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Home Page: https://social-science-data-editors.github.io/guidance/
License: Other
Guidance by Data Editors
Home Page: https://social-science-data-editors.github.io/guidance/
License: Other
GIS code often is a challenge. Need some pointers to guidance to not have to do it by hand.
Suggested by @abogdanoski
guidance/addtl-data-citation-guidance.md
Line 250 in 51f0bc7
Some code commingles the data early on, and prevents the creation of analysis and output even if that analysis does not rely on confidential data.
This may involve creating empty variables, placeholder values, synthetic data, or simply good programming practices.
https://journals.plos.org/plosbiology/s/authorship#loc-author-contributions and https://casrai.org/credit/. While not typical in social sciences (in particular in economics) might warrant a discussion.
The data (and code) authorship != paper authorship is not at all common in Economics. One example I can think of is https://www.aeaweb.org/articles?id=10.1257/pol.20170704, with replication package https://doi.org/10.3886/E110642V1 explicitly single-authored by one of the authors. I've also seen some papers (need to dig out references) where Github with code might be "Author A + RA" whereas paper is "Author A + Author B".
The generic guidance I have in mind is https://casrai.org/credit/, which has been adopted by some journals, such as PLOS https://journals.plos.org/plosbiology/s/authorship#loc-author-contributions, where it applies to the paper. If considering the data + the code as distinct first-class research objects, the CRediT taxonomy directly applies there as well, and there's nothing that requires that the two be the same.
Question received:
For the access date for cited datasets, what is the definition of “access date”? I believe it is the date the data were actually accessed. Is this correct? The wording of the question was whether it is "the dates we first accessed the actual data itself” or, instead, “the date we accessed the websites describing the data.” I believe it is the former.
It is, in fact the former.
Need to add this to relevant FAQ and guidance.
I read this comment in the FAQ:
If not, then what I suggest is to do the following
clean up the repo (possibly in a branch)
on Github, there is no way to fork to your own space, and a fork would carry the entire history anyway. So this assumes manual interaction (I’m going to assume you use the command line for this, this works in git-bash, or bash on Linux/OSX).
create a new clone of your (now cleaned) repo, and switch to the clean branch ``` git clone (WHATEVER) cd whatever git branch “cleaned”
now wipe out all git information: ``` rm -rf .git
create a new repo ``` git init
Add all files ``` git add *
I think there are two things going on here, which can be handled differently:
In the latter case, I'd recommend suggesting the git filter-branch
approach, which is essentially a way to split off a subset of a repo into a new repo (e.g. a subdirectory paper-1
). It will conveniently inherit the commit history as well. See guide:
https://help.github.com/en/articles/splitting-a-subfolder-out-into-a-new-repository
I think it would help if the FAQ delineated these two competing goals and stated more clearly the differences/approaches here.
Maybe Github made an update to their minimal theme?
In the template, we should make clearer where data citations go. I often have authors send me a ReadMe that is very good, but none of them put data citations in the References. Lars explained: in the DAS, the author will explain how to get access to the data, and cite it, but then the reference goes in the reference list. I don't think this is explicit enough.
Example:
" Trade data for 1974-2000 were downloaded from the NBER-UN World Trade Flows dataset (Feenstra & Lipsey, 2005) originally created by Feenstra et al (2005). Data can be directly downloaded using https://cid.econ.ucdavis.edu/nberus.html. We use fileswtf74 through wtf00. The data are in the public domain"
And then later in the references:
Feenstra & Lipsey. 2005. "NBER-United Nations Trade Data 1962-2000". Center for International Trade, UC Davis [distributor]. https://cid.econ.ucdavis.edu/nberus.html accessed on 2021-03-24.
https://gatesopenresearch.org/for-authors/data-guidelines#prepareyourdata
Note that Gates uses F1000 for publication, so there's likely duplication there.
In a case where the authors cannot name the company, you would cite as follows:
"Anonymous Firm (DATE OF FILE CREATION)"
and have in the references the following entry:
Anonymous Firm. (DATE OF FILE CREATION), Property Insurance claims. Accessed via Cornell Restricted Access Data Center (CRADC). Last accessed on (DATE).
where
See https://datakit.ap.org/ - seems quite flexible, and could be easily adapted to specific labs or work groups.
Downside: requires python (for command line aficionados)
Example IZA
Others should be added to this issue, or a separate pull request made referencing this issue.
See draft here: https://github.com/social-science-data-editors/guidance/blob/master/docker-guidelines.md
@floswald Any additions/comments?
The DOI is thus public, and all repositories will provide a suggested citation. One can also use https://www.doi2bib.org/ or https://citation.crosscite.org/ to get a citation.
This is trickier. The data does not necessarily have a title that is related to the paper. Some repositories allow authors to "reserve" a DOI (Zenodo) or to delay publication. For some repositories, the DOI, while not officially reserved, can be derived from information already available (see this FAQ for openICPSR, something similar may be possible at Dataverse).
In some cases, authors may be able to delay publication, and coordinate it with the publication of the article (openICPSR, possibly Zenodo).
See links in Twitter thread.
discussion about setwd() and similar:
Responses: 1 Jenny Bryant's take
https://doi.org/10.1371/journal.pcbi.1007007 (via @darribas) but also pointer to https://github.com/jupyter/nbdime
https://jupyterbook.org/en/stable/publish/gh-pages.html might do the trick - ability to classify articles by type, search efficiently, and possibly include some computational examples.
Add to to guidance on Citing Data and Code
The DOI is thus public, and all repositories will provide a suggested citation. One can also use https://www.doi2bib.org/ or https://citation.crosscite.org/ to get a citation.
This is trickier. The data does not necessarily have a title that is related to the paper. Some repositories allow authors to "reserve" a DOI (Zenodo) or to delay publication. For some repositories, the DOI, while not officially reserved, can be derived from information already available (see this FAQ for openICPSR, something similar may be possible at Dataverse).
In some cases, authors may be able to delay publication, and coordinate it with the publication of the article (openICPSR, possibly Zenodo).
The data deposit should be cited in the main manuscript, and referenced in the data availability statement (some journals) or the README (other journals).
Example:
https://geobgu.xyz/py/
https://github.com/rdahis/paper_template
Downside: requires Python
https://twitter.com/OmerOzakEcon/status/1359882541977714692
In case it is useful to someone, I have written an intro to python and GIS for my students
https://github.com/ozak/CompEcon
You can even try it out and use the system online for basic stuff. Or follow instructions to have a working GIS environment on your computer.
Some computations, as presented in the paper, may run for a very long time.
Authors should be encouraged (required?) to provide instructions on how to run "feasible" computations (instead of 1,000 bootstraps, run only 10; instead of full sample, run with a 1% sample) and how it might impact the output.
This is different from creating synthetic data.
Many researchers "copy the ado file to your personal directory" - this should be replaced with guidance on how to have a (persistent) project-specific directory for ADO files (using adopath
or better wholesale replacement of the sysdir
areas).
https://help.figshare.com/article/how-to-connect-figshare-with-your-github-account
wherever the existing Github-Zenodo connector shows up.
https://twitter.com/mberesewicz/status/1359943661245657090
For such tutorials, I highly recommend #geocomputation with R (https://geocompr.robinlovelace.net) by @jakub_nowosad
and @Robinlovelace
which I think is the most up-to-date reference for GIS with R.
Nature's list of repositories is the only one [...] that a requirement that the repository support anonymous sharing for the purpose of double-blind peer review. [...] The discovery of such an existing list of repositories was very helpful for me in drafting our own data policy because many journals and publishers that are currently requiring data sharing either (a) use single-blind or transparent peer review or (b) only require data and code sharing at the publication stage, not at the submission stage.
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