FAIRPoints
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| Anzahl der Teile | 542 | |
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| Lizenz | CC-Namensnennung 2.0 Belgien: Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen. | |
| Identifikatoren | 10.5446/61495 (DOI) | |
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00:00
Divergente ReiheEreignishorizontImplementierungFokalpunktMetrisches SystemAbstraktionsebeneDatenverwaltungBildverstehenProzess <Informatik>StandardabweichungDatenverwaltungDivergente ReiheImplementierungEinflussgrößeEreignishorizontMengeVirtuelle MaschineBildverstehenProzess <Informatik>ComputerspielTopologieTabelleSchlussregelPRINCE2COMDiagrammComputeranimation
01:04
Computeranimation
01:26
GoogolSystemprogrammierungFormale SpracheSoftwareProgrammbibliothekMathematikInformationTheoretische PhysikNeuroinformatikSolar-terrestrische PhysikComputerspielGenerizitätDienst <Informatik>Formale SemantikProzess <Informatik>ProgrammierumgebungNatürliche SpracheOISCEreignishorizontImplementierungGruppenoperationFunktion <Mathematik>Virtuelle MaschineDivergente ReiheElektronisches ForumObjekt <Kategorie>DigitalsignalPunktwolkeOffene MengeVorzeichen <Mathematik>PunktOntologie <Wissensverarbeitung>EntscheidungstheorieExogene VariableAuswahlaxiomPerspektiveEin-AusgabeTwitter <Softwareplattform>Vollständiger VerbandRelativitätstheorieVierzigTranslation <Mathematik>Cloud ComputingDialektSchlüsselverwaltungTermAdditionVerschlingungGemeinsamer SpeicherFlächeninhaltUmsetzung <Informatik>DatensatzProjektive EbeneVererbungshierarchiePunktWiederherstellung <Informatik>Wort <Informatik>Physikalisches SystemGraphfärbungGesetz <Physik>InformationAuswahlaxiomImplementierungPerspektiveEreignishorizontGruppenoperationVirtuelle MaschineOffene MengeDivergente ReiheAutomatische HandlungsplanungGewicht <Ausgleichsrechnung>RichtungDatenfeldPRINCE2AggregatzustandElektronisches ForumSystemplattformEntscheidungstheoriePermanenteBildschirmmaskeGeradeSoftwareentwicklerExtreme programmingZahlenbereichFramework <Informatik>Selbst organisierendes SystemPhysikalische TheorieInteraktives FernsehenEinsOrdnung <Mathematik>Innerer PunktVorzeichen <Mathematik>Inklusion <Mathematik>RandwertDifferenteFunktion <Mathematik>Reelle ZahlMultiplikationsoperatorSprachsyntheseExpandierender GraphKollaboration <Informatik>Inhalt <Mathematik>ComputerspielEin-AusgabeIdentifizierbarkeitUmwandlungsenthalpieDateiformatSupercomputerKartesische KoordinatenComputeranimation
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Flussdiagramm
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PunktReelle ZahlVirtuelle MaschineMultiplikationsoperatorWeb-SeiteAdressraumVorzeichen <Mathematik>Domain <Netzwerk>Gemeinsamer SpeicherGruppenoperationQuellcodeZweiInformationDifferenteNabel <Mathematik>Leistung <Physik>BitMomentenproblemIntegralEreignishorizontVerschlingungDivergente ReiheNeuroinformatikVererbungshierarchieMathematikOffene MengeNewsletterInverser LimesKonsistenz <Informatik>Formale SemantikMaschinenschreibenEinsBesprechung/Interview
13:45
MaschinenschreibenBesprechung/Interview
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Besprechung/Interview
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Besprechung/Interview
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Flussdiagramm
Transkript: Englisch(automatisch erzeugt)
00:08
Hi, my name is Sarah Elgibaly. Today I'll be presenting FAIR coins, the events series highlighting pragmatic measures developed by the community towards the implementation of the FAIR data principles.
00:22
FAIR stands for Findable, Accessible, Interoperable and Reusable. These principles were first coined in 2016 in a landmark paper summarizing the fundamental concepts to improve the infrastructure to enable the reuse of data. And to provide guidance to enhance reusability. So when I say principles, they are an effort
00:46
to define the best practices for data to facilitate discovery, access and reuse by humans and machines. So, in essence, FAIR is not a set of rules, it's not a standard, it's not a have to guide, it's an evolving process and a vision.
01:04
This also makes it very difficult to understand, so how do these principles translate to real life? How do they translate to solutions? And this is the question that inspired us to explore a different approach to understanding what the FAIR principles mean to the community and how they are applied in reality.
01:27
In addition to that, the applications and the development in the realm of FAIR have been evolving at an extremely rapid pace and expanding to include more aspects beyond just data. So we realized that in order to find those solutions, we need to really pool our knowledge
01:45
together and learn from each other and bring in a diverse community from all over the place. And this is really where FAIR points comes into the picture. We offer a platform for those conversations to happen.
02:00
We offer a platform to understand what are the realistic and pragmatic FAIR implementations. And our main goal is to bring together the research community, the ultimate users and producers of the data, as well as the policy and decision makers who shape research practices in
02:21
the broader research support community, the people that are going to help in the development of those solutions. So to that end, we offer a framework for these conversations in different formats. We have community discussions where our community members come together, share experiences, identify solutions.
02:45
But we also produce an output to build up the resource to disseminate this knowledge further. So essentially, we want to capture the conversation summary. Also, we have keynote events where we tap into the expertise available of specific researchers or folks in the field of FAIR implementations.
03:12
And we highlight the solutions from those diverse fields. And again, we provide conversation summaries in the form of bite-sized reusable content that includes some practical advice and how-tos.
03:27
And that can hopefully be easily adopted by others in a cross-disciplinary fashion. Most recently, we've also launched a series of Ask Me Anything events.
03:42
And these events are meant to bring together speakers from the Research Data Alliance and speakers from a related European Open Science Cloud service. And the aim here is to have the respective speakers to describe how their work aligns, how it connects with the research community
04:05
and how it benefits the research community in a way that is directly related to the implementation of FAIR principles and open research practices. The main point here is that these events are beginner-friendly, they're laid-back, easy to follow, and they're moderator and audience-driven.
04:26
Meaning that our moderators from our community, as well as you, the audience, can drive the discussion in the direction that you want through your questions. So, if you're interested in joining us, please sign up to the whole series or to the specific event you're interested in.
04:52
I have to go back because we have five different themes on identifier, FAIR software, regulatory processes, machine actionability, as well as equitable and transparent access to information and knowledge.
05:06
So, please sign up to either one of those or the full series and also to receive the recording. And please feel free to send us your questions that you want answered. So, besides these events, we also have ongoing projects such as the FAIR Open Science Forum.
05:24
And this is something we see as a community hub. The people can come together, share information, experiences, find answers to some questions, find topics that are related to their interests, and share their and present their work as well.
05:41
So, we want to expand different channels of dissemination of knowledge about FAIR practices. We also have ongoing collaboration with the Machine Centering Podcast, where we offer FAIR Point Choicers Challenge.
06:03
This is where we ask the community a question about one of the FAIR implementation principles. And what we want to hear from you is either a challenge you face in implementing that principle or maybe something you've tried that worked out for you and how that choice affected your work.
06:28
And how is it going now? So, this is really a two-minute free recording. You can do it multiple times. You can send it in on the Speak By link here I'm showing.
06:43
And if you want to delve into the topic deeper, you can be our guest on one of the Machine Centering Podcasts, driven by Donny Winston, who is our co-founder. So, join us. Let us know how it's going for you.
07:01
One thing that is really important in all of this is to include diverse voices. So, we need to make FAIR accessible to the broader audience. And as it develops, we need to develop it together with a global community where we connect and collect heterogeneous input from a global perspective, supporting equitable access
07:25
and working towards advancing FAIR beyond to everywhere. So, we also came to learn from experience that FAIR might look different in real life for researchers in different places.
07:41
And that's where we're really keen to include relatable examples from research practices from different regions in the world. And not only do we want to extend beyond fields and disciplines, but beyond geographical boundaries and learn how FAIR translates into practice and what it means to our global community.
08:03
How does it look like for you? So, join our conversation, sign up to community discussions, our event series, join us in our Slack and interact in different ways. We'd love to hear from you. And big thanks to my whole team, Chris Erdmann, Donnie Winston, Nabil, CB, and
08:24
Julian Schneider, as well as all the organizations that are supporting this work, including Cyla's Lab, Go4US, Cindy's Supercomputing Center, recently Research Data Alliance, EOS Futures, and FAIR Digital Objects. And thank you all so much for listening. I hope to see you in different formats.
08:44
And if you have any questions, please feel free to ask them. Thank you. Bye.
09:03
Excellent. Thank you so much, Sarah, for that fantastic talk. It's great to have you here today. So we have a few questions that have come in from the audience. I think one of the first ones that we have from Celia is, could you explain more information about what machine actionability means? Maybe give us some examples.
09:27
Did we just lose Sarah? I'm sure she'll be back in a moment. I'm going to try and gently fill the silence until Sarah returns.
09:55
I noticed that Celia had another question saying, depending on disciplines, the way is the
10:01
way of considering data very different and how do you work with diverse research communities? And I actually can just answer a little bit from my own personal experience that occasionally I used to work in data integration. We found that when you are a primary data source, you have a lot more power to make data fair than when you work in integrating multiple different data sources.
10:24
So I think it's probably fair to say that different domains definitely treat and can address FAIR differently. And that you need to assess each other on each one on its own merits, depending on what capabilities they have. And recognizing that some people may have more practical limits or some data sources may have more practical limits around FAIR than others will.
10:45
I noticed also Sarangit asks, are there any workshops or sessions where a researcher or individual can learn more about FAIR? I will happily suggest go to the FAIRpoints website because FAIRpoints really is all about making everything practical and approachable for people who may be applying it for the right time.
11:06
And I've just noticed that Sarah is still in the main channel, but can't access as a speaker, which is a real shame. Folks, I'm going to recommend, if you can, in the open research tools and technology online, Devrin, please post
11:24
your questions there in a written fashion so that Sarah can answer and she'll catch up as she has time. I now have two minutes of dead air to continue.
11:41
Thank you for bearing with us. I'm going to read out what Sarah's also said about machine actionability. What is needed to achieve machine actionability, things like semantics and metadata, why automation is required in research. And as a researcher, I think this is a really great point, how you can create something that's machine actionable.
12:03
I'll maybe rephrase that myself as the ability to allow a computer to read it and to manage data rather than it just being something that a human can read and explain or process and work with. We also have a few really good links here.
12:22
fairpoints.org. These are in the chat at the moment, you can sign up to the event series, and there's also a slack and a newsletter where you can go and you can learn a bit more about fair points and how to apply it. Okay, one minute left, I'm going to keep on rambling about.
12:46
Thanks for bearing with us. One thing that I've always wondered, or I've noticed a lot of people talk about open research and fair, but don't necessarily recognize that fair can be fair is about being findable, which doesn't require open and accessible.
13:04
You have to access it, but that still doesn't mean it's open, it might be that you have to ask someone or do it some other way. Interoperable and reusable again don't require open so there are times when it's appropriate for something let's say medical data to be private, and it can still be fair. Thank you, sir.
13:22
Okay, we have another question and we still have a few seconds to read out. Depending on disciplines. Okay, we try and find commonalities between different discipline disciplines and fair Sarah says hi. Yay, you got back just in time for eight seconds left.
13:43
Thank you so much for doing this, folks, please get in touch. I'm so sorry we missed all your q&a Sarah. Yeah, please feel free to send me any questions and stay in touch. I've posted
14:03
some of the links, and I hope I managed to answer some of your questions. All right, thank you. I'm gonna hop off to the next. Bye.