information literacy and science
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Science and Technology
Information Seeking, Scholarly Communication, and Open Access
Florence M. Paisey
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The Web is an information resource of extraordinary size and depth, yet it is also an
information reproduction and dissemination facility of great reach and capability; it is at
once one of the world’s largest libraries and surely the world’s largest copying machine.
“The Digital Dilemma: Intellectual Property in the Information Age”
National Research Council: Committee on Intellectual Property Rights in the Emerging Information Infrastructure, 2000
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Table of Contents
Preface................................................................................................................................4
Information Literacy in Science: The ACRL Definition....................................................6
The ACRL Science and Technology Standards and IS Models........................................13
Unique Characteristics of the ACRL Science and Technology Standards........................15
Communication and Flow of Scientific Information.........................................................17
Patents, Intellectual Property, and Digital Data Depositories............................................22
References..........................................................................................................................27
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Preface
The following essay explores information seeking and the search process characteristic
of academic professionals in the sciences. In addition, it discusses key issues that relate to
scientific information: its ownership, its exchange and production or communication, and
some of the legislation and policy issues that have come into play with the large scale
research needs of megasciences and open access depositories such as SPARC. The essay
commences with the ACRL definition of information literacy in the sciences and a
description of generic information skills that, potentially, bring about effective information
management in the sciences.
It is difficult to speak about information seeking in a generic sense, particularly in the
sciences. Context alone does not explain the differences in information seeking among
scientific disciplines. The force of habit, whether given to optimal results or not, often
plays into how scientists construct their thought and interact with information sources.
While humanities scholars consult definitive works on potential research topics, scientists,
on the whole, first engage in discourse with their peers, and then develop research
methodology and strategy. For those scientists working in applied fields, such as medicine,
roles generate discrete tasks and demand knowledge with the possibility of an information
need and query.
The topics of communication in the sciences as well as property rights to data are also
briefly explored. Communication models are evolving first through modernization with a
view to transformation. Property rights to data, generated by those involved in the
megasciences, are complex with grayed and ambiguous boundaries; there is no simple
answer. Each aspect of information behavior and information seeking explored here is a
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deeply engaging topic in its own right. I have merely identified points, at issue, in an effort
to understand how information impacts individual interaction with sources, the flow of
scholarly communication, and research policy.
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Information Literacy in Science: The ACRL Definition
Information literacy involves recognizing an information need and seeking answers to
that need through questions one asks. It may be viewed as a “generic ability of citizens in a
democratic society to make well informed choices based on the critical evaluation of a
wide range of information sources” (Alexandersson & Limberg, 2005). Fundamentally,
information literacy and information literacy in science involve the same goals: identifying
and satisfying information needs.
Information literacy identifies a general set of cognitive, physical, and technical skills
applicable to unscientific information; information literacy in science deals with cognitive,
physical, and technical skills characteristic of specific disciplines in science and scientific
thought. The generic ACRL Information Literacy Standards set forth broad information
skills and behaviors that will foster the ability to manage and use information in general.
The ACRL Science and Technology Standards distinguish information skills that are
distinctive to the nature of and goals of science.
The ACRL provides a definition of information literacy in science and engineering.
This definition directed the development of the information literacy standards and
performances specific to science and engineering/technology. The ACRL defines
information literacy in science as:
…a set of abilities to identify the need for information,
procure the information, evaluate the information and
subsequently revise the strategy for obtaining information,
to use the information, and to use it in an ethical and legal
manner, and to engage in lifelong learning (ACRL, 2006).
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The Association of College and Research Libraries (ACRL) developed the Science and
Technology Information Literacy Standards (ACRL, 2006) as an outcomes-based
framework for students and professionals in higher education. They include five Standards
and 26 performances or performance indicators with associated outcomes, all intended to
implement the practice of information literacy in science and engineering/technology, as
conceived and described by the ACRL. These Standards are scaffolding upon which
specific, granular performances will bring about meaningful and ethical individual
information management and use as conducted by students, scholars, and researchers in
higher education.
They support in-depth, inquiry-based information searching. This is the fundamental
intent underlying the ACRL Science and Technology Standards. Like the general ACRL
Information Literacy Standards, the science and technology standards derive from
cognitive task analyses of experts or the “analysis of specific sequences of action and
cognitive processes” employed by experts when satisfying an information need (Vakkari,
2003). In the case of the ACRL Science and Technology Standards, the information need
would relate to science and require information searching appropriate to the nature of a
scientific discipline.
Wilson’s model of information behavior and information searching (1999) offers a
cohesive representation or grounding of information behavior and the search process
regardless of the discipline. His description of information behaviors (2000) as
information seeking, information searching, and information use also corresponds to levels
of goal-directed information behavior whether the informational goal is of a scientific
nature or otherwise. The search process itself follows from an information need to the
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satisfying of that need. However, as Vakkari (2003) has pointed out, the search process
and outcome are dependent variables – consistent across searchers, disciplines, and
domains. The independent variable involves user characteristics and those factors in users
that cause “systematic variation in search process and outcome.” The ACRL Science and
Technology Standards may be viewed, basically, as the dependent variable or the search
process, with specific features that directly relate to specialized sources of information and
the character of scientific communication. The independent variable, the user and those
factors, such as the need for scientific information, may be viewed as process variables
that cause “systematic variation” in the search process.
In general, the Standards provide the dependent variable, a search process, though
specific features of the process such as “search tactics” or “term choices” could be viewed
as either dependent or independent variables, depending on the formulation of the problem
(ibid). For example, as a search process, the ACRL Standards for Science and Technology
serve as a template; the characteristics of the user and the information need will determine
variation in the use of the Standards. Within the Standards, Standard 2 includes 5
Performance Indicators. They include the selection of investigative methodology, the
construction of a search strategy, the retrieval of information, refinement of the search (if
necessary), and the use of appropriate technology to record pertinent information. These
performances are consistent; they are dependent variables. However, the methodology one
selects to investigate an inquiry, the terms or vocabulary a user employs or the formulation
of how to pursue a search – whether to refine a search or not, the search strategy – would
be characterized by the user and, as such, would be independent variables.
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The ACRL Science and Technology Standards support the search process in scientific
disciplines. However, they do not identify specific work roles and tasks that would
characterize the user’s information need, interaction with sources of information,
communication cycle, or the outcomes of the search process: the search result. It is this
aspect of information searching, user characteristics, that “cause systematic variation in
search process and outcome – that is, that are systematically connected to searching and
search results” (Vakkari, 2003).
Leckie & Pettigrew (1996) studied the information habits and flow of three professional
groups– engineers, health care workers, and lawyers. Based upon their findings, they
developed a general model of information seeking for professionals, the ISP model. Six
components form the basis of the model, including:
1. Work roles
2. Associated Tasks
3. Information Needs
4. Awareness
5. Sources
6. Outcomes
The first component specifies the varied roles a professional assumes in professional
life. A physician may assume multiple roles such as diagnostician, therapist, medical
administrator, examiner, counselor, researcher, teacher, colleague, manager, medication
therapist, and crisis caretaker. Each of these roles engenders tasks that, in turn, trigger
information needs, giving rise to an information search process (ibid). The information
search process will be shaped by the characteristics of the information need. These needs
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will vary according to many possible factors; both independent and intervening or covert
variables will come to bear.
A note of wariness is worth mentioning here. The independent variable is the user;
intervening variables will affect the user, producing an effect on the dependent variable –
the search process. The independent variable is known, controlled for, and manipulated so
that one can determine its effect on the dependent variable – in this case the search
process. Intervening variables will also produce an effect on the dependent variable or
search process, but will not be immediately accessible; intervening variables are internal,
covert, unobserved factors that can be inferred and identified only by manipulating the
independent variable. A clear distinction between independent and intervening variables
was not clear in Leckie & Pettigrew (1996).
Independent variables might be age, years of medical experience, hours on the job,
specialization, and the frequency with which the physician encounters the task as well as
the information need. Intervening variables might be the degree of information needed, the
confidence level of the physician, the clarity of the information need – does the physician
understand clearly what question to follow or is research required to identify possible
diagnoses, then possible courses of treatment. Personality and preferences in research will
also affect the search process and may be viewed as intervening variables until an
assessment is conducted and one controls for specific variables (Heinström, 2003). An
awareness of one’s expertise or limitations will affect recognition of the information need
as well as the ability to diagnose an ailment accurately; these would be intervening
variables until they become known quantities.
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An awareness of the information sources and one’s perception of the ease or difficulty
in accessing these sources will also affect the search process (Leckie & Pettigrew, 1996).
This level of awareness may be affected by accessibility, familiarity, prior experience,
cost, and timeliness. Where does one find information relating to the need, what is the
currency of the information, how accessible are sources? These are a few of the issues
involved in how information sources are perceived. Leckie & Pettigrew (ibid) identify
sources of information as channels or “formats” of information. These channels or formats
are distinguished as formal and informal, internal and external, oral and written.
Formal channels include conferences and journals; informal channels include
colleagues. Leckie & Pettigrew (ibid) describe the stratification of channels further in
distinguishing between internal (sources within an organization – corporate engineers
frequently utilize internal sources or channels) and external (conference proceedings,
medical literature, the Internet). In addition to these distinctions, sources are viewed as
either oral channels or written channels. One of the characteristics of a user will be their
preference for particular channels or sources of information. These are all variables that
can be controlled for, so user characteristics and the search process can be examined with
greater clarity. However, in order to associate particular user characteristics with a path in
the search process, these variables should not be estimated; rather, they require
identification and control.
The final component in the Leckie & Pettigrew ISP model is outcomes. Outcomes are
defined as the “end-point” of work-related information requirements of “specific roles and
tasks” (ibid). The satisfaction of the information need is recognized as the “optimal
outcome” such as diagnosing an ailment, completing paperwork, submitting an appeal, or
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producing a product. If the satisfaction of the need has not been met, a feedback loop
provides the path to iterate another search process. As repeated searches are carried out,
the user may alter some of the independent variables such as search terms or specific
sources.
The Leckie & Pettigrew model of the information seeking of professionals (ISP) has
been applied to various professions in the sciences, including medicine, biomedicine,
dentistry, nursing, engineering, and software engineering, among others. The notion of
specific roles and associated tasks that give rise to information needs grounds the context
of information seeking by recognizing task goals or in complex tasks the “series of actions
undertaken in pursuit of a goal” (Vakkari, 2003).
The model at issue also discusses the effect of independent and intervening variables on
the search process, though the construct falls short of including likely points of static along
the information search continuum or flow. Its strength lies with recognition that the
interaction of roles and tasks plays a significant role in the formulation of an information
problem, and subsequent need. The Leckie & Pettigrew model (1996) has held up in
limited studies, particularly those studies looking specifically at the relationship between
roles, tasks, and information needs. The model does not provide for a situational context,
reportedly a significant factor in a medical search process (Gruppen, 1990).
However, the Leckie & Pettigrew model provides a valuable dimension to Wilson’s
expansive model. It does not stand an as alternative to Wilson’s information behavior
model; it may be subsumed within this model, along with Ellis’ behavioral micro-search,
in providing further understanding and representation of information behavior, in
particular, the information behavior of professionals.
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The ACRL Science and Technology Standards and IS Models
If one were to layer the Leckie & Pettigrew ISP model (1996) within Wilson’s
information behavior model (1999), one could identify a particular context in which a role
is performed, tasks associated with that role, and the characteristics of the information
needs. Wilson’s model provides for independent and intervening variables along the path
to the search process. This search process, as Vakkari (2003) has pointed out, is the
dependent variable that user characteristics will act on, causing variation in search process
and outcome. As traits of the user become known, a search path becomes more predictable
and systematic. Such knowledge will facilitate the searcher’s understanding of the process,
but will also inform systems designers in their effort to create systems that support search
features with typified search processes.
The ACRL Science and Technology Standards specify standard behaviors,
performances, and performance outcomes that demonstrate competences in a standard or
activities carried out in a search process, particular to disciplines in science. As previously
stated, Wilson’s macro-model (1999) can be layered with dimensions of information
behavior. While the Leckie & Pettigrew (1996) model nests from context to information
need and three aspects of a search, Ellis’ behavioral model of chaining fosters an
understanding of micro-search habits of scientists. The ACRL Science and Technology
Standards offer behaviors, strategies, and values that scientific investigation or the search
process in science requires. These standards can be integrated with Wilson’s expansive
model (1999), Leckie & Pettigrew’s (1996) ISP model, and Ellis’ search chain.
This is not an either-or situation. None of these models or standards is a complete
representation of information behavior and the search process. Each supplies a dimension
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of the search process – in this instance, the search process with features to support
scientific investigation. The ACRL Science and Technology Standards stand as a thorough
account of performances required in such a search process. The degree to which one aspect
of the standards is applied relates to user characteristics, intervening variables, and the
information need.
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Unique Characteristics of the ACRL Science and Technology Standards
Baldwin (2005) identifies several unique characteristics of performances and their
outcomes to information literacy in science and engineering/technology. In Standard 1,
Performance Indicator 2, Baldwin points out that types and formats of sources for
information will be subject specific. She lists several types of source information essential
to study in most of the science disciplines. These sources include “handbooks, patent
literature, standards, specifications, and product literature” (ibid). Each science discipline,
both applied and pure, will have indispensable core reference sources.
In medicine, indispensable sources would include the Physician’s Desk Reference
(PDR), the Merck Manual, Stedmen’s Medical Dictionary, and Mosby's Medical, Nursing,
and Allied Health Dictionary, to name a few. In psychology, the Corsini Encyclopedia of
Psychology and Behavioral Science has been core for several years, while the Mental
Measurements Yearbook and the Diagnostic and Statistical Manual of Mental Disorder
(DSM IV) have been standard information manuals for decades, with periodic updates. In
physics and general sciences, a few core handbooks and manuals would include the CRC
Handbook of Chemistry and Physics, Handbook of Physical Quantities, and the Handbook
of Physics. The Periodic Table is viewed as one of the most important classifications of
the natural world and would be included in all science collections, as would the
Encyclopedia of Associations and Organizations, both National and International. OSHA’s
standards and specifications for safety would also be relevant across all science disciplines
and some social sciences.
In addition to the types and formats of scientific information, Baldwin underscores the
importance of recognizing how “scientific, technical, and related information is formally
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and informally produced, organized, and disseminated” as well as “understanding the flow
of scientific information and the scientific information life cycle” (Baldwin, 2005). This
aspect of Standard 1, Performance Indicator 2, is particularly significant in determining the
credibility and currency of scientific information. What was credible and definitive a
decade ago in physics, chemistry, or medicine may well have changed considerably.
Furthermore, as information and communication technologies have spread and advanced,
communication and the flow of scientific information have altered, though not
fundamentally or universally yet. As this aspect of science will potentially effect dramatic
changes in the information search process of scientists, it merits a bit of discussion.
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Communication and Flow of Scientific Information
Information and communication technologies have changed the way scientists
communicate. However, as Hurd (2000) points out, these changes are incremental and
with a few modifications, fundamental practices still reflect the dominant use of
communication channels established generations ago – weakening the argument for
technological determinism. Scientific communities have integrated ICTs, but such
integration has developed upon existing practice, selectively and gradually. Recent studies
of communication practice support this assertion (Leckie & Fullerton, 1999; Fidel &
Green, 2003).
Hurd (2000) recognizes that ICTs are catalysts that both modernize and will,
ultimately, transform the social networks, information flow, and dissemination of scientific
information. The earlier scientific communication practice referenced above alludes to the
Garvey & Griffith (1964) foundational model of scientific communication, a model that
emerged during the “print-on-paper” era. This model continues to characterize scientific
communication with technologies that support traditional channels or functions, adding
capabilities to an evolving communication system (Hurd, 2000).
More than forty years ago, Garvey & Griffith (1964) conducted a study of the
communication and information needs of scientists. Their initial study focused on the
information behaviors of psychologists (ibid). Their findings were accepted as
representative information searching, exchange, production, and dissemination in both the
physical and social sciences. Garvey & Griffith’s groundbreaking study (ibid), and their
involvement in it, emerged from what had been described as a “scientific information
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crisis” (ibid). They ultimately found, contrary to their initial thought, that “the exchange of
information in research evolves predictably and can be experimentally modified” (ibid).
Garvey & Griffith’s study looked at the information behaviors of scientists as they
attempted to satisfy their information needs. They traced the dissemination process from
the time a researcher starts work until reports of that work have appeared in secondary
publication sources. The processes of scientific communication and information exchange
revealed a dynamic interaction among informal and formal media or information sources.
This interchange typified the means by which scientists mapped investigation that would
satisfy their information needs; it also formed the basis for the study of scientific
communication as a social system or from a sociological perspective. Scientific
communication, exchange and flow, was found to be well organized and predictable with
established communication channels and venues for discourse, review, and eventual
inclusion into an official body of literature.
The Garvey & Griffith model recognizes that the information need precipitates
scientific investigation, but unlike general models of information behavior, it does not look
at individual information seeking and searching or the cognitive field of information
processing. Garvey & Griffith looked at the larger picture, a macro-depiction, identifying
the channels, purposes, and results of discourse within the context of scientific endeavor.
The outcome of these social, contextualized channels of information exchange would be
publication in peer-reviewed journals, then secondary sources. Their model is sociological
in nature, rather than psychological. Such a model is of fundamental importance in
understanding information behavior as a cultural phenomenon as well as an event within a
context, situation, or a profession – in this case, the sciences and the scientific community.
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Information literacy in science, as in all domains, requires an awareness of the culture,
profession, and community in order to understand the nature of a specific – in this case,
scientific – communication interchange and cycle. Such understanding will facilitate all
interaction with information channels and sources within scientific disciplines or contexts,
as well as general information skills. Within the scientific disciplines, knowledge of the
striation or structures of sources that distinguish scientific literature is essential. Of
particular significance is gray literature or “newsletters, reports, working papers, theses,
government documents, bulletins, fact sheets, conference proceedings, and other
publications distributed free, available by subscription, or for sale” (Weintraub, 2000).
Other sources important to distinguish are published monographs, primary and secondary
sources and their place in the flow of information within the cycle. Understanding the
place of an information source within the cycle enables one to formulate sound evaluative
criteria, relevant to one’s information needs.
The sociological aspect of information flow and communication in science has
analogous aspects to individual information behavior. Analogues involve the perception of
one’s information need, formulation of a precise question, the search for information,
development of search strategy, metacognition, and potential revision of the hypothesis,
thesis, or concept. All of these elements of information searching require interaction with
information sources, formal and informal. Generally, the initial phase, or that which
characterizes information seeking, involves informal discussion and debate with one’s
colleagues as a means of negotiating the information question, assessing its value,
identifying pre-existing information sources related to the issue, operationalizing the
question, and devising a methodology to investigate the question. The information need or
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task uncertainty (Fuchs, 1993) may arise out of one’s observation, a diagnostic report,
research report, reflection, or in a tight social network, small world or “invisible college”
(Crane, 1972).
Newman (2001) looked at the social network or collaboration network of scientists,
finding that they form small worlds in which scientists cluster within the proximity of a
few connected acquaintances. The notion that scientists chat, informally, within social
networks, small worlds, or “invisible colleges” supports the Garvey & Griffith model that
scientific investigations, generally, start with the information need, but that the pursuit of
the issue is discussed and hypotheses formulated, informally, collaboratively, or within
small worlds or “invisible colleges” as preliminary dialogue that will direct a course of
scientific investigation. Again, this observation holds up in current practice, as the findings
of numerous studies have concluded (Leckie & Fullerton (1999; Fidel & Green, 2003;
Tenopir & King, 2004).
The Garvey & Griffith model of scientific communication continues to represent the
sociology of scientific communication despite widespread use of ICTs. While scientists
may not write letters as previously, they employ the same means of communicating, but in
a modernized way. Hurd (2000) distinguishes between a modernized and a transformed
communication system. She states:
Modernized features are those that employ
technology to support and update traditional
functions that endure because they continue
to be valued by a community of
scientists (ibid).
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Communication features such as telephones, e-mail, fax, audio and video capabilities
via the Web, and expeditious travel for collaboration update the communication channels
of the Garvey & Griffith model, but the fundamental channels and norms in
communication have not altered. The “invisible college” is broadening its membership to a
“virtual invisible college” where communication relies on the Internet (Hurd, 2000),
including scientists who have, heretofore, been unable to participate in elite collaborative
networks. Yet, these are functional modifications that have simply enhanced
communication. The essential social structure and communication habits remain
unchanged; they are built upon a long-standing scientific communication model, initially
described by Garvey & Griffith.
The fundamental paradigm of scientific communication will transform when scientific
organizations redefine roles and extend collaboration for services. This reorganization is
under way, particularly in big science, characterized by enormous facilities, with vast data
banks. The Scholarly Publishing and Academic Resources Coalition (SPARC) is an
example of “a redefined role for organizations” (ibid); this coalition emerged as an effort
to reduce costs of serials. Gradually, transformation of roles and channels of
communication will materialize, but, as yet, the scientific communication system that has
been in place for generations continues, with updated communication features.
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Patents, Intellectual Property, and Digital Data Depositories
Baldwin (2005) observes a few other unique features of scientific disciplines. Of note is
her reference to Standard 1, Performance Indicator 2, and Standard 2, Performance
Indicator 2. Both of these performances relate to intellectual property – Standard 1 lists
“patent literature”; Standard 2 lists “research data as intellectual property” (ACRL, 2006).
These two performance indicators are closely related and have become critical issues
among scientists, particularly those involved in “megascience.”
The United States Patent and Trade Office (USPTO), an agency of the U.S. Department
of Commerce, includes intellectual property in its concept of a patent. Its purpose “serves
the interest of inventors and businesses with respect to their inventions and corporate
products, and service identifications” (USPTO, 2006). The United States Patent and Trade
Office defines intellectual property as:
Creations of the mind – creative works or ideas embodied
in a form that can be shared or can enable others to
recreate, emulate, or manufacture them (USPTO, 2006).
More fundamentally, intellectual property is a claim to property; it is similar to ownership
of physical property – one has rights of entitlement and controls what happens to such
property within legal parameters. Intellectual property differs from physical property by its
term limits – intellectual property is limited to a specific number of years, depending on
the way one has protected one’s invention, production, or authorship.
According to the USPTO, there are four ways to protect intellectual property – patents,
trademarks, copyrights, and trade secrets (2006). Each form of intellectual property is
purposed to protect a specific form of creation. In science, patents, trademarks, and trade
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secrets apply most frequently. “Raw” scientific data or primary data in science are the
material of scientific discovery and invention; these data have been generated through
scientific investigation and experimentation (often funded by organizational or national
agencies), and is regarded as the wellspring of scientific achievement. Data are
scientifically raw as the producer, the scientist, has not published the findings and
interpreted them within a theoretical or conceptual framework. Customarily, scientists can
protect their data by patenting it.
Patents on data occur most frequently in individual-investigator driven research such as
chemistry or psychology. If patented, such data are granted protection like physical
property. Once acquired, a patent (or trademark) grants a property right for the data to the
inventor, discoverer, or scientist. This grant offers “the right to exclude others from
making, using, offering for sale, selling or importing the invention” (ibid). There is legal
ownership and precedent to claim all rights to the property, though it is incumbent upon
the patentee to enforce the patent, usually with the aid of legal counsel.
There are three types of patents: utility patents, design patents, and plant patents. The
utility patent is defined as “any new and useful process, machine, article of manufacture,
or composition of matter, or any new and useful improvement thereof” (ibid). It is the
utility patent that applies to scientific and technological discovery or invention, in other
words, raw data. Design patents relate to “ornamental design for an article of manufacture”
(ibid), and plant patents relate to the reproduction of “any distinct and new variety of
plant” (ibid).
Information literacy in science requires an understanding of intellectual property as it
pertains to science. The laws and conventions of patents relating to science have now
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become not only arcane, but also rather murky. Over the last two decades, law relating to
patents, particularly when government funding has supported investigation, has become
subtle and unstable. In 1996, the Human Genome Project (HGP) adopted data release
principles, preventing a scientist from acquiring a patent on his/her possible discovery in
genome sequencing. The central principle states,
"human genomic sequence information
generated by centers funded for large-scale
sequencing should be freely available and
in the public domain...."
This policy supports active research and development (R&D), but it is controversial.
The potential for merit and collaborative work is enormous. However, while an open
access depository, in other words data in the public domain, is desirable for large-scale
scientific investigation and advancement, those who have produced valuable data, but have
not finalized interpretation of the data, basically have no legal entitlement as producers of
these data. “There are no formal restrictions on its use” – no proprietary rights (Rowen,
Wong, Lane, & Hood, 2000). All investigative findings, or primary data, deposited in
GenBank, the Humane Genome Project database or depository, are available to the public
as well as the scientific community without any rights of ownership.
Similar conditions apply to several projects that would be classified as “big science” or
“megascience” (Reichman & Uhlir, 2006). Such sciences use large research facilities with
“facility class” instruments usually characterized as “observational and experimental”
(ibid). A notable example of a large-scale observational investigation would be NASA’s
Apollo program. Examples of large observational facilities would include space science
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satellites, earth observation satellites, and automated genome decoding machines. In the
experimental sciences, examples of large-scale facilities include large lasers, high-field
magnet labs, and supercolliders for high-energy particle physics.
Scientific investigation in small, independent, investigator research differs in character
from megascience endeavors. Sciences such as psychology, microbiology, anthropology,
environmental science, or biodiversity do not require a large-scale facility. They are labor
intensive, depend on replicable experiments with replicable findings, require relatively
small samples that are collected individually, and produce small data sets that are analyzed
individually and independently. In this labor-intensive situation, primary data are
traditionally proprietary and rarely deposited in open access databases. Patented or not,
independent, investigative research and data collected are tacitly proprietary.
The Organization for Economic Cooperation and Development (OECD) is actively
involved in setting policy or international rules and guidelines with regard to the exchange
of scientific data, information, and knowledge (OECD, 2004). It is specifically addressing
the establishment of access regulations for digital research data from public funding, and
protecting intellectual property rights including trade secrets with international and
national law. This involves creating new mechanisms and practices supporting
international collaboration in access to digital research data (ibid).
The National Research Council (NRC) has recognized the difficulty inherent in
protecting the rights of those who create information products and services as well as those
scientists contributing to open access depositories. The balance between maximizing
access to digital information and protecting owners is viewed as a significant legal issue,
with “broad implications” (NRC, 2000). Within this context, the federal government has
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added an “Information Sector” (ibid) to the classification of industries. While the
unprecedented production of and access to digital information enriches society, this
enrichment can also be exploited, violating the rights of those who have contributed to
scientific discovery or produced information of use. As stated in The Digital Dilemma:
Intellectual Property in the Information Age:
The Web is an information resource of extraordinary
size and depth, yet it is also an information
reproduction and dissemination facility of great
reach and capability; it is at once one of the world’s
largest libraries and surely the world’s largest
copying machine.
Intellectual property law regarding projects that receive public funding and open
access depositories is still an open issue. No explicit, clear-cut answers or legal recourse
has been established to protect contributing scientists, though legislation, international
lawmakers, and organizations are looking carefully at this dilemma and proposing
solutions. Within the scientific community, tacit law and ethics govern those who would
observe community. Nonetheless, should an imposter claim ownership, the tale would not
be one of an idiot – it would be a word to the wise. The “dark lady” of DNA, Rosalind
Franklin, looms large when one speculates on the meaning of proprietary rights and
scientific data.
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