Download to read offline
Uploaded byPeter McQuilton
How to share useful data
The document discusses the importance of data sharing, reusability, and reproducibility in scientific research, emphasizing how deficiencies in these areas can hinder scientific accountability and progress. It outlines recommendations for documenting and reporting experimental data effectively, including adherence to content standards and ensuring data is formatted for discoverability and reuse. The document also provides guidance on using community standards and resources to facilitate better data management and sharing practices.
More Related Content
Similar to How to share useful data
More from Peter McQuilton
How to share useful data
- 1. How to shareuseful data Peter McQuilton Biosharing.org @drosophilic
- 2. Outline • Data sharing •Reusability and reproducibility • How the lack of these affects scientific accountability and progress • Experimental context • What to report – what level of granularity • How to report it – what format, structure • Content standards • How to find them • Complying with repositories, funders and publishers
- 3. Outline • Data sharing •Reusability and reproducibility • How the lack of these affects scientific accountability and progress • Experimental context • What to report – what level of granularity • How to report it – what format, structure • Content standards • How to find them • Complying with repositories, funders and publishers
- 4. Research data lifecycle Image credit to:
- 5. Credit to: ttps://projects.ac/blog/five-top-reasons-to-protect-your-data-and-practise-safe-science/2014 Better data = better science
- 6. A community mobilizationfor “openness” image by Greg Emmerich http://discovery.urlibraries.org/ https://okfn.org Open data is a means to do better science more efficiently http://pantonprinciples.org https://creativecommons.org
- 7. Growing movement forFAIR data and research outputs
- 8. But in allfairness, not much data is FAIR!
- 9. But in allfairness, not much data is FAIR!
- 10. But in allfairness, not much data is FAIR!
- 11. “Reproducing the methodtook several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results” Unfairness in both experimental and computation areas
- 12. • Not alwayswell cited, stored o Software, codes, workflows are hard(er) to get hold of • Poorly described for third party reuse o Different level of detail and annotation • Curation activities are perceived as time consuming o Collection and harmonization of detailed methods and experimental steps is rushed at the publication stage Not very FAIR: low findability and understandability
- 13. • Effectively documentyour data so that it can be understood in the future • Periodically move data to new storage media (drives degrade over time) • Keep more than one copy of data (local and cloud) • Migrate data to new software versions • Use a well documented and supported format Ideally this should be covered in a data management plan at the start of a project, so that you can factor any associated time and resources into your budget. What can I do to ensure my data are shareable/usable in the future?
- 14. Outline • Data sharing •Reusability and reproducibility • How the lack of these affects scientific accountability and progress • Experimental context - standards • What to report – what level of granularity • How to report it – what format, structure • Content standards • How to find them • Complying with repositories, funders and publishers
- 15. Do you knowwhat this is? LS1_C2_LD_TP2_P1 file1-fastq.gz
- 16. …how NOT toreport the experimental information! LS1_C2_LD_TP2_P1 file1-fastq.gz
- 17. …how NOT toreport the experimental information! Sample name (?!) Data file LS1_C2_LD_TP2_P1 file1-fastq.gz
- 18. We need toclearly describe the information • LS1 liver sample 1 • C2 compound 2 • LD low dose • TP2 time point 2 • P1 protocol 1 • file1-fastq.gz compressed data file for sequence information corresponding to this sample Sample name (?!) Data file LS1_C2_LD_TP2_P1 file1-fastq.gz
- 19. Without context datais meaningless
- 20. Without context datais meaningless
- 21. Without context datais meaningless
- 22. Without context datais meaningless
- 23. • We needto report sufficient information to reuse the dataset • We must strike a balance between depth and breadth of information Information intensive experiments
- 24. Information intensive experiments •Not too much • Not too little • ….just right
- 25. Seven week oldC57BL/6N mice were treated with low-fat diet. Liver was dissected out, hepatocytes prepared… From natural language to ‘computable’ concepts
- 26. Age value? Unit? Strain name Subjectof the experiment Type of diet and experimental condition Anatomy part Seven week old C57BL/6N mice were treated with low-fat diet. Liver was dissected out, hepatocytes prepared … From natural language to ‘computable’ concepts
- 27. Age value Unit Strain name? Subjectof the experiment? Type of diet and experimental condition Anatomy part Seven week old C57BL/6N mice were treated with low-fat diet. Liver was dissected out, hepatocytes prepared … From natural language to ‘computable’ concepts
- 28. Age value Unit Strain name Subjectof the experiment Type of diet and experimental condition? Anatomy part Seven week old C57BL/6N mice were treated with low-fat diet. Liver was dissected out, hepatocytes prepared … From natural language to ‘computable’ concepts
- 29. Age value Unit Strain name Subjectof the experiment Type of diet and experimental condition Anatomy part? Seven week old C57BL/6N mice were treated with low-fat diet. Liver was dissected out, hepatocytes prepared … From natural language to ‘computable’ concepts
- 30. Age value Unit Strain name Subjectof the experiment Type of diet and experimental condition Anatomy part Seven week old C57BL/6N mice were treated with low-fat diet. Liver was dissected out, hepatocytes prepared … From natural language to ‘computable’ concepts
- 31. Age value Unit Strain name Subjectof the experiment Type of diet and experimental condition Anatomy part Seven week old C57BL/6N mice were treated with low-fat diet. Liver was dissected out, hepatocytes prepared … From natural language to ‘computable’ concepts Type of protocol – cell preparation Type of protocol - sample treatment Type of protocol – liver preparation
- 32. How do youknow what to report, or how to structure it? • Data/content standards: • Structure, enrich and report the description of the datasets and the experimental context under which they were produced • Facilitate the discovery, sharing, understanding and reuse of datasets
- 33. Outline • Data sharing •Reusability and reproducibility • How the lack of these affects scientific accountability and progress • Experimental context • What to report – what level of granularity • How to report it – what format, structure • Content standards • How to find them • Complying with repositories, funders and publishers
- 34.
- 35. de jure defacto grass-roots groups standard organizations Nanotechnology Working Group Community mobilisation to develop content standards
- 36. Databases have theirown standards, e.g. at EBI:
- 37. Enablers: to betterdescribe, share and query data
- 38. Enablers: to betterdescribe, share and query data • Minimum information reporting requirements, or checklists o Report the same core, essential information
- 39. • Minimum information reportingrequirements, or checklists o Report the same core, essential information • Controlled vocabularies, taxonomies, thesauri, ontologies etc. o Use the same word and refer to the same ‘thing’ Enablers: to better describe, share and query data
- 40. • Minimum information reportingrequirements, or checklists o Report the same core, essential information • Controlled vocabularies, taxonomies, thesauri, ontologies etc. o Use the same word and refer to the same ‘thing’ • Conceptual model, conceptual schema, or exchange formats o Allow data to flow from one system to another Enablers: to better describe, share and query data
- 41. A web-based, curatedand searchable registry ensuring that biological standards and databases are registered, informative and discoverable; also monitoring the development and evolution of standards, their use in databases and the adoption of both in data policies.
- 42. Researchers, developers andcurators lack support and guidance on how to best navigate and select content standards, understand their maturity, or find databases that implement them; Funders, journals and librarians do not have enough information to make informed decisions on which content standards or database to recommended in policies, or fund or implement Our mission: To help people make the right choice
- 43.
- 44. Work out whichformat your data should be in for submission to a particular database
- 45. STANDARD DATABASE Standards anddatabases (and policies) cross-linked
- 46. From simple andadvanced searches
- 47. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project Search and filter to find what is relevant to your type of data
- 48. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project Tracking evolution, e.g. deprecations and substitutions
- 49. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project Tracking evolution, e.g. deprecations and substitutions
- 50. Create your ownCollection
- 53. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 5 3 User profiles populated from ORCID...
- 54. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project 5 4 ... credit for creating, contributing to, maintaining standards, databases and policies Ownership of open standards can be problematic in broad, grass-root collaborations It requires improved models, to encourage maintenance of and contributions to these efforts, rewards and incentives need to be identified for all contributors to supporting the continued development of standards
- 55. What you cando with BioSharing… “Which standard should I use for this data, considering I’d like to publish in journal X? “Are we using the most up-to-date version of this standard?” “My data is in X format, which databases take that format?
- 56. How can youuse community-standards? model and related formats These tools and formats will help you to:
- 57. The International Conferenceon Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project ISA powers data collection, curation resources and repositories, e.g.: ISA model and related formats
- 59. 1 Create template(s) tofit the type of experiments to be described Create templates detailing the steps to be reported for different investigations, complying to community standards in e.g. configuring the value(s) allowed for each field to be • text (with/without regular expressions), • ontology terms, • numbers etc. We have ‘ready to use’ community standards compliant configurations and can create more according to user needs
- 60. • The ISAmodel records the data’s provenance, how it was generated and where it is located. • Published Data Descriptors are indexed in all major bibliographic indexing services (incl. PubMed) • However, accompanying every Data Descriptor article there are metadata files, specifically created to aid discovery and understanding of the data itself. • Using the ISA (Investigation, Study, Assay) model, these metadata files provide a machine readable overview of the study that generated the data.
- 61. • Filter datasetsby data repository or metadata • Boolean searches • Future enhancements: - Statistics - Richer queries based on semantics of the data ISA-explorer: A demo tool for discovering and exploring Scientific Data’s ISA-tab metadata
- 62. ISA-explorer: A demotool for discovering and exploring Scientific Data’s ISA-tab metadata Visualise the data associated with a paper http://tinyurl.com/isaexplorer
- 63. • Reusability andreproducibility o Is pivotal to drive science and discoveries o Do your best to make your digital research outputs FAIR • Experimental context o Report the experimental context of your findings o Do to your data what you wish that others would do to theirs • Content standards o Continuously evolving o Make use of tools implementing standards, such as ISAtools o Use biosharing.org to explore repositories, standards and policies Summary
- 64.
- 65. Find the rightdatabase for your data, and which data standard to use – https://www.biosharing.org Checking your data conforms to a standard, or making your own templates – http://www.isa-tools.org Where to keep research data: DCC checklist for evaluating data repositories (DCC) - http://tinyurl.com/DCCResearchData How and why you should manage your research data (JISC) - http://tinyurl.com/JISCDMP Useful links