cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.5tjeus
title: Occurrence Download

description: A dataset containing 1239 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dillenia indica)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1239 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054189-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cwt8dg
title: Occurrence Download

description: A dataset containing 801 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Material sample, Material citation, Fossil, Specimen, Living Specimen, Occurrence evidence, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Spizocorys sclateri"
 ]
}

 The dataset includes 801 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054193-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n8b8eq
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is University of Minnesota Insect Collection",
 "TaxonKey is Bombus flavidus"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054194-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nbkmhc
title: Occurrence Download

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

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054195-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17095993
title: Data: Towards scalable quantum computations of atomic nuclei

description: Data and Python scripts to produce the figures of "Towards scalable quantum computations of atomic nuclei" by C. Gu, M. Heinz, O. Kiss, and T. Papenbrock.
DOI: 10.5281/zenodo.17279920
title: Data: Towards scalable quantum computations of atomic nuclei

description: Data and Python scripts to produce the figures of "Towards scalable quantum computations of atomic nuclei" by C. Gu, M. Heinz, O. Kiss, and T. Papenbrock.
DOI: 10.6084/m9.figshare.29724074
title: Original Data.xlsx

description: QTP植被和土壤数据
DOI: 10.6084/m9.figshare.29724074.v2
title: Original Data.xlsx

description: QTP植被和土壤数据
DOI: 10.6084/m9.figshare.29724074.v1
title: Original Data.xlsx

description: QTP植被和土壤数据
DOI: 10.5281/zenodo.17264585
title: FedStenoNet X-ray Coronary Angiography Dataset for Stenosis Detection

description: This dataset is released to support research on the automatic detection of coronary stenosis in X-ray coronary angiography (XCA).

It contains 1565 XCA images acquired at Ospedali Riuniti (Ancona, Italy) using Azurion Clarity IQ (Philips) and Allura Xper FD10 (Philips) scanners, from 244 patients who underwent XCA procedures during 2020. All patients included in the dataset were diagnosed with significant coronary stenosis, as reported in their examination records. According to the European Society of Cardiology Guidelines, a stenosis was considered significant if presenting an area reduction ≥70% measured with quantitative coronary angiography (QCA). When necessary, diagnosis was confirmed by intravascular ultrasound (IVUS), where a stenosis was considered significant if the minimum lumen area was ≤4 mm².

Images were selected considering high-contrast dye visibility, varied viewpoints, and the diastolic phase of the cardiac cycle. Three expert clinicians supervised the acquisition and provided manual annotations of stenotic regions along the left coronary artery.  Data collection complied with the Helsinki Declaration, was approved by the Local Ethics Committee (CET 59/2024), and informed consent was obtained from all patients. 

This dataset corresponds to Dataset A described and used in the article:Di Cosmo, M., Migliorelli, G., Villani, F.P., Francioni, M., Muçaj, A., Frontoni, E., Moccia, S., Fiorentino, M.C. "FedStenoNet: Tackling domain shift in x-ray coronary angiography through a personalized federated detection framework", Computers in Biology and Medicine (2025).
DOI: 10.5281/zenodo.17264586
title: FedStenoNet X-ray Coronary Angiography Dataset for Stenosis Detection

description: This dataset is released to support research on the automatic detection of coronary stenosis in X-ray coronary angiography (XCA).

It contains 1565 XCA images acquired at Ospedali Riuniti (Ancona, Italy) using Azurion Clarity IQ (Philips) and Allura Xper FD10 (Philips) scanners, from 244 patients who underwent XCA procedures during 2020. All patients included in the dataset were diagnosed with significant coronary stenosis, as reported in their examination records. According to the European Society of Cardiology Guidelines, a stenosis was considered significant if presenting an area reduction ≥70% measured with quantitative coronary angiography (QCA). When necessary, diagnosis was confirmed by intravascular ultrasound (IVUS), where a stenosis was considered significant if the minimum lumen area was ≤4 mm².

Images were selected considering high-contrast dye visibility, varied viewpoints, and the diastolic phase of the cardiac cycle. Three expert clinicians supervised the acquisition and provided manual annotations of stenotic regions along the left coronary artery.  Data collection complied with the Helsinki Declaration, was approved by the Local Ethics Committee (CET 59/2024), and informed consent was obtained from all patients. 

This dataset corresponds to Dataset A described and used in the article:Di Cosmo, M., Migliorelli, G., Villani, F.P., Francioni, M., Muçaj, A., Frontoni, E., Moccia, S., Fiorentino, M.C. "FedStenoNet: Tackling domain shift in x-ray coronary angiography through a personalized federated detection framework", Computers in Biology and Medicine (2025).
DOI: 10.15468/dl.e3mbsq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is "
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054198-250920141307145/datasets/export for details.
DOI: 10.15468/dl.9r3ynt
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dillenia turbinata)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 118 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054197-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/kam1-acc1-fq0r
title: DC3: NASA DC-8 Solar Spectral Flux Radiometer (SSFR) Upwelling and Downwelling Solar Spectral Irradiance Data. Version 1.0

description: This data set contains NASA DC-8 Solar Spectral Flux Radiometer (SSFR) Upwelling and Downwelling Solar Spectral Irradiance data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 11 May 2012 through 22 June 2012. This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments. The data reported are only selected wavelengths, as well as integrated solar irradiance in two ranges (SSFR). Use of these data requires PRIOR OK from the PI. For full SSFR spectra from 350-2150 nm, please contact the PI.
DOI: 10.15468/dl.88asu9
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Borysthenia naticina",
 "Country is Germany",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054199-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ls/1fx6pi
title: Trimbos-institute (2019-2022) NEMESIS-3 Baseline Wave (T0) Netherlands Mental Health Survey and Incidence Study-3
title: NEMESIS-3 Baseline Wave (T0)

description: NEMESIS is een landelijke studie naar de psychische gezondheid en het welzijn van volwassen Nederlanders van 18 tot en met 75 jaar. Doel ervan is om meer inzicht te krijgen in het vóórkomen, ontstaan, beloop en gevolgen van psychische aandoeningen in de Nederlandse bevolking. In NEMESIS maken we gebruik van een klinische instrument, de CIDI (Composite International Diagnostic Interview) versie 3.0, om veelvoorkomende psychische aandoeningen volgens de DSM (Diagnostic and Statistical Manual of Mental Disorders) te bepalen. Versie 3.0 is voor dit onderzoek enigszins aangepast zodat de criteria voor As-1 stoornissen voor zowel DSM-IV als DMS-5 konden worden vastgesteld. &lt;br&gt;
Naast de CIDI nemen interviewers een aanvullende vragenlijst af waarin onderwerpen zoals demografie, zorggebruik en algemeen functioneren aan de orde komen. De interviews duren gemiddeld ongeveer anderhalf uur en vinden bijna altijd thuis bij de respondent plaats.&lt;br&gt;
In NEMESIS-3 is het klinisch instrument en de aanvullende vragenlijst zo min mogelijk aangepast waardoor een goede vergelijking met NEMESIS-2 mogelijk is.
NEMESIS is een cohort studie waarbij dezelfde respondenten gedurende meerdere metingen worden gevolgd. De baseline meting is uitgevoerd, in 2019-2022 en in 2023-2024 is er bij dezelfde respondenten een tweede meting uitgevoerd. Een derde meting zal in 2026 starten. NEMESIS levert een schat aan gegevens op over de psychische gezondheidstoestand en het zorggebruik van de Nederlandse bevolking. De studie geeft inzicht in de aanwezigheid (prevalentie), het ontstaan (incidentie) en het beloop van psychische aandoeningen en de risicofactoren daarvan, het functioneren bij psychische aandoeningen, ziekteverzuim vanwege psychische en somatische aandoeningen en het zorggebruik. ( zie ook- https://cijfers.trimbos.nl/nemesis/kerncijfers-psychische-aandoeningen/samenvatting-kerncijfers/ )&lt;br&gt;
Met de vervolgmetingen wordt het mogelijk het langere beloop (in termen van o.a. terugval en chroniciteit) van psychische aandoeningen in de bevolking te onderzoeken.
Deze doi bevat informatie van de eerste (baseline) meting van NEMESIS-3. De data is op aanvraag beschikbaar. &lt;br&gt;
Het betreft hier data van de derde NEMESIS studie. In de periode 1996-1999 is in drie metingen NEMESIS-1 uitgevoerd en in de periode 2007-2018 in vier metingen NEMESIS-2. (zie Relations).
description: NEMESIS
description: SPSS
DOI: 10.17028/rd.lboro.30287107
title: Supplementary information files for "Food insecurity and maternal mental health during the COVID-19 pandemic: associations over time among low income communities"

description: Supplementary files for article "Tracking and changes in the clustering of physical activity, sedentary behavior, diet, and sleep across childhood and adolescence: A systematic review"<br><br><b>Introduction</b>Clusters of health behaviors (e.g. physical activity/sedentary behavior/diet/sleep) can exert synergistic influences on health outcomes, such as obesity. Understanding how clusters of health behaviors change throughout childhood and adolescence is essential for developing interventions aimed at uncoupling unhealthy behaviors. This review synthesizes prospective studies examining changes in clusters of physical activity, sedentary behavior, diet, and sleep through childhood and adolescence.<b>Methods</b>Electronic searches (PubMed, Embase, Web of Science, Scopus) identified prospective studies, published in English up to/including January 2024, of children/adolescents (0-19 years) which used data-driven methods to identify clusters of 2/more behaviors (physical activity, sedentary behaviors, diet, sleep) at multiple timepoints. A narrative synthesis was conducted due to methodological heterogeneity.<b>Results</b>Eighteen studies reporting data from 26,772 individual participants were included. Eleven studies determined clusters at each timepoint (i.e. identified clusters at T1 and T2, respectively), while seven determined clusters longitudinally using behavioral data across multiple timepoints. Among studies that identified clusters at each timepoint, participants commonly transitioned to similarly characterized clusters between timepoints. Where cluster tracking was examined, 64% of clusters had stable transition probabilities of 60-100%. The most prevalent longitudinal cluster trajectories were characterized by co-occurring healthy behaviors which remained stable. Remaining within unhealthy clusters at multiple timepoints was associated with higher markers of adiposity.<b>Conclusion</b>‘Healthy’ and ‘unhealthy’ clusters remained highly stable over time, suggesting behavioral patterns developed early can become entrenched and resistant to change. Interventions focused on instilling healthy behaviors early are required to provide a strong foundation for behavioral stability throughout life.<br><br>© The Author(s), CC BY 4.0
DOI: 10.17028/rd.lboro.30287107.v1
title: Supplementary information files for "Food insecurity and maternal mental health during the COVID-19 pandemic: associations over time among low income communities"

description: Supplementary files for article "Tracking and changes in the clustering of physical activity, sedentary behavior, diet, and sleep across childhood and adolescence: A systematic review"<br><br><b>Introduction</b>Clusters of health behaviors (e.g. physical activity/sedentary behavior/diet/sleep) can exert synergistic influences on health outcomes, such as obesity. Understanding how clusters of health behaviors change throughout childhood and adolescence is essential for developing interventions aimed at uncoupling unhealthy behaviors. This review synthesizes prospective studies examining changes in clusters of physical activity, sedentary behavior, diet, and sleep through childhood and adolescence.<b>Methods</b>Electronic searches (PubMed, Embase, Web of Science, Scopus) identified prospective studies, published in English up to/including January 2024, of children/adolescents (0-19 years) which used data-driven methods to identify clusters of 2/more behaviors (physical activity, sedentary behaviors, diet, sleep) at multiple timepoints. A narrative synthesis was conducted due to methodological heterogeneity.<b>Results</b>Eighteen studies reporting data from 26,772 individual participants were included. Eleven studies determined clusters at each timepoint (i.e. identified clusters at T1 and T2, respectively), while seven determined clusters longitudinally using behavioral data across multiple timepoints. Among studies that identified clusters at each timepoint, participants commonly transitioned to similarly characterized clusters between timepoints. Where cluster tracking was examined, 64% of clusters had stable transition probabilities of 60-100%. The most prevalent longitudinal cluster trajectories were characterized by co-occurring healthy behaviors which remained stable. Remaining within unhealthy clusters at multiple timepoints was associated with higher markers of adiposity.<b>Conclusion</b>‘Healthy’ and ‘unhealthy’ clusters remained highly stable over time, suggesting behavioral patterns developed early can become entrenched and resistant to change. Interventions focused on instilling healthy behaviors early are required to provide a strong foundation for behavioral stability throughout life.<br><br>© The Author(s), CC BY 4.0
DOI: 10.25625/3a5dfl/m5achq
title: P1EL.021.png

description: :unav
DOI: 10.25625/3a5dfl/scrzku
title: P1EL.012.png

description: :unav
DOI: 10.25625/3a5dfl/0w3bfl
title: P1EL.003.png

description: :unav
DOI: 10.25625/3a5dfl/gpcqcr
title: P1EL.013.png

description: :unav
DOI: 10.25625/3a5dfl/2r8fcv
title: P1EL.002.png

description: :unav
DOI: 10.25625/3a5dfl/6pdgsb
title: P1EL.014.png

description: :unav
DOI: 10.25625/3a5dfl/ufvvut
title: P1EL.001.png

description: :unav
DOI: 10.25625/3a5dfl/daxjqc
title: P1EL.015.png

description: :unav
DOI: 10.25625/3a5dfl/bbchxb
title: P1EL.029.png

description: :unav
DOI: 10.25625/3a5dfl/oy7oym
title: P1EL.030.png

description: :unav
DOI: 10.25625/3a5dfl/1dfqxo
title: P1EL.034.png

description: :unav
DOI: 10.25625/3a5dfl/2wrzny
title: P1EL.028.png

description: :unav
DOI: 10.25625/3a5dfl/7sspkr
title: P1EL.005.png

description: :unav
DOI: 10.25625/3a5dfl/vnmxpk
title: P1EL.018.png

description: :unav
DOI: 10.25625/3a5dfl/kk4hau
title: P1EL.004.png

description: :unav
DOI: 10.25625/3a5dfl/hlgnx5
title: P1EL.006.png

description: :unav
DOI: 10.25625/3a5dfl/wy9hba
title: P1EL.010.png

description: :unav
DOI: 10.25625/3a5dfl/eolp37
title: P1EL.007.png

description: :unav
DOI: 10.25625/3a5dfl/rxqxpt
title: P1EL.017.png

description: :unav
DOI: 10.25625/3a5dfl/5ale7y
title: P1EL.019.png

description: :unav
DOI: 10.25625/3a5dfl/arghwo
title: P1EL.009.png

description: :unav
DOI: 10.25625/3a5dfl/vpivlm
title: P1EL.032.png

description: :unav
DOI: 10.25625/3a5dfl/w713ys
title: P1EL.011.png

description: :unav
DOI: 10.25625/3a5dfl/y9r4qq
title: P1EL.031.png

description: :unav
DOI: 10.25625/3a5dfl/xsbp0i
title: P1EL.023.png

description: :unav
DOI: 10.25625/3a5dfl/42qpli
title: P1EL.027.png

description: :unav
DOI: 10.25625/3a5dfl/tdw8gt
title: P1EL.020.png

description: :unav
DOI: 10.25625/3a5dfl/leyhza
title: P1EL.035.png

description: :unav
DOI: 10.25625/3a5dfl/vglh4j
title: P1EL.024.png

description: :unav
DOI: 10.25625/3a5dfl/gpz1za
title: P1EL.022.png

description: :unav
DOI: 10.25625/3a5dfl/07onjc
title: P1EL.025.png

description: :unav
DOI: 10.25625/3a5dfl/qaeive
title: P1EL.033.png

description: :unav
DOI: 10.25625/3a5dfl/f2ls6t
title: P1EL.016.png

description: :unav
DOI: 10.25625/3a5dfl/pjmkku
title: P1EL.026.png

description: :unav
DOI: 10.15468/dl.ypwtn5
title: Occurrence Download

description: A dataset containing 4190 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dimocarpus longan)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4190 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054200-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25625/3a5dfl/3ttzmg
title: P1EL.008.png

description: :unav
DOI: 10.25625/3a5dfl/u2di8w
title: P1EL v2.0.031.png

description: :unav
DOI: 10.15468/dl.se9366
title: Occurrence Download

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

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054201-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25625/3a5dfl/sbrgxp
title: P1EL v2.0.010.png

description: :unav
DOI: 10.25625/3a5dfl/c4cavz
title: P1EL v2.0.005.png

description: :unav
DOI: 10.25625/3a5dfl/ird1km
title: P1EL v2.0.015.png

description: :unav
DOI: 10.25625/3a5dfl/rxjzhg
title: P1EL v2.0.007.png

description: :unav
DOI: 10.25625/3a5dfl/kolh6l
title: P1EL v2.0.016.png

description: :unav
DOI: 10.25625/3a5dfl/nw9sjj
title: P1EL v2.0.009.png

description: :unav
DOI: 10.25625/3a5dfl/3yho2x
title: P1EL v2.0.026.png

description: :unav
DOI: 10.25625/3a5dfl/b40eom
title: P1EL v2.0.017.png

description: :unav
DOI: 10.25625/3a5dfl/tswhfh
title: P1EL v2.0.011.png

description: :unav
DOI: 10.25625/3a5dfl/xyi5so
title: P1EL v2.0.004.png

description: :unav
DOI: 10.15468/cdma93
title: Diversity of geometrid moths (Lepidoptera: Geometroidea) from northernmost Western Ghats of Maharashtra, India, with notes on range extension.

description: We present the first comprehensive checklist of geometrid moths from the northernmost Western Ghats of Maharashtra, India a biogeographically significant yet underexplored region. Surveys conducted across eight localities between 2022 and 2024 recorded 286 individuals representing 77 species and 9 genus-level morphotypes within the superfamily Geometroidea. These taxa span 46 genera, 20 tribes, and 7 subfamilies under two families, namely Geometridae (73 species) and Uraniidae (4 species). Sampling was carried out across an elevational gradient of 309–1243 m asl using non-invasive light-sheet methods supplemented with opportunistic photographic observations. The most species-rich subfamilies were Ennominae (29 species), Sterrhinae (22 species), and Geometrinae (17 species). Genera such as Scopula, Chiasmia, and Comibaena were frequently encountered, indicating ecological generalism and strong phototactic behavior. Notably, three species—Idaea protensa, Cleora cornaria, and Ophthalmitis herbidaria—are reported for the first time from Peninsular India, thus extending their known distributional ranges. In addition, range extensions of eight species to the northernmost Western Ghats in Maharashtra and a total of thirty species to the northern Western Ghats were recorded.
DOI: 10.25625/3a5dfl/shztad
title: P1EL v2.0.018.png

description: :unav
DOI: 10.25625/3a5dfl/khehyq
title: P1EL v2.0.030.png

description: :unav
DOI: 10.25625/3a5dfl/mcfixw
title: P1EL v2.0.032.png

description: :unav
DOI: 10.25625/3a5dfl/1pyvdk
title: P1EL v2.0.029.png

description: :unav
DOI: 10.25625/3a5dfl/jelkxb
title: P1EL v2.0.027.png

description: :unav
DOI: 10.25625/3a5dfl/do2mvj
title: P1EL v2.0.023.png

description: :unav
DOI: 10.25625/3a5dfl/abk6dz
title: P1EL v2.0.024.png

description: :unav
DOI: 10.25625/3a5dfl/80rmga
title: P1EL v2.0.002.png

description: :unav
DOI: 10.25625/3a5dfl/d6gdqo
title: P1EL v2.0.012.png

description: :unav
DOI: 10.25625/3a5dfl/zkjdlz
title: P1EL v2.0.014.png

description: :unav
DOI: 10.25625/3a5dfl/ejbsgx
title: P1EL v2.0.008.png

description: :unav
DOI: 10.25625/3a5dfl/hgjrhk
title: P1EL v2.0.021.png

description: :unav
DOI: 10.25625/3a5dfl/wwrrbw
title: P1EL v2.0.022.png

description: :unav
DOI: 10.25625/3a5dfl/ls6we0
title: P1EL v2.0.001.png

description: :unav
DOI: 10.25625/3a5dfl/zunwnf
title: P1EL v2.0.006.png

description: :unav
DOI: 10.25625/3a5dfl/ceuka8
title: P1EL v2.0.003.png

description: :unav
DOI: 10.25625/3a5dfl/iofxew
title: P1EL v2.0.025.png

description: :unav
DOI: 10.25625/3a5dfl/dm4rp5
title: P1EL v2.0.020.png

description: :unav
DOI: 10.25625/3a5dfl/isnwfk
title: P1EL v2.0.013.png

description: :unav
DOI: 10.25625/3a5dfl/xvoyba
title: P1EL v2.0.028.png

description: :unav
DOI: 10.25625/3a5dfl/t970gu
title: P1EL v2.0.019.png

description: :unav
DOI: 10.25625/3a5dfl/jb9qtk
title: P1EL v3.0.006.png

description: :unav
DOI: 10.25625/3a5dfl/xrshuc
title: P1EL v3.0.025.png

description: :unav
DOI: 10.25625/3a5dfl/umxe1n
title: P1EL v3.0.027.png

description: :unav
DOI: 10.25625/3a5dfl/8ajah0
title: P1EL v3.0.004.png

description: :unav
DOI: 10.25625/3a5dfl/6iurzt
title: P1EL v3.0.024.png

description: :unav
DOI: 10.25625/3a5dfl/tslptm
title: P1EL v3.0.007.png

description: :unav
DOI: 10.25625/3a5dfl/nzq5zm
title: P1EL v3.0.030.png

description: :unav
DOI: 10.25625/3a5dfl/m2e2we
title: P1EL v3.0.011.png

description: :unav
DOI: 10.25625/3a5dfl/p044d9
title: P1EL v3.0.031.png

description: :unav
DOI: 10.25625/3a5dfl/skb8yy
title: P1EL v3.0.029.png

description: :unav
DOI: 10.25625/3a5dfl/wyyaiu
title: P1EL v3.0.026.png

description: :unav