cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.z5wbsd
title: Occurrence Download

description: A dataset containing 41960 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Solidago canadensis)",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "Year is greater than or equal to 2010",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 50",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 41960 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006708-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/60e67262-a8bc-4e7c-b4fb-fc569b33eb04
title: Seismic data for Sierra Negra volcano, Galapagos Islands 2022-25

description: Seismic data for four stations located on Seirra Negra volcano, Galapagos Islands, between 2022 and 2025. The stations were maintained and operated by NERC grant NE/W007274/1. The instruments (SN07, SN12, SN14, and SN17) are four GeoBit GeoTiny 'wideband' seismometers. SN17 replaced SN07 in 2023, at a new site ~200m from the old location. The data cover a period of surface uplift at Sierra Negra, driven by magma accumulation following the 2018 eruption.
Data are uploaded as miniseed files, in folder structured by station/channel. Files follow a standard naming format e.g. 'EC.SN12..HHZ.D.2022.001'
SN07: -0.80719, -91.17005, 1067m
SN12: -0.811195, -91.134960, 954m
SN14: -0.843680, -91.152610, 917m
SN17: -0.805954, -91.170256, 1068m
An xml file is also included which contains useful metadata about the station locations and instrument responses.
DOI: 10.15468/dl.4pr5b2
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aegokeras caespitosa",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006712-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/9wom0m
title: PDF_BASE_ONERA_468_CRM-WBPN_ComputersAndFluids302-2025.pdf

description: Scientific reference article (author version) about the database and the associated 2025 regression challenge
DOI: 10.57745/u1yoyi
title: ReadMe_6.pdf

description: :unav
DOI: 10.57745/alspnf
title: BASEa_WTLowRe.gz

description: :unav
DOI: 10.57745/uzggnm
title: BASEb_WTDPW6.gz

description: :unav
DOI: 10.57745/enisbn
title: BASEc_WTHighRe.gz

description: :unav
DOI: 10.57745/qp0csf
title: ELSA_FILES.gz

description: :unav
DOI: 10.57745/z9ldy8
title: FILES_OF_2025_REGRESSION_CHALLENGE.gz

description: :unav
DOI: 10.57745/ilauoz
title: ReadMe_7.pdf

description: :unav
DOI: 10.57745/k1uk6z
title: ONERA 468 CRM: CFD wall wall fields about the Common Reserch Model (WBPN) for 468 flow conditions

description: The database contains the wall distributions of 468 Reynolds-Averaged Navier–Stokes simulations using the Spalart–Allmaras turbulence model, performed on the NASA/Boeing Common Research Model wing-body-pylon-nacelle configuration. The database spans a wide range of flow conditions, varying Mach number (including transonic regimes), angle of attack (capturing flow separation), and Reynolds number (based on three stagnation pressures, with one setting matching wind tunnel experiments). The numerical quality of the database is assessed, through checking the convergence level of each computation.
DOI: 10.15468/dl.r9cfhw
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.8265 25.604751, 88.748638 25.599174, 88.672714 25.582581, 88.600617 25.555385, 88.534137 25.518264, 88.47492 25.472138, 88.42442800000001 25.418154, 88.3839 25.357651, 88.35432400000001 25.292125, 88.336414 25.223198, 88.33059 25.15257, 88.336977 25.081982, 88.355396 25.013171, 88.38537599999999 24.947826, 88.426163 24.887549, 88.476744 24.833818, 88.535872 24.787945, 88.602093 24.751051, 88.67378600000001 24.724036, 88.749202 24.707559, 88.8265 24.702022, 88.90379799999999 24.707559, 88.979214 24.724036, 89.050907 24.751051, 89.11712799999999 24.787945, 89.176256 24.833818, 89.226837 24.887549, 89.267624 24.947826, 89.29760400000001 25.013171, 89.316023 25.081982, 89.32241 25.15257, 89.316586 25.223198, 89.298676 25.292125, 89.26909999999999 25.357651, 89.228572 25.418154, 89.17807999999999 25.472138, 89.118863 25.518264, 89.05238300000001 25.555385, 88.98028600000001 25.582581, 88.90436200000001 25.599174, 88.8265 25.604751))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006713-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3nrnru
title: Occurrence Download

description: A dataset containing 661 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euphausia vallentini",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLatitude is less than 10",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2015"
 ]
}

 The dataset includes 661 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006714-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57841/casdc.0007030
title: 自适应视觉感知器件明暗双模态调控亮度范围
DOI: 10.15468/dl.7wrwee
title: Occurrence Download

description: A dataset containing 28591 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pecten maximus"
 ]
}

 The dataset includes 28591 records from 222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006715-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57841/casdc.0007031
title: 多组份界面的构筑方法的数据集
DOI: 10.57841/casdc.0007032
title: 多尺度原位表界面表征方法数据集
DOI: 10.15468/dl.6mg7uy
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.88925 24.487058, 88.81209 24.481481, 88.73685 24.464889, 88.665401 24.437694, 88.599515 24.400573, 88.540824 24.354447, 88.490776 24.300461, 88.45059999999999 24.239954, 88.42127499999999 24.174423, 88.40351 24.105489, 88.39772499999999 24.034851, 88.40404100000001 23.964252, 88.422285 23.895427, 88.451989 23.830068, 88.49240899999999 23.769777, 88.542541 23.716031, 88.60114799999999 23.670145, 88.66679000000001 23.63324, 88.73786 23.606216, 88.81262099999999 23.589734, 88.88925 23.584195, 88.965879 23.589734, 89.04064 23.606216, 89.11171 23.63324, 89.177352 23.670145, 89.23595899999999 23.716031, 89.286091 23.769777, 89.326511 23.830068, 89.35621500000001 23.895427, 89.374459 23.964252, 89.380775 24.034851, 89.37499 24.105489, 89.357225 24.174423, 89.3279 24.239954, 89.287724 24.300461, 89.23767599999999 24.354447, 89.178985 24.400573, 89.11309900000001 24.437694, 89.04165 24.464889, 88.96641 24.481481, 88.88925 24.487058))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006716-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18160/fgrd-9pwh
title: European Obspack compilation of atmospheric carbon dioxide data from ICOS stations for the period 1972-2026; obspack_co2_466_ICOSFT2026.1_20260123

description: This data package contains high accuracy CO2 dry air mole fractions from 40 ICOS European observatories at in total 98 observation levels, collected by the ICOS Atmosphere Thematic Centre (ATC) and provided by the station contributors. The package includes the 2026.1 FastTrack update of the Globalview EU data product and is intended for use in carbon cycle inverse modeling, model evaluation, and satellite validation studies. Please report errors and send comments regarding this product to the ObsPack originators. Please read carefully the ObsPack Fair Use statement and cite appropriately. Please review the release notes for the associated Obspack product at www.esrl.noaa.gov/gmd/ccgg/obspack/release_notes.html. Metadata for this product are available at https://commons.datacite.org/doi.org/10.18160/FGRD-9PWH. Please visit http://www.gml.noaa.gov/ccgg/obspack/ for more information on Obspack.
DOI: 10.5281/zenodo.18347293
title: Dataset used in the publication entitled "Challenges in the Proper Metrological Verification of Smart Energy Meters"

description: Dataset obtained from preliminary studies of AMI meters for a test signal containing a fundamental and an additional component with a fundamental component share equal to ui [%] and frequency fi [Hz]. Individual columns contain results for a Class A power quality analyzer (PQA) and subsequent AMI meters (3 meters from 3 different manufacturers). The tests presented the results of measurements of the fundamental frequency f0, the short-term flicker indicator Pst, and the total harmonic distortion (THD). The results of preliminary studies are presented and discussed in the publication: Bracale A., Janowicz J., Kuwałek P., Wiczyński G., "Challenges in the Proper Metrological Verification of Smart Energy Meters." The research results are part of work carried out as part of the project entitled "Proper metrological verification of smart energy meters" financed by the National Science Centre – 2024/55/D/ST7/00441.
description: This research was funded in whole or in part by National Science Centre, Poland – 2024/55/D/ST7/00441. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.
DOI: 10.5281/zenodo.18347294
title: Dataset used in the publication entitled "Challenges in the Proper Metrological Verification of Smart Energy Meters"

description: Dataset obtained from preliminary studies of AMI meters for a test signal containing a fundamental and an additional component with a fundamental component share equal to ui [%] and frequency fi [Hz]. Individual columns contain results for a Class A power quality analyzer (PQA) and subsequent AMI meters (3 meters from 3 different manufacturers). The tests presented the results of measurements of the fundamental frequency f0, the short-term flicker indicator Pst, and the total harmonic distortion (THD). The results of preliminary studies are presented and discussed in the publication: Bracale A., Janowicz J., Kuwałek P., Wiczyński G., "Challenges in the Proper Metrological Verification of Smart Energy Meters." The research results are part of work carried out as part of the project entitled "Proper metrological verification of smart energy meters" financed by the National Science Centre – 2024/55/D/ST7/00441.
description: This research was funded in whole or in part by National Science Centre, Poland – 2024/55/D/ST7/00441. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.
DOI: 10.15468/dl.wx7ajg
title: Occurrence Download

description: A dataset containing 4272 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Crepis biennis, Crepis biennis, Crepis biennis, Crepis biennis, Crepis biennis)",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "Year is greater than or equal to 2010",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 50",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 4272 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006717-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bj49vq
title: Occurrence Download

description: A dataset containing 5683 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aegopodium podagraria",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 5683 records from 258 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006718-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18347579
title: ГИПОТАЛАМО-ГИПОФИЗАРНАЯ ДИСФУНКЦИЯ ПОСЛЕ ЧЕРЕПНО-МОЗГОВОЙ ТРАВМЫ У ДЕТЕЙ МЛАДШЕГО ШКОЛЬНОГО ВОЗРАСТА: ЭПИДЕМИОЛОГИЯ, ПАТОГЕНЕЗ, ДИАГНОСТИКА И ТЕРАПЕВТИЧЕСКИЕ ПОДХОДЫ

description: Цель: изучить частоту гипоталамо-гипофизарной дисфункции у детей младшего школьного возраста после черепно-мозговой травмы (ЧМТ).

Материалы и методы: исследование включало 58 детей с ЧМТ. Оценивались уровни гормонов в крови, МРТ головного мозга, нейроэндокринные осложнения в динамике (Т0, Т3, Т6, Т12).

Результаты: гормональные нарушения выявлены у 17,6% пациентов, наиболее частыми были пангипопитуитаризм (25,8%) и несахарный диабет (20,6%).

Выводы: тяжесть ЧМТ и развитие СНСАДГ/НД в остром периоде являются ключевыми факторами риска эндокринной дисфункции.
DOI: 10.5281/zenodo.18347580
title: ГИПОТАЛАМО-ГИПОФИЗАРНАЯ ДИСФУНКЦИЯ ПОСЛЕ ЧЕРЕПНО-МОЗГОВОЙ ТРАВМЫ У ДЕТЕЙ МЛАДШЕГО ШКОЛЬНОГО ВОЗРАСТА: ЭПИДЕМИОЛОГИЯ, ПАТОГЕНЕЗ, ДИАГНОСТИКА И ТЕРАПЕВТИЧЕСКИЕ ПОДХОДЫ

description: Цель: изучить частоту гипоталамо-гипофизарной дисфункции у детей младшего школьного возраста после черепно-мозговой травмы (ЧМТ).

Материалы и методы: исследование включало 58 детей с ЧМТ. Оценивались уровни гормонов в крови, МРТ головного мозга, нейроэндокринные осложнения в динамике (Т0, Т3, Т6, Т12).

Результаты: гормональные нарушения выявлены у 17,6% пациентов, наиболее частыми были пангипопитуитаризм (25,8%) и несахарный диабет (20,6%).

Выводы: тяжесть ЧМТ и развитие СНСАДГ/НД в остром периоде являются ключевыми факторами риска эндокринной дисфункции.
DOI: 10.15468/dl.q7tgqp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.5562 25.689746, 89.478283 25.684168, 89.402305 25.667576, 89.330157 25.64038, 89.26363000000001 25.603258, 89.20437200000001 25.557133, 89.153845 25.503149, 89.11329000000001 25.442646, 89.08369399999999 25.37712, 89.065772 25.308194, 89.059946 25.237567, 89.066339 25.16698, 89.084771 25.098169, 89.114772 25.032825, 89.15558799999999 24.97255, 89.206205 24.91882, 89.265373 24.872948, 89.33163999999999 24.836054, 89.40338199999999 24.80904, 89.478849 24.792564, 89.5562 24.787027, 89.633551 24.792564, 89.709018 24.80904, 89.78076 24.836054, 89.847027 24.872948, 89.906195 24.91882, 89.956812 24.97255, 89.99762800000001 25.032825, 90.027629 25.098169, 90.04606099999999 25.16698, 90.052454 25.237567, 90.046628 25.308194, 90.028706 25.37712, 89.99911 25.442646, 89.958555 25.503149, 89.908028 25.557133, 89.84877 25.603258, 89.78224299999999 25.64038, 89.710095 25.667576, 89.634117 25.684168, 89.5562 25.689746))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006719-260120142942310/datasets/export for details.
DOI: 10.34740/kaggle/dsv/14591484
title: Financial and regulatory impact simulator
DOI: 10.15468/dl.yumwsh
title: Occurrence Download

description: A dataset containing 203 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hansarsia megalops",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLatitude is less than 10",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 2015"
 ]
}

 The dataset includes 203 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006720-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14591450
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.5517/ccdc.csd.cc2n1596
title: CCDC 2443987: Experimental Crystal Structure Determination

description: Related Article: Chavakula Nagababu, Takuya Muramatsu, Muhamad Zulfaqar Bacho, Shiwei Wu, Seishu Ochiai, Jorge Escorihuela, Norio Shibata|2025|Chemical Science|||doi:10.1039/d5sc08177g
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.nxvt5b
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.77606 24.488828, 88.698899 24.483251, 88.62365800000001 24.466659, 88.55220799999999 24.439464, 88.486321 24.402342, 88.427629 24.356217, 88.37757999999999 24.302231, 88.33740400000001 24.241724, 88.30807900000001 24.176193, 88.290314 24.107259, 88.28452799999999 24.036621, 88.29084400000001 23.966022, 88.309088 23.897197, 88.338793 23.831838, 88.379214 23.771547, 88.42934700000001 23.717801, 88.487955 23.671915, 88.553597 23.63501, 88.624668 23.607986, 88.69943000000001 23.591504, 88.77606 23.585965, 88.85269 23.591504, 88.927452 23.607986, 88.99852300000001 23.63501, 89.064165 23.671915, 89.122773 23.717801, 89.172906 23.771547, 89.21332700000001 23.831838, 89.243032 23.897197, 89.261276 23.966022, 89.26759199999999 24.036621, 89.26180600000001 24.107259, 89.244041 24.176193, 89.214716 24.241724, 89.17453999999999 24.302231, 89.12449100000001 24.356217, 89.065799 24.402342, 88.99991199999999 24.439464, 88.928462 24.466659, 88.853221 24.483251, 88.77606 24.488828))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006722-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hd2zq2
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Moehringia intricata tejedensis"
 ]
}

 The dataset includes 55 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006723-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31132751
title: Unveiling internal drivers of corporate greenwashing: a machine learning approach

description: Environmental, social, and governance (ESG) greenwashing has emerged as a critical issue, yet research on its prediction remains underdeveloped. This study builds a model to predict corporate greenwashing using machine learning. Using data on Chinese listed companies from 2009 to 2023, we examine features across four dimensions: finance, governance, chief executive officer (CEO), and chairman. The results indicate that organizational variables (finance and governance) outperform managerial individual traits (CEO and chairman) in both explanatory power and predictive accuracy. Non-linear models perform better than linear regression, especially eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). We further use the SHapley Additive exPlanations (SHAP) algorithm to evaluate feature importance, finding that total assets play a key role in predicting greenwashing. This study advances the literature on corporate greenwashing from a machine learning perspective, offering novel evidence that can inform regulatory oversight and promote corporate sustainability.
DOI: 10.6084/m9.figshare.31132751.v1
title: Unveiling internal drivers of corporate greenwashing: a machine learning approach

description: Environmental, social, and governance (ESG) greenwashing has emerged as a critical issue, yet research on its prediction remains underdeveloped. This study builds a model to predict corporate greenwashing using machine learning. Using data on Chinese listed companies from 2009 to 2023, we examine features across four dimensions: finance, governance, chief executive officer (CEO), and chairman. The results indicate that organizational variables (finance and governance) outperform managerial individual traits (CEO and chairman) in both explanatory power and predictive accuracy. Non-linear models perform better than linear regression, especially eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). We further use the SHapley Additive exPlanations (SHAP) algorithm to evaluate feature importance, finding that total assets play a key role in predicting greenwashing. This study advances the literature on corporate greenwashing from a machine learning perspective, offering novel evidence that can inform regulatory oversight and promote corporate sustainability.
DOI: 10.18112/openneuro.ds005559.v2.0.1
title: Item and spatial pattern separation