cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10566438
title: Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Diary Matrices - Accompanying material

description: These files are accompanying material for our submission "Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Diary Matrices" to MDPI Processes:

Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices. The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight. For the prediction of the Kmg in dairy matrices, R2 scores of up to 0.99 were reached. For 37.0 °C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 ◦C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the Kmg value of aroma compounds in different dairy matrices.

We provided two folders with the results and scripts, described by documentation:



Results_Aroma_Regression (Feature_Importance, Histogram_Plots)

Scripts_Aroma_Regression (Pipeline.py, regr_simple.py, regr_preprocessed.py)
DOI: 10.5281/zenodo.10566439
title: Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Diary Matrices - Accompanying material

description: These files are accompanying material for our submission "Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Diary Matrices" to MDPI Processes:

Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices. The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight. For the prediction of the Kmg in dairy matrices, R2 scores of up to 0.99 were reached. For 37.0 °C, which resembles the temperature of the mouth, RFR delivered the best results, and, at lower temperatures of 7.0 ◦C, typical for a household fridge, XGB performed best. The results from the models work as a proof of concept and show the applicability of a data-driven approach with machine learning to predict the Kmg value of aroma compounds in different dairy matrices.

We provided two folders with the results and scripts, described by documentation:



Results_Aroma_Regression (Feature_Importance, Histogram_Plots)

Scripts_Aroma_Regression (Pipeline.py, regr_simple.py, regr_preprocessed.py)
DOI: 10.18465/0e123e39-ca1b-4554-a154-68c8ac57c83e
title: MNT Jard-sur-Mer 2013

description: Modèle Numérique de Terrain obtenu par LiDAR sur la côte vendéenne au niveau de Jard-sur-Mer
DOI: 10.18465/d3ee998f-618c-4d8f-aeea-7c407650bd04
title: MNS Tranche-sur-Mer 2016

description: Modèle Numérique de Surface obtenu par LiDAR sur la côte vendéenne au niveau de la Tranche-sur-Mer
DOI: 10.18465/3495c0fa-d5ac-4e85-a15f-f75d14b0a99e
title: MNT Tranche-sur-Mer 2016

description: Modèle Numérique de Terrain obtenu par LiDAR sur la côte vendéenne au niveau de la Tranche-sur-Mer
DOI: 10.57710/alba-es-14
title: Test

description: Yet another test
DOI: 10.18465/4fe6b40e-8fd1-4923-8f48-6f8b8322dcbb
title: MNS Faute-sur-Mer 2013

description: Modèle Numérique de Surface obtenu par LiDAR sur la côte vendéenne au niveau de la Faute-sur-Mer
DOI: 10.18465/5a6dce14-23c2-41cf-a227-814dcee8974a
title: MNT Faute-sur-Mer 2013

description: Modèle Numérique de Terrain obtenu par LiDAR sur la côte vendéenne au niveau de la Faute-sur-Mer
DOI: 10.5519/qd.dofoyp2g
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (142 records)
DOI: 10.18465/bd79218d-7fa7-41fc-b5ff-2feb0a23a8fe
title: MNS Faute-sur-Mer 2016

description: Modèle Numérique de Surface obtenu par LiDAR sur la côte vendéenne au niveau de la Faute-sur-Mer
DOI: 10.5281/zenodo.10508244
title: Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows- Xenium mouse brain datasets

description: Four mouse brain coronal datasets generated for with Xenium's mouse brain panel.
DOI: 10.5281/zenodo.10508245
title: Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows- Xenium mouse brain datasets

description: Four mouse brain coronal datasets generated for with Xenium's mouse brain panel.
DOI: 10.5281/zenodo.10566172
title: Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows- Xenium mouse brain datasets

description: Four mouse brain coronal datasets generated for with Xenium's mouse brain panel.
DOI: 10.5281/zenodo.10534844
title: Temperature and Relative Humidity monitoring

description: Temperature and relative humidity has been monitored before and after the installation of nature-based solutions during the proGIreg project. Specifically, NBS3 in Dortmund, NBS2, NBS3 and NBS5 in Turin, and NBS5 in Zagreb have been monitored. Continuous measurements have been made inside the NBS and in a control site over three years; for each monitoring site, 6 temperature sensors are used (3 for the NBS site and 3 for the control site).For NBS5 outdoor green walls in Turin and Zagreb, the temperature has been measured inside the building and only for one year, and the temperature measured by a reference weather station in the same district has been used as control.

More details are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.
DOI: 10.5281/zenodo.5138080
title: Temperature and Relative Humidity monitoring

description: Temperature and relative humidity has been monitored before and after the installation of nature-based solutions during the proGIreg project. Specifically, NBS3 in Dortmund, NBS2, NBS3 and NBS5 in Turin, and NBS5 in Zagreb have been monitored. Continuous measurements have been made inside the NBS and in a control site over three years; for each monitoring site, 6 temperature sensors are used (3 for the NBS site and 3 for the control site).For NBS5 outdoor green walls in Turin and Zagreb, the temperature has been measured inside the building and only for one year, and the temperature measured by a reference weather station in the same district has been used as control.

More details are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.
DOI: 10.18465/cec02a16-9af3-43f7-93d8-09332b8eadae
title: MNT Faute-sur-Mer 2016

description: Modèle Numérique de Terrain obtenu par LiDAR sur la côte vendéenne au niveau de la Faute-sur-Mer
DOI: 10.48323/qcx3g-p3m39
title: Networks of Anglophone LGBTQ+ Exile Writers from 1900 to 1969

description: Data sets on meetings and creative exchanges between Anglophone LGBTQ+ writers (1900-69) for series of network visulalizations (graphs, maps, and timeline).
Austrian Science Fund (FWF): 10.55776/P35199
DOI: 10.5281/zenodo.10534857
title: Air Quality

description: NO2 and O3 air concentration data aquired as baseline before the NBS implementation. The data were recorded in June 2019 in the proximity of the NBS and in a control site. In total 74 passive diffusion tubes (Gradko International Ltd) were installed in selected NBS of the three Front Runner Cities (Dortmund, Turin and Zagreb), in particular 16 in Dortmund (NBS3), 28 in Zagreb (NBS3 and NBS5)  and 30 in Turin (NBS2, NBS3, NBS5). Half of the tubes monitored NO2 while the other half O3. The placed samplers were removed after 21 days and sent for analysis.

More details are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.
DOI: 10.5281/zenodo.3572499
title: Air Quality

description: NO2 and O3 air concentration data aquired as baseline before the NBS implementation. The data were recorded in June 2019 in the proximity of the NBS and in a control site. In total 74 passive diffusion tubes (Gradko International Ltd) were installed in selected NBS of the three Front Runner Cities (Dortmund, Turin and Zagreb), in particular 16 in Dortmund (NBS3), 28 in Zagreb (NBS3 and NBS5)  and 30 in Turin (NBS2, NBS3, NBS5). Half of the tubes monitored NO2 while the other half O3. The placed samplers were removed after 21 days and sent for analysis.

More details are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.
DOI: 10.5281/zenodo.10534946
title: Bees and Butterflies Monitoring

description: Biodiversity monitoring surveys of selected pollinator species (bees and butterflies) performed according to specific protocols adapted to NBS and observers, along fixed transects, once a week during the pollinators’ season, for 3 years.

Reference methods: Underwood, Darwin, Gerritsen, (2017), Pollinator initiatives in EU Member States: Success factors and gaps. Report for European Commission under contract for provision of technical support related to Target of the EU Biodiversity Strategy to 2020 – maintaining and restoring ecosystems and their services. ENV.B.2/SER/2016/0018. Institute for European Environmental Policy, Brussels; Potts et al. (2020), Proposal for an EU Pollinator Monitoring Scheme, EUR 30416 EN, Publications Office of the European Union, Luxembourg.
DOI: 10.5281/zenodo.10534947
title: Bees and Butterflies Monitoring

description: Biodiversity monitoring surveys of selected pollinator species (bees and butterflies) performed according to specific protocols adapted to NBS and observers, along fixed transects, once a week during the pollinators’ season, for 3 years.

Reference methods: Underwood, Darwin, Gerritsen, (2017), Pollinator initiatives in EU Member States: Success factors and gaps. Report for European Commission under contract for provision of technical support related to Target of the EU Biodiversity Strategy to 2020 – maintaining and restoring ecosystems and their services. ENV.B.2/SER/2016/0018. Institute for European Environmental Policy, Brussels; Potts et al. (2020), Proposal for an EU Pollinator Monitoring Scheme, EUR 30416 EN, Publications Office of the European Union, Luxembourg.
DOI: 10.5281/zenodo.10535031
title: District level health and well-being data

description: Self-reported health and well-being data collected in the Living Labs district and in a control district of the Front Runner Cities of proGIreg project (Dortmund: Huckarde and Mengede districts; Turin: Mirafiori Sud and Barriera di Milano districts; Zagreb: Sesvete and Špansko-Jug districts), before and after the project NBS implementations (2019 and 2022). More details about methodology are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.

More details about results are reported in: Baldacchini, C., Calfapietra, C. (2023): Living Labs impact at the district level, Deliverable No.4.8, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, pp 93.
DOI: 10.5281/zenodo.10535032
title: District level health and well-being data

description: Self-reported health and well-being data collected in the Living Labs district and in a control district of the Front Runner Cities of proGIreg project (Dortmund: Huckarde and Mengede districts; Turin: Mirafiori Sud and Barriera di Milano districts; Zagreb: Sesvete and Špansko-Jug districts), before and after the project NBS implementations (2019 and 2022). More details about methodology are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.

More details about results are reported in: Baldacchini, C., Calfapietra, C. (2023): Living Labs impact at the district level, Deliverable No.4.8, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, pp 93.
DOI: 10.18465/b3fb3449-cfe7-46c2-817c-3dcd19c25f49
title: MNS Aiguillon-sur-Mer 2016

description: Modèle Numérique de Surface obtenu par LiDAR sur la côte vendéenne au niveau de l'Aiguillon-sur-Mer
DOI: 10.5281/zenodo.10535100
title: Self-reported health and well-being data from NBS users

description: The users of the NBS implementations realized in the cities of Dortmund, Ningbo, Turin, and Zagreb within the proGIreg project have been monitored. Data about NBS use have been collected observationally by SOPARC (McKenzie et al., (2006), J. Phys. Act. Health 3 Suppl 1, S208-S222) and self-reported health and well-being data have been collected from the NBS users by means of a in-person survey called the NBS-visitor questionnaire (reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124).
DOI: 10.5281/zenodo.10535101
title: Self-reported health and well-being data from NBS users

description: The users of the NBS implementations realized in the cities of Dortmund, Ningbo, Turin, and Zagreb within the proGIreg project have been monitored. Data about NBS use have been collected observationally by SOPARC (McKenzie et al., (2006), J. Phys. Act. Health 3 Suppl 1, S208-S222) and self-reported health and well-being data have been collected from the NBS users by means of a in-person survey called the NBS-visitor questionnaire (reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124).
DOI: 10.18465/04e8a8a9-3b3f-4516-8b45-7b8bf327442f
title: MNT Aiguillon-sur-Mer 2016

description: Modèle Numérique de Terrain obtenu par LiDAR sur la côte vendéenne au niveau de l'Aiguillon-sur-Mer
DOI: 10.5281/zenodo.10566727
title: Code and data for Buzan et al., 2024, Winter climate response to glacial maximum orbits and ice sheet heights in CESM1.2

description: These directories contain the data to make the figures 1-4 and 6-11 in: Buzan et al., 2024, "Winter climate response to glacial maximum orbits and ice sheet heights in CESM1.2." Figure 5 is generated using Tierney et al., 2020 and Rayner et al., 2003.
DOI: 10.5281/zenodo.10566728
title: Code and data for Buzan et al., 2024, Winter climate response to glacial maximum orbits and ice sheet heights in CESM1.2

description: These directories contain the data to make the figures 1-4 and 6-11 in: Buzan et al., 2024, "Winter climate response to glacial maximum orbits and ice sheet heights in CESM1.2." Figure 5 is generated using Tierney et al., 2020 and Rayner et al., 2003.
DOI: 10.5281/zenodo.10554808
title: Economic data at NBS level

description: Economic data recorded from NBS managers about the NBS implementations realized in the cities of Dortmund, Ningbo, Turin, and Zagreb within the proGIreg project. See proGIreg project Deliverable 4.5 for further details (Baldacchini, C. (2021): Report on benefits produced by implemented NBS, Deliverable No.4.5, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 146.)
DOI: 10.5281/zenodo.10554809
title: Economic data at NBS level

description: Economic data recorded from NBS managers about the NBS implementations realized in the cities of Dortmund, Ningbo, Turin, and Zagreb within the proGIreg project. See proGIreg project Deliverable 4.5 for further details (Baldacchini, C. (2021): Report on benefits produced by implemented NBS, Deliverable No.4.5, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 146.)
DOI: 10.5281/zenodo.10566755
title: Thermographic Imaging Data
DOI: 10.5281/zenodo.10566756
title: Thermographic Imaging Data
DOI: 10.5281/zenodo.10534249
title: NDVI and Walkability index

description: NDVI calculated at city, district and NBS level from 2018 to 2022 for the cities of Dortmund (Huckarde district), Ningbo (Moon Lake district), Turin (Mirafiori Sud district) and Zagreb (Sesvete district). Walkability index calculated at city and district level in 2018 and in 2022 for the cities of Dortmund (Huckarde district), Ningbo (Moon Lake district), Turin (Mirafiori Sud district) and Zagreb (Sesvete district).
DOI: 10.5281/zenodo.10534250
title: NDVI and Walkability

description: NDVI calculated at city, district and NBS level from 2018 to 2022 for the cities of Dortmund, Ningbo, Turin and Zagreb. Walkability index calculated at city and district level in 2018 and in 2022 for the cities of Dortmund, Ningbo, Turin and Zagreb.
DOI: 10.5281/zenodo.10551942
title: NDVI and Walkability index

description: NDVI calculated at city, district and NBS level from 2018 to 2022 for the cities of Dortmund, Ningbo, Turin and Zagreb. Walkability index calculated at city and district level in 2018 and in 2022 for the cities of Dortmund, Ningbo, Turin and Zagreb.
DOI: 10.5281/zenodo.10562524
title: NDVI and Walkability index

description: NDVI calculated at city, district and NBS level from 2018 to 2022 for the cities of Dortmund (Huckarde district), Ningbo (Moon Lake district), Turin (Mirafiori Sud district) and Zagreb (Sesvete district). Walkability index calculated at city and district level in 2018 and in 2022 for the cities of Dortmund (Huckarde district), Ningbo (Moon Lake district), Turin (Mirafiori Sud district) and Zagreb (Sesvete district).
DOI: 10.5281/zenodo.10566818
title: data set and trees used in "Redefining Ormyridae (Hymenoptera, Chalcidoidea) with establishment of subfamilies and description of new genera". van Noort et al. 2024. Syst. Entomol.

description: ormyridae_trees.nex : contains the 3 phylogenetic trees included in the paper : the IQ-TREE tree obtained from the concatenated UCE data set without partitioning; the IQ-TREE tree obtained from the concatenated UCE data set with partitioning using the SWSC method; the ASTRAL tree. The file can be opened in Figtree to navigate between trees. Statistical supports can be visualised as branch or node labels.

ormyridae_dataset.tar.gz : contains the final 671 UCE files aligned with MAFFT-linsi and cleaned with seqtools. These files were analysed individually or as a concatenated matrix to obtained the provided trees.
DOI: 10.5281/zenodo.10566819
title: data set and trees used in "Redefining Ormyridae (Hymenoptera, Chalcidoidea) with establishment of subfamilies and description of new genera". van Noort et al. 2024. Syst. Entomol.

description: ormyridae_trees.nex : contains the 3 phylogenetic trees included in the paper : the IQ-TREE tree obtained from the concatenated UCE data set without partitioning; the IQ-TREE tree obtained from the concatenated UCE data set with partitioning using the SWSC method; the ASTRAL tree. The file can be opened in Figtree to navigate between trees. Statistical supports can be visualised as branch or node labels.

ormyridae_dataset.tar.gz : contains the final 671 UCE files aligned with MAFFT-linsi and cleaned with seqtools. These files were analysed individually or as a concatenated matrix to obtained the provided trees.
DOI: 10.6084/m9.figshare.24161061
title: Rock magnetic dataset, accumulation rates and astrochronology age model for the Trowbarrow section

description: Page 1: rock magnetic datasetPage 2: transformed rock magnetic datasetPage 3: abundance and mineralogy PCA datasetPage 4: sediment accumulation rate modelsPage 5: Astrochronology age modelsPage 6: Horbury allochem data for the sectionPage 7: Microfacies datasetsPage 8 : Separate word file of R-scripts
DOI: 10.6084/m9.figshare.24161061.v1
title: Rock magnetic dataset, accumulation rates and astrochronology age model for the Trowbarrow section

description: Page 1: rock magnetic datasetPage 2: transformed rock magnetic datasetPage 3: abundance and mineralogy PCA datasetPage 4: sediment accumulation rate modelsPage 5: Astrochronology age modelsPage 6: Horbury allochem data for the sectionPage 7: Microfacies datasets
DOI: 10.57745/bskcnv
title: READ_ME_ACIDIF_DATA.txt

description: :unav
DOI: 10.57745/hsblfo
title: Experimental_Acidification_Data_(phage_attack).tab

description: :unav
DOI: 10.18742/25043606
title: Social media, Smartphone use and Self-Harm (3S-YP) study data

description: The Social media, Smartphone use and Self-Harm (3S-YP) study is a prospective observational cohort study to investigate the associations between social media and smartphone use and self-harm in young people. Young people aged 13–25 years old from secondary mental health services were recruited and followed for up to 6 months. Data collected in the study includes questionnaire data and data extracted from electronic health records (EHR) and user generated data sources.
DOI: 10.5517/ccdc.csd.cc2dr32c
title: CCDC 2226732: Experimental Crystal Structure Determination

description: Related Article: Clément Denhez, Hatice Berber, Pedro Lameiras|2022|CSD Communication|||
description: Related Article: Clément Denhez, Pedro Lameiras, Hatice Berber|2023|Org.Biomol.Chem.|21|9572|doi:10.1039/D3OB01617J
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.