Category Archives: Science

Embrace logic

Let him who is not come to logic be plagued with continuous and everlasting filth.

Metalogicon II (1159)
John of Salisbury (1120-1180)

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Is the Universe a simulation?

From an article by Edward Frenkel in today’s New York Times:

Many mathematicians, when pressed, admit to being Platonists. The great logician Kurt Gödel argued that mathematical concepts and ideas “form an objective reality of their own, which we cannot create or change, but only perceive and describe”. But if this is true, how do humans manage to access this hidden reality?

We don’t know. But one fanciful possibility is that we live in a computer simulation based on the laws of mathematics — not in what we commonly take to be the real world. According to this theory, some highly advanced computer programmer of the future has devised this simulation, and we are unknowingly part of it. Thus when we discover a mathematical truth, we are simply discovering aspects of the code that the programmer used.

 
This hypothesis is by no means new; in Are you living in a computer simulation, Nick Bostrum argues that one of the following propositions is true:

  1. the human species is very likely to go extinct before reaching a “posthuman” stage;
  2. any posthuman civilisation is extremely unlikely to run a significant number of simulations of their evolutionary history (or variations thereof);
  3. we are almost certainly living in a computer simulation.

Also see: Constraints on the Universe as a Numerical Simulation.

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This Article Should Not Be Rejected

In 1990, Spanish philosopher Jon Perez Laraudogoitia submitted an article to the journal Mind entitled “This Article Should Not Be Rejected by Mind”. In it, he argued:

  1. If statement 1 in this argument is trivially true, then this article should be accepted.
  2. If statement 1 were false, then its antecedent (“statement 1 in this argument is trivially true”) would be true, which means that statement 1 itself would be true, a contradiction. So statement 1 must be true.
  3. But that seems wrong, since Mind is a serious journal and shouldn’t publish trivial truths.
  4. That means statement 1 must be either false or a non-trivial truth. We know it can’t be false (#2), so it must be a non-trivial truth, and its antecedent (“statement 1 in this argument is trivially true”) is false.
  5. What then is the truth value of its consequent, “this article should be accepted”? If this were false then Mind shouldn’t publish the article; that can’t be right, since the article consists of a non-trivial truth and its justification.
  6. So the consequent must be true, and Mind should publish the article.

They published it. “This is, I believe, the first article in the whole history of philosophy the content of which is concerned exclusively with its own self, or, in other words, which is totally self-referential”, Laraudogoitia wrote. “The reason why it is published is because in it there is a proof that it should not be rejected and that is all”.

(reblogged from Futility Closet)

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Grant applications, early 20th century style

warburggrant

Facsimile of a research proposal submitted by Otto Warburg to the Notgemeinschaft der Deutschen Wissenschaft (Emergency Association of German Science), c.1921.

The application, which consisted of a single sentence, “I require 10,000 marks“, was funded in full.

(read the full Nature Reviews Cancer article)

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2014 Software Sustainability Institute Fellowship

SSI

I’m delighted to have been named today as one of the sixteen Software Sustainability Institute Fellows for 2014.

The Software Sustainability Institute (SSI) is an EPSRC-funded project based at the universities of Edinburgh, Manchester, Oxford and Southampton, and draws on a team of experts with a breadth of experience in software development, project and programme management, research facilitation, publicity and community engagement. It’s a national facility for cultivating world-class research through software, whose goal is to make it easier to rely on software as a foundation of research; see their manifesto. The SSI works with researchers, developers, funders and infrastructure providers to identify the key issues and best practice surrounding scientific software.

During my fellowship, I’m particularly keen to work closely with Software Carpentry and Mozilla Science Lab to highlight the importance of software skills across the STEM disciplines. I’m also interested in a broader open science/open computation agenda; see the Recomputation Manifesto and the recently established recomputation.org project.

More to follow in 2014!

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Colloquial definitions of Big, Open and Personal Data

Here’s a useful (draft) set of colloquial definitions for Big, Open and Personal Data on GitHub from the Open Data Institute.

Why is this a worthwhile exercise? Well, Open Data gets conflated with Personal Data, everyone talks about Big Data (yet no-one is exactly sure what it is, but many have tried to define it)…and we all should be concerned about Personal Data.

typesofdata

1. Big Data is (i) data that you cannot handle with conventional tools or (ii) a term used as a vague metaphor for solving problems with data.

2. Open Data is data that anyone can use; without legal, technical or financial barriers.

3. Personal Data is data derived from people, where you can distinguish a person from other people in the group.

(also, can Big Open Personal (BOP) Data exist?)

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Science rules of thumb


If an elderly but distinguished scientist says that something is possible he is almost certainly right, but if he says that it is impossible he is very probably wrong.

Arthur C. Clarke

 

When, however, the lay public rallies around an idea that is denounced by distinguished but elderly scientists and supports that idea with great fervor and emotion — the distinguished but elderly scientists are then, after all, probably right.

Isaac Asimov

(reblogged from Futility Closet)

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Ten Simple Rules for Reproducible Computational Research

In a paper published last week in PLoS Computational Biology, Sandve, Nekrutenko, Taylor and Hovig highlight the issue of replication across the computational sciences. The dependence on software libraries, APIs and toolchains, coupled with massive amounts of data, interdisciplinary approaches and the increasing complexity of the questions being asked are complicating replication efforts.

To address this, they present ten simple rules for reproducibility of computational research:
 

Rule 1: For Every Result, Keep Track of How It Was Produced

Rule 2: Avoid Manual Data Manipulation Steps

Rule 3: Archive the Exact Versions of All External Programs Used

Rule 4: Version Control All Custom Scripts

Rule 5: Record All Intermediate Results, When Possible in Standardized Formats

Rule 6: For Analyses That Include Randomness, Note Underlying Random Seeds

Rule 7: Always Store Raw Data behind Plots

Rule 8: Generate Hierarchical Analysis Output, Allowing Layers of Increasing Detail to Be Inspected

Rule 9: Connect Textual Statements to Underlying Results

Rule 10: Provide Public Access to Scripts, Runs, and Results


The rationale underpinning these rules clearly resonates with the work of the Software Sustainability Institute: better science through superior software. Based at the universities of Edinburgh, Manchester, Oxford and Southampton, it is a national facility for cultivating world-class research through software (for example, Software Carpentry). An article that caught my eye in July was the Recomputation Manifesto: computational experiments should be recomputable for all time. In light of the wider open data and open science agenda, should we also be thinking about open software and open computation?

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Winchester Science Festival 2013

Yesterday I spoke at the 2013 Winchester Science Festival, a fantastic weekend of science communication and science education with some excellent speakers. My talk was entitled “Computing: The Science of Nearly Everything” (slides), which attempted to reset the perception of computer science: highlighting the importance of computer science education (in particular the wide utility of programming) and how modern science and engineering increasingly leverages computation.

Précis: We have seen how computational techniques have moved on from assisting scientists in doing science, to transforming both how science is done and what science is done (also see this Royal Society report). Thus, perhaps we should value the increasingly cross-cutting and interdisciplinary field of computer science, as well as computational literacy from school through to postgraduate research skills training.

Dr Tom Crick opening slide

9335493620_0c3b2f4df5_o

(you can also see other photos from the 2013 Winchester Science Festival, including me doing silly gestures)

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