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3 materials found

Target audience: computational scientists 


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
Next Generation Sequencing (NGS) wikibook

Next generation sequencing (NGS) has become a commodity. With the commercialization of various affordable desktop sequencers, NGS will be of reach by more traditional wet-lab biologists . As seen in recent years, genome-wide scale computational analysis is increasingly being used as a backbone to...

Keywords: Bioinformatics, Next generation sequencing, Ngs

Next Generation Sequencing (NGS) wikibook https://tess.elixir-europe.org/materials/next-generation-sequencing-ngs-wikibook Next generation sequencing (NGS) has become a commodity. With the commercialization of various affordable desktop sequencers, NGS will be of reach by more traditional wet-lab biologists . As seen in recent years, genome-wide scale computational analysis is increasingly being used as a backbone to foster novel discovery in biomedical research. However, as the quantities of sequence data increase exponentially, the analysis bottle-neck is yet to be solved. The current sources for NGS informatics are extremely fragmented. A novice could read review articles in various journals, follow discussion threads on forums such as Biostar[1] or SEQanswers [2], or sign up for courses organized by various institutes. Finding a centralized synthesis is much more difficult. Books are available, but the development of the field is so fast that book chapters risk being obsoleted by the time they are even printed. Moreover, cost for a handful of authors to continually update their text would presumably take up a lot of their schedule. Drawing from the obvious goodwill and community spirit displayed on discussion forums, and exploiting the collaborative tools made available by the Wikimedia foundation, we propose to initiate the editing of a collaborative WikiBook on NGS. Our plan is to collect a sufficient amount of text that people will be incentivized to contribute to it, essentially providing the same information as a forum but in a tidier form. Ultimately, our goal is to create a collective lab book that explains the key concepts and describes best practices in NGS. Parnell LD, Lindenbaum P, Shameer K et al. BioStar: an online question & answer resource for the bioinformatics community, PLoS Comput Biol 2011;7:e1002216. Li JW, Schmieder R, Ward RM et al. SEQanswers: an open access community for collaboratively decoding genomes, Bioinformatics 2012;28:1272-1273 Join this initiative now and contribute to further expand and improve the contents and case studies in the NGS wikibook! Read about this initiative and the nine rules for NGS data analysis here: The NGS WikiBook: a dynamic collaborative online training effort with long-term sustainability Bioinformatics, Next generation sequencing, Ngs Biologists bioinformaticians computational scientists 2013-07-15 2017-10-09