Top Tweets for #tpdl2016
#TPDL2016
https://t.co/fDK2Q5cjbn
@ibnesayeed et al. proposed Random Searcher Model (RSM) to discover the holding of web archives using their profiles

How to turn an abstract into a video abstract #VideosinDigitalLibraries #TPDL2016 @TIBHannover #openaccess see: https://t.co/SGCHMx3fLM /ab
Videos in digital libraries workshop at #TPDL2016: slidedecks online, videos will be soon: https://t.co/mk73xHCKei

One of the highlights of last week's #TPDL2016: workshop on video in science dissemination. Useful food for thought! https://t.co/x0Iw6d7FaV
#nkos slides by @hugo_manguinhas about @eu_sounds's #lodlam work: https://t.co/SNaBEHHeSp #tpdl2016 @EuropeanaTech https://t.co/O5esGJbb7g
Capturing your research on film: hints and tips from the #tpdl2016 conference
https://t.co/ZDzbzca83Y

Capturing your research on film: hints and tips from the #tpdl2016 conference
https://t.co/ZDzbzca83Y

Markus presenting executable research compendium + publication process @RepScience2016 workshop #TPDL2016

Sünje Dallmeier-Tiessen on reproducibility at @CERN at @RepScience2016 workshop #TPDL2016 #hardreproducibleresearch

While Hugo @hugo_manguinhas is giving talk on alignment to MIMO vocabulary, I created https://t.co/DzKbkYClz0 #NKOS #TPDL2016
Congratulation to the winners of the #tpdl2016 Best Paper Award and Best Poster/demo Award https://t.co/o8hZEQt3MO

Looking forward to hear more interesting talks on reproducibility during first RepScience workshop #TPDL2016
#NKOS bibliography (2000-2015) presented by @Philipp_Mayr
full paper: https://t.co/807WGT1ix8
data: https://t.co/LGk3zqYXkN #tpdl2016
o2r team members Markus and Daniel (@nordholmen) present executable research containers today @RepScience2016 workshop at #TPDL2016 today!
Welcome back Sawood! Anticipate joyful talking #digitalarchive #TPDL2016 @oducs @ODUnow @ODUIntlPrograms @StromeEC https://t.co/SKSyvhahwn
We're happy to welcome @ibnesayeed from @WebSciDL tomorrow on #Stendal Campus @hs_magdeburg @ProfvonVelsen @maherzog

@ststaab presenting Text Mining using LDA with Context #tpdl2016

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