Transcript Trust and Epistemic Communities in Biodiversity Data Sharing

Trust and Epistemic Communities
in Biodiversity Data Sharing
Nancy Van House
SIMS, 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 DLs: ready access to unpublished information by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
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 Unpublished (unreviewed) information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 doto-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
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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.
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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)
CalFlora Target Users
 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 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
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
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
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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 firsthand 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
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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
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