contexere 1.1.4

Systematic tracking and referencing of digital artefacts for postgraduate students and early career researchers

Scientists and engineers create a multitude of digital artefacts during their daily work [1]:

• experimental results,

• simulation results,

• literate programming notebooks analysing experiments and simulations

• statistical models,

• machine learning models,

• figures,

• tables, etc

In order to trace and track these multiple interconnected research artefacts, hierarchical naming schemes are a powerful tool to document the connection between research artefacts, find previous research outputs, and enable reproducible research [2].

The following naming scheme has evolved over several years to track research artefacts of all kinds:

The general scheme is: PIyymDc[_x]__keyword

• PI: [a-zA-Z]{2,} is the project identifier, which consists of at least two letters.

• yy: [0-9][0-9] are the last two digits of the years in the 21st century. I won’t live beyond that. So, I do not care for following centuries.

• m: [o-z] these letters map to the respective months.

• D: [1-9,A-V] represent the 31 days of a month. Digits and upper-case characters have approximately the same height, such that this element gives a visual structure to the name, which divides the date from the daily counter.

• c: [a-z] daily counter as lower-case letter enumerating the respective database or dataset.

• x: Optional attribute being the last significant characters of the dataset, from which DSyymde is derived.

• keyword: One or more keywords separated by __.

m	month	D	day	D	day	D	day
o	January	1	1	B	11	L	21
p	February	2	2	C	12	M	22
q	March	3	3	D	13	N	23
r	April	4	4	E	14	O	24
s	May	5	5	F	15	P	25
t	June	6	6	G	16	Q	26
u	July	7	7	H	17	R	27
v	August	8	8	I	18	S	28
w	September	9	9	J	19	T	29
x	October	A	10	K	20	U	30
y	November					V	31
z	December

• The first dataset created on Friday 01.01.2021 would be named DS21o1a.

• The second dataset created on the same day would be named DS21o1b.

• An analysis (e.g. Jupyter notebook) of the first data set started after the second data set had been created would be named DS21o1c_a. Exported figures of this analysis should be named DS21o1c_a__[plottype].[filetype].

• An analysis of data set DS21o1b started on 2nd January should be named DS21o2a_1b.

• An meta analysis of DS21o1c_a and DS21o2a_1b started on 11th February should be named DS21pBa_o1c_2a.

Installation

The module contexere can be installed from PyPi:

pip install contexere

Usage

The project provides the command line tool nxt:

usage: nxt [-h] [--version] [-g GROUP] [-k KEYWORDS [KEYWORDS ...]] [-l] [-p] [-r [REFERENCE]] [-s] [-u] [-v] [-vv] [target]

Suggest name for research artefact

positional arguments:
  target                Either a project identifier, filename, or folder

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -g, --group GROUP     Project identifier for which the next research artefact GROUP will be suggested
  -k, --keywords KEYWORDS [KEYWORDS ...]
                        Optional argument for --clone adding one or more keywords to the filename
  -l, --local           Inspect files in current working dir only
  -p, --project         Create new project directory structure
  -r, --reference [REFERENCE]
                        Optional argument indicating reference of cloned file if used without arguments or accepting comma
                        separated list of references.
  -s, --summary         Summarise files following the naming convention
  -u, --utc             Generate timestamp with respect to UTC (default is local timezone)
  -v, --verbose         set loglevel to INFO
  -vv, --very-verbose   set loglevel to DEBUG

Calling the tool without any arguments in an empty directory returns the date abbreviation of today appended by the first daily counter (a):

$ nxt
26s6a

The date abbreviation indicates that the nxt command in this example was called on 6 May 2026. The following examples assume that all following commands of nxt were also called on 6 May 2026.

Calling the nxt command at the root of a directory structure, which contains files following the naming scheme, finds the latest research artefact group (RAG) and uses the respective project identifier to suggest a new RAG:

$ nxt --project  # Followed by a dialog specifying the new project ERP (Example Research Project)
$ cd ERP
$ nxt
ERP26s6b

The suggested RAG ERP26s6b is the second RAG (b) of 9 May 2026, because nxt --project creates template files starting with ERP26s6a like notebooks/ERP26s6a__template_notebook.ipynb.

An overview of the existing files associated with RAGs gives the command nxt --summary, which provides a tabular output of all RAGs in the directory hierarchy:

$ nxt --summary
Project RAGs Files Latest
ERP        1     4  26s6a
KM         1     1  26oOa

Let’s assume that ERP project has been put under git revision control and we want to create a clone of the Jupyter notebook template:

$ nxt notebooks/ERP26s6a__template_notebook.ipynb --keywords poc
[main .......] Cloned from ERP26s6a__template_notebook.ipynb
 1 file changed, 54 insertions(+)
 create mode 100755 notebooks ERP2RP26s6b_poc.ipynb
Added cloned file ERP2RP26s6b_poc.ipynb to the git repository.

The cloned file is added automatically to the git repository of the project such that all edits can be tracked systematically.

Let’s assume that we want to follow-up with a visualisation of data or results generated by ERP26s6b__poc.ipynb. In this case, we might want to continue from the configuration provided in ERP26s6b__poc.ipynb. The command:

$ nxt notebooks/ERP26s6b__poc.ipynb --reference --keywords visualisation

Creates a copy of ERP26s6b__poc.ipynb in directory notebooks named:

ERP26s6c_b__visualisation.ipynb

Note, that the cloned RAG references the original RAG but abbreviates the reference. In another rapid development cycle, we might want to continue from ERP26s6b, but this time using additional data provided by a completely different project DS25zAa. The commands:

$ cd notebooks
$ nxt ERP26s6b__poc.ipynb --reference s6b,DS25zAa --keywords simulation

Create a fourth notebook named:

ERP26s6d_b_DS25zAa__simulation.ipynb

Again providing an efficient referencing of the input RAGs and thus creating a directed graph of RAGs. Note, that the provided reference s6b is an abbreviation of ERP26s6b.

[1] Martin Kühne and Andreas W. Liehr. Improving the traditional information management in natural sciences. Data Science Journal, 8(1):18–26, 2009, doi: 10.2481/dsj.8.18

[2] Andreas W. Kempa-Liehr. Contexere—Systematic Tracking and Referencing of Digital Artefacts for Postgraduate Students and Early Career Researchers. Preprint, 2026, doi: 10.20944/preprints202605.0993.v1