cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7086698
title: Data QJ paper

description: https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.4325: data used for paper.
DOI: 10.25824/redu/3pz1cq
title: data1.json

description: Este arquivo contém as características extraídas pela técnica MFCC das gravações de áudio nomeado como caso 1 (amassadas em conjunto).
DOI: 10.25824/redu/g3amsw
title: dataset_audios.zip

description: Esse arquivo contém todas as gravações de áudio utilizada na pesquisa do caso 1 (amassadas em conjunto) e caso 2 (amassadas por peça).
DOI: 10.25824/redu/sirw24
title: data2.json

description: Este arquivo contém as características extraídas pela técnica MFCC das gravações de áudio nomeado como caso 2 (amassadas por peça).
DOI: 10.25824/redu/ugmpjr
title: Banco de dados de áudio: classificação de resinas plásticas para reciclagem usando processamento de sinais acústico e redes neurais artificiais

description: Esse banco de dados contém as gravações de áudio das sete resinas plásticas de reciclagem (PET, PEAD, PVC, PP, PS e Outros) sendo amassadas de duas formas, em conjunto dentro de um bag de pano (chamado de caso 1) e por peça (chamado de caso 2). Essas gravações passaram por um processamento de sinais, e suas características foram extraídas utilizando a técnica coeficientes cepstrais de frequência mel (MFCC) que foram armazenados em um arquivo adjacente no formato JSON. Esse arquivo contém informações como os gêneros, que são os tipos de resinas plásticas recicláveis, os vetores MFCC para cada segmento (como exemplo: [-338.15258789][49.42590332][-41.34419632]), que foram utilizados como entrada nas redes e os labels (rótulos) que são as categorias pertencentes. Tendo como índice 0 – PET, índice 1 – PEAD, índice 2 – PVC, índice 3 – PEBD, índice 4 – PP, índice 5 – PS e índice 6 – Outros.
DOI: 10.5281/zenodo.7086867
title: Primary NMR fid data for Abas, M. et al.

description: Primary data fid files for 1H NMR and 13C{1H} NMR spectra of compounds 1a, 1b, 1c, 1d, 1e, 2a, 2b, 3, 5, 6, 7, and 8 from the manuscript "Truce-Smiles Rearrangement of Carbanion Nucleophiles Stabilized by Various Functional Groups".
DOI: 10.5281/zenodo.7086866
title: Primary NMR fid data for Abas, M. et al.

description: Primary data fid files for 1H NMR and 13C{1H} NMR spectra of compounds 1a, 1b, 1c, 1d, 1e, 2a, 2b, 3, 5, 6, 7, and 8 from the manuscript "Truce-Smiles Rearrangement of Carbanion Nucleophiles Stabilized by Various Functional Groups".
DOI: 10.48432/yzves0
title: Macroinvertebrados bentônicos em viveiros escavados de aquicultura

description: A planilha depositada representa o conjunto de dados de macroinvertebrados bentônicos coletados em viveiros escavados localizados nos municípios de Conchal, Mogi Mirim, e Itapira, no estado de São Paulo, durante o projeto “Uso de bioindicadores para avaliação da qualidade da água no cultivo da tilápia” (2016-2018). A matriz apresenta as datas de coleta (de fevereiro a maio de 2016), em cada piscicultura avaliada (representada pela segunda coluna com as iniciais dos produtores). A seguir é descrito o local de coleta (região do viveiro onde foi instalado o coletor com substrato artificial). E depois, nas outras colunas, os táxons identificados, com seu respectivo número de abundância. Neste projeto também foi elaborado um sistema web para gestão de um banco de dados, acessível e amigável para o público leigo (Bio Aqua), principalmente produtores de tilápia em viveiros escavados, incluindo informações sobre organismos bentônicos, parasitos de peixes e variáveis físicas e químicas de qualidade de água, bem como de suas respectivas coletas nas propriedades da Associação Paulista de Piscicultores (ASPI), parceira do projeto. Isso permite o diagnóstico da qualidade da água do viveiro assim como a qualidade do peixe criado.
DOI: 10.5517/ccdc.csd.cc2czsx2
title: CCDC 2204345: Experimental Crystal Structure Determination

description: Related Article: W. Ren, J. Li|2022|IUCrData|7|x220869|doi:10.1107/S2414314622008690
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d0pfk
title: CCDC 2205198: Experimental Crystal Structure Determination

description: Related Article: R. Shimazaki, M. Sadakiyo|2022|IUCrData|7|x220884|doi:10.1107/S2414314622008847
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: 10.5281/zenodo.6948324
title: Data of Figure 11 ("Photonic and Optomechanical Thermometry", Optics 2022, 3, 159–176, doi.org/10.3390/opt3020017)

description: Data of figure 11 includes: (11a.csv) Simulated temperature distribution plot with an incident power P = 64.39 μW; (11b.csv) thermomechanical noise spectra measured at a temperature of 5 K for different laser powers. The x-axis is the difference in frequency from the resonance frequency measured with a laser power of 64.39 μW, equal to 1.331 MHz. (11c.csv) measured resonance frequency difference versus laser power with respect to the resonance frequency acquired with a laser power of 64.39 μW; (11d.csv) temperature extracted from the measurements in (11b.csv) by integrating the area under the peak versus laser power.
DOI: 10.5281/zenodo.6948325
title: Data of Figure 11 ("Photonic and Optomechanical Thermometry", Optics 2022, 3, 159–176, doi.org/10.3390/opt3020017)

description: Data of figure 11 includes: (11a.csv) Simulated temperature distribution plot with an incident power P = 64.39 μW; (11b.csv) thermomechanical noise spectra measured at a temperature of 5 K for different laser powers. The x-axis is the difference in frequency from the resonance frequency measured with a laser power of 64.39 μW, equal to 1.331 MHz. (11c.csv) measured resonance frequency difference versus laser power with respect to the resonance frequency acquired with a laser power of 64.39 μW; (11d.csv) temperature extracted from the measurements in (11b.csv) by integrating the area under the peak versus laser power.
DOI: 10.5281/zenodo.6948299
title: Data of Figure 9 ("Photonic and Optomechanical Thermometry", Optics 2022, 3, 159–176, doi.org/10.3390/opt3020017)

description: Data of figure 9 includes: (9b.csv) thermomechanical noise spectra at different temperatures obtained using a spectrum analyzer. The x-axis is centered at the fundamental resonance frequency of each spectrum for comparison. (9c.csv) temperature extracted by integrating the area under the peak at each temperature with the standard deviation.
DOI: 10.5281/zenodo.6948300
title: Data of Figure 9 ("Photonic and Optomechanical Thermometry", Optics 2022, 3, 159–176, doi.org/10.3390/opt3020017)

description: Data of figure 9 includes: (9b.csv) thermomechanical noise spectra at different temperatures obtained using a spectrum analyzer. The x-axis is centered at the fundamental resonance frequency of each spectrum for comparison. (9c.csv) temperature extracted by integrating the area under the peak at each temperature with the standard deviation.
DOI: 10.5281/zenodo.7086466
title: Data of Figure 2 - "Photonic and Optomechanical Thermometry"

description: <strong>"Fig_2a_Spectra_Backround.csv"</strong> - Raw data of PIC transmittance and estimated background <strong>"Figure_2b,2c - Spectra Section,Fits,Residuals.xls"</strong> - PIC transmittance, normalised by the background - Dataset of the lorentz fit for each peak (R1, R2, R3) - Fit residuals of peak fit
DOI: 10.5281/zenodo.7086467
title: Data of Figure 2 - "Photonic and Optomechanical Thermometry"

description: <strong>"Fig_2a_Spectra_Backround.csv"</strong> - Raw data of PIC transmittance and estimated background <strong>"Figure_2b,2c - Spectra Section,Fits,Residuals.xls"</strong> - PIC transmittance, normalised by the background - Dataset of the lorentz fit for each peak (R1, R2, R3) - Fit residuals of peak fit
DOI: 10.5281/zenodo.7086433
title: Data of Figure 3 - "Photonic and Optomechanical Thermometry"

description: data of Figure 3: Q-factor (a) and extinction ratio (b) of the three ring resonators (R1, R2, R3) between 1530 nm to 1570 nm<br> for different temperatures (20°C, 50°C, 80°C)
DOI: 10.5281/zenodo.7086434
title: Data of Figure 3 - "Photonic and Optomechanical Thermometry"

description: data of Figure 3: Q-factor (a) and extinction ratio (b) of the three ring resonators (R1, R2, R3) between 1530 nm to 1570 nm<br> for different temperatures (20°C, 50°C, 80°C)
DOI: 10.5281/zenodo.7086191
title: Fig8.c--Photonic and Optomechanical Thermometry

description: Data of figure 10.b depicting the optical resonance shift due to the thermorefractive and thermal expansion effects. The folder contains the raw optical spectra for each temperature and the analyzis python script.
DOI: 10.5281/zenodo.7086190
title: Fig8.c--Photonic and Optomechanical Thermometry

description: Data of figure 10.b depicting the optical resonance shift due to the thermorefractive and thermal expansion effects. The folder contains the raw optical spectra for each temperature and the analyzis python script.
DOI: 10.5281/zenodo.6625502
title: Datasets for computational counterselection manuscript

description: Training datasets for currently unpublished manuscript titled: "Computational counterselection identifies nonspecific therapeutic biologic candidates".
DOI: 10.5281/zenodo.6625501
title: Datasets for computational counterselection manuscript

description: Training and raw datasets for manuscript titled: "Computational counterselection identifies nonspecific therapeutic biologic candidates". Training data is in "data.zip". Raw data with Excel file for label identities in "raw_data.zip". If you use this data in your work, please cite: Saksena SD, Liu G, Banholzer C, Horny G, Ewert S, Gifford DK. 2022. Computational counterselection identifies nonspecific therapeutic biologic candidates. <em>Cell Rep Methods</em> <strong>2</strong>: 100254. http://dx.doi.org/10.1016/j.crmeth.2022.100254.
DOI: 10.5281/zenodo.6625509
title: Datasets for computational counterselection manuscript

description: Training datasets for currently unpublished manuscript titled: "Computational counterselection identifies nonspecific therapeutic biologic candidates".
DOI: 10.5281/zenodo.7084268
title: Datasets for computational counterselection manuscript

description: Training and raw datasets for manuscript titled: "Computational counterselection identifies nonspecific therapeutic biologic candidates". Training data is in "data.zip". Raw data with Excel file for label identities in "raw_data.zip". If you use this data in your work, please cite: Saksena SD, Liu G, Banholzer C, Horny G, Ewert S, Gifford DK. 2022. Computational counterselection identifies nonspecific therapeutic biologic candidates. <em>Cell Rep Methods</em> <strong>2</strong>: 100254. http://dx.doi.org/10.1016/j.crmeth.2022.100254.
DOI: 10.6084/m9.figshare.21136279.v1
title: butastur_liventer_zanfona_1.fasta.gz

description: .........
DOI: 10.6084/m9.figshare.21136279
title: butastur_liventer_zanfona_1.fasta.gz

description: .........
DOI: 10.5281/zenodo.7086999
title: Weamyl radar and precipitation data related to quantitative precipitation estimation

description: This dataset contains radar data and precipitation data provided for research during the Weamyl project (NO Grants 2014-2021, under Project contract no. 26/2020) specifically used for quantitative precipitation estimation research. Radar data contains two radar products: Reflectivity (R): a base radar product, measured in dBZ, it is the product used for estimating precipitation. R data for the lowest 2 elevation angles. One-hour precipitation (OHP): a derivate radar product, it is the precipitation estimation that the radar makes automatically; to be used as comparison All radar data are in netcdf format. It is data from a WSR-98D Doppler weather radar situated in central Transylvania, provided in the form of a 557x800 grid with the cell size of ~1x1 km. Precipitation data contains 1-hour precipitation values collected from ground weather station, measured in mm; with the purpose to be used as ground truth. The precipitation data contains a csv file that contains the actual precipitation data, first value mentions the station code, the second the time and date and the third the 1-hour accumulated precipitation value, in mm. The second file provides the lat/long values for the ground weather stations, to be able to match the values of a station to a location on the grids from the radar data.
DOI: 10.5281/zenodo.7086998
title: Weamyl radar and precipitation data related to quantitative precipitation estimation

description: This dataset contains radar data and precipitation data provided for research during the Weamyl project (NO Grants 2014-2021, under Project contract no. 26/2020) specifically used for quantitative precipitation estimation research. Radar data contains two radar products: Reflectivity (R): a base radar product, measured in dBZ, it is the product used for estimating precipitation. R data for the lowest 2 elevation angles. One-hour precipitation (OHP): a derivate radar product, it is the precipitation estimation that the radar makes automatically; to be used as comparison All radar data are in netcdf format. It is data from a WSR-98D Doppler weather radar situated in central Transylvania, provided in the form of a 557x800 grid with the cell size of ~1x1 km. Precipitation data contains 1-hour precipitation values collected from ground weather station, measured in mm; with the purpose to be used as ground truth. The precipitation data contains a csv file that contains the actual precipitation data, first value mentions the station code, the second the time and date and the third the 1-hour accumulated precipitation value, in mm. The second file provides the lat/long values for the ground weather stations, to be able to match the values of a station to a location on the grids from the radar data.
DOI: 10.5281/zenodo.7087009
title: Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data

description: The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book. The input data refer to a subset of single sample data file for 1.5 m temperature as part of the Met Office contribution to the COVID 19 modelling effort. The full dataset was available for download from the Met Office Azure (https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/). The full dataset was available for download under the terms of non-commercial purposes. <strong>Contributions</strong> <em>Notebook</em> Samantha V. Adams (author), Met Office Informatics Lab, @svadams Alejandro Coca-Castro (reviewer), The Alan Turing Institute, @acocac <em>Dataset originator/creator</em> Met Office Informatics Lab (creator) Microsoft (support) European Regional Development Fund (support) <em>Dataset authors</em> Met Office <em>Dataset documentation</em> Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f. <strong>Note this data should be used only for non-commercial purposes.</strong>
DOI: 10.5281/zenodo.7087008
title: Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data

description: The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book. The input data refer to a subset of single sample data file for 1.5 m temperature as part of the Met Office contribution to the COVID 19 modelling effort. The full dataset was available for download from the Met Office Azure (https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/). The full dataset was available for download under the terms of non-commercial purposes. <strong>Contributions</strong> <em>Notebook</em> Samantha V. Adams (author), Met Office Informatics Lab, @svadams Alejandro Coca-Castro (reviewer), The Alan Turing Institute, @acocac <em>Dataset originator/creator</em> Met Office Informatics Lab (creator) Microsoft (support) European Regional Development Fund (support) <em>Dataset authors</em> Met Office <em>Dataset documentation</em> Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f. <strong>Note this data should be used only for non-commercial purposes.</strong>
DOI: 10.5066/p9r8ykl0
title: Principal components of climate variation in the Desert Southwest (ver. 2.0, September 2019)

description: Five principal components are used to represent the climate variation in an original set of 12 climate variables reflecting precipitation and temperature gradients. The dataset provides coverage for four regions (the Sonoran Desert, Mojave Desert, Colorado Plateau, and Southern Great Basin) and three time periods including current climate (defined as the 1980-2010 normal period) and future climate (2010-2040 and 2040-2070 normal periods) under the RCP4.5 and RCP8.5 emission scenarios. Climate variables were chosen based on their known influence on local adaptation in plants, and include: mean annual temperature, summer maximum temperature, winter minimum temperature, annual temperature range, temperature seasonality (coefficient of variation in monthly average temperatures), mean annual precipitation, winter precipitation, summer precipitation, proportion of summer precipitation, precipitation seasonality (coefficient of variation in monthly precipitation totals), long-term winter precipitation variability, and long-term summer precipitation variability. The conversion to principal components both standardizes and accounts for covariation in climate variables, while emphasizing the most important climate gradients across the landscape. Raster layers representing each principal component form the input to Climate Distance Mapper (https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/), an interactive R Shiny application for matching seed sources with restoration sites. Plant populations are commonly adapted to local climate gradients and frequently exhibit a home-site advantage. For this reason, climate information may serve as a proxy for local adaptation in restoration designs. Climate Distance Mapper allows users to rank the suitability of seed sources for restoration sites by displaying multivariate climate distances (incorporating climate principal components) from user-supplied input points to the surrounding landscape. The application provides functions to match seed sources with current or future climate, guide sampling effort for large scale seed collections, and partition the landscape into suitable areas for different seed sources. These data support the following publication: Shryock, D.F., DeFalco, L.A., and T.C. Esque. 2018. Spatial decision-support tools to guide restoration and seed sourcing in the Desert Southwest. Ecosphere 9(10):e02453.
DOI: 10.17909/sh0j-ym03
title: Kelt-15b Tess
DOI: 10.22003/xfel.eu-data-003049-00
title: XPCS of strongly coupled nanocrystal superlattice precursors in electrolyte
DOI: 10.6084/m9.figshare.21136462
title: phenganax_marumi_zanfona_1.fasta.gz

description: .....
DOI: 10.6084/m9.figshare.21136462.v1
title: phenganax_marumi_zanfona_1.fasta.gz

description: .....
DOI: 10.5517/ccdc.csd.cc2b8sn2
title: CCDC 2153404: Experimental Crystal Structure Determination

description: Related Article: Shurong He, Jie Wang, Jinfeng Zheng, Qing-Qing Luo, Haijun Leng, Sixiang Zheng, Cheng Peng, Bo Han, Gu Zhan|2022|New J.Chem.|46|11617|doi:10.1039/D2NJ01949C
description: Related Article: Shurong He,  Jie Wang, Jinfeng Zheng, Qing-Qing Luo, Haijun Leng, Sixiang Zheng, Cheng Peng, Bo Han, Gu Zhan|2022|CSD Communication|||
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.3761562.v2343
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.19487706
title: Multiple Group Structural Equation Modeling of the Social Relations Model

description: The social relations model is a statistical tool that allows the analysis of group dynamics as dyadic interactions between individuals. Within a multiple group structural equation modeling framework, Wald-tests and likelihood ratio tests based on (1) equality constraints among model parameters and (2) Lagrange multipliers for restrictions among non-linear parameter transformations are presented as methods for group-comparisons of various quantifications of group dynamics that hold different interpretations. The methods are illustrated with an empirical example. A simulation study investigates the performance of the methods with regard to Type I error-rate recoverability and statistical power and displays overall promising results. Implications and limitations are discussed.
DOI: 10.6084/m9.figshare.19487706.v1
title: Multiple Group Structural Equation Modeling of the Social Relations Model

description: The social relations model is a statistical tool that allows the analysis of group dynamics as dyadic interactions between individuals. Within a multiple group structural equation modeling framework, Wald-tests and likelihood ratio tests based on (1) equality constraints among model parameters and (2) Lagrange multipliers for restrictions among non-linear parameter transformations are presented as methods for group-comparisons of various quantifications of group dynamics that hold different interpretations. The methods are illustrated with an empirical example. A simulation study investigates the performance of the methods with regard to Type I error-rate recoverability and statistical power and displays overall promising results. Implications and limitations are discussed.
DOI: 10.6084/m9.figshare.21136495
title: cacatua_moluccensis_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21136495.v1
title: cacatua_moluccensis_zanfona_1.fasta.gz

description: .......