Preface

This dataset contains experimental data that supplement the article Thermal fault detection by changes in electrical behaviour in lithium-ion cells (10.1016/j.jpowsour.2021.229572) in the Journal of Power Sources. This dataset extends the already published cell characteristics (see 10.17632/g443f7cn7p.2) by all measured quantities associated with the conducted study. Therefore, the dataset includes sensor readings that have not been described in the before mentioned documents due to space limitations.

The published data belongs to the master thesis Development of a model-based method for the early detection of safety-critical heating of lithium-ion cells (transl.), Klink (2020), TU Clausthal that is connected to a study thankfully funded by the European Automobile Manufacturers' Association (ACEA).

Structure

The repository is subdivided in four directories (.zip) based on the content. Within these directories, the individual datasets can be found. While every dataset contains three different file types, the corresponding files can be identified based on the identical filenames. The following file types are provided:

File type Content Comment

*.png Simple graph of the provided data. Missing values are interpolated.

*.csv Tabular data of the dataset. Columns are separated by ";", the decimal point is ".".

*.pickle Pickled object of a pandas dataframe (Python) of the data. Preserve index and data types. Pickled with pandas version 2.2.2 using the pickle protocol 5

The index and column names of the tabular time series have the following name scheme: X_Y_Z

Placeholder Description Example

X Quantity symbol U for voltage, I for current

Y [optional] Additional index meas for measured quantities

Z Unit s for seconds, V for volt

Content

The dataset contains the data of both experiments for validation and for investigation of the fault characteristics of the conducted thermal abuse test. While the electrical quantities have been recorded using a battery test stand from Keysight/Scienlab (SL60/200/12BT4C) the temperature readings have been measured by type K thermocouples and recorded with data logger from PCE instruments. For all tests, the temperature sample rate has been set to 1 Hz. Please refer to the attached schematics in SensorPositions.zip for the placement of the individual thermocouples. In addition, T_5 represents the surrounding and T_2 is on the backside of T_1. The sensor positions T_7 and T_8 are added only for the uniform heating where T_7 is located between heating element and cell and T_8 central at the heating plate. Within the referenced article, only T_1 has been used.

For details on the experimental setup, please refer to the method section of the linked article.

1. Validation

Description

The data contains the electrical load of the cell with an extended WLTC driving cycle that has been scaled to approx. 400 A as well as the corresponding temperature at T_1. The test was conducted within a climatic chamber at 20°C. This data can be used to either parameterize a model of the cell or to validate a model based on other parameter such as the linked parameter set.

Columns t_s Test time in seconds

I_meas_A Applied current for WLTC emulation

U_meas_V Voltage response of cell

T_meas_C Cell surface temperature

1. ThermalCalibration

Description

For each heating setup (uniform, local) this directory contains one data set. Within this experiment, the cell was pulsed with short high current (150 A) pulses to achieve a constant thermal heating power without changing the SOC. Based on the temperature response, a thermal model can be parameterized for both heating setups. Please note, that the electrical sample rate was higher and no interpolation was conducted.

Columns t_s Test time in seconds

I_meas_A Applied current

U_meas_V Voltage response of cell

T_?_C Temperature reading of sensor ?. See above for description of the individual sensor positions.

1. UniformThermalFault

Description

During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, the cell went into thermal runaway during a charging procedure. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. Temperature readings of 9999°C (Upper range) due to sensor failure have been replaced by NaN. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.

Columns t_s Test time in seconds

I_meas_A Applied current

U_meas_V Voltage response of cell

T_?_C Temperature reading of sensor ?. See above for description of the individual sensor positions.

1. LocalThermalFault

Description

During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, a charging process and observation, no thermal runaway occurred. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. It seems that the heat transfer into the cell could have been optimized, as shown by the relatively low cell temperature despite the hot heating element. Nevertheless, this experiment can be used to investigate online detection of small cell changes due to local heating - even without thermal runaway. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.

Columns t_s Test time in seconds

I_meas_A Applied current

U_meas_V Voltage response of cell

T_?_C Temperature reading of sensor ?. See above for description of the individual sensor positions.

Matlab file, excel files with datasets, Labview file and Solidworks file from the mechanical analysis of the eye phantom (from the Master thesis).

About the project:

'The present research work aims at designing a cataract surgery eye phantom for capsulorhexis and globe fixation, which can replicate the movement of the eyeball in the orbit coupled with the inherent passive stiffness. The project culminates in the design of a 2-degree-of-freedom anterior human eye phantom with anatomically similar features of the human eye needed for training the aforementioned surgical steps. A significant part of the prototype structure is 3D printed using Draft resin V2 on the Formlabs Form 3+ printer to create minute yet almost anatomically impeccable components. Mechanical analyses were performed to tune the passive stiffness of the compliant mechanisms, and materials such as PlatSil Gel-00, hydrogel, and eggshell membrane were chosen and utilized to replicate the interactions between the tools and various tissues in the human eye. Clinical evaluations were conducted by a surgeon performing capsulorhexis on the prototype in a wet lab environment and several validation tests by an academic trainer for the suitability and practicality of the prototype.'

The method to use the files attached to this dataset has been explained in the "Experimental setup, protocols, and validation" chapter of the thesis report.

Abstract:

In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.

Research:

As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary:

• Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, In 2024 Symposium on Eye Tracking Research and Applications. ETRA.
• Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence. Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany
• Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In ICERI2023 Proceedings (pp. 5153-5162). IATED.
• Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 138-147). ECSEE.

The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly.

Experimental Setup:

In the following section, crucial notes on the circumstances and the experiment itself as well as the equipment are given.
In order to reduce the external influence on the experiment, variables such as:

• order, number, and presentation of the stimuli,
• instruction to the participant prior to the experiment,
• position of the participant in respect to the Eye Tracking equipment,
• environment such as illuminance and ambient noise for the participant,
• Eye Tracking equipment, software, settings such as sampling frequency and latency as well as calibration

were attempted to keep constant and consistent throughout the experiment.

Equipment:

In this study, the Tobii Pro Fusion (https://go.tobii.com/tobii-pro-fusion-user-manual) eye tracker is utilized without a chin rest along with the Tobii IVT filter for fixation detection and Tobii Pro Lab software for data collection. The Tobii Pro Fusion is categorised as a video-based combined pupil and corneal reflection technology. This tracker provides several advantages, such as the collection of comprehensive data, comprising gaze, pupil, and eye-opening metrics. The eye tracker captures up to 250 images per second (250Hz), enhancing its precision and eye movement analysis. In addition, Tobii Pro Fusion is capable of performing under different lighting conditions, thus making this portable device ideal for off-site studies.

Ensuring consistent quality across all experiment participants is crucial. Prior to each individual experiment, eye trackers are calibrated, aiming for a maximum reproduction error of less or equal than 0.2 degree during calibration to minimize deviations. The calibration is excluded from the experiment recording. Each participant is given the same instructions for their single trial of the experiment. The stimuli is displayed on a 24-inch monitor in a 16:9 format, positioned approximately 65cm away from the participants' eyes. Any effect related to the characteristics of the participants, such as age, visual acuity, eye colour, pupil size, etc., are considered in the experiment design.

Procedure:

Initially, the participants are requested to confirm their ability to conduct the experiment based on their current condition. Subsequently, the participant must be positioned comfortably and accurately in relation to the eye tracker. The eye tracker calibration is carried out for each participant to ensure a suitable experimental configuration. Once a successful calibration is achieved, the Eye Tracking experiment begin with introductions prior to each task. The stimuli presentation is unrestricted by time constraints, and no prior knowledge of the stimuli contents is necessary. Employing a within-subject design, each stimulus is exposed to each subject. Following completion of the experiment, participants anonymously answer the ILS questionnaire. To prevent any impact on the experiment, it is important that the questionnaire only be seen and completed after the experiment.

Stimuli:

The specially designed stimuli shown to participants during the study are illustrated in the left-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. For this research, only specific regions of a stimulus, referred to as AOI, are taken into consideration. The size of the AOI depends on both stimulus information and distance between multiple AOIs. Adequate results are ensured by not overlapping AOIs and appropriate spacing. The AOIs of the various stimuli employed in this research are illustrated in the right-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. The stimuli are presented in German language, ensuring reliable Eye Tracking measurements without any interference from language barriers. Each stimulus comprises diverse learning materials to engage students with varying learning styles, with some general information about the quantitative research cycle. Some stimuli feature identical type of material, e.g. illustrations or key words, but with different contexts and positions on the stimuli. Rearranging the identical material reduces the influence of reading style and enhances the impact of the learning style, producing a more reliable experiment. These identical types of material or AOIs on different stimuli can be grouped together, identified by the same colour and title, and referred to as AOI groupings.
There are ten different AOI groupings in total, as illustrated in the figure in the "[Documentation]stimuli_preview.pdf" file, where each grouping consists of several AOIs.
In detail, the AOI grouping regarding:

• table of contents and summary contain only a single AOI each,
• illustrations, key words, theory, exercise, example and additional material contain three AOIs each,
• supporting text and multiple choice question contain two AOIs each.

Research data management:

To ensure the transparency and reproducibility of this study, effective management of research data is essential. This section provides details on the management, storage and analysis of the extensive dataset collected as part of the study. Importantly, this research, the study and its processes adhered to ethical guidelines at all times, including informed consent, participant anonymity and secure data handling. The data collected will only be kept for a specific period of time as defined in the research project guidelines. The collection itself involves the recording of participants' eye movements during the ET study and the collection of their demographic data and responses to the ILS questionnaire.

Floating structures have developed significantly in recent years. As land is becoming scarce, the use the water surface will contribute to ease this scarcity. Therefore in recent years, floating structures covering large areas have been developed, also called flexible floating structures.

In this project the focus is set on the mooring system very flexible floating structures (VFFS). At the TU Delft, two towing tanks can be used to investigate the mooring system of VFFS, however first a reliable measuring system is required that is able to examine a specific part of the mooring system. Conventional setups that measure the mooring forces consist of large instruments, as these instruments only have a small effect on their investigated structure (vessels). The response of VFFS is dominated by elastic deformations and differs from conventional rigid structures. For VFFS, these type of instruments will have a large effect on the structure motions and thus these conventional setups cannot be used. Therefore, a new measuring system is required to conduct small scale experiments with VFFS, and the following objective is formulated: Develop an instrumented mooring system for VFFS at model scale for the towing tank at the TU Delft and determine its accuracy.

A new concept is developed in this project. This concept resulted from an extensive concept development where all functions of the system were analyzed. With the use of a Morphological Chart and a Multi Criteria Analysis the best concept was selected. For this concept, it was determined that the focus should be on the sensor configuration and calibration procedure.

First, the optimal sensor configuration of the concept was specified by analysing the working principle of the concept. Second, the calibration procedure was further analyzed. From this analysis, three calibration procedures were developed: the single sensor calibration matrix, the full fixed calibration matrix and the full rotated calibration matrix. From literature and theory, it was not possible to determine in advance what calibration procedure should be selected, and therefore the performance of the procedures were verified with experiments. All calibration procedures were executed, whereafter the performance of the different procedures were compared. The two main considerations for the comparison were the accuracy and the usability of the procedures. After performing the comparison, the main conclusion was that the full upright calibration procedure is the optimal procedure.

To verify the concept under realistic conditions, an example application was performed in the towing tank No.1 at the TU Delft. By doing this, the concept has proven to be suitable to measure the mooring force and transform them into usable data.

In this project a new concept was developed into a working system. This system forms an excellent base for extensive research into the mooring system of VFFS, and is a good addition to the measurement instruments for the towing tank at the TU Delft. It is concluded that the system is able to measure the mooring forces and the direction. The accuracy of the system still has to be improved, and with additional research the working concept can be further developed.

This dataset includes the data of the electronic appendix for the dissertation 'Effects of pyrolytic biochar on soil properties, Collembola, mites and seed performance' by Olivia Roithmeier (2025) conducted at the University of Bremen (Germany). The dissertation investigated the effects of a biochar produced from plant-material (wood) via pyrolysis (pmpBC), which was bio-activated with compost for several weeks before being applied into the soil of a temperate grassland in Germany. The effects of the BCa on soil properties, soil mesofauna and early seed performance of grassland plants were assessed by a field experiment (run May 2014 to September 2015) and three laboratory experiments (run between November 2014 and June 2016). Please note that to access the tables and text files, you will need the LibreOffice (The Document Foundation) programs. To access the data exploration and modelling files, you will need the R© statistical and programming environment (R Core Team). For more details on material and methods behind the dataset (e.g. software and statistics), please refer to Chapter 3.8 and 3.9 of the dissertation (Roithmeier, O. (2025). Effects of pyrolytic biochar on soil properties, Collembola, mites and seed performance. DOI: /10.26092/elib/4060). The dataset is comprised of eleven folders (Appendix 1e to 11e):

Appendix 1e: Raw data on the physico-chemical properties of the raw substrates

• Appendix 1e_a: raw data on the field soil of the grassland in Borgfeld, Germany
• Appendix 1e_b: analysis data sheet of the BC as provided by Carbon Terra
• Appendix 1e_c: grain sizes of compost, soil, pmpBCC, BCa
• Appendix 1e_d: pH values of compost, soil, pmpBC, BCa
• Appendix 1e_e: data of the raw substrates as determined during the bio-activation of the biochar, the field and laboratory experiments 1‑3

Appendix 2e: Biochar bio-activation procedure

• Detailed documentation of the biochar bio-activation procedure

Appendix 3e: Recalculation of the application rates

• Documentation of the recalculation of the application rates from fresh weight to dry weight for the field experiment and from the field experiment application rates to the rates to be applied in experiments 1-3, as these three experiments were to apply the higher application rate of the field study for the compost (C5) and biochar (BCa5) treatments

Appendix 4e: Logbook of the field experiment test area

• Logbook of the field experiment test area in Borgfeld, Germany, from the biochar bio-activation procedure prior to the start of the field study to the last sampling taken at the test area for this thesis

Appendix 5e: Documentation of plant performance during experiments 1-3

• Documentation of the seedling performance as investigated once a week and at the test termination (test vessel-specific raw data and photographs)

Appendix 6e: Logbook and photographs of experiments 1‑3

• Logbook of experiment 1 (no logbook was written for experiments 2 and 3 as these two experiments followed the procedure of their its precursor experiment 1)
• Documentation of mesofauna at test start of experiments 1‑3
• Photographical documentation of experiments 1‑3
  

Appendix 7e: Mesofauna identification key and identification card template

This appendix includes two documents developed by me for this thesis:

• an identification card template to document the identified specimens per extracted sample in a standardised way,
• a one-page identification key illustrating the most common taxa of the test area, used for preliminary mesofauna identification.

Appendix 8e: Photographs of the mesofauna

• Example photographs (raw data, adjusted ones) of the mesofauna found in the test area throughout the thesis

Appendix 9e: Statistical evaluation with R

This appendix is split in an Appendix a) for the field experiment and an Appendix b) for experiments 1-3, each containing the following data:

• Data per indicator summarised in tabular master-files (LibreOffice© Calc): e.g. "alpha_borgfeld_field_experiment_boku_smf_alle_termine.odt" for data on soil properties (buko) and soil mesofauna (SMF) of the field experiment for all sampling dates, or "THESIS_daten_smf_labvers1bis3" for all mesofauna data of laboratory experiments 1-3:
• Folder 'r_scripe_csv':
  • csv_files csv-files for data upload into R©: extracted from master-files mentioned before, e.g. for "glmer_boku_6weeks.csv" to analyse the soil property data of the samples collected six weeks after the start of the field experiment to be analysed by a GLMER,
  • R©-script files run by me: e.g. in folder "scripte_67weeks", the R-file "b_67_weeks_sum_coll_26oct.R" for the mesofauna indicator "total sum of Collembola", based on samples collected at week 67. For the field experiment, R© master scripts were written individually for each sampling date and separately for soil property and mesofauna data each (e.g. "b_6_weeks_boku_master", "b_6_weeks_SMF_master"). Based on these master scripts, individual scripts were created per indicator (e.g. "b_6_weeks_boku_ph", "b_6_weeks_sum_coll"). For experiments 1‑3, R© scripts were written individually for each of experiments 1‑3, sampling date, soil property and seed performance data (e.g. "cn_etal_CNratio_vers3_dry_wet" contained the R© script for the soil property C/N‑ratio, experiment 3, with a code implemented in the script to switch between the 'dry soil' and 'wet soil' sub-data set
• Folder 'results':
  
  • Result files (LibreOffice Writer©) of the statistical evaluation: e.g. "b_6weeks_sum_collembola_1bis2mm" for the mesofauna indicator sum of Collembola, body size 1‑2 mm, sampled at week six after the start of the field experiment). These files summarise the outputs of the statistical analysis run for each indicator to be analysed specifically (e.g. model evaluations and outputs).

Appendix 10e: Mesofauna extraction and insertion of experiment 1-3 (raw data)

• Experiments 1-2: Raw data of mesofauna control extractions carried out at the start of the test and during the tests
• Experiment 3: Raw data from the mesofauna extraction and Collembola hand-insertion performed at the start of the test and of the mesofauna control extractions performed during the test

Appendix 11e: Desoria trispinata breeding

• Documentation of the breeding (raw data photographs, notes on the breeding)
• See also Roithmeier et al. 2018. Desoria trispinata (MacGillivray, 1896), a promising model Collembola species to study biological invasions in soil communities. Pedobiologia, Vol. 67, 45-56, doi.org/10.10166/j.pedobi.20167.11.003