cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/inscriptifact-ouc112048743
title: Cuneiform Tablet 1913.14.1506, Spurlock 1913.14.1506

description: [Text Description:] Account concerning silver, barley, and refined bitumen ❧ [Text or Publication Number Note:] Spurlock 1913.14.1506; CDLI P421358 ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Yellowish White (10YR 9/2) Light Brown (5YR 6.5/4) Dark Gray (N 3/ 6.6% R) ❧ [Physical Object Note:] 10.1 x 6.9 x 2.9 cm (213g)
DOI: 10.15468/dl.g2txkv
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Isthmura gigantea (Taylor, 1939)",
 "Year 1960-2022"
 ]
}

 The dataset includes 79 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0409972-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e2a4d3
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cuprella homalocarpa (Fisch. & C.A.Mey.) Salmerón-Sánchez, Mota & Fuertes"
 ]
}

 The dataset includes 50 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036728-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/inscriptifact-ouc112048778
title: Cuneiform Tablet 1913.14.1506, Spurlock 1913.14.1506

description: [Text Description:] Account concerning silver, barley, and refined bitumen ❧ [Text or Publication Number Note:] Spurlock 1913.14.1506; CDLI P421358 ❧ [Medium:] Clay ❧ [Script Note:] Non-Alphabetic Cuneiform ❧ [Physical Object Description:] Yellowish White (10YR 9/2) Light Brown (5YR 6.5/4) Dark Gray (N 3/ 6.6% R) ❧ [Physical Object Note:] 10.1 x 6.9 x 2.9 cm (213g)
DOI: 10.25549/inscriptifact-ouc112053684
title: Baal Myth Fifth Tablet

description: [Text Description:] Baal enters underworld, his body is discovered, El and Anat mourn him ❧ [Text or Publication Number Note:] KTU 1.5; CTA 5; CAT 1.5; KTU2 1.5; CTU 1.5; RS 2.[022]; RS 3.[565] ❧ [Excavation Description:] Excavations at Ras Shamra 1930. Found in Library of the High Priest, p. t. 210-264, Acropolis, excavated by Claude F.-A. Schaeffer, Mission Archeologique Francaise. ❧ [Medium:] Clay ❧ [Script Note:] Alphabetic Cuneiform ❧ [Physical Object Description:] Clay tablet, 14.5 cm. X 15.6 cm., 6 fragments ❧ [Physical Object Note:] Grey clay, six columns, incomplete
DOI: 10.15468/dl.6xccj3
title: Occurrence Download

description: A dataset containing 12065 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sirystes sibilator (Vieillot, 1818)"
 ]
}

 The dataset includes 12065 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009079-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j9fwun
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ponticola gorlap",
 "Country is Russian Federation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 63 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011876-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rpqzzc
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ranunculus ficaria",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 22 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054528-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cb6xs3
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argemone ochroleuca",
 "Country is Yemen",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054529-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yfza35
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argemone ochroleuca",
 "Country is Zambia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 15 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054530-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2rjtmd
title: Occurrence Download

description: A dataset containing 101 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nylanderia pubens",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 101 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054532-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zmg5e8
title: Occurrence Download

description: A dataset containing 3749 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Paraswammerdamia nebulella",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3749 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054531-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.czu93w
title: Occurrence Download

description: A dataset containing 976 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeagnus angustifolia",
 "Country is Czechia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 976 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054533-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8ckj2f
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ranunculus lingua",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054535-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c6hgvh
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeagnus angustifolia",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054536-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vy72j2
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Parlatoria pergandii"
 ]
}

 The dataset includes 24 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054534-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n9wjwt
title: Occurrence Download

description: A dataset containing 216 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Reynoutria sachalinensis",
 "Country is Canada",
 "HasCoordinate is true"
 ]
}

 The dataset includes 216 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054539-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n782yx
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Reynoutria sachalinensis",
 "Country is Belarus",
 "HasCoordinate is true"
 ]
}

 The dataset includes 22 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054538-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mg4mb3
title: Occurrence Download

description: A dataset containing 11426 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeagnus angustifolia",
 "Country is France",
 "HasCoordinate is true"
 ]
}

 The dataset includes 11426 records from 147 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054537-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.skkjmk
title: Occurrence Download

description: A dataset containing 705 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeagnus angustifolia",
 "Country is Germany",
 "HasCoordinate is true"
 ]
}

 The dataset includes 705 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054540-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xhrezy
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Reynoutria sachalinensis",
 "Country is Croatia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054541-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s25kbz
title: Occurrence Download

description: A dataset containing 867052 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cygnus buccinator)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "License is one of (CC-BY 4.0, CC0 1.0)"
 ]
}

 The dataset includes 867052 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054508-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15796289
title: Rooftop Height Estimation from Orthophotos Using ML (Vienna use case)

description: This dataset contains materials supporting a machine learning pipeline developed for estimating rooftop building heights from orthophoto imagery in Vienna, Austria. The task is part of FAIRiCUBE Use Case 4 (UC4), which aims to assess urban building stock and geometry for energy and infrastructure analysis.

The true rooftop heights were computed using LOD2.1 building data obtained from Vienna's open governmental portal. Each building's geometry was analyzed as MULTIPOLYGON Z objects, from which the highest z-value was extracted for each unique (x, y) coordinate. Rooftop heights were then determined by calculating the difference between the maximum and minimum heights within each polygon.

The dataset includes:



A set of cropped and masked rooftop images (used for training and inference)

A CSV file with image filenames and corresponding reference rooftop heights

A CSV file with predicted heights using the trained model

A trained model file (.pkl) based on a VGG18 → XGBoost architecture

A scatter plot showing predicted vs true heights (RMSE = 1.14 m, R² = 0.57)

A poster summarizing the methodology and results


Feature extraction is performed using VGG18, a convolutional neural network pretrained on ImageNet. These features are fed into an XGBoost regressor to predict continuous rooftop height values. The model was trained and tested on approximately 26,000 rooftop images from Vienna.

This dataset enables reproducible research in geospatial ML, urban analytics, and building stock modeling.
DOI: 10.5281/zenodo.15796288
title: Rooftop Height Estimation from Orthophotos Using ML (Vienna use case)

description: This dataset contains materials supporting a machine learning pipeline developed for estimating rooftop building heights from orthophoto imagery in Vienna, Austria. The task is part of FAIRiCUBE Use Case 4 (UC4), which aims to assess urban building stock and geometry for energy and infrastructure analysis.

The true rooftop heights were computed using LOD2.1 building data obtained from Vienna's open governmental portal. Each building's geometry was analyzed as MULTIPOLYGON Z objects, from which the highest z-value was extracted for each unique (x, y) coordinate. Rooftop heights were then determined by calculating the difference between the maximum and minimum heights within each polygon.

The dataset includes:



A set of cropped and masked rooftop images (used for training and inference)

A CSV file with image filenames and corresponding reference rooftop heights

A CSV file with predicted heights using the trained model

A trained model file (.pkl) based on a VGG18 → XGBoost architecture

A scatter plot showing predicted vs true heights (RMSE = 1.14 m, R² = 0.57)

A poster summarizing the methodology and results


Feature extraction is performed using VGG18, a convolutional neural network pretrained on ImageNet. These features are fed into an XGBoost regressor to predict continuous rooftop height values. The model was trained and tested on approximately 26,000 rooftop images from Vienna.

This dataset enables reproducible research in geospatial ML, urban analytics, and building stock modeling.
DOI: 10.5281/zenodo.15789925
title: Building Height Estimation in Halle Using Geoclimate and Machine Learning

description: This GeoTIFF dataset contains estimated building heights for Halle, Germany, produced using the Geoclimate model—an open-source geospatial processing toolbox implementing a random forest regression algorithm. The model was trained on a dataset covering 14 French communes, using ground truth building heights provided by BDTopo (IGN France).

For inference, the model automatically retrieves building footprints from OpenStreetMap (OSM) and computes a set of 62 geospatial indicators describing each building's local environment (e.g. density, proximity to other structures, land use context). These indicators serve as input features to the trained random forest, which predicts the building height.

This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.
DOI: 10.5281/zenodo.15789926
title: Building Height Estimation in Halle Using Geoclimate and Machine Learning

description: This GeoTIFF dataset contains estimated building heights for Halle, Germany, produced using the Geoclimate model—an open-source geospatial processing toolbox implementing a random forest regression algorithm. The model was trained on a dataset covering 14 French communes, using ground truth building heights provided by BDTopo (IGN France).

For inference, the model automatically retrieves building footprints from OpenStreetMap (OSM) and computes a set of 62 geospatial indicators describing each building's local environment (e.g. density, proximity to other structures, land use context). These indicators serve as input features to the trained random forest, which predicts the building height.

This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.
DOI: 10.15468/dl.jg962s
title: Occurrence Download

description: A dataset containing 822 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeagnus angustifolia",
 "Country is Italy",
 "HasCoordinate is true"
 ]
}

 The dataset includes 822 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054544-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15789829
title: Estimation of building height in Halle using digital elevation data

description: This GeoTIFF file contains estimated building heights for Halle, Germany, derived by calculating the difference between a Digital Surface Model (DSM) and a Digital Terrain Model (DTM). For each building footprint, the mean value of this difference—representing the canopy height—was used as the estimated building height.

This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.
DOI: 10.5281/zenodo.15789830
title: Estimation of building height in Halle using digital elevation data

description: This GeoTIFF file contains estimated building heights for Halle, Germany, derived by calculating the difference between a Digital Surface Model (DSM) and a Digital Terrain Model (DTM). For each building footprint, the mean value of this difference—representing the canopy height—was used as the estimated building height.

This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.
DOI: 10.15468/dl.t5uujf
title: Occurrence Download

description: A dataset containing 800 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elaeagnus angustifolia",
 "Country is Hungary",
 "HasCoordinate is true"
 ]
}

 The dataset includes 800 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054543-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15789744
title: Building Height in Halle using Number of Levels

description: This GeoTIFF file contains estimated building heights for Halle, Germany, derived from the number of building levels available in OpenStreetMap. Heights were calculated by multiplying the number of levels by a constant value (c), representing the average height of a single floor.

The constant c = 3.0 meters was determined empirically through a brute-force calibration approach using available ground truth building height data. A range of values was tested to minimize the mean absolute error (MAE) and root mean squared error (RMSE).

This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.
DOI: 10.5281/zenodo.15789745
title: Building Height in Halle using Number of Levels

description: This GeoTIFF file contains estimated building heights for Halle, Germany, derived from the number of building levels available in OpenStreetMap. Heights were calculated by multiplying the number of levels by a constant value (c), representing the average height of a single floor.

The constant c = 3.0 meters was determined empirically through a brute-force calibration approach using available ground truth building height data. A range of values was tested to minimize the mean absolute error (MAE) and root mean squared error (RMSE).

This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.
DOI: 10.5281/zenodo.15789453
title: Building Energy Estimation using Machine Learning: Barcelona use case

description: This CSV file contains building-level energy demand estimations for the city of Barcelona, computed using the XGboost, a tree based Machine Learning approach. The model uses buildings' 'height', 'construction_year' and 'shared_wall_area' as input features.

This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.
DOI: 10.5281/zenodo.15789452
title: Building Energy Estimation using Machine Learning: Barcelona use case

description: This CSV file contains building-level energy demand estimations for the city of Barcelona, computed using the XGboost, a tree based Machine Learning approach. The model uses buildings' 'height', 'construction_year' and 'shared_wall_area' as input features.

This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.
DOI: 10.15468/dl.hud8a5
title: Occurrence Download

description: A dataset containing 374 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Reynoutria sachalinensis",
 "Country is Czechia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 374 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054545-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15789520
title: Building Energy Estimation using Machine Learning: Rennes use case

description: This CSV file contains building-level energy demand estimations for the city of Rennes, computed using the XGboost, a tree based Machine Learning approach. The model uses buildings "number_of_levels", "area_of_heat_loss_opaque_vertical_walls", "year_of_construction" as input features.

This dataset is an output of FAIRiCUBE Use Case 4 (UC4): Spatial and temporal assessment of neighbourhood building stock, which aims to evaluate energy consumption and material stocks in urban environments using harmonized methods across European cities.