cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8185870
title: Landslide susceptibility maps using ensemble machine learning models on basin and regional level in Lombardy, Italy

description: A selection of landslide susceptibility maps computed through ensemble machine learning models for the basins of Val Tartano, Upper Valtellina and Valchiavenna, and on a regional level for the Lombardy region in Italy.

A list of the used base machine learning methods:



Random Forest,

AdaBoost,

Neural Networks.


A list of the used ensemble models:



Stacking,

Blending,

Soft Voting.


A full list of the model combinations can be found in the "Case Studies" document.

The maps are in WGS 84/ UTM zone 32N (EPSG:32632).

The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:

Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a casestudy in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263

The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.

The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.
DOI: 10.5281/zenodo.8185887
title: The best performing landslide susceptibility maps using ensemble machine learning models and precipitation data on basin and regional level in Lombardy, Italy

description: A selection of landslide susceptibility maps computed through ensemble machine learning models with included precipitation data for the basin of Valchiavenna, and the Lombardy region in Italy.

A list of the used base machine learning methods:



Neural Networks.


A list of the precipitation data included in the models:



Average hourly precipitation for the year of 2020,

90th percentile for the hourly precipitation for the year of 2020 ,

Averaged + 90th percentile for the hourly precipitation for the year of 2020.


A full list of the model combinations can be found in the "Case Studies" document.

The maps are in WGS 84/ UTM zone 32N (EPSG:32632).

The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:

Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a casestudy in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263

The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.

The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.
DOI: 10.5281/zenodo.8185907
title: Estimated No Landslide Zone for the Lombardy region, Italy

description: Areas with a very low probability of shallow landslide occurrence according to a geological criterion. The zonation considers as variables the terrain slope and the Intact Uniaxial Compressive Strength (IUCS), an index dependent on the lithology that describes the maximum stress that a material can take before failing. In broad terms, areas with very low inclination (<5°) were included with no other constraint. On the other hand, terrains with low (5°-20°) or very high (>70°) inclination required a resistant terrain (with an IUCS of at least 100 MPa) in order to be included.

The vector layers is in WGS 84/ UTM zone 32N (EPSG:32632), covering the whole region of Lombardy, Italy, with an area of 13,325 km2.

The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:

Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a casestudy in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263

The layer is produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.

The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.
DOI: 10.5287/ora-yrq0dv7wg
title: Bacterial lifestyle shapes pangenomes (data & code)

description: This repository contains code and data for the research article 'Bacterial lifestyle shapes pangenomes'.
DOI: 10.6084/m9.figshare.27302049
title: Supplementary Material for: GLOBAL ASSESSMENT OF TRAINING NEEDS IN ADDICTION MEDICINE

description: Introduction
A minority of addiction patients receive appropriate treatment directly from trained professionals in addiction medicine. Most countries have not recognized addiction medicine as a speciality in its own right or within psychiatry/other specialities. Therefore, the effectiveness and organization of addiction medicine training around the world need to be improved. Unfortunately, standard instruments are rarely used in most studies to assess training needs. This study aimed to determine international competencies in addiction medicine (AM) among professionals in as many countries as possible using a standard instrument such as the AM Training Needs Assessment (AM-TNA).
Method We examined competencies in addiction medicine with the AM-TNA using an online survey. A General Competency Level in Addiction Medicine (GLOCIAM30) was calculated by dividing the total score by the number of 30 items. This GLOCIAM30 was used to measure the general level of competency in addiction medicine and to compare individual competencies. 
Results One hundred ninety-nine respondents from 45 countries completed the survey. Ninety-five of the 199 respondents (49.0%) had a GLOCIAM30 higher or equal to 4 (fairly competent). The highest skill level was found for the competency "Assessing substance use problems by taking the patient's history.” Nine of the 45 countries had 8 or more respondents (n=129). After post hoc analysis, there was no difference between these countries. Respondents could reasonably estimate whether the competency level in their country was higher or lower than the world average.
Conclusions This study is the first international attempt to examine competencies in addiction medicine. Although a much larger study population is needed to establish an overall goal for competencies in addiction medicine, our study provided an initial direction for such a gold standard in benchmarking procedures.
DOI: 10.13020/d6jp4s
title: Minnesota Land Cover Classification and Impervious Surface Area by Landsat and Lidar: 2013 Update

description: This is a 15-meter raster dataset of a land cover and impervious surface classification for 2013-14, level two classification. The classification was created using a combination of multitemporal Landsat 8 data and LiDAR data with Object-based image analysis. By using objects instead of pixels we were able to utilize multispectral data along with spatial and contextual information of objects such as shape, size, texture and LiDAR-derived metrics to distinguish different land cover types. While OBIA has become the standard procedure for classification of high resolution imagery we found that it works equally well with Landsat imagery. For the objects classified as urban or developed, a regression model relating the Landsat greenness variable to percent impervious was developed to estimate and map the percent impervious surface area at the pixel level.
DOI: 10.15468/dfqxwo
title: Azores chondrichthyes updated checklist

description: This updated checklist of the Chondrichthyans already identified within the Azores EEZ follows the last revised version published by Barreiros & Gadig (2011). Although no new records are now registered the taxonomy has been updated. New information regarding the specific occurrence of the rare Odontaspis ferox was recently published (see Barcelos et al. 2018). As for the updated checklist of Azores' Actinopterygii, the first comprehensive chondrichthyan checklist was published by Santos et al. (1997) and later updated by Porteiro et al. (2010).
DOI: 10.15468/dmrkx9
title: Azores cetaceans updated checklist

description: All 28 species of Cetaceans known to occurr within the Azores' EEZ (corresponding to ca. 36% of all 86 cetacean species - including 6 freshwater ones) are listed in this updated checklist. Following the last update (Prieto & Silva, 2010) this "situation point" is presently the basis for further eventual new records. Three species listed here are certainly occasional and/or vagrant (i.e. Eubalaena glacialis, Lagenodelphis hosei and Phocoena phocoena). For an updated cheklist on the world's cetaceans see Perrin (2019).
DOI: 10.15468/godgmq
title: Azores actinopterygii updated checklist

description: Since the first published comprehensive checklist of Azorean fishes - covering the whole EEZ region - (Santos et al. 1997) several new records have been published and an updated checklist published (Porteiro et al. 2010). This new dataset covers all confirmed species of bony fish for the Azorean EEZ and is currently sequenced with an updated checklist (Barreiros & Azevedo, 2019) for the region's Chondrichthyes (last revised by Barreiros & Gadig 2011).
DOI: 10.15468/mz8r7v
title: Occurrences of Sea turtles in Azores Archipelago

description: Sea turtles are the best-known and more widespread marine reptiles. However, information on their distribution and the occurrence of most species, except for nesting beaches, remains scarce and sporadic, depending on sightings from fishing vessels, tourist activities and occurrences in coastal areas as well as fishing bycatch. Since the last updated species’ list for the Azores (Santos et al, 2010), no new species’ record was known for Azorean waters, until October 2020, with the confirmed sighting of an Olive Ridley, Lepidochelys olivacea (Eschscholtz, 1829) (Barcelos et al. 2021). After that, in February 2021, a second individual was found stranded on Pico Island, already in an advanced state of decomposition. This increased the number of species present in Azores EEZ (Exclusive Economic Zone) to six out of the seven extant worldwide. The remaining one, Natator depressus (Garman, 1880), is native to the Indo-pacific (see Red List Standards & Petitions Subcommittee, 1996).
DOI: 10.15468/tc5n4y
title: Odonata observations from SE Asia

description: Database containing all published georeferenced distribution records of dragonflies and damselflies of Malaysia, Brunei, Indonesia, Timor Leste, Papua New Guinea and the Solomon islands. The names used reflect the taxonomy as used in 2022. Data entry & organisation: Rory Dow and Vincent Kalkman
DOI: 10.1594/pangaea.754751
title: Fossil plant species described in Flora Fossilis Arctica, Volume 3
DOI: 10.17632/8wpsfxhfcg.1
title: INFORMED CONSENT FORM

description: For participation in the study "Psychosocial risk factors: identification, analysis and prevention".

The purpose of this study is "To know the perception of workers about psychosocial risk factors in the industry of Baja California". 
For this purpose we would like to ask for your support in answering the questions of the questionnaire and interview.
We will not take into account your name, telephone number, address, what we talk about in the meeting will be anonymous and confidential. 
Your participation in this study is completely voluntary. It will not generate any cost, harm or risk to you. If you decide not to participate, your decision will be of no consequence. 
Now that you have this information, are you willing to participate? Remember that your participation is completely voluntary.
DOI: 10.17632/wzndkbc6fk.1
title: Data for paper: Psychosocial risk factors identification in Mexican workers and RGIII validation  

description: REFERENCE GUIDE III (RGIII). Translation of Reference Guide III to NOM-035-STPS-2018 of Mexico. Questionnaire to identify psychosocial risk factors and assess the organizational environment in workplaces
DOI: 10.18462/iir.gl.2018.1367
title: Design recommendations for R-718 heat pumps in high temperature applications.

description: The transition towards carbon neutral industry requires sustainable and climate friendly heat supply. Heat pumps can meet these requirements, but currently achievable temperatures for supply of process heat are limited to around 100 °C. Using water (R-718) as refrigerant shows a good thermodynamic and environmental performance at higher temperatures, while a sophisticated design is required to compensate increased investment cost due to relatively large volume flows. This work analyses the design of two-stage R-718 heat pumps with turbo compressors with a focus on desuperheating and suggests design recommendations to ensure a competitive thermodynamic and economic performance. The results have shown that the economic performance becomes especially competitive at evaporation temperatures above 100 °C with COPs of up to 4.3 and specific investment cost below 250 €/kW of supplied heat. The suggested approach to realize the desuperheating was found as the optimal solution with respect to pressure drop and space requirements.
DOI: 10.24381/cds.0ab27596
title: Downscaled bioclimatic indicators for selected regions from 1950 to 2100 derived from climate projections
DOI: 10.25921/11da-3x54
title: Physical, chemical, and biological water quality monitoring data to support detection of Harmful Algal Blooms (HABs) in western Lake Erie, collected by the Great Lakes Environmental Research Laboratory and the Cooperative Institute for Great Lakes Research since 2012

description: Blooms of nuisance and toxic cyanobacteria, referred to as cyanobacteria harmful algal blooms (cHABs), occur annually in Lake Erie and pose a threat to human health, affect the quality of life, and significantly degrade the ecosystem. NOAA Great Lakes Environmental Research Laboratory and the Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan, started regular water quality monitoring of the western basin of Lake Erie in 2012. Since that time the monitoring effort has expanded to incorporate additional parameters and sample locations.

Physical, chemical, and biological water quality data were collected during repeated weekly sampling trips to a set of stations before, during, and after HAB events (from May - October). Data for these discrete sampling events include: wind speed, wave height, secchi depth, sample temperature, Conductivity, Temperature and Depth (CTD) Sonde temperature, CTD specific conductivity, CTD beam attenuation, CTD transmission, CTD dissolved oxygen, CTD photosynthetically active radiation, turbidity, particulate microcystin, dissolved microcystin, extracted phycocyanin, extracted chlorophyll-a, total phosphorus, total dissolved phosphorus, soluble reactive phosphorus, ammonia, nitrate + nitrite, urea, particulate organic carbon, particulate organic nitrogen, dissolved organic carbon, colored dissolved organic material absorbance at 400 nm, total suspended solids, and volatile suspended solids. The bulk water quality parameters were analyzed via established techniques and procedures for routine water quality monitoring and analysis (APHA 1992, 1998, 2017).

In 2014, CIGLR deployed moored buoys to collect real-time physical, chemical, and biological water quality data during the summer season. There are now four moored buoys deployed during the open water season in western Lake Erie that collect in situ data for the following parameters, average wind speed, maximum wind speed, air temperature, barometric pressure, depth, water temperature, specific conductivity, pH, turbidity, dissolved oxygen, chlorophyll-a, phycocyanin, dissolved organic matter, nitrate, and phosphate.
DOI: 10.5281/zenodo.12193630
title: MESA files for "She's Got Her Mother's Hair: How Black Holes Acquire their Magnetic Fields in Jetted Stellar Explosions"

description: MESA input files, output, and scripts to reproduce Fig. 1 and the appendix figure in Gottlieb, Renzo,  et al. 2024
DOI: 10.5281/zenodo.6802158
title: Dataset of a Study of Computational reproducibility of Jupyter notebooks from biomedical publications

description: This repository contains the dataset for the study of the computational reproducibility of Jupyter notebooks from biomedical publications. We analyzed the reproducibility of Jupyter notebooks from GitHub repositories associated with publications indexed in the biomedical literature repository PubMed Central. The dataset includes the metadata information of the journals, publications, the Github repositories mentioned in the publications and the notebooks present in the Github repositories.
DOI: 10.5281/zenodo.8185734
title: Landslide Influencing Factors for Landslide Susceptibility Mapping in Lombardy, Italy

description: A selection of  Landslide Influencing Factors used for Landslide susceptibility mapping through machine learning models covering the Lombardy region in Italy.

A list of the factors:



Averaged Hourly Precipitation for the year of 2020. Source: Precipitation data from ARPA Lombardia.

Digital Elevation Model [meters], 5m/pix. Source: GeoPortale Lombardia.

Eastness [unitless], 5m/pix. Source: DTM derived.

Faults buffer (50, 100, 200, 500, >500), [meters], 1:10 000. Source: GeoPortale Lombardia, derived.

Land Use Land Cover Map. Source: GeoPortale Lombardia.

Lithology, 5m/pix. Source: GeoPortale Lombardia.

Normalized Difference Vegetation Index, [unitless], 10m/pix. Source: Sentinel-2 derived for the year 2020.

Northness [unitless], 5m/pix. Source: DTM derived.

Orientation of the slope faces [degrees], 5m/pix. Source: DTM derived.

Plan Curvature [unitless], 5m/pix. Source: DTM derived.

Profile Curvature [unitless], 5m/pix. Source: DTM derived.

River buffer (50, 100, 200, 500, >500), [meters], 1:10 000. Source: GeoPortale Lombardia, derived.

Road buffer (50, 100, 200, 500, >500), [meters], 1:10 000. Source: GeoPortale Lombardia, derived.

The 90th percentile of hourly precipitation for the year of 2020. Source: Precipitation data from ARPA Lombardia.

Topographic Wetness Index, 5m/pix . Source: DTM derived


The maps are in WGS 84/ UTM zone 32N (EPSG:32632).

The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:

Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a casestudy in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263

The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.

The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.
DOI: 10.5281/zenodo.8185806
title: Landslide susceptibility maps using base machine learning models on basin and regional level in Lombardy, Italy

description: A selection of landslide susceptibility maps computed through base machine learning models for the basins of Val Tartano, Upper Valtellina and Valchiavenna, and on a regional level for the Lombardy region in Italy.

A list of the used machine learning methods:



Bagging,

Random Forest,

AdaBoost,

Gradient Tree Boosting,

Neural Networks.


A full list of the model combinations can be found in the "Case Studies" document.

The maps are in WGS 84/ UTM zone 32N (EPSG:32632).

The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:

Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a casestudy in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263

The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.

The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.
DOI: 10.5281/zenodo.8211761
title: EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 1: Introduction and Methodology

description: This document is Part 1 of the EnergyPROSPECTS Factsheet Series summarising the methodology used for collecting and presenting the data as well as introducing the database. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe. EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.
DOI: 10.5439/2333879
title: Project ICEBall: Cryo-SEM images of Captured Cirrus Ice Particles. ARM SGP, October 21-November 1, 2021

description: This dataset includes a unique set of cryo-SEM images of captured cirrus ice particle from ARM Project ICEBall, which was conducted at and around the Southern Great Plains site from October 21- November 1, 2021.  The instrument used for particle collection, Ice Crystal Encapsulation by Ball (ICE-Ball), is described in Magee et al. 2021, 'Captured cirrus ice particles in high definition", Atmos. Chem. Phys.  The ice particles were captured on balloon flights directly from cirrus cloud formations between 7-12 km on seven ICEBall flights on these dates: October 21, 23, 24, 25, 26, 31, and Nov. 1.
DOI: 10.6084/m9.figshare.24161229
title: Supplementary Information, Supplementary Tables, R Code, FASTA file and OTU tables from DNA metabarcoding, additional data tables for the publication "Shift in diet composition of a riparian predator along a stream pollution gradient" by Huszarik et al. 2024, doi:10.1098/rspb.2024.2104

description: Flying insects that emerge from streams are important food sources for insectivores living on the shore, such as spiders. However, chemical pollution present in the water can affect these insects and the spiders relying on them. In this study we investigated changes in the diet of spiders near streams exposed to different levels of wastewater and pesticide pollution. We found slight changes in the flying insect community, and found that spiders consumed more midges and fewer other flies at more polluted streams. This shows that effects of stream pollution can induce changes in the food web, including for terrestrial organisms.