4 ms·

It's not a terribly good assumption. With programming and git (or any version control system), everything is submitted to the repository, functional features, t

It's not a terribly good assumption. With programming and git (or any version control system), everything is submitted to the repository, functional features, tests, data, etc. With scientific papers, the only thing submitted is the final published paper itself. There is a countless amount of data, settings, computer code, etc, that is not included with a scientific paper. The ability to inspect, comment, and modify a scientific publication doesn't mean much if the underlying work is unavailable.

beojan 8y ago
That doesn't mean there isn't a git repository (or multiple) somewhere with all the code and TeX source for the paper. It's just that these aren't usually published.
- speedplane 8y ago
  Your focus on the Tex source is pretty amusing. Imagine working for a year on a project, building countless experiments that your write code for, edit, annotate in random files or on a notepad, build huge datasets, spin up and down servers or various equipment. Then, when you finally get a result, you put write a nicely formatted document in Tex or Microsoft Word or whatever. That final document represents a tiny fraction of the work and thought you put into it. The ability to mess with that final product without the underlying 99% of thinking and effort that went into it gets you nowhere.
  - COGlory 8y ago
    Seriously, this whole git idea shows the level of ignorance surrounding the actual scientific process and workflow. The data files for my work (protein crystallography) are terabytes upon terabytes large. There's no reasonable way to distribute them. They go through multiple layers of data paring. Any final raw data is included in the supplemental material, but it has already been heavily curated and reduced. And also biased. The solution to this problem is not iteration and revision.
  - beojan 8y ago
    I'm not focusing on the TeX source. I said "code (i.e. simulation and/or analysis code) and TeX source (including the original images for plots and the data those were made from)".
marcus_holmes 8y ago
> There is a countless amount of data, settings, computer code, etc, that is not included with a scientific paper. And that, in itself, is a problem. How can others identify flaws in the paper if they can't see the data you used? Open Source software includes all the code you need, not just the final binary executable. Any proposed "Github for Science" needs to do the same. And any arguments that "the data is expensive to collect and proprietary" can be countered with "code is expensive to write and proprietary, yet open source still works".
- Dayshine 8y ago
  Some data cannot be shared due to patient confidentially. In fact, almost all medical trials or epidemiological studies use data that can't be shared outside the study.
  - marcus_holmes 8y ago
    surely there are methods of concealing patient identity? We do this with lots of other confidential personal information...
    - Dayshine 8y ago
      But then the research isn't reproducible...
      - stevemadere 8y ago
        Hashing identity solves this. All of the data can be anonymized while retaining the linkage of measurements and procedures performed on the same subject. e.g. We don't know who this guy is, but we do know that every measurement of him was linked to this same enormous random string.
        Dayshine 8y ago
        Only if your data is very limited in depth. When doing data linking I can usually identify one person in a dataset of almost 1 million from three innocent seeming pieces of data. Pseudonimisation doesn't really work.

beojan 8y ago

That doesn't mean there isn't a git repository (or multiple) somewhere with all the code and TeX source for the paper. It's just that these aren't usually published.

speedplane 8y ago
Your focus on the Tex source is pretty amusing. Imagine working for a year on a project, building countless experiments that your write code for, edit, annotate in random files or on a notepad, build huge datasets, spin up and down servers or various equipment. Then, when you finally get a result, you put write a nicely formatted document in Tex or Microsoft Word or whatever. That final document represents a tiny fraction of the work and thought you put into it. The ability to mess with that final product without the underlying 99% of thinking and effort that went into it gets you nowhere.
- COGlory 8y ago
  Seriously, this whole git idea shows the level of ignorance surrounding the actual scientific process and workflow. The data files for my work (protein crystallography) are terabytes upon terabytes large. There's no reasonable way to distribute them. They go through multiple layers of data paring. Any final raw data is included in the supplemental material, but it has already been heavily curated and reduced. And also biased. The solution to this problem is not iteration and revision.
- beojan 8y ago
  I'm not focusing on the TeX source. I said "code (i.e. simulation and/or analysis code) and TeX source (including the original images for plots and the data those were made from)".

marcus_holmes 8y ago

> There is a countless amount of data, settings, computer code, etc, that is not included with a scientific paper. And that, in itself, is a problem. How can others identify flaws in the paper if they can't see the data you used? Open Source software includes all the code you need, not just the final binary executable. Any proposed "Github for Science" needs to do the same. And any arguments that "the data is expensive to collect and proprietary" can be countered with "code is expensive to write and proprietary, yet open source still works".

Dayshine 8y ago
Some data cannot be shared due to patient confidentially. In fact, almost all medical trials or epidemiological studies use data that can't be shared outside the study.
- marcus_holmes 8y ago
  surely there are methods of concealing patient identity? We do this with lots of other confidential personal information...
  - Dayshine 8y ago
    But then the research isn't reproducible...
    - stevemadere 8y ago
      Hashing identity solves this. All of the data can be anonymized while retaining the linkage of measurements and procedures performed on the same subject. e.g. We don't know who this guy is, but we do know that every measurement of him was linked to this same enormous random string.
      - Dayshine 8y ago
        Only if your data is very limited in depth. When doing data linking I can usually identify one person in a dataset of almost 1 million from three innocent seeming pieces of data. Pseudonimisation doesn't really work.