In the context of Open Science, data are just one, albeit important, part of the chain of scientific outputs, ranging from samples, to data, software, and research articles. The Specialized Information Service for Geosciences FID GEO has adopted this holistic view and considers the areas of text and data publications as two complementary components of Open Science.

Since 2016, FID GEO is offering information and publication services for the geoscientific community and is increasingly acknowledged as a competence center for the transformation of the publication culture in the geosciences. FID GEO’s services focus on electronic publishing of texts and research data, as well as digitization through the participating repositories GEO-LEOe-docs, hosted at the Göttingen State and University library (SUB), and GFZ Data Services, hosted at the GFZ German Research Centre for Geosciences, but also a comprehensive consulting portfolio.

FID GEO’s services were developed with the active participation of the geoscience community. The website, social media accounts and our newsletter are tools for actively connecting with the community. Informations on practical aspects of Open Science practices are regularly published in the journal “GMIT – Geowissenschaftliche Mitteilungen”, on the website and partner websites (e.g. forschungsdaten.info). Workshops and talks are successful tools to enable discussions, to address questions or uncertainties directly and to provide the appropriate framework to address specific requirements of individual research groups. FID GEO collaborates with strategic (inter)national initiatives (like NFDI4Earth), with several German Geosciences societies and other library-related projects to ensure the ongoing successful shift towards Open Science practices.

The Department of Earth Sciences at Freie Universität Berlin has recently established a concept and workflow for the promotion of research data and its publishing. The project, funded with seed money from central university administration, was developed in close cooperation with the research data management (RDM) team of the university library.

A key element in the project is the RDM-team proactively advertising support for data publishing to researchers of the department. This offer is very well received by the researchers, as they are often willing to publish data, but lack time as well as knowledge of best practice in data publication. Several data publishing projects have been kicked-off this way.

Another thrust of the project is to feature data publications on the department´s website. This fosters the recognition of data publications as important resarch output of the scientists of the Department of Earth Sciences. The data are presented on the website in an easily accessible and understandable context, to makes them available to potenially new user groups like lay persons, citizen scientists, or pupils. Before published on the website, researchers are asked by the RDM-team to proof-read short descriptive drafts about their data publications, and this opens up a great opportunity for researchers to engage with data publication specialists about best practices and standards of „FAIR and open“ data practices.

This project facilitates cultural change towards FAIR and open data publication at an university department and may be used as a blueprint for departments in other universities and research institutions.

The Geothermal Heat Utilization in Industrial Processes in SICA Member Countries (GEO II) project, implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ) for SICA, proposes a methodology for the elaboration of geothermal favorability maps for the member countries of the Central American Integration System (SICA). The methodology is a spatial analysis tool, based on a simple weighted overlay model that assigns a favorability index in the range of 0 to 10, with the objective of locating suitable sites to initiate the geothermal exploration stage. It considers geoscientific evidence defined as input criteria of the model, assuming that they indicate the probable presence of volcanic geothermal systems typical of the region. It is performed in Geographic Information Systems (GIS): QGIS and ArcGIS, through the stages: definition, derivation, transformation, and superposition. The model combines the transformed data, applying a weighted overlay to the geological, geochemical, and geophysical criteria, resulting in a raster surface that represents the suitability index, indicating the geothermal favorability for each country, allowing a comparison between them according to their variation in the input criteria.

Finally, an analysis stage, based on the comparison of the favorability map with the location of geothermal projects in the region to ensure that the result is a starting point for the identification of favorable areas in the exploration stage for the development of geothermal projects through a platform and database accessible at the regional scale of SICA.

Living benthic foraminifera are used as environmental proxies to evaluate the quality of marine ecosystems. This evaluation is usually based on diversity indices and/or on a group of indicative species with specific ecological requirements (tolerant to sensitive species). For this purpose, sorting and identification of living benthic foraminifera are needed which is time-consuming and requires taxonomical expert knowledge. In this study, we present an approach to automatically identify living (Rose Bengal stained) benthic foraminifera using the "ParticleTrieur" software (Marchant et al. 2020) and test its applicability for biomonitoring purposes. Samples from the intertidal mudflat of the Atlantic coast and the French Mediterranean coast were photographed by an automated machine consisting of a 3D printer moving in X, Y, and Z space and a camera connected to an objective with a ring light. Focus stacked images were always taken with the same exposure, stack height, and stack step. Through initial manual segmentation in the "Computer Vision Annotation Tool" (CVAT) and later training in the segmentation by artificial intelligence, living benthic foraminifera are cropped as individual images from full-field images. Using "ParticleTrieur", foraminifera images are manually labelled at the species level and a model is trained to automatically classify future images of interest. Convolutional Neural Network training is performed using Tensorflow libraries. The trained models will be applied to unclassified datasets to compare human and artificial intelligence classification concerning different ecological indices in both contrasted study areas.

Metadata, i.e. 'data about data', are fundamental for making research data findable, usable and understandable by researchers and all interested parties now and in the future. Moreover, with the advent of new, data-driven technologies and instrumentation leading to constantly increasing data volumes, and scientific research becoming increasingly interdisciplinary, research data has to be inseparably linked to standardized, machine readable and complete metadata in order to foster data reuse and reproducibility of research results, e.g. by provenance metadata. Only by supporting standards and tools for metadata handling, interoperability with national and international research data infrastructures can be ensured.

The mission of the Helmholtz Metadata Collaboration (HMC, https://helmholtz-metadaten.de/en) is to facilitate the discovery, access, machine readability, and reuse of research data of the Helmholtz Association. Concepts and services are developed and established, allowing the enrichment of research data with standardized metadata in the various phases of their creation. The aim of HMC is to co-ordinate these services with the national and international scientific community in order to establish widely accepted practices in the handling of research data.

In this presentation we will introduce to the current HMC activities and outcome: working groups, recommendations on specific metadata elements (e.g. different persistent identifiers), and standards for sharing metadata and tools to do so. Both activities, our discussion and jointly agreed recommendations take place on a "Community Platform" website that is currently being developed.

Persistent identifiers (PIDs) are an integral element of the FAIR principles (Wilkinson et al. 2016) as they are recommended to refer to data sets and metadata. They are, however, also considered to be used to refer to other data entities, like people, organizations, projects, laboratories, repositories, publications, vocabularies, samples, instruments, licenses, methods and others. Consistently integrating these PIDs into data infrastructures can create a high level of interoperability allowing to build connections between data sets from different repositories according to common meta information.

For developers and maintainers of repositories it is very difficult to decide, which PID systems to integrate, and how to implement the PIDs into the repositories metadata schema. Many decisions have to be made, e.g. where is the reference information, in my repositories metadata or the PID metadata? Who is responsible for a PID record, who registers is and who maintains it, in case the meta-information changes?

In this presentation we will shed some light on selected PID systems we recommend to use within the Helmholtz Association, and how we envision to solve some of the mentioned challenges. We develop and outline procedures to implement PIDs in a harmonized way, in order to achieve a level of interoperability across data infrastructures, based on metadata of commonly referenced PIDs.