Reviewer has chosen not to be AnonymousOverall Impression:
UndecidedTechnical Quality of the paper:
Limited noveltyData availability:
Not all used and produced data are FAIR and openly available in established data repositories; authors need to fix thisLength of the manuscript:
The authors need to elaborate more on certain aspects and the manuscript should therefore be extended (if the general length limit is already reached, I urge the editor to allow for an exception)
Summary of paper in a few sentences:
The paper presents the latest version of OSCAR (OpenCitation RDF Search Application), a tool developed to enable Semantic web non-experts to query RDF triplestore. It extends a previous workshop paper by demonstrating how OSCAR can be configured to work with Wikidata SPARQL endpoint. It describes the latest version functionalities (e.g., an advanced query interface to create multi-field queries, novel preprocessing functions, conversion rules and a restructuration of the configuration files). It analyses the usage statistics retrieved from the OpenCitations website logs to demonstrate the usefulness of OSCAR.
Reasons to accept:
OSCAR makes SPARQL endpoints usable by a broad audience. It provides a helpful tool which might enrich a large number of data portals. It works with any SPARQL endpoints, and once configured it offers a fairly simple interface to the end users. The paper provides application examples in the domain of scholarly documents, but as far as I can understand it could be used in any other domain.
The paper is fairly well written and comprehensible.
In the context of data science, it is important to let developers and scientists advertise and get credit for tools like OSCAR that are made available for third-party reuse.
Reasons to reject:
Papers describing tools hardly fit with the usual review criteria (e.g., the novelty and validation of contribution) but I think some extra effort can be made to clear the contribution of OSCAR from the scientific point of view.
in relation with the novelty of contribution, what does OSCAR provide that the others do not? A deeper comparison between OSCAR and the tools mentioned in the related work would substantiate the novelty of contribution from the scientific perspective.
In relation to OSCAR usefulness, I have no problem believing it is useful. However, showing the Usage statistics retrieved from the OpenCitations website logs is a very weak proof of its usefulness. It does not distinguish between the usefulness of the content served by OpenCitations and the actual usefulness of OSCAR. Of course, it is better than nothing, but actual user satisfaction using OSCAR should be more systematically investigated.
Further suggestions and comments follow:
- Selected Keywords (i.e., OpenOffice, ODT to RASH) do not relate to the content of the paper.
- please state explicitly that OSCAR can be applied beyond scholarly data portals, as it is configurable on any SPARQL endpoint and RDF schema.
- In the related works, I would consider citing YASGUI as an example of an interface for semantic web literate. Laurens Rietveld, Rinke Hoekstra, The YASGUI family of SPARQL clients. Semantic Web 8(3): 373-383 (2017)
- In the caption of figure 1, "workshop .The", there is a missing space.
- Configuration examples might be not easy to grasp, I think a reference to the configuration instructions and the inclusion of some comments in the configuration file might help.
-I suggest adding a discussion of the limitations and applicability of OSCAR. For example, in a separate section. In such a section, the authors might want to answer the following questions: When configuring OSCAR is less handy than writing a custom user interface? Under what kind of licence OSCAR is made available? is there any assistance in case one has any difficulties when configuring/using OSCAR? etc.
- One thing I've noticed playing with the results from http://opencitations.net/search?text=machine+learning : if one sorts the results by the number of citations, and limits the number of results visualized, he gets the first ten results in the result set, not the first ten most-cited papers. This is extremely counterintuitive! I suggest to fix it in the next release of OSCAR.
- The Data science journal requires that all used and produced data are openly available in established data repositories, as mandated by FAIR and the data availability guidelines (https://journals.plos.org/plosone/s/data-availability). As far as I understand the guidelines have not followed for the statistics regarding the accesses to OSCAR used in section 5. Please fix it or make clear how you have met the availability guidelines.