cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e171541v1
title: Stigma towards mental illness and help-seeking behaviors among adult and child psychiatrists in Hungary: a cross-sectional study

description: <b>Objective:</b> Stigma towards people with mental health problems is a growing issue across the world, to which healthcare providers might contribute. The aim of the present study was to explore psychiatrists’ attitudes towards their patients and link them to psychosocial and professional factors. <b>Methods: </b>An online questionnaire was used to approach the in- and outpatient psychiatric services across Hungary. A total of 211 trainees and specialists in adult and child psychiatry participated in our study. Their overall stigmatizing attitudes were measured, with focus on attitude, disclosure and help-seeking, and social distance dimensions by using the self-report Opening Minds Stigma Scale for Health Care Providers (OMS-HC). Multiple linear regression analyses were performed to elucidate the dimensions of stigma and its association with sociodemographic, professional and personal traits. <b>Results:</b> Stigmatizing attitudes of close colleagues towards patients were statistically significant predictors of higher scores on the attitude [B=0.235 (0.168-0.858), p=0.004], the disclosure and help-seeking subscales [B=0.169 (0.038-0.908), p=0.033], and the total score of the OMS-HC [B=0.191 (0.188-1.843), p=0.016]. Psychiatrists who had already sought help for their own problems had lower scores on the disclosure and help-seeking subscale [B=0.202 (0.248-1.925), p=0.011]. The overall stigmatizing attitude was predicted by the openness to participate in case discussion, supervision or Balint groups [B=0.166 (0.178-5.886), p=0.037] besides the more favorable attitudes of their psychiatrist colleagues [B=0.191 (0.188-1.843), p=0.016]. <b>Conclusions:</b> The favorable attitudes of psychiatrists are associated with their own experiences with any kind of psychiatric condition, previous help-seeking behavior and the opportunity to work together with fellow psychiatrists, whose attitudes are less stigmatizing. The perception of fellow colleagues’ attitudes towards patients and the openness to case discussion, supervision and Balint groups were the main two factors that affected the overall attitudes towards patients; therefore, these should be considered when tailoring anti-stigma interventions for psychiatrists.
DOI: 10.5281/zenodo.6591864
title: Dataset and Puppet Issues Mining

description: Mining Puppet and Dataset
DOI: 10.5281/zenodo.6591822
title: Dataset and Puppet Issues Mining

description: Mining Puppet and Dataset
DOI: 10.6084/m9.figshare.19919696
title: Calixto et al. 2022 AJB

description: This Readme_file.txt file was generated on 2022-05-29 by Eduardo Calixto <br> GENERAL INFORMATION <br> 1. Title of Dataset: Revisiting the ant-plant-pollinator interaction: new insight into the importance of mutualist partners identity for plant fitness <br> 2. Author Information Eduardo S Calixto1,5*; Danilo B Santos; Denise Lange2; Helena M Torezan-Silingardi3; Robert J Marquis4; Kleber Del-Claro1,3 1 Programa de Pos-Graduacao em Entomologia. Faculdade de Filosofia, Ciências e Letras. Universidade de São Paulo. Av. Bandeirantes, 3900, 14040-901, Ribeirão Preto, SP, Brazil. ESC – calixtos.edu@gmail.com, DBS – santosdanilo499@gmail.com 2 Universidade Tecnológica Federal do Paraná, Campus Santa Helena, Rua Cerejeira, s/n, 85892-000, Santa Helena, PR, Brazil. DL – deniselange@yahoo.com.br 3 Laboratório de Ecologia Comportamental e de Interações. Instituto de Biologia. Universidade Federal de Uberlândia, Cx.P. 593, 38400-920, Uberlândia, MG, Brazil. HMTS – hmtsilingardi@gmail.com, KDC – delclaro@ufu.br 4 Department of Biology and the Whitney R. Harris World Ecology Center, University of Missouri-St. Louis, 1 University Boulevard, St. Louis, Missouri 63121-4499, USA. RJM – Robert_Marquis@umsl.edu Corresponding author. Eduardo S. Calixto. ORCID: 0000-0003-3617-2464. E-mail: calixtos.edu@gmail.com <br> <br> DATA & FILE OVERVIEW <br> 1. File List: 1.1 data_raw: contains all the data collected. Each sheet within this excel file is related to a prediction. When runing the R code, it will automatically load the correct data_sheet. 1.2 R_scripts: Each R code is related to a prediction to be tested. Check methods and data collection in Dryad website to reference each code to each prediction <br> METHODOLOGICAL INFORMATION <br> 1. All methodological approach has been uploaded in the Dryad webpage, in which readers can check how we collected and analysed our data.
DOI: 10.34740/kaggle/dsv/3715902
title: YouTube Daily
DOI: 10.18112/openneuro.ds002239.v1.1.1
title: A pilot test-retest study of resting state activity using multiecho fMRI
DOI: 10.34740/kaggle/dsv/3716324
title: Preprocessed Slices Balanced(0.4)
DOI: 10.6084/m9.figshare.19919056
title: JSS - The Role of Scrum Master - Survey Questionnaire

description: This contains the survey questionnaire of the paper: The Role of the Scrum Master in an Industry based University Course (Journal of Systems and Software, 2022)
DOI: 10.34740/kaggle/ds/2222522
title: Base de dados de CEPs Brasileiros (2018)
DOI: 10.5517/ccdc.csd.cc1ldg8q
title: CCDC 1471733: Experimental Crystal Structure Determination

description: Related Article: Yan Jiang, Ren-Yong Yang, Zhao-Xia Qu, Gui-Ge Hou, Wei Cong, Chun-Hua Wang, Feng Zhao|2021|Chin.Chem.Lett.|33|2919|doi:10.1016/j.cclet.2021.10.072
DOI: 10.5517/ccdc.csd.cc27khcp
title: CCDC 2072392: Experimental Crystal Structure Determination

description: Related Article: Qingqing Guo, Nanzhu Li, Qian Zou, Jiage Jia, Yifan Wei, Songsong Bao, Limin Zheng|2021|Chin.Chem.Lett.|33|3259|doi:10.1016/j.cclet.2021.10.091
DOI: 10.5517/ccdc.csd.cc27khdq
title: CCDC 2072393: Experimental Crystal Structure Determination

description: Related Article: Qingqing Guo, Nanzhu Li, Qian Zou, Jiage Jia, Yifan Wei, Songsong Bao, Limin Zheng|2021|Chin.Chem.Lett.|33|3259|doi:10.1016/j.cclet.2021.10.091
DOI: 10.17608/k6.auckland.19450631
title: Supplementary Information

description: <strong>Supplementary figure 1</strong>. Percentage (%) of cells with multiple centrioles (&gt;2) in chondrocytes cultured on 5 kPa, 50 kPa gels and coverslips for 48 hours in low serum media. Data represents the mean and standard error of the mean (n &gt; 150 cells per stiffness). <br> <strong>Supplementary Tables 1-9:</strong> Tables summarising the Pearson's correlation data examining the corrections between cilia length, centriole position, cell area, cell height, cell circularity and nucleus area on cells cultured on 5kPa, 50kPa or glass coverslips at 12, 24 or 48 hours of low serum culture. <br> <br> <br> <br>
DOI: 10.17608/k6.auckland.19450631.v2
title: Supplementary Information

description: <strong>Supplementary figure 1</strong>. Percentage (%) of cells with multiple centrioles (&gt;2) in chondrocytes cultured on 5 kPa, 50 kPa gels and coverslips for 48 hours in low serum media. Data represents the mean and standard error of the mean (n &gt; 150 cells per stiffness). <br> <strong>Supplementary Tables 1-9:</strong> Tables summarising the Pearson's correlation data examining the corrections between cilia length, centriole position, cell area, cell height, cell circularity and nucleus area on cells cultured on 5kPa, 50kPa or glass coverslips at 12, 24 or 48 hours of low serum culture. <br> <br> <br> <br>
DOI: 10.17608/k6.auckland.19450631.v1
title: Supplementary tables 1-9.pdf

description: Tables summarising the Pearson's correlation data examining the corrections between cilia length, centriole position, cell area, cell height, cell circularity and nucleus area on cells cultured on 5kPa, 50kPa or glass coverslips at 12, 24 or 48 hours of low serum culture.
DOI: 10.34740/kaggle/dsv/3716441
title: Preprocessed Slices Balanced(0.0)
DOI: 10.34740/kaggle/dsv/3716446
title: Preprocessed Slices Balanced(0.6)
DOI: 10.34740/kaggle/dsv/3716495
title: Preprocessed Slices Balanced(all)
DOI: 10.6084/m9.figshare.19688950
title: Research data (erosion rates and figure 3)

description: Excel data file containing erosion rates for both study sites, as well as Figure 3 from the research paper
DOI: 10.6084/m9.figshare.19688950.v1
title: Research data (erosion rates and figure 3)

description: Excel data file containing erosion rates for both study sites, as well as Figure 3 from the research paper
DOI: 10.6084/m9.figshare.19688944
title: Table 1 (satellite image tiles)

description: Drew Point, Alaska & 212106-1003 & 7/3/2017\\ & 212517-1022 & 10/24/2017\\ & 213358-1005 & 6/26/2018\\ & 213708-101e & 10/2/2018\\ & 214630-1021 & 6/19/2019\\ & 214625-0f25 & 10/13/2019\\ & 214852-0f4e & 6/4/2020\\ & 214757-100c & 9/17/2020\\ & 221214-51-2426 & 6/11/2021\\ & 214507-103c & 9/18/2021\\ Cape Halkett, Alaska & 211551-1021 & 7/3/2017\\ & 211819-0f31 & 10/24/2017\\ & 191634-104b & 8/12/2018\\ & 213254-0f4e & 10/2/2018\\ & 064231-1105 & 6/19/2019\\ & 213240-1025 & 9/26/2019\\ & 211752-68-2259 & 6/4/2020\\ & 002613-0f4c & 9/17/2020\\ & 211020-45-220b & 6/22/2021\\ & 213436-1014 & 9/18/2021\\
DOI: 10.6084/m9.figshare.19688944.v1
title: Table 1 (satellite image tiles)

description: Drew Point, Alaska & 212106-1003 & 7/3/2017\\ & 212517-1022 & 10/24/2017\\ & 213358-1005 & 6/26/2018\\ & 213708-101e & 10/2/2018\\ & 214630-1021 & 6/19/2019\\ & 214625-0f25 & 10/13/2019\\ & 214852-0f4e & 6/4/2020\\ & 214757-100c & 9/17/2020\\ & 221214-51-2426 & 6/11/2021\\ & 214507-103c & 9/18/2021\\ Cape Halkett, Alaska & 211551-1021 & 7/3/2017\\ & 211819-0f31 & 10/24/2017\\ & 191634-104b & 8/12/2018\\ & 213254-0f4e & 10/2/2018\\ & 064231-1105 & 6/19/2019\\ & 213240-1025 & 9/26/2019\\ & 211752-68-2259 & 6/4/2020\\ & 002613-0f4c & 9/17/2020\\ & 211020-45-220b & 6/22/2021\\ & 213436-1014 & 9/18/2021\\
DOI: 10.25919/p5a7-v349
title: Parkes observations for project PX500 semester 2022APRS_07

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.25919/17en-br84
title: Parkes observations for project PX500 semester 2022APRS_08

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.5281/zenodo.6591921
title: ground truths for mesmerize-core tests

description: ground truths for mesmerize-core tests
DOI: 10.6084/m9.figshare.14575382
title: Transformation models for the compressibility properties of Finnish clays using a multivariate database

description: The compression index obtained from an oedometer test is often used to estimate the settlements of clayey subsoil, but compressibility parameters are rarely available during the preliminary geotechnical design phase. Various empirical correlations linking compressibility to other properties such as water content have been proposed. However, as Scandinavian clays are soft and exhibit greater compressibility, the existing transformation models for compressibility can be biased when applied to Finnish clays. This paper compiles a partial multivariate database of Finnish clayey soils and demonstrates that the existing transformation models tend to underestimate the compressibility of Finnish clays. The new transformation models are constructed by means of a 2-degree polynomial regression applied to the natural logarithms of the soil properties. Finally, the transformation uncertainties are quantified via the standard deviation of errors and the coefficient of variation. The best predictors for the compressibility of Finnish clayey soils were found to be the void ratio and water content. When the void ratio was combined with a secondary predictor, such as the ratio between undrained shear strength and preconsolidation pressure or plastic limit, the transformation uncertainty decreased notably.
DOI: 10.6084/m9.figshare.14575382.v1
title: Transformation models for the compressibility properties of Finnish clays using a multivariate database

description: The compression index obtained from an oedometer test is often used to estimate the settlements of clayey subsoil, but compressibility parameters are rarely available during the preliminary geotechnical design phase. Various empirical correlations linking compressibility to other properties such as water content have been proposed. However, as Scandinavian clays are soft and exhibit greater compressibility, the existing transformation models for compressibility can be biased when applied to Finnish clays. This paper compiles a partial multivariate database of Finnish clayey soils and demonstrates that the existing transformation models tend to underestimate the compressibility of Finnish clays. The new transformation models are constructed by means of a 2-degree polynomial regression applied to the natural logarithms of the soil properties. Finally, the transformation uncertainties are quantified via the standard deviation of errors and the coefficient of variation. The best predictors for the compressibility of Finnish clayey soils were found to be the void ratio and water content. When the void ratio was combined with a secondary predictor, such as the ratio between undrained shear strength and preconsolidation pressure or plastic limit, the transformation uncertainty decreased notably.
DOI: 10.6084/m9.figshare.19753879
title: staahl2016visualization_ST_BreastCancer_Layer

description: staahl2016visualization_ST_BreastCancer_Layer are spatial transcriptomics datasets of mouse olfactory bulb and human breast cancer, published with the paper presenting spatial transcriptomics (ST) technology4. It was generated by ST, that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. The staahl2016visualization_ST_BreastCancer_Layer dataset contains 4 sub-dataset with ~14000 genes expression features for 4 human breast cancer layers, which has 254 (Layer1), 251 (Layer2), 264 (Layer3) and 262 (Layer4) spots, respectively. The datasets was downloaded from https://www.spatialresearch.org/resources-published-datasets/, containing H&E images, spot-wise region annotation and count matrix of each sub-dataset, as well as other spatial transcriptomics resource published datasets.
DOI: 10.6084/m9.figshare.19753879.v1
title: staahl2016visualization_ST_BreastCancer_Layer

description: staahl2016visualization_ST_BreastCancer_Layer are spatial transcriptomics datasets of mouse olfactory bulb and human breast cancer, published with the paper presenting spatial transcriptomics (ST) technology4. It was generated by ST, that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. The staahl2016visualization_ST_BreastCancer_Layer dataset contains 4 sub-dataset with ~14000 genes expression features for 4 human breast cancer layers, which has 254 (Layer1), 251 (Layer2), 264 (Layer3) and 262 (Layer4) spots, respectively. The datasets was downloaded from https://www.spatialresearch.org/resources-published-datasets/, containing H&E images, spot-wise region annotation and count matrix of each sub-dataset, as well as other spatial transcriptomics resource published datasets.
DOI: 10.6084/m9.figshare.19753963
title: staahl2016visualization_ST_MOB

description: staahl2017visualization_ST_MOB are spatial transcriptomics datasets of mouse olfactory bulb and human breast cancer, published with the paper presenting spatial transcriptomics (ST) technology. It was generated by ST, that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. staahl2017visualization_ST_MOB and staahl2016visualization_ST_BreastCancer_Layer are the most widely used benchmark datasets among SE methods, including SPRI64, SpaGCN45, GPcounts65, sepal66, SPARK36, trendseek35 and SpatialDE34. The staahl2017visualization_ST_MOB dataset contains 12 sub-datasets with ~16000 genes expression features, and the number of spots for the 12 replicates are 267 (Rep1), 280 (Rep2), 269 (Rep3), 264 (Rep4), 267 (Rep5), 242 (Rep6), 231 (Rep7), 234 (Rep8), 237 (Rep9), 281 (Rep10), 262 (Rep11) and 282 (Rep12).The datasets was downloaded from https://www.spatialresearch.org/resources-published-datasets/, containing H&E images, spot-wise region annotation and count matrix of each sub-dataset, as well as other spatial transcriptomics resource published datasets.
DOI: 10.6084/m9.figshare.19753963.v1
title: staahl2016visualization_ST_MOB

description: staahl2017visualization_ST_MOB are spatial transcriptomics datasets of mouse olfactory bulb and human breast cancer, published with the paper presenting spatial transcriptomics (ST) technology. It was generated by ST, that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. staahl2017visualization_ST_MOB and staahl2016visualization_ST_BreastCancer_Layer are the most widely used benchmark datasets among SE methods, including SPRI64, SpaGCN45, GPcounts65, sepal66, SPARK36, trendseek35 and SpatialDE34. The staahl2017visualization_ST_MOB dataset contains 12 sub-datasets with ~16000 genes expression features, and the number of spots for the 12 replicates are 267 (Rep1), 280 (Rep2), 269 (Rep3), 264 (Rep4), 267 (Rep5), 242 (Rep6), 231 (Rep7), 234 (Rep8), 237 (Rep9), 281 (Rep10), 262 (Rep11) and 282 (Rep12).The datasets was downloaded from https://www.spatialresearch.org/resources-published-datasets/, containing H&E images, spot-wise region annotation and count matrix of each sub-dataset, as well as other spatial transcriptomics resource published datasets.
DOI: 10.6084/m9.figshare.19768351
title: staahl2016visualization_ST_Breast_Cancer

description: staahl2016visualization_ST_Breast_Cancer is a spatial transcriptomics datasets of human breast cancer, published with the paper presenting spatial transcriptomics (ST) technology. It was generated by ST, that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. The staahl2016visualization_ST_Breast_Cancer dataset has 68 sub-datasets with almost 19000 gene expression features, which contains 23 breast cancer patients with 5 breast cancer subtypes with 3 replicates. There are 14 HER2_luminal, 15 HER2_non_luminal, 12 Luminal_A, 15 Luminal_B and 12 TNBC sub-datasets in the dataset. The original paper provided the spot-wise gene expression matrix in https://data.mendeley.com/datasets/29ntw7sh4r/5, paired with H&E images and spot-wise tumor annotation of each sub-dataset.
DOI: 10.5281/zenodo.6591690
title: Historical IVT 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.6591689
title: Historical IVT 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: 10.6084/m9.figshare.19768360
title: berglund2018spatial_ST_melanoma

description: berglund2018spatial_ST_melanoma is a spatial transcriptomics datasets of stage III cutaneous malignant melanoma67. The published paper adapted the technology of spatial transcriptomics (ST) to melanoma lymph node biopsies and successfully sequenced the transcriptomes of over 2,200 tissue domains. There are 8 sub-datasets in berglund2018spatial_ST_melanoma with ~16000 gene expression features, which contains 4 stage III cutaneous malignant melanoma patients in duplicate. The number of spots inf each sub-dataset are 279 (mel1_rep1), 293 (mel1_rep2), 383 (mel2_rep1), 380 (mel2_rep2), 256 (mel3_rep1), 294 (mel3_rep2), 212 (mel4_rep1) and 248 (mel4_rep2). The count matrix of berglund2018spatial_ST_melanoma dataset was downloaded from https://www.spatialresearch.org/resources-published-datasets/.
DOI: 10.6084/m9.figshare.19768363
title: moncada2020integrating_ST_PancreaticCancer

description: moncada2020integrating_ST_PancreaticCancer is a spatial transcriptomics datasets of primary pancreatic tumors used by SpaGCN45 method, published with a study presenting a method for multimodal intersection analysis of Single-cell RNA data and spatial transcriptomics data68. The moncada2020integrating_ST_PancreaticCancer dataset contains 12 sub-datasets with ~19000 gene expression features , and the number of spots for the 12 sub-datasets are 288 (GSM4100728_PDAC-G), 316 (GSM4100721_PDAC-A), 359 (GSM4100727_PDAC-F), 359 (GSM4100726_PDAC-E), 460 (GSM4100725_PDAC-D), 428 (GSM3036911_PDAC-A-filtered), 224 (GSM3405534_PDAC-B-filtered), 288 (GSM4100722_PDAC-A), 321 (GSM4100723_PDAC-B), 996 (GSM3405534_PDAC-B) and 242 (GSM4100724_PDAC-B). The original paper provided the spot-wise gene expression matrix in https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111672, paired with H&E images and single cell sequencing data of each sub-dataset.
DOI: 10.6084/m9.figshare.19768363.v1
title: moncada2020integrating_ST_PancreaticCancer

description: moncada2020integrating_ST_PancreaticCancer is a spatial transcriptomics datasets of primary pancreatic tumors used by SpaGCN45 method, published with a study presenting a method for multimodal intersection analysis of Single-cell RNA data and spatial transcriptomics data68. The moncada2020integrating_ST_PancreaticCancer dataset contains 12 sub-datasets with ~19000 gene expression features , and the number of spots for the 12 sub-datasets are 288 (GSM4100728_PDAC-G), 316 (GSM4100721_PDAC-A), 359 (GSM4100727_PDAC-F), 359 (GSM4100726_PDAC-E), 460 (GSM4100725_PDAC-D), 428 (GSM3036911_PDAC-A-filtered), 224 (GSM3405534_PDAC-B-filtered), 288 (GSM4100722_PDAC-A), 321 (GSM4100723_PDAC-B), 996 (GSM3405534_PDAC-B) and 242 (GSM4100724_PDAC-B). The original paper provided the spot-wise gene expression matrix in https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111672, paired with H&E images and single cell sequencing data of each sub-dataset.