cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15124/85cd4daf-de96-4192-9712-1180496c51f9
title: Dataset associated with publication 'Catch-and-Release: The Assembly, Immobilisation and Recycling of Redox-reversible Artificial Metalloenzymes'

description: Raw data and processed data associated with DOI: 10.1021/acscatal.3c05294
DOI: 10.5281/zenodo.10638628
title: Database micrometeorological station of Cogne

description: Hourly micrometeorological and soil data, Cogne eddy covariance site. Growing seasons 2014, 2015, 2016, 2017.
DOI: 10.6084/m9.figshare.24722232
title: Microservices Dataset - Filtered version

description: This is a microservices dataset. For an exclusive explanation, please take a look at the <b>paper</b> and at the <b>online appendix</b>: https://github.com/darioamorosodaragona-tuni/Microservices-Dataset<br><br>In particular, this file contains all the projects labeled as:<br>- <i>Is it a microservices?:</i> <i>Yes</i><br>- <i>Archived:</i> <i>No</i><br><br><b>Copyright:</b><br><i>Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). MSR ’24, April 15–16, 2024, Lisbon, Portugal © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0587-8/24/04</i><i> </i><i>https://doi.org/10.1145/3643991.3644890</i>
DOI: 10.6084/m9.figshare.24722232.v3
title: Microservices Dataset - Filtered version

description: This is a microservices dataset. For an exclusive explanation, please take a look at the paper.<br><br>In particular, this file contains all the projects labeled as:<br>- <i>Is it a microservices?:</i> <i>Yes</i> <br>- <i>Archived:</i> <i>No</i>
DOI: 10.6084/m9.figshare.22764866
title: Data set: Matchette et al. 2023

description: Title: 
Predatory trumpetfish conceal themselves from their prey by swimming alongside other fish
<br>
Author list: 
Samuel R Matchette, Christian Drerup, Isla Keesje Davison, Stephen D Simpson, Andrew N Radford and James E Herbert-Read
<br>
Corresponding data file: 
Data_Matchette_et_al_2023.txt
<br>
Journal: 
Current Biology 
<br>
- Day: Day number (1 - 18)
- Colony_No: Relates to presentations conducted for an independent colony (1 - 36)
- Treatment: The model treatment that was presented (3 levels: Trumpetfish [alone, non-shadowing], Parrotfish [alone, non-predatory], Shadowing [trumpetfish shadowing parrotfish])
- Date: Shorthand notation for the date (D/MM/YY)
- Location: The location of a given colony (3 levels: Cas Abao, Kokomo, Porto Mari)
- Treatment_Order: The order of model presentation for a given colony (1-3)
- Shadowing_Position: Shadowing treatment only, this relates to the side of the parrotfish that the trumpetfish gets attached to (2 levels: 1 = left, 2 = right)
- Maximum_Colony_Size: The maximum number of damselfish observed throughout a colony visit at any one time
- Maximum_Inspect_No: The maximum number of damselfish observed inspecting at any one time
- Maximum_Inspect_Proportion: Maximum_Inspect_No / Maximum_Colony_Size
- Total_Inspect_Duration: The total amount of time (s) that at least one damselfish inspects a given treatment
- Total_Avoidance: The total number of individual avoidance responses observed for a given treatment
- Speed_Of_Treatment: The reeling speed for a given treatment (m/s)
- Distance_First_Avoidance: The distance (m) that the treatment is from the colony when the first avoidance response is observed
- Distance_First_Inspection: The distance (m) that the treatment is from the colony when the first inspection behaviour is observed
DOI: 10.6084/m9.figshare.22764866.v1
title: Data set: Matchette et al. 2023

description: Title: 
Predatory trumpetfish conceal themselves from their prey by swimming alongside other fish
<br>
Author list: 
Samuel R Matchette, Christian Drerup, Isla Keesje Davison, Stephen D Simpson, Andrew N Radford and James E Herbert-Read
<br>
Corresponding data file: 
Data_Matchette_et_al_2023.txt
<br>
Journal: 
Current Biology 
<br>
- Day: Day number (1 - 18)
- Colony_No: Relates to presentations conducted for an independent colony (1 - 36)
- Treatment: The model treatment that was presented (3 levels: Trumpetfish [alone, non-shadowing], Parrotfish [alone, non-predatory], Shadowing [trumpetfish shadowing parrotfish])
- Date: Shorthand notation for the date (D/MM/YY)
- Location: The location of a given colony (3 levels: Cas Abao, Kokomo, Porto Mari)
- Treatment_Order: The order of model presentation for a given colony (1-3)
- Shadowing_Position: Shadowing treatment only, this relates to the side of the parrotfish that the trumpetfish gets attached to (2 levels: 1 = left, 2 = right)
- Maximum_Colony_Size: The maximum number of damselfish observed throughout a colony visit at any one time
- Maximum_Inspect_No: The maximum number of damselfish observed inspecting at any one time
- Maximum_Inspect_Proportion: Maximum_Inspect_No / Maximum_Colony_Size
- Total_Inspect_Duration: The total amount of time (s) that at least one damselfish inspects a given treatment
- Total_Avoidance: The total number of individual avoidance responses observed for a given treatment
- Speed_Of_Treatment: The reeling speed for a given treatment (m/s)
- Distance_First_Avoidance: The distance (m) that the treatment is from the colony when the first avoidance response is observed
- Distance_First_Inspection: The distance (m) that the treatment is from the colony when the first inspection behaviour is observed
DOI: 10.25549/usctheses-ouc11291112
title: submit_update1.pdf
DOI: 10.6084/m9.figshare.25196841
title: How predictable are macroscopic traffic states: a perspective of uncertainty quantification

description: Traffic condition forecasting is fundamental for Intelligent Transportation Systems. Besides accuracy, many services require an estimate of uncertainty for each prediction. Uncertainty quantification must consider the inherent randomness in traffic dynamics, the so-called aleatoric uncertainty, and the additional distrust caused by data shortage, the so-called epistemic uncertainty. They together depict how predictable macroscopic traffic is. This study uses deep ensembles of graph neural networks to estimate both types of uncertainty in network-level speed forecasting. Experimental results given by the used model reveal that, although rare congestion patterns arise randomly, the short-term predictability of traffic states is mainly restricted by the irreducible stochasticity in traffic dynamics. The predicted future state bifurcates into congested or free-flowing cases. This study suggests that the potential for improving prediction models through expanding speed and flow data is limited while diversifying data types is crucial.
DOI: 10.6084/m9.figshare.25196841.v1
title: How predictable are macroscopic traffic states: a perspective of uncertainty quantification

description: Traffic condition forecasting is fundamental for Intelligent Transportation Systems. Besides accuracy, many services require an estimate of uncertainty for each prediction. Uncertainty quantification must consider the inherent randomness in traffic dynamics, the so-called aleatoric uncertainty, and the additional distrust caused by data shortage, the so-called epistemic uncertainty. They together depict how predictable macroscopic traffic is. This study uses deep ensembles of graph neural networks to estimate both types of uncertainty in network-level speed forecasting. Experimental results given by the used model reveal that, although rare congestion patterns arise randomly, the short-term predictability of traffic states is mainly restricted by the irreducible stochasticity in traffic dynamics. The predicted future state bifurcates into congested or free-flowing cases. This study suggests that the potential for improving prediction models through expanding speed and flow data is limited while diversifying data types is crucial.
DOI: 10.6084/m9.figshare.24722163
title: Microservices Dataset - Complete Version

description: This is a microservices dataset. For an exclusive explanation, please take a look at the <b>paper</b> and at the <b>online appendix</b>: https://github.com/darioamorosodaragona-tuni/Microservices-Dataset<br><br>In particular, this file contains all the projects labeled as:<br>- <i>Is it a microservices?:</i> <i>Yes</i> | <i>Uknown</i><br>- <i>Archived:</i> <i>Yes</i> | <i>No</i><br><b>Copyright:</b><br><i>Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). MSR ’24, April 15–16, 2024, Lisbon, Portugal © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0587-8/24/04</i><i> </i><i>https://doi.org/10.1145/3643991.3644890</i>
DOI: 10.6084/m9.figshare.24722163.v3
title: Microservices Dataset - Complete Version

description: This is a microservices dataset. For an exclusive explanation, please take a look at the paper.<br><br>In particular, this file contains all the projects labeled as: <br>- <i>Is it a microservices?:</i> <i>Yes</i> | <i>Uknown</i><br>- <i>Archived:</i> <i>Yes</i> | <i>No</i>
DOI: 10.6084/m9.figshare.16557021.v3
title: Avian species richness and tropical urbanization gradient - effects of woodland retention and human disturbance

description: Dataset accompany research article Thaweepworadej and Evans entitiled Avian species richness and tropical urbanization gradient - effects of woodland retention and human disturbance including List of bird species detected during the field surveys in Bangkok region in 2018, bird survey data in Bangkok region during March to July 2018, and predictor variables using for regressions and mixed effects analyses.
DOI: 10.25405/data.ncl.16950880
title: Gas Chromatography Raw Data - CO2 and Water Splitting Systems

description: Gas chromatography raw data to accompany the manuscript "Ruthenium Assemblies for CO2Reduction and H2 Generation: TimeResolved Infrared Spectroscopy,Spectroelectrochemistry and aPhotocatalysis Study in Solution and onNiO"
DOI: 10.5281/zenodo.5608558
title: JNK inhibition in Rett Syndrome models

description: Datasets produced and used during the current study. Data are mean ± SEM.
DOI: 10.5281/zenodo.5608557
title: JNK inhibition in Rett Syndrome models

description: Datasets produced and used during the current study. Data are mean ± SEM.
DOI: 10.25405/data.ncl.16953979
title: UV-vis Measurements - CO2 and Water Splitting Systems

description: UV-vis absorption measurements on thin films raw data to accompany the manuscript "Ruthenium Assemblies for CO2Reduction and H2 Generation: TimeResolved Infrared Spectroscopy,Spectroelectrochemistry and aPhotocatalysis Study in Solution and onNiO"
DOI: 10.6084/m9.figshare.14847147.v2
title: R codes and data for analyses in "2021. Cascading effects of sand stabilization on pathogen communities: connecting global and local processes"

description: R codes and data for analyses in <i>2021. Cascading effects of sand stabilization on pathogen communities: connecting global and local processes</i>
DOI: 10.25405/data.ncl.16950988.v1
title: Electrochemistry Data - CO2 and Water Splitting Systems

description: Electrochemical measurements raw data to accompany the manuscript "Ruthenium Assemblies for CO2Reduction and H2 Generation: TimeResolved Infrared Spectroscopy,Spectroelectrochemistry and aPhotocatalysis Study in Solution and onNiO"
DOI: 10.25405/data.ncl.16950988
title: Electrochemistry Data - CO2 and Water Splitting Systems

description: Electrochemical measurements raw data to accompany the manuscript "Ruthenium Assemblies for CO2Reduction and H2 Generation: TimeResolved Infrared Spectroscopy,Spectroelectrochemistry and aPhotocatalysis Study in Solution and onNiO"
DOI: 10.5281/zenodo.5657797
title: Yang_et_al_2021_SCS_DATASET

description: This dataset contains the underlying data for the following publication: <strong>Yang, X., Lin, W., Gong, R., Zhu, M., & Springer, C. (2021). Transport decarbonization in big cities: An integrated environmental co-benefit analysis of vehicles purchases quota-limit and new energy vehicles promotion policy in Beijing. <em>Sustainable Cities and Society, 71</em>, 102976. </strong><strong>https://doi.org/10.1016/j.scs.2021.102976</strong><strong>. </strong>Full details of methods used to create the dataset and provided within this publication.
description: Aside from "PARIS REINFORCE" (Grant No. 820846), the authors also gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant no. 71704187), the Beijing Social Science Fund (Grant no. 17GLC045), and the Research Foundation Flanders (Grant No. 76473).
DOI: 10.5281/zenodo.5657796
title: Yang_et_al_2021_SCS_DATASET

description: This dataset contains the underlying data for the following publication: <strong>Yang, X., Lin, W., Gong, R., Zhu, M., & Springer, C. (2021). Transport decarbonization in big cities: An integrated environmental co-benefit analysis of vehicles purchases quota-limit and new energy vehicles promotion policy in Beijing. <em>Sustainable Cities and Society, 71</em>, 102976. </strong><strong>https://doi.org/10.1016/j.scs.2021.102976</strong><strong>. </strong>Full details of methods used to create the dataset and provided within this publication.
description: Aside from "PARIS REINFORCE" (Grant No. 820846), the authors also gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant no. 71704187), the Beijing Social Science Fund (Grant no. 17GLC045), and the Research Foundation Flanders (Grant No. 76473).
DOI: 10.21227/a9h9-wq94
title: 3D hand pose dataset RGB

description: this is a test
DOI: 10.5281/zenodo.5657807
title: Time series of simulated ionospheric fountain effect along with real-data TEC map and station-pair ionospheric gradients

description: This dataset was used to generate a simulated Total Electron Content (TEC) distribution due to the Fountain Effect in equatorial and low-latitude regions. It is in Figure 6 from the article "Ground-Based Augmentation System Operation in Low Latitudes - Part 2: Space Weather, Ionospheric Behavior and Challenges" currently <em>in press</em> by the <em>Journal of Aerospace Technology and Management</em> (JATM). The same dataset also contains a TEC map over Brazil and station-pair ionospheric gradients measured from two stations in São José dos Campos/SP Brazil. Both data are from the night between November 21 and 22, 2014, and are part of Figure 10 of the same article cited above.
DOI: 10.5281/zenodo.5657806
title: Time series of simulated ionospheric fountain effect along with real-data TEC map and station-pair ionospheric gradients

description: This dataset was used to generate a simulated Total Electron Content (TEC) distribution due to the Fountain Effect in equatorial and low-latitude regions. It is in Figure 6 from the article "Ground-Based Augmentation System Operation in Low Latitudes - Part 2: Space Weather, Ionospheric Behavior and Challenges" currently <em>in press</em> by the <em>Journal of Aerospace Technology and Management</em> (JATM). The same dataset also contains a TEC map over Brazil and station-pair ionospheric gradients measured from two stations in São José dos Campos/SP Brazil. Both data are from the night between November 21 and 22, 2014, and are part of Figure 10 of the same article cited above.
DOI: 10.6084/m9.figshare.16960732
title: Alexnet-ABeffect: predicted image features from fMRI data

description: The 320 files (corresponding to 4 subjects, 8 CNN layers and 10 brain regions) are predicted image features from fMRI data with a trained linear regression model used in the conference paper <b>Categorical Difference and Related Brain Regions of the Attentional Blink Effect by Renzhou Gui and Xiaohong Ji </b>(see reference for full paper). Due to the unexpected damage of subject2-PPA-CNN7.pkl and subject2-PPA-CNN8.pkl, subject2 related image features are all removed.<br>Files are generated using source codes from Horikawa and Kamitani (2017) Generic decoding of seen and imagined objects using hierarchical visual features. Nature Communications 8:15037.
DOI: 10.5281/zenodo.5657758
title: Sporobolus alterniflorus SPAdes preassembly

description: ..
DOI: 10.5281/zenodo.5657757
title: Sporobolus alterniflorus SPAdes preassembly

description: ..
DOI: 10.6084/m9.figshare.16964786
title: Inference in Sparsity-Induced Weak Factor Models

description: In this paper, we consider statistical inference for high-dimensional approximate factor models. We posit a weak factor structure, in which the factor loading matrix can be sparse and the signal eigenvalues may diverge more slowly than the cross-sectional dimension, <i>N</i>. We propose a novel inferential procedure to decide whether each component of the factor loadings is zero or not, and prove that this controls the false discovery rate (FDR) below a pre-assigned level, while the power tends to unity. This “factor selection” procedure is primarily based on a debiased version of the SOFAR estimator of Uematsu and Yamagata (2021), but is also applicable to the principal component (PC) estimator. After the factor selection, the <i>re-sparsified</i> SOFAR and <i>sparsified</i> PC estimators are proposed and their consistency is established. Finite sample evidence supports the theoretical results. We apply our method to the FRED-MD dataset of macroeconomic variables and the monthly firm-level excess returns which constitute the S&P 500 index. The results give very strong statistical evidence of sparse factor loadings under the identification restrictions and exhibit clear associations of factors and categories of the variables. Furthermore, our method uncovers a very weak but statistically significant factor in the residuals of Fama-French five factor regression.
DOI: 10.6084/m9.figshare.16964786.v1
title: Inference in Sparsity-Induced Weak Factor Models

description: In this paper, we consider statistical inference for high-dimensional approximate factor models. We posit a weak factor structure, in which the factor loading matrix can be sparse and the signal eigenvalues may diverge more slowly than the cross-sectional dimension, <i>N</i>. We propose a novel inferential procedure to decide whether each component of the factor loadings is zero or not, and prove that this controls the false discovery rate (FDR) below a pre-assigned level, while the power tends to unity. This “factor selection” procedure is primarily based on a debiased version of the SOFAR estimator of Uematsu and Yamagata (2021), but is also applicable to the principal component (PC) estimator. After the factor selection, the <i>re-sparsified</i> SOFAR and <i>sparsified</i> PC estimators are proposed and their consistency is established. Finite sample evidence supports the theoretical results. We apply our method to the FRED-MD dataset of macroeconomic variables and the monthly firm-level excess returns which constitute the S&P 500 index. The results give very strong statistical evidence of sparse factor loadings under the identification restrictions and exhibit clear associations of factors and categories of the variables. Furthermore, our method uncovers a very weak but statistically significant factor in the residuals of Fama-French five factor regression.
DOI: 10.5281/zenodo.5658014
title: Data related to the article "A molecular perspective on induced charges on a metallic surface"

description: Contains input files and data used to generate the figures of the article: A molecular perspective on induced charges on a metallic surface<br> (Giovanni Pireddu, Laura Scalfi, and Benjamin Rotenberg) arXiv: https://arxiv.org/abs/2110.11103 The folder EXAMPLE_INPUT_FILES contains typical MetalWalls (repository) input files used to perform the simulations. The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper. The data files are named according to the ion distance considered:<br> - 'd1' corresponds to 1.50 Å;<br> - 'd2' corresponds to 3.14 Å;<br> - 'd3' corresponds to 5.40 Å;<br> - 'd4' corresponds to 7.03 Å;<br> - 'd5' corresponds to 15.00 Å. The data files are reported in three formats:<br> - induced charge density maps are in matrix format (arranged in several rows with each value corresponding to the values on the map). The coordinates of each point on the map are stored in the first row and first column.<br> - Solvent charge density maps are arranged in columns: (i) x coordinate, (ii) y coordinate, (iii) solvent charge density.<br> - Radial profiles are arranged in columns: (i) r, (ii) charge density, (iii) radial integral.
description: {"references": ["https://arxiv.org/abs/2110.11103"]}
DOI: 10.5281/zenodo.5658013
title: Data related to the article "A molecular perspective on induced charges on a metallic surface"

description: Contains input files and data used to generate the figures of the article: A molecular perspective on induced charges on a metallic surface<br> (Giovanni Pireddu, Laura Scalfi, and Benjamin Rotenberg) arXiv: https://arxiv.org/abs/2110.11103 The folder EXAMPLE_INPUT_FILES contains typical MetalWalls (repository) input files used to perform the simulations. The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper. The data files are named according to the ion distance considered:<br> - 'd1' corresponds to 1.50 Å;<br> - 'd2' corresponds to 3.14 Å;<br> - 'd3' corresponds to 5.40 Å;<br> - 'd4' corresponds to 7.03 Å;<br> - 'd5' corresponds to 15.00 Å. The data files are reported in three formats:<br> - induced charge density maps are in matrix format (arranged in several rows with each value corresponding to the values on the map). The coordinates of each point on the map are stored in the first row and first column.<br> - Solvent charge density maps are arranged in columns: (i) x coordinate, (ii) y coordinate, (iii) solvent charge density.<br> - Radial profiles are arranged in columns: (i) r, (ii) charge density, (iii) radial integral.
description: {"references": ["https://arxiv.org/abs/2110.11103"]}
DOI: 10.18462/iir.pcm.2021.2204
title: Experimental study on the role of metal foams in a vertical PCM-based thermal energy storage tube.
DOI: 10.18462/iir.pcm.2021.2203
title: Numerical modelling of a PCM embedded in periodic structures.
DOI: 10.18462/iir.pcm.2021.2116
title: Numerical study of PCM based TMS for Li-Ion cells.
DOI: 10.18462/iir.pcm.2021.2201
title: A numerical analysis of latent thermal energy storage for refrigerated trucks.
DOI: 10.18462/iir.pcm.2021.2081
title: Numerical simulation of latent and sensible concrete thermal energy storage system.
DOI: 10.18462/iir.pcm.2021.2074
title: Wood impregnated with a multicomponent bio-phase change material for energy storage in buildings.
DOI: 10.18462/iir.pcm.2021.2051
title: Compatibility of plastics and metals with latent heat storage media: methodologies for evaluation.
DOI: 10.18462/iir.pcm.2021.2044
title: Employment of dynamic temperature-time-transformation (DTTT) diagrams on phase change materials (PCM).
DOI: 10.18462/iir.pcm.2021.2042
title: Reduction of rotation phase supercooling in n-docosanol nano phase change slurries.
DOI: 10.18462/iir.pcm.2021.2034
title: Experimental investigation of melting and solidification processes of phase change material heat exchanger.
DOI: 10.18462/iir.pcm.2021.2032
title: Heat storage and release characteristics of sodium acetate trihydrate-based phase change materials.
DOI: 10.5517/ccdc.csd.cc27gd9f
title: CCDC 2069414: Experimental Crystal Structure Determination

description: Related Article: Friedrich Schwotzer, Irena Senkovska, Volodymyr Bon, Stefanie Lochmann, Jack D. Evans, Darius Pohl, Bernd Rellinghaus, Stefan Kaskel|2021|Inorg.Chem.Front.|8|3308|doi:10.1039/D1QI00349F