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In short, Aletheia is software for getting science published and into the hands of everyone, for free. It's a decentralised and distributed database used as a publishing platform for scientific research. So, Aletheia is software. But software without people is nothing. To comprehensively answer the question what is Aletheia, Aletheia is software surrounded by a community of people who want to change the world through open access to scientific knowledge. For a more in depth explanation, Aletheia is an Ethereum Blockchain application utilising IPFS for decentralised storage that anyone can upload documents to, download documents from, that also handles the academic peer review process. The application runs on individual PCs, all forming part of the IPFS database. This gives us an open source platform that cannot be bought out by the large publishers (and any derivitive works must also be open source) that should also be hard to take down due to the database being spread across the globe in multiple legal jurisdictions. Aletheia is designed to be a resilient platform run transparently by the community, not some black box corporation or editorial board, meaning all users can see the decisions Aletheia is making and have a stake in that decision making process if they so desire. By this nature, Aletheia is decentralised, it has no key person risk. Should the core group who invented Aletheia dissapear Aletheia won't cease to exist, it will continue to be run by the community. The community moderates content through various mechanisms (peer review, reputation scores etc.,) to ensure quality of content.
'Apophenia' is a statistical library for C. It provides functions on the same level as those of the typical stats package (OLS, probit, singular value decomposition, &c.) but doesn't tie the user to an ad hoc language or environment. It uses the GNU Scientific Library for number crunching and SQLite for data management, so the library itself focuses on model estimation and quickly processing data.
AutoClass solves the problem of automatic discovery of classes in data (sometimes called clustering or unsupervised learning), as distinct from the generation of class descriptions from labeled examples (called supervised learning). It aims to discover the 'natural' classes in the data. AutoClass is applicable to observations of things that can be described by a set of attributes, without referring to other things. The data values corresponding to each attribute are limited to be either numbers or the elements of a fixed set of symbols. With numeric data, a measurement error must be provided.
Bc Heckert gnu.small.png
'Bc' is an arbitrary precision numeric processing language. Its syntax is similar to C, but differs in many substantial areas. It supports interactive execution of statements. 'Bc' is a utility included in the POSIX P1003.2/D11 draft standard. This version does not use the historical method of having bc be a compiler for the dc calculator (the POSIX document doesn't specify how bc must be implemented). This version has a single executable that both compiles the language and runs the resulting 'byte code.' The byte code is not the dc language.
cl-ana is a library of modular utilities for reasonably high performance data analysis & visualization using Common Lisp. (Reasonably means I have to be able to use it for analyzing particle accelerator data). The library is made of various sublibraries and is designed in a very bottom-up way so that if you don't care about some feature you don't have to load it. The functionality support so far are
  • Tabular data analysis: Read-write of large datasets stored in HDF5 files are supported, along with ntuple datasets, CSVs, and in-memory data tables. Users can add their own table types by defining 4 methods and extending the table CLOS type.
  • Histograms: Binned data analysis is supported with both contiguous and sparse histogram types; functional interface is provided via map (which allows reduce/fold) and filter.
  • Plotting: Uses gnuplot for plotting dataset samples, plain-old lisp functions, histograms, strings-as-formulae, and anything else the user wishes to add via methods on a couple of generics.
  • Fitting: Uses GSL for non-linear least squares fitting. Uses plain-old lisp functions as the fit functions and can fit against dataset samples, histograms, and whatever the user adds.
  • Generic mathematics: CL doesn't provide extendable math functions, so cl-ana provides these as well as a convenient mechanism (a single function) for using these functions instead of the non-extendable versions. Already included are error propogation and quantities (values with units, e.g. 5 meters) as well as a GNU Octave-style handling of sequences (e.g. (+ (1 2) (3 4)) --> (4 6)).
Dap Heckert gnu.small.png
Dap is a small statistics and graphics package, based on C, that provides core methods of data management, analysis, and graphics commonly used in statistical consulting practice. Anyone familiar with basic C syntax can learn Dap quickly and easily from the manual and the examples in it. Advanced features of C are not necessary, although they are available. As of Version 3.0, Dap can read SBS programs, thereby freeing the user from having to learn any C at all to run straightforward analyses. The manual contains a brief introduction to the C syntax needed for C-style programming for Dap. Because Dap processes files one line at a time, rather than reading entire files into memory, it can be, and has been, used on data sets that have very many lines and/or very many variables.
Data Frame
In the R language, a dataframe object is a way to group tabular data. The functions in this package allow the manipulation of data in a similar way in Octave. Dataframe objects in Octave can be created in a variety of ways (from other objects or from tabular data in a file) and then can be accessed either as matrix or by column name. This Octave add-on package is part of the Octave-Forge project.
DataStatix is a free software for GNU/Linux and Windows useful to manage data of every kind (although it has been written to manage biomedical data), to create descriptive statistics and graphs and to export items easily to R environment or to other statistic softwares. In order to handle properly big amount of data and many concurrent users, DataStatix works with MySql database and it has been developed and tested with MySql community edition 5.5. Some features of the software are: users management (create, delete, modify password) within the software; different users levels of data access (administrator, default, read only); user defined templates (models) of data, to create new databases easily; importation and esportation of data in CSV format (used also by Calc and Excel); updating of existing data from a CSV file created with DataStatix; descriptive statistics from every data (some more kind of statistics to come); graphs from every data.
Datamash Heckert gnu.small.png
Datamash is a command-line program which performs basic numeric, textual and statistical operations on input textual data files. it is designed to be portable and reliable, and aid researchers to easily automate analysis pipelines, without writing code or even short scripts.
Primary aim of the dinrhiw is to be linear algebra library and machine learning library. For this reason dinrhiw implements PCA and neural network codes. Currently, the neural network code only supports:
  • hamiltonian monte carlo sampling (HMC) and simple bayesian neural network
  • second order L-BFGS search
  • gradient descent (backpropagation)
As well as mathematical routines for arbitrary precision mathematics, hermite curve interpolation and many other things.

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