Trust and Epistemic Communities

in Biodiversity Data SharingNancy Van HouseSIMS, UC Berkeley

www.sims.berkeley.edu/~vanhouse

Trust and Epistemic Communities in Biodiversity Data Sharing

DLs: ready access to unpublished information by variety of users - crossing sociotechnical boundaries Raises issues of trust and credibility

Knowledge is social What we know, whom we believe is determined by/within

epistemic cultures Biodiversity data

Great variety of information, sources, purposes CalFlora: an example of a user-oriented DL

Incorporating users’ practices of trust and credibility Negotiating differences x epistemic cultures

Implications

DLs Facilitate Access To greater variety of information:

Unpublished (unreviewed) information “Raw” data such as reports of observations Information from outside own reference

group Problems:

Which info, sources do we believe? How do we evaluate info from unfamiliar sources? Which info do we use for what purposes?

By people from outside own reference group Inappropriate use of information? Burden on data owner of making data available, usable,

and understandable to reduce misuse

Examples of Risks– Botanical Information

Unreliable Info Erroneous, duplicative observations >> belief

that a species is prevalent >> not preserving a population of a rare species

Chasing after erroneous reported sighting of a rare species –or discounting significant sighting as amateur’s error

Inappropriate Use of Info Private landowners destroying specimens of a

rare plant to avoid legal limits on land development

Collectors (over-)collecting specimens of rare species

Knowledge is Social What we know comes primarily from others.

Cognitive efficiency: we don’t have time, resources Expertise: we don’t have sufficient knowledge in all

areas Have to decide whom we trust, what we

believe. What we consider “good“ work, whom we

believe and, how we decide are determined and learned in epistemic communities

DLs need to support the diverse practices of epistemic communities

Social Nature of Knowledge is of Concern in Many Areas

Science studies Inquires into the construction of scientific

knowledge & authority Social epistemology

Asks: How should the collective pursuit of knowledge be organized?

Situated action/learning Posits knowledge, action, identity, and

community to be mutually constituted Knowledge management

Is concerned with how to share knowledge

Cognitive Trust and DLs For people to use a DL:

Information must be credible Sources must be trustworthy DL itself must be perceived to be

trustworthy How can DLs be designed to:

Facilitate users’ assessments of trust and credibility of info and sources?

Demonstrate their own trustworthiness?

Epistemic Cultures “…those amalgams of arrangements

and mechanisms … which, in a given field, make up how we know what we know.”

“Epistemic cultures…create and warrant knowledge, and the premier knowledge institution throughout the world is, still, science.”

Karen Knorr-Cetina, Epistemic Cultures

Culture Context of history and on-going events Practice: how people actually do their

do-to-day work Artifacts

Info artifacts include documents, images, thesauri, classification systems

Diversity If all the same, no culture Including diversity x areas of science

Epistemic Cultures Differ Practices of work

Practices of trust Artifacts – e.g. genres Methods of data collection and analysis Meanings, interpretations, understandings Tacit knowledge and understandings Values Methods, standards, and information for

evaluating other participants’ work and values

Institutional arrangements

Communities and Knowledge

Becoming a member of a community of practice = identity learning practices, values, orientation to the

world We learn what to believe, whom to believe,

how to decide in epistemic communities. We tend to trust people from within our own

epistemic communities. Similar values, orientation, practices, standards Ability to assess their credibility

DLs and Epistemic Cultures DLs enable information to cross epistemic

communities. More easily, more often than before. Raw data, not just syntheses, analyses – e.g. publications

Crossing communities often undermines our practices of trust. Who are these people? How did they collect the data? What do they know? What are their goals, values, priorities?

DLs need to be designed to support practices of assessing trustworthiness and credibility.

Biodiversity Data Biodiversity: studies diversity of life and ecosystems

that maintain it Central question: change over space and time Uses large quantities of data that vary in:

Precision and accuracy Methods of data collection, description, storage

Old data particularly valuable Broad range of datasets: biological, geographical,

meteorological, geological… Created and used by different professions, disciplines,

types of institutions…for different purposes Politically, economically, sensitive data

“Citizen Science” Fine-grained data from observers in the

field Observers with varying levels and types

of expertise E.g., expert on an area, habitat, taxon…

Expert amateurs Private-public cooperation

Government agencies, environmental action groups, university herbaria, membership organizations, concerned individuals…

CalFlora

http://www.calflora.org Comprehensive web-accessible

database of plant distribution information for California

Independent non-profit organization Designed/managed by people from

botanical community, not librarians or technologists

Free In conjunction with UC Berkeley Digital

Library (http://elib.cs.berkeley.edu)

Researchers & prof’ls in land management Ready access to data for

Addressing critical issues in plant biodiversity Analyzing consequences of land use alternatives and

environmental change on distribution of native and exotic species

The public: promoting interest in biodiversity Active engagement in biodiversity issues/work Wildflowers as “charismatic”

CalFlora Target Users

CalFlora Priorities

Focus on people; put technology in the back seat

Pay attention to how the world works for the people who produce and use information

Honor existing traditions of data exchange

Botanists at Work

Components of Interest Today

CalPhotos

CalFlora Occurrence Database

CalPhotos In conjunction with the UC Berkeley Digital

Library Project http://elib.cs.berkeley.edu > 28,000 images of California plants

Approx. half of all Calif. species are represented Sources

Some institutions – e.g. Cal Academy of Sciences Many from “native plant enthusiasts” Currently accepting/soliciting contributions from

users Major reported uses

Plant identification Illustrations

CalFlora Occurrence Database

> 800,000 geo-referenced reports of observations Specimens in collections Reports from literature Reports from field Checklists

Sources 19 institutions About to begin accepting reports from

registered contributors via Internet

CalFlora Occurrence Database Users can

“Click through the map to underlying data” Download data for own analyses, tools

Uses Land management decisions Legally-mandated environmental reports (NEPA,

CEQA) Identify plants (though not designed for this)

Common analyses Which species are present in an area Which are common, which are rare Which species are restricted to a habitat affected by

proposed actions Analyze various species in combination, by geo

area

CalFlora Occurrence Database: Significance

Most comprehensive source by far (for Calif) Common as well as rare taxa

Biodiversity beginning to be interested in all populations, not just rare -- requires vastly more data

Data downloadable, manipulable Easy to use (for professionals, anyway) Remote access via Internet

E.g. botanist in remote National Forest… About to accept observations from “the public”

Source of valuable data re rare and esp’ly common species

Dilemmas and Conflicts Useful place to see tensions,

breakdowns, conflicts across epistemic cultures

Not whose right, wrong but underlying differences in values, priorities, practices, understandings

CalFlora Dilemmas Quality filtering: made centrally vs. pushed down

to user Inclusiveness of observations vs. selectivity Speed of additions vs. review, filtering Labelling data for quality vs. providing info for users

Access Benefits vs. dangers of wide access to information

Free vs. fee Cost recovery Discourage frivolous use

Who bears the costs? Externalities

Dilemmas, Cont. Institutional independence:

Autonomy, ability to be responsive to multiple stake-holder communities vs. security and credibility of institutional sponsorship

How (Some) Experts Assess Occurrence Reports

The evidence: Type of report (specimen, field observation,

list) Type of search (casual, directed)

The source: Personal knowledge of contributor’s expertise Examination of other contributions, same

person Annotations by trusted others

Ancillary conditions: Likelihood of that species appearing at that

time, habitat, geographical location Other, similar reports

How CalFlora Presents Occurrence Data

Links to data source(s) – personal and institutional

Compliance with institutional source’s requirements Fuzzed locations Links to institutional source’s caveats,

explanations Publicly-contributed observations

Info about observer Info about observation Annotations by experts

Contributor Registration Biography, credentials (free text) Expertise/interests (free text) Affiliation Contact info/web site “I will submit only my own observations of wild

plants. I realize that this system is only for first-hand reports about plants, native and introduced, that are growing without deliberate planting or cultivation.”

“I will…make sure I have the correct scientific name…I will submit uncertain identifications only if I believe them to be very important and time sensitive, and will label such reports ‘uncertain.’”

Contributor Registration (cont) Experience level (self-assessment; check one)

I am a professional biologist/botanist, or have professional training in botany.

Although I do not have formal credentials, I am recognized as a peer by professional botanists.

Although I do not consider myself to have professional-level knowledge, I am quite experienced in the use of keys and descriptions, and/or have expertise with the plants for which I will be submitting observations.

I do not have extensive experience or background in botany, but I am confident that I can accurately identify the plants for which I will be submitting observations.

Occurrence Form Species identification, habitat, location, date Method of identification

“I recognize …from prior determinations and experience” “I compared this plant with herbarium specimens” “I keyed this plant in a botanical reference” “I compared … with published taxonomic descriptions” “An expert reviewed and confirmed this identification”

Certainty of identification “I am confident of this identification, and submit this as a

positive observation.” “I am not certain of this identification but believe it to be a

significant observation and submit it here as an alert only.”

Annotations Herbarium practice: experts annotate

records with corrections, comments. CalFlora: registered experts can

annotate photos and occurrence records. Annotation by an expert raises the

credibility of a record. Actually – how often?

CalFlora Data and Trust Trusting data

Every observation trackable to source(s) Detailed info & contact info for source, observer Detailed info about observation Observations categorized by type Annotation

Trusting users NOT registering or charging users Respecting source’s limits, caveats on data Leaving quality decisions to the users

Trusting CalFlora Detailed list of contributing organizations, advisors NOT affiliated with another organization

Concerns CalFlora relies on record-by-record examination

Looking at methods of classifying records in collections CalFlora relies on voluntary contributions of data

Experts with lots of data and no time to contribute Well-meaning volunteers with time but not expertise

Users need to be able to track back to source of each record, each data point Concern about “modalities,” uncertainties being lost

Archiving Concern about dynamicism of CalFlora Stability of electronic media Stability of the organization

Delegating decisions about quality of observations to (inexpert) users

Implications for DLs, Other Info Systems

The social nature of knowledge We have to decide on whom we will depend We learn from others whom and what we can

depend on Information must be credible to be used The importance of culture in constituting

knowledge Practice, values, orientations…

Epistemic cultures differ Not simply a matter of experts vs. public

Therefore: DLs need to accommodate practices

Incl. practices of trust and credibility Users need to know provenance of data

Users differ and not just experts vs. nonexperts

DLs serve multiple, varied epistemic cultures Same person,multi cultures

Users need flexibility to accommodate the DL to their needs, practices Some users need decisions made for them

>> involvement of users in design

Implications for DL Creation and Management

Different epistemic cultures participate in the design and management of DLs, as well Librarians Technologists Various, differing user groups

Differences in practices, understandings, values >> differences in priorities and decisions

A continual process of negotiation and translation

`