Outline

2. Outline • What’s new about MIIDI, and how has it been developed? • What is the structure of MIIDI? • The nature of the MIIDI Excel spreadsheet • A detailed look at the existing MIIDI standard and its application to a particular research article and to a research dataset

3. What is new about MIIDI? • MIIDI extends the scope of previous MIBBI standards, which are focused on metadata for research datasets, largely of laboratory origin • It is designed for use in describing both datasetsand publications • For the latter, it has fields not found in any other MIBBI standard • e.g. permitting entry of investigation conclusions • Because the range of infectious disease investigations is large, MIIDI is specifically designed to deal with a diversity of investigation types and a variety of study types • As in any Minimal Information Standard, there is a tension between being too minimal and too inclusive, and a potential for conflicts and overlaps with other standards • Today, I will simply report on the standard as it now exists, without inviting discussion • Tomorrow we will look together at such tensions, conflicts and overlaps, and discuss how MIIDI can be improved

4. How have we arrived at MIIDI version 0.2? • With help from Lucy Mason, MIIDI drafts were developed earlier this summer, by analysing the metadata required to summarize different types of infectious disease reports and research articles • For convenience, we encoded the draft in Excel • Our work resulted in Draft 8 of the MIIDI standard, which was circulated to all workshop participants on 22 July, together with completion notes and exemplar implementations, and a request that you undertake ‘homework’ • Many of you put considerable effort into fitting your own work into the MIIDI draft, and reporting back where the draft was inadequate • Over the last few weeks, we have been updating MIIDI to accommodate this feedback, primarily by adding extra requested metadata fields • The resulting artefact, MIDDI version 0.1, was posted on the MIIDI wiki last Thursday, 27 August • Over the weekend, I have made a few additional changes, and posted version 0.2 this morning

5. What is the structure of MIIDI? • MIIDI adopts the “ISA” hierarchical structure: • At the top level is the Investigation, which can be a multi-faceted research activity • An Investigation comprises one or more Studies, each Study looking at a single aspect of the overall Investigation • e.g. environmental, sociological, serological • A Study may have one or more Assays, each measuring one thing • e.g. rainfall, family income, immunity • The Investigation – Study – Assay hierarchical structureof MIIDI is generic, permitting future repurposing for use in other areas • e.g. non-infectious diseases, crop husbandry, fisheries research

6. The Investigation – Study – Assay hierarchy of MIIDI Investigation Study #1 Assay 1a Assay 1b Study #2 Assay 2b Study #3 Assay 3a Assay 3b

7. Advantages of adopting the ISA hierarchy • It clarifies the metadata structure, preventing muddled thinking and avoiding muddling metadata at different levels of detail • It enables things defined at one level to be ‘inherited’ by lower levels • e.g. the source of research funding defined for the Investigation applies to all that Investigation’s individual Studies • It provides flexibility within the MIIDI Standard, in that different types of Investigation can employ different Study templates as appropriate, ignoring others that are not relevant • This makes for a lighter-weight standard • It fits well with other standards-related activities, particularly tools such as ISA-Creator developed under the ISA-Infrastructure, which Susanna Sansone will demonstrate after this session

8. Types of infectious disease Investigations and Studies Study type • Study of disease host (Human, animal or plant) • Study of disease vector or animal reservoir • Study of disease pathogen or parasite • Study of disease habitat • Study of disease model Investigationtype • Outbreak investigation / Clinical report • Epidemiological or observational investigation • Interventionist investigation / Clinical trial • Molecular, cellular or genetic investigation • Systematic review or meta-analysis • Mathematical predictive modelling investigation

9. Types of infectious disease Investigations and Studies

10. The nature of the MIIDI Excel worksheets Investigation summary Investigation details Study of disease host Assay Assay Assay Study of disease vector Assay Assay Assay Study of disease pathogen Assay Assay Assay Study of disease habitat Assay Assay Assay Study of disease model

11. Encoding MIIDI in Excel • The fact that MIIDI v0.2 is presently encoded in Excel is purely for convenience – it could be in any representation, or completely hidden from the user by a metadata entry tool such as ISA-Creator • Ideally, such a tool would allow the user to specify • the object of study (host, vector, pathogen, etc.) • the nature of the Studies involved (host, vector, pathogen, etc.) • and would then construct user interfaces that only ask the user for relevant metadata • One goal is to encode MIIDI in RDF, so that metadata created using it could become part of the Web of Linked Data

12. So, now let’s look at the existing MIIDI standard . . . • MIIDI version 0.2 with completion notes • MIDDI v0.2 applied to Reis et al. (2008) PLoS Neglected Tropical Diseases • MIIDI v0.2 applied to a research video investigating T-cell cytotoxicity • MIIDI v0.2 applied to Attaran et al. (1999) investigating T-cell cytotoxicity • MIIDI v0.2 applied to a ProMED-mail disease outbreak report

13. Regularizing MIIDI • MIDDI version 0.2 contains ~110 unique fields • We now need to regularize its structure • Fields that should be constrained by data type • integer, date • Fields whose content should be drawn only from controlled vocabularies • disease names, place names • Fields whose content should be drawn from defined ontology terms • ‘outbreak’, ‘journal article’

14. end