cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.16904/slf.2
title: Snow and the water cycle in a changing climate

description: This report was prepared as one of the synthesis report chapters of the Hydro-CH2018 project of the Federal Office for the Environment (FOEN). An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is nothing other than a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1% from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months.
Due to the great dependence on minus temperatures and precipitation, the snow cover reacts sensitively to temperatures above 0° Celsius and more or less precipitation. Due to climate change and the associated warming, the proportion of precipitation that falls as snow decreases measurably. In addition to this reduction in snowfall, the warmer temperatures also cause the snow cover to melt more quickly. The decline in snowfall has so far mainly affected lower altitudes, where winter temperatures often reach positive levels. 
As climate change progresses, this trend is likely to continue and above all affect higher zones. Even at higher altitudes, the snow cover will then start later, melt away earlier and is increasingly no longer permanently present. This development will also have an effect on the water bodies. Today nival regimes, i.e. regimes shaped by snow, are shifting towards pluvial regimes, i.e. regimes dominated by rain. Overall, winter runoff increases, summer runoff decreases. By the end of the century, the proportion of runoff from snowmelt will decrease throughout Switzerland, albeit to a lesser extent than the proportion from glacier melt.
description: Resource Title: Synthesis report chapter
 Size: 1271280 bytes
 Format: application/pdf
 URL: https://www.envidat.ch/dataset/b9bc50a3-b45a-4afb-8468-5ad7feda33d8/resource/d3f00a2f-f06a-463d-975a-a05ce0036bfa/download/snow-hydro-ch2018-synthesis-report-chapters-future-changes-in-hydrology.pdf
 Description:
description: Resource Title: DORA Persistent URL
 Size: 1 bytes
 Format: No Info
 URL: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:23389
 Description:
DOI: 10.16904/15
title: GCOS SWE data from 11 stations in Switzerland

description: This dataset contains long-term snow water equivalent and corresponding snow depth data 11 observer sites in Switzerland between 1200 and 2500 m a.s.l. compiled for the Global Climate Observing System (GCOS) and supported by MeteoSwiss. Snow depth (cm) and snow water equivalent (mm) are manually recorded every 2 weeks since the 1947 (depending on station). The attached metadata file gives details for each station.
The measurement series agree with GCOS objectives according to the GCOS Implementation Plan: This inlcudes:
• Raw data are archived in the snow and avalanche database at SLF.
• Measuring techniques are traceable and documented as snow depth and snow water equivalent have in general remained the same since beginning up to now. When planning new systems or changes of existing systems in the future, their impact will be assessed prior to implementation.
• Historical data of these 11 stations have been digitized and all data have been quality controlled. 
• Detailed metadata (location of measurements) are available.
• Public availability of the data has been ensured by publishing the data on the Envidat portal (https://www.envidat.ch/dataset/gcos-swe-data).
description: Resource Title: STATION METADATA
 Size: 8049 bytes
 Format: .txt
 URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/15/GCOS/metadata.txt
 Description:
description: Resource Title: DATA ACCESS
 Size: 343040 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?prefix=doi/15/GCOS/
 Description:
DOI: 10.16904/17
title: IRKIS Soil moisture measurements Davos

description: Meteorological and soil moisture measurements from soil moisture stations installed from October 2010 - October 2013 in the area surrounding Davos, in particular in the Dischma catchment.
There are in total 7 stations: 1202, 1203, 1204, 1205, 222, 333 and SLF2. For each of the stations, there is a:
* vwc_[stn].smet: containing the soil moisture measurements
* station_[stn].smet: in-situ measured meteorlogical parameters. Note, the quality of these measurements for stations 1202, 1203, 1204 and 1205 is very low, with data gaps. Use this data with care. For stations 222, 333 and SLF2, data quality is high and only the default cautiousness should be applied.
* interpolatedmeteo_[stn].smet contains per stations a dataset derived by interpolating from several stations in the Davos area to the stations location. This dataset was generated from the output of the Alpine3D model, of which simulations are presented in the Wever et al. (2017) manuscript.
At the soil moisture measurement sites, Decagon 10HS sensors were installed, at 10, 30, 50, 80 and 120 cm depth. Per depth 2 sensors were installed, labelled A and B in the datafiles. Note that at stations 1203, 1204 and 1205, sensors were only installed at 10, 30 and 50 cm depth.
The files follow the SMET format: https://models.slf.ch/docserver/meteoio/SMET_specifications.pdf and metadata for the stations can be found in the header of the smet files.
Please cite the Wever et al. (2017) reference when using this data in publications.
For a more detailed description, please refer to: Wever, N., Comola, F., Bavay, M., and Lehning, M.: Simulating the influence of snow surface processes on soil moisture dynamics and streamflow generation in an alpine catchment, Hydrol. Earth Syst. Sci., 21, 4053-4071, https://doi.org/10.5194/hess-21-4053-2017, 2017.
description: Resource Title: IRKIS soil moisture data (zip file)
 Size: 82313216 bytes
 Format: .zip
 URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/17/irkis/IRKISsoilmoisturedata.zip
 Description: Compressed archive (zip) containing soil moisture measurements, as well as meteorological measurements and interpolated meteorological time series.
DOI: 10.16904/envidat.113
title: Snow Drift Station - Micro Rain Radar

description: The instrument (MRR, Metek) was mounted at Gotschnagrat (LON: 46.859 LAT: 9.849) at a height of 1 m above snow surface (at the beginning of the campaign) with an orientation of 22° with respect to North and a horizontal viewing direction. The sampling time was either 5 s or 10 s, depending on the settings at the specific period. The MRR produces standard outputs like radar reflectivity, doppler velocity, etc., and additional information can be found [here](https://metek.de/de/product/mrr-2/).
description: Resource Title: Parameter MRR
 Size: 93093 bytes
 Format: application/pdf
 URL: https://www.envidat.ch/dataset/898d94a9-7a48-4fdc-9eda-43f536e39285/resource/cc39db24-81eb-4110-8a71-d7b04d5f56e6/download/mrr_parameters.pdf
 Description: The PDF contains additional information on parameters which were set for the certain time periods (time in UTC).
description: Resource Title: MMR Data Period 1
 Size: 2576980378 bytes
 Format: .zip
 URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.113/raclets/Period1.zip
 Description: The dataset contains measurements between March 1 and March 5, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.
description: Resource Title: MRR data Period 2
 Size: 720723546 bytes
 Format: .zip
 URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.113/raclets/Period2.zip
 Description: The dataset contains measurements between March 5 and March 8, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.
description: Resource Title: MRR data of period 3
 Size: 340845550 bytes
 Format: .zip
 URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.113/raclets/Period3.zip
 Description: The dataset contains measurements between March 8 and March 13, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.
description: Resource Title: MRR data of period 4
 Size: 517071441 bytes
 Format: .zip
 URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.113/raclets/Period4.zip
 Description: The dataset contains measurements between March 13 and March 22, 2019, with time in UTC. The parameters and specifications are given in MRR_parameters.pdf. The zip folder contains hourly data files in netCDF format and a log file, reporting the instrument status.
DOI: 10.16904/21
title: Wind crust formation: SnowMicroPen data

description: This dataset contains the SnowMicroPen (SMP) data from 38 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winters 2015/16 and 2016/17 and include more than 1000 SMP measurements. 
The SMPs are organized per experiment. Each experiment subfolder contains the processed SMP profiles and some additional files.
Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.
This dataset is the basis of the following publication:
Sommer C.G., Lehning M., & Fierz C. (2017). Wind tunnel experiments: Saltation is necessary for wind-packing. Journal of Glaciology, 63(242), 950-958. doi:10.1017/jog.2017.53
description: Resource Title: SMP data
 Size: 536870912 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?prefix=doi/21/windCurstFormation_smpData/
 Description:
description: Resource Title: README
 Size: 2701 bytes
 Format: text/plain
 URL: https://www.envidat.ch/dataset/9918d77c-3176-453f-b34c-062ba979144a/resource/551fe7aa-af53-44a5-8d6f-615b5891a696/download/readme.txt
 Description: Describes the dataset in detail
description: Resource Title: Processing scripts
 Size: 12976 bytes
 Format: application/zip
 URL: https://www.envidat.ch/dataset/9918d77c-3176-453f-b34c-062ba979144a/resource/f4d23fd3-364b-4574-bc54-5fc07aae4d35/download/processingscripts.zip
 Description: Contains the R-scripts used to process the SMPs
DOI: 10.16904/envidat.30
title: Expedition to Princess Elisabeth Antarctica Station, 2016/2017

description: This dataset contains the data acquired during the expedition to Princess Elisabeth Antarctica Station in December 2016 and January 2017. The dataset consits of meterorological data, drifting snow mass flux data, SnowMicroPen data and Terrestrial Laser Scanning data.
Please refer to the README for more information about the data.
This dataset is the basis of the following publication:
Sommer, C. G., Wever, N., Fierz, C., and Lehning, M.: Wind-packing of snow in Antarctica, The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-36, in review, 2018.
description: Resource Title: Dataset
 Size: 697932186 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?prefix=doi/envidat.30/princessElisabethStation_2016_2017/
 Description:
description: Resource Title: README
 Size: 3534 bytes
 Format: text/plain
 URL: https://www.envidat.ch/dataset/b5c9438a-83c3-4afb-8f37-b3ec18d3db6e/resource/f5b4224d-6a68-4090-b0d6-24234f698bb5/download/readme.txt
 Description: Describes the dataset in detail
DOI: 10.16904/22
title: Wind crust formation: Microsoft Kinect data

description: This data sets contains the Microsoft Kinect data from 15 wind tunnel experiments on wind-packing / wind crust formation. These experiments were performed in the winter 2016/17. The Kinect measures distributed snow depth.
The Kinect data is organized per experiment. Each experiment subfolder contains the processed Kinect depth images and some additional files.
Please refer to the README for more details on the data. The processing scripts are available for download as well. The scripts are mainly provided as documentation and would need to be adjusted to be used.
This dataset is the basis of the following publication:
Sommer C.G., Lehning M. & Fierz C. (2018). Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow. Front. Earth Sci. 6:4. doi: 10.3389/feart.2018.00004
description: Resource Title: Kinect data
 Size: 3221225472 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?prefix=doi/22/windCrustFormation_kinectData/
 Description:
description: Resource Title: README
 Size: 967 bytes
 Format: text/plain
 URL: https://www.envidat.ch/dataset/d57b3332-804d-463b-bbf8-0ed38593e180/resource/76c0e984-004d-4524-9f2f-30005a955d83/download/readme.txt
 Description: Describes the dataset in detail
description: Resource Title: Processing scripts
 Size: 35366 bytes
 Format: application/zip
 URL: https://www.envidat.ch/dataset/d57b3332-804d-463b-bbf8-0ed38593e180/resource/28bfe352-df8a-423e-ae2c-d938b0c2a4c1/download/processingscripts.zip
 Description: Contains the Matlab- and R-scripts used to process the Kinect data
DOI: 10.5281/zenodo.17561470
title: Underwater images collected by a Kite surf in Le-Morne-Manawa, Mauritius - 2019-09-14

description: This dataset was collected by a Kite surf in Le-Morne-Manawa, Mauritius - 2019-09-14. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.15468/dl.6pt9vm
title: Occurrence Download

description: A dataset containing 2851213865 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 2851213865 records from 57534 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023391-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/iw73uu/8ershj
title: KPC_U_hour.nc

description: Uploaded with pyDataverse 2025-06-01 14:06
DOI: 10.22008/fk2/iw73uu/yfagaw
title: KPC_U_month.csv

description: Uploaded with pyDataverse 2025-06-01 14:06
DOI: 10.22008/fk2/iw73uu/jlj7ul
title: KPC_U_day.csv

description: Uploaded with pyDataverse 2025-06-01 14:06
DOI: 10.22008/fk2/iw73uu/ocpk6b
title: RED_L_month.nc

description: Uploaded with pyDataverse 2025-06-01 14:07
DOI: 10.22008/fk2/iw73uu/unzbk8
title: RED_L_hour.csv

description: Uploaded with pyDataverse 2025-06-01 14:07
DOI: 10.22008/fk2/iw73uu/igdiyp
title: RED_L_month.csv

description: Uploaded with pyDataverse 2025-06-01 14:07
DOI: 10.22008/fk2/iw73uu/571sro
title: RED_L_hour.nc

description: Uploaded with pyDataverse 2025-06-01 14:08
DOI: 10.22008/fk2/iw73uu/im8ohg
title: RED_L_day.nc

description: Uploaded with pyDataverse 2025-06-01 14:08
DOI: 10.22008/fk2/iw73uu/nbr90o
title: RED_L_day.csv

description: Uploaded with pyDataverse 2025-06-01 14:08
DOI: 10.22008/fk2/iw73uu/azyavy
title: TAS_A_month.nc

description: Uploaded with pyDataverse 2025-06-01 14:08
DOI: 10.5281/zenodo.18673939
title: ON THE CHARACTERISTICS OF CARDIOVASCULAR DISEASES IN PATIENTS WITH PULMONARY TUBERCULOSIS WITH VARIOUS CLINICAL PROGRAMS

description: Currently, due to the deteriorating epidemiological situation for tuberculosis, patients with tuberculosis combined with cardiovascular pathology, both as a primary manifestation of the disease and as a result of pulmonary tuberculosis, deserve special attention. The cardiovascular system is well known to be highly sensitive to tuberculosis intoxication, leading to impaired automatism, myocardial excitability, and vasomotor disturbances in the pulmonary and systemic circulations.
DOI: 10.22008/fk2/iw73uu/wiwoq2
title: TAS_A_month.csv

description: Uploaded with pyDataverse 2025-06-01 14:09
DOI: 10.5281/zenodo.18673938
title: ON THE CHARACTERISTICS OF CARDIOVASCULAR DISEASES IN PATIENTS WITH PULMONARY TUBERCULOSIS WITH VARIOUS CLINICAL PROGRAMS

description: Currently, due to the deteriorating epidemiological situation for tuberculosis, patients with tuberculosis combined with cardiovascular pathology, both as a primary manifestation of the disease and as a result of pulmonary tuberculosis, deserve special attention. The cardiovascular system is well known to be highly sensitive to tuberculosis intoxication, leading to impaired automatism, myocardial excitability, and vasomotor disturbances in the pulmonary and systemic circulations.
DOI: 10.22008/fk2/iw73uu/ydprar
title: TAS_A_day.nc

description: Uploaded with pyDataverse 2025-06-01 14:09
DOI: 10.22008/fk2/iw73uu/fhsxj1
title: TAS_A_hour.csv

description: Uploaded with pyDataverse 2025-06-01 14:09
DOI: 10.22008/fk2/iw73uu/hdym36
title: TAS_A_hour.nc

description: Uploaded with pyDataverse 2025-06-01 14:10
DOI: 10.22008/fk2/iw73uu/k7nbcx
title: TAS_A_day.csv

description: Uploaded with pyDataverse 2025-06-01 14:10
DOI: 10.22008/fk2/iw73uu/8x2aua
title: NUK_N_hour.nc

description: Uploaded with pyDataverse 2025-06-01 14:10
DOI: 10.22008/fk2/iw73uu/6uaieo
title: NUK_N_day.csv

description: Uploaded with pyDataverse 2025-06-01 14:11
DOI: 10.15468/dl.buvqqh
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Atriplex portulacoides)",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025058-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/iw73uu/ryjh4n
title: NUK_N_hour.csv

description: Uploaded with pyDataverse 2025-06-01 14:11
DOI: 10.22008/fk2/iw73uu/lkehn4
title: NUK_N_month.csv

description: Uploaded with pyDataverse 2025-06-01 14:11
DOI: 10.5281/zenodo.18673967
title: Dataset: Air Quality Dataset for Qingdao (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Qingdao covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Qingdao, compare Qingdao with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.22008/fk2/iw73uu/ii0gm5
title: NUK_N_day.nc

description: Uploaded with pyDataverse 2025-06-01 14:11
DOI: 10.22008/fk2/iw73uu/mzjqpr
title: NUK_N_month.nc

description: Uploaded with pyDataverse 2025-06-01 14:12
DOI: 10.5281/zenodo.18673966
title: Dataset: Air Quality Dataset for Qingdao (2022-08-01 to 2026-02-18)

description: This dataset contains historical air quality time series for Qingdao covering 2022-08-01 to 2026-02-18. It includes key air pollution indicators such as AQI indices and major pollutants, plus city metadata including coordinates, administrative areas, and population. Use this dataset to analyze long-run air quality trends in Qingdao, compare Qingdao with other cities, and build models that link air quality to urban characteristics. Air quality values are retrieved from the Open-Meteo Air Quality service using the cams_global domain, and city metadata is derived from the GeoNames city list. Nitiraj Kulkarni
DOI: 10.22008/fk2/iw73uu/jp9qtk
title: KAN_M_day.nc

description: Uploaded with pyDataverse 2025-06-01 14:12
DOI: 10.22008/fk2/iw73uu/75wwjt
title: KAN_M_hour.csv

description: Uploaded with pyDataverse 2025-06-01 14:12