Knowledge infrastructure for integrated data management and analysis supporting new approach methods in predictive toxicology and risk assessment

Thesis/Objective – The profession of data steward is not fully defined. In particular, in Polish scientific institutions, where even the name of this position is still hard to find, this is a new phenomenon that requires a deeper analysis. It is difficult to specify not only the definition of this profession, or rather the role, but also the scope of tasks and required competencies. All this depends on the role of the data steward in the unit, at the university or in the research team. The aim of the article is to present the origins of the creation of a new specialisation in Poland, which is the role of a data steward. In addition, the forms of education for data stewards in Poland are discussed, as well as initiatives and projects aimed at defining the professional profile, qualifications and career paths of data stewards. Research methods: The critical literature review was used to analyse the content of foreign and Polish LIS literature published in the years 2018-2022. Also, official documents and websites, project descriptions, working group’s, and Task Force’s working papers were analysed. In addition, a survey was carried out and analysed among the members of the Polish data stewards working group (DSCCIN PL) on organisational structures and positions dedicated to research data management existing at Polish universities. Results and conclusions – The first data stewards appeared in libraries. This resulted directly from the policy of the National Science Centre (NCN) but above all also from the fact that the first unit providing support for researchers in the field of data management – the Open Science Competence Center – was established in the Library of the Gdańsk University of Technology (Gdańsk Tech). The Gdańsk Tech Library has established numerous contacts with international networks and organizations, which resulted in active cooperation and placing it in international structures. In particular as a national chapter of the GO-FAIR network (DSCC-IN PL: Data Stewardship Competence Centers Implementation Network – Poland). This initiated the creation of the Polish working group of data stewards, which is now the foundation of the nascent profession and provides a field for the exchange of knowledge and experience. The lack of educational programs at university or in any other form of vocational education has been filled in a sense through the training activities of the Competence Center and the only Data Steward School in Poland. It was only in 2022 that the increased activity of the National Science Center and the Ministry of Education and Science began. In conjunction with the work on the national policy for open data as well as activities within the European Open Science Cloud, a coherent environment began to emerge for the development of a new specialization at the level of Polish universities and scientific institutions.

The development of data management services capable to cope with very large data resources is a key challenge to allow the future einfrastructures to address the needs of the next generation extreme scale scientific experiments. To face this challenge, in November 2017 the H2020 eXtreme DataCloud - XDC project has been launched. Lasting for 27 months and combining the expertise of eight large European research organisations, the project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The targeted platforms are the current and next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC), the European Grid Infrastructure (EGI), and the Worldwide LHC Computing Grid (WLCG). The project is use-case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: High Energy Physics, Astronomy, Photon and Life Science, Medical research. XDC is aimed at implementing data management scalable services, combining already established data management and orchestration tools, to address the following high level topics: policy driven data management based on Quality-of-Service, Data Life-cycle management, smart placement of data with caching mechanisms to reduce access latency, meta-data with no predefined schema handling, execution of pre-processing applications during ingestion, data management and protection of sensitive data in distributed e-infrastructures, intelligent data placement based on access patterns. This contribution introduces the project, presents the foreseen overall architecture and the developments that are being carried on to implement the requested functionalities.

Explore methods to accurately reflect the risk level of ferrous metal foundry workplaces when the silica dust concentration exceeds the limit, and provide a basis for the application of risk assessment techniques in key industries with occupational exposure to silica dust. The survey was conducted in 25 ferrous metal casting industries in Henan Province, China. Five occupational health risk assessment (OHRA) methods, including Risk index method, Hazard grading method, International Council on Mining and Metals model, The synthesis index method, and The exposure ratio method, were used to assess the occupational health risk of the workplaces that produced silica dust and the concentration of silica dust exceeded the occupational exposure limits (OELs), and to compare the results of the different methods. The risk index assessment method yielded one job of mild hazard, seven job of moderate hazard, fifteen jobs of high hazard, and forty-four jobs of extreme hazard. The hazard classification method resulted in two jobs of mild hazard, six jobs of moderate hazard, and fifty-nine jobs of high hazard. The ICMM qualitative method identified fifteen jobs of moderate risk and fifty-two jobs of high risk. The synthesis index method revealed nine jobs of moderate risk and fifty-eight jobs of high risk. The exposure ratio method identified ten jobs of high risk and fifty-seven jobs of extremely high risk. The results obtained from the synthesis index method showed relatively lower levels, except for the index method, there was a certain correlation (r: 0.541-0.798, P < 0.05) and consistency (kapa: 0.521-0.561, P < 0.05) with the remaining four methods. This study shows that although there are some differences in the results of different OHRA methods, there is also some correlation between them, which can corroborate each other and enhance the reliability of the assessment results. In practical application, appropriate assessment methods should be selected according to specific situations and the results of multiple methods should be combined and analyzed comprehensively to ensure accurate identification and assessment of occupational hazards and provide a scientific basis for improving occupational safety and health management.

Open Science is directly linked to the present trend of Research Data Management. To have fully open research data, systems that allow free access to the data need to be present. As a result, research institutions have tried to adapt to the changes required to be deemed as Open Science supporters. Several institutions implement a Current Research Information System (CRIS) and an Open Science Policy for all their published research data.Interoperable data between different systems implemented is a key feature of Open Science. In Europe, the European Open Science Cloud provides a set of standards and guidelines that must be followed so the implemented systems offer an interoperable service to their researchers.Previous research revealed that Albania is ready to take its first steps toward Open Science, and future ideas for how to contribute to the field were proposed. There were two significant findings: first, there is no CRIS implementation in Albanian institutions, and second, there is no strategy or policy to support Open Science. The research also proposed an Albanian Open Science Digital Repository.In this paper, we show how the implementation of the system is done and how it relates to the newly proposed Open Science Policy. The goal is to demonstrate how Albanian institutions are implementing advances towards Open Science.In conclusion, this paper demonstrates Albanian researchers' support for Open Science and the European Open Science Cloud.

SeaDataNet is a major pan-European infrastructure for managing and providing access to marine data sets, acquired by European organisations from research cruises and other observational activities in European coastal marine waters, regional seas and the global ocean. Founding partners are National Oceanographic Data Centres (NODCs), and major marine research institutes. The SeaDataNet network gradually expanded its network of data centres and infrastructure, during a series of dedicated EU RTD projects, and by engaging as core data management infrastructure and network in leading European Commission initiatives such as the European Marine Observation and Data network (EMODnet), Copernicus Marine Service (CMS), and the European Open Science Cloud (EOSC).SeaDataNet develops, governs and promotes common standards, vocabularies, software tools, and services for marine data management, which are widely adopted. A core service is the CDI data discovery and access service which provides online unified discovery and access to vast resources of data sets, managed by 115+ connected SeaDataNet data centres from 34 countries around European seas, both from research and monitoring organisations. Currently, it gives access to more than 3 Million data sets, originating from 1000+ organisations in Europe, covering physical, geological, chemical, biological and geophysical data, acquired in European waters and global oceans. Standard metadata and data formats are used, supported by an ever-increasing set of controlled vocabularies, resulting in rich and highly FAIR metadata and data sets. SeaDataNet provides core services in EMODnet Chemistry, Bathymetry, and Physics for bringing together and harmonizing large amounts of marine data sets, which are used by EMODnet groups for generating thematic data products.EMODnet Bathymetry is active since 2008 and maintains a Digital Terrain Model (DTM) for the European seas. This is published every 2 years, each time extending coverage, and improving quality and precision. The DTMs are produced from surveys and aggregated data sets that are referenced with metadata via the SeaDataNet Catalogue services. Bathymetric survey data sets are gathered and populated by national hydrographic services, marine research institutes, and companies in the SeaDataNet CDI Data Discovery &amp; Access service. Currently, this amounts to more than 45.000 datasets from 78 data providers. A major selection of these datasets has been used for preparing the 2024 release of the EMODnet DTM for all European waters and Caribbean, which has been published on the EMODnet portal. Currently, work is ongoing for a new 2026 version. The EMODnet DTM has a grid resolution of 1/16 * 1/16 arc minutes (circa 115 * 115 m), covering all European seas. It is based upon circa 22.000+ in situ datasets. It can be downloaded in tiles and viewed as map layers in the EMODnet portal. The maps are derived from EMODnet Bathymetry OGC WMS, WMTS, and WFS services. The EMODnet Bathymetry products are very popular and in 2024 – 2025 more than 100.000 EMODnet DTM files were downloaded, and more than 60 million OGC service requests were registered over the 2 years. EMODnet Bathymetry is also managing the European contribution to the international Seabed 2030 project.

SeaDataNet is a pan-European infrastructure that manages and provides access to marine datasets collected by European organisations through research cruises and observational activities in coastal waters, regional seas, and the global ocean. It was founded by National Oceanographic Data Centres (NODCs) and major marine research institutes. The network has expanded through successive EU-funded RTD projects and by contributing to major European initiatives such as EMODnet, Copernicus Marine Service, ENVRI, and the European Open Science Cloud (EOSC).SeaDataNet develops and promotes widely adopted standards, vocabularies, software tools, and services that support FAIR marine data management. Its core service, the CDI (Common Data Index), provides unified online discovery and access to in situ marine observation data managed by more than 115 data centres in 34 countries. The service currently offers access to over 3 million datasets from more than 1,000 European organisations, covering physical, chemical, biological, geological, and geophysical data from European waters and the global ocean. The use of standard metadata, formats, and controlled vocabularies ensures rich, highly FAIR datasets.SeaDataNet also delivers core data services for EMODnet Chemistry, Bathymetry, and Physics, harmonising large volumes of marine data that support the production of thematic data products, including Essential Climate Variable (ECV) and Essential Ocean Variable (EOV) datasets.Environmental science increasingly relies on large, heterogeneous, and rapidly growing data collections that must be efficiently accessed, subsetted, and harmonised for use in models, digital twins, AI workflows, and Virtual Research Environments (VREs). The fully open-source Beacon software, developed by MARIS https://beacon.maris.nl/, addresses these challenges by enabling cloud-native, high-performance data lakes that are fast to deploy and access. Beacon supports parameter harmonisation using metadata annotations based on NERC vocabularies, ECV vocabularies, and the I-ADOPT methodology adopted in ENVRI-HUB Next.To improve the ease of access to subsets of the SeaDataNet CDI data collection, a Beacon instance containing all the open SeaDataNet data was set-up. This now allows users to obtain real-time access to data subsets in multiple data formats (NetCDF, Parquet, Zarr) and flexible querying from Jupyter Notebooks or a newly developed Beacon studio (user interface) for non-technical users. Within ENVRI-HUB Next, this SeaDataNet instance enables on-the-fly access to ECV-related subsets from millions of files via Jupyter Notebooks, ready for use in the Analytical Framework.The presentation focuses on this use case, the technical solution, and its potential applicability for other Research Infrastructures supporting EOSC use cases.

The eXtreme-DataCloud (XDC) is an EU H2020 funded project aimed at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. XDC software stack is based on existing tools, whose technical maturity is proved, that the project enriches with new functionalities and plugins to address real life requirements from user communities belonging to a variety of scientific domains: Life Science, Astrophysics, High Energy Physics, Photon Science and Clinical Research. The XDC toolbox includes services like ONEDATA (AGH), dChace (DESY), EOS (CERN), the INDIGO-PaaS Orchestrator (INFN), DYNAFED (CERN). The targeted platforms are the current and next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC), the European Grid Infrastructure (EGI), the Worldwide LHC Computing Grid (WLCG) and the computing infrastructures funded by other public and academic initiatives. The focus of the project is on policy driven data management, on orchestration based on Quality of Services and on pre-processing of ingested data with user-defined applications. Smart caching and storage federation technologies for the creation of the so-called “DataLakes” are also important topics for XDC. In this manuscript, we present the recent advancements in the project services architecture and the developments carried on in preparation of the first XDC release planned for the end of 2018.

The concept of risk assessment became important with economic and technological developments in the 1920s. After World War II, damage became more important with the fact that potential accidents and all risks resulting in damage and injury could be controlled and managed. With this importance, organizations in developed countries have focused on risk assessment activities in their respective fields. Risk assessment is one of the most important and critical steps of risk management. While the concept of risk assessment is new in our lives, its content and methods are not new. Risk assessment and analysis methods have been applied around the world since the 1930s. The lack of practice in the companies that produce medical devices related to risk assessment reveals the importance of working on this issue. This study was conducted using Risk Assessment Method in a medium-sized factory operating in the medical device sector in Istanbul. This assessment was carried out in conjunction with the company’s quality team. The results of the evaluation were examined and the risk outcomes were determined by taking into consideration the events, the returns of the patients and the possible risks.

Introduction. In the pharmaceutical field, the quality management is a priority and the risk management is a valuable component of an effective quality system. It involves anticipating hazards and controlling risk through a process of risk awareness, reduction, and review. The aim of this paper was to reveal the usefulness of modern risk management strategies and to assess the impact of risk management in the provision of quality services by community pharmacies. Material and methods. The causes that can lead to medication errors in community pharmacies were identified by applying the CNAM Method combined with the Delphi Technique, later the risks were categorized by the Ishikawa diagram, analyzed and evaluated by applying the expert analysis method (MAE) in risk assessment, graphically represented by the Matrix risks and ranked, highlighting pharmacy activities during which medication errors may occur. Results. The risk factor was calculated for 56 potential risks that can cause medication errors, of which 8 for the activity of ordering medicines, 8 potential risks for the activity of receiving products, 6 risks for the activity of storing products, 17 risks for the preparation of drugs and 17 risks for drug dispensing activity. Applying the MAE method in risk assessment, risk occurrence probability and outcome impact values were obtained for each risk. The potential risks of error were graphically represented using the Risk Matrix. Thus, the following activities with a major level of risk were highlighted, for which urgent measures to minimize the impact are required: illegible writing in prescriptions (15.5), fatigue of pharmaceutical staff (15.48), overwork/ multitasking (14.99), prescriptions containing errors (14.26), insufficient staff knowledge (13.81), ordering inappropriate quantities of products in pharmacies (13.59), incomplete prescriptions (12.86) and similar packaging of medicines (12.63). Conclusions. The results of this paper provide the basis for further research in order to develop a Risk Management Plan in community pharmacy. The causes that can lead to errors in the pharmacy have been identified, by highlighting pharmaceutical activities, during which risks of errors may occur. The work has practical use, and the research results can be applied by pharmacies, contributing to the improvement of their performance.

EOSC-Life brings together the 13 Life Science ‘ESFRI’ research infrastructures (LS RIs) to create an open, digital and collaborative space for biological and medical research. The project is starting to publish ‘FAIR’ data and a catalogue of services provided by participating RIs for the management, storage and reuse of data in the European Open Science Cloud (EOSC). This space will be accessible to European research communities.

BackgroundDue to recent advances in data storage and sharing for further data processing in predictive toxicology, there is an increasing need for flexible data representations, secure and consistent data curation and automated data quality checking. Toxicity prediction involves multidisciplinary data. There are hundreds of collections of chemical, biological and toxicological data that are widely dispersed, mostly in the open literature, professional research bodies and commercial companies. In order to better manage and make full use of such large amount of toxicity data, there is a trend to develop functionalities aiming towards data governance in predictive toxicology to formalise a set of processes to guarantee high data quality and better data management. In this paper, data quality mainly refers in a data storage sense (e.g. accuracy, completeness and integrity) and not in a toxicological sense (e.g. the quality of experimental results).ResultsThis paper reviews seven widely used predictive toxicology data sources and applications, with a particular focus on their data governance aspects, including: data accuracy, data completeness, data integrity, metadata and its management, data availability and data authorisation. This review reveals the current problems (e.g. lack of systematic and standard measures of data quality) and desirable needs (e.g. better management and further use of captured metadata and the development of flexible multi-level user access authorisation schemas) of predictive toxicology data sources development. The analytical results will help to address a significant gap in toxicology data quality assessment and lead to the development of novel frameworks for predictive toxicology data and model governance.ConclusionsWhile the discussed public data sources are well developed, there nevertheless remain some gaps in the development of a data governance framework to support predictive toxicology. In this paper, data governance is identified as the new challenge in predictive toxicology, and a good use of it may provide a promising framework for developing high quality and easy accessible toxicity data repositories. This paper also identifies important research directions that require further investigation in this area.

Assuring safety without animal testing: The case for the human testis in vitro

External service integration and adherence to industry standards has become ever more important for collections data management platforms. External APIs (Application Programming Interfaces), allow for the development of bi-directional data flows critical to service integration. In contrast to service-oriented backend APIs, public APIs must have continually up-to-date, comprehensive documentation that covers common use cases, on-the-fly request validation, and meaningful error messages. OpenAPI (OpenAPI Initiative 2021), a machine-readable API documentation specification can help significantly with testing and maintenance, and libraries can be used to automate common maintenance tasks. Specify 7 is a biological collections data management platform developed by the Specify Collections Consortium (Specify Software Consortium 2021). This presentation summarizes the challenges and lessons learned with publishing the existing backend Specify 7 API to a public-facing external API. Each Specify 7 API is composed of 200 resources. A standard set of CRUD (Create, Read, Update, Delete) operations is provided for each resource for client interaction with a group of service-based endpoints for bulk operations such as file uploads, file-based data imports, and attachment manipulation. To support the migration, we developed a custom library to enhance request validation. Parameter validation is extended through a real-time comparison against the existing schema and data. The library is available to the community under a MIT license on GitHub (https://github.com/specify/open_api_tools/). In this presentation, we will close with an overview of the next steps for the Specify 7 public API. These include: An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects. An update to the latest OpenAPI specification, version 3.1. The latest version aims to increase compatibility with the Javascript Object Notation (JSON) Schema specification, and thus would allow us to use JSON Schema (IETF Trust 2021) validation frameworks. An in-depth evaluation of GraphQL for its ability to force all endpoints to be strongly typed and automatic validation of request parameters and response objects.

A new object-oriented interface to MDSplus

In recent years, ammonia leakage accidents occur frequently, which causes a great concern in China. Based on traditional risk analysis methods and the equipment characteristics, a risk evaluation method for small ammonia refrigeration units is proposed. The method includes a risk assessment model based on area division, a failure probability assessment method based on expert scoring system, a failure consequence assessment method based on regional population density and consequence mitigation measures, and a HAZOP risk correction method. Based on the proposed method, a risk assessment system of small ammonia refrigeration units is developed. This risk assessment method and system will provide a scientific basis to carry out the cold storage management in China.