cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17026/dans-xtg-dvkn
title: Ijsselstein Walkade Walkade, gemeente Ijsselstein. Een archeologische begeleiding

description: &lt;p&gt;ADC ArcheoProjecten heeft in de periode van 22 t/m 26 maart 2012 een archeologische begeleiding uitgevoerd aan de Walkade naast nr. 58 te Ijsselstein. Binnen dit circa 340 m2 grote plangebied werd een circa 130 m2 grote bouwkuip ten behoeve van een nieuwbouwwoning aangelegd.&lt;br&gt;Voorafgaand aan de aanvang van het onderzoek was de ontgraving van de bouwkuip reeds gestart. Hierbij was de (zuid)oostzijde van de kuip vrijwel tot op de beoogde einddiepte ontgraven waarbij tevens een in de zuidoosthoek van de kuip gesitueerde betonnen kelder was verwijderd.&lt;br&gt;Inspectie van de bouwkuip leverde geen aanwijzingen op dat daarbij archeologische waarden zouden zijn vernietigd. Tijdens de begeleiding van het verdere ontgraven van de bouwkuip zijn vervolgens 23 begravingen aangetroffen.&lt;br&gt;De aanwezigheid van de begravingen sluit aan op het feit dat de kadastrale minuut van 1811-1832 ter hoogte van het plangebied melding maakt van de aanwezigheid van een kerkhof, behorend bij de Rooms-Katholieke (schuil)kerk gelegen aan de Havenstraat. De oorsprong van die kerk gaat terug tot 1635. De aanleg van het kerkhof dateert op basis van historische gegevens evenwel pas uit 1822. In 1829 werd het vervolgens alweer buiten gebruik gesteld vanwege het feit dat toen door de overheid de regel werd uitgevaardigd dat niet meer binnen de stad begraven mocht worden.&lt;br&gt;Alle tijdens de begeleiding aangetroffen skeletten bevonden zich aan de noordwestzijde van de bouwkuip en het kerkhof werd hier door een noord-zuidgerichte muur begrensd te worden.&lt;br&gt;Afgezien van de begravingen&lt;/p&gt;
DOI: 10.82364/138.2/lsu/ref-nr99/metadata/rnacentral
title: SILVA Release 138.2 LSU Ref NR 99 export for RNAcentral
DOI: 10.5281/zenodo.15198657
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2004—2005

Description

Global annual maps of median vegetation height for 2000—2022 produced within the scope of the Global Pasture Wath initiative, integrating multi-sensor remote sensing data (LIDAR ICESat-2, Landsat, MODIS and DTM) and based on spatiotemporal Machine Learning (Gradient boosting trees). While the primary focus is on improving monitoring in short vegetation ecosystems, the dataset provides wall-to-wall coverage to all terrestrial ecosystems and is organized in 70 global mosaics in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 (STAC and GEE) and 0.00075 degrees (Zenodo), including:



Median vegetation height: 



Mean predicted value: 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 5th value (upper prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 95th value (lower prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)



Trend analysis (2000—2022):



Linear regression intercept (filtered p-value < 0.05, Mann-Kendall test): 1 long-term map with values range from -0.92–0.7 (embedded scale factor 0.0001)




All raster files are in 16-bit integer format and use -3200 as no-data value (pixels covering ocean waters, lakes and water bodie), following an specific naming convention:



Project name: Global Pasture Watch (gpw)

Class name: Short vegetation height (short.veg.height)

Procedure combination:  Ensemble Gradient Boosting Trees (egbt),  trend/intercept derived per-pixel via linear regression (trend), Mann-Kendall test (mk).

Variable type: mean predicted value (m), 5th (p.05) and 95th percentiles (p.95)

Spatial resolution: 30m

Begin of time reference: date of first Landsat composite used by the modeling (20220101)

End of time reference: date of last Landsat composite used by the modeling (20221231)

Spatial extent: global (go)

Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)

Version: v1


Related resources



Maps of median vegetation height:2000-2001 2002-2003 2004-2005 2006-2007 2008-2009 2010-2011 2012-2013 2014-2015 2016-2017 2018-2019 2020-2021 2022

Trend analysis map:2000—2022

Global reference samples and machine learning models:Parquet and joblib python files
DOI: 10.5281/zenodo.15198658
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2004—2005

Description

Global annual maps of median vegetation height for 2000—2022 produced within the scope of the Global Pasture Wath initiative, integrating multi-sensor remote sensing data (LIDAR ICESat-2, Landsat, MODIS and DTM) and based on spatiotemporal Machine Learning (Gradient boosting trees). While the primary focus is on improving monitoring in short vegetation ecosystems, the dataset provides wall-to-wall coverage to all terrestrial ecosystems and is organized in 70 global mosaics in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 (STAC and GEE) and 0.00075 degrees (Zenodo), including:



Median vegetation height: 



Mean predicted value: 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 5th value (upper prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 95th value (lower prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)



Trend analysis (2000—2022):



Linear regression intercept (filtered p-value < 0.05, Mann-Kendall test): 1 long-term map with values range from -0.92–0.7 (embedded scale factor 0.0001)




All raster files are in 16-bit integer format and use -3200 as no-data value (pixels covering ocean waters, lakes and water bodie), following an specific naming convention:



Project name: Global Pasture Watch (gpw)

Class name: Short vegetation height (short.veg.height)

Procedure combination:  Ensemble Gradient Boosting Trees (egbt),  trend/intercept derived per-pixel via linear regression (trend), Mann-Kendall test (mk).

Variable type: mean predicted value (m), 5th (p.05) and 95th percentiles (p.95)

Spatial resolution: 30m

Begin of time reference: date of first Landsat composite used by the modeling (20220101)

End of time reference: date of last Landsat composite used by the modeling (20221231)

Spatial extent: global (go)

Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)

Version: v1


Related resources



Maps of median vegetation height:2000-2001 2002-2003 2004-2005 2006-2007 2008-2009 2010-2011 2012-2013 2014-2015 2016-2017 2018-2019 2020-2021 2022

Trend analysis map:2000—2022

Global reference samples and machine learning models:Parquet and joblib python files
DOI: 10.15468/dl.srn25b
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bauhinia monandra",
 "Country is Somalia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030049-250827131500795/datasets/export for details.
DOI: 10.15468/dl.g9wcmg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bauhinia monandra",
 "Country is Timor-Leste",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030050-250827131500795/datasets/export for details.
DOI: 10.15468/dl.238tva
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Garrulax castanotis",
 "Country is one of (Myanmar, Thailand, Lao People’s Democratic Republic, Cambodia, Viet Nam, Malaysia)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 53 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030048-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.82364/138.2/ssu/ref-nr99/metadata/quality
title: SILVA Release 138.2 SSU Ref NR 99 metadata: quality
DOI: 10.17026/dans-278-6jr3
title: Ijsselstein Koningshof Ijsselstein, Koningshof, Schuttersgracht en Molenstraat

description: &lt;p&gt;ADC ArcheoProjecten heeft een Archeologische Begeleiding (conform protocol Opgraven)1 uitgevoerd. Dit ten behoeve van de stadsvernieuwing binnen het gebied van de Koningsstraat, Schuttersgracht en Molenstraat in Ijsselstein. De Archeologische Begeleiding behelsde het toezicht houden op het uitgraven van kabels- en leidingsleuven, zie hiervoor PVE fase2.&lt;br&gt;Het onderzoeksgebied heeft een oppervlakte van ca. 6100 m² en was vóór de werkzaamheden in gebruik als openbare weg en parkeerterrein. Het gebied behelst het Koningshof, het zuidoost deel van de Molenstraat en de Schuttersgracht.&lt;br&gt;Tijdens de begeleiding zijn sporen van bewoning uit de Late Middeleeuwen B en de Nieuwe Tijd B aangetroffen. In dit gebied komen dergelijke resten veel voor en geven aan dat hier in deze periode twee verschillende bewoningsfase te onderscheiden zijn.&lt;/p&gt;
DOI: 10.5281/zenodo.15198662
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2008—2009

Description

Global annual maps of median vegetation height for 2000—2022 produced within the scope of the Global Pasture Wath initiative, integrating multi-sensor remote sensing data (LIDAR ICESat-2, Landsat, MODIS and DTM) and based on spatiotemporal Machine Learning (Gradient boosting trees). While the primary focus is on improving monitoring in short vegetation ecosystems, the dataset provides wall-to-wall coverage to all terrestrial ecosystems and is organized in 70 global mosaics in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 (STAC and GEE) and 0.00075 degrees (Zenodo), including:



Median vegetation height: 



Mean predicted value: 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 5th value (upper prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 95th value (lower prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)



Trend analysis (2000—2022):



Linear regression intercept (filtered p-value < 0.05, Mann-Kendall test): 1 long-term map with values range from -0.92–0.7 (embedded scale factor 0.0001)




All raster files are in 16-bit integer format and use -3200 as no-data value (pixels covering ocean waters, lakes and water bodie), following an specific naming convention:



Project name: Global Pasture Watch (gpw)

Class name: Short vegetation height (short.veg.height)

Procedure combination:  Ensemble Gradient Boosting Trees (egbt),  trend/intercept derived per-pixel via linear regression (trend), Mann-Kendall test (mk).

Variable type: mean predicted value (m), 5th (p.05) and 95th percentiles (p.95)

Spatial resolution: 30m

Begin of time reference: date of first Landsat composite used by the modeling (20220101)

End of time reference: date of last Landsat composite used by the modeling (20221231)

Spatial extent: global (go)

Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)

Version: v1


Related resources



Maps of median vegetation height:2000-2001 2002-2003 2004-2005 2006-2007 2008-2009 2010-2011 2012-2013 2014-2015 2016-2017 2018-2019 2020-2021 2022

Trend analysis map:2000—2022

Global reference samples and machine learning models:Parquet and joblib python files
DOI: 10.5281/zenodo.15198661
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2008—2009

Description

Global annual maps of median vegetation height for 2000—2022 produced within the scope of the Global Pasture Wath initiative, integrating multi-sensor remote sensing data (LIDAR ICESat-2, Landsat, MODIS and DTM) and based on spatiotemporal Machine Learning (Gradient boosting trees). While the primary focus is on improving monitoring in short vegetation ecosystems, the dataset provides wall-to-wall coverage to all terrestrial ecosystems and is organized in 70 global mosaics in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 (STAC and GEE) and 0.00075 degrees (Zenodo), including:



Median vegetation height: 



Mean predicted value: 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 5th value (upper prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 95th value (lower prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)



Trend analysis (2000—2022):



Linear regression intercept (filtered p-value < 0.05, Mann-Kendall test): 1 long-term map with values range from -0.92–0.7 (embedded scale factor 0.0001)




All raster files are in 16-bit integer format and use -3200 as no-data value (pixels covering ocean waters, lakes and water bodie), following an specific naming convention:



Project name: Global Pasture Watch (gpw)

Class name: Short vegetation height (short.veg.height)

Procedure combination:  Ensemble Gradient Boosting Trees (egbt),  trend/intercept derived per-pixel via linear regression (trend), Mann-Kendall test (mk).

Variable type: mean predicted value (m), 5th (p.05) and 95th percentiles (p.95)

Spatial resolution: 30m

Begin of time reference: date of first Landsat composite used by the modeling (20220101)

End of time reference: date of last Landsat composite used by the modeling (20221231)

Spatial extent: global (go)

Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)

Version: v1


Related resources



Maps of median vegetation height:2000-2001 2002-2003 2004-2005 2006-2007 2008-2009 2010-2011 2012-2013 2014-2015 2016-2017 2018-2019 2020-2021 2022

Trend analysis map:2000—2022

Global reference samples and machine learning models:Parquet and joblib python files
DOI: 10.82364/138.2/ssu/ref/metadata/quality
title: SILVA Release 138.2 SSU Ref metadata: quality
DOI: 10.15468/dl.7muum9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bambusicola sonorivox",
 "Country is one of (Myanmar, Thailand, Lao People’s Democratic Republic, Cambodia, Viet Nam, Malaysia)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030051-250827131500795/datasets/export for details.
DOI: 10.5281/zenodo.15198664
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2010—2011

Description

Global annual maps of median vegetation height for 2000—2022 produced within the scope of the Global Pasture Wath initiative, integrating multi-sensor remote sensing data (LIDAR ICESat-2, Landsat, MODIS and DTM) and based on spatiotemporal Machine Learning (Gradient boosting trees). While the primary focus is on improving monitoring in short vegetation ecosystems, the dataset provides wall-to-wall coverage to all terrestrial ecosystems and is organized in 70 global mosaics in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 (STAC and GEE) and 0.00075 degrees (Zenodo), including:



Median vegetation height: 



Mean predicted value: 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 5th value (upper prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 95th value (lower prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)



Trend analysis (2000—2022):



Linear regression intercept (filtered p-value < 0.05, Mann-Kendall test): 1 long-term map with values range from -0.92–0.7 (embedded scale factor 0.0001)




All raster files are in 16-bit integer format and use -3200 as no-data value (pixels covering ocean waters, lakes and water bodie), following an specific naming convention:



Project name: Global Pasture Watch (gpw)

Class name: Short vegetation height (short.veg.height)

Procedure combination:  Ensemble Gradient Boosting Trees (egbt),  trend/intercept derived per-pixel via linear regression (trend), Mann-Kendall test (mk).

Variable type: mean predicted value (m), 5th (p.05) and 95th percentiles (p.95)

Spatial resolution: 30m

Begin of time reference: date of first Landsat composite used by the modeling (20220101)

End of time reference: date of last Landsat composite used by the modeling (20221231)

Spatial extent: global (go)

Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)

Version: v1


Related resources



Maps of median vegetation height:2000-2001 2002-2003 2004-2005 2006-2007 2008-2009 2010-2011 2012-2013 2014-2015 2016-2017 2018-2019 2020-2021 2022

Trend analysis map:2000—2022

Global reference samples and machine learning models:Parquet and joblib python files
DOI: 10.5281/zenodo.15198663
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2010—2011

Description

Global annual maps of median vegetation height for 2000—2022 produced within the scope of the Global Pasture Wath initiative, integrating multi-sensor remote sensing data (LIDAR ICESat-2, Landsat, MODIS and DTM) and based on spatiotemporal Machine Learning (Gradient boosting trees). While the primary focus is on improving monitoring in short vegetation ecosystems, the dataset provides wall-to-wall coverage to all terrestrial ecosystems and is organized in 70 global mosaics in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 (STAC and GEE) and 0.00075 degrees (Zenodo), including:



Median vegetation height: 



Mean predicted value: 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 5th value (upper prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)

Percentile 95th value (lower prediction boundary): 23 annual maps with values range from 0–20 (embedded scale factor 0.1)



Trend analysis (2000—2022):



Linear regression intercept (filtered p-value < 0.05, Mann-Kendall test): 1 long-term map with values range from -0.92–0.7 (embedded scale factor 0.0001)




All raster files are in 16-bit integer format and use -3200 as no-data value (pixels covering ocean waters, lakes and water bodie), following an specific naming convention:



Project name: Global Pasture Watch (gpw)

Class name: Short vegetation height (short.veg.height)

Procedure combination:  Ensemble Gradient Boosting Trees (egbt),  trend/intercept derived per-pixel via linear regression (trend), Mann-Kendall test (mk).

Variable type: mean predicted value (m), 5th (p.05) and 95th percentiles (p.95)

Spatial resolution: 30m

Begin of time reference: date of first Landsat composite used by the modeling (20220101)

End of time reference: date of last Landsat composite used by the modeling (20221231)

Spatial extent: global (go)

Coordinate system: World Geodetic System 1984, used in GPS (epsg.4326)

Version: v1


Related resources



Maps of median vegetation height:2000-2001 2002-2003 2004-2005 2006-2007 2008-2009 2010-2011 2012-2013 2014-2015 2016-2017 2018-2019 2020-2021 2022

Trend analysis map:2000—2022

Global reference samples and machine learning models:Parquet and joblib python files
DOI: 10.5281/zenodo.17035732
title: Enhanced-sampling MD simulations of the RAD51-BRC4 complex

description: Molecular dynamics simulations of the RAD51-BRC4 complex, using enhanced sampling methods (steered MD and metadynamics). The simulations integrate experimental information, namely SAXS (in the steered MD) and XL-MS (in the metad). All input files necessary to perform the simulations are provided, including the initial model of the full RAD51-BRC4 complex generated via AlfaFold2 after proper equilibration, used to start the steered MD, as well as the output files (including MD trajectories). In the notebook folder, we also provide a Jupyter Notebook to analyze results and perform ensemble reweighting through the maximum entropy principle; the folder also gathers all files necessary to run the notebook and a lighter version of the trajectories.

The weights associated to the reconstructed conformational ensemble can be found in the notebook/output_check subfolder, both in binary and human-readable formats; the weights can also be easily recomputed and saved using dedicated instructions in the notebook. In the notebook/output_check/out_cluster_analysis subfolder, we supply representative structures, i.e. cluster centroids, for highest-weighted clusters from the reweighted ensemble in the form of individual PDBs, labelled with the corresponding cluster weight in the REMARK header line of each PDB; instructions to identify and save these structure files can be found inside the notebook as well.

The notebook can also be easily consulted at https://github.com/CompMedChemLab/project_saxs-xlms-md_rad
DOI: 10.5281/zenodo.17061715
title: Living in the Afterlife

description: Materials and Data related to the study "Living in the afterlife: clues from direct experiencers".

Preprint available at: https://osf.io/preprints/psyarxiv/7n9gr_v1