The rapid increase in lipidomic studies has led to a collaborative effort within the community to establish standards and criteria for producing, documenting, and disseminating data. Creating a dynamic easy-to-use checklist that condenses key information about lipidomic experiments into common terminology will enhance the field\'s consistency, comparability, and repeatability. Here, we describe the structure and rationale of the established Lipidomics Minimal Reporting Checklist to increase transparency in lipidomics research.

OBJECTIVE: Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct.
METHODS: A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe.
RESULTS: A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype.
CONCLUSIONS: A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.

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UNASSIGNED: Within the scope of the Helmholtz Metadata Collaboration (HMC), the ADVANCE project - Advanced metadata standards for biodiversity survey and monitoring data: supporting of research and conservation - aimed at supporting rich metadata generation with interoperable metadata standards and semantic artefacts that facilitate data access, integration and reuse across terrestrial, freshwater and marine realms. HMC\'s mission is to facilitate the discovery, access, machine-readability, and reuse of research data across and beyond the Helmholtz Association.
UNASSIGNED: We revised, adapted and expanded existing metadata schemas, vocabularies and thesauri to build a FAIR metadata schema and a metadata entry form built on it for users to provide their metadata instances focused on biodiversity monitoring data. The schema is FAIR because it is both machine-interpretable and follows domain-relevant community standards. This report provides a general overview of the project results and instructions on how to access, re-use and complete the metadata form.

Data stand as the foundation for studying, evaluating, and addressing the multifaceted challenges within environmental health research. This chapter highlights the contributions of the Canadian Urban Environmental Health Research Consortium (CANUE) in generating and democratizing access to environmental exposure data across Canada. Through a consortium-driven approach, CANUE standardizes a variety of datasets - including air quality, greenness, neighborhood characteristics, and weather and climatic factors - into a centralized, analysis-ready, postal code-indexed database. CANUE\'s mandate extends beyond data integration, encompassing the design and development of environmental health-related web applications, facilitating the linkage of data to a wide range of health databases and sociodemographic data, and providing educational training and events such as webinars, summits, and workshops. The operational and technical aspects of CANUE are explored in this chapter, detailing its human resources, data sources, computational infrastructure, and data management practices. These efforts collectively enhance research capabilities and public awareness, fostering strategic collaboration and generating actionable insights that promote physical and mental health and well-being.

UNASSIGNED: Publication of data from past field studies on invertebrate populations is of high importance, as there is much added value for them to be used as baselines to study spatiotemporal population and community dynamics in these groups. Therefore, a dataset consisting of occurrence data on epigaeic invertebrates collected in 1996 was standardised into the Darwin core format and cross-checked in order to make it publicly available following FAIR data principles. With publication, it can contribute to the biodiversity assessment of terrestrial invertebrates, thereby improving the availability and accessibility of much-needed historical datasets on macro-invertebrates.Here, we present sampling event data on invertebrates from four grasslands taken out of agricultural production over the span of several decades, effectively displaying a chronosequence on the effects of agricultural extensification. The data were collected by means of a standardised sampling design using pyramid traps, pitfall traps and soil samples.
UNASSIGNED: The raw data presented in this data paper have not been published before. They consist of 20,000+ records of nearly 70,000 specimens from 121 taxonomic groups. The data were collected using a standardised field study set-up and specimens were identified by taxonomic specialists. Most groups were identified up to family level, with eight groups identified up to species level. The occurrence data are complemented by information on plant composition, meteorological data and soil physical characteristics. The dataset has been registered in the Global Biodiversity Information Facility (GBIF): http://doi.org/10.15468/7n499e.

UNASSIGNED: Duchenne and Becker muscular dystrophy lack curative treatments. Registers can facilitate therapy development, serving as a platform to study epidemiology, assess clinical trial feasibility, identify eligible candidates, collect real-world data, perform post-market surveillance, and collaborate in (inter)national data-driven initiatives.
UNASSIGNED: In addressing these facets, it\'s crucial to gather high-quality, interchangeable, and reusable data from a representative population. We introduce the Dutch Dystrophinopathy Database (DDD), a national registry for patients with DMD or BMD, and females with pathogenic DMD variants, outlining its design, governance, and use.
UNASSIGNED: The design of DDD is based on a system-independent information model that ensures interoperable and reusable data adhering to international standards. To maximize enrollment, patients can provide consent online and participation is allowed on different levels with contact details and clinical diagnosis as minimal requirement. Participants can opt-in for yearly online questionnaires on disease milestones and medication and to have clinical data stored from visits to one of the national reference centers. Governance involves a general board, advisory board and database management.
UNASSIGNED: On November 1, 2023, 742 participants were enrolled. Self-reported data were provided by 291 Duchenne, 122 Becker and 38 female participants. 96% of the participants visiting reference centers consented to store clinical data. Eligible patients were informed about clinical studies through DDD, and multiple data requests have been approved to use coded clinical data for quality control, epidemiology and natural history studies.
UNASSIGNED: The Dutch Dystrophinopathy Database captures long-term patient and high-quality standardized clinician reported healthcare data, supporting trial readiness, post-marketing surveillance, and effective data use using a multicenter design that is scalable to other neuromuscular disorders.

Open science (OS) awareness and skills are increasingly becoming an essential part of everyday scientific work as e.g., many journals require authors to share data. However, following an OS workflow can seem challenging at first. Thus, instructions by journals and other guidelines are important. But how comprehensive are they in the field of ecology and evolutionary biology (Ecol Evol)? To find this out, we reviewed 20 published OS guideline articles aimed for ecologists or evolutionary biologists, together with the data policies of 17 Ecol Evol journals to chart the current landscape of OS guidelines in the field, find potential gaps, identify field-specific barriers for OS and discuss solutions to overcome these challenges. We found that many of the guideline articles covered similar topics, despite being written for a narrow field or specific target audience. Likewise, many of the guideline articles mentioned similar obstacles that could hinder or postpone a transition to open data sharing. Thus, there could be a need for a more widely known, general OS guideline for Ecol Evol. Following the same guideline could also enhance the uniformity of the OS practices carried on in the field. However, some topics, like long-term experiments and physical samples, were mentioned surprisingly seldom, although they are typical issues in Ecol Evol. Of the journals, 15 out of 17 expected or at least encouraged data sharing either for all articles or under specific conditions, e.g. for registered reports and 10 of those required data sharing at the submission phase. The coverage of journal data policies varied greatly between journals, from practically non-existing to very extensive. As journals can contribute greatly by leading the way and making open data useful, we recommend that the publishers and journals would invest in clear and comprehensive data policies and instructions for authors.
Avoimen tieteen ymmärrys ja taitojen hallinta on yhä tärkeämpi osa tutkijan arkea, sillä esimerkiksi monet tieteelliset lehdet odottavat aineiston avointa jakamista. Avoimen tieteen työtapojen noudattaminen voi kuitenkin tuntua alkuun haastavalta, minkä vuoksi esimerkiksi tieteellisten lehtien ja muiden tahojen laatimat ohjeet ovat tärkeitä. Mutta kuinka kattavia ne ovat ekologian ja evoluutiobiologian alalla? Kävimme läpi 20 julkaistua ekologeille tai evoluutiobiologeille suunnattua avoimen tieteen ohjeistusta sekä 17 ekologian ja evoluutiobiologian tieteellisen lehden datakäytännöt, tarkoituksenamme kartoittaa alojen avoimen tieteen ohjeiden nykytilaa, löytää mahdollisia puutteita, tunnistaa alakohtaisia esteitä avoimen tieteen käytäntöjen toteutumiselle sekä keskustella ratkaisuista, joilla nämä haasteet voitaisiin ratkaista. Havaitsimme, että monet ohjeistukset käsittelivät samankaltaisia aiheita, vaikka ne oli tarkoitettu kapealle erityisalalle tai suunnattu hyvin rajoitetulle kohderyhmälle. Samoin monissa ohjeistuksissa mainittiin samankaltaisia aineistojen avoimen jakamisen hidastamista tai estämistä aiheuttavia haasteita. Toiset aiheet, kuten pitkäaikaiskokeet ja fyysiset näytteet, sen sijaan mainittiin yllättävän harvoin, vaikka niissä on tyypillisiä ekologian ja evoluutiobiologian alojen haasteita. Tieteellisistä lehdistä 15:ssä 17:sta vaadittiin tai vähintään kannustettiin jakamaan aineisto avoimesti joko kaikkien artikkelien osalta tai tietyin edellytyksin, esim. rekisteröityjen tutkimusraporttien osalta. Lisäksi 10 näistä lehdistä edellytti aineiston avointa jakamista jo submittointivaiheessa. Tieteellisten lehtien aineisto‐ohjeiden kattavuus vaihteli suuresti lehtien välillä, käytännössä olemattomasta hyvin laajaan. Koska tieteellisillä lehdillä on suuri vaikutusvalta avoimen tieteen käytäntöjen edistämiseen, suosittelemme kustantajia ja lehtiä panostamaan selkeisiin ja kattaviin aineistolinjauksiin ja ohjeistuksiin.

The Facility for Antiproton and Ion Research (FAIR) is in its final construction stage next to the campus of the Gesellschaft für Schwerionenforschung Helmholtzzentrum for heavy-ion research in Darmstadt, Germany. Once it starts its operation, it will be the main nuclear physics research facility in many basic sciences and their applications in Europe for the coming decades. Owing to the ability of the new fragment separator, Super-FRagment Separator, to produce high-intensity radioactive ion beams in the energy range up to about 2 GeV/nucleon, these can be used in various nuclear reactions. This opens a unique opportunity for various nuclear structure studies across a range of fields and scales: from low-energy physics via the investigation of multi-neutron systems and halos to high-density nuclear matter and the equation of state, following heavy-ion collisions, fission and study of short-range correlations in nuclei and hypernuclei. The newly developed reactions with relativistic radioactive beams (R3B) set up at FAIR would be the most suitable and versatile for such studies. An overview of highlighted physics cases foreseen at R3B is given, along with possible future opportunities, at FAIR. This article is part of the theme issue \'The liminal position of Nuclear Physics: from hadrons to neutron stars\'.

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.