Perspectives on Cross-Disciplinary Research and Research Writing in Computer Science & the Biological Sciences
Kristopher J. Carroll
Looking at the specific cross-section of research between the disciplines of the biological sciences and computer science, it’s clear that, as with many other disciplines in which computer science has begun to overlap, the nuances and areas of cross-over are ever-increasing. As computer science continues to develop, the various uses and applications of computer-based modeling and programs expands to encompass many more tasks, easing the burden of analog versions of similar processes. Study in writing in the disciplines (WID) serves as an entire branch of academic interest that focuses on the evolution of research writing and writing in general across the academic disciplines, noting agreement and disagreement amongst scholars as to what aspects are necessary or focused upon in specific disciplines. A major area of distinction in WID is the concept of writing within the disciplines, or participating in the academic discourse that shapes the way knowledge is understood and discussed, versus writing outside the disciplines, which serves more as a form of academic knowledge repository [1]. When looking at areas of cross-disciplinary writing, it’s important to focus on the expected backgrounds and formations of expectations in both the research being done and the writing and presentation of that research from each discipline. Ultimately, the two disciplines (or more, if the research project is actually crossing between many different disciplines) must be evaluated in the final form of the research to determine which form is most appropriate for the proposed forum and audience to which the research will be presented. Let us look at the literature discussing writing in each discipline, the biological sciences and computer science, individually, and then attempt to glimpse the discourse currently discussing cross-disciplinary writing and research.
Biology
Research and research writing in the biological sciences commonly results as the distillation of a large amount of collected and interpreted data from experiments whether they be performed in the field (such as in the specialties of ecology and environmental sciences) or in a laboratory (where much of the research of areas such as microbiology and anatomy and physiology occur). Research writing in the biological sciences depends heavily on succinct presentation of this large quantity of data in a manner that can be appropriately consumed by the proposed audience of the research paper, whether it be to the general layperson or fellow scholars within the field. Carter points out distinct goals in the area of research in microbiology to ask pertinent questions within the field (being aware of the current academic discourse in the topic of choice), apply the scientific method in order to outline and perform an experiment to test those questions, organize, analyze and summarize the data collected during the experimental phase, and finally interpret and discuss the results and their impact or reflection on the questions originally posed for the research [1]. This outline of research in the biological sciences is largely considered a standard and uniform approach to research and research writing within the larger category of the discipline of biological science, as can be seen through the writing expectations of a number of different educational institutions such as the University of Connecticut [2], Columbia University [3], or the California State University, Bakersfield [4]. Looking at the writing guides for the departments of biology amongst a range of universities such as these, it becomes very clear that the general consensus behind the methodology and goals of writing within the biological sciences has been cemented and agreed upon.
In addition to the overall format and goals of scientific writing in the biological sciences, certain guidelines have been established as to what qualifies as appropriate and respectable methods of achieving an effective biological science paper. Burks and Todd identify that there are a number of different types or aims for writing in the biological sciences, largely due to specific goals of the writer themselves and the audience they wish to present the information to [5]. As there are a huge range of sub-disciplines within the umbrella of biology, general formatting and style guidelines are largely dependent on the journal in which the author(s) choose to attempt to publish their research. Additionally, there are many different types or reasons for writing within biology, and format and approach differs between each of these. Whether the authors be writing for a research proposal, publishing laboratory reports, or synthesizing a range of research within the field or fields they are pursuing, each of these different base concepts for research writing determines the overall approach and appearance of research writing in the biological sciences [5]. Regardless of the type of research writing being conducted, the biological sciences all agree that any academic writing in the disciplines must be based around and supported by previous and current peer-reviewed research. Nearly all research in biology, as in many other disciplines, begins with a question that is then pursued through review of the academic literature surrounding the topic of the question. Determining whether or not there is value in further research on the topic is paramount to producing effective, valuable research.
Computer Science
The discipline of computer science is a relatively new and growing field with dramatic breakthroughs in technology and understanding happening at an extremely rapid pace. As a result, much of the research being done in the discipline is in a race to maintain pace with the advancements in the machinery and technical equipment used to conduct such research. As the field as been growing quickly, much of the basis for research in the field draws stylistic constructs from the other sciences, such as the biological sciences. O’Leary, when discussing approaching research in computer science at the graduate level, expresses the importance in ensuring research conducted be “timely” with previous research having provided the context to “ripe[n]…the area [of the research topic] for future work and employment” [6]. However, unlike the biological sciences, where research has had plenty of time to expand laterally and important, respected institutions to be formed in where research is frequently sought (such as the major peer-reviewed journals), computer science research is most often consumed through conferences and conference write-ups. This is expected, due to the fast advancing nature of the field, where publication in journals in intervals might lead to untimely release of research. Whereas much of the prestige in the biological sciences goes towards being published in respected peer-reviewed journals, many scholars in the field of computer science look towards presentations and demonstrations at major conferences worldwide [7].
Interdisciplinary Writing
As computer science is still a relatively recent field of study as compared to some of the other, much older sciences such as biology and chemistry, there still remains a relatively small amount of time for interdisciplinary writing involving computer science to stabilize and therefore there is little discourse on the topics of what kinds of expectations should be had for scholars who choose to pursue research in topics that rely heavily on computer science in addition to another of the sciences. However, there is a growing web of specific interdisciplinary study programs at many major universities such as MIT where majors have been created for the specific study of computer science combined with fields such as molecular biology or economics [8]. Additionally, a study of dissertations by the National Science Foundation found that a little over 19% of all computer science primary field dissertations were interdisciplinary in nature and nearly 88% of responding graduate students reported a distantly related secondary research field involved in their dissertation [9].
As computer science has developed largely due to a mathematics-based field, those other fields that are also mathematics-centric have had the most time to develop and expand in interdisciplinary studies with computer science. The fields of engineering, physics, astronomy and some branches of chemistry have all found major support and integration with computer science as increases in computing power directly translated to increased ability to process previously unreachable (or at least very difficult to reach) computational goals common to advances in these fields [10]. However, as computer science is still relatively new and growing, other less mathematically based fields, termed the medicine-related fields by Leydesdorff, such as biology and the social sciences including psychology are beginning to find support and application of advanced computing capabilities [10]. A few of these newly developing fields will be reviewed below, with discussion about interdisciplinary research and research writing implications in each.
Bioinformatics
Bioinformatics is the umbrella term for the interdisciplinary field of applied computer science software and algorithms for understanding a range of biological data. Relevant fields to bioinformatics include the biological sciences (microbiology, genetics, physiology, cellular biology, plant biology, etc – the full range of biological sciences are represented in bioinformatics), computer science (software engineering primarily), engineering (usually computer engineering but some chemical engineering may explain interactions in biological systems and provide useful modeling algorithms or formulas), and mathematics (primarily concerned with providing useful algorithms and formulas for software development). Bioinformatics was largely seen to be coevolved with biophysics and biochemistry (both of which already had deep involvement with computer science due to a largely mathematical basis as previous noted) [11].
Research in bioinformatics has largely had the computer science portions of the research concealed or conformed to the area of biology in which the research was conducted. Most bioinformatics studies, though many contain a significant portion of methodology and basis in computer science, largely constrain the computational portions of the research to a small section in the “Materials and Methods” sections of a formal scientific lab write up. Computational models developed for theoretical biology applications are largely derived from a perceived need in biological inquiry first and then developed according to the perceived need [11]. This trend is commonly seen in other interdisciplinary research involving computer science, with the computer science and engineering portions being utilized largely as a tool or method for applying a theoretical approach to a data set.
Health or medical informatics is a growing sub-discipline of bioinformatics, utilizing the fields of computer and information science combined with behavioral or social sciences to produce useful medical modeling and informational processing systems for healthcare and other medicine-related fields. Health informatics is an example of interdisciplinary research being harnessed for efficient application in a working environment in order to streamline and manage complex systems with high data flow such as those commonly seen in the healthcare industry [12][13]. Computational health informatics is the area of computer science that focuses on developing algorithms, methods and software that can be appropriately used in a healthcare setting without focusing on specific handling of patient-based data. Examples can be seen in the development of automated handwashing systems for dementia patients (Hoey et al) [14]. This distinction between systems that primarily establish into already existing healthcare structures and those that are developed for healthcare concerns that have not been appropriately met thus far demonstrates the range of interdisciplinary study currently available in bioinformatics.
Cybernetics
Cybernetics is a growing interdisciplinary field involving a huge range of disciplines from the biological sciences and the computer sciences and engineering fields. The term is broadly used to encompass any study of human, animal or machine control of closed systems and investigates the interaction between portions of these systems. Although there is a good portion of cybernetics (ecology, for example) that does not necessarily involve computer science (including a large chunk of historical basis of the field), much of the field utilizes computational models now to track and produce models of complex systems analysis through simulation and advanced computation. Many peer-reviewed journals such as Cybernetics and Systems [18] and The Journal of Computer Science and Cybernetics [19] have formally acknowledged the use of computer-based systems and computational models in the process of cybernetics research.
Essentially, cybernetics as a whole encompasses the evaluation and analysis of complex systems and how humans and machines interact to make changes upon those systems. Those systems may be organic in nature or, in recent times, may be more digital and dynamic in nature such as what can be seen in the growing internet and digitally-connected and enabled systems seem in a number of applications such as in human social interaction via digital assistance to digitally assisted mechanisms for handling complex systems such as ecological advancements during construction, environmental modeling and human-assisted natural rehabilitation programs.
Artificial Intelligence
Artificial intelligence has been an interdisciplinary field of great interest since the dawn of computers and automated systems. Today, artificial intelligence is largely considered to be the study of developing any device or electronic system that can react to its environment in order to maximize success and the ultimate goal of artificial intelligence research is to develop systems that can mimic or perform “natural intelligence” as demonstrated by humans and animals. The fields involved in artificial intelligence research include computer science, mathematics, psychology and related behavioral sciences, philosophy, neuroscience and linguistics. The field was originally coined and focused on per the Dartmouth proposal, in which it was stated that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” [15].
Research in artificial intelligence has largely been subdivided into many different specific goals, each representing a defined aspect of natural intelligence or perceived intelligent function in machine processing. These categories include reasoning, planning, learning, natural language processing, social interaction, and creativity. There are numerous academic journals dedicated to discussing and reporting artificial intelligence research and in contrast to other interdisciplinary studies involving computer science, much of the literature is dense in explanation of development and implementation of computer methods and software in order to attain breakthroughs in the above intelligence categories. The Journal of Artificial Intelligence Research (JAIR), a refereed journal founded in 1993 to serve as a collection of academic discourse on all aspects of artificial intelligence, requires submissions to be concise, referential to the work of predecessors in the fields in which they are working, and clearly articulated in how their work advances the current state of the academic field [16]. Articiles in the JAIR follow formatting guidelines very similar to those found in the biological sciences, with an introduction providing background on the topic of discussion, a section to allow the author to explain their reasoning for pursuing the project, detailed outlines on methodology used (which are usually fairly dense in computer science terminology and concepts without showing actual lines of code), empirical results of the study and a discussion of how these results impact the academic discussion. Many of the papers published in JAIR show an extensive methodology section, as can be seen in Yang and Nenkova’s paper on detecting content-dense texts in news to determine the categories or topics those texts are discussing where approximately 5 pages of the paper is spent discussing the various computer science formats and methods they used to program the pattern recognition used to determine topic of discussion in the texts they tested[17].
Other rising journals in the field such as Artificial Intelligence Research (AIR), Artificial Intelligence (an international journal) and many others that are peer reviewed or refereed have similar requirements. Most focus heavily on the computer science components of the research published within, with much of the methodology and discussion directly focused on the formation and applications of the computer science constructed discussed and researched. Articles found in these journals tend to have multiple subsections in their methods sections outlining the computational framework behind the research and how it was proposed as a different alternative to other ongoing propositions behind the artificial intelligence-based task being researched.
Discussion
Looking at the range of formats and expectations for research in the biological sciences to strictly computer science, it’s easy to see why there may be so much variation in the different expectations of interdisciplinary research between these two major fields of study. Depending on the interdisciplinary nature of the research topic, the format and expectations of computer science and engineering components of the research varies dramatically. Much of the expectation is largely formally assigned through the various forums of publication chosen for submitting research topics, as each journal or conference or collaborative text has their own expectations in both explicit form of general layout of research writing as well as implicit form through the culture of the forum itself and how other researchers publishing in the forum have coalesced to create a general “type” of form for the research found within. As the computer science field is continuing to grow and expand into increasingly diverse interdisciplinary fields, the various forms and expectations of researchers will continue to diversify as well, requiring a computer scientist to be mindful and flexible of the expectations of the audience and community they choose to write for. Those fields which already have interdisciplinary interaction with computer science have already begun establishing guidelines and general form for performing and presenting interdisciplinary research and looking towards the already established discourse communities found in these areas is a value resource for researchers new to researching in these areas.
As computer science continues to expand into other disciplinary fields either in a central capacity, such as in computational modeling of complex systems or behaviors, or a simply supportive role as in general data handling and upkeep, research communities must come together to form a general understanding of how to acknowledge and present the new component to their field. This appears to have been done in the past by generally determining how vital understanding of the computational methods is to comprehension of research findings and to inspiring further inquiry in the topics of research performed. As seen in most of the biological sciences, computational methods and functions are largely accessory to the general biological inquiry, and thus large portions of computer-based data handling and manipulation are generally summed into a short paragraph or explanation of a program used to generate and handle data. However, in fields where computer science plays a much larger basis in the research being performed, as seen in areas like artificial intelligence, the computational methods and components are the larger focus and outlining the composition and use of these portions of the research serves as the primary focus of presenting the research performed. Understanding of the relevance of comprehension of these computer science and engineering portions is vital to establishing guidelines in new interdisciplinary ventures.
References
[1] M. Carter, “Ways of Knowing, Doing, and Writing in the Disciplines,” College Composition and Communication, vol. 58, no. 3, pp. 385-418, Feb. 2007.
[2] University of Connecticut, “Writing in Biology,” The University of Connecticut, 2017. [Online] Available:
https://writingcenter.uconn.edu/writing-in-biology/ [Accessed: 1-Nov-2017]
[3] Columbia University, “Writing a Scientific Research Article,” Columbia University, N.D. [Online] Available: http://www.columbia.edu/cu/biology/ug/research/paper.html [Accessed: 3-Nov-2017]
[4] California State University, Bakersfield Department of Biology, “Biology Research Paper Format,” California State University, Bakersfield, 2014. [Online] Available: https://www.csub.edu/biology/_files/How%20to%20Write_14.pdf [Accessed: 3-Nov-2017]
[5] R. Burks & M. Todd, “Guide for Writing in Biology,” Southwestern University, N.D. [Online] Available: https://www.southwestern.edu/live/files/4637-biology-department-writing-guide [Accessed: 5-Nov-2017]
[6] D. O’Leary, “Graduate Study in the Computer and Mathematical Sciences: A Survival Manual,” University of Maryland, 2016. [Online] Available: http://www.cs.umd.edu/~oleary/gradstudy/gradstudy.html [Accessed 6-Nov-2017]
[7] R. Andonie & I. Dzitac, “How to Write a Good Paper in Computer Science and How Will It Be Measured by ISI Web of Knowledge,” International Journal of Computers, Communications & Control (IJCCC), vol. 5, pp. 432-446, 2010
[8] Massachusetts Institute of Technology, “Interdisciplinary Programs,” Massachusetts Institute of Technology, 2017. [Online] Available: http://catalog.mit.edu/interdisciplinary/ [Accessed: 9-Nov-2017]
[9] M. Millar & D. Dillman, “Trends in Interdisciplinary Dissertation Research: An Analysis of the Survey of Earned Doctorates,” National Science Foundation, National Center for Science and Engineering Statistics, 2012. [Online] Available: http://www.nsf.gov/statistics/ncses12200/ [Accessed: 9-Nov-2017]
[10] L. Leydesdorff, “A global map of science based on the ISI subject categories,” Journal for the American Society for Information Science and Technology, vol. 60, no. 2, pp. 348-362, 2009
[11] P. Hogeweg, “The Roots of Bioinformatics in Theoretical Biology,” PLoS Computation Biology, vol. 7, no. 3, March, 2011
[12] T. Mettler & D. Raptis, “What constitutes the field of health information systems? Fostering a systematic framework and research agenda,” Health Informatics Journal, vol. 18, no. 2, pp. 147-156, 2012
[13] National Information Center on Health Services Research and Health Care Technology (NICHSR), “HSRIC: Health Informatics,” National Institutes of Health, 2017. [Online] Available: https://www.nlm.nih.gov/hsrinfo/informatics.html [Accessed: 21-Nov-2017]
[14] J. Hoey et al, “Automated handwashing assistance for persons with dementia using video and partially observable Markov decision process,” Computer Vision and Image Understanding, vol. 114, no. 5, pp. 503-519, May 2010.
[15] J. McCarthy, M. Minsky, N. Rochester, C. Shannon, “A proposal for the Dartmouth summer research project on artificial intelligence,” Dartmouth College, 1955.
[16] Journal for Artificial Intelligence Research, “Submission Info,” The Journal for Artificial Intelligence Research(JAIR), 2009. [Online] Available: http://www.jair.org/submission_info.html [Accessed: 23-Nov-2017]
[17] Y. Yang & A. Nenkova, “Combining Lexical and Syntactic Features for Detecting Content-Dense Texts in News,” The Journal of Artificial Intelligence Research, vol. 60, pp. 179-219, Sept 2017.
[18] Cybernetics and Systems: An International Journal, “Aims and scope,” Taylor and Francis, Inc., 2017. [Online] Available: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=ucbs20 [Accessed 24-Nov-2017]
[19] Journal of Computer Science and Cybernetics, “Editorial Policies: Focus and Scope,” Vietnam Academy of Science and Technology, n.d. [Online] Available: http://vjs.ac.vn/index.php/jcc/about/editorialPolicies#focusAndScope [Accessed: 24-Nov-2017]
Biology
Research and research writing in the biological sciences commonly results as the distillation of a large amount of collected and interpreted data from experiments whether they be performed in the field (such as in the specialties of ecology and environmental sciences) or in a laboratory (where much of the research of areas such as microbiology and anatomy and physiology occur). Research writing in the biological sciences depends heavily on succinct presentation of this large quantity of data in a manner that can be appropriately consumed by the proposed audience of the research paper, whether it be to the general layperson or fellow scholars within the field. Carter points out distinct goals in the area of research in microbiology to ask pertinent questions within the field (being aware of the current academic discourse in the topic of choice), apply the scientific method in order to outline and perform an experiment to test those questions, organize, analyze and summarize the data collected during the experimental phase, and finally interpret and discuss the results and their impact or reflection on the questions originally posed for the research [1]. This outline of research in the biological sciences is largely considered a standard and uniform approach to research and research writing within the larger category of the discipline of biological science, as can be seen through the writing expectations of a number of different educational institutions such as the University of Connecticut [2], Columbia University [3], or the California State University, Bakersfield [4]. Looking at the writing guides for the departments of biology amongst a range of universities such as these, it becomes very clear that the general consensus behind the methodology and goals of writing within the biological sciences has been cemented and agreed upon.
In addition to the overall format and goals of scientific writing in the biological sciences, certain guidelines have been established as to what qualifies as appropriate and respectable methods of achieving an effective biological science paper. Burks and Todd identify that there are a number of different types or aims for writing in the biological sciences, largely due to specific goals of the writer themselves and the audience they wish to present the information to [5]. As there are a huge range of sub-disciplines within the umbrella of biology, general formatting and style guidelines are largely dependent on the journal in which the author(s) choose to attempt to publish their research. Additionally, there are many different types or reasons for writing within biology, and format and approach differs between each of these. Whether the authors be writing for a research proposal, publishing laboratory reports, or synthesizing a range of research within the field or fields they are pursuing, each of these different base concepts for research writing determines the overall approach and appearance of research writing in the biological sciences [5]. Regardless of the type of research writing being conducted, the biological sciences all agree that any academic writing in the disciplines must be based around and supported by previous and current peer-reviewed research. Nearly all research in biology, as in many other disciplines, begins with a question that is then pursued through review of the academic literature surrounding the topic of the question. Determining whether or not there is value in further research on the topic is paramount to producing effective, valuable research.
Computer Science
The discipline of computer science is a relatively new and growing field with dramatic breakthroughs in technology and understanding happening at an extremely rapid pace. As a result, much of the research being done in the discipline is in a race to maintain pace with the advancements in the machinery and technical equipment used to conduct such research. As the field as been growing quickly, much of the basis for research in the field draws stylistic constructs from the other sciences, such as the biological sciences. O’Leary, when discussing approaching research in computer science at the graduate level, expresses the importance in ensuring research conducted be “timely” with previous research having provided the context to “ripe[n]…the area [of the research topic] for future work and employment” [6]. However, unlike the biological sciences, where research has had plenty of time to expand laterally and important, respected institutions to be formed in where research is frequently sought (such as the major peer-reviewed journals), computer science research is most often consumed through conferences and conference write-ups. This is expected, due to the fast advancing nature of the field, where publication in journals in intervals might lead to untimely release of research. Whereas much of the prestige in the biological sciences goes towards being published in respected peer-reviewed journals, many scholars in the field of computer science look towards presentations and demonstrations at major conferences worldwide [7].
Interdisciplinary Writing
As computer science is still a relatively recent field of study as compared to some of the other, much older sciences such as biology and chemistry, there still remains a relatively small amount of time for interdisciplinary writing involving computer science to stabilize and therefore there is little discourse on the topics of what kinds of expectations should be had for scholars who choose to pursue research in topics that rely heavily on computer science in addition to another of the sciences. However, there is a growing web of specific interdisciplinary study programs at many major universities such as MIT where majors have been created for the specific study of computer science combined with fields such as molecular biology or economics [8]. Additionally, a study of dissertations by the National Science Foundation found that a little over 19% of all computer science primary field dissertations were interdisciplinary in nature and nearly 88% of responding graduate students reported a distantly related secondary research field involved in their dissertation [9].
As computer science has developed largely due to a mathematics-based field, those other fields that are also mathematics-centric have had the most time to develop and expand in interdisciplinary studies with computer science. The fields of engineering, physics, astronomy and some branches of chemistry have all found major support and integration with computer science as increases in computing power directly translated to increased ability to process previously unreachable (or at least very difficult to reach) computational goals common to advances in these fields [10]. However, as computer science is still relatively new and growing, other less mathematically based fields, termed the medicine-related fields by Leydesdorff, such as biology and the social sciences including psychology are beginning to find support and application of advanced computing capabilities [10]. A few of these newly developing fields will be reviewed below, with discussion about interdisciplinary research and research writing implications in each.
Bioinformatics
Bioinformatics is the umbrella term for the interdisciplinary field of applied computer science software and algorithms for understanding a range of biological data. Relevant fields to bioinformatics include the biological sciences (microbiology, genetics, physiology, cellular biology, plant biology, etc – the full range of biological sciences are represented in bioinformatics), computer science (software engineering primarily), engineering (usually computer engineering but some chemical engineering may explain interactions in biological systems and provide useful modeling algorithms or formulas), and mathematics (primarily concerned with providing useful algorithms and formulas for software development). Bioinformatics was largely seen to be coevolved with biophysics and biochemistry (both of which already had deep involvement with computer science due to a largely mathematical basis as previous noted) [11].
Research in bioinformatics has largely had the computer science portions of the research concealed or conformed to the area of biology in which the research was conducted. Most bioinformatics studies, though many contain a significant portion of methodology and basis in computer science, largely constrain the computational portions of the research to a small section in the “Materials and Methods” sections of a formal scientific lab write up. Computational models developed for theoretical biology applications are largely derived from a perceived need in biological inquiry first and then developed according to the perceived need [11]. This trend is commonly seen in other interdisciplinary research involving computer science, with the computer science and engineering portions being utilized largely as a tool or method for applying a theoretical approach to a data set.
Health or medical informatics is a growing sub-discipline of bioinformatics, utilizing the fields of computer and information science combined with behavioral or social sciences to produce useful medical modeling and informational processing systems for healthcare and other medicine-related fields. Health informatics is an example of interdisciplinary research being harnessed for efficient application in a working environment in order to streamline and manage complex systems with high data flow such as those commonly seen in the healthcare industry [12][13]. Computational health informatics is the area of computer science that focuses on developing algorithms, methods and software that can be appropriately used in a healthcare setting without focusing on specific handling of patient-based data. Examples can be seen in the development of automated handwashing systems for dementia patients (Hoey et al) [14]. This distinction between systems that primarily establish into already existing healthcare structures and those that are developed for healthcare concerns that have not been appropriately met thus far demonstrates the range of interdisciplinary study currently available in bioinformatics.
Cybernetics
Cybernetics is a growing interdisciplinary field involving a huge range of disciplines from the biological sciences and the computer sciences and engineering fields. The term is broadly used to encompass any study of human, animal or machine control of closed systems and investigates the interaction between portions of these systems. Although there is a good portion of cybernetics (ecology, for example) that does not necessarily involve computer science (including a large chunk of historical basis of the field), much of the field utilizes computational models now to track and produce models of complex systems analysis through simulation and advanced computation. Many peer-reviewed journals such as Cybernetics and Systems [18] and The Journal of Computer Science and Cybernetics [19] have formally acknowledged the use of computer-based systems and computational models in the process of cybernetics research.
Essentially, cybernetics as a whole encompasses the evaluation and analysis of complex systems and how humans and machines interact to make changes upon those systems. Those systems may be organic in nature or, in recent times, may be more digital and dynamic in nature such as what can be seen in the growing internet and digitally-connected and enabled systems seem in a number of applications such as in human social interaction via digital assistance to digitally assisted mechanisms for handling complex systems such as ecological advancements during construction, environmental modeling and human-assisted natural rehabilitation programs.
Artificial Intelligence
Artificial intelligence has been an interdisciplinary field of great interest since the dawn of computers and automated systems. Today, artificial intelligence is largely considered to be the study of developing any device or electronic system that can react to its environment in order to maximize success and the ultimate goal of artificial intelligence research is to develop systems that can mimic or perform “natural intelligence” as demonstrated by humans and animals. The fields involved in artificial intelligence research include computer science, mathematics, psychology and related behavioral sciences, philosophy, neuroscience and linguistics. The field was originally coined and focused on per the Dartmouth proposal, in which it was stated that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it” [15].
Research in artificial intelligence has largely been subdivided into many different specific goals, each representing a defined aspect of natural intelligence or perceived intelligent function in machine processing. These categories include reasoning, planning, learning, natural language processing, social interaction, and creativity. There are numerous academic journals dedicated to discussing and reporting artificial intelligence research and in contrast to other interdisciplinary studies involving computer science, much of the literature is dense in explanation of development and implementation of computer methods and software in order to attain breakthroughs in the above intelligence categories. The Journal of Artificial Intelligence Research (JAIR), a refereed journal founded in 1993 to serve as a collection of academic discourse on all aspects of artificial intelligence, requires submissions to be concise, referential to the work of predecessors in the fields in which they are working, and clearly articulated in how their work advances the current state of the academic field [16]. Articiles in the JAIR follow formatting guidelines very similar to those found in the biological sciences, with an introduction providing background on the topic of discussion, a section to allow the author to explain their reasoning for pursuing the project, detailed outlines on methodology used (which are usually fairly dense in computer science terminology and concepts without showing actual lines of code), empirical results of the study and a discussion of how these results impact the academic discussion. Many of the papers published in JAIR show an extensive methodology section, as can be seen in Yang and Nenkova’s paper on detecting content-dense texts in news to determine the categories or topics those texts are discussing where approximately 5 pages of the paper is spent discussing the various computer science formats and methods they used to program the pattern recognition used to determine topic of discussion in the texts they tested[17].
Other rising journals in the field such as Artificial Intelligence Research (AIR), Artificial Intelligence (an international journal) and many others that are peer reviewed or refereed have similar requirements. Most focus heavily on the computer science components of the research published within, with much of the methodology and discussion directly focused on the formation and applications of the computer science constructed discussed and researched. Articles found in these journals tend to have multiple subsections in their methods sections outlining the computational framework behind the research and how it was proposed as a different alternative to other ongoing propositions behind the artificial intelligence-based task being researched.
Discussion
Looking at the range of formats and expectations for research in the biological sciences to strictly computer science, it’s easy to see why there may be so much variation in the different expectations of interdisciplinary research between these two major fields of study. Depending on the interdisciplinary nature of the research topic, the format and expectations of computer science and engineering components of the research varies dramatically. Much of the expectation is largely formally assigned through the various forums of publication chosen for submitting research topics, as each journal or conference or collaborative text has their own expectations in both explicit form of general layout of research writing as well as implicit form through the culture of the forum itself and how other researchers publishing in the forum have coalesced to create a general “type” of form for the research found within. As the computer science field is continuing to grow and expand into increasingly diverse interdisciplinary fields, the various forms and expectations of researchers will continue to diversify as well, requiring a computer scientist to be mindful and flexible of the expectations of the audience and community they choose to write for. Those fields which already have interdisciplinary interaction with computer science have already begun establishing guidelines and general form for performing and presenting interdisciplinary research and looking towards the already established discourse communities found in these areas is a value resource for researchers new to researching in these areas.
As computer science continues to expand into other disciplinary fields either in a central capacity, such as in computational modeling of complex systems or behaviors, or a simply supportive role as in general data handling and upkeep, research communities must come together to form a general understanding of how to acknowledge and present the new component to their field. This appears to have been done in the past by generally determining how vital understanding of the computational methods is to comprehension of research findings and to inspiring further inquiry in the topics of research performed. As seen in most of the biological sciences, computational methods and functions are largely accessory to the general biological inquiry, and thus large portions of computer-based data handling and manipulation are generally summed into a short paragraph or explanation of a program used to generate and handle data. However, in fields where computer science plays a much larger basis in the research being performed, as seen in areas like artificial intelligence, the computational methods and components are the larger focus and outlining the composition and use of these portions of the research serves as the primary focus of presenting the research performed. Understanding of the relevance of comprehension of these computer science and engineering portions is vital to establishing guidelines in new interdisciplinary ventures.
References
[1] M. Carter, “Ways of Knowing, Doing, and Writing in the Disciplines,” College Composition and Communication, vol. 58, no. 3, pp. 385-418, Feb. 2007.
[2] University of Connecticut, “Writing in Biology,” The University of Connecticut, 2017. [Online] Available:
https://writingcenter.uconn.edu/writing-in-biology/ [Accessed: 1-Nov-2017]
[3] Columbia University, “Writing a Scientific Research Article,” Columbia University, N.D. [Online] Available: http://www.columbia.edu/cu/biology/ug/research/paper.html [Accessed: 3-Nov-2017]
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Kristopher J. Carroll is pursuing a Baccalaureate of Computer Science.
This piece was selected by Professor Jacqueline Cason.
This piece was selected by Professor Jacqueline Cason.