FAIR data Sample Clauses

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. 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. 5.1.1 Making data findable, including provisions for metadata 5.1.2 Making data openly accessible

FAIR data. F - Making data findable A - Making data openly accessible

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. 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. 6.1 Findable Data Making data findable, including provisions for metadata: 6.2 Accessibility Making data openly accessible: 6.3 Interoperability Making data interoperable: 6.4 Reuse Increase data re-use (through clarifying licenses):

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. 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 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.

FAIR data. The FAIR Data Principles are a set of guiding principles to make data findable, accessible, inter- operable and reusable (Xxxxxxxxx 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 (xxxxx://xxxxxxxxxx.xxx/) 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://xxx.xx/0XXxX0X) 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 (xxxxx://xxx.xxxx.xx/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:// xxxx.0xx.xx/xxxx/) 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...