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W@_#oew&UtNekAsPFUbDy=l=ju4G%5= literal 0 HcmV?d00001 diff --git a/about/index.html b/about/index.html new file mode 100644 index 0000000..f636c69 --- /dev/null +++ b/about/index.html @@ -0,0 +1,198 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + diff --git a/blog/2023-building-snowpack/index.html b/blog/2023-building-snowpack/index.html new file mode 100644 index 0000000..713caa8 --- /dev/null +++ b/blog/2023-building-snowpack/index.html @@ -0,0 +1,265 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+ +
+
+ + +
+ +
+ +

Building SNOWPACK in a container

+ +

+ +

+ A step-by-step guide on how we built it inside a SingularityCE/Apptainer container +

+ + November 02, 2023 + + - Radovan Bast + +

+ +
+ +

For a project with the Department of Geosciences +at UiT we needed to build and test +SNOWPACK, an open source snowpack and ground +surface model.

+

Tools

+

We decided to do this using a SingularityCE/Apptainer container +with the motivation:

+
    +
  • It encourages us to document the installation process.
    • +
  • We have a baseline which we can refer to from different computers and +operating systems.
    • +
  • Later, once we decide to move computations to a cluster/supercomputer, it +will be easier to deploy there having it already containerized.
    • +
+

Building the container

+

We use the following container definition file and call this file snowpack.def:

+
Bootstrap: docker
+From: ubuntu:22.04
+
+%post
+    export DEBIAN_FRONTEND=noninteractive
+    apt-get update -y
+    apt-get install -y wget
+
+    wget https://gitlabext.wsl.ch/snow-models/snowpack/-/package_files/428/download -O Snowpack-3.6.0-x86_64.deb
+    dpkg -i Snowpack-3.6.0-x86_64.deb
+
+%runscript
+    snowpack $@
+
+%help
+    Example usage:
+    $ ./snowpack.sif -c io_res1exp.ini -e 1996-06-17T00:00
+
+

From the above definition file we build the image snowpack.sif, either using singularity or +apptainer (at the time of writing both are equivalent in functionality for +our purposes here):

+
singularity build --fakeroot snowpack.sif snowpack.def
+
+

The generated image snowpack.sif can now be moved to other computers that +have +SingularityCE/Apptainer +installed and run there.

+

Example run

+

First we need to download a configuration file and two input files:

+
wget https://code.wsl.ch/snow-models/snowpack/-/raw/master/doc/examples/cfgfiles/io_res1exp.ini
+
+mkdir -p input output
+
+wget https://code.wsl.ch/snow-models/snowpack/-/raw/master/doc/examples/input/MST96.sno  -O input/MST96.sno
+wget https://code.wsl.ch/snow-models/snowpack/-/raw/master/doc/examples/input/MST96.smet -O input/MST96.smet
+
+

With the above commands, our directory now looks like this:

+
.
+├── input
+│   ├── MST96.smet
+│   └── MST96.sno
+├── io_res1exp.ini
+├── output
+├── snowpack.def
+└── snowpack.sif
+
+

Now we can run the example

+
./snowpack.sif -c io_res1exp.ini -e 1996-06-17T00:00
+
+

This generates the following output:

+
[i] [2023-11-02T10:48:57] ---> Start SNOWPACK in RESEARCH mode
+[i] []                 snowpack compiled on Sep  1 2020 at 22:29:53
+[i] []                 Experiment : res
+[i] []                 Output dir : ./output
+
+[i] []                 Run on meteo station MST96
+[i] []                 Reading snow cover data for station MST96
+[i] []                 Finished initializing station MST96
+[i] []                 No file ./output/MST96_res.ini to erase
+[i] [2023-11-02T10:48:57] ---> Start simulation for MST96 on 1995-11-01T00:15:00+01:00
+[i] []                 End date specified by user: 1996-06-17T00:00:00+01:00
+[i] []                 Integration step length: 15.000000 min
+[i] [1995-11-16T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 16.11.1995 00:00:00 station time
+[i] [1995-12-01T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 01.12.1995 00:00:00 station time
+[i] [1995-12-16T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 16.12.1995 00:00:00 station time
+[i] [1995-12-31T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 31.12.1995 00:00:00 station time
+[i] [1996-01-15T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 15.01.1996 00:00:00 station time
+[i] [1996-01-30T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 30.01.1996 00:00:00 station time
+[i] [1996-02-14T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 14.02.1996 00:00:00 station time
+[i] [1996-02-29T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 29.02.1996 00:00:00 station time
+[i] [1996-03-15T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 15.03.1996 00:00:00 station time
+[i] [1996-03-30T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 30.03.1996 00:00:00 station time
+[i] [1996-04-14T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 14.04.1996 00:00:00 station time
+[i] [1996-04-29T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 29.04.1996 00:00:00 station time
+[i] [1996-05-14T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 14.05.1996 00:00:00 station time
+[i] [1996-05-29T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 29.05.1996 00:00:00 station time
+[i] [1996-06-13T00:00:00] ---> Station MST96 (1 slope(s)): advanced to 13.06.1996 00:00:00 station time
+[i] []                 Total time to read meteo data : 0.304980 s
+[i] []                 Runtime for station MST96: 9.455373 s
+
+[i] []                 STARTED  running SLF RESEARCH Snowpack Model on Thu Nov  2 10:48:57 2023
+                       ========================================================================
+                       FINISHED running SLF RESEARCH Snowpack Model on Thu Nov  2 10:49:07 2023
+
+ + +
+ +
+ +
+ + + + +
+ + diff --git a/blog/2023-end-of-year-post/index.html b/blog/2023-end-of-year-post/index.html new file mode 100644 index 0000000..0a2c0be --- /dev/null +++ b/blog/2023-end-of-year-post/index.html @@ -0,0 +1,196 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+ +
+
+ + +
+ +
+ +

2023 in review: A milestone year with our first office hour

+ +

+ +

+ As we are wrapping up the year, it's a great moment to look back at what we have achieved in 2023 +

+ + December 23, 2023 + + - Bente Barge + +

+ +
+ +

New team members

+

Our team has grown, welcoming two new team members at the start of the year: +Magnar Bjørgve and Bente Barge. Both Magnar and Bente have a background in +theoretical chemistry and hands-on experience with research software, enriching +our group with their knowledge and fresh perspectives.

+

Office hours

+

We started the year with our first office hours, which we held on January +25th. Since then, we have been available for researchers and students to drop +by with their programming challenges most Wednesdays. The consistent turnout +underscores a clear need for this service on campus. This format has proven to +be a great way to attract new users and to communicate with UiT researchers and +students. The feedback from our users has been incredibly encouraging, and we +are proud of what we have achieved so far!

+

Projects

+

Each challenge brought to us has been a learning opportunity, both for the user +and for ourselves. Our users are associated with a range of departments at UiT, +including the Department of Language and Culture, the Department of Arctic and +Marine Biology, the Department of Physics and Technology, the Department of +Chemistry, and the School of Business and Economics. Throughout the year, we +initiated a total of almost 20 projects, ranging from helping users with Python +environments, to building graphical user interfaces, to translating a legacy +Visual Basic program to Python. +As the year is coming to an end, we still have 3 exciting projects in +the pipeline.

+

What's next for 2024?

+

As we enter 2024, our aim is to expand our reach and impact within the UiT +community. We're excited about welcoming new users and launching into +innovative projects. Building on our experiences from the past year, we are +confident that we can continue to provide a valuable service. While continuing +our established office hours, we're also looking forward to engaging in +long-term projects beyond office hours.

+ + +
+ +
+ +
+ + + + +
+ + diff --git a/blog/2023-help-with-automation-and-efficiency/index.html b/blog/2023-help-with-automation-and-efficiency/index.html new file mode 100644 index 0000000..a797f2b --- /dev/null +++ b/blog/2023-help-with-automation-and-efficiency/index.html @@ -0,0 +1,413 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+ +
+
+ + +
+ +
+ +

Help with automation and efficiency with an R project

+ +

+ +

+ Some lessons learned from working with R scripts used in arctic and marine biology research +

+ + May 14, 2023 + + - Radovan Bast + +

+ +
+ +

In this project we have worked on five issues which are very typical for many R +and Python projects and which we consider "the bread and butter" of our +research software engineering support work:

+ + + + + +

Below we will summarize our steps, lessons learned, and recommendations for +follow-up steps.

+

Too much manual work is needed to process each of the many analyses

+

The analyses have to be carried out by varying a number of parameters but for +each new variation a new script was created and manually adjusted. We have +addressed this by first moving all variables to the top of the script. A good +next step will be to introduce a command-line interface (example is below) so +that parameters can be changed without changing the code.

+

Another problem was that the consistency of data was previously verified +visually and not automatically. We have reformulated this as a function and now +the code can check this "by itself":

+
library(Seurat)
+
+# function which checks whether ident is set correctly
+# correctly here means that the values of the seurat_clusters
+# are all represented in the levels of the integrated_samples
+check_ident <- function(seurat_object) {
+    unique_levels <- unique(as.numeric(levels(integrated_samples)))
+    unique_clusters <- unique(integrated_samples@meta.data$seurat_clusters)
+ 
+    if (!all(unique_clusters %in% unique_levels)) {
+        stop("ERROR: Ident is not correctly set up")
+    }
+}
+
+# load the sample
+integrated_samples <- readRDS("sample.rds")
+
+# if ident is not set correctly, script will stop here
+check_ident(integrated_samples)
+
+

Code repetition

+

The code to create a grid of plots was repetitive:

+
plot <- cowplot::plot_grid(
+    scater::plotReducedDim(cluster_0_SCE, 'Method 1', colour_by = I(clusters$clusters[, 'k.10_cluster'])) + ggtitle('k = 10'),
+    scater::plotReducedDim(cluster_0_SCE, 'Method 1', colour_by = I(clusters$clusters[, 'k.20_cluster'])) + ggtitle('k = 20'),
+    scater::plotReducedDim(cluster_0_SCE, 'Method 1', colour_by = I(clusters$clusters[, 'k.30_cluster'])) + ggtitle('k = 30'),
+    scater::plotReducedDim(cluster_0_SCE, 'Method 1', colour_by = I(clusters$clusters[, 'k.40_cluster'])) + ggtitle('k = 40'),
+    scater::plotReducedDim(cluster_0_SCE, 'Method 1', colour_by = I(clusters$clusters[, 'k.50_cluster'])) + ggtitle('k = 50'),
+    nrow = 2, ncol = 3, rel_heights = c(0.5, 0.5)
+    )
+plot
+
+plot <- cowplot::plot_grid(
+    scater::plotReducedDim(cluster_0_SCE, 'Method 2', colour_by = I(clusters$clusters[, 'k.10_cluster'])) + ggtitle('k = 10'),
+    scater::plotReducedDim(cluster_0_SCE, 'Method 2', colour_by = I(clusters$clusters[, 'k.20_cluster'])) + ggtitle('k = 20'),
+    scater::plotReducedDim(cluster_0_SCE, 'Method 2', colour_by = I(clusters$clusters[, 'k.30_cluster'])) + ggtitle('k = 30'),
+    scater::plotReducedDim(cluster_0_SCE, 'Method 2', colour_by = I(clusters$clusters[, 'k.40_cluster'])) + ggtitle('k = 40'),
+    scater::plotReducedDim(cluster_0_SCE, 'Method 2', colour_by = I(clusters$clusters[, 'k.50_cluster'])) + ggtitle('k = 50'),
+    nrow = 2, ncol = 3, rel_heights = c(0.5, 0.5))
+plot
+
+

We have helped rewriting this to:

+
# explanation why a for loop does not work
+# https://stackoverflow.com/questions/39799886/r-assigning-ggplot-objects-to-list-in-loop
+
+k_values <- c(10, 20, 30, 40, 50)
+
+plot_list = lapply(k_values, function(k)
+    scater::plotReducedDim(cluster_SCE, "Method 1", colour_by = I(clusters$clusters[, paste0("k.", k, "_cluster")])) +
+    ggtitle(paste0("k = ", k))
+    )
+plot_grid <- cowplot::plot_grid(plotlist = plot_list, nrow = 2, ncol = 3, rel_heights = c(0.5, 0.5))
+plot_grid
+
+plot_list = lapply(k_values, function(k)
+    scater::plotReducedDim(cluster_SCE, "Method 2", colour_by = I(clusters$clusters[, paste0("k.", k, "_cluster")])) +
+    ggtitle(paste0("k = ", k))
+    )
+plot_grid <- cowplot::plot_grid(plotlist = plot_list, nrow = 2, ncol = 3, rel_heights = c(0.5, 0.5))
+plot_grid
+
+

Memory bottleneck

+

One of the scripts fills up the memory (over 30 GB). Using memory +profiling we have identified +that this happens when de-duplicating records with duplicate row names.

+

This is currently done like this:

+
duplicated_rows <- duplicated(rownames(data))
+
+data <- data[!duplicated_rows, ]
+
+

The dataset is +relatively large and the above step fills the memory, fills the swap disk and +then the script crashes.

+

As next steps we recommend to try dplyr to +filter the duplicate rows and/or to process the huge dataset not in one go but +in batches and to later recombine results.

+

Code takes too long to run

+

One analysis script takes over a week to run and this is prohibitively long not +only on a laptop but also on a supercomputing cluster. The time consuming step +happens inside a triple-loop (this is a simplified code, not the actual code):

+
time_consuming_function <- function(i, j, k) {
+    result <- 0
+    # here is some time consuming computation
+    # ...
+    # result <- result + ...
+    # ...
+    result
+}
+
+i_parameters <- c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
+j_parameters <- c(2, 4, 6, 8, 10, 12, 14, 16, 18, 20)
+k_parameters <- c(5, 10, 15, 20, 25, 30, 35, 40, 45, 50)
+
+result <- 0
+num_computations <- 0
+
+for(i in i_parameters)
+{
+    for(j in j_parameters)
+    {
+        for(k in k_parameters)
+        {
+            result <- result + time_consuming_function(i, j, k)
+            num_computations <- num_computations + 1
+        }
+    }
+}
+
+cat("we performed", num_computations, "computations\n")
+
+

This is a very common example and one strategy is to move one or several loops +to the "outside" and to introduce a command line interface:

+
library(optparse)
+
+option_list <- list(
+    make_option(c("--i-parameter", "-i"), type = "integer", help = "help text about the i parameter", default = 1),
+    make_option(c("--j-parameter", "-j"), type = "integer", help = "help text about the j parameter", default = 1)
+)
+
+args <- parse_args(OptionParser(option_list = option_list))
+
+time_consuming_function <- function(i, j, k) {
+    result <- 0
+    # here is some time consuming computation
+    # ...
+    # result <- result + ...
+    # ...
+    result
+}
+
+# iterating over i and j moved "outside"
+# i_parameters <- c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
+# j_parameters <- c(2, 4, 6, 8, 10, 12, 14, 16, 18, 20)
+
+k_parameters <- c(5, 10, 15, 20, 25, 30, 35, 40, 45, 50)
+
+result <- 0
+num_computations <- 0
+
+for(k in k_parameters)
+{
+    result <- result + time_consuming_function(args$i, args$j, k)
+    num_computations <- num_computations + 1
+}
+
+cat("we performed", num_computations, "computations\n")
+
+

Note how we have changed i and j to args$i and args$j.

+

By introducing a command line interface (here using the optparse library) +we make it possible to set i and j from the outside:

+
$ Rscript example.R -i 10 -j 2
+
+

Now we can call the script with a specific i,j-pair and reduce the number of +expensive function calls from 1000 to 10. Of course we still need to iterate +over all i,j-combinations somewhere so the total amount of computing to be +done did not magically reduce. However, now we can do all those calculations in +parallel if we have enough cores available, for instance using Slurm job +arrays.

+

We also get a help text:

+
$ Rscript example.R --help
+
+
Usage: example.R [options]
+
+Options:
+	-i I-PARAMETER, --i-parameter=I-PARAMETER
+		help text about the i parameter
+
+	-j J-PARAMETER, --j-parameter=J-PARAMETER
+		help text about the j parameter
+
+	-h, --help
+		Show this help message and exit
+
+

Reproducibility

+

In order to simplify sharing the examples and document dependencies we have +introduced renv into the +project. We have also developed an Apptainer/Singularity container for +reproducible R environments.

+

This is the Apptainer/Singularity container definition file that we have used +for testing and debugging:

+
Bootstrap: docker
+From: satijalab/seurat:latest
+
+%post
+    apt-get update -qq
+    export DEBIAN_FRONTEND=noninteractive
+
+    # possibly not all of these are needed
+    apt-get install -y libfontconfig1-dev libharfbuzz-dev libfribidi-dev libcairo2-dev libfreetype6-dev libpng-dev libtiff5-dev libjpeg-dev libgif-dev libwebp-dev libpoppler-glib-dev libpoppler-private-dev libssl-dev libcurl4-openssl-dev libxml2-dev
+
+    R --quiet -e "options(repos = c(CRAN = 'http://cran.us.r-project.org'))"
+    R --quiet -e "install.packages('BiocManager')"
+
+    R --quiet -e "BiocManager::install(c('systemfonts'))"
+    R --quiet -e "BiocManager::install(c('clustree'))"
+    R --quiet -e "BiocManager::install(c('scater'))"
+    R --quiet -e "BiocManager::install(c('cowplot'))"
+    R --quiet -e "BiocManager::install(c('scran'))"
+    R --quiet -e "BiocManager::install(c('scmap'))"
+
+

The container is based on the container image provided +here.

+ + +
+ +
+ +
+ + + + +
+ + diff --git a/blog/2023-software-licensing-guide/index.html b/blog/2023-software-licensing-guide/index.html new file mode 100644 index 0000000..9904d82 --- /dev/null +++ b/blog/2023-software-licensing-guide/index.html @@ -0,0 +1,527 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+ +
+
+ + +
+ +
+ +

Draft: Research software licensing guide

+ +

+ +

+ A practical guide on why software licenses matter and how to choose one and what practical steps to take +

+ + June 21, 2023 + + - Radovan Bast and Kathleen A. Smart + +

+ +
+ +
+

This is a preliminary draft of a proposed research software licensing guide +to be used by researchers at UiT. We are sharing this draft with colleagues +and various stakeholders for review and comments. Please send comments, +suggestions, and corrections to +radovan.bast@uit.no.

+
+
+

This guide is shared under the Creative Commons CC0 +waiver.

+
+ + + + + +

What is "research software"?

+

We consider any code, script, notebook, or file, regardless of size, as +"research software" if it is needed to generate, visualize, or reproduce +data/results that are part of, or will form part of a publication. You don't +need to be a "proper software engineer" to produce software. Most research +software is not written by "proper software engineers" (whatever that means).

+

Examples of "research software" for the purpose of this guide:

+
    +
  • Script to convert data from one format to another
    • +
  • Script to read data and visualize it
    • +
  • Program that generates data
    • +
  • Analysis script
    • +
  • Set of scripts that form an analysis pipeline
    • +
  • Code that is compiled
    • +
  • Code that is dynamically interpreted and not compiled
    • +
  • Web app
    • +
+

Why software licenses matter

+

Imagine the following frequent scenario: You find some great code or data that +you want to reuse for your own publication. This is good for the original +author - you will cite them. Maybe other people who cite you will cite them. +You need to modify the code a little bit, or you remix the data a bit. But +then, when it comes time to publish, you realize there is no license to the +original work. Thus, you can't release the new stuff you made!

+

Now you have a problem:

+
    +
  • You manage to publish the paper without the software/data but others cannot +build on your software and data and +you don't get as many citations as you could.
    • +
  • Or, you cannot publish it at all if the journal requires that papers should +come with data and software so that they are reproducible.
    • +
+

Next time you are smarter and check the license before building on someone +else's work. And others may approach your work the same way.

+

Open science is built upon sharing of research data and software in a FAIR +(findable, accessible, interoperable, and reusable) manner. For research data, +reuse entails applying an open data license (generally using Creative Commons +licenses), but sharing of software requires licenses with more specific +terms.

+

In open science we strive towards reproducibility (results can be verified by +other researchers) and reusability (results can be reused in follow-up research +by you and/or others without starting over from scratch).

+

Without a license that allows reuse and redistribution, and additionally clarifies terms +under which code can be reused and redistributed:

+
    +
  • others may not be able to publish derivative work based on your code
    • +
  • you may not be able to publish derivative work based on somebody else's code +or even your own code (e.g. after changing jobs)
    • +
+

This means that clarifying terms of use is essential for derivative work +based on your research to be even publishable. Choosing an open source +license can also be good insurance for you against being locked out of your +own code after changing affiliation/group or job.

+

When should I add a license?

+

Choose a license early in the project, even before you publish it. Later in the +project it may become complicated to change it. Agreeing on a software license +does not mean that you have to make it open immediately. You can also follow +the "open core" approach: You don't have to open source all your work. Core can +be open and on a public branch. Unpublished code can be on a private +repository.

+

However, we recommend to work as if the code is public even though it still may +be private: This is to avoid surprises about code in the history with +incompatible license years later when you decide to open the project.

+

How do I add a license to my work?

+

If your work is derivative work

+

(here we will add a decision tree/ flow-chart)

+

Your code is derivative work if you have started (partially) from an existing code and +made changes to it or if you incorporated an existing code into your code.

+

If your code is derivative work, then you need to check the license of the +original code. Depending on the license, your choices might be limited. In +this case we recommend to use these two resources:

+ +

If the original code does not have a license, you may not be able to distribute your +derivative code. +Even if it doesn't have a license, it still might have terms of use, which might or might not be compatible with a standard license. +You can try to contact the authors and ask them to clarify +the license of their code.

+

Practical steps for incorporating something small into your own project with a license +that allows you to do so (as +an example incorporating a function or two from another project):

+
    +
  • Create a LICENSES/ folder in your project and "put the unmodified license text +(i.e., the license text template without any copyright notices) in your +LICENSES/ folder" (https://reuse.software/faq/#license-templates). This +way if you reuse code from multiple projects, you can keep there multiple +license files.
    • +
  • Put the code that you incorporate into a separate file or separate files. This makes +it later easier to see what was incorporated, and what was written from scratch. +On top of the file(s) which you have incorporated into your project add (and +adapt) the following header (more examples):
    # SPDX-FileCopyrightText: 2023 Jane Doe <jane@example.com>
+#
+# SPDX-License-Identifier: MIT
+
    +The REUSE initiative was started by the Free +Software Foundation Europe to make licensing of software +projects easier. It is OK if you prefer to not follow this strict format but +the advantage of following it is that the +reuse-tool makes it then easy to verify +and update license headers if you have many files from different sources.
    • +
  • If it does not make sense to have several files in your project (e.g. when incorporating +something into a notebook), then add a note/comment +about the license and where the code came from on top of the function.
    • +
  • Although it is not dictated by the license but it can still be nice to +acknowledge the incorporated functions/code in your README/documentation and to cite +their work if you publish a paper about your code.
    • +
  • Some licenses are more permissive (you can keep your changes private) but some licenses +require you to publish the changes ("share-alike" or "copy-left").
    • +
+

Practical steps for making changes to an existing project with a license +that allows you to do so:

+
    +
  • If the project is on GitHub or GitLab or similar, first fork the project +(copy it into your user space where you can make changes).
    • +
  • For the BSD and MIT licenses you are not obliged to state your changes but it can +still be helpful for others if you do. You can state your changes in the +header of the files you have modified. It can be helpful to state +bigger-picture changes in the README file of the project.
    • +
  • Some licenses are more permissive (you can keep your changes private) but some licenses +require you to publish the changes (share-alike).
    • +
+

If your work is not derivative work

+

If you have started "from scratch", and not used any existing code, or +incorporated existing code into your code, then you may consider your code to +be not derivative work. Note that if you only use (but not incorporate or +change) libraries, plug-ins, and packages, which are distributed via platforms +like PyPI, Conda, CRAN, Crates, ... this is typically not considered derivative work.

+

Before you may choose a license, clarify the following points with, for +example, your supervisor, principal investigator, collaborators, or research +support service:

+
    +
  • Does your work contract, grant, or collaboration agreement dictate a +specific license?
    • +
  • Is there an intent to commercialize the code?
    • +
  • When there is unknown or mixed ownership: If there are multiple persons or +organizations as owners of the code, all must agree to the license.
    • +
+

Do not invent your own license. Choose one of the standard licenses, otherwise +compatibility is not clear:

+ +

If there are no restrictions from the above points, and no overriding guidelines exist, +we recommend one of the following two licenses:

+
    +
  • European Union Public Licence, Version 1.2 or later (EUPL) +(https://data.europa.eu/doi/10.2799/77160): this license is an official +license of the European Union (EC Decision, part of European law). It is +interoperable, reciprocal (derivative work must publish and provide back +the modified source code), and compatible with many standard licenses. It +is a good choice if you want to make sure that you have access to changes +and improvements applied to your code so that you can integrate them back +and reuse them in your own work or you want some competitive advantage +(right to relicense) over other users of your work.
    • +
  • MIT License: Choose this license to prioritize ease of reuse +over requiring derivative work to provide modifications back to you and the +community: Others will be able to do whatever they want with your code as +long as they include the original copyright and licence notice in any copy +of the software/source. For small and short-lived projects this can be a +good choice.
    • +
+

Note that with the exception of CC0 (i.e. public domain dedication) +CC licenses are not appropriate for software +(although they can be used for software documentation).

+

Practical steps:

+
    +
  • Create a LICENSES/ folder (example).
    • +
  • Put the unmodified license text +(i.e., the license text template without any copyright notices) in plain +text format into the folder (example). Here are +the two above licenses in plain text: +EUPL +and MIT (we recommend +to use the MIT license text without the copyright header).
    • +
  • Add copyright and license information to each file following +https://reuse.software/tutorial/ which uses a standard format with +so-called SPDX identifiers. Example below +(example):
    # SPDX-FileCopyrightText: 2023 Jane Doe <jane@example.com>
+#
+# SPDX-License-Identifier: EUPL-1.2
+
    +The REUSE initiative was started by the Free +Software Foundation Europe to make licensing of software +projects easier. It is OK if you prefer to not follow this strict format but +the advantage of following it is that the +reuse-tool makes it then easy to verify +and update license headers if you have many files from different sources.
    • +
  • Create a CITATION.cff file +(example; more about it below).
    • +
  • For really small projects with one or two files the above may seem excessive +and some projects choose to not have copyright information on top of their +files and they only have one LICENSE file and a CITATION.cff file and that is +OK for really small projects.
    • +
+

Make it persistent and citable

+

Choosing a license, adding a license file, and putting your code on GitHub or +GitLab is good start, but to ensure FAIR (findable, accessible, interoperable, +and reusable) research software for long term access, we recommend to go two +steps further:

+ +

The reason why we currently recommend Zenodo over +DataverseNO for software products is that it is +easier to update versions. DataverseNO currently does not allow to assign +different licenses on a file-level which may result in license conflicts +(possible case: publishing software under CC0 on DataverseNO and having it +under a more restrictive license on GitHub at the same time). We believe that +linking from the DataverseNO entry to the software as "related dataset" is a +better option.

+

Software Heritage and +CodeMeta exist as an alternative ecosystem that +is currently receiving some attention on a European level. Comparison and links +to converters can be found in https://zenodo.org/record/8086413.

+

Additionally, UiT and other major national (RCN Open Science) and European +funders (EU Open Science) require open access to publications and research data +(including research software) as early as possible, so self-archiving your +research software satisfies these requirements. For more information on these +policies, please see: UiT Research Data +Portal, UiT Regulations on +Research Data, or +contact the University Library at +researchdata@uit.no. 

+

How about data?

+

Citing from the Principles and guidelines for management of research data at +UiT:

+
    +
  • "The researcher shall make research data openly available for further use to +all relevant users, providing there are no legal, ethical, security or +commercial reasons for not doing so."
    • +
  • "Research data shall be equipped with licenses for access, reuse, and +redistribution."
    • +
  • "The licenses should be internationally recognised and place as few +restrictions as possible on the access, reuse and redistribution of the +data."
    • +
+

By default, the UiT collection in DataverseNO recommends the Creative Commons +CC0 waiver. +The UiT Research Data Portal +contains information and guides on why and how to add such a license to your +data.

+

The Horizon Europe +guideline +specifies that data should be CC0 or CC-BY. Metadata must be CC0. The +recommendations of the +Norwegian Research council can be found in the report +"Hvordan skal vi dele forskningdata" (p. 40). +The overall principle +rule is "as open as possible, as closed as necessary".

+

We wish to emphasize that using a restrictive license with the intention to +protect privacy of the data is not a good substitute for having the data +sufficiently protected in other ways.

+

Questions and contact

+

Unsure which license to choose? Unsure how to proceed with sharing your code +or reusing somebody else's code? The research software engineering (RSE) +group at UiT is here to +help you. You can contact us at rse@uit.no or come to our +office hours. For questions regarding sharing of publications and research +data, contact us at researchdata@uit.no.

+

Great resources

+ +

Acknowledgements

+

We are very grateful to +Korbinian Michael Bösl, +Philipp Conzett, +Richard Darst, +Luca Ferranti, +Noortje Haugstvedt, +and +Jenny Ostrop, +for their comments and suggestions which +significantly improved this document.

+ + +
+ +
+ +
+ + + + +
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+ + + +
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+ +
+ +

Supporting our university community

+ +

+ +

+ Magnar's first quarter at UiT's Research Software Engineering Office Hours +

+ + May 10, 2023 + + - Magnar Bjørgve + +

+ +
+ +

At UiT, we understand the importance of offering support and guidance in research software development and usage. To address this need, our IT department has recently introduced "Research Software Engineering" (RSE) office hours in room A209 at Modulbygget, held every Wednesday from 14:00 to 16:00. In this blog post, I'll share some of the projects and services I've personally worked on as part of our RSE team and how we strive to assist students and staff at UiT. +Establishing a Point of Contact:

+

To ensure smooth communication and timely assistance, we've created a dedicated email address (rse@uit.no) for RSE-related inquiries. Don't hesitate to reach out with questions, concerns, or to schedule a consultation.

+

Notable Projects and Services I've Contributed To: +As a member of the RSE team, I have had the opportunity to contribute to several exciting projects, demonstrating our commitment to providing valuable support to the university community. Here are a few highlights:

+
    +
  1. Exploring Ansys and OpenFOAM on National HPC Resources: +I've investigated the potential of utilizing Ansys and OpenFOAM, two popular +computational fluid dynamics (CFD) tools, on the national HPC resources. +Currently, I'm collaborating with a user to implement OpenFOAM in their +research, streamlining the process and offering valuable insights.
    2. +
  2. Assessing Mathematica on National HPC Resources: +I'm also looking into the feasibility of using Mathematica, a powerful +computational software for various scientific disciplines, on the national +HPC resources. This project is ongoing, and I'm excited to share our +findings in the future.
    3. +
  3. Guiding R Code Users and a Biology Student: +I've provided guidance on using the R programming language, including +assistance with debugging code. In one instance, I've worked with a biology +student, discussing how data and code can be presented for their master +project. Our team is equipped to help researchers with diverse programming +backgrounds and offers tailored support for their specific needs.
    4. +
  4. Assisting with GitHub Copilot Setup: +As a team, we've helped users set up GitHub Copilot, an AI-powered code +completion tool that can significantly improve programming efficiency. +Researchers can take advantage of this cutting-edge technology to enhance +their work.
    5. +
  5. Supporting Python Code Debugging and Optimization: +I've been involved in debugging and optimizing Python code, ensuring that +researchers can work more efficiently and produce accurate, high-quality +results.
    6. +
+

My experience as part of the RSE team at UiT's Research Software Engineering office hours has allowed me to contribute valuable support to students and staff, helping them navigate the complexities of research software and programming. I'm proud to be part of a team committed to empowering our university community by providing assistance in a wide range of areas, from software setup to code optimization.

+

If you're a student or staff member at UiT and need assistance with research software or programming, don't hesitate to visit us during office hours in room A209 at Modulbygget on Wednesdays from 14:00 to 16:00, or reach out to us at rse@uit.no. We look forward to helping you achieve your research goals! Remember to stay tuned for more blog posts from my colleagues, detailing their own experiences and the projects they've been working on.

+AI art based on blogpost + + +
+ +
+ +
+ + + + +
+ + diff --git a/blog/2023-synchronizing-git-repo-via-email/index.html b/blog/2023-synchronizing-git-repo-via-email/index.html new file mode 100644 index 0000000..664fd09 --- /dev/null +++ b/blog/2023-synchronizing-git-repo-via-email/index.html @@ -0,0 +1,306 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
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+ + +
+ +
+ +

Synchronizing Git repositories via email

+ +

+ +

+ Sometimes GitHub/GitLab/own-server is not an option - good old email to the rescue +

+ + November 27, 2023 + + - Radovan Bast + +

+ +
+ +

For a project with the Department of Language and +Culture at UiT we had the +challenge to find a way to synchronize two Git repositories where we wish to +end up with a as-similar-as-possible history, but where we cannot communicate +changes through GitHub or GitLab or similar servers.

+

To have a concrete example, let us assume that we have two computers +(computer A and computer B) which are far away and they can only talk +to each other via email. Both hold a Git repository which tracks some data or +code and both Git repository have the same starting point (they have a common +history).

+
+
+

On computer A we have the following Git history:

+
* 69bf25c (HEAD -> main) another improvement
+* b202aa5 adding more data to my repo
+* 5670bc4 initial commit
+
+

We got this information with git log --oneline --graph. +69bf25c is the newest commit and 5670bc4 the oldest.

+
+
+

On computer B we have the following Git history:

+
* e6287b6 (HEAD -> main) another local modification
+* 1120a75 one local modification
+* 69bf25c another improvement
+* b202aa5 adding more data to my repo
+* 5670bc4 initial commit
+
+

The Git history on computer B has the same starting point (first 3 commits) +but we created two more commits which we wish to later share with the other +computer:

+
+
+

Below I will discuss 3 options to do this, but I recommend the first (using +git bundle) since that one preserves commit hashes (commit identifiers), +whereas the other two option don't.

+ + + + + + +

On computer B we create a bundle from the entire main branch:

+
$ git bundle create project.bundle main
+
+

It can be useful to also tag the commit from which we created the bundle +so that we know where to bundle from next time:

+
$ git tag -f last-export-on-computer-B
+
+

The generated project.bundle file is the one that we can email over to the other computer.

+

On computer A we can now pull changes from the bundle into the main branch:

+
$ git pull project.bundle main --rebase
+
+

The --rebase part makes sure that we move any local changes we might have to +the end. We can verify the result with git log --oneline --graph. If +computer A has commits that computer B does not have, we can do the +same in reverse: create a bundle, tag the commit from which we created the +bundle, email the bundle over and apply it there (pull from it there).

+

Using the tags allows you to bundling commits only since that tag instead of +all commits:

+
$ git bundle create project.bundle last-export-on-computer-B..main
+
+

But don't forget to tag again after each bundling.

+

If both computers changed the same portion of the code/data in different ways +it is possible to get a conflict. Here it might be necessary that the humans +who created the two changes communicate and decide which of the two versions +they wish to keep and base their further work on. You can minimize the chance +of conflicts by emailing bundles right after making a new commit and not +waiting for too long.

+

Using git format-patch

+

Also here we start with computer B having two commits more. So let's create patches +from the two most recent commits:

+
$ git format-patch -2
+
+0001-one-local-modification.patch
+0002-another-local-modification.patch
+
+

Note that the patches are numbered. We can now email +0001-one-local-modification.patch and 0002-another-local-modification.patch +and try to apply them on the other computer. If you need to send very many +files, you can zip them into one archive and email that one archive instead.

+

Now we move to computer A and try to apply the patches but we need to apply them in order:

+
$ git am 0001-one-local-modification.patch
+$ git am 0002-another-local-modification.patch
+
+

One disadvantage of git format-patch compared to git bundle is however that +we will end up with different Git commit hashes. +To see this, compare histories on both. The content is the same, the commit messages +are the same but the commit hashes differ:

+
+
+

Git history on computer A:

+
* 87fe580 (HEAD -> main) another local modification
+* 175d42c one local modification
+* 69bf25c another improvement
+* b202aa5 adding more data to my repo
+* 5670bc4 initial commit
+
+
+
+

Git history on computer B:

+
* e6287b6 (HEAD -> main) another local modification
+* 1120a75 one local modification
+* 69bf25c another improvement
+* b202aa5 adding more data to my repo
+* 5670bc4 initial commit
+
+
+
+

Comparing and applying patches "manually"

+

If the data/software that is tracked in Git is not too large to be sent itself, +then one way to share changes is to send the data or software over and on each +side merge changes visually using programs like Meld +and then commit the new changes:

+
    +
  • Email the data/software to computer A
    • +
  • With Meld compare the new version with the last commit and merge changes
    • +
  • Commit
    • +
  • Email the merged result back to computer B
    • +
  • With Meld compare the new version with the last commit and merge changes
    • +
  • Commit
    • +
  • ...
    • +
+

However, with this strategy we will end up with different Git commit hashes.

+ + +
+ +
+ +
+ + + + +
+ + diff --git a/blog/index.html b/blog/index.html new file mode 100644 index 0000000..6e46151 --- /dev/null +++ b/blog/index.html @@ -0,0 +1,240 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+ +
+
+ + +
+ +

Blog posts

+ + + + +
+ +
+ + + + +
+ + diff --git a/contact/index.html b/contact/index.html new file mode 100644 index 0000000..39172ef --- /dev/null +++ b/contact/index.html @@ -0,0 +1,170 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
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Contact

+

How to reach us via email

+

Write to rse@uit.no.

+

Newsletter

+

We plan to send out an email newsletter about courses and events every 1-3 months.

+
    +
  • +

    You can subscribe like this: +Send an email to sympa@list.uit.no with the subject subscribe rse-news Firstnames Lastname (replace Firstnames and Lastname) and an empty email +body.

    +
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    Or unsubscribe like this: +Send an email to sympa@list.uit.no with the subject unsubscribe rse-news and an empty email body.

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Where to find us on the campus

+ + + +
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+ + diff --git a/courses/index.html b/courses/index.html new file mode 100644 index 0000000..8e2b64a --- /dev/null +++ b/courses/index.html @@ -0,0 +1,180 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Courses

+

Most courses we do are developed and organized together with +CodeRefinery.

+

You can also join our email newsletter to stay up-to-date about +courses and events.

+

Upcoming courses and events we recommend

+ +

Recent courses we co-organized

+ +

Lesson material and recordings

+ + + +
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+ + + +
+
+ +
+
+ + +
+ +

+ Research software engineering (RSE) group at + UiT +

+ +
+
+ + RSE logo +
+ +
+ +

We want to help all researchers and students at UiT to develop and improve +their research software. This can span from simple R or Python scripts to +complex GPU enabled software packages.

+

Save time on your project by getting quick and comprehensive help by +experts.

+ +
+ +
+
+
    + + +

    "The Research Software Engineering Group has been of great help to me in +starting to use Git and organizing my code. +"

    + +

    "The execution time for my script went down from 4 weeks to below an hour which +was really cool! +"

    + +
+
+

+ (more testimonials) +

+
+
+ +
+ +

Welcome to office hours Wednesdays 13-15

+

Need help with anything code related? Visit us during our office hours, +(almost) every Wednesday 13:00 - 15:00 in room A210 in +Modulbygget.

+

Office hours before summer 2024: Mar 20, Apr 3, Apr 10, Apr 17, Apr 24, May 8, May 15, May 29, Jun 5, Jun 12, Jun 19

+ +
+ +

What we offer

+
+ +
    +
  • Help with improving your scripts/code +
      +
    • Code review: we look together at your code/script and give constructive feedback and advice
      • +
    • Making code more reproducible and reusable
      • +
    • Advice and help how to best document code
      • +
    • Advice about whether to use YAML or CSV or JSON or TOML or something else
      • +
    • Consulting and help with web development (static websites, JavaScript, HTML, CSS frameworks)
      • +
    +
    • +
  • Help with organising your code +
      +
    • Help with modularizing your code
      • +
    • Help with moving your work/project/code/data to Git
      • +
    • Help with Git, GitHub, and GitLab
      • +
    • Help with organization of reusable and reproducible Jupyter notebooks and +Binder
      • +
    +
    • +
  • Help with sharing your code +
      +
    • Help publishing your code
      • +
    • Help with software licenses and open sourcing
      • +
    • Packaging and sharing software
      • +
    • Containerization (Singularity, Docker)
      • +
    • Help with pip, PyPI, and Conda
      • +
    +
    • +
  • Help with running your code faster +
      +
    • Improving scaling, CPU, and memory footprint of research codes
      • +
    • Porting to GPU
      • +
    • Moving from local computer to cloud or high-performance computing (using e.g. NRIS resources)
      • +
    +
    • +
+

If you have questions about code you wrote or want to write, please contact +us or come and talk to us. +Or check our short presentation slide deck for research groups. +Or try the even shorter slide deck.

+ +
+ +
+ +
+ + + + +
+ + diff --git a/index/index.html b/index/index.html new file mode 100644 index 0000000..7a3f72e --- /dev/null +++ b/index/index.html @@ -0,0 +1,151 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + diff --git a/index/office-hours/index.html b/index/office-hours/index.html new file mode 100644 index 0000000..40a4f32 --- /dev/null +++ b/index/office-hours/index.html @@ -0,0 +1,156 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Welcome to office hours Wednesdays 13-15

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Need help with anything code related? Visit us during our office hours, +(almost) every Wednesday 13:00 - 15:00 in room A210 in +Modulbygget.

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Office hours before summer 2024: Mar 20, Apr 3, Apr 10, Apr 17, Apr 24, May 8, May 15, May 29, Jun 5, Jun 12, Jun 19

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+ + diff --git a/index/slogan/index.html b/index/slogan/index.html new file mode 100644 index 0000000..b9e6c19 --- /dev/null +++ b/index/slogan/index.html @@ -0,0 +1,156 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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We want to help all researchers and students at UiT to develop and improve +their research software. This can span from simple R or Python scripts to +complex GPU enabled software packages.

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Save time on your project by getting quick and comprehensive help by +experts.

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+ + diff --git a/index/what-we-offer/index.html b/index/what-we-offer/index.html new file mode 100644 index 0000000..f53c7ef --- /dev/null +++ b/index/what-we-offer/index.html @@ -0,0 +1,191 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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  • Help with improving your scripts/code +
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    • Code review: we look together at your code/script and give constructive feedback and advice
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    • Making code more reproducible and reusable
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    • Advice and help how to best document code
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    • Advice about whether to use YAML or CSV or JSON or TOML or something else
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    • Consulting and help with web development (static websites, JavaScript, HTML, CSS frameworks)
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  • Help with organising your code +
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    • Help with modularizing your code
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    • Help with moving your work/project/code/data to Git
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    • Help with Git, GitHub, and GitLab
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    • Help with organization of reusable and reproducible Jupyter notebooks and +Binder
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  • Help with sharing your code +
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    • Help publishing your code
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    • Help with software licenses and open sourcing
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    • Packaging and sharing software
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    • Containerization (Singularity, Docker)
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    • Help with pip, PyPI, and Conda
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  • Help with running your code faster +
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    • Improving scaling, CPU, and memory footprint of research codes
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    • Porting to GPU
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    • Moving from local computer to cloud or high-performance computing (using e.g. NRIS resources)
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If you have questions about code you wrote or want to write, please contact +us or come and talk to us. +Or check our short presentation slide deck for research groups. +Or try the even shorter slide deck.

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+ + + + +
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+ + + +
+
+ +
+
+ + +
+ +

Projects

+

+ +Here we summarize ongoing and completed projects, small and large. If you want to +learn more about what we do check out our blog. +
+

+
+

Speeding up a Python code from 4 weeks to below an hour

+

In this project we reworked a Python code (in a Jupyter Notebook) which was +originally written in MATLAB. The code was processing 18000 files containing +satellite data and this took 4 weeks. At the end of the project we could do +this in below an hour, with less code.

+

Let us summarize our steps:

+
    +
  • Files were processed in pairs but instead of going through all pairs we +decided to focus on one pair only and re-introduce the iteration over all +pairs at the end. This gave us better focus, shorter test and debug loop, and +more flexibility to potentially parallelize over the pairs.
    • +
  • We moved the project to GitHub to simplify collaboration and to introduce a +code review workflow.
    • +
  • We started tracking issues and ideas in GitHub issue threads instead of via email.
    • +
  • We identified the computationally intensive steps.
    • +
  • Most of the code consisted of merging large tables which we re-implemented using a Pandas +inner join. This significantly simplified the code.
    • +
  • We moved filtering steps to earlier in the code so that we do computations on +filtered data instead of first computing data which we might throw away +later.
    • +
  • We abstracted repeating code into functions and moved the functions outside +of the notebook so that they can be tested and improved independently. The +functions got then imported into the notebook.
    • +
  • We delegated data cleaning to Pandas instead of performing it "manually".
    • +
  • We parallelized over data chunks which can be processed independently.
    • +
+

Helping a student getting an R script to run

+

In this short project we helped with the dependency installation step.

+

Helping two students installing Python software for a course

+

Two students approached us to help installing software and dependencies which +they needed to attend a computational course. We solved this with Conda and by +adjusting wrong paths. It also required a non-trivial work-around with a +symbolic link to a library which could not be found by the software.

+

Translating a legacy Visual Basic program to Python

+

In this ongoing project we help a researcher to translate and restructure an +old legacy VB6 code base for biological food web modelling to modern object +oriented python. The goals of this project are to improve maintainability and +ease extensibility of the code by developing a new structure.

+

Running bioinformatics workflows on data hosted on NIRD

+

Together with the researcher, we are evaluating different methods to run +genomic and transcriptomic workflows with large datasets with regards to ease +of use, performance and extensibility. We plan to offer the solution to other +researchers as a quick and easy way of getting started with NRIS resources.

+

Solving a Python problem with regular expressions

+

This was a 2 hours project where we helped writing a prototype Python code +which would traverse a file tree and using a regular expression would extract a +set of variables from the file names which would later be used for data +queries.

+

Porting a Fortran project to CMake

+

The goal of this project is to compile a code that was written 4 years ago and +to port the configuration and build setup to CMake. Here we apply "code +archaeology" and at the same time we wish to document the build process better +and to make it more portable from a very specific setup (visual studio with a +particular compiler) towards in principle any Fortran compiler and any editor +and operating system.

+

Optimizing an R script which takes too much time and consumes too much memory

+

This project is ongoing. The R script currently fills the available memory and +times out during one particular step. Our approach is to profile the memory +demands and profile the individual steps. Once the problem is isolated and +understood, we will try to overcome the bottleneck, possibly by rewriting the +library call or even the library function(s).

+

Help with setting up a Python development environment

+

Here we have helped a researcher who is moving their development environment +from Matlab to Python with setting up an editor, configuring it with GitHub +Copilot, installing Anaconda, and setting up a Conda environment for their +project.

+

Helping out with a Python course for Bioinformatics

+

We have contributed one lesson to a two-weeks Python course "Introduction to +Python in the Life +Sciences".

+

Helping out with running OpenFOAM on National HPC Resources

+

In this project, we're collaborating with a group from the Department of +Automation and Process Engineering to streamline their computational work. +After assessing their specific needs and the capabilities of various tools, +it was concluded that OpenFOAM was the better option for this group's work +on the national High-Performance Computing (HPC) system.

+

Our current focus is on guiding them in effectively utilizing OpenFOAM on the +HPC system. This includes providing them with the necessary skills to navigate +the HPC environment and optimize the capabilities of OpenFOAM within it.

+

The ultimate goal of this project is to empower the group to independently +conduct their production calculations using OpenFOAM on the HPC system. +We aim to enhance the efficiency of their computational work and facilitate +a more powerful research outcome within the Department of +Automation and Process Engineering.

+

R Code Guidance for a Biology Student

+

This project involved assisting a student with their R code during our office +hours. The student, conducting biology-related research, encountered some +challenges with their code. Our task was to provide the necessary guidance +to help the student understand and resolve these issues, enabling them to +continue with their research more effectively. This is an example of how we +support individual needs, even on a one-to-one basis, during our dedicated office hours.

+

Setting up GitHub Copilot for a Student

+

In a specific case, we assisted a student with setting up GitHub Copilot, +an AI-powered code completion tool. We provided step-by-step guidance +throughout the setup process, resolving any issues that arose. By doing so, +the student was able to utilize this cutting-edge technology to enhance their +programming efficiently.

+

Building a GUI

+

In this project we are helping a researcher in creating a Graphical User +Interface (GUI). The researcher have an existing Python program with command +line interface but wish to make the program more accessible for users +unfamiliar with command line. Together with the researcher, we have decided on +creating the GUI using the Python library ‘PyQt’ and we are sharing our +experience with research software development with the researcher.

+

Getting started with Git, best practices and Xarray.

+

The user is getting started with Git and we have provided support and answered +Git-related questions. We have also discussed best practices and introduced the +user to Black, Flake8, and docstring guides for cleaning up code, as well as +creating top-level functions in scripts and importing functions via symlink. In +addition, the user had challenges with Python and Xarray in their research, +which we together solved.

+

Electron Density Calculations with Custom Python Scripting

+

A researcher needed help in writing a Python script to read electron +density data from a CHGCAR file produced by VASP software. We +provided guidance on setting up Python using an environment.yml file +and advised on the optimal structure of the code. As a result, the +researcher successfully calculated the integral of the electron +density within a specified radius around an atom.

+

Unlocking Advanced Research Capabilities through Customized Software Installation

+

A researcher required specialized support for setting up a Python environment to run and +modify the vampyr and mrcpp software packages. We provided comprehensive guidance on +environment setup and facilitated the installation of these software packages from source. +This enabled the researcher to go beyond the limitations of the official releases, offering +them the flexibility to run and modify the codes for their specific research needs. The +collaboration enhanced the researcher's computational capabilities, paving the way for more +sophisticated and tailored research outcomes

+

Using autogenerated multiple choice questions in teaching

+

We helped a course instructor getting started with using autogenerated multiple +choice questions in their course. The instructor was introduced to new +functionality within the Canvas learning management system, such as question +banks and quizzes. In addition we provided a hands-on tutorial on how to use +R/exams to efficiently generate a large amount of multiple choice questions and +how to upload them to Canvas question banks.

+

Help with speeding up an R script by dividing it into independent chunks

+

The challenge here is to compute in parallel but using random numbers. This is +not completely trivial since we need to carefully use the random number seed. +This project is on-going.

+

Code to position a medical device

+

In this project we write code to help with device positioning for Transcranial +magnetic +stimulation.

+

We do this in Python and Rust but also use containers. Soon we will write a +blog post and a paper on this.

+

Organizing, speeding up, and packaging a Python code which analyses island shapes

+

This involves profiling and organizing the code. Soon we will work on making +the code more robust, faster, and more re-usable. Finally we will together work +on packaging the project to PyPI.

+

Help with Matplotlib

+

This was a small project but also small things can take lots of time.

+

Help with R and ggplot2 when plotting water depths

+

Here we sat together and studied and simplified an R Studio project for +plotting measurements along water depths.

+

Moving a machine learning pipeline from the laptop to the cluster

+

This project involved re-organizing the Python code so that independent runs +can run concurrently. To make the project easier to move from one computer to +another, we have containerized the run. We have also added a command-line +interface to the code to make it easier to study parameter-sweeps without +editing the source code every time.

+
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+ + diff --git a/resources/index.html b/resources/index.html new file mode 100644 index 0000000..2157c60 --- /dev/null +++ b/resources/index.html @@ -0,0 +1,160 @@ + + + + Research software engineering group at UiT + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+ +
+
+ + +
+ +

Useful resources

+ + + +
+ +
+ + + + +
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https://research-software.uit.no/blog/2023-synchronizing-git-repo-via-email/ + 2023-11-27 + + + https://research-software.uit.no/contact/ + + + https://research-software.uit.no/courses/ + + + https://research-software.uit.no/index/ + + + https://research-software.uit.no/index/office-hours/ + + + https://research-software.uit.no/index/slogan/ + + + https://research-software.uit.no/index/what-we-offer/ + + + https://research-software.uit.no/projects/ + + + https://research-software.uit.no/resources/ + + + https://research-software.uit.no/testimonials/ + + + https://research-software.uit.no/vision/ + + diff --git a/style.css b/style.css new file mode 100644 index 0000000..712972e --- /dev/null +++ b/style.css @@ -0,0 +1 @@ +html{font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color 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Testimonials

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    "The Research Software Engineering Group has been of great help to me in +starting to use Git and organizing my code. I joined the Code Refinery +workshops, which provided a good introduction to Git. In the time that +followed, the RSE group was available to answer Git-related questions, +making it easier for me to apply Git to my own research work. The RSE group +also introduced me to tools like Black, Flake8, and docstring guides for +cleaning up code, as well as creating top-level functions in scripts and +importing functions via symlink. The sessions have been a combination of +in-person help and group discussions, both of which have worked out well. +The group is always welcoming and eager to help with any problems you may +have."

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    "I had approached the RSE group seeking help to improve the efficiency and +performance of my python code which was taking approximately 4 weeks to run +even when using 8 cores of a server.

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    The objective was to go through 8 years of data stored in approximately +18000 txt files and extract certain parameters based on a set of given +conditions. Even though I had used python before, I got the opportunity to +learn about new functionalities and features of pandas dataframes. Using +Black and other extensions, I was taught to organize my code more +efficiently thereby improving its readability. Additionally, Radovan also +showed me that is possible to import my own functions as separate modules +without the need to populate my script with a lot of functions. These were +so powerful that the execution time for the script slid down from 4 weeks +to below an hour which was really cool!

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    Radovan also motivated me to use GitHub, which I had never used before, by +introducing features that could be utilized by a research student like me +who uses a lot of data and scripts. I am starting to explore more features +of GitHub and python on my own now. I am really happy with the guidance I +had received from the RSE group especially that from Radovan who helped me +re-think the way I program. I felt that the RSE group members are so +welcoming and excited to help learn and relearn new things together."

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Vision

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Our vision is to put UiT on the map as a place where research groups have +access to best in class RSE services. So that researchers and students can get +the help they need to be able to focus on excellent research. Over time we +wish to attract first class staff, research groups, funding, collaborations, +and attention.

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We are starting a RSE group at UiT, following the footsteps of UK-based +groups and the RSE group at Aalto +University.

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About us

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Together, we have many years of experience programming for science, mentoring, +and teaching computing.

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Radovan Bast is a research software engineer with +background in theoretical chemistry. He leads the +CodeRefinery project. At UiT he leads the +high-performance computing group and the research software engineering group. +These days mostly programming in Python, Rust, R, and JavaScript but has spent +many years with Fortran, C(++), and CMake.

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Magnar Bjørgve has a background in applied mathematics and theoretical chemistry. +He is working in the GPU and training team at NRIS +at the University of Tromsø, Norway.

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Gregor Decristoforo gained experience in parallel programming and research +software engineering during his PhD in computational physics. As a Postdoc at +UiT he set up and maintained the Complex Systems +Modelling Github organization. Gregor has +experience in Python, C++ and CUDA and is currently learning R and Rust.

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Jørn Dietze is almost finished with a Ph.D in bioinformatics. +He is working with high-performance computing systems and +has experience with programming in Python, Julia, and a little bit of C. +He is part of NRIS +at the University of Tromsø, Norway, and the +LUMI User Support Team.

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And you? If you are interested in this work, please contact us.

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+photo of the cake from the group inauguration event +
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Former group members

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Bente Barge is a Ph.D student in computational chemistry and part-time +member of the HPC group at the University of Tromsø, Norway. Coding has been an +important part of her Ph.D and her interest in research software development +has evolved during her time in academia.

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Geir Villy Isaksen was in our group when we started this when we were only +two persons. We are grateful for his contributions to the group, the artwork, +work on the website, and outreach.

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