cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2cb4gb
title: CCDC 2184491: Experimental Crystal Structure Determination

description: Related Article: Zhi-Hong Du|2022|CSD Communication|||
description: Related Article: Xiao-Xiong Lv, Ning Liu, Fei Chen, Hao Zhang, Zhi-Hong Du, Pei Wang, Meng Yuan, Chao Shan Da|2023|Org.Biomol.Chem.|21|8695|doi:10.1039/D3OB01227A
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: 10.48328/tudatalib-1261
title: Crosskonkordanz zwischen DFG-Fächern 2020 und DNB-Sachgruppen

description: Die Crosskonkordanz bildet die Fächer der Fachsystematik der Deutschen Forschungsgemeinschaft (DFG) (Stand 2020-2024) auf die DDC-Sachgruppen (Stand 2004) ab, wie sie in der Deutschen Nationalbibliografie (DNB) verwendet werden. <br> <br> Diese Konkordanz ist gedacht für die automatisierte Zuordnung von Ressourcen, die nach dem einen System erschlossen sind, zum jeweils anderen System. Diese Zuordnungen sind nicht eins-zu-eins möglich, insbesondere da die DNB-Sachgruppen meist weniger differenziert sind als die DFG-Fächer. <br> <br> Nützlich ist die Crosskonkordanz vor allem für eine Zuordnung von nach DFG-Fächern klassifizierten Ressourcen zu den DNB-Sachgruppen, wie sie z.B. vom DINI-Zertifikat gefordert wird. <br> <br> Die Crosskonkordanz liegt in den Dateiformaten .csv, .json (beide Richtungen) und .xlsx (DFG zu DNB) vor. Ein Vorschlag zur Überführung von Fächern aus der DFG-Klassifikation 2016-2019 in die Klassifikation 2020-2024 ist enthalten.
DOI: 10.15131/shef.data.24523903.v1
title: Truss bridge under dynamic loading: A dataset containing the reaction of a laboratory-scale truss bridge to dynamic loading

description: This dataset is designed to identify the response of a truss bridge when subjected to dynamic forces. The bridge was excited across various damage scenarios, and the effect of detaching and reattaching a single strut was also assessed. The tests were carried out at the Laboratory for Verification and Validation (LVV) at the University of Sheffield.
DOI: 10.15131/shef.data.24523903
title: Truss bridge under dynamic loading: A dataset containing the reaction of a laboratory-scale truss bridge to dynamic loading

description: This dataset is designed to identify the response of a truss bridge when subjected to dynamic forces. The bridge was excited across various damage scenarios, and the effect of detaching and reattaching a single strut was also assessed. The tests were carried out at the Laboratory for Verification and Validation (LVV) at the University of Sheffield.
DOI: 10.5281/zenodo.10078968
title: Case Study Scenarios (CSS) 5 for Stratospheric Sudden Warming
DOI: 10.5281/zenodo.10078969
title: Case Study Scenarios (CSS) 5 for Stratospheric Sudden Warming
DOI: 10.5281/zenodo.10079355
title: Data underpinning "Quantum Effects on Unconventional Pinch Point Singularities"

description: Raw data set and evaluation scripts used for publication.
To reproduce the plots, it is recommended to execute individual code cells in the .jl files in a notebook workflow.
DOI: 10.5281/zenodo.10079354
title: Data underpinning "Quantum Effects on Unconventional Pinch Point Singularities"

description: Raw data set and evaluation scripts used for publication.
To reproduce the plots, it is recommended to execute individual code cells in the .jl files in a notebook workflow.
DOI: 10.5281/zenodo.8410136
title: ToolBoxSF: Robustly interrogating machine learning-based scoring functions: what are they learning?

description: This Zenodo repository provides comprehensive resources for the pre-print research paper titled "Robustly interrogating machine learning-based scoring functions: what are they learning?" Our collection includes Singularity containers containing pre-trained models, benchmark datasets, and training/test CSV files, offering valuable insights into the inner workings of machine learning-based scoring functions.
Key Components:
Singularity Containers:

Machine Learning Models: Explore state-of-the-art scoring models used in the study, enabling reproducibility and in-depth analysis.
Environment Setup: Simplify model deployment and experimentation by utilizing our pre-configured environments.
Benchmark Datasets:

Curated benchmark datasets used in the pre-print, facilitating validation and evaluation of scoring functions.
Training and Test CSV Files:

Training and test data in CSV format, along with associated metadata.
Facilitate model testing and comparison using the provided data.
This Zenodo collection is a valuable resource for researchers, data scientists, and machine learning enthusiasts seeking to replicate the study's findings, explore model behaviors, and conduct further investigations into machine learning-based scoring functions. Detailed documentation and usage instructions are included to support your research efforts at https://github.com/guydurant/toolboxsf.
Citation Information: Please cite this Zenodo repository when using our resources in your work, and consider acknowledging the original pre-print when publishing research based on these materials.
DOI: 10.5281/zenodo.8410135
title: ToolBoxSF: Robustly interrogating machine learning-based scoring functions: what are they learning?

description: This Zenodo repository provides comprehensive resources for the pre-print research paper titled "Robustly interrogating machine learning-based scoring functions: what are they learning?" Our collection includes Singularity containers containing pre-trained models, benchmark datasets, and training/test CSV files, offering valuable insights into the inner workings of machine learning-based scoring functions.
Key Components:
Singularity Containers:

Machine Learning Models: Explore state-of-the-art scoring models used in the study, enabling reproducibility and in-depth analysis.
Environment Setup: Simplify model deployment and experimentation by utilizing our pre-configured environments.
Benchmark Datasets:

Curated benchmark datasets used in the pre-print, facilitating validation and evaluation of scoring functions.
Training and Test CSV Files:

Training and test data in CSV format, along with associated metadata.
Facilitate model testing and comparison using the provided data.
This Zenodo collection is a valuable resource for researchers, data scientists, and machine learning enthusiasts seeking to replicate the study's findings, explore model behaviors, and conduct further investigations into machine learning-based scoring functions. Detailed documentation and usage instructions are included to support your research efforts at https://github.com/guydurant/toolboxsf.
Citation Information: Please cite this Zenodo repository when using our resources in your work, and consider acknowledging the original pre-print when publishing research based on these materials.
DOI: 10.5281/zenodo.10082166
title: ToolBoxSF: Robustly interrogating machine learning-based scoring functions: what are they learning?

description: This Zenodo repository provides comprehensive resources for the pre-print research paper titled "Robustly interrogating machine learning-based scoring functions: what are they learning?" Our collection includes Singularity containers containing pre-trained models, benchmark datasets, and training/test CSV files, offering valuable insights into the inner workings of machine learning-based scoring functions.
Key Components:
Singularity Containers:

Machine Learning Models: Explore state-of-the-art scoring models used in the study, enabling reproducibility and in-depth analysis.
Environment Setup: Simplify model deployment and experimentation by utilizing our pre-configured environments.
Benchmark Datasets:

Curated benchmark datasets used in the pre-print, facilitating validation and evaluation of scoring functions.
Training and Test CSV Files:

Training and test data in CSV format, along with associated metadata.
Facilitate model testing and comparison using the provided data.
This Zenodo collection is a valuable resource for researchers, data scientists, and machine learning enthusiasts seeking to replicate the study's findings, explore model behaviors, and conduct further investigations into machine learning-based scoring functions. Detailed documentation and usage instructions are included to support your research efforts at https://github.com/guydurant/toolboxsf.
Citation Information: Please cite this Zenodo repository when using our resources in your work, and consider acknowledging the original pre-print when publishing research based on these materials.
DOI: 10.5281/zenodo.10037351
title: Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020

description: The dataset "Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020" contains the emissions analysed in the manuscript "Updated Land Use and Land Cover Information Improves Biomass Burning Emission Estimates", published in Fire 2023, 6(11), 426; https://doi.org/10.3390/fire6110426.
DOI: 10.5281/zenodo.10037352
title: Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020

description: The dataset "Daily Emission of Fine Particulate Matter (PM2.5) Associated with Biomass Burning in South America During 2002-2020" contains the emissions analysed in the manuscript "Updated Land Use and Land Cover Information Improves Biomass Burning Emission Estimates", published in Fire 2023, 6(11), 426; https://doi.org/10.3390/fire6110426.
DOI: 10.5281/zenodo.10082466
title: Campi Flegrei  cGPS Weekly Positions Time Series

description: Weekly positions time series for the 21 Campi Flegrei cGPS stations from January 2000 to October 2023.
A full description of cGPS network and time series analysis is reported in:

De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.
Please cite this when using the dataset
DOI: 10.5281/zenodo.10082465
title: Campi Flegrei  cGPS Weekly Positions Time Series

description: Weekly positions time series for the 21 Campi Flegrei cGPS stations from January 2000 to October 2023.
A full description of cGPS network and time series analysis is reported in:

De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.
Please cite this when using the dataset
DOI: 10.5281/zenodo.10082644
title: HARBIY AVTOMOBIL TEXNIKALARINI OG'IR IQLIMLI SHAROITLARDA ISHLASHGA TAYYORLASH
DOI: 10.5281/zenodo.10078995
title: Case Study Scenarios (CSS) 7 for Southern Hemisphere Winter
DOI: 10.5281/zenodo.10078996
title: Case Study Scenarios (CSS) 7 for Southern Hemisphere Winter
DOI: 10.5281/zenodo.10082645
title: HARBIY AVTOMOBIL TEXNIKALARINI OG'IR IQLIMLI SHAROITLARDA ISHLASHGA TAYYORLASH
DOI: 10.5281/zenodo.10082670
title: AVTOMOBILLARNI ISHLAB CHIQARISH JARAYONIDAGI NAZORAT TEKSHIRISH JIHOZLARINI O'RGANISH
DOI: 10.5281/zenodo.10082671
title: AVTOMOBILLARNI ISHLAB CHIQARISH JARAYONIDAGI NAZORAT TEKSHIRISH JIHOZLARINI O'RGANISH
DOI: 10.5281/zenodo.10016249
title: Land Use/cover dataset of the Guangdong-Hong Kong Macao Greater Bay Area at 30 m Resolution from 1976 to 2020

description: High spatiotemporal resolution, large-scale, multi-category land use data are crucial for studying regional ecological and environmental changes, urbanization impacts, and sustainable development planning.
In this study, the Google Earth Engine platform, Landsat satellite imagery, and a substantial number of manually interpreted samples were used to develop a dataset of annual land use in the Guangdong-Hong Kong Macao Greater Bay Area (GBA) at a 30 m resolution from 1976 to 2020. This dataset, termed Annual Land Use/Cover of the Greater Bay Area (LUC-GBA), was used to analyze the annual land use variation in 11 cities within the GBA. The high level accuracy achieved with the LUC-GBA dataset was evidenced by an overall accuracy (OA) of 93.98% and a kappa coefficient of 0.92 in 2020. The OA of interannual classification models ranges from 83.9% to 93.9%, and the kappa coefficients from 0.805 to 0.923, thus demonstrating the good performance of the model in multi-category land use classification within the GBA.
Resolution: 30m
Time span: 1976-2020 (excluding 1981-1985)
DOI: 10.5281/zenodo.10016250
title: Land Use/cover dataset of the Guangdong-Hong Kong Macao Greater Bay Area at 30 m Resolution from 1976 to 2020

description: High spatiotemporal resolution, large-scale, multi-category land use data are crucial for studying regional ecological and environmental changes, urbanization impacts, and sustainable development planning.
In this study, the Google Earth Engine platform, Landsat satellite imagery, and a substantial number of manually interpreted samples were used to develop a dataset of annual land use in the Guangdong-Hong Kong Macao Greater Bay Area (GBA) at a 30 m resolution from 1976 to 2020. This dataset, termed Annual Land Use/Cover of the Greater Bay Area (LUC-GBA), was used to analyze the annual land use variation in 11 cities within the GBA. The high level accuracy achieved with the LUC-GBA dataset was evidenced by an overall accuracy (OA) of 93.98% and a kappa coefficient of 0.92 in 2020. The OA of interannual classification models ranges from 83.9% to 93.9%, and the kappa coefficients from 0.805 to 0.923, thus demonstrating the good performance of the model in multi-category land use classification within the GBA.
Resolution: 30m
Time span: 1976-2020 (excluding 1981-1985)
DOI: 10.5281/zenodo.10082686
title: FREEDOM AND SOCIAL CONTROL IN SCIENTIFIC RESEARCH
DOI: 10.5281/zenodo.10082687
title: FREEDOM AND SOCIAL CONTROL IN SCIENTIFIC RESEARCH
DOI: 10.5281/zenodo.10082695
title: 6bus_system
DOI: 10.5281/zenodo.10082696
title: 6bus_system
DOI: 10.6084/m9.figshare.24524854
title: Structural_and_functional_annotations_<i>Oryzias curvinotus</i> genomic

description: 的基因组、结构和功能注释 <i>Oryzias curvinotus</i>
DOI: 10.6084/m9.figshare.24524911
title: Supp B: Multi-element geochemical analyses on ultrafine soils in Western Australia – towards establishing abundance ranges in mineral exploration settings

description: Supplementary Data B
DOI: 10.6084/m9.figshare.24524911.v1
title: Supp B: Multi-element geochemical analyses on ultrafine soils in Western Australia – towards establishing abundance ranges in mineral exploration settings

description: Supplementary Data B
DOI: 10.5281/zenodo.10082697
title: FUNCTIONAL STATE OF ENDOTHELIUM IN PATIENTS WITH ANKYLOSING SPONDYLITIS AFTER COVID-19
DOI: 10.5281/zenodo.10082698
title: FUNCTIONAL STATE OF ENDOTHELIUM IN PATIENTS WITH ANKYLOSING SPONDYLITIS AFTER COVID-19
DOI: 10.6084/m9.figshare.24524923
title: Supp C: Multi-element geochemical analyses on ultrafine soils in Western Australia – towards establishing abundance ranges in mineral exploration settings

description: Supplementary Data C
DOI: 10.6084/m9.figshare.24524923.v1
title: Supp C: Multi-element geochemical analyses on ultrafine soils in Western Australia – towards establishing abundance ranges in mineral exploration settings

description: Supplementary Data C
DOI: 10.6084/m9.figshare.24524899
title: Table S2.1.xlsx

description: Thesis Table S2.1 Number of Shigella and E. coli genomes encoding ARGs to commonly-used antibiotics, analyzed by the NCBI Pathogen Detection Database.
DOI: 10.6084/m9.figshare.24524899.v1
title: Table S2.1.xlsx

description: Thesis Table S2.1 Number of Shigella and E. coli genomes encoding ARGs to commonly-used antibiotics, analyzed by the NCBI Pathogen Detection Database.
DOI: 10.6084/m9.figshare.22353517
title: Seed masting is stronger in tall plants

description: Two economies of scale, predator satiation and pollination efficiency, have been proposed to explain the evolutionary mechanisms of seed masting adopted by some long-lived plants. Plant height is strongly selected by pollination vectors and therefore may also provide economies of scale; however, it remains unknown whether there is a close relationship between plant height and seed masting intensity. Here, we analyzed seed masting intensity of 158 perennial plant species to test if plant height can select for seed masting. We show that seed masting intensities are higher in tall plants irrespective of phylogeny, life form, pollination vector, and type of spermatophytes. We also show that anemophily rather than entomophily selects for taller plant species and higher seed masting intensities. The linear correlations and evolutionary links between plant height and seed masting intensity provide further evidence that seed masting could have evolved as an adaptation to plant height selected by pollination vectors, which paves a novel avenue for better understanding masting evolution across plant species.
DOI: 10.6084/m9.figshare.22353517.v1
title: Seed masting is stronger in tall plants

description: Two economies of scale, predator satiation and pollination efficiency, have been proposed to explain the evolutionary mechanisms of seed masting adopted by some long-lived plants. Plant height is strongly selected by pollination vectors and therefore may also provide economies of scale; however, it remains unknown whether there is a close relationship between plant height and seed masting intensity. Here, we analyzed seed masting intensity of 158 perennial plant species to test if plant height can select for seed masting. We show that seed masting intensities are higher in tall plants irrespective of phylogeny, life form, pollination vector, and type of spermatophytes. We also show that anemophily rather than entomophily selects for taller plant species and higher seed masting intensities. The linear correlations and evolutionary links between plant height and seed masting intensity provide further evidence that seed masting could have evolved as an adaptation to plant height selected by pollination vectors, which paves a novel avenue for better understanding masting evolution across plant species.
DOI: 10.5281/zenodo.10081573
title: SEQC2 CNV raw call sets
DOI: 10.34740/kaggle/dsv/6920560
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/6920574
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.17630/4f2c7298-3ef3-4c54-b1f2-38e73f745871
title: Data underpinning Pengfei Hou's thesis (3)
DOI: 10.5281/zenodo.10081574
title: SEQC2 CNV raw call sets
DOI: 10.5281/zenodo.10054835
title: Radarcape ADS-B messages captured by antenna installed at the roof of the EETAC during Nov'2023

description: Messages collected from the ADS-B antenna at the roof of the EETAC-UPC school at Castelldefels, Barcelona, from 15th of Sep 2023 to 31st of Oct 2023
DOI: 10.5281/zenodo.10054834
title: Radarcape ADS-B messages captured by antenna installed at the roof of the EETAC during Nov'2023

description: Messages collected from the ADS-B antenna at the roof of the EETAC-UPC school at Castelldefels, Barcelona, from 15th of Sep 2023 to 31st of Oct 2023
DOI: 10.5281/zenodo.10036387
title: Acoustic data and Loading Regime of Two Rock Physics Experiments carried out on INOVA-1000 test-complex, Borok, Russia

description: Dataset linked to a manuscript under revision: "Source parameters of laboratory acoustic emission events estimated from the coda of waveforms". Two experiments carried out on cilindrical samples (D30xH60mm) of Berea sandstone and granite of Voronezh crystal massif (Russia). Each experimental dataset contains: acoustic records from 16 sensors, the appearance of acoustic emission events during the experiment, velocities of P-waves changing in time, loading regime, i.e. confining pressure and axial loading curves, results of source characteristics analyses from coda-waves: corner frequencies, relative seismic moments and relative stress-drops.
DOI: 10.5281/zenodo.10036388
title: Acoustic data and Loading Regime of Two Rock Physics Experiments carried out on INOVA-1000 test-complex, Borok, Russia

description: Dataset linked to a manuscript under revision: "Source parameters of laboratory acoustic emission events estimated from the coda of waveforms". Two experiments carried out on cilindrical samples (D30xH60mm) of Berea sandstone and granite of Voronezh crystal massif (Russia). Each experimental dataset contains: acoustic records from 16 sensors, the appearance of acoustic emission events during the experiment, velocities of P-waves changing in time, loading regime, i.e. confining pressure and axial loading curves, results of source characteristics analyses from coda-waves: corner frequencies, relative seismic moments and relative stress-drops.
DOI: 10.6084/m9.figshare.24503980
title: Supplementary Material for: CLEAR CELL CARCINOMA ARISING IN LOW-GRADE MULLERIAN ADENOSARCOMA: FIRST REPORTED CASE WITH INSIGHT INTO MOLECULAR PROFILE

description: Uterine adenosarcoma is a rare biphasic neoplasm composed of a malignant, usually low-grade stromal component and benign epithelial component, usually endometrioid. Pathogenesis is unknown; some cases are undoubtably associated with tamoxifen use. Endometrial clear cell carcinoma (CCC) is an aggressive subtype of endometrial cancer, accounting for less than 10% of all uterine carcinomas. The etiology is unknown but can rarely be associated with Lynch syndrome and tamoxifen administration. The development of a composite neoplasm consisting of adenocarcinoma in adenosarcoma is extremely rare. Endometrioid carcinoma typically represents the epithelial component of the composite tumor. Here we present the very first case of composite tumor, namely adenosarcoma with clear cell carcinoma in which next-generation sequencing was performed. Patient was an 85-years-old woman treated with tamoxifen for five years. To better understand the pathobiology of two tumors, a targeted genomic analysis of both components was performed. We found seven identical somatic variants in the samples of both tumors, indicating that the tumors have a high probability of having the same origin. Dual amplification of CDK4 and MDM2 was the most likely primary cause of tumor formation, but also one driver variant in the DHX15 gene that was present in both tumor components, suggesting that DHX15 may play an important role in the initiation and development of sarcoma and carcinoma. The patient is followed by regular clinical controls and is alive without signs of disease recurrence 18 months after surgery.