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.

Austin, Timothy · do Valle Chagas Azaneu, Marina · Roughan, Moninya

26 files · 29 MB · netcdf, pdf, pngdeclared

Data collected from a temperature mooring at Lord Howe Island maintained by UNSW Sydney and funded by Parks Australia. The mooring position is longitude = 158.97°E and latitude = -31.51°, and local depth of approximately 52 m. The data were sampled using a series of thermistors (aqualogger 520PTs) deployed on a mooring line at 4m intervals through the water column, with shallowest instrument at 13 m and deepest at 53 m. The time period spans between 14-05-2025 and 22-04-2026. IMOS standard data quality assurance and quality control processes have been followed and the data formatted following IMOS conventions. Data quality control includes automated routines and visual inspection (expert QC) and flagging of obvious errors. File are c.f. compliant NetCDF files, and file name format follows IMOS conventions and includes sampling period in the format: UNSW_Lord_Howe_Marine_Park_TZ_ yyyymmddThhmmss Z_LH050_FV01_ LH050-2511-Aqualogger-AQUAlogger-520PT16-max160m-13_END- yyyymmddThhmmssZ.

Wang, Chang · Lu, Xingcheng

2 files · 222 MB · netcdfdeclared

Machine-learning-inferred monthly anthropogenic NOx emission over the 2026 Strait-of-Hormuz disruption (global, 0.1 degree, January 2025 - May 2026). This dataset is the top-down NOx emission product underlying the companion manuscript on the 2026 Strait-of-Hormuz shipping-emission collapse. A LightGBM estimator trained on the CAMS-GLOB-ANT v6.2 inventory (2018-2024), with the observed TROPOMI NO2 column and GEOS-CF chemistry/meteorology as predictors, is applied month by month to 2025-01 through 2026-05 to infer the anthropogenic NOx emission flux. Provided as a single self-describing CF-1.8 NetCDF containing: the total anthropogenic NOx flux (kg m-2 s-1, reported as NO) and a per-pixel cross-validation uncertainty (log-space). The estimator resolves the total emission only; no sector decomposition is distributed, because over open ocean the total is essentially ship emission while on land a sector split would only re-apply the CAMS-GLOB-ANT prior shares and is not constrained by the observations. Coverage is land and ocean within +/-60 degrees latitude on a regular 0.1-degree global grid. All units and coordinates are embedded in the file.

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

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

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.

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

Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo

100 files · 49 GB · netcdf, tardeclared

Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625°. The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components for soil moisture layers 4, 5 and 6, consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ). The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch (https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Spin-up: 30-year spin-up using 1990-2019 ERA5 climatology Model version: v1.0 Setup Scope: Model run for domain 1020011530, post-processed and clipped. Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline (https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables sm_l04: Volumetric soil moisture layer 4 (300-500 mm depth) [mm mm-1, fraction between 0 and 1] sm_l05: Volumetric soil moisture layer 5 (500-1000 mm depth) [mm mm-1, fraction between 0 and 1] sm_l06: Volumetric soil moisture layer 6 (1000-2000 mm depth) [mm mm-1, fraction between 0 and 1] 📫 Contact Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de Institution Helmholtz Centre for Environmental Research - UFZ, Department of Computational Hydrosystems

Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo

70 files · 34 GB · netcdfdeclared

Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625°. The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components for soil water content layers 3 and 4, consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ). The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch (https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Spin-up: 30-year spin-up using 1990-2019 ERA5 climatology Model version: v1.0 Setup Scope: Model run for domain 1020011530, post-processed and clipped. Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline (https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables swc_l03: Soil water content layer 3 (150-300 mm depth) [mm] swc_l04: Soil water content layer 4 (300-500 mm depth) [mm] 📫 Contact Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de Institution Helmholtz Centre for Environmental Research - UFZ, Department of Computational Hydrosystems

van Gestel, Cornelis

25 files · 746 KB · csv, pdfdeclared

Data from binary mixture toxicity tests with springtails exposed to Cd-Pb, Cd-Cu and Cd-Zn mixtures, including: Data used to construct Langmuir sorption isotherms relating total metal concentrations to CaCl2 or H2O extractable concentrations in soil, for each of the three mixtures PDF files showing the Results of the Langmuir sorption isotherm calculations Data on the survival of springtails in exposures to each of the three mixtures Data on the growth of the springtails in exposures to each of the three mixtures Results of the mixture toxicity analysis for growth effects for each of the three mixtures Data on the reproduction of the springtails in exposures to each of the three mixtures Results of the mixture toxicity analysis for reproduction effects for each of the three mixtures Results of the analysis of chloride concentrations in H2O extracts of the soil of the three mixture exposures Data on the pH of the test soil dosed with the metals, for each of the three mixtures

Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo

70 files · 34 GB · netcdfdeclared

Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625°. The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components for soil moisture layers 2 and 3, consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ). The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch (https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Spin-up: 30-year spin-up using 1990-2019 ERA5 climatology Model version: v1.0 Setup Scope: Model run for domain 1020011530, post-processed and clipped. Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline (https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables sm_l02: Volumetric soil moisture layer 2 (50-150 mm depth) [mm mm-1, fraction between 0 and 1] sm_l03: Volumetric soil moisture layer 3 (150-300 mm depth) [mm mm-1, fraction between 0 and 1] 📫 Contact Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de Institution Helmholtz Centre for Environmental Research - UFZ, Department of Computational Hydrosystems

Allaert, Reinoud A. · Stienen, Eric W.M. · Lens, Luc · et al.

6 files · 125 MB · csv, gzipdeclared

HG_JUVENILE - Juvenile herring gulls (Larus argentatus, Laridae) hatched at the southern North Sea coast (Belgium) is a bird tracking dataset published by the Centre for Research on Ecology, Cognition and Behaviour of Birds at Ghent University and the Research Institute for Nature and Forest (INBO) . It contains animal tracking data for the project/study HG_JUVENILE , using trackers developed by Interrex ( http://www.interrex-tracking.com ). The study has been operational since 2022. In total 204 individuals of European herring gull ( Larus argentatus ) have been tagged. 150 individuals were raised from egg by Ghent University researchers at the Wildlife Rescue Center in Ostend, completed several cognitive and behavioural tests when approximately three weeks old, and were released in the IJzermonding, Nieuwpoort (Belgium). 54 additional individuals were tagged and released in the wild to collect baseline data. The main goal of the study is to link cognitive performance in the lab to behaviour in the wild. Data are automatically synced with Movebank and from there periodically archived on Zenodo (see https://github.com/inbo/bird-tracking ). Files Data in this package are exported from Movebank study 2217728245 . Fields in the data follow the Movebank Attribute Dictionary and are described in datapackage.json . Files are structured as a Frictionless Data Package . You can access all data in R via https://zenodo.org/records/21279427/files/datapackage.json using frictionless . datapackage.json : technical description of the data files. HG_JUVENILE-reference-data.csv : reference data about the animals, tags and deployments. HG_JUVENILE-gps-yyyy.csv.gz : GPS data recorded by the tags, grouped by year. Acknowledgements This dataset was collected using infrastructure provided by the ERC and Ghent University.

van Gestel, Kees

14 files · 192 KB · csvdeclared

Data files on cadmium, copper, lead and zinc concentrations in soil, H2O- and CaCl2-extracts of soil and in springtails exposed to complex mixtures of chloride salts of these metals, before and after leaching the soil to remove the chloride counterion. Also included are data on the effects of the metals, single and in mixtures on the survival, growth and reproduction of the springtails.

Zhang, Yunwei

6 files · 40 GB · hdf5, zipdeclared

Files related to INVNET, a deep learning model for surface wave dispersion spectrum inversion in geophysics.