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FAIR co creation or FAIR herding? eNanoMapper database, tools and workflows

Nina Jeliazkova, Nikolay Kochev, Luchesar Iliev, Vedrin Jeliazkov, Martine Bakker
2022 Zenodo  
Despite the wide agreement FAIR guiding principles are helping consistent curation and data reuse, the current adoption mostly addresses findability and accessibility, and less the interoperability and  ...  For this reason the FAIR advocates argue a cultural change is necessary and/or transfer the data to "data shepherds" or "data stewards".  ...  FAIR adoption in general mostly addresses findability and accessibility, and less the interoperability and "machine actionability", which is the core reason of introducing FAIR by the data professionals  ... 
doi:10.5281/zenodo.6655950 fatcat:xocdq4is4zcd7filzzlfdytequ

FAIR for research software [article]

Michelle Barker
2021 figshare.com  
Keynote presentation for Collaborations Workshop 2021 (CW21, https://software.ac.uk/cw21)Research Software AllianceFAIR for Research Software Working Group  ...  Findable The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers.  ...  Findable The first step in (re)using software is to find it. Metadata and software should be easy to find for both humans and computers.  ... 
doi:10.6084/m9.figshare.14401355.v1 fatcat:yzpa7kksonbi7kr3l7qsubjyay

FAIR Computational Workflows

Sarah COHEN-BOULAKIA
2020 Zenodo  
Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products.  ...  The paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.  ...  • The distinction between a workflow and its component steps is blurred → FAIR can be applied simultaneously on multiple levels • Findable composite workflows = findable involved tools and data types  ... 
doi:10.5281/zenodo.4025294 fatcat:dy4dhpusffdevlkjks4ddssp7e

Fair Persistent Connections

Adam Vials Moore, Josh Brown, Christopher Brown, Alice Meadows, Fiona Murphy, Phill Jones
2021 Zenodo  
We present an overview of those PIDs along with a discussion of how embedding them within the wider open research workflows will have a positive impact on data and metadata, enabling the FAIR (Findable  ...  Machine to machine processes decrease costs – effort, attention and financial  ...  We present an overview of those PIDs along with a discussion of how embedding them within the wider open research workflows will have a positive impact on data and metadata, enabling the FAIR (Findable  ... 
doi:10.5281/zenodo.4701207 fatcat:iisjhxyx7zb6ppnu2k3aq5b2xy

The Turing Way Workshop: Reproducible, Open and FAIR Research

Malvika Sharan, Emma Karoune
2022 Zenodo  
between FAIR and open data/research Learn how to implement a reproducible workflow Shared notes This workshop uses a shared document, you can see/reuse the template provided here: https://hackmd.io/@turingway  ...  Use The Turing Way chapter for README to guide your assignment https://the-turing-way.netlify.app/project-design/project-repo/project-repo-readme.html  ...  Adapted from talk by Philippe Rocca-Serra ( 2020 ) R in an online data catalogue / archive / portal findable by humans and by machines • ENA for DNA sequences • GBif and OBIS for biodiversity data • BioImage  ... 
doi:10.5281/zenodo.6337938 fatcat:nexmgqerjfhjlafugw5p2ic2we

Do I have to make my models FAIR? Current practices in making models and data Findable, Accessible, Interoperable, and Reusable [article]

Leslie Hsu
2019 Figshare  
"Do I have to make my models FAIR? Current practices in making models and data Findable, Accessible, Interoperable, and Reusable."  ...  FAIR means for your models and data?  ...  Three decisions you can make to increase the FAIRness of your models/data in proposals and publications 17 Where to share your code What license to use What information would you need for your code to  ... 
doi:10.6084/m9.figshare.8035868 fatcat:eub5v4y2xndhfazm42qdxqudre

No country for old data: Increasing FAIR-ness of research outcomes through standardization and community-driven development - Brainhack Global Marburg 2020 [article]

Peer Herholz
2020 figshare.com  
and machines (machine readable metadata essential for automatic discovery) Accessible • data can be easily obtained by humans as well as by machines, through well-defined and ideally standardized  ...  acquisition project creation not sufficient not sufficient not sufficient not sufficient not sufficient sufficient if done right Findabledata and metadata easily findable for both humans  ...  BIDS Apps -MRIQC Thank you very much for your attention. I'm happy to answer questions during the chat and/or via twitter (@peerherholz) or email (herholz dot peer at gmail dot com).  ... 
doi:10.6084/m9.figshare.13480191.v1 fatcat:rialq5cqxrhwrkwfky5ujfrjua

How to implement (scientific) FAIR principles in my work?

Ammar Ammar
2022 Zenodo  
- Describe your data with metadata/controlled vocabularies - Put it online (Data repositories/registries) - Make sure to use non ambiguous IDs - Use global identifiers (DOI, ORCID, Inchi ..etc). - Data  ...  format, structure and organization - Licensing/Data citation Tools/Platforms: Zenodo, Figshare, GitHub, ORCID, re3data.org, Fairsharing.org, CC-BY, FAIR assessment tools How do you assess the FAIRness  ...  • For datasets/document: Zenodo, Figshare • For code: GitHub, SourceForge More examples: https://www.nature.com/sdata/policies/repositories Findable research output Make sure to deposit your data in an  ... 
doi:10.5281/zenodo.6381648 fatcat:pmel5ydy3ra33jconjmdgcg2pa

FAIRification Efforts of Clinical Researchers: The Current State of Affairs [chapter]

Martijn G. Kersloot, Philip van Damme, Ameen Abu-Hanna, Derk L. Arts, Ronald Cornet
2021 Studies in Health Technology and Informatics  
In order to make machine-readable, FAIR data a reality, researchers require proper training, support, and tools to help them understand the importance of data FAIRification and guide them through the FAIRification  ...  machine-readability (31.2%).  ...  in 2016, stating that scholarly output should be Findable, Accessible, Interoperable, and Reusable, both for machines and for people [1] .  ... 
doi:10.3233/shti210807 pmid:34795075 fatcat:gwelx32fgvfflbwbvidsmdymli

Biodiversity Literature Repository: Building the customized FAIR repository by using custom metadata

Alexandros Ioannidis-Pantopikos, Donat Agosti
2021 Biodiversity Information Science and Standards  
Given Zenodo's long tradition of making research artifacts FAIR (Findable, Accessible, Interoperable, and Reusable), there are still challenges in applying these principles effectively when serving the  ...  In the landscape of general-purpose repositories, Zenodo was built at the European Laboratory for Particle Physics' (CERN) data center to facilitate the sharing and preservation of the long tail of research  ...  and both human-and machine-readable output provided by Zenodo, and accessible via the Biodiversity Literature Repository community at Zenodo.  ... 
doi:10.3897/biss.5.75147 fatcat:vpsjbnh54je5tbgqkr2dnkhc74

Digivet deliverable 2.1

Fernanda Dórea, Ivana Rodriguez Ewerlöf, Wonhee Cha, Stefan Widgren, Petter Hopp, Arvo Viltrop, Matt Denwood, Jessica Enright
2022 Zenodo  
It describes, in particular, a discussion on data FAIRness for each individual data source across all case studies and countries involved. NordForsk project ID 97424 – 2021-2023  ...  This deliverable reports the results of "Task 2.1 Data Collection, Curation, Preservation"; and the first sub-task in "Task 2.2 Data FAIRness".  ...  Accessible Accessibility to the datasets is low for both humans and machines.  ... 
doi:10.5281/zenodo.6393053 fatcat:cciqcbrgzvbmlbmzhaw54mddyu

Fair Data and Data Management Plans

Paola Masuzzo
2019 Zenodo  
We explore the FAIR principles in detail and highlight best practices, tools, and tips for implementing FAIR Data.  ...  Managing research data effectively and efficiently is crucial for the success of any research project. In the modern eScience ecosystem, enabling optimal (re)use of data is a major challenge.  ...  Researchers AND machines need to find/discover data having features of interest, for which they will be using links, metadata, as well as actual data Once found, machines need to access/retrieve data of  ... 
doi:10.5281/zenodo.2637895 fatcat:slu43meoava6fpi76nmopkxaxa

Be FAIR to your data

Dörte Solle
2020 Analytical and Bioanalytical Chemistry  
FAIR describes a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable.  ...  A challenge of science is to facilitate knowledge discovery by assisting humans and machines in their discovery of scientific data and their associated algorithms and workflows.  ...  Let us start our journey to the internet of data for knowledge discovery by humans and machines. Be FAIR to your data and digitalize your laboratory. Make science for the future.  ... 
doi:10.1007/s00216-020-02526-7 pmid:32300841 pmcid:PMC7320032 fatcat:rwbbcpjakze7jpufl3hltapsui

Reproducible Big Data Science: A Case Study In Continuous Fairness

Kyle Chard, Ravi Madduri, Michael D'Arcy, Segun Jung, Alexis Rodriguez, Dinanath Sulakhe, Eric Deutsch, Cory Funk, Ben Heavner, Matthew Richards, Paul Shannon, Gustavo Glusman (+3 others)
2018 Zenodo  
Big biomedical data create exciting opportunities for discovery but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR).  ...  In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle.  ...  Reproducibility requires continuous FAIRness • Make all data findable, accessible, interoperable, reusable at every stage, via pervasive use of simple identifier and exchange format conventions • Build  ... 
doi:10.5281/zenodo.1484403 fatcat:mqztdeowdff6tb4vpy7km54rn4

FAIR Computational Workflows

Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober
2019 Zenodo  
Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products.  ...  These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right.  ...  Acknowledgements Carole Goble acknowledges funding by BioExcel2 (H2020 823830), IBISBA1.0 (H2020 730976) and EOSCLife (H2020 824087) .  ... 
doi:10.5281/zenodo.3268653 fatcat:xpcie77nvbasvikgbsfsv7f4im
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