cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25952/4rb4-c230
title: Multi-modifier strategies to improve rumen health, growth and carcass attributes of grain-fed cattle - Dataset

description: Dataset relating to multi-modifier strategies to improve rumen health, growth and carcass attributes of grain-fed cattle.
description: Dataset relating to multi-modifier strategies to improve rumen health, growth and carcass attributes of grain-fed cattle.
DOI: 10.6084/m9.figshare.24579139
title: Data on Eco-friendly product purchase behavior model: Theory of planned behavior among generation Z consumers

description: <i>This dataset analyzed (1) the relationship between consumer attitudes, subjective norms, and perceived controls with the purchase intention of eco-friendly products in Generation Z consumers</i>
DOI: 10.5281/zenodo.10145530
title: Machine Learning-Based Bridge Maintenance Optimization Model for Maximizing Performance within Available Annual Budgets

description: Effective maintenance planning for bridges is crucial for maintaining their performance, safety, and minimizing maintenance costs. Timely implementation of interventions can improve the performance of bridges and avoid the need for costly interventions. However, bridge maintenance is often delayed due to inadequate planning and budget allocation, as well as resource constraints such as funding. With availability of historical condition data of bridges in databases such as the National Bridge Inventory (NBI) and National Bridge Elements (NBE), there is an opportunity to use data-driven methods to predict deterioration of bridge elements and optimize their maintenance interventions to maximize performance of bridges. This paper presents the development of a novel system that uses Machine Learning (ML) techniques to predict condition of concrete bridge elements and binary linear programming optimization method to identify the optimal selection of maintenance interventions and their timing to maximize the performance of bridges while complying with available annual budgets. Four ML methods are explored: decision tree, random forest, gradient boosting, and support vector machines. The results of the ML evaluation show that, while the values of the predictive performance metrics varied for different elements, random forest method had the best performance for all elements. A case study of a concrete bridge is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The case study results show that the developed model identifies optimal maintenance interventions for various annual budgets over a 50-year study period. The primary contributions of this research to the body of knowledge are: (1) development of a novel system that integrates machine learning techniques and linear programming for predicting bridge element conditions and optimizing maintenance interventions; (2) modeling and predicting the deterioration of bridge elements based on health index metric; and (3) generating long-term maintenance plans for each of bridge elements to maximize the performance of bridges within available annual budgets. The present system is expected to support decision makers, such as highway agencies, in allocating limited financial resources for bridge maintenance more efficiently and cost-effectively.
DOI: 10.5281/zenodo.10145531
title: Machine Learning-Based Bridge Maintenance Optimization Model for Maximizing Performance within Available Annual Budgets

description: Effective maintenance planning for bridges is crucial for maintaining their performance, safety, and minimizing maintenance costs. Timely implementation of interventions can improve the performance of bridges and avoid the need for costly interventions. However, bridge maintenance is often delayed due to inadequate planning and budget allocation, as well as resource constraints such as funding. With availability of historical condition data of bridges in databases such as the National Bridge Inventory (NBI) and National Bridge Elements (NBE), there is an opportunity to use data-driven methods to predict deterioration of bridge elements and optimize their maintenance interventions to maximize performance of bridges. This paper presents the development of a novel system that uses Machine Learning (ML) techniques to predict condition of concrete bridge elements and binary linear programming optimization method to identify the optimal selection of maintenance interventions and their timing to maximize the performance of bridges while complying with available annual budgets. Four ML methods are explored: decision tree, random forest, gradient boosting, and support vector machines. The results of the ML evaluation show that, while the values of the predictive performance metrics varied for different elements, random forest method had the best performance for all elements. A case study of a concrete bridge is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The case study results show that the developed model identifies optimal maintenance interventions for various annual budgets over a 50-year study period. The primary contributions of this research to the body of knowledge are: (1) development of a novel system that integrates machine learning techniques and linear programming for predicting bridge element conditions and optimizing maintenance interventions; (2) modeling and predicting the deterioration of bridge elements based on health index metric; and (3) generating long-term maintenance plans for each of bridge elements to maximize the performance of bridges within available annual budgets. The present system is expected to support decision makers, such as highway agencies, in allocating limited financial resources for bridge maintenance more efficiently and cost-effectively.
DOI: 10.5517/ccdc.csd.cc2gnvpp
title: CCDC 2284163: Experimental Crystal Structure Determination

description: Related Article: Ameni Ghabi, Hasan Mtiraoui, Nesrine Amiri, Hanan Al-Ghulikah, Taha Guerfel, Morgane Sanselme, Melek Hajji|2024|J.Mol.Struct.|1296|136799|doi:10.1016/j.molstruc.2023.136799
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.cc2gp9p5
title: CCDC 2284597: Experimental Crystal Structure Determination

description: Related Article: Alexey V. Dobrydnev, Maria V. Popova, Andrii V. Yatsymyrskyi, Svitlana V. Shishkina, Yaroslav O. Chuchvera, Yulian M. Volovenko|2024|J.Mol.Struct.|1295|136745|doi:10.1016/j.molstruc.2023.136745
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.cc2gp9q6
title: CCDC 2284598: Experimental Crystal Structure Determination

description: Related Article: Alexey V. Dobrydnev, Maria V. Popova, Andrii V. Yatsymyrskyi, Svitlana V. Shishkina, Yaroslav O. Chuchvera, Yulian M. Volovenko|2024|J.Mol.Struct.|1295|136745|doi:10.1016/j.molstruc.2023.136745
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.cc2gpz38
title: CCDC 2285230: Experimental Crystal Structure Determination

description: Related Article: Tomislav Stolar, Jasna Alić, Gregor Talajić, Nikola Cindro, Mirta Rubčić, Krešimir Molčanov, Krunoslav Užarević, José G. Hernández|2023|Chem.Commun.|59|13490|doi:10.1039/D3CC04448C
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.cc2gqmrl
title: CCDC 2285870: Experimental Crystal Structure Determination

description: Related Article: Zheng Zhang, Tony M. Pugliano, Da Cao, Doup Kim, Roshan S. Annam, Dilruba A. Popy, Tamanna Pinky, Ge Yang, Jivtesh Garg, Mario F. Borunda, Bayram Saparov|2023|J.Mater.Chem.C|11|15357|doi:10.1039/D3TC02787B
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: 10.60520/ieda/113064
title: Carbon Isotope Chemostratigraphy and Paleoclimatology of Hensel Formation Paleosols in Kimble County, Texas

description: The Cretaceous Hensel Formation located in Kimble County, Texas reveal continental deposits including paleosol carbonates. This data set provide carbon and oxygen isotope values for paleosol carbonates that can be used to understand paleoclimate parameters.
description: Cuellar, J. M., (2022), Carbon Isotope Chemostratigraphy and Paleoclimatology of Hensel Formation Paleosols in Kimble County, Texas. M.S. Thesis, The University of Kansas
DOI: 10.26208/jxze-px92
title: A Temperature-Dependent Length-Scale for Transferable Local Density Potentials

description: Recent coarse-grained (CG) models have often supplemented conventional pair potentials with potentials that depend upon the local density around each particle. In this work, we investigate the temperature-dependence of these local density (LD) potentials. Specifically, we employ the multiscale coarse-graining (MS-CG) force-matching variational principle to parameterize pair and LD potentials for one-site CG models of molecular liquids at ambient pressure. The accuracy of these MS-CG LD potentials quite sensitively depends upon the length-scale, rc, that is employed to define the local density. When the local density is defined by the optimal length-scale, rc*⁠, the MS-CG potential often accurately describes the reference state point and can provide reasonable transferability across a rather wide range of temperatures. At ambient pressure, the optimal LD length-scale varies linearly with temperature over a very wide range of temperatures. Moreover, if one adopts this temperature-dependent LD length-scale, then the MS-CG LD potential appears independent of temperature, while the MS-CG pair potential varies linearly across this temperature range. This provides a simple means for predicting pair and LD potentials that accurately model new state points without performing additional atomistic simulations. Surprisingly, at certain state points, the predicted potentials provide greater accuracy than MS-CG potentials that were optimized for the state point.
DOI: 10.34740/kaggle/dsv/6984923
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.34740/kaggle/dsv/6984930
title: BBC News
DOI: 10.17903/fk2/1ewkvt
title: Αξίες στην κρίση της πανδημίας Covid-19 (πρώτο κύμα)

description: Η κρίση λόγω της πανδημίας COVID-19 είναι πολλαπλή και θέτει μείζονες προκλήσεις για την υγεία, την οικονομία και την κοινωνία. Η μακροχρόνια παρακολούθηση των κεντρικών αξιών και των στάσεων των πολιτών σε περιόδους κρίσεων συμβάλλει στην αντιμετώπιση των τρεχουσών κοινωνικών και πολιτικών εντάσεων. Η παροχή συγκρίσιμων δεδομένων μεταξύ των χωρών σε παγκόσμια κλίμακα είναι ο κύριος στόχος της έρευνας: "Αξίες στην Κρίση / Value in Crisis Study" (VIC). Ο Christian Welzel, μαζί με γνωστούς ερευνητές από τη Γερμανία, το Ηνωμένο Βασίλειο και τη Σουηδία ξεκίνησαν την έρευνα και τελικά 16 χώρες έλαβαν μέρος στον πρώτο γύρο. Επί του παρόντος, η έρευνα Values In Crisis είναι το μοναδικό διεθνές έργο διαχρονικής έρευνας σχετικά με τις στάσεις και τις αξίες που παρέχει δεδομένα σε παγκόσμια κλίμακα. Μέσω του Αποθετηρίου του EKKE στον Κατάλογο Δεδομένων του SoDaNet είναι διαθέσιμο τόσο το παρόν πρώτο κύμα της έρευνας για την Ελλάδα όσο και το δεύτερο κύμα. Και στα δυο κύματα διατίθενται τα δεδομένα και τα ερωτηματολόγια στα Ελληνικά και στα Αγγλικά. Θα πραγματοποιηθεί άλλο ένα τρίτο και τελευταίο κύμα το 2022 όπου θεωρείται ότι θα βρισκόμαστε στο τέλος της πανδημίας, οπότε και θα υφίσταται μία πλήρως συγκριτική εικόνα των αξιών των Ελλήνων στην αρχή, στο μέσο και στο τέλος της πανδημίας.
DOI: 10.25345/c5cc0v458
title: MassIVE MSV000093411 - GNPS_U19_ADRC_Human_Fecal_Samples
title: 764 Human stool samples from ADRC (longitudinal samples, complimenting previous 637 samples MSV000091380). QiitaID 14736. Untargeted LC-MS/MS acquisition was performed on a Vanquish UHPLC system coupled to a Q-Exactive Hybrid Quadrupole-Orbitrap (Thermo Fisher Scientific, Bremen, Germany). Chromatographic separation was performed on a Kinetex 1.7 um 100 A pore size C18 reversed phase UHPLC column 50 x 2.1 mm (Phenomenex, Torrance, CA).
DOI: 10.6084/m9.figshare.24579187
title: data_train

description: Understanding the demand predictability of bike share systems: A station-level analysis
DOI: 10.6084/m9.figshare.24579187.v1
title: data_train

description: Understanding the demand predictability of bike share systems: A station-level analysis
DOI: 10.6084/m9.figshare.24579178
title: data_test

description: Understanding the demand predictability of bike share systems: A station-level analysis
DOI: 10.6084/m9.figshare.24579178.v1
title: data_test

description: Understanding the demand predictability of bike share systems: A station-level analysis
DOI: 10.6084/m9.figshare.24579142
title: Updated data on elk study

description: Treponema genomes from elk
DOI: 10.6084/m9.figshare.24579142.v2
title: Updated data on elk study

description: Treponema genomes from elk
DOI: 10.6084/m9.figshare.24579142.v1
title: Updated data on elk study

description: Treponema genomes from elk
DOI: 10.25493/dsy2-w2p
title: The Swedish National Facility for Magnetoencephalography Parkinson's Disease Dataset (v1.1)

description: Parkinson's disease (PD) is characterised by a loss of dopamine and dopaminergic cells. The consequences hereof are widespread network disturbances in brain function. It is still an ongoing topic of investigation how these disease-related changes in brain function manifest in PD relate to clinical symptoms in the motor-, cognitive-, and emotional domains. We present The Swedish National Facility for Magnetoencephalography Parkinson's Disease Dataset (NatMEG-PD) as an Open Science contribution to identify the functional neural signatures of Parkinson's disease and contribute to diagnosis and treatment. The dataset consists of whole-head magnetoencephalographic (MEG) recordings from 66 well-characterized PD patients and 68 age- and sex-matched healthy controls. NatMEG-PD was acquired for investigating beta-band activity and contains resting-state MEG, MEG during an active movement task, and MEG during passive movements. The data includes anonymised MRI for source analysis and clinical scores for motor, cognitive and depressive symptoms. MEG data is rich in nature and can be used to explore numerous kinds of functional brain changes in PD. By sharing these data, we hope other researchers will contribute to advancing our understanding of the relationship between brain activity and disease state or symptoms.
DOI: 10.26195/jjaa-7p04
title: Generative AI in the Australian Education System: An Open Dataset of Stakeholder Recommendations and Emerging Analysis from a Public Inquiry

description: Dataset accompanying AJET paper of the same name. The launch of new tools in late 2022 heralded significant growth in attention to the impacts of generative AI (genAI) in education. Claims of the potential impact on education are contested, but there are clear risks of inappropriate use particularly where genAI aligns poorly with learning aims. In response, in mid-2023 the Australian Federal Government held an inquiry, calling for public submissions. This inquiry offers a lens onto the policy framing of genAI in education and provides the object of investigation for this paper. We use the inquiry submissions, extracting structured claims from each. This extraction is provided as an open data set for further research.
DOI: 10.25345/c57m04b01
title: MassIVE MSV000093412 - Co-Culture Penicillium spp BuOH
title: Co-culture Penicillium spp., Indonesian tropical peat swamp soil isolates using BuOH solvent
DOI: 10.5281/zenodo.10129624
title: NextClone and CloneDetective: An Integrated Nextflow Pipeline and R Package for Clonal Barcode Extraction and Quantification

description: BAM file (chunk 26-50 of 50) for the scRNAseq data required to replicate the analyses presented at: https://phipsonlab.github.io/NextClone-analysis/.
Chunk 1-25 can be downloaded from https://zenodo.org/records/10129134.
The original BAM file was too big to fit in an entry. Thus it was split into 50 using picard:

SplitSamByNumberOfReads -I possorted_genome_bam.bam -O split_sam/ -N_FILES 50 --CREATE_MD5_FILE
Before re-running all the analyses in https://phipsonlab.github.io/NextClone-analysis/, make sure you merge all 50 chunks first using picard (change xxx to point to the directory storing all the 50 chunks you have downloaded):

outdir="xxx"
args=""
# Loop through each BAM file
for file in ${outdir}/*.bam; do
    args+="-I $file "
done
# Do the actual merging
MergeSamFiles $args -O $outdir/merged_v2/merged_bam_v2.bam --USE_THREADING --CREATE_MD5_FILE
Picard can be downloaded from: https://github.com/broadinstitute/picard
DOI: 10.5281/zenodo.10129625
title: NextClone and CloneDetective: An Integrated Nextflow Pipeline and R Package for Clonal Barcode Extraction and Quantification

description: BAM file (chunk 26-50 of 50) for the scRNAseq data required to replicate the analyses presented at: https://phipsonlab.github.io/NextClone-analysis/.
Chunk 1-25 can be downloaded from https://zenodo.org/records/10129134.
The original BAM file was too big to fit in an entry. Thus it was split into 50 using picard:

SplitSamByNumberOfReads -I possorted_genome_bam.bam -O split_sam/ -N_FILES 50 --CREATE_MD5_FILE
Before re-running all the analyses in https://phipsonlab.github.io/NextClone-analysis/, make sure you merge all 50 chunks first using picard (change xxx to point to the directory storing all the 50 chunks you have downloaded):

outdir="xxx"
args=""
# Loop through each BAM file
for file in ${outdir}/*.bam; do
    args+="-I $file "
done
# Do the actual merging
MergeSamFiles $args -O $outdir/merged_v2/merged_bam_v2.bam --USE_THREADING --CREATE_MD5_FILE
Picard can be downloaded from: https://github.com/broadinstitute/picard
DOI: 10.5281/zenodo.10129133
title: NextClone and CloneDetective: An Integrated Nextflow Pipeline and R Package for Clonal Barcode Extraction and Quantification

description: BAM file (chunk 1-25 of 50) for the scRNAseq data required to replicate the analyses presented at: https://phipsonlab.github.io/NextClone-analysis/.
Chunk 26-50 can be downloaded from https://zenodo.org/uploads/10129625
The original BAM file was too big to fit in an entry. Thus it was split into 50 using picard:

SplitSamByNumberOfReads -I possorted_genome_bam.bam -O split_sam/ -N_FILES 50 --CREATE_MD5_FILE
Before re-running all the analyses in https://phipsonlab.github.io/NextClone-analysis/, make sure you merge all 50 chunks first using picard (change xxx to point to the directory storing all the 50 chunks you have downloaded):

outdir="xxx"
args=""
# Loop through each BAM file
for file in ${outdir}/*.bam; do
    args+="-I $file "
done
# Do the actual merging
MergeSamFiles $args -O $outdir/merged_v2/merged_bam_v2.bam --USE_THREADING --CREATE_MD5_FILE
Picard can be downloaded from: https://github.com/broadinstitute/picard
DOI: 10.5281/zenodo.10129134
title: NextClone and CloneDetective: An Integrated Nextflow Pipeline and R Package for Clonal Barcode Extraction and Quantification

description: BAM file (chunk 1-25 of 50) for the scRNAseq data required to replicate the analyses presented at: https://phipsonlab.github.io/NextClone-analysis/.
Chunk 26-50 can be downloaded from https://zenodo.org/uploads/10129625
The original BAM file was too big to fit in an entry. Thus it was split into 50 using picard:

SplitSamByNumberOfReads -I possorted_genome_bam.bam -O split_sam/ -N_FILES 50 --CREATE_MD5_FILE
Before re-running all the analyses in https://phipsonlab.github.io/NextClone-analysis/, make sure you merge all 50 chunks first using picard (change xxx to point to the directory storing all the 50 chunks you have downloaded):

outdir="xxx"
args=""
# Loop through each BAM file
for file in ${outdir}/*.bam; do
    args+="-I $file "
done
# Do the actual merging
MergeSamFiles $args -O $outdir/merged_v2/merged_bam_v2.bam --USE_THREADING --CREATE_MD5_FILE
Picard can be downloaded from: https://github.com/broadinstitute/picard
DOI: 10.25958/jgb4-8842
title:  Determinants of social loneliness and its association with negative coping styles

description: The original study linked to this dataset argued that underlying antecedents of social loneliness maintain negative behaviour, whereby it becomes chronic and problematic. Four intrinsic antecedents of social loneliness are examined (Secure and Insecure Attachment, Emotional Regulation Problems – Expressive Suppression, Unclear Self-Concept, and Neuroticism) along with negative (Ignoring and Withdrawing) behavioural coping styles. Attachment type, Neuroticism, and to a lesser extent, Self-Concept Clarity, Expressive Suppression and Withdrawal coping styles may play an important role in averting or predicting Social Loneliness. Behaviourally, those who are higher in Social Loneliness, particularly young adult females, may adopt less Withdrawal coping styles once a threshold of loneliness has been attained.
DOI: 10.5517/ccdc.csd.cc2grnfb
title: CCDC 2286852: Experimental Crystal Structure Determination

description: Related Article: Eshani Das, Mark Aldren M. Feliciano, Pavel Yamanushkin, Xinsong Lin, Brian Gold|2023|Org.Biomol.Chem.|21|8857|doi:10.1039/D3OB01559A
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.cc2grngc
title: CCDC 2286853: Experimental Crystal Structure Determination

description: Related Article: Eshani Das, Mark Aldren M. Feliciano, Pavel Yamanushkin, Xinsong Lin, Brian Gold|2023|Org.Biomol.Chem.|21|8857|doi:10.1039/D3OB01559A
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.