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.
3.2.1 Open data produced by MULTI-STR3AM
3.2.2 Non-open data produced by MULTI-STR3AM
FAIR data. 3.1.1 Making data findable, including provisions for metadata
3.1.2 Making data openly accessible
FAIR data. 7.1.1 Making data findable, including provisions for metadata
7.1.2 Making data openly accessible
7.1.3 Making data interoperable
7.1.4 Increase data re-use (through clarifying licences)
FAIR data. Data management in MeMAD is guided by the set of guiding principles labelled FAIR. The purpose of these principles is to make data Findable, Accessible, Interoperable and Reusable. In order to be findable according to these principles, the research data has to be described using a rich set of metadata. This metadata must then be assigned a unique, specified identifier, which will be registered in an indexed or searchable resource. According to the accessibility principle, this set of metadata has to be accessible using standardized communications protocols that is free and universally implementable, and that allows for the authentication and authorization procedures when needed. The principle also dictates, that this metadata has to remain accessible through these means even though the dataset itself is not or no longer available. The interoperability principles dictates that the metadata must use a formal and accessible language for knowledge representation that is at the same time also shared and broadly applicable. The vocabularies used in describing the data should also follow FAIR principles, and include qualified references to other metadata. In order to further the re-usability of the data, the FAIR principles dictate that the metadata should be composed of a plurality of accurate and relevant attributes that are associated with their provenance and follow domain-specific standards. The metadata must be accompanied with a clear and accessible license for the use of the data. It is understood, that data management as practiced currently in MeMAD does not fully conform to the FAIR principles. This document describes the current adopted practices within the project, in order to facilitate the integration of practices during the successive iteration of data management practices. The aim of MeMAD is to create an integrated set of data management practices during the project, and the FAIR principles will be used to guide the process of data management practice development.
2.1. Making data findable, including provisions for metadata AALTO UH EURECOM SURREY YLE Limecraft
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).
2.1. Making data findable, including provisions for metadata Software files or other document types used to process or generate data (e.g. Excel documents, MATLAB files, Lab view projects) should be named in a descriptive manner for easy identification and should be provided with the data if necessary to access, treat or interpret the data.
2.2. Making data openly accessible Data storage (closed) Host Comment Open access publishing Host Comment Repository (archiving) Host Comment
(i) data supporting peer reviewed publications. The data supporting the publication shall remain closed and protected until the date of publication of the peer reviewed journal article. Upon publication, the supporting data will be made openly accessible, as previously described.
(ii) data detrimental for the application of a patent. The data sets supporting the patent application shall remain closed and protected until the date of publication of the patent.
(iii) data comprising a substantial amount of protected background information, such that the data cannot reasonably be disclosed without disclosing the incorporated background information. In this case, the beneficiary can choose to publish part of the data set that does not jeopardise the protection of the incorporated background information. If the extent of the data set that cannot be disclosed is too large – decided in good conscience by the involved beneficiary – the data set is exempt from open access publication. Note that in the case that multiple beneficiaries are involved with the generation of the data, and a such share the ownership of the intellectual property, a consensus needs to be reached between the beneficiaries. In case no consensus is reached, the abovementioned data will not be made openly available if one of the involved beneficiaries opts not to disclose the data. The raw data obtained from materials characterization (e.g. spectroscopy and thermal analysis) will be very heterogeneous and dedicated software is required to open and treat the data. The data can be exported in commonly available formats (.txt, .csv, .xls) or treated by Matlab programs. For open access publication of data and for depositing data in repositories for long term preservation (archiving), the data would be made available in open source formats. In ...
FAIR data. In this section the compliance with the FAIR data principles is reported.
3.1 Findable Will data be identified by a persistent identifier? All non-confidential datasets will be assigned a unique persistent identifier (e.g. DOI). The DOIs will be referenced in related publications on journals and within presentations at Conferences. Furthermore, project deliverables are assigned a unique identifier QUANTIFY_[number of Deliverable]_[Title]_[version]_[date of submission, when submitted], e.g. QUANTIFY_D1.1_ProjectManagementHandbook_v0.1_202402 28 (already submitted). All files made publicly available reference QUANTIFY in their name. In particular: meeting documents (agenda, minutes, presentation), conference presentations, and deliverables, with the recommendation to follow the instructions reported within the Project management handbook (xxxxx://xxx.xxx/10.5281/zenodo.10849415) Will rich metadata be provided to allow discovery? What metadata will be created? What disciplinary or general standards will be followed? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how. All data will have an associated metadata document (stored as a .txt file) which describes key aspects of the data. In details, Annex 1 gives more information about created metadata. Will search keywords be provided in the metadata to optimize the possibility for discovery and then potential re-use? Search keywords will be implemented for optimizing possibilities for re-use. Will metadata be offered in such a way that it can be harvested and indexed? Yes, they will.
3.2 Accessible Will the data be deposited in a trusted repository? Data will be available to all the consortium through the SharePoint. In addition, accessibility of data/research outputs will be guaranteed, within the scope of IPR protection, by using ZENODO, as trusted platform, which assign a Digital Object Identifier (DOI) to each dataset. All will be published without restrictions, for all the data and deliverable labeled as Public. Public deliverable will be also uploaded within the Dissemination area of the QUANTIFY website (xxxxxxxx-xxxxxxx.xx). The only data which will not made openly accessible will be data which contain personally identifiable information and data underlying deliverables that are identified as “Sensitive” – SEN. Each publication will cite the DOI of related datasets, thus connecting them directly to the research outputs. The IPR will ...
FAIR data. 2.1. Making data findable, including provisions for metadata [FAIR data] Outline the discoverability of data (metadata provision) 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? Outline naming conventions used
FAIR data. CREMLINplus It is dedicated to promoting FAIR (findable, accessible, interoperable and reusable) data, and - if possible - also research software.
2.1 Making data findable, including provisions for metadata Documents produced as part of the project will adhere to a clear naming conversion based on best practices. All project related document can be assigned to a work package within the project and include document information as part of version control. A template for deliverables, milestones as well as other materials is available to all project participants.
2.2 Making data openly accessible CREMLINplus publications will be allocated a DOI, stored in a database [Zenodo or PUBDB] and will be made openly accessible via the project’s website. Public deliverables will also be made openly accessible via the project’s website, while confidential deliverables and materials will be securely stored in the project’s collaboration space with restricted access rights.
2.3 Making data interoperable CREMLINplus contributes to making data interoperable by applying best practices in documenting its data and providing [machine-readable] metadata.
2.4 Increase data re-use (through clarifying licenses) CREMLINplus intends to use open licences where possible, such as Creative Commons licences (CC) BY 4.0, for public project materials. With reference to source code written as part of the project, the use of open licences will be encouraged.
FAIR data. Making BEACONING’s data Findable, Accessible, Interoperable and Reusable (FAIR) is important for the project as part of the ORDP. Data is published on the research-sharing platform Zenodo, as described in D1.9. Zenodo can be used by third parties without the need of an account, which means that publications are exposable for anyone interested. BEACONING is represented at the platform as community. This means, that researchers can search for the project name in the community section. Here, all data and publications project members have uploaded are listed. Additionally, Zenodo automatically allocates a digital object identifier (DOI) number for easy re-finding certain articles. Moreover, when uploading publications, a set of keywords are set for better findability. The first one always has to be the project name. The others depend on the context. Project members are advised to use common terminology within the field of the publication. For instance, if a researcher wants to find articles about game-based education but the article is claimed with “game-based learning” only, the researcher might miss the project’s publication. Therefore, it is important to have well-thought keywords which describe the same thing in different words and match the common terminology in the field.
FAIR data. The BIG project attaches great importance to making its research data findable, discoverable and identifiable. Therefore following the GA and guidelines for working on documents FAIR data follows the principle that research data should be findable, accessible, interoperable, and reusable.
