cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8jd5zsf8m9.1
title: Floristic Inventory of Ankasamudra Bird Conservation reserve: A wetland of Kalyana Karnataka, India

description: Ankasamudra Bird Conservation Reserve (ABCR) is located near Hampi & is one of three wetlands from Karnataka that have been added to the list of Ramsar sites on the eve of World Wetland Day 2024 (February 2). Floristic inventory is essential to know the site for large colonially nesting waterbirds.
DOI: 10.16904/envidat.317
title: Observer-driven pseudoturnover in vegetation surveys

description: This dataset was used to analyze the inter-observer error (i.e. pseudoturnover) in vegetation surveys for the publication Boch S, Küchler H, Küchler M, Bedolla A, Ecker KT, Graf UH, Moser T, Holderegger R, Bergamini A (2022) Observer-driven pseudoturnover in vegetation monitoring is context dependent but does not affect ecological inference. Applied Vegetation Science. 
In the framework of the project "Monitoring the effectiveness of habitat conservation in Switzerland", we double-surveyed a total of 224 plots that were marked once in the field and then sampled by two observers independently on the same day. Both observers conducted full vegetation surveys, recording all vascular plant species, their cover, and additional plot information. We then calculated mean ecological indicator values and pseudoturnover.
The excel file contains two sheets: 
1) Raw species lists of the 224 plots conducted by two different observers. Woody species are distinguished in three layers: H (herb layer; woody species -0.5 m in height), S (shrub layer; woody species 0.5–3 m in height) and T (tree layer; woody species -3 m in height). "cf." indicates uncertain identification, "aggr." indicates that the plant was identified only to the aggregate level. Cover was estimated for each species using a modified Braun-Blanquet scale (r ≙ -0.1%, + ≙ 0.1% to -1%, 1 ≙ 1% to -5%, 2 ≙ 5% to -25%, 3 ≙ 25% to -50%, 4 ≙ 50% to -75%, 5 ≙ 75% to -100%).
2) File used for the linear mixed effects model.
description: Resource Title: Boch et al. 2022 Applied Vegetation Science - Raw species list and file used for lmer
 Size: 129048 bytes
 Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
 URL: https://www.envidat.ch/dataset/b641780a-ae67-473c-a4f0-22386a606a72/resource/c5bcb041-6bd7-4cc5-9a05-1e48413d06a1/download/envidat-raw-species-list-and-file-used-for-lmer.xlsx
 Description: The dataset was used for the paper Boch S, Küchler H, Küchler M, Bedolla A, Ecker KT, Graf UH, Moser T, Holderegger R, Bergamini A (2022) Observer-driven pseudoturnover in vegetation monitoring is context dependent but does not affect ecological inference. Applied Vegetation Science.
DOI: 10.16904/envidat.311
title: L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory

description: Dataset from the publication "L-Band Radiometry of Alpine Seasonal Snow Cover: 4 Years at the Davos-Laret Remote Sensing Field Laboratory", under review in IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. volume and issue TBD. 
Dataset specifics are described in the publication.
description: Resource Title: Publications
 Size: 93441479 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/384d588d-06d3-44f2-92a5-cdd49da0ff23/resource/fdbeb1dc-a8b9-4179-aa01-7d7debfce003/download/publications.zip
 Description:
description: Resource Title: Snowpit data from SLF
 Size: 23783 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/384d588d-06d3-44f2-92a5-cdd49da0ff23/resource/a461f8ef-077e-4827-8f2c-533636e6d9df/download/snowpit_c1-c4.zip
 Description: In-situ characterization data from Davos-Laret snowpits.
description: Resource Title: Davos-Laret Data 2016-2017 Winter
 Size: 475560722 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/384d588d-06d3-44f2-92a5-cdd49da0ff23/resource/cfcf69a0-9f71-452c-9487-bc4ef23b65fe/download/c1__winter2016_2017.zip
 Description:
description: Resource Title: Davos-Laret Data 2017-2018 Winter
 Size: 316269368 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/384d588d-06d3-44f2-92a5-cdd49da0ff23/resource/a5104047-a61f-4625-b8f7-9b41c5281852/download/c2__winter2017_2018.zip
 Description:
description: Resource Title: Davos-Laret Data 2018-2019 Winter
 Size: 864734454 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/384d588d-06d3-44f2-92a5-cdd49da0ff23/resource/b407b13d-2883-41cf-8fe3-e30f50eb837c/download/c3__winter2018_2019.zip
 Description:
description: Resource Title: Davos-Laret Data 2019-2020 Winter
 Size: 922489429 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/384d588d-06d3-44f2-92a5-cdd49da0ff23/resource/788fd33f-602d-4c5f-98ef-8ddf20f08fa7/download/c4__winter2019_2020.zip
 Description:
DOI: 10.16904/envidat.315
title: Infestation of Scots pines with different vitalities by bark and wood boring insects

description: After a major dieback of Scots pines in the Valais, an inner Alpine valley in Switzerland, the colonization of differently vigorous pines by stem and branch insects was investigated to assess their role in tree mortality. At 2 locations, the needle loss (defoliation) of some 500 pine trees was assessed twice a year. Of these trees, 34-36 trees were cut each year between 2001-2005 across all defoliation classes. From each tree, two 75-cm bolts were cut from both the stem and thick branches. They were incubated in photo-eclectors (metal cabinets) set up in a greenhouse where the insects could develop under the bark. The emerged adults were collected in water-filled eclector boxes and identified to species level by specialists. Attack time was estimated from the development time of each insect species emerged. The colonisation densities of the trees were related to the transparency level of each host tree at the time of attack.
description: Resource Title: FoeWallis_data
 Size: 21157 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/31238741-1c24-4c18-b454-fecc4f31fb6c/resource/c636b42c-3901-440f-b8ae-420d78b17536/download/foewallis_data.zip
 Description: see ReadMe
DOI: 10.17632/psspn8h5dg
title: Mingzhang Zuo，2025 Central China Normal University students data
DOI: 10.16904/envidat.313
title: Dynamics of insect natural enemies of bark beetles

description: In 1994 a large area of mountain spruce forest was infested by the European spruce bark beetle (Ips typographus) in the Gandberg forest near Schwanden, canton Glarus, Switzerland (46.99145 N, 9.10768 E, 1300 m a.s.l.). In a perimeter of approx. 13 ha, 50 infested dead spruce trees were selected and labelled in 1994. The trees were randomly distributed across the whole perimeter and attributed to 5 groups of 10 trees of approx. 25-40 cm diameter each. In each of the following 5 years (1995-1999), the trees of one of these groups were cut in early spring and transported by helicopter to a vehicle-accessible road. Of each log, two bolts of 1.5 m length were cut, one from the base and one from the beginning of the crown. The bolts were transported by truck to the institute WSL and exposed in emergence eclectors (metal cabinets of approx. 2.0x0.5x0.5 m) in a greenhouse to let the insects emerge. Each tree was left 2 years in the eclectors to allow insects with more than 1 year development time to emerge. During 2 months in the winter between the two exposure years the bolts were removed from the eclectors and exposed to ambient winter temperatures for chilling. They were then moved back to the eclectors in the greenhouse.
Additionally, 18 living unattacked trees were provided with a pheromone lure in early spring 1995 to induce new bark beetle attack. 10 infested trees were then cut and processed as described above.
The water-filled emergence traps of the eclectors were emptied monthly-bimonthly and the insects were separated to taxonomic groups and eventually identified by specialists. Before disposing the logs, tree age was recorded by tree-ring-counting.
description: Resource Title: Data_Antagonists.csv
 Size: 39830 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/a8128568-6978-421e-9abd-1344a2339af0/resource/fb75f405-9823-4dd6-aa73-f79733d0574f/download/antago.zip
 Description: See ReadMe file
DOI: 10.16904/envidat.314
title: Survey of spruce seed and cone insects in Switzerland

description: In 1989 a nation-wide survey on spruce seed and cone insects was carried out at 29 locations distributed across the 5 main geographic regions of Switzerland. The cones were incubated in a controlled environment chamber and the emerging insects were collected and identified. The cones were kept for three years to allow diapausing insects to emerge. The methods are described in more detail in the corresponding publications.
description: Resource Title: FiZa_data.zip
 Size: 6480 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/48458af4-759c-45bc-9954-3c517f5ed392/resource/6aaedef7-e88a-47e8-8cf8-073b9509f495/download/fiza_data.zip
 Description: See ReadMe
DOI: 10.17632/psspn8h5dg.1
title: Mingzhang Zuo，2025 Central China Normal University students data
DOI: 10.16904/envidat.122
title: Modeling snow failure with DEM

description: This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.
description: Resource Title: Read-me
 Size: 174785 bytes
 Format: application/pdf
 URL: https://www.envidat.ch/dataset/a3ace234-0794-4b75-bf17-3790744ed665/resource/8142e5f2-74cd-49ae-b64f-87d9600b8b53/download/read_me.pdf
 Description: The read-me file contains a description of all the data provided in the zip file.
description: Resource Title: Dataset
 Size: 268629 bytes
 Format: application/zip
 URL: https://www.envidat.ch/dataset/a3ace234-0794-4b75-bf17-3790744ed665/resource/73b649d7-b035-4815-b97a-bd8ecdf97a27/download/data.zip
 Description: The zip file contains the data underlying the figures presented in Bobillier et al. (2019). The figures can be reproduced with the python file, which is also provided.
description: Resource Title: Python file
 Size: 32003 bytes
 Format: text/x-python
 URL: https://www.envidat.ch/dataset/a3ace234-0794-4b75-bf17-3790744ed665/resource/bcd1cf92-6c73-4bf4-83fb-96b8fc7c93b9/download/plot_figures.py
 Description: Python file to reproduce the figures in Bobillier et al. (2019)
DOI: 10.16904/envidat.306
title: LiDAR metrics predict suitable forest foraging areas of endangered Mouse-eared bats (Myotis myotis)

description: Habitat shift caused by human impact on vegetation structure poses a great threat to species which are special- ized on unique habitats. Single layered beech forests, the main foraging habitat of Greater Mouse-eared Bats (My- otis myotis), are threatened by recent changes in forest structure. After this species suffered considerable popula- tion losses until the 1970s, their roosts in buildings are strictly protected. However, some populations are still de- clining. Thus, the spatial identification of suitable foraging habitat would be essential to ensure conservation pol- icy. The aim of this study was (a) to verify the relevance of forest structural variables for the activity of M. myotis and (b) to evaluate the potential of LiDAR (Light Detection and Ranging) in predicting suitable foraging habitat of the species. We systematically sampled bat activity in forests close to 18 maternity roosts in Switzerland and applied a generalized linear mixed model (GLMM) to fit the activity data to forest structure variables recorded in the field and derived from LiDAR. We found that suitable forest foraging habitat is defined by single layered for- est, dense canopy, no shrub layer and a free flight space. Most importantly, this key foraging habitat can be well predicted by airborne LiDAR data. This allows for the first time to create nationwide prediction maps of potential foraging habitats of this species to inform conservation management. This method has a special significance for endangered species with large spatial use, whose key resources are hard to identify and widely distributed across the landscape.
description: Resource Title: Fielddata_2020-02-14_Guilds.csv
 Size: 241361 bytes
 Format: application/vnd.ms-excel
 URL: https://www.envidat.ch/dataset/b64e697c-bef8-455f-a2ca-96e269fdfd1c/resource/8b121238-5319-4a48-ad15-a0382e8e37f8/download/fielddata_2020-02-14_guilds.csv
 Description: Complete set of field data and LiDAR data including bat activity measurements
description: Resource Title: Variable description_20220411.csv
 Size: 3254 bytes
 Format: application/vnd.ms-excel
 URL: https://www.envidat.ch/dataset/b64e697c-bef8-455f-a2ca-96e269fdfd1c/resource/121b2a4d-d2a0-416e-bd23-3a744cb57b1b/download/variable-description_20220411.csv
 Description: Description of all variables given in the file 'Felddaten_2020-02-14_Guilde.csv'
description: Resource Title: R_Script_Publication_20220405.R
 Size: 27971 bytes
 Format: .r
 URL: https://www.envidat.ch/dataset/b64e697c-bef8-455f-a2ca-96e269fdfd1c/resource/50bc0f40-a3ab-49d6-82bb-35ffe03d387f/download/r_script_publication_20220405.r
 Description: R scripts for all analyses
DOI: 10.16904/envidat.308
title: Species distribution maps of Fagales and Pinales (GDPlants)

description: This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022). 
To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details).
description: Resource Title: Species range maps of species in Fagales and Pinales (with enough occurrences)
 Size: 37517772 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/aee8c2bd-ca0b-4de0-a41f-940dc11984e6/resource/6c946cd2-e08b-44e0-8fda-4ab01b6b43b9/download/species_maps.zip
 Description: species_map.zip includes species 1690 range maps generated based on the integrated method described in Lyu et al., (2022). File names with (polygon) indicate that this map is only generated by polygon mapping, as there are not enough occurrences for SDMs.
description: Resource Title: Occurrence maps for species of Fagales and Pinales with few records
 Size: 32315283 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/aee8c2bd-ca0b-4de0-a41f-940dc11984e6/resource/9239265c-dba9-413d-9fc6-40beb3cb7368/download/point_buffers.zip
 Description: Point_buffer.zip includes 267 occurrence maps of species that do not have enough occurrence data for polygon mapping.
DOI: 10.16904/envidat.134
title: Snow avalanche data Davos, Switzerland, 1999-2019

description: These data include all avalanches that were mapped in the region of Davos, Switzerland during the winters 1998-1999 to 2018-2019 (21 years), in total 13,918 avalanches, and the corresponding forecast danger level valid on the day of avalanche occurrence, 3533 days and danger ratings in total.
This avalanche activity data set was analysed and results published by Schweizer et al. (2020). They found that the 
number of avalanches per day strongly increased with increasing danger level, but avalanche size was poorly related to avalanche danger level.
The data are provided in two files: the first includes the avalanche data (13,918 records); the second includes the avalanche activity per day (3533 records).
Please refer to the Read-me file for further details on the data.
These data are the basis of the following publication:
Schweizer, J., Mitterer, C., Techel, F., Stoffel, A. and Reuter, B., 2020. On the relation between avalanche occurrence and avalanche danger level. The Cryosphere, 14, 737-750, https://doi.org/10.5194/tc-14-737-2020.
description: Resource Title: Readme
 Size: 159745 bytes
 Format: application/pdf
 URL: https://www.envidat.ch/dataset/ce11efbe-4dac-4ff5-9a3d-f01e2c573292/resource/f8d6835f-3ef6-4fe6-94a2-6b7f7b5827ce/download/read_me.pdf
 Description: The Readme file contains the description of the two data files.
description: Resource Title: Snow avalanche data
 Size: 936455 bytes
 Format: text/csv
 URL: https://www.envidat.ch/dataset/ce11efbe-4dac-4ff5-9a3d-f01e2c573292/resource/4c2b7c38-a874-45fc-9833-fdf83823067b/download/data_set_1_avalanche_observations_wi9899_to_wi1819_davos.csv
 Description: This data file includes data on the avalanches mapped in the region of Davos.
description: Resource Title: Avalanche activity data
 Size: 929518 bytes
 Format: text/csv
 URL: https://www.envidat.ch/dataset/ce11efbe-4dac-4ff5-9a3d-f01e2c573292/resource/39e6dd9e-9b52-439e-a4d1-5e17a8e18eca/download/data_set_2_danger_avalanches.csv
 Description: This data file includes data on the avalanche activity per day aggregated from the avalanche data.
DOI: 10.16904/envidat.307
title: Napf ERT monitoring data

description: The dataset contains the electrical resistivity tomography (ERT) monitoring data from the publication Wicki and Hauck (2022). It contains the unprocessed monitoring data and the filtered monitoring data prior to the inversion process.
The data is organized in two zip-files:
* Napf_Raw_BIN.zip: Raw monitoring data in bin-format
* Napf_Filtered_DAT.zip: Filtered monitoring data in dat-format including topography of the monitoring line
The zip files contain the apparent resistivity measurements (ohm m) of the individual measurements. The naming convention of the files is according to following convention: site_profile_configuration_date_time.format
The file names contain following abbreviations:
* Site: Napf 
* Profile: Hor (horizontal profile), Ver (vertical profile)
* Configuration: WS (Wenner-Schlumberger configuration)
* Date: Format YYYY-MM-DD
* Time: Format hhmm
description: Resource Title: ERT raw monitoring data
 Size: 6664841 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/370d354c-e662-4fc2-8d1d-f46755cbed33/resource/93a0c6a6-d2f6-441b-ad7c-efedb19a7583/download/napf_raw_bin.zip
 Description: ERT raw monitoring data in bin format
description: Resource Title: ERT filtered monitoring data
 Size: 2820513 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/370d354c-e662-4fc2-8d1d-f46755cbed33/resource/52ece757-ff8f-4fe9-8405-0c077be5f3d9/download/napf_filtered_dat.zip
 Description: ERT filtered monitoring data in dat format
DOI: 10.16904/envidat.240
title: Source code for: Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

description: This is the source code to compute rainfall thresholds for debris flows or landslides following Hirschberg et al. (2021).
 How to install and run the example
Pyhton has to be installed to run the codes. To make sure it works correctly, it is easiest to install Anaconda and create an environment with the right packages from the yml-file. To this end, in a command-line interpreter, change the working directory to where you saved this project and run the following:
`$ conda env create -f environment.yml`
`$ conda activate thresholds` or `$ source activate thresholds`
To run an example:
`$ python run_example()`
It will save a dat-file and a figure as Fig. 7 in Hirschberg et al. (2021), where more information can be found.
 License
GNU General Public License v3.0
description: Resource Title: Threshold code and example data
 Size: 19390 bytes
 Format: application/x-zip-compressed
 URL: https://www.envidat.ch/dataset/6fe790e2-d008-4dd5-a7c8-2a7849d05596/resource/9fc14677-b6c7-410f-8943-4771b7410c9e/download/threshold_example.zip
 Description: .zip file with code and example data. See the README.md for how to use it.
DOI: 10.16904/envidat.275
title: Planning intentions in strategic plans of European urban regions

description: The present dataset is part of the report titled Gradinaru S.R., Hersperger A.M., Schmid F. (2021). Deriving Planning Intentions from written planning documents. Report on CONCUR Project- From plans to land change: how strategic spatial planning contributes to the development of urban regions. 
The data corresponds to the data collected as part of the DPI Method for deriving all PIs contained in a plan (open coding) as detailed in section 4 of the report. The method involved reading the plans to break down of information in meaningful discrete “incidents” or planning intentions. To identify the planning intentions, the starting points were represented by a) the structuring of the plans in chapters and sub chapters and b) the themes that the plans addressed. Thus, the collected information was not grouped according to pre-defined categories of planning intentions, but rather put together as a list of intentions as revealed by each plan. As a result, we provide, for each case study, a document (named [Urban region name] PI as defined in the plan) which contains:
 Date when the information was filled in.
 Name of the urban region and analysed strategic spatial plan .
 A list of all planning intentions contained in a plan, with each PI being addresses as follows:
 Name of PI as it appears in the plan
 Translated name of the PI (i.e. short name for easy understanding of the meaning)
 Explanation regarding the meaning of the PI
 Why the PI is considered a priority for the urban region
 Spatial information on the PI (text and cartographic representations).
In total, 14 documents are available, one for each case study. Documents contain up to 20 pages of information extracted from the plans together with explanations and notes taken during plan reading.
description: Resource Title: Planning intentios
 Size: 30037608 bytes
 Format: application/x-compressed
 URL: https://www.envidat.ch/dataset/60c8c0cd-ab8b-4261-9877-5da75717f355/resource/382c572e-6ef6-4c8d-865e-78ec63040d3b/download/planning_intentions.7z
 Description: In total, 14 documents are available, one for each case study. Documents contain up to 20 pages of information extracted from the plans together with explanations and notes taken during plan reading.
DOI: 10.15468/dl.t9auqs
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-5.22687 51.72106,-5.22287 51.72343,-5.21929 51.72252,-5.21598 51.7228,-5.21183 51.72162,-5.20854 51.72023,-5.20651 51.71905,-5.2046 51.71809,-5.20449 51.71705,-5.20252 51.71555,-5.20168 51.71443,-5.19902 51.71489,-5.1998 51.71729,-5.20096 51.71905,-5.20296 51.71964,-5.20407 51.72058,-5.20762 51.7221,-5.21001 51.72299,-5.21069 51.724,-5.21269 51.72509,-5.21451 51.72569,-5.2172 51.72478,-5.21786 51.72478,-5.21896 51.72437,-5.22036 51.72461,-5.22132 51.725,-5.22239 51.72484,-5.22382 51.72461,-5.22532 51.72417,-5.22651 51.7236,-5.22714 51.7233,-5.22732 51.72252,-5.22797 51.72228,-5.22839 51.72105,-5.22687 51.72106)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi",
 "Year 2015-2025"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057302-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.