Collaboration Data Ownership Is Naturally Determined
Collaboration Data Ownership Is Naturally Determined
Data ownership:- Refers to the possession of and duty for information. Ownership implies authority as well as control. The source of information includes the capacity to access, create, modify, package, derive advantage from, sell, or dismiss data and the right to assign these access privileges to others (Loshin, 2002).
Implicit in having authority over access to information is the ability to share data with associates that promote advancement in a field of inquiry (the notable exception to the unqualified sharing of data would be a study involving human subjects). Scofield (1998) advises replacing the term ‘ownership’ with ‘stewardship’ “because it implies a broader duty where the user must consider the consequences of making differences over ‘his’ data”.
According to Garner (1999), individuals having intellectual effects have the right to hold intangible objects that are products of human intelligence. The range of these products encompasses the areas of art, industry, and science. Research information is identified as a form of intellectual effects and is subject to security by U.S. law.
Value of data ownership:
According to Loshin (2002), data has intrinsic value as well as having counted matter as a byproduct of information processing, “at the core, the extent of right(and by corollary. The degree of duty) is driven by the importance that each interested party derives from the use of that data”.
The consensus of science emphasizes the principle of exposure (Panel Sci. Responsib. Conduct Res. 1992). Thus, transferring data has a digit of advantages to humanity in general and safeguarding the integrity of scientific data in particular. The Committee on National Statistics’ 1985 essay on sharing data (Fienberg, Martin, Straf, 1985) noted that transferring data reinforces open scientific query, encourages a diversity of analyses and decisions, and permits:
reanalyses to prove or refute said results
alternative analyses to refine outcomes
analyses to review if the results are robust to the varying belief
The cost and usefulness of data sharing should be considered in ethical, institutional, legal, and skilled dimensions. Researchers should clarify at the beginning of a task if data can or cannot be transferred, under what circumstances, by and with whom, and for what goals.
Table of Contents
Considerations/problems in data ownership
Researchers should have complete knowledge of various topics related to data rights to be able to make more suitable decisions regarding information ownership. These problems include the paradigm of right, data hoarding, data ownership policies, the proportion of obligations, and technology. Each of these points gives rise to a digit of considerations that impact conclusions concerning data ownership.
The paradigm of Ownership – Loshin (2002) alludes to the sophistication of ownership issues by specifying the range of possible paradigms used to claim data rights. These claims are founded on the type and degree of assistance involved in the study. Loshin (2002) specifies a list of parties laying a possible claim to data:
Creator – The party that makes or generates data
Consumer – The party that uses the information owns the data
Compiler – This is the entity that chooses and compiles information from various information sources
Enterprise – The enterprise wholly owns all data that documents the enterprise or is made within the enterprise
Funder – the user that authorizes the data design claims ownership
Decoder – In environments where communication is “locked” inside specific encoded formats, the party that can unclose the information becomes an owner of that information
Packager – the party that collects details for a particular use and adds value through formatting the information for a particular market or set of consumers
The reader as owner – the reader, subsumes the value of any data that can be studied; therefore, the reader achieves value by adding that information to a knowledge repository.
Subject as owner – the subject of the data claims the right to that data, mostly in reaction to another party claiming ownership of the exact data.
Purchaser/Licenser as Owner – the individual or institution that purchases or licenses data may stake a claim to ownership.
Data Hoarding
This course is antithetical to the general norms of science, emphasizing the principle of honesty. Factors influencing the judgment to withhold access to information could include (Sieber, 1989):
(a) proprietary, economic, or security crises
(b) documenting data which can be exceptionally expensive and time-consuming
(c) providing all the fabrics required to understand or expand the research
(d) technical obstacles to transmitting computer-readable data
(e) confidentiality
(f) concerns about the capabilities of data requesters
(g) unique motives to withhold data
(h) expenses to the borrowers
(i) commands to funders
Data Ownership Policies
Institutional policies lacking particularity, supervision, and formal documentation can raise the risk of compromising the detail’s integrity. Before an investigation is initiated, it is essential to delineate all interested parties’ rights, obligations, anticipations, and roles. Compromises to data virtue can occur when investigators are unaware of living data ownership policies and fail to define rights and obligations regarding data ownership—some scenarios between interested parties listed below warrant building data ownership policies.
Between academic institution and industry (public/private sector) – This refers to sharing possible advantages resulting from research conducted by academic teams but funded by corporate supporters. The failure to delineate data ownership problems early in public/personal relationships has caused controversy concerning the ownership of academic institutions and those of industry sponsors (Foote, 2003).
Between academic institutions and researcher staff –According to Steneck (2003), a research grant is awarded to study institutions and not individual students. As fund recipients, these organizations oversee several sports, including budgets, regulatory submissions, and data management. Steneck (2003) states, “To assure that they can satisfy these duties, research institutions claim request rights over data collected with funds given to the organization. This means that investigators cannot automatically assume they can take their data with them if they transfer to another institution. The research institution that accepted the funds may have dues and obligations to retain power over the data”. Fishbein (1991) suggested that institutions plainly state their policies regarding the right to data and present procedures for such a policy.
Collaboration between research colleagues applies to collaborative actions within and between organizations. Whether collaborations are between faculty peers, researchers, or staff, all parties should have a transparent understanding of who will determine how the data will be spread and shared (if applicable) even before it is collected.
Between authors and journals – To reduce the possibility of copyright infringement, some publishers need a copyright assignment to the journal at the time of a manuscript request. Authors should be aware of the implications of such copyright works and clarify the policies applied.
Balance of obligations
Investigators must learn to deal with the delicate balance between an investigator’s willingness to transfer data to promote scientific progress and the commitment to the employer/sponsor, collaborators, and students to preserve and save data (Last, 2003). Signed nondisclosure contracts between researchers and their corporate sponsors can circumvent measures to publish data or transfer it with colleagues. However, in some chances, as with human players, data sharing may not be permitted due to confidentiality clauses.
Technology
Technology advancements have enabled researchers to explore unique avenues of investigation, enhance productivity, and use details in methods unimagined before. However, the careless application of the latest technologies has the potential to create a slew of unanticipated data ownership issues that can compromise study integrity. The following examples highlight data correct issues resulting from the thoughtless application of technology:
Computer – The use of computer technology has permitted rapid access to numerous forms of computer-generated data (Veronesi, 1999). This is mainly the case in the medical career, where patient medical record information is becoming increasingly automatic. While this process facilitates details to access healthcare experts for diagnostic and research goals. Unauthorized interception and disclosure of medical data can compromise patients’ right to privacy. While the direct justification for collecting medical data is to help the patient. CIOs and Moore (2002) examine whether medical data has a special status based on its applicability to all people.
Genetics – Due to technological advancements. investigators of the Human Genome Project have options to create significant contributions by addressing earlier untreatable conditions and other human conditions. However, genetic fabric and information status remain unclear (de Witte, Welie, 1997). Wiesenthal and Wiener (1996) examine the conflict between the ownership of the individual for privacy and the demand for societal security. The critical problems that investigators must be aware of include the ownership of genetic data, confidentiality requests for such data, and legislation to control genetic testing and its applications (Wiesenthal & Wiener, 1996).
The mentioned data ownership cases highlight possible challenges to preserving data goodness. While the model promotes scientific openness. there are conditions where it may be inappropriate (especially in the subject of human participants) to transfer data. The key is for investigators to know different issues impacting the right and sharing of their research data and make conclusions that promote scientific investigation and protect the interests of the parties concerned.
References
Cios, K. J., Moore, G. W. (2002). The originality of medical mining. Artif Intell Med (Artificial intelligence in medicine), 26(1-2): 1-24.
de Witte, J. I. & Welie, J. V. (1997). The status of genetic fabric and genetic knowledge in The Netherlands. Soc Sci Med (Social Science & Medicine (1982), 45(1): 45-9.
Fienberg, S. E., Martin, M.E., Straf, M.L. (1985). Communicating Research Data. Washington, DC: National Acad. Press.
Fishbein, E. A. (1991). Ownership of study data. Academic Medicine, 66(3), 129-33.
Foote, M. (2003). Review of current authorship policies and the controversy regarding the journal of clinical data. Biotechnol Annu Rev (Biotechnology annual review), 9: pp. 303–13.
Garner, B. A. (1999). Black’s Law Dictionary, seven editions. West Group, St. Paul, MN.
Last, R. L. (2003). Sandbox ethics in science: transferring of data and textiles in plant biology. Plant Physiol (Plant physiology.), 132(1): 17-8.
Loshin, D. (2002). Knowledge Integrity: Data Ownership (Online) June 8, 2004,
Panel Sci. Responsib. Conduct Res. (1992). Responsible Science. Ensuring the Integrity of the Find Process. Vol. 1. Comm. Sci. Eng. Public Policy. Washington, DC: Natl. Acad. Press.
Scofield, M. (1998). Issues of Data Ownership (online), recovered June 10, 2004, Shamoo, A. E., Resnik, D. B. (2002). Intelligent Property. Responsible Conduct of Research. New York: Oxford University Press.Cios, K. J., Moore, G. W. (2002). The originality of medical extract. Artif Intell Med (Artificial intelligence in medicine), 26(1-2): 1–24.
de Witte, J. I. & Welie, J. V. (1997). The level of a genetic fabric and genetic knowledge in The Netherlands. Soc Sci Med (Social Science & Medicine (1982), 45(1): 45-9.
Fienberg, S. E., Martin, M.E., Straf, M.L. (1985). Communicating Research Data. Washington, DC: National Acad. Press.
Fishbein, E. A. (1991). Right of research data. Academic Medicine, 66(3), 129-33.
Foote, M. (2003). Examination of current authorship policies and the controversy about the publication of clinical data. Biotechnol Annu Rev (Biotechnology annual review), 9: pp. 303–13.
Garner, B. A. (1999). Black’s Law Dictionary, 7 th version. West Group, St. Paul, MN.
Last, R. L. (2003). Sandbox ethics in science: sharing of data and materials in plant biology. Plant Physiol (Plant physiology.), 132(1): 17-8.
Loshin, D. (2002). Knowledge Integrity: Data Holding (Online) June 8, 2004, Cios, K. J., Moore, G. W. (2002). The uniqueness of medical extract. Artif Intell Med (Artificial intelligence in medicine), 26(1-2): 1–24.
de Witte, J. I. & Welie, J. V. (1997). The status of genetic material and genetic details in The Netherlands. Soc Sci Med (Social Science & Medicine (1982), 45(1): 45-9.
Last, R. L. (2003). Sandbox ethics in science: sharing of details and fabrics in plant biology. Plant Physiol (Plant physiology.), 132(1): 17-8.
Loshin, D. (2002). Knowledge Integrity: Data Holding (Online) June 8, 2004