FAIR data. This section brings the concept of FAIR data – findable, accessible, interoperable and re-usable. It is important to remark that when dealing with advances on the technology frontier, the equilibrium between disclosure and confidentiality is key: to guarantee that products and processes will reach the market, benefiting the society with more sustainable e better quality products, generating taxes; at the same time that the revelation of scientific knowledge will benefit society showing advances and promoting a “fast-track” to more technological developments. The MULTI-STR3AM consortium will play an effort to reach successful results, launching innovative processes and products. To be economically viable, the consortium partners will evaluate which kind of data will be disclosed and which will be considered strategic for the development of a successful business model.
FAIR data. Core partners that produce new data within simulations and the analysis of existing data are urged to publish their data according to FAIR (Findable, Accessible, Interoperable, and Re- usable) principles. With respect to the associate partners, CompBioMed does not have control over how this data is published and made available. Nevertheless, CompBioMed will encourage partners to follow the FAIR principles. These efforts are promoted by task 3.4 “Data Curation” of the project, which will offer support to the core and associate partners of the CompBioMed2 XxX.
FAIR data. In the following sections, distinctions will be made between the storage and curation of data during the project (work in progress) and the long-term preservation of data (archiving).
FAIR data. 7 2.1. Making data findable, including provisions for metadata [FAIR data] 7
FAIR data. 2.1. Making data findable, including provisions for metadata [FAIR data] Outline the discoverability of data (metadata provision) Some of the data (such as design documents) is searchable as text. Other types of data, such as schematics, layouts and measurements are not directly searchable but are described by attributes such as filename, or are linked from searchable documents Re. TUBS SiMoNe data: The data TUBS is mainly using and archiving is background and simulation data implemented in the in-house-developed tool Simulator for Mobile Networks (SiMoNe). This tool stores its data in an SQL data base which is searchable both via SQL commands and through the Graphical User Interface of SiMoNe. For the exchange of data with partners import and export routines (with converters) exist enabling the exchange of ASCII data, e.g. which is searchable through filenames. Outline the identifiability of data and refer to standard identification mechanism. Do you make use of persistent and unique identifiers such as Digital Object Identifiers? The data will have standard identifiers. Outline naming conventions used All partners will use the agreed ThoR naming conventions are used as defined in the project management manual (D1.1, section 7.3): "In a project of this size, many hundreds of documents will be generated. For ease of use a register of all project documents based on a unique reference based on the document creation date will be included on the website. The initial version will be A; updates B, C etc. will retain the original creation date so that this acts as an identifier.
FAIR data. As a project participating in the Open Research Data Pilot (ORD-Pilot) in Horizon 2020, NEP will work to make its Research Data Findable, Accessible, Interoperable, and Reusable (FAIR)5. Work Package 16, Implementing FAIR Data approach within NEP, is fully devoted to this challenging task and aims at consolidating what was achieved within NFFA-Europe and at further developing new tools and services to provide guidelines and procedures for a FAIR Data approach. This specific activity will strongly benefit from the suggestions and contributions of the EOSC experts within the executive and strategy committee (ESC) of NEP. This joint activity is actively working to provide data services and support to Recipients. For each of the Proposals approved within the infrastructure, the objective of NEP is to provide Research Users and Access Providers with tools ensuring that the Research Data are managed in a FAIR by design way. 1 NFFA-Europe Research Data Policy, art 5.1.1 and 5.1.2 xxxxx://xxx.xxxx.xx/apply/data-policy 2 NFFA-Europe Research Data Policy, art 5.1.3 xxxxx://xxx.xxxx.xx/apply/data-policy 3 NFFA-Europe Research Data Policy, art 5.1.4 xxxxx://xxx.xxxx.xx/apply/data-policy 4 NFFA-Europe Research Data Policy, art 5.1.5 xxxxx://xxx.xxxx.xx/apply/data-policy 5 The FAIR Guiding Principles for scientific data management and stewardship xxxxx://xx.xxx.xxx/10.1038/sdata.2016.18 Making data FAIR ensures they can be found, understood and reused by the creators as well as by others. A useful tool for researchers and providers is the FAIR Data checklist (xxxxx://xxx.xxx/10.5281/zenodo.5111307). General scheme of FAIR principles6: Findable ● Persistent ID ● Metadata online Accessible ● Data online ● Restrictions where needed Interoperable ● Use standards, controlled vocabularies ● Common (open) formats Reusable ● Rich documentation ● Clear usage licence Every Recipient will have the possibility to choose whether to adopt tools provided by the project or to use their own tools and good practices that have to be compliant with the FAIR principles.
FAIR data. This DMP follows the EU guidelines1 and describes the data management procedures according to the FAIR principles2. The acronym FAIR identifies the main features that the project research data must have in order to be findable, accessible, interoperable and re-useable, allowing thus for maximum knowledge circulation and return of investment.
FAIR data. 3.1 Making data openly accessible We discuss next the accessibility strategy for each type of data mentioned in section 2. As indicated earlier, Publications are the most discussed and understood data. PRESENT adheres to Open Access publication as a matter of principle, and compliance with Horizon 2020 rules for Open Access to scientific publications needs to be ensured. Each partner will choose the most suitable approach (either “green” OA or “gold” OA) for each publication concerned. The e-repository, which is the institutional repository of the UPF, the coordinator of the PRESENT project, is among the top 20 providers of publications and projects H2020 and ERC: it meets all the requirements established by the European Union within the framework of Open Access publishing, and will be used for UPF generated publications. Inria has its own repository (HAL, xxxxx://xxx.xxxxx.xx/), has been fully supporting Open Science strategies (including an ‘obligation to deposit’ since 2015), and it is a major source of publications, which includes both publications and software. UAu has an open repository as well (OPUS-Datenbank). UPF as coordinator will check the compliance with the H2020 rules, will make available the e-repository for the partners and that all publications resulting from the project are referenced on the web site, to ensure its widest dissemination, linking to the appropriate repositories. With respect to Software, several academic partners promote Open Access. For instance, UPF-GTI makes available web based 3D graphics software through GitHub (mostly through updated versions of WebGLStudio xxxxx://xxxxxxxxxxx.xxx/), as well as part of the results of EU supported current projects (HDR4EU, SAUCE). Continuing with this option seems a good alternative to make software as widely accessible as possible. A similar strategy is also used by UAu with respect to their Social Signal Interpretation Framework xxxxx://xxx-xxx.xx/projects/ssi/, which is going to be enhanced within PRESENT. We have already mentioned that HAL, Inria’s repository, also includes software as well as publications. As indicated earlier, professional Data of high quality not covered by IP is scarce; PRESENT partners will discuss whether it makes sense and is possible to make the high quality data character captured widely available. Other content generated, beyond using publicly available datasets, will be made available by the academic institutions as in the past. With respect to Results,...
FAIR data. F - Making data findable The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of making data "FAIR". During the MIP-Frontiers life cycle, ESRs and their supervisors should ensure and try to make data and metadata findable by means of: • Assigning (meta)data a globally unique and persistent identifier. • Describing data with rich metadata. • Registering or indexing (meta)data in a searchable resource. • Specifying the metadata using standard identifiers.
FAIR data. 3.1.1 Making data findable, including provisions for metadata In order to ensure the comparability of data, standard naming conventions will be used whenever suitable. For the waste materials, the European list of waste10 will be used as a standard nomenclature. The plastic resins resulting from sorting and reprocessing will be classified following the naming convention used in PlastEurope’s Plastics Exchange11. Due to the nature of the project, which focuses on development of new prototypes of plastic products based on innovative, new plastic resin mixtures and production processes, it is not expected that the use of standard naming conventions will always be suitable. In these cases, a suitable categorisation will be developed in collaboration with the experts involved in the project. In addition, some plastic resin mixtures might be considered as a trade secret by the involved companies. In this case, the project management will engage in productive dialogue with the companies about which data can and cannot be published.
