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    <title>[2205.14430] Angle-Uniform Parallel Coordinates</title>
    <dc:date>2024-05-07T11:14:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2205.14430</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present angle-uniform parallel coordinates, a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot. Despite being a common method for visualizing multidimensional data, parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable estimations. To address this issue, we introduce a transformation that bounds all points horizontally using an angle-uniform mapping and shrinks them vertically in a structure-preserving fashion; polygonal lines become smooth curves and a symmetric representation of data correlations is achieved. We further propose a combined subsampling and density visualization approach to reduce visual clutter caused by overdrawing. Our method enables accurate visual pattern interpretation of data correlations, and its data-independent nature makes it applicable to all multidimensional datasets. The usefulness of our method is demonstrated using examples of synthetic and real-world datasets.
]]></description>
<dc:subject>data-analysis visualization parallel-coordinates multiobjective-optimization rather-interesting statistics data-science scientific-communication</dc:subject>
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<item rdf:about="https://orsociety.tandfonline.com/doi/abs/10.1057/jors.2013.78">
    <title>The mangle of OR practice: towards more informative case studies of ‘technical’ projects: Journal of the Operational Research Society: Vol 65, No 8</title>
    <dc:date>2019-04-25T15:37:44+00:00</dc:date>
    <link>https://orsociety.tandfonline.com/doi/abs/10.1057/jors.2013.78</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Case studies of interventions involving ‘technical’ OR have traditionally been published in the style of scientific papers. Most are well written and technically sound but few explain the process of intervention, the story of what actually happened. Those interested in the process of OR would like know about the organizational hurdles that had to be surmounted, the changes in direction that were made, the influences of the people involved and technology available on the path taken. The physicist turned sociologist Andrew Pickering has suggested that by conceiving scientific practice as a dynamic process of intertwined elements a more insightful account is obtained, leading to a better understanding and giving rise to more interesting questions. In order to explore this claim the paper describes an OR project that already features in the OR literature, and then discusses it in terms of Pickering’s concept of the mangle of practice. The project examined is the development and use of a model of the UK energy market. The mangle perspective places the emphasis on the interaction through time of material, human and conceptual components of a research programme. It is concluded that the concept of mangle can indeed help case writers produce a more realistic description and help them make better sense of what occurred. Such cases could provide a useful source of material for some academic research programmes.

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<dc:subject>the-mangle-in-practice science-studies anthropology academic-culture data-science modeling-is-not-mathematics</dc:subject>
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    <title>[1702.01522] Inverse statistical problems: from the inverse Ising problem to data science</title>
    <dc:date>2017-11-17T13:25:26+00:00</dc:date>
    <link>https://arxiv.org/abs/1702.01522</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Inverse problems in statistical physics are motivated by the challenges of `big data' in different fields, in particular high-throughput experiments in biology. In inverse problems, the usual procedure of statistical physics needs to be reversed: Instead of calculating observables on the basis of model parameters, we seek to infer parameters of a model based on observations. In this review, we focus on the inverse Ising problem and closely related problems, namely how to infer the coupling strengths between spins given observed spin correlations, magnetisations, or other data. We review applications of the inverse Ising problem, including the reconstruction of neural connections, protein structure determination, and the inference of gene regulatory networks. For the inverse Ising problem in equilibrium, a number of controlled and uncontrolled approximate solutions have been developed in the statistical mechanics community. A particularly strong method, pseudolikelihood, stems from statistics. We also review the inverse Ising problem in the non-equilibrium case, where the model parameters must be reconstructed based on non-equilibrium statistics.]]></description>
<dc:subject>data-science statistics inverse-problems complexology rather-interesting inference to-write-about review to-simulate philosophy-of-science</dc:subject>
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<item rdf:about="https://github.com/MimiOnuoha/missing-datasets/blob/master/README.md">
    <title>missing-datasets/README.md at master · MimiOnuoha/missing-datasets · GitHub</title>
    <dc:date>2017-09-25T13:03:12+00:00</dc:date>
    <link>https://github.com/MimiOnuoha/missing-datasets/blob/master/README.md</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>data-science data activism public-policy open-access</dc:subject>
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    <title>[1602.03926] Modelling the level of adoption of analytical tools; An implementation of multi-criteria evidential reasoning</title>
    <dc:date>2016-09-14T13:18:49+00:00</dc:date>
    <link>http://arxiv.org/abs/1602.03926</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In the future, competitive advantages will be given to organisations that can extract valuable information from massive data and make better decisions. In most cases, this data comes from multiple sources. Therefore, the challenge is to aggregate them into a common framework in order to make them meaningful and useful. This paper will first review the most important multi-criteria decision analysis methods (MCDA) existing in current literature. We will offer a novel, practical and consistent methodology based on a type of MCDA, to aggregate data from two different sources into a common framework. Two datasets that are different in nature but related to the same topic are aggregated to a common scale by implementing a set of transformation rules. This allows us to generate appropriate evidence for assessing and finally prioritising the level of adoption of analytical tools in four types of companies. A numerical example is provided to clarify the form for implementing this methodology. A six-step process is offered as a guideline to assist engineers, researchers or practitioners interested in replicating this methodology in any situation where there is a need to aggregate and transform multiple source data.
]]></description>
<dc:subject>multiobjective-optimization decision-making management data-science data-analysis ergonomics user-experience to-write-about</dc:subject>
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    <title>[1505.05211] Principles of Dataset Versioning: Exploring the Recreation/Storage Tradeoff</title>
    <dc:date>2016-01-29T11:12:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.05211</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The relative ease of collaborative data science and analysis has led to a proliferation of many thousands or millions of versions of the same datasets in many scientific and commercial domains, acquired or constructed at various stages of data analysis across many users, and often over long periods of time. Managing, storing, and recreating these dataset versions is a non-trivial task. The fundamental challenge here is the storage−recreationtrade−off: the more storage we use, the faster it is to recreate or retrieve versions, while the less storage we use, the slower it is to recreate or retrieve versions. Despite the fundamental nature of this problem, there has been a surprisingly little amount of work on it. In this paper, we study this trade-off in a principled manner: we formulate six problems under various settings, trading off these quantities in various ways, demonstrate that most of the problems are intractable, and propose a suite of inexpensive heuristics drawing from techniques in delay-constrained scheduling, and spanning tree literature, to solve these problems. We have built a prototype version management system, that aims to serve as a foundation to our DATAHUB system for facilitating collaborative data science. We demonstrate, via extensive experiments, that our proposed heuristics provide efficient solutions in practical dataset versioning scenarios.
]]></description>
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    <title>Growing need for data heads</title>
    <dc:date>2011-05-22T12:03:27+00:00</dc:date>
    <link>http://flowingdata.com/2011/05/20/growing-need-for-data-heads/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["I've said it before, but if digging into data is your idea of fun, there's a whole mess of excitement and adventure headed your way. There are lots of opportunities already out there in marketing, journalism, tech, the Web, government, and pretty much everywhere you look. And more importantly, there are lots of opportunities that you can make for yourself. This is a great time for data heads."]]></description>
<dc:subject>data-science data-mining statistics jobs advice</dc:subject>
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<item rdf:about="http://www.r-bloggers.com/review-of-2011-data-scientist-summit/?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29">
    <title>Review of 2011 Data Scientist Summit | (R news &amp; tutorials)</title>
    <dc:date>2011-05-14T13:16:46+00:00</dc:date>
    <link>http://www.r-bloggers.com/review-of-2011-data-scientist-summit/?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This was the first annual Data Scientist Summit, and I will no doubt be back. With that said, discussion of technical topics had a bit of an introductory flavor to them, which made the discussion of the technology seem dated. For example, “Vanilla” Hadoop was introduced as a tool for processing vast amounts of data. I would expect that most Data Scientists have worked with Hadoop, or at least know what it is. Hadoop is somewhat old news in terms of “cutting-edge technology.” Tools like Pig, Cascalog, HBase, Hive, Cascading, etc. would have been a better discussion topic. I was also disappointed with how little coverage of tools (except for Hadoop, NoSQL, and enterpise databases) there was. It seemed as if R had gone M.I.A. and I was surprised that there was such little discussion of visualization tools like Tableau, Processing, Gephi, D3, Polymaps, etc.

]]></description>
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