The goal of the OneNet System is to facilitate data exchanges among existing platforms, services, applications, and devices by the power of interoperability techniques. To ensure that system requirements are technically -implementable and widely adopted, internationally standardized file formats, metadata, vocabularies and identifiers - are required. The OneNet “Cross-Platform Access” pattern is the fundamental characteristic of an interoperable ecosystem, leading to the definition of the exposed list OneNet Cross-Platform services (CPS). The pattern entails that an application accesses services or resources (information or functions) from multiple platforms through the same interface. For example, a “grid monitoring” application gathers information on different grid indicators provided by different platforms that conduct measurements or state estimations. The challenge of realizing this pattern lies in allowing applications or services within one platform to interact with other platforms (eventually from different providers) with relevant services or applications via the same interface and data formats. Thereby, reuse and composition of services as well as easy integration of data from different platforms are enabled. Based on the defined concept for CPS, an extensive analysis has been performed regarding data exchange patterns and roles involved for system use cases (SUCs) from other H2020 projects and the OneNet demo clusters. This has resulted into a first list of CPS, that has been thereafter taxonomized into 10 categories. The different entries have been defined providing a set of classes such as service description, indicative data producer/consumer etc. Each CPS can be assigned with multiple business objects describing the context of it. For a specific set of widely used by the Demo CPS, there have been formal semantic definitions provided in the "CrossPlatformServices-Semantic" excel worksheet.

Rapid technological advances coupled with modern analysis methods has increased the quantity of geographical data and has therefore improved monitoring of local, regional and global environmental phenomena at much finer detail. Despite these advances, insufficient data interoperability remains to be a barrier to reusability, discovery and access to geographical information. To overcome this heterogeneity it is crucial to generate syntactic interoperability, which determines fixed standards in data exchange, but also semantic interoperability, a possibility to generate comparability by using shared descriptions with unambiguous meaning stored in semantic systems. In the field of nature conservation, semantic heterogeneity is a big challenge since national and regional data acquisition methodologies vary broadly. Moreover, trans-national data (which are required for multi-national legal processes like the EU Habitats Directive (HabDir), the Water Framework Directive or INSPIRE) are generated bottom-up, using mostly national or regional acquisition guidelines. This dissertation addresses four aspects of interoperability in nature conservation. The first part provides a methodology for semantic mediation of remote-sensing based data products, which were generated in different countries in Europe by taking into account differing sensor types and base classification schemes. The results indicate that automated, semantic-based data transformation is feasible, but is highly dependent on the conceptualisation of the respective nomenclatures. Therefore transparent, hierarchical nomenclatures are far more important for transferability than the sensor or study area. The second part applies the developed method in an up-scaling application to generate a comparable automated delineation of selected habitats in different countries by generating transferable aggregation rules. For the different habitats in the two sites an accuracy of above 70% was achieved in regard to a manual, expert-based delineation. This meets approximately the percentages of the comparison of two manual delineations since the process of manual delineation is always subjective and highly dependent on the personal qualification and perception of the surveyor and therefore inherits a high degree of uncertainty. The third part addresses the challenge of generating reproducible and formalized information in remote-sensing analysis in a semantic, ontology-based classification approach. This approach combines advanced machine learning algorithms and ontological data management and classification. It produces results with similar quality to established machine learning algorithms like the Extra Tree Classifier (ET) but preserves transferable classification rules and ontological formalism. The fourth part evaluates the automated aggregation approach of part two in respect to manual, expert-based delineation and gives recommendations for the international guidelines in terms of scale effects, minimum mapping units and the potential of the usage of remote sensing-based data sets in automated up-scaling procedures for European legal purposes. Semantic systems inherit great potential for nature conservation in terms of data storage, information retrieval and derivation and comparability of data. This thesis shows this potential by proving feasibility of semantic transformation between different nature conservation data sets, the application of this transformation procedure in up-scaling processes, and the ability to use semantic-based technologies in classification procedures. It therefore indicates that using semantic systems for data interoperability in nature conservation is possible but underlies, up to now, certain limitations. From a technical point of view the main restrictions are the absence of theme-specific controlled vocabularies and semantic infrastructures which are increasingly developed and provided by regional and international authorities. With regard to the content of the nature conservation data, limitations occur because of the high degree of uncertainty in data acquisition, semantic impreciseness of data descriptions and natural gradients in the composition of habitats.

This reference data provides a standard list of values for all Countries, Territories and Geographic areas. This list is intended to standardize the way Countries, Territories and Geographic areas are described in datasets to enable data interoperability and improve data quality. The data dictionary explains what each column means in the list.

This reference data provides a standard list of values for all Countries, Territories and Geographic areas. This list is intended to standardize the way Countries, Territories and Geographic areas are described in datasets to enable data interoperability and improve data quality. The data dictionary explains what each column means in the list.

The Structure Definition resource describes a structure - a set of data element definitions, and their associated rules of usage. These structure definitions are used to describe both the content defined in the Fast Healthcare Interoperability Resources (FHIR) specification itself - resources, data types, the underlying infrastructural types, and also are used to describe how these structures are used in implementations.

SeaLiT Knowledge Graphs is an RDF dataset of maritime history data that has been transcribed (and then transformed) from original archival sources in the context of the SeaLiT Project (Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s). The underlying data model is the SeaLiT Ontology, an extension of the ISO standard CIDOC-CRM (ISO 21127:2014) for the modelling and integration of maritime history information.

The knowledge graphs integrate data of totally 16 different types of archival sources:

• Crew Lists
  • Crew and displacement list (Roll)
  • Crew List (Ruoli di Equipaggio)
  • General Spanish Crew List
• Registers / Lists
  • Students Register
  • Civil Register
  • Register of Maritime Personnel
  • Register of Maritime Workers (Matricole della gente di mare)
  • Sailors Register (Libro de registro de marineros)
  • Naval Ship Register List
  • Seagoing Personnel
  • Lists of ships
• Censuses
  • Census La Ciotat
  • First National all-Russian Census of the Russian Empire
• Payrolls
  • Payrolls of private archives and libraries in Greece
  • Payrolls of Russian Steam Navigation and Trading Company
• Employment records
  • Shipyards of Messageries Maritimes, La Ciotat

More information about the archival sources are available through the SeaLiT website. Data exploration applications over these sources are also publicly available (SeaLiT Catalogues, SeaLiT ResearchSpace).

Data from these archival sources has been transcribed in tabular form and then curated by historians of SeaLiT using the FAST CAT system. The transcripts (records), together with the curated vocabulary terms and entity instances (ships, persons, locations, organizations), are then transformed to RDF using the SeaLiT Ontology as the target (domain) model. To this end, the corresponding schema mappings between the original schemata and the ontology were defined using the X3ML mapping definition language, that were subsequently used for delivering the RDF datasets.

More information about the FAST CAT system and the data transcription, curation and transformation processes can be found in the following paper:

P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. https://doi.org/10.1145/3461460 [pdf, bib]

The RDF dataset is provided as a set of TriG files per record per archival source. For each record, the dataset provides: i) one trig file for the record's data (records.trig), ii) one trig file for the record's (curated) vocabulary terms (vocabularies.trig), and iii) four trig files for the record's (curated) entity instances (ships.trig, persons.trig, persons.trig, organizations.trig).

We also provide the RDFS files of the used ontologies (SeaLiT Ontology verson 1.0, CIDOC-CRM version 7.1.1).

Kaggle Card: FHIR Profiles-Resources JSON File Overview Fast Healthcare Interoperability Resources (FHIR, pronounced "fire") is a standard developed by Health Level Seven International (HL7) for transferring electronic health records. The FHIR Profiles-Resources JSON file is an essential part of this standard. It provides a schema that defines the structure of FHIR resource types, including their properties and attributes.

Dataset Structure This file is structured in the JSON format, known for its versatility and human-readable nature. Each JSON object corresponds to a unique FHIR resource type, outlining its structure and providing a blueprint for the properties and attributes each resource type should contain.

Fields Description While the precise properties and attributes differ for each FHIR resource type, the typical elements you may encounter in this file include:

Id: The unique identifier for the resource type. Url: A global identifier URI for the resource type. Version: The business version of the resource. Name: The human-readable name for the resource type. Status: The publication status of the resource (draft, active, retired). Experimental: A boolean value indicating whether this resource type is experimental. Date: The date of the resource type's last change. Publisher: The individual or organization that published the resource type. Contact: Contact details for the publishers. Description: A natural language description of the resource type. UseContext: A list outlining the usability context for the resource type. Jurisdiction: Identifies the region/country where the resource type is defined. Purpose: An explanation of why the resource type is necessary. Element: A list defining the structure of the properties for the resource type, including data types and relationships with other resource types. Potential Use Cases Schema Validation: Use the schema to validate FHIR data and ensure it aligns with the defined structure and types for each resource. Interoperability: Facilitate the exchange of healthcare information with other FHIR-compatible systems by providing a standardized structure. Data Mapping: Utilize the schema to map data from other formats into the FHIR format, or vice versa. System Design: Aid the design and development of healthcare systems by offering a template for data structure.

The region is the top tier of local government in New Zealand. There are 16 regions of New Zealand (Part 1 of Schedule 2 of the Local Government Act 2002). Eleven are governed by an elected regional council, while five are governed by territorial authorities (the second tier of local government) who also perform the functions of a regional council and thus are known as unitary authorities. These unitary authorities are Auckland Council, Nelson City Council, Gisborne, Tasman, and Marlborough District Councils. The Chatham Islands Council also perform some of the functions of a regional council, but is not strictly a unitary authority. Unitary authorities act as regional councils for the purposes of a wide range of Acts and regulations. Regional council areas are based on water catchment areas. Regional councils are responsible for the administration of many environmental and public transport matters.Regional Councils were established in 1989 after the abolition of the 22 local government regions. The local government act 2002, requires the boundaries of regions to confirm as far as possible to one or more water catchments. When determining regional boundaries, the local Government commission gave consideration to regional communities of interest when selecting water catchments to included in a region. It also considered factors such as natural resource management, land use planning and environmental matters. Some regional boundaries are conterminous with territorial authority boundaries but there are many exceptions. An example is Taupo District, which is split between four regions, although most of its area falls within the Waikato Region. Where territorial local authorities straddle regional council boundaries, the affected area have been statistically defined in complete area units. Generally regional councils contain complete territorial authorities. The unitary authority of the Auckland Council was formed in 2010, under the Local Government (Tamaki Makarau Reorganisation) Act 2009, replacing the Auckland Regional Council and seven territorial authorities.The seaward boundary of any costal regional council is the twelve mile New Zealand territorial limit. Regional councils are defined at meshblock and area unit level.Regional Councils included in the 2013 digital pattern are:Regional Council CodeRegional Council Name01Northland Region02Auckland Region03Waikato Region04Bay of Plenty Region05Gisborne Region06Hawke's Bay Region07Taranaki Region08Manawatu-Wanganui Region09Wellington Region12West Coast Region13Canterbury Region14Otago Region15Southland Region16Tasman Region17Nelson Region18Marlborough Region99Area Outside RegionAs at 1stJuly 2007, Digital Boundary data became freely available.Deriving of Output FilesThe original vertices delineating the meshblock boundary pattern were digitised in 1991 from 1:5,000 scale urban maps and 1:50,000 scale rural maps. The magnitude of error of the original digital points would have been in the range of +/- 10 metres in urban areas and +/- 25 metres in rural areas. Where meshblock boundaries coincide with cadastral boundaries the magnitude of error will be within the range of 1–5 metres in urban areas and 5 - 20 metres in rural areas. This being the estimated magnitude of error of Landonline.The creation of high definition and generalised meshblock boundaries for the 2013 digital pattern and the dissolving of these meshblocks into other geographies/boundaries were completed within Statistics New Zealand using ESRI's ArcGIS desktop suite and the Data Interoperability extension with the following process: 1. Import data and all attribute fields into an ESRI File Geodatabase from LINZ as a shapefile2. Run geometry checks and repairs.3. Run Topology Checks on all data (Must Not Have Gaps, Must Not Overlap), detailed below.4. Generalise the meshblock layers to a 1m tolerance to create generalised dataset. 5. Clip the high definition and generalised meshblock layers to the coastline using land water codes.6. Dissolve all four meshblock datasets (clipped and unclipped, for both generalised and high definition versions) to higher geographies to create the following output data layers: Area Unit, Territorial Authorities, Regional Council, Urban Areas, Community Boards, Territorial Authority Subdivisions, Wards Constituencies and Maori Constituencies for the four datasets. 7. Complete a frequency analysis to determine that each code only has a single record.8. Re-run topology checks for overlaps and gaps.9. Export all created datasets into MapInfo and Shapefile format using the Data Interoperability extension to create 3 output formats for each file. 10. Quality Assurance and rechecking of delivery files.The High Definition version is similar to how the layer exists in Landonline with a couple of changes to fix topology errors identified in topology checking. The following quality checks and steps were applied to the meshblock pattern:Translation of ESRI Shapefiles to ESRI geodatabase datasetThe meshblock dataset was imported into the ESRI File Geodatabase format, required to run the ESRI topology checks. Topology rules were set for each of the layers. Topology ChecksA tolerance of 0.1 cm was applied to the data, which meant that the topology engine validating the data saw any vertex closer than this distance as the same location. A default topology rule of “Must Be Larger than Cluster Tolerance” is applied to all data – this would highlight where any features with a width less than 0.1cm exist. No errors were found for this rule.Three additional topology rules were applied specifically within each of the layers in the ESRI geodatabase – namely “Must Not Overlap”, “Must Not Have Gaps” and “"Area Boundary Must Be Covered By Boundary Of (Meshblock)”. These check that a layer forms a continuous coverage over a surface, that any given point on that surface is only assigned to a single category, and that the dissolved boundaries are identical to the parent meshblock boundaries.Topology Checks Results: There were no errors in either the gap or overlap checks.GeneralisingTo create the generalised Meshblock layer the “Simplify Polygon” geoprocessing tool was used in ArcGIS, with the following parameters:Simplification Algorithm: POINT_REMOVEMaximum Allowable Offset: 1 metreMinimum Area: 1 square metreHandling Topological Errors: RESOLVE_ERRORSClipping of Layers to CoastlineThe processed feature class was then clipped to the coastline. The coastline was defined as features within the supplied Land2013 with codes and descriptions as follows:11- Island – Included12- Mainland – Included21- Inland Water – Included22- Inlet – Excluded23- Oceanic –Excluded33- Other – Included.Features were clipped using the Data Interoperability extension, attribute filter tool. The attribute filter was used on both the generalised and high definition meshblock datasets creating four meshblock layers. Each meshblock dataset also contained all higher geographies and land-water data as attributes. Note: Meshblock 0017001 which is classified as island, was excluded from the clipped meshblock layers, as most of this meshblock is oceanic. Dissolve meshblocks to higher geographiesStatistics New Zealand then dissolved the ESRI meshblock feature classes to the higher geographies, for both the full and clipped dataset, generalised and high definition datasets. To dissolve the higher geographies, a model was built using the dissolver, aggregator and sorter tools, with each output set to include geography code and names within the Data Interoperability extension. Export to MapInfo Format and ShapfilesThe data was exported to MapInfo and Shapefile format using ESRI's Data Interoperability extension Translation tool. Quality Assurance and rechecking of delivery filesThe feature counts of all files were checked to ensure all layers had the correct number of features. This included checking that all multipart features had translated correctly in the new file.

BackgroundAccording to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), scientific research data should be findable, accessible, interoperable, and reusable. The COVID-19 pandemic has led to massive research activities and an unprecedented number of topical publications in a short time. However, no evaluation has assessed whether this COVID-19-related research data has complied with FAIR principles (or FAIRness).ObjectiveOur objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness.MethodsWe conducted a comprehensive search and retrieved all open-access articles related to COVID-19 from journals indexed in PubMed, available in the Europe PubMed Central database, published from January 2020 through June 2023, using the metareadr package. Using rtransparent, a validated automated tool, we identified articles with links to their raw data hosted in a public repository. We then screened the link and included those repositories that included data specifically for their pertaining paper. Subsequently, we automatically assessed the adherence of the repositories to the FAIR principles using FAIRsFAIR Research Data Object Assessment Service (F-UJI) and rfuji package. The FAIR scores ranged from 1–22 and had four components. We reported descriptive analysis for each article type, journal category, and repository. We used linear regression models to find the most influential factors on the FAIRness of data.Results5,700 URLs were included in the final analysis, sharing their data in a general-purpose repository. The mean (standard deviation, SD) level of compliance with FAIR metrics was 9.4 (4.88). The percentages of moderate or advanced compliance were as follows: Findability: 100.0%, Accessibility: 21.5%, Interoperability: 46.7%, and Reusability: 61.3%. The overall and component-wise monthly trends were consistent over the follow-up. Reviews (9.80, SD = 5.06, n = 160), articles in dental journals (13.67, SD = 3.51, n = 3) and Harvard Dataverse (15.79, SD = 3.65, n = 244) had the highest mean FAIRness scores, whereas letters (7.83, SD = 4.30, n = 55), articles in neuroscience journals (8.16, SD = 3.73, n = 63), and those deposited in GitHub (4.50, SD = 0.13, n = 2,152) showed the lowest scores. Regression models showed that the repository was the most influential factor on FAIRness scores (R2 = 0.809).ConclusionThis paper underscored the potential for improvement across all facets of FAIR principles, specifically emphasizing Interoperability and Reusability in the data shared within general repositories during the COVID-19 pandemic.

Software-Defined Weapon System Market Outlook

The global Software-Defined Weapon System market size was valued at USD 8.7 billion in 2024, according to our latest research. The market is experiencing robust growth, registering a CAGR of 13.6% from 2025 to 2033. By the end of the forecast period, the market is expected to reach USD 26.7 billion. The primary growth driver for this market is the increasing integration of advanced digital technologies, such as artificial intelligence and IoT, into modern defense systems to enhance operational flexibility, real-time responsiveness, and interoperability.

The surge in demand for software-defined weapon systems is fundamentally rooted in the need for adaptable, upgradeable, and interoperable defense solutions. Unlike traditional weapon systems that rely heavily on hardware for functionality, software-defined solutions enable rapid upgrades, customization, and integration with various platforms. This flexibility is crucial as militaries worldwide face increasingly dynamic and complex threat environments. The growing adoption of digital transformation initiatives across defense forces is further accelerating the shift toward software-centric architectures, allowing for improved situational awareness, faster decision-making, and enhanced mission effectiveness.

Another key growth factor is the proliferation of emerging technologies such as artificial intelligence, machine learning, and big data analytics within the software-defined weapon system market. These technologies empower defense organizations to process large volumes of data in real time, automate complex processes, and facilitate predictive threat analysis. As adversaries employ more sophisticated tactics, the need for intelligent, adaptive, and networked weapon systems becomes paramount. Governments are investing heavily in research and development to ensure their military capabilities remain at the cutting edge, resulting in increased procurement and deployment of software-defined weapon systems across multiple domains.

The expanding role of joint and coalition operations is also fueling market growth. Modern military engagements often require seamless interoperability between allied forces, necessitating weapon systems that can be easily integrated and reconfigured to work with diverse platforms and communication protocols. Software-defined architectures offer the necessary agility and scalability to support these requirements, enabling real-time information sharing and coordinated response across air, land, naval, and space domains. This trend is particularly pronounced in NATO countries and other defense alliances, where standardization and interoperability are critical to mission success.

From a regional perspective, North America continues to dominate the software-defined weapon system market, driven by substantial defense budgets, ongoing modernization programs, and the presence of leading technology providers. However, the Asia Pacific region is witnessing the fastest growth, fueled by rising defense expenditures, geopolitical tensions, and rapid technological advancements in countries such as China, India, and Japan. Europe remains a significant market, supported by collaborative defense initiatives and a focus on upgrading legacy systems. Meanwhile, the Middle East & Africa and Latin America are gradually increasing their investments in advanced weapon systems to address evolving security challenges.

Component Analysis

The Component segment of the software-defined weapon system market is categorized into hardware, software, and services, each playing a pivotal role in shaping the industry landscape. Hardware components include sensors, processors, communication modules, and other physical elements that form the foundation of these advanced systems. The ongoing trend toward miniaturization and increased processing power is enabling the development of more compact and efficient hardware solutions, which are critical for deployment across diverse platf

FAIRness score for each journal subject area (mean and SD).

FAIRness score for each article type (mean and SD).