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. ID.Repository Repository Short description Provider Link 1 ADRIPLAN Data Portal Data portal of the ADRiatic Ionian maritime spatial PLANning project RITMARE project http://catalogue.msp-supreme.eu/it/dataset/adriplan-data-portal 2 Algaebase Global algal database for taxonomic, nomenclatural and distributional information Ryan Institute, National University of Ireland https://www.algaebase.org 3 Bold Systems Cloud-based data storage for DNA barcode data International Barcode of Life (iBOL) consortium https://boldsystems.org/ 4 CNR research institutes databases Repository of data provided by the CNR institutes Consiglio Nazionale delle Ricerche https://www.CNR.it/en/databases-institutes 5 Copernicus European Union's Earth observation programme European Commission https://www.copernicus.eu/en 6 DataONE Data Observation Network for Earth DataOne Projects https://www.dataone.org 7 Dati Arpae Arpae portal providing public data produced by the Agency Arpae Emilia-Romagna https://dati.arpae.it 8 Dati Gov Public administration open data AGID - Agenzia per l'Italia Digitale https://www.dati.gov.it 9 DEIMS-SDR Dynamic ecological information management system eLTER Research Infrastructure https://deims.org 10 Dext3r Data from the regional meteorological data survey network Arpae Emilia-Romagna (Servizio IdroMeteoClima) https://simc.arpae.it/dext3r/ 11 EDMED European Directory of Marine Environmental Data BODC (British Oceanographic Data Centre) https://www.bodc.ac.uk/resources/inventories/edmed/ 12 EMODnet European Marine Observation and Data Network European Commission https://emodnet.ec.europa.eu/en 13 EurOBIS European Node of the international Ocean Biodiversity Information System (OBIS) Flanders Marine Institute (VLIZ) https://www.eurobis.org 14 FAD Fisheries and Aquaculture data Fisheries and Aquaculture sector at JRC (Joint Research Center), European Commission https://data.jrc.ec.europa.eu/collection/id-0075 15 Fishbase Global information system for fishes GEOMAR Helmholtz Centre for Ocean Research https://www.fishbase.se/search.php 16 GBIF Global Biodiversity Information Facility GBIF Project https://www.gbif.org 17 Geoportale Nazionale Italian data geoportal MASE (Ministero dell'Ambiente e della Sicurezza Energetica) http://www.pcn.minambiente.it/mattm/ 18 Linked ISPRA ISPRA Indicator Database ISPRA https://www.isprambiente.gov.it/it/banche-dati/banche-dati-folder/mare 19 MinERva Portal for sharing information held by the Emilia-Romagna Region's General Directorate for Territorial and Environmental Care Regione Emilia-Romagna https://datacatalog.regione.emilia-romagna.it/catalogCTA/ 20 OpenDataER Open Data Service of the Emilia-Romagna Region Regione Emilia-Romagna https://dati.emilia-romagna.it/ 21 Polytraits Global database on biological traits of polychaetes Hellenic Centre for Marine Research http://polytraits.lifewatchgreece.eu 22 RCMed U-CEM Reef Check Med Underwater Coastal Environment Monitoring Protocol Reef Check Italia https://www.reefcheckmed.org/english/underwater-monitoring-protocol/webgis-map/ 23 RNDT National Directory of Spatial Data AGID - Agenzia per l'Italia Digitale https://geodati.gov.it/geoportale/ 24 SeaDataNet Pan European infrastructure for ocean & marine data management European Commission https://www.seadatanet.org 25 WebGIS ISMAR WebGiS database on geophysical and geologic data of Italian sea ISMAR - CNR Bologna http://gismargrey.bo.ismar.CNR.it:8080/mokaApp/apps/ismarBoApp/index.html?null 26 WoRMS Word Register of Marine Species Flanders Marine Institute (VLIZ) https://www.marinespecies.org/index.php 27 Zenodo Data centre-backed research data repository CERN https://zenodo.org 28 iNaturalist Online citizen science platform for sharing biodiversity observation iNaturalist Network https://www.inaturalist.org/ 29 MarineTraffic Global ship traffic intelligence AIS (Automatic Identification System) Marine Traffic company https://www.marinetraffic.com 30 ADRIREEF ADRIREEF map of Adriatic reefs ADRIREEF project https://adrireef.github.io/sandbox3/ 31 COL Catalogue of Life Species 2020 and ITIS https://www.catalogueoflife.org 32 ITIS Integrated Taxonomic Information System United States Geological Survey (USGS) https://www.itis.gov 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. Field Description ID.Repository Unique number which identifies the repository. Repository Name of the repository. Short description Brief explanation of the repository. Provider Entity, institution or agency that maintains and manages the data infrastructure. Link Direct link to the repository web page. Geographic scope Extent of coverage of data available in the repository. It varies from regional (data cover only the area of the Emilia-Romagna Region), national (data refer to the whole Italian territory), European (data are collected within Europe), and international (data refer to the global scale). Language Spoken language used in the repository. Notes Deeper explanation of the context in which the repository was created, its content and its purpose. Findable Description of the extent to which data in the repository is easily located and identified. Accessible Description of the extent to which data in the repository is easily retrievable. Interoperable Description of the extent to which data in the repository can be easily integrated and combined with other data. Reusable Description of the extent to which data in the repository can be easily applied for different purposes beyond their original context. Findability The calculated value of Findability (between 0 and 1), obtained by averaging the scores assigned to the different criteria ad sub-criteria. Accessibility The calculated value of Accessibility (between 0 and 1), obtained by averaging the scores assigned to the different criteria ad sub-criteria. Interoperability The calculated value of Interoperability (between 0 and 1), obtained by averaging the scores assigned to the different criteria ad sub-criteria. Reusability The calculated value of Reusability (between 0 and 1), obtained by averaging the scores assigned to the different criteria ad sub-criteria. FAIRness The calculated value of FAIRness (between 0 and 1), obtained by averaging the values of Findability, Accessibility, Interoperability, Reusability. 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. ID.Repository Repository Datasets 1 ADRIPLAN Data Portal 2 2 Algaebase 1 3 Bold Systems 1 4 CNR research institutes databases 4 5 Copernicus 67 6 DataONE 36 7 Dati Arpae 35 8 Dati Gov 15 9 DEIMS-SDR 1 10 Dext3r 1 11 EDMED 1 12 EMODnet 4 13 EurOBIS 1 14 FAD 2 15 Fishbase 1 16 GBIF 3 17 Geoportale Nazionale 2 18 Linked ISPRA 2 19 MinERva 7 20 OpenDataER 6 21 Polytraits 1 22 RCMed U-CEM 1 23 RNDT 1 24 SeaDataNet 5 25 WebGIS ISMAR 1 26 WoRMS 1 27 Zenodo 1 28 iNaturalist 1 29 MarineTraffic 1 30 ADRIREEF 1 31 COL 1 32 ITIS 1 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. Field Description ID.Repository Unique number which identifies the repository. ID.Dataset Unique identifier for each dataset entry. Dataset name Dataset name as assigned within the repository. Data format File format or structure in which the dataset is stored. Data typology Nature or category of the data (e.g., environmental parameters, biotic resources). Access mode Specifies the accessibility of the dataset. “Open access” means the data is freely available, while “Open access on request” implies limited access subject to approval. Additionally, “Restricted access” indicates that the dataset has limited accessibility, typically with specific constraints or conditions that need to be met before obtaining permission to access the data. License Describes the terms and conditions governing the use of the dataset (e.g., Creative Commons Attribution-NonCommercialShareAlike). Geo-referenced Indicates whether the dataset contains geographical references (Yes/No). Lat min Minimum latitude (or northing) value for the dataset. Lat max Maximum latitude (or northing) value for the dataset. Lon min Minimum longitude (or easting) value for the dataset. Lon max Maximum longitude (or easting) value for the dataset. CRS Defines the geodetic datum (and projection) used for geographical (or kilometric) coordinates. Short description Brief overview or summary of the dataset. Notes Additional notes or remarks about the dataset. 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. Data typology Description Environmental parameters Abiotic data, including water temperature, pH, salinity, dissolved oxygen concentrations, wave height, current intensity and direction. These data also encompass precise locations of sea-based instruments employed by ARPAE Emilia Romagna for collecting chemical-physical data. Bathing water parameters Water quality data derived from bathing water monitoring networks, encompassing information on toxic algae, cyanobacteria and phytoplankton. Biotic resources Data on specific species of interest (e.g., keystone species, protected and non-indigenous species, commercially relevant species). This data may include occurrence details, natural growth areas, production locations for harvested species meant for human consumption. Coastal protection works Data concerning coastal protection initiatives, such as beach nourishment activities and efforts to mitigate coastal hazards' impacts on adjacent lands. These data cover sand sampling points (locations for sand removal used in beach nourishment) and indicators of coastal evolution and criticality. Geomorphological features Topographic and bathymetric features of the Adriatic Sea off the Emilia-Romagna coast. This dataset incorporates continental shelf geology, shoreline mapping and classification, bathymetric curves and the continental shelf's delimitation line. Offshore Works Information includes precise locations of active and disused offshore platforms and methane wells off the coast of Emilia-Romagna, as well as methane and oil pipelines connecting with the mainland. Sedimentological parameters Data encompass sedimentology sampling points and related sedimentological parameters such as mean diameter, sorting coefficient, asymmetry coefficient and the percentage of sand and mud. Other This category encompasses data that has not been grouped into the previous categories and spans across various topics. 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). The FAIR Guiding Principles To be Findable: F1. (meta)data are assigned a globally unique and persistent identifierF2. data are described with rich metadata (defined by R1 below)F3. metadata clearly and explicitly include the identifier of the data it describes F4. (meta)data are registered or indexed in a searchable resource To be Accessible: A1. (meta)data are retrievable by their identifier using a standardised communication protocol A1.1 the protocol is open, free, and universally implementableA1.2 the protocol allows for an authentication and authorization procedure, where necessary A2. metadata are accessible, even when the data are no longer available To be Interoperable: I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation I2. (meta)data use vocabularies that follow FAIR principlesI3. (meta)data include qualified references to other (meta)data To be Reusable: R1. meta(data) are richly described with a plurality of accurate and relevant attributes R1.1. (meta)data are released with a clear and accessible data usage licenceR1.2. (meta)data are associated with detailed provenanceR1.3. (meta)data meet domain-relevant community standards 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. Rank Index value Overall evaluation Considerations 5 <0.01 Not FAIR Data are not findable, accessible, interoperable, or reusable. This data may be poorly described, difficult to access, or incompatible with other data sources, making it of limited use for research or other purposes. 4 >0.01-0.25 Minimally FAIR Data are minimally findable, accessible, interoperable, and reusable. Data are not indexed in a searchable resource, making it difficult to discover. Data may have significant limitations in terms of metadata or accessibility, which hinders their effective use. A policy on data reuse is not expressed. 3 >0.25-0.50 Partially FAIR Data are partially findable, accessible, interoperable, and reusable. Data may have limited metadata or accessibility, making it more difficult to find or use effectively. A policy on data reuse is not expressed. 2 >0.50-0.75 Generally FAIR Data are mostly findable, accessible, interoperable, and reusable. Data may have some limitations in terms of metadata or accessibility, but they are generally well-described and can be used for research or other purposes (clear licenses terms on data reuse are provided). 1 >0.75-1.00 Highly FAIR Data are easily findable, accessible, interoperable, and reusable. Data are indexed in a searchable resource, openly available, provided with rich metadata that conform to international standards, clear licenses terms, and can be easily integrated with other data sources. 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.