cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2790zk
title: CCDC 2064258: Experimental Crystal Structure Determination

description: Related Article: Yuanyuan Hu, Zhen Wang, Honggen Luo, Hongwei Jin, Yunkui Liu, Bingwei Zhou|2021|Org.Biomol.Chem.|19|3834|doi:10.1039/D1OB00443C
DOI: 10.5517/ccdc.csd.cc2794nc
title: CCDC 2064372: Experimental Crystal Structure Determination

description: Related Article: Hui Liu, Yingkun Yan, Min Li, Xiaomei Zhang|2021|Org.Biomol.Chem.|19|3820|doi:10.1039/D1OB00374G
DOI: 10.5517/ccdc.csd.cc2794pd
title: CCDC 2064373: Experimental Crystal Structure Determination

description: Related Article: Hui Liu, Yingkun Yan, Min Li, Xiaomei Zhang|2021|Org.Biomol.Chem.|19|3820|doi:10.1039/D1OB00374G
DOI: 10.5517/ccdc.csd.cc27dr5l
title: CCDC 2067829: Experimental Crystal Structure Determination

description: Related Article: Natalia Guranova, Pavel Golubev, Olga Bakulina, Dmitry Dar'in, Grigory Kantin, Mikhail Krasavin|2021|Org.Biomol.Chem.|19|3829|doi:10.1039/D1OB00534K
DOI: 10.5517/ccdc.csd.cc270z5f
title: CCDC 2056514: Experimental Crystal Structure Determination

description: Related Article: Yuan-Hui Zhong, Yonghe He, Hua-Qun Zhou, Sai-Li Zheng, Qi Zeng, Lai-Hon Chung, Wei-Ming Liao, Jun He|2021|J.Mater.Chem.C|9|5088|doi:10.1039/D1TC01043C
description: Related Article: Wei-Ming Liao|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27dr6m
title: CCDC 2067830: Experimental Crystal Structure Determination

description: Related Article: Natalia Guranova, Pavel Golubev, Olga Bakulina, Dmitry Dar'in, Grigory Kantin, Mikhail Krasavin|2021|Org.Biomol.Chem.|19|3829|doi:10.1039/D1OB00534K
DOI: 10.5281/zenodo.4635976
title: Supplementary dataset: Local Number Fluctuations in Hyperuniform and Nonhyperuniform Systems: Higher-Order Moments and Distribution Functions

description: Supplementary dataset of the publication "Local Number Fluctuations in Hyperuniform and Nonhyperuniform Systems: Higher-Order Moments and Distribution Functions" by the same authors: S. Torquato, J. Kim, M. A. Klatt. <em>Phys. Rev. X</em> <strong>11</strong>, 021028 (2021)<br> https://link.aps.org/doi/10.1103/PhysRevX.11.021028
description: {"references": ["S. Torquato, J. Kim, M. A. Klatt. Phys. Rev. X 11, 021028 (2021) https://link.aps.org/doi/10.1103/PhysRevX.11.021028"]}
DOI: 10.5281/zenodo.4739297
title: Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"

description: <br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems" <br> Description of the content: <br> 1) "1_Issues-Data" folder contains all the main data concerning the issues collected from all the Projects considered in the study. <br> <br> - We report the issue data from the 15 projects organized with sub-folders for each Domain (containing the project issue relevant data):<br> - Robotics<br> - Machinery<br> - Drones<br> - Automotive<br> - Arduino <br> 2) "2_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_ Sampled-Issues":<br> - We report the sampled issues from all the projects organized with sub-folders for each Domain (containing the project issue relevant data):<br> - Robotics<br> - Machinery<br> - Drones<br> - Automotive<br> - Arduino<br> - Under "the sub-folder "2_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps <br> 3) "3_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: <br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 4) "4_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.<br>
DOI: 10.5286/isis.e.rb2010231-1
title: Inorganic Nanoparticles interacting with membrane models of lamellar and non-lamellar nature

description: Despite the efforts towards the design of inorganic nanoparticles (NP) for Nanomedicine, the poor understanding of their interaction with biological barriers limits translation into medical practice. Artificial cell-membrane models are useful to identify key determinants of NP cytotoxicity under controlled conditions. We recently performed Neutron Reflectometry experiment at ISIS (June 2019, OFFSPEC) to determine the structural effects of AuNPs' surface features and shape on model membranes of different geometry, i.e. cubic and lamellar lipid films. Here, we propose to complete the previous NR dataset and to extend it to NP of a different composition: this will represent one of the first attempt to study the impact of NP on non-lamellar membranes, as compared to lamellar ones. The results will contribute to the assessment of NP cytotoxicity and implementation of their medical potential.
DOI: 10.6084/m9.figshare.13714061
title: Self-organising map-based dynamic decision-making algorithm for heterogeneous wireless sensor network

description: Wireless sensor networks (WSNs) are applicable in most of the domains of engineering. Presently, Heterogeneous WSN (HWSN) is gaining more importance than homogeneous WSN. One of the major challenges of a HWSN is efficient deployment which can provide good coverage. The deployment strategy in WSN can be static or dynamic. It is found that there is a need of dynamic deployment that intelligently places the sensor nodes in the deployment area. This intelligence is possible by one of the artificial neural networks concept like self-organizing map (SOM). This work presents a dynamic decision-making algorithm (DDMA) for deployment of sensors using SOM such that the nodes will adjust their locations to uncovered area by failed sensor node in the changing sensing environment. The DDMA is compared with LEA2C and ECBS which are variants of LEACH. The results show that proposed DDMA performs better than the existing algorithm in-terms of decreased node depletion rate by 35% and 25% more coverage. The experimental analysis also show that SOM-based DDMA performs better than the non-SOM-based DDMA deployment for HWSN in terms of 5% better energy conservation, 30% better network lifetime, 75% more coverage in the deployment area and 90% reduced node depletion rate. This work presents a dynamic decision-making algorithm (DDMA) for deployment of sensors using self-organising map (SOM) such that the nodes will adjust their locations to uncovered area by failed sensor node in the changing sensing environment. The DDMA is compared with variants of LEACH with existing LEA2C and ECBS. The results show that proposed DDMA performs better than the existing algorithm in-terms of decreased node depletion rate by 35% and 25% more coverage. The experimental analysis is performed between SOM-based and non-SOM-based DDMA algorithms. Results show that SOM-based DDMA performs better than the non-SOM-based DDMA deployment for heterogeneous WSN in terms of 5% better energy conservation, 30% better network lifetime, 75% more coverage in the deployment area and 90% reduced node depletion rate.
DOI: 10.6084/m9.figshare.13714061.v1
title: Self-organising map-based dynamic decision-making algorithm for heterogeneous wireless sensor network

description: Wireless sensor networks (WSNs) are applicable in most of the domains of engineering. Presently, Heterogeneous WSN (HWSN) is gaining more importance than homogeneous WSN. One of the major challenges of a HWSN is efficient deployment which can provide good coverage. The deployment strategy in WSN can be static or dynamic. It is found that there is a need of dynamic deployment that intelligently places the sensor nodes in the deployment area. This intelligence is possible by one of the artificial neural networks concept like self-organizing map (SOM). This work presents a dynamic decision-making algorithm (DDMA) for deployment of sensors using SOM such that the nodes will adjust their locations to uncovered area by failed sensor node in the changing sensing environment. The DDMA is compared with LEA2C and ECBS which are variants of LEACH. The results show that proposed DDMA performs better than the existing algorithm in-terms of decreased node depletion rate by 35% and 25% more coverage. The experimental analysis also show that SOM-based DDMA performs better than the non-SOM-based DDMA deployment for HWSN in terms of 5% better energy conservation, 30% better network lifetime, 75% more coverage in the deployment area and 90% reduced node depletion rate. This work presents a dynamic decision-making algorithm (DDMA) for deployment of sensors using self-organising map (SOM) such that the nodes will adjust their locations to uncovered area by failed sensor node in the changing sensing environment. The DDMA is compared with variants of LEACH with existing LEA2C and ECBS. The results show that proposed DDMA performs better than the existing algorithm in-terms of decreased node depletion rate by 35% and 25% more coverage. The experimental analysis is performed between SOM-based and non-SOM-based DDMA algorithms. Results show that SOM-based DDMA performs better than the non-SOM-based DDMA deployment for heterogeneous WSN in terms of 5% better energy conservation, 30% better network lifetime, 75% more coverage in the deployment area and 90% reduced node depletion rate.
DOI: 10.7294/14504913
title: The environmental footprint of data centers in the United States

description: Much of the world’s data are stored, managed, and distributed by data centers. Data centers re-quire a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center’s water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint..
DOI: 10.5519/qd.2z84b44j
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (185 records)
DOI: 10.5526/erdr-00000135
title: Diverse Physiological and Physical Responses among Wild, Landrace and Elite Barley Varieties Point to Novel Breeding Opportunities
DOI: 10.6084/m9.figshare.12849650.v1
title: Validation of the ToMas-child Scale for the Assessment of the Theory of Mind in a Group of Spanish Speaking Children Aged 3 to 7 Years from Spain

description: This study aimed to determine the psychometric properties of the Theory of Mind assessment scale in children (ToMas-child). Two hundred and fifty-two Spanish children from 3 to 7 years were assessed in school settings. Rasch analysis showed the ToMas-child is a unidimensional scale valid for the assessment of the main components of the ToM in children. Data of hierarchical distribution of six items (seven tasks) are discussed as milestones in the latent construct of ToM development in childhood, and construct validity of the scale is examined. A reliability index of PSI = 0.7 indicated the usefulness of the scale as a screening tool.
DOI: 10.6084/m9.figshare.12849650
title: Validation of the ToMas-child Scale for the Assessment of the Theory of Mind in a Group of Spanish Speaking Children Aged 3 to 7 Years from Spain

description: This study aimed to determine the psychometric properties of the Theory of Mind assessment scale in children (ToMas-child). Two hundred and fifty-two Spanish children from 3 to 7 years were assessed in school settings. Rasch analysis showed the ToMas-child is a unidimensional scale valid for the assessment of the main components of the ToM in children. Data of hierarchical distribution of six items (seven tasks) are discussed as milestones in the latent construct of ToM development in childhood, and construct validity of the scale is examined. A reliability index of PSI = 0.7 indicated the usefulness of the scale as a screening tool.
DOI: 10.6084/m9.figshare.14346995
title: all_fia_presences.csv

description: Presence locations information for 108 trees species of western North America modeled. Data come from Forest Inventory and Analysis program.<br>Column 1: SPCD (species code that corresponds with REF_SPECIES.csv table)Column 2: STATECD (numerical state code that corresponds to nomenclature used by FIA program)Column 3: LON (longitude)Column 4: LAT (latitude)
DOI: 10.6084/m9.figshare.14346992
title: all_fia_locations

description: Forest Inventory and Analysis program data plot locations (fuzzed and swapped) across entire study extent.<br>Column 1: LON (longitude)Column 2: LAT (latitdue)
DOI: 10.25500/edata.bham.00000643
title: Research data Word2Vec Models-Experiment2
DOI: 10.6084/m9.figshare.14269973
title: Parkinson-posture

description: Files showing the raw data<br>
DOI: 10.34740/kaggle/dsv/2197647
title: Vietnam SARS-CoV-2 | COVID-19 Data
DOI: 10.6084/m9.figshare.14541894
title: Hyperspectral Image Dataset

description: Curated hyperspectral image datase we used in our analysis. <br>Based on original data from (Nascimento SM, Ferreira FP, Foster DH, 2002); and (Chakrabarti A, Zickler T, 2011).<br><br>
DOI: 10.5281/zenodo.4739277
title: Lunar samples record an impact 4.2 billion years ago that may have formed the Serenitatis Basin

description: These files serve as publicly available data to follow the publication titled 'Lunar samples record an impact 4.2 billion years ago that may have formed the Serenitatis Basin' accepted in <em>Communications Earth & Environment (Nature Springer). </em> <strong>Supplementary Data 1. </strong>A zip folder containing iSALE code modelling input parameters for 1) Serenitatis Basin formation at 4.5 and 4.0 Gyr; 2) Dawes crater and ejecta formation, and 3) Dawes ejecta landing. Details on the modelling are provided in Supplementary Methods 1.2 and Supplementary Discussion 2.3. of the published article. <strong>Supplementary Data 2. </strong>An Excel spreadsheet containing U-Pb and Pb-Pb dating results presented in Supplementary Table 2. <strong>Supplementary Movie 1.</strong> Mg distribution in the Atom Probe Tomography microtip M3 of shocked lunar apatite. Polygonal grains of lunar apatite can be recognized by the magnesium decoration of grain boundaries. <strong>Supplementary Movie 2.</strong> Mg-decorated grain boundaries in the Atom Probe Tomography microtip M3 of shocked lunar apatite. Mg atoms (blue dots) and a 0.13 at. % magnesium isosurface (purple) in a 5 nm slice of the apatite data as it is moved through the needle from one side to the other, show the shape of the grain boundaries.
DOI: 10.25500/edata.bham.00000644
title: Research data related to NeuralNetwork-Experiment2
DOI: 10.6084/m9.figshare.14542434
title: RGB Image Dataset

description: Curated RGB image dataset for our analysis, splited into training and evalutaion set. <br>Based on ImageNet ILSVRC dataset (Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, 2015).
DOI: 10.25386/genetics.14533956
title: Supplemental Material for Cizeron et al., 2021

description: <b>Table S1: List of strains </b>
DOI: 10.25386/genetics.14533956.v1
title: Supplemental Material for Cizeron et al., 2021

description: <b>Table S1: List of strains </b>
DOI: 10.6084/m9.figshare.14542923
title: CRT Display

description: The parameters of the CRT display we used in our analysis.
DOI: 10.6084/m9.figshare.14543481
title: Sparse Prior

description: Learned sparse prior distribution (i.e., the set of basis function and mean vector of pixel valeus) we used in our analysis.
DOI: 10.6084/m9.figshare.14522487
title: Gravity-magnetic Study to Reappraise the Crustal Configuration of East Geothermal Province Along the Mahanadi Rift Basin, India: Implication Towards Non-volcanic Hot Springs

description: Acquired land gravity, magnetic, and elevation data of Mahanadi rift basin area around Atri and Tarabalo hot springs.<br>
DOI: 10.6084/m9.figshare.14522487.v2
title: Gravity-magnetic Study to Reappraise the Crustal Configuration of East Geothermal Province Along the Mahanadi Rift Basin, India: Implication Towards Non-volcanic Hot Springs

description: Acquired land gravity, magnetic, and elevation data of Mahanadi rift basin area around Atri and Tarabalo hot springs.<br>
DOI: 10.6084/m9.figshare.14522487.v1
title: Gravity-magnetic Study to Reappraise the Crustal Configuration of East Geothermal Province Along the Mahanadi Rift Basin, India: Implication Towards Non-volcanic Hot Springs

description: Acquired land gravity, magnetic, and elevation data of Mahanadi rift basin area around Atri and Tarabalo hot springs.<br>
DOI: 10.6084/m9.figshare.14524119
title: Zipped Raw Fasta File for Contaminant Filtering Script from CROMO, California, USA CSW1.1 well depth profile May 2016

description: This is the original 16S rRNA gene sequence fasta file processed with mothur and used for removing common contaminant taxa and counts information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016.
DOI: 10.6084/m9.figshare.14524119.v1
title: Zipped Raw Fasta File for Contaminant Filtering Script from CROMO, California, USA CSW1.1 well depth profile May 2016

description: This is the original 16S rRNA gene sequence fasta file processed with mothur and used for removing common contaminant taxa and counts information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016.
DOI: 10.6084/m9.figshare.14524110
title: Meta Data File used for Contaminant Filtering Script from CROMO, California, USA CSW1.1 well depth profile May 2016

description: This is the meta data file used in the phyloseq package within R to remove common contaminant taxa and counts information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016.
DOI: 10.6084/m9.figshare.14524110.v1
title: Meta Data File used for Contaminant Filtering Script from CROMO, California, USA CSW1.1 well depth profile May 2016

description: This is the meta data file used in the phyloseq package within R to remove common contaminant taxa and counts information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016.
DOI: 10.6084/m9.figshare.14524098
title: Raw 16S rRNA Taxonomy Table File for Contaminant Filtering Script from CROMO, California, USA CSW1.1 well depth profile May 2016

description: This is the original 16S rRNA gene sequence taxonomy table file processed with mothur and used for removing common contaminant taxa and counts information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016.
DOI: 10.6084/m9.figshare.14524098.v1
title: Raw 16S rRNA Taxonomy Table File for Contaminant Filtering Script from CROMO, California, USA CSW1.1 well depth profile May 2016

description: This is the original 16S rRNA gene sequence taxonomy table file processed with mothur and used for removing common contaminant taxa and counts information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016.
DOI: 10.6084/m9.figshare.14524065
title: Raw 16S rRNA Count Table File for Contaminant Filtering Script from CROMO, California, USA CSW1.1 well depth profile May 2016

description: This is the original 16S rRNA gene sequence count table file processed with mothur and used for removing common contaminant taxa and counts information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016.
DOI: 10.6084/m9.figshare.14524065.v1
title: Raw 16S rRNA Count Table File for Contaminant Filtering Script from CROMO, California, USA CSW1.1 well depth profile May 2016

description: This is the original 16S rRNA gene sequence count table file processed with mothur and used for removing common contaminant taxa and counts information from five depths in the CSW1.1 well at the Coast Range Ophiolite Microbial Observatory, California, USA collected during a one-day sampling event in May 2016.
DOI: 10.6084/m9.figshare.14489190
title: Data Sheet S7 - CROMO CSW1.1 May 2016 depth profile MAG bin completeness contamination information from CROMO, California, USA

description: Data Sheet S7 contains completeness and contamination percentages for May 2016 CROMO CSW1.1 depth profile metagenome assembled genomes. Data sheet was created to better understand the quality of bins created and used in this study.
DOI: 10.5517/ccdc.csd.cc21pc7c
title: CCDC 1897362: Experimental Crystal Structure Determination

description: Related Article: B. M. Omkaramurthy, G. Krishnamurthy, Sabine Foro|2020|SN Applied Sciences|2|342|doi:10.1007/s42452-020-2051-6
DOI: 10.6084/m9.figshare.14543694
title: Render Matrix, Typical Observer

description: The pre-computed render matrix (i.e., likelihood function). It describes the linear transformation between linear RGB pixel values and the average cone isomerization for a 1-deg foveal mosaic of a typical human observer.
DOI: 10.6084/m9.figshare.14489190.v1
title: Data Sheet S7 - CROMO CSW1.1 May 2016 depth profile MAG bin completeness contamination information from CROMO, California, USA

description: Data Sheet S7 contains completeness and contamination percentages for May 2016 CROMO CSW1.1 depth profile metagenome assembled genomes. Data sheet was created to better understand the quality of bins created and used in this study.
DOI: 10.5517/ccdc.csd.cc24fwr7
title: CCDC 1979559: Experimental Crystal Structure Determination

description: Related Article: Shazia Soobrattee, Xiaofang Zhai, Kudzanai Nyamayaro, Carlos Diaz, Paul Kelley, Tannaz Ebrahimi, Parisa Mehrkhodavandi|2020|Inorg.Chem.|59|5546|doi:10.1021/acs.inorgchem.0c00250
DOI: 10.5517/ccdc.csd.cc25nwpf
title: CCDC 2016075: Experimental Crystal Structure Determination

description: Related Article: Heng-Shi Li, Sheng-Mei Zhang, Ping Ye, Tao Sun, Kai Wang, Xiu-Qing Zhang, Yan Li|2021|J.Coord.Chem.|74|927|doi:10.1080/00958972.2020.1861602