cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15198672
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2016—2017

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/lsu/ref-nr99/metadata/quality
title: SILVA Release 138.2 LSU Ref NR 99 metadata: quality
DOI: 10.5281/zenodo.17055470
title: Dataset related to article "Prospective evaluation of a multimodal tumor-agnostic plasma ctDNA approach for the clinical management of pleural mesothelioma"

description: This record contains data related to article "Prospective evaluation of a multimodal tumor-agnostic plasma ctDNA approach for the clinical management of pleural mesothelioma"
DOI: 10.5281/zenodo.17061718
title: Dataset related to article "Prospective evaluation of a multimodal tumor-agnostic plasma ctDNA approach for the clinical management of pleural mesothelioma"

description: This record contains data related to article "Prospective evaluation of a multimodal tumor-agnostic plasma ctDNA approach for the clinical management of pleural mesothelioma"
DOI: 10.5281/zenodo.15198673
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2018—2019

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.15198674
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2018—2019

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/geolocation
title: SILVA Release 138.2 SSU Ref metadata: geolocation
DOI: 10.82364/138.2/lsu/parc/metadata/quality
title: SILVA Release 138.2 LSU Parc metadata: quality
DOI: 10.5281/zenodo.16999176
title: آخرین روزهای امپراتوری شیعی خامنه ای رهبر جمهوری اسلامی ایران

description: رهبری علی خامنه‌ای در جمهوری اسلامی ایران بیش از سه دهه دوام آورده و در این مدت، ساختاری سیاسی و ایدئولوژیک را شکل داده که از آن با عنوان «امپراتوری شیعی» یاد می‌شود. این امپراتوری بر پیوند میان ایدئولوژی دینی، شبکه‌های امنیتی و گسترش نفوذ منطقه‌ای استوار بوده است. با این حال، در سال‌های اخیر نشانه‌های متعددی از افول و بحران درونی آن آشکار شده است؛ از جمله اعتراضات اجتماعی فراگیر، فرسایش پایگاه ایدئولوژیک، بحران اقتصادی ساختاری، و انزوای منطقه‌ای و بین‌المللی. این مقاله با بهره‌گیری از نظریه‌های افول هژمونیک و گذار رژیم‌های اقتدارگرا، نشانه‌ها و روندهای افول امپراتوری شیعی خامنه‌ای را تحلیل می‌کند. پرسش اصلی آن است که آیا جمهوری اسلامی در واپسین روزهای خود قرار دارد و چه سناریوهایی برای دوران پس از خامنه‌ای محتمل است. یافته‌ها نشان می‌دهد که گرچه نظام همچنان از ظرفیت‌های سرکوب و بازتولید قدرت برخوردار است، اما هم‌زمان با بحران مشروعیت، تضعیف روحانیت، شکاف نسلی و فشارهای خارجی، وارد مرحله‌ای بحرانی از بقای تاریخی خود شده است.
DOI: 10.5281/zenodo.17061746
title: آخرین روزهای امپراتوری شیعی خامنه ای رهبر جمهوری اسلامی ایران

description: رهبری علی خامنه‌ای در جمهوری اسلامی ایران بیش از سه دهه دوام آورده و در این مدت، ساختاری سیاسی و ایدئولوژیک را شکل داده که از آن با عنوان «امپراتوری شیعی» یاد می‌شود. این امپراتوری بر پیوند میان ایدئولوژی دینی، شبکه‌های امنیتی و گسترش نفوذ منطقه‌ای استوار بوده است. با این حال، در سال‌های اخیر نشانه‌های متعددی از افول و بحران درونی آن آشکار شده است؛ از جمله اعتراضات اجتماعی فراگیر، فرسایش پایگاه ایدئولوژیک، بحران اقتصادی ساختاری، و انزوای منطقه‌ای و بین‌المللی. این مقاله با بهره‌گیری از نظریه‌های افول هژمونیک و گذار رژیم‌های اقتدارگرا، نشانه‌ها و روندهای افول امپراتوری شیعی خامنه‌ای را تحلیل می‌کند. پرسش اصلی آن است که آیا جمهوری اسلامی در واپسین روزهای خود قرار دارد و چه سناریوهایی برای دوران پس از خامنه‌ای محتمل است. یافته‌ها نشان می‌دهد که گرچه نظام همچنان از ظرفیت‌های سرکوب و بازتولید قدرت برخوردار است، اما هم‌زمان با بحران مشروعیت، تضعیف روحانیت، شکاف نسلی و فشارهای خارجی، وارد مرحله‌ای بحرانی از بقای تاریخی خود شده است.
DOI: 10.82364/138.2/ssu/ref-nr99/metadata/geolocation
title: SILVA Release 138.2 SSU Ref NR 99 metadata: geolocation
DOI: 10.5281/zenodo.15198699
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2022 & Trend analysis (2000—2022)

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.15198700
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2022 & Trend analysis (2000—2022)

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/lsu/ref-nr99/metadata/geolocation
title: SILVA Release 138.2 LSU Ref NR 99 metadata: geolocation
DOI: 10.15468/dl.m4ddfm
title: Occurrence Download

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

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030054-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3naq6n
title: Occurrence Download

description: A dataset containing 152 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Syzygium lineatum"
 ]
}

 The dataset includes 152 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030056-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/dans-xqv-m4qv
title: Sleeg 15 te Zevenaar Sleeg 15 te Zevenaar

description: &lt;p&gt;In oktober 2018 (en fase twee in april 2019) is door ADC ArcheoProjecten een Inventariserend Proefsleuven Onderzoek uitgevoerd op de locatie Sleeg 15 te Zevenaar. Hier zijn plannen om huizen te bouwen. Vooronderzoek heeft aangetroond dat hier vanaf de eerste helft van de 18e eeuw bebouwing aanwezig is geweest.&lt;br&gt;Tijdens het onderzoek zijn er in totaal drie proefsleuven aangelegd. In werkputten één en twee werden alleen een nagenoeg noord oost-zuidwest georiënteerde perceelsgreppel aangetroffen, die vermoedelijk vanaf de 14e eeuw dienst heeft gedaan, afgaand op een fragment steengoed afkomstig uit dit spoor. Doordat het plangebied in oktober 2018 nog deels bebouwd was konden de proefsleuven enkel aan de noordelijke zijde worden aangelegd. Op basis van de bevindingen adviseerde ADC ArcheoProjecten om aan de noordzijde van het plangebied geen verder archeologisch onderzoek uit te voeren. Voor een deel van de zuidzijde van het plangebied adviseerde ADC ArcheoProjecten een additionele proefsleuf. Dit zou werkput drie worden en deze had ten doel om de aan- of afwezigheid van de 18e-eeuwse bebouwing alhier vast te stellen. In deze werkput zijn direct onder de bouwvoor enkele uitbraaksleuven met mortelresten herkent. Deze houden vermoedelijk verband met de 18e-eeuwse bebouwing die hier verondersteld wordt. Zij zijn echter grotendeels in de bouwvoor opgegaan door later grondgebruik en er is op deze locatie geen opgaand muurwerk aangetroffen. Op een dieper niveau zijn er vijf grondsporen aangetroffen waarvan de ouderdom niet bepaald kon worden door het ontbreken van vondstmateriaal. Het betreft vier paalkuilen en een greppel die de zelfde oriëntatie heeft als de bestaande weg ten zuiden van het plangebied. Met in acht name van de lage spoordichtheid adviseert ADC ArcheoProjecten ook voor de zuidzijde van het plangebied geen aanvullend archeologisch onderzoek.&lt;/p&gt;
DOI: 10.82364/138.2/ssu/parc/metadata/geolocation
title: SILVA Release 138.2 SSU Parc metadata: geolocation
DOI: 10.15468/dl.yn8ze4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Afrithelphusa monodosa, Astacoides betsileoensis, Astacoides caldwelli, Astacoides crosnieri, Astacoides granulimanus, Astacoides hobbsi, Astacoides madagascarensis, Astacoides petiti, Astacus astacus, Atya africana, Atya gabonensis, Atya intermedia, Atya scabra, Atyaephyra desmarestii, Atyella brevirostris, Atyella longirostris, Atyoida serrata, Boreathelphusa uglowi, Caridella cunningtoni, Caridella minuta, Caridella paski, Caridina africana, Caridina amnicolizambezi, Caridina angulata, Caridina belazoniensis, Caridina brachydactyla, Caridina bunyonyiensis, Caridina calmani, Caridina crurispinata, Caridina evae, Caridina gabonensis, Caridina gaesumi, Caridina gordonae, Caridina gracilirostris, Caridina hova, Caridina isaloensis, Caridina isaloensis grandidieri, Caridina isaloensis isaloensis, Caridina kaombeflutilis, Caridina lamiana, Caridina lanzana, Caridina lineorostris, Caridina lipalmaria, Caridina lovoensis, Caridina lumilympha, Caridina malawensis, Caridina mauritii, Caridina messofluminis, Caridina moeri, Caridina natalensis, Caridina nilotica, Caridina norvestica, Caridina okiamnis, Caridina parvocula, Caridina pseudonilotica, Caridina richtersi, Caridina serratirostris, Caridina similis, Caridina sodenensis, Caridina spathulirostris, Caridina steineri, Caridina subventralis, Caridina togoensis, Caridina troglophila, Caridina typus, Caridina umtatensis, Caridina unca, Caridina xiphias, Caridinopsis chevalieri, Cherax cainii, Cherax quadricarinatus, Deckenia imitatrix, Deckenia mitis, Desmocaris bislineata, Desmocaris trispinosa, Dugastella marocana, Erimetopus brazzae, Erimetopus vandenbrandeni, Euryrhynchina edingtonae, Euryrhynchina puteola, Euryrhynchoides holthuisi, Foza ambohitra, Vahatra ambohitra, Foza goudoti, Agora goudoti, Foza manonae, Hydrothelphusa agilis, Hydrothelphusa bombetokensis, Hydrothelphusa madagascariensis, Hydrothelphusa vencesi, Liberonautes chaperi, Liberonautes grandbassa, Liberonautes latidactylus, Liberonautes lugbe, Liberonautes nanoides, Liberonautes nimba, Liberonautes paludicolis, Liberonautes rubigimanus, Limnocaridella alberti, Limnocaridina iridinae)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((-16.8319 22.8052,-16.8618 22.7792,-16.8618 20.0599,-7.8618 20.0599,-7.8618 29.0599,-11.3716 29.0599,-11.7891 28.737,-13.2712 28.3165,-13.6289 27.8814,-13.9674 27.0555,-14.8908 26.4573,-15.3388 24.986,-16.3247 24.0887,-16.8319 22.8052))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030055-250827131500795/datasets/export for details.
DOI: 10.82364/138.2/lsu/parc/metadata/geolocation
title: SILVA Release 138.2 LSU Parc metadata: geolocation
DOI: 10.17026/dans-2bv-ypp2
title: Dinkelland, Klein Agelo Dusinkweg Reconstructie Dusinksweg, Klein Agelo, gemeente Dinkelland. Een archeologische opgraving

description: &lt;p&gt;De aanleiding tot het opgraven van het plangebied vormde de reconstructie van het tweede (oostelijke) deel van de Dusinksweg. Ter hoogte van de aansluiting op de Denekampstraat zal de huidige weg omgelegd en een gescheiden fietspad gerealiseerd worden. Vooronderzoek heeft aangetoond dat de randzone van het historische erve Dusink zich binnen het plangebied bevindt.&lt;br&gt;De kern bevindt zich echter net ten zuidwesten van de huidige opgraving. Dit blijkt uit het hoofdzakelijk aangetroffen corpus van zogenoemde off site sporen als greppels en sloten. Het aangetroffen materiaal dateert hoofdzakelijk in de Nieuwe tijd met enkele vroegere, laatmiddeleeuwse, sporen en vondsten (zie tabel 1 voor een verduidelijking van deze historische perioden). 14C analyse van het grondmonster uit één van de sporenclusters heeft een datering in de 14e eeuw opgeleverd. Samen met het aanwezige vondstmateriaal uit deze periode is aangetoond dat er een laatmiddeleeuwse voorganger moet zijn geweest van het boerenbedrijf Dusink. Een deel van de sporen uit de Late Middeleeuwen is vergraven door activiteiten in de Nieuwe tijd. Dit blijkt uit laatmiddeleeuws materiaal dat is aangetroffen in sporen die in de Nieuwe tijd dateren.&lt;/p&gt;
DOI: 10.34740/kaggle/dsv/12960266
title: The EduAffect Dataset