cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15309323
title: ImageGrains 2.0 models

description: Model weights for the default segmentation model of ImageGrains 2.0 (https://github.com/dmair1989/imagegrains), i.e., the fine-tuned Cellpose-SAM default, and the other Cellpose-2-based models.

The ImageGrains 2 dataset is available here: 10.5281/zenodo.17866827

If you use these models, please cite:



Mair, D., Witz, G., Do Prado, A., Garefalakis, P., Wild, A., Ville, F., Schuster, B., Horn, M., Österle, J., Fabbri, S. C., Litty, C., Achleitner, S., Leistner, S., Hiller, C., and Schlunegger, F. (2026): ImageGrains 2.0: Improved precision and generalization for grain segmentation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6346.



Pachitariu, M., Rariden, M., Stringer, C.. Cellpose-SAM: superhuman generalization for cellular segmentation. bioRxiv 2025.04.28.651001; https://doi.org/10.1101/2025.04.28.651001.



If you use legacy versions of Cellpose (e.g., by using ImageGrains v1.x models), please cite:



Mair, D., Witz, G., Do Prado, A.H., Garefalakis, P. & Schlunegger, F. (2023) Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning. Earth Surface Processes and Landforms, 1–18. https://doi.org/10.1002/esp.5755.

Stringer, C.A., Pachitariu, M., (2021). Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106. https://doi.org/10.1038/s41592-020-01018-x.


If you use ImageGrains to calculate percentile uncertainties, please also cite:



Mair, D., Henrique, A., Prado, D., Garefalakis, P., Lechmann, A., Whittaker, A., and Schlunegger, F. (2022): Grain size of fluvial gravel bars from close-range UAV imagery-uncertainty in segmentation-based data, Earth Surf. Dyn., 10,953-973. https://doi.org/10.5194/esurf-10-953-2022.
DOI: 10.5281/zenodo.15728186
title: ImageGrains 2.0 models

description: Model weights for the default segmentation model of ImageGrains 2.0 (https://github.com/dmair1989/imagegrains), i.e., the fine-tuned Cellpose-SAM default, and the other Cellpose-2-based models.

The ImageGrains 2 dataset is available here: 10.5281/zenodo.17866827

If you use these models, please cite:



Mair, D., Witz, G., Do Prado, A., Garefalakis, P., Wild, A., Ville, F., Schuster, B., Horn, M., Österle, J., Fabbri, S. C., Litty, C., Achleitner, S., Leistner, S., Hiller, C., and Schlunegger, F. (2026): ImageGrains 2.0: Improved precision and generalization for grain segmentation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6346.



Pachitariu, M., Rariden, M., Stringer, C.. Cellpose-SAM: superhuman generalization for cellular segmentation. bioRxiv 2025.04.28.651001; https://doi.org/10.1101/2025.04.28.651001.



If you use legacy versions of Cellpose (e.g., by using ImageGrains v1.x models), please cite:



Mair, D., Witz, G., Do Prado, A.H., Garefalakis, P. & Schlunegger, F. (2023) Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning. Earth Surface Processes and Landforms, 1–18. https://doi.org/10.1002/esp.5755.

Stringer, C.A., Pachitariu, M., (2021). Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106. https://doi.org/10.1038/s41592-020-01018-x.


If you use ImageGrains to calculate percentile uncertainties, please also cite:



Mair, D., Henrique, A., Prado, D., Garefalakis, P., Lechmann, A., Whittaker, A., and Schlunegger, F. (2022): Grain size of fluvial gravel bars from close-range UAV imagery-uncertainty in segmentation-based data, Earth Surf. Dyn., 10,953-973. https://doi.org/10.5194/esurf-10-953-2022.
DOI: 10.15468/dl.27v6ss
title: Occurrence Download

description: A dataset containing 745 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((104.219 2.08518, 104.148691 2.079612, 104.080115 2.063043, 104.01496 2.035884, 103.954834 1.998803, 103.901216 1.952713, 103.855427 1.89875, 103.818594 1.838244, 103.791623 1.772685, 103.775177 1.703687, 103.769661 1.632949, 103.775208 1.562215, 103.791682 1.493224, 103.818675 1.427675, 103.855522 1.367183, 103.901316 1.313236, 103.954929 1.267161, 104.015042 1.230093, 104.080174 1.202945, 104.148722 1.186384, 104.219 1.180818, 104.289278 1.186384, 104.357826 1.202945, 104.422958 1.230093, 104.483071 1.267161, 104.536684 1.313236, 104.582478 1.367183, 104.619325 1.427675, 104.646318 1.493224, 104.662792 1.562215, 104.668339 1.632949, 104.662823 1.703687, 104.646377 1.772685, 104.619406 1.838244, 104.582573 1.89875, 104.536784 1.952713, 104.483166 1.998803, 104.42304 2.035884, 104.357885 2.063043, 104.289309 2.079612, 104.219 2.08518))"},{"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 745 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002705-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bkawew
title: Occurrence Download

description: A dataset containing 3356 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Vicia cracca, Vicia cracca)",
 "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 3356 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002707-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jst3kx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((102.965 -1.283818, 102.894719 -1.289384, 102.826168 -1.305945, 102.761034 -1.333093, 102.700919 -1.37016, 102.647303 -1.416234, 102.601506 -1.470181, 102.564657 -1.530673, 102.537661 -1.596221, 102.521186 -1.665212, 102.515637 -1.735946, 102.521153 -1.806683, 102.537599 -1.875681, 102.56457 -1.941241, 102.601405 -2.001748, 102.647196 -2.055711, 102.700817 -2.101801, 102.760947 -2.138883, 102.826105 -2.166043, 102.894686 -2.182611, 102.965 -2.188179, 103.035314 -2.182611, 103.103895 -2.166043, 103.169053 -2.138883, 103.229183 -2.101801, 103.282804 -2.055711, 103.328595 -2.001748, 103.36543 -1.941241, 103.392401 -1.875681, 103.408847 -1.806683, 103.414363 -1.735946, 103.408814 -1.665212, 103.392339 -1.596221, 103.365343 -1.530673, 103.328494 -1.470181, 103.282697 -1.416234, 103.229081 -1.37016, 103.168966 -1.333093, 103.103832 -1.305945, 103.035281 -1.289384, 102.965 -1.283818))"},{"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/0002709-260119153935675/datasets/export for details.
DOI: 10.5281/zenodo.14177009
title: MST NDSI Collection: A Cloud-free MODIS NDSI Dataset (2001–2022) for Tibetan Plateau Generated by a LightGBM-Based Method Using Multivariate Features

description: 1. Cloud-free MODIS normalized difference snow index (NDSI) dataset for Tibetan Plateau from 2001 to 2022 is generated.

2. This dataset is derived from daily 500-m MODIS NDSI datasets (MOD10A1 and MYD10A1).

3. This dataset is provided using a WGS_1984_UTM_45N projection, with the data format of TIFF images. 

4. The NDSI value ranges from -10,000-10,000, corresponding to the raw NDSI band of MOD10A1 and MYD10A1. The fill value is set to -32768.

5. Due to the large amount of data, the data for each year has been divided into several split archives (YYYY.partX.rar).

6. Each RAR contains NDSI data for one year (January to December). After uncompressing into the TIFF format, the files are named as "NDSI_Daily_YYYY_mm_dd.tif" for NDSI data and "NDSI_Daily_YYYY_mm_dd.tif".

7. The accuracy of this collection has been well validated by simulation experiments.

8. This version includes data from 2017 to 2022 (https://doi.org/10.5281/zenodo.14177009). Version 1 includes data from 2001 to 2004 (https://doi.org/10.5281/zenodo.14027672). Version 2 includes data from 2005 to 2010(https://doi.org/10.5281/zenodo.14038846). Version3 includes data from 2011 to 2016 (https://doi.org/10.5281/zenodo.14089925).

9. For the specific processing steps and further details, please refer to our published paper: A novel approach for cloud-free MODIS NDSI reconstruction on the Tibetan Plateau combining spatiotemporal cube and environmental features, IEEE Transactions on Geoscience and Remote Sensing, doi:10.1109/TGRS.2025.3542095.
DOI: 10.5281/zenodo.14027671
title: MST NDSI Collection: A Cloud-free MODIS NDSI Dataset (2001–2022) for Tibetan Plateau Generated by a LightGBM-Based Method Using Multivariate Features

description: 1. Cloud-free MODIS normalized difference snow index (NDSI) dataset for Tibetan Plateau from 2001 to 2022 is generated.

2. This dataset is derived from daily 500-m MODIS NDSI datasets (MOD10A1 and MYD10A1).

3. This dataset is provided using a WGS_1984_UTM_45N projection, with the data format of TIFF images. 

4. The NDSI value ranges from -10,000-10,000, corresponding to the raw NDSI band of MOD10A1 and MYD10A1. The fill value is set to -32768.

5. Due to the large amount of data, the data for each year has been divided into several split archives (YYYY.partX.rar).

6. Each RAR contains NDSI data for one year (January to December). After uncompressing into the TIFF format, the files are named as "NDSI_Daily_YYYY_mm_dd.tif" for NDSI data and "NDSI_Daily_YYYY_mm_dd.tif".

7. The accuracy of this collection has been well validated by simulation experiments.

8. This version includes data from 2017 to 2022 (https://doi.org/10.5281/zenodo.14177009). Version 1 includes data from 2001 to 2004 (https://doi.org/10.5281/zenodo.14027672). Version 2 includes data from 2005 to 2010(https://doi.org/10.5281/zenodo.14038846). Version3 includes data from 2011 to 2016 (https://doi.org/10.5281/zenodo.14089925).

9. For the specific processing steps and further details, please refer to our published paper: A novel approach for cloud-free MODIS NDSI reconstruction on the Tibetan Plateau combining spatiotemporal cube and environmental features, IEEE Transactions on Geoscience and Remote Sensing, doi:10.1109/TGRS.2025.3542095.
DOI: 10.5281/zenodo.18299617
title: Socio-economic status in a rural Pakistani village

description: Background

Socioeconomic status is an important driver of health outcomes, but the drivers of change in household status over time are often overlooked in favor of cross-sectional metrics of convenience. This is especially challenging in remote populations in low- and middle-income settings. We report changes in socioeconomic status over two decades.

Methods

During two studies on childhood health outcomes (1989-1996 and 2011-2014), socio-economic data were collected through surveys and interviews in Oshikhandass village, a remote population in northern Pakistan. Observations included metrics of population demography, occupation and incomes, household structure and self-reported adult illness. Individual measurements are reported with summary statistics comparing the two time periods and multi-dimensional socio-economic constructs are constructed from factor analyses to compare changes in relative household socio-economic position over time.

Files

Study 1, 1989-1996

Study 2, 2011-2014




Data
Codebook
Description


Study 1 HH_Family Folder _Jan17_2026
Study 1 Codebook HH_Family folder_Jan 17_2026
Family information including parental education, occupation and income in Study 1


Study 1 HHFamilyFolderOnly for HH_withChildrenUnder5years_Jan17_2026
Study 1 Codebook HH_Family folder_Jan 17_2026
Subset of the above for parents with children under 5y


Study 1 Data on Individuals in Households Jan17 2026
Study 1 Codebook Individuals in Households Jan 17_2026
Age of individuals in Study 1


Study 2 Mapping Data_Final_January 17_2026
Study 2 Mapping Codebook_January 17 2026
Household factors, e.g., construction and assets, from mapped data in Study 2


Study 2 Individual Members Data_January 16_2026
Study 2 Individual Members Codebook_January 16_2026
Ages and self-reported illness data in Study 2


Study 2 Data for Parents of U5 Children Jan17_2026
Study 2 Codebook for Parents of U5 Children Jan 17_2026
Occupations and education of parents of children <5y in Study 2


Study 2 SES Data Occupations All Earning Household Members_January 17_2026
Study 2 SES Codebook Occupations_January 17_2026
Occupation data for Study 2


Study 2 SES Data_Jan 18 2026
Study 2 SES Codebook_January 18 2026
Data from the socio-economic status questionnaire in Study 2 data


SES Factor Analysis Data_Jan16_2026
SES Factor Analysis Codebook Jan16_2026
Factor analysis of both Study 1 and 2
DOI: 10.5281/zenodo.18299618
title: Socio-economic status in a rural Pakistani village

description: Background

Socioeconomic status is an important driver of health outcomes, but the drivers of change in household status over time are often overlooked in favor of cross-sectional metrics of convenience. This is especially challenging in remote populations in low- and middle-income settings. We report changes in socioeconomic status over two decades.

Methods

During two studies on childhood health outcomes (1989-1996 and 2011-2014), socio-economic data were collected through surveys and interviews in Oshikhandass village, a remote population in northern Pakistan. Observations included metrics of population demography, occupation and incomes, household structure and self-reported adult illness. Individual measurements are reported with summary statistics comparing the two time periods and multi-dimensional socio-economic constructs are constructed from factor analyses to compare changes in relative household socio-economic position over time.

Files

Study 1, 1989-1996

Study 2, 2011-2014




Data
Codebook
Description


Study 1 HH_Family Folder _Jan17_2026
Study 1 Codebook HH_Family folder_Jan 17_2026
Family information including parental education, occupation and income in Study 1


Study 1 HHFamilyFolderOnly for HH_withChildrenUnder5years_Jan17_2026
Study 1 Codebook HH_Family folder_Jan 17_2026
Subset of the above for parents with children under 5y


Study 1 Data on Individuals in Households Jan17 2026
Study 1 Codebook Individuals in Households Jan 17_2026
Age of individuals in Study 1


Study 2 Mapping Data_Final_January 17_2026
Study 2 Mapping Codebook_January 17 2026
Household factors, e.g., construction and assets, from mapped data in Study 2


Study 2 Individual Members Data_January 16_2026
Study 2 Individual Members Codebook_January 16_2026
Ages and self-reported illness data in Study 2


Study 2 Data for Parents of U5 Children Jan17_2026
Study 2 Codebook for Parents of U5 Children Jan 17_2026
Occupations and education of parents of children <5y in Study 2


Study 2 SES Data Occupations All Earning Household Members_January 17_2026
Study 2 SES Codebook Occupations_January 17_2026
Occupation data for Study 2


Study 2 SES Data_Jan 18 2026
Study 2 SES Codebook_January 18 2026
Data from the socio-economic status questionnaire in Study 2 data


SES Factor Analysis Data_Jan16_2026
SES Factor Analysis Codebook Jan16_2026
Factor analysis of both Study 1 and 2
DOI: 10.5281/zenodo.18311952
title: Tensile test of steel specimen using digital image correlation

description: The dataset contains measurements from a tensile test on a 1 mm thick tensile specimen. The material tested is the steel alloy IID 1.4016 (X6Cr17). It includes raw experimental displacement data as well as the image sequence acquired during the test.
DOI: 10.5281/zenodo.18311953
title: Tensile test of steel specimen using digital image correlation

description: The dataset contains measurements from a tensile test on a 1 mm thick tensile specimen. The material tested is the steel alloy IID 1.4016 (X6Cr17). It includes raw experimental displacement data as well as the image sequence acquired during the test.
DOI: 10.15468/dl.ms63vp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((111.202 2.11718, 111.13169 2.111611, 111.063112 2.095043, 110.997956 2.067884, 110.937829 2.030802, 110.88421 1.984712, 110.83842 1.930749, 110.801587 1.870243, 110.774616 1.804684, 110.75817 1.735686, 110.752654 1.664948, 110.758202 1.594214, 110.774676 1.525223, 110.801669 1.459675, 110.838517 1.399182, 110.884312 1.345235, 110.937926 1.299161, 110.998039 1.262093, 111.063172 1.234945, 111.131721 1.218384, 111.202 1.212818, 111.272279 1.218384, 111.340828 1.234945, 111.405961 1.262093, 111.466074 1.299161, 111.519688 1.345235, 111.565483 1.399182, 111.602331 1.459675, 111.629324 1.525223, 111.645798 1.594214, 111.651346 1.664948, 111.64583 1.735686, 111.629384 1.804684, 111.602413 1.870243, 111.56558 1.930749, 111.51979 1.984712, 111.466171 2.030802, 111.406044 2.067884, 111.340888 2.095043, 111.27231 2.111611, 111.202 2.11718))"},{"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/0002711-260119153935675/datasets/export for details.
DOI: 10.15468/dl.m869rj
title: Occurrence Download

description: A dataset containing 3551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Vicia cracca, Vicia cracca, Vicia tenuifolia, Vicia tenuifolia, Vicia tenuifolia)",
 "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 3551 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002712-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18312471
title: Silene pendula L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:54
DOI: 10.5281/zenodo.18312472
title: Silene pendula L. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:54
DOI: 10.15468/dl.bhsz5k
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((110.673 -1.271818, 110.60272 -1.277384, 110.534169 -1.293945, 110.469035 -1.321093, 110.40892 -1.35816, 110.355304 -1.404234, 110.309508 -1.458181, 110.272659 -1.518673, 110.245664 -1.584221, 110.229189 -1.653212, 110.22364 -1.723947, 110.229156 -1.794684, 110.245602 -1.863682, 110.272573 -1.929241, 110.309407 -1.989748, 110.355198 -2.043711, 110.408819 -2.089801, 110.468949 -2.126883, 110.534106 -2.154043, 110.602687 -2.170611, 110.673 -2.176179, 110.743313 -2.170611, 110.811894 -2.154043, 110.877051 -2.126883, 110.937181 -2.089801, 110.990802 -2.043711, 111.036593 -1.989748, 111.073427 -1.929241, 111.100398 -1.863682, 111.116844 -1.794684, 111.12236 -1.723947, 111.116811 -1.653212, 111.100336 -1.584221, 111.073341 -1.518673, 111.036492 -1.458181, 110.990696 -1.404234, 110.93708 -1.35816, 110.876965 -1.321093, 110.811831 -1.293945, 110.74328 -1.277384, 110.673 -1.271818))"},{"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/0002713-260119153935675/datasets/export for details.
DOI: 10.5281/zenodo.18231600
title: Data of manuscript "Effect of cation's alkyl chain length of imidazolium-driven PVDF-based supported ionic liquid membranes on the CO2 separation performance from biogas and flue gas."

description: Data of manuscript

 



Title: Effect of cation’s alkyl chain length of imidazolium-driven PVDF-based supported ionic liquid membranes on the CO2 separation performance from biogas and flue gas 




Authors: Marco-Velasco G, Martínez-Soria V, Izquierdo M, Chafer A, Badia-Valiente JD

Journal: Carbon Capture and Technology, Elsevier (2026)




Affiliations:

Research Group in Materials Technology and Sustainability (MATS), Department of Chemical Engineering, School of Engineering, University of Valencia, Avda. Universitat s/n, 46100 Burjassot, Spain. 




* Correspondence: jose.badia@uv.es (J.D.B-V.)


Funding: This research was supported by project PID2021-1224950A-I00 and TED2021-131276A-I00, funded by Ministerio de Ciencia, Innovación y Universidades (MCIU, Spain) (MCIN/AEI/10.13039/501100011033) and by European Union NextGenerationEU/PRTR and ERDF/EU. The PhD grant of G. Marco-Velasco was also funded by MCIU (FPU23/02636).