Debian Science Data Management packages

DataLad is a data management and distribution platform providing access to a wide range of data resources already available online. Using git-annex as its backend for data logistics it provides following facilities built-in or available through additional extensions

• command line and Python interfaces for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get)

• extract, aggregate, and search through various sources of metadata (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI support)

• crawl web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc (install datalad-crawler)

This extension enhances DataLad (http://datalad.org) for working with computational containers.

git-annex allows managing large files with git, without storing the file contents in git. It can sync, backup, and archive your data, offline and online. Checksums and encryption keep your data safe and secure. Bring the power and distributed nature of git to bear on your large files with git-annex.

It can store large files in many places, from local hard drives, to a large number of cloud storage services, including S3, WebDAV, and rsync, with dozens of cloud storage providers usable via plugins. Files can be stored encrypted with gpg, so that the cloud storage provider cannot see your data. git-annex keeps track of where each file is stored, so it knows how many copies are available, and has many facilities to ensure your data is preserved.

git-annex can also be used to keep a folder in sync between computers, noticing when files are changed, and automatically committing them to git and transferring them to other computers. The git-annex webapp makes it easy to set up and use git-annex this way.

The external filter mechanism introduced with HDF5 1.8.12 allows applications to utilize custom filters not shipped by the HDF5 core library without recompiling your application. This package provides external filters for HDF5 for

• the LZ4 compression algorithm
• BZip2 compression

This package contains a filter for HDF5 that uses the Blosc compressor. By installing this filter, you can read and write HDF5 files with Blosc-compressed datasets.

H5Z-ZFP is a compression filter for HDF5 using the ZFP compression library, supporting lossy and lossless compression of floating point and integer data to meet bitrate, accuracy, and/or precision targets.

NeXus is a common data format for neutron, X-ray, and muon science. It is being developed as an international standard by scientists and programmers representing major scientific facilities in Europe, Asia, Australia, and North America in order to facilitate greater cooperation in the analysis and visualization of neutron, X-ray, and muon data.

This is the package containing some applications for reading and writing NeXus files.

The plfit software fits power-law distributions to empirical (discrete or continuous) data, according to the method of Clauset, Shalizi and Newman [SIAM Review 51, 661-703 (2009)].

This package provides two command line utilities, plfit and plgen.

The JData Specification (https://github.com/fangq/jdata/) defines a lightweight language-independent data annotation interface targeted at storing and sharing complex data structures across different programming languages such as MATLAB, JavaScript, python etc. Using JData formats, a complex python data structure can be encoded as a dict object that is easily serialized as a JSON/binary JSON file and share such data between programs of different languages.

Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers.

NeXus is a common data format for neutron, X-ray, and muon science. It is being developed as an international standard by scientists and programmers representing major scientific facilities in Europe, Asia, Australia, and North America in order to facilitate greater cooperation in the analysis and visualization of neutron, X-ray, and muon data.

This is the package containing the Python 3 bindings.

PyZoltan is as the name suggests, is a Python wrapper for the Zoltan data management library.

In PyZoltan, only specific routines and objects are wrapped. The following features of Zoltan are currently supported:

• Dynamic load balancing using geometric algorithms
• Unstructured point-to-point communication
• Distributed data directories

OpenLink Virtuoso is a high-performance object-relational SQL database. It provides transactions, a smart SQL compiler, hot backup, SQL:1999 support, a powerful stored-procedure language supporting server-side Java or .NET, and more. It supports all major data-access interfaces, including ODBC, JDBC, ADO.NET, and OLE/DB.

Virtuoso supports SPARQL embedded into SQL for querying RDF data stored in its database. SPARQL benefits from low-level support in the engine itself, such as SPARQL-aware type-casting rules and a dedicated IRI data type.

Install this metapackage for the full suite of packages that make up Virtuoso OSE ("Open-Source Edition").

NeXus is a common data format for neutron, X-ray, and muon science. It is being developed as an international standard by scientists and programmers representing major scientific facilities in Europe, Asia, Australia, and North America in order to facilitate greater cooperation in the analysis and visualization of neutron, X-ray, and muon data.

This is the package containing the development libraries.

NeXus is a common data format for neutron, X-ray, and muon science. It is being developed as an international standard by scientists and programmers representing major scientific facilities in Europe, Asia, Australia, and North America in order to facilitate greater cooperation in the analysis and visualization of neutron, X-ray, and muon data.

This is the package containing the java libraries.

The plfit software fits power-law distributions to empirical (discrete or continuous) data, according to the method of Clauset, Shalizi and Newman [SIAM Review 51, 661-703 (2009)].

This package contains the header files, static libraries and symbolic links that developers using the plfit library will need.

Modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE), a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings, massive speed improvements, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations.

The plfit software fits power-law distributions to empirical (discrete or continuous) data, according to the method of Clauset, Shalizi and Newman [SIAM Review 51, 661-703 (2009)].

This package provides a Python module.