Soumettre la recherche
Mettre en ligne
The road from good software engineering to good science...is a two way street
•
Télécharger en tant que ODP, PDF
•
0 j'aime
•
352 vues
University of Minnesota, Duluth
Suivre
Invited talk at SETQA-2009 workshop http://compbio.uchsc.edu/SETQA-NLP2009/index.shtml
Lire moins
Lire la suite
Technologie
Signaler
Partager
Signaler
Partager
1 sur 196
Télécharger maintenant
Recommandé
The Relationship Between Development Problems and Use of Software Engineering...
The Relationship Between Development Problems and Use of Software Engineering...
SoftwarePractice
Water fall model
Water fall model
Maria Saleem
Market Basket Analysis Algorithm with Map/Reduce of Cloud Computing
Market Basket Analysis Algorithm with Map/Reduce of Cloud Computing
Jongwook Woo
Advantages of java
Advantages of java
xxx007008
Oracle database introduction
Oracle database introduction
Mohammad Javad Beheshtian
Design Issue(Reuse) in Software Engineering SE14
Design Issue(Reuse) in Software Engineering SE14
koolkampus
How can we reduce open defecation in rural India?
How can we reduce open defecation in rural India?
Yogesh Upadhyaya
Esoft Metro Campus - Certificate in c / c++ programming
Esoft Metro Campus - Certificate in c / c++ programming
Rasan Samarasinghe
Recommandé
The Relationship Between Development Problems and Use of Software Engineering...
The Relationship Between Development Problems and Use of Software Engineering...
SoftwarePractice
Water fall model
Water fall model
Maria Saleem
Market Basket Analysis Algorithm with Map/Reduce of Cloud Computing
Market Basket Analysis Algorithm with Map/Reduce of Cloud Computing
Jongwook Woo
Advantages of java
Advantages of java
xxx007008
Oracle database introduction
Oracle database introduction
Mohammad Javad Beheshtian
Design Issue(Reuse) in Software Engineering SE14
Design Issue(Reuse) in Software Engineering SE14
koolkampus
How can we reduce open defecation in rural India?
How can we reduce open defecation in rural India?
Yogesh Upadhyaya
Esoft Metro Campus - Certificate in c / c++ programming
Esoft Metro Campus - Certificate in c / c++ programming
Rasan Samarasinghe
Introduction & Manual Testing
Introduction & Manual Testing
VenkateswaraRao Siddabathula
Algorithm analysis
Algorithm analysis
sumitbardhan
Algorithm analysis (All in one)
Algorithm analysis (All in one)
jehan1987
RSA Algorithm
RSA Algorithm
West University of Timisoara
UML Case Tools
UML Case Tools
Ashesh R
Introduction to Java Programming
Introduction to Java Programming
Ravi Kant Sahu
Software Development Life Cycle (SDLC)
Software Development Life Cycle (SDLC)
Alaa' Amr Amin
Introduction to java
Introduction to java
Veerabadra Badra
Embedded System Basics
Embedded System Basics
Dr M Muruganandam Masilamani
Deep C
Deep C
Olve Maudal
LinkedIn SlideShare: Knowledge, Well-Presented
LinkedIn SlideShare: Knowledge, Well-Presented
SlideShare
AliceVision : pipeline de reconstruction 3D open source
AliceVision : pipeline de reconstruction 3D open source
Open Source Experience
Better, Stronger, Faster Failures
Better, Stronger, Faster Failures
gnat
IbelongInCSsample.pdf
IbelongInCSsample.pdf
Angela DeHart
Limits of Machine Learning
Limits of Machine Learning
Meir Maor
Scientific method
Scientific method
Toni Legg
Unstructure: Smashing the Boundaries of Data (SxSWi 2014)
Unstructure: Smashing the Boundaries of Data (SxSWi 2014)
Ian Varley
Artificial intelligence
Artificial intelligence
Umesh Meher
fuzzy logic,proposition with types and example
fuzzy logic,proposition with types and example
stellan7
fuzzy.ppt
fuzzy.ppt
AtmacaDevrim
Exponential tech.pptx
Exponential tech.pptx
RajalakshmiRamu1
Intoduction of Artificial Intelligence
Intoduction of Artificial Intelligence
Babasaheb Bhimrao Ambedakar University
Contenu connexe
En vedette
Introduction & Manual Testing
Introduction & Manual Testing
VenkateswaraRao Siddabathula
Algorithm analysis
Algorithm analysis
sumitbardhan
Algorithm analysis (All in one)
Algorithm analysis (All in one)
jehan1987
RSA Algorithm
RSA Algorithm
West University of Timisoara
UML Case Tools
UML Case Tools
Ashesh R
Introduction to Java Programming
Introduction to Java Programming
Ravi Kant Sahu
Software Development Life Cycle (SDLC)
Software Development Life Cycle (SDLC)
Alaa' Amr Amin
Introduction to java
Introduction to java
Veerabadra Badra
Embedded System Basics
Embedded System Basics
Dr M Muruganandam Masilamani
Deep C
Deep C
Olve Maudal
LinkedIn SlideShare: Knowledge, Well-Presented
LinkedIn SlideShare: Knowledge, Well-Presented
SlideShare
En vedette
(11)
Introduction & Manual Testing
Introduction & Manual Testing
Algorithm analysis
Algorithm analysis
Algorithm analysis (All in one)
Algorithm analysis (All in one)
RSA Algorithm
RSA Algorithm
UML Case Tools
UML Case Tools
Introduction to Java Programming
Introduction to Java Programming
Software Development Life Cycle (SDLC)
Software Development Life Cycle (SDLC)
Introduction to java
Introduction to java
Embedded System Basics
Embedded System Basics
Deep C
Deep C
LinkedIn SlideShare: Knowledge, Well-Presented
LinkedIn SlideShare: Knowledge, Well-Presented
Similaire à The road from good software engineering to good science...is a two way street
AliceVision : pipeline de reconstruction 3D open source
AliceVision : pipeline de reconstruction 3D open source
Open Source Experience
Better, Stronger, Faster Failures
Better, Stronger, Faster Failures
gnat
IbelongInCSsample.pdf
IbelongInCSsample.pdf
Angela DeHart
Limits of Machine Learning
Limits of Machine Learning
Meir Maor
Scientific method
Scientific method
Toni Legg
Unstructure: Smashing the Boundaries of Data (SxSWi 2014)
Unstructure: Smashing the Boundaries of Data (SxSWi 2014)
Ian Varley
Artificial intelligence
Artificial intelligence
Umesh Meher
fuzzy logic,proposition with types and example
fuzzy logic,proposition with types and example
stellan7
fuzzy.ppt
fuzzy.ppt
AtmacaDevrim
Exponential tech.pptx
Exponential tech.pptx
RajalakshmiRamu1
Intoduction of Artificial Intelligence
Intoduction of Artificial Intelligence
Babasaheb Bhimrao Ambedakar University
Discovery Posters
Discovery Posters
adepaolis
PRACTICAL RESEARCH 1- MODULE (3 Days).docx
PRACTICAL RESEARCH 1- MODULE (3 Days).docx
MARVANLLANTO2
Ratcheting evolution
Ratcheting evolution
Joshua Knowles
Lecture 1 Slides -Introduction to algorithms.pdf
Lecture 1 Slides -Introduction to algorithms.pdf
RanvinuHewage
Technical+writing1
Technical+writing1
guestb27fae
Arthur Weglein, Houston Physics Professor Interview
Arthur Weglein, Houston Physics Professor Interview
Arthur Weglein
STEM Day
STEM Day
Walkersville Middle School
The Kipling-Zachman lens
The Kipling-Zachman lens
Richard Veryard
1. The Game Of The Century
1. The Game Of The Century
Alexandre Linhares
Similaire à The road from good software engineering to good science...is a two way street
(20)
AliceVision : pipeline de reconstruction 3D open source
AliceVision : pipeline de reconstruction 3D open source
Better, Stronger, Faster Failures
Better, Stronger, Faster Failures
IbelongInCSsample.pdf
IbelongInCSsample.pdf
Limits of Machine Learning
Limits of Machine Learning
Scientific method
Scientific method
Unstructure: Smashing the Boundaries of Data (SxSWi 2014)
Unstructure: Smashing the Boundaries of Data (SxSWi 2014)
Artificial intelligence
Artificial intelligence
fuzzy logic,proposition with types and example
fuzzy logic,proposition with types and example
fuzzy.ppt
fuzzy.ppt
Exponential tech.pptx
Exponential tech.pptx
Intoduction of Artificial Intelligence
Intoduction of Artificial Intelligence
Discovery Posters
Discovery Posters
PRACTICAL RESEARCH 1- MODULE (3 Days).docx
PRACTICAL RESEARCH 1- MODULE (3 Days).docx
Ratcheting evolution
Ratcheting evolution
Lecture 1 Slides -Introduction to algorithms.pdf
Lecture 1 Slides -Introduction to algorithms.pdf
Technical+writing1
Technical+writing1
Arthur Weglein, Houston Physics Professor Interview
Arthur Weglein, Houston Physics Professor Interview
STEM Day
STEM Day
The Kipling-Zachman lens
The Kipling-Zachman lens
1. The Game Of The Century
1. The Game Of The Century
Plus de University of Minnesota, Duluth
Muslims in Machine Learning workshop (NeurlPS 2021) - Automatically Identifyi...
Muslims in Machine Learning workshop (NeurlPS 2021) - Automatically Identifyi...
University of Minnesota, Duluth
Automatically Identifying Islamophobia in Social Media
Automatically Identifying Islamophobia in Social Media
University of Minnesota, Duluth
What Makes Hate Speech : an interactive workshop
What Makes Hate Speech : an interactive workshop
University of Minnesota, Duluth
Algorithmic Bias - What is it? Why should we care? What can we do about it?
Algorithmic Bias - What is it? Why should we care? What can we do about it?
University of Minnesota, Duluth
Algorithmic Bias : What is it? Why should we care? What can we do about it?
Algorithmic Bias : What is it? Why should we care? What can we do about it?
University of Minnesota, Duluth
Duluth at Semeval 2017 Task 6 - Language Models in Humor Detection
Duluth at Semeval 2017 Task 6 - Language Models in Humor Detection
University of Minnesota, Duluth
Who's to say what's funny? A computer using Language Models and Deep Learning...
Who's to say what's funny? A computer using Language Models and Deep Learning...
University of Minnesota, Duluth
Duluth at Semeval 2017 Task 7 - Puns upon a Midnight Dreary, Lexical Semantic...
Duluth at Semeval 2017 Task 7 - Puns upon a Midnight Dreary, Lexical Semantic...
University of Minnesota, Duluth
Puns upon a midnight dreary, lexical semantics for the weak and weary
Puns upon a midnight dreary, lexical semantics for the weak and weary
University of Minnesota, Duluth
The horizon isn't found in a dictionary : Identifying emerging word senses a...
The horizon isn't found in a dictionary : Identifying emerging word senses a...
University of Minnesota, Duluth
Screening Twitter Users for Depression and PTSD
Screening Twitter Users for Depression and PTSD
University of Minnesota, Duluth
Duluth : Word Sense Discrimination in the Service of Lexicography
Duluth : Word Sense Discrimination in the Service of Lexicography
University of Minnesota, Duluth
Pedersen masters-thesis-oct-10-2014
Pedersen masters-thesis-oct-10-2014
University of Minnesota, Duluth
MICAI 2013 Tutorial Slides - Measuring the Similarity and Relatedness of Conc...
MICAI 2013 Tutorial Slides - Measuring the Similarity and Relatedness of Conc...
University of Minnesota, Duluth
What it's like to do a Master's thesis with me (Ted Pedersen)
What it's like to do a Master's thesis with me (Ted Pedersen)
University of Minnesota, Duluth
Pedersen naacl-2013-demo-poster-may25
Pedersen naacl-2013-demo-poster-may25
University of Minnesota, Duluth
Pedersen semeval-2013-poster-may24
Pedersen semeval-2013-poster-may24
University of Minnesota, Duluth
Talk at UAB, April 12, 2013
Talk at UAB, April 12, 2013
University of Minnesota, Duluth
Feb20 mayo-webinar-21feb2012
Feb20 mayo-webinar-21feb2012
University of Minnesota, Duluth
Ihi2012 semantic-similarity-tutorial-part1
Ihi2012 semantic-similarity-tutorial-part1
University of Minnesota, Duluth
Plus de University of Minnesota, Duluth
(20)
Muslims in Machine Learning workshop (NeurlPS 2021) - Automatically Identifyi...
Muslims in Machine Learning workshop (NeurlPS 2021) - Automatically Identifyi...
Automatically Identifying Islamophobia in Social Media
Automatically Identifying Islamophobia in Social Media
What Makes Hate Speech : an interactive workshop
What Makes Hate Speech : an interactive workshop
Algorithmic Bias - What is it? Why should we care? What can we do about it?
Algorithmic Bias - What is it? Why should we care? What can we do about it?
Algorithmic Bias : What is it? Why should we care? What can we do about it?
Algorithmic Bias : What is it? Why should we care? What can we do about it?
Duluth at Semeval 2017 Task 6 - Language Models in Humor Detection
Duluth at Semeval 2017 Task 6 - Language Models in Humor Detection
Who's to say what's funny? A computer using Language Models and Deep Learning...
Who's to say what's funny? A computer using Language Models and Deep Learning...
Duluth at Semeval 2017 Task 7 - Puns upon a Midnight Dreary, Lexical Semantic...
Duluth at Semeval 2017 Task 7 - Puns upon a Midnight Dreary, Lexical Semantic...
Puns upon a midnight dreary, lexical semantics for the weak and weary
Puns upon a midnight dreary, lexical semantics for the weak and weary
The horizon isn't found in a dictionary : Identifying emerging word senses a...
The horizon isn't found in a dictionary : Identifying emerging word senses a...
Screening Twitter Users for Depression and PTSD
Screening Twitter Users for Depression and PTSD
Duluth : Word Sense Discrimination in the Service of Lexicography
Duluth : Word Sense Discrimination in the Service of Lexicography
Pedersen masters-thesis-oct-10-2014
Pedersen masters-thesis-oct-10-2014
MICAI 2013 Tutorial Slides - Measuring the Similarity and Relatedness of Conc...
MICAI 2013 Tutorial Slides - Measuring the Similarity and Relatedness of Conc...
What it's like to do a Master's thesis with me (Ted Pedersen)
What it's like to do a Master's thesis with me (Ted Pedersen)
Pedersen naacl-2013-demo-poster-may25
Pedersen naacl-2013-demo-poster-may25
Pedersen semeval-2013-poster-may24
Pedersen semeval-2013-poster-may24
Talk at UAB, April 12, 2013
Talk at UAB, April 12, 2013
Feb20 mayo-webinar-21feb2012
Feb20 mayo-webinar-21feb2012
Ihi2012 semantic-similarity-tutorial-part1
Ihi2012 semantic-similarity-tutorial-part1
Dernier
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
The Digital Insurer
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
naman860154
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
Sinan KOZAK
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Enterprise Knowledge
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
debabhi2
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
gurkirankumar98700
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
Pooja Nehwal
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Maria Levchenko
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Miguel Araújo
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
Martijn de Jong
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
Allon Mureinik
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
Enterprise Knowledge
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
The Digital Insurer
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Neo4j
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Delhi Call girls
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
Delhi Call girls
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024
Results
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Katpro Technologies
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
Michael W. Hawkins
Dernier
(20)
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
A Call to Action for Generative AI in 2024
A Call to Action for Generative AI in 2024
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
The road from good software engineering to good science...is a two way street
1.
The road from
good software engineering
2.
to good science
3.
...is a two
way street...
4.
Good
5.
6.
Quality
7.
Science
8.
Develop theories or
models that let us make predictions about the world
9.
Our world is
language...
10.
Do our work
so that others can reproduce our results
11.
Science
12.
Reproduce Results
13.
Good Software Engineering
14.
...are those methods
that result in software that anyone can use, anytime, anywhere...
15.
...to reproduce our
results...
16.
Experimental Results
in NLP/CL Papers
17.
Results are output
from test cases
18.
Empirical / experimental
results that you publish are the test cases for your ideas
19.
...and your software...
20.
Can't discount role
of softwawe
21.
...although many try...
22.
“It's really the
ideas that count...”
23.
“Well, the algorithm
is described in the paper...”
24.
“It's really just
a prototype...”
25.
“Well, I got
a new computer and I don't think the software made it to the new one...”
26.
“Ummm....my student left
and I don't quite know how he did all this...”
27.
I did this
experiment on X
28.
Here are the
results...
29.
Accept them
30.
No, the software
isn't available
31.
Neither is the
data
32.
I simply assume
you have 8 months available to reinvent my method
33.
And that you
can do that from an incomplete description
34.
Cheers!
35.
That's many things
36.
It's not science
37.
Empiricism is Not
a Matter of Faith Computational Linguistics September 2008
38.
Software and NLP
39.
40.
41.
Good Software
42.
It should work
43.
Anytime
44.
Anywhere
45.
For Anyone
46.
...and it should
certainly work for you 6 months in the future
47.
...or 5 years
from now...
48.
...and it should
work for someone else now, and 5 years from now..
49.
...even after you've
moved on and aren't answering email, even after the project is over
50.
If your software
can do that, it's pretty well engineered
51.
Will your software
work in 40 years?
52.
You should hope
so
53.
Make choices that
make that at least possible
54.
Think of your
software as a time capsule
55.
Think of it
as your chance for immortality
56.
57.
How many hours
have you spent away from loved ones, friends, adventure, nature, romance, and life...to create software?
58.
At least make
it last...
59.
Let someone 100
years from now unpack your code and data, and be able to read it, understand it, run it, and modify it
60.
Maybe it's your
grandchildren, wondering why they never saw you?
61.
Let yourself be
able to do the same thing in 10 years
62.
Will the Linux
Kernel be available and running in X years?
63.
There's a good
chance
64.
Company won't go
out of business
65.
ANSI C will
be around a long time
66.
Virtualization will keep
architectures alive even when hardware is gone
67.
Make choices that
give your code (and your legacy) a chance too
68.
Don't rely on
the newest priceiest weirdest goofball proprietary bleeding edge hardware and software
69.
70.
71.
Anyone
72.
...with $200?
73.
...with $20,000
74.
...with a PhD
in Computer Science?
75.
...and a staff
of 10?
76.
...with 4 weeks
available to debug?
77.
...and another 6
months to reimplement?
78.
Good
79.
Software won't stop
children from dying of war and disease...
80.
So be a
little humble
81.
Appreciate your good
fortune
82.
And push yourself
a little harder
83.
Don't act all
whiny and put-upon when people ask you for code or data
84.
Especially since a
lot of that work is funded by people working very hard making less in a month than we make a in a week
85.
...or that we
spend going to NAACL
86.
Appreciate our good
fortune
87.
Good Science
88.
Produces theories that
make reliable predictions about the world
89.
Reproducible Experimental Results
90.
Anytime
91.
Anywhere
92.
For Anyone
93.
Gravity
94.
A Good Theory
95.
Works now
96.
Will work in
10 years
97.
Works here
98.
Works on the
moon
99.
Works for me
100.
Works for you
101.
Falling Objects
102.
Gravity is a
force, not an artifact
103.
Telescope
104.
Works anytime,
anywhere, for anyone
105.
The old ones
still work
106.
107.
We share the
big ones...
108.
109.
If we have
access to the same resources, we can reproduce each other's results
110.
If we have
access to the same resources, we can reproduce each other's results
111.
We need to
work a lot harder (and engineer systems a lot better) to make that happen
112.
Not convinced?
113.
Conduct the following
experiment
114.
Randomly select
1 of your papers
115.
Reproduce your results
116.
If you can't...
117.
Do you think
anyone else can?
118.
What if nobody
could have reproduced Galileo's falling objects experimental results? Would we simply believe him?
119.
They barely believed
him at the time
120.
If you can,
try it for someone else's papers
121.
Lessons from Science
122.
We don't get
it right the first time
123.
If I have
seen further it is only by standing on the shoulders of giants
124.
125.
(who were mostly
wrong)
126.
Aristotle (384 –
322 BC)
127.
There are 4
elements
128.
The heavens are
different
129.
Different rules apply
130.
Before the telescope,
the heavens really were different
131.
Other planets were
balls of fire, like the stars, like the sun
132.
133.
Ptolemy (90 –
168)
134.
135.
136.
Crazy?
137.
Very reliably predicts
the movement of heavenly bodies
138.
Instrumentalist
139.
A theory that
reliably explains and predicts the existing data
140.
Realistic
141.
A theory that
describes things as they “really” are
142.
143.
144.
A great instrumentalist
A great data preserver and collector
145.
Copernicus (1473 -
1543)
146.
147.
Wasn't much of
an observer
148.
149.
Found Ptolmey's model
overly complicated
150.
Wanted a simpler
explanation
151.
Wanted a simpler
explanation
152.
Came up with
another model that was consistent with Ptolmey's data
153.
154.
Great!
155.
(Well, better)
156.
Uniform Motion
157.
Perfect circles
158.
159.
160.
Brahe (1546
- 1601)
161.
162.
A great observational
astronomer, the last naked eye astronomer
163.
164.
Galileo (1564
- 1642)
165.
166.
167.
1609 Telescope
168.
1610 Observed
4 moons of Jupiter
169.
Back to Tycho
170.
Made remarkably accurate
observations for 20 years
171.
Knew about Copernicus
172.
Arrived at his
own theory
173.
174.
A hybrid model
175.
Fits and predicts
the observed data
176.
Data Sharing
177.
178.
Kepler (1571 -
1630)
179.
180.
Why are there
6 planets?
181.
Why are they
so positioned?
182.
Geometry and Perfect
Solids
183.
184.
In 1601 Tycho
bequeathed his data...
185.
Kepler's Laws of
Planetary Motion
186.
Varying velocity
187.
Elliptical Orbits
188.
...around the Sun
189.
It was left
to Newton to work out what held the planets in place and made them move...
190.
History of Science?
191.
We are wrong
many many times before we are right
192.
Progress happens
when people leave their data and instruments behind
193.
Ptolemy (90
- 168) Copernicus (1473 - 1543) Tycho (1546 – 1601) Galileo (1564 - 1642) Kepler (1571 - 1630) Newton (1642 - 1727)
194.
Good science and
good software assume you don't get it right at first
195.
Leave something behind
for your successors to build on
196.
Here lies our
fried Ted We're sad that's he dead http://www.d.umn.edu/~tpederse
Télécharger maintenant