Differences

This shows you the differences between two versions of the page.

--- wiki:basic_recommendations [2025/06/19 09:03] – [Motivation for this Recommendation:] dkottmeier
+++ wiki:basic_recommendations [2025/06/20 07:57] (current) – [Description] dkottmeier
@@ Line 6: / Line 6: @@
-[Status: Under development, Date: 2023-08-25, Version: 001]
+[Status: Under development, Date: 2025-06-19, Version: 001]
 ==== Motivation for this Recommendation: ====
@@ Line 25: / Line 25: @@
 The activities may be prioritized for implementation in a specific order or for a particular stakeholder group, either because they are simple to set up, or because they are prerequisites for other recommended activities. These decisions have to be made on a case by case basis, in order to move forward establishing the envisioned data space.
-Suggestion for improvement of this paragraph (DK):
+-------
+**Comment DK:** //I think in this paragraph needs some modifications to put equal weight on all aspects of FAIR. The above version particularly addresses interoperability in its formulation. I also think that there is too much redundancy in the motivation paragraph with the recommendation paragraph, therefore I would here really only focus on the motivation. The suggested measures can come in the paragraph below. I suggest to write the following://
+The Helmholtz Association is committed to making its data available in accordance with the FAIR principles — ensuring that data are Findable, Accessible, Interoperable, and Reusable. This commitment is outlined in the "Recommendations for Policies of the Helmholtz Centers on Research Data Management" [1].
+To achieve FAIR data across the various data infrastructures (DIS) within the Helmholtz Association, a number of coordinated efforts are required. The following recommendations aim to describe procedures to achieve the above-mentioned goals, and identify activities and the relevant stakeholder groups to implement these. This implies that the identified stakeholders will take on specific responsibilities to ensure the success of the FAIR-aligned approach.
+Since achieving these goals requires deliberate decision-making, the document outlines decision processes from high-level strategy to operational detail, resulting in agreed procedures. It further describes how these procedures are to be implemented, specifying the roles, responsibilities, and actions required of each stakeholder group.
+Activities may be prioritized for implementation based on factors such as ease of deployment or their role as prerequisites for other efforts. These decisions should be made case by case to effectively progress toward the envisioned Helmholtz data space.
 ==== Basic FAIR Implementation Recommendations ====
@@ Line 47: / Line 56: @@
 A very important precondition for the implementation of these recommendations is the **presence of skilled and highly trained data management personnel**, such as data stewards, data curators, and developers for data management and processing tools.
+-----
+**Comment/Suggestion DK**: //I would suggest to rewrite this paragraph in a way that it sounds more like a recommendation and is less redundant with the motivation paragraph. I therefore merged and extended the bullet sections from both paragraphs. My suggestion is to write the following//:
+It is recommended that coordinated efforts across the Helmholtz Association be undertaken to achieve the above-mentioned goals, with the implementation of a common set of **FAIR building blocks**. These building blocks are operational measures required to support the findability, accessibility, interoperability, and reusability of research data. They include:
+  * Common and agreed procedures to refer to shared entities and metadata values, particularly through the use of **metadata of persistent identifiers** (**PID**s), to reduce redundancy and improve consistency in referencing key entities such as people, organizations, instruments, and datasets (**rename to PID**?).
+  * Harmonized use of **semantic resources**, including controlled vocabularies and mapping tables, to standardize metadata element names and their meanings, reduce ambiguity, and ensure consistent interpretation across disciplines and systems (**S**).
+  * Agreements on **mandatory and optional metadata elements**, covering both domain-independent and domain-specific needs, to support accurate description, discovery, exchange, and reuse of data across infrastructures, user communities, and tools (**M**?).
+  * Common metadata or **exchange formats**, such as DataCite, DCAT, or ISO 19115, to ensure consistent structuring and exchange of metadata across systems (**E**)
+  * Common data **exchange containers**, such as FAIR Digital Objects or DataCrates, to enable machine-actionable reuse and portability (**C**).
+  * Common **interfaces**, to provide standardized, open mechanisms for data and metadata access and harvesting (**I**).
+**Other important topics** where harmonization procedures are important are:
+  * Inclusion of **provenance information**, capturing data origin, transformation steps, and responsible agents, to enable assessment of data reliability and reproducibility (**P**).
+  * Clear **license information**, using standardized, machine-readable licenses, to define conditions of use and promote legal clarity (**L**).
+  * Documented procedures for assessing and communicating **data quality**, including uncertainty, validation, completeness, and versioning, to ensure data are fit for purpose (**Q**).
+  * Clear definition of **stakeholder roles** and responsibilities, including who is accountable for metadata provision, data stewardship, infrastructure maintenance, and policy implementation (**R**).
+  * **Valuating research data management** (RDM) engagement, through citation of datasets with DOIs, inclusion of author contributions, and formal acknowledgment of data curation efforts (**V**).
+These elements form the structural foundation for the detailed recommendations presented in this wiki.
+A key precondition for the implementation of these recommendations is the availability of skilled personnel, such as data stewards, data curators, and developers, who support data management and the technical and semantic infrastructure required to implement FAIR.
 ==== References ====