I have a robust method to quickly analyze histologically stained sections (suitable for ImageJ/MatLab) that I wish to publish, including sharing of the file/code that achieves this. I also have a good example of data that has been processed using this method. Any suggestions of a good journal for this?
In my opinion, the most user-friendly way to publish raw data and source code along with your research paper is to
- upload raw data to a free repository such as Zenodo (https://zenodo.org) or Figshare (https://figshare.com) where it is assigned a DOI
- share your source code on GitHub (https://github.com) and use Zenodo to create a DOI for the code
Then, in your research paper, you can simply cite the DOIs for your code and your raw data. If you (or anyone else) later uses the same raw data or code for a different project, it is very easy to refer to the DOIs. Plus, these platforms offer much better user interfaces for anyone who is interested in your code or data, as compared to the supplemental materials sections of most journals.
For your research paper describing the new method, I would suggest PLOS ONE, PLOS Computational Biology, Diagnostic Pathology or Oncotarget. These are all open access journals with a reasonable impact factor that are open to digital pathology manuscripts.
You might be interested in our latest digital pathology paper where we published our code on GitHub and Zenodo, used an open access dataset as raw data and published the article in PLOS ONE (Link attached).
Best regards, Jakob
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145572
The program ImageJ was reported in at least two publications:
Schneider, C.A., Rasband, W.S., Eliceiri, K.W. "NIH Image to ImageJ: 25 years of image analysis". Nature Methods 9, 671-675, 2012.
Abramoff, M.D., Magalhaes, P.J., Ram, S.J. "Image Processing with ImageJ". Biophotonics International, volume 11, issue 7, pp. 36-42, 2004.
I've seen other code (matlab, imageJ plugins) reported/published in journals, but the code itself (like ImageJ) is usually hosted elsewhere (like author's website). They have been in all sorts of journals.
So, I think the field is wide open for where you could publish it. Maybe pick a journal that you respect in the field and search the archives to see how many papers they publish on this topic.
I would love to see more codes published, especially ones that automate. When I do see them, they are usually included as supplementary files, and I have seen executable files included there as well.
As for journals, they tend to vary in (5-yr) impact factors as some of them can be rather specific:
PLoS ONE: http://www.ncbi.nlm.nih.gov/pubmed/24647355
Signal Processing, IEEE: http://dx.doi.org/10.1109/MSP.2014.2346443
BioTechniques: http://www.ncbi.nlm.nih.gov/pubmed/17936939 and http://www.ncbi.nlm.nih.gov/pubmed/26260089
Cellular Oncology: http://www.ncbi.nlm.nih.gov/pubmed/21538025
Histology and Histopathology: http://www.ncbi.nlm.nih.gov/pubmed/19012244
Computer Methods and Programs in Biomedicine: http://www.ncbi.nlm.nih.gov/pubmed/26093386
Computers in Biology and Medicine: http://www.ncbi.nlm.nih.gov/pubmed/18163985
In my opinion, the most user-friendly way to publish raw data and source code along with your research paper is to
- upload raw data to a free repository such as Zenodo (https://zenodo.org) or Figshare (https://figshare.com) where it is assigned a DOI
- share your source code on GitHub (https://github.com) and use Zenodo to create a DOI for the code
Then, in your research paper, you can simply cite the DOIs for your code and your raw data. If you (or anyone else) later uses the same raw data or code for a different project, it is very easy to refer to the DOIs. Plus, these platforms offer much better user interfaces for anyone who is interested in your code or data, as compared to the supplemental materials sections of most journals.
For your research paper describing the new method, I would suggest PLOS ONE, PLOS Computational Biology, Diagnostic Pathology or Oncotarget. These are all open access journals with a reasonable impact factor that are open to digital pathology manuscripts.
You might be interested in our latest digital pathology paper where we published our code on GitHub and Zenodo, used an open access dataset as raw data and published the article in PLOS ONE (Link attached).
Best regards, Jakob
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145572
Maybe in BioMedical Engineering Online, with ISSN: 1475925X, have a h-index of 38, its advantages is that in less than three weeks you'll have a result of the evaluation of your article.
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