Following the Italian National Guidelines ("Linee guida nazionali per la valorizzazione del patrimonio informativo pubblico"), developed in the context of the Data & Analytics Framework project by AgID and the Digital Transformation Team, we have performed a quality analysis of our source datasets to ensure their good condition and their suitability for the intended use.
Specifically, there are four main factors to look for when analysing data quality:

• Accuracy (syntactic and semantic): the data and its attributes correctly represent the real value of the concept or event they refer to
• Coherence: the data and its attributes do not present any contradictions with respect to other data in the context of use by the administration owner
• Completeness: the data are exhaustive for what concerns every expected value and with respect to the related entities (sources) that contribute to the definition of the procedure
• Timeliness (or promptness of updating): the data and its attributes refer to the "correct time" (up to date) with respect to the procedure they refer to

The legal analysis of the source datasets is fundamental to obtain sustainability over time of the production process and of the publication of datasets and to guarantee a balanced service in compliance with the public function and with individual rights.

This analysis was carried out using a reference checklist consisting of a series of binary questions regarding the topics of: privacy issues, IPR policy, licenses, limitations on public access, economical conditions, and temporal aspects.

Publication license

Finally, a fundamental step of the legal analysis is the choice of the publication license for the new mashup datasets. Of course, this choice must consider the original licenses of the source datasets, which, in our case, were all published under CC BY 3.0.
To better navigate the alternatives we made use of the Licensing Assistant tool provided by the European Commission, and we decided to publish all nine mashup datasets under a CC BY 4.0 license.

The table below showcases the original licenses of the original datasets and the final publication license of the mashup datasets:

For the ethical analysis of our project's data, we considered the Data Ethics Principles and Guidelines and the Odi Project's detailed canvas for evaluating the ethical aspects of our data processing.

Since both our source and mashup datasets contain Information provided exclusively by the Italian National Institute of Statistics, we initially focused on analysing the fairness of data collection and management by ISTAT, and then established guidelines for addressing ethical concerns when processing the source datasets for our project.

Data Ethics Principles

• Human being at the center As stated on the institution's website, ISTAT's policy is aligned with Ethical and legislative principles, with the primary aim to publish and communicate effectively statistical information and results of analyses conducted in order to foster awareness of Italy's conditions and to improve decision-making processes on the part of private subjects and public institutions. Furthermore, ISTAT is committed to conducting methodological, applied research with the aim of improving statistical production processes and enhancing Italy's statistical literacy.
• Equality In line with what is mentioned above, ISTAT uses statistical methodologies to publish data on important equality issues in various domains, such as diversity management in enterprises in Italy, gender discrimination, labour discrimination and poverty, and many more, that can be browsed in their dedicated archive. We do note however, that there is room for improvement especially regarding the representation of ethnic and gender and sexuality minority groups in their data.
• Transparency The data gathered is transparently managed, with ample documentation made available to final users. This documentation describes the data collection methods, and the significance of specific vocabulary in the dataset, as well as related licenses and policies to avoid misinterpretations.
• Accountability Istat quality policy is coherent with the European framework developed by Eurostat, adhering to the principles of the European Statistics Code of Practice, that ensures and strengthens both accountability and governance of the European Statistical System and the National Statistical Systems inside it.
• Individual data protection Istat's datasets are anonymised, and as stated in their their regulations and privacy page, the institute respects the privacy of respondents, protects the confidentiality of the data gathered, and carries out its activities in a transparent and independent manner.
  To be more specific, the information collected is protected by statistical confidentiality (Article 9 of Legislative Decree No. 322/1989) and subject to the legislation on the protection of personal data (Regulation (EU) 2016/679, Legislative Decree n. 196/2003, Legislative Decree n. 101/2018).

Ethical concerns and their management

Despite ISTAT's compliance with the ethics principles of data collection and management, the team placed special importance

Data related to age, residence, religious observance and reproductive health are indeed sensible and the ethical aspect of their handling was carefully considered through the following steps:

• Data integrity and privacy are furtherly respected through the process of aggregating source dataset values and providing them in percentage values, in order to avoid any correlation with real individuals.
• To ensure equality and protection of more vulnerable groups, clear boundaries were set by intentionally omitting available data by ISTAT, such as the citizenship distinction in all of our data, which could lead to possible discriminatory behavior.
• The team's purpose was to discover the existence of possible patterns and not make any inferences or interpretations. Throughout our results and conclusion documentation, we also stress that any observed patterns in our data do not call for generalisations regarding the parameters studied, given the inconsistencies in our data and the absence of other, possibly relevant socioeconomic factors.
• All relevant documentation regarding the data processing for creating the mashup datasets and visualisations is provided in our github repository.

Source datasets

All source dataset have been evaluated based on the metadata model provided by AGID that classifies metadata quality on a range of 4 levels according to two factors: data-metadata bond and detail level

N.B. Additional information and reconstructed metadata about source datasets can be found in the metadata analysis table below or in our general documentation.

RDF Assertion and Metadata for the mashup datasets

All produced mashup dataset have been thoroughly described with metadata, following the specification of DCAT-AP_IT standard as recommended by AGID's public information heritage valorization guidelines.

Since all our datasets contain data of specific national interest and are derived from Istat datasets, which is an italian public research institution we decided to adopt DCAT-AP_IT (2016), the national standard.
Although it is based on the first version of the european standard DCAT and has more constraints, compared to the more flexible and recent european standard DCAT-AP 2.0, we considered DCAT-AP_IT the more suitable standard for our mashuo datasets: on the one hand, because in the italian public sector an increasing number of Public Administrations are adopting DCAT-AP_IT; on the other hand, because this allowed us to follow more detailed national guidelines and therfore ensure interoperability and harmonization with other data on a national level.

Moreover:

• To describe in full transparency the sources and the activities underlying the creation of our mashup datasets we adopted PROV-O - the provenance ontology as strongly recommended on a european level and also allowed by DCAT-AP_IT metadata model
• Since all our mashup datasets are series containing individual datasets for each year (2017, 2018, 2019) and only DCAT-AP 3.0 currently provides a dcat:DatasetSeries with related properties, again, we followed AGID's metadata model instruction about how to handle relationships between datasets.
  We emphasized individual elements of the serie and created, inside each mashup dataset RDF assertion, a triple with a Serie Dataset subject, connected through the Dublin Core property dct:type to the value <http://inspire.ec.europa.eu/metadata-codelist/ResourceType/series> .
  Then we specified which dataset belonged to the Serie by means of dct:hasPart property.
  Finally, every individual yearly Mashup dataset, it's connected in its turn with the related Serie by means of dct:isPartOf

Additional Metadata for source datasets

In providing the metadata for the source datasets, information have been retreived from original sources. When missing, they have been induced and integrated according to the same guidelines adopted for mashup datasets (ex. assignining a theme to source dataset has been done according to the same european authority).

It should be mentioned that, in case of D1 and D2, our source datasets were downloaded from demo database Endpoint where very few and non structured metadata were available. Nonetheless, the same datasets are also published in the IstatDati database Endpoint where the Standard protocol Statistical Data and Metadata eXchange (SDMX) is adopted. For more information about Istat Data and SDMX metadata incorporation carried out by Istat see SDMX web service for Istat data.

FAIR principles

During the development of the project, we strived to adhere to the FAIR principles, as published by the GO FAIR Initiative.
These recommendations are a set of guiding principles proposed by a consortium of scientists and organizations to support the Findability, Accessibility, Interoperability, Reusability of digital assets, emphasizing, in particular, machine-actionability.

In the following table we have employed the overview of FAIR Principles provided by GO Fair as a checklist to thoroughly examine our project.