cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12849569
title: Bayesian inference for high-dimensional nonstationary Gaussian processes

description: In spite of the diverse literature on nonstationary spatial modelling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian inference for moderately sized nonstationary spatial data sets on a personal laptop. For statisticians and data scientists who wish to conduct posterior inference and prediction with appropriate uncertainty quantification, the lack of such approaches and software is a limitation. Here, we develop methodology for implementing formal Bayesian inference for a general class of nonstationary GPs. Our novel approach uses pre-existing frameworks for characterizing nonstationarity in a new way while utilizing via modern GP likelihood approximations. Posterior sampling is implemented using flexible MCMC methods, with nonstationary posterior prediction conducted as a post-processing step. We demonstrate our novel methods on two data sets, ranging from several hundred to several thousand locations. All of our methods are implemented in the freely available <b>BayesNSGP</b> software package for R.
DOI: 10.6084/m9.figshare.12849569.v1
title: Bayesian inference for high-dimensional nonstationary Gaussian processes

description: In spite of the diverse literature on nonstationary spatial modelling and approximate Gaussian process (GP) methods, there are no general approaches for conducting fully Bayesian inference for moderately sized nonstationary spatial data sets on a personal laptop. For statisticians and data scientists who wish to conduct posterior inference and prediction with appropriate uncertainty quantification, the lack of such approaches and software is a limitation. Here, we develop methodology for implementing formal Bayesian inference for a general class of nonstationary GPs. Our novel approach uses pre-existing frameworks for characterizing nonstationarity in a new way while utilizing via modern GP likelihood approximations. Posterior sampling is implemented using flexible MCMC methods, with nonstationary posterior prediction conducted as a post-processing step. We demonstrate our novel methods on two data sets, ranging from several hundred to several thousand locations. All of our methods are implemented in the freely available <b>BayesNSGP</b> software package for R.
DOI: 10.6084/m9.figshare.13217479.v1
title: Permutation testing in high-dimensional linear models: an empirical investigation

description: Permutation testing in linear models, where the number of nuisance coefficients is smaller than the sample size, is a well-studied topic. The common approach of such tests is to permute residuals after regressing on the nuisance covariates. Permutation-based tests are valuable in particular because they can be highly robust to violations of the standard linear model, such as non-normality and heteroscedasticity. Moreover, in some cases they can be combined with existing, powerful permutation-based multiple testing methods. Here, we propose permutation tests for models where the number of nuisance coefficients exceeds the sample size. The performance of the novel tests is investigated with simulations. In a wide range of simulation scenarios our proposed permutation methods provided appropriate type I error rate control, unlike some competing tests, while having good power.
DOI: 10.6084/m9.figshare.14531385.v2
title: Rare and endangered plants in serpentine forest in Poland - data

description: Phytosociological data to paper proposed to publication in Diversity journal coce
DOI: 10.21227/ye12-qd31
title: Optimization of Radiation Search

description: Data from simulations in which different radiation search strategies are compared.
DOI: 10.5281/zenodo.4738936
title: Datasets corresponding to publication: Label-free imaging flow cytometry: analysis and sorting of enzymatically dissociated tissues

description: Datasets corresponding to publication: Label-free imaging flow cytometry: analysis and sorting of enzymatically dissociated tissues. This repository contains data to reproduce each figure contained in the manuscript<br> "Label-free imaging flow cytometry: analysis and sorting of enzymatically dissociated tissues".<br> The folders:<br> - 20151028_TiagoF_MaikH_Ader_Retina_Nrl-GFP-P10<br> - 20190114_Maik_Ahsan_retina_sorting<br> - 20190204_MaikH_Retina_Nrl_P04<br> - 20190624_Maik_Ahsan_Neutrophils_AI_Sorting<br> - 20191021_MaikH_Nrl_P05<br> - 20210304_MaikH_Retina_mGluR6-GFP_P04<br> - 20210305_MaikH_Nrl_P04_Sorting<br> - DataSet_Cones_Gated<br> - DataSet_Cones_Raw<br> - DataSet_HRO_Labelled<br> - DataSet_Nrl-eGFP_Gated<br> - DataSet_Nrl-eGFP_Raw<br> contain data (either original, or readily gated/labelled)<br> <br> The folders:<br> - Figure 2...Figure S8<br> contain Python scripts and further resources to reproduce plots and analyses.<br> There are folders for each subpanel in each figure and a "HowTo.txt" in each directory describes<br> how to proceed. <br> To provide a Python environment that contains all the required Python packages of the correct version,<br> we designed PyBox 0.1.0. PyBox is essentially a readily installed Python environment in a zip file.<br> Alternatively, the Python environment can manually be installed as described here:<br> https://github.com/maikherbig/PyBox#which-packages-are-contained-in-pybox
DOI: 10.5281/zenodo.4738935
title: Datasets corresponding to publication: Label-free imaging flow cytometry: analysis and sorting of enzymatically dissociated tissues

description: Datasets corresponding to publication: Label-free imaging flow cytometry: analysis and sorting of enzymatically dissociated tissues. This repository contains data to reproduce each figure contained in the manuscript<br> "Label-free imaging flow cytometry: analysis and sorting of enzymatically dissociated tissues".<br> The folders:<br> - 20151028_TiagoF_MaikH_Ader_Retina_Nrl-GFP-P10<br> - 20190114_Maik_Ahsan_retina_sorting<br> - 20190204_MaikH_Retina_Nrl_P04<br> - 20190624_Maik_Ahsan_Neutrophils_AI_Sorting<br> - 20191021_MaikH_Nrl_P05<br> - 20210304_MaikH_Retina_mGluR6-GFP_P04<br> - 20210305_MaikH_Nrl_P04_Sorting<br> - DataSet_Cones_Gated<br> - DataSet_Cones_Raw<br> - DataSet_HRO_Labelled<br> - DataSet_Nrl-eGFP_Gated<br> - DataSet_Nrl-eGFP_Raw<br> contain data (either original, or readily gated/labelled)<br> <br> The folders:<br> - Figure 2...Figure S8<br> contain Python scripts and further resources to reproduce plots and analyses.<br> There are folders for each subpanel in each figure and a "HowTo.txt" in each directory describes<br> how to proceed. <br> To provide a Python environment that contains all the required Python packages of the correct version,<br> we designed PyBox 0.1.0. PyBox is essentially a readily installed Python environment in a zip file.<br> Alternatively, the Python environment can manually be installed as described here:<br> https://github.com/maikherbig/PyBox#which-packages-are-contained-in-pybox
DOI: 10.17037/data.00002236
title: Therapy codelist - CPRD Aurum - Pneumococcal vaccine

description: Aurum therapy codes for pneumococcal polysaccaride vaccine.
DOI: 10.6084/m9.figshare.8874380
title: PET PODs: A Tool to Assist with Good Welfare Provisions during Emergent Conditions

description: The PETS Act amended the Robert T. Stafford Disaster Relief and Emergency Assistance Act to ensure state and local emergency preparedness operational plans included provisions for individuals with household pets and service animals following a major disaster or emergency. While the national newsworthy events are usually large scope disasters, there are also local events which do not meet the minimal conditions to qualify for state or federal level welfare support. Local governments should be prepared to house pets at evacuation shelters, regardless of the breadth of the event, and especially for local events that do not reach the level of state or federal disasters. Wake County, North Carolina developed a concept of prepared sustainment “PET PODs” equipped with inventory and operational instructions required to create and operate an animal evacuation shelter, which could be, or not be, closely associated with the human evacuation shelter. The Wake County Animal Center “PET PODs” provided supplies for animals which arrived with their owners at evacuation shelters during Hurricane Florence, which in addition to volunteer personnel and donated feed provisions, established the foundation for good animal welfare for dozens of animals.
DOI: 10.6084/m9.figshare.8874380.v1
title: PET PODs: A Tool to Assist with Good Welfare Provisions during Emergent Conditions

description: The PETS Act amended the Robert T. Stafford Disaster Relief and Emergency Assistance Act to ensure state and local emergency preparedness operational plans included provisions for individuals with household pets and service animals following a major disaster or emergency. While the national newsworthy events are usually large scope disasters, there are also local events which do not meet the minimal conditions to qualify for state or federal level welfare support. Local governments should be prepared to house pets at evacuation shelters, regardless of the breadth of the event, and especially for local events that do not reach the level of state or federal disasters. Wake County, North Carolina developed a concept of prepared sustainment “PET PODs” equipped with inventory and operational instructions required to create and operate an animal evacuation shelter, which could be, or not be, closely associated with the human evacuation shelter. The Wake County Animal Center “PET PODs” provided supplies for animals which arrived with their owners at evacuation shelters during Hurricane Florence, which in addition to volunteer personnel and donated feed provisions, established the foundation for good animal welfare for dozens of animals.
DOI: 10.25879/rw72-rq38
title: DS with iHub domain in fabrica
title: A DataShare
DOI: 10.17037/data.00002237
title: Therapy codelist - CPRD GOLD - Pneumococcal vaccine

description: BNF codes for pneumococcal polysaccaride vaccine. Developed by the NIHR Health Protection Research Unit in Immunisation.
DOI: 10.17037/data.00002238
title: Clinical codelist - CPRD Aurum - spleen dysfunction

description: SNOMED codes for a clinical diagnosis of, or clinical review specific to, asplenia or dysfunction of the spleen (including sickle cell disease but not sickle cell trait).
DOI: 10.6084/m9.figshare.13366430
title: Metadata record for the manuscript: Targeting the Epichaperome As an Effective Precision Medicine Approach in a Novel PML-SYK Fusion Acute Myeloid Leukemia

description: <b>Summary</b> This metadata record provides details of the data supporting the claims of the related manuscript: “Targeting the Epichaperome As an Effective Precision Medicine Approach in a Novel PML-SYK Fusion Acute Myeloid Leukemia ”. The related study aimed to prospectively identify patients likely to respond to epichaperome inhibitors to measure target engagement and dependency during treatment by developing a flow cytometry-based assay for evaluation and monitoring of epichaperome abundance at the single-cell level. Subject of data: leukaemia cells from a patient with an unclassified myeloproliferative neoplasm harbouring a novel PML-SYK fusion that transformed to AML Sample size: case report containing data from a single patient <b>Data access</b> All data are shared openly. The RNA sequencing and whole exome sequencing data are shared in the <i>European Genotype-phenotype Archive </i>under accession https://identifiers.org/ega.study:EGAS00001004992. All other data are shared as part of this figshare metadata record. A list of which datafiles underlie which figures in the manuscript is also included in Excel .xlsx format in the file ‘sugita_datafiles_underlying_figures_lookup.xlsx’.<br> <b>Corresponding authors</b><br> Monica L. Guzman, Ph.D., Weill Cornell Medicine, 1300 York Ave, C-610C, Box 113, New York, NY 10065. Email: mlg2007@med.cornell.edu Juan Miguel Mosquera, M.D, M.Sc., Weill Cornell Medicine, 1300 York Ave, Box 69, New York, NY 10065. Email: jmm9018@med.cornell.edu <br> <b>Name of Institutional Review Board or ethics committee that approved the study </b> All experimental procedures were carried out in accordance with approved guidelines and were approved by the Institutional Review Board (IRB) at Weill Cornell Medicine. Patients in the study signed informed written consent under an IRB-approved protocol (IRB #1305013903).<br>
DOI: 10.17037/data.00002239
title: Clinical codelist - CPRD GOLD - spleen dysfunction

description: Read codes for a clinical diagnosis of, or clinical review specific to, asplenia or dysfunction of the spleen (including sickle cell disease but not sickle cell trait). Based on code list developed by the NIHR Health Protection Research Unit in Immunisation.
DOI: 10.17037/data.00002191
title: Clinical codelist - CPRD Aurum - acute respiratory infection and influenza/influenza-like-illness codes

description: SNOMED codes for clinically diagnosed acute respiratory infections, including influenza/ILI and pneumonia.
DOI: 10.17037/data.00002193
title: Clinical codelist - HES - acute respiratory infection and influenza/influenza-like-illness codes

description: ICD-10 codes for clinically diagnosed acute respiratory infections, including influenza/ILI and pneumonia.
DOI: 10.17037/data.00002192
title: Clinical codelist - CPRD GOLD - acute respiratory infection and influenza/influenza-like-illness codes

description: Read codes for clinically diagnosed acute respiratory infections, including influenza/ILI and pneumonia.
DOI: 10.17037/data.00002208
title: Clinical codelist - CPRD Aurum - asthma

description: SNOMED codes for a clinical diagnosis of, or clinical review specific to, asthma or hospitalisation for asthma.
DOI: 10.6084/m9.figshare.14350871
title: Metadata record for the manuscript: The CINSARC signature predicts the clinical outcome in patients with Luminal B breast cancer

description: <b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related manuscript: “The CINSARC signature predicts the clinical outcome in patients with Luminal B breast cancer”. The related study tested the prognostic value for disease-free survival (DFS) of CINSARC, a multigene expression signature originally developed in sarcomas and shown to have prognostic impact in various cancers, in a series of 6035 early-stage invasive primary breast cancers. Type of data: prognostic value for DFS of CINSARC Subject of data: <i>Homo sapiens</i> Sample size: 6035 Population characteristics: All cases were invasive breast carcinomas profiled using DNA microarrays or RNA-sequencing with expression and clinicopathological data available. All samples are pre-treatment samples (operative specimen or diagnostic biopsy before neo-adjuvant chemotherapy). The detailed characteristics of patients and tumours analysed in the present study are available in Supplementary Table 10. Recruitment: publicly available transcriptomic data of invasive primary breast cancer enrolled in 36 retrospective studies published over a 10-year period between 2002 and 2012. <b>Data access</b> All data sets of primary breast cancer were downloaded from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), ArrayExpress (https://www.ebi.ac.uk/arrayexpress/), Genomic Data Commons (GDC, https://portal.gdc.cancer.gov/) and cBioPortal (https://www.cbioportal.org/) databases. All accession IDs are provided in Supplementary Table 10 (Table S10 revised.xlsx), which is included with this data record. The data underlying the figures and tables of the related article are contained in the files ‘Goncalves_supporting_data.xlsx’ and ‘Table S8.xlsx’, which are included with this data record. A detailed list of the data underlying each figure and table of the related article is available in the file ‘Goncalves_2021_underlying_data_list.xlsx’, which is included with this data record. <b>Corresponding author(s) for this study</b> Pr François BERTUCCI, MD PhD, Département d’Oncologie Médicale, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13009 Marseille, France e-mail:bertuccif@ipc.unicancer.fr ; Phone : +33 4 91 22 35 37 ; Fax : +33 4 91 22 36 70 <br> <b>Study approval </b> The details of Institutional Review Board and Ethical Committee approval and patients’ consent for the 36 studies analysed in the related study are present in their corresponding publications, which are listed in Supplementary Table 10 of the related article.
DOI: 10.6084/m9.figshare.14350871.v1
title: Metadata record for the manuscript: The CINSARC signature predicts the clinical outcome in patients with Luminal B breast cancer

description: <b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related manuscript: “The CINSARC signature predicts the clinical outcome in patients with Luminal B breast cancer”. The related study tested the prognostic value for disease-free survival (DFS) of CINSARC, a multigene expression signature originally developed in sarcomas and shown to have prognostic impact in various cancers, in a series of 6035 early-stage invasive primary breast cancers. Type of data: prognostic value for DFS of CINSARC Subject of data: <i>Homo sapiens</i> Sample size: 6035 Population characteristics: All cases were invasive breast carcinomas profiled using DNA microarrays or RNA-sequencing with expression and clinicopathological data available. All samples are pre-treatment samples (operative specimen or diagnostic biopsy before neo-adjuvant chemotherapy). The detailed characteristics of patients and tumours analysed in the present study are available in Supplementary Table 10. Recruitment: publicly available transcriptomic data of invasive primary breast cancer enrolled in 36 retrospective studies published over a 10-year period between 2002 and 2012. <b>Data access</b> All data sets of primary breast cancer were downloaded from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), ArrayExpress (https://www.ebi.ac.uk/arrayexpress/), Genomic Data Commons (GDC, https://portal.gdc.cancer.gov/) and cBioPortal (https://www.cbioportal.org/) databases. All accession IDs are provided in Supplementary Table 10 (Table S10 revised.xlsx), which is included with this data record. The data underlying the figures and tables of the related article are contained in the files ‘Goncalves_supporting_data.xlsx’ and ‘Table S8.xlsx’, which are included with this data record. A detailed list of the data underlying each figure and table of the related article is available in the file ‘Goncalves_2021_underlying_data_list.xlsx’, which is included with this data record. <b>Corresponding author(s) for this study</b> Pr François BERTUCCI, MD PhD, Département d’Oncologie Médicale, Institut Paoli-Calmettes, 232 Bd. Ste-Marguerite, 13009 Marseille, France e-mail:bertuccif@ipc.unicancer.fr ; Phone : +33 4 91 22 35 37 ; Fax : +33 4 91 22 36 70 <br> <b>Study approval </b> The details of Institutional Review Board and Ethical Committee approval and patients’ consent for the 36 studies analysed in the related study are present in their corresponding publications, which are listed in Supplementary Table 10 of the related article.
DOI: 10.48329/fbjh-es87
title: CPRD COVID-19 symptoms and risk factors synthetic dataset April 2021

description: This synthetic dataset is based on anonymised real primary care patient data extracted from the CPRD Aurum database. The dataset focuses on patients presenting to primary care with symptoms indicative of COVID-19 (confirmed/suspected COVID-19) and control patients with negative COVID-19 test results. The dataset includes data on sociodemographic and clinical risk factors. The development of this dataset was funded by NHSX using the synthetic data generation and evaluation framework developed under a grant from the Regulators’ Pioneer Fund launched by The Department for Business, Energy and Industrial Strategy (BEIS) and managed by Innovate UK. The dataset includes 779,546 patients. Further information is available at https://www.cprd.com/content/synthetic-data.
DOI: 10.17037/data.00002209
title: Clinical codelist - CPRD GOLD - Asthma

description: Read codes for a clinical diagnosis of, or clinical review specific to, asthma or hospitalisation for asthma. Based on previously published code list https://doi.org/10.17037/DATA.210.
DOI: 10.17037/data.00002210
title: Therapy codelist - CPRD Aurum - Asthma

description: Aurum therapy codes for inhaled steroid treatment.
DOI: 10.17037/data.00002211
title: Therapy codelist - CPRD GOLD - Asthma

description: BNF codes for inhaled steroid treatment.
DOI: 10.17037/data.00002213
title: Clinical codelist - CPRD GOLD - chronic liver disease

description: Read codes for a clinical diagnosis of, or clinical review specific to, chronic liver disease including cirrhosis, oesophageal varices, biliary atresia and chronic hepatitis. Based on code list developed by the NIHR Health Protection Research Unit in Immunisation https://doi.org/10.17037/DATA.00001659.
DOI: 10.17037/data.00002212
title: Clinical codelist - CPRD Aurum - chronic liver disease

description: SNOMED codes for a clinical diagnosis of, or clinical review specific to, chronic liver disease including cirrhosis, oesophageal varices, biliary atresia and chronic hepatitis.
DOI: 10.17037/data.00002214
title: Clinical codelist - CPRD Aurum - chronic respiratory disease

description: SNOMED codes for a clinical diagnosis of, or clinical review specific to, chronic respiratory disease without asthma, including chronic obstructive pulmonary disease (COPD), emphysema, bronchitis, cystic fibrosis, or fibrosing interstitial lung diseases.
DOI: 10.17037/data.00002215
title: Clinical codelist - CPRD GOLD - chronic respiratory disease

description: Read codes for a clinical diagnosis of, or clinical review specific to, chronic respiratory disease without asthma, including chronic obstructive pulmonary disease (COPD), emphysema, bronchitis, cystic fibrosis, or fibrosing interstitial lung diseases. Based on code list developed by the NIHR Health Protection Research Unit in Immunisation https://doi.org/10.17037/DATA.00001659.
DOI: 10.48329/adkk-m317
title: CPRD GOLD April 2021

description: The CPRD GOLD database contains longitudinal routinely-collected electronic health records (EHR data) from UK primary care practices using Vision® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care. Access to anonymised data from CPRD is subject to a full licence agreement containing detailed terms and conditions of use. Anonymised patient datasets can be extracted for researchers against specific study specifications, following protocol approval. Further information is available at https://www.cprd.com/data-access.
DOI: 10.6084/m9.figshare.3413851
title: MAMEM EEG SSVEP Dataset III (14 channels, 11 subjects, 5 frequencies presented simultaneously)

description: EEG signals with 14 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the Emotiv EPOC, using 14 wireless channels has been used for capturing the signals.<br>Check <b>https://github.com/MAMEM/ssvep-eeg-processing-toolbox </b>for the processing toolbox.
DOI: 10.6084/m9.figshare.3413851.v2
title: MAMEM EEG SSVEP Dataset III (14 channels, 11 subjects, 5 frequencies presented simultaneously)

description: EEG signals with 14 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the Emotiv EPOC, using 14 wireless channels has been used for capturing the signals.<br>Check <b>https://github.com/MAMEM/ssvep-eeg-processing-toolbox </b>for the processing toolbox.