Existing databases and dataset repositories on marine abiotic and biotic resources in the Emilia-Romagna Region (Ver. 3.0)

This version provides an assessment of the 32 repositories (Table 1) included in the version 2.0 of the ‘Existing databases and dataset repositories on marine abiotic and biotic resources in the Emilia-Romagna Region’. This assessment is based on a new FAIRness index (see section 2.1.1 at pag. 14) that measures how well repositories, and the datasets they deposit, adhere to the FAIR (Findability, Accessibility, Interoperability, and Reusability) guiding principles according to the criteria proposed by Wilkinson et al. (2016), which was specifically developed for the aims of the project ‘Ecosystem for Sustainable Transition in Emilia-Romagna’ under Work Package 3 - Spoke 5.

Table 1. List of the 32 repositories described and analysed.

The structure and organization of the dataset have remained consistent with the previous versions 1.0 (DOI: 10.5281/zenodo.7965539) and 2.0 (DOI: 10.5281/zenodo.10568888). For a comprehensive overview of the dataset fields, we refer the reader to the corresponding reports titled “Database of marine abiotic and biotic resources readily usable for scientific and management purposes”, Deliverable D5.3.2 (DOI: 10.5281/zenodo.7885755) Deliverable D5.3.5 (10.5281/zenodo.10598477).

These version of the dataset includes two Microsoft Excel files (.xlsx):

·         Existing databases and dataset repositories on marine abiotic and biotic resources in the Emilia-Romagna Region.xlsx

Otherwise similar to the previous version, this file has been added with the average Findability, Accessibility, Interoperability, Reusability, and FAIRness values calculated for the different repositories and a new spreadsheet with the graphs reported in this analysis. The included spreadsheets are:

o   Repositories (32 records)

o   Datasets (208 records)

o   Variables (1157 records)

o   Legend

o   FAIRness plots

·         FAIRness index.xlsx

Contains the evaluation elements and calculation formulas for the FAIRness index, as well as the proposed scale of FAIRness. The included spreadsheets are:

o   Index calculation

o   Scale of FAIRness

Each of the 32 repositories has been described providing the information summarised in Table 2.

Table 2. Structure of the Repositories spreadsheet.

Since the previous version, for each repository, datasets containing information relevant to the Emilia-Romagna Region were selected and analysed. The number of considered datasets may be lower than the total number within the repository, especially for repositories covering a larger geographical area than Emilia-Romagna. The number of retrieved and analysed datasets for each repository is indicated in Table 3.

Table 3. Number of considered datasets for each repository.

Moreover, for repositories with broader geographical coverage, such as European ones (e.g., Copernicus and Zenodo) or international ones (e.g., DEIMS-SDR and ITIS), housing a substantial volume of data, only datasets deemed most relevant to the ECOSISTER project were included in this deliverable. For a comprehensive understanding of data availability in these repositories, direct reference is made to the repositories themselves. Access and utilization are facilitated by the previous deliverable, which, through version 1 of the "Existing databases and dataset repositories on marine abiotic and biotic resources in the Emilia-Romagna Region'' dataset, provides a tool for optimal exploration of each repository, understanding its potential and limitations through an assessment of FAIR data guiding principles, along with a direct link to the portal page.

Overall, for the purposes of this deliverable, we conducted a comprehensive analysis of 208 public datasets from 32 different repositories and databases. These datasets include a wide array of different variables, encompassing both abiotic and biotic factors, thus providing researchers with a wealth of information to improve the management, conservation and sustainable use of marine resources in the Emilia-Romagna Region.

The analysis of these variables holds a major importance in the context of the blue economic growth of the Emilia-Romagna Region and the advancement of sustainable development objectives. The strategic assessment of abiotic factors such as water quality, temperature and sediment composition, together with biotic factors such as biodiversity and species distribution, provides a basis for informed decision-making. This, in turn, not only supports the economic prosperity of the region, but also ensures responsible management of its marine ecosystems. Collaborative efforts in data analysis and dissemination, as highlighted by this report, exemplify the commitment to evidence-based policymaking and emphasize the role of research in promoting positive environmental and socio-economic outcomes.

The Excel sheet (and the corresponding CSV file) named "Datasets" includes the fields outlined in Table 4, which provide information on various aspects of each dataset such as the file format or structure in which the data is stored, the accessibility of the dataset, the terms governing the use of the dataset, its geographical coverage, etc.

Table 4. Structure of the Datasets spreadsheet.

The set of analysed datasets covers a wide range of biotic and abiotic data typologies, which have been grouped into data type categories, as shown in Table 5, to facilitate the user in their search.

Table 5. Data typology categories.

2.1.1 FAIRness index

Researchers, service providers and policy makers are asking for access to as much data and products as possible to allow in-depth studies and specific applications of present and old data in new contexts (Pouliquen et al., 2010). Sharing data fosters collaboration among scientists and stakeholders, allowing them to combine resources, expertise and datasets. Data sharing, indeed, includes the deposition and preservation of data, allowing their use and reuse by others, making digital data not only the outputs of research but also inputs to new hypotheses that enable new scientific insights (Tenopir et al., 2011) and thus holds a great potential for scientific progress, knowledge discovery and innovation (Fecher et al., 2015; Wilkinson et al., 2016).

There is a growing need for data sharing, so that potential users can access data that have been previously acquired and processed by other providers, reducing data collection and thus optimising the use of resources. However, the promotion of data sharing and reuse can only take place if good practices are ensured at all stages of the data life cycle, such as data generation and collection, data management and data analysis (Tenopir et al., 2011). Well-founded data management is of crucial importance for using and analysing data as it ensures that essential data are retained and made accessible for applications by users, guaranteeing local and interoperable discovery and access (Tahnua et al., 2019).

However, the past existing digital ecosystem surrounding data publication has prevented the community from gaining optimal benefit from data obtained through research investments in private and public institutions. This is since “good data management” was largely undefined for decades until Wilkinson and colleagues defined the FAIR (Findability, Accessibility, Interoperability, and Reusability) guiding principles in 2016 (Wilkinson et al., 2016; see Table 6). Consequently, standards for good data management were left as a decision for the data, database or repository owner, although it is well known that standards for metadata and data formats are essential for interoperability. However, while in the past researchers have wondered what metadata should always accompany data to guarantee their interoperability (Keely et al., 2010), at present there is no general agreement; nevertheless, the principles defined by the European INSPIRE Directive (2007/2/EC) are of great support. Furthermore, it has been claimed that, lacking proper training in data management, researchers may be unfamiliar with the best practices for proper data archiving (Roche et al., 2015). This results in the publication of data that do not follow univocal principles and are therefore hardly reusable since they are not clearly described using standardised schemas. Data providers, indeed, are different parties, such as research institutes, meteorological and environment agencies, private associations and consortia, with different knowledge of data processing and sharing. These groups usually organise their data system and distribution channels to serve their users, but it comes without saying that being able to serve a wider range of users needs additional effort and harmonisation (Pouliquen et al., 2010). Indeed, to better support research, operational and commercial users, data made available through web services should be FAIR: findable (F), accessible (A), interoperable (I) and reusable through thematic integrated products and services (R).

Table 6. Summary of the FAIR guiding principles as defined by Wilkinson et al. (2016).

About abiotic and biotic data of the Emilia-Romagna Region, to date data management systems and repositories have been mainly developed in isolation and with different goals to serve a specific interest or founding routes (e.g. particular monitoring networks or research programs carried out by different institutes and agencies). This has led to the collection of data that only partially meet the FAIR principles, making it difficult for users to find and process data on the web.

The elements of the FAIR principles are related, but independent and separable. These principles define the characteristics that data should have to support discovery and reuse by current and future users (Wilkinson et al., 2016).

To assess how well public repositories adhere to the FAIR principles, we assigned scores from 0 to 1 to the various components that describe the four principles. The scores, averaged together, provide a score from 0 to 1 for each principle. Averaging the values for the different principles provide a final single FAIRness index. The assigned values are based on a detailed analysis of the characteristics of each repository and, although some subjectivity is inevitable, taken together they provide a useful comparative numerical analysis for evaluating the repositories and their potential room for improvement. Figure 1 and 2 provide a comparison of the findability, accessibility, interoperability and reusability of the analysed repositories.

Table 7. FAIRness index scale based on Wilkinson et al. (2016) principles and descriptors.

Overall, it can be observed that the Emilia-Romagna Region's marine abiotic and biotic resource repositories and databases exhibit a high degree of FAIRness, the result of years of experience in the sector. Only the older repositories, less oriented to a general audience, present some critical issues, especially regarding the findability and reusability of their datasets.

As a result of the abundance of knowledge acquired through this work, the Emilia-Romagna Region is in a considerably better position to manage the complex balance between economic development and environmental sustainability. In conclusion, this comprehensive analysis represents a fundamental step towards a more resilient and ecologically aware future for the Emilia-Romagna region, in line with global imperatives for sustainable development and responsible resource management.