cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22284688.v1
title: A comparison of photographic and spectrometric methods to quantify the colors seen by animal eyes

description: This is the complete data set and MATLAB code used in the paper entitled "A comparison of photographic and spectrometric methods to quantify the colors seen by animal eyes" by Cynthia Tedore, published in <i>Methods in Ecology and Evolution</i>.
DOI: 10.6084/m9.figshare.24715959
title: D1&D2 images

description: Image datasets of population genetics
DOI: 10.6084/m9.figshare.24715959.v2
title: D1&D2 images

description: Image datasets of population genetics
DOI: 10.6084/m9.figshare.24715959.v1
title: D1&D2 images

description: Image datasets of population genetics
DOI: 10.5281/zenodo.10251196
title: Delving into the Impact of Stress on Mental Health: A Data-Driven Approach

description: This comprehensive dataset delves into the intricate relationship between stress and mental health, providing valuable insights for researchers, healthcare professionals, and individuals alike. The dataset encompasses a diverse range of variables, including self-reported stress levels, standardized mental health assessments, demographic information, and lifestyle factors. This rich data empowers researchers to conduct in-depth analyses and uncover the multifaceted connections between stress and mental Health.
DOI: 10.5281/zenodo.10251195
title: Delving into the Impact of Stress on Mental Health: A Data-Driven Approach

description: This comprehensive dataset delves into the intricate relationship between stress and mental health, providing valuable insights for researchers, healthcare professionals, and individuals alike. The dataset encompasses a diverse range of variables, including self-reported stress levels, standardized mental health assessments, demographic information, and lifestyle factors. This rich data empowers researchers to conduct in-depth analyses and uncover the multifaceted connections between stress and mental Health.
DOI: 10.5281/zenodo.10251162
title: Supplementary Data: Uncovering DFG-out sequence propensity determinants of kinases with machine learning

description: General description
This submission accompanies the paper "Uncovering DFG-out sequence propensity determinants of kinases with machine learning" and covers:

Trained models (train/models_latest). A subset of these is also published as a part of the KinActive tool; each model is of `KinactiveClassifier` type and can be loaded using this tool as well via `kinactive.io.load()` function).
Patched sequences (patched_seqs). PDB sequences, with missing regions patched by UniProt sequences. This is a collection of lXtractor ChainSequence objects.
All labels, variables, and datasets (datasets and labels).
Initial chains and predictions for SwissProt proteins (SP_predictions).
Note on model abbreviations
The main text emphasized datasets that yielded more interpretable results. These were constructed from domain sequences, labeled as apo, inactive, DFG-in, or DFG-out, and further divided into TK and STK subsets. We refer to these datasets with the abbreviation AAIO (Apo All In or Out) to distinguish them from additional datasets.
In addition to AAIO, we explored two alternative labeling strategies:

AHAO (Apo/Holo Any Out): This dataset includes sequences from ligand-bound entries. All sequences within 95\% identity clusters are labeled as DFG-out if the cluster contains at least one sequence in this state. All others are labeled as DFG-in.
AAO (Apo Any Out): This dataset excludes sequences corresponding to ligand-bound entries but includes those with conflicting conformational tendencies within 95\% identity clusters.
Each of these datasets had two versions:

A seed version, denoted by a "*" symbol.
A version enriched with orthologous sequences (no special designation).
Together with the TkST datasets (encompassing TK and STK labels) used for testing the methodology, a total of 14 datasets were used, and both RF and XGB models were applied to each, using the same initial settings, resulting in 28 different models. This additional information is provided for completeness.
DOI: 10.5281/zenodo.10251163
title: Supplementary Data: Uncovering DFG-out sequence propensity determinants of kinases with machine learning

description: General description
This submission accompanies the paper "Uncovering DFG-out sequence propensity determinants of kinases with machine learning" and covers:

Trained models (train/models_latest). A subset of these is also published as a part of the KinActive tool; each model is of `KinactiveClassifier` type and can be loaded using this tool as well via `kinactive.io.load()` function).
Patched sequences (patched_seqs). PDB sequences, with missing regions patched by UniProt sequences. This is a collection of lXtractor ChainSequence objects.
All labels, variables, and datasets (datasets and labels).
Initial chains and predictions for SwissProt proteins (SP_predictions).
Note on model abbreviations
The main text emphasized datasets that yielded more interpretable results. These were constructed from domain sequences, labeled as apo, inactive, DFG-in, or DFG-out, and further divided into TK and STK subsets. We refer to these datasets with the abbreviation AAIO (Apo All In or Out) to distinguish them from additional datasets.
In addition to AAIO, we explored two alternative labeling strategies:

AHAO (Apo/Holo Any Out): This dataset includes sequences from ligand-bound entries. All sequences within 95\% identity clusters are labeled as DFG-out if the cluster contains at least one sequence in this state. All others are labeled as DFG-in.
AAO (Apo Any Out): This dataset excludes sequences corresponding to ligand-bound entries but includes those with conflicting conformational tendencies within 95\% identity clusters.
Each of these datasets had two versions:

A seed version, denoted by a "*" symbol.
A version enriched with orthologous sequences (no special designation).
Together with the TkST datasets (encompassing TK and STK labels) used for testing the methodology, a total of 14 datasets were used, and both RF and XGB models were applied to each, using the same initial settings, resulting in 28 different models. This additional information is provided for completeness.
DOI: 10.34740/kaggle/dsv/7105759
title: Bangla Textual Emotion Dataset (BMotion)
DOI: 10.5517/ccdc.csd.cc2dhbx5
title: CCDC 2219287: Experimental Crystal Structure Determination

description: Related Article: Hongchao Yin, Yuan Tian, Chenxi Guo, Ran Song, Daoshan Yang, Jian Lv|2023|J.Org.Chem.|88|15862|doi:10.1021/acs.joc.3c01941
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: 10.6084/m9.figshare.24716010.v1
title: Underlying data files with manuscript PONE-D-23-22353

description: <b><i>Anaerobutyricum soehngenii</i></b><b> reduces hepatic lipogenic pathways and increases intestinal gluconeogenic gene expression in metabolic-dysfunction associated steatotic liver disease (MASLD) mice</b><b>All supporting data files</b>
DOI: 10.6084/m9.figshare.24716010
title: Underlying data files with manuscript PONE-D-23-22353

description: <b><i>Anaerobutyricum soehngenii</i></b><b> reduces hepatic lipogenic pathways and increases intestinal gluconeogenic gene expression in metabolic-dysfunction associated steatotic liver disease (MASLD) mice</b><b>All supporting data files</b>
DOI: 10.21227/8ce5-5p09
title: Design Methodology for Conformal U-Shaped Series Fed Microstrip Antenna Array

description: The paper proposes a systematic approach for the  design of a conformal U-shaped series-feed microstrip antenna array (US-SFMA) using true time delay phase shifters. The presented study describes the design procedure of conformal SFMA without the use of additional element to tune its spectral and radiation performance. This restores the resonant frequency and hence the S11 response of the array on conforming it on a cylindrical surface by phase compensation technique, thereby enhancing its gain optimally. The paper proposes a systematic approach for the  design of a conformal U-shaped series-feed microstrip antenna array (US-SFMA) using true time delay phase shifters. The presented study describes the design procedure of conformal SFMA without the use of additional element to tune its spectral and radiation performance. This restores the resonant frequency and hence the S11 response of the array on conforming it on a cylindrical surface by phase compensation technique, thereby enhancing its gain optimally.
DOI: 10.5281/zenodo.10251233
title: An empirical approach and practical framework for a decentralized Ethereum Ecosystem Index (EEI) datasets and codes
DOI: 10.5281/zenodo.10251234
title: An empirical approach and practical framework for a decentralized Ethereum Ecosystem Index (EEI) datasets and codes
DOI: 10.5281/zenodo.10245522
title: Data from: "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in non-stationary climate with changing risk of flooding"

description: In the paper "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in a non-stationary climate with changing risk of flooding", life cycle assessment is used to compare two ways to maintain the state of a coastal urban area in a changing climate with increasing flood risk. On one side, the construction of a dike, a hard and proactive scenario, is modeled using a bottom-up approach. On the other, the systematic repair of houses flooded by sea surges, a post-disaster measure, is assessed using a Monte Carlo simulation allowing for aleatory uncertainties in predicting future sea level rise and occurrences of extreme events. Two metrics are identified, normalized mean impacts and probability of dike being most efficient. The methodology is applied to three case studies in Denmark representing three contrasting areas, Copenhagen, Frederiksværk, and Esbjerg. For all case studies the distribution of the calculated impact of repairing houses is highly right skewed, which in some cases has implications for the comparative LCA. 
This dataset contains the underlying data to support the findings of the paper. In particular, two sets of characterized environmental impacts are reported: (1) the impacts of flood-related repairs summed over a century, for each Monte Carlo simulation and (2) the impacts of building a dam. Both sets of results are reported for each of the three cities studied.
DOI: 10.5281/zenodo.10245521
title: Data from: "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in non-stationary climate with changing risk of flooding"

description: In the paper "A framework for performing comparative LCA between repairing flooded houses and construction of dikes in a non-stationary climate with changing risk of flooding", life cycle assessment is used to compare two ways to maintain the state of a coastal urban area in a changing climate with increasing flood risk. On one side, the construction of a dike, a hard and proactive scenario, is modeled using a bottom-up approach. On the other, the systematic repair of houses flooded by sea surges, a post-disaster measure, is assessed using a Monte Carlo simulation allowing for aleatory uncertainties in predicting future sea level rise and occurrences of extreme events. Two metrics are identified, normalized mean impacts and probability of dike being most efficient. The methodology is applied to three case studies in Denmark representing three contrasting areas, Copenhagen, Frederiksværk, and Esbjerg. For all case studies the distribution of the calculated impact of repairing houses is highly right skewed, which in some cases has implications for the comparative LCA. 
This dataset contains the underlying data to support the findings of the paper. In particular, two sets of characterized environmental impacts are reported: (1) the impacts of flood-related repairs summed over a century, for each Monte Carlo simulation and (2) the impacts of building a dam. Both sets of results are reported for each of the three cities studied.
DOI: 10.5281/zenodo.10245853
title: Data from: "Integrating variability in life cycle assessments of products, cities, and sectors"

description: In my thesis "Integrating variability in life cycle assessments of products, cities, and sectors", I aimed to integrate sources of variability in the life cycle assessments of products, cities, and sectors. In this data repository, data underlying the findings of Chapters 2 and 5 were published.
In Chapter 2, the mitigation potential of a new chemical agent used for tire manufacturing was investigated. ZnO nanoparticles anchored on SiO2 NPs (ZnO@SiO2) is a new vulcanization activator currently being developed at the pilot scale to minimize environmental impacts by decreasing the zinc content and rolling resistance of tires. We assessed the environmental impacts of synthesizing and utilizing ZnO@SiO2 on human health, ecosystems, and resource scarcity. Prospective LCA was used to integrate temporal variability and forecast the environmental impacts of the synthesis at the industrial scale. Two industrial predictions were made based on two sets of experimental data available at TRLs 5 and 6. Explorative scenarios were used to estimate the environmental consequences of using the new activator to manufacture tires. 
In this repository, two sets of results were reported: (1) endpoint impacts for TRLs 5, 6, 9a, and 9b, per kg of activator synthesized and (2) endpoint impacts of an average passenger car tire and a tire made with the new activator.
In Chapter 5, the GHG footprints of economic sectors were assessed. Intermediate producer bear responsibility for emissions embodied in their supply chains. Environmental accounts describing the supply chain emissions of sectors at a local level and with granular sector aggregation are urgently needed to inform relevant policy action to drive climate change mitigation efforts at the regional level in the EU. We aimed to build such a dataset for the European Union (EU) and focused on quantifying the subnational variation of sector GHG supply chain emissions. We used an environmentally extended multi-region input-output (EEMRIO) analysis to examine the GHG footprints of 63 sectors in 264 subnational regions across the EU. This approach inherently integrated the spatial variability of the GHG footprints examined. We used a top-down MRIO database with subnational European information on production, and trade structures, accounting for trade inside and outside Europe. We added a subnational environmental extension for GHG emissions from an air emission database at the NUTS 2 level. 
In this repository, the dataset created for the study was published, including the supply chain emissions reported per sector and region.
DOI: 10.5281/zenodo.10245854
title: Data from: "Integrating variability in life cycle assessments of products, cities, and sectors"

description: In my thesis "Integrating variability in life cycle assessments of products, cities, and sectors", I aimed to integrate sources of variability in the life cycle assessments of products, cities, and sectors. In this data repository, data underlying the findings of Chapters 2 and 5 were published.
In Chapter 2, the mitigation potential of a new chemical agent used for tire manufacturing was investigated. ZnO nanoparticles anchored on SiO2 NPs (ZnO@SiO2) is a new vulcanization activator currently being developed at the pilot scale to minimize environmental impacts by decreasing the zinc content and rolling resistance of tires. We assessed the environmental impacts of synthesizing and utilizing ZnO@SiO2 on human health, ecosystems, and resource scarcity. Prospective LCA was used to integrate temporal variability and forecast the environmental impacts of the synthesis at the industrial scale. Two industrial predictions were made based on two sets of experimental data available at TRLs 5 and 6. Explorative scenarios were used to estimate the environmental consequences of using the new activator to manufacture tires. 
In this repository, two sets of results were reported: (1) endpoint impacts for TRLs 5, 6, 9a, and 9b, per kg of activator synthesized and (2) endpoint impacts of an average passenger car tire and a tire made with the new activator.
In Chapter 5, the GHG footprints of economic sectors were assessed. Intermediate producer bear responsibility for emissions embodied in their supply chains. Environmental accounts describing the supply chain emissions of sectors at a local level and with granular sector aggregation are urgently needed to inform relevant policy action to drive climate change mitigation efforts at the regional level in the EU. We aimed to build such a dataset for the European Union (EU) and focused on quantifying the subnational variation of sector GHG supply chain emissions. We used an environmentally extended multi-region input-output (EEMRIO) analysis to examine the GHG footprints of 63 sectors in 264 subnational regions across the EU. This approach inherently integrated the spatial variability of the GHG footprints examined. We used a top-down MRIO database with subnational European information on production, and trade structures, accounting for trade inside and outside Europe. We added a subnational environmental extension for GHG emissions from an air emission database at the NUTS 2 level. 
In this repository, the dataset created for the study was published, including the supply chain emissions reported per sector and region.
DOI: 10.6084/m9.figshare.24716076
title: ECA1

description: Source code, models and datasets.
DOI: 10.6084/m9.figshare.24716076.v1
title: ECA1

description: Source code, models and datasets.
DOI: 10.5281/zenodo.10251249
title: Sized Files
DOI: 10.5281/zenodo.10251250
title: Sized Files
DOI: 10.5281/zenodo.10251244
title: Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms (supplementary material)

description: Supplementary material for Sawi et al., 2023, Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms (The Seismic Record). Catalog of repeating earthquakes in sequences on a 10-km long segment of the San Andreas Fault in California from 1984-2019. 
 
Catalog Header
YR/MO/DY...........Date of event
HR/MN/SC...........Time of event
LAT/LON/DEP........Location of event
EX/EY/EZ...........Relative location uncertainty (in m)
MAG................NCSN magnitude
evID.................NCSN event ID
seqID................Repeating earthquake sequence ID
isRESp............Is quasi-periodic RES (bool)
 
References: 
Sawi T., Waldhauser F., Holtzman B. K., Groebner, N. (2023) Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms. The Seismic Record. 
Waldhauser, F., and Schaff, D. P. (2021). A Comprehensive Search for Repeating Earthquakes in Northern California: Implications for Fault Creep, Slip Rates, Slip Partitioning, and Transient Stress. J Geophys Res B Solid Earth, 126(11), 1–22. https://doi.org/10.1029/2021JB022495
DOI: 10.5281/zenodo.10251243
title: Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms (supplementary material)

description: Supplementary material for Sawi et al., 2023, Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms (The Seismic Record). Catalog of repeating earthquakes in sequences on a 10-km long segment of the San Andreas Fault in California from 1984-2019. 
 
Catalog Header
YR/MO/DY...........Date of event
HR/MN/SC...........Time of event
LAT/LON/DEP........Location of event
EX/EY/EZ...........Relative location uncertainty (in m)
MAG................NCSN magnitude
evID.................NCSN event ID
seqID................Repeating earthquake sequence ID
isRESp............Is quasi-periodic RES (bool)
 
References: 
Sawi T., Waldhauser F., Holtzman B. K., Groebner, N. (2023) Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms. The Seismic Record. 
Waldhauser, F., and Schaff, D. P. (2021). A Comprehensive Search for Repeating Earthquakes in Northern California: Implications for Fault Creep, Slip Rates, Slip Partitioning, and Transient Stress. J Geophys Res B Solid Earth, 126(11), 1–22. https://doi.org/10.1029/2021JB022495
DOI: 10.5281/zenodo.10251230
title: INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells

description: Long non-coding RNAs (lncRNAs) are a diverse group of transcripts with poorly understoodfunctionality. To address this gap, we developed INFLAMeR, an advanced machine learningmodel trained on CRISPRi screening data, to predict functional lncRNAs using comprehensivegenetic features. We experimentally validated the predictions by assessing their impact on cellproliferation and anticancer drug resistance. Among the selected lncRNAs, 85% showedsignificant effects upon knockdown, while low-scoring lncRNAs had no discernible impact.Notably, our study elucidated the functional role of SNHG6 in hematopoietic differentiation.INFLAMeR greatly enhances the prediction of functional lncRNAs, providing valuable insightsinto their regulatory landscape. By integrating INFLAMeR with experimental validation, we canidentify and characterize functional lncRNAs in a cell-type-specific manner, contributing to adeeper understanding of their involvement in cellular processes. Our findings revealed insightsinto lncRNA biology and a framework for improving the identification of functional lncRNAs.
DOI: 10.5281/zenodo.10251229
title: INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells

description: Long non-coding RNAs (lncRNAs) are a diverse group of transcripts with poorly understoodfunctionality. To address this gap, we developed INFLAMeR, an advanced machine learningmodel trained on CRISPRi screening data, to predict functional lncRNAs using comprehensivegenetic features. We experimentally validated the predictions by assessing their impact on cellproliferation and anticancer drug resistance. Among the selected lncRNAs, 85% showedsignificant effects upon knockdown, while low-scoring lncRNAs had no discernible impact.Notably, our study elucidated the functional role of SNHG6 in hematopoietic differentiation.INFLAMeR greatly enhances the prediction of functional lncRNAs, providing valuable insightsinto their regulatory landscape. By integrating INFLAMeR with experimental validation, we canidentify and characterize functional lncRNAs in a cell-type-specific manner, contributing to adeeper understanding of their involvement in cellular processes. Our findings revealed insightsinto lncRNA biology and a framework for improving the identification of functional lncRNAs.
DOI: 10.5281/zenodo.10251281
title: The Role of Research University Libraries in Research Data Management: The Case of Türkiye
DOI: 10.5281/zenodo.10251282
title: The Role of Research University Libraries in Research Data Management: The Case of Türkiye