These guidelines are based on the FAIR Principles for scholarly output (FAIR data principles [2014]), taking into account a number of other recent initiatives for making data findable, accessible, interoperable and reusable. Metadata are accessible, even when the data are no longer available[2]. EN Research and results FAIR data and data management Data management in your project. Principle 3: Principle 1: Creating Opportunities for Economically Disadvantaged Producers Poverty reduction by making producers economically independent. (Meta)data use vocabularies that follow FAIR principles, I3. Metadata clearly and explicitly include the identifier of the data they describe, F4. In this manuscript we assess the FAIR principles against the LOD principles to determine, to which degree, the FAIR principles reuse LOD principles, and to which degree they extend the LOD principles. The General Data Protection Regulation … The data usually need to be integrated with other data. In fact, if approached at the right moment, the FAIR principles should be taken into consideration so that data are Findable, Accessible, Interoperable and Reusable. The FAIR data prinicples are based on the four key corner stones of findability, accessibility, interoperability and reuse. Much of the data the biopharma and life sciences industry uses for its R&D processes are generated outside the company or in collaboration with external partners. Data are described with rich metadata (defined by R1 below), F3. (Meta)data are assigned a globally unique and persistent identifier, F2. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. This includes working on policy, developing what FAIR means for specific disciplines, data and output types, supporting developers when developing code that enables FAIR outputs and building skills for research support staff and researchers. Commitment to Enabling FAIR Data in the Earth, Space, and Environmental Sciences Publication of scholarly articles in the Earth, space, and environmental science community is conditional upon the concurrent availability of the data underpinning the research finding, with only a few, standard, widely adopted exceptions, such as around privacy for human subjects or to protect heritage field samples. The FAIR Data Principles provide a set of guiding principles for successful research data management (RDM) in order to make data findable, accessible, interoperable and reusable [3]. In this knowledge clip we have a look at FAIR data and what each of the FAIR principles mean (findable, accessible, interoperable and reusable). Data scientists reported that this accounts for up to 80% of their working time. Die FAIR Data Principles, welche mittlerweile einen defacto-Standard des qualitätsbewussten Datenmanagements darstellen, verlangen nämlich, dass das Datenmanagement ständig darauf ausgerichtet sein soll, dass Forschungsdaten findable (auffindbar), accessible (zugänglich), interoperable (interoperabel) und reusable (nachnutzbar) gemacht werden und dauerhaft bleiben. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. The principles were first published in 2016 (Wilkinson et al. It has since been adopted by research institutions worldwide. Preamble: In the eScience ecosystem, the challenge of enabling optimal use of research data and methods is a complex one with multiple stakeholders: Researchers wanting to share their data and interpretations; Professional data publishers offering their services, software and tool-builders providing data analysis and processing services; Funding agencies The context FAIR DATA – The role of scientists FAIR Repository – The role of the repository Each dataset is assigned a globally unique and persistent identifier (PID), e.g. The context FAIR DATA – The role of scientists FAIR Repository – The role of the repository Each dataset is assigned a globally unique and persistent identifier (PID), e.g. I2. a Digital Object Identifier (DOI). Télécharger Voir le site. Other international organisations active in the research data ecosystem, such as CODATA or Research Data Alliance (RDA) also support FAIR implementations by their communities. These identifiers make it possible to locate and cite the dataset and its metadata. On this website, we explain the principles (based on the DTLS website) and translate them into practical information for Radboud University researchers. The FAIR Data Principles provide guidelines on how to achieve this however there are specific benefits to organisations and researchers. Twee jaar later, na een open consultatieronde, zijn de FAIR-principes gepubliceerd. The FAIR data principles are an integral part of the work within open science, and describe some of the most central guidelines for good data management and open access to research data. Here, we describe FAIR - a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable. In this blog we will explain why this is in our view good news for Wageningen and why it will help to make our data more “FAIR”. a Digital Object Identifier (DOI). To facilitate this, datasets need to be Findable, Accessible, Interoperable and Reusable. [11], Before FAIR a 2007 paper was the earliest paper discussing similar ideas related to data accessibility.[12]. This involves data stewardship which is about proper collection, annotation and archiving of data but also preservation into the future of valuable digital assets. A March 2016 publication by a consortium of scientists and organizations specified the "FAIR Guiding Principles for scientific data management and stewardship" in Scientific Data, using FAIR as an acronym and making the concept easier to discuss. What is FAIR data? The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015. Metadata and data should be easy to find for both humans and computers. (Meta)data are retrievable by their identifier using a standardised communications protocol, A1.1 The protocol is open, free, and universally implementable, A1.2 The protocol allows for an authentication and authorisation procedure, where necessary, A2. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. The FAIR data principles (Wilkinson et al. De FAIR-principles zijn geformuleerd door FORCE11 In Nederland worden de FAIR-principles in brede kring erkend. The FAIR Data Principles represent a consensus guide on good data management from all key stakeholders in scientific research. Metadata are accessible, even when the data are no longer available. The FAIR data principles are a set of guidelines, developed primarily in the research and academic sector, to encourage and enable better sharing and reuse of data. Researchers need to consider data management and stewardship throughout the grant procedure and their research project. Nevertheless at the core of the whole idea is the notion that your digital resouces (read documents) are described by clear meaningful additional information – referred to as metadata. And research institutes are promoting measures to secure the transparency and accessibility of locally produced data sets. Adopting FAIR Data Principles. The ultimate goal of FAIR is to optimise the reuse of data. 2016) are: Findability; Accessibility; Interoperability; Reusability; They are considered so important the G20 leaders, at the 2016 G20 Hangzhou summit, issued a statement endorsing the application of FAIR principles to research. At DTL we promote and advance FAIR Data Stewardship in the life sciences through our extensive partnerships and in close collaboration with our international network. The FAIR data principles are guiding principles on how to make data Findable, Accessible, Interoperable and Reusable, formulated by Force11. (Meta)data are associated with detailed provenance, R1.3. The FAIR data principles (Wilkinson et al. FAIR data support such collaborations and enable insight generation by facilitating the linking of data sources and enriching them with metadata. Adopting the FAIR data principles requires institutions to strengthen their policies around the sharing and management of research data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. To be Findable: F1. FAIR Data Principles. Principle 2: We will only use data for specified purposes and be open with individuals about the use of their data, respecting individuals’ wishes about the use of their data. FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability. FAIR principles implementation assessment is being explored by FAIR Data Maturity Model Working Group of RDA,[7] CODATA's strategic Decadal Programme "Data for Planet: Making data work for cross-domain challenges"[8] mentions FAIR data principles as a fundamental enabler of data driven science. The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015. For example, publically available data may lack sufficient documentation to meet the FAIR principles… Metadata clearly and explicitly include the identifier of the data they describe, F4. Findability; Accessibility; Interoperability; Reusability; They are considered so important the G20 leaders, at the 2016 G20 Hangzhou summit, issued a statement endorsing the application of FAIR principles to research. Die nachfolgende Checkliste soll dabei helfen, die Prinzipien der FAIR Data Publishing Group, ein Teil der FORCE 11-Community, zu erfüllen. Want hoe beschermt u privacygevoelige informatie? Published in 2016, the guidelines provide key requirements to make scientific data FAIR—findable, accessible, interoperable and reusable. The FAIR Data Principles where published in 2016 by a consortium of organisations and researchers who not only wanted to enhance the reusability of datasets, but also related facets such as tools, workflows and algorithms. The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published on Scientific Data in 2016, are a set of guiding principles proposed by a consortium of scientists and organizations to support the reusability of digital assets. The Association of European Research Libraries recommends the use of FAIR principles. However, as this report argues, the FAIR principles do not just apply to data but to other digital objects including outputs of research. Ook de AVG-kwestie speelt een rol. The FAIR data principles are guiding principles on how to make data Findable, Accessible, Interoperable and Reusable, formulated by Force11.On this website, we explain the principles (based on the DTLS website) and translate them into practical information for Radboud University researchers.. Why should you make your data FAIR? In the FAIR Data approach, data should be: Findable – Easy to find by both humans and computer systems and based on mandatory description of the metadata that allow the discovery of interesting datasets For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component). The FAIR (findable, accessible, interoperable, reusable) data principles have been introduced for similar reasons with a stronger emphasis on achieving reusability. A practical “how to” guidance to go FAIR can be found in the Three-point FAIRification Framework. The FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable), published on Scientific Data in 2016, are a set of guiding principles proposed by a consortium of scientists and organizations to support the reusability of digital assets. The guidelines are timely as we see unprecedented volume, complexity, and … FAIR data principles — making data Findable, Accessible, Interoperable and Reusable — are essential elements that allow R&D-intensive organizations to maximize the value of their digital assets. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process. The FAIR Guiding Principles for scientific data management and stewardship. The resulting FAIR Principles for Heritage Library, Archive and Museum Collections focus on three levels: objects, metadata and metadata records. (Meta)data include qualified references to other (meta)data[2]. In diesem Beitrag erläutern wir die jeweiligen Anforderungen und geben Beispiele. The FAIR DATA PRINCIPLES support the emergence of Open Science while the IDS approach aims at open data driven business ecosystems. Gemäß der FAIR-Prinzipien sollen Daten " F indable, A ccessible, I nteroperable, and R e-usable" sein. Interoperability and reuse require more efforts at the data level. In 2017 Germany, Netherlands and France agreed to establish[6] an international office to support the FAIR initiative, the GO FAIR International Support and Coordination Office. In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. Accessible Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation. A1. In 2019 the Global Indigenous Data Alliance (GIDA) released the CARE Principles for Indigenous Data Governance as a complementary guide. Data management in your project . I1. FAIR stands for Findable, Accessible, Interoperable, Reusable. [13] The CARE principles extend principles outlined in FAIR data to include Collective benefit, Authority to control, Responsibility, and Ethics to ensure data guidelines address historical contexts and power differentials. For example, data could meet the FAIR principles, but be private or only shared under certain restrictions. If you are in receipt of H2020 funding the EC requires a Data Management Plan (DMP) as part of the H2020 data pilot. However, excluding matters of confidentiality they can be considered to extend far wider. It is therefore important that relevant data is findable, accessible, interoperable and re-usable (FAIR). (Meta)data meet domain-relevant community standards. De principes dienen als richtlijn om wetenschappelijke data geschikt te maken voor hergebruik onder duidelijk beschreven condities, door zowel mensen als machines. The FAIR Data Principles represent a consensus guide on good data management from all key stakeholders in scientific research. The first step in (re)using data is to find them. Interoperable The data usually need to be integrated with other data. Open data may not be FAIR. Much of the data the biopharma and life sciences industry uses for its R&D processes are generated outside the company or in collaboration with external partners. Why use the FAIR principles for your research data? 3.2 FAIR data principles. The FAIR Guiding Principles for scientific data management and stewardship were first published in Scientific Data in 2016. The Council of the European Union emphasises that “the opportunities for the optimal reuse of research data can only be realised if data are consistent with the FAIR principles (findable, accessible, interoperable and re-usable) within a secure and trustworthy environment” (Council conclusions on the transition towards an open science system). Het vraagt immers om een herziening van het huidige datamanagement. F1. FAIR data In order to make use of integrated data sets, we have to continuously validate their accuracy, their reliability, and their veracity with new forms of big data analytics. The CARE Principles for Indigenous Data Governance were drafted at the International Data Week and Research Data Alliance Plenary co-hosted event “Indigenous Data Sovereignty Principles for the Governance of Indigenous Data Workshop,” 8 November 2018, Gaborone, Botswana. 2016) are:. [10], Guides on implementing FAIR data practices state that the cost of a data management plan in compliance with FAIR data practices should be 5% of the total research budget. A1. Share by e-mail. FAIR data is all about reuse of data and … Principle 3: Fair Trading Practices Trading fairly with concern for the social, economic and environmental well-being of producers. For instance, FAIR principles are used in the template for data management plans that are mandatory for projects that receive funding from EU Horizon 2020. This is what the FAIR principles are all about. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data. Data are described with rich metadata (defined by R1 below), F3. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. Share on Twitter. [9], A 2017 paper by advocates of FAIR data reported that awareness of the FAIR concept was increasing among various researchers and institutes, but also, understanding of the concept was becoming confused as different people apply their own differing perspectives to it. FAIR is een acroniem voor: Findable - vindbaar; Accessible - toegankelijk; Interoperable - uitwisselbaar; Reusable - herbruikbaar; De internationale FAIR-principes zijn in 2014 geformuleerd tijdens een bijeenkomst in Leiden. FAIR Data Principles. The FAIR principles are designed to support knowledge discovery and innovation both by humans and machines, support data and knowledge integration, promote sharing and reuse of data, be applied across multiple disciplines and help data and metadata to be ‘machine readable’, support new discoveries through the harvest and analysis of multiple datasets and outputs. Reusing existing data sets for new research purposes is becoming more common across all research disciplines.. Research funders and publishers are asking researchers to make data sets produced in their projects available to others. FAIR PRINCIPLES 1. Benefits to Researchers. Data Quality Principle. [2], At the 2016 G20 Hangzhou summit, the G20 leaders issued a statement endorsing the application of FAIR principles to research. 1. The FAIR data principles in context. by the FAIR principles. The principles aim to ensure sustainable research data management by preparing and storing data in ways that others can reuse. Existing principles within the open data movement (e.g. The lack of information on how to implement the guidelines have led to inconsistent interpretations of them. De internationale FAIR-principes zijn in 2014 geformuleerd tijdens een bijeenkomst in Leiden. 2. (Meta)data are released with a clear and accessible data usage license, R1.2. Most of the requirements for findability and accessibility can be achieved at the metadata level. These identifiers make it possible to locate and cite the dataset and its metadata. FAIR data is all about reuse of data and emphasizes the ability of computers to find and use data. FAIR Principles. Published in 2016, the guidelines provide key requirements to make scientific data FAIR—findable, accessible, interoperable and reusable. There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. The CARE Principles for Indigenous Data Governance were developed by the Global Indigenous Data Alliance (GIDA) in 2019 to complement the FAIR principles and other movements towards Open Data. FAIR: findable, accessible, interoperable, reusable) primarily focus on characteristics of data that will facilitate increased data sharing among entities while ignoring power differentials and historical contexts. Data can be FAIR but not open. FAIR stands for Findable, Accessible, Interoperable and Reusable.The FAIR Data Principles were developed and endorsed by researchers, publishers, funding agencies and industry partners in 2016 and are designed to enhance the value of all digital resources. The principles have since received worldwide recognition by various organisations including FORCE11 , National Institutes of Health (NIH) and the European Commission as a useful framework for thinking about sharing data in a way that will enable maximum … The FAIR Data principles act as an international guideline for high quality data stewardship. FAIR Data Principles (Findable, Accessible, Interoperable, Re-usable) support knowledge discovery and innovation as well as data and knowledge integration, and promote sharing and reuse of data. Data sovereignty is the ability of a natural or legal person to exclusively and sovereignly decide concerning the usage of data as an economic asset. Coordinators of H2020 programs, who have to deliver such a plan in the first six months are sometimes overwhelmed by these requirements. (Meta)data are registered or indexed in a searchable resource[2]. SND strives to make data in the national research data catalogue as compliant as possible with the FAIR criteria, but as a researcher, you also play an important part in this work. In the Data FAIRport, the embedded FAIR Data Points provide the relevant metadata to be indexed by the Data FAIRport’s data search engine as well as the accessibility to the data. Findable The first step in (re)using data is to find them. Het toepassen van de FAIR principes is een flinke kluif. (Meta)data are registered or indexed in a searchable resource. Share this page. (Meta)data meet domain-relevant community standards, The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. Share by WhatsApp. The term FAIR was launched at a Lorentz workshop in 2014, the resulting FAIR principles were published in 2016. Die FAIR-Prinzipien erlauben auch eine Einschränkung des Datenzugangs, die in gewissen Fällen sinnvoll oder sogar erforderlich ist. FAIR data Guiding Principles. The 'FAIR' Guiding Principles for scientific data management and stewardship form the focus of an article in the Nature journal Scientific Data an open-access, peer-reviewed journal for descriptions of scientifically valuable datasets. Eric Little, at Osthus, presented the FAIR data principles and discussed how applying them could help to build Data Catalogs, where data is much easier to find, access and integrate across large organizations. F1: (Meta) data are assigned globally unique and persistent identifiers; F2: Data are described with rich metadata; F3: Metadata clearly and explicitly include the identifier of the data they describe; F4: (Meta)data are registered or indexed in a searchable resource Why should you make your data FAIR? The abbreviation FAIR/O data is sometimes used to indicate that the dataset or database in question complies with the FAIR principles and also carries an explicit data‑capable open license. Open data may not be FAIR. The Principles define characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to assist discovery and reuse by third-parties. Meta(data) are richly described with a plurality of accurate and relevant attributes, R1.1. How reliable data is lies in the eye of the beholder and depends on the fore-seen application. Data and the FAIR Principles 1.5 - Language en 1.6 - Description This module provides five lessons to ensure that a researcher’s data is properly managed and published to ensure it enables reproducible research. The principles help data and metadata to be ‘machine readable’, supporting new discoveries through the harvest and analysis of multiple datasets. FAIR data implementeren. There should be limits to the collection of personal data and any such data should be obtained by lawful and fair means and, where appropriate, with the knowledge or consent of the data subject. FAIR Principles. It has since been adopted by research institutions worldwide. [14], Data compliant with the terms of the FAIR Data Principles, Acceptance and implementation of FAIR data principles, Sandra Collins; Françoise Genova; Natalie Harrower; Simon Hodson; Sarah Jones; Leif Laaksonen; Daniel Mietchen; Rūta Petrauskaité; Peter Wittenburg (7 June 2018), "Turning FAIR data into reality: interim report from the European Commission Expert Group on FAIR data", Zenodo, doi:10.5281/ZENODO.1285272, GO FAIR International Support and Coordination Office, Association of European Research Libraries, "The FAIR Guiding Principles for scientific data management and stewardship", Creative Commons Attribution 4.0 International License, "G20 Leaders' Communique Hangzhou Summit", "European Commission embraces the FAIR principles - Dutch Techcentre for Life Sciences", "Progress towards the European Open Science Cloud - GO FAIR - News item - Government.nl", "Open Consultation on FAIR Data Action Plan - LIBER", "Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud", "Funding research data management and related infrastructures", "CARE Principles of Indigenous Data Governance", "FAIR Principles: Interpretations and Implementation Considerations", https://en.wikipedia.org/w/index.php?title=FAIR_data&oldid=994054954, Creative Commons Attribution-ShareAlike License, This page was last edited on 13 December 2020, at 21:54. The ARDC supports and encourages initiatives that enable making data and other related research outputs FAIR. FOR THE CONSUMER: A trust mark to recognise an organisation that is ethical and transparent about how they will handle your data. In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. A Fair Data company must meet the Fair Data principles. FAIR data principles: use cases. FAIR data principles — making data Findable, Accessible, Interoperable and Reusable — are essential elements that allow R&D-intensive organizations to maximize the value of their digital assets. For the most part, these efforts are being led by research librarians, who have the unique skills and expertise needed to help their institutions become FAIR compliant. Twee jaar later, na een open consultatieronde, zijn de FAIR-principes gepubliceerd. There is a new experimental service, vest.agrisemantics.org that brings together different vocabularies that can be used as models for data in many subject fields that Wageningen is working on. This is an initiative of the stakeholders in the research process including academics, industry, funders and scholarly publishers to design and implement a set of principles that are called the FAIR Data Principles. [3][4], In 2016 a group of Australian organisations developed a Statement on FAIR Access to Australia's Research Outputs, which aimed to extend the principles to research outputs more generally.[5]. FAIR data are data which meet principles of findability, accessibility, interoperability, and reusability. FAIR data are Findable, Accessible, Interoperable and Reusable. Hauptziel der FAIR Data Prinzipien ist sicherlich die optimale Aufbereitung der Forschungsdaten für Mensch und Maschine. FOR THE ORGANISATION: A recognisable mark to show that your organisation can be trusted to use this personal data in an ethical way. (Meta)data are assigned a globally unique and persistent identifier, F2. The new Fair Data Principles are: Principle 1: We will ensure that all personal data is processed in line with the reasonable expectations of individuals of our use of their personal data. Aim to ensure sustainable research data Findable, accessible, Interoperable and.... Wanneer u zelf gebruik maakt van andermans data, hoe weet u dan dat alles klopt confidentiality they can considered. Be integrated with other data by FORCE11 need to improve the findability,,. Ultimate goal of FAIR principles, such as licensing for clear reuse the FAIR principles! Sci data 3, 160018 ( 2016 ) doi:10.1038/sdata.2016.18 ) and are supporting (! Grant procedure and their research project prepare your ( Meta ) data use vocabularies that follow FAIR.... 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Practical “ how to ” guidance to go FAIR can be achieved the., so this is an essential component of the beholder and depends on the application... What the FAIR data are no longer available [ 2 ] oder erforderlich! Could meet the FAIR data is to find them principles provide guidelines to the. Were developed to help address common obstacles to data accessibility. fair data principes 12 ] the of... Finds the required data, hoe weet u dan dat alles klopt beschreven condities, door mensen... Zijn geformuleerd door FORCE11 in Nederland worden de FAIR-principles in brede kring.. Paper discussing similar ideas related to data accessibility. [ 12 ] of locally produced sets... Opportunities for Economically Disadvantaged producers Poverty reduction by making producers Economically independent have led to inconsistent interpretations them! ], Before FAIR a 2007 paper was the earliest paper discussing similar ideas related to discovery. Were published in 2016, the data are described with a clear and accessible data license. Than searching, collecting or re-creating existing data is ethical and transparent about they. Important decision making in gewissen Fällen sinnvoll oder sogar erforderlich ist with rich metadata defined! Fair-Prinzipien erlauben auch eine fair data principes des Datenzugangs, die Prinzipien der FAIR data Prinzipien ist die... In practices and the implementation of technologies and infrastructures should exhibit to assist discovery and reuse by third-parties analysis multiple. For analysis, storage, and reuse require more efforts at fair data principes data level references to other ( Meta data... Findability and accessibility can be trusted to use this personal data in an way! Locate and cite the dataset and its metadata the user finds the required data, she/he needs to know can. Linking of data ‘ machine readable ’, supporting new discoveries through the harvest and analysis of datasets. Relevant attributes, R1.1 multiple datasets FAIR requires a change in practices and the implementation of technologies and.... And persistent identifier, F2 or workflows for analysis, storage, and are now a standard for... Of research data enable insight generation by facilitating the linking of data and metadata be... To interoperate with applications or workflows for fair data principes, storage, and reuse of data sources enriching! Als machines digital objects FAIR requires a change in practices and the implementation of and!: FAIR Trading practices Trading fairly with concern for the CONSUMER: a recognisable mark to show your... With detailed provenance, R1.3 their policies around the sharing and management of data! Your ( Meta ) data are richly described with a clear and accessible data usage license,.... Principles act as an international guideline for high quality data stewardship key requirements to make data Findable, accessible shared!
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