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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.The brain is an immensely complex organ which regulates many of the behaviors that animals need to survive. To understand how the brain works, scientists monitor and record brain activity under different conditions using a variety of experimental techniques. These neurophysiological studies are often conducted on multiple types of cells in the brain as well as a variety of species, ranging from mice to flies, or even frogs and worms. Such a range of approaches provides us with highly informative, complementary ‘views’ of the brain. However, to form a complete, coherent picture of how the brain works, scientists need to be able to integrate all the data from these different experiments. For this to happen effectively, neurophysiology data need to meet certain criteria: namely, they must be findable, accessible, interoperable, and re-usable (or FAIR for short). However, the sheer diversity of neurophysiology experiments impedes the ‘FAIR’-ness of the information obtained from them. To overcome this problem, researchers need a standardized way to communicate their experiments and share their results – in other words, a ‘standard language’ to describe neurophysiology data. Rübel, Tritt, Ly, Dichter, Ghosh et al. therefore set out to create such a language that was not only FAIR, but could also co-evolve with neurophysiology research. First, they produced a computer software program (called Neurodata Without Borders, or NWB for short) which generated and defined the different components of the new standard language. Then, other tools for data management were created to expand the NWB platform using the standardized language. This included data analysis and visualization methods, as well as an ‘archive’ to store and access data. Testing the new language and associated tools showed that they indeed allowed researchers to access, analyze, and share information from many different types of experiments, in organisms ranging from flies to humans. The NWB software is open-source, meaning that anyone can obtain a copy and make changes to it. Thus, NWB and its associated resources provide the basis for a collaborative, community-based system for sharing neurophysiology data. Rübel et al. hope that NWB will inspire similar developments across other fields of biology that share similar levels of complexity with neurophysiology.

The DDI Training Group organized a series of tutorials around the 2021 European DDI Users Conference. This included a general overview of the DDI standards on Friday 26 November, followed by a series of more detailed tutorials and a session describing the software tools and services available for implementing DDI, all on Monday 29 November 2021. A short description of each session is available here, along with recordings and presentations. DDI Lifecycle addresses the needs of metadata management not only for archives and data disseminators, but also throughout the data lifecycle, from data collection and survey design through the end stages. It is also a useful tool for understanding how related data sets fit together, whether as a longitudinal or repeat cross-sectional effort, or through ad-hoc comparison and harmonization. A range of topics will cover a broad description of the specification, as well as providing focus on specific aspects of the standard for metadata and data management. Presenters include Wendy Thomas, Hayley Mills, Kaia Kulla, and Benjamin Beuster.

The DDI Training Group organized a series of tutorials around the 2021 European DDI Users Conference. This included a general overview of the DDI standards on Friday 26 November, followed by a series of more detailed tutorials and a session describing the software tools and services available for implementing DDI, all on Monday 29 November. A short description of each session is available here, along with recordings and presentations. DDI Codebook is a metadata specification for providing detailed, machine-actionable and human-readable metadata about individual studies and their data files in an XML format. Instructors are Jane Fry of Carleton University and Adrian Dusa of the University of Bucharest, who will introduce you to this popular metadata standard. Please note that the date on the title slide is incorrect: the presentation actually took place on Mon 29 November 2021.

The Operational Data Model (ODM) is a data standard for interchanging clinical trial data. ODM contains the metadata definition of a study, i.e., case report forms, as well as the clinical data, i.e., the answers of the participants. The portal of medical data models is an infrastructure for creation, exchange, and analysis of medical metadata models. There, over 23000 metadata definitions can be downloaded in ODM format. Due to data protection law and privacy issues, clinical data is not contained in these files. Access to exemplary clinical test data in the desired metadata definition is necessary in order to evaluate systems claiming to support ODM or to evaluate if a planned statistical analysis can be performed with the defined data types. In this work, we present a web application, which generates syntactically correct clinical data in ODM format based on an uploaded ODM metadata definition. Data types and range constraints are taken into account. Data for up to one million participants can be generated in a reasonable amount of time. Thus, in combination with the portal of medical data models, a large number of ODM files including metadata definition and clinical data can be provided for testing of any ODM supporting system. The current version of the application can be tested at https://cdgen.uni-muenster.de and source code is available, under MIT license, at https://imigitlab.uni-muenster.de/published/odm-clinical-data-generator.

Background Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. Objective The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. Methods We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. Results We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. Conclusions Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.

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. publishedVersion

To accelerate the clinical translation of photoacoustic (PA) imaging, IPASC aims to provide open and publicly available reference datasets for testing of data reconstruction and spectral processing algorithms in a widely accepted data format. The International Photoacoustic Standardisation Consortium (IPASC) has identified and agreed on a list of essential metadata parameters to describe raw time series PA data and used it to develop an initial prototype of a standardized PA data format. We aim to apply the proposed format in an open database that provides reference datasets for testing of processing algorithms, thereby facilitating and advancing PA research and translation.

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0 [13], a modern data standard for collaborative science across the neurophysiology community.

Museums and their collections have specially customized databases in order to optimally gather and record their contents and associated metadata associated with their specimens. To share, exchange, and publish data, an appropriate data standard is essential. ABCD (Access to Biological Collection Data) is a standard for biological collection units, including living and preserved specimen, together with field observation data. Its extension, EFG (Extension for Geoscience), enables sharing and publishing data related to paleontological, mineralogical, and petrological objects. The standard is very granular and allows detailed descriptions, including information about the collection event itself, the holding institution, stratigraphy, chemical analysis, and host rock. The standard extension was developed in 2006 and has been used since then by different initiatives and applied for the publication of collection-related data in domain-specific and interdisciplinary portals.

Open government data (OGD) is a valuable resource for both policy transparency and government accountability. All levels of the United States government are working hard to promote open data and its portals. However, there is still a lack of stud-ies on local-level OGD portals in the United States, particularly on the quality of metadata adopted by these portals. By ex-amining 200 US cities, a list of 112 local-level portals is sampled and we investigate the current usages of open data plat-forms for building local-level OGD portals. This study further investigates and discusses the adoption and potential issues of metadata on those OGD portals. Our result findings discuss the platform distributions among US local-level OGD portals, and also highlight several critical issues associated with metadata on the portals. We anticipate the results will inspire further studies on identifying solutions to improve the metadata and enhance the usability of open government data portals

UMR BFP - Equipe Métabolisme; International audience; NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters and, where available, spectral metadata such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex bio-mixtures i.e. metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker and Agilent/Varian vendor formats. In addition, easy-to-use web-based spectral viewing, processing and spectral assignment tools that read and write nmrML have been developed. Software libraries and web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g. serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI) and we here encourage user participation and feedback to increase usability and make it a successful standard.

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.

The data management landscape associated with the Global Ocean Observing System is distributed, complex, and only loosely coordinated. Yet interoperability across this distributed landscape is essential to enable data to be reused, preserved, and integrated and to minimize costs in the process. A building block for a distributed system in which component systems can exchange and understand information is standardization of data formats, distribution protocols, and metadata. By reviewing several data management use cases we attempt to characterize the current state of ocean data interoperability and make suggestions for continued evolution of the interoperability standards underpinning the data system. We reaffirm the technical data standard recommendations from previous OceanObs conferences and suggest incremental improvements to them that can help the GOOS data system address the significant challenges that remain in order to develop a truly multidisciplinary data system. Fil: Snowden, Derrick. No especifíca; Fil: Tsontos, Vardis M.. NASA Jet Propulsion Laboratory; Estados Unidos Fil: Handegard, Nils Olav. Norwegian Institute of Marine Research; Noruega Fil: Zárate, Marcos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina Fil: O' Brien, Kevin M.. University of Washington; Estados Unidos Fil: Casey, Kenneth S. National Centers for Environmental Information; Estados Unidos Fil: Smith, Neville. No especifíca; Fil: Sagen, Helge. Norwegian Institute of Marine Research; Noruega Fil: Bailey, Kathleen. No especifíca; Fil: Lewis, Mirtha Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina Fil: Arms, Sean. University Corporation for Atmospheric Research; Estados Unidos

Decentralized original and disordered management of atmospheric lidar data are seriously obstructing regional atmospheric environment research. Though the analysis of the characteristics of atmospheric lidar data in this paper, it shows that it is very necessary to create the data standard based on metadata. After the analysis of the basic functions and the main content of the atmospheric lidar metadata standard, it gives a descriptive method and atmospheric lidar metadata model to based on meteorological metadata standard.

The current paper presents a theoretical framework for standardizing Peace Data as a means of understanding the conditions under which people’s technology use results in positive engagement and peace. Thus, the main point of our paper is that Big Data can be conceptualized in terms of its value to peace. We define peace as a set of positive, prosocial behaviors that maximize mutually beneficial positive outcomes resulting from interactions with others. To accomplish this goal, we present hypothetical and real-world, data driven examples that illustrate our thinking in this domain and present guidelines for how to identify, collect, utilize, and evaluate Peace Data generated during mediated interactions and further suggest that Peace Data has four primary components: group identity information, behavior data, longitudinal data, and metadata. This paper concludes with a call for participation in a Peace Data association and suggested for guidelines for how scholars and practitioners can identify Peace Data in their own domains. Ethical considerations and suggestions for future research are also discussed.

mzTab is the most recent standard format developed by the Proteomics Standards Initiative. mzTab is a flexible tab-delimited file that can capture identification and quantification results coming from MS-based proteomics and metabolomics approaches. We here present an open-source Java application programming interface for mzTab called jmzTab. The software allows the efficient processing of mzTab files, providing read and write capabilities, and is designed to be embedded in other software packages. The second key feature of the jmzTab model is that it provides a flexible framework to maintain the logical integrity between the metadata and the table-based sections in the mzTab files. In this article, as two example implementations, we also describe two stand-alone tools that can be used to validate mzTab files and to convert PRIDE XML files to mzTab. The library is freely available at http://mztab.googlecode.com.

Water quality is a multi-source, multi-purpose problem that needs exploiting observations, often taken by a number of heterogeneous bodies. This problem has been tackled within the Italian Long Term Ecological research network (LTER-Italy) in an experiment aimed at testing how ecological observations of mountain lakes water can be shared by OGC (Open Geospatial Consortium) standard services of the Sensor Web Enablement (SWE) initiative. A friendly and easy implementation of these services is fostered by the usage of the open source software Geoinformation Enabling Toolkit StarterKit ® (GET-IT). It has been used in the experiment to create SOS services, upload observations and create SensorML metadata of the involved sensors. This contribution describes the experiment and presents its results.

The ESO Phase 3 process allows the upload, validation, storage, and publication of reduced data through the ESO Science Archive Facility. Since its introduction, ~2 million data products have been archived and published; 80% of them are one-dimensional extracted and calibrated spectra. Central to Phase3 is the ESO science data product standard that defines metadata and data format of any product. This contribution describes the ESO data standard for 1d-spectra, its adoption by the reduction pipelines of selected instrument modes for in-house generation of reduced spectra, the enhanced archive legacy value. Archive usage statistics are provided.

The complexities of the intricate geospatial resources and formats make preservation and distribution of GIS data difficult even among experts. The proliferation of, for instance, KML, Internet map services, etc, reflects the need for sharing geodata but a comprehensive solution when having to deal with data and metadata of a certain complexity is not currently provided. Original geospatial data is usually divided into several parts to record its different aspects (spatial and thematic features, etc), plus additional files containing, metadata, symbolization specifications and tables, etc; these parts are encoded in different formats, both standard and proprietary. To simplify data access, software providers encourage the use of an additional element that we call generically "map project", and this contains links to other parts (local or remote). Consequently, in order to distribute the data and metadata refereed by the map in a complete way, or to apply the Open Archival Information System (OAIS) standard to preserve it for the future, we need to face the multipart problem. This paper proposes a package allowing the distribution of real (comprehensive although diverse and complex) GIS data over the Internet and for data preservation. This proposal, complemented with the right tools, hides but keeps the multipart structure, so providing a simpler but professional user experience. Several packaging strategies are reviewed in the paper, and a solution based on ISO 29500-2 standard is chosen. The solution also considers the adoption of the recent Open Geospatial Consortium Web Services common standard (OGC OWS) context document as map part, and as a way for also combining data files with geospatial services. Finally, and by using adequate strategies, different GIS implementations can use several parts of the package and ignore the rest: a philosophy that has proven useful (e.g. in TIFF). Geospatial Preservation is solved by implementing the OAIS information package.The ISO 29500-2 standard is adapted to the multipart geospatial storages.OPC adds compression to OGC OWS context standard.

The SAS Clinical Standards Toolkit provides a framework for validating and documenting CDISC-compliant data. It is a free add-on package for SAS software available under Microsoft Windows and UNIX. At present, it contains the validation of datasets according to the SDTM model (inclusive of published WebSDM™ and Janus checks) plus the generation of a data definition document following CRT-DDS (define. xml) for the submission. As all metadata and rules reside in SAS tables, other standards or new versions of existing standards can be retrofitted. To begin with, this paper will outline the meaning of such a tool within the scope of CDISC-compliant data management. Following that, the concept of the SAS Clinical Standards Toolkit will be presented and some examples for usage and results will be given. Finally, the adaptability of the tool will be expanded upon and some practical hints will be given.

The Human Proteome Organisation's Proteomics Standards Initiative has developed the GelML (gel electrophoresis markup language) data exchange format for representing gel electrophoresis experiments performed in proteomics investigations. The format closely follows the reporting guidelines for gel electrophoresis, which are part of the Minimum Information About a Proteomics Experiment (MIAPE) set of modules. GelML supports the capture of metadata (such as experimental protocols) and data (such as gel images) resulting from gel electrophoresis so that laboratories can be compliant with the MIAPE Gel Electrophoresis guidelines, while allowing such data sets to be exchanged or downloaded from public repositories. The format is sufficiently flexible to capture data from a broad range of experimental processes, and complements other PSI formats for MS data and the results of protein and peptide identifications to capture entire gel-based proteome workflows. GelML has resulted from the open standardisation process of PSI consisting of both public consultation and anonymous review of the specifications.

The communication of experimental results is common to scientific investigation. The method of presentation varies widely across investigation technique, and may stymie fundamental scientific goals such as sharing of results and replication of experiments. Even within a limited community, such as small-angle scattering, the choice of how to organize stored information is fragmented, with the result that often, the data are deposited in ad hoc form. To increase access to data produced from publicly-funded research, funding agencies are now requiring that proposals describe how data will be made available. With the increases in data volume due to higher-efficiency collection, increased experimental complexity, and larger and faster detectors, the plethora of ad hoc formats is a burden to the scientific community. Reliance on a few, well-considered standards facilitates automated processes for analysis and correlation of scientific data. Furthermore, it leads to development of common tools for data visualization and analysis, and data catalogues for access, reference, and data mining. The Worldwide Protein Data Bank is such an example where, by the provision of a standardized data format, scientific data and metadata from a vast range of methods has become available for research on the structures of proteins. Guidelines for structural modelling of smallangle scattering data from biomolecules in solution were adopted by the IUCr Commission on Small-Angle Scattering in 2012. NXcanSAS is a standard to store reduced small-angle scattering data of any dimension. It is sufficiently general that it may be used for any form of reduced SAS data in different scientific applications. The hierarchical structure of the canSAS (http://www.cansas.org) standard has been designed within the NeXus (http://www.nexusformat.org) data format. This presentation will describe the NXcanSAS standard, underlying concepts, and will give some examples.

This article proposes a comprehensive set of data standards to address the submission of clinical data based on the work of the Clinical Data Interchange Standards Consortium (CDISC). The present components of the CDISC submission standards are described and related to Food and Drug Administration guidelines on submission as expressed in the 1999 guidance documents on regulatory submissions and the 2005 guidance on the electronic Common Technical Document (eCTD). The relative roles of the CDISC Study Data Tabulation Model (SDTM); the Case Report Tabulation Data Description Specification (defme.xml), which is based on the CDISC Operational Data Model (ODM); and the Analysis Dataset Models (ADaM) in meeting submission data requirements are next described. Finally, this article discusses implementation alternatives for sponsors, future directions, and the potential benefit of current projects to improve the submission process.

To complete large-scale clinical research, organizations must share data. Because institutional database schemas are inherently heterogeneous, they need a standard metadata representation in order to exchange and combine data for multi-center research. The AGNIS application (A Growable Network Information System) facilitates the exchange of hematopoietic cell transplantation outcomes data using data standards. However, adoption rates remain low due to a significant mapping burden. The AGNIS experience shows that developing a data standard is not enough. Tools and resources are needed to facilitate utilization of the standard.

This article discusses a small scale pilot to harmonise three Bank of England statistical and regulatory data forms. The primary purpose of the pilot was to assess opportunities for improved operational efficiency in regulatory reporting. The broader purpose was to demonstrate how common data standards can be created from heterogeneous data sets. In the course of discussing the pilot, the article explains the history of how data has been collected at the Bank of England; how that process is changing in light of the Bank’s Strategic Plan; and explains why a common data standard is a critical financial market infrastructure fundamental to the success of global regulatory reform.

Background Although rare disease patients make up approximately 6–8% of all patients in Europe, it is often difficult to find the necessary expertise for diagnosis and care and the patient numbers needed for rare disease research. The second French National Plan for Rare Diseases highlighted the necessity for better care coordination and epidemiology for rare diseases. A clinical data standard for normalization and exchange of rare disease patient data was proposed. The original methodology used to build the French national minimum data set (F-MDS-RD) common to the 131 expert rare disease centers is presented. Methods To encourage consensus at a national level for homogeneous data collection at the point of care for rare disease patients, we first identified four national expert groups. We reviewed the scientific literature for rare disease common data elements (CDEs) in order to build the first version of the F-MDS-RD. The French rare disease expert centers validated the data elements (DEs). The resulting F-MDS-RD was reviewed and approved by the National Plan Strategic Committee. It was then represented in an HL7 electronic format to maximize interoperability with electronic health records. Results The F-MDS-RD is composed of 58 DEs in six categories: patient, family history, encounter, condition, medication, and questionnaire. It is HL7 compatible and can use various ontologies for diagnosis or sign encoding. The F-MDS-RD was aligned with other CDE initiatives for rare diseases, thus facilitating potential interconnections between rare disease registries. Conclusions The French F-MDS-RD was defined through national consensus. It can foster better care coordination and facilitate determining rare disease patients’ eligibility for research studies, trials, or cohorts. Since other countries will need to develop their own standards for rare disease data collection, they might benefit from the methods presented here.

Digital Imaging and Communications in Medicine (DICOM) files are an international data standard for storing, distributing and processing medical images of all types. DICOM files include a header file containing Metadata on details which may include information on the patient. This often inhibits the free distribution of DICOM files due to issues relating to the confidentiality of data on identifiable living people, thereby limiting the potential for other radiologists to provide a diagnosis, for example, through distribution of the data over the Internet. This problem is a current limiting condition with regard to the development of Tele-medical imaging. Thus in this paper we consider a method of encrypting and embedding (or Stegacrypting) DICOM Metadata into the DICOM image, thereby providing a solution to a problem that currently inhibits the distribution of medical images using a file type that is an established international standard. The proposed method removes or `anonymises' the private data, encrypts it and then embeds it into the DICOM image in an imperceptible way. The specific algorithm developed retains the private data attached to a DICOM image even when the image is converted into a standard image file format.

The VPH/Physiome project is developing tools and model databases for computational physiology based on three primary model encoding standards: CellML, SBML and FieldML. For the modelling community these standards are the equivalent of the DICOM standard for the clinical imaging community and it is important that the tools adhere to these standards to ensure that models from different groups can be curated, annotated, reused and combined. This chapter discusses the development and use of the VPH/Physiome standards, tools and databases, and also discusses the minimum information standards and ontology-based metadata standards that are complementary to the markup language standards. Data standards are not as well developed as the model encoding standards (with the DICOM standard for medical image encoding being the outstanding exception) but one new data standard being developed as part of the VPH/Physiome suite is BioSignalML and this is described here also. The PMR2 (Physiome Model Repository 2) database for CellML and FieldML files is also described, together with the Application Programming Interfaces (APIs) that facilitate access to the models from the visualization (cmgui and GIMIAS) or computational (OpenCMISS, OpenCell/OpenCOR and other) software.

CytometryML is an XML schema based translation, extension and amalgamation of the DICOM and ISAC standards. CytometryML consists of 4 major XML schemas: Series, Instance, Instrument, and Specimen; it also includes Image and List-Mode schemas. Series metadata, which is specific for an entire collection of images and/or list-mode files produced by a single instrument and derived from a single specimen, is stored together with associated metadata files in a container (ZIP) file. Each Instance container file includes binary image and/or list-mode files together with associated metadata files that are specific for a single or closely related group of instrument runs from a single specimen. The Archival Cytometry Standard (ACS) proposed Table of Contents schema including its Resource Description Framework (RDF) capabilities has been extended and modified for use in the Instance schema.

The U.S. Navy established the Mine Warfare (MIW) Community of Interest (COI) in 2007 to develop data strategies in support of a future information-based architecture for naval MIW. The MIW COI is actively pursuing standard data exchange as a mechanism to facilitate the creation of a future service oriented architecture (SOA) and improved interoperability within the MIW system-of-systems. Initiatives include publishing data standards to facilitate implementation of open architecture (OA) and net-centric approaches as well as the development of a Data Standards Guidebook to facilitate the generation of future standards. During the past year, the COI published the data standard for MIW Contacts and prepared a foundational structure and schema for a MIW sensor data format using the hierarchical data format (HDF) specification as a common approach to managing unstructured sensor data in support of post mission analysis (PMA). Looking forward, the COI is implementing a data model roadmap to manage the data standards generation process for MTW-relevant tactical, meteorological and environment data, and sensor data. The development of data standards directly enables technology insertion by providing inputs to algorithms and modules. The use of a standard format also reduces integration costs and risk through early communication of available data and associated metadata. The access to common data standards allows new technologies to be more easily analyzed and incorporated during the verification and validation (V&V) process. An agile approach to technology insertion, enabled by common data standards, will ultimately result in an improved future MIW capability.

The requirements for faster return-to-service time and higher data rates in space system design and operations have greatly contributed to a refocus on data and information models to ensure that Space and Ground segments can benefit from data representation standards or best practices in commercial IT industries. Collectively, the space industry members have come to a realization that cost and competitiveness can be in the design of the system operations, but not in the ground product data integration or data exchange processing. Data translation and integration is behind the scene. The space system users can only understand the benefit of a data standard compliance, when the information for every decision in the daily operations is represented correctly and provided in automation. When complicated data mining is a daily routine of operations in order to bring the system back to service, there is room for improvement. XML is the latest language widely recognized for its benefits, delivering metadata for Space Systems data integration. The XML Telemetry and Command Exchange (XTCE) Standard, by the Object Management Group (OMG)’s Space Domain Task Force (SDTF) and adopted by the CCSDS Spacecraft Monitor and Control Working Group, is a collaborated effort to evolve the space and ground software product development using XML technologies. XTCE provides an abstraction of common operational use database and metadata vocabulary. XTCE, an industry first effort, provides a set of vocabulary that is common to the space system operations. However, the expectations need to be managed. Technology insertion is only one of three key elements for mission assurance. People and processes are the other two key elements, as the James Webb Telescope project found out during its success in adopting the XTCE standard. Adopting XTCE is like learning a foreign language, first we learn the vocabulary. Once we recognize the word patterns and can map them to the meaning in our native languages, we then can be ready to associate the words with our intents. After that, we use the learned vocabulary and phrases for communicating messages and event status for appropriate actions. This presentation describes how the XTCE schema may have a bumpy ride to be adopted with commercial members in the industry. Like many other new standards, if the user communities are not aware of the benefits of XTCE, they do not request its compliance in contracts and the system integrators and subcontractors will not be interested in allocating investment in the adoption. What is the next step? Should OMG’s SDTF facilitate a broader user participation and collaboration opportunity to encourage the open standard adoption? Can the synergy be converged to ensure that the operational customers are not again lost in translation? Is the XTCE schema a stepping stone to a ground domain specific ontology standard?

The primary purpose of data standards or metadata schemas is to improve the interoperability of data created by multiple standard users. Given the high cost of developing data standards, it is desirable to assess the quality of data standards. We develop a set of metrics and a framework for assessing data standard quality. The metrics include completeness and relevancy. Standard quality can also be indirectly measured by assessing interoperability of data instances. We evaluate the framework using data from the financial sector: the XBRL (eXtensible Business Reporting Language) GAAP (Generally Accepted Accounting Principles) taxonomy and US Securities and Exchange Commission (SEC) filings produced using the taxonomy by approximately 500 companies. The results show that the framework is useful and effective. Our analysis also reveals quality issues of the GAAP taxonomy and provides useful feedback to taxonomy users. The SEC has mandated that all publicly listed companies must submit their filings using XBRL. Our findings are timely and have practical implications that will ultimately help improve the quality of financial data.

Objective For the purpose of establishing electronic health record (EHR), business-oriented health data distributed in different systems should be integrated to focus on individuals. This study is aimed at collecting health data items that are now nationally available in various health information systems, and harmonizing them by modeling and defining the data elements. Methods This study followed a bottom-up strategy in data standard development. Health data items were identified and collected by referring to national health service regulations, consulting domain experts and performing field investigations. Data items were classified and modeled based on recognized domain knowledge, information standards and specifications developed by standard development organizations (SDOs) of other countries. Data elements were extracted from data items and defined according to ISO/IEC 11179 – Metadata registries (MDR) and confirmed needs. Results 1588 data items were collected from 33 recording forms that have been used nationally in health services, and were classified with a conceptual data model that was composed of 7 super classes (healthcare clients, healthcare providers, birth registry, health event/act, healthcare process, death, and others) and 15 classes (person's identification, person's socio-demographic characteristics, address, communication, provider-organization, provider-individual, birth, health event/act, observation, procedure, drug and material administration, recommendation, evaluation, expenditure, death, others). By normalizing the concepts and representations of data items, data elements were derived and defined as the attributes of classes in the data model. Data items were specified as instances of corresponding data elements. Conclusions A large number of health data have been collected nationwide but person's life-long health record is incomplete and inconsistent now. To integrate such massive quantity of health data from various sources, a conceptual data model was established to organize data items, avoiding conflicts and duplications in between. For data consistency, data elements should be extracted from the data items and defined as attributes of classes in the data model by choosing essential metadata attributes. Treating data items as instances of well defined data elements might make data in different contexts manageable and agreeable. To be semantically unambiguous, further study should be performed to deal with the standardization of detailed medical information, and perfect the approach of data harmonization.

The chapter introduces the challenges of storing, sharing, and querying proteomics data caused by the complexity of the experimental techniques, and the speed with which the techniques evolve. Public proteome databases are difficult to develop and populate because of the range of data types and queries that must be supported, and the quantity of metadata required to validate results. There are several data standards under development that should alleviate some of the challenges, and databases that utilize the standards are becoming more widely supported. The chapter describes a model of a complete proteomics pipeline, including the metadata that should be captured to allow confidence to be placed on the results. Software is also required, which can produce data conforming to the standards and that can be used to query proteomics data repositories. The chapter outlines the requirements for software and presents two exemplars developed at the University of Manchester. Finally, there is a description of the likely future developments in standardization for proteomics.