2024-03-28T19:24:20Z
https://zenodo.org/oai2d
oai:zenodo.org:7291749
2022-11-06T02:26:27Z
openaire
user-hmc-conference_2022
Pfeil, Andreas
Chelbi, Sabrine
Jejkal, Thomas
2022-10-05
<p>The FAIR Digital Object Lab is an extendable and adjustable software stack for generic FAIR Digital Object (FAIR DO) tasks. It consists of a set of interacting components with services and tools for creation, validation, discovery, curation, and more.<br>
<br>
The creation and maintenance of FAIR DOs is not trivial, as their PIDs contain typed record information. They are meant to be machine-actionable, not human-readable. Easing the creation and maintenance of FAIR DOs, as well as making FAIR DOs searchable and human-accessible, are functions of the FAIR DO Lab. While it started as the “FAIR DO Testbed”, development now focuses on production-readiness and user interfaces.</p>
https://doi.org/10.5445/IR/1000151868
oai:zenodo.org:7291749
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
info:eu-repo/semantics/openAccess
Other (Open)
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
FAIR Digital Object
Software Stack
Service-based infrastructure
FAIR DO Lab – A FAIR Digital Object Lab for your research
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7244684
2022-10-25T07:00:37Z
openaire
user-hmc-conference_2022
user-nfdi4ing
Hartmann, Volker
Heinrichs, Benedikt
2022-10-05
<p>The Metadata Hub provides a generic service for metadata repositories. Based on this, different kinds of metadata repositories can be accessed with uniform tools without the researchers having to deal with the complex details. In the domain of research data management, there are a variety of repositories that offer metadata management services to researchers. This poses the challenge that these repositories usually have different interfaces and in nature are not very interoperable with each other, violating one of the FAIR Principles. Our work aims to build a bridge between these repositories as a generic service for metadata repositories, the Metadata Hub. It’s accessible via the Turntable API, which defines a uniform interface for metadata repositories. To validate metadata documents, a definition of the document structure has to be available. For JSON/XML, there is JSON/XML Schema for this purpose. In case of JSON-LD, JSON Schema is not sufficient. Therefore, so-called application profiles are used, which are defined by using Shapes Constraint Language (SHACL). In general, the Turntable API is split in two parts: The first part is about managing (CRUD) ‘schemas/application profiles’ which are describing the structure of metadata documents, extended by the ability to validate a metadata document against a registered ‘schema/application profile/…’.</p>
<p>The second part is about managing the metadata documents itself based on one of registered ‘schemas/application profiles’. Currently, we build the Metadata Hub as a Demonstrator mapping two completely different repositories (Coscine, MetaStore) as a showcase. The Metadata Hub is powered by the Metadata Hub Framework and provides a web interface to make the service available to a broad mass. This work has been supported by the research program ‘Engineering Digital Futures’ of the Helmholtz Association of German Research Centers, the Helmholtz Metadata Collaboration Platform and the German National Research Data Infrastructure (NFDI).</p>
https://doi.org/10.5445/IR/1000151732
oai:zenodo.org:7244684
eng
Zenodo
https://zenodo.org/communities/nfdi4ing
https://zenodo.org/communities/hmc-conference_2022
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
Services
Research Data Management
Metadata
Metadata Hub - One for All
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7197596
2022-10-14T14:26:34Z
openaire
user-hmc-conference_2022
Gilein, Astrid
Walter, Konstantin Pascal
Glodowski, Tempest
Preuß, Gabriel
Schmidt, Alexander
Serve, Vivien
Mannix, Oonagh
Kubin, Markus
2022-10-06
<p>Making research data reusable in an open and FAIR<sup><a href="https://www.nature.com/articles/sdata201618">[1]</a></sup> way is part of good scientific practice and is increasingly becoming part of the scientific workflow. Where and how "FAIR" research data is published alongside a research paper, is often not yet tracked by<br>
research institutes.<br>
In a pilot project of the Helmholtz Metadata Collaboration (HMC) Hub Matter we developed an approach to automatically find and<br>
catalogue publicly accessible datasets published by researchers of selected Helmholtz centers. These datasets are assessed with<br>
respect to the FAIRsFAIR metrics<sup><a href="https://zenodo.org/record/6461229#.Y0kbVYTP2Uk">[2]</a></sup> using the F-UJI tool<sup><a href="https://zenodo.org/record/4063720#.Y0kbeoTP2Uk">[3]</a></sup>. The results are gathered and visualized in an interactive pilot<br>
dashboard.<br>
This assists HMC Hub Matter to identify and characterize repositories used by the Matter community and to identify key<br>
actions for engaging with repository infrastructure and research communities. In this poster, we discuss the different steps of the<br>
data collection and the first results.</p>
<p> </p>
<p>[1] M Wilkinson et al. (2016). The FAIR Guiding Principles for scientific data (...). Sci Data 3, 160018. DOI: 10.1038/sdata.2016.18<br>
[2] A Devaraju et al. (2020). FAIRsFAIR Data Object Assessment Metrics (0.5). Zenodo. DOI: 10.5281/zenodo.6461229<br>
[3] A Devaraju & R Huber. (2020). F-UJI - An Automated FAIR Data Assessment Tool (v1.0.0). Zenodo. DOI: 10.5281/zenodo.4063720</p>
https://doi.org/10.5281/zenodo.7197596
oai:zenodo.org:7197596
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7197595
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
Helmholtz Metadata Collaboration
FAIR
Pilot Dashboard for Open and FAIR Data Metrics by HMC Hub Matter
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7180896
2022-10-11T02:26:23Z
openaire
user-hmc-conference_2022
Süß Wolfgang
Stucky Karl-Uwe
Schweikert Jan
Koubaa Mohamed Anis
Ballani Felix
Steinmeier Leon
2022-10-10
<p>Five Helmholtz Centers are participating in the Research Field Energy, three of them are directly contributing to Hub Energy. To be well prepared for their supporting tasks in establishing a FAIR data ecosystem within the energy research community at Helmholtz, the team members of Hub Energy study relevant use cases and develop software tools in close cooperation with FAIR Data Commons. This poster presents four examples for this work: A photovoltaic system requires ontology development and data models based on standards like IEC 61850 or SensorML as well as on FAIR Digital Objects (FDO). In another use case, RO-Crates are automatically generated for data of the KIT Campus North energy and water consumption. The aim is to study methods for a detailed metadata desciption in data publication processes. In the field of software development, an FDO browser offers cascading search for metadata and application data entities and a metadata editor supports users in creating and editing schemas and instances as well. The presented activities foster close contact between Hub Energy and Helmholtz energy researchers and, thus, essentially support the formation of a FAIR energy data management. Use cases feed technical details into the Hub's energy knowledge pool and they are also a nearly perfect training programme for the Hub personnel. In doing the presented software development work, deep insights into energy data landscapes and an improved sense for user requirements are induced, even if in the end more elaborated and harmonized solutions from FAIR Data Commons may be adopted.</p>
https://doi.org/10.5281/zenodo.7180896
oai:zenodo.org:7180896
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7180895
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration - Conference 2022, Virtual Event, 05-06. October 2022
FAIR Digital Objects
Photovoltaics Ontology
RO Crates
Use Cases and Tools in HMC Hub Energy
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7291939
2022-11-06T02:26:28Z
openaire
user-hmc-conference_2022
Scholz, Jonas
Tzotchev, Nikola
Pfeil, Andreas
2022-10-05
<p>Research Object Crate (RO-Crate) is an open, community-driven data package specification to describe all kinds of file-based data, as well as entities outside the package. In order to do so, it uses the widespread JSON format, representing Linked Data (JSON-LD), allowing to link to external information. This makes the format flexible and machine-readable. These packages are referred to as (RO-)crates.<br>
<br>
We developed the ro-crate-java library, which allows creating, modifying, and validating crates using the Java programming language. The focus of development was the ease of use: We aimed to make it intuitive and easy to create valid crates, without knowing the specification too well.</p>
https://doi.org/10.5445/IR/1000151861
oai:zenodo.org:7291939
eng
Zenodo
https://github.com/kit-data-manager/ro-crate-java
https://github.com/kit-data-manager/ro-crate-benchmarks
https://zenodo.org/communities/hmc-conference_2022
info:eu-repo/semantics/openAccess
Other (Open)
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
Research Data Management
Research Data Package
Software Library
Benchmark
Research Object Crates – Bundling Research (Meta-)Data
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7180648
2022-10-11T02:26:22Z
openaire
user-hmc-conference_2022
Knodel, Oliver
2022-10-10
<p>The HELIPORT project aims to make the components or steps of the entire life cycle of a research project at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the Helmholtz-Institute Jena (HIJ) discoverable, accessible, interoperable and reusable according to the FAIR principles. In particular, this data management solution deals with the entire lifecycle of research experiments, starting with the generation of the first digital objects, the workflows carried out and the actual publication of research results. For this purpose, a concept was developed that identifies the different systems involved and their connections. By integrating computational workflows (CWL and others), HELIPORT can automate calculations that work with metadata from different internal systems (application management, Labbook, GitLab, and further). This presentation will cover the first year of the project, the current status and the path taken so far in the life cycle of the project.</p>
https://doi.org/10.5281/zenodo.7180648
oai:zenodo.org:7180648
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7180647
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
data management
workflows
data provenance
metadata
The Integrated Research Data Lifecycle of the HELIPORT Project
info:eu-repo/semantics/lecture
oai:zenodo.org:7148360
2022-10-11T14:26:19Z
openaire
user-hmc-conference_2022
Gerlich, Silke Christine
Hofmann, Volker
Kubin, Markus
Kulla, Lucas
Lemster, Christine
Mannix, Oonagh
Rink, Katharina
Nolden, Marco
Schweikert, Jan
Shankar, Sangeetha
Söding, Emanuel
Steinmeier, Leon
Süß, Wolfgang
2022-10-06
<p>Annotation of research data with rich metadata is important to make that data findable, accessible, interoperable and reusable (<a href="https://doi.org/10.1038/sdata.2016.18">Wilkinson et al., 2016</a>), thereby rendering the carried out research more sustainable. Within the Helmholtz Association, the Helmholtz Metadata Collaboration (HMC) coordinates the mission to enrich Helmholtz-based research data with metadata by providing (information about) technical solutions, advise and ensuring uniform scientific standards for the use of metadata.</p>
<p>In 2021, HMC conducted its first community survey to align its services with the needs of Helmholtz researchers. A question catalogue with 49 (sub-)questions was designed and disseminated among researchers in all six Helmholtz research fields. The conditional succession of the questions was aligned with predetermined expertise levels ("no prior knowledge", "intermediate prior knowledge", "high level of prior knowledge"). 631 completed survey replies were obtained for analysis.</p>
<p>The HMC Community Survey 2021 provides insight into the management of research data as well as the data publication practices of researchers in the Helmholtz Association. The characterization of research-field-dependent communities will enable HMC to further develop targeted, community-directed support for the documentation of research data with metadata.</p>
https://doi.org/10.5281/zenodo.7148360
oai:zenodo.org:7148360
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7148359
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, 5-6 October 2022
Survey
Helmholtz Metadata Collaboration
Metadata
Data management practices among Helmholtz's research communities – A survey on the status quo and on community-specific demands.
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7189770
2022-10-12T14:26:20Z
openaire
user-hmc-conference_2022
Bienhold, Christina
Bayer, Till
Harms, Lars
Neuhaus, Stefan
Koppe, Roland
2022-10-12
<p>Biomolecules, such as DNA and RNA, provide a wealth of information about the distribution and function of marine organisms, and biomolecular research in the marine realm is pursued across several Helmholtz Centers. Biomolecular metadata, i.e. DNA and RNA sequences and all steps involved in their creation, exhibit great internal diversity and complexity. However, high-quality (meta)data management is not yet well developed and harmonized in environmentally focused Helmholtz Centers. As part of the HMC Project HARMONise, we aim to develop sustainable solutions and digital cultures to enable high-quality, standards-compliant curation and management of marine biomolecular metadata at AWI and GEOMAR to better embed biomolecular science into broader digital ecosystems and research domains. Our approach builds on a relational database that aligns metadata with community standards such as the MIxS (Minimum Information about any (x) sequence) supported by the International Nucleotide Sequence Database Collaboration (INSDC) to promote global interoperability. At the same time, we ensure the harmonization of metadata with existing Helmholtz repositories (e.g. PANGAEA). A web-based hub will enable the standardized export and exchange of core metadata, in line with domain-specific standards and using standard conventions such as JSON(-LD). Here we will present the current status of the database scheme, highlight the use of standards and fields that promote interoperability, and outline the planned development of an exchange hub for sharing and validating biomolecular metadata across Helmholtz Centers. Enabling sustainable data stewardship, export and publication routines will support researchers in delivering Helmholtz biomolecular data to national European and global repositories in alignment with community standards and the FAIR principles.</p>
https://doi.org/10.5281/zenodo.7189770
oai:zenodo.org:7189770
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7189769
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, online, 5-6 October 2022
sequence data management
interoperability
metadata harmonization
FAIR principles
HARMONise – Enhancing the interoperability of marine biomolecular (meta)data across Helmholtz Centres
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7313840
2022-11-11T14:26:25Z
openaire
user-hmc-conference_2022
user-helmholtz_hmc
Kubin, Markus
Günther, Gerrit
Gilein, Astrid
Preuß, Gabriel
Cristiano, Luigia
Walter, Konstantin Pascal
Serve, Vivien
Görzig, Heike
Mannix, Oonagh
2022-10-05
<p>Supporting Helmholtz’s research communities in making their data FAIR is one of the key missions of HMC. A multi-method approach combining quantitative and qualitative methods was developed to understand current data management practices in research field Matter. Quantitative information was obtained from data that was self-reported by Helmholtz’s researchers in the HMC Community Survey 2021. Complementary data on Open and FAIR data practices in research field Matter, gathered in a data mining approach, is visualized in a dashboard. Qualitative understanding of community-specific FAIR data practices was obtained from a manual FAIR assessment based on the FAIR Data Maturity Model. Here we report on a combined interpretation of HMC Hub Matter’s findings from this multi-method approach. Three key areas for future action by HMC Hub Matter are discussed, such as (1) bridging policy and practicability, (2) creating a culture of data reuse, and (3) monitoring and engaging with technical infrastructure.</p>
https://doi.org/10.5281/zenodo.7313840
oai:zenodo.org:7313840
eng
Zenodo
https://zenodo.org/communities/helmholtz_hmc
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7313839
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
HMC Conference 2022, 5-6 October 2022
Helmholtz Metadata Collaboration
Hub Matter
Research Data Management
Understanding Data Management Practices in Research Field Matter: Conclusions from a Multi-Method Approach
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7115759
2023-10-24T06:37:57Z
openaire
user-heliport
user-hmc-conference_2022
user-derse23
Knodel, Oliver
Pape, David
Voigt, Martin
Gruber, Thomas
Kelling, Jeffrey
Lokamani, Mani
Müller, Stefan
Juckeland, Guido
Kessler, Alexander
Hein, Joachim
Lee, Chien-Li
Kaluza, Malte
Schuller, Bernd
2022-09-27
<p><a href="https://heliport.hzdr.de">HELIPORT</a> is a data management solution that aims at making the components and steps of the entire research experiment’s life cycle discoverable, accessible, interoperable and reusable according to the FAIR principles.<br>
Among other information, HELIPORT integrates documentation, scientific workflows, and the final publication of the research results - all via already established solutions for proposal management, electronic lab notebooks, software development and devops tools, and other additional data sources. The integration is accomplished by presenting the researchers with a high-level overview to keep all aspects of the experiment in mind, and automatically exchanging relevant metadata between the experiment’s life cycle steps.<br>
Computational agents can interact with HELIPORT via a REST API that allows access to all components, and landing pages that allow for export of digital objects in various standardized formats and schemas. An overall digital object graph combining the metadata harvested from all sources provides scientists with a visual representation of interactions and relations between their digital objects, as well as their existence in the first place. Through the integrated computational workflow systems, HELIPORT can automate calculations using the collected metadata.<br>
By visualizing all aspects of large-scale research experiments, HELIPORT enables deeper insights into a comprehensible data provenance with the chance of raising awareness for data management.</p>
https://doi.org/10.5281/zenodo.7115759
oai:zenodo.org:7115759
eng
Zenodo
https://doi.org/10.14278/rodare.946
https://doi.org/10.5281/zenodo.7054582
https://zenodo.org/communities/heliport
https://zenodo.org/communities/hmc-conference_2022
https://zenodo.org/communities/derse23
https://doi.org/10.5281/zenodo.7104941
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
deRSE23, Conference for Research Software Engineering in Germany, Paderborn, Germany, 20-23 February 2023
Data management
FAIR
workflows
scientific project
project lifecycle
HELIPORT — An Integrated Research Data Lifecycle
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7220686
2023-05-22T08:58:18Z
openaire
user-hmc-conference_2022
user-helmholtz_hmc
Ernst, Felix
Götzelmann, Germaine
Jha, Vandana
Tögel, Philipp
Tonne, Danah
Frank, Laura
2022-10-05
<p>Annotation is one of the oldest cultural techniques of mankind. While in past centuries pen and paper were the means of choice to add annotations to a source, this activity has increasingly shifted to the digital world in recent years. With the W3C recommendation 'Web Annotation Data Model', a powerful tool has been available since 2017 to model annotations in a wide variety of disciplines and to enable cross-disciplinary analysis. </p>
<p> </p>
<p>In this poster, we give an insight into our annotation infrastructure, which is in use in three humanities research projects. The focus is on a custom-developed annotation server with RDF backend (fully compliant to the 'Web Annotation Protocol') as well as our annotation interfaces. The interaction of these components with each other but also with other infrastructure components such as a research data repository or a vocabulary editor as well as the daily work of researchers with these components is illustrated.</p>
<p> </p>
<p>Special attention is paid to the modeling of annotations in our different use cases. Examples range from labeling of logical diagrams in medieval Aristotle manuscripts, to the analysis of metaphors in religious meaning-making, to the capture of particular Hebrew letter variations in Torah scrolls. The discussion of similarities and differences in these use cases holds great potential for transferability and fruitfulness in further scientific disciplines and is thus a main part of our contribution.</p>
https://doi.org/10.5445/IR/1000151590
oai:zenodo.org:7220686
eng
Zenodo
https://zenodo.org/communities/helmholtz_hmc
https://zenodo.org/communities/hmc-conference_2022
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Annotation, Digital Humanities, HMC
Annotating Humanities Research Data - Episteme, Metaphors and the Torah
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7274813
2022-11-04T14:26:31Z
openaire
user-hmc-conference_2022
Pörsch, Andrea
Buttigieg, Pier Luigi
Söding, Emanuel
Weinelt, Martin
2022-11-02
<p>The desired Interoperability of data as outlined by the FAIR principles, requires a harmonization of data handling processes among data infrastructures. To support the adoption of agreements on such processes and thus further develop the “ROAD TO FAIR”, HMC is currently establishing a FAIR-IMP-lementation Network (F-IMP). With this communication network we encourage the data management community to present suitable use cases, develop solutions and make recommendations for the implementation of data handling procedures not only within the Helmholtz Association but also beyond.<br>
<br>
To activate the F-IMP a web-based information hub for all interested parties in the form of a communication portal was set up. It aims to enable a dialogue for the exchange of information leading to agreements across all participating parties, from Helmholtz Centers, over national initiatives to international actors, if desired. The platform will allow anyone to actively participate in discussions and decisions, not only the F-IMP, and track the ongoing coordination processes as they happen. The HMC Community Portal will initially be developed jointly by HMC and Helmholtz-Zentrum Hereon.<br>
<br>
The following activities are currently planned to be conducted on the portal: Working Groups (WG) on 1. the usage of PIDs in data infrastructures, 2. the interoperability of data infrastructures through harmonized interfaces and APIs, and 3. the implementation of common semantic concepts. Other topics will emerge as required by the participants. Other information sources will deal with community recommendations and the establishment of a knowledge base to cover some general information.<br>
<br>
The portal’s design will be updated according to the needs of the community as it is developed. We hope that the F-IMP and the portal will support the harmonization and improvement of data management processes towards the further implementation of a FAIR data space.</p>
https://doi.org/10.5281/zenodo.7274813
oai:zenodo.org:7274813
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7274812
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Conference title: Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
metadata
FAIR principles
harmonization
HMC Earth and Environment - Community Involvement - the FAIR Implementation Network and the Community Portal
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7243871
2022-10-24T14:26:27Z
openaire
user-hmc-conference_2022
Blumenröhr Nicolas
Aversa, Rossella
2022-10-24
<p>Scientific image data sets can be continuously enriched by labels describing new features which are relevant for some specific task. This process can be automated by means of Machine Learning (ML) techniques. Although such an approach shows clear advantages, especially when it is applied to large datasets, it also poses an important challenge:<br>
Relabeling image data sets curated by different scientists, in order to collectively use them for ML, requires a common agreement on the labels which can be used. This can be achieved thanks to the use of a standardized way to describe the label information: a metadata schema including vocabularies. Furthermore, machine-actionable decisions on the label information for relabeling can be enabled by the representation of images and schema-based metadata as FAIR Digital Objects (DOs).<br>
We introduce a metadata schema including vocabularies to describe ML image data represented as FAIR DOs that can be accessed for relabeling. The specifications of the metadata schema are presented. The relevance of a standardized metadata description including vocabularies for relabeling ML image data is emphasized. It is shown how the metadata is accessed with FAIR DOs and how vocabularies support automated relabeling. This contribution supplements the content of “FAIR DO Application Case for Composing Machine Learning Training Data” with a focus on the semantic aspects for relabeling.<br>
This work has been supported by the research program ‘Engineering Digital Futures’ of the Helmholtz Association of German Research Centers and the Helmholtz Metadata Collaboration Platform. This project has received funding from the ‘European Union’s Horizon 2020‘ research and innovation program under grant agreement No. 101007417 within the framework of the ‘NFFA-Europe Pilot‘ (NEP) Joint Activities.</p>
https://doi.org/10.5281/zenodo.7243871
oai:zenodo.org:7243871
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7243870
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
Using Schema-based Metadata for Image Labels accessed with FAIR Digital Objects
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7194276
2022-10-13T14:26:21Z
openaire
user-hmc-conference_2022
Grimm-Seyfarth, Annegret
Menger, Juliana
2022-10-13
<p>In an ever-changing world, field surveys, inventories and monitoring data are essential for prediction of biodiversity responses to global drivers such as land use and climate change. This knowledge provides the basis for appropriate management. However, field biodiversity data collected across terrestrial, freshwater and marine realms are highly complex and heterogeneous. The successful integration and re-use of such data depends on how FAIR (<em>Findable, Accessible, Interoperable, Reusable</em>) they are. ADVANCE aims at underpinning rich metadata generation with interoperable metadata standards using semantic artefacts. These are tools allowing humans and machines to locate, access and understand (meta) data, and thus facilitating integration and reuse of biodiversity monitoring data across terrestrial, freshwater and marine realms. To this end, we revised, adapted and expanded existing metadata standards, thesauri and vocabularies. We focused on the most comprehensive database of biodiversity monitoring schemes in Europe (DaEuMon) as the base for building a metadata schema that implements quality control and complies with the FAIR principles. In a further step, we will use biodiversity data to test, refine and illustrate the strength of the concept in cases of real use. ADVANCE thus complements semantic artefacts of the Hub Earth & Environment and other initiatives for FAIR biodiversity research, enabling assessments of the relationships between biodiversity across realms and associated environmental conditions. Moreover, it will facilitate future collaborations, joint projects and data-driven studies among biodiversity scientists of the Helmholtz Association and beyond.</p>
https://doi.org/10.5281/zenodo.7194276
oai:zenodo.org:7194276
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7194275
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ADVANCE - Advanced metadata standards for biodiversity survey and monitoring data: Supporting of research and conservation
info:eu-repo/semantics/lecture
oai:zenodo.org:7194514
2022-10-13T14:26:23Z
openaire
user-hmc-conference_2022
Algergawy, Alsayed
Hamed, Hamdi
Thiel, Sven
Totsche, Kai Uwe
König-Ries, Birgitta
2022-10-13
<p>The Collaborative Research Centre AquaDiva is a large collaborative project spanning a variety of domains, such as biology, geology, chemistry, and computer science, with the common goal of better understand the Earth’s critical zone, in particular, how environmental conditions and surface properties shape the structure, properties, and functions of the subsurface. Within AquaDiva, large volumes of heterogeneous observational data are being collected. Besides this structured data, knowledge is also encoded in an unstructured form in scientific publications. To support search and dataset discovery, standard metadata is recommended to describe data. However, due to the heterogeneity in AquaDiva datasets, one metadata standard does not fit all. In the first phase, we made use of EML and ABCD. However, both of them are not adequate for AquaDiva. Therefore, we develop and introduce AquaDiva specific metadata to effectively describe AquaDiva data and support dataset search and discovery. In particular, the proposed metadata consists of four main components: a general component to provide information about the dataset, such as title, description and a set of main keywords; the second component to introduce information about the project(s) involved in collecting and generating the dataset; the third component to describe information about persons, such as dataset owner, dataset curators; the last and the most important component to present AquaDiva-specific metadata information, such as sampling location, sample object, sample type and the data types generated from these samples. As a next step, we plan to link our metadata concepts (or to annotate our metadata concepts with them) to appropriate controlled vocabularies and ontologies. This not only contributes to interoperability but also ensures a well-understood, common definition of the fields.</p>
https://doi.org/10.5281/zenodo.7194514
oai:zenodo.org:7194514
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7194513
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
AuqaDiva
Metadata
FAIR Data
AquaDiva Metadata: Towards Achieving FAIRness in the AquaDiva Data Portal
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7054583
2023-07-06T08:38:11Z
openaire
user-heliport
user-hmc-conference_2022
Knodel, Oliver
Voigt, Martin
Pape, David
Gruber, Thomas
Kelling, Jeffrey
Deinert, Jan-Christoph
Mueller, Stefan. E.
Juckeland, Guido
Lokamani, Mani
2022-09-27
<p>At the High-Field High-Repetition-Rate Terahertz facility @ ELBE (<a href="https://doi.org/10.1063/1.4978042">TELBE</a>), ultrafast terahertz-induced dynamics can be probed in various states of matter with highest precision. The TELBE sources offer both, stable and tunable narrowband THz radiation with pulse energies of several microjoules at high repetition rates and a synchronized coherent diffraction radiator,that provides broadband single-cycle pulses. The measurements at TELBE are data intensive, which can be as high as 20GB per experiment, that can lasts up to several minutes. As a result, the current data aquisition and data analysis stages are decoupled, where in the first step the primary data is processed and stored at HZDR and in a later step, restricted data access is made available to the user for post-processing.</p>
<p>In this poster contribution, we present an integrated workflow for post-processing of the experimental data at TELBE with in-built exchange of metadata between the experiment control software LabView and the workflow execution engine <a href="https://doi.org/10.1109/HPCSim.2016.7568392">UNICORE</a>. We also present the guidance system <a href="https://doi.org/10.1145/3456287.3465477">HELIPORT</a> which manages the metadata of the associated project proposal and job information from UNICORE, and integrates with the electronic lab notebook (<a href="https://www.mediawiki.org/wiki/Project:About">MediaWiki</a>), providing a user-friendly interface for monitoring the actively running experiments at TELBE.</p>
https://doi.org/10.5281/zenodo.7054583
oai:zenodo.org:7054583
eng
Zenodo
https://zenodo.org/communities/heliport
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7054582
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
data management
terahertz
High-Field High-Repetition-Rate
workflow
metadata
Intergrated Data Workflow using HELIPORT at TELBE
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7188049
2022-10-12T14:26:18Z
openaire
user-daphne4nfdi
user-hmc-conference_2022
Hannemann, Moritz
2022-10-12
<p>Recording data with the help of photons and neutrons is limited to bigger institutes. Besides the limited time slots, this process is also quite expensive. To save resources, DAPHNE4NFDI focuses on creating ontologies and infrastructure to make all data from its participants FAIR. This enables users not only to use existing data but also to automatically fetch data for analysis. This analysis process can also be started in the institute context. This way, analysis can be made repeatable as well, because the used software is stored and versioned at the institutes.<br>
Three big building blocks for this project are ontologies, metadata catalogs and a common search across all institutes. Onthologies are used to have the same names for the same variable or technique. Meta-data catalogs essentially are databases that store meta-data of the collected data. This meta-data describes the environment the data was collected in. The common search, along with the ontologies and meta-data catalogs, then enable users around the world to search for data by its meta-data.<br>
A problem that other projects share is the sharing and tracking of data across multiple instances. If a sample is created in one institute and then taken to another one, the data has to be shared. Should the sample be altered or destroyed, this change has to be communicated to everyone else in order to save the whole life-cycle.<br>
At JCNS we use the instrument control software Nicos. Nicos implements the concepts of data-sinks, which enables us to save the recorded data in multiple ways. To decouple Nicos and the meta-data catalog, we plan to use a structure that buffers every request. The two biggest advantages are network connection independence and logging of all operations.</p>
https://doi.org/10.5281/zenodo.7188049
oai:zenodo.org:7188049
eng
Zenodo
https://zenodo.org/communities/daphne4nfdi
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7188048
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
HMC Conference 2022, Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
daphne
nfdi
dpahne4nfdi
metadata
fair
DAPHNE4NFDI
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7274860
2022-11-04T14:26:31Z
openaire
user-hmc-conference_2022
Buttigieg, Pier Luigi
Söding, Emanuel
Weinelt, Martin
Pörsch, Andrea
2022-11-02
<p>In pursuit of deep and expressive semantic interoperability, the Earth and Environment Hub is adopting a three-pillared approach to develop strategically and technically aligned capacity within the Helmholtz Association and globally.<br>
<br>
The first pillar is implementation of high-quality, future-oriented semantic solutions for Earth and environmental applications. HMC E&E personnel lead the development of the Environment Ontology (ENVO), an internationally recognised, highly expressive, and adopted community ontology for environmental research, management, and operations. Leveraging the practices, technologies, and interoperability architecture of the Open Biomedical and Biological Ontologies (OBO) Foundry, ENVO hosts machine-friendly representations of classification systems including the World Wildlife Fund's biomes and ecoregions, the Global Platform for Marine Litter's litter and debris classification for reporting towards Sustainable Development Goal (SDG) 14, and the UNEP World Conservation Monitoring Centre's mountain classification. Current activities are deepening links to the SDGs through collaboration with UN Environment, the UN Statistical Division, and UN Data, particularly on environmental hazards and disasters.<br>
<br>
Our second pillar is harmonisation of existing semantic resources to enhance interoperability amongst them. Through the work of an Earth Science Information Partners' (ESIP) cluster for semantic harmonisation, we are supporting efforts to harmonise semantics for vocabularies, glossaries, thesauri, and ontologies describing the cryosphere, the marine realm, natural hazards and disasters, and heliosphere. This activity engages major global stakeholders - including the WMO and NASA - and fosters collaborative interoperation between formerly competing standards.<br>
<br>
Our third pillar is the co-development and deployment of lightweight semantic solutions for knowledge graph creation and maintainence by multiple parties. Leveraging ESIP's Science on Schema (SoSo) approaches, our personnel are leading co-development of the UNESCO Intergovernmental Oceanographic Commission's Ocean Data and Information System (ODIS) and Ocean InfoHub (OIH), requested by the Member States. As it matures, we seek to merge this graph with its counterparts emerging in the Polar community and others, as well as ontological graphs noted in the other pillars.<br>
<br>
In conclusion, our efforts are addressing local and global needs in environmental semantics through broad, multilateral collaboration while creating fluid capacity exchange between all actors. This approach will support the creation of Helmholtz data spaces bearing intrinsic semantic compatibility with external systems and ready to transfer Helmholtz data to address global challenges.</p>
https://doi.org/10.5281/zenodo.7274860
oai:zenodo.org:7274860
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7274859
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Conference title: Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
ontology
earth and environment
semantic
biological
biomedical
UNESCO
ESIP
HMC Earth and Environment - Knowledge representation for globally-oriented semantic interoperability
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7185671
2022-10-13T14:16:37Z
openaire
user-ta3-m5
user-hmc-conference_2022
user-konsortswd
user-nfdi
Xiaoyao Han
Claudia Saalbach
2022-10-05
<p>Researchers in the social sciences use various software for statistical analysis of rectangular, structured data . The various data formats which are only partially compatible impede data exchange and reuse. In particular, proprietary data formats endanger those in the FAIR principles enshrined demand for interoperability. The project <a href="https://www.diw.de/en/diw_01.c.813498.en/projects/konsortswd_-_consortium_for_the_social__behavioural__educati___sciences_in_the_national_research_data_infrastructure__nfdi.html">Open Data Format</a> aims to develop a non-proprietary Open Data Format enriched with multi-level metadata that can be collectively used in popular statistical software. At the same time, The Open Data Format can be enriched with multilingual metadata as well as further links to data portals, which allows direct access to online documentation materials via the statistical software itself. The project is in the frame of <a href="https://zenodo.org/record/4446457#.Yv_LWXZBzIV">KonsortSWD</a>, consortium for the social, behavioural, educational and economic sciences and is funded by NFDI. In this conference we would like to introduce our work from current stage, including specification of metadata profile and development of technical import and export filters for the statistical program R and Stata.</p>
https://doi.org/10.5281/zenodo.7185671
oai:zenodo.org:7185671
Zenodo
https://zenodo.org/communities/nfdi
https://zenodo.org/communities/ta3-m5
https://zenodo.org/communities/hmc-conference_2022
https://zenodo.org/communities/konsortswd
https://doi.org/10.5281/zenodo.7185670
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, virtual, 05-06 October 2022
metadata, FAIR, Interoperability
Open, Metadata-Enriched, Non-Proprietary Data Format for Data Dissemination
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7148767
2022-10-05T14:33:00Z
openaire
user-hmc-conference_2022
user-helmholtz_hmc
Jens Bröder
Pedro Videgain Barranco
Volker Hofmann
Stefan Sandfeld
2022-10-05
<p>For research data to be reusable by scientists or machines, the data and associated meta-data should comply with the so-called "FAIR principles", i.e. it should be findable, accessible, interoperable, and reusable [1]. To realize this, is not a straightforward task, as researchers do not know how FAIR or un-fair their data actually is and how to improve their FAIRness. A quantitative measure, which is easy to apply could help. The Helmholtz Metadata Collaboration (HMC) works on improving tools to automate the assessment of the FAIRness of publications.</p>
<p>The F-UJI framework [2] originating from the FAIRsFAIR project is a powerful tool that provides a score for the FAIRness for machine findable and readible metadata of a given publication with respect to the FAIRsFAIR metric [3]. We co-develop F-UJI to explore and evaluate ways to apply it in user-sided tools.</p>
<p>On this poster we present a FAIR assessment through F-UJI of Helmholtz data repositories. With our work, we want to identify gaps in the Helmholtz data infrastructure with respect to the FAIRness of (meta)data and how these gaps can be closed effectively. We also provide an outlook on possible research into the development of FAIRness over time within communities.</p>
<p>[1] Wilkinson, M.D.et al. Sci Data 3, 160018 (2016)<br>
[2] Devaraju, A., Huber, R. (2020). F-UJI, Zenodo. https://doi.org/10.5281/zenodo.4063720<br>
[3] Devaraju, A., et al. (2020). FAIRsFAIR Metrics. Zenodo.<br>
https://doi.org/10.5281/zenodo.6461229</p>
https://doi.org/10.5281/zenodo.7148767
oai:zenodo.org:7148767
eng
Zenodo
https://zenodo.org/communities/helmholtz_hmc
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7148766
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration Conference 2022, 05-06.10.2022
FAIR
FAIR assessment
F-UJI
FAIR principles
Quantifying FAIRness: evaluating Helmholtz data repositories using F-UJI
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7120694
2022-11-14T08:27:18Z
user-orcid-de
openaire
user-hmc-conference_2022
Vierkant, Paul
Schrader, Antonia
Pampel, Heinz
2022-09-28
<p>The Open Researcher and Contributor ID <a href="http://orcid.org/">ORCID</a> strives to enable transparent and trustworthy connections between researchers, their contributions, and their affiliations by providing a unique, persistent identifier for individuals to use as they engage in research, scholarship, and innovation activities. ORCID is therefore an essential piece of the puzzle for increasing the discoverability of researchers by disambiguating them from all the other researchers with the same or even a similar name and definitively connecting them with their research contribution metadata (e.g. their scholarly record). Furthermore, ORCID specifically addresses each of the FAIR findability principle components. The international non-profit organization <a href="http://orcid.org/">ORCID</a>, on which the initiative is based, already connects over 14 million persons worldwide with their research outcomes. </p>
<p>In order to promote the widespread implementation of ORCID at universities and non-university research institutions in Germany, the project "ORCID DE" was launched and is funded by the German Research Foundation (DFG). </p>
<p>With the development of claiming services for the national bibliography of the German National Library and the BASE (Bielefeld Academic Search Engine), one of the world's most voluminous search engines especially for academic web resources, ORCID DE provides central tools for shaping the distribution and quality of metadata of scholarly communication from Germany.</p>
<p>Furthermore the project fosters the interconnection of ORCID with other persistent identification (PID) systems and therefore contributes to more discoverability, accessibility, and visibility of research outcomes.</p>
<p>As coordinator of the project, the <a href="https://os.helmholtz.de/en/">Helmholtz Open Science Office</a> makes an important contribution to improving scientific information management in the context of Open Science </p>
<p>Further project partners of ORCID DE are <a href="https://www.dnb.de/">Deutsche Nationalbibliothek</a>, <a href="https://www.uni-bielefeld.de/ub/">Universitätsbibliothek Bielefeld</a>, <a href="http://v/">DataCite - International Data Citation Initiative e. V.</a> and <a href="https://www.tib.eu/en/">Technische Informationsbibliothek (TIB) Hannover</a>.</p>
https://doi.org/10.5281/zenodo.7120694
oai:zenodo.org:7120694
eng
Zenodo
https://zenodo.org/communities/orcid-de
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7120693
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration l Conference 2022, WWW, October 5-6, 2022
ORCID
Open Researcher and Contributor ID
Open Science
PID
Open Access
Persistent Identifier
ORCID in Germany - A project-driven success story
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7092348
2022-11-30T09:06:39Z
openaire
user-hermes
user-hmc-conference_2022
Druskat, Stephan
Bertuch, Oliver
Knodel, Oliver
Juckeland, Guido
Meinel, Michael
Kelling, Jeffrey
Schlauch, Tobias
2022-09-19
<p>Software as an important method and output of research should follow the RDA "FAIR for Research Software Principles". In practice, this means that research software, whether open, inner or closed source, should be published with rich metadata to enable FAIR4RS. For research software practitioners, this currently often means following an arduous and mostly manual process of software publication. <a href="https://software-metadata.pub">HERMES</a>, a project funded by the Helmholtz Metadata Collaboration, aims to alleviate this situation. We develop configurable, executable workflows for the publication of rich metadata for research software, alongside the software itself. These workflows follow a push-based approach: they use existing continuous integration solutions, integrated in common code platforms such as GitHub or GitLab, to harvest, unify and collate software metadata from source code repositories and code platform APIs. They also manage curation of unified metadata, and deposits on publication platforms. These deposits are based on deposition requirements and curation steps defined by a targeted publication platform, the depositing institution, or a software management plan. In addition, the HERMES project works to make the widely-used publication platforms InvenioRDM and Dataverse "research software-ready", i.e., able to ingest software publications with rich metadata, and represent software publications and metadata in a way that supports findability, assessability and accessibility of the published software versions. Beyond the open source workflow software, HERMES will openly provide templates for different continuous integration solutions, extensive documentation, and training material. Thus, researchers are enabled to adapt automated software publication quickly and easily. Our poster presents a high-level overview of the HERMES concept, its status and an outlook.</p>
https://doi.org/10.5281/zenodo.7092348
oai:zenodo.org:7092348
eng
Zenodo
https://zenodo.org/communities/hermes
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7092347
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
software publication
FAIR4RS
software metadata
Automated FAIR4RS software publication with HERMES
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7186601
2023-07-18T15:26:36Z
openaire
user-hmc-conference_2022
user-helmholtz_hmc
Eschke, Catriona
Kirchner, Fabian
Sahim, Sayed Ahmad
Held, Martin
Jung, Nicole
2022-10-11
<p>Electronic lab notebooks (ELNs) serve as means to gather analog metadata, e.g. experimental parameters, that would otherwise be hard to digitalize. However, different systems are often used within the same research institution or community, especially when covering a long, interdisciplinary process chain. The use of different systems in the same institution - each addressing distinct requirements for discipline-specific needs - enables the availability of a broad functionality but results in challenges due to an often missing interoperability of the metadata. We are addressing this lack of interoperability for the two ELNs Herbie and Chemotion with an API-based data exchange.</p>
<p>A specific use-case in membrane research is treated as a starting point. As a first step, the necessary metadata for the use case were defined in both ELNs and their data fields implemented. A mapping of the corresponding data fields and the adaptation of general metadata schemes lead to a discipline-specific metadata exchange format being processed via the ELNs’ APIs.</p>
<p>The entire process will be generalized in a guideline, motivating other ELN developers to implement interconnections for metadata transfer. The envisaged reduction of boundaries between different disciplines will enable the creation of large and coherent data sets in experimental research.</p>
https://doi.org/10.5281/zenodo.7186601
oai:zenodo.org:7186601
eng
Zenodo
https://zenodo.org/communities/helmholtz_hmc
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7186600
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
Electronic Lab Notebook
Membrane
ELN-DIY-Meta: Creating Interoperability for ELNs
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7153323
2022-10-10T06:44:35Z
openaire
user-hmc-conference_2022
Marco Dallavalle
Ronny Gey
Jeroen Staab
Jan Bumberger
Hannes Taubenböck
Maike Ferland
Marie Standl
Kathrin Wolf
2022-10-06
<p>Cross-domain research is often hampered by the lack of harmonized metadata schemas and standards. Metadata of different domains vary in origin, format and scope, so they cannot be merged routinely. In the interdisciplinary field of environmental epidemiology, an efficient linkage of health data with the multitude of environmental and earth observation data is crucial to quantify human exposures. To bridge the gap between the metadata of our different research fields, we aim to compile machine-readable and interoperable metadata schemas for exemplary data of our three domains Health (HMGU), Earth & Environment (UFZ), and Aeronautics, Space & Transport (DLR).</p>
<p>As use cases for metadata compilation, enrichment, and pooling, the project partners contributed with metadata of the child cohorts GINI and LISA (HMGU), drought monitor (UFZ) and land cover (DLR). UFZ and DLR will adopt as common standard ISO 19115: Geographic Metadata Information. For HMGU, we reviewed several metadata standards for health data (e.g. CDISC ODM, Snomed CT, HL7 FHIR) and started to upload our metadata to the NFDI4health StudyHub, an inventory of German health studies on COVID-19. In addition, we have developed a workflow to transform base cohort information in an ISO 19115 compliant manner without exposing sensitive information about participants’ data. </p>
<p>Spatial and time coverage will be the main mapping criteria. The metadata of our three domains will be uploaded into an instance of GeoNetwork, a catalog application that we are currently setting up in a testing environment, where they can be jointly queried and searched. We aim to have a server version ready by the end of the project that can be augmented with additional metadata from our domains, but also from other fields, to facilitate interdisciplinary research.</p>
https://doi.org/10.5281/zenodo.7153323
oai:zenodo.org:7153323
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7153322
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Health
Environment
Earth observation
Metadata generation
Mapping
Metadata generation, enrichment and linkage across the three domains health, environment and earth observation
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7243865
2022-10-24T14:26:27Z
openaire
user-hmc-conference_2022
Blumenröhr, Nicolas
Jejkal, Thomas
Pfeil, Andreas
Stotzka, Rainer
2022-10-24
<p>The application case for implementing and using the FAIR Digital Object (FAIR DO) concept aims to simplify usage of label information for composing Machine Learning (ML) training data.<br>
Image data sets curated by different domain experts usually have non-identical label terms. This prevents images with similar labels from being easily assigned to the same category. Therefore, using the images collectively for application as training data in ML comes with the cost of laborious relabeling. To automate this process, machine-actionable decisions for label information must be enabled. For this purpose the FAIR DO concept is used. A FAIR DO is a representation of scientific data and requires at least a globally unique Persistent Identifier (PID), relevant metadata, and a type.<br>
We show the requirements for specifying and using FAIR DOs when applied to ML data. Based on an application case with Scanning Electron Microscopy (SEM) image data, a Proof-of-Principle approach shows the potential of the concept for usage in ML related data management.<br>
This work has been supported by the research program ‘Engineering Digital Futures’ of the Helmholtz Association of German Research Centers and the Helmholtz Metadata Collaboration (HMC) Platform.</p>
https://doi.org/10.5281/zenodo.7243865
oai:zenodo.org:7243865
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7243864
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
FAIR DO Application Case for Composing Machine Learning Training Data
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7152931
2022-10-06T14:26:18Z
openaire
user-hmc-conference_2022
user-fair_wish
Elger, Kirsten
Brauser, Alexander
Wieczorek, Mareike
Heim, Birgit
Frenzel, Simone
Baldewein, Linda
Kleeberg, Ulrike
2022-10-06
<p>Physical samples or specimen are often at the beginning of the “research chain” as they are the source for many data described in scholarly literature. The International Generic Sample Number (IGSN) is a globally unique and persistent identifier (PID) for physical samples and collections with discovery function in the internet. IGSNs enable to directly link data and publications with samples they originate from and thus close the last gap in the full provenance of research results. The modular IGSN metadata schema has a small number of mandatory and recommended metadata elements that can be individually extended with discipline-specific elements.</p>
<p>Based on three use cases that represent all states of digitisation - from individual scientists, collecting sample descriptions in their field books to digital sample management systems fed by an app that is used in the field - FAIR WISH will (1) develop standardised and discipline specific IGSN metadata schemes for different sample types from the Earth and Environment Sciences, (2) develop workflows to generate machine-readable IGSN metadata from different states of digitisation, (3) develop workflows to automatically register IGSNs and (4) prepare the resulting workflows for further use in the Earth Science community.</p>
<p>"FAIR WISH - FAIR Workflows to establish IGSN for Samples in the Helmholtz Association" is a pilot project of the Helmholtz Metadata Collaboration, funded by the Helmholtz Association. This presentation was presented during the HMC Connference on 5th October 2022.</p>
https://doi.org/10.5281/zenodo.7152931
oai:zenodo.org:7152931
eng
Zenodo
https://doi.org/10.5281/zenodo.6787199
https://doi.org/10.5281/zenodo.7147531
https://zenodo.org/communities/hmc-conference_2022
https://zenodo.org/communities/fair_wish
https://doi.org/10.5281/zenodo.7152930
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
FAIR samples, metadata workflows, controlled vocabularies, IGSN, International Generic Sample Number, persistent identifier
FAIR WISH - FAIR Workflows to establish IGSN for Samples in the Helmholtz Association - Status Presentation
info:eu-repo/semantics/lecture
oai:zenodo.org:7291556
2022-11-05T02:26:37Z
openaire
user-hmc-conference_2022
Pfeil, Andreas
Jejkal, Thomas
2022-10-05
<p>Working in the realm of FAIR Digital Objects can be very abstract and sometimes overwhelming. There are so many aspects which have to be addressed in order to create a first FAIR Digital Object. The FAIR DO Cookbook aims to guide researchers and explain all required knowledge, ingredients, and steps to execute. The target audience are people building, controlling or maintaining infrastructure or software that should work with FAIR DOs in come way, as well as people interested in types, profiles, and PIDs work and how they can be created.</p>
https://doi.org/10.5445/IR/1000151864
oai:zenodo.org:7291556
Zenodo
https://zenodo.org/communities/hmc-conference_2022
info:eu-repo/semantics/openAccess
Other (Open)
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
Research Data Management
FAIR Digital Object
FAIR DO Cookbook – Recipes for FAIR Digital Objects
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7157694
2022-10-10T06:48:36Z
openaire
user-hmc-conference_2022
Günther, Gerrit
Schweikert, Jan
Mannix, Oonagh
Süß, Wolfgang
2022-10-06
<p>We present three use cases which showcase methods of providing a detailed metadata description with the goal of increasing the reusability of data.<br>
First, Hub Energy presents a photovoltaic system which required ontology development and the implementation of data models based on standards like <a href="https://doi.org/10.1109/PES.2006.1709546">IEC 61850</a> or <a href="http://docs.ogc.org/is/12-000r2/12-000r2.html">SensorML</a> as well as on <a href="https://doi.org/10.2777/54599">FAIR Digital Objects</a> (FDO). The backend was realized using the <a href="https://doi.org/10.5445/IR/1000131498">Metastore</a> software from the Fair Data Commons while a FDO browser was implemented for visualization which offers a cascading search for metadata and application data.<br>
In a second use case of Hub Energy, time series data of the energy consumption of the buildings on KIT's Campus North are described by automatically generated <a href="https://doi.org/10.3233/DS-210053">RO-Crates</a>. This allows energy researchers to use these time series data without any knowledge about the structure of the database and provides a case study on working with RO-Crate technology.</p>
<p>The third use case is provided by Hub Matter, in the research field of high energy physics, and shows the optimization of a typical data set for data publication. To increase FAIRness of the distributed file set, (meta)data is (i) enriched by metadata, (ii) converted to a machine- as well as human-readable format and (iii) linked to a central file to create scientific context. By abstracting from community-specific details these measures can serve as a general approach to make data publishable.<br>
The variety of use cases presented provides a menu of technologies and approaches implemented in diverse contexts to enhance the reusability of data, along with general advice for anyone looking to do the same.</p>
https://doi.org/10.5281/zenodo.7157694
oai:zenodo.org:7157694
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7157693
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Virtual Event, 05.-06.10.2022
FAIR, Use Case, FAIR Digital Object, RO-Crate, HMC, Metadata
Use Cases in HMC – from Generation to Reuse of Data
info:eu-repo/semantics/lecture
oai:zenodo.org:7274845
2022-11-04T14:26:31Z
openaire
user-hmc-conference_2022
Söding, Emanuel
Weinelt, Martin
Buttigieg, Pier Luigi
Pörsch, Andrea
2022-11-02
<p>PIDs (Persistent Identifiers) are a core concept at the center of FAIR data architectures such as FAIR Digital Objects. They point to a digital resource such as a publication, dataset or a set of information in a distinctive and lasting fashion and are assured to persist over longer, defined periods of time.<br>
<br>
We looked into six established PID systems (ROR, ORCID, PIDINST, IGSN, DataCite DOI, Crossref DOI) to map the interconnection (graph) and overlap between systems. This was carried out by inspecting and comparing the metadata schemas of these PID systems in their current version to find out, to what extent they support each other as PID systems and how this is done.<br>
<br>
We expected these PID schemas not to overlap in too many elements, but we expected some of the systems / schemas to recognize and make use of each another.<br>
<br>
The number of external PID systems supported varies considerably for the six PID systems investigated, with ROR at 4 and up to 49 systems at ORCID. The system mostly implemented as a reference is DOI (4 other systems do refer to DOI in their metadata schema), while ROR is only referenced by DataCite DOIs yet.<br>
<br>
Interconnected PID systems can be visualized as graphs of relationships (PID graphs) between for instance scientists, datasets, publications, institutions etc. They can be machine actionable and thus be tailored to specific questions or fields of interest, as was shown by EU programme FREYA (<a href="https://www.project-freya.eu">https://www.project-freya.eu</a>).<br>
<br>
Our findings show, that PIDs act as an important part of the data space we are constructing. They allow to link meta information of different data sets in a uniform manner. Consistently implementing PIDs will allow a high level of informational data interoperability among distributed data sets, which should complement other interoperability measures, e.g. the semantic interoperability.</p>
https://doi.org/10.5281/zenodo.7274845
oai:zenodo.org:7274845
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7274844
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
strategy
standards
PID
earth and environment
HMC Earth and Environment - Using PIDs in Helmholtz Earth and Environment Data Infrastructures
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7274877
2022-11-04T14:26:30Z
openaire
user-hmc-conference_2022
Söding, Emanuel
Weinelt, Martin
Pörsch, Andrea
Buttigieg, Pier Luigi
Lorenz, Sören
2022-11-02
<p>HMC Earth and Environment (E&E) strives to define, create and activate a Helmholtz FAIR Data Space (HFDS) as a "decentralized infrastructure for trustworthy data sharing and exchange in data ecosystems based on commonly agreed principles" (Nagel L., Lycklama D., 2021). Within HMC E&E the data space consists of common agreements to implement the FAIR building blocks (see below), leading to internal interoperability of data. In addition a data integration system is needed, which will act as a data broker between data infrastructures, providing internal and external integration and data access opportunities.<br>
<br>
Unlike the concept for the European data space, which is largely tailored towards commercial data, the Helmholtz Association's data space covers primarily research data, which may or may not be openly accessible. For such research data, we envision four major building blocks required, to implement and activate the data space:</p>
<p>1. the consistent usage of high-resolution PID referable metadata elements, e.g. ORCID, DOI, ROR, InstPID and others (see other poster).<br>
2. The implementation of consistent semantic concepts within data repositories and infrastructures (see other poster).<br>
3. The containerization datasets and metadata within machine actionable FAIR digital objects (FDOs)<br>
4. The agreement and implementation of standardized interfaces to access and address containerized data through common APIs.</p>
<p>To implement these features a co-design and implementation process needs to be set up. Within this co-design process procedures should be agreed upon, implemented and maintained by data stewards and data infrastructures together, in order to support data producers, data maintainers and data re-users and ease their handling of research data.<br>
<br>
In HMC we plan to conduct the following actions, in order to develop the HFDS together with our scientific and technical communities:</p>
<p>1. Define the concept and requirements of a Helmholtz data space, which is in-line with other data spaces in preparation.<br>
2. Establish a communication platform allowing us to define and agree upon the building blocks required to set up the data space (see above and other poster).<br>
3. Work with data repositories and data stewards to implement and document the building blocks required to establish the data space.<br>
4. Build a data integration system, connecting the different decentralized parts of the data space and connect it to other data spaces.<br>
These activities will ultimately lead to the establishment of an well-formed interoperable FAIR Data Space, which anyone interested is welcome to join and shape.<br>
<br>
(1) Nagel L., Lycklama D. (2021): Design Principles for Data Spaces. Position Paper. Version 1.0. Berlin, DOI: <a href="http://doi.org/10.5281/zenodo.5105744">http://doi.org/10.5281/zenodo.5105744</a></p>
https://doi.org/10.5281/zenodo.7274877
oai:zenodo.org:7274877
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7274876
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Conference title: Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
strategy
data space
earth and environment
HMC Earth and Environment - Overall Strategy and Implementation of a FAIR Helmholtz Data Space
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7227714
2022-10-21T02:26:26Z
openaire
user-nfdi-matwerk
user-hmc-conference_2022
user-nfdi4ing
Stotzka, Rainer
Abdildina, Gulzaure
Aversa, Rossella
Blumenröhr, Nicolas
Chelbi, Sabrine
Duda, Leonhard
Ernst, Felix
Frank, Laura
Götzelmann, Germaine
Hartmann, Volker
Inckmann, Maximilian
Jejkal, Thomas
Jha, Vandana
Joseph, Reetu
Ost, Philipp
Pfeil, Andreas
Shakeel, Yusra
Soysal, Mehmet
Tögel, Philipp
Vitali, Elias
2022-10-05
<p>A sophisticated ensemble of services and tools enables high-level research data and research metadata management in science. On a technical level, research datasets need to be registered, preserved, and made interactively accessible using repositories that meet the specific requirements of scientists in terms of flexibility and performance. These requirements are fulfilled by the Base Repo and the MetaStore of the KIT Data Manager Framework.<br>
In our data management architecture, data and metadata are represented as FAIR Digital Objects that are machine actionable. The Typed PID Maker and the FAIR Digital Object Lab provide support for the creation and management of data objects. Other tools enable editing of metadata documents, annotation of data and metadata, building collections of data objects, and creating controlled vocabularies.<br>
Information systems such as the Metadata Standards Catalog and the Data Collections Explorer help researchers select domain-specific metadata standards and schemas and identify data collections of interest.<br>
Infrastructure developers search the Catalog of Repository Systems for information on modern repository systems, and the FAIR Digital Object Cookbook for recipes for creating FAIR Digital Objects.<br>
<br>
Existing knowledge about metadata management, services, tools, and information systems has been applied to create research data management architectures for a variety of fields, including digital humanities, materials science, biology, and nanoscience. For Scanning Electron Microscopy, Transmission Electron Microscopy and Magnetic Resonance Imaging, metadata schemas were developed in close cooperation with the domain specialists and incorporated in the research data management architectures.<br>
This research has been supported by the research program ‘Engineering Digital Futures’ of the Helmholtz Association of German Research Centers, the Helmholtz Metadata Collaboration (HMC) Platform, the German National Research Data Infrastructure (NFDI), the German Research Foundation (DFG) and the Joint Lab “Integrated Model and Data Driven Materials Characterization (MDMC)”. Also, this project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101007417 within the framework of the NFFA-Europe Pilot (NEP) Joint Activities.</p>
https://doi.org/10.5445/IR/1000151569
oai:zenodo.org:7227714
eng
Zenodo
https://zenodo.org/communities/nfdi-matwerk
https://zenodo.org/communities/nfdi4ing
https://zenodo.org/communities/hmc-conference_2022
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Metadata
FAIR Digital Objects
FAIR Data Commons / Essential Services and Tools for Metadata Management Supporting Science
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7432171
2023-01-10T07:59:59Z
openaire
user-hmc-conference_2022
Ihsan, Ahmad Zainul
Fathalla, Said
Aversa, Rossella
Jalali, Mehrdad
Panighel, Mirco
Osmenaj, Elda
Hofmann, Volker
Sandfeld, Stefan
2022-12-13
<p>How can a computer understand the relations of data or objects from the real world? Ontologies are semantic artifacts that capture knowledge about their domain of interest in a machine-understandable form. The main goal of developing ontologies is to formalize concepts and their relations through which humans express meaning and to use them as a communication interface to machines. Thus, ontology development is an important step towards generating linked and FAIR data.</p>
<p>Within HMC we support and co-develop domain and application-level ontologies. Here we present two developments: Dislocation Ontology (DISO) and Model and Data-Driven Materials Characterization Provenance (MDMC-PROV).</p>
<p> </p>
<p><strong>DISO: </strong> An important class of materials is crystalline materials, e.g., metals and semiconductors, which nearly always contain defects, the “dislocations”. This type of defect determines many important material properties, e.g., strength and ductility. Over the past years, significant effort has been put into understanding dislocation behavior across different length scales via experimental characterization techniques and simulations. However, there is still a lack of common standards to formally describe and represent disclocations. Thus, in this work we develop the dislocation ontology (DISO), which is a domain ontology that defines the concepts and relationships related to linear defects in crystalline materials. DISO is published<sup>1</sup> through a persistent URL following W3C best practices for publishing Linked data.</p>
<p><strong>MDMC-Prov: </strong>The rapid development of science and technology in everyday large data generation does not match the data understanding. These days, understanding how experiments are performed and results are derived become more complex due to a lack of provenance documentation. Therefore, the provenance must be tracked, described, and managed over the research process. Thus, in this work, we report an application ontology that can capture provenance information in materials science experiments. The ontology is based on the MDMC glossary<sup>2</sup>, which defines the common terms in the materials science experiments. From each term, we map to PROV-O<sup>3</sup>. These ensure the validity, reproducibility, and reusability of the data.</p>
<p> </p>
<p>[1] https://purls.helmholtz-metadaten.de/disos/diso</p>
<p>[2] https://jl-mdmc-helmholtz.de</p>
<p>[3] https://www.w3.org/TR/2013/NOTE-prov-primer-20130430/</p>
https://doi.org/10.5281/zenodo.7432171
oai:zenodo.org:7432171
https://doi.org/10.5445/IR/1000152174
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7432170
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ontology
ontology design
provenance
dislocation ontology
Domain level ontology design: DISO and MDMC-NEP Prov
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7144022
2022-11-30T09:06:37Z
openaire
user-hermes
user-hmc-conference_2022
Druskat, Stephan
Bertuch, Oliver
Knodel, Oliver
Meinel, Michael
Kelling, Jeffrey
Schlauch, Tobias
Juckeland, Guido
2022-10-04
<p>Presentation given at HMC Conference online on 2022-10-05.</p>
<p>Software as an important method and output of research should follow the RDA "FAIR for Research Software Principles". In practice, this means that research software, whether open, inner or closed source, should be published with rich metadata to enable FAIR4RS. For research software practitioners, this currently often means following an arduous and mostly manual process of software publication. HERMES, a project funded by the Helmholtz Metadata Collaboration, aims to alleviate this situation. We develop configurable, executable workflows for the publication of rich metadata for research software, alongside the software itself.<br>
These workflows follow a push-based approach: they use existing continuous integration solutions, integrated in common code platforms such as GitHub or GitLab, to harvest, unify and collate software metadata from source code repositories and code platform APIs. They also manage curation of unified metadata, and deposits on publication platforms. These deposits are based on deposition requirements and curation steps defined by a targeted publication platform, the depositing institution, or a software management plan. In addition, the HERMES project works to make the widely-used publication platforms InvenioRDM and Dataverse "research software-ready", i.e., able to ingest software publications with rich metadata, and represent software publications and metadata in a way that supports findability, assessability and accessibility of the published software versions.<br>
Beyond the open source workflow software, HERMES will openly provide templates for different continuous integration solutions, extensive documentation, and training material. Thus, researchers are enabled to adapt automated software publication quickly and easily. In this presentation, we provide an overview of the project aims, its current status, and an outlook on future development.</p>
https://doi.org/10.5281/zenodo.7144022
oai:zenodo.org:7144022
eng
Zenodo
https://doi.org/10.5281/zenodo.7092348
https://zenodo.org/communities/hermes
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7144021
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
HMC Conference 2022, Helmholtz Metadata Collaboration | Conference 2022, online, 05-06 October 2022
software publication
software metadata
FAIR4RS
Project HERMES: Automated FAIR4RS software publication with HERMES
info:eu-repo/semantics/lecture
oai:zenodo.org:7244696
2022-10-24T14:26:31Z
openaire
user-hmc-conference_2022
Hartmann, Volker
Jejkal, Thomas
Chelbi, Sabrine
2022-10-05
<p>MetaStore is a metadata repository for managing metadata documents. It supports communities in storing metadata documents in a predefined schema. It is therefore an important building block for more precise automated evaluation and/or retrieval of digital objects. With the help of the metadata documents, digital objects can also be evaluated/compared according to content-related aspects. XML and JSON are very common as data formats for such machine-interpretable documents. However, they are only meaningful if they adhere to a certain structure and are correctly filled in. MetaStore supports the use of XML and JSON schema as the definition for the document structure. It allows you to register your own and/or existing schemas in these two formats to ensure that the documents have the appropriate structure. When ingesting metadata documents, the structure is checked and invalid documents are rejected. All valid documents are assigned a persistent identifier and can be automatically indexed for search. Public documents can be harvested via a standardized protocol (OAI-PMH). The supplied web interface also provides a low-threshold entry point for managing documents and also allows documents to be created/edited without additional tools. This work has been supported by the research program ‘Engineering Digital Futures’ of the Helmholtz Association of German Research Centers and the Helmholtz Metadata Collaboration Platform.</p>
https://doi.org/10.5445/IR/1000151733
oai:zenodo.org:7244696
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
Metadata
Research Data Management
FAIR Digital Object
MetaStore - Managing Metadata for Digital Objects
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7147703
2023-02-13T08:50:59Z
openaire
user-hmc-conference_2022
user-ro
user-eu
Goble, Carole
Soiland-Reyes, Stian
2022-10-05
<p>HMC Keynote: Carole Goble, The University of Manchester</p>
<p>Title: RO-Crate: packaging metadata love notes into FAIR Digital Objects</p>
<p> </p>
<p>Abstract</p>
<p>The Helmholtz Metadata Collaboration aims to make the research data [and software] produced by Helmholtz Centres FAIR for their own and the wider science community by means of metadata enrichment [1]. Why metadata enrichment and why FAIR? Because the whole scientific enterprise depends on a cycle of finding, exchanging, understanding, validating, reproducing), integrating and reusing research entities across a dispersed community of researchers.</p>
<p> </p>
<p>Metadata is not just “a love note to the future” [2], it is a love note to today’s collaborators and peers. Moreover, a FAIR Commons must cater for the metadata of all the entities of research – data, software, workflows, protocols, instruments, geo-spatial locations, specimens, samples, people (well as traditional articles) – and their interconnectivity. That is a lot of metadata love notes to manage, bundle up and move around. Notes written in different languages at different times by different folks, produced and hosted by different platforms, yet referring to each other, and building an integrated picture of a multi-part and multi-party investigation. We need a crate!</p>
<p> </p>
<p>RO-Crate [3] is an open, community-driven, and lightweight approach to packaging research entities along with their metadata in a machine-readable manner. Following key principles - “just enough” and “developer and legacy friendliness - RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility and citability. As a self-describing and unbounded “metadata middleware” framework RO-Crate shows that a little bit of packaging goes a long way to realise the goals of FAIR Digital Objects (FDO)[4], and to not just overcome platform diversity but celebrate it while retaining investigation contextual integrity.</p>
<p> </p>
<p>In this talk I will present the why, and how Research Object packaging eases Metadata Collaboration using examples in big data and mixed object exchange, mixed object archiving and publishing, mass citation, and reproducibility. Some examples come from the HMC, others from EOSC, USA and Australia, and from different disciplines.</p>
<p> </p>
<p>Metadata is a love note to the future, RO-Crate is the delivery package.</p>
<p> </p>
<p>[1] <a href="https://helmholtz-metadaten.de/en">https://helmholtz-metadaten.de/en</a></p>
<p>[2] Scott, Jason The Metadata Mania, <a href="http://ascii.textfiles.com/archives/3181">http://ascii.textfiles.com/archives/3181</a>, June 2011</p>
<p>[3] Soiland-Reyes, Stian et al. “Packaging Research Artefacts with RO-Crate”. <em>Data Science</em>, 2022; 5(2):97-138, DOI: 10.3233/DS-210053</p>
<p>[4] De Smedt K, Koureas D, Wittenburg P. “FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units”. <em>Publications</em>. 2020; 8(2):21. https://doi.org/10.3390/publications8020021</p>
<p> </p>
<p> </p>
<p> </p>
https://doi.org/10.5281/zenodo.7147703
oai:zenodo.org:7147703
Zenodo
https://zenodo.org/communities/ro
https://zenodo.org/communities/hmc-conference_2022
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7147702
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
RO-Crate
Research Objects
data packaging
FAIR digital objects
metadata
RO-Crate: packaging metadata love notes into FAIR Digital Objects
info:eu-repo/semantics/lecture
oai:zenodo.org:7313153
2022-11-11T14:26:25Z
openaire
user-hmc-conference_2022
user-helmholtz_hmc
Videgain Barranco, Pedro
Kubin, Markus
Bröder, Jens
Gilein, Astrid
Günther, Gerrit
Preuß, Gabriel
Hofmann, Volker
Mannix, Oonagh
Sandfeld, Stefan
2022-10-05
<p>Publishing data in a FAIR <sup>1</sup> way is already part of good scientific practice. While institutional policy as well as funding and publishing guidelines support this, scientist, technicians, and data stewards struggle to realize it when handling their research data. The reason is that the FAIR principles are high level principles and guidelines rather than concrete implementations. This is one of the key missions of HMC: support the Helmholtz community in making their data FAIR in an easy and comparable way. Developing a sustainable strategy for this requires a detailed understanding of practices, strengths, and deficiencies with respect to applying each of the FAIR principles. Here, tools that assess data FAIRness in comparison to a set of specific implementations in a quantitative fashion can help. When handling a dataset, such measures can aid the understanding of how FAIR a dataset actually is, as well as how to improve its FAIRness.</p>
<p>In this Blitzlicht-Talk, HMC Hub Matter and Hub Information will jointly present insights, benefits, and pitfalls from applying and further developing such metrics. For this we used the F-UJI tool <sup>2,3</sup>, a python-based development by the FAIRsFAIR project, in two complementary projects.</p>
<p>In a first, "repository-based" approach, we evaluate data repositories based on the data contained. The analyzed results are then used towards informing infrastructural development towards improving data FAIRness.</p>
<p>In a second, "publication-based" approach, data publications from individual research centers or specific fields are evaluated with F-UJI. The results are gathered and visualized in an interactive pilot dashboard. This helps to identify and quantify the usage of repositories by Helmholtz‘s research communities as well as to better support the development of relevant infrastructure for FAIR data practices.</p>
<p>We discuss our experience from these automatic FAIR assessment approaches and compare them to complementary insights from a manual FAIR assessment of a particular data pipeline <sup>4</sup> using the FAIR Data Maturity Model <sup>5</sup>. We discuss future plans for metric development and the potential use of such metrics in user-sided tooling.</p>
<p>[1] <a href="https://doi.org/10.1038/sdata.2016.18">https://doi.org/10.1038/sdata.2016.18</a><br>
[2] <a href="https://doi.org/10.5281/zenodo.4063720">https://doi.org/10.5281/zenodo.4063720</a><br>
[3] <a href="https://doi.org/10.5281/zenodo.6461229">https://doi.org/10.5281/zenodo.6461229</a><br>
[4] <a href="https://doi.org/10.5281/zenodo.6059994">https://doi.org/10.5281/zenodo.6059994</a><br>
[5] <a href="https://doi.org/10.15497/rda00050">https://doi.org/10.15497/rda00050</a></p>
https://doi.org/10.5281/zenodo.7313153
oai:zenodo.org:7313153
eng
Zenodo
https://zenodo.org/communities/helmholtz_hmc
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7313152
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, 5-6 November 2022
FAIR data
Helmholtz Association
FAIR Assessment
F-UJI
FAIR Data Maturity Model
Helmholtz Metadata Collaboration
HMC
How FAIR is my data? Benefits and pitfalls of quantitative assessment of FAIRness
info:eu-repo/semantics/lecture
oai:zenodo.org:7198861
2022-10-14T14:26:35Z
openaire
user-hmc-conference_2022
Lehmann, Jos
Schader, Philipp
Kulla, Lucas
Maier-Hein, Klaus
Nolden, Marco
2022-10-14
<p>The Helmholtz Metadata Collaboration (HMC) promotes the use of metadata in Research Data Management as a means to achieving data findability, accessibility, interoperability, reusability (FAIR). These in turn enable or optimize software functionalities essential to automated research processes, such as multi-, inter- and transdisciplinary indexing and retrieval, versioning, provenance tracking, data contextualization, workflow reproduction, compliance assessment, publication. Metadata are also key to Hybrid Artificial Intelligence, i.e. the integration of sub-symbolic and symbolic techniques, which improves Machine Learning systems’ trainability and explainability.</p>
<p>In this context, Hub Health is developing a framework for the identification and specification of metadata use cases in health data analysis workflows. This will provide stakeholders, e.g. researchers or developers, with insight into types and roles of metadata in a given workflow phase. For instance, metadata that are more extrinsic to the data, such as data format, are mostly needed during data acquisition. Metadata that are more intrinsic to the data, such as terminologies, may also contribute to the data analysis itself, for instance during feature extraction.</p>
<p>The initial case study for the framework is Medical Imaging. This offers a template for workflows that are ubiquitous in medical research and practice (e.g. diagnostics and prognostics) and it can be extended to the analysis of data other than images, e.g. natural language or diagnostic test data.</p>
<p>The toolkit KAAPANA, which supports AI-based medical data analysis workflows, is being used to benchmark the use of metadata in medical imaging workflows, to test new ideas and to integrate HMC resources, such as the Hub Health Information Portal or other tools from FAIR Data Commons or other Hubs.</p>
<p>In particular, image segmentation in KAAPANA, a phase shared by many imaging workflows, is currently being reviewed from the perspective of the functionalities targeted in HMC (from indexing and retrieval to trainability and explainability).</p>
https://doi.org/10.5281/zenodo.7198861
oai:zenodo.org:7198861
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7198860
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
Medical Imaging
Radiomics
Segmentation
Standards
Semantics
Software Tools
Medical Imaging as a Case Study of the Use of Metadata in Health Research Data Management
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7313873
2022-11-17T08:16:07Z
openaire
user-hmc-conference_2022
user-helmholtz_hmc
Kubin, Markus
Günther, Gerrit
Cristiano, Luigia
Görzig, Heike
Krahl, Rolf
Mannix, Oonagh
2022-10-05
<p>A central mission of HMC is to support the data producers of the Helmholtz community in making their data FAIR. Developing a sustainable strategy for doing so requires a detailed understanding of community-specific practices, strengths, and limitations related to the application of each FAIR data guideline. We have applied the FAIR Data Maturity Model, developed by the respective RDA working group, to a prototypical data pipeline in the research field Matter. In our poster presentation, we would like to provide an overview of our approach and discuss the lessons learned that helped us identify key activities for meeting community needs.</p>
https://doi.org/10.5281/zenodo.7313873
oai:zenodo.org:7313873
eng
Zenodo
https://zenodo.org/communities/helmholtz_hmc
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7313872
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
HMC Conference 2022, 5-6 October 2022
FAIR Data Maturity Model
FAIR Assessment
Helmholtz Metadata Collaboration
Lessons learned from applying the FAIR Data Maturity Model to a prototypical data pipeline in Matter
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7104942
2023-06-21T11:57:23Z
openaire
user-heliport
user-hmc-conference_2022
user-derse23
Voigt, Martin
Gruber, Thomas
Lokamani, Mani
Müller, Stefan E.
Kelling, Jeffrey
Juckeland, Guido
Schuller, Bernd
Kessler, Alexander
Hein, Joachim
Lee, Chien-Li
Kaluza, Malte C.
Pape, David
Knodel, Oliver
2022-09-22
<p><a href="https://heliport.hzdr.de">HELIPORT</a> is a data management solution that aims at making the components and steps of the entire research experiment’s life cycle discoverable, accessible, interoperable and reusable according to the FAIR principles.<br>
Among other information, HELIPORT integrates documentation, scientific workflows, and the final publication of the research results - all via already established solutions for proposal management, electronic lab notebooks, software development and devops tools, and other additional data sources. The integration is accomplished by presenting the researchers with a high-level overview to keep all aspects of the experiment in mind, and automatically exchanging relevant metadata between the experiment’s life cycle steps.<br>
Computational agents can interact with HELIPORT via a REST API that allows access to all components, and landing pages that allow for export of digital objects in various standardized formats and schemas. An overall digital object graph combining the metadata harvested from all sources provides scientists with a visual representation of interactions and relations between their digital objects, as well as their existence in the first place. Through the integrated computational workflow systems, HELIPORT can automate calculations using the collected metadata.<br>
By visualizing all aspects of large-scale research experiments, HELIPORT enables deeper insights into a comprehensible data provenance with the chance of raising awareness for data management.</p>
https://doi.org/10.5281/zenodo.7104942
oai:zenodo.org:7104942
eng
Zenodo
https://doi.org/10.14278/rodare.946
https://zenodo.org/communities/heliport
https://zenodo.org/communities/hmc-conference_2022
https://zenodo.org/communities/derse23
https://doi.org/10.5281/zenodo.7104941
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Data management
FAIR
workflows
scientific project
project lifecycle
HELIPORT — An Integrated Research Data Lifecycle
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7198682
2022-10-14T14:26:34Z
openaire
user-hmc-conference_2022
user-nfdi4ing
Ost, Philipp
2022-10-05
<p>For research data to be used efficiently, it must be easy to find and access. This is a requirement in all areas of science. The Data Collections Explorer, developed within NFDI4Ing for the engineering sciences, targets these needs. It is an information system that provides an overview of research data repositories, archives, databases as well as individual datasets published in the field. Two use cases are considered:</p>
<p>1. Scientists searching for data sets. Are there datasets available to aid in your research? Are there benchmarks available to check your results? Are these datasets available under an open access license?<br>
2. Scientists aiming to publish data sets: Among community-specific repositories, which ones are suitable to publish the research data? Do repositories restrict the size of the datasets that can be uploaded and if so, what are the limits? Are publication fees charged and if so, how much is charged?</p>
<p>To facilitate answering these questions, the Data Collections Explorer provides both a free text search and filters for type of service, subject area, and access license. Where appropriate and available, information on data size limits and publishing fees is provided.<br>
The Data Collections Explorer complements re3data, as it includes entries which are outside its scope or which are not listed.</p>
<p>This concept is not limited to the engineering sciences. To broaden the impact, we are currently working on expanding the Data Collections Explorer to the material sciences and engineering community within NFDI- MatWerk.</p>
<p>This work is supported by the NFDI4Ing consortium, the German Research Foundation (DFG), the research program ‘Engineering Digital Futures’ by the Helmholtz Research Association and the Helmholtz Metadata Collaboration (HMC) Platform.</p>
https://doi.org/10.5445/IR/1000151429
oai:zenodo.org:7198682
eng
Zenodo
https://zenodo.org/communities/nfdi4ing
https://zenodo.org/communities/hmc-conference_2022
info:eu-repo/semantics/openAccess
Helmholtz Metadata Collaboration | Conference 2022, Online, 5-6 October 2022
NFDI4Ing
NFDI
FAIR
Engineering Sciences
Data Collections Explorer
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7221235
2023-02-28T13:42:01Z
openaire
user-hmc-conference_2022
Pirogov, Anton
D'Mello, Fiona
Hofmann, Volker
Sandfeld, Stefan
2022-10-05
<p>Improving research data management practices is both an organizational and a technical challenge: even in the same research field, (meta)data is often created, stored and processed in an ad-hoc manner. This results in a lack of a clear structure and standardization and makes the metadata “unFAIR”. We present two tools that assist scientists in their research workflows to enrich, structure and validate their data and metadata. This increases machine interpretability and reusability, e.g. to ease (automatic) data analysis or metadata harvesting pipelines.</p>
<p><strong>Metador</strong> is a web-based structured submission interface for uploading research data and linking it to predefined metadata in a structured form. Metadata is supplied by completing a form for each uploaded file. The form is configurable by JSON Schemas and can adjusted by the user depending on the type of the uploaded file. This ensures that captured metadata is specific to the uploaded file type and appropriate to the scientific domain. It is intended for deployment in research groups and designed for quick and easy integration into existing scientific workflows.<br>
Currently we are extending Metador architecture and functionality into a versatile RDM platform focused on metadata standardization and harmonization. It will be designed as an open and extensible ecosystem of reusable generic building blocks and ready-to-deploy services. Combined, they will cover aspects from initial collection of metadata up to improved search, data extraction and data visualization.</p>
<p><strong>DirSchema</strong> is a specification and validation tool that enforces requirements concerning the directory structure and metadata provided in datasets. It is intended to be used by researchers and research groups during dataset generation or preparation to harmonize metadata in datasets across users and groups. DirSchema can be used by individual researchers or research groups to validate their dataset directory structures against an agreed-upon JSON Schema based specification that is provided as a YAML file. Further it can be deployed as a building block in other local or web-based scenarios to perform the validation automatically.</p>
https://doi.org/10.5281/zenodo.7221235
oai:zenodo.org:7221235
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7221234
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022, online, 5-6 October 2022
metadata
validation
jsonschema
yaml
Metadata in the Research Workflow: Tools for Enrichment and Validation of Structured Metadata
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7125644
2023-07-19T09:18:03Z
openaire
user-hmc-conference_2022
Garabedian, Nikolay
Bagov, Ilia
Weber, Karlheinz
Greiner, Christian
Klusemann, Benjamin
Bock, Frederic
Held, Martin
Wieland, Florian
Eschke, Catriona
2022-09-28
<p>One of the prerequisites for FAIR data publication is the use of FAIR vocabularies. Currently, tools for the collaborative composition of such vocabularies are missing. For this reason, a universal manual and software for user-friendly vocabulary assembly is being composed in the HMC-funded MetaCook project. The project includes 4 separate test cases from 4 labs across KIT and Hereon, which will help strengthen the software's universality and applicability to various domains.</p>
<p>The components described in MetaCook will be implemented in the form of multiple software tools. The first one, a Python-based web application called VocPopuli, is the entry point for domain experts. The software, whose first version is being developed at the time of writing, enables the collaborative definition, and editing of metadata terms. Additionally, it annotates each term, as well as the entire vocabulary, with the help of the PROV Data Model (PROV-DM) - a schema used to describe the provenance of a given object. Finally, it assigns a unique ID to each term in the vocabulary, as well as a hash-based ID the vocabulary itself.</p>
<p>The second software tool will facilitate the transformation of the vocabularies developed with the help of VocPopuli into ontologies. It will handle two distinct use cases – the from-scratch conversion of vocabularies into ontologies, and the augmentation of existing ontologies with the terms from a given thesaurus. Both software tools will be used by two semi-overlapping user groups: domain experts will input, edit, and discuss vocabulary terms in their area of interest, while vocabulary and ontology administrators will oversee the vocabulary creation, and ontology transformation.</p>
<p>Both the controlled vocabularies and the corresponding ontologies offer the possibility to enrich data documented in Electronic Laboratory Notebooks (ELNs). As the simplest solution, terms used within the ELN are linked to the IDs of the related vocabulary and ontology for an unambiguous definition. Additionally, an export of the defined schemes can be used to automatically create a structured form in the ELNs for documenting the described processes. The output from the developed tools will be exemplarily integrated into the ELNs Herbie and Kadi4Mat.</p>
https://doi.org/10.5281/zenodo.7125644
oai:zenodo.org:7125644
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7125643
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Helmholtz Metadata Collaboration | Conference 2022
Controlled Vocabularies, FAIR, Ontologies
MetaCook: FAIR Vocabularies Cookbook
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7185423
2022-12-08T07:59:37Z
openaire
user-hmc-conference_2022
Simson, Anna
Boxberg, Marc S.
Kowalski, Julia
2022-10-11
<p>In geodisciplines such as the <strong>cryosphere</strong> sciences, a large variety of data is available in data repositories provided on platforms such as Pangaea. In addition, many computational process models exist that capture various physical, geochemical, or biological processes at a wide range of spatial and temporal scales and provide corresponding simulation data. A natural thought is to <strong>hybridize measured and simulated data</strong> into comprehensive data sets that complement each other and provide a joint basis for subsequent model-based interpretation. Two aspects remain challenging, namely <em>a)</em> we are lacking a <strong>unified metadata</strong> approach that is ready to use for hybrid data compilations comprising both measured and simulated data each with their own characteristics and natural limitations, and <em>b)</em> we are not providing these data compilations in an <strong>‘analysis-ready’</strong> format, for instance, including uncertainties.<br>
In this contribution, we present an example from cryosphere science, where much potential remains in a joint interpretation of several field tests and simulation studies to generate an integrated, holistic representation of the ice body. Yet, to date, this joint interpretation is often not feasible because metadata of the measurements lack <strong>cross-repository consistency and completeness</strong>, and simulated data are often not equipped with metadata at all. We discuss these challenges in light of FAIR, while focusing on the example of <strong>sea ice core data</strong>. Specifically, we introduce our in-house Ice Data Hub (IDH) as a <strong>flexible data management</strong> tool that aims to overcome these challenges. We use the IDH to <em>a)</em> store measurement data sets together with enriched, consistent metadata, <em>b)</em> display, add, and plot data sets through its web browser-based GUI, and <em>c)</em> directly couple simulation environments to facilitate <strong>interdisciplinary dataflow and interoperability</strong>. Lastly, we present an example of an ‘analysis-ready’ sea ice core data set that is merged from individual ice cores stored in the IDH.</p>
https://doi.org/10.5281/zenodo.7185423
oai:zenodo.org:7185423
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7185422
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
metadata enrichment
FAIR
hybrid data
cryosphere
reusability
sea ice
Enriched metadata for hybrid data compilations with applications to cryosphere research
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7120472
2022-09-29T14:26:25Z
openaire
user-hmc-conference_2022
Schrader, Antonia
Bertelmann, Roland
Bruch, Christoph
Ferguson, Lea Maria
Pampel, Heinz
Schultze-Motel, Paul
Weisweiler, Nina Leonie
2022-09-28
<p>The Helmholtz Open Science Office (OS Office) embraces this mission since it was founded by the Helmholtz Association in 2005. It supports the Helmholtz Association as a service provider in shaping the cultural change towards open science. Furthermore, it promotes dialogue on open science within and beyond Helmholtz and regularly offers events on open science developments to provide impulses and guidelines for the Helmholtz Association.</p>
<p>In the area of open research data, the Helmholtz Open Science Office supports the Helmholtz Centers in developing policies and implementing corresponding practices for handling digital research data. Furthermore, the OS Office supports the discussion on the re-use and thus also reproducibility of research data in Helmholtz and the interdisciplinary exchange in this field.</p>
<p>The <a href="https://www.nfdi.de/en-gb">National Research Data Infrastructure (NFDI)</a> and the <a href="https://ec.europa.eu/info/research-and-innovation/strategy/strategy-2020-2024/our-digital-future/open-science/european-open-science-cloud-eosc_en">European Open Science Cloud (EOSC) </a>represent central infrastructures in this context. Numerous NFDI consortia are being implemented with substantial Helmholtz participation, and Helmholtz Centers are also actively involved in the implementation of the EOSC. The OS Office actively animates and supports these activities. </p>
<p>Helmholtz's participation in the international <a href="https://rd-alliance.org/">Research Data Alliance (RDA)</a> and in <a href="https://www.rda-deutschland.de/">Research Data Alliance Germany (RDA-DE)</a> is also accompanied and coordinated by the Helmholtz Open Science Office. Moreover, the OS Office is active in <a href="https://www.helmholtz.de/open-science-subsite/en/newsroom/projects/">third-party funded projects</a> on open research data and persistent identifiers, such as <a href="https://os.helmholtz.de/de/projekte/re3data-coref/">re3data COREF</a> and <a href="https://os.helmholtz.de/de/projekte/orcid-de/">ORCID DE</a>.</p>
<p>The work of the Helmholtz Open Science Office thus complements the developments of platforms in the Helmholtz Incubator and contributes to the utilization of the <a href="https://www.go-fair.org/fair-principles/">FAIR principles</a> in Helmholtz, such as the <a href="https://helmholtz-metadaten.de/de">Helmholtz Metadata Collaboration (HMC)</a>.</p>
https://doi.org/10.5281/zenodo.7120472
oai:zenodo.org:7120472
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7120471
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
open science
open research data
FAIR
open access
open research software
Enabling Open Science practices in Helmholtz!
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7085952
2022-09-18T02:26:31Z
openaire
user-re3data
user-hmc-conference_2022
Weisweiler, Nina Leonie
Bertelmann, Roland
2022-09-16
<p>The year 2022 marks the 10th anniversary of the Registry of Research Data Repositories, re3data. The global index currently lists almost 3000 digital repositories across all scientific disciplines – critical infrastructures to enable the global exchange of research data. The openly accessible service is used by researchers and services worldwide. It provides extensive descriptions of repositories based on a detailed and publicly available metadata schema. New suggestions and change requests for re3data are managed by an experienced team of editors. The editorial process includes a multi-stage review. An international team of experts thoroughly analyzes the repositories and takes care of metadata completeness and quality.</p>
<p>The poster presents the growth, development, and accomplishments of re3data over the past 10 years that have resulted in re3data becoming the most comprehensive and largest information and metadata resource on research data repositories. The poster highlights and summarizes important activities like the initial projects funded by the German Research Foundation (DFG), the collaboration and merger with DataBib, and the partnership with DataCite. It also details collaborations like the EU Open Science Monitor, SNF, and FAIR-enabling measures together with the European Community. The poster addresses activities with different research communities in order to initiate intensive talks about best practices and experience made. Furthermore, it highlights the current projects re3data COREF (Community Driven Open Reference for Research Data Repositories / DFG funded) and FAIR Impact (EU funded).</p>
https://doi.org/10.5281/zenodo.7085952
oai:zenodo.org:7085952
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://zenodo.org/communities/re3data
https://doi.org/10.5281/zenodo.7085951
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
HMC Conference, 5-6 October 2022
research data, repositories, re3data, anniversary
Celebrating 10 Years of re3data
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7143158
2022-10-04T14:26:18Z
openaire
user-hmc-conference_2022
Anniés, Jeannette
Dobrokhotova, Ekaterina
Sautter, Johannes
Wuchner, Andrea
2022-10-04
<p>With new specialisations such as Data Science driven by digitisation, efficiency potentials of a digital transformation are raised in both empirical research and data governance processes. Here, one challenge is to establish open and interoperable datasets, recognising the FAIR criteria (cf. Wilkinson et al., 2016) as a standard of that process. Data – as well as metadata – should comply to this standard. However, traditional methodological research processes (cf. Brosius, Haas, & Koschel, 2012, p. 28; Friedrichs, 1990, p. 119) lack the support of information technology which would lever the process into the digital age. Therefore, we propose a digital research process that closes ranks between the traditional process and the opportunities of a digital world.<br>
The digital research process was established as a concept for a data model with corresponding roles (cf. Wuchner, & Sautter, 2020; Sautter, & Wuchner, 2020; Sautter et al., 2018). We found that a data governance process depends less on the specific method and much more on a common cross-method research process (cf. also UK Data Service). As a result, the digital research process needed to be highly adaptive to the purposes of different kinds of research fields.<br>
The digital research process we propose consists of nine activities, terminated by data filing points (DFPs). The obligatory DFPs consider projects that focus on data search, acquisition, and archiving only. The optional DFPs represent the process of obtaining new (research) data. Additionally, optional data analysis may play a role in projects that merely reuse existing data. The optional DFPs represent the adaptability of research objectives in humanities. Equally unique to the digital research process is the frequent update of metadata throughout the research cycle, to create FAIR metadata throughout the time frame of the research and data processing.</p>
https://doi.org/10.5281/zenodo.7143158
oai:zenodo.org:7143158
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7143157
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Research Data Management
FAIR Metadata
Digital Research Process
A Digital Research Process for FAIR Data and Metadata
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:7179875
2022-10-11T02:26:31Z
openaire
user-hmc-conference_2022
Dallavalle, Marco
Gey, Ronny
Staab, Jeroen
Standl, Marie
Ferland, Maike
Bumberger, Jan
Taubenböck, Hannes
Wolf, Kathrin
2022-10-10
<p>Digital metadata solutions for epidemiological cohorts are lacking since most schemas and standards in the Health domain are clinically oriented and cannot be directly transferred. In addition, the environment plays an increasingly important role for human health and efficient linkage with the multitude of environmental and earth observation data is crucial to quantify human exposures. There are however currently no harmonized metadata standards for the different areas, so they cannot be merged routinely. Therefore, we aim to compile machine-readable and interoperable metadata schemas for exemplary data of our three domains Health (HMGU), Earth & Environment (UFZ), and Aeronautics, Space & Transport (DLR).</p>
<p>We will present our data use cases (HMGU: GINI/LISA cohort; UFZ: drought monitor; DLR: land cover), their current metadata formats and our strategy for metadata compilation, enrichment and mapping. UFZ and DLR will converge their metadata to the standard ISO 19115: Geographic Metadata Information. For HMGU, we reviewed several metadata standards for health data (e.g. CDISC ODM, Snomed CT, HL7 FIHR) and started to upload our metadata to the NFDI4health StudyHub, an inventory of German health studies on COVID-19 which is based on the Maelstrom catalogue. In addition, we have developed a workflow to transform base cohort information in an ISO 19115 compliant manner. The respective metadata sheet increases accessibility to researchers from other domains without exposing sensitive information about participants’ data.</p>
<p>The metadata mapping will be performed by location (spatial coverage) and date (time coverage) within GeoNetwork, a catalog application that we are currently setting up in a testing environment. We aim to have a server version ready by the end of the project that can be augmented with additional metadata from our domains, but also from other fields, to facilitate interdisciplinary research.</p>
https://doi.org/10.5281/zenodo.7179875
oai:zenodo.org:7179875
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://doi.org/10.5281/zenodo.7179874
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Health
Environment
Earth Observation
Metadata generation
Mapping
Epidemiology
MetaMap³: Metadata generation, enrichment and linkage across the three domains health, environment and earth observation
info:eu-repo/semantics/lecture
oai:zenodo.org:7189121
2022-11-02T17:05:15Z
openaire
user-hmc-conference_2022
user-dlr_de
Mohr, Daniel
Brötz, Björn
2022-10-06
<p><a href="https://riaf-data.org/">RIAF</a> is a repository infrastructure to accommodate files. It enables to hold the data with the <a href="https://doi.org/10.3289/HMC_publ_01">FAIR<br>
principles</a>. RIAF is designed to enable provenance and reproducibility of the research data in the early part of the data life cycle, i. e. prior to publication. It further is designed to enable checks on metadata relevant to research data management as defined e. g. in a machine actionable data management plan (maDMP). In our concept most data is stored in a repository and can be easily distributed. This allows the data genesis in a private environment (e. g. aircraft, campaigns, ...) without network access and later share the data using a central server instance. The primary focus is to work as an in-house solution to handle digital assets. For this purpose we use open source software in a composability design (Unix philosophy).</p>
https://doi.org/10.5281/zenodo.7189121
oai:zenodo.org:7189121
eng
Zenodo
https://zenodo.org/communities/hmc-conference_2022
https://zenodo.org/communities/dlr_de
https://doi.org/10.5281/zenodo.7189120
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
data repository
fair
provenance
reproducibility
machine actionable data management plan
maDMP
Helmholtz Metadata Collaboration
HMC
RIAF – a Repository Infrastructure that Accommodates Files
info:eu-repo/semantics/conferencePoster