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1Two perspectives on the future of computer sciences - YouTube
https://www.youtube.com/watch?v=EOuGmRXsb4g
9 months ago by @hotho
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science
computer
informatik
future
perspective
sciencecomputerinformatikfutureperspective
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1https://8530121.fs1.hubspotusercontent-na1.net/hubfs/8530121/Website%20Relaunch%202022/Blogbeiträge/DASC-PM_v1.1.pdf
https://8530121.fs1.hubspotusercontent-na1.net/hubfs/8530121/Website%20Relaunch%202022/Blogbeitr%C3%A4ge/DASC-PM_v1.1.pdf
11 months ago by @hotho
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science
Modell
process
data
model
scienceModellprocessdatamodel
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1Compilers: Principles, Techniques, and Tools (Dragon Book)
https://suif.stanford.edu/dragonbook/
a year ago by @jaeschke
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science
computer
parser
book
dragon
compiler
programming
sciencecomputerparserbookdragoncompilerprogramming
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1Titanic Model with 90% accuracy | Kaggle
https://www.kaggle.com/code/vinothan/titanic-model-with-90-accuracy
a year ago by @becker
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sciencekaggletitanicdata
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1kaggle-titanic/titanic-solution.ipynb at master · minsuk-heo/kaggle-titanic
https://github.com/minsuk-heo/kaggle-titanic/blob/master/titanic-solution.ipynb
a year ago by @becker
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titanic
data
sciencetitanicdata
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1Data Science Club | Becker Lab
Hallo
a year ago by @becker
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science
dsc
dts
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club
dtsclub
sciencedscdtsdataclubdtsclub
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3Network Science by Albert-László Barabási
http://networksciencebook.com/
2 years ago by @jaeschke
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science
social
sna
book
analysis
network
sciencesocialsnabookanalysisnetwork
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1Tired: PCA + kmeans, Wired: UMAP + GMM | R-bloggers
https://www.r-bloggers.com/2021/06/tired-pca-kmeans-wired-umap-gmm/
2 years ago by @becker
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umap
science
comparison
data
gmm
kmeans
pca
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umapsciencecomparisondatagmmkmeanspcacombination
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1leon-thomm/Ryven: Flow-based visual scripting for Python
https://github.com/leon-thomm/Ryven
2 years ago by @becker
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science
jupyter
python
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dag
notebook
programming
sciencejupyterpythondatadagnotebookprogramming
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1wonderworks-software/PyFlow: Visual scripting framework for python - https://wonderworks-software.github.io/PyFlow
https://github.com/wonderworks-software/PyFlow
2 years ago by @becker
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science
jupyter
python
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dag
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programming
sciencejupyterpythondatadagnotebookprogramming
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1DAG Based notebooks? - JupyterLab - Jupyter Community Forum
https://discourse.jupyter.org/t/dag-based-notebooks/11173
2 years ago by @becker
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science
jupyter
python
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dag
notebook
programming
sciencejupyterpythondatadagnotebookprogramming
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1NEXTGEN JANE
https://www.nextgenjane.com/
2 years ago by @becker
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science
period
bio
startup
women
health
scienceperiodbiostartupwomenhealth
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1Technology
https://www.bighatbio.com/technology
2 years ago by @becker
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bio
startup
sciencebiostartup
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1BigScience Research Workshop
https://bigscience.huggingface.co/
2 years ago by @hotho
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lm
science
lingual
language
model
big
multi
research
lmsciencelinguallanguagemodelbigmultiresearch
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2CORNELL CS4780 "Machine Learning for Intelligent Systems" - YouTube
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
3 years ago by @becker
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science
mining
boosting
data
machine
kilian
course
lecture
learning
weinberger
scienceminingboostingdatamachinekiliancourselecturelearningweinberger
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119: Boosting
https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote19.html
3 years ago by @becker
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mining
boosting
data
machine
kilian
course
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learning
weinberger
scienceminingboostingdatamachinekiliancourselecturelearningweinberger
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1JMIR Medical Informatics
https://medinform.jmir.org/
3 years ago by @nosebrain
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science
journal
computer
medical
sciencejournalcomputermedical
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1JBHI Homepage – EMBS
https://www.embs.org/jbhi/
3 years ago by @nosebrain
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science
journal
computer
medical
sciencejournalcomputermedical
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1Wayback Machine: A Practical Introduction to DataStructures and Algorithm Analysis
https://web.archive.org/web/20180403090554/http://home.deib.polimi.it/morzenti/Info3/notes.pdf
4 years ago by @jaeschke
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science
computer
data
mk3.3
ddm
algorithm
cs
structure
sciencecomputerdatamk3.3ddmalgorithmcsstructure
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1ACS Webinars | Active vs. Passive Voice in Scientific Writing
https://www.acs.org/content/dam/acsorg/events/professional-development/Slides/2015-04-09-active-passive.pdf
4 years ago by @becker
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science
writing
voice
scientific
academic
active
passive
sciencewritingvoicescientificacademicactivepassive
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1Python Data Science Handbook
J. VanderPlas. O'Reilly Media, Inc., (2016)
10 days ago by @jaeschke
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science
python
book
data
daa_botw
handbook
sciencepythonbookdatadaa_botwhandbook
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2Computer Systems: A Programmer's Perspective
R. Bryant, and D. O'Hallaron. Pearson, 2nd edition, (2011)
2 months ago by @jaeschke
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science
computer
book
cpu
cs
programming
sciencecomputerbookcpucsprogramming
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2Real-Time Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features
T. Nguyen, H. Nguyen, M. Sartipi, and M. Fisichella. Transportation Research Record: Journal of the Transportation Research Board, 2678 (1): 296–308 (May 2023)
2 months ago by @l3s
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myown
detection
data
automatic
vehicle
from:erichoang
recognition
identification
science
systems
pattern
machine
vision
myowndetectiondataautomaticvehiclefrom:erichoangrecognitionidentificationsciencesystemspatternmachinevision
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2Real-Time Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features
T. Nguyen, H. Nguyen, M. Sartipi, and M. Fisichella. Transportation Research Record: Journal of the Transportation Research Board, 2678 (1): 296–308 (May 2023)
2 months ago by @erichoang
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myown
detection
data
automatic
vehicle
recognition
identification
science
systems
pattern
machine
vision
myowndetectiondataautomaticvehiclerecognitionidentificationsciencesystemspatternmachinevision
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1Rebuttal How-To: Strategies, Tactics, and the Big Picture in Research
D. Yao. Commun. ACM, 67 (1): 47–49 (Dec 21, 2023)
3 months ago by @jaeschke
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science
writing
review
phd
rebuttal
research
sciencewritingreviewphdrebuttalresearch
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2Abstractions, their algorithms, and their compilers
A. Aho, and J. Ullman. Communications of the ACM, 65 (2): 76--91 (January 2022)
a year ago by @jaeschke
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science
computer
abstraction
parser
language
turing
lecture
compiler
algorithm
cs
programming
sciencecomputerabstractionparserlanguageturinglecturecompileralgorithmcsprogramming
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1Compilers: Principles, Techniques, and Tools
A. Aho, M. Lam, R. Sethi, and J. Ullman. Pearson Education, Inc, 2nd edition, (2006)
a year ago by @jaeschke
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science
computer
parser
book
dragon
compiler
cs
sciencecomputerparserbookdragoncompilercs
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2Analyzing the Web: Are Top Websites Lists a Good Choice for Research?
T. Alby, and R. Jäschke. Proceedings of the International Conference on Theory and Practice of Digital Libraries, page 11--25. Cham, Springer, (2022)
2 years ago by @jaeschke
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science
myown
web
tpdl
commoncrawl
archive
2022
alexa
crawl
research
sciencemyownwebtpdlcommoncrawlarchive2022alexacrawlresearch
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1Vision and natural language for metadata extraction from scientific PDF documents
Z. Boukhers, and A. Bouabdallah. Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries, ACM, (June 2022)
2 years ago by @jaeschke
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nlp
pdf
science
unknowndata
metadata
citation
social
extraction
research
nlppdfscienceunknowndatametadatacitationsocialextractionresearch
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2The scientific body of knowledge--Whose body does it serve? A spotlight on oral contraceptives and women’s health factors in neuroimaging
C. Taylor, L. Pritschet, and E. Jacobs. Frontiers in neuroendocrinology, (2021)
2 years ago by @becker
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imaging
hormones
sex
citedby:scholar:timestamp:2022-5-5
neuro
citedby:scholar:count:8
m:id:contraception:node
contraception
m:
m:id:contraception:coordinate:y:-535.9444732666016
brain
science
project:contraception
m:id:contraception:coordinate:x:-223.6855764389038
oral
imaginghormonessexcitedby:scholar:timestamp:2022-5-5neurocitedby:scholar:count:8m:id:contraception:nodecontraceptionm:m:id:contraception:coordinate:y:-535.9444732666016brainscienceproject:contraceptionm:id:contraception:coordinate:x:-223.6855764389038oral
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5Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
S. Raschka, J. Patterson, and C. Nolet. Information, 11 (4): 193 (2020)
2 years ago by @becker
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python
rapids
artifical
data
ai
py
learning
gpu
intelligence
paper:fastcor
science
library
machine
pythonrapidsartificaldataaipylearninggpuintelligencepaper:fastcorsciencelibrarymachine
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1RAPIDS: Collection of Libraries for End to End GPU Data Science
R. Team. (2018)
2 years ago by @becker
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science
library
python
rapids
data
py
gpu
paper:fastcor
sciencelibrarypythonrapidsdatapygpupaper:fastcor
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1Towards a Reference Model for Scientific Visualization Systems
W. Felger, M. Frühauf, M. Göbel, R. Gnatz, and G. Hofmann. Visualization in Scientific Computing, page 63--74. Berlin, Heidelberg, Springer, (1994)
2 years ago by @jaeschke
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science
reference
visualization
model
ddm
mk5.4
research
sciencereferencevisualizationmodelddmmk5.4research
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4Googleology is Bad Science
A. Kilgarriff. Computational Linguistics, 33 (1): 147--151 (March 2007)
2 years ago by @jaeschke
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science
web
google
sciencewebgoogle
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2Developing Open Source Educational Resources for Machine Learning and Data Science
L. Bothmann, S. Strickroth, G. Casalicchio, D. Rügamer, M. Lindauer, F. Scheipl, and B. Bischl. (2021)cite arxiv:2107.14330Comment: 6 pages.
2 years ago by @jaeschke
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science
data
machine
learning
oer
aiskills
ml
sciencedatamachinelearningoeraiskillsml
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1New media, familiar dynamics: academic hierarchies influence academics' following behaviour on Twitter
R. Jäschke, S. Linek, and C. Hoffmann. (October 2017)
3 years ago by @jaeschke
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follower
science
myown
twitter
social
sna
academic
analysis
network
blog
research
followersciencemyowntwittersocialsnaacademicanalysisnetworkblogresearch
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1Machine learning as an experimental science (revisited)
C. Drummond. In AAAI Workshop on Evaluation Methods for Machine Learning, page 1--5. (2006)
3 years ago by @jaeschke
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science
experiment
evaluation
machine
learning
ml
research
scienceexperimentevaluationmachinelearningmlresearch
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2The fourth paradigm: data-intensive scientific discovery
A. Hey, S. Tansley, and K. Tolle (Eds.) Microsoft research Redmond, WA, (October 2009)
3 years ago by @jaeschke
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science
datascience
discovery
ddm
mk5.4
research
sciencedatasciencediscoveryddmmk5.4research
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1Warcbase
J. Lin, I. Milligan, J. Wiebe, and A. Zhou. Journal on Computing and Cultural Heritage, 10 (4): 1--30 (October 2017)
3 years ago by @parismic
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science
social
warc
base
sciencesocialwarcbase
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1Eleven grand challenges in single-cell data science
D. Lähnemann, J. Köster, E. Szczurek, D. McCarthy, S. Hicks, M. Robinson, C. Vallejos, K. Campbell, N. Beerenwinkel, A. Mahfouz and 1 other author(s). Genome biology, 21 (1): 1--35 (2020)
4 years ago by @becker
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cytometry
citedby:scholar:count:36
data
learning
grand
cell
citedby:scholar:timestamp:2020-8-16
science
machine
challenge
challenges
cyto
single
cytof
cytometrycitedby:scholar:count:36datalearninggrandcellcitedby:scholar:timestamp:2020-8-16sciencemachinechallengechallengescytosinglecytof
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