1 / 31

R4L: The Repository for the Laboratory

This article discusses the challenges of data generation, management, and publication in laboratories, and proposes the "Repository for the Laboratory" (R4L) as a solution. It highlights the need for better ways to store, retrieve, and validate research data, and emphasizes the importance of reproducible experiments. The article also explores the design and features of the R4L repository.

deanmiller
Télécharger la présentation

R4L: The Repository for the Laboratory

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. This work is licensed under a Creative Commons LicenceAttribution-ShareAlike 3.0 http://creativecommons.org/licenses/by-sa/3.0/ • Experiences with Repositories and Blogs in Laboratories • or • ‘R4L: The Repository for the Laboratory’ • http://r4l.eprints.org • Leslie Carr, Simon Coles & Jeremy Frey • University of Southampton, U.K. • s.j.coles@soton.ac.uk

  2. The Problem: Data Generation Synthesis Characterisation

  3. The Problem: Data Management “Data from experiments conducted as recently as six months ago might be suddenly deemed important, but those researchers may never find those numbers – or if they did might not know what those numbers meant” “Lost in some research assistant’s computer, the data are often irretrievable or an undecipherable string of digits” “To vet experiments, correct errors, or find new breakthroughs, scientists desperately need better ways to store and retrieve research data” “Data from Big Science is … easier to handle, understand and archive. Small Science is horribly heterogeneous and far more vast. In time Small Science will generate 2-3 times more data than Big Science.” ‘Lost in a Sea of Science Data’ S.Carlson, The Chronicle of Higher Education (23/06/2006)

  4. The Problem: Data Deluge • 40 years ago a PhD student would determine about 3 crystal structures during the course of their study – this can now be easily achieved in a day • There are approx. 30 million known chemical compounds • Approx. 2 million crystal structures have been determined • There are less than 0.5 million published crystal structures residing in (licensed) curated databases • There are just a few thousand ‘open’ crystal structures • The primary cause of this is the current data publication process, which is tied to journal articles and peer review

  5. The Problem: Publishing Data Spectroscopic analysis is often performed to ensure a reaction is proceeding according to plan – as a result <5% are published (via a process with heavy information loss)

  6. The Problem: Reproducible Experiments • Poor availability and description of experiments and arising data in the current literature • How can we validate this data? • Open data will need to be self explanatory and prove its own ‘correctness’ • Requirement for an ‘experiment audit trail’ • (Published) Science should be reproducible • Requirement to provide sufficient data and metadata to back up an experiment description

  7. The Solution Intellect & Interpretation (Journal article, report, etc) Underlying data (Institutional data repository)

  8. Fitting into the Information Environment Institutional Data Sources

  9. High Level Relationships

  10. Repository Design • Scenarios – assist design team in understanding each other • Feedback from SPECTRa – based questionnaire • First design: build one to throw away (out of the box EPrints) • Population of disposable repository informed design of actual repository • Population informed workflow capture and analysis • Manufacturer discussions • Requirements capture with publishing community

  11. Questionnaire Results #1 • Respondents comprised PhD students (55%), postdoctoral workers (18%) and faculty staff (19%) and totalled 110 people. • Primary use of computers and the internet is for information researching, writing papers and reports, working up data and instrument control. • Computers are used regularly for everyday work, but much less so for social networking and other ‘modern’ uses. • Mainly highly established community standard applications, software and file formats are used, with less use of modern data sources. • There is still extensive use of printed paper copies of PDF files, which are generally stored on personal computers without any structure or use of reference software. A researcher will have 100-1000 PDF files on their computer and prefers to communicate them by sending PDF’s to collaborators.

  12. Questionnaire Results #2 • About 66% have had to generate electronic supplementary information to supplement their journal articles. • There is a predominance for self teaching use of software, as opposed to being taught professionally. • Supplementary information is mainly generated and stored in proprietary formats, although there is considerable use of ‘popular’ formats (eg Microsoft Office). • There is a preference, or requirement, to keep a hardcopy of data as well as an electronic one. • Experimental and analysed data are generally kept on a group or instrument controlling computer, however there is often a need to keep a hardcopy (eg in a lab notebook). • About 66% have not heard of ‘InChI’, ‘metadata’ or ‘JCAMP’ format, whilst around 50% have not heard of ‘DOI’, ‘Open Access’, ‘Semantic Web’ or ‘RDF’.

  13. Questionnaire Results #3 • There is a considerable lack of awareness surrounding repositories and their function. • There is a requirement for search and discovery to be based predominantly on structure, formula, author or keyword. • The most attractive purpose of a repository would be for the storage of a ‘permanent record’. • Most chemists would comply if deposit in a repository was a mandatory requirement of funding or publication, however virtually all are ignorant of what the position of these organisations is with respect to open access and deposit in a repository.

  14. The Plan

  15. Workflow Analysis UV-Vis Powder XRD Mass Spec NMR Sufficient similarity to design a generic deposit / ingest process

  16. The R4L Repository Deposit / Ingest Add new experiment type Create new compound (parent record) Add metadata and upload data files

  17. The Probity Service • Process to assert originality of a piece of work / repository record • Incorporate into ePrints core software?

  18. Repository Search / Browse Crucial record metadata for Data Management, Search/Browse and Discovery: Date; Instrument; Location; Compound Name; Experiment Type; Researcher Search / Browse

  19. Report Generation • Too cumbersome and inflexible (revisit?) • Requirement for ‘familiar’ software • Suitable for informatics, but not routine reporting

  20. Report Generation • Ability to import repository data into software and easily edit • Need to bring the repository to the researchers ‘desktop’ • Demonstrator employed Sharepoint to store templates • (functionality to be incorporated into repository software?) • Does this really bring anything new to reporting research?

  21. Analysis & Discussion: Blogging Experiments • A repository can… • Allow one to put, store and get digital objects • Provide minimal search and browse functions • NOT provide the presentation and discussion functions essential to working up a scientific study • Social networking tools and approaches can provide a way…

  22. Getting data into Blogs • Developing relationship between Blog and R4L repository • Repository back-end, Blog for sharing data with collaborators and developing ideas / conclusions based on data • ‘Live copy’ application only – <SWORD /> development?

  23. Enabling Research • Enables ‘geographically distributed collaborative research’ • Useful approach for sharing ‘failed’ experiments?

  24. Open Notebook Science

  25. Automatic Blogging by Machines

  26. Automatic Logging of Sensor Data • Timeline visualisation – instant detection of erroneous event • Assists in analysing inconsistencies in datasets

  27. Comments and Annotation • Chemists need to scribble! • A picture says a thousand words! • Need for more advanced Blog tools / technology

  28. R4L End-to-End Overview

  29. Usability • Low barrier to use; familiarity; flexibility; quick gain • A specification/requirements for data repository based software?

  30. Problems Encountered • Over ambitious in affecting the attitudes of instrument manufacturers at such an early stage • Attitudes of chemists towards changes in laboratory working procedure • Attitudes of chemists towards change in the publication system • Input from journal publishers before demonstrator / prototype available • Blog software restrictive • Extreme diversity and number of file formats employed for analytical chemistry

  31. Future Directions • A useful demonstrator to the practising scientist of the value of a laboratory repository… • Further advocacy required in preservation and data management areas • Feasibility of a departmental data repository? • An exemplar for: • The institution - towards an institutional data preservation policy? • Publishers – improved handling of supplementary information • Instrument manufacturers – will respond to the demands of their customer base • Follow on funding: eChemistry; myExperiment • Best practice in validation and reproducibility of experiments • Develop relationship between data repository & ‘Blog approach’

More Related