and collaboration'. All departments within the coal enterprise work together with industry and government regulatory departments to formulate data standards and management specifications. The data structure and data transmission link of coal enterprises are reshaped to break through the data barriers of smart mines and ensure data quality and data security. The smart mine technology linkage and business collaboration mechanism are built to realize the process of digital-driven decision-making and flexible production. This paper discusses the development goal of data governance for smart mine from three aspects, including overall goal, development strategy and phased tasks. It is pointed out that the key point of data governance for smart mine is to solve the problem of information isolated island in the process of smart mine construction. Therefore, the data sharing and business collaboration are realized so as to make smart mine become an organic whole to promote the value enhancement of coal enterprises. Smart mine data governance needs to properly handle the five aspects relationships, including local coordination and overall coordination, physical integration and logical integration, intelligent business system and data governance platform, group-level data governance platform and mine-level data governance platform, and local processing and cloud processing. It is proposed that smart mine data governance needs to break through six key technologies, including data standard, global data modeling, data aggregation, data development, data management and data center architecture.

The digital twin concept describes a comprehensive model used to monitor and simulate an existing asset in nearly realtime. In a maritime context, digital twins do not yet have a wide selection of standards to support interoperability among systems, undermining users' ability to create a cohesive digital twin that combines multiple sources of data. This study proposes an open framework for modelling and organizing ship, sensor, and operating context data in digital twins. The framework is web-driven, allowing digital twin content and simulations to be accessed by distributed users. Two case studies based on the framework are presented. The first one explains how to manage ship and sensor data using taxonomies suitable across different stages of the lifecycle. A dashboard for browsing a digital model of the research vessel Gunnerus according to taxonomies for early design and operation is developed as a proof of concept. The second case study illustrates how the proposed data standards can be used to develop simulations accounting for some aspects of the vessel's operating context. The digital Gunnerus model is used to develop a navigational simulator including surrounding terrain and nearby vessels. The challenges anticipated in scaling the framework toward industrial applications are discussed in terms of standards' capabilities, compatibility, and of data governance. Plans of integrating the case studies with sensor streams to obtain true digital twins are outlined. [ABSTRACT FROM AUTHOR]

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

Objective The global COVID pandemic has highlighted the need for the reporting of enhanced serology data from laboratory testing data. Qualitative results alone do not provide detailed data to understand levels of SARS-CoV-2 seroprevalence within a populace. To enable this, a minimum data set is required to support standardised data reporting. Approach The CO-CONNECT project is concerned with the response to COVID within the UK and how datasets can be made Findable, Accessible, Interoperable and Reusable (FAIR). One aspect of this is a focus upon improving granular levels of COVID serology testing data being reported by national laboratories to the National Pathology Exchange (NPEx). A study was conducted to ascertain the essential data elements that could formulate a COVID Serology Data Standard (CSDS). Three NHS laboratories acting as pilot studies and NPEx have been actively involved with the development of the CSDS, together with clinicians and academics from the CO-CONNECT project. Results Feedback from clinicians, academics and NPEx has been collected, together with NHS laboratory feasibility analysis to derive a minimum set of data elements that form a proposed data standard to standardise the reporting of laboratory COVID serology data for the UK. The proposed CSDS comprises twelve data elements in total, grouped into four main areas, (1) subject identifier and test date and time, (2) analyser types, test kits and samples, (3) qualitative results and (4) quantitative results. To further support the standardisation effort, a proposed Health Level Seven (HL7) message structure has been created to enable the reporting of the CSDS data elements to NPEx. Conclusion The CSDS has been created to help augment levels of reported granular data for laboratory COVID serology testing. If adopted, this could enable healthcare professionals to better understand the calibration of assays across multiple analyser types and thus antibody response levels.

Keywords: Analytical chemistry; cheminformatics: data and standards; data standard; file format; mass spectrometry; NMR EN Analytical chemistry cheminformatics: data and standards data standard file format mass spectrometry NMR 725 736 12 08/09/22 20220601 NES 220601 Introduction The amount of research data is growing in all disciplines, and chemistry is no exception. The NMR record in NMReDATA format includes the instrument (raw) data, a SDFile and, since version 2, also spectral data in JCAMP format in a folder, which can be compressed in the zip format for data exchange. Data standards for NMR spectroscopy NMR is an indispensable analytical technique providing rich information on bonding and structure as well as molecule interaction and abundance of molecules in samples. Data formats for analytical data When developing infrastructures for chemistry, one of the major tasks is to adopt or define data models and formats for exchange, storage and archival of analytical data. [Extracted from the article]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NEF and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB restraint violation report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints. [Display omitted] • The wwPDB validation report has been expanded to include restraint analysis • NMR-STAR and NEF serve as the standardized format for NMR data at wwPDB • Standardized restraint format facilitates model versus data assessment • More comprehensive and better assessment of biomolecular NMR structures Baskaran et al. outlined the rationale for model-versus-data assessment of NMR structures by wwPDB, along with a summary of validation tools for NMR distance and dihedral-angle restraints implemented in the wwPDB validation pipeline. These tools enhance the assessment of biomolecular NMR structure quality, benefiting users of the wwPDB archive. [ABSTRACT FROM AUTHOR]

• Allotrope Data Model ensures data integrity through validation. • Allotrope Framework's foundation is built upon the versatile HDF5. • Multiple tools enable easy access to Allotrope Data Format datasets. • Advances in AF technologies made it easy for users to adopt the standard. Recent Allotrope Foundation (AF) Connect Workshops (2021–2023) showcased some of the latest advancements in data standardization for analytical data in the pharmaceutical industry. These workshops demonstrated the adaption of two key technologies, the Allotrope Data Format (ADF) and the Allotrope Simple Model (ASM), which streamline instrument data representation and terminology to enhance interoperability across systems. Notably, ASM has facilitated broader adoption of the standard. The increasing significance of data-driven decision-making in the life sciences is underscored by the evolving landscape of open-source solutions and commercial implementations, as demonstrated by industry leaders adopting these standards. Here, we highlight selected examples that illustrate the collective efforts of the community in advancing data standards and data management in the life sciences. [ABSTRACT FROM AUTHOR]

To be computationally reproducible and efficient, integration of disparate data depends on shared entities whose matching meaning (semantics) can be computationally assessed. For biodiversity data one of the most prevalent shared entities for linking data records is the associated taxon concept. Unlike Linnaean taxon names, the traditional way in which taxon concepts are provided, phylogenetic definitions are native to phylogenetic trees and offer well-defined semantics that can be transformed into formal, computationally evaluable logic expressions. These attributes make them highly suitable for phylogeny-driven comparative biology by allowing computationally verifiable and reproducible integration of taxon-linked data against Tree of Life-scale phylogenies. To achieve this, the first step is transforming phylogenetic definitions from the natural language text in which they are published to a structured interoperable data format that maintains strong ties to semantics and lends itself well to sharing, reuse, and long-term archival. To this end, we developed the Phyloreference Exchange Format (Phyx), a JSON-LD-based text format encompassing rich metadata for all elements of a phylogenetic definition, and we created a supporting software library, phyx.js, to streamline computational management of such files. Together they form a foundation layer for digitizing and computing with phylogenetic definitions of clades. [ABSTRACT FROM AUTHOR]

To be computationally reproducible and efficient, integration of disparate data depends on shared entities whose matching meaning (semantics) can be computationally assessed. For biodiversity data one of the most prevalent shared entities for linking data records is the associated taxon concept. Unlike Linnaean taxon names, the traditional way in which taxon concepts are provided, phylogenetic definitions are native to phylogenetic trees and offer well-defined semantics that can be transformed into formal, computationally evaluable logic expressions. These attributes make them highly suitable for phylogeny-driven comparative biology by allowing computationally verifiable and reproducible integration of taxon-linked data against Tree of Life-scale phylogenies. To achieve this, the first step is transforming phylogenetic definitions from the natural language text in which they are published to a structured interoperable data format that maintains strong ties to semantics and lends itself well to sharing, reuse, and long-term archival. To this end, we developed the Phyloreference Exchange Format (Phyx), a JSON-LD-based text format encompassing rich metadata for all elements of a phylogenetic definition, and we created a supporting software library, phyx.js, to streamline computational management of such files. Together they form a foundation layer for digitizing and computing with phylogenetic definitions of clades.

The nuclear magnetic resonance extracted data (NMReDATA) format has been proposed as a way to store, exchange, and disseminate nuclear magnetic resonance (NMR) data and physical and chemical metadata of chemical compounds. In this paper, we report on analytical workflows that take advantage of the uniform and standardized NMReDATA format. We also give access to a repository of sample data, which can serve for validating software packages that encode or decode files in NMReDATA format. [ABSTRACT FROM AUTHOR]

The global movement of people and goods has increased the risk of biosecurity threats and their potential to induce large economic, social, and environmental harm. Integration of biosafety monitoring networks has become a top priority for addressing biosafety issues. In order to resolve the data standards and integration problems in the field of biosafety in China, the Biosafety Surveillance Conceptual Data Model (BSCDM), which is an object-oriented, hierarchically designed, flexible and scalable biosafety surveillance concept data model, is proposed in this article. This model is based on the integration of business process management and data resources of disease surveillance, animal disease surveillance and potential invasive biological monitoring. In reference to the Public Health Conceptual Data Model (PHCDM) and Federal Enterprise Architecture (FEA), BSCDM conducts a thorough analysis of biosafety monitoring activities, records basic information requirements of biosafety monitoring and provides data set standards for biosafety-related activities. It is developed with the Unified Modeling Language (UML) and could be applied as an open standard for accelerating data analysis and promoting collaboration. This study attempts to integrate biosafety monitoring data and the presented model has been tested in the detection of dengue fever in China and will be applied to other biosafety fields.

Natural history collections are invaluable tools for various questions regarding biodiversity, environmental, and cultural studies. All object metadata thus need to be findable, reachable and interoperable for the scientific community and beyond. This requires a good structuration of data, appropriate exchange formats, and web sites or portals making all necessary information accessible. Collection managers, curators, and scientist from various institutions and nationalities were surveyed in order to understand the importance of open geoscientific collections for the respective holding institution and their daily work. In addition, particular requirements for the publication of geoscientific collection object metadata were gathered in a two-day workshop with international experts working with paleontological, mineralogical, petrological and meteorite collections. The survey and workshop revealed that common data standards are of crucial importance though insufficiently used by most institutions. The extent and type of information necessary for the publication and discussed during the workshop will be considered for domain specific application schema facilitating the publication and exchange of geoscientific object metadata. There is a high demand for comprehensive data portals covering all geoscientific disciplines. Gathered portal requirements will be taken into account when improving the already running GeoCASe aggregator platform.

Although plot or monitoring data are quite often associated with objects collected in the plot and stored in specific collections, controlled vocabularies currently available do not cover both disciplines. This situation limits the possibility to publish common data sets and consequently brings a loss of significant information by combining plot-based research with collection object associated data. To facilitate the exchange and publication of these important data sets, experts in natural history collection data, ecological research, and environmental science met for a one-day workshop in Berlin. The participants discussed data standards and ontologies relevant for each discipline and collected requirements for a first application schema covering terms important for both, collection object related data and plot-based research.

The flow cytometry data file standard provides the specifications needed to completely describe flow cytometry data sets within the confines of the file containing the experimental data. In 1984, the first Flow Cytometry Standard format for data files was adopted as FCS 1.0. This standard was modified in 1990 as FCS 2.0 and again in 1997 as FCS 3.0. We report here on the next generation flow cytometry standard data file format. FCS 3.1 is a minor revision based on suggested improvements from the community. The unchanged goal of the standard is to provide a uniform file format that allows files created by one type of acquisition hardware and software to be analyzed by any other type. The FCS 3.1 standard retains the basic FCS file structure and most features of previous versions of the standard. Changes included in FCS 3.1 address potential ambiguities in the previous versions and provide a more robust standard. The major changes include simplified support for international characters and improved support for storing compensation. The major additions are support for preferred display scale, a standardized way of capturing the sample volume, information about originality of the data file, and support for plate and well identification in high throughput, plate based experiments. Please see the normative version of the FCS 3.1 specification in Supporting Information for this manuscript (or at http://www.isac-net.org/ in the Current standards section) for a complete list of changes.

Abstract Background To truly achieve personalized medicine in oncology, it is critical to catalog and curate cancer sequence variants for their clinical relevance. The Somatic Working Group (WG) of the Clinical Genome Resource (ClinGen), in cooperation with ClinVar and multiple cancer variant curation stakeholders, has developed a consensus set of minimal variant level data (MVLD). MVLD is a framework of standardized data elements to curate cancer variants for clinical utility. With implementation of MVLD standards, and in a working partnership with ClinVar, we aim to streamline the somatic variant curation efforts in the community and reduce redundancy and time burden for the interpretation of cancer variants in clinical practice. Methods We developed MVLD through a consensus approach by i) reviewing clinical actionability interpretations from institutions participating in the WG, ii) conducting extensive literature search of clinical somatic interpretation schemas, and iii) survey of cancer variant web portals. A forthcoming guideline on cancer variant interpretation, from the Association of Molecular Pathology (AMP), can be incorporated into MVLD. Results Along with harmonizing standardized terminology for allele interpretive and descriptive fields that are collected by many databases, the MVLD includes unique fields for cancer variants such as Biomarker Class, Therapeutic Context and Effect. In addition, MVLD includes recommendations for controlled semantics and ontologies. The Somatic WG is collaborating with ClinVar to evaluate MVLD use for somatic variant submissions. ClinVar is an open and centralized repository where sequencing laboratories can report summary-level variant data with clinical significance, and ClinVar accepts cancer variant data. Conclusions We expect the use of the MVLD to streamline clinical interpretation of cancer variants, enhance interoperability among multiple redundant curation efforts, and increase submission of somatic variants to ClinVar, all of which will enhance translation to clinical oncology practice.

This paper explores the data challenges of a major collection method in the field of ecology: using infrared-activated cameras to detect wildlife. One such solution, eMammal, is now available to address these struggles. We delineate the key reason behind its success: a data curator who manages an established data standard and communicates with eMammal’s users and stakeholders. We outline the tasks of this data curator, mention how they can work with data librarians, and demonstrate that the data curator position is already applicable in several biological science fields with a few examples. We end by emphasizing the growth of such a position and how it contributes to the research field. The substance of this article is based upon a lightning talk presentation at RDAP Summit 2018.