The following glossary intends to standardise the terms used in the context of the NFFA-Europe Infrastructure. Its content is constantly being updated.
Access Provider: a Beneficiary or linked Third Party that is in charge of providing access to one or more research infrastructures or Installations, or part of them.
(adapted from NFFA-Europe PILOT Consortium Agreement)
Acquisition Software: any software designed to interface directly with an Instrument in order to control and monitor Data Acquisition parameters, and to collect Raw Data. Regardless of whether the Acquisition Software is integrated with the Instrument or operates as a standalone application (e.g., installed on a separate computer or packaged as a virtual machine), it is considered part of the Instrument.
Additional Data: any other data that is not Publication Data but is directly related to it as specified in the Data Management Plan (for instance curated data not directly attributable to a publication, or related Raw Data).
Analyzed Data: specific type of Research Data, primary output of any kind of Data Analysis performed on Research Data, typically on Processed Data.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Beneficiary: legal entity part of NFFA-Europe research infrastructure that signed the Grant Agreement with the European Union, represented by the European Commission, and which therefore undertakes to complete the actions envisaged in the funded project within the terms (temporal and legal) provided for.
Calculation: computational Data Acquisition performed by one or more Research Users on a Model to process its input Settings into output calculated properties using a specific computational and/or theoretical Technique based on a theory accepted by the scientific community (e.g., Density Functional Theory, Conformal Field Theory).
(adapted from https://doi.org/10.1007/978-3-030-62466-8_14)
Consortium: the beneficiaries collectively.
(from NFFA-Europe PILOT Consortium Agreement)
Consumable: auxiliary entity, normally acquired from third-party manufacturers, used during Processing and Treatment or Measurement which has a limited time capacity or is limited in its number of uses before it is disposed of. Consumables are not part of the Instrument. Examples of Consumable are: gloves, syringes, wipes, etching solutions, glass slides, spatulas, weighing paper, two-sided tape.
Correlative Characterization: set of actions carried out by one or more Research Users, possibly using Research Software, with the intent of characterizing and connecting the different types of information from co-referenced (in time or space) multimodal Research Data obtained using different Techniques. This may include the output of multiple Data Acquisitions and/or Data Analyses to obtain complementary insights on a region of interest, as well as to put into relation features of different Systems across multiple length scales over time. While Correlative Characterization by definition focuses on one location, it is frequently combined with statistical methods for Raw Data from multiple locations.
(adapted from https://doi.org/10.1038/s41563-019-0402-8)
Data Acquisition: set of actions carried out by one or more Research Users, performed on a System or a set of them to generate a single self-consistent unit of Raw Data using a Technique, an Instrument and possibly other Equipment under constant or varying conditions described by Settings, depending on the particular research context. Data Acquisition may be an experimental (Measurement) or a computational (Calculation, Simulation) process. Data Acquisition is specific to a Technique: an investigation on the same System conducted using a different Technique implies a different Data Acquisition. The output of Data Acquisition is Raw Data.
Data Analysis Software: software used for analysis of Raw Data or previously Analysed Data and yielding Analysed Data as an output. If software is used for simulation (computer Experiment), is it considered an Instrument and should be described as such.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Data Analysis: set of actions performed by one or more Research Users, possibly using a Technique and Research Software, on Research Data, typically Processed Data, to extract insights that support the answer to some scientific research question. Data Analysis may include: linear combination fitting, least-squares curve fitting, data modelling, pattern extraction and/or segmentation, data visualization. The output of Data Analysis is Analyzed Data.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Data Collaboration Platform: operational information system which allows Research Users to keep their Research Data, Datasets, and related documents (e.g., notes or drafts of Scientific Publications) synchronized and up to date and to share them with other Research Users, who are typically members of the same Project. The access rights granted to the resources may be determined by the User Role. The system is intended for the long-tail, unstructured, and still volatile data, which may change and is still subject to active research. Therefore, a Data Collaboration Platform offers versioning of all ingested files but does not usually assign them Persistent Identifiers. A Data Collaboration Platform may be associated with an Institution or a group of them, or may be run by a third party.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Data Curation: et of actions performed by one or more Research Users on Research Data and/or Datasets using a Technique to cleanse, document, standardize, format and inter-relate them, with the goal of ensuring they are fit for the purpose and available for discovery and reuse. This includes (but is not limited to): forming a new Dataset from several data sources, annotating Research Data and/or a Dataset with Metadata, maintaining links with published materials (Publication Data and/or Scientific Publications). For dynamic Datasets this may mean continuous enrichment or updating. Special forms of Data Curation (e.g., versioning) may be available in Data Repositories.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Data Management Plan (DMP): a formal document that outlines what to do with data during and after a research project. It describes the type of data that will be used for a research, how this data is collected, organized, and stored, and in which formats. It details how data will be accessible and documented for sharing and reuse during and after the project is finished.
(from https://researchdatamanagement.harvard.edu/data-management-plans)
Data Processing: set of actions performed by one or more Research Users, possibly using a Technique and Research Software, on Research Data, typically Reference Data or Raw Data, to prepare it for one or more further processes, e.g., Model Preparation, computational Data Acquisition, and/or Data Analysis. Data Processing usually consists of routine actions. It may include: filtering, denoising, transformation, fusion and/or compression of Research Data, as well as calibration, normalization, statistical data reduction, background subtraction and/or correction of artifacts. The output of Data Processing is Processed Data.
Data Policy: an identifiable expression of rules and regulations and sharing within NFFA-Europe PILOT project. Data Policy may be applicable to Publication Data, Raw Data or/and Analysed Data.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Data Repository: information system used to store, manage, and provide access to digital resources, following a set of rules that define storage and access norms. A Data Repository is particularly suitable for (but not limited to) Publication Data, which is not likely to be altered again. Many Data Repositories automatically assign globally unique Persistent Identifiers to deposited resources. A Data Repository may be associated with an Institution or a group of them, or may be run by a third party. A Data Repository may or may not be directly used by Research Users. The access rights granted to the resources may be determined by the User Role.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Data Research Infrastructure Support Professional: ICT expert who manages and operates research infrastructures and the necessary services for the storage, preservation and processing of Research Data.
(from https://op.europa.eu/en/publication-detail/-/publication/af7f7807-6ce1-11eb-aeb5-01aa75ed71a1)
Data Scientist/Data Analyst: expert on data processing, not necessarily from a specific discipline, who is capable of evaluating data quality, extracting relevant knowledge from data and representing such knowledge. A Data Scientist or Data Analyst could be an expert who develops a general-purpose machine learning algorithm that could efficiently run on the EOSC federated research infrastructures that are consuming data from EOSC services.
(from https://op.europa.eu/en/publication-detail/-/publication/af7f7807-6ce1-11eb-aeb5-01aa75ed71a1)
Data Steward: expert on the preparation and treatment of data including data selection, storage, preservation, annotation provenance and other Metadata maintenance, and dissemination. Data librarians are professional library staff who are experts on RDM, using Research Data as a resource or supporting researchers dealing with data (description, archiving and dissemination). Other closely related roles will also be considered under this category. A Data Steward could be an expert who validates, recodes, trims or applies any other action on each source Dataset to guarantee that they can be properly used and integrated according to domain-specific standard formats.
(from https://op.europa.eu/en/publication-detail/-/publication/af7f7807-6ce1-11eb-aeb5-01aa75ed71a1)
Database: organised collection of data allowing storage and retrieval of data by means of a computer system.
Dataset: collection of scientifically related (depending on the research context) Research Data, along with their respective descriptive Metadata, formed after some kind of Data Curation. Datasets are typically stored in a Data Collaboration Platform and/or in a Data Repository. A Dataset may consist of other Datasets. The components of a Dataset remain individually identifiable.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Electronic Laboratory Notebook (ELN): computer program designed to replace paper Laboratory notebooks. It is used by Instruments Scientist and Research Users to document research, procedures, workflow performed during an Experiment and typically related to a particular Instrument.
(adapted from https://en.wikipedia.org/wiki/Electronic_lab_notebook)
EOSC - European Open Science Cloud: European Commission initiative aiming at developing an infrastructure providing its users with services promoting open science practices.
Equipment: any kind of physical or virtual item, device, machine or other tool located in a Laboratory hosted by an Institution, which can be physically, virtually, and/or remotely accessed to perform any of the processes during the course of a Study, by applying a Technique. Equipment is usually an investment. According to this definition, an Instrument is a particular type of Equipment.
Experiment: identifiable activity with a clear start time and finish time conducted by Research User who uses one or more Instruments to investigate or produce one or more Samples and collects Raw Data about it. Experiment consists of (or includes – in case of Sample Preparation) one or a series of Measurements. Experiments can be a computer simulation (computational Experiment), or a combination of it with physical Measurements.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
External User: person who hasn't registered to the NFFA-Europe Portal, so has not a username/password.
FAIR Data: data which meet the FAIR principles of findability, accessibility, interoperability, and reusability. The FAIR principles emphasize machine-actionability, i.e. the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention.
¶ Head of Laboratory: person who has the overall responsibility of technical, scientific and administrative operations of the Laboratory/research group. She/He is responsible for assuring that the Laboratory complies with EU obligations and regulations concerning Research Data. In accordance with these, will be in charge of drafting and regularly updating the Lab-DMP related to the management of the Research Data for the respective Laboratory or research group. This person is usually someone that is experienced with one or (possibly) more of the techniques offered, and should have a clear knowledge of the data produced and how this data is handled during the research process. Each Laboratory may have one or more Head of Laboratories.
Installation: a part or a service of a research infrastructure that could be used independently from the rest. A research infrastructure consists of one or more Installations.
(from NFFA-Europe PILOT Consortium Agreement)
Institution: hierarchical entity which hosts one or more Laboratories.
¶ Instrument: physical or virtual identifiable piece of Equipment used to perform a Data Acquisition and to generate Raw Data. The Instrument is located in a (physical or virtual) Laboratory hosted by an Institution and can be physically, virtually, and/or remotely accessed. A virtual Instrument may be any computational resource or HPC infrastructure (cloud infrastructure or supercomputer) needed to perform Calculations or Simulations.
Instrument Datasheet: technical specification of the Instrument, organised according to a Metadata Schema.
Instrument Scientist: person, or a group of them, who manage a particular Instrument, or a set of them (ref. NFFA D11.2). This is the person who usually performs the Measurement or Experiment and possibly the Data Analysis.
(from https://doi.org/10.1007/978-3-319-57135-5_18)
Laboratory: physical or virtual place hosted by an Institution, where the Equipment (including Instruments), is located and can be physically, virtually, and/or remotely accessed to perform any of the processes during the course of a Study.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Laboratory Information Management System (LIMS): software-based solution with features supporting Laboratory's operations, including - but not limited to - workflow and data tracking support, Sample management, Instrument integration (adapted from https://en.wikipedia.org/wiki/Laboratory_information_management_system).
License: official permission or permit to do, use, or own something (as well as the document of that permission or permit)
Material: broad term used to define a physical System, typically a substance or mixture of substances, in different states of matter or phases, of which something is composed or can be made. According to this definition, a Sample is constituted by at least one Material. Material(s) may undergo some kind of Processing and Treatment and/or Measurement.
Measurement: experimental Data Acquisition performed by one or more Research Users on one or more physical Systems (Materials or Samples) using an experimental Technique. One or more Measurements may also be performed during Processing and Treatment, e.g., to characterize the intermediate stages and/or the final resulting physical System(s). A Measurement may require the use of Consumables.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Metadata: any descriptive data intended to contextualize or otherwise qualify Research Data and/or Datasets and/or Publication Data and their management through time. Typically, Metadata is created in the course of a Study and then integrated, organized, and refined during Data Curation. Examples of Metadata are: Sample properties and/or treatment history recorded in an Electronic Laboratory Notebook, Instrument parameters saved with Raw Data, analysis processes saved with Analyzed Data, Settings of a Model, but also data license, affiliation of Research Users, descriptions of how files are named, structured and stored. Metadata may be registered in a Metadata Repository and assigned a Persistent Identifier.
(partially adapted from ISO 15489-1 s 3.12, from https://doi.org/10.2777/1524 and from https://helmholtz-metadaten.de/glossary)
Metadata Document: the actual document, written in JSON or XML format, containing the Metadata, following a Metadata Schema.
Metadata Repository: information system used to store, manage and provide access to Metadata, following a policy or a set of rules that define storage and access norms. Many Data Repositories automatically assign globally unique Persistent Identifiers to deposited resources. A Metadata Repository may be associated with an Institution or a group of them, or may be run by a third party. A Metadata Repository may or may not be directly used by Research Users. The access rights granted to the resources may be determined by the User Role.
Metadata Schema: a logical plan showing the relationships between Metadata, normally through establishing rules for the use and management of Metadata specifically as regards the semantics, the syntax and the cardinality (mandatory, optional, recommended) of values. (from ISO 23081.1 s3 Terms and Definitions). It can be written, e.g., in XSD (XML Schema Definition) or in JSON format, and may be implemented as machine actionable through consistent data entries and the inclusion of access points using controlled vocabularies. A Metadata Schema that gains wide acceptance from a reference user community and has been formally approved by Standards organizations, becomes a Metadata Standard.
Metadata Standard: a Metadata Schema that fulfills the needs of a scientific community, has obtained consensus, and has been ratified as a standard by some official bodies, such as the National Institute of Standards and Technology (NIST), the Dublin Core Metadata Initiative or the NeXus Data Format. A Metadata Standard describes the information and the terms needed to properly define specific data and it favors interoperability. In NeXus Data Format, a Metadata Standard is called “Application Definition”.
Model: digital representation of a System, primary output of any kind of Model Preparation, aimed to be used in one or more Calculations or Simulations for its description or for predictions of its behaviour. A Model represents the System by direct similitude (e.g., small-scale replica) or by capturing in a logical framework the relations between its properties (e.g., mathematical Model). A Model typically consists of Settings which may be stored in one or more files.
Model Preparation: set of actions carried out by one or more Research Users, possibly using Research Software, to define and/or formulate a Model making use of Research Data (typically, but not limited to, Reference Data after Data Processing) as input for parametrization, boundary conditions, or validation. A Model Preparation may require the use of Equipment. The output of Model Preparation is Model.
NFFA-Europe Portal: website accessible at www.nffa.eu, which is the starting point of a broad array of information resources and services. It represents the Single-Entry Point for NFFA-Europe infrastructure proposal submission and management.
Ontology: formal representation of knowledge, typically in a graph or network structure, with both human and machine-readable definitions, with logical relationships (axioms) between the terms, which together define a domain of knowledge.
Open Access (OA): practice of providing online access to scientific information that is free of charge to the end-user and reusable. 'Scientific' refers to all academic disciplines. In the context of research and innovation, 'scientific information' can mean: peer-reviewed scientific research articles (published in scholarly journals), or Research Data (data underlying publications, curated data and/or Raw Data). Open Access is granted by providing a suitable open License such as Creative Commons Licences (CC BY or CC0).
Open Format: open standard which specifies a file format. An Open Format is a file format for storing digital data, defined by a published specification, usually maintained by a standards organization, and which can be used and implemented by anyone. Open Formats are also called free file formats if they are not encumbered by any copyrights, patents, trademarks or other restrictions so that anyone may use the format at no monetary cost for any desired purpose.
Persistent Identifier (PID): long-lasting reference which provides the information required to reliably identify, verify, and locate a digital resource, e.g., Research Data (typically, but not limited to, Publication Data), Metadata, Scientific Publications, but also Systems, Research Users, Equipment, and/or Institutions.
(partially adapted from https://doi.org/10.2777/1524 and from https://helmholtz-metadaten.de/glossary)
Processed Data: specific type of Research Data, primary output of any kind of Data Processing performed on Research Data (typically, but not limited to, Raw Data or Reference Data). Processed Data is usually an intermediate result, to be used as input of one or more further processes, e.g., Model Preparation or Data Analysis.
Processing and Treatment: set of actions (physical changes or chemical reactions) carried out by one or more Research Users, a commercial enterprise, or a third party, performed on (or between) one or more physical Systems to produce them, treat them, or prepare them for a subsequent Measurement, under controlled conditions described by Settings. Processing and Treatment may require the use of Equipment and Consumables. One or more Measurements may also be performed during Processing and Treatment, e.g., to characterize the intermediate stages and/or the final resulting physical System(s). The output of Processing and Treatment is one or more physical Systems. Examples of Processing and Treatment are: synthesis, fabrication, any type of Material or Sample treatment (e.g., thermo-mechanical or surface treatment), and Sample preparation.
Project: Enterprise (potentially individual but typically collaborative) of one or more Research Users, planned to perform one or more Studies.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18 and from https://schema.org/Project)
Proposal: application of one (usually the Team Leader) or more Registered Users to get User Access in order to perform one or more activities, in one or more Laboratories using one or more Instruments for taking one or more Measurements of one or more Samples during one or more Experiments. Instrument, Measurement, Experiment and Sample can refer to computer simulation environments.
Publication Data: Dataset(s) generated in the course of a Study, that has undergone quality assessment and Data Curation, and can be cited as a reliable source (i.e., a Persistent Identifier is assigned to it) to, e.g., reproduce the results and/or support the conclusions presented in a Scientific Publication or appearing in it. Publication Data may be attributed to some or to all the Research Users who are members of the Project.
Raw Data: Specific type of Research Data, primary output of a Data Acquisition performed on a System, before any subsequent Data Processing.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Recipient: Research User or researcher affiliated with a Beneficiary or a Third Party who performs activities related to NFFA-Europe PILOT project.
Reference Data: any Research Data from prior Studies or from literature, which is used in the course of a Study to, e.g., define a Model, compare Analyzed Data, and/or validate findings and results. Reference Data is typically retrieved from publicly accessible Data Repositories. Depending on the research context, Reference Data usually undergo some kind of Data Processing before being used.
Registered User: person registered as user of the NFFA-Europe Portal.
Research Data: data collected, created, or examined by one or more Research Users to be analyzed or considered as a basis for reasoning, discussion or calculation in a research context, with the purpose of generating, verifying, and/or validating original scientific claims that support the answer to some specific research question. According to this definition, Raw Data, Processed Data, Analyzed Data and Reference Data are particular types of Research Data. Research Data is typically in the form of one or more data files, but it may potentially be a data stream or any other form of data which is relevant in a particular data management context. Research Data may be described by Metadata and may be stored in a Data Collaboration Platform and/or in a Data Repository. Research Data may undergo some kind of Data Curation and be aggregated into a Dataset.
(adapted from https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf)
Research Digital Object: any computer processable or computer processed object that results from research activity, including, but not limited to, Dataset, Research Software, Laboratory workflows and notebooks, data services, publications.
(adapted from https://doi.org/10.2777/1524)
Research Software Engineer: ICT expert who designs, implements, maintains and/or integrates services and software in the EOSC ecosystem to enable FAIR and open science, ensuring the fulfilment of software quality, reproducibility and sustainability. A Research Software Engineer could be designing, building and maintaining software that is compiled and installed by someone else. Research Software Engineers may require other ICT skills of different roles such as ICT Managers, Development Operations Engineers or Database Programmers.
(from https://op.europa.eu/en/publication-detail/-/publication/af7f7807-6ce1-11eb-aeb5-01aa75ed71a1)
Research Software: any software used to process, analyze, or visualize Research Data (including data rendering and or plotting).
Research User: person, usually member of a Project, who conducts any part of the Study to collect and/or to analyze Research Data, and/or reuses Reference Data to extract insights that support the answer to some specific research question, usually reported in one or more Scientific Publications. A Research User may be assigned with a User Role.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Result: any (tangible or intangible) output of the Project such as data, knowledge, or information — whatever its form or nature, whether it can be protected or not — that is generated in the NFFA-Europe PILOT project, as well as any rights attached to it, including intellectual property rights.
(adapted from NFFA-Europe PILOT Consortium Agreement)
Sample: physical System, composed by one or more Materials, exposed to the Instrument during a Measurement, typically after some kind of Processing and Treatment. Sample may be held by a Sample Holder and/or carried by a Sample Carrier.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Sample Carrier: piece of Equipment used for carrying one or more Samples and/or one or more Sample Holders (depending on the scientific use case), which is helpful for, e.g., referencing, handling, or height adjustment. Examples of Sample Carrier are: a naked wafer, a glass slide, an individually designed metal frame, but also a Si crystal carrying a Sample of exfoliated graphene.
Sample Component: identifiable piece of material with distinctive properties (structural, chemical, dimensional and others), used in Sample Preparation to produce a Sample.
Sample Component Production: production of a Sample Component in controlled conditions, performed by a commercial enterprise, a Research User or someone else, typically before the User Access begins.
Sample Holder: piece of Equipment used for making one or more Samples accessible for a Measurement or for holding them in place in the predefined position to be mounted inside the Instrument. Examples of Sample Holder are: a glass slide, a TEM grid, or a tilting support. Depending on the scientific use case, a Sample Holder may carry or be carried by a Sample Carrier.
Sample Preparation: preliminary actions (physical changes or chemical reactions) typically carried out by a Research User or Instrument Scientist on (or between) one (or more) Sample Component(s) to produce a Sample, in order to perform a Measurement. May be done before the User Access begins.
Scientific Publication: any of the following contributions, peer-reviewed or not: article in a scientific journal (and related supporting information), monograph, book or book chapter, conference proceedings, “grey literature” (informally published material having not gone through a standard publishing process, e.g., reports and highlights). A Scientific Publication typically reports the results and the conclusions of a Study, may be supplemented by Publication Data, and may be assigned a Persistent Identifier. A Scientific Publication may be attributed to some or to all the Research Users who are members of the Project.
Settings: any set of configuration parameters which determine the operational conditions of Equipment (including Instrument), configure Research Software, and/or describe a Model (e.g., by specifying the type of solver user).
Single-Entry Point: system that enables consumers to access long term and supportive services through one agency or organization.
Single Sign-On (SSO): authentication scheme that allows a user to log in with a single ID and password to several related, yet independent, software systems.
Simulation: computational Data Acquisition performed by one or more Research Users on a Model to manipulate its Settings using a specific computational and/or theoretical Technique in order to study, predict, or optimize the behaviour and performance of existing or proposed features and properties of a physical System that would otherwise be too complex, too large/small, too fast/slow, too dangerous, inaccessible, or unacceptable to engage and/or control. Examples of Simulation are: multiscale simulation, finite-element simulation, molecular dynamics simulation, discrete dislocation dynamics simulation.
(adapted from https://dl.acm.org/doi/10.1145/268437.268440)
System: physical or digital entity or set of entities with distinctive properties (structural, chemical, dimensional, functional, or others) which is the subject of one or more actions or investigations. According to this definition, Sample, Material, and Model are particular types of System.
Site: specific geographical location where one or more Institutions with one or more Laboratories are located.
Study: set of all the processes and activities performed by one or more Research Users, who are part of the same Project, with the purpose of verifying, falsifying, or establishing the validity of a hypothesis and supporting the answer to some scientific research question. The output of a Study is usually reported in one or more Scientific Publications and may be supplemented by Publication Data.
(adapted from https://doi.org/10.1007/978-3-319-57135-5_18)
Team Leader (also known as Principal Investigator): Research User officially designated as head of Proposal team
(from DataCite ProjectLeader)
Team Member: Research User on the membership list of a designated Proposal.
(from DataCite)
Technique: any experimental, theoretical, or computational method with a specific aim, including but not limited to: process and/or treat Samples, prepare Models, acquire and/or analyze Research Data, using some Equipment.
Third Party: any legal entity part of NFFA-Europe research infrastructure that has not signed the Grant Agreement. If it is necessary to implement the Project, Beneficiaries may involve Third Parties as defined in Article 8 of the Grant Agreement.
(adapted from NFFA-Europe PILOT Consortium Agreement)
TLNet Node: person representing one or more Institutions providing Transnational Access within NFFA-Europe.
Transnational Access (TA): free of charge access to the Institutions which are part of NFFA-Europe Infrastructure through the NEP proposal evaluation system. Transnational Access can be in-presence or remote and both academic and industrial users can apply. A contribution for reimbursement of travel, accommodation and subsistence costs can be granted to a maximum of two Team Members per accepted Proposal.
User Access (UA): research activity performed according to a Proposal after its approval and carried out within a defined period of time. The User Access may include, all or in part, the Data Analysis following the Experiment. NEP User Access can be Transnational Access and/or Virtual Access.
User Role: the role of the Research User in a Project, in the context of a specific Study. Examples of User Role are: data curator, project leader, project member. User Role also determines the access rights which might be granted in the information system(s) used for data management (i.e., Data Collaboration Platform, Data Repository, Metadata Repository).
Virtual Access (VA): free of charge access to e-infrastructure, namely: sophisticated computer services; online data analysis tools; powerful computers, networks, grids, repositories, databanks.
Vocabulary: on the Semantic Web, vocabularies define the concepts and relationships (also referred to as “terms”) used to describe and represent an area of concern. Vocabulary is used to classify the terms that can be used in a particular application, characterize possible relationships, and define possible constraints on using those terms.
(from https://www.w3.org/standards/semanticweb/Ontology)
