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I'm happy to announce that DASH (the Harvard open-access repository) just passed the milestone of 10 million downloads.

The business-as-usual DASH home page.
https://dash.harvard.edu/

The real-time download counter with confetti.
https://dash.harvard.edu/downloads.html

#oa #openaccess 

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Check out the latest Tomviz release, #OpenSource 3D tomography for materials science with a reproducible data pipeline. Lots of bug fixes, new features, improved installers, new support for macOS retinal resolution displays, and more

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Open Knowledge International (where I work) has funding to work with researchers on trialing our "Frictionless Data" approach across various research disciplines. Read the link below to find out more :).

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A new science publisher seems to have appeared recently, or publisher is probably not the right word… science.ai is apparently neither a journal nor a publisher per se. Rather, they seem to be focusing on developing a new publishing platform that provides…

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OpenCon2015 shows how to make a conference open
I am currently following OpenCon2015 online and really enjoying the experience. There are two main components: A YouTube live feed and a Twitter feed (#opencon) .   The Twitter feed and the high number of live Tweets is really what makes this work!  You not...

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Slides for a talk I gave in Sydney on how openness boosts research. Feel free to use/adapt under CC-BY. I think it's an important message to get across: it's not that we "ought" to be more open, but that if we are open it's an advantage, because the research is then better/faster/more robust. I know everyone here gets this.

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A stumbling block for  #openscience  is the need to anonymise participant identities before posting their data. While many people have written about how they integrate open science into lab practices, I haven't seen much on anonymising data.

My R program anonymises my participants' data using an encryption key that I read in from a second file. This allows me to keep a project's experiment code, analysis code, and raw data all in a single git repository that github keeps track of and makes available online.  

The "loadAnonymiseSaveData.R" https://github.com/alexholcombe/MOTcircular/blob/master/dataRaw/loadAnonymiseSaveData.R file finds all the raw data files and de-identifies them when combining them. The participants are still kept separate but they are assigned new names using an encryption key read from a separate, non-public file (its name is listed in .gitignore so that git won't archive it). The use of an encryption key means the participants can be re-identified later by those who are given the key, so I can track things back all the way to the original untouched files generated by the experiment. A full audit trail like this is important for checking for errors (and fraud).

After this anonymisation step, the R files in my analysis folder https://github.com/alexholcombe/MOTcircular/tree/master/analysis analyse the anonymised data file, and all this can be viewed or downloaded by anyone, even while I am still collecting data.

Obviously, if you aren't ready to use git, this isn't going to help you. Software Carpentry (https://software-carpentry.org/) provides free classes to bring researchers up to speed.

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New OA mandate from the US Agency for Healthcare Research and Quality (AHRQ).

The Agency for Healthcare Research and Quality (AHRQ) just became the second US federal agency to adopt an OA mandate under the Obama White House directive of February 2013. 
http://www.ahrq.gov/funding/policies/publicaccess/index.html

The first such OA mandate came from the Department of Energy (DOE). For the reasons why I found it deeply flawed, see my blog post from August 2014. 
https://plus.google.com/+PeterSuber/posts/ZHRXEvLoq4n

On the plus side, the AHRQ avoids one of the biggest mistakes of the DOE policy. AHRQ will use OA repositories independent of publishers. As much as it can, the DOE will use publisher-hosted OA. AHRQ will disregard CHORUS, while DOE will depend on CHORUS. 

On the minus side, the AHRQ shares one of the biggest mistakes of the DOE policy. It is silent on open licensing and reuse, even though the White House guidelines explicitly require agency policies to "maximize the potential for...creative reuse." (To be more precise, the AHRQ wants reuse for data, but is silent on reuse for articles.)

We know that the White House approved the DOE policy. I can't tell yet whether it has approved the AHRQ policy. If it has, that will confirm the conclusion that +The White House will not enforce its own guidelines. If it hasn't yet approved the new policy, and is still deliberating, then there's hope that public comments can persuade it to send agency policies back to the drawing board to comply with the reuse requirement.

Here's my quick take on the AHRQ policy strengths and weaknesses:

Strengths
* The AHRQ policy does not rely on CHORUS or publisher-hosted OA. For articles, it will use PubMed Central. For data, it will outsource to a still-unnamed commercial repository.
* It covers data as well as articles.
* It wants data to be freely available at time of publication, without embargo.
* It adopts the NIH mechanism to enforce the deposit requirement, including the potential withholding of funds to non-compliant grantees.

Weaknesses
* It's silent on the timing of the deposit of articles. For example, it doesn't require deposit at the time of acceptance or before the time of publication.
* It's silent on open licensing and reuse.

The AHRQ permits embargoes up to 12 months. That's a weakness, but unfortunately it's one allowed, even encouraged, by the White House guidelines. 

For background, see the Obama White House directive itself from February 2013.
http://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf

Also see my March 2013 article on the Obama directive and FASTR. 
http://dash.harvard.edu/handle/1/10528299

Also see the AHRQ home page.
http://www.ahrq.gov/

#oa #openaccess #ahrq  
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