cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sf88fz9zc6.2
title: Entropy Variability Factor Test

description: The proposed Entropy Variability Factor (EVF) framework can reliably detect subtle structural changes in the local irregularity of time-series data. Specifically, we hypothesize that:
Rolling Shannon entropy can serve as a sensitive local measure of disorder.
Combining this with a two-sample Z-test and an empirically derived ±2% null zone provides a robust statistical test to distinguish genuine changes from random background variation.
The EVF framework performs consistently across synthetic and real-world datasets and remains stable under a range of practical parameter settings.This shared dataincludes all MATLAB scripts and input data files used to implement the proposed Entropy Variability Factor (EVF) framework as presented in the manuscript.
The data includes:
Synthetic signals: These are generated under controlled conditions to simulate known structural patterns, constant variance scenarios (stable conditions), and abrupt variance shifts (change detection scenarios).
Real-world data: Includes a time series from publicly available COVID-19 community mobility datasets (Google Community Mobility Reports), segmented around the onset of COVID-19 to test the framework on real behavioral changes.
Contents:
1. Benchmark 1: MATLAB script demonstrating the intuitive response of rolling Shannon entropy for simple signals with known structures.

2. Benchmark 2: MATLAB script for testing the EVF framework under constant-variance conditions to establish the empirical ±2% null zone.

3. Benchmark 3: MATLAB script for detecting controlled synthetic variance shifts, showing the test’s detection performance.

4. Benchmark 4: MATLAB script for applying the EVF framework to real-world COVID-19 mobility data, including signal segmentation and test output.

5. Sensitivity Analysis: Scripts that systematically vary the number of histogram bins, rolling window sizes, and segment lengths to assess the framework’s robustness across realistic parameter ranges 
Notes: 
 1. All codes were developed and run using MATLAB R2022a.
 2. Some scripts and functions may require additional time to execute, especially when processing large time-series with high-resolution rolling windows and multiple bin configurations.
 3. The provided code and data have been tested for reproducibility and transparency. Users are encouraged to adjust the window size, bin count, and other parameters as needed to replicate the benchmarks or adapt the Entropy Variability Factor (EVF) test for their own datasets at different variables.
DOI: 10.17632/w293stycvv
title: AJP Supplementary Figures 1-4

description: Supplementary Figures 1-4 to correspond with AJP-Endocrinology Manuscript
DOI: 10.17632/w293stycvv.1
title: AJP Supplementary Figures 1-4

description: Supplementary Figures 1-4 to correspond with AJP-Endocrinology Manuscript
DOI: 10.17632/2xxw2f28p6
title: IsomiR Utility in ALS Prognostication

description: Codes for running statistical analysis on RNA-seq data and processed data
DOI: 10.17632/2xxw2f28p6.2
title: IsomiR Utility in ALS Prognostication

description: Codes for running statistical analysis on RNA-seq data and processed data
DOI: 10.5281/zenodo.15741981
title: Eruptive Regime-Shift Detection at Steamboat Geyser – Code and Data for Clustering and Monitoring Framework

description: This repository accompanies the conference paper "Eruptive Regime-Shift Detection at Steamboat Geyser: An Integrated ML Pipeline and Its Implications for New Zealand Hydrothermal Monitoring" (Ardid, Dempsey, Cronin). It contains all necessary data, feature sets, and scripts to reproduce the PCA–DBSCAN clustering pipeline used to detect hydrothermal regime transitions at Steamboat Geyser.

Specifically, the dataset includes:





Seismic data from station YNM (YNM_seismic_data.csv)




Eruption metadata (YNM_eruptive_periods.txt)




Feature files (2016–2022) precomputed across multiple frequency bands (rsamF, mfF, hfF, lfF, vlfF, lrarF, rmarF, vlarF, dsarF) using a 12-hour window and z-score normalization




Python scripts:





sensitivity_clustering_dbscan.py: performs sensitivity analysis across PCA dimensions and clustering hyperparameters, outputs visual diagnostics




realtime_cluster_detection.py: simulates forward-in-time detection of new seismic regimes, mimicking a real-time monitoring system






Environment file (environment.yml): includes dependencies required to run the pipeline (Python ≥3.8, scikit-learn, pandas, tsfresh, matplotlib, etc.)



The methodology combines dimensionality reduction (PCA) with unsupervised density-based clustering (DBSCAN) to identify shifts in seismic behavior that precede or accompany changes in eruption dynamics. The same framework is adaptable to real-time implementation, and this dataset supports future transfer to geothermal monitoring sites in New Zealand.

Usage Notes

To replicate the analysis:





Install dependencies using conda env create -f environment.yml




Run sensitivity_clustering_dbscan.py to explore the parameter space




Use realtime_cluster_detection.py to simulate sequential regime detection




Plots and results are saved in the corresponding output directories



Please cite the accompanying paper when using this dataset.
DOI: 10.5281/zenodo.15373392
title: Breaking Ground: The effect of leachates from conventional and biodegradable mulching films on plants
DOI: 10.5281/zenodo.15783462
title: 3D ED/MicroED of the class Ia ribonucleotide reductase R2 subunit in oxidized FeIII-FeIII form (metR2a)

description: This upload includes the individual converted datasets (IMG format) and XDS output files from data reduction. Datasets were originally recorded as MRC files and converted to IMG format using an in-house script. Datasets were processed with XDS and merged using edtools. The final merged dataset included these 22 crystals with data to 2.00 Å resolution, 88.1% completeness, CC1/2 of 98.4%, and I/σ(I) of 5.73.

All MicroED datasets were collected on a Titan Krios cryo-TEM (ThermoFisher Scientific) operating at 300 kV with a Ceta-D CMOS detector. The software EPU-D was used for MicroED data collection. 






Dataset



Tilt step [°/frame]



Exposure time [s]



No. frames

[-] 



Tilt range [°]



Flux

[eÅ-2s-1]



Fluence [eÅ-2]



C2

[µm]



CL [m]



Beam Size [µm]



Spot size

[-]





1



0.5



0.5



129



64.5



0.21



13.3



50



1.5



3



8





2



0.5



1



199



99.5



0.01



2.0



20



1.2



2.5



8





3



0.5



0.5



199



99.5



0.01



1.0



20



1.2



2.5



8





4



0.5



0.5



199



99.5



0.01



1.0



20



1.2



2.5



8





5



0.5



1



199



99.5



0.01



2.0



20



1.2



2.5



8





6



0.2



0.5



99



19.8



0.21



10.2



50



1.5



3



8





7



0.2



0.5



99



19.8



0.21



10.2



50



1.5



3



8





8



0.2



0.5



99



19.8



0.05



2.6



50



1.5



3



10





9



0.2



0.5



99



19.8



0.05



2.6



50



1.5



3



10





10



0.2



0.5



99



19.8



0.05



2.6



50



1.5



3



10





11



0.2



0.5



99



19.8



0.05



2.6



50



1.5



3



10





12



0.2



0.5



99



19.8



0.05



2.6



50



1.5



3



10





13



0.2



0.5



99



19.8



0.05



2.6



50



1.5



3



10





14



0.2



0.5



99



19.8



0.05



2.6



50



1.5



3



10





15



0.2



0.5



99



19.8



0.21



10.2



50



1.5



3



8





16



0.2



0.5



99



19.8



0.21



10.2



50



1.5



3



8





17



0.2



0.5



99



19.8



0.21



10.2



50



1.5



3



8





18



0.2



0.5



99



19.8



0.21



10.2



50



1.5



3



8





19



0.2



0.5



99



19.8



0.21



10.2



50



1.5



3



8





20



0.2



0.5



99



19.8



0.05



2.6



50



1.5



3



10





21



0.5



0.5



159



79.5



0.21



16.4



50



1.5



3



8





22



0.5



0.5



159



79.5



0.21



16.4



50



1.5



3



8
DOI: 10.5281/zenodo.17142319
title: Dataset and code for "Experimental quantification of capillary and adsorbed water in active clays for hydro-mechanical modelling"

description: This repository supports the journal paper "Experimental quantification of capillary and adsorbed water in active clays for hydro-mechanical modelling"

It includes the raw data used to generate the figures in the paper, as well as the code developed for data processing.

For further details, please refer to the accompanying Readme.txt file.
DOI: 10.5281/zenodo.17117613
title: Data for "Rapid intracortical AAV delivery in neonatal mice enables stable adult imaging of genetically encoded sensors" (Wisser, Blackwood et al., 2025)
DOI: 10.5281/zenodo.10855065
title: Nutrients revisited: Sources, variability, and contribution of nutrient sources to the nutrient budget of freshwater aquaculture systems

description: Abstract



This study assesses the relevance of fish feed, source water, and alkalinity supplements as inputs into closed freshwater aquaculture systems in Europe. Emphasis is given to N, P, K, Ca, Mg, S, Fe, Cu, and Zn due to their relevance for biorefinery and plant production. Results show that feed contributes almost all N and P and at least beyond 70 %, and 60 % of K, and Zn, respectively, below 100 L makeup water kg−1 feed. Ca, Mg, S, Fe, and Cu, meanwhile, are more dependent on the water source and volume. If alkalinity supplements based on K, Ca, or Mg are used, then they are the main input source of those substances, regardless of feed or water used. Optimising fish feed for N, P, K, and Zn appears reasonable but site-specific solutions will be necessary for the management of Ca, Mg, S, Fe, and Cu.
DOI: 10.5281/zenodo.10855066
title: Nutrients revisited: Sources, variability, and contribution of nutrient sources to the nutrient budget of freshwater aquaculture systems

description: Abstract



This study assesses the relevance of fish feed, source water, and alkalinity supplements as inputs into closed freshwater aquaculture systems in Europe. Emphasis is given to N, P, K, Ca, Mg, S, Fe, Cu, and Zn due to their relevance for biorefinery and plant production. Results show that feed contributes almost all N and P and at least beyond 70 %, and 60 % of K, and Zn, respectively, below 100 L makeup water kg−1 feed. Ca, Mg, S, Fe, and Cu, meanwhile, are more dependent on the water source and volume. If alkalinity supplements based on K, Ca, or Mg are used, then they are the main input source of those substances, regardless of feed or water used. Optimising fish feed for N, P, K, and Zn appears reasonable but site-specific solutions will be necessary for the management of Ca, Mg, S, Fe, and Cu.
DOI: 10.5281/zenodo.8283281
title: Understanding the differential impacts of two antidepressants on locomotion of freshwater snails (Lymnaea stagnalis)

description: Data and scripts used for the plots and data analysis for the manuscript Understanding the differential impacts of two antidepressants on locomotion of freshwater snails (Lymnaea stagnalis), experiments were conducted in 2021.
DOI: 10.5281/zenodo.14104497
title: D4.3_Probefield_Dataset

description: Origin: WP4. This dataset contains VISNIR spectra from in situ measurements and laboratory analyses of air-dried and sieved samples (<2 mm), including pXRF analysis, all conducted by CSIC (Spain). The dataset includes samples collected from various field locations, with corresponding laboratory data from Italy and Turkey. In total, 136 samples were collected from experimental agricultural fields managed by CREA (IT) and TAGEM (TR). 

The dataset contains information on sampling date, sample_ID, site, crop, depth (cm), coordinates (in LAEA projection), and the corresponding analyzed values for the following parameters: texture (%, sand, silt, clay), pH (Ext 1:2.5), EC (Ext 1:5) (mS/cm), carbonate (% CaCO₃), active carbonate (%), organic carbon (%), total nitrogen (%), available phosphorus (mg/kg), cation exchange capacity (CEC, meq/100g), exchangeable bases (meq/100g), elemental analysis (pXRF), and VNIR-pretreated spectra (350–2500 nm). An additional sheet contains metadata on the lab methods used. The following pretreatments were applied to the absorbance value of VNIR spectral data: Standardization and correction factor with lucky bay sand following internal soil standard (ISS); Code Trimming; splice Correction; Savitzky-Golay smoothing filter; aggregation side 8 wavelength resampling
description: Purpose and objectives: The main purpose was to conduct a cost-benefit analysis to evaluate the improvement of multivariate VNIR models by integrating variables from elementary pXRF analysis for predicting soil parameters. For the in-situ data the protocol from Task 2.4 for the best surface preparation and the protocol from Task 3.2 for moisture effect removal are followed.
DOI: 10.5281/zenodo.17041843
title: Subglacial Hydrology Model Output for "Subglacial discharge effects on Antarctic ice-shelf basal melt and the Southern Ocean in a global, coupled ocean–sea-ice model"

description: Antarctic-wide subglacial hydrology model simulation using the MPAS-Albany Land Ice (MALI) model. The simulation is part of "Subglacial discharge effects on Antarctic ice-shelf basal melt and the Southern Ocean in a global, coupled ocean–sea-ice model", currently in review at the Journal of Geophysical Research: Oceans. The simulation uses only distributed subglacial discharge (no subglacial channels) and was run on a 4 – 20 km resolution two-dimensional Voronoi mesh. Subglacial water enters the system through frictional and geothermal basal melting assuming the bed thermal state is temperate everywhere. Archived model output is a snapshot at steady state. Please see the forthcoming publication for more details (relevant model information also copied into README file). Descriptions of model variables included in the output file can be found here:

 https://github.com/MALI-Dev/E3SM/blob/develop/components/mpas-albany-landice/src/Registry_subglacial_hydro.xml

and 

https://github.com/MALI-Dev/E3SM/blob/develop/components/mpas-albany-landice/src/Registry.xml
DOI: 10.5281/zenodo.17128325
title: DataSource Supplementary File 1 (A critical review of American trypanosomiasis (AT) in dogs with the current distribution of cases in Brazil and a diagnostic approach for veterinary clinicians)
DOI: 10.5281/zenodo.15966533
title: Gas exchange, hyperspectral reflectance, and chlorophyll fluorescence measurements for a field-grown maize MAGIC population

description: Biological material

This repository contains gas exchange and hyperspectral measurements for a maize MAGIC population grown in the field across different growth periods.

The field trials were conducted at the National Institute of Agricultural Botany (NIAB, Cambridge, UK) over three consecutive seasons (2021, 2022, and 2023) with genotypes of a maize MAGIC population (Dell Acqua et al. 2015, doi: 10.1186/s13059-015-0716-z).

The experimental design of the field trial has been described in Ferguson et al. 2025, doi: 10.1093/plcell/koaf158. 

Gas exchange measurements

The experimental details of gas exchange measurements are described in Xu et al. 2025, doi: 10.1101/2025.07.16.665083.

A/Ci and A/Q curves were obtained for the genotypes grown in 2022 and 2023. For the genotypes grown in 2021, measurements of photosynthetic rates at saturating light (Asat) and A/Ci curves are available.

Leaf hyperspectral reflectance data

The experimental details of hyperspectral reflectance measurements are detailed in Xu et al. 2025, doi: 10.22541/au.175100405.52153616/v1. 

Chlorophyll fluorescence

Chlorophyll fluorescence measurements are available for genotypes grown in 2021, 2022, and 2023.

The experimental details and measurements for 2021 and 2022 have been described in Ferguson et al. 2025, doi: 10.1093/plcell/koaf158.

Specific leaf area (SLA)

Measurements of specific leaf area are available for genotypes grown in 2021, 2022, and 2023.

Isotope measurements

Carbon and Nitrogen isotope measurements are available for genotypes grown in 2021, 2022, and 2023.

 

Contact

John Ferguson: jfergu@essex.ac.uk

Johannes Kromdijk: jk417@cam.ac.uk
DOI: 10.5281/zenodo.17421378
title: HN-LULC

description: The HN-LULC dataset is a pixel-level dataset, which was created by gathering images from the Sentinel. The Iimages weare acquired from Sentinel-2A with Red, Green, Blue, and NIR channels. The labels areis human annotated, which includinge water, urban, meadow, forest, and farmland.
DOI: 10.5281/zenodo.10060926
title: Construction Cost to Completion for Reinforced Concrete Building Project

description: The dataset includes 19 variables obtained from 10 historical RC construction projects in Taiwan
DOI: 10.5281/zenodo.15307415
title: Current and future scenarios of EUNIS habitats distribution in Europe

description: The EUNIS habitat classification is a key framework for categorising European habitats and supporting biodiversity policies, including the implementation of the nature restoration law. It organises habitat types into a hierarchical system, with over 200 distinct habitat classes at level 3, grouped into broader formations at level 1—such as saltmarshes (MA2), coastal habitats (N), wetlands (Q), grasslands (R), shrublands (S), forests (T), sparsely vegetated areas (U), and man-made habitats (V) (Chytrý et al., 2020).

To support growing demands for spatially explicit and forward-looking habitat data, we applied habitat distribution modelling (HDM) to produce projections of over 260 EUNIS level 3 habitats at 1 km resolution across Europe. These projections cover both current conditions and multiple future scenarios, including three Shared Socioeconomic Pathways (SSP1, SSP3, SSP5). For SSP1, we also include land use outcomes from the three Nature Futures Framework perspectives: Nature-for-Nature, Nature-for-Society, and Nature-as-Culture.

We used ensemble machine learning models that integrate climate projections, land use scenarios, and environmental variables such as topography and soil to estimate the most probable EUNIS habitat type at each grid cell. The dataset includes not only habitat predictions but also assessments of model uncertainty and the dominant habitat types within each EUNIS level 1 category.

These spatial outputs are valuable for informing long-term conservation and restoration strategies, and can be refined further with local or high-resolution land cover data for more detailed planning and implementation.
DOI: 10.5281/zenodo.17235447
title: Whispers of Dissent? Analyzing the Rhetorical Alignment of the Elites with the Authoritarian Incumbent through Parliamentary Discourse in the Kazakhstani Senate (Replication Materials)

description: The replication materials folder contains all the relevant data (incl., links to a raw data set of Senate protocols), scripts, and draft analytical write-ups unrelated to the dissertation but relevant for the analyses in the paper with the same title.

Any errors in the data or coding are solely mine and should be reported to me for corrections via Daminov_Ildar@phd.ceu.edu (or my current institutional address in ORCID, if this email address has changed).
DOI: 10.16904/envidat.513
title: GEM2: Meteorological and snow station at Gemsstock (3021 m asl), Canton Uri, Switzerland

description: Meteorological station at Gemstock (3021 m asl) in Canton Uri. The station includes in/out LW/SW and a snow height sensor.
Data from this station is managed by the permos.ch project. More information: https://www.permos.ch/permafrost-monitoring/field-sites
description: Resource Title: DATA ACCESS
 Size: 1024 bytes
 Format: No Info
 URL: https://www.permos.ch/data-portal
 Description: Meteo data at Gemsstock [Level 2 dataset](http://models.slf.ch/p/dataset-processing/).

fields = timestamp TSS HS VW DW VW_MAX OSWR ISWR ILWR RECORD AUX_VOLTAGE LOGGER_VOLTAGE LW_SENSOR_TEMP VENTILATION_FLAG OLWR
DOI: 10.15468/dl.xq9s4x
title: Occurrence Download

description: A dataset containing 359 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Coccoloba liebmannii"
 ]
}

 The dataset includes 359 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010383-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.16904/envidat.345
title: Seilaplan Tutorial: Load WMS layers as background maps

description: In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German!
Link for the integration of WMS data: https://wms.geo.admin.ch/
Link to the description on the Swisstopo website:
https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html
Link to the Seilaplan website: https://seilaplan.wsl.ch
**************************
Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI. 
Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/
Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html
Link zur Seilaplan-Website: https://seilaplan.wsl.ch
description: Resource Title: WMS-Layer als Hintergrundkarten laden
 Size: 946355714 bytes
 Format: video/mp4
 URL: https://www.envidat.ch/dataset/96da5435-fcc6-4a25-ba24-234b90cf3dfb/resource/18d13f78-0149-42bc-b962-fdd7bd972e4d/download/seilaplan_tutorial_4_wms_v2.mp4
 Description:
description: Resource Title: View video on youtube!
 Size: 1024 bytes
 Format: No Info
 URL: https://youtu.be/6ILeTPlOG6A
 Description: