6 ms·

The Extraordinary Growing Impact of the History of Science

12 points by joubert 11y ago

shalmanese 11y ago
This is an essay on the ease of accessing historical archives of science, not the discipline involved studying science from a historical lens. Those are two very different things that scientists routinely conflate.
- agumonkey 11y ago
  Similarly I heard about an historian with a strong scientific view on history, he even coined a name for his field, unfortunately I can't remember it for my life.
  - benbreen 11y ago
    Cliometrics? At any rate, this is my favorite take on the concept of scientific history, courtesy of Isaiah Berlin: http://berlin.wolf.ox.ac.uk/published_works/cc/scihist.pdf http://berlin.wolf.ox.ac.uk/published_works/cc/scihist.pdf The parent comment makes a good point here. Although I did appreciate the nod at the end to the larger archive of historical sources that digitization has "presentized" or whatever we want to call the process discussed here. I just wish there were more dialogue between practicing scientists and historians of science - in my experience there is virtually no professional interaction.
    - agumonkey 11y ago
      I remember a two word name, something like "fluctuative" history, but I may be wrong. I'll happily take your suggestion though it looks fascinating.
    - dalke 11y ago
      I really enjoyed http://cacm.acm.org/magazines/2015/1/181633-the-tears-of-donald-knuth/fulltext http://cacm.acm.org/magazines/2015/1/181633-the-tears-of-don... ("The Tears of Donald Knuth"), which describes Knuth's reasons to study the history of a field, and also describes some of the disciplinary and institutional difficulties involved.
      - agumonkey 11y ago
        I'm fascinated by the drift of teaching and its limited span. How in 20 years people will have forgotten about fringe or non mainstream ideas and will probably have them again thinking they're something new to dive into.
        dalke 11y ago
        Here's one I've been researching for the last year or so (of on-and-mostly-off research) - superimposed coding. Remember about 5-10 years when Bloom filters popped up often on HN and other tech sites? They are one type of superimposed coding. The basic idea is that a given word or descriptor gets $k$ bits, out of $N$ in a bitset. In a Bloom filter, $k$ is constant, but in the general case it can vary. In the 1940s, Calvin Mooers worked out one system of choosing $k$ based on the information content of the descriptors. He called it Zatocoding. He applied this to selecting punched cards, including punched cards with chemical data; that being the field I work in. Knuth has a bit about the approach in The Art of Computer Programming, v 3, with "false drop cookies." This technique was never mainstream, in part I think because one has to trust in random numbers, but also in practice it was applied to manual punched card searches, and it's difficult to memorize random punch codes. As computers became machines instead of people, this approach disappeared. There's a chemistry paper in about 1975 which considers it, with the phrases "old technique" and "this may appear to be a throwback", then a paper in 1976 which gives a much needed improvement for how to apply Zatocoding to hierarchical data. I think that 1976 paper is amazing. It helped me understand the problem. But it was never part of mainstream understanding. That is to say, very few modern practitioners know of the concept, it's not in any of the text books (they have a very shallow coverage of the topic), and the few papers that have referenced the 1976 article don't seem to have understood the paper, but are citing it for completeness sake. That said, it doesn't solve the problem. An alternate approach was developed in the late 1980s that's more directly related to the Bloom filter sense of using $k$ hash functions. As a result, it's detuned for the specific information in the data set, but it's able to reject new descriptors that weren't part of the training set. Based on the documentation for the method (it was never published in the scientific literature; everyone references the manual page), it comes across like a Bloom filter. However, in talking with people in the know, a specific detail was not clarified, which is that $k$ is a function of the size of the chemical feature used as the descriptor. This makes it a proper superimposed coding system. No one has looked at this information-theoretical approach in 25 years. Almost no one knows that this can even be a useful approach.
dalke 11y ago
This came out last year, and was discussed on HN. One of the things I said then, which I'll repeat now, concerns these lines: > But many of the subject areas that experienced a decline in older citations were part of two broader areas: chemical and materials sciences, and engineering. > Consequently, these broad disciplines show almost no increase in old citations. Just why citation trends should differ in these disciplines isn’t clear. If you look at the early history of information retrieval you'll see that chemical documentation was tightly connected with it. Several of the IR pioneers (Luhn, Mooers, Taube) worked with the American Chemical Society or presented at ACS conferences. I like to point out that "information retrieval" as a term was first presented at an ACS meeting. By the 1960s, CAS (the Chemical Abstracts Service of the ACS), the Institute for Scientific Information, and other organizations provided pretty complete indexing of the chemical literature. While it's improved since then, I believe the old literature for chemistry has been searchable longer than for other fields, and in large part because the chemical structure give a uniquely indexable key (usually) for indexing the literature.
toddkaufmann 11y ago
There are tools and data available at the Science of Science (Sci^2) site [1], part of the Cyberinfrastructure for Network Science Center, funded by NSF. I haven't looked closely in a couple years, but my impression is NSF hopes such tools can help show the effects (and "ROI") of research grant money, connection with PI's and institutions, and their impact both on publication citation and economic impact (development of technology etc.). Ideally this could also be used to measure growth in technical fields to determine whether more (or less) funding is required to answer bigger questions in basic science (which may not have economic incentives yet), methodologies, public policy, and education (will there be enough Ph.D's in the pipeline to meet demands for fields that will exist in ten years?). Scientometrics [2] (the journal) has been around for nearly 40 years, and I assume people were thinking about such issues then. Sci^2 looks to me like a more "big data" approach to not only understanding this, but seeing if it is possible to "push" the frontiers (but I admit I don't know anything that goes on at NSF or how their decision-making process works). Another tool, Publish Or Perish [3], is aimed at individual academics to understand their (or another's) impact in terms of citation metrics that are used in the games for academic (and other) hiring purposes. I stumbled on Sci^2 when trying to learn some new fields (computer vision, hpc / parallel computing, network science, sensemaking) and wanted to quickly find seminal papers (ie highly cited, or literature reviews) to quickly get a broad overview. Not having the patience or time to read lots, playing with interesting tools and trying to extract data from Google Scholar and the like was more attractive. Being impatient, I wanted a way to process knowledge like data. To measure something like growth of a field, it seems something like scientometrics with some natural language processing and ontology engineering is needed. The Google paper seems to be more about an analysis of Google Scholar data and what can be gleaned there. Maybe an update of Google Scholar Metrics is coming? I am surprised no reference to scientometrics in the arXiv paper (maybe they aren't familiar with the literature?). 1. https://sci2.cns.iu.edu/user/index.php https://sci2.cns.iu.edu/user/index.php 2. http://en.wikipedia.org/wiki/Scientometrics_%28journal%29 http://en.wikipedia.org/wiki/Scientometrics_%28journal%29 3. http://www.harzing.com/pop.htm http://www.harzing.com/pop.htm

12 points by joubert 11y ago

This is an essay on the ease of accessing historical archives of science, not the discipline involved studying science from a historical lens. Those are two very different things that scientists routinely conflate.

agumonkey 11y ago
Similarly I heard about an historian with a strong scientific view on history, he even coined a name for his field, unfortunately I can't remember it for my life.
- benbreen 11y ago
  Cliometrics? At any rate, this is my favorite take on the concept of scientific history, courtesy of Isaiah Berlin: http://berlin.wolf.ox.ac.uk/published_works/cc/scihist.pdf http://berlin.wolf.ox.ac.uk/published_works/cc/scihist.pdf The parent comment makes a good point here. Although I did appreciate the nod at the end to the larger archive of historical sources that digitization has "presentized" or whatever we want to call the process discussed here. I just wish there were more dialogue between practicing scientists and historians of science - in my experience there is virtually no professional interaction.
  - agumonkey 11y ago
    I remember a two word name, something like "fluctuative" history, but I may be wrong. I'll happily take your suggestion though it looks fascinating.
  - dalke 11y ago
    I really enjoyed http://cacm.acm.org/magazines/2015/1/181633-the-tears-of-donald-knuth/fulltext http://cacm.acm.org/magazines/2015/1/181633-the-tears-of-don... ("The Tears of Donald Knuth"), which describes Knuth's reasons to study the history of a field, and also describes some of the disciplinary and institutional difficulties involved.
    - agumonkey 11y ago
      I'm fascinated by the drift of teaching and its limited span. How in 20 years people will have forgotten about fringe or non mainstream ideas and will probably have them again thinking they're something new to dive into.
      - dalke 11y ago
        Here's one I've been researching for the last year or so (of on-and-mostly-off research) - superimposed coding. Remember about 5-10 years when Bloom filters popped up often on HN and other tech sites? They are one type of superimposed coding. The basic idea is that a given word or descriptor gets $k$ bits, out of $N$ in a bitset. In a Bloom filter, $k$ is constant, but in the general case it can vary. In the 1940s, Calvin Mooers worked out one system of choosing $k$ based on the information content of the descriptors. He called it Zatocoding. He applied this to selecting punched cards, including punched cards with chemical data; that being the field I work in. Knuth has a bit about the approach in The Art of Computer Programming, v 3, with "false drop cookies." This technique was never mainstream, in part I think because one has to trust in random numbers, but also in practice it was applied to manual punched card searches, and it's difficult to memorize random punch codes. As computers became machines instead of people, this approach disappeared. There's a chemistry paper in about 1975 which considers it, with the phrases "old technique" and "this may appear to be a throwback", then a paper in 1976 which gives a much needed improvement for how to apply Zatocoding to hierarchical data. I think that 1976 paper is amazing. It helped me understand the problem. But it was never part of mainstream understanding. That is to say, very few modern practitioners know of the concept, it's not in any of the text books (they have a very shallow coverage of the topic), and the few papers that have referenced the 1976 article don't seem to have understood the paper, but are citing it for completeness sake. That said, it doesn't solve the problem. An alternate approach was developed in the late 1980s that's more directly related to the Bloom filter sense of using $k$ hash functions. As a result, it's detuned for the specific information in the data set, but it's able to reject new descriptors that weren't part of the training set. Based on the documentation for the method (it was never published in the scientific literature; everyone references the manual page), it comes across like a Bloom filter. However, in talking with people in the know, a specific detail was not clarified, which is that $k$ is a function of the size of the chemical feature used as the descriptor. This makes it a proper superimposed coding system. No one has looked at this information-theoretical approach in 25 years. Almost no one knows that this can even be a useful approach.

dalke 11y ago

This came out last year, and was discussed on HN. One of the things I said then, which I'll repeat now, concerns these lines: > But many of the subject areas that experienced a decline in older citations were part of two broader areas: chemical and materials sciences, and engineering. > Consequently, these broad disciplines show almost no increase in old citations. Just why citation trends should differ in these disciplines isn’t clear. If you look at the early history of information retrieval you'll see that chemical documentation was tightly connected with it. Several of the IR pioneers (Luhn, Mooers, Taube) worked with the American Chemical Society or presented at ACS conferences. I like to point out that "information retrieval" as a term was first presented at an ACS meeting. By the 1960s, CAS (the Chemical Abstracts Service of the ACS), the Institute for Scientific Information, and other organizations provided pretty complete indexing of the chemical literature. While it's improved since then, I believe the old literature for chemistry has been searchable longer than for other fields, and in large part because the chemical structure give a uniquely indexable key (usually) for indexing the literature.

toddkaufmann 11y ago

There are tools and data available at the Science of Science (Sci^2) site [1], part of the Cyberinfrastructure for Network Science Center, funded by NSF. I haven't looked closely in a couple years, but my impression is NSF hopes such tools can help show the effects (and "ROI") of research grant money, connection with PI's and institutions, and their impact both on publication citation and economic impact (development of technology etc.). Ideally this could also be used to measure growth in technical fields to determine whether more (or less) funding is required to answer bigger questions in basic science (which may not have economic incentives yet), methodologies, public policy, and education (will there be enough Ph.D's in the pipeline to meet demands for fields that will exist in ten years?). Scientometrics [2] (the journal) has been around for nearly 40 years, and I assume people were thinking about such issues then. Sci^2 looks to me like a more "big data" approach to not only understanding this, but seeing if it is possible to "push" the frontiers (but I admit I don't know anything that goes on at NSF or how their decision-making process works). Another tool, Publish Or Perish [3], is aimed at individual academics to understand their (or another's) impact in terms of citation metrics that are used in the games for academic (and other) hiring purposes. I stumbled on Sci^2 when trying to learn some new fields (computer vision, hpc / parallel computing, network science, sensemaking) and wanted to quickly find seminal papers (ie highly cited, or literature reviews) to quickly get a broad overview. Not having the patience or time to read lots, playing with interesting tools and trying to extract data from Google Scholar and the like was more attractive. Being impatient, I wanted a way to process knowledge like data. To measure something like growth of a field, it seems something like scientometrics with some natural language processing and ontology engineering is needed. The Google paper seems to be more about an analysis of Google Scholar data and what can be gleaned there. Maybe an update of Google Scholar Metrics is coming? I am surprised no reference to scientometrics in the arXiv paper (maybe they aren't familiar with the literature?). 1. https://sci2.cns.iu.edu/user/index.php https://sci2.cns.iu.edu/user/index.php 2. http://en.wikipedia.org/wiki/Scientometrics_%28journal%29 http://en.wikipedia.org/wiki/Scientometrics_%28journal%29 3. http://www.harzing.com/pop.htm http://www.harzing.com/pop.htm