Kim, Ah Young · Kim, Jin Su · Ko, Jung Yun · et al.

3 files · 307 KB · pdf, xlsxdeclared

This dataset contains the source data associated with the manuscript entitled "Overexpression of SlMPBQMT (VTE3) enhances α-tocopherol accumulation and reduces oxidative stress in tomato." The uploaded files include the original experimental data used to generate the figures and analyses presented in the manuscript, including: HPLC quantification of α-tocopherol. Plant phenotype measurements. qRT-PCR expression data. DAB and NBT staining quantification data. These data are provided to ensure transparency, reproducibility, and compliance with the data availability policy of BMC Plant Biology .

Lafleur, Trevor · Rawoof, Shaik · Bowden, George · et al.

13 files · 762 KB · xlsxdeclared

Datasets correspond to data used/presented in the open-access paper: T. Lafleur, R. Shaik, G. Bowden, D. Petty, and C. Corr, "Frequency-Agile Matching of Inductively Coupled Plasmas", Plasma Sources Science and Technology, accepted (2026) Specific files correspond to the data presented in the relevant figures of the above paper: Sweep_Prf_56ugs.xlsx: Data for the thruster at a fixed mass flow rate of 56 ug/s and different powers. The columns indicate different system properties. Used in Figures 3 and 4. Sweep_mdot_7W.xlsx: Data for the thruster at a fixed power of 7 W and different mass flow rates. The columns indicate different system properties. Used in Figures 3 and 4. Sweep_freq_56ugs_7W.xlsx: Data for the thruster at a fixed power and mass flow rate of 7 W and 56 ug/s respectively, and different frequencies. The columns indicate different system properties. Used in Figure 5. Agile_Sweep_Prf_56ugs.xlsx: Data for the thruster at a fixed mass flow rate of 56 ug/s and different powers, and with frequency-agile matching used. The columns indicate different system properties. Used in Figure 6. Agile_Sweep_Prf_56ugs_Fixed.xlsx: Data for the thruster at a fixed mass flow rate of 56 ug/s and different powers, and with the frequency fixed. The columns indicate different system properties. Used in Figure 6. Sweep_2D.xlsx: Data for the thruster at difference powers and mass flow rates, and with frequency-agile matching used. Each sheet indicates 2D data for the relevant property in that sheet. Used in Figure 7. Sweep_2D_Fixed.xlsx: Data for the thruster at difference powers and mass flow rates, and with the frequency fixed. Each sheet indicates 2D data for the relevant property in that sheet. Used in Figure 7. Torch_Sweep_mdot_800W.xlsx: Data for the torch at a fixed power of 800 W and different mass flow rates. The columns indicate different system properties. Used in Figure 8. Torch_Sweep_freq_100mgs_800W.xlsx: Data for the torch at a fixed power and mass flow rate of 800 W and 100 mg/s respectively, and different frequencies. The columns indicate different system properties. Used in Figure 9. Agile_Torch_Sweep_mdot_800W.xlsx: Data for the torch at a fixed power of 800 W and different mass flow rates, and with frequency-agile matching used. The columns indicate different system properties. Used in Figure 10. Agile_Torch_Sweep_mdot_800W_Fixed.xlsx: Data for the torch at a fixed power of 800 W and different mass flow rates, and with the frequency fixed. The columns indicate different system properties. Used in Figure 10. Torch_Sweep_2D.xlsx: Data for the torch at difference powers and mass flow rates, and with frequency-agile matching used. Each sheet indicates 2D data for the relevant property in that sheet. Used in Figure 11. Torch_Sweep_2D_Fixed.xlsx: Data for the torch at difference powers and mass flow rates, and with the frequency fixed. Each sheet indicates 2D data for the relevant property in that sheet. Used in Figure 11.

Anonymous

6 files · 4.5 MB · xlsxdeclared

Replication package for the paper submitted to SE4FP 2026

Madhusudan, Gujral

6 files · 29 MB · parquetdeclared

Large language models (LLMs) are trained on massive, publicly available text datasets comprising trillions of tokens, enabling them to excel at general language tasks like next-token prediction. However, LLMs often struggle with domain-specific prompts, exhibiting reduced accuracy or generating inaccurate information (hallucinations). This is because they lack sufficient subject matter expertise. Two primary approaches exist to address this limitation for augmenting LLMs knowledge: Retrieval-Augmented Generation (RAG) and fine-tuning. This presentation focuses on fine-tuning smaller LLMs with domain-specific instruct datasets using the LoRA (Low-Rank Adaptation) technique on Gaudi hardware. We will leverage publicly available LLMs and datasets from the Hugging Face Hub for this demonstration. Though it is possible to fine tune LLMs with plain text data - sourced from documents, articles, and other materials.

Krajnc, Matej · Comi, Troy · Miao, Siqi · et al.

10 files · 25 GB · csv, zipdeclared

This dataset accompanies the manuscript "A Controlled in Silico Benchmark for GNN Prediction of Tissue Dynamics." It contains model prediction outputs, trained checkpoints, train/validation/test splits, spring-embedding outputs, generated analysis figures, analysis tables, and manuscript-specific diagnostic outputs used to reproduce the post-prediction analyses and figures. The dataset is distributed as logical ZIP archives with file-level and archive-level SHA-256 checksums. For questions, contact Tomer Stern at tomers@umich.edu.

MENEGUZZO, FRANCESCO · Zabini, Federica

4 files · 146 KB · csvdeclared

This record contains the de-identified, analysis-ready dataset and Python analysis code associated with a school-based quasi-experimental pilot study on therapist-guided forest therapy and persistent anxiety symptoms in adolescents. The study involved two fourth-year high-school classrooms in Cecina, Tuscany, Italy: one intervention classroom that attended four therapist-guided forest therapy sessions in a coastal pine forest, and one control classroom that followed usual school activities. The dataset includes anonymized student codes, classroom allocation, SCAS total raw scores and derived reduction scores across repeated assessments, POMS-A acute mood-state variables for the intervention classroom, exploratory post-intervention nature-exposure and connectedness variables, and exploratory school-performance variables. The repository includes four files: 1. forest_therapy_adolescent_anxiety_dataset.csv: de-identified analysis-ready dataset. 2. README_forest_therapy_adolescent_anxiety_dataset.txt: dataset metadata and variable descriptions. 3. analyze_forest_therapy_adolescent_anxiety.py: Python script used to reproduce the main analyses and generate analysis outputs. 4. README_analyze_forest_therapy_adolescent_anxiety.txt: operational guide for running the analysis script and interpreting the output files. The dataset uses semicolon-separated values. Missing values are encoded as NaN and must not be interpreted as zero. Because the study involved minors, item-level questionnaire responses and more granular school records are not shared; only de-identified and analysis-ready variables compatible with privacy, ethical approval, and consent constraints are provided.

Li, Mei-Ling · Hsieh, Cheng-Hong · Huang, Shang-Ming · et al.

1 files · 394 KB · xlsxdeclared

Supporting dataset for the manuscript "Quantum Mechanical Insights into the Inhibition of Xanthine Oxidase by Dietary Flavonoids: Ligand-to-Metal Charge Transfer at the Molybdenum Cofactor Governs Inhibitory Potency" (submitted to Inorganic Chemistry). Contains 10 worksheets: - README: Version history and sheet descriptions - Per_Flavonoid_Summary: Mean ± SD per compound (Ki, IC50, CT_lig, ΔE_Coul) - Table_S1_Mulliken_All: Mulliken atomic charges for all 50 QM/MM snapshots - Table_S1_Mo_Charges: Molybdenum Mulliken charges (gas-phase vs bound) - Table_S2_QM_Energies: QM/MM total energies and interaction energies - Gas_Phase_References: Gas-phase MoCo reference calculations - Correlation_Analysis: Pearson/Spearman correlations (10 metrics) - Table_S4_Hirshfeld_CT: Hirshfeld CT_lig and ΔE_Coulomb for all 50 snapshots - Table_S5_IGMH: IGMH δg_inter analysis (5 representative snapshots) - Hirshfeld_Summary: Cross-method validation (Mulliken vs Hirshfeld) QM/MM level: B3LYP-D3BJ/def2-SVP with RIJCOSX and electrostatic embedding (ORCA 6.1.0). Five flavonoids: apigenin, kaempferol, quercetin, morin, naringenin. 10 MD snapshots per compound (10-100 ns), 50 calculations total.

Rodrigues Brás, Gonçalo · Mamede, Renato

1 files · 37 KB · xlsxdeclared

Publications (ten original research articles and one review article) addressing themes at the intersection of the blue bioeconomy and its proposed "grey side", published between 2016 and 2026

Soltes, Julian

3 files · 12 MB · csv, pdfdeclared

This paper presents a non-parametric topography of the cosmological landscape, following the principle of maximum entropy to map the relative probability of all isotropic spacetime configurations. The topography is defined as an 11-D probability distribution, derived from a maximally unbiased ensemble of solutions to the Einstein Field Equations (EFE). Uncertainty prevents infinite precision, blurring the ensemble to quantify the relative likelihood of any physical microstate. The result provides a statistical foundation for the manifestation of our universe and its contents, described by probabilities and their respective gradients. This assumes unconstrained numerical coverage of the EFE landscape, mapping mathematically valid solutions that may violate parametric energy conditions. Uncertainty distorts the boundaries between these exotic states and the canonical phase, rationalizing their existence within our universe as quantifiable improbabilities. Resources: The computational implementation and main data ensemble are attached here, as well as maintained at: https://github.com/jgsoltes/Universe-KDE Researchers are encouraged to apply the QUASAR optimizer to their own high-dimensional or non-convex function landscapes. Source code and documentation are maintained at: https://github.com/jgsoltes/hdim-opt

Hackl, Jürgen

6 files · 132 MB · parquet, tiffdeclared

EuroFlood is an open, cloud-native index over the JRC/Copernicus CEMS-EFAS Satellite-Derived Flood Depth Maps for Europe (Betterle & Salamon, 2025; CC-BY-4.0) - ~3,280 satellite-derived observed flood-depth maps across Europe, 2015-2024. The bundle is a sparse Cloud-Optimized GeoTIFF encoding, per pixel, the set of flood events that inundated it, plus a combo_id -sorted GeoParquet dictionary and a small events table. Query by region and time via HTTP range reads (GDAL /vsicurl + DuckDB) to retrieve matching events, then fetch only the source depth rasters needed. Built with the open-source EuroFlood Python package ( pip install euroflood ).

Fobbe, Sean

16 files · 1.4 GB · csv, pdf, zipdeclared

Überblick Das Corpus des deutschen Bundesrechts (C-DBR) ist eine möglichst vollständige Sammlung der konsolidierten Fassungen aller Gesetze und Verordnungen auf Bundesebene. Der Datensatz nutzt als seine Datenquelle das amtliche Internetangebot www.gesetze-im-internet.de des Bundesministeriums der Justiz und wertet dieses vollständig aus. Bitte lesen Sie zuerst das beiliegende Codebook! Es enthält wichtige Informationen zur korrekten Nutzung des Datensatzes. Es hilft auch bei der Entscheidung, welche Variante für Sie am besten geeignet ist. In der Regel empfehle ich für quantitative Forschung die CSV-Dateien und für traditionelle Forschung die PDF-Sammlung. Um das Gesetzgebungsverfahren näher zu beleuchten können Sie zusätzlich auf folgende Datensätze zurückgreifen (jeweils mit Links auf vergleichbare Datensätze anderer Autor:innen): Corpus der Drucksachen des Deutschen Bundestages (CDRS-BT) Corpus der Plenarprotokolle des Deutschen Bundestages (CPP-BT) Aktualisierung Dieser Datensatz wird ca. alle 3 Monate aktualisiert. Benachrichtigungen über neue und aktualisierte Datensätze veröffentliche ich immer zeitnah auf Mastodon unter @seanfobbe@fediscience.org NEU in Version 2026-07-09 Vollständige Aktualisierung der Daten Ausführung der Pipeline in Userland im Container Neues Skript für Zenodo Upload Eckdaten Stichtag: 9. Juli 2026 Umfang: 6.124 Bundesgesetze und -verordnungen der Bundesrepublik Deutschland Formate: CSV, PDF, EPUB, TXT und XML Features Einfache Nutzung für statistische Analysen mit CSV-Dateien Bis zu 42 Variablen in den CSV-Varianten Fortlaufende Aktualisierung Urheberrechtsfreiheit Sowohl für traditionelle Rechtsanwender als auch für Legal Tech-Anwendungen geeignete Formate (CSV, PDF, EPUB, TXT und XML) Umfangreicher Compilation Report um den Erstellungs-Prozess zu erläutern Hochauflösende Diagramme und deskriptive Tabellen für alle Zwecke Diagramme in PDF (Druck) und PNG (Web) verfügbar, Tabellen als menschen- und maschinenlesbares CSV Vollständiges tabellarisches Verzeichnis aller Rechtsakte und der vom BMJV gebrauchten Abkürzungen Netzwerk-Strukturen für alle Rechtsakte und Visualisierungen für über 1000 Rechtsakte (experimentell) Veröffentlichung des Source Codes Source Code und Compilation Report Der gesamte Erstellungs-Prozess ist vollautomatisiert und detailliert dokumentiert. Mit jeder Kompilierung des vollständigen Datensatzes wird auch ein umfangreicher Compilation Report in einem attraktiv designten PDF-Format erstellt (ähnlich dem Codebook). Zudem werden Robustness Checks auf Vollständigkeit und Plausibilität durchgeführt und in einem separaten Bericht dokumentiert. Der Compilation Report enthält den Code für die vollständige Pipeline, dokumentiert relevante Rechenergebnisse, gibt sekundengenaue Zeitstempel an und ist mit einem klickbaren Inhaltsverzeichnis versehen. Er ist zusammen mit dem Source Code hinterlegt. Wenn Sie sich für Details des Erstellungs-Prozesses interessieren, lesen Sie diesen bitte zuerst. Der vollständige Source Code - sowohl für die Erstellung des Datensatzes, als auch für das Codebook - ist öffentlich einsehbar und dauerhaft erreichbar im wissenschaftlichen Archiv des CERN unter diesem Link hinterlegt: https://zenodo.org/doi/10.5281/zenodo.4072934 Kryptographische Signaturen Die Integrität und Echtheit der einzelnen Archive des Datensatzes sind durch eine Zwei-Phasen-Signatur sichergestellt. In Phase I werden während der Kompilierung für jedes ZIP-Archiv, das Codebook und die Robustness Checks Hash-Werte in zwei verschiedenen Verfahren (SHA2-256 und SHA3-512) berechnet und in einer CSV-Datei dokumentiert. In Phase II werden diese CSV-Datei und der Compilation Report mit meinem persönlichen geheimen GPG-Schlüssel signiert. Dieses Verfahren stellt sicher, dass die Kompilierung von jedermann durchgeführt werden kann, insbesondere im Rahmen von Replikationen, die persönliche Gewähr für Ergebnisse aber dennoch vorhanden ist. Die während der Kompilierung des Datensatzes erstellte CSV-Datei mit den Hash-Prüfsummen ist mit meiner persönlichen GPG-Signatur versehen. Der mit dieser Version korrespondierende Public Key ist sowohl mit dem Datensatz als auch mit dem Source Code hinterlegt. Er hat folgende Kenndaten: Name: Sean Fobbe (fobbe-data@posteo.de) Fingerabdruck: FE6F B888 F0E5 656C 1D25 3B9A 50C4 1384 F44A 4E42 Kein Urheberrecht: Public Domain An den Normtexten und Metadaten besteht gem. § 5 Abs. 1 UrhG kein Urheberrecht, da sie amtliche Werke sind. § 5 UrhG ist auf amtliche Datenbanken analog anzuwenden (BGH, Beschluss vom 28.09.2006 - I ZR 261/03, "Sächsischer Ausschreibungsdienst"). Alle eigenen Beiträge (z.B. durch Zusammenstellung und Anpassung der Metadaten) und damit den gesamten Datensatz stelle ich gemäß einer CC0 1.0 Universal Public Domain License vollständig urheberrechtsfrei. Disclaimer Dieser Datensatz ist eine private wissenschaftliche Initiative und steht in keiner Verbindung zu Behörden, Gerichten oder anderen öffentlichen Stellen der Bundesrepublik Deutschland. Alternativen [Ab 10.06.2019, nur XML] Beckedorf, Janis/Coupette, Corinna/Hartung, Dirk. 2020. "gesetze-im-internet: A daily archive of https://www.gesetze-im-internet.de". GitHub. https://github.com/QuantLaw/gesetze-im-internet [Änderungsgesetze] Wehrmeyer, Stefan/Semsrott, Arne/Filter, Johannes. 2021. "OffeneGesetze.de ist eine zivilgesellschaftliche, ehrenamtliche Plattform für amtliche Gesetzesblätter". Open Knowledge Foundation. https://offenegesetze.de/ [Alte Rechtsakte] Open Knowledge Foundation. 2013. "Bundesgit". GitHub. https://github.com/bundestag/gesetze Weitere Open Access Veröffentlichungen (Fobbe) Website - www.seanfobbe.de Open Data - zenodo.org/communities/sean-fobbe-data/ Source Code - zenodo.org/communities/sean-fobbe-code/ Volltexte regulärer Publikationen - zenodo.org/communities/sean-fobbe-publications/ Kontakt Fehler gefunden? Anregungen? Kommentieren Sie gerne im Issue Tracker oder kontaktieren Sie mich über www.seanfobbe.de

Alvarez, Ibis M.

1 files · 176 KB · xlsxdeclared

This supplementary material provides anonymised Mission Control records, selected anonymised transcript excerpts and a coding matrix developed for a methodological pilot study on the transformation of immersive VR simulation records into pedagogically interpretable assessment evidence in teacher education. The material illustrates the procedure used to transform Mission Control records into assessment evidence through sequence-based multimodal coding. It includes session-level variables, event-level variables, intervention-window coding, exploratory vocal and movement indicators, coding definitions and pilot observations. Video recordings and identifiable audiovisual data are not shared for ethical and privacy reasons.

Vera, Lourdes

31 files · 4.6 MB · csv, geojson, pngdeclared

Data, analysis scripts, and derived outputs reproducing the setback analysis between occupied buildings and active oil and gas wells in Karnes County, Texas, using public data (Texas Railroad Commission well locations; FEMA/ORNL USA Structures building footprints; and U.S. Census TIGER/Line block groups and 2020-2024 American Community Survey). The pipeline runs offline in Python (geopandas) and reproduces every reported distance, summary statistic, table, and figure in the associated article. This package reproduces and updates Chapter 3 of the author's doctoral dissertation: Vera, Lourdes (2022), "Environmental Data Justice in Action: Civically Valid Air Monitoring Near Oil and Gas Extraction in the Eagle Ford Shale Play," Ph.D. dissertation, Northeastern University, Boston, MA.

Wareesri, Prapassorn · Ieamvijarn, Subunn

14 files · 1.7 MB · csvdeclared

Dataset and replication code for the paper "The Regime-Dependent Value of Macroeconomic Information in Gold Futures Volatility Forecasting: A HAR-Machine Learning Comparison on COMEX" submitted to Investment Management and Financial Innovations. Data sourced from Yahoo Finance covering January 2014 to December 2025.

Listewnik, Marta

1 files · 57 KB · xlsxdeclared

Dataset containing proper names transliterated from Russian and Ukrainian Cyrillic alphabet found in articles from Welsh-language media and Welsh Wikipedia. The analysed corpus consisted of three samples: 1) three major Welsh magazines published between November 2021 and August 2022: the weekly Golwg (25 issues, 800 pages), the monthly Barn (9 issues, 547 pages), and the quarterly O'r Pedwar Gwynt (3 issues, 126 pages); 2) the second sample consisted of articles published in the main Welsh-language online news service, BBC Cymru Fyw, between 1 February 2022 and 30 June 2022 available on the WaybackInternetArchive, which were related to Ukraine or Russia (N=39); 3) the third sample included articles from Welsh Wikipedia (N=75) connected with Russia and Ukraine as of August 2022. The corpus was manually annotated by extracting examples of Romanized Ukrainian and Russian proper names into the spreadsheet. Occurrences of Romanised items other than proper names were also noted but not used in the final study analysis.

Haghbin, Kourosh

7 files · 121 KB · csv, xlsxdeclared

Supplementary code and data for the Master's thesis "The Influence of Green Infrastructure on the Urban Acoustic Environment: A Seasonal Noise Analysis in Bochum" (TU Dortmund University, 2026). The repository contains Python scripts implementing the full statistical analysis pipeline: site-level data assembly, Ordinary Least Squares (OLS) regression, and Multiscale Geographically Weighted Regression (MGWR) of BirdNET-derived normalised Shannon bird diversity (BN_H) against acoustic and structural green infrastructure predictors across 118 spring morning monitoring sites in Bochum, Germany. The primary script ( run_ols_mgwr_dawn.py ) implements the four-predictor model (dB, NDSI, AEI, and GI Tier; OLS R² = 0.319, MGWR R² = 0.796), selected as the primary model based on AICc (ΔAICc = -105.70 over the five-predictor alternative). A comparison script ( run_ols_mgwr_area.py ) implements the five-predictor model including log-transformed GI polygon area (OLS R² = 0.333, MGWR R² = 0.895). All analyses are implemented from scratch in Python 3 using NumPy and Pandas, without proprietary GIS or statistical libraries. The site-level analytical dataset (Dawn_4to9_Analysis_Data.xlsx, n = 118 sites, 04:00-09:00 dawn chorus window) is included to enable full reproduction of reported results.

Hagen, Karl · Zieher, Thomas · Stary, Ulrike · et al.

21 files · 6.6 MB · csv, pdfdeclared

Groundwater and landslide displacement records of the Eggerberg slope (Gradenbach landslide, Carinthia, Austria) Austrian Research Centre for Forests (BFW) Institute for Natural Hazards, Unit of Torrent Process & Hydrology K. Hagen, T. Zieher, U. Stary‚ E. Lang, S. Riedl, G. Priesch, J. Pichler, J. Rojacher First published June 2026 Contact: wasser.naturgefahren@bfw.gv.at Overview This dataset documents long-term hydrogeological and displacement monitoring at the deep-seated rock slide Eggerberg, situated in the Gradenbach catchment (Carinthia, Austria). The active mass movement covers approximately 2 km² and reaches depths exceeding 130 m. Owing to its interaction with the Gradenbach torrent system, the instability represents a significant natural hazard for nearby settlements in the Möll Valley. The dataset includes groundwater level and temperature measurements, as well as landslide displacement records collected by the Austrian Research Centre for Forests (BFW). It represents one of the longest continuous hydrogeological and geotechnical monitoring programs of a deep-seated gravitational slope deformation in the European Alps. It extends the existing dataset published in Hormes et al. (2026) by data of several boreholes, not used in the publication. Available groundwater temperature records were compiled and added in the borehole records. Furthermore, the displacement record of the extensometer was reset to zero after the data gap from 1995 to 1999. Dataset Description Groundwater monitoring Groundwater levels were monitored in 15 boreholes including several paired installations designed to observe groundwater conditions in different depth horizons and aquifer systems. Monitoring of the goundwater levels started in 1979, using manual cable light-plummet measurements with an accuracy of approximately 1 cm, and ended in 2024. In the beginning, measurements were generally performed every two weeks, since 1998 usually weekly. Groundwater temperature was measured between 1998 and 2015 with a sensor accuracy of 0.1°C. Continuous digital monitoring of groundwater level and temperature is additionally available for selected boreholes from July 2007 to December 2024 using OTT Orpheus Mini pressure probes. The observation series reveal the presence of approximately four hydrogeologically distinct aquifers within the moving rock mass. Landslide displacement monitoring Landslide displacement was monitored using a wire extensometer installed across the Gradenbach ravine. The instrument measured changes in the distance between the moving landslide mass and the comparatively stable opposite valley flank. The observation record extends from May 1979 to April 2024 and includes the same principal data gap between 1996 and 1998. Initially, measurements were recorded using analogue strip-chart systems and later digitized. In 2006, the monitoring station was upgraded with a continuous digital acquisition system (Thalimedes). To reduce short-term thermal effects caused by steel-wire expansion and contraction, the published dataset contains daily aggregated displacement values. All records (groundwater level and temperature, landslide displacement) were generally quality-controlled and checked for outliers and plausibility before publication. However, no warranty is given regarding their accuracy, completeness, or fitness for any particular purpose. Data Structure Groundwater Files Each borehole is provided as an individual CSV file (GRD-GWL-[borehole number]): • Column 1: Date (YYYY-MM-DD) • Column 2: Groundwater temperature (°C) • Column 3: Groundwater level below ground surface (m) Missing or unreliable values are coded as 9999. Groundwater levels are reported as negative values relative to the ground surface. Extensometer File The landslide displacement dataset is provided as a single CSV file (GRD_EXT.csv): • Column 1: Date (YYYY-MM-DD) • Column 2: Landslide displacement (cm), expressed as reduction of the distance between canyon slopes • Column 3: Measurement and data-quality code The PDF document 'GRD_Zenodo-V1-20260701.pdf' provides site information, methods, data gaps, and corrected observations. The ancillary document 'monitoring_data_gradenbach.html' provides minimal code snippets for working with the data in R. It further includes interactive plots of the data for visual inspection. Scientific R elevance The Gradenbach-Eggerberg dataset provides a unique long-term record enables the investigations of groundwater-controlled slope acceleration processes, and temporal trends in aquifer behavior. The dataset therefore constitutes an important resource for landslide process research, hazard assessment, hydrogeological investigations, and model validation.

Robert Koch-Institut

9 files · 54 MB · csv, pdf, zipdeclared

Der Datensatz "Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland" des Robert Koch-Instituts dokumentiert die tägliche intensivmedizinische Versorgungslage seit der COVID-19-Pandemie. Basierend auf Meldungen aller intensivbettenführenden Krankenhäuser in Deutschland erfasst das DIVI-Intensivregister Echtzeitdaten zu belegten und freien Intensivbetten. Die Erhebung differenziert nach Altersgruppen, Regionen und Versorgungsstufen. COVID-19-Fälle auf Intensivstationen werden gesondert ausgewiesen. Die Daten stehen aggregiert auf Bundes-, Landes- und Kreisebene zur Verfügung. Damit bildet der Datensatz eine Grundlage für die Überwachung von Kapazitäten, die Koordination von Behandlungskapazitäten und politische Entscheidungsprozesse während der Pandemie und darüber hinaus.

Pifferi, Gabriele · Thulinsson, Felix · Söderlund, Niclas · et al.

9 files · 165 KB · csvdeclared

# Camera Monitor System (CMS) Dataset and Analysis Scripts Version 4 Dataset and analysis scripts associated with the manuscript: "Effects of Camera Height, Field of View, and Driver Age on Depth Judgement and Lane-Change Decisions in Camera Monitor Systems" ## Changelog ### Version 4 - Added the R analysis scripts for the analyses reported in the associated manuscript (`CMS_continuous_age_analysis.R`, `Within_subject_figures.R`, `confidence_analysis.R`, `unsigned_error_analysis.R`); see the Analysis scripts section below. - Data files unchanged from Version 3. ### Version 3 - Corrected a file export error that caused row truncation in `distance_estimation_cleaned.csv` and `lane_change_dataset.csv` (earlier versions). Most data columns were missing from each row in those files due to a comma/semicolon delimiter collision during export. Files have been rebuilt from the original source data. - Corrected a mixed decimal notation issue (comma vs dot) in the `Confidence` column of `distance_estimation_cleaned.csv`, which caused the column to be read as a string rather than a numeric type in standard CSV parsers. - Updated `variable_dictionary.csv` to accurately reflect the column names used in all three data files (earlier versions listed intended names that did not match the actual file contents). - Added two participants (P48, P49) to `participant_metadata.csv` whose records were missing from the earlier export due to a data extraction error. Their experimental task data were present in `lane_change_dataset.csv` but lacked corresponding metadata rows. - Clarified that the N=56 figure in the preprocessing section applies specifically to the distance estimation task (task-specific outlier exclusion). The lane-change task and participant metadata retain the full N=58. ### Version 2 Same datafiles as in Version was uploaded by mistake ### Version 1 Initial release. --- ## Licence This dataset and the accompanying analysis scripts are shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. https://creativecommons.org/licenses/by/4.0/ --- ## Included files ### Data #### participant_metadata.csv Participant-level demographic and background information. N=58. #### distance_estimation_cleaned.csv Cleaned participant-level data from the distance estimation task. N=56 after task-specific outlier exclusion (see Data preprocessing below). #### lane_change_dataset.csv Participant-level data from the lane-change task. N=58. #### variable_dictionary.csv Definitions and descriptions of all variables included across the three data files. ### Analysis scripts (R) #### CMS_continuous_age_analysis.R #### Within_subject_figures.R #### confidence_analysis.R #### unsigned_error_analysis.R See the Analysis scripts section below for descriptions, requirements, and run order. --- ## Experimental overview A controlled laboratory experiment combining two aligned data collections was conducted using dynamic rearward driving scenarios in a Camera Monitor System (CMS) environment. Participants completed: - a distance estimation task - a lane-change task (last safe gap, LSG) Experimental factors: - Field of View (FOV): 40°, 76°, 112° - Camera height: High / Low - Driver age: continuous variable (range 22-64 years, mean 38.2 years). The variable `AgeGroupMedianSplit` is retained in the data files for continuity with prior analyses (Pifferi, 2025), but is not used in the primary analysis reported in the associated manuscript. --- ## Data preprocessing Preliminary analyses examined the distribution of the dependent variables for the distance estimation (Dist) and lane-change (LSG) tasks. The LSG data did not show substantial skewness or kurtosis, whereas the Dist data contained several extreme values reflecting substantial underestimation relative to the actual target distances. Outliers in the distance estimation data were identified using a threshold of ±2.5 standard deviations from the mean of the dependent variables (absolute and relative distance estimation errors). Exclusion limits: - Absolute distance error: -84 m to 79 m - Relative distance error: -2.76 to 2.55 Two participants were excluded from the distance estimation task because more than half of their trials fell outside the ±2.5 SD range (participants P48 and P49). These participants are retained in `lane_change_dataset.csv` and `participant_metadata.csv`, as the exclusion criterion was specific to the distance estimation task. Two additional participants contained isolated outlying trials (one and two trials, respectively); these isolated outlying trials were replaced using mean-value imputation within the corresponding dependent variable. Following preprocessing, the distance estimation dataset contains 56 participants and the lane-change dataset contains 58 participants. --- ## Analysis scripts R scripts for the analyses reported in the associated manuscript. All scripts expect the dataset CSV files in the same folder as the scripts, or edit `data_dir` at the top of each script. Requirements: R >= 4.0. Each script checks for and installs its required packages (lme4, lmerTest, emmeans, tidyverse, performance, ggplot2, ggsignif, dplyr, rmcorr) from CRAN if missing. Run order: 1. **CMS_continuous_age_analysis.R** - Primary analyses. Linear mixed-effects models with driver age as a continuous predictor for signed distance estimation error (DistErr), relative error (RelErr), and time-to-contact (TTC), including simple-slope (estimated marginal trend) analyses, the quadratic age check, the gap-split sensitivity analysis, and model-based predictions with confidence intervals. Writes three CSV files of model predictions to the working directory. 2. **Within_subject_figures.R** - Publication figures for the within-subject effects. Must be run in the same R session after script 1, as it uses the emmeans objects created there. Figure export lines (`ggsave`) are provided but commented out. 3. **confidence_analysis.R** - Confidence rating analyses: condition effects via linear mixed-effects models, repeated-measures correlations (rmcorr) between trial-level confidence and objective performance, and participant-level Spearman correlations. Self-contained; can be run independently. 4. **unsigned_error_analysis.R** - Unsigned (absolute) error analyses for the distance estimation task, using the same model structure as the primary analyses, with partial eta squared computed from the F-based approximation used in the manuscript. Self-contained; can be run independently. --- ## Ethics and anonymisation The shared datasets do not contain directly identifying personal information. Participant IDs are anonymised. Raw interview recordings, interview transcripts, and any potentially identifying qualitative material are not included in the shared repository for ethical and privacy reasons. --- ## Suggested citation Pifferi, G., Thulinsson, F., Söderlund, N., Brunnström, K., Rafiei, S., Schenkman, B., Djupsjöbacka, A., Sperandio, I., & Andrén, B. (2026). Dataset for: *Effects of Camera Height, Field of View, and Driver Age on Depth Judgement and Lane-Change Decisions in Camera Monitor Systems* [Data set]. Zenodo. DOI: https://doi.org/10.5281/zenodo.20055125