cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25574/31872
title: Chandra X-ray Observatory ObsId 31872

description: A detailed view of the rare large scale sloshing cold front in the outskirts of Abell 85
DOI: 10.34740/kaggle/dsv/13070972
title: Booru.org Top Leaderboard
DOI: 10.5281/zenodo.11288787
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Netta Elizabeth Gray, http://www.wikidata.org/entity/Q17330665. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q17330665
DOI: 10.5281/zenodo.17128022
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Vasco M. Tanner, http://www.wikidata.org/entity/Q20675737. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q20675737
DOI: 10.5281/zenodo.17128023
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Netta Elizabeth Gray, http://www.wikidata.org/entity/Q17330665. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q17330665
DOI: 10.5281/zenodo.17128024
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Moritz Goldschmidt, http://www.wikidata.org/entity/Q1947922. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q1947922
DOI: 10.16904/envidat.151
title: WFJ_RHOSSA: Multi-instrument stratigraphy data for the seasonal evolution of an alpine snowpack

description: The WFJ_RHOSSA dataset contains multi-instrument, multi-resolution snow stratigraphy measurements for the seasonal evolution of the snowpack from the Weissfluhjoch research site, Davos, Switzerland.
The measurements were initiated during the RHOSSA field campaign conducted in the winter season 2015–2016 with a focus on density (RHO) and specific surface area (SSA) measurements.
The Instruments and methods used in the campaign at different spatial and temporal resolution are: SnowMicroPen, Density Cutter, IceCube, Traditional profiles, Stability tests and X-ray tomography. The measurements are complemented by simulation data from the model SNOWPACK.
description: Resource Title: wfj_rhossa_data
 Size: 427819008 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?prefix=doi/envidat.151/2020_rhossa_paper_calonne_tc
 Description: Link to data
description: Resource Title: README
 Size: 827 bytes
 Format: No Info
 URL: https://www.envidat.ch/dataset/eca24fe5-ed00-4ff6-9482-414ff98161d1/resource/312e0c4c-e5c6-447b-8c39-5fd9c7ba4adc/download/readme-2
 Description: Top level README file explaining the organization of the data
DOI: 10.5281/zenodo.8233945
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Abel Joel Grout, http://www.wikidata.org/entity/Q21338717. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21338717
DOI: 10.5281/zenodo.17128025
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Abel Joel Grout, http://www.wikidata.org/entity/Q21338717. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21338717
DOI: 10.16904/envidat.305
title: Meteorological data for investigation of rain-on-snow events in 58 catchments in Switzerland

description: Meteorological data used to run SNOWPACK for 58 catchments in the Swiss Alps. The data consists of a 2 km grid of "virtual meteorological stations" for each catchment. It was used to simulate snow cover processes during rain-on-snow events, therefore meteorological data of each catchment contains at least one rain-on-snow event. Further information can be found in the attached readme.txt and in Würzer & Jonas et al. (2017), currently under review in Hydrological Processes.
description: Resource Title: 
 Size: 1534677078 bytes
 Format: .zip
 URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.305/ros_data/ros_data.zip
 Description: The file contains all data needed to run simulations with the snow cover model SNOWPACK.
description: Resource Title: 
 Size: 768 bytes
 Format: .txt
 URL: https://os.zhdk.cloud.switch.ch/envicloud/doi/envidat.305/ros_data/readme.txt
 Description: Readme.txt
DOI: 10.5281/zenodo.17128026
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Robert L. Minckley, https://orcid.org/0000-0002-1217-7693. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.16904/envidat.211
title: CHELSA-TraCE21k: Downscaled transient temperature and precipitation data since the last glacial maximum

description: High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm. 
The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.
description: Resource Title: Data Access
 Size: 3848290697216 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fchelsav1%2F&prefix=chelsa_trace%2F
 Description: High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm.

The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.
description: Resource Title: Technical documentation 
 Size: 478853 bytes
 Format: application/pdf
 URL: https://www.envidat.ch/dataset/c88baa58-dbae-452c-8c2c-343e4c9fb885/resource/613fcb27-3649-4b5f-8880-984403cccd62/download/chelsa-trace21k_technical_documentation.pdf
 Description: Technical documentation containing file metadata and variable descriptions.
DOI: 10.5281/zenodo.8316598
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Wanda Elaine Triplehorn, http://www.wikidata.org/entity/Q113640300. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q113640300
DOI: 10.5281/zenodo.17128027
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Wanda Elaine Triplehorn, http://www.wikidata.org/entity/Q113640300. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q113640300
DOI: 10.5281/zenodo.17128028
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Eduard Wenck, http://www.wikidata.org/entity/Q130386109. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q130386109
DOI: 10.5281/zenodo.15805672
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Eduard Wenck, http://www.wikidata.org/entity/Q130386109. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q130386109
DOI: 10.16904/envidat.77
title: SPOT6 Avalanche outlines 24 January 2018

description: Outlines of 18'737 avalanches mapped from SPOT6 satellite data over the Swiss Alps on 24 January 2018. The outlines have different attributes described in the data example key (ExampleKey_AvalMapping.pdf)
The generation of the data is described in:
Bühler, Y., E. D. Hafner, B. Zweifel, M. Zesiger, and H. Heisig (2019), Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps, The Cryosphere, 13(12), 3225-3238, doi:10.5194/tc-13-3225-2019.
Abstract. 
Accurate and timely information on avalanche occurrence are key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability considering accuracy, completeness and reliability of the reported avalanche events is limited. In this study we present the spatial continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12’500 km2). 
We tested different satellite sensors available for rapid mapping during a first avalanche period. Based on these experiences, we tasked SPOT6/7 data for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18’737 individual avalanche events, applying image enhancement techniques to analyze regions in cast shadow as well as brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having a unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period. 
This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data is of great value to validate avalanche bulletins, complete existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.
Koordinate System: CH1903+ LV95 LN02
description: Resource Title: SPOT6 Avalanche outlines 24 January 2018
 Size: 157117977 bytes
 Format: application/zip
 URL: https://www.envidat.ch/dataset/fa4adf13-d0e5-4479-9b46-cbb07233999f/resource/309d5260-f13e-4fd1-881e-8968c829941b/download/aval_outlines2018.zip
 Description: Shapefile of the manually mapped avalanche outlines. Please only use this data together with the example key document provided with this dataset.
DOI: 10.5281/zenodo.8121725
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Charles A. Triplehorn, http://www.wikidata.org/entity/Q32287694. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q32287694
DOI: 10.5281/zenodo.17128029
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Charles A. Triplehorn, http://www.wikidata.org/entity/Q32287694. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q32287694
DOI: 10.16904/envidat.568
title: A high-resolution snow and hydrometeorological dataset for the Dischma region in Switzerland

description: This repository contains data from the alpine Dischma watershed and its surroundings in eastern Switzerland and includes: (a) hourly hydrometeorological data with 100m spatial resolution, (b) remotely sensed snow depth data with 100m spatial resolution obtained at peak of winter, and (c) station measurements of variables such as snow depth and catchment runoff.
This dataset is particularly suited for (a) different simulation experiments using distributed and process-based models, including physics-based snow and hydrological models, (b) testing various snow data assimilation schemes, and (c) developing models representing snow-forest interactions.
The dataset is described in detail in:
Magnusson, J., Bühler, Y., Quéno, L., Cluzet, B., Mazzotti, G., Webster, C., Mott, R., and Jonas, T.: High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-374, in review, 2024.
This dataset was published within the INARCH project:
https://inarch.usask.ca/
description: Resource Title: Dischma dataset - v20241219 and v20250821
 Size: 37580963840 bytes
 Format: No Info
 URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.568%2F
 Description: The inarch_dischma_20241219.zip file contains data for the period from 2016 to 2023, as described in the publication linked to this repository. The inarch_dischma_20250821.zip file contains data for the period from 2023 to 2024, and thus ammends the previous file with an additional year of data. Both zip files contain a readme describing the content in detail. Static files (e.g., landuse data) are only available in the inarch_dischma_20241219.zip file. There is a short period of overlapping data in both files. The newest file contains the most up-to-date data. Data last updated 2025-08-21.
DOI: 10.16904/envidat.441
title: Greater observer expertise leads to higher estimates of bryophyte species richness

description: This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2332 relevés conducted from 2011 to 2021 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relevés as sites are revisited every six years. **The dataset was prepared for usage in an unpublished article with the title of this dataset. Due to improvements in the analysis, results turned out to be more nuanced and it was found that the title of the data set is no longer valid.** A new dataset was created and is available here: [https://www.doi.org/10.16904/envidat.683](https://www.doi.org/10.16904/envidat.683)
description: Resource Title: Releve data
 Size: 413711 bytes
 Format: text/csv
 URL: https://www.envidat.ch/dataset/a4f3730d-2b36-4248-abea-5923d9bcdadc/resource/35e4d27a-87ab-4f0d-8244-5b2eaeb13665/download/20240808_bryophyte-observer-bias-data.csv
 Description: The CSV file contains all releves considered in the mentioned study. The columns
include plot identification, survey period, survey year, habitat category of
the surveyed object, vegetation type of the plot, observer expertise category,
scaled mean indicator values (i.e., light, nutrients, reaction, temperature and
wetness), counts for the four groups (all species, liverworts, mosses exkl.
_Sphagnum_ spp. and _Sphagnum_ spp.), the size categories (small, medium,
large) and the modified cover classes (NA, r, +, 1, 2, 3-5).
DOI: 10.60873/fk2/poedqc
title: Prefeitura da Cidade do Rio de Janeiro - Bilhetagem JAÉ - Histórico [2023-2025]

description: Número das transações do JAÉ (ônibus) no Rio de Janeiro agregadas por hora do dia e pela linha do ônibus.
DOI: 10.34740/kaggle/dsv/13070982
title: Forests_Dongling_Mountain
DOI: 10.15468/dl.p7xn3q
title: Occurrence Download

description: A dataset containing 29526867 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "Year 1990-2025"
 ]
}

 The dataset includes 29526867 records from 2500 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004895-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.su8yc7
title: Occurrence Download

description: A dataset containing 54661 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Tringa nebularia (Gunnerus, 1767))",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 54661 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002560-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4226/77/57216f7c5940e
title: NERP Marine Biodiversity Hub, Theme 1 - Tasman Fracture Commonwealth Marine Reserve Pilot Study - Baited remote underwater stereo video (BRUV) Component
DOI: 10.15468/dl.383tep
title: Occurrence Download

description: A dataset containing 5114 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ulmus rubra Muhl.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5114 records from 133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002561-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qsdze8
title: Occurrence Download

description: A dataset containing 17663 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Ephippiorhynchus senegalensis (Shaw, 1800))",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 17663 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002562-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x3rvv7
title: Occurrence Download

description: A dataset containing 138 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ficopomatus enigmaticus (Fauvel, 1923)",
 "Country is France",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 138 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002563-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d5r2na
title: Occurrence Download

description: A dataset containing 140604 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lynx Kerr, 1792"
 ]
}

 The dataset includes 140604 records from 416 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002547-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28235258
title: Data and code for Poverty Alleviation Resettlement in China Reduces Deforestation

description: Deforestation frequently accompanies poverty, yet various anti-poverty programs in many countries have exhibited mixed results in addressing deforestation. Poverty Alleviation Resettlement (PAR) stands out as one of the few government-led resettlement programs designed to alleviate poverty, offering comprehensive follow-up support for quality of life and employment after relocation. Our study uncovered empirical evidence of the PAR program’s impact on forest quality. Through a multiperiod difference-in-differences analysis of remote sensing and household survey data, we discovered that the PAR program significantly curbed deforestation in the participating counties and reduced forest-clearing activities among the resettled households, even those relocating to rural villages. Mechanism analysis revealed that the program discouraged deforestation by enhancing market accessibility, fostering non-farm employment opportunities, and elevating income levels. The study underscores that altering livelihood strategies and lifestyles is essential for resettlement programs to effectively mitigate ecological degradation.
DOI: 10.6084/m9.figshare.28235258.v1
title: Data and code for Poverty Alleviation Resettlement in China Reduces Deforestation

description: Deforestation frequently accompanies poverty, yet various anti-poverty programs in many countries have exhibited mixed results in addressing deforestation. Poverty Alleviation Resettlement (PAR) stands out as one of the few government-led resettlement programs designed to alleviate poverty, offering comprehensive follow-up support for quality of life and employment after relocation. Our study uncovered empirical evidence of the PAR program’s impact on forest quality. Through a multiperiod difference-in-differences analysis of remote sensing and household survey data, we discovered that the PAR program significantly curbed deforestation in the participating counties and reduced forest-clearing activities among the resettled households, even those relocating to rural villages. Mechanism analysis revealed that the program discouraged deforestation by enhancing market accessibility, fostering non-farm employment opportunities, and elevating income levels. The study underscores that altering livelihood strategies and lifestyles is essential for resettlement programs to effectively mitigate ecological degradation.