cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1285/i27241688n2p115
title: Radio Magica's community Talking Maps®: Digital, linguistic and cognitive accessibility for cultural heritage

description: The Talking Maps® project aims at developing a "digital common good" based on multimodality to promote the dissemination of Italian Cultural Heritage through a narrative language, enjoyable and accessible by a wide range of users, including non-experts such as children, and individuals with cognitive disabilities or linguistic special needs. Maps are both digital and paper-based. Digital contents connected to each Talking Map® comprise multimodal stories and fun-facts developed by a multidisciplinary team of experts and artists, in line with the principles of universal design. Maps are conceived as community maps, since the production methodology involves citizens of all ages. Empirical investigation conducted between 2018 and 2023 made it possible to point out the strengths of the project and analyze its development on a longitudinal basis.
DOI: 10.1285/i27241688n2p131
title: English audio description: Selected linguistic features and applications for ESL classrooms

description: This chapter focuses on audio description (AD), a form of accessible translation delivered orally and consumed aurally that was originally conceived to make visual content accessible to people who are blind and visually impaired. Recently, AD has started to be used successfully by several secondary audiences, and especially in the language classromm, where it has a great potential. In this chapter, we take into account diverse classroom applications of both art and screen AD. In the first part of the chapter, we set the foundations of this discipline and highlight a selection of linguistic aspects of professional English AD for the art. Then, we show some possible applications of art AD in the English language classroom. In the second part of the chapter, we describe AD as a versatile resource for university students learning English and illustrate a university course aimed at teaching how to write English ADs for the screen. Finally, we showcase an example of art AD simplification targeted at audiences with cognitive difficulties.
DOI: 10.1285/i27241688n2p165
title: Exploring multimodal discourse in sustainable tourism. A case study of the GO GREEN website

description: This chapter explores the use of multimodal resources and the promotion of extended reality (XR) technologies in the GO GREEN project, a sustainable tourism initiative, centred on the Gorizia province in the Friuli-Venezia Giulia (FVG) region of Italy, promoted by Fondazione Cassa di Risparmio di Gorizia (CARIGO, for short). The analysis investigates how various meaning-making resources are employed on the GO GREEN website to engage the viewer through compelling sustainability narratives. By promoting on-site access to XR tools, the website illustrates nine distinct "narrative itineraries", or projects, within the region's natural and cultural heritage, encouraging both locals and tourists to undertake immersive experiences. Using Baldry and Thibault's (2006, Ch. 3) framework for multimodal website analysis, this chapter examines the synergic interplay between visuals and text to enhance persuasiveness in one of these itineraries – viz. the one in the Collio region – drawing on key themes and master narratives emphasising the integration of past and present and the role of young people as active participants in digital experiences. The findings reveal how the website shapes viewer experiences and encourages young people to engage in sustainable practices, ultimately raising questions about the potential of XR technologies as tools for environmental education.
DOI: 10.1285/i27241688n2p189
title: Colophon
DOI: 10.15468/dl.apeap4
title: Occurrence Download

description: A dataset containing 332 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus bungeana Zucc. ex Endl.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 332 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063482-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zp72ay
title: Occurrence Download

description: A dataset containing 2431 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((12.382 45.43809,12.35569 45.41502,12.32149 45.37068,12.30045 45.34156,12.35175 45.32723,12.35701 45.34711,12.38923 45.38177,12.40699 45.40994,12.42212 45.4321,12.382 45.43809))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2020-2025"
 ]
}

 The dataset includes 2431 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063484-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ad4hbf
title: Occurrence Download

description: A dataset containing 34271 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-10.6876 51.24968,-5.09258 51.24968,-5.09258 55.61658,-10.6876 55.61658,-10.6876 51.24968))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Haematopus ostralegus Linnaeus, 1758"
 ]
}

 The dataset includes 34271 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063483-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.serp59
title: Occurrence Download

description: A dataset containing 4226 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus canariensis C.Sm.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4226 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063485-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10183691
title: LAVESI-FIRE simulation output at Lake Satagay, Central Yakutia, Siberia

description: Simulation output data for long-term fire-vegetation simulations with the individual-based, spatially explicit model LAVESI-FIRE. The individual, numbered simulation folders are described in the included .docx file, as well as in the related research paper. In each folder, following files can be found (_18224XX refers to the climate input used for the simulation):



datatrees_currencies_18224XX.csv: Timeseries data. Simulation area-wide summarized data on tree abundance, climate, environment and fire occurrence. Each row of the table represents an individual annual simulation timestep.

databiomassgrid_1_18224XX_XX00_1_X.csv: Spatial data. Each file includes tree abundance for one species (_1 = Larix gmelinii; _2 = Larix sibirica; _3 = Larix cajanderi; _4 = Picea obovata; _5 = Pinus sylvestris; _6 = Pinus sibirica), summarized in grid cells with x- and y-coordinates.

datatrees_TreedensityXX00_18224XX.csv: Spatial data. Each file includes tree density, environment, and fire occurrence, summarized in grid cells with x- and y-coordinates.


For more detail, please refer to the linked research paper:

Glückler, R., Gloy, J., Dietze, E., Herzschuh, U., & Kruse, S. (2024). Simulating long-term wildfire impacts on boreal forest structure in Central Yakutia, Siberia, since the Last Glacial Maximum. Fire Ecology, 20(1), 1. https://doi.org/10.1186/s42408-023-00238-8
DOI: 10.5281/zenodo.8092897
title: LAVESI-FIRE simulation output at Lake Satagay, Central Yakutia, Siberia

description: Simulation output data for long-term fire-vegetation simulations with the individual-based, spatially explicit model LAVESI-FIRE. The individual, numbered simulation folders are described in the included .docx file, as well as in the related research paper. In each folder, following files can be found (_18224XX refers to the climate input used for the simulation):



datatrees_currencies_18224XX.csv: Timeseries data. Simulation area-wide summarized data on tree abundance, climate, environment and fire occurrence. Each row of the table represents an individual annual simulation timestep.

databiomassgrid_1_18224XX_XX00_1_X.csv: Spatial data. Each file includes tree abundance for one species (_1 = Larix gmelinii; _2 = Larix sibirica; _3 = Larix cajanderi; _4 = Picea obovata; _5 = Pinus sylvestris; _6 = Pinus sibirica), summarized in grid cells with x- and y-coordinates.

datatrees_TreedensityXX00_18224XX.csv: Spatial data. Each file includes tree density, environment, and fire occurrence, summarized in grid cells with x- and y-coordinates.


For more detail, please refer to the linked research paper:

Glückler, R., Gloy, J., Dietze, E., Herzschuh, U., & Kruse, S. (2024). Simulating long-term wildfire impacts on boreal forest structure in Central Yakutia, Siberia, since the Last Glacial Maximum. Fire Ecology, 20(1), 1. https://doi.org/10.1186/s42408-023-00238-8
DOI: 10.34740/kaggle/ds/6457739
title: Countries' Land and Water Areas with ISO3 Codes
DOI: 10.15468/dl.ebzg6u
title: Occurrence Download

description: A dataset containing 20955 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus cembra L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 20955 records from 195 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063487-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ru3bm4
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((11.35277 -13.75579,47.64723 -13.75579,47.64723 0.75579,11.35277 0.75579,11.35277 -13.75579))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lophiobagrus brevispinis Bailey & Stewart, 1984"
 ]
}

 The dataset includes 17 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063488-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13801738
title: TP53 ALTERATIONS ASSOCIATE WITH POOR RESPONSE TO LENVATINIB IN PATIENTS WITH ADVANCED THYROID CANCER

description: This set of raw data cannot be made publicly available as including sensitive information, but might be available to interested researchers upon reasonable requests. Please, forward you request to: l.fugazzola@auxologico.it
DOI: 10.5281/zenodo.13801739
title: TP53 ALTERATIONS ASSOCIATE WITH POOR RESPONSE TO LENVATINIB IN PATIENTS WITH ADVANCED THYROID CANCER

description: This set of raw data cannot be made publicly available as including sensitive information, but might be available to interested researchers upon reasonable requests. Please, forward you request to: v.cirello@auxologico.it
DOI: 10.5281/zenodo.14288301
title: TP53 ALTERATIONS ASSOCIATE WITH POOR RESPONSE TO LENVATINIB IN PATIENTS WITH ADVANCED THYROID CANCER

description: This set of raw data cannot be made publicly available as including sensitive information, but might be available to interested researchers upon reasonable requests. Please, forward you request to: l.fugazzola@auxologico.it
DOI: 10.5281/zenodo.12680172
title: PDAC_Morpho-Biotype_Study

description: Reference files, count matrices, meta data tables and R objects from LMD-seq and single-cell RNA-seq data of PDAC human samples (Di Chiaro et al., Cancer Cell 2024; DOI: 10.1016/j.ccell.2024.02.017).



Mapping and read counting using laser microdissetion (LMD) coupled to RNAseq data

Motif analysis using laser microdissetion (LMD) coupled to RNAseq data

Inference of gene regulatory networks through VIPER using laser microdissetion (LMD) coupled to RNAseq data

single-cell RNA-seq data
DOI: 10.6084/m9.figshare.27194964
title: Dataset of; Amazon rainforest ecosystem exchange of CO2 and H2O through turbulent understory ejections. Described in: Moonen et al. 2025 (ACP)

description: This data supports the 2025 study titled: High frequency isotopic composition measurements to classify cloud induced turbulent patterns above the Amazon rain forest. It consists of windfield, mole fraction, PAR, and isotopic composition data gathered using an eddy covariance system (IRGASON EC-100), open path gas analyser (LICOR 7500), PAR sensor, and Picarro L-2130i water vapour isotope analyser respectively.Three data-sets are shared, of differing depth of analysis, duration, and frequencies (10Hz, 10s, 30min). The units of all included variables can are detailed in the units.csv file.4Hz 'raw' data example of a 30 minute data interval containing a clear ejection event (Fig1).<br>Limitted to wind field and molefraction information.<br>10s processed data containing variables on ejections and cloud fields (Fig 4).<br>In addition, 10s agregated data on scalars, wind fiels, and isotopic compositions is also available.<br>30min processed flux data which include derived source compositions and quadrant specific fluxes (Fig 2 & 3).<br>In addition, isotopic flux data for the dD and d18O isotopes is shared.
DOI: 10.6084/m9.figshare.27194964.v4
title: Dataset of; Amazon rainforest ecosystem exchange of CO2 and H2O through turbulent understory ejections. Described in: Moonen et al. 2025 (PNAS)

description: This data supports the 2025 study titled: High frequency isotopic composition measurements to classify cloud induced turbulent patterns above the Amazon rain forest. It consists of windfield, mole fraction, PAR, and isotopic composition data gathered using an eddy covariance system (IRGASON EC-100), open path gas analyser (LICOR 7500), PAR sensor, and Picarro L-2130i water vapour isotope analyser respectively.Three data-sets are shared, of differing depth of analysis, duration, and frequencies (10Hz, 10s, 30min). The units of all included variables can are detailed in the units.csv file.4Hz 'raw' data example of a 30 minute data interval containing a clear ejection event (Fig1).<br>Limitted to wind field and molefraction information.<br>10s processed data containing variables on ejections and cloud fields (Fig 4).<br>In addition, 10s agregated data on scalars, wind fiels, and isotopic compositions is also available.<br>30min processed flux data which include derived source compositions and quadrant specific fluxes (Fig 2 & 3).<br>In addition, isotopic flux data for the dD and d18O isotopes is shared.
DOI: 10.5281/zenodo.11172569
title: High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany

description: Open access publication under review.

Visit this Earth Engine app to explore the data interactively.

 

Abstract:

Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study  develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.

We propose a two-step approach that estimates five canopy fuel variables at the point level, first using measurements from NASA’s GEDI instrument, before predicting high-resolution raster maps. Instead of using field measurements, we generate plot-level estimates for Canopy (Base) Height (CH, CBH), Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass models. To predict plot-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching. Predictions at >1.6 M GEDI points and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.

The agreement with test data (from the same population) was strong for plot-level predictions and moderate for map predictions. A validation with independent estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH) rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of model prediction standard errors. Contributing factors may be simplification through allometric equations  and spatial and temporal mismatch of data inputs. The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.

 

Data:

This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.
DOI: 10.5281/zenodo.14549613
title: High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany

description: Open access publication under review.

Visit this Earth Engine app to explore the data interactively.

 

Abstract:

Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.

We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomassmodels. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.

Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validationwith estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation ofmodel prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.

 

 

Data:

This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.
DOI: 10.5281/zenodo.8285856
title: High-Resolution Canopy Fuels for Germany Based on GEDI

description: Forest fuels are an essential component of wildfire behavior and risk assessment, but difficult to quantify accurately. As fire spreads into new regions such as Central Europe, the demand for fuels information is increasing. The aim of this study was to develop a methodology to map canopy fuels over large areas and at high spatial resolution, using only open and accessible data. We propose a two-step approach that i) predicts five canopy fuel variables at point-level using measurements from NASA's Global Ecosystem Dynamics Investigation (GEDI) instrument, before ii) interpolating them to wall-to-wall raster maps. Instead of conducting field measurements we generate reference data for Canopy (Base) Height (CH, CBH), Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting point clouds from airborne laser scanning and processing tree-level metrics to plot-level fuels estimates via allometric crown biomass models. Point-level Random Forest models were tuned to select optimal hyperparameters and cross-validated using k-fold Nearest Neighbor Distance Matching. Predictions at extgreater1.6 M GEDI measurement locations and multiple biophysical raster covariates serve as input to Universal Kriging to produce 20 meter wall-to-wall maps. Random Forest models showed favourable results, especially for CC (R² = 0.76; RMSE = 9.95%), followed by CBH (R² = 0.68; RMSE = 2.41 m) and CH (R² = 0.67; RMSE = 3.86 m). Performance was slightly weaker for CFL (R² = 0.63; RMSE = 4.03 t/ha) and CBD (R² = 0.55; RMSE = 0.03 kg/m³). Wall-to-wall interpolation achieved less accurate yet acceptable results (0.24 &lt; R² &gt; 0.42), except for CC(R² = 0.65). All models could benefit from residual Kriging over only applying linear regression. We show that canopy fuel layers can be sufficiently derived from openly accessible data at country level without extensive field measurements. The proposed workflow may support regions where wildfire is an emerging issue and fuels information is scarce.