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+Summary
+-------
+
+This paper presents and studies data spaces, which can be thought of a specific
+kind of data exchange/market. The defining feature of a data space, compared to
+generic data marketplaces, appears to be a greater focus on interoperability,
+allowing value to be extracted from data more easily, typically within
+a specific application domain.
+
+A (fictional?) use case that is used as a running example throughout the paper
+is “Green twin”: a data exchange pooling together data about a city's
+buildings, vehicles, inhabitants and network infrastructure with the goal of
+improving energy efficiency and quality of life. A survey of existing efforts
+in the realm of data spaces (led by two related non-profit organizations, IDSA
+and Gaia-X) is presented, highlighting the challenges of data interoperability
+(with possible solutions involving the creation of data ontologies and the use
+of standardized protocols) and generating value from data (with possible
+solutions involving the use of automated machine learning techniques with
+transfer of knowledge).
+
+Comments
+--------
+
+While the concept of data spaces seems promising and an interesting object of
+study, this paper suffers from a somewhat ambiguous scope and its contributions
+are not immediately apparent:
+
+1. As an overview of existing techniques and solutions, many of the
+   explanations were lacking in precision. For example, I was not able to find
+   a clear definition of data spaces and how they differ from data
+   marketplaces, and had to rely instead on slowly discovering the concept over
+   the course of the entire paper. The closest to this were the following
+   sentences in the introduction:
+
+		This concept serves as an abstraction for data management in case where
+		many stakeholders are involved and exchange data with each other. The
+		easy data exchange between the stakeholders will generate value,
+		especially in combination with data analytics. New trading mechanisms
+		can allow stakeholders to cooperate with each other based on the value
+		of the exchanged data and the analytics services.
+
+2. As a position paper, I found that the description of future challenges lacks
+   in concreteness. What are the concrete open questions that researchers in
+   the data economy community should focus on? Do we need new algorithms? new
+   data structures? new machine learning techniques? What are specific ways in
+   which currently existing techniques unable to solve this challenges?
+
+Overall, I think the paper would benefit from focusing on at most two of the
+following three kind of contributions:
+1. A systematic description and documentation of currently existing
+   technologies, protocols, and standards that can be used to build a data
+   space.
+2. A concrete proposal for the “Green twin” use case following the standards of
+   the systems community: a clear description of a system that would solve this
+   use case, the different pieces it will contain and how they interact with
+   other.
+3. Concrete open questions and conjectures and an invitation to the research
+   community to work on them.