cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8046479
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.8046500
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.8046611
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.8316043
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.6020480
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.8316050
title: Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction

description: (DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning
description: The authors acknowledge Junta de Andalucía for the postdoctoral contract of Carlos Loucera (PAIDI2020- DOC_00350) co-funded by the European Social Fund (FSE) 2014-2020.
DOI: 10.5281/zenodo.8315009
title: Europe Gross Primary Productivity (GPP) Dataset 2018 (.NetCDF)

description: Europe Gross Primary Productivity (GPP) Dataset 2018 (in .NetCDF Files) Product: Gross Primary Productivity (GPP) Year: 2018 Region: Europe Temporal Scale: 8 Days Spatial Resolution: 500 meters Method: Light-use-efficiency (LUE) approach. Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149 https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261
description: Please contact the authors for further details: S.bandopadhyay@soton.ac.uk j.dash@soton.ac.uk b.o.ogutu@soton.ac.uk
DOI: 10.5281/zenodo.8315010
title: Europe Gross Primary Productivity (GPP) Dataset 2018 (.NetCDF)

description: Europe Gross Primary Productivity (GPP) Dataset 2018 (in .NetCDF Files) Product: Gross Primary Productivity (GPP) Year: 2018 Region: Europe Temporal Scale: 8 Days Spatial Resolution: 500 meters Method: Light-use-efficiency (LUE) approach. Referred Publication: https://www.sciencedirect.com/science/article/pii/S0048969718307149 https://onlinelibrary.wiley.com/doi/10.1111/gcb.12261
description: Please contact the authors for further details: S.bandopadhyay@soton.ac.uk j.dash@soton.ac.uk b.o.ogutu@soton.ac.uk
DOI: 10.5281/zenodo.8237371
title: Early-Stage Bifurcation of Crystallization in a Sphere

description: This is the raw data for the manuscript: Early-Stage Bifurcation of Crystallization in a Sphere All data are sorted in folders according to their appearance in the figures of the main manuscript. Data types:<br> * Particle position files (text format): .pos<br> * Data files (text format): .dat, .txt, .rdf<br> * Tables: .xlsx<br> * Images/Graphics: .tif, .tiff, .png, .eps, .pdf<br> * Gnuplot scripts: .gnu, .plot<br> * Shell scripts: .sh
description: This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 416229255—SFB 1411. S.E. acknowledges support by the DFG within the framework of the research training group GRK 1896 (Project-ID 218975129), SFB 1452 (Project-ID 431791331) and the project SP648/8 (Project-ID 316992193). N.V. and M.E. also acknowledge funding by the DFG under projects VO 1824/7-1 and EN 905/2-1, respectively. Computational resources and support provided by the Erlangen Regional Computing Center (RRZE) are gratefully acknowledged.
DOI: 10.5281/zenodo.8237372
title: Early-Stage Bifurcation of Crystallization in a Sphere

description: This is the raw data for the manuscript: Early-Stage Bifurcation of Crystallization in a Sphere All data are sorted in folders according to their appearance in the figures of the main manuscript. Data types:<br> * Particle position files (text format): .pos<br> * Data files (text format): .dat, .txt, .rdf<br> * Tables: .xlsx<br> * Images/Graphics: .tif, .tiff, .png, .eps, .pdf<br> * Gnuplot scripts: .gnu, .plot<br> * Shell scripts: .sh
description: This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 416229255—SFB 1411. S.E. acknowledges support by the DFG within the framework of the research training group GRK 1896 (Project-ID 218975129), SFB 1452 (Project-ID 431791331) and the project SP648/8 (Project-ID 316992193). N.V. and M.E. also acknowledge funding by the DFG under projects VO 1824/7-1 and EN 905/2-1, respectively. Computational resources and support provided by the Erlangen Regional Computing Center (RRZE) are gratefully acknowledged.
DOI: 10.5281/zenodo.7670645
title: Preclinical to Clinical Translation for Intervertebral Disc Repair: Effects of Species-Specific Scale, Metabolism and Matrix Synthesis Rates on Cell-Based Regeneration

description: Supporting datasets for journal article funded by the ERC
DOI: 10.5281/zenodo.7670644
title: Preclinical to Clinical Translation for Intervertebral Disc Repair: Effects of Species-Specific Scale, Metabolism and Matrix Synthesis Rates on Cell-Based Regeneration

description: Supporting datasets for journal article funded by the ERC
DOI: 10.5517/ccdc.csd.cc271mvs
title: CCDC 2057155: Experimental Crystal Structure Determination

description: Related Article: Li‐Yan Xu, Yong‐Mei Chai, Cheng‐Guo Li, Lan‐Qin Chai|2021|Appl.Organomet.Chem.|35|e6279|doi:10.1002/aoc.6279
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: 10.5517/ccdc.csd.cc271mwt
title: CCDC 2057156: Experimental Crystal Structure Determination

description: Related Article: Li‐Yan Xu, Yong‐Mei Chai, Cheng‐Guo Li, Lan‐Qin Chai|2021|Appl.Organomet.Chem.|35|e6279|doi:10.1002/aoc.6279
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: 10.5281/zenodo.8255783
title: IndEmoVis: An Indian Multimodal Audio-Visual Emotion Dataset for Conversational Interactions

description: The dataset comprises a total of 122 videos, involving 61 participants, consisting of 25 females and 36 males, with ages ranging from 18 to 21 years. Each video captures a conversational interaction between two participants, with two cameras mounted on the table to record separate videos of the speaker and the listener. These recorded videos are subsequently segmented into clips, each showcasing different emotions expressed by the participants. The dataset comprises a total of 122 videos, involving 61 participants, consisting of 25 females and 36 males, with ages ranging from 18 to 21 years. Each video captures a conversational interaction between two participants, with two cameras mounted on the table to record separate videos of the speaker and the listener. These recorded videos are subsequently segmented into clips, each showcasing different emotions expressed by the participants. The data collection process involved conversational interactions between pairs of participants within a controlled environment. The recordings captured a combination of spontaneous and guided conversations. The dataset consists of 45 recordings of guided conversations and 77 recordings of natural conversations, resulting in a total of 216 video clips portraying various emotions from the guided interactions and 379 video clips displaying emotions from the natural conversations. To facilitate further analysis and research, the video clips were further processed to extract image frames. From each clip, the frame with the highest intensity, capturing the peak expression of the corresponding emotion, was selected for subsequent analysis. These peak images serve as representative snapshots of the emotional expressions and can be used for facial emotion recognition tasks. IndEmoVis dataset is annotated for six basic emotion categories (Happiness, Sadness, Surprise, Disgust, Anger, Fear), along with complex emotion categories of Awe and Sympathy, and a Neutral emotion class. Additionally, the dataset is annotated for the presence of braces, spectacles, and prominent hand gestures, providing valuable context for emotion analysis. Furthermore, intensity and confidence levels are annotated on a scale of 0 to 5 and 0 to 7, respectively, by the clinical psychologist to further enrich the dataset with nuanced emotional expressions.
DOI: 10.5281/zenodo.8255782
title: IndEmoVis: An Indian Multimodal Audio-Visual Emotion Dataset for Conversational Interactions

description: The dataset comprises a total of 122 videos, involving 61 participants, consisting of 25 females and 36 males, with ages ranging from 18 to 21 years. Each video captures a conversational interaction between two participants, with two cameras mounted on the table to record separate videos of the speaker and the listener. These recorded videos are subsequently segmented into clips, each showcasing different emotions expressed by the participants. The dataset comprises a total of 122 videos, involving 61 participants, consisting of 25 females and 36 males, with ages ranging from 18 to 21 years. Each video captures a conversational interaction between two participants, with two cameras mounted on the table to record separate videos of the speaker and the listener. These recorded videos are subsequently segmented into clips, each showcasing different emotions expressed by the participants. The data collection process involved conversational interactions between pairs of participants within a controlled environment. The recordings captured a combination of spontaneous and guided conversations. The dataset consists of 45 recordings of guided conversations and 77 recordings of natural conversations, resulting in a total of 216 video clips portraying various emotions from the guided interactions and 379 video clips displaying emotions from the natural conversations. To facilitate further analysis and research, the video clips were further processed to extract image frames. From each clip, the frame with the highest intensity, capturing the peak expression of the corresponding emotion, was selected for subsequent analysis. These peak images serve as representative snapshots of the emotional expressions and can be used for facial emotion recognition tasks. IndEmoVis dataset is annotated for six basic emotion categories (Happiness, Sadness, Surprise, Disgust, Anger, Fear), along with complex emotion categories of Awe and Sympathy, and a Neutral emotion class. Additionally, the dataset is annotated for the presence of braces, spectacles, and prominent hand gestures, providing valuable context for emotion analysis. Furthermore, intensity and confidence levels are annotated on a scale of 0 to 5 and 0 to 7, respectively, by the clinical psychologist to further enrich the dataset with nuanced emotional expressions.
DOI: 10.6084/m9.figshare.24081702
title: Orginal data of "Joint Geophysical Detection Techniques of Large-Scale Heavy Metal-Organic Compound Contamination: From Laboratory Experiments to Site Validation"

description: Original data of "Joint Geophysical Detection Techniques of Large-Scale Heavy Metal-Organic Compound Contamination: From Laboratory Experiments to Site Validation". Including Electrical properties of heavy metal solutions, Formation factor and resistivity index, Relationship between resistivity and heavy metal ion concentration, Resistivity and chargeability of contaminated medium with different compound proportions, media, and compound proportions, Resistivity of the miscellaneous fill and silty clay on survey lines L1 and L5, Variation of apparent resistivity at different depths for R1 and I1, R2 and I2.
DOI: 10.6084/m9.figshare.24081702.v1
title: Orginal data of "Joint Geophysical Detection Techniques of Large-Scale Heavy Metal-Organic Compound Contamination: From Laboratory Experiments to Site Validation"

description: Original data of "Joint Geophysical Detection Techniques of Large-Scale Heavy Metal-Organic Compound Contamination: From Laboratory Experiments to Site Validation". Including Electrical properties of heavy metal solutions, Formation factor and resistivity index, Relationship between resistivity and heavy metal ion concentration, Resistivity and chargeability of contaminated medium with different compound proportions, media, and compound proportions, Resistivity of the miscellaneous fill and silty clay on survey lines L1 and L5, Variation of apparent resistivity at different depths for R1 and I1, R2 and I2.
DOI: 10.6084/m9.figshare.24081438.v4
title: lunar_surface_chemistry_maps

description: The data files depict the Mg# values and the abundances of SiO2, TiO2, Al2O3, FeO, MgO and CaO observed by the KAGUYA multiband imager. The data files are in the format of .tif and can be opend by the software ArcGIS.
DOI: 10.5281/zenodo.8316122
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Arthur Mansion, http://www.wikidata.org/entity/Q21519912. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21519912
DOI: 10.5281/zenodo.8316121
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Arthur Mansion, http://www.wikidata.org/entity/Q21519912. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21519912
DOI: 10.5281/zenodo.8316128
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Walter Friedrick Otto Marasas, http://www.wikidata.org/entity/Q21519932. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21519932
DOI: 10.5281/zenodo.8316127
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Walter Friedrick Otto Marasas, http://www.wikidata.org/entity/Q21519932. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21519932
DOI: 10.5281/zenodo.8316130
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Alice Elizabeth Margesson, http://www.wikidata.org/entity/Q118869519. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q118869519
DOI: 10.5281/zenodo.8316129
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Alice Elizabeth Margesson, http://www.wikidata.org/entity/Q118869519. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q118869519
DOI: 10.5281/zenodo.8316134
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ernest David Marquand, http://www.wikidata.org/entity/Q5836702. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q5836702
DOI: 10.5281/zenodo.8316133
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ernest David Marquand, http://www.wikidata.org/entity/Q5836702. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q5836702
DOI: 10.21227/abdd-2y20
title: Cellular measurements (4G) onboard a moving train

description: This dataset provides valuable insights into Received Signal Reference Power (RSRP) measurements collected from four User Equipment (UE) devices strategically positioned within a moving train. Additionally, it includes RSRP values obtained from an external reference source using the rooftop train antenna, all systematically interpolated on a cell and frequency basis, with both datasets available.The dataset presents not only the RSRP values but also their corresponding standard deviations at each measurement location. Interpolated mean RSRP values form the foundation for calculating the Vehicle Path Loss (VPL).All the data in this dataset corresponds to the research conducted in our work titled  "Measuring the Effects of Angle of Arrival (AoA) on Vehicle Penetration Loss in Cellular Networks." It also encompasses calculated elevation and azimuth AoA, along with all the supporting variables used in their computation.  This dataset serves as a valuable resource for researchers and practitioners interested in optimizing cellular network  performance in moving vehicles.
DOI: 10.25934/pr00004870.1
title: Available IPD datapackage for study 'Phase 3 Study Comparing Daratumumab, Lenalidomide, and Dexamethasone (DRd) vs Lenalidomide and Dexamethasone (Rd) in Subjects With Relapsed or Refractory Multiple Myeloma'

description: The purpose of this study is to compare the effectiveness of daratumumab when combined with lenalidomide and dexamethasone (DRd) to that of lenalidomide and dexamethasone (Rd), in terms of progression-free survival in participants with relapsed or refractory multiple myeloma.
DOI: 10.5517/ccdc.csd.cc29wk3w
title: CCDC 2141638: Experimental Crystal Structure Determination

description: Related Article: Weize Wang, Weixuan Chen, Wenke Yuan, Hai-Qun Xu, Bo Liu|2022|Inorg.Chem.|61|17937|doi:10.1021/acs.inorgchem.2c03144
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: 10.25835/iweoct2f
title: RePTiL test rig snow motion

description: Rubber sample in contact with prepared artificial snow track in test rig RePTiL (Realistic Pattern Testing in Lab)
DOI: 10.5281/zenodo.8316125
title: (Data Set) Shielding Factors for a Fission-Powered Mars Exploration Rover

description: Data Set to Accompany Technical Paper: A. BENDOYRO et al., Shielding Factors for a Fission-Powered Mars Exploration Rover, 2023
DOI: 10.5281/zenodo.8316079
title: TeCoPhy: A Text Corpus of German Physics Texts

description: TeCoPhy is a Text Corpus of German Physics Texts. Most of the texts are taken from textbooks at school and university level, but other sources were included as well. The corpus is a collection of sentences. From each book, at most 14% of the text is included in the corpus. The corpus consists of 236,278 sentences with 5,32 Million tokens collected from 223 different sources. The distribution consists of two files: an XML file with metadata on the sources and a file with the sentences taken from those sources.
DOI: 10.5281/zenodo.8316078
title: TeCoPhy: A Text Corpus of German Physics Texts

description: TeCoPhy is a Text Corpus of German Physics Texts. Most of the texts are taken from textbooks at school and university level, but other sources were included as well. The corpus is a collection of sentences. From each book, at most 14% of the text is included in the corpus. The corpus consists of 236,278 sentences with 5,32 Million tokens collected from 223 different sources. The distribution consists of two files: an XML file with metadata on the sources and a file with the sentences taken from those sources.
DOI: 10.6084/m9.figshare.23963556.v6
title: <b>Supplementary material for "Thyroid Function and Preeclampsia: A Two-Sample Bidirectional Mendelian Randomization Study"</b>

description: Our study suggests that hyper-/hypo-thyroidism are causal effects on preeclampsia, while preeclampsia is not causally associated with thyroid dysfunction.
DOI: 10.5281/zenodo.8316173
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by George Maxwell, http://www.wikidata.org/entity/Q5542253. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q5542253
DOI: 10.5281/zenodo.8316172
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by George Maxwell, http://www.wikidata.org/entity/Q5542253. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q5542253
DOI: 10.5281/zenodo.7701531
title: Code and data for "Telomere length vary with sex, hatching order and year of birth in little owls, Athene noctua"

description: Datafile and R code of the statistical analyses published in the article "telomere length vary with sex, hatching order and year of birth in little owls, <em>Athene noctua</em>".
DOI: 10.5281/zenodo.8316158
title: Code and data for "Telomere length vary with sex, hatching order and year of birth in little owls, Athene noctua"

description: Datafile and R code of the statistical analyses published in the article "telomere length vary with sex, hatching order and year of birth in little owls, <em>Athene noctua</em>".