cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.16904/envidat.348
title: Bryophytes of Europe Traits (BET) dataset

description: The Bryophytes of Europe Traits (BET) dataset includes values for 65 biological and ecological traits and 25 bioclimatic variables for all 1816 bryophytes included in the European Red List (Hodgetts et al. 2019). The traits are compiled from several regional trait datasets and manually complemented using Floras, species-specific literature and expert knowledge. The bioclimatic variables are calculated using the European range of each species. Details regarding the trait compilation and extraction of bioclimatic variables can be found in the corresponding data paper (Van Zuijlen et al. 2023).
description: Resource Title: BET_data_explanations
 Size: 981939 bytes
 Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
 URL: https://www.envidat.ch/dataset/4865a082-169d-40d1-920b-fc20ad0acad2/resource/6367f57f-1894-4f1b-997d-068d4d7511cc/download/betdata.xlsx
 Description: Excel file including the BET dataset and (trait) explanations.
description: Resource Title: BET_data
 Size: 1072488 bytes
 Format: text/plain
 URL: https://www.envidat.ch/dataset/4865a082-169d-40d1-920b-fc20ad0acad2/resource/d2d2f958-051c-4638-a808-88547cc64d92/download/betdata.txt
 Description: Text file including the BET dataset.
description: Resource Title: BET_explanations
 Size: 249953 bytes
 Format: application/pdf
 URL: https://www.envidat.ch/dataset/4865a082-169d-40d1-920b-fc20ad0acad2/resource/9ac4a865-5bb2-479b-93f4-b3bfd99c7ada/download/bet_explanations.pdf
 Description: PDF including the (trait) explanations.
DOI: 10.16904/envidat.375
title: Photogrammetric Drone Data Wolfgang Arelen

description: We conducted four drone flights in Davos Wolfgang Arelen, in 2020/21 and 2022 to obtain data for the generation of DSMs and orthomosaics at a high resolution. The data was processed with the Agisoft Metashape Professional Software.
description: Resource Title: Data Access 
 Size: 616 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?bucket=https://s3-zh.os.switch.ch/drone-data/&prefix=Wingtra/Wolfgang_Arelen/
 Description: - DSM 10cm resolution 
- Orthomosaic 3cm resolution 
- Agisoft Metashape Processing Report
- original RGB images

All generated products are also available as a cloud optimized geotiff.
DOI: 10.15468/dl.rku6p8
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-4.8052 51.64372,-4.80486 51.64333,-4.80418 51.64282,-4.80384 51.64216,-4.80311 51.64206,-4.8014 51.64193,-4.80018 51.64185,-4.80044 51.64281,-4.80019 51.64384,-4.8009 51.64459,-4.80268 51.64465,-4.80477 51.64436,-4.80654 51.64475,-4.80688 51.64458,-4.80722 51.64418,-4.8052 51.64372)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi",
 "Year 2015-2025"
 ]
}

 The dataset includes 25 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057300-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.16904/envidat.373
title: Photogrammetric Drone Data Schürlialp

description: The data was collected on 16.04.2021 and on 28.05.2021 with a Wingtra Gen II and a Sony RX1 II RGB sensor to obtain snow depth and distribution data. Following the data collection, the data was processed with Agisoft Metashape. A 10cm DSM, a 10cm snow depth raster, a 3mm orthophoto and the original drone images are available for download.
description: Resource Title: Data Access
 Size: 614 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?bucket=https://s3-zh.os.switch.ch/drone-data/&prefix=Wingtra/Schuerlialp/
 Description: - 10 cm DSM 
- 10 cm HS 
- 3 cm orthophoto 
- original RGB images
- Agisoft Metashape Processing Report

All of the products are also available as a cloud optimized geotiff.
DOI: 10.16904/envidat.378
title: Illgraben debris-flow characteristics 2019-2022

description: List of key debris flow variables from the WSL Illgraben monitoring station (2019-2022) such as occurrence date and time, peak flow depth, peak flow velocity, total volume and bulk density.
This table contains values based on our current analysis methods. The list will be updated annually after each debris flow season, and as our methods continue to improve, individual values may change slightly in the future.
description: Resource Title: Illgraben debris-flow characteristics 2019-2022
 Size: 2252 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/119e182c-01f5-44c2-96d7-4fba7133a844/resource/6a5bb9d8-f572-4571-a6b3-0858883c20f5/download/df_characteristics_illgraben_2019-2022.zip
 Description: List of key debris flow variables from the WSL Illgraben monitoring station (2019-2022)
DOI: 10.16904/envidat.172
title: Ecological properties of urban ecosystems. Biodiversity dataset of Zurich

description: Richness, site occurrence and abundance data of bees, beetles, birds, hoverflies, net-wingeds, true bugs, snails, spiders, milipides, wasps collected in the city of Zurich using different sampling techniques, and the environmental variables for each sampling site.
Data are provided on request to contact person against bilateral agreement.
description: Resource Title: Dataset_1_SAD_SOD.txt
 Size: 73478 bytes
 Format: text/plain
 URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/a7a96a85-dc46-4b5f-bcf4-4d91485e3d03/download/dataset_1_sad_sod.txt
 Description: abundance and occurrence data of the 1446 species studied, used to investigate species-abundance distributions and species-occurrence distributions
description: Resource Title: Dataset_2_SDM_data.txt
 Size: 65679611 bytes
 Format: text/plain
 URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/0e2e7fb0-88ba-4aa4-92ef-079191b4a668/download/dataset_2_sdm_data.txt
 Description: contains the raw data to perform species distribution models, only for common species
description: Resource Title: Dataset_3_Species_richness_hyper.txt
 Size: 1373081 bytes
 Format: text/plain
 URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/0ef3b508-b8e7-4404-a7b9-edc51c7f2375/download/dataset_3_species_richness_hyper.txt
 Description: contains the raw data to perform the species richness models of the hypercommon species
description: Resource Title: Dataset_4_Species_richness_rare.txt
 Size: 2274863 bytes
 Format: text/plain
 URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/3bbbc78a-92c3-4832-b51b-61d65346460e/download/dataset_4_species_richness_rare.txt
 Description: contains the raw data to perform the species richness models of the rare species
description: Resource Title: Dataset_5_Species_richness_all.txt
 Size: 2452421 bytes
 Format: text/plain
 URL: https://www.envidat.ch/dataset/0f81429a-204e-4720-929e-5b251e9ab1c4/resource/f3abc8d4-c92e-4a57-aea4-39ba8c93158d/download/dataset_5_species_richness_all.txt
 Description: contains the raw data to perform the species richness models of all species
DOI: 10.17632/y7x39rvrwm.1
title: Computational thinking related datasets in educational robotic-supported programming activities

description: Research Hypothesis:
This study hypothesizes that students’ computational thinking (CT) development in educational robotics-supported programming learning environments is influenced by both internal psychological factors (learning motivation, cognitive engagement, cognitive load, and prior programming knowledge) and external technology-related factors (task-technology fit, perceived usefulness, and perceived ease of use). The study integrates the Cognitive-Affective Theory of Multimedia Learning (CATLM), the Technology Acceptance Model (TAM), and the Task-Technology Fit (TTF) model into a unified theoretical framework.

Data Summary:
The dataset includes responses to 54 items measuring seven constructs:

Computational Thinking (CT)

Learning Motivation

Cognitive Load

Cognitive Engagement

Prior Programming Knowledge (measured via a Python programming test)

Perceived Usefulness (PU)

Perceived Ease of Use (PEU)

Task-Technology Fit (TTF)

All Likert-scale responses were rated on a 5-point scale (1 = strongly disagree, 5 = strongly agree). The Python programming test consists of 10 multiple-choice questions assessing basic programming skills (e.g., loops, conditions), scored from 0 to 10.

Data Collection Method:
The data were collected online using the Wenjuanxing platform in March 2025. Participants completed a structured questionnaire and a Python fundamentals test. All participants had prior experience with educational robotics in programming learning. The instruments used were adapted from validated scales and reviewed by experts. Participation was anonymous and voluntary, with informed consent obtained from all participants and guardians.

Notable Findings:

The proposed model explains 54.1% of the variance in computational thinking.

Learning motivation and task-technology fit are the strongest predictors of computational thinking.

Task-technology fit significantly influences perceived usefulness and ease of use, enhancing technology acceptance.

Prior programming knowledge reduces cognitive load and positively impacts computational thinking.

Cognitive engagement positively mediates learning outcomes, while cognitive load exerts a negative effect on engagement.

How to Interpret the Data:

Each row represents an individual student’s complete response.

Items are grouped by construct and follow a consistent coding scheme.

Higher scores reflect more positive perceptions or stronger abilities (e.g., higher CT or motivation).

Factor scores can be used to compute latent variables or replicate the structural equation modeling (PLS-SEM) and artificial neural network (ANN) analyses.

Potential Uses:

Reproducing or extending the SEM/ANN analyses presented in the study.

Exploring the relationships between cognitive, motivational, and technological factors in programming education.

Informing instructional design and technology integration strategies in STEM or robotics education contexts.
DOI: 10.16904/envidat.210
title: Flowering Plants (Angiospermae) in Urban Green Areas in five European Cities

description: Data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland) sampled between April and July 2018.
description: Resource Title: Floral_1_occurrence.csv
 Size: 1535390 bytes
 Format: application/vnd.ms-excel
 URL: https://www.envidat.ch/dataset/cf6885b4-0705-415b-983b-13b118c3cc95/resource/3c30a1f0-d0c3-4323-891b-646f3d94378f/download/floral_1_occurrence.csv
 Description: List of plant species sampled in the five cities (Antwerp, Paris, Poznan, Tartu and Zurich) during the different sampling perionds (four for Zurich, three for the other cities) with their frequency (oc1-oc16) within a 100 m radius
description: Resource Title: Floral_2_counts.csv
 Size: 260871 bytes
 Format: application/vnd.ms-excel
 URL: https://www.envidat.ch/dataset/cf6885b4-0705-415b-983b-13b118c3cc95/resource/380c0645-2665-4f9e-a8a1-bcc75bc714b8/download/floral_2_counts.csv
 Description: Floral units, mean number of flowers per floral unit and floral abundance of the different plants counted in quadrats in the study sites in Zurich during the four sampling periods
description: Resource Title: Floral_Traits.csv
 Size: 248377 bytes
 Format: application/vnd.ms-excel
 URL: https://www.envidat.ch/dataset/cf6885b4-0705-415b-983b-13b118c3cc95/resource/31270de2-4fe9-416f-b0c0-5d871505aec9/download/floral_traits.csv
 Description: Trait values for the sampled plants
DOI: 10.16904/envidat.226
title: Novel methods to correct for observer and sampling bias in presence-only species distribution models

description: Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence-only SDMs that often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence-only models. 
Innovation: Recent methods correct for observer bias by using covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1’900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.
Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as bias covariate (Target-BCC) displayed most realistic modelled species distributions, with a clear positive correlation (r≃0.5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.
description: Resource Title: PPM_bias_correction
 Size: 410411202 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/a09eb932-e181-47a3-945a-7f7cde5d2393/resource/d2665a9b-a7de-4bff-8f6e-6dbf0406fd0b/download/nc_data_n_scripts.zip
 Description: Non-copyright data, scripts, diversity outputs
DOI: 10.16904/envidat.334
title: Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity

description: This dataset comprises a large array of ecological data for the European Alps:
 (1) Current soil and climate predictors at various resolutions.
 (2) GBIF observations of the European Alps Flora (~4,000 species).
 (3) Species habitat suitability maps (1,109 species; based on species observations filtered at 40x40-km)
 at various resolutions used in the study to generate (4); except 'expert'...
 (4) Expert, Taxonomic, phylogenetic and functional diversity of the study region at various resolutions
 (from 100-m to 40-km --- 100-m aggregated & mean to km + non-aggregated/predicted) for CLIM,
 SOIL and CLIM-SOIL models.
 (5) Ecological and altitudinal preferences of the European Alps Flora.
 (6) Data outputs of the related published article.
 (7) All scripts used for analyses.
 (8) Additional files used for analyses.
 (9) Improved set of species habitat suitability maps (~2,600 species; based on species observations
 filtered at 1x1-km) and related taxonomic diversity at 100-m resolution (aggregated to km +
 non-aggregated/predicted) for CLIM, SOIL and CLIM-SOIL models ---- not incorporated in the study.
description: Resource Title: Data access
 Size: 60301432 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?prefix=doi/envidat.334
 Description:
DOI: 10.16904/envidat.369
title: Napf soil wetness monitoring data

description: The dataset contains the soil wetness monitoring data from the publication Wicki et al. (2022). It was collected in Wasen i.E. (Napf area, Switzerland).
The monitoring data is quality-controlled and aggregated to hourly values and it is provided for the study period 2019-04-05 to 2022-04-30.
The following information is contained (by column):
* Timestamp (UTC+1 time zone)
* Site: Slope (47.02486 N, 7.81960 E), Flat (47.02302 N, 7.81760 E)
* Sensor type
* Measure: VWC = volumetric water content [m3 m-3], MP = matric potential [hPa], TEMP = temperature [°C], PREC = precipitation [mm]
* Sensor number (per site each sensor is provided a unique identifier)
* Installation depth [m]
* Homogenization flag: If the data is considered homogeneous, it is given the flag 1, else the flag 0 is given
* Sensor value
* Normalized value: Normalization was conducted for VWC (saturation) and MP values
Wicki, A., Lehmann, P., Hauck, C., and Stähli, M.: Impact of topography on in-situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-211, in review, 2022.
description: Resource Title: Napf soil wetness monitoring data
 Size: 88475061 bytes
 Format: application/vnd.ms-excel
 URL: https://www.envidat.ch/dataset/11c8642a-eefe-4a9b-97cf-15313ee82cf8/resource/2bc3cdd4-20c5-47c1-affe-21dd45ca063b/download/soilwetness_napf_wsl_20190401_20210430.csv
 Description: Soil wetness monitoring data (soil moisture, matric potential) and additional time series (air temperature, precipitation, ground temperature).
Quality-controlled and aggregated to hourly values.
DOI: 10.17632/yschyymgck
title: Milkyway Cookies Consumer Buying History Data from November 2023 until 2024

description: Raw data of Milkyway cookies consumer buying history from November 2023 until 2024
DOI: 10.16904/envidat.364
title: An ice oxygen K-edge NEXAFS spectroscopy data set on gas-phase processing

description: Data are compiled that have been used to demonstrate the impact of high water partial pressure on X-ray absorption spectra of ice.
description: Resource Title: 
 Size: 3858 bytes
 Format: application/vnd.ms-excel
 URL: https://www.envidat.ch/dataset/316d5fcf-76f0-4921-807f-2ea1d3e9f7bc/resource/4523fcaa-78bf-456c-8c56-f60196724043/download/datalist.csv
 Description: The table lists all NEXAFS raw data with the following columns:

* name: raw file name
* xRayEkinRange_eV: Smallest and largest X-ray photon energy in eV
* scientaPassEnergy_eV: Pass energy of electron analyzer in eV
* scientaDwellTime_ms: Dwell time of electron analyzer in ms
* regionName: O K-edge for all data
* scientaAcquisitionMode: Fixed energy (snapshot) mode
* scientaEkinRange_eV: Smallest and largest energy of the kinetic energy window used for detection in this work
* scientaEkinStep_eV Not defined in fixed energy mode
* scientaLensMode: Tune table
* scientaRegionIterations: Iterations (sweeps)
* scientaSequenceIterations: Iterations (sweeps)
* sampleTemp_dC: Temperature as monitored at the sample holder in °C
* cellPressure_mbar: Total pressure in measurement cell in mbar
* iceTemp_dC: Temperature of ice sample calculated based on vapour pressure (assuming total pressure equals partial pressure of water) in °C
* smplX_mm: positon of measurement spot, x dimension in mm
* smplY_mm: positon of measurement spot, y dimension in mm
* smplZ_mm: positon of measurement spot, z dimension in mm
* sealingTemp: temperature at base of sample manipulator
* lastModifiedDatestr: time and date when data aquisition was finished
* sample: sample description
description: Resource Title: 
 Size: 434478 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/316d5fcf-76f0-4921-807f-2ea1d3e9f7bc/resource/bb9c5895-b2f0-4451-9869-b869dc1e081e/download/spectraandphotonfluxdata.zip
 Description: Near Ambient Pressure partial Auger-Meitner electron yield X-ray absorption spectra and photon flux data. X-ray energy in eV in column 1, analyzer electron count intensity in arb. units in column 2.
description: Resource Title: 
 Size: 3057 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/316d5fcf-76f0-4921-807f-2ea1d3e9f7bc/resource/e654832d-1c8e-4d9c-a140-e19a29dbebea/download/processeddata.zip
 Description: Processed data as shown in Figure 4 of publication. X-ray energy in eV in column one, electron count intensity in arb. units in column 2.
DOI: 10.16904/envidat.360
title: Implementing the energy transition at municipal level in Switzerland: A regional survey

description: The dataset contains data from a survey, which was conducted in a periurban region close to Berne, Switzerland. The survey was conducted in Fall 2018 and contained opinion questions about the energy transition. Additionally, spatial data was collected using a PPGIS. While the opinion data is included in the data set, the spatial data is not. For more explanation, please consider the information sheet, the related publications or to contact the authors.
description: Resource Title: Surveydata_withoutspatialdata
 Size: 1078613 bytes
 Format: application/vnd.ms-excel
 URL: https://www.envidat.ch/dataset/69a97cec-2062-464c-bd18-565d347e87cf/resource/07932f6b-27b2-4150-86a0-f33bbbfdc717/download/surveydata_withoutspatialdata.csv
 Description:
description: Resource Title: Survey_Information_Abbreviations
 Size: 215077 bytes
 Format: application/pdf
 URL: https://www.envidat.ch/dataset/69a97cec-2062-464c-bd18-565d347e87cf/resource/b110fd73-5c13-4f6c-aec4-95c8887be65b/download/survey_information_abbreviations.pdf
 Description:
DOI: 10.17632/yschyymgck.1
title: Milkyway Cookies Consumer Buying History Data from November 2023 until 2024

description: Raw data of Milkyway cookies consumer buying history from November 2023 until 2024
DOI: 10.16904/envidat.284
title: Potential driving factors of urban transformations of Austin over 25 years

description: In this study, the Austin metropolitan area, Texas, U.S., one of the fastest urban transformations and transformations regions, is selected to test the hypothesis that spatial planning and policies are important factors of urban transformations. Despite ample previous work in understanding the interactions between human and urban form transformation at specific areas, the actual interventions and outcomes of planning and policies (e.g., ‘smart growth’) on urban forms have been poorly measured. In this study, the potential influencing factors of urban transformations of Austin over 25 years were selected and collected.
description: Resource Title: Potential driving factors of urban transformations of Austin over 25 years
 Size: 140698644 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/f2879751-44d7-4039-bc51-843f82adc0ae/resource/82700559-e5c2-4f2f-ba0f-b8ac1ee54abf/download/potential-driving-factors-of-urban-transformations-of-austin-over-25-years.zip
 Description:
DOI: 10.16904/envidat.361
title: 3D_Snow_Models

description: The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel).
description: Resource Title: Description of snowtypes, filenames and other metadata
 Size: 2170683 bytes
 Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
 URL: https://www.envidat.ch/dataset/f5163bdf-657d-43f8-a907-5a87e440f06a/resource/2092babe-4033-4cd3-bacc-857bce65cbb1/download/3d_snow_models_metadata.xlsx
 Description: This file contains information about the stl-files of the different snow models. Snowtype (Schneeart), International Snow Classification, preview picture, notes about the origin of the snow, filename of the stl-data, and physical size of the model are given.
description: Resource Title: Snow models in STL-format
 Size: 382570967 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/f5163bdf-657d-43f8-a907-5a87e440f06a/resource/59fbf858-ae63-4067-9c51-4cd21817e22c/download/3d_snow_stl.zip
 Description: The zipped file contains all data described in the file 3d_snow_models_metadata. The stl-files should be ready for additive manufacturing.