Description: The Amon mud volcano (MV), located at 1250 m water depth on the Nile deep-sea fan, is known for its active emission of methane and non-methane hydrocarbons into the hydrosphere. Previous investigations showed a low efficiency of hydrocarbon-degrading anaerobic microbial communities inhabiting the Amon MV center in the presence of sulfate and hydrocarbons in the seeping subsurface fluids. By comparing spatial and temporal patterns of in situ biogeochemical fluxes, temperature gradients, pore water composition, and microbial activities over 3 yr, we investigated why the activity of anaerobic hydrocarbon degraders can be low despite high energy supplies. We found that the central dome of the Amon MV, as well as a lateral mud flow at its base, showed signs of recent exposure of hot subsurface muds lacking active hydrocarbon degrading communities. In these highly disturbed areas, anaerobic degradation of methane was less than 2% of the methane flux. Rather high oxygen consumption rates compared to low sulfide production suggest a faster development of more rapidly growing aerobic hydrocarbon degraders in highly disturbed areas. In contrast, the more stabilized muds surrounding the central gas and fluid conduits hosted active anaerobic hydrocarbon-degrading microbial communities. The low microbial activity in the hydrocarbon-vented areas of Amon MV is thus a consequence of kinetic limitations by heat and mud expulsion, whereas most of the outer MV area is limited by hydrocarbon transport.

Description: n the Arctic, under-ice primary production is limited to summer months and is restricted not only by ice thickness and snow cover but also by the stratification of the water column, which constrains nutrient supply for algal growth. Research Vessel Polarstern visited the ice-covered eastern-central basins between 82° to 89°N and 30° to 130°E in summer 2012, when Arctic sea ice declined to a record minimum. During this cruise, we observed a widespread deposition of ice algal biomass of on average 9 grams of carbon per square meter to the deep-sea floor of the central Arctic basins. Data from this cruise will contribute to assessing the effect of current climate change on Arctic productivity, biodiversity, and ecological function.

Description: Underwater images are used to explore and monitor ocean habitats, generating huge datasets with unusual data characteristics that preclude traditional data management strategies. Due to the lack of universally adopted data standards, image data collected from the marine environment are increasing in heterogeneity, preventing objective comparison. The extraction of actionable information thus remains challenging, particularly for researchers not directly involved with the image data collection. Standardized formats and procedures are needed to enable sustainable image analysis and processing tools, as are solutions for image publication in long-term repositories to ascertain reuse of data. The FAIR principles (Findable, Accessible, Interoperable, Reusable) provide a framework for such data management goals. We propose the use of image FAIR Digital Objects (iFDOs) and present an infrastructure environment to create and exploit such FAIR digital objects. We show how these iFDOs can be created, validated, managed and stored, and which data associated with imagery should be curated. The goal is to reduce image management overheads while simultaneously creating visibility for image acquisition and publication efforts.

Description: Modern digital scientific workflows - often implying Big Data challenges - require data infrastructures and innovative data science methods across disciplines and technologies. Diverse activities within and outside HGF deal with these challenges, on all levels. The series of Data Science Symposia fosters knowledge exchange and collaboration in the Earth and Environment research community. We invited contributions to the overarching topics of data management, data science and data infrastructures. The series of Data Science Symposia is a joint initiative by the three Helmholtz Centers HZG, AWI and GEOMAR Organization: Hela Mehrtens and Daniela Henkel (GEOMAR)

Description: 〈jats:title〉Abstract〈/jats:title〉 〈jats:p〉This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.〈/jats:p〉

Description: 〈jats:title〉Abstract〈/jats:title〉〈jats:p〉Natural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing the considerable amount of original data generated during the process of naming 15,000–20,000 species every year. From the perspective of alpha-taxonomists, we provide a review of the properties and diversity of taxonomic data, assess their volume and use, and establish criteria for optimizing data repositories. We surveyed 4113 alpha-taxonomic studies in representative journals for 2002, 2010, and 2018, and found an increasing yet comparatively limited use of molecular data in species diagnosis and description. In 2018, of the 2661 papers published in specialized taxonomic journals, molecular data were widely used in mycology (94%), regularly in vertebrates (53%), but rarely in botany (15%) and entomology (10%). Images play an important role in taxonomic research on all taxa, with photographs used in >80% and drawings in 58% of the surveyed papers. The use of omics (high-throughput) approaches or 3D documentation is still rare. Improved archiving strategies for metabarcoding consensus reads, genome and transcriptome assemblies, and chemical and metabolomic data could help to mobilize the wealth of high-throughput data for alpha-taxonomy. Because long-term—ideally perpetual—data storage is of particular importance for taxonomy, energy footprint reduction via less storage-demanding formats is a priority if their information content suffices for the purpose of taxonomic studies. Whereas taxonomic assignments are quasifacts for most biological disciplines, they remain hypotheses pertaining to evolutionary relatedness of individuals for alpha-taxonomy. For this reason, an improved reuse of taxonomic data, including machine-learning-based species identification and delimitation pipelines, requires a cyberspecimen approach—linking data via unique specimen identifiers, and thereby making them findable, accessible, interoperable, and reusable for taxonomic research. This poses both qualitative challenges to adapt the existing infrastructure of data centers to a specimen-centered concept and quantitative challenges to host and connect an estimated $ \le $2 million images produced per year by alpha-taxonomic studies, plus many millions of images from digitization campaigns. Of the 30,000–40,000 taxonomists globally, many are thought to be nonprofessionals, and capturing the data for online storage and reuse therefore requires low-complexity submission workflows and cost-free repository use. Expert taxonomists are the main stakeholders able to identify and formalize the needs of the discipline; their expertise is needed to implement the envisioned virtual collections of cyberspecimens. [Big data; cyberspecimen; new species; omics; repositories; specimen identifier; taxonomy; taxonomic data.]〈/jats:p〉

Description: The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.

Description: In the Arctic, under-ice primary production is limited to summer months and is not only restricted by ice thickness and snow cover but also by the stratification of the water column, which constrains nutrient supply for algal growth. RV Polarstern visited the ice-covered Eastern Central basins between 82 to 89°N and 30 to 130°E in summer 2012 when Arctic sea ice declined to a record minimum. During this cruise, we observed a widespread deposition of ice algal biomass of on average 9 g C per m**2 to the deep-sea floor of the Central Arctic basins. Data from this cruise will contribute to assessing the impact of current climate change on Arctic productivity, biodiversity, and ecological function.