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Open neuroscience and the meaning of FAIR

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İçerik Future Science Group tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Future Science Group veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.
This episode, our second recorded at Neuroscience 2022 (13-19th April 2022; San Diego, CA, USA), delves into the importance of open data in neuroscience and the FAIR guidelines, which encourage researchers to make their data Findable, Accessible, Interoperable and Reusable. Sharing her considerable expertise in this area is one of the authors of the FAIR guidelines, Maryann Martone, who provides some key examples of the successes that open data practices have delivered so far and cautionary tales for how current practices are damaging the field. Listen on to find out how to implement open data practices, how they can help your lab and why Maryann sees it as our responsibility to resolve! Contents: Introduction: 00:00-02:00Introducing open neuroscience and the meaning of FAIR 02:00-03:00Exposing outdated paradigms in science: academic targets, publications, reproducibility and data accessibility: 03:00-06.30Incentivizing open data and reassigning value restructuring academia: 06:30-08.40The impact of FAIR within labs: 08:40-09:50Challenges of establishing and barriers to Open and FAIR neuroscience 09:50-12:00The reception of these guidelines in the field: 12:00-16:50Examples of the impact of open data in the spinal cord injury community: 16:50-18:10Marryann’s experience of enacting changes early in her career: 18:10-21:20Judging value in scientific research and understanding your purpose: 21:20-24:20The importance of investment: 24:20-26:30The impact of industry on lab data: 26:30-27:50Practical tips for addressing your lab data: 27:50-31:10Key tips for preparing data for an open-source repository 31:10-33.15The FAIR data principles explained: 33.15-37:50
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iconPaylaş
 
Manage episode 354374813 series 1258384
İçerik Future Science Group tarafından sağlanmıştır. Bölümler, grafikler ve podcast açıklamaları dahil tüm podcast içeriği doğrudan Future Science Group veya podcast platform ortağı tarafından yüklenir ve sağlanır. Birinin telif hakkıyla korunan çalışmanızı izniniz olmadan kullandığını düşünüyorsanız burada https://tr.player.fm/legal özetlenen süreci takip edebilirsiniz.
This episode, our second recorded at Neuroscience 2022 (13-19th April 2022; San Diego, CA, USA), delves into the importance of open data in neuroscience and the FAIR guidelines, which encourage researchers to make their data Findable, Accessible, Interoperable and Reusable. Sharing her considerable expertise in this area is one of the authors of the FAIR guidelines, Maryann Martone, who provides some key examples of the successes that open data practices have delivered so far and cautionary tales for how current practices are damaging the field. Listen on to find out how to implement open data practices, how they can help your lab and why Maryann sees it as our responsibility to resolve! Contents: Introduction: 00:00-02:00Introducing open neuroscience and the meaning of FAIR 02:00-03:00Exposing outdated paradigms in science: academic targets, publications, reproducibility and data accessibility: 03:00-06.30Incentivizing open data and reassigning value restructuring academia: 06:30-08.40The impact of FAIR within labs: 08:40-09:50Challenges of establishing and barriers to Open and FAIR neuroscience 09:50-12:00The reception of these guidelines in the field: 12:00-16:50Examples of the impact of open data in the spinal cord injury community: 16:50-18:10Marryann’s experience of enacting changes early in her career: 18:10-21:20Judging value in scientific research and understanding your purpose: 21:20-24:20The importance of investment: 24:20-26:30The impact of industry on lab data: 26:30-27:50Practical tips for addressing your lab data: 27:50-31:10Key tips for preparing data for an open-source repository 31:10-33.15The FAIR data principles explained: 33.15-37:50
  continue reading

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