cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17026/dans-xp4-x56n
title: Gevonden te Odijk: een crematiegrafveld uit de Romeinsetijd en een hofstede uit de Late Middeleeuwen Gevonden te Odijk: een crematiegrafveld uit de Romeinse tijd en een hofstede uit de Late Middeleeuwen

description: &lt;p&gt;Tussen 31 oktober 2016 en 10 november 2016 heeft ADC ArcheoProjecten een inventariserend proefsleuvenonderzoek uitgevoerd op twee locaties ten noorden (De Heuveltjes) en zuiden (Het Burgje) van de Vinkenburgweg te Odijk. Tijdens het onderzoek zijn twee vindplaatsen aangetroffen en grotendeels begrensd. Het betreft een grafveld uit de Romeinse tijd en hofstede Vinkenburg uit de Late Middeleeuwen. Gebleken is dat de hofstede zich alleen op De Heuveltjes manifesteert, terwijl het grafveld zich uitstrekt tot in de boomgaard van Het Burgje en aan de noordwestzijde tot buiten het plangebied.&lt;br&gt;Het grafveld is gelegen op een ca. 20-30m smalle stroomrug in het landschap, welke NNW-ZZO in het landschap is gelegen. Zowel crematie- als inhumatiegraven komen voor. De aangetroffen graven zijn vaak sterk aangetast door nivellering van de percelen en vervolgens door agrarische activiteiten. Ooit in de grafmonumenten geplaatste giften zijn stuk geploegd of ontbreken en zijn verspreid geraakt in de bouwvoor. Op de flanken van de stroomrug zijn de graven het best bewaard gebleven en zijn kringgreppels aangetroffen. Aardewerk en metaalvondsten dateren de graven in de 1e tot en met de 3e eeuw n. Chr.&lt;br&gt;Direct ten oosten van de stroomrug is een geul gelegen. De geul heeft ter plekke van De Heuveltjes een opvulling met zware komklei. Op deze klei is in de 13e eeuw een huisplaats gesticht, welke op later tijdstip verbouwd is tot hofstede. Van de hofstede zijn enkele funderingen aangetroffen van al hergebruikte baksteen, met gereconstrueerde afmetingen van 32 x 24m. De 36 x 36 m grote huisplaats is omgracht met 2 tot 4 m brede grachten, waarbinnen zich tevens een beerput bevindt.&lt;br&gt;Vanwege de aanwezigheid van grachten ten zuiden van de huisplaats en hofstede, lijkt er een zuidelijke toegangsweg aanwezig geweest te zijn vanaf de Vinkenburgweg.&lt;br&gt;Het aardewerk en metaal dat is aangetroffen dateert de gehele vindplaats van de 13e eeuw tot eerste helft 17e eeuw, doch bestaat er een duidelijke 14e-eeuwse fase, met een mogelijke uitloop naar de 15e eeuw. Het aardewerk uit de Nieuwe tijd is hoofdzakelijk afkomstig uit de grachten, iets wat mogelijk wijst op (verdere) demping van de grachten gedurende deze periode. Dit materiaal betreft vermoedelijk stadsafval dat van elders afkomstig is.&lt;br&gt;Ten zuidoosten van de Stenen Kamer Het Burgje, nu nog aanwezig als gelijkgenaamde 17eeeuwse boerderij, is in het proefsleuvenonderzoek een 10m brede gracht aangetroffen, welke zich oriënteert op de huidige opstallen.&lt;/p&gt;
DOI: 10.15468/dl.pjmmvq
title: Occurrence Download

description: A dataset containing 3 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 ((-16.8295 11.8376,-16.8618 11.7189,-16.8618 11.0599,-7.8618 11.0599,-7.8618 20.0599,-16.8618 20.0599,-16.8618 19.0648,-16.6653 18.7702,-16.5447 17.7591,-16.8618 16.9131,-16.8618 11.8636,-16.8295 11.8376))"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030046-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15198659
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2006—2007

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.15198660
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2006—2007

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/parc/metadata/rnacentral
title: SILVA Release 138.2 SSU Parc export for RNAcentral
DOI: 10.15468/dl.xxfumr
title: Occurrence Download

description: A dataset containing 104659 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phalacrocorax aristotelis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((40.81373 29.26705, 40.81373 50.94, -6.6 50.94, -6.6 29.26705, 40.81373 29.26705))",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Material sample)"
 ]
}

 The dataset includes 104659 records from 285 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029933-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/dans-z4b-jr2z
title: Boomplant Markt, Wijk bij Duurstede Boomplant Markt, Wijk bij Duurstede

description: &lt;p&gt;ADC ArcheoProjecten heeft op 23 april 2019 een Opgraving Landbodems - variant Archeologische Begeleiding uitgevoerd op de locatie Markt te Wijk bij Duurstede (afb. 1 en 2). Het archeologisch onderzoek is uitgevoerd in verband met de voorgenomen ontgraving tot ca. 1,5 m –mv ten behoeve van de aanplant van een grote boom.&lt;br&gt;Tijdens de begeleiding zijn tegen verwachting in geen graven of menselijk resten gevonden, gezien de aanwezigheid van twee recente ophogingspakketten is het mogelijk dat de aanwezige graven op een eerdere moment zijn afgegraven.&lt;/p&gt;
DOI: 10.82364/138.2/lsu/parc/metadata/rnacentral
title: SILVA Release 138.2 LSU Parc export for RNAcentral
DOI: 10.5281/zenodo.15198653
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2000—2001

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.15198654
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2000—2001

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: 10.17026/dans-2xa-wkas
title: Vierhouten, gemeente Nunspeet - van Bronstijd tot moesbed Vierhouten, Nunspeet - van Bronstijd tot moesbed

description: &lt;p&gt;ADC ArcheoProjecten heeft op 14 en 15 november 2018 een Archeologische Begeleiding (conform protocol opgraven) uitgevoerd ten behoeve van nieuwe groepsverblijven op de locatie ’t Frusselt te Vierhouten. De Archeologische Begeleiding behelsde het toezicht houden op het uitgraven van twee bouwputten.&lt;br&gt;Aan de hand van de sporen en vondsten, die zijn aangetroffen tijdens de archeologische begeleiding, kan slechts worden vastgesteld dat er vanaf de prehistorie mensen actief zijn geweest in het plangebied.&lt;/p&gt;
DOI: 10.82364/138.2/lsu/ref/metadata/rnacentral
title: SILVA Release 138.2 LSU Ref export for RNAcentral
DOI: 10.5281/zenodo.17061664
title: TranscriboQuest 2025 -- Ancient Greek Team

description: This digital corpus contains twenty pages of ancient Greek manuscripts (4th-1st century BCE) processed via eScriptorium HTR and manually corrected. The collection features dramatic fragments from Menander's Sicyonians, Apollodorus, and Epicharmus's Meropis, Timotheus's lyrical works, and a medical treatise, representing key genres of Hellenistic literature.
DOI: 10.5281/zenodo.15198655
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2002—2003

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.15198656
title: Global Pasture Watch - Annual short vegetation height maps at 30-m spatial resolution (2000—2022)

description: Sub-dataset: Median vegetation height, 2002—2003

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.14678130
title: What Pulls International Students to Indonesia? Factors Exploration across Student Groups
DOI: 10.5281/zenodo.14678131
title: What Pulls International Students to Indonesia? Factors Exploration across Student Groups