Introduction In scientific blogging, references and citations are essential. Unfortunately, Hugo’s support for citations is limited, see e.g. this discourse . So here’s a workflow for spacemacs that works for me. Editing Export to org with org-ref-export Export to hugo compatible markdown Workflow Editing I write my posts in spacemacs org-mode.

This post demonstrates the NFDI4BIOIMAGE Knowledge Graph (N4BIKG). Many aspects of this project are still very much in the flow including a consensus on which ontologies and terms to employ, how to define various namespaces and much more.

I was at the 2024 Open Science Ambassodors Meeting in Berlin and gave a presentation on reproducibility in scientific and high performance computing. The presentation slides are at https://mpievolbio-scicomp.pages.gwdg.de/presentations/osa2024.pdf, the permalink is https://dx.doi.org/10.5281/zenodo.14051129.

Introduction As researchers, we give presentations. Over the last years, I gave somewhere between 2 and 10 presentations a year. What I need is a html document that lists all my presentations, their title, date of presentation and a link to the pdf document in a table. This table can then be included on my institutional home page. As a plus, I want the table to be searchable and each column to be sortable.

Am 30.1.2024 gab ich im Rahmen der Veranstaltungsreihe "MPI Winter Talks" einen Vortrag zum Thema "Künstliche Intelligenz und Evolution". Die Vortragsfolien sind auf zenodo unter https://dx.doi.org/10.5281/zenodo.10599178 erhältlich.

Background The SLURM array directive provides a simple and intuitive syntax to submit a job with multiple similar tasks to the underlying HPC system. To fully exploit this capability, a few things should be kept in mind. In the following, we will, starting from a simple, serial job, explore how the total run time of our job behaves when the `array` option is applied.

Introduction The python library numba provides (among others) just-in-time (jit) compilation for python. Python code can gain tremendous speed-ups without the need to rewrite a single line of code. Simply adding a decorator to a function such as illustrated in this example: import numba @njit def accelerate_me(): ... can lead to run times comparable to C or Fortran code.

Experimenting with the tripalv3 JSON+LD API Introduction In this post I'm experimenting with querying the JSON-LD (LD stands for Linked Data) API of my Tripal3 genome database for Pseudomonas fluorescens SBW25. A little bit of background: Tripal is a content management system (CMS) for genomic data, based on the Drupal CMS framework.

A colleague needed a table listing image ID, image filename, and number or ROIs for all images in a project.

Working on a multiple sequence alignment project, I wanted to calculate a distance matrix from a MSA dataset generated by progressiveMauve. The MSA file is in xmfa format. I could not find a tool that calculates a distance matrix directly from the xmfa file, so I searched for conversion utilities to other MSA formats, such as phylip, maf, or msa. This biostars forum entry always came out at top but the links provided there are dead.

Introduction In this post I will present example SPARQL queries against the Pseudomonas fluorescens SBW25 knowledge graph (SBW25KG). The knowlegde graph was derived from the manually created annotation in gff3 format, as explained in a previous post. The queries are run against a local instance of the apache-jena-fuseki triplestore.