Find research datasets worth reusing

Data set - Practical teacher-training program on STEAM activity planning

Data set of answers of 14 Brazilian teachers who took part in a practical teacher-training program on STEAM activity planning.

opencc-by-4.0Mar 2021View details →

Data set for publication: Extended SINDICOMP: Characterizing MV Voltage Transformers with Sine Waves

This is dataset for paper published: Crotti, G.; D’Avanzo, G.; Giordano, D.; Letizia, P.S.; Luiso, M. Extended SINDICOMP: Characterizing MV Voltage Transformers with Sine Waves. Energies 2021, 14, 1715. https://doi.org/10.3390/en14061715   Excel file provides data for Table 2, Table 4 and Figure 10 a.

opencc-by-4.0Jul 2021View details →

Data set for publication: A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers

This is dataset for paper published: G. Crotti, D. Giordano, G. D'Avanzo, P.S. Letizia, M. Luiso, "A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers", Measurement, Volume 182, 2021, 109674, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2021.109674.   Excel file provides data for Figure 3, 4, 6 and 7.

SEVIRI derived ET data sets

There are two relevant SEVIRI derived ET data sets distributed by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF) (<a href="https://landsaf.ipma.pt/en/data/">https://landsaf.ipma.pt/en/data/</a>; last access: 1 December 2021); (i) SEVIRI- actual ET (SEVIRI-ETa) that accounts for the water evaporated from the soil surface, vegetation canopy and water bodies, and (ii) SEVIRI- &lrm;reference ET (SEVIRI-ET0) that accounts for the (local) atmospheric demand and corresponds to the ET from a hypothetical well-watered green grass having 12 cm height and 0.23 albedo under the given down-welling short-wave radiation (Allen et al., 1998). These data sets are being provided in near real-time through the LSA SAF operational system and we used them in the GEOEssential project for water stress workflow development. For more information, please contact Dr. Bagher Bayat (bagher.bayat@gmail.com and b.bayat@fz-juelich.de).

SEVIRI derived ET data sets V2

This data set is SEVIRI derived ET distributed by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF) (<a href="https://landsaf.ipma.pt/en/data/">https://landsaf.ipma.pt/en/data/</a>; last access: 1 December 2021).  There are two relevant SEVIRI derived ET data sets (i) SEVIRI- actual ET (SEVIRI-ETa) that accounts for the water evaporated from the soil surface, vegetation canopy and water bodies, and (ii) SEVIRI- &lrm;reference ET (SEVIRI-ET0) that accounts for the (local) atmospheric demand and corresponds to the ET from a hypothetical well-watered green grass having 12 cm height and 0.23 albedo under the given down-welling short-wave radiation (Allen et al., 1998). These data sets are being provided in near real-time through the LSA SAF operational system and we used them in the GEOEssential project for water stress workflow development. For more information, please contact Dr. Bagher Bayat (bagher.bayat@gmail.com and b.bayat@fz-juelich.de).

opencc-by-nc-sa-4.0Feb 2022View details →

AIT Log Data Set V2.0

AIT Log Data Sets This repository contains synthetic log data suitable for evaluation of intrusion detection systems, federated learning, and alert aggregation. A detailed description of the dataset is available in [1]. The logs were collected from eight testbeds that were built at the Austrian Institute of Technology (AIT) following the approach by [2]. Please cite these papers if the data is used for academic publications. In brief, each of the datasets corresponds to a testbed representing a small enterprise network including mail server, file share, WordPress server, VPN, firewall, etc. Normal user behavior is simulated to generate background noise over a time span of 4-6 days. At some point, a sequence of attack steps is launched against the network. Log data is collected from all hosts and includes Apache access and error logs, authentication logs, DNS logs, VPN logs, audit logs, Suricata logs, network traffic packet captures, horde logs, exim logs, syslog, and system monitoring logs. Separate ground truth files are used to label events that are related to the attacks. Compared to the <a href="../record/4264796">AIT-LDSv1.1</a>, a more complex network and diverse user behavior is simulated, and logs are collected from all hosts in the network. If you are only interested in network traffic analysis, we also provide the <a href="../record/6610489">AIT-NDS</a> containing the labeled netflows of the testbed networks. We also provide the <a href="../records/8263181">AIT-ADS</a>, an alert data set derived by forensically applying open-source intrusion detection systems on the log data. The datasets in this repository have the following structure: <ul> <li>The gather directory contains all logs collected from the testbed. Logs collected from each host are located in gather/<host_name>/logs/.</li> <li>The labels directory contains the ground truth of the dataset that indicates which events are related to attacks. The directory mirrors the structure of the gather directory so that each label files is located at the same path and has the same name as the corresponding log file. Each line in the label files references the log event corresponding to an attack by the line number counted from the beginning of the file ("line"), the labels assigned to the line that state the respective attack step ("labels"), and the labeling rules that assigned the labels ("rules"). An example is provided below.</li> <li>The processing directory contains the source code that was used to generate the labels.</li> <li>The rules directory contains the labeling rules.</li> <li>The environment directory contains the source code that was used to deploy the testbed and run the simulation using the <a href="https://github.com/ait-aecid/kyoushi-environment">Kyoushi Testbed Environment</a>.</li> <li>The dataset.yml file specifies the start and end time of the simulation.</li> </ul> The following table summarizes relevant properties of the datasets: <ul> <li>fox <ul> <li>Simulation time: 2022-01-15 00:00 - 2022-01-20 00:00</li> <li>Attack time: 2022-01-18 11:59 - 2022-01-18 13:15</li> <li>Scan volume: High</li> <li>Unpacked size: 26 GB</li> </ul> </li> <li>harrison <ul> <li>Simulation time: 2022-02-04 00:00 - 2022-02-09 00:00</li> <li>Attack time: 2022-02-08 07:07 - 2022-02-08 08:38</li> <li>Scan volume: High</li> <li>Unpacked size: 27 GB</li> </ul> </li> <li>russellmitchell <ul> <li>Simulation time: 2022-01-21 00:00 - 2022-01-25 00:00</li> <li>Attack time: 2022-01-24 03:01 - 2022-01-24 04:39</li> <li>Scan volume: Low</li> <li>Unpacked size: 14 GB</li> </ul> </li> <li>santos <ul> <li>Simulation time: 2022-01-14 00:00 - 2022-01-18 00:00</li> <li>Attack time: 2022-01-17 11:15 - 2022-01-17 11:59</li> <li>Scan volume: Low</li> <li>Unpacked size: 17 GB</li> </ul> </li> <li>shaw <ul> <li>Simulation time: 2022-01-25 00:00 - 2022-01-31 00:00</li> <li>Attack time: 2022-01-29 14:37 - 2022-01-29 15:21</li> <li>Scan volume: Low</li> <li>Data exfiltration is not visible in DNS logs</li> <li>Unpacked size: 27 GB</li> </ul> </li> <li>wardbeck <ul> <li>Simulation time: 2022-01-19 00:00 - 2022-01-24 00:00</li> <li>Attack time: 2022-01-23 12:10 - 2022-01-23 12:56</li> <li>Scan volume: Low</li> <li>Unpacked size: 26 GB</li> </ul> </li> <li>wheeler <ul> <li>Simulation time: 2022-01-26 00:00 - 2022-01-31 00:00</li> <li>Attack time: 2022-01-30 07:35 - 2022-01-30 17:53</li> <li>Scan volume: High</li> <li>No password cracking in attack chain</li> <li>Unpacked size: 30 GB</li> </ul> </li> <li>wilson <ul> <li>Simulation time: 2022-02-03 00:00 - 2022-02-09 00:00</li> <li>Attack time: 2022-02-07 10:57 - 2022-02-07 11:49</li> <li>Scan volume: High</li> <li>Unpacked size: 39 GB</li> </ul> </li> </ul> The following attacks are launched in the network: <ul> <li>Scans (nmap, WPScan, dirb)</li> <li>Webshell upload (CVE-2020-24186)</li> <li>Password cracking (John the Ripper)</li> <li>Privilege escalation</li> <li>Remote command execution</li> <li>Data exfiltration (DNSteal)</li> </ul> Note that attack parameters and their execution orders vary in each dataset. Labeled log files are trimmed to the simulation time to ensure that their labels (which reference the related event by the line number in the file) are not misleading. Other log files, however, also contain log events generated before or after the simulation time and may therefore be affected by testbed setup or data collection. It is therefore recommended to only consider logs with timestamps within the simulation time for analysis. The structure of labels is explained using the audit logs from the intranet server in the russellmitchell data set as an example in the following. The first four labels in the labels/intranet_server/logs/audit/audit.log file are as follows: <blockquote> {"line": 1860, "labels": ["attacker_change_user", "escalate"], "rules": {"attacker_change_user": ["attacker.escalate.audit.su.login"], "escalate": ["attacker.escalate.audit.su.login"]}} {"line": 1861, "labels": ["attacker_change_user", "escalate"], "rules": {"attacker_change_user": ["attacker.escalate.audit.su.login"], "escalate": ["attacker.escalate.audit.su.login"]}} {"line": 1862, "labels": ["attacker_change_user", "escalate"], "rules": {"attacker_change_user": ["attacker.escalate.audit.su.login"], "escalate": ["attacker.escalate.audit.su.login"]}} {"line": 1863, "labels": ["attacker_change_user", "escalate"], "rules": {"attacker_change_user": ["attacker.escalate.audit.su.login"], "escalate": ["attacker.escalate.audit.su.login"]}} </blockquote> Each JSON object in this file assigns a label to one specific log line in the corresponding log file located at gather/intranet_server/logs/audit/audit.log. The field "line" in the JSON objects specify the line number of the respective event in the original log file, while the field "labels" comprise the corresponding labels. For example, the lines in the sample above provide the information that lines 1860-1863 in the gather/intranet_server/logs/audit/audit.log file are labeled with "attacker_change_user" and "escalate" corresponding to the attack step where the attacker receives escalated privileges. Inspecting these lines shows that they indeed correspond to the user authenticating as root: <blockquote> type=USER_AUTH msg=audit(1642999060.603:2226): pid=27950 uid=33 auid=4294967295 ses=4294967295 msg='op=PAM:authentication acct="jhall" exe="/bin/su" hostname=? addr=? terminal=/dev/pts/1 res=success' type=USER_ACCT msg=audit(1642999060.603:2227): pid=27950 uid=33 auid=4294967295 ses=4294967295 msg='op=PAM:accounting acct="jhall" exe="/bin/su" hostname=? addr=? terminal=/dev/pts/1 res=success' type=CRED_ACQ msg=audit(1642999060.615:2228): pid=27950 uid=33 auid=4294967295 ses=4294967295 msg='op=PAM:setcred acct="jhall" exe="/bin/su" hostname=? addr=? terminal=/dev/pts/1 res=success' type=USER_START msg=audit(1642999060.627:2229): pid=27950 uid=33 auid=4294967295 ses=4294967295 msg='op=PAM:session_open acct="jhall" exe="/bin/su" hostname=? addr=? terminal=/dev/pts/1 res=success' </blockquote> The same applies to all other labels for this log file and all other log files. There are no labels for logs generated by "normal" (i.e., non-attack) behavior; instead, all log events that have no corresponding JSON object in one of the files from the labels directory, such as the lines 1-1859 in the example above, can be considered to be labeled as "normal". This means that in order to figure out the labels for the log data it is necessary to store the line numbers when processing the original logs from the gather directory and see if these line numbers also appear in the corresponding file in the labels directory. Beside the attack labels, a general overview of the exact times when specific attack steps are launched are available in gather/attacker_0/logs/attacks.log. An enumeration of all hosts and their IP addresses is stated in processing/config/servers.yml. Moreover, configurations of each host are provided in gather/<host_name>/configs/ and gather/<host_name>/facts.json. Version history: <ul> <li><a href="https://doi.org/10.5281/zenodo.3723082">AIT-LDS-v1.x</a>: Four datasets, logs from single host, fine-granular audit logs, mail/CMS.</li> <li><a href="http://doi.org/10.5281/zenodo.5789064">AIT-LDS-v2.0</a>: Eight datasets, logs from all hosts, system logs and network traffic, mail/CMS/cloud/web.</li> </ul> Acknowledgements: Partially funded by the FFG projects INDICAETING (868306) and DECEPT (873980), and the EU projects GUARD (833456) and PANDORA (SI2.835928). If you use the dataset, please cite the following publications: [1] M. Landauer, F. Skopik, M. Frank, W. Hotwagner, M. Wurzenberger, and A. Rauber. <a href="https://ieeexplore.ieee.org/abstract/document/9866880">"Maintainable Log Datasets for Evaluation of Intrusion Detection Systems"</a>. IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 4, pp. 3466-3482, doi: 10.1109/TDSC.2022.3201582. [<a href="https://arxiv.org/pdf/2203.08580.pdf">PDF</a>] [2] M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner and A. Rauber, <a href="https://ieeexplore.ieee.org/document/9262078">"Have it Your Way: Generating Customized Log Datasets With a Model-Driven Simulation Testbed,"</a> in IEEE Transactions on Reliability, vol. 70, no. 1, pp. 402-415, March 2021, doi: 10.1109/TR.2020.3031317. [<a href="https://www.skopik.at/ait/2020_trel.pdf">PDF</a>]

Data set figures 1-3 article Reaux et al; Communications Biology 2022

The files attached are : - the manuscript and figures of our article entitled ".." accepted for publication in Communication Biology in February 2022.  - A supplementary material, which is an Excel file is the dataset of the figures 1 to 3. It is composed of two sheets in which the values of cerebral blood volume are given for every single animal included in our study:  before and during each stimulation (been mechanical or chemical stimulation in sheet 1 - figure 2 or mechanical stimulations of either the ophthalmic or maxillary territories - 2d sheet - figure 3).  - A Matlab file with dataset of the figure 4 (ULM of the saggital brain showing the rich and complex vasculature in the trigeminal ganglion)

Data sets for assessing potential CC impacts on the activity of the Vögelsberg landslide

Bias-corrected air temperature and precipitation time series (RCM sample from EURO CORDEX) for Kleinvolderberg station near the Vögelsberg landslide (OAL-AT) under RCP8.5, monthly water balance components derived from an empirical model for six elevation steps under current and potential land cover conditions for 1950-2100, median monthly displacement and current hydrological forcing of the Vögelsberg landslide

In silico data-set used to train the PGNNIV

The folder contains the data used to train PGNNIVs to unravel the go or grow behaviour of glioblastoma under 4 different parametric models. These data consist on the solutions (in time and space) of eleven simulations with different oxygen boundary conditions. Each folder is named as DATA_"ModelName", where "ModelName" can be: "Sigmoid", "ReLU", "MichaelisMenten" or "Heaviside". Inside each folder, the multidimensional arrays for input and output data for the network training can be found. These arrays have dimension [nExp,TimeStep,x,field], where: <ul> <li>nExp = 11  and corresponds to the number of different configurations or experiments simulated.</li> <li>TimeStep = 1000 and correspond to the different temporal frames where the solution is given.</li> <li>x = 51 and corresponds to the different spatial points where the solution is given.</li> <li>field = 2 and correspond to the different solution fields (1: cells, 2: oxygen).</li> </ul>

1440° (4 turn) data set from cubic insulin, with slight radiation damage

X-ray diffraction data set recorded on beamline i03 at Diamond Light Source, as part of a training workshop. Data were collected from a cubic insulin crystal prepared following standard methods with an Eiger 2XE 16M detector at 250Hz.   Data Collection Parameters <table> <tbody> <tr> <td>Wavelength</td> <td>1.2399Å</td> </tr> <tr> <td>Oscillation angle</td> <td>0.1°</td> </tr> <tr> <td>Nominal resolution (inscribed circle)</td> <td>1.5</td> </tr> <tr> <td>Total rotation</td> <td>1440°</td> </tr> <tr> <td>Nominal flux</td> <td>5e11 ph/s</td> </tr> <tr> <td>Beam size</td> <td>80x20µm</td> </tr> </tbody> </table>   Merging stats from xia2 / DIALS automated online processing <pre>For mx30951v8/xins162/SAD Overall Low High High resolution limit 1.32 3.58 1.32 Low resolution limit 54.95 55.00 1.34 Completeness 98.7 100.0 83.3 Multiplicity 132.9 152.8 33.7 I/sigma 41.7 200.6 0.4 Rmerge(I) 0.072 0.044 4.027 Rmerge(I+/-) 0.071 0.043 3.977 Rmeas(I) 0.072 0.044 4.088 Rmeas(I+/-) 0.071 0.044 4.094 Rpim(I) 0.006 0.004 0.675 Rpim(I+/-) 0.008 0.005 0.940 CC half 1.000 1.000 0.360 Wilson B factor 19.890 Anomalous completeness 98.4 100.0 79.3 Anomalous multiplicity 69.1 84.3 17.7 Anomalous correlation 0.760 0.646 -0.050 Anomalous slope 0.613 dF/F 0.026 dI/s(dI) 1.013 Total observations 2426172 151841 26018 Total unique 18257 994 772 Assuming spacegroup: I 2 3 Unit cell (with estimated std devs): 77.71580(7) 77.71580(7) 77.71580(7) 90.0 90.0 90.0 </pre>   Data are made available for any purpose including methods developers trying to optimize their software for higher multiplicity data sets.

Settings and data files from Silva et al (Scientific Reports 2022)

Neolithic Greece mtDNA sequences The 47 mitochondrial sequences generated for this work are available in the .arp format for the program ARLEQUIN (http://cmpg.unibe.ch/software/arlequin35/). Simulated Data and Simulation Program This dataset permits to simulate the scenarios investigated in the article submitted by Silva et al, using the modified version of the program SPLATCHE2 provided here (http://www.splatche.com). There is a zipped folder Silva_et_al_SimulationSettings" that contains: i) SPLATCHE2 executable called "SPLATCHE2-VariableAdmixture". ii) a folder "DanubeRouteExpansion" including the settings used for the simulation of the four scenarios of the Neolithic expansion along the Danubian route. iii) a folder "GreeceContinuity" including the settings used to perform the structured population continuity test. A "ReadMe.txt" file is available in both settings folders with the instructions to launch the simulations.

Impact of water on the solubility of Si in Fe alloys_data sets

Original data for the manuscript "Possible formation of hydrogen-rich layer in the topmost outer core by deeply subducted water". All relevant data in the manuscript and supplementary information will be uploaded. Codes in the manuscript are available at https://zenodo.org/record/6383354#.Yj3lZedBwuX.

Data from 'Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience'

Aquatic deoxygenation has been flagged as an overlooked but key factor driving mass bleaching-induced coral mortality as oxygen supplies lower to concentrations that can elicit an aerobic metabolic crisis i.e., hypoxia. Surprisingly little is known of the fundamental hypoxia responsive gene set inventory corals possess to respond to deoxygenation. It is unclear whether variation in gene copy number across species exist that potentially affect gene expression with subsequent differences in the effectiveness of a given stress response. Here, we used an ortholog-based meta-analysis to investigate how hypoxia gene inventories differed amongst coral species to assess putative copy number variation (CNV) across 24 coral protein sets from species with a sequenced genome that span corals from the robust and complex clade. We found approximately a third of the investigated genes exhibited copy number differences, and these differences were species-specific rather than the robust-complex split. Zipped folders of OrthoFinder results: 'Results_Feb16' contains results including all 24 coral species from 7 genera (Acropora, Pocillopora, Stylophora, Montastrea, Montipora, Obricella, Porites). 'gene_sets_acropora_acuminata_only' contains results including just one species per genera with Acropora acuminata. 'gene_sets_acropora_cytherea_only' contains results including just one species per genera with Acropora cytherea. 'gene_sets_acropora_digitifera_only' contains results including just one species per genera with Acropora digitifera.   Results and Interpretations from these analyses are published open access here: <a href="https://doi.org/10.3389/fmars.2022.834332">https://doi.org/10.3389/fmars.2022.834332</a> Full citation: Alderdice R, Hume BCC, Kühl M, Pernice M, Suggett DJ, Voolstra CR. Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience. Front Mar Sci. 2022;9. doi:10.3389/fmars.2022.834332 Scripts are available here: <a href="https://zenodo.org/record/6396671#.YoYpoS8RoZg">https://github.com/didillysquat/alderdice_2021</a>

opencc-by-4.0Jun 2019View details →

ISC19 IO500 Data Sets

Full, 10 node, and standard IO500 lists

Data set from long-term wind and acceleration monitoring of the Gjemnessund Bridge

The Gjemnessund Bridge has been monitored by accelerometers and anemometers for almost ten years. The data collected between 2013 and 2018 are now available in this open-access research entry, for free access and download. The data is collected in two h5-files (hierachical data format), with sampling rates 2 Hz and 10 Hz, downsampled from the raw sampling rate of 200 Hz. Some minimal signal processing is applied to the data in line with that applied to the Hardanger Bridge data described in Fenerci et al. (2021) and the Bergsøysund Bridge data described in Kvåle et al. (2022). The structure of the data is identical to that of the latter reference, which is described in a preprint appended to that research entry. The Python package opyndata available on GitHub contains useful tools compatible with the format of the dataset, for data import, processing and visualization.

opencc-zeroApr 2022View details →

dryad40/100

Data from: Let's stick together: infection enhances preferences for social settings in a songbird species

Acute infections can alter foraging and movement behaviours relevant to sociality and pathogen spread. However, few studies have examined how infection with directly-transmitted pathogens influences host social preferences. Juvenile house finches are gregarious and particularly susceptible to infection by the bacterial pathogen Mycoplasma gallisepticum (MG). Changes in sociality during infection are likely to have important consequences for MG transmission throughout majority-juvenile flocks, but it remains unknown how infection influences sociality in house finches. To test this, we inoculated 33 wild-caught juvenile house finches with MG or media (sham control). At peak infection, birds were given a choice assay to assess preference for associating near a flock versus an empty cage. Infected birds were significantly more likely than controls to spend time near the flock while eating, and marginally so while perching. These results indicate augmented social preferences during infection, potentially as a form of behavioural tolerance. Notably, infected birds showed strong social preferences regardless of individual variation in disease severity or pathogen loads, with 14/19 harbouring high loads (log10 5-6) at time of assay. Overall, our results show that infection with a directly-transmitted pathogen can augment social preferences, with potential implications for MG spread in natural populations.

opencc-by-nc-sa-4.0Apr 2022View details →

DEBS 2022 Grand Challenge Data Set: Trading Data

The data provided here as part of the DEBS 2022 Grand Challenge is based on real tick data captured by Infront Financial Technology GmbH for the complete week of November 8th to 14th, 2021 (i.e., five trading days Monday to Friday + Saturday and Sunday). The data set contains 289 million tick data events covering 5504 equities and indices that are traded on three European exchanges: Paris (FR), Amsterdam (NL), and Frankfurt (ETR).  Some event notifications appear to come with no payload. This is due to the fact that the 2022 GC requires only a small subset of attributes to be evaluated; other attributes have been eliminated from the data set to minimize its overall size while keeping the amount of events to process unchanged. Further details on the data set, its syntax and its semantics can be found in the official DEBS 2022 Grand Challenge paper as part of the DEBS 2022 conference proceedings (please use this for citation):  Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, and Hans-Arno Jacobsen. 2022. The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data. In The 16th ACM International Conference on Distributed and Event-based Systems (DEBS ’22), June 27-June 30, 2022, Copenhagen. ACM, New York, NY, USA. All files of the DEBS 2022 Grand Challenge Data Set “Trading Data” are provided as-is. By downloading and using this data you agree to the terms and conditions of the licensing agreement (CC by-nc-sa).

opencc-by-4.0Apr 2022View details →

Thetis Baltic Sea simulation: model and observation data sets

Model and observation data sets used in article "Adjoint-based optimization of a regional water elevation model".

opencc-by-4.0Apr 2022View details →

Data set for: Toggle-like current-induced Bloch point dynamics of 3D skyrmion strings in a room-temperature nanowire

This data set contains both the experimental data and the simulation scripts to reproduce the results of [1]: Toggle-like current-induced Bloch point dynamics of 3D skyrmion strings in a room-temperature nanowire by M. T. Birch, D. Cortés-Ortuño, K. Litzius, S. Wintz, F. Schulz, M. Weigand, A. &Scaron;tefančič, D. Mayoh, G. Balakrishnan, P.D. Hatton, G. Schütz. A preprint of this publication is available at <a href="https://www.researchsquare.com/article/rs-1235546/v1">https://www.researchsquare.com/article/rs-1235546/v1</a>. This data set is also hosted in Github: <a href="https://github.com/davidcortesortuno/paper-2022_toggle-like_current_induced_bp_dynamics_3d_skyrmion_strings">https://github.com/davidcortesortuno/paper-2022_toggle-like_current_induced_bp_dynamics_3d_skyrmion_strings</a> If you find this material useful please cite us <pre><code>@Misc{Birch2022, author = {M. T. Birch and D. Cort\'es-Ortu\~no}, title = {{Data set for: Toggle-like current-induced Bloch point dynamics of 3D skyrmion strings in a room-temperature nanowire}}, howpublished = {Zenodo \url{doi:10.5281/zenodo.6393340}. Github: \url{https://github.com/davidcortesortuno/paper-2022_toggle-like_current_induced_bp_dynamics_3d_skyrmion_strings}}, year = {2022}, doi = {10.5281/zenodo.6393340}, url = {https://doi.org/10.5281/zenodo.6393340}, }</code></pre>