Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and (...) an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond. Depending on graph type, raw data can be displayed along with standard measures of central tendency and dispersion. The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization. (shrink)

The large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of data production and (...) circulation is then introduced to problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis. (shrink)

This guide accompanies the following article(s): ‘Full Disclosure of the “Raw Data” of Research on Humans: Citizens’ Rights, Product Manufacturer’s Obligations and the Quality of the Scientific Database.’Philosophy Compass 6/2 (2011): 90–99. doi: 10.1111/j.1747‐9991.2010.00376.x Author’s Introduction Securing consent (and informed consent) from patients and research study participants is a key concern in patient care and research on humans. Yet, the legal doctrines of consent and informed consent differ in their applications. In patient care, the judicial doctrines of consent and (...) informed consent are disclosure doctrines based on the obligation of physicians to inform their patients of the following types of information: the nature of the procedure the physician is recommending in the patent’s care (P), the alternatives to the physician‐recommendation (A), and the risks of procedure and its alternatives (R). In addition, the physician must provide truthful answers to the patient’s questions (Q) to the best of the physician’s abilities. In research on humans, the onus of disclosure by study sponsors and principal investigators is much greater than in patient care precisely because the purpose of research is not patient care. The purpose of research is the identification and development of new generalizable knowledge. Thus, participation in a research study or trial may not benefit the study volunteer at all. In addition, the participant may carry a heavy risk burden in relationship to any testing of a newly designed drug or device as part of their experiences in the research study. Presently, despite the risks borne by the research study participant, the raw data collected from volunteers in research trials are treated by courts as if they were the private property of the study sponsor rather than generally owed to the public as would be expected by an effort like research participation aimed at contributing to the development of generalizable knowledge. This paper reviews the obligations that study sponsors owe to their study participants based on the very definition of research as a systematic activity whose purpose is to develop generalizable knowledge to help all humans. Author Recommends Court cases Canterbury v. Spence, United States Court of Appeals, District of Columbia Circuit 464 F.2d 772 (1972). (Federal appellate decision heard in the District of Columbia, USA) Reibl v. Hughes, 2 S.C.R. 880 (1980). (Supreme Court of Canada) Rogers v. Whitaker, 175 C.L. R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Canterbury v. Spence is the landmark Federal informed consent case in the United States heard in the District of Columbia. In this case, Judge Spottswood Robinson articulated the reasonable person standard of informed consent. This standard was subsequently adopted by the Supreme Court of Canada (Reibl v. Hughes, 2 S.C.R. 880 (1980)) and the High Court of Australia (Rogers v. Whitaker, 175 C.L. R. 479 (1992)). The House of Lords rejected the reasonable person standard in England in Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985). Book Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. This book is a detailed guide for the review of scientific and ethical aspects of research on humans contained in the research study protocols and research informed consent forms which need to be submitted by study sponsors and principal investigators to institutional review boards (IRBs) for IRB review before a research study can receive approval to be conducted within a human study population in those institutions over which the IRB has authority. It also presents basic definitions in the area of research on humans and basic approaches to help conduct a deep review of the underlying scientific and ethical issues that are present within a research study protocol and its accompanying research informed consent form. Report National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). This U.S. National Commission in the Belmont Report argued for the necessity of a reasonable volunteer standard in research on humans in the United States. Arguing that the professional standard and the reasonable person standard of consent and informed consent were insufficient to base a disclosure standard in research on humans, this National Commission developed the reasonable volunteer standard where a study participant is to be approached in research informed consent with that information that a reasonable volunteer would want to know. Online Materials Belmont Report: Syllabus Topics for Lecture and Discussion Week I: Introduction and Overview Readings: Rogers v. Whitaker, 175 C.L.R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week II: The Different Standards of Consent and Informed Consent Readings: Canterbury v. Spence, 464 F.2d 772 (1972). (Federal appellate opinion heard in the District of Columbia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week III: Definition of Research Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 2 and 3) Week IV: How to Review a Research Study Protocol and Research Informed Consent Form Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 4–6, 10, and 12–14) Week V: Obligations in Research of Humans Versus Patient Care Reading: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). Focus Questions • How do the judicial doctrines of consent in England and Australia compare to the judicial doctrines of informed consent in the United States and Canada? • How does a professional standard of disclosure differ from a reasonable person standard of disclosure? • What is the definition of research? • How is a research study (study proposal and research informed consent form) reviewed from scientific and ethical bases for considerations of approval by an IRB? • How do the obligations of a study sponsor, a principal investigator, and a research team conducting a research trial on human study participants differ from the obligations of a physician caring for a patient? • Should all raw data of research derived from humans be made available to the public for use in developing generalizable knowledge to help others, or should raw data be considered the private property of the study sponsors (for example, prescription drug manufacturers), who fund the research trials? Seminar/project Idea Draw a timeline of the development of the basic concepts of consent and informed consent in patient care and in patient research. On this timeline, first place the dates of the key court cases in England, the United States, Canada, and Australia on consent and informed consent. Then place the date of the development of the Belmont Report. Why do differences exist between these four countries in terms of the legal requirements of informedness of a patient (in medical care) and a study participant (in medical research)? What roles if any do the concepts of self‐determination and self‐decision have in consent and informed consent in patient care and in informed consent in research on humans among the four countries of interest? What are the realities of research on human participants that require a new standard of informed consent, the reasonable volunteer standard, to be developed beyond the two standards in patient care, the professional standard, and the reasonable person standard? Counterpoint Arguments The following are counterpoint arguments that need to be appreciated to more fully understand the concepts that impact the thesis that ‘raw data’ should be open to the public to allow the opportunity of checking of the results of its associated scientific paper and for further analysis. De‐Identified (Anonymized) Raw Data A current approach to identifying (anonymizinig) raw data is based on the Health Insurance Portability and Accountability Act (HIPAA) of 1996 (P.L.104‐191). HIPAA was enacted by the U.S. Congress in 1996 to keep a person’s medical information private. In a research study, a person’s private medical information can become research data. HIPAA’s Privacy Rule allows a covered entity to de‐identify data by removing all 18 elements that could be used to identify the individual or the individual’s relatives, employers, or household members; these elements are enumerated in the Privacy Rule. The covered entity must also have no actual knowledge that the remaining information could be used alone or in combination with other information to identify the individual who is the subject of the information. Under this method, the identifiers that must be removed are the following: 1 Names. 2 All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP Code, and their equivalent geographical codes, except for the initial three digits of a ZIP Code if, according to the current publicly available data from the Bureau of the Census: 2a. The geographic unit formed by combining all ZIP Codes with the same three initial digits contains more than 20,000 people. 2b. The initial three digits of a ZIP Code for all such geographic units containing 20,000 or fewer people are changed to 000. 3 All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, and date of death, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older. 4 Telephone numbers. 5 Facsimile numbers. 6 Electronic mail addresses. 7 Social security numbers. 8 Medical record numbers. 9 Health plan beneficiary numbers. 10 Account numbers. 11 Certificate/license numbers. 12 Vehicle identifiers and serial numbers, including license plate numbers. 13 Device identifiers and serial numbers. 14 Web universal resource locators (URLs). 15 Internet protocol (IP) address numbers. 16 Biometric identifiers, including fingerprints and voiceprints. 17 Full‐face photographic images and any comparable images. 18 Any other unique identifying number, characteristic, or code, unless otherwise permitted by the Privacy Rule for re‐identification. Even though ‘raw data’ can be de‐identified (anonymized) so that there is no way to trace back the data to the study participant from which it was drawn, there are still questions whether the ‘de‐identified raw data’ can be given to other researchers or made public without the original study participant’s approval. It may be the case that this approval could be done at the start of any research study where the individual considering study participation would be counseled on what the process of de‐identification consists of and would be asked whether the participant would allow use of the data by other researchers or made available to the public in a de‐identified state. However, there may be questions of the study participants as to whether the data are truly de‐identified and untraceable back to the study participant since the participant would not be able to see the process actually being carried out in detail in all relevant circumstances. Using Raw Data in Context Simply being in possession of raw data is never enough to build on that data from a research perspective. Researchers need to understand the context of the scientific research study in which the data were collected. This research context includes (but is not limited to): (i) the scientific hypothesis of the research study, (ii) the inclusion and exclusion criteria selected by the research designers of the study to determine which study participants were included and which were excluded from the study, (iii) the methods used to collect the data and possibilities of errors in the data collection processes, and (iv) answers to questions regarding whether any data were excluded from the study data and why these data were excluded. Ownership Rights Over Data There are ownership rights over data. Ownership rights include court decision making related to the question which parties are to be considered as the owners of the data. Possible owners here include (but are not limited to) (i) the study sponsor whose only role was to fund that study, (ii) the principal investigator(s) who developed the scientific hypothesis, and (iii) individual study participants through special contracts made by individual participants who negotiate such contracts with study sponsors and principal investigators to share in the ownership of data. Examples of such data are participant sources of unique gene lines or unique cell lines to be used in future research. Objections to Data Sharing Finally, individual researchers may simply object to sharing data. A researcher’s arguments here may be simply stating that he or she has a right to fully analyze the data since the collection of the data was the researchers’ idea in the first place. These researchers may argue that they have the right to mine the data as fully as they can before deciding to make the data open to the public for others to analyze. Works Cited 1 Kaiser, J. ‘Making Clinical Data Widely Available.’Science 322.5899 (10 Oct. 2008): 217–8. 2 de Lusignan, S., J.F. Metsemakers, P. Houwink, V. Gunnarsdottir, and J. van der Lei. ‘Routinely Collected General Practice Data: Goldmines for Research? A Report of the European Federation for Medical Informatics Primary Care Informatics Working Group (EFMI PCIWG) from MIE2006, Maastricht, The Netherlands.’Informatics in Primary Care 14.3 (2006): 203–9. 3 Sim, I. ‘Human Studies Database Project’, CTSA Data Repositories Symposium (17 Oct. 2008). 23 Nov. 2010. 4 Sim, I., C.G. Chute, H. Lehmann, R. Nagarajan, M. Nahm, and R.H. Scheuermann. ‘Keeping Raw Data in Context.’Science 323.5915 (6 Feb. 2009): 713. 5 U.S. Department of Health and Human Services, National Institutes of Health, HIPAA Privacy Rule, Information for Researchers. 13 Nov. 2010. 6 Wilczynski, N.L., R.B. Haynes, and Hedges Team. ‘EMBASE Search Strategies for Identifying Methodologically Sound Diagnostic Studies for Use by Clinicians and Researchers.’BMC Medicine 29.3 (March 2005): 7. (shrink)

As Next Generation Sequencing technologies are increasingly implemented in biomedical research and care, the number of study participants and patients who ask for release of their genomic raw data is set to increase. This raises the question whether research participants and patients have a legal and moral right to receive their genomic raw data and, if so, how this right should be implemented into practice. In a first step we clarify some central concepts such as “raw data”; (...) in a second step we sketch the international legal framework. The third step provides an extensive ethical analysis which comprehends two parts: an evaluation of whether there is a prima facie moral right to receive one’s raw data, and a contextualization and discussion of the right in light of potentially conflicting interests and rights of the data subject herself and third parties; in a last fourth step we emphasize the main practical consequences of the ethical analyses and propose recommendations for the release of raw data. In several legislations like the new European General Data Protection Regulation, patients do in principle have the right to receive their raw data. However, the procedural implementation of this right and whether it involves genetic counselling is at the discretion of the Member States. Even more questions remain with respect to the research context. The ethical analysis suggests that patients and research subjects have a moral right to receive their genomic raw data and addresses aspects which are also of relevance for the legal discussion such as the costs of release of raw data and its impact on academic freedom. Taking into account the specific nature and implications of genomic raw data and the contexts of research and health care, several concerns and potentially conflicting interests of the data subjects themselves and involved researchers, physicians, biomedical institutions and relatives arise. Instead of using them to argue in favor of restrictions of the data subjects’ legal and moral right to genomic raw data, the concerns should be addressed through provision of information and other measures. To this end, we propose relevant recommendations. (shrink)

This guide accompanies the following article(s): ‘Full Disclosure of the “Raw Data” of Research on Humans: Citizens’ Rights, Product Manufacturer’s Obligations and the Quality of the Scientific Database.’Philosophy Compass 6/2 (2011): 90–99. doi: 10.1111/j.1747‐9991.2010.00376.x Author’s Introduction Securing consent (and informed consent) from patients and research study participants is a key concern in patient care and research on humans. Yet, the legal doctrines of consent and informed consent differ in their applications. In patient care, the judicial doctrines of consent and (...) informed consent are disclosure doctrines based on the obligation of physicians to inform their patients of the following types of information: the nature of the procedure the physician is recommending in the patent’s care (P), the alternatives to the physician‐recommendation (A), and the risks of procedure and its alternatives (R). In addition, the physician must provide truthful answers to the patient’s questions (Q) to the best of the physician’s abilities. In research on humans, the onus of disclosure by study sponsors and principal investigators is much greater than in patient care precisely because the purpose of research is not patient care. The purpose of research is the identification and development of new generalizable knowledge. Thus, participation in a research study or trial may not benefit the study volunteer at all. In addition, the participant may carry a heavy risk burden in relationship to any testing of a newly designed drug or device as part of their experiences in the research study. Presently, despite the risks borne by the research study participant, the raw data collected from volunteers in research trials are treated by courts as if they were the private property of the study sponsor rather than generally owed to the public as would be expected by an effort like research participation aimed at contributing to the development of generalizable knowledge. This paper reviews the obligations that study sponsors owe to their study participants based on the very definition of research as a systematic activity whose purpose is to develop generalizable knowledge to help all humans. Author Recommends Court cases Canterbury v. Spence, United States Court of Appeals, District of Columbia Circuit 464 F.2d 772 (1972). (Federal appellate decision heard in the District of Columbia, USA) Reibl v. Hughes, 2 S.C.R. 880 (1980). (Supreme Court of Canada) Rogers v. Whitaker, 175 C.L. R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Canterbury v. Spence is the landmark Federal informed consent case in the United States heard in the District of Columbia. In this case, Judge Spottswood Robinson articulated the reasonable person standard of informed consent. This standard was subsequently adopted by the Supreme Court of Canada (Reibl v. Hughes, 2 S.C.R. 880 (1980)) and the High Court of Australia (Rogers v. Whitaker, 175 C.L. R. 479 (1992)). The House of Lords rejected the reasonable person standard in England in Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985). Book Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. This book is a detailed guide for the review of scientific and ethical aspects of research on humans contained in the research study protocols and research informed consent forms which need to be submitted by study sponsors and principal investigators to institutional review boards (IRBs) for IRB review before a research study can receive approval to be conducted within a human study population in those institutions over which the IRB has authority. It also presents basic definitions in the area of research on humans and basic approaches to help conduct a deep review of the underlying scientific and ethical issues that are present within a research study protocol and its accompanying research informed consent form. Report National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). This U.S. National Commission in the Belmont Report argued for the necessity of a reasonable volunteer standard in research on humans in the United States. Arguing that the professional standard and the reasonable person standard of consent and informed consent were insufficient to base a disclosure standard in research on humans, this National Commission developed the reasonable volunteer standard where a study participant is to be approached in research informed consent with that information that a reasonable volunteer would want to know. Online Materials Belmont Report: Syllabus Topics for Lecture and Discussion Week I: Introduction and Overview Readings: Rogers v. Whitaker, 175 C.L.R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week II: The Different Standards of Consent and Informed Consent Readings: Canterbury v. Spence, 464 F.2d 772 (1972). (Federal appellate opinion heard in the District of Columbia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week III: Definition of Research Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 2 and 3) Week IV: How to Review a Research Study Protocol and Research Informed Consent Form Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 4–6, 10, and 12–14) Week V: Obligations in Research of Humans Versus Patient Care Reading: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). Focus Questions • How do the judicial doctrines of consent in England and Australia compare to the judicial doctrines of informed consent in the United States and Canada? • How does a professional standard of disclosure differ from a reasonable person standard of disclosure? • What is the definition of research? • How is a research study (study proposal and research informed consent form) reviewed from scientific and ethical bases for considerations of approval by an IRB? • How do the obligations of a study sponsor, a principal investigator, and a research team conducting a research trial on human study participants differ from the obligations of a physician caring for a patient? • Should all raw data of research derived from humans be made available to the public for use in developing generalizable knowledge to help others, or should raw data be considered the private property of the study sponsors (for example, prescription drug manufacturers), who fund the research trials? Seminar/project Idea Draw a timeline of the development of the basic concepts of consent and informed consent in patient care and in patient research. On this timeline, first place the dates of the key court cases in England, the United States, Canada, and Australia on consent and informed consent. Then place the date of the development of the Belmont Report. Why do differences exist between these four countries in terms of the legal requirements of informedness of a patient (in medical care) and a study participant (in medical research)? What roles if any do the concepts of self‐determination and self‐decision have in consent and informed consent in patient care and in informed consent in research on humans among the four countries of interest? What are the realities of research on human participants that require a new standard of informed consent, the reasonable volunteer standard, to be developed beyond the two standards in patient care, the professional standard, and the reasonable person standard? Counterpoint Arguments The following are counterpoint arguments that need to be appreciated to more fully understand the concepts that impact the thesis that ‘raw data’ should be open to the public to allow the opportunity of checking of the results of its associated scientific paper and for further analysis. De‐Identified (Anonymized) Raw Data A current approach to identifying (anonymizinig) raw data is based on the Health Insurance Portability and Accountability Act (HIPAA) of 1996 (P.L.104‐191). HIPAA was enacted by the U.S. Congress in 1996 to keep a person’s medical information private. In a research study, a person’s private medical information can become research data. HIPAA’s Privacy Rule allows a covered entity to de‐identify data by removing all 18 elements that could be used to identify the individual or the individual’s relatives, employers, or household members; these elements are enumerated in the Privacy Rule. The covered entity must also have no actual knowledge that the remaining information could be used alone or in combination with other information to identify the individual who is the subject of the information. Under this method, the identifiers that must be removed are the following: 1 Names. 2 All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP Code, and their equivalent geographical codes, except for the initial three digits of a ZIP Code if, according to the current publicly available data from the Bureau of the Census: 2a. The geographic unit formed by combining all ZIP Codes with the same three initial digits contains more than 20,000 people. 2b. The initial three digits of a ZIP Code for all such geographic units containing 20,000 or fewer people are changed to 000. 3 All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, and date of death, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older. 4 Telephone numbers. 5 Facsimile numbers. 6 Electronic mail addresses. 7 Social security numbers. 8 Medical record numbers. 9 Health plan beneficiary numbers. 10 Account numbers. 11 Certificate/license numbers. 12 Vehicle identifiers and serial numbers, including license plate numbers. 13 Device identifiers and serial numbers. 14 Web universal resource locators (URLs). 15 Internet protocol (IP) address numbers. 16 Biometric identifiers, including fingerprints and voiceprints. 17 Full‐face photographic images and any comparable images. 18 Any other unique identifying number, characteristic, or code, unless otherwise permitted by the Privacy Rule for re‐identification. Even though ‘raw data’ can be de‐identified (anonymized) so that there is no way to trace back the data to the study participant from which it was drawn, there are still questions whether the ‘de‐identified raw data’ can be given to other researchers or made public without the original study participant’s approval. It may be the case that this approval could be done at the start of any research study where the individual considering study participation would be counseled on what the process of de‐identification consists of and would be asked whether the participant would allow use of the data by other researchers or made available to the public in a de‐identified state. However, there may be questions of the study participants as to whether the data are truly de‐identified and untraceable back to the study participant since the participant would not be able to see the process actually being carried out in detail in all relevant circumstances. Using Raw Data in Context Simply being in possession of raw data is never enough to build on that data from a research perspective. Researchers need to understand the context of the scientific research study in which the data were collected. This research context includes (but is not limited to): (i) the scientific hypothesis of the research study, (ii) the inclusion and exclusion criteria selected by the research designers of the study to determine which study participants were included and which were excluded from the study, (iii) the methods used to collect the data and possibilities of errors in the data collection processes, and (iv) answers to questions regarding whether any data were excluded from the study data and why these data were excluded. Ownership Rights Over Data There are ownership rights over data. Ownership rights include court decision making related to the question which parties are to be considered as the owners of the data. Possible owners here include (but are not limited to) (i) the study sponsor whose only role was to fund that study, (ii) the principal investigator(s) who developed the scientific hypothesis, and (iii) individual study participants through special contracts made by individual participants who negotiate such contracts with study sponsors and principal investigators to share in the ownership of data. Examples of such data are participant sources of unique gene lines or unique cell lines to be used in future research. Objections to Data Sharing Finally, individual researchers may simply object to sharing data. A researcher’s arguments here may be simply stating that he or she has a right to fully analyze the data since the collection of the data was the researchers’ idea in the first place. These researchers may argue that they have the right to mine the data as fully as they can before deciding to make the data open to the public for others to analyze. Works Cited 1 Kaiser, J. ‘Making Clinical Data Widely Available.’Science 322.5899 (10 Oct. 2008): 217–8. 2 de Lusignan, S., J.F. Metsemakers, P. Houwink, V. Gunnarsdottir, and J. van der Lei. ‘Routinely Collected General Practice Data: Goldmines for Research? A Report of the European Federation for Medical Informatics Primary Care Informatics Working Group (EFMI PCIWG) from MIE2006, Maastricht, The Netherlands.’Informatics in Primary Care 14.3 (2006): 203–9. 3 Sim, I. ‘Human Studies Database Project’, CTSA Data Repositories Symposium (17 Oct. 2008). 23 Nov. 2010. 4 Sim, I., C.G. Chute, H. Lehmann, R. Nagarajan, M. Nahm, and R.H. Scheuermann. ‘Keeping Raw Data in Context.’Science 323.5915 (6 Feb. 2009): 713. 5 U.S. Department of Health and Human Services, National Institutes of Health, HIPAA Privacy Rule, Information for Researchers. 13 Nov. 2010. 6 Wilczynski, N.L., R.B. Haynes, and Hedges Team. ‘EMBASE Search Strategies for Identifying Methodologically Sound Diagnostic Studies for Use by Clinicians and Researchers.’BMC Medicine 29.3 (March 2005): 7. (shrink)

It is obligatory to educate student researchers before they start their work by teaching them about the various types of plagiarism and how to avoid them. It is also vital that research supervisors take into account the sources of data that are explored in their students’ manuscripts. This article tries to draw the reader’s attention to the importance of avoiding all types of plagiarism in their research.

This article looks at some common assumptions and associated work practices within a military intelligence community. There intelligence practitioners use the term “raw data” as a common sense category, as a word that describes information they want or have gained access to. The practical and organizational processes that inform the construction of the term raw data are unpacked here. Examples presented are based on interviews, field observations, and document reviews. Theoretical descriptions and models of work, for example, the (...) intelligence cycle, construction, and the use of computer databases, assume that work in this community starts with raw data. However, this data has already been processed by the work practices, political, practical, and other decisions even before data collection occurs. The technology and attendant categories and practices instrumentalize a certain world view. The model used frames not just the collection and organization of the institution’s knowledge but those categories that inform how this institution organizes, legitimizes, and enacts its work, data, and knowledge. Given the kinds of opponents nations have to face today, intelligence practitioners and those who study their work have to understand practice, action, and contexts often quite different from ones own. (shrink)

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include (...) class='Hi'>data conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data. (shrink)

Background: Scientific research entails systematic investigation. Publishing the findings of research in peer reviewed journals implies a high level of confidence by the authors in the veracity of their interpretation. Therefore it stands to reason that researchers should be prepared to share their raw data with other researchers, so that others may enjoy the same level of confidence in the findings. Method: In a prospective study, 29 corresponding authors of original research articles in a medical journal (the British Medical (...) Journal) were contacted to ascertain their preparedness to share the data from their research. The email contact was in one of two forms, a general request and a specific request. The type of request a researcher received was randomly allocated. Findings: Researchers receiving specific requests for data were less likely, and slower, to respond than researchers receiving general requests. Only one researcher released data. Most researchers were reluctant to release their data. Some required further information, clarification, or authorship. Interpretation: The general reluctance of researchers to consider requests for their data is of concern. It raises questions about the level of confidence that should be placed on their interpretations of the data. It also highlights an unfortunate situation where researchers are more concerned with losing an advantage than advancing science. (shrink)

Video data has now become the most common form of data for educational researchers studying classroom interaction and school culture. Software protocols for analysing vast archives of video data are deployed regularly, allowing researchers to annotate, code and sort images. These protocols are often applied by researchers without reflection or reference to the extensive philosophical work in film and media studies. Without exception, this research treats the video image as movement-image or picture, a recording of ‘raw (...) class='Hi'>data’, indexical of a given time-space relationship. In this article I situate this kind of research within the history of scientific cinema, drawing on Deleuze's books on cinema, as well as his ideas on colour and figure from The Logic of Sensation, to propose an alternative way of analysing video data. One of the central claims of Deleuze in Cinema 2 is that the time-image reconfigures bodies as expressions of force – the body becomes a ‘shock of forces’. I argue that such an approach allows us to study the student body as less a phenomenological organism with built-in ‘I can’ cognitive and motor capacities, and more an indeterminate crystalline contraction and expansion of intensity. I present an example of how to study classroom video data as time-image, and explore the implications of such work for education research. (shrink)

Many decades ago Patrick Suppes argued rather convincingly that theoretical hypotheses are not confronted with the direct, raw results of an experiment, rather, they are typically compared with models of data. What exactly is a data model however? And how do the interactions of particles at the subatomic scale give rise to the huge volumes of data that are then moulded into a polished data model? The aim of this paper is to answer these questions by (...) presenting a detailed case study of the construction of data models at the LHCb for testing Lepton Flavour Universality in rare decays of B-mesons. The close examination of the scientific practice at the LHCb leads to the following four main conclusions: raw data in their pure form are practically useless for the comparison of experimental results with theory, and processed data are in some cases epistemically more reliable, real and simulated data are involved in the co-production of the final data model and cannot be easily distinguished, theory-ladenness emerges at three different levels depending on the scope and the purpose for which background theory guides the overall experimental process and the overall process of acquiring and analysing data in high energy physics is too complicated to be fully captured by a generic methodological description of the experimental practice. (shrink)

Most, if not all, philosophers agree that computers cannot learn what words refers to from raw text alone. While many attacked Searle’s Chinese Room thought experiment, no one seemed to question this most basic assumption. For how can computers learn something that is not in the data? Emily Bender and Alexander Koller ( 2020 ) recently presented a related thought experiment—the so-called Octopus thought experiment, which replaces the rule-based interlocutor of Searle’s thought experiment with a neural language model. The (...) Octopus thought experiment was awarded a best paper prize and was widely debated in the AI community. Again, however, even its fiercest opponents accepted the premise that what a word refers to cannot be induced in the absence of direct supervision. I will argue that what a word refers to _is_ probably learnable from raw text alone. Here’s why: higher-order concept co-occurrence statistics are stable across languages and across modalities, because language use (universally) reflects the world we live in (which is relatively stable). Such statistics are sufficient to establish what words refer to. My conjecture is supported by a literature survey, a thought experiment, and an actual experiment. (shrink)

This paper offers an account of data manipulation in scientific experiments. It will be shown that in many cases raw, unprocessed data is not produced, but rather a form of processed data that will be referred to as a data model. The language of data models will be used to provide a framework within which to understand a recent debate about the status of data and data manipulation. It will be seen that a (...) description in terms of data models allows one to understand cases in which data acquisition and data manipulation cannot be separated into two independent activities. (shrink)

I respond to H. M. Collins's claim (1985, 1990, 1993) that experimental inquiry cannot be objective because the only criterium experimentalists have for determining whether a technique is "working" is the production of "correct" (i.e., the expected) data. Collins claims that the "experimenters' regress," the name he gives to this data-technique circle, cannot be broken using the resources of experiment alone. I argue that the data-technique circle, can be broken even though any interpretation of the raw (...) class='Hi'>data produced by techniques is theory-dependent. However, it is possible to break this circle by eliminating dependence on even those theoretical presuppositions that are shared by an entire scientific community through the use of multiple independently theory-dependent techniques to produce robust bodies of data. Moreover, I argue, that it is the production of robust bodies of data that convinces experimentalists of the objectivity of their data interpretations. (shrink)

We compare our model of unsupervised learning of linguistic structures, ADIOS [1], to some recent work in computational linguistics and in grammar theory. Our approach resembles the Construction Grammar in its general philosophy (e.g., in its reliance on structural generalizations rather than on syntax projected by the lexicon, as in the current generative theories), and the Tree Adjoining Grammar in its computational characteristics (e.g., in its apparent affinity with Mildly Context Sensitive Languages). The representations learned by our algorithm are truly (...) emergent from the (unannotated) corpus data, whereas those found in published works on cognitive and construction grammars and on TAGs are hand-tailored. Thus, our results complement and extend both the computational and the more linguistically oriented research into language acquisition. We conclude by suggesting how empirical and formal study of language can be best integrated. (shrink)

There is a growing chorus of voices in the scientific community calling for greater openness in the sharing of raw data that lead to a publication. In this commentary, we discuss the merits of sharing, common concerns that are raised, and practical issues that arise in developing a sharing policy. We suggest that the cognitive science community discuss the topic and establish a data-sharing policy.

This article investigates the work of processors who curate and “clean” the data sets that researchers submit to data archives for archiving and further dissemination. Based on ethnographic fieldwork conducted at the data processing unit of a major US social science data archive, I investigate how these data processors work, under which status, and how they contribute to data sharing. This article presents two main results. First, it contributes to the study of invisible technicians (...) in science by showing that the same procedures can make technical work invisible outside and visible inside the archive, to allow peer review and quality control. Second, this article contributes to the social study of scientific data sharing, by showing that the organization of data processing directly stems from the conception that the archive promotes of a valid data set—that is, a data set that must look “pristine” at the end of its processing. After critically interrogating this notion of pristineness, I show how it perpetuates a misleading conception of data as “raw” instead of acknowledging the important contribution of data processors to data sharing and social science. (shrink)

This article explores the ways in which data centre operators are currently reconfiguring the systems of energy and heat supply in European capitals, replacing conventional forms of heating with data-driven heat production, and becoming important energy suppliers. Taking as an empirical object the heat generated from server halls, the article traces the expanding phenomenon of ‘waste heat recycling’ and charts the ways in which data centre operators in Stockholm and Paris direct waste heat through metropolitan district heating (...) systems and urban homes, and valorise it. Drawing on new materialisms, infrastructure studies and classical theory of production and destruction of value in capitalism, the article outlines two modes in which this process happens, namely infrastructural convergence and decentralisation of the data centre. These modes arguably help data centre operators convert big data from a source of value online into a raw material that needs to flow in the network irrespective of meaning. In this conversion process, the article argues, a new commodity is in a process of formation, that of computation traffic. Altogether data-driven heat production is suggested to raise the importance of certain data processing nodes in Northern Europe, simultaneously intervening in the global politics of access, while neutralising external criticism towards big data by making urban life literally dependent on power from data streams. (shrink)

The practice of modern craniomaxillofacial surgery has been defined by emergent technologies allowing for the acquisition, storage, utilization, and transfer of massive amounts of sensitive and identifiable patient data. This alone has thrust providers into an unlikely and unprecedented role as the stewards of vast databases of digital information. This data powers the potent surgical tool of virtual surgical planning, a method by which craniomaxillofacial surgeons plan and simulate procedural outcomes in a digital environment. Further complicating this new (...) terrain is the involvement of third‐party contractors—a necessary presence in bringing raw data to bear in the office, virtual space, and operating room. The individual privileges and responsibilities of patients, providers, and vendors towards data are situated within the most recent U.S. court rulings and regulations. This paper offers guidance for overseeing the safe and responsible transfer to third‐party contractors, and provides suggestions for negotiating the trinary relationship between physicians, their patients, and the vendors offering this transformative technology. (shrink)

This article shows how activists in the open data movement re-articulate notions of democracy, participation, and journalism by applying practices and values from open source culture to the creation and use of data. Focusing on the Open Knowledge Foundation Germany and drawing from a combination of interviews and content analysis, it argues that this process leads activists to develop new rationalities around datafication that can support the agency of datafied publics. Three modulations of open source are identified: First, (...) by regarding data as a prerequisite for generating knowledge, activists transform the sharing of source code to include the sharing of raw data. Sharing raw data should break the interpretative monopoly of governments and would allow people to make their own interpretation of data about public issues. Second, activists connect this idea to an open and flexible form of representative democracy by applying the open source model of participation to political participation. Third, activists acknowledge that intermediaries are necessary to make raw data accessible to the public. This leads them to an interest in transforming journalism to become an intermediary in this sense. At the same time, they try to act as intermediaries themselves and develop civic technologies to put their ideas into practice. The article concludes with suggesting that the practices and ideas of open data activists are relevant because they illustrate the connection between datafication and open source culture and help to understand how datafication might support the agency of publics and actors outside big government and big business. (shrink)

Georgios Pentzaropoulos continues his investigation undertaken in 'Generating Stable Knowledge via Reduction in Entropy' (Philosophy Pathways Issue 167, 28 November 2011) looking at knowledge not as something we 'possess' -- the knowledge contained in a book, for example -- but rather as an active, or interactive process of increasing understanding through the judicious sifting and processing of raw information. As Macmurray argued in his Gifford Lectures, 'All knowledge is for the sake of action.' Instead of seeking to define some abstract (...) notion of knowledge in a vacuum, what philosophers should be getting to grips with is the notion of actionable knowledge. - Geoffrey Klempner, Editor. (shrink)

Benjamin Libet compared the perceived time of direct brain stimulation to the perceived time of skin stimulation. His results are among the most controversial experiments at the interface between psychology and philosophy. The new element that I bring to this discussion is a reanalysis of Libet's raw data. Libet's original data were difficult to interpret because of the manner in which they were presented in tables. Plotting the data as psychometric functions shows that the observers have great (...) uncertainty about the relative timing of events, as seen the shallow psychometric slopes. A second indication of uncertainty comes from Libet's use of three response categories, A first; B first; and A and B simultaneous. The large number of “perceptually simultaneous” responses provides a further measure of the difficulty of the judgment. There are thus a very broad range of stimulus delays in which the subject is unable to make an accurate ordering response. These points provide evidence that there is no compelling reason to invent exotic or ad hoc mechanisms to account for Libet's data since the uncertainty window is large enough to allow simple mechanism such as memory shifts. Libet argued that his data provide evidence for a backward referral in time. I argue that even though Libet's own data are weak, there are good arguments for a backward referral mechanism to help the subject make sense out of the tangled chaos of asynchronous information associated with experienced events. (shrink)

The internal structure of ability emotional intelligence tests at item level has been hardly studied, and if studied often the predicted structure did not show. In the present study, an a priori model for responses to EI ability items using Likert response scales with a Situational Judgement Test format is investigated with confirmatory factor analysis. The model consists of a target EI ability factor, an acquiescence factor, which is a method factor induced by the Likert response scales, and design-based error (...) covariances, which are induced by the SJT format. It is investigated whether this a priori model can account for the observed associations between the raw item responses of the Components of Emotion Understanding Test-24. The CEUT-24 is a new test developed to assess emotion understanding, a key aspect of the EI ability construct, based on the componential emotion framework. The sample consisted of 1184 participants from four European countries speaking four different languages. Findings showed that the a priori model fitted the data well in all four languages. Furthermore, measurement invariance testing gave evidence for a well-fitting configural, metric, and partial scalar invariance model. The conclusion is that within a regular CFA framework using raw observed items responses, method factors can be disentangled from the targeted EI ability factor. (shrink)

The emergence of Big Data has added a new aspect to conceptualizing the use of digital technologies in the delivery of public services and for realizing digital governance. This article explores, via the ‘value-chain’ approach, the evolution of digital governance research, and aligns it with current developments associated with data analytics, often referred to as ‘Big Data’. In many ways, the current discourse around Big Data reiterates and repeats established commentaries within the eGovernment research community. This (...) body of knowledge provides an opportunity to reflect on the ‘promise’ of Big Data, both in relation to service delivery and policy formulation. This includes, issues associated with the quality and reliability of data, from mixing public and private sector data, issues associated with the ownership of raw and manipulated data, and ethical issues concerning surveillance and privacy. These insights and the issues raised help assess the value of Big Data in government and smart city environments. (shrink)

SmartData is a research program to develop web-based intelligent agents that will perform two tasks: securely store an individual’s personal and/or proprietary data, and protect the privacy and security of the data by only disclosing it in accordance with instructions authorized by the data subject. The vision consists of a web-based SmartData agent that would serve as an individual’s proxy in cyberspace to protect their personal or proprietary data. The SmartData agent (which ‘houses’ the data (...) and its permitted uses) would be transmitted to, or stored in a database, not the personal data itself. In effect, there would be no personal or proprietary raw data out in the open—it would instead be housed within a SmartData agent, much like we humans carry information in our heads; extending the analogy, it would be the human-like clone that would be transmitted or stored, not the raw data. The binary string representative of a SmartData agent would be located in local or central databases. Organizations requiring access to any of the data resident within the agent would query it once it had been activated. In this paper, we provide a preliminary overview of the SmartData concept, and describe the associated research and development that must be conducted in order to actualize this vision. (shrink)

When a technique purports to provide information that is not available to the unaided senses, it is natural to think that the only way to validate that technique is by appealing to a theory of the processes that lead from the object of study to the raw data. In fact, scientists have a variety of strategies for validating their techniques. Those strategies can yield multiple independent arguments that support the validity of the technique. Thus, it is possible to produce (...) a robust body of data with a single technique. I illustrate and support these claims with a historical case study. *Received September 2009; revised October 2009. †To contact the author, please write to: Department of History and Philosophy of Science, 1017 Cathedral of Learning, University of Pittsburgh, Pittsburgh, PA 15260; e‐mail: [email protected]. (shrink)

Refining big data is a new multipurpose way to find, collect, and analyze information obtained from the web and off-line information sources about any research subject. It gives the opportunity to investigate (with an assumed level of statistical significance) the past and current status of information on a subject, and it can even predict the future. The refining of big data makes it possible to quantitatively investigate a wide spectrum of raw information on significant human issues—social, scientific, political, (...) business, and others. Refining creates a space for new, rich sources of information and opens innovative ways for research. The article describes a procedure for refining big data and gives examples of its use. (shrink)

Nowadays, Artificial Intelligence (AI) is present in all sectors of the economy. Consequently, both data-the raw material used to build AI systems- and AI have an unprecedented impact on society and there is a need to ensure that they work for its benefit. For this reason, the European Union has put data and trustworthy AI at the center of recent legislative initiatives. An important element in these regulations is transparency, understood as the provision of information to relevant stakeholders (...) to support their understanding of AI systems and data throughout their lifecycle. In recent years, an increasing number of approaches for documenting AI and datasets have emerged, both within academia and the private sector. In this work, we identify the 36 most relevant ones from more than 2200 papers related to trustworthy AI. We assess their relevance from the angle of European regulatory objectives, their coverage of AI technologies and economic sectors, and their suitability to address the specific needs of multiple stakeholders. Finally, we discuss the main documentation gaps found, including the need to better address data innovation practices (e.g. data sharing, data reuse) and large-scale algorithmic systems (e.g. those used in online platforms), and to widen the focus from algorithms and data to AI systems as a whole. (shrink)

Note?takers in early modern Europe mixed a number of scribal practices. Not only did they write down extracts of texts, they also collected data from observation or from accounting. Practices such as commonplacing were part of sometimes communal, rather informal personal practices that laid the foundations for personal diaries. Other note?taking was prescriptive, fact?establishing technical data entry. Yet both the personal, sentimental and technical forms of note?taking were interrelated. It was during this period that merchants, administrators, scholars and (...) scientists sought methods to transform raw information such as notes into formalized knowledge for practical use. Notes had formed a part of larger writing and archival projects since the dawn of writing and literacy, but during the seventeenth and eighteenth centuries, figures such as Bacon, Boyle and Pepys not only mixed note?taking practices from both scholarly and mercantile traditions; they also thought about the very act of note?taking and how to transform it into a tool for the management of large?scale government, industry and research. Most of all, in the seventeenth century, scholars, merchants and naturalists began thinking in formal terms about how to use notes as part of larger information systems which they called collections, compilations and even archives. (shrink)

different spaces and places, therefore construct the way in which architecture operates in Western society. The use and occupation of architecture also ...

There is much effort nowadays to protect communication networks against different cybersecurity attacks (which are more and more sophisticated) that look for systems’ vulnerabilities they could exploit for malicious purposes. Network Intrusion Detection Systems (NIDSs) are popular tools to detect and classify such attacks, most of them based on ML models. However, ML-based NIDSs cannot be trained by feeding them with network traffic data as it is. Thus, a Feature Engineering (FE) process plays a crucial role transforming network traffic (...) raw data onto derived one suitable for ML models. In this work, we study the effects of applying one such FE technique in different ways on the performance of two ML models (linear and non-linear) and their selected features. This the Feature as a Counter approach. The derived observations are computed from either with the same number of raw samples, (batch-based approaches) or by aggregating them by time intervals (timestamp-based approach). Results show that there is no significant differences between the proposed approaches neither in the performance of the models nor in the selected features that validate our proposal making it feasible to be widely used as a standard FE method. (shrink)

Recent literature on the role of pictorial representation in the life sciences has focused on the relationship between detailed representations of empirical data and more abstract, formal representations of theory. The standard argument is that in both a historical and epistemic sense, this relationship is a directional one: beginning with raw, unmediated images and moving towards diagrams that are more interpreted and more theoretically rich. Using the neural network diagrams of Warren McCulloch and Walter Pitts as a case study, (...) I argue that while in the empirical sciences, pictorial representation tends to move from data to theory, in areas of the life sciences that are predominantly theoretical, when abstraction occurs at the outset, the relationship between detail and abstraction in pictorial representations can be of a different character. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Architectural Theory, Volume 1: An Anthology from Vitruvius to 1870Peg RawesArchitectural Theory, Volume 1: An Anthology from Vitruvius to 1870, edited by Harry Francis Mallgrave. Malden MA, Oxford, Victoria: Blackwell Publishing, 2006, 590 pp., $49.95.This anthology is a rich and comprehensive documentation of the key stages that construct Western architectural theory, from Vitruvius's classical writing to Gottfried Semper's theories in late-nineteenth-century Europe. Comprised of 229 texts by these (...) and other significant writers of architectural theory, it represents an extremely valuable resource for architectural design, history, and theory education and, more broadly, for aesthetic education, art history, aesthetics, and visual culture. Professor Mallgrave's career as an architectural historian and theorist at the Illinois Institute of Technology, and editor of Architecture and Aesthetics for the Texts and Documents Series at the Getty Research Institute, has enabled him to bring together a large number of canonical texts by architects and writers, including Alberti, Palladio, Vasari, Perrault, Wotton, Wren, Soufflot, Blondel, Boullée, Ledoux, Soane, Reynolds, Pugin, Viollet-le-Duc, Ruskin, and Greenough. In addition, Mallgrave has selected important contextualizing texts that inform the philosophical, cultural, and aesthetic development of the discipline by writers such as Rousseau, Voltaire, Montesquieu, Morris, Chambers, Winckelmann, Locke, Hume, Ramsey, Burke, Walpole, Price, von Schlegel, Wolff, Jefferson, Emerson, Hugo, Zola, and Leibnitz.Six stages of development are laid out, beginning with the Classical and Renaissance periods in part I. In part II the debates about Classicism in France and Britain develop, leading into part III, in which texts from the Neoclassical and Enlightenment debates in Europe take precedence. In part IV, Picturesque and Sublime theories are documented, from which part V turns to consider the importance of Historicism in the nineteenth century and, finally, in part VI the impact of Historicism is examined in relation to developments in industrialization.Introducing each part, Mallgrave succinctly highlights the historical, cultural, and political structures that underpin the development of architectural ideas and debates during the period. Within each part, internal sections further distinguish the particular ideas that preoccupied specific individuals and groups to comprise different theoretical positions. Part II, for example, is divided into the nationalist differences between "French Classicism: Ancients and Moderns" and "British Classicism and Palladianism," and part IV identifies key texts that contribute to the theorization of eighteenth-century aesthetics in three stages: "Sources of the Picturesque," "Toward a Relativist Aesthetics," and "Consolidation of Picturesque Theory." In addition, Mallgrave introduces each individual text, drawing attention to the author's intellectual ambitions and the contemporary debates in which their contribution is constructed. Architectural [End Page 111] theory is therefore characterized as a discipline that is made up of dynamic discussions, which are frequently unresolved and irregular in their formation. Diverse and opposing agendas are pursued, both within and between different institutions, academies, and communities, including the late-sixteenth-century "stylistic battles" of Alberti and Serlio's treatises, which were developed out of Vitruvius's ten "books" of architecture; the pluralism of styles in the United States in the mid-nineteenth century that was comprised of puritanical influential theories, Gothic and French Classicism, and the new generation of American architectural theorists; and the emergence of a German architectural theory within the political disputes that led to the unification of Germany in 1871. Architectural theory is therefore firmly constructed in relation to social, material, cultural, and political systems and frameworks.This colorful construction of architectural theory is evident, especially in those sections that are central to Mallgrave's ongoing research interests, in the tension between the rational, technical, and empirical discussions that inform seventeenth-, eighteenth-, and nineteenth-century aesthetics. Running through parts II and III, for example, is one of the most drawn-out and bitter disputes, which dominated architectural theory in the seventeenth- and eighteenth-century French Academy of Architecture. Focused on the value of Vitruvius's Roman origins of Classicism, versus revivalist ambitions to retrieve the ornamental Classicism of Greek and Gothic styles, this great quarrel occupies the texts, letters, essays, and articles by architects and writers, including Blondel, Perrault, Piranesi, Soufflot, Winckelmann, Le Roy, Boullée, and Ledoux. But this debate is also important in the context of... (shrink)

The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take (...) the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises. (shrink)

Peg Rawes examines a "minor tradition" of aesthetic geometries in ontological philosophy. Developed through Kant’s aesthetic subject she explores a trajectory of geometric thinking and geometric figurations--reflective subjects, folds, passages, plenums, envelopes and horizons--in ancient Greek, post-Cartesian and twentieth-century Continental philosophies, through which productive understandings of space and embodies subjectivities are constructed. Six chapters, explore the construction of these aesthetic geometric methods and figures in a series of "geometric" texts by Kant, Plato, Proclus, Spinoza, Leibniz, Bergson, Husserl and Deleuze. In (...) each text, geometry is expressed as a uniquely embodies aesthetic activity because each respective geometric method and figure is imbued with aesthetic sensibility and geometric sense (rather than as disembodies scientific methods). An ontology of aesthetic geometric methods and figures is therefore traced from Kant’s Critical writings, back to Plato and Proclus Greek philosophy, Spinoza and Leibniz’s post-Cartesian philosophies, and forwards to Bergson’s "duration" and Husserl’s "horizons" towards Deleuze’s philosophy of sense. (shrink)

The first section discusses natural intelligence, and notes two major branches of the animal kingdom in which it evolved independently, and several offshoots. The suggestion is that intelligence need not be so difficult to construct as is sometimes assumed.

Numbers have been identified with symbolic data forever. The profound association of both with acoustics, music, and sonic art from Pythagoras to current work is beyond reproach. Recently, sonification looks for ways to realize symbolic data (representing results or measurements) as well as “raw” data (signals, impulses, images, etc.) into compositions. In the strictest sense, everything in a computer is symbolic, that is, represented by 0s and 1s. In the arts, the digital age has broadened and enhanced (...) the conceptual landscape not simply through its servitude to the creative process, but as its partner. However, there is a rich history of the use of data that no doubt has paved the way for many of today’s experiments including my own. (shrink)

Neurodevices that collect neural (or brain activity) data have been characterised as having the ability to register the inner workings of human mentality. There are concerns that the proliferation of such devices in the consumer-directed realm may result in the mass processing and commercialisation of neural data (as has been the case with social media data) and even threaten the mental privacy of individuals. To prevent this, some argue that all raw neural data should be conceptualised (...) and regulated as “medical data” even if it is collected from consumer-directed devices in obviously non-clinical settings. In this paper, we argue that without a clearer formulation of what does and does not count as medical data, this approach might also uncritically enlarge the scope for medical influence and an unwarranted medicalisation of everyday aspects of mental life. Indeed, if we were to accept the position that all neural data is medical data because it offers insights into personally sensitive information (such a person’s thoughts, emotions, or intentions) then this could even unnecessarily expand the boundaries of medical data to other forms of data that otherwise seem to be non-clinical. If all brain data (neural activity data) is considered to be medical data even when collected from consumer-directed devices then we might unintentionally reduce meaningful distinctions between what is and what is not rightfully in the purview of medicine. (shrink)

This article explores how post-war documentary film-makers negotiated complex social, formal, and autobiographical issues associated with representing mental illness and its treatments, and the extent to which their respective approaches helped to challenge conventional attitudes to alternative psychotherapies – especially within the context of advances in new documentary film-making technologies, alongside a wider culture of social activism. Focussing on A Look at Madness ( Regard sur la folie; Mario Ruspoli, 1962, France) and Now Do You Get It Why I Am (...) Crying? ( Begrijpt u nu waarom ik huil?; Louis van Gasteren, 1969, Netherlands), the article discusses how the collaborative, democratic aims of cinéma direct coincided with the ethos of institutional psychotherapy, and compares this with the relations between the documentary form and the subject of LSD-assisted psychotherapeutic techniques in Van Gasteren's film. (shrink)

Advances in information and communication technologies enable more decentralized and individualized mechanisms for coordination and for managing societal complexity. This has important consequences for the role of conditionality and the idea of individual responsibility in two seemingly unrelated policy areas. First, the changing information infrastructure enables an extension of conditionality in the area of welfare through greater activation, enhanced self-management, and a personalization of risks. Second, conditionality and personal responsibility also form an important ideational template and a legitimatory basis for (...) facilitating value creation that is based on data as a raw material. This argument is illustrated looking at the trajectories of the digital strategies in the United Kingdom and Germany. In both cases, data protection is depicted as a question of individual responsibility and tied to certain forms of individual conduct. (shrink)