cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19525480.v2
title: NMF_metasamples

description: Data for the basis coefficient matrices, reflecting the probability of allocation of the 2,372 samples (y-axis) to 25 different groups (x-axis) based on their plasmid gene profiles using NMF. The legend below shows the probability of allocation (high in red, low in yellow) to the different groups (“basis”). Many of the <i>Enterobacterales </i>were allocated to the same rank (9).
DOI: 10.6084/m9.figshare.19525597.v2
title: NMF_mixtureCoefficientMatrix

description: NMF mixture coefficient matrices, reflecting the probability of allocation of 6,779 plasmid-associated genes to 25 different groups based on their prevalence in 2,372 samples using NMF.
DOI: 10.25625/r73dcj/xvluua
title: info_plot_selection_data.txt

description: :unav
DOI: 10.25625/r73dcj/95vafo
title: LA_plot_selection_data.tab

description: :unav
DOI: 10.25625/r73dcj
title: LA plot metadata of plot selection phase (EFForTS CRC 990 - Landscape Assessment)

description: Preliminary plot metadata collected during plot selection phase to inform selection process.
DOI: 10.5281/zenodo.6642285
title: Young People's Use of Digital Tools to Support Their Mental Health During Covid-19 Restrictions

description: Young adulthood represents a sensitive period for young people's mental health. The lockdown restrictions associated with the COVID-19 pandemic have reduced young people's access to traditional sources of mental health support. This exploratory study aimed to investigate the online resources young people were using to support their mental health during the first lockdown period in Ireland. It made use of an anonymous online survey targeted at young people aged 18–25. Participants were recruited using ads on social media including Facebook, Twitter, Instagram, and SnapChat. A total of 393 respondents completed the survey. Many of the respondents indicated that they were using social media (51.4%, 202/393) and mental health apps (32.6%, 128/393) as sources of support. Fewer were making use of formal online resources such as charities (26%, 102/393) or professional counseling services (13.2%, 52/393). Different social media platforms were used for different purposes; Facebook was used for support groups whilst Instagram was used to engage with influencers who focused on mental health issues. Google search, recommendations from peers and prior knowledge of services played a role in how resources were located. Findings from this survey indicate that digital technologies and online resources have an important role to play in supporting young people's mental health. The COVID-19 pandemic has highlighted these digital tool's potential as well as how they can be improved to better meet young people's needs
DOI: 10.5281/zenodo.6642284
title: Young People's Use of Digital Tools to Support Their Mental Health During Covid-19 Restrictions

description: Young adulthood represents a sensitive period for young people's mental health. The lockdown restrictions associated with the COVID-19 pandemic have reduced young people's access to traditional sources of mental health support. This exploratory study aimed to investigate the online resources young people were using to support their mental health during the first lockdown period in Ireland. It made use of an anonymous online survey targeted at young people aged 18–25. Participants were recruited using ads on social media including Facebook, Twitter, Instagram, and SnapChat. A total of 393 respondents completed the survey. Many of the respondents indicated that they were using social media (51.4%, 202/393) and mental health apps (32.6%, 128/393) as sources of support. Fewer were making use of formal online resources such as charities (26%, 102/393) or professional counseling services (13.2%, 52/393). Different social media platforms were used for different purposes; Facebook was used for support groups whilst Instagram was used to engage with influencers who focused on mental health issues. Google search, recommendations from peers and prior knowledge of services played a role in how resources were located. Findings from this survey indicate that digital technologies and online resources have an important role to play in supporting young people's mental health. The COVID-19 pandemic has highlighted these digital tool's potential as well as how they can be improved to better meet young people's needs
DOI: 10.5281/zenodo.6642286
title: VCFs of Chromosome 17 of Case 5 from the EGAD00001008392 Dataset

description: This synthetic data is a subset of the VCFs (chr17) from case 5 from the Rare Disease Synthetic Dataset (EGAD00001008392) dataset and the Human genomic and phenotypic synthetic data for the study of rare diseases (EGAS00001005702) study. For more info go to https://ega-archive.org/. All this data was created with the support of the RD-Connect GPAP (https://platform.rd-connect.eu/), EC H2020 project EJP-RD (grant # 825575), EC H2020 project B1MG (grant # 951724) and Generalitat de Catalunya VEIS project (grant # 001-P-001647).
DOI: 10.5281/zenodo.6642269
title: Dataset of "Parallel InAs nanowires for Cooper pair splitters with Coulomb repulsion"

description: Raw data of CPS realized in parallel InAs nanowires with Coulomb repulsion.
DOI: 10.6084/m9.figshare.19525480.v3
title: Figure_S9_NMF_metasamples

description: Figure S9. The frequency distributions of the rate of allocation of the 389 samples to each of seven ranks from NMF based on their presence-absence rates in 989 plasmid-encoded genes. The x-axes show the log10-scaled probability of allocation to each rank. 33, 74, 125, 102, 100, 99 and 180 samples had high probabilities of being allocated to each rank, respectively.
DOI: 10.5281/zenodo.6642304
title: Dataset of "Parallel InAs nanowires for Cooper pair splitters with Coulomb repulsion"

description: Raw data of CPS realized in parallel InAs nanowires with Coulomb repulsion.
DOI: 10.5281/zenodo.6642268
title: Dataset of "Parallel InAs nanowires for Cooper pair splitters with Coulomb repulsion"

description: Raw data of CPS realized in parallel InAs nanowires with Coulomb repulsion.
DOI: 10.5517/ccdc.csd.cc28cfhl
title: CCDC 2096359: Experimental Crystal Structure Determination

description: Related Article: Troy Luster, Hannah J. Van de Roovaart, Kyle J. Korman, Georgia G. Sands, Kylie M. Dunn, Anthony Spyker, Richard J. Staples, Shannon M. Biros, John E. Bender|2022|Dalton Trans.|51|9103|doi:10.1039/D2DT01570F
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.20066982
title: A step-by-step investigation of sodium chloride clusters: accurate references, assessment of low-cost methods, and convergence from molecule to salt

description: In the present study, we use high-level coupled-cluster-based methods to obtain benchmark geometries and lattice energy for small sodium chloride clusters up to (NaCl)<sub>15</sub>. Reliable geometries can be obtained with the CCSD(T)-F12b method in conjunction with a small double-<i>ζ</i> basis set, while accurate energies can be obtained with relatively economical methods (e.g. DLPNO-CCSD(T1)-F12) by adjusting for systematic bias against higher-level methods (e.g. W2). For the calculation of larger clusters, we find PBE0-D3BJ and GFN1-XTB to be suitable for geometry optimisation, while B97M-V and GFN1-XTB provide an adequate means for energy calculations once they are calibrated against high-level references. For much larger systems, a Born–Landé-type model can be used to obtain fairly good lattice energies. The results show the progression from a single NaCl molecule to bulk salt, with full convergence expected for cluster sizes in the range of 5–10 nm. The protocol for obtaining highly accurate lattice energies and calculating large clusters may be used to examine the energetics of ionic clusters more generally.
DOI: 10.6084/m9.figshare.20066982.v1
title: A step-by-step investigation of sodium chloride clusters: accurate references, assessment of low-cost methods, and convergence from molecule to salt

description: In the present study, we use high-level coupled-cluster-based methods to obtain benchmark geometries and lattice energy for small sodium chloride clusters up to (NaCl)<sub>15</sub>. Reliable geometries can be obtained with the CCSD(T)-F12b method in conjunction with a small double-<i>ζ</i> basis set, while accurate energies can be obtained with relatively economical methods (e.g. DLPNO-CCSD(T1)-F12) by adjusting for systematic bias against higher-level methods (e.g. W2). For the calculation of larger clusters, we find PBE0-D3BJ and GFN1-XTB to be suitable for geometry optimisation, while B97M-V and GFN1-XTB provide an adequate means for energy calculations once they are calibrated against high-level references. For much larger systems, a Born–Landé-type model can be used to obtain fairly good lattice energies. The results show the progression from a single NaCl molecule to bulk salt, with full convergence expected for cluster sizes in the range of 5–10 nm. The protocol for obtaining highly accurate lattice energies and calculating large clusters may be used to examine the energetics of ionic clusters more generally.
DOI: 10.5066/p9yeuntm
title: Field observations and spectral evolution of wind waves in Upper Delaware Bay with living shorelines

description: This dataset contains measured (interval=0.5 hour) water depth, wave height, and peak wave period during February 2 to April 4, 2018 at six wave gage locations along the Gandy's Beach, New Jersey in upper Delaware Bay. These data were used for the analysis of spectral evolution of wind waves along the coast in upper Delaware Bay with oyster-reef based living shoreline.
DOI: 10.5066/p92tlpu2
title: Fault data collected between 1996 and 2019 from the Buffalo River watershed area, northern Arkansas

description: Information on the locations and characteristics of faults were collected while conducting fieldwork between 1996 and 2019 in support of geologic mapping in the Buffalo River watershed area of northern Arkansas. A total of 1274 entries on faults were recorded at 452 sites. The observations associated with faults include seven categories of increasing completeness of information.
DOI: 10.18419/darus-2793
title: Replication Data for: Quantum-assisted distortion-free audio signal sensing

description: Datasets to reproduce all plots in the paper. Each spreadsheet contains the horizontal and vertical data of a subfigure. The first few lines describe the axis legends and the legends of curves that are plotted. Audio signals are presented in *.wav files.
DOI: 10.18419/darus-2793/1
title: Fig2c.xlsx

description: :unav
DOI: 10.18419/darus-2793/2
title: Fig2d.xlsx

description: :unav
DOI: 10.18419/darus-2793/3
title: Fig3c.xlsx

description: :unav
DOI: 10.18419/darus-2793/4
title: Fig2f.xlsx

description: :unav
DOI: 10.18419/darus-2793/5
title: Fig2a.xlsx

description: :unav
DOI: 10.18419/darus-2793/6
title: fig4d.xlsx

description: :unav
DOI: 10.18419/darus-2793/7
title: Fig4a.xlsx

description: :unav
DOI: 10.18419/darus-2793/8
title: Fig3d.xlsx

description: :unav
DOI: 10.18419/darus-2793/9
title: Fig2b.xlsx

description: :unav
DOI: 10.18419/darus-2793/10
title: Fig3b.xlsx

description: :unav
DOI: 10.18419/darus-2793/11
title: Fig2e.xlsx

description: :unav
DOI: 10.18419/darus-2793/12
title: fig4b.xlsx

description: :unav
DOI: 10.18419/darus-2793/13
title: fig4c.xlsx

description: :unav
DOI: 10.18419/darus-2793/14
title: Audio-S4_det_voice.wav

description: speech reconstructed from the NV signal
DOI: 10.6084/m9.figshare.14806311
title: Economy-Wide Material Flow Accounts of Canton Vaud and Geneva, Switzerland

description: The attached tables report results of the Economy-Wide Material Flow Accounts (EW-MFA) questionnaires compiled for the canton of Vaud and Geneva, Switzerland. These latter were created in the framework of a preliminary study to regionalise an EW-MFA to NUTS 3 scale (according to EEA's classification), based on the case study of Swiss cantons. The EW-MFA method, developed by Eurostat in 2001 and updated in 2018, is a systemic tool to analyse the material flows of an urban area. Originally, it was designed to be applied on a national scale. However, by analysing material flows at a reduced scale a finer understanding of regional dynamics, revealing more operational insights on resources needs and use, and of circularity prospects. In our research, the cantons of Vaud and Geneva were selected as case studies. The spatial boundaries of the system therefore correspond to the cantonal limits (NUTS 3 regions). Regarding the temporal boundaries, the reference year is 2018, but an overview between 2000 and 2019 is proposed depending on the available data. Data sources, data quality and method are directly provided in the questionnaires. Versions 1-3 present some errors in the questionnaires, which have been corrected.
DOI: 10.6084/m9.figshare.14806311.v4
title: Economy-Wide Material Flow Accounts of Canton Vaud and Geneva, Switzerland

description: The attached tables report results of the Economy-Wide Material Flow Accounts (EW-MFA) questionnaires compiled for the canton of Vaud and Geneva, Switzerland. These latter were created in the framework of a preliminary study to regionalise an EW-MFA to NUTS 3 scale (according to EEA's classification), based on the case study of Swiss cantons. The EW-MFA method, developed by Eurostat in 2001 and updated in 2018, is a systemic tool to analyse the material flows of an urban area. Originally, it was designed to be applied on a national scale. However, by analysing material flows at a reduced scale a finer understanding of regional dynamics, revealing more operational insights on resources needs and use, and of circularity prospects. In our research, the cantons of Vaud and Geneva were selected as case studies. The spatial boundaries of the system therefore correspond to the cantonal limits (NUTS 3 regions). Regarding the temporal boundaries, the reference year is 2018, but an overview between 2000 and 2019 is proposed depending on the available data. Data sources, data quality and method are directly provided in the questionnaires. Versions 1-3 present some errors in the questionnaires, which have been corrected.
DOI: 10.18419/darus-2793/15
title: Audio-S2_det_melody.wav

description: melody reconstructed from the NV detected signal
DOI: 10.18419/darus-2793/16
title: Audio-S3_voice.wav

description: speech signal for detection
DOI: 10.18419/darus-2793/17
title: Audio-S1_melody.wav

description: melody signal sent for detection
DOI: 10.5281/zenodo.6642333
title: VCFs of Chromosome 17 of Case 5 from the EGAD00001008392 Dataset

description: This synthetic data is a subset of the VCFs (chr17) from case 5 from the Rare Disease Synthetic Dataset (EGAD00001008392) dataset and the Human genomic and phenotypic synthetic data for the study of rare diseases (EGAS00001005702) study. For more info go to https://ega-archive.org/. All this data was created with the support of the RD-Connect GPAP (https://platform.rd-connect.eu/), EC H2020 project EJP-RD (grant # 825575), EC H2020 project B1MG (grant # 951724) and Generalitat de Catalunya VEIS project (grant # 001-P-001647).
DOI: 10.6084/m9.figshare.20043131
title: DigitalAppendix.pdf

description: Digital Appendix of the paper <br> „Making Third-Party Sellers More Attractive – The Case of Amazon“
DOI: 10.6084/m9.figshare.20043131.v2
title: DigitalAppendix.pdf

description: Digital Appendix of the paper <br> „Making Third-Party Sellers More Attractive – The Case of Amazon“
DOI: 10.5281/zenodo.6631454
title: Mineral liberation by free surface area of the target Au-Ag-Te bearing minerals

description: This is the dataset from Figure 4 from the article "A particle-based approach to predict the success and selectivity of leaching processes using ethaline - Comparison of simulated and experimental results". Chandra Widyananda Winardhi, Jose Ricardo da Assuncao Godinho, Cindy Rachmawati, Isabelle Duhamel Achin, Ainhoa Unzurrunzaga Iturbe, Gero Frisch, Jens Gutzmer<br> https://doi.org/10.1016/j.hydromet.2022.105869
DOI: 10.5281/zenodo.6631456
title: Cumulative mineral leaching over 96 h of leaching simulation of acanthite, electrum, hessite, petzite, galena and chalcopyrite for 322,842 particles.

description: This is the dataset from Figure 7 from the article "A particle-based approach to predict the success and selectivity of leaching processes using ethaline - Comparison of simulated and experimental results". Chandra Widyananda Winardhi, Jose Ricardo da Assuncao Godinho, Cindy Rachmawati, Isabelle Duhamel Achin, Ainhoa Unzurrunzaga Iturbe, Gero Frisch, Jens Gutzmer<br> https://doi.org/10.1016/j.hydromet.2022.105869
DOI: 10.5281/zenodo.6631453
title: Mineral liberation by free surface area of the target Au-Ag-Te bearing minerals

description: This is the dataset from Figure 4 from the article "A particle-based approach to predict the success and selectivity of leaching processes using ethaline - Comparison of simulated and experimental results". Chandra Widyananda Winardhi, Jose Ricardo da Assuncao Godinho, Cindy Rachmawati, Isabelle Duhamel Achin, Ainhoa Unzurrunzaga Iturbe, Gero Frisch, Jens Gutzmer<br> https://doi.org/10.1016/j.hydromet.2022.105869
DOI: 10.5281/zenodo.6631455
title: Cumulative mineral leaching over 96 h of leaching simulation of acanthite, electrum, hessite, petzite, galena and chalcopyrite for 322,842 particles.

description: This is the dataset from Figure 7 from the article "A particle-based approach to predict the success and selectivity of leaching processes using ethaline - Comparison of simulated and experimental results". Chandra Widyananda Winardhi, Jose Ricardo da Assuncao Godinho, Cindy Rachmawati, Isabelle Duhamel Achin, Ainhoa Unzurrunzaga Iturbe, Gero Frisch, Jens Gutzmer<br> https://doi.org/10.1016/j.hydromet.2022.105869
DOI: 10.5281/zenodo.6633830
title: Historical AR data of SPEAR-LE (ens05)

description: The repository contains the historical scenario simulation (5th ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.
DOI: 10.5281/zenodo.6633829
title: Historical AR data of SPEAR-LE (ens05)

description: The repository contains the historical scenario simulation (5th ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.
DOI: 10.5281/zenodo.6629478
title: Historical AR data of SPEAR-LE (ens02)

description: The repository contains the historical scenario simulation (2nd ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.
DOI: 10.5281/zenodo.6629477
title: Historical AR data of SPEAR-LE (ens02)

description: The repository contains the historical scenario simulation (2nd ensemble) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.
DOI: 10.6084/m9.figshare.20066990
title: Dataset.zip

description: This dataset is the original data of the manuscript "high phylogenetic diversity promotes the function and stability of synthetic denitrification community"
DOI: 10.21258/1547210
title: Inventaire forestier national suisse - Tableau no 859823
DOI: 10.21258/1547211
title: Inventaire forestier national suisse - Tableau no 849900
DOI: 10.21258/1547212
title: Inventaire forestier national suisse - Tableau no 829491
DOI: 10.21258/1547213
title: Inventaire forestier national suisse - Tableau no 840109
DOI: 10.21258/1547204
title: Inventaire forestier national suisse - Tableau no 831133
DOI: 10.21258/1547205
title: Inventaire forestier national suisse - Tableau no 889856
DOI: 10.21258/1547206
title: Inventaire forestier national suisse - Tableau no 794341
DOI: 10.21258/1547207
title: Inventaire forestier national suisse - Tableau no 807204
DOI: 10.21258/1547208
title: Inventaire forestier national suisse - Tableau no 872021
DOI: 10.21258/1547209
title: Inventaire forestier national suisse - Tableau no 850078
DOI: 10.21258/1547199
title: Inventaire forestier national suisse - Tableau no 873873
DOI: 10.21258/1547200
title: Inventaire forestier national suisse - Tableau no 884413
DOI: 10.21258/1547201
title: Inventaire forestier national suisse - Tableau no 882619
DOI: 10.21258/1547202
title: Inventaire forestier national suisse - Tableau no 847736
DOI: 10.21258/1547203
title: Inventaire forestier national suisse - Tableau no 868013
DOI: 10.21258/1547194
title: Inventaire forestier national suisse - Tableau no 862871
DOI: 10.21258/1547195
title: Inventaire forestier national suisse - Tableau no 814764
DOI: 10.21258/1547196
title: Inventaire forestier national suisse - Tableau no 848931
DOI: 10.21258/1547197
title: Inventaire forestier national suisse - Tableau no 901928
DOI: 10.21258/1547198
title: Inventaire forestier national suisse - Tableau no 878848
DOI: 10.21258/1547189
title: Inventaire forestier national suisse - Tableau no 894015
DOI: 10.21258/1547190
title: Inventaire forestier national suisse - Tableau no 802282
DOI: 10.21258/1547191
title: Inventaire forestier national suisse - Tableau no 811001
DOI: 10.21258/1547192
title: Inventaire forestier national suisse - Tableau no 890011
DOI: 10.21258/1547193
title: Inventaire forestier national suisse - Tableau no 802143
DOI: 10.21258/1547185
title: Inventaire forestier national suisse - Tableau no 823355
DOI: 10.21258/1547186
title: Inventaire forestier national suisse - Tableau no 861046
DOI: 10.21258/1547187
title: Inventaire forestier national suisse - Tableau no 840407
DOI: 10.21258/1547188
title: Inventaire forestier national suisse - Tableau no 898118
DOI: 10.21258/1547179
title: Inventaire forestier national suisse - Tableau no 797890
DOI: 10.21258/1547180
title: Inventaire forestier national suisse - Tableau no 807691
DOI: 10.21258/1547181
title: Inventaire forestier national suisse - Tableau no 890187
DOI: 10.21258/1547182
title: Inventaire forestier national suisse - Tableau no 823903
DOI: 10.21258/1547183
title: Inventaire forestier national suisse - Tableau no 866619
DOI: 10.21258/1547184
title: Inventaire forestier national suisse - Tableau no 846920
DOI: 10.21258/1547172
title: Inventaire forestier national suisse - Tableau no 863730
DOI: 10.21258/1547173
title: Inventaire forestier national suisse - Tableau no 891465
DOI: 10.21258/1547174
title: Inventaire forestier national suisse - Tableau no 865870
DOI: 10.21258/1547175
title: Inventaire forestier national suisse - Tableau no 821933
DOI: 10.21258/1547176
title: Inventaire forestier national suisse - Tableau no 899277
DOI: 10.21258/1547177
title: Inventaire forestier national suisse - Tableau no 862076
DOI: 10.21258/1547178
title: Inventaire forestier national suisse - Tableau no 832575
DOI: 10.21258/1547166
title: Inventaire forestier national suisse - Tableau no 868213
DOI: 10.21258/1547167
title: Inventaire forestier national suisse - Tableau no 890283
DOI: 10.21258/1547168
title: Inventaire forestier national suisse - Tableau no 840524
DOI: 10.21258/1547169
title: Inventaire forestier national suisse - Tableau no 887106
DOI: 10.21258/1547170
title: Inventaire forestier national suisse - Tableau no 812065
DOI: 10.21258/1547171
title: Inventaire forestier national suisse - Tableau no 850501
DOI: 10.21258/1547160
title: Inventaire forestier national suisse - Tableau no 819242
DOI: 10.21258/1547161
title: Inventaire forestier national suisse - Tableau no 798815
DOI: 10.21258/1547162
title: Inventaire forestier national suisse - Tableau no 892503
DOI: 10.21258/1547163
title: Inventaire forestier national suisse - Tableau no 842609