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The presentation is part of documentation elaborated by INFO/RAC for supporting Contracting Parties in reporting data into the IMAP Info System under the Integrated Monitoring and Assessment Programme of the Mediterranean Sea and Coast and Related Assessment Criteria (IMAP), in the framework of Barcelona Convention. This presentation explains structure and functions of the IMAP Info System, providing a general overview about the reporting process for each IMAP Common Indicator (CI) using Data Standards and Data Dictionaries (DSs&DDs).

Next generation sequencing (NGS) is being applied for HLA typing in research and clinical settings. NGS HLA typing has made it feasible to sequence exons, introns and untranslated regions simultaneously, with significantly reduced labor and reagent cost per sample, rapid turnaround time, and improved HLA genotype accuracy. NGS technologies bring challenges for cost-effective computation, data processing and exchange of NGS-based HLA data. To address these challenges, guidelines and specifications such as Genotype List (GL) String, Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING), and Histoimmunogenetics Markup Language (HML) were proposed to streamline and standardize reporting of HLA genotypes. As part of the 17th International HLA and Immunogenetics Workshop (IHIW), we implemented standards and systems for HLA genotype reporting that included GL String, MIRING and HML, and found that misunderstanding or misinterpretations of these standards led to inconsistencies in the reporting of NGS HLA genotyping results. This may be due in part to a historical lack of centralized data reporting standards in the histocompatibility and immunogenetics community. We have worked with software and database developers, clinicians and scientists to address these issues in a collaborative fashion as part of the Data Standard Hackathons (DaSH) for NGS. Here we report several categories of challenges to the consistent exchange of NGS HLA genotyping data we have observed. We hope to address these challenges in future DaSH for NGS efforts.

The article reveals with the logic, mechanism and main parameters for using passenger data (API/PNR). The interrelation of the concepts of “Advance Passenger Information” and “Passenger Name Record” has been revealed. It has been determined that the systems of simplification of formalities also have a positive effect on security. For example, the purpose of data collection in the EU is to effectively combat illegal migration and strengthen border control, as well as to prevent, detect, investigate and prosecute terrorist and serious crimes. This applies to both the Passenger Information System (API) and additional passenger data, such as Passenger Name Record (PNR) data. There is no doubt that the successful application of API and PNR data exchange depends on a unified approach of all participants in information relations (both border agencies and airlines in different countries) regarding the question of data standard and standard of their transmission. Ensuring such a unified approach is a problem even when there is unity of legal regulation on data format and data exchange procedures. There are no special regulations in the national legislation that would regulate the relationship regarding the circulation of passenger registration data, nor is there a specific entity that processes this type of information. Thus, there is a need for special legal regulation of the circulation of information about airline passengers crossing the state border of Ukraine. In particular, the legislation must regulate the procedure, grounds, purpose of obtaining, processing, transmission, storage and destruction of information about passengers (its content), protection of the right to confidential information of persons operating international flights, establish a responsible controller of such information.

Application of Building Information Modeling (BIM) in construction industry has been applied for many years back. This because BIM can provide a better advantage in construction industry in term of controlling and managing construction project during their life cycle. The advantages that can be provide by BIM is focusing on the indoor planning tasks. But, when the construction project involves, besides indoor planning, outdoor planning also is important part that need to be look up. To cover the outdoor planning in construction project, Geographic Information System (GIS) need to be applied. GIS can overcome this problem because GIS mainly for outdoor planning by using their spatial analysis. GIS can offer a high degree of geospatial information and can provide the detailed geometrical and semantic information of building to assisted across improve automation. Towards produce the improved preparation in construction project, BIM and GIS should be integrated. To integrate both domains, the data interoperability between them need to be investigate because they used different data standard. This study focusses on solving the data interoperability through the data integration between BIM and GIS to solve the problem of data mismatch and data missing during data translation process. Industry Foundation Classes (IFC) was used as a data standard to perform the data integration between BIM and GIS. The outcomes from this study show that when the data interoperability applied between BIM and GIS, the problem above can be solved, and the data dimension and their coordinate system also can be control.

The concept of Essential Biodiversity Variables (EBVs) was conceived to study, report, and manage biodiversity change. The EBV netCDF structure was developed in order to support publication and interoperability of biodiversity data. This standard is based on the Network Common Data Format (netCDF). Additionally, it follows the Climate and Forecast Conventions (CF, version 1.8) and the Attribute Convention for Data Discovery (ACDD, version 1.3). The standard allows several datacubes per netCDF file (see Fig. 1). These cubes have four dimensions: longitude, latitude, time and entity, whereby the last dimension can, for example, encompass different species or groups of species, ecosystem types or other aspects. The usage of hierarchical groups enables the coexistence of multiple EBV cubes (see Fig. 2). The first level (netCDF group) are scenarios, e.g., the modelling for different Shared Socioeconomic Pathways (SSP) scenarios. The second level (netCDF group) are metrics, e.g., the percentage of protected area per pixel and its proportional loss over a certain time span per pixel. All metrics are repeated per scenario, if any are present. The result is a rather complex raster dataset (see example dataset in Fig. 3). This is where the ebvcube R package comes into play. This R package enables scientists to create their own netCDFs in the EBV cube standard. Its functionality covers the creation, opening/reading and visualizing the EBV netCDFs. The ebvcube package is part of the overall EBV infrastructure and works together with the EBV Data Portal. Users can work with the downloaded EBV netCDFs or upload their own EBV netCDFs to the portal. Generally, the package aims to condense the output for the users and assist in the understanding of the file structure to overcome the complexity. The output is reduced to the necessary information, e.g., not displaying coordinate variables or any technical attributes. Moreover, functionality for a quick data exploration is implemented.

This workshop report reflects the discussions and outputs of the first joint LIFE MICA - LIFE RIPARIAS workshop, organized virtually on March 25, 2022. This workshop represented the kick-off of a longer term process towards the development of a data exchange format for reporting on the management of Invasive Alien Species. The aim of the workshop was to discuss the reporting on management interventions against invasive alien species (IAS), building on the experiences of project managers. Participants to the workshop were scientists, practitioners, authorities and other parties involved in IAS management. Break-out groups discussed requirements for reporting on IAs mangement, relevant terms and available controlled vocabularies for them. Presentations at the workshop are available upon request with their respective authors., The workshop was performed within the framework of the LIFE projects RIPARIAS: Reaching Integrated and Prompt Action in Response to Invasive Alien Species (LIFE19 NAT/BE/000953) and MICA: Management of Invasive Coypu and muskrAt in Europe (LIFE18 NAT/NL/001047), co-funded by the the European Commission LIFE programme.

Chronological uncertainty is a hallmark of the paleoenvironmental sciences and geosciences. While many tools have been made available to researchers to quantify age uncertainties suitable for various settings and assumptions, disparate tools and output formats often discourage integrative approaches. In addition, associated tasks like propagating age-model uncertainties to subsequent analyses, and visualizing the results, have received comparatively little attention in the literature and available software. Here, we describe geoChronR, an open-source R package to facilitate these tasks. geoChronR is built around an emerging data standard (Linked PaleoData, or LiPD) and offers access to four popular age-modeling techniques (Bacon, BChron, OxCal, BAM). The output of these models is used to conduct ensemble data analysis, quantifying the impact of chronological uncertainties on common analyses like correlation, regression, principal component, and spectral analyses by repeating the analysis across a large collection of plausible age models. We present five real-world use cases to illustrate how geoChronR may be used to facilitate these tasks, visualize the results in intuitive ways, and store the results for further analysis, promoting transparency and reusability.

PURPOSES : The purpose of this study is to contribute to the utilization of standards while considering the possible upgrade of a local system as a subject of the application. Therefore, this study aims to explore the possible application of LandInfra for a local road management (maintenance) system in the context of enabling the basis of 3D geospatial road information management in Korea. METHODS : Based on a review of related literature and international standards, an analysis of the current system is performed. After reviewing the LandInfra standard, an examination of corresponding classes between each data model (HMS and LandInfra) is performed for the mapping process. After the mapping process, a data model of the LandInfra-based HMS pavement data model is proposed. RESULTS : To apply the LandInfa to the HMS pavement part, an examination of each data model is performed. After this procedure, a LandInfra-based HMS pavement database schema is proposed in the context of enabling 3D geospatial road information management and maintenance, particularly for pavement management information. CONCLUSIONS : This paper presents how the LandInfra international open geospatial standard can be applied to the local road management system (HMS pavement part). As a result of this study, the LandInfra standard could be applied to the HMS; however, an encoding of the standard is required for conformance. Thus, further studies would be the encoding of the proposed data model for conformance with InfaGML encoding standards. In addition, a system prototype may be needed for complete application.

The US Food and Drug Administration (FDA) and the National Center for Advancing Translational Sciences (NCATS) have collaborated to publish rigorous scientific descriptions of substances relevant to regulated products. The FDA has adopted the global ISO 11238 data standard for the identification of substances in medicinal products and has populated a database to organize the agency's regulatory submissions and marketed products data. NCATS has worked with FDA to develop the Global Substance Registration System (GSRS) and produce a non-proprietary version of the database for public benefit. In 2019, more than half of all new drugs in clinical development were proteins, nucleic acid therapeutics, polymer products, structurally diverse natural products or cellular therapies. While multiple databases of small molecule chemical structures are available, this resource is unique in its application of regulatory standards for the identification of medicinal substances and its robust support for other substances in addition to small molecules. This public, manually curated dataset provides unique ingredient identifiers (UNIIs) and detailed descriptions for over 100 000 substances that are particularly relevant to medicine and translational research. The dataset can be accessed and queried at https://gsrs.ncats.nih.gov/app/substances.

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.

A digital twin is a digital asset that simulates the behaviours of a physical counterpart. Digital twin ship literature identifies that the concept is already being applied to specialised problems, but no clear guide exists for creating broader interdisciplinary digital twins. Relevant dimensions of product data modelling and previous attempts at standardizing ship data elucidate the requirements for effective data modelling in a digital twin context. Such requirements are placed in a broader perspective for digital twin implementation that encompasses challenges and directions for future development of services, networks, and software. Finally, an open standardization for digital twin data is proposed based on lessons extracted from this panorama, proposing its application to a research vessel.

Objective In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease. Methods We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study. Results We found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%). Conclusion The mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.

Passive ultra-high frequency (UHF) radio frequency identification (RFID) technology has been widely adopted by retail and other industries for serialized item-level identification and data sharing. This article introduces the standards that support and define all procedures in various passive UHF RFID applications. Electronic Product Code (EPC) Radio-Frequency Identity Protocols Generation-2 UHF RFID Standard, or C1G2, is a foundational standard that defines the format, encoding, and procedures within the air interfaces of RFID systems. Low Level Reader Protocol provides a standard and portable interface between different applications and RFID readers from different vendors. The format and encoding of the EPC information are defined by the EPC Tag Data Standard, which enables each tag to be uniquely identified (e.g., item-level identity of many goods in a warehouse). Additional protocols, such as Discovery, Configuration and Initialization and Reader Management, specify and clarify more processes (e.g., the management of many readers) that can be deployed in various business applications. We provide a general introduction of passive UHF RFID technology standards and review each protocol's features and procedures.

Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.

Natural history specimens constitute physical evidence for past observations of nature. They hold further value as the backbone of taxonomy and as historical samples that can be subjected to further analysis. Yet, as physical objects scattered across collections around the world, their scientific use cases are limited by an overall lack of FAIRness, i.e. not easily Findable, Accessible, Interoperable or Reusable. Digitization of these specimens through imaging and categorical metadata capture can improve this FAIRness and has been done to some extent for decades already, but only recently have technical developments in the field of imaging and information technology made it possible for the fruits of these digitization efforts to be widely distributed and utilized. Digitization can be done in many different ways and while protocols may be well formulated during a project or within the responsible digitization team, they are often not communicated beyond to users, get lost with time and are not available for analysts to assess the state of digitization or make requests concerning material for which further information may be available. Hence, as digitization is ongoing, it is a difficult exercise to estimate how much has been digitized, and to what extent, at the collection level or on a larger scale. The Minimum Information about a Digital Specimen (MIDS) standard that is currently under development by a Working Group of Biodiversity Information Standards (TDWG) aims to address this problem by defining hierarchical levels of digitization, each associated with a set of criteria for a level to be achieved by an individual specimen. MIDS has been in development since work in the ICEDIG project in 2019 and its earlier drafts have been used in surveys to try and determine digitization status, often through coarse estimates based on the experience of curators. As a result, these scores cannot be considered reproducible or particularly reliable. Ideally, MIDS scores can be calculated automatically based on a mapping made between the data model of the source and the MIDS criteria. These mappings should also take into account any data value that is known not to be reflective of digitization status. While in an ideal world there would be only one accepted mapping for any data model, different practices causing interoperability conflicts and different kinds of specimens will likely continue to require slight modifications. To make this concrete, we constructed a few JSON schemas that specify such mappings, based on the current specification of the MIDS standard and the state of biodiversity data in a few sources, including Darwin Core archives for occurrence data as produced by the Global Biodiversity Information Facility (GBIF). These schemas could be incorporated into existing data publication workflows to automatically calculate MIDS levels. We have also developed an R Shiny app with a user interface to make calculations and simple adjustments of the schemas. We welcome anyone interested to further develop the syntax and philosophy behind the schemas and their integration into other systems.

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. published

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.

Darwin Core, the data standard used for sharing modern biodiversity and paleodiversity occurrence records, has previously lacked proper mechanisms for reporting what is known about the estimated age range of specimens from deep time. This has led to data providers putting these data in fields where they cannot easily be found by users, which impedes the reuse and improvement of these data by other researchers. Here we describe the development of the Chronometric Age Extension to Darwin Core, a ratified, community-developed extension that enables the reporting of ages of specimens from deeper time and the evidence supporting these estimates. The extension standardizes reporting about the methods or assays used to determine an age and other critical information like uncertainty. It gives data providers flexibility about the level of detail reported, focusing on the minimum information needed for reuse while still allowing for significant detail if providers have it. Providing a standardized format for reporting these data will make them easier to find and search and enable researchers to pinpoint specimens of interest for data improvement or accumulate more data for broad temporal studies. The Chronometric Age Extension was also the first community-managed vocabulary to undergo the new Biodiversity Informatics Standards (TDWG) review and ratification process, thus providing a blueprint for future Darwin Core extension development.

An international system should be established to support personal protective equipment (PPE) inventory monitoring, particularly within the healthcare industry. In this article, the authors discuss the development and 15-week deployment of a proof-of-concept prototype that included the use of a Healthcare Trust Data Platform to secure and transmit PPE-related data. Seventy-eight hospitals participated, including 66 large hospital systems, 11 medium-sized hospital systems, and a single hospital. Hospitals reported near-daily inventory information for N95 respirators, surgical masks, and face shields, ultimately providing 159 different PPE model numbers. Researchers cross-checked the data to ensure the PPE could be accurately identified. In cases where the model number was inaccurately reported, researchers corrected the numbers whenever possible. Of the PPE model numbers reported, 74.2% were verified-60.5% of N95 respirators, 40.0% of face shields, and 84.0% of surgical masks. The authors discuss the need to standardize how PPE is reported, possible aspects of a PPE data standard, and standards groups who may assist with this effort. Having such PPE data standards would enable better communication across hospital systems and assist in emergency preparedness efforts during pandemics or natural disasters.

CitSci.org is a global citizen science software platform and support organization housed at Colorado State University. The mission of CitSci is to help people do high quality citizen science by amplifying impacts and outcomes. This platform hosts over one thousand projects and a diverse volunteer base that has amassed over one million observations of the natural world, focused on biodiversity and ecosystem sustainability. It is a custom platform built using open source components including: PostgreSQL, Symfony, Vue.js, with React Native for the mobile apps. CitSci sets itself apart from other Citizen Science platforms through the flexibility in the types of projects it supports rather than having a singular focus. This flexibility allows projects to define their own datasheets and methodologies. The diversity of programs we host motivated us to take a founding role in the design of the PPSR Core, a set of global, transdisciplinary data and metadata standards for use in Public Participation in Scientific Research (Citizen Science) projects. Through an international partnership between the Citizen Science Association, European Citizen Science Association, and Australian Citizen Science Association, the PPSR team and associated standards enable interoperability of citizen science projects, datasets, and observations. Here we share our experience over the past 10+ years of supporting biodiversity research both as developers of the CitSci.org platform and as stewards of, and contributors to, the PPSR Core standard. Specifically, we share details about: the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. the origin, development, and informatics infrastructure for CitSci our support for biodiversity projects such as population and community surveys our experiences in platform interoperability through PPSR Core working with the Zooniverse, SciStarter, and CyberTracker data quality data sharing goals and use cases. We conclude by sharing overall successes, limitations, and recommendations as they pertain to trust and rigor in citizen science data sharing and interoperability. As the scientific community moves forward, we show that Citizen Science is a key tool to enabling a systems-based approach to ecosystem problems.

The utopian vision is of a future where a digital representation of each object in our collections is accessible through the internet and sustainably linked to other digital resources. This is a long term goal however, and in the meantime there is an urgent need to share data about our collections at a higher level with a range of stakeholders (Woodburn et al. 2020). To sustainably achieve this, and to aggregate this information across all natural science collections, the data need to be standardised (Johnston and Robinson 2002). To this end, the Biodiversity Information Standards (TDWG) Collection Descriptions (CD) Interest Group has developed a data standard for describing collections, which is approaching formal review for ratification as a new TDWG standard. It proposes 20 classes (Suppl. material 1) and over 100 properties that can be used to describe, categorise, quantify, link and track digital representations of natural science collections, from high-level approximations to detailed breakdowns depending on the purpose of a particular implementation. The wide range of use cases identified for representing collection description data means that a flexible approach to the standard and the underlying modelling concepts is essential. These are centered around the ‘ObjectGroup’ (Fig. 1), a class that may represent any group (of any size) of physical collection objects, which have one or more common characteristics. This generic definition of the ‘collection’ in ‘collection descriptions’ is an important factor in making the standard flexible enough to support the breadth of use cases. For any use case or implementation, only a subset of classes and properties within the standard are likely to be relevant. In some cases, this subset may have little overlap with those selected for other use cases. This additional need for flexibility means that very few classes and properties, representing the core concepts, are proposed to be mandatory. Metrics, facts and narratives are represented in a normalised structure using an extended MeasurementOrFact class, so that these can be user-defined rather than constrained to a set identified by the standard. Finally, rather than a rigid underlying data model as part of the normative standard, documentation will be developed to provide guidance on how the classes in the standard may be related and quantified according to relational, dimensional and graph-like models. So, in summary, the standard has, by design, been made flexible enough to be used in a number of different ways. The corresponding risk is that it could be used in ways that may not deliver what is needed in terms of outputs, manageability and interoperability with other resources of collection-level or object-level data. To mitigate this, it is key for any new implementer of the standard to establish how it should be used in that particular instance, and define any necessary constraints within the wider scope of the standard and model. This is the concept of the ‘collection description scheme,’ a profile that defines elements such as: which classes and properties should be included, which should be mandatory, and which should be repeatable; which controlled vocabularies and hierarchies should be used to make the data interoperable; how the collections should be broken down into individual ObjectGroups and interlinked, and how the various classes should be related to each other. which classes and properties should be included, which should be mandatory, and which should be repeatable; which controlled vocabularies and hierarchies should be used to make the data interoperable; how the collections should be broken down into individual ObjectGroups and interlinked, and how the various classes should be related to each other. Various factors might influence these decisions, including the types of information that are relevant to the use case, whether quantitative metrics need to be captured and aggregated across collection descriptions, and how many resources can be dedicated to amassing and maintaining the data. This process has particular relevance to the Distributed System of Scientific Collections (DiSSCo) consortium, the design of which incorporates use cases for storing, interlinking and reporting on the collections of its member institutions. These include helping users of the European Loans and Visits System (ELViS) (Islam 2020) to discover specimens for physical and digital loans by providing descriptions and breakdowns of the collections of holding institutions, and monitoring digitisation progress across European collections through a dynamic Collections Digitisation Dashboard. In addition, DiSSCo will be part of a global collections data ecosystem requiring interoperation with other infrastructures such as the GBIF (Global Biodiversity Information Facility) Registry of Scientific Collections, the CETAF (Consortium of European Taxonomic Facilities) Registry of Collections and Index Herbariorum. In this presentation, we will introduce the draft standard and discuss the process of defining new collection description schemes using the standard and data model, and focus on DiSSCo requirements as examples of real-world collection descriptions use cases.

Food microbiology researchers, risk assessment agencies and food business operators rely heavily on the reuse of knowledge that is available as data, models and tools. Unfortunately, such knowledge reuse remains challenging, as food safety data sets, models and tools are usually only available in platform-dependent or software-dependent formats that rarely comply to the Findability, Accessibility, Interoperability, and Reusability data principles. In recent years, the Risk Assessment Modelling and Knowledge Integration Platform (RAKIP) Initiative developed the so-called Food Safety Knowledge Exchange (FSKX) format. This development was accompanied by the creation of open-source software that facilitates the adoption of FSKX. Future work within RAKIP will focus on creating semantic interoperability in FSKX-related solutions and on the extension of the FSKX format towards other food microbiology knowledge.

This paper collects a large number of cases and makes a comparative analysis of the typical application of Chinese and American open government data for public governance. Through comparison, this paper finds the gap between China's open government data and the United States, and then analyzes the reasons. On this basis, through the investigation of advanced experience, this paper puts forward the suggestions of open government data to innovate public governance, including data catalogue compilation, data standard formulating, data quality assessment and open government data sharing cooperation, in order to improve Chinese open government data to innovate the public governance level.

NIH requestedpublic comment on the use of CDEs, particularly in the context of COVID-19 research, including opportunities for advancing research with CDEs, challenges to adopting CDEs, and guidance or tools that could facilitate use of CDEs. Here, members of theNCI funded Center for Cancer Data Harmonization, the Pediatric Data Commons, the NCATS funded Center for Data to Health, describe our extensive use ofCDEs for prospective studies and data harmonization efforts. We participate in many SDOs such as HL7, ISO, CDISC, GA4GH, WHO, etc. and try to promote computational encoding and use of CDEs in these contexts.

Advances in multi-omics technologies have led to an explosion of multimodal datasets to address questions ranging from basic biology to translation. While these rich data provide major opportunities for discovery, they also come with data management and analysis challenges, thus motivating the development of tailored computational solutions to deal with multi-omics data.Here, we present a data standard and an analysis framework for multi-omics — MUON — designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible data structure, supporting an arbitrary number of omics layers. The MUON data structure is interoperable with existing community standards for single omics, and it provides easy access to both data from individual omics as well as multimodal dataviews. Building on this data infrastructure, MUON enables a versatile range of analyses, from data preprocessing, the construction of multi-omics containers to flexible multi-omics alignment.

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.