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Target audience: computational scientists 

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Licence: MIT License 


DE-Sim examples, tutorials, and documentation

*DE-Sim* is an open-source, Python-based object-oriented discrete-event simulation (DES) tool that makes it easy to use large, heterogeneous datasets and high-level data science tools such as [NumPy](https://numpy.org/), [Scipy](https://scipy.org/scipylib/index.html),...

Scientific topics: Computational biology, Mathematics, Computer science, Simulation experiment

Operations: Visualisation, Modelling and simulation

Keywords: data-driven modeling, Computational modelling, discrete-event simulation, DES, object-oriented programming, Python, data visualization, Data Science

Resource type: examples, Tutorial, Jupyter notebook, API reference

DE-Sim examples, tutorials, and documentation https://tess.elixir-europe.org/materials/de-sim-examples-tutorials-and-documentation *DE-Sim* is an open-source, Python-based object-oriented discrete-event simulation (DES) tool that makes it easy to use large, heterogeneous datasets and high-level data science tools such as [NumPy](https://numpy.org/), [Scipy](https://scipy.org/scipylib/index.html), [pandas](https://pandas.pydata.org/), and [SQLAlchemy](https://www.sqlalchemy.org/) to build and simulate complex computational models. Similar to [Simula](http://www.simula67.info/), *DE-Sim* models are implemented by defining logical process objects which read the values of a set of shared variables and schedule events to modify their values at discrete instants in time. This website provides examples, tutorials, and documentation for *DE-Sim*. Computational biology Mathematics Computer science Simulation experiment data-driven modeling, Computational modelling, discrete-event simulation, DES, object-oriented programming, Python, data visualization, Data Science computational scientists Computational biologists bioinformaticians software engineers programmers
ObjTables Python tutorials

ObjTables is a toolkit for creating re-usable datasets that are both human and machine-readable, combining the ease of spreadsheets (e.g., Excel workbooks) with the rigor of schemas (classes, their attributes, the type of each attribute, and the possible relationships between instances of...

Scientific topics: Data submission, annotation and curation, Data quality management, Data integration and warehousing, Data management

Operations: Validation

Keywords: spreadsheet, schema, table, workbook, worksheet, XLSX, Excel, standard, reuse, compose, integrate, quality control

Resource type: Jupyter notebook

ObjTables Python tutorials https://tess.elixir-europe.org/materials/objtables-python-tutorials ObjTables is a toolkit for creating re-usable datasets that are both human and machine-readable, combining the ease of spreadsheets (e.g., Excel workbooks) with the rigor of schemas (classes, their attributes, the type of each attribute, and the possible relationships between instances of classes). ObjTables consists of a format for describing schemas for spreadsheets, numerous data types for science, a markup format for indicating the class and attribute represented by each table and column in a workbook, and software for using schemas to rigorously validate, merge, split, compare, and revision datasets. ObjTables is ideal for supplementary materials of journal article, as well as for emerging domains which need to quickly build new formats for new types of data and associated software with minimal effort. The tutorials provide a brief introduction to the ObjTables format for schemas for spreadsheets, the ObjTables markup syntax for spreadsheets, and the ObjTables Python package for parsing, validating, querying, editing, comparing, merging, splitting, revisioning, migrating, and analyzing spreadsheets. Data submission, annotation and curation Data quality management Data integration and warehousing Data management spreadsheet, schema, table, workbook, worksheet, XLSX, Excel, standard, reuse, compose, integrate, quality control Researchers Scientists Data scientists computational scientists
Introductory image processing on biological images using python.

A jupyter notebook python practical designed to give students a introduction to opening and processing image files derived from biological samples.

Resource type: Jupyter notebook, PDF

Introductory image processing on biological images using python. https://tess.elixir-europe.org/materials/introductory-image-processing-on-biological-images-using-python A jupyter notebook python practical designed to give students a introduction to opening and processing image files derived from biological samples. Anatole Chessel Volker Baecker Bioimage Analysts Image Analysts Computer Vision scientists bioinformaticians computational scientists Biophysicists Biologists Microscopists Python for Biologists PhD students