Basics of collaborative research data management: Requirements for a Schleswig-Holstein state initiative on research data management

Das Papier "Grundlagen eines partnerschaftlichen Forschungsdatenmanagements - Anforderungen an eine schleswig-holsteinische Landesinitiative zum Forschungsdatenmanagement" umreißt die Anforderungen für eine schleswig-holsteinische Landesinitiative zum Forschungsdatenmanagement (FDM-SH). Hierfür wird zunächst das Umfeld, in dem eine solche Initiative entstehen und agieren soll, beschrieben. So beeinflussen sowohl die Eigenheiten der regionalen Forschungslandschaft wie auch die Entwicklungen im Bereich der Nationalen Forschungsdateninfrastruktur (NFDI) die Ausprägungen von Landesinitiativen. Die speziellen Anforderungen werden durch den Vergleich mit anderen Landesinitiativen, die Analyse von spezifischen Umfrageergebnissen aus Schleswig-Holstein sowie die Berücksichtigung der Anforderungen der NFDI gesammelt. Der Ansatz des partnerschaftlichen Forschungsdatenmanagements (FDM) spiegelt das Anliegen Schleswig-Holsteins wider, die Herausforderungen für ein zeitgemäßes FDM vor Ort gemeinsam zu bewältigen und dabei sowohl Know-how zu teilen als auch Ressourcen zu schonen

FAIR WISH "FAIR SAMPLES Template"

<p>This template presents the suggested IGSN metadata elements for sample descriptions of different sample types relevant in the context of the FAIR WISH project and shall serve as the basis for batch uploading sample-metadata to the IGSN server (FAIR SAMPLES TEMPLATE). The modular template was developed as an all-in-one solution for the varying use cases, with the ability for users to select only the variables needed for their specific sample type. This template can serve for the description of individual samples and for hierarchical sample structures later during the project (i.e. vegetation plot > soil pit > soil horizon > soil sample).</p> <p>The FAIR SAMPLES TEMPLATE was developed as Microsoft Excel spreadsheet and will be the source for semi-automated generation of standardised XML files required for the IGSN registration and IGSN landing pages. The development strategy of the template was explicitly followed to meet the practice of researchers who mainly organise their sample descriptions in tables. Furthermore, as often information for 100 and more samples are to be registered, submitting information via bulk uploads is favoured compared to submitting information for each sample individually via a webform. We develop the template for Excel, as this is widely used in the community. It will further be possible to work with the template with Open Source programs, like Libre Office or organise the information as ASCII csv files.</p> <p>The FAIR SAMPLES TEMPLATE includes a “Guide” which describes all available fields and additionally enables the selection of those variables which are necessary to describe one’s sample data. For this deliverable, we provide the full metadata schema, including the metadata fields and (sub)properties required for the IGSN registration schema (that will be provided by the allocating agent) and metadata fields that can be derived automatically and do not need to be provided by the users (e.g. the ROR identifier that will be included via an API). In addition, we adjusted the field names for the user template [column "Variable (Column name)" in the “Guide”] so that they are understandable by researchers and more intuitive than the original metadata field names in the IGSN metadata schema.</p> <p>The template will be updated on a regular basis.</p> <p>Version history:</p> <p>(10 January 2023) publication of the first version 1.0</p> <p>(30 April 2023) metadata update: change of title and exchange of first and second authors</p&gt

Towards an interoperable digital ecosystem in Earth System Science research

Earth System Science (ESS) relies on the availability of data from varying resources and ranging over different disciplines. Hence, data sources are rich and diverse, including observatories, satellites, measuring campaigns, model simulations, case studies, laboratory experiments as well as citizen science etc. At the same time, practices of professional research data management (RDM) are differing significantly among various disciplines. There are many well-known challenges in enabling a free flow of data in the sense of the FAIR criteria. Such are data quality assurance, unique digital identifiers, access to and integration of data repositories, just to mention a few. The Helmholtz DataHub Earth&Environment is addressing digitalization in ESS by developing a federated data infrastructure. Existing RDM practices at seven centers of the Helmholtz Association working together in a joint research program within the Research Field Earth and Environment (RF E&E) are harmonized and integrated in a comprehensive way. The vision is to establish a digital research ecosystem fostering digitalization in geosciences and environmental sciences. Hereby, issues of common metadata standards, digital object identifiers for samples, instruments and datasets, defined role models for data sharing certainly play a central role. The various data generating infrastructures are registered digitally in order to collect metadata as early as possible and enrich them along the flow of the research cycle. Joint RDM bridging several institutions relies on professional practices of distributed software development. Apart from operating cross-center software development teams, the solutions rely on concepts of modular software design. For example, a generic framework has been developed to allow for quick development of tools for domain specific data exploration in a distributed manner. Other tools incorporate automated quality control in data streams. Software is being developed following guiding principles of open and reusable research software development. A suite of views is being provided, allowing for varying user perspectives, monitoring data flows from sensor to archive, or publishing data in quality assured repositories. Furthermore, high-level data products are being provided for stakeholders and knowledge transfer (for examples see https://datahub.erde-und-umwelt.de). Furthermore, tools for integrated data analysis, e.g. using AI approaches for marine litter detection can be implemented on top of the existing software stack. Of course, this initiative does not exist in isolation. It is part of a long-term strategy being embedded within national (e.g. NFDI) and international (e.g. EOSC, RDA) initiatives

Serum concentrations of DKK-1 correlate with the extent of bone disease in patients with multiple myeloma.

OBJECTIVES: Lytic bone disease is a hallmark of multiple myeloma (MM) and is caused by osteoclast activation and osteoblast inhibition. Secretion of Dickkopf (DKK)-1 by myeloma cells is a major factor which causes inhibition of osteoblast precursors. So far, there is no study showing a significant difference in serum DKK-1 levels in MM patients with or without lytic bone lesions. METHODS: DKK-1 serum levels were quantified in 184 untreated MM patients and 33 monoclonal gammopathy of undetermined significance (MGUS) patients by ELISA, using a monoclonal anti-DKK-1 antibody. RESULTS: Serum DKK-1 was elevated in MM as compared with MGUS (mean 11 963 pg/mL vs. 1993 pg/mL; P 3 lesions: 3114 pg/mL vs. 3559 pg/mL vs. 24 068 pg/mL; P = 0.002). CONCLUSION: Using a large series of myeloma patients, we could show for the first time a correlation between DKK-1 serum concentration and the amount of lytic bone disease, indicating that DKK-1 is an important factor for the extent of bone disease and supporting the hypothesis of DKK-1 as a therapeutic target in myeloma bone disease

Serum Levels of Total-RANKL in Multiple Myeloma

Background: Receptor activator of nuclear factor-kappa B ligand (RANKL) plays a key role in osteoclast activation in myeloma bone disease. The increased expression of RANKL in the bone marrow microenvironment was demonstrated in several studies, but there are only rare data on circulating RANKL levels in patients with multiple myeloma (MM). Patients and Methods: In the current study, we investigated the clinical significance of serum RANKL levels, using an enzyme-linked immunosorbent assay test that detects both free and osteoprotegerin (OPG)-bound RANKL (total-RANKL, tRANKL) in patients with newly diagnosed MM (n = 93) and monoclonal gammopathy of undetermined significance (MGUS; n = 20) compared with healthy controls (n = 20). Results: Circulating serum tRANKL was significantly elevated in patients with MM compared with controls (P < .001) or MGUS (P < .001). Furthermore, tRANKL levels were higher in smoldering MM versus MGUS (P = .031) and in symptomatic versus smoldering MM (P < .001). Serum tRANKL increased parallel to International Staging System stages I to III (P = .004) and correlated with the presence of lytic bone lesions (P < .001). Total-RANKL was a prognostic factor for overall survival in symptomatic MM (P = .043). A significantly longer progression-free survival was observed in patients with a > 50% decrease in tRANKL levels after 3 months of combined chemotherapy and bisphosphonate treatment. Conclusion: Our study demonstrates for the first time that serum tRANKL reflects advanced disease, lytic bone destruction, and poor prognosis in MM

Geschäftsordnung : Schleswig-Holsteinische Landesinitiative zum Forschungsdatenmanagement

Die Geschäftsordnung der schleswig-holsteinischen Landesinitiative zum Forschungsdatenmanagement (FDM-SH) dient den Gremien als Leitfaden zur Erfüllung ihrer Aufgaben. Die grundsätzlichen Anforderungen an sowie das Konzept zur Umsetzung von FDM-SH wurden partnerschaftlich gemeinsam mit den schleswig-holsteinischen Hochschulen und Forschungseinrichtungen im Vorprojekt „FDM-SH“ erarbeite

Ansatz für ein partnerschaftliches Forschungsdatenmanagement: Konzept für eine schleswig-holsteinische Landesinitiative zum Forschungsdatenmanagement

Das Papier „Ansatz für ein partnerschaftliches Forschungsdatenmanagement – Konzept für eine schleswig-holsteinische Landesinitiative zum Forschungsdatenmanagement“ stellt Aufgaben, Arbeitsweise, Organisation sowie Zuständigkeiten für die schleswig-holsteinische Landesinitiative zum Forschungsdatenmanagement FDM-SH vor