FAIR data Sample Clauses
The FAIR data clause establishes requirements for data to be Findable, Accessible, Interoperable, and Reusable. In practice, this means that any data generated or shared under the agreement must be organized and described in a way that allows others to easily locate, access, and use it, often by following established metadata standards and using open formats. This clause ensures that data is managed in a way that maximizes its value for future research, collaboration, and compliance with open science principles.
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 Making data openly accessible
3.2 Making data findable, including provisions of metadata
FAIR data. 8.1.1 Making data findable, including provisions for metadata
8.1.2 Making data openly accessible
8.1.3 Making data interoperable
8.1.4 Increase data re-use (through clarifying licences)
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.
2.1 Making data findable, including provisions for metadata
2.2 Making data openly accessible Partne r Repository name URL Type
2.3 Making data interoperable
2.4 Increase data re-use (licensing)
FAIR data. The FAIR Data Principles are a set of guiding principles to make data findable, accessible, inter- operable and reusable (▇▇▇▇▇▇▇▇▇ et al., 2016). In this section, we explain how these principles are implemented in SORTEDMOBILITY.
2.2.1 Making data findable, including provisions for metadata The supply data will be accompanied by a detailed description of their fields, variables, charac- teristics, and units of measurement. The description will also provide an explanation of the sources and the methods used to collect the data. The supply data description document will be uploaded on the 4TU.ResearchData centre along- side with the supply data in the form of ‘readme’ text file provided for each of the different cat - egories of produced data. Input and output supply data will be made findable, accessible, interoperable and reusable (FAIR), by adopting the DublinCore (▇▇▇▇▇://▇▇▇▇▇▇▇▇▇▇.▇▇▇/) metadata standard. Specific headers will be provided to describe content, context, variables and characteristics for each of the data categories. The supply data will be stored on the certified 4TU.ResearchData repository in the Netherlands which fully satisfies international FAIR data policies. Preliminary drafts of supply data and metadata will be stored in the TU Delft project drive (ht- tps://▇▇▇.▇▇/▇▇▇▇▇▇▇) which is a FAIR data archive having a storage limit up to 5 TB. For data ex- change with the other project partners, the Surfdrive cloud repository (▇▇▇▇▇://▇▇▇.▇▇▇▇.▇▇/en) will instead be adopted which has a storage size of 500 GB. A password or encrypted code will be shared with the other project partners to allow the access to raw and preliminary supply data during the progress of the project. Revised and verified supply data and metadata will be then made publicly available at the end of each related task and/or work package by means of the 4TU.ResearchData (https:// ▇▇▇▇.▇▇▇.▇▇/▇▇▇▇/) repository which is a trusted certified data storage centre in the Netherlands complying with international FAIR data policies. The 4TU.ResearchData repository has a storage size of up to 1 TB in total per year with a limit of 100 GB per project partner. As for the demand data, four main types of data will be considered:
1. Observed disaggregate demand (trip records) data
2. Demand-related context data
3. Simulated disaggregated demand data
4. Simulated aggregated data The first two types of demand data (Observed disaggregate demand (trip records) and demand- related context ...
FAIR data. 3.1 MAKING DATA FINDABLE, INCLUDING PROVISIONS FOR METADATA
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. 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 (▇▇▇▇▇://▇▇▇.▇▇▇/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 (▇▇▇▇▇▇▇▇-▇▇▇▇▇▇▇.▇▇). 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 be...
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. F - Making data findable
A - Making data openly accessible