1. Richard White Biodiversity Informatics Projects

2. Thoughts Role of biodiversity data in bioinformatics – assisting with organising and retrieving bioinformatic (molecular) data – a separate area with different users (taxonomy, ecology, conservation, resource management …) Demand from users for taxonomic and species diversity information on the Web Pressure on the taxonomic community to deliver Demand for more sophisticated use of available data: interoperability = online analysis, not just browsing

3. Assembling biodiversity information sources Delivering species diversity information by assembling, merging & linking databases and publishing on the Web, with special emphasis on linking

4. Issues in assembling and linking biodiversity information sources Assembling a web-site (ERMS) Assembling databases by merging (ILDIS) Linking on-line databases through a gateway (Species 2000 and SPICE) Onward links to related information Checking the reliability of links (LITCHI) Intelligent linking Persistent identifiers

5. Assembling species databases First of all, before we start merging and linking databases, let’s assemble a database from scratch: ERMS (European Register of Marine Species) Now at www.marbef.org/data/erms.php

6. ERMS

7. Incoming data Approximately 100 separate lists for different taxonomic groups Mostly compiled as spreadsheets Scientific names, synonyms, geography (at least Atlantic or Mediterranean) Some optional fields Objective to create a book and a web-site, partially supported by a database

8. List conversion was carried out in several stages: Excel spreadsheets were exported to text files Tab-delimited text files were imported into a client- server database (MySQL) Database queries results are passed through templates to generate either RTF (for the printed publication) or HTML (for the Web site)

9. Variations on a theme Fields may be combined or separated e.g. genus species authority date Higher taxa may be: –repeated in fields of the species record –given once in separate preceding records in various different formats Synonyms may be: –in a separate field of the species record, or mixed with other remarks, with various delimiters and separators – in separate records, linked by code or by name or even abbreviated –implied, e.g. Genus1 specname (Smith as Genus2) Geographical information is often free text

10. ERMS book page

11. Osteichthyes: brief checklist

12. Reptilia: full details

13. Taxonomic hierarchy for Reptilia

14. Merging versus linking Merging databases to create a single larger database Linking databases to create a distributed information system

15. Merging species databases 1The original databases are physically copied into a new combined database. 2The user interacts with the new combined database.

16. Linking 1The user interacts with an access system which does not itself contain data. 2When the user requests data, it is fetched from the appropriate database.

17. Assembling databases by merging Now we have some databases, let’s build a bigger one by merging: ILDIS (International Legume Database and Information Service)

18. ILDIS International Legume Database and Information Service International collaborative project –10 Regional Centres –30 Taxonomic Coordinators Its goals include –building, maintaining and enhancing the ILDIS World Database of Legumes –designing and providing services from it to users, including: ILDIS LegumeWeb via Species 2000

19. ILDIS World Database of Legumes v. 7.00 Taxa Species 15,500 Subspecies 1,600 Varieties 2,400 19,500 Names Accepted names 19,500 Synonyms 19,000 39,500

20. ILDIS’s data model: core data A core taxonomic checklist, assembled from regional data sets and nearing completion, provides a consensus taxonomy - a unified taxonomic treatment or backbone on which other data can be hung Various kinds of additional data may be attached to this backbone (see later)

21. Features of ILDIS LegumeWeb We’ll look at examples of the use of LegumeWeb, to show a couple of features: Two-stage access with “synonymic indexing” A gateway to external information - “onward links” (direct species name links) to further sources of information

24. User access to LegumeWeb: Step 1 The user types in a name, which may be incomplete (or wrong!) LegumeWeb responds by showing a list of the species names which fit the user’s specification

26. User access to LegumeWeb: Step 2 The user chooses one of the species names provided (which may be synonym or an accepted name) –In this example, the user chooses Abrus cyaneus (a synonym for Abrus precatorius) LegumeWeb responds by showing a standard set of information about the chosen species

28. Synonymic indexing Automated synonymic indexing synonym entered  accepted name found (name  taxon) taxon found  synonyms listed Types of synonyms –Unambiguous –Ambiguous pro parte homonyms misapplied names In these cases an explanation is offered to the user

29. Assembling databases by linking Now we have some biggish databases, let’s build something even bigger by linking databases together: Species 2000 –SPICE –Species 2000 Europa

30. Linking 1The user interacts with an access system which does not itself contain data. 2When the user requests data, it is fetched from the appropriate database.

31. The Catalogue of Life (Species 2000) An international collaborative project to provide access to an authoritative and up-to-date checklist of all the world’s species A distributed array of Global Species Databases (GSDs) can be accessed through a Web gateway or Central Access System (CAS) The array of GSDs provide an index to a further range of information about each species, using onward links (see later) www.sp2000.org

32. Species 2000 organisation Taxonomic hierarchy (or hierarchies) Species Global species databases (GSDs) and interim checklists: the species index GSD interim checklists Species information sources (SISs): regional faunas and floras, specialist or sectoral databases, web pages etc. SIS

33. Architecture of Species 2000 User interface Data collector (CAS) Wrapper GSD Wrapper GSD Wrapper GSD

34. Species 2000’s Common Access System Species 2000 gives users a single point of access to GSDs Access involves a two-stage search process similar to that used in LegumeWeb In the second stage, the user sees a screen of “standard data” about a species

35. The “standard data” This comprises the information about a species which Species 2000 wishes to provide: – Accepted name (with references) – Synonyms (with references) – Common Names (with references) – Family or other higher taxon – Geography – Comment – Scrutiny information – URL or URLs linking to further data sources for this species

36. Need for communication Different people are building the various components of the system: –GSDs –wrappers –CAS –user interface We need to ensure they all have a common understanding of the data to avoid mistakes

37. Common Data Model We use a Common Data Model (CDM) –A definition of the information being passed to and fro –Human-readable, not machine-readable –Helps to manage complexity –Used to create specific machine-readable implementations for Corba (IDL), CGI/XML (DTD, XML Schema), Web Services, etc.

38. What does the CDM look like? It defines the input (“request”) and output (“response”) for six fundamental operations which the system needs to be able to carry out

39. Request Types 0-6 –Type 0: Get CDM version supported by a GSD’s wrapper –Type 3: Get information about a GSD –Type 1: Search for a name in a GSD –Type 2: Fetch “standard data” about a chosen species –Type 4: Move up the taxonomic hierarchy (towards the root of the tree) –Type 5: Move down the taxonomic hierarchy (towards the species level)

40. Spice CAS in use Screen-shots of an old version of the Spice system in use:

41. Spice 1 CAS

44. Onward links to related external data Species databases such as ILDIS and federated systems such as Species 2000 envisage providing links from their data to external sources of related data, so- called “onward links” Example from ILDIS...

45. “Onward links” The user may follow a hyperlink to some other data source for further information, not managed by ILDIS –In this example, the user chooses to go to W 3 Tropicos at Missouri Botanical Garden to see more information In this way LegumeWeb acts as a gateway to other information about legume species

46. LegumeWeb page with onward links

47. Destination of an onward link

48. Further information obtained

49. Checking the reliability of links Whether in –merging data sets to construct a species database like ILDIS, or in –linking from one data set to another, it is necessary to ensure that the species concepts in the different databases do not conflict

50. Example 1 Database A Caragana arborescens Lam. [accepted name] Caragana sibirica Medikus [synonym] Database B Caragana sibirica Medikus [accepted name] Caragana arborescens Lam. [synonym]

51. Example 2 Database A Caesalpinia crista L. [accepted name] Database B Caesalpinia crista L. [accepted name] Caesalpinia bonduc (L.) Roxb. [accepted name] Caesalpinia crista L., p.p. [synonym]

52. LITCHI project We modelled the knowledge integrity rules in a taxonomic treatment The knowledge tested is implicit in the assemblage of scientific names and synonyms used to represent each taxon Practical uses include –helping a taxonomist to detect and resolve taxonomic conflicts when merging or linking two databases –helping a non-taxonomist user follow links from one database to another, in which the species may be differently classified

53. Conflict display

54. Outcome of LITCHI project A prototype tool for merging checklists & checking integrity of individual checklists was implemented In the Species 2000 Europa project, we are now creating a completely new second version with a view to allowing: –dynamic linking (so-called “taxonomically intelligent links”) –Presentation of “attached data” to be organised, merged and used to support conflict resolution

55. “Intelligent” linking The Catalogue of Life (Species 2000) is –not just a catalogue (which lists things) –it is an index (which points to things) GSDs, and gateways to them such as the Catalogue of Life, can serve not only as catalogues of species but also as indexes giving access, potentially, to all species information on the Internet

56. “Intelligent” linking Species 2000 plans to provide links to take a user –from a species entry (from a GSD) –to further sources of information about that particular species (Species Information Sources or SISs)

57. Species 2000 organisation Taxonomic hierarchy (or hierarchies) Species Global species databases (GSDs) and interim checklists: the species index GSD interim checklists Species information sources (SISs): regional faunas and floras, specialist or sectoral databases, web pages etc. SIS

58. “Intelligent” species links Given that it is possible to detect many cases of potential taxonomic conflict when linking species databases, how can such links be managed? There are a number of choices in the ways links may be made and handled

59. Cross-mapping So how can we make intelligent links work, especially in the difficult cases where a species in one database does not have an exact match in the other ? –One way is to create and maintain “cross- maps” which describe how one or more taxa in one resource (such as the Species 2000 index) relate to one or more taxa in another resource

60. A dream A system for managing intelligent species links would maximise the potential of the plethora of species-based catalogues, indexes and rich species resources currently being assembled all over the world Perhaps on the Web, as with the current Spice/Species 2000 prototypes Or...

61. The Grid The Grid is often thought of as a new toy for particle physicists, with –very high bandwidth –distributed computational resources But it also provides opportunities for more structured and reliable access to data and information sources, using improved protocols with metadata –For example, access to such knowledge sources as these cross-maps

62. Using biodiversity information resources Helping Biodiversity Researchers to do their Work Collaborative e-Science and Virtual Organisations

63. Biodiversity analysis and modelling Scientists working with biodiversity information employ a wide variety of resources: data sources statistical analysis and modelling tools presentation or visualisation software which may be available on various local and remote computer platforms.

64. Examples of biodiversity resources Data sources: Names: Species 2000 & ITIS Catalogue of Life Data: GBIF, sequence databases Geography: Gazetteers Collections and distributions: BioCASE, MaNIS Analysis tools: Statistical and multivariate analysis Modelling Visualisation

65. Use of resources together Scientists frequently need to use several of these resources in sequence to carry out their research. Much effort is currently expended in initially acquiring resources installing and sometimes adapting them to run on the user’s own machine converting and transporting data sets between stages of the analysis process

66. Biodiversity research Biologists are working to understand the adaptation of organisms to their environmental niche, eventually by combining knowledge of all the levels of biological organisation and to predict their interactions with their environment genome transcription proteome metabolic pathways cell tissue organ individual whole organism population species evolutionary pathways

67. Workflows Resources are called into use in an appropriate sequence from an interactive workflow. The facility for scientists to be able to create their own workflows, without the need for regular assistance from computer scientists, is an essential part of the BDWorld system. Accessible tools for resource discovery and for workflow design, enactment and re- use are therefore required.

68. For example Changes in distribution in response to climate changes brought about by global warming

69. CSM: Climate-space modelling Modelling and predicting changes in distribution in response to climate changes such as those brought about by global warming An unreasonably brief explanation: Get current distribution of a species (e.g. specimen records) Get current or recent climate data for those localities Calculate a model for the climate space the species can occupy Predict the distribution the species would have in any specified climate (may be different to the climate used above) Project back on world map

70. Example work-flow (Climate-space Modelling) Projection Prediction SPICE Localities Climate Space Model Base Maps Climate Submit scientific name; retrieve accepted name & synonyms for species Retrieve distribution maps for species of interest Climate surfaces Model of climatic conditions where species is currently found Possibly different climate surfaces (e.g. predicted climate) World or regional maps Prediction of suitable regions for species of interest Projection of predicted distribution on to base map

71. Triana screen-shots 1 Creation (design, editing)

72. Triana screen-shots

75. Triana screen-shots 2 Execution (enactment, run-time)

76. Triana screen-shots

80. And finally …

81. Triana screen-shots

82. Elements of the BDWorld system What did the system have to do to make that example happen?

83. Role of the work-flow engine Create and edit a workflow –locate an appropriate resource –check interoperability –arrange any necessary transformations –record provenance of generated data sets Execute a workflow, passing data sets to and fro Create a log or ‘lab book’ for user

84. Difficulties with resources Finding the resources Knowing how to use these heterogeneous resources –Originally constructed for various reasons, often with little attention to standards or interoperability –Have to pass data sets from one to another –Some involve user interaction

85. Role of metadata Metadata is needed to enable discovery of resources and to indicate how they are to be used. Properties to help locate appropriate resources Check interoperability, suggest transformations Provenance of data sets Log of work-flows executed

86. What is biodiversity informatics? The preceding project, among others, shows that the challenges facing biodiversity informatics include not only – Describing the diversity of life at all levels of organisation, so that biologists can understand, conserve and exploit it, But also – Inventing ways to describe the ever-increasing diversity of information resources and analysis tools available, so that users can find and use them

87. A challenge to link resources It is potentially very difficult to link all these resources together Much attention is currently being given to: –Providing unique identifiers for data objects –Which can return metadata about themselves –Which can be stitched together into a distributed collaborative information system: see the biodiversity informatics organisations TDWG and GBIF (later)

88. End