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opencc-by-4.0Jun 2023View details →

NewSOC, supplementary information to WT2.5.4 "Cells with honeycomb structured oxygen electrodes": electrochemical and post-mortem data sets.

These are data set, related to participation of the IEN in project NewSOC. It includes results of the SEM-EDS analysis of the cells with hexagonal current collecting net and electrochemical performance data, both EIS and C-V characteristics. Results are grouped in zip archives, named according to cell design, “infill-net”. Compositions: LNF – LaNi0.6Fe0.4O3 (net) LSC – La0.6Sr0.4CoO3-d (infill) LSF – La0.5Sr0.5FeO3-d (infill) LSCCF – La0.6Sr0.4Co0.15Cu0.05Fe0.8O3-d (net) PCM – Pr0.5Ca0.5MnO3 (net) BSCFM – Ba0.5Sr0.5Co0.725Fe0.2Mo0.075O3-d (infill)   Data presentation. C-V characteristics: This is text files, generated by Zahner galvanostat, with self-decriptional titles. “05iv_650c_100h2+100h2o_500air.txt” –  measurement at 650°C, 100 mL/min H2 and 100 mL/min H2O on fuel side, 500 mL/min of air on air side. EIS data: EIS results were extracted from proprietary binary files, generated by Zahner galvanostat, and raw data is generally meaningless except the owners of such hardware. So, extracted EIS can be found in Excel files, used in analysis, in sheet “Experimental”. Other sheets in xlsx include some metadata (“info”), results of the equivalent circuit fit (“fit”) and some plots. Fit results might not be relevant.   SEM Post-mortem results presented as SEM images (tif or jpg files) and pdf files with results of the EDS analysis. LSC-LNF (air flow 1 L/min, current density 0.25 A/cm2) test_1_07:   03_eis20200917142808.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H2; 05_eis20200917123105.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H2+ 0.1 H2O; 08_eis20200917123857.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.1 H2+ 0.1 H2O; 10_eis20200917131537.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.1 H2+ 0.1 H2O; test_1_08: 03_eis20200917125707.xlsx – EIS, SOFC, 700°C, Flows L/min: F:0.2 H2; 09_eis20200917130019.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.09 H2+ 0.21 H2O; 10_eis20200917130600.xlsx – EIS, SOFC, 700°C, flows L/min: F:0.06 H2+ 0.14 H2O; 12_eis20200917130737.xlsx – EIS, SOEC, 700°C, flows L/min: F:0.12 H2+ 0.28 H2O; test_3_14: 01_eis20210517102042.xlsx– EIS, SOFC, 700°C, Flows L/min: F:0.1 H2+0.1 N2; 05_eis20210517102625.xlsx– EIS, SOFC, 700°C, flows L/min: F:0.1 H2+ 0.1 H2O; 07_eis20210517102305.xlsx– EIS, SOEC, 700°C, flows L/min: F:0.06 H2+ 0.14 H2O; SEM (post-mortem after test_1_07) ogniwo_310_2020  ****.jpg - surface   BSCMF-PCM (SOFC, air flow 0.5 L/min) test_2_14 01eis_700c_cc4a_100h2+100n2_500air_eqc20220110112836.xlsx –700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 02eis_700c_cc4a_100h2+100h2o_500ai_eqc20220110112724.xlsx –700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 04eis_650c_cc3a_100h2+100h2o_500ai_eqc20220110112503.xlsx–650°C, 0.1875 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 06eis_650c_cc3a_100h2+100n2_500air_eqc20220110112332.xlsx –650°C, 0.1875 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 07eis_625c_cc3a_100h2+100n2_500air_eqc20220110112214.xlsx –625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 08eis_625c_cc3a_100h2+100h2o_500ai_eqc20220110111834.xlsx–625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; test_1_67 01_eqc20220831134628.xlsx–700°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 02_eqc20220831134724.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 04_eqc20220831135442.xlsx–650°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 06_eqc20220831135622.xlsx - 650°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 08_eqc20220831135801.xlsx - 625°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 09_eqc20220831135933.xlsx –650°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; SEM (post-mortem of the test_2_14) 493-2021-1.pdf, 493-2021-1i.pdf, 493-2021-2.pdf, 493-2021-2-2.pdf, 493-2021-2-3.pdf –cross-sections with EDS 493_2021_*_**.tif  - cross-sections   BSCMF-LSCCF (SOFC, air flow 0.5 L/min)                                test_2_08 01_eis20211104144342.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 03eis_700c__eis20211108102241.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 04eis_700c__eis20211108102401.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 08eis__eis20211110094642.xlsx - 650°C, 0.1875  A/cm2 Flows L/min: F:0.1 H2+0.1 N2 10eis__eis20211110094945.xlsx - 650°C, 0.1875 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 11eis__eis20211110101358.xlsx - 625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 13eis__eis20211110100837.xlsx- 625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2;                                test_2_26 04_eqc20220817114103.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 06_eqc20220817121529.xlsx - 700°C, 0.25 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 07_eqc20220802084119.xlsx - 650°C, 0.1875  A/cm2 Flows L/min: F:0.1 H2+0.1 08_eqc20220802084616.xlsx - 650°C, 0.1875 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 10_eqc20220802150705.xlsx - 625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 11_eqc20220802150108.xlsx - 625°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; SEM (post-mortem of the test_2_26) 661_BSCMF_LSCCF_p.pdf  – cross-section with EDS 661_BSCMF_LSCCF_PM.pdf - surface with EDS 661_BSCMF_LSCCF_p_01.tif, 661_BSCMF_LSCCF_p_02.tif, 661_BSCMF_LSCCF_p_03.tif, 661_BSCMF_LSCCF_p_04.tif  -  cross-section, infill zone 661_BSCMF_LSCCF_p_05.tif, 661_BSCMF_LSCCF_p_06.tif - cross-section, net zone zone 661_BSCMF_LSCCF_PM_**.tif  - surface   LSF-LSCCF (SOFC, air flow 0.5 L/min) test_1_65 01_eqc20220816105347.xlsx - 700°C, 0. 1875 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 02_eqc20220816105941.xlsx - 700°C, 0. 1875 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 04_eqc20220816112311.xlsx - 650°C, 0. 125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 06_eqc20220816111640.xlsx - 650°C, 0.125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 07_eqc20220816142436.xlsx- 625°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 08_eqc20220816142842.xlsx-625°C, 0.625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; SEM (post-mortem) 663_LSF_LSCCF_p.pdf – cross-section with EDS 663_LSF_LSCCF_PM.pdf - surface with EDS 663_LSF_LSCCF_P_GR_**.tif – cross-section of the cell 663_LSF_LSCCF_P_LSCCF_**.tif – surface of the LSCCF grid 663_LSF_LSCCF_PM_**.tif - surface of the LSF infill     LSF-PCM (SOFC, air flow 0.5 L/min) tests_1_66 01_eqc20220831133210.xlsx - 700°C, 0. 125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 02_eqc20220831133409.xlsx - 700°C, 0. 125 A/cm2 Flows L/min: F:0.1 H2+0.1 N2 03_eqc20220831133928.xlsx - 700°C, 0. 125 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 05_eqc20220831134048.xlsx - 650°C, 0.0625  A/cm2 Flows L/min: F:0.1 H2+0.1 N2; 06_eqc20220831134142.xlsx - 650°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 08_eqc20220831134237.xlsx - 625°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 H2O; 10_eqc20220831134412.xlsx - 625°C, 0.0625 A/cm2 Flows L/min: F:0.1 H2+0.1 N2; SEM (post-mortem) 658_LSF_PCM_p.pdf – cross-section with EDS 658_LSF_PCM_PM.pdf - surface with EDS 658_LSF_PCM_P_LSF_**.tif  – cross-section, infill zone 658_LSF_PCM_P_pcm_**.tif - cross-section, net zone 658_LSF_PCM_PM_**.tif - surface description.pdf - pdf version of this information.

The first 10m resolution thermokarst lake and pond data set in the Lena basin during 2020 thawing season

We present the first 10m resolution thermokarst lake and pond data set in the Lena basin during 2020 thawing season. A mapping workflow was proposed and implemented on the Google Earth Engine (GEE) platform. The accuracy assessment demonstrates a satisfactory overall accuracy of 93.63%, and comparing with several land cover and waterbody products, our results exhibited better consistency with TLPs under real conditions.

zebrafish GSE223922 scRNA data set objects

scRNA data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223922 (Sur et al. 2023), see a detailed description of the study here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055256/ Data were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223922 to create a R Seurat object and converted into AnnData (h5ad) file to be able to analyse with e.g. python scanpy package. If you use this data, please cite Sur et al. 2023.

Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes

Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles: <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> The spatial reference system is EPSG 25830.

UCI and OpenML Data Sets for Ordinal Quantification

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data. With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label. We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample. Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed. Usage You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python. Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments. Data Extraction: In your terminal, you can call either <pre><code>make</code></pre> (recommended), or <pre><code>julia --project="." --eval "using Pkg; Pkg.instantiate()" julia --project="." extract-oq.jl</code></pre> Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class. Further Reading Implementation of our experiments: <a href="https://github.com/mirkobunse/regularized-oq">https://github.com/mirkobunse/regularized-oq</a>

opencc-by-4.0Jan 2023View details →

Codes and data set for Cryoconite hole model (CryHo)

Codes and data set for cryoconite hole model (CryHo) (Onuma et al., 2023, The Cryosphere). The content is as below. - cryho_disclose_v1: readme, the model codes, parameter files for the model, model outputs and shell scripts for sensitivity tests  - data: model input data (meteorological conditions), model data of extinction coefficient for ice* and observational data of cryoconite hole depths - python: Python scripts for the visualization - figure: png files created by the Python scripts *If you have any questions about extinction coefficients for ice, please contact Dr. Teruo Aoki, a co-author in this dataset.

opencc-by-4.0Aug 2023View details →

AIT Alert Data Set

This repository contains the AIT Alert Data Set (AIT-ADS), a collection of synthetic alerts suitable for evaluation of alert aggregation, alert correlation, alert filtering, and attack graph generation approaches. The alerts were forensically generated from the <a href="https://zenodo.org/record/5789064">AIT Log Data Set V2 (AIT-LDSv2)</a> and origin from three intrusion detection systems, namely Suricata, Wazuh, and AMiner. The data sets comprise eight scenarios, each of which has been targeted by a multi-step attack with attack steps such as scans, web application exploits, password cracking, remote command execution, privilege escalation, etc. Each scenario and attack chain has certain variations so that attack manifestations and resulting alert sequences vary in each scenario; this means that the data set allows to develop and evaluate approaches that compute similarities of attack chains or merge them into meta-alerts. Since only few benchmark alert data sets are publicly available, the AIT-ADS was developed to address common issues in the research domain of multi-step attack analysis; specifically, the alert data set contains many false positives caused by normal user behavior (e.g., user login attempts or software updates), heterogeneous alert formats (although all alerts are in JSON format, their fields are different for each IDS), repeated executions of attacks according to an attack plan, collection of alerts from diverse log sources (application logs and network traffic) and all components in the network (mail server, web server, DNS, firewall, file share, etc.), and labels for attack phases. For more information on how this alert data set was generated, check out our paper accompanying this data set [1] or our <a href="https://github.com/ait-aecid/alert-data-set">GitHub repository</a>. More information on the original log data set, including a detailed description of scenarios and attacks, can be found in [2]. The alert data set contains two files for each of the eight scenarios, and a file for their labels: <ul> <li><scenario>_aminer.json contains alerts from AMiner IDS</li> <li><scenario>_wazuh.json contains alerts from Wazuh IDS and Suricata IDS</li> <li>labels.csv contains the start and end times of attack phases in each scenario</li> </ul> Beside false positive alerts, the alerts in the AIT-ADS correspond to the following attacks: <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) and stopped service</li> </ul> The total number of alerts involved in the data set is 2,655,821, of which 2,293,628 origin from Wazuh, 306,635 origin from Suricata, and 55,558 origin from AMiner. The numbers of alerts in each scenario are as follows. fox: 473,104; harrison: 593,948; russellmitchell: 45,544; santos: 130,779; shaw: 70,782; wardbeck: 91,257; wheeler: 616,161; wilson: 634,246. Acknowledgements: Partially funded by the European Defence Fund (EDF) projects AInception (101103385) and NEWSROOM (101121403), and the FFG project PRESENT (FO999899544).  Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. The European Union cannot be held responsible for them. If you use the AIT-ADS, please cite the following publications: [1] Landauer, M., Skopik, F., Wurzenberger, M. (2024): <a href="https://doi.org/10.1145/3675741.3675748">Introducing a New Alert Data Set for Multi-Step Attack Analysis.</a> Proceedings of the 17th Cyber Security Experimentation and Test Workshop. [<a href="https://dl.acm.org/doi/pdf/10.1145/3675741.3675748">PDF</a>] [2] Landauer M., Skopik F., Frank M., Hotwagner W., Wurzenberger M., Rauber A. (2023): <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. [<a href="https://arxiv.org/pdf/2203.08580.pdf">PDF</a>]

opencc-by-4.0Aug 2023View details →

Data Sets: The Influence of Synoptic Wind on Land-Sea Breezes

DATA & FILE OVERVIEW This dataset contains the dimensional results of large-eddy simulations in .nc file format conducted over the thirty one simulations detailed in Allouche et al. (2023) (https://doi.org/10.1002/qj.4552) with differing synoptic pressure forcings and alignment angles of the latter with the shoreline. The patterns are altered as to conduct the analysis followed in Allouche et al. (2023) (https://doi.org/10.1002/qj.4552).

opencc-by-4.0Oct 2021View details →

UWB Positioning and Tracking Data Set

# UWB Positioning and Tracking Data Set UWB positioning data set contains measurements from four different indoor environments. The data set contains measurements that can be used for range-based positioning evaluation in different indoor environments.   # Measurement system The measurements were made using 9 DW1000 UWB transceivers (DWM1000 modules) connected to the networked RaspberryPi computer using in-house radio board SNPN_UWB. 8 nodes were used as positioning anchor nodes with fixed locations in individual indoor environment and one node was used as a mobile positioning tag. Each UWB node is designed arround the RaspberryPi computer and are wirelessly connected to the measurement controller (e.g. laptop) using Wi-Fi and MQTT communication technologies. All tag positions were generated beforehand to as closelly resemble the human walking path as possible. All walking path points are equally spaced to represent the equidistand samples of a walking path in a time-domain. The sampled walking path (measurement TAG positions) are included in a downloadable data set file under downloads section.   # Folder structure Folder structure is represented below this text. Folder contains four subfolders named by the indoor environments measured during the measurement campaign and a folder raw_data where raw measurement data is saved. Each environment folder has a anchors.csv file with anchor names and locations, .json file data.json with measurements, file walking_path.csv file with tag positions and subfolder floorplan with floorplan.dxf (AutoCAD format), floorplan.png and floorplan_track.jpg. Subfolder raw_data contains raw data in subfolders named by the four indor environments where the measurements were taken. Each location subfolder contains a subfolder data where data from each tag position from the walking_path.csv is collected in a separate folder. There is exactly the same number of folders in data folder as is the number of measurement points in the walking_path.csv. Each measurement subfolder contains 48 .csv files named by communication channel and anchor used for those measurements. For example: ch1_A1.csv contains all measurements at selected tag location with anchor A1 on UWB channel ch1. The location folder contains also anchors.csv and walking_path.csv files which are identical to the files mentioned previously. The last folder in the data set is the technical_validation folder, where results of technical validation of the data set are collected. They are separated into 8 subfolders: - cir_min_max_mean - los_nlos - positioning_wls - range - range_error - range_error_A6 - range_error_histograms - rss   The organization of the data set is the following: data_set + location0 - anchors.csv - data.json - walking_path.csv + floorplan - floorplan.dxf - floorplan.png - floorplan_track.jpg - walking_path.csv + location1 - ... + location2 - ... + location3 - ... + raw_data + location0 + data + 1.07_9.37_1.2 - ch1_A1.csv - ch7_A8.csv - ... + 1.37_9.34_1.2 - ... + ... + location1 + ... + location2 + ... + location3 + ... + technical validation + cir_min_max_mean + positioning_wls + range + range_error + range_error_histograms + rss - LICENSE - README   # Data format Raw measurements are saved in .csv files. Each file starts with a header, where first line represents the version of the file and the second line represents the data column names. The column names have a missing column name. Actual column names included in the .csv files are:   TAG_ID ANCHOR_ID X_TAG Y_TAG Z_TAG X_ANCHOR Y_ANCHOR Z_ANCHOR NLOS RANGE FP_INDEX RSS RSS_FP FP_POINT1 FP_POINT2 FP_POINT3 STDEV_NOISE CIR_POWER MAX_NOISE RXPACC CHANNEL_NUMBER FRAME_LENGTH PREAMBLE_LENGTH BITRATE PRFR PREAMBLE_CODE CIR (starts with this column; all columns until the end of the line represent the channel impulse response)   # Availability of CODE Code for data analysis and preprocessing of all data available in this data set is published on GitHub: https://github.com/KlemenBr/uwb_positioning.git The code is licensed under the Apache License 2.0.   # Authors and License Author of data set in this repository is Klemen Bregar, klemen.bregar@ijs.si. This work is licensed under a Creative Commons Attribution 4.0 International License.   # Funding The research leading to the data collection has been partially funded from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116, the Slovenian Research Agency under Grant numbers P2-0016, J2-2507 and bilateral project with Grant number BI-ME/21-22-007.

opencc-by-4.0Aug 2023View details →

Research generated data supporting the article manuscript "Setting Grounds for Data Literacy in the Sector of Agriculture: Learning About and with Open Data"

In the research 345 MS courses and 216 MS courses data from the ECTS catalogue (2019) of University of Zagreb Faculty of Agriculture were mapped onto the data literacy competence areas (theme) and DL competence areas sub-themes adapted ODI Data Skills Framework (2020) expanding the term “skill” to “competence” to include knowledge and attitudes. Teaching staff was interviewed in semi-structured interviews on the data literacy competences covered in their courses and open data use and teaching in their courses as well as their perceived importance for the sector of the course. The upload consists of the following .csv files: <table> <tbody> <tr> <td>readme_DL_OD_Salamonetal.csv</td> </tr> <tr> <td>01DL_OD_Salamonetal.csv</td> </tr> <tr> <td>02DL_OD_Salamonetal.csv</td> </tr> <tr> <td>03DL_OD_Salamonetal.csv</td> </tr> <tr> <td>04DL_OD_Salamonetal.csv</td> </tr> <tr> <td>05DL_OD_Salamonetal.csv</td> </tr> <tr> <td>06DL_OD_Salamonetal.csv</td> </tr> <tr> <td>07DL_OD_Salamonetal.csv</td> </tr> </tbody> </table>

opencc-by-4.0Sep 2023View details →

H2020 Platone German Demonstrator - Use Case Setting Data

This dataset belongs to the German demonstrator of the H2020 Platone project (WP5). The dataset contain information that have been set for Use Cases (UCs) parameterization during the project phase. UC have been parameterized along a grafical user interface (GUI) named "Use Case Selector". Each parameterized UC triggered has been logged in the dataset. Background - Field Test Setup The field test setup consists of a Low Voltage (LV) community with 450 kW installed generation capacity. The power exchange between the LV grid and Medium Voltage (MV) grid takes place along a single point of common coupling (PCC). i.e., a secondary substation that includes a transformer with senors on the LV busbar, to measure the net power exchange. The community consists of 89 households, 450kW of installed PV generation capacity, a Community Battery Energy Storage (CBES) connected to the LV busbar with 300 kW and 850 kWh capacity.  Definition of data: RequID - Request ID - Identifier for each triggered UC Alert - Indicates, whether UC has been executed successfull ( " "and " true" indacates successfull implementation by Energy Management System (EMS); "false" indicates that UC has not been implemented by EMS) Submission - timestamp of UC submission  Note - Annotations entered by UseCase operator Priority - Defines UC priority set by operator (priority: 1 - high , 2 - medium, 3 - low, 4 - very low) (only relevant for UC 2) Status - Indicates the status of the UC (closed - UC has been executed, cancelled, UC has been has been canceled before or during application) Start - Point of time set for the beginning of UC End - Point of time set for the end of UC Type- Triggered Type of UC (1 - "Virtual Islanding of LV community" (UC 1); 2 - "Coordination of Flex Request" (UC 2); 3 - "Energy Import in Bulk" (UC 3); 4 - "Bulk-based Energy Export" (UC 4) Subtype - 0 - Rule-Based Operation Mode with 15-minutes control cycles of battery (CBES in the field) ;1 - Day-ahead forecast-based control; 2.0 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC within 24h period ; 21 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC and achieving a requested State of Charge (of CBES) at the end of UC_End; bulkStart - Point of time of start of energy bulk import or export (only relevant for UC 3 and 4) bulkEnd - Point of time of end of energy bulk import or export (only relevant for UC 3 and 4) bulk Energy - Amount of energy triggered to be imported or exported as bulk (only relevant for UC 3 and 4) Final_SOF - State of Charge (SOC) of CBES that should be achieved at end of UC (End) FlexDemand - Requested power exchange that should be achieved at MV/LV PCC (Only relevant for UC 2) Ptcb - Measured CBES charging power at poin of time of UC submission  Ptei - Measured power exchange at PCC at point of time of UC submission  SoC - State of Charge of CBES at point of time of UC submission  SoE - State of Energyof CBES at point of time of UC submission  ActiveSet - State of Energyof CBES at point of time of UC submission  maxSoC - Maximum SoC set for CBES at point of time of UC submission  minSoc - MinimumSoC set for CBES at point of time of UC submission  This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300.

Benchmark movement data set for trust assessment in human robot collaboration

In the Drapebot project, a worker is supposed to collaborate with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from a standard Trust questionnaire (Trust perception scale - HRI, Schaefer 2016). Data has been collected in the transport and draping tasks (counterbalanced) from 20 participants,  7 female and 13 male, average age 25 (SD = 4.0). Average height was 1.74 meters (SD = 0.1). One session consists of 24 trials on average for the transport and draping task resulting in 951 trials across all conditions. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements). Data organization There are 20 files for 20 participants of each task accordingly (transport and draping). The name of the files is P01SD, where the number 01 is the participant the D stands for draping. Accordingly, P01ST stands for transport. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data: <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a> For more information on each specific data and/or sensors please see the xsens manual (Link above) Data Annotation For each procedure there is an annotation file called sorted_draping.xlsx and sorted_transport.xlsx. In these files the first column is the frame and from column 2 until column 21 are the annotations for each procedure for each participant. The annotations describe the different phases during the procedures for each data frame recorded by xsens: <ul> <li>Transport phases: pick, transport, drop, return</li> <li>Draping phases: approach, draping, return</li> </ul> The file trustscores.xlsx includes some demographic data as well as the results of the trust questionaire for each participant and each task, including the scores for the individual items as well as the calculated trust score. The different columns are: <ul> <li>Subject: participant number for crossreferencing with annotation and movement data</li> <li>Transport.Speed: denoting the robot speed (fast or slow)</li> <li>Age: age of the participant</li> <li>Gender: gender of the participant</li> <li>DominantHand: dominant hand of the participant (left or right)</li> <li>Height: height of the participant</li> <li>Score for answers of the participant in related questions category.</li> </ul> This is followed by the trust questionaire items: <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> The last two columns are <ul> <li>TrustScore – Final trust score calculated from all questions</li> <li>Task – Which task is being performed (Transport/Draping)</li> </ul>

MediaFutures Open Calls Data-Set

The Data-Set includes the data collected through the cascade funding open calls carried out during the H2020 MediaFutures project with the title “MediaFutures, Data-driven innovation hub for the media value chain”. The responsible and innovative use of data is instrumental in today's digitalised media industry. The EU-funded MediaFutures project has addressed this challenge by reshaping the media value chain. It has set up a virtual European data innovation hub to support entrepreneurial and innovative projects. It has also established a participatory inclusive innovation program encouraging synergies between businesses and creators and organised a competition to identify innovative digital entrepreneurs, creatives and data-empowered solutions. By delivering data and experimentation facilities to the winners, the project has showcased and improved their ideas. It has also facilitated the technical, legal, business and sustainability mentoring of businesses and artists and helped them achieve further access to funding. The virtual European data innovation hub has been supported by an international network of European organisations. The project has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement 951962. The file contains data from the open calls, webinars, matchmaking events and help-desk activities carried out during the MediaFutures project. It includes data such as the number of applications received, data regarding eligibility and in-eligibility of applications, from which country the applications came, how many projects were evaluated and funded, data on the gender and ethnicity of applicants, etc. For more information and context related to the data, see also the following deliverables published on the MediaFutures website (https://mediafutures.eu/resources/): D1.4: Summary of Calls v1, D1.5: Summary of Open Calls v2, D1.6: Summary of Calls v3.

Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. Data Set for Final Data Report

The 43 txt-files included in this dataset relate to the report: Ryhl-Svendsen, Jensen, Bøhm, and Klenz Larsen (2012): Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. UMTS Research Project 2007–2011: Final Data Report, Kgs. Lyngby: National Museum of Denmark, 122 pp. The document <a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/00_List-of-data-files.pdf?versionId=a3e9691f-6e73-4a7b-aaab-c8ccee7c419b">00_List-of-data-files.pdf</a> contain a full list of the data files with a description of their structure and content, and is the key to how the individual data files relate to the report.  The research project focussed on four modern museum storage facilities in Denmark, for which the indoor climate, air quality, and the energy consumption of the climate control systems was measured at several locations, typically for a period of between two and four years. The storage facilities were Museum of Southwest Jutland’s storage building in Ribe (‘Ribe’), The Shared Storage Facility at The Centre for Preservation of Cultural Heritage in Vejle (‘Vejle’), The Joint Storage Facility for museums in East Jutland/ Museum Østjylland (‘Randers’), and from The National Museum of Denmark the storage building Hall P at the Ørholm Storage Facility (‘Ørholm’). For description of the sites, monitoring campaigns, and graphed data, the report should be consulted. For completeness, the report is included with the dataset (<a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/Report_low-energy-museum-storage-buildings.pdf?versionId=44097d39-775b-4031-9e07-6978c68912a9">Report_low-energy-museum-storage-buildings.pdf</a>).

Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"

Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization". Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a>

Software Plagiarism Detection on Intermediate Representation Data Set

This data set contains the necessary files used for the bachelor's thesis Software Plagiarism Detection on Intermediate Representation. This includes the data sets for the tests, the implemented code and scripts for the evaluation as well as referenced work.