Why do commercial companies contribute to open source software?

This is the post-print version of the Article. The official published version can be accessed from the link belowMany researchers have pointed out that the opensource movement is an interesting phenomenon that is difficult to explain with conventional economic theories. However, while there is no shortage on research on individuals’ motivation for contributing to opensource, few have investigated the commercial companies’ motivations for doing the same. A case study was conducted at three different companies from the IT service industry, to investigate three possible drivers: sale of complimentary services, innovation and open sourcing (outsourcing). We offer three conclusions. First, we identified three main drivers for contributing to opensource, which are (a) selling complimentary services, (b) building greater innovative capability and (c) cost reduction through open sourcing to an external community. Second, while previous research has documented that the most important driver is selling complimentary services, we found that this picture is too simple. Our evidence points to a broader set of motivations, in the sense that all our cases exhibit combinations of the three drivers. Finally, our findings suggest that there might be a shift in how commercial companies view opensource software. The companies interviewed have all expressed a moral obligation to contribute to open source

The Free Software Movement and the GNU/Linux Operating System

Richard Stallman will speak about the purpose, goals, philosophy, methods, status, and future prospects of the GNU operating system, which in combination with the kernel Linux is now used by an estimated 17 to 20 million users world wide.BiographyRichard Stallman is the founder of the Gnu Project, launched in 1984 to develop the free operating system GNU (an acronym for ''GNU's Not Unix''), and thereby give computer users the freedom that most of them have lost. GNU is free software: everyone is free to copy it and redistribute it, as well as to make changes either large or small. Today, Linux-based variants of the GNU system, based on the kernel Linux developed by Linus Torvalds, are in widespread use. There are estimated to be some 20 million users of GNU/Linux systems today. Richard Stallman is the principal author of the GNU Compiler Collection, a portable optimizing compiler which was designed to support diverse architectures and multiple languages. The compiler now supports over 30 different architectures and 7 programming languages. Stallman also wrote the GNU symbolic debugger (gdb), GNU Emacs, and various other GNU programs. Stallman graduated from Harvard in 1974 with a BA in physics. During his college years, he also worked as a staff hacker at the MIT Artificial Intelligence Lab, learning operating system development by doing it. He wrote the first extensible Emacs text editor there in 1975. In January 1984 he resigned from MIT to start the GNU project. Stallman received the Grace Hopper award for 1991 from the Association for Computing Machinery, for his development of the first Emacs editor. In 1990 he was awarded a Macarthur foundation fellowship, and in 1996 an honorary doctorate from the royal institute of Technology in Sweden. In 1998 he received the Electronic Frontier Foundation's pioneer award along with Linus Torvalds. In 1999 he received the Yuri Rubinski award. In 2001 he received a second honorary doctorate, from the University of Glasgow, and shared the Takeda award for social/economic betterment with Torvalds and Ken Sakamura. In 2002 he was elected to the National Academy of Engineering

Spherical k-means clustering is good for interpreting multivariate species occurrence data

1. Clustering multivariate species data can be an effective way of showing groups of species or samples with similar characteristics. Most current techniques classify the samples first and then the species. A disadvantage of classifying the samples first is that relatively subtle differences between occurrence profiles of species can be obscured. 2. The k-means method of clustering minimizes the sum of squared distances between cluster centres and cluster members. If the entities to be clustered are projected on the unit sphere, then a natural measure of dispersion is the sum of squared chord distances separating the entities from their cluster centres; k-means clustering with this measure of dispersion is called spherical k-means (SKM). We also consider a variant in which the sum of squared perpendicular distances to a central ray is minimized. 3. Unweighted SKM is liable to produce clusters of very rare species. This feature can be avoided if each point on the unit sphere is weighted by the length of the ray that produced it. The standard SKM algorithm converges to very numerous local optima. To avoid this problem, we have developed a computationally intensive algorithm that uses multiple randomizations to select high-quality seed species. 4. The species clustering can be used to define simplified attributes for the samples. If the samples are then classified using the same technique, the resulting matrix of clustered species and clustered samples provides a biclustering of the data. The strength of the relationship between clusters can be measured by their mutual information, which is effectively the entropy of the biclustering. 5. The technique was tested on five ecological and biogeographical datasets ranging in size from 30 species in 20 samples to 1405 species in 3857 samples. Several variants of SKM were compared, together with results from the established program Twinspan. When judged by entropy, SKM always performed adequately and produced the best clustering in all datasets but the smallest

Local frequency as a key to interpreting species occurrence data when recording effort is not known

1. Data on the occurrence of species in grid cells are collected by biological recording schemes, typically with the intention of publishing an atlas. Interpretation of such data is often hampered by the lack of information on the effort that went into collecting them. This is the ‘recorder effort problem’. 2. One measure of recorder effort is the proportion of a suite of common species (‘benchmark species’) found at a given location and time. Benchmark species have in the past been taken as a uniform set across a territory. However, if records are available from a neighbourhood surrounding a given location, then a local set benchmark species can be defined by pooling records from the neighbourhood and selecting the commonest species in the pooled set. 3. Neighbourhoods differ in species richness, so that the list of species that ‘ought’ to be found in one location may be longer than that for another. If the richness of a neighbourhood can be estimated, then a suite of benchmark species can be standardized to be the commonest of a fixed proportion of the total expected for the neighbourhood. Recording effort is then defined as the proportion of benchmark species that were found. 4. A method of estimating species richness is proposed here, based on the local frequencies fj of species in neighbouring grid cells. Species discovery is modelled as a Poisson process. It is argued that when a neighbourhood is well sampled, the frequency-weighted mean frequency Σfj2/Σfj of species in the neighbourhood will assume a standard value. 5. The method was applied to a data set of 2 000 000 records detailing the occurrence of bryophytes in 3695 out of the total 3854 hectads (10-km squares) in Great Britain, Ireland, the Isle of Man and the Channel Islands. 6. Three main applications are outlined: estimation of recording effort, scanning data for unexpected presences or absences and measurement of species trends over time. An explicit statistical model was used to estimate trends, modelling the probability of species j being found at location i and time t as the outcome of Poisson process with intensity Qijt xjt, where xjt is a time factor for species j, and Qijt depends on recording effort at location i and time t and on the time-independent probability of species j being found in hectad i