cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3648231
title: LEN-DB - Local earthquakes detection: a benchmark dataset of 3-component seismograms built on a global scale

description: In this study ( The paper ) we present a large dataset of 1,249,411 3-component seismograms, recorded along the vertical, north, and east components of 1487 broad-band or very broad-band receivers distributed worldwide, including 631,105 3-component seismograms generated by 304,878 local earthquakes and labeled as earthquakes (EQ), and 618,306 ones labeled as noise (AN). The choice of collecting only local earthquake-data is motivated by the fact that small-magnitude events, which generate relatively small amplitudes and are easily attenuated, are often problematic to detect but provide valuable information about earthquake processes. The labeled data are split into HDF5-Groups: <em>EQ</em> and <em>AN</em>. Each of these groups contains as many HDF5-Datasets as the number of 3-component seismograms; these are labeled in accordance to the format <em>net_sta_starttime</em>, where <em>net</em>, <em>sta</em>, and <em>starttime</em> represent the seismic network, station, and start time of the seismograms. Each HDF5-Dataset (i.e. each triplet of seismograms) has an attribute, which allows accessing the respective metadata. In addition, the HDF5-Group <em>Stations</em> allows accessing stations’ metadata through as many HDF5-Datasets (which are labeled in accordance to the format <em>net_sta)</em> as the number of receivers employed for collecting the waveforms. This global dataset is intended to be used for carrying out a multitude of seismological and signal processing tasks on single-station recordings, and its size particularly suits machine learning (ML) applications.. Application of ML to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings with high accuracy (93.2%), even if applied in regions not investigated by the training set. We make the dataset publicly available as a unique file in HDF5 data format, intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in seismology and signal processing.
DOI: 10.6084/m9.figshare.13153877
title: split_for_training.zip

description: Data for Timi
DOI: 10.6084/m9.figshare.13153877.v1
title: split_for_training.zip

description: Data for Timi
DOI: 10.6084/m9.figshare.13176761
title: SCA analysis on setA using kinase targets as latent space

description: We developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). SCA analysis on setA was also done using a latent space based on kinase targets, but we cannot find any robust association with reference cluster.
DOI: 10.6084/m9.figshare.13176761.v1
title: SCA analysis on setA using kinase targets as latent space

description: We developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). SCA analysis on setA was also done using a latent space based on kinase targets, but we cannot find any robust association with reference cluster.
DOI: 10.6084/m9.figshare.13176770
title: Integrative bioinformatics analysis of DNA copy number variation and differentially expressed genes to identify the essential genes involved in kidney clear cell carcinoma.

description: We made an integrated bioinformatics analysis of the TCGA database and found the differential mRNA expression and Copy number variation (CNV) of killer cell immunoglobulin like receptor 2DL4 (KIR2DL4) gene in RCC.<br><br>
DOI: 10.6084/m9.figshare.13176770.v1
title: Integrative bioinformatics analysis of DNA copy number variation and differentially expressed genes to identify the essential genes involved in kidney clear cell carcinoma.

description: We made an integrated bioinformatics analysis of the TCGA database and found the differential mRNA expression and Copy number variation (CNV) of killer cell immunoglobulin like receptor 2DL4 (KIR2DL4) gene in RCC.<br><br>
DOI: 10.17037/data.00001938
title: Internal monitoring within MUII-plus for research capacity development

description: A dataset on 25 projects that provided data on an internal monitoring evaluation of the Makerere University-Uganda Virus Research Institute Centre of Excellence for Infection and Immunity Research and Training (MUII-plus) research programme. The dataset contains 75 variables on projects adherence to the approved protocol, Standard Operating Procedures (SOPs), Good Clinical Practices (GCP) and Good Clinical Laboratory Practices (GCLP), other applicable regulatory requirements, study related training and presence of adequate facilities required for study related procedures.
DOI: 10.6084/m9.figshare.13007954
title: Metadata record for the manuscript: Unmasking the immune microecology of ductal carcinoma in situ with deep learning

description: <b>Summary</b> This metadata record provides details of the data supporting the claims of the related manuscript: “Unmasking the immune microecology of ductal carcinoma in situ with deep learning”. The data consist of immunohistochemistry (IHC) haematoxylin and eosin (H&E) staining images of grade 2-3 pure ductal carcinoma in situ (DCIS) and DCIS adjacent to invasive cancer (adjacent DCIS) samples. The related study aimed to characterise tissue spatial architecture and the microenvironment of DCIS via design and validation of a new deep learning pipeline. <b> </b> <b>Data access</b> All training data, including the fully anonymised raw H&E image tiles and pathological annotations as binary marks, as well as Python code, are available in the corresponding author’s GitHub: https://github.com/pathdata/HE_Tissue_Segmentation. Requests for data access for the Duke samples can be submitted to E. Shelley Hwang (shelley.hwang@duke.edu)<sup> </sup>and Yinyin Yuan (yinyin.yuan@icr.ac.uk). Data underlying Figures 4 and 6 are in the files “Ext_validData_DCIS_DAVE_Fig4_data.csv” and “Ext_validData_DCIS_DAVE_Fig6_data.csv”, included with this metadata record. The images used as representative examples in Figure 8 are listed in the file “Figure 8 image details.xlsx”, included with this metadata record. <b>Name of Institutional Review Board or ethics committee that approved the study</b> The study was approved by the institutional review board of Duke with a waiver of the requirement to obtain informed consent.
DOI: 10.6084/m9.figshare.13007954.v1
title: Metadata record for the manuscript: Unmasking the immune microecology of ductal carcinoma in situ with deep learning

description: <b>Summary</b> This metadata record provides details of the data supporting the claims of the related manuscript: “Unmasking the immune microecology of ductal carcinoma in situ with deep learning”. The data consist of immunohistochemistry (IHC) haematoxylin and eosin (H&E) staining images of grade 2-3 pure ductal carcinoma in situ (DCIS) and DCIS adjacent to invasive cancer (adjacent DCIS) samples. The related study aimed to characterise tissue spatial architecture and the microenvironment of DCIS via design and validation of a new deep learning pipeline. <b> </b> <b>Data access</b> All training data, including the fully anonymised raw H&E image tiles and pathological annotations as binary marks, as well as Python code, are available in the corresponding author’s GitHub: https://github.com/pathdata/HE_Tissue_Segmentation. Requests for data access for the Duke samples can be submitted to E. Shelley Hwang (shelley.hwang@duke.edu)<sup> </sup>and Yinyin Yuan (yinyin.yuan@icr.ac.uk). Data underlying Figures 4 and 6 are in the files “Ext_validData_DCIS_DAVE_Fig4_data.csv” and “Ext_validData_DCIS_DAVE_Fig6_data.csv”, included with this metadata record. The images used as representative examples in Figure 8 are listed in the file “Figure 8 image details.xlsx”, included with this metadata record. <b>Name of Institutional Review Board or ethics committee that approved the study</b> The study was approved by the institutional review board of Duke with a waiver of the requirement to obtain informed consent.
DOI: 10.6084/m9.figshare.13176785
title: DEG between KIR2DL4 overexpressed Caki-1 and Control cells

description: To further understand the mechanism of KIR2DL4 promotes RCC progression, we performed RNA-sequencing to identify the differential expression genes between parallel-controlled and KIR2DL4-overexpressed Caki-1 cells.
DOI: 10.6084/m9.figshare.13176785.v1
title: DEG between KIR2DL4 overexpressed Caki-1 and Control cells

description: To further understand the mechanism of KIR2DL4 promotes RCC progression, we performed RNA-sequencing to identify the differential expression genes between parallel-controlled and KIR2DL4-overexpressed Caki-1 cells.
DOI: 10.5523/bris.cpunp7ml1s5t2owhutk6hj7gd
title: In-situ CT gaps

description: Data for JCOMA-20-816
DOI: 10.6084/m9.figshare.13176806
title: Supplementary Material for: A Metabolically Unhealthy Phenotype Is Associated with <b><i>ADIPOQ</i></b> Genetic Variants and Lower Serum Adiponectin Levels

description: <b><i>Background:</i></b> Even though excessive adipose tissue is related to chronic metabolic disturbances, not all subjects with excess weight (EW) display metabolic alterations, and not all normal-weight (NW) subjects have a metabolically healthy (MH) phenotype, probably due to gene-environment interactions. The aim of this study was to investigate the interaction effects of <i>ADIPOQ</i> and <i>PPARG</i> genetic variants in NW and EW individuals with different metabolic phenotypes. <b><i>Methods:</i></b> Data on 345 adults from western Mexico were analyzed. The individuals were classified into NW and EW groups according to body mass index, and were categorized as MH or metabolically unhealthy (MUH), considering homeostatic model assessment insulin resistance (HOMA-IR) and National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) cut-off points for glucose, triglycerides, high-density lipoprotein cholesterol, and blood pressure. Subjects with ≤1 altered parameter were classified as MH. The single nucleotide polymorphisms (SNPs) –11377C&gt;G, –11391G&gt;A, +45T&gt;G, and +276G&gt;T for <i>ADIPOQ</i> and Pro12Ala for <i>PPARG</i> were analyzed by allelic discrimination. High-molecular-weight adiponectin isoform levels were measured by ELISA. <b><i>Results:</i></b> Lower serum adiponectin levels were associated with the MUH phenotype in EW subjects. NW subjects with the GG or TG genotype for the +45T&gt;G SNP had reduced odds of the MUH phenotype. Individuals who carried two copies of the GG haplotype at the –11391G&gt;A and –11377C&gt;G SNPs for <i>ADIPOQ</i> had lower serum adiponectin levels than those with zero copies. <b><i>Conclusion:</i></b> In this population, lower serum adiponectin levels were found in the EW-MUH phenotype, and no differences were observed between the NW-MH and the EW-MH phenotype. In addition, the +45T&gt;G SNP was associated with reduced odds of the MUH phenotype.
DOI: 10.5281/zenodo.3755040
title: HidroData

description: This is a discontinued project. There is a Python package with an improvement in these functions. You can see it clicking on here
DOI: 10.5281/zenodo.3755065
title: HidroData

description: This is a discontinued project. There is a Python package with an improvement in these functions. You can see it clicking on here
DOI: 10.5281/zenodo.3749303
title: HidroData

description: This is a discontinued project. There is a Python package with an improvement in these functions. You can see it clicking on here
DOI: 10.5281/zenodo.3749304
title: HidroData

description: This is a discontinued project. There is a Python package with an improvement in these functions. You can see it clicking on here
DOI: 10.17632/54kwdrjdcw.1
title: Data for: Hybrid Multi-Stage Decomposition Applied to Wind Speed Forecasting by Means of a Parametric Model

description: Daily wind speed sampled by 10min, at 90m on the Northewest region of Brazil
DOI: 10.5519/qd.34v4a9ju
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1612 records)
DOI: 10.6084/m9.figshare.13175120
title: R0219_20180227a_videos

description: Rat 0219, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13175120.v1
title: R0219_20180227a_videos

description: Rat 0219, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.5281/zenodo.4191037
title: data postprocessing for the EMEP local city source contribution

description: - First edition by A. Valdebenito (Norwegian Meteorological Institute) similar codes were used in the country source contribution calculations: https://doi.org/10.5194/gmd-13-1787-2020 -updated for the analysis and the publication by M. Pommier
DOI: 10.5517/ccdc.csd.cc24mztl
title: CCDC 1985420: Experimental Crystal Structure Determination

description: Related Article: Qin Xiang,  Jing Guo,  Jun Xu,  Shuaishuai Ding,  Zhaoyang Li,  Guangwu Li,  Hoa Phan,  Yanwei Gu,  Yanfeng Dang,  Zhanqiang Xu,  Zongcheng Gong,  Wenping Hu,  Zebing Zeng,  Jishan Wu,  Zhe Sun|2020|J.Am.Chem.Soc.|142|11022|doi:10.1021/jacs.0c02287
DOI: 10.5517/ccdc.csd.cc24n083
title: CCDC 1985434: Experimental Crystal Structure Determination

description: Related Article: Qin Xiang,  Jing Guo,  Jun Xu,  Shuaishuai Ding,  Zhaoyang Li,  Guangwu Li,  Hoa Phan,  Yanwei Gu,  Yanfeng Dang,  Zhanqiang Xu,  Zongcheng Gong,  Wenping Hu,  Zebing Zeng,  Jishan Wu,  Zhe Sun|2020|J.Am.Chem.Soc.|142|11022|doi:10.1021/jacs.0c02287
DOI: 10.5517/ccdc.csd.cc251mvq
title: CCDC 1997573: Experimental Crystal Structure Determination

description: Related Article: Jan-Lucas Wree, Engin Ciftyurek, David Zanders, Nils Boysen, Aleksander Kostka, Detlef Rogalla, Maren Kasischke, Andreas Ostendorf, Klaus Schierbaum, Anjana Devi|2020|Dalton Trans.|49|13462|doi:10.1039/D0DT02471F
DOI: 10.5281/zenodo.4190987
title: Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity

description: The deposited files contain balanced data sets of multi-target (MT) and single-target (ST) compounds (CPDs) used for machine learning studies (https://dx.doi.org/10.1021/acs.molpharmaceut.0c00901). The first file (st_mt_data.tsv) contains 15,142 MT- and 15,081 ST-CPDs and the second (st_dt_data.tsv) 1828 DT- and 1776 ST-CPDs. For each CPD, a nonstereo_aromatic_SMILES representation, the original ChEMBL_cid, UniProt (target) IDs, and CPD category (CPD_CAT) (i.e. DT/MT/ST) is provided. DT stands for 'diverse-target' and denotes a subset of MT-CPDs (as detailed in the publication). In addition, a CPD is tagged “Y” if it continued to be present in the data set after removal of 50% randomly selected CPDs or 50% CPD nearest neighbors (NN), respectively.
description: {"references": ["https://dx.doi.org/10.1021/acs.molpharmaceut.0c00901"]}
DOI: 10.5517/ccdc.csd.cc257nhl
title: CCDC 2003359: Experimental Crystal Structure Determination

description: Related Article: Sergey Shuvaev, Elizaveta A. Suturina, Nicholas J. Rotile, Andrei Astashkin, Christopher J. Ziegler, Alana W. Ross, Tia L. Walker, Peter Caravan, Ian S. Taschner|2020|Dalton Trans.|49|14088|doi:10.1039/D0DT02787A
DOI: 10.5517/ccdc.csd.cc257nkn
title: CCDC 2003361: Experimental Crystal Structure Determination

description: Related Article: Sergey Shuvaev, Elizaveta A. Suturina, Nicholas J. Rotile, Andrei Astashkin, Christopher J. Ziegler, Alana W. Ross, Tia L. Walker, Peter Caravan, Ian S. Taschner|2020|Dalton Trans.|49|14088|doi:10.1039/D0DT02787A
DOI: 10.5517/ccdc.csd.cc25dm18
title: CCDC 2008119: Experimental Crystal Structure Determination

description: Related Article: Bin Zhu, Guimei Huang, Yanni He, Jisheng Xie, Tao He, Junli Wang, Ziao Zong|2020|RSC Advances|10|34943|doi:10.1039/D0RA06529C
DOI: 10.5517/ccdc.csd.cc25dnr0
title: CCDC 2008172: Experimental Crystal Structure Determination

description: Related Article: Bin Zhu, Guimei Huang, Yanni He, Jisheng Xie, Tao He, Junli Wang, Ziao Zong|2020|RSC Advances|10|34943|doi:10.1039/D0RA06529C
DOI: 10.5519/qd.ee43rvnp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (365830 records)
DOI: 10.5517/ccdc.csd.cc25gnt4
title: CCDC 2010096: Experimental Crystal Structure Determination

description: Related Article: Swarup Ghosh, Eduard Glöckler, Christoph Wölper, Alexander Tjaberings, André H. Gröschel, Stephan Schulz|2020|Dalton Trans.|49|13475|doi:10.1039/D0DT02831B
DOI: 10.5517/ccdc.csd.cc25gns3
title: CCDC 2010095: Experimental Crystal Structure Determination

description: Related Article: Swarup Ghosh, Eduard Glöckler, Christoph Wölper, Alexander Tjaberings, André H. Gröschel, Stephan Schulz|2020|Dalton Trans.|49|13475|doi:10.1039/D0DT02831B
DOI: 10.5517/ccdc.csd.cc25k7gg
title: CCDC 2012565: Experimental Crystal Structure Determination

description: Related Article: Stanislav K. Petrovskii, Aleksandra V. Paderina, Anastasia A. Sizova, Andrey Yu. Baranov, Alexander A. Artem'ev, Vladimir V. Sizov, Elena V. Grachova|2020|Dalton Trans.|49|13430|doi:10.1039/D0DT02583F
DOI: 10.5517/ccdc.csd.cc25k7hh
title: CCDC 2012566: Experimental Crystal Structure Determination

description: Related Article: Stanislav K. Petrovskii, Aleksandra V. Paderina, Anastasia A. Sizova, Andrey Yu. Baranov, Alexander A. Artem'ev, Vladimir V. Sizov, Elena V. Grachova|2020|Dalton Trans.|49|13430|doi:10.1039/D0DT02583F
DOI: 10.5517/ccdc.csd.cc25k7jj
title: CCDC 2012567: Experimental Crystal Structure Determination

description: Related Article: Stanislav K. Petrovskii, Aleksandra V. Paderina, Anastasia A. Sizova, Andrey Yu. Baranov, Alexander A. Artem'ev, Vladimir V. Sizov, Elena V. Grachova|2020|Dalton Trans.|49|13430|doi:10.1039/D0DT02583F
DOI: 10.5517/ccdc.csd.cc25mz1v
title: CCDC 2015187: Experimental Crystal Structure Determination

description: Related Article: Juan-Ramón Jiménez, Maxime Poncet, Benjamin Doistau, Céline Besnard, Claude Piguet|2020|Dalton Trans.|49|13528|doi:10.1039/D0DT02872J
DOI: 10.5517/ccdc.csd.cc25mz2w
title: CCDC 2015188: Experimental Crystal Structure Determination

description: Related Article: Juan-Ramón Jiménez, Maxime Poncet, Benjamin Doistau, Céline Besnard, Claude Piguet|2020|Dalton Trans.|49|13528|doi:10.1039/D0DT02872J
DOI: 10.5517/ccdc.csd.cc25mz3x
title: CCDC 2015189: Experimental Crystal Structure Determination

description: Related Article: Juan-Ramón Jiménez, Maxime Poncet, Benjamin Doistau, Céline Besnard, Claude Piguet|2020|Dalton Trans.|49|13528|doi:10.1039/D0DT02872J
DOI: 10.5517/ccdc.csd.cc25mz4y
title: CCDC 2015190: Experimental Crystal Structure Determination

description: Related Article: Juan-Ramón Jiménez, Maxime Poncet, Benjamin Doistau, Céline Besnard, Claude Piguet|2020|Dalton Trans.|49|13528|doi:10.1039/D0DT02872J
DOI: 10.6084/m9.figshare.13176647
title: MRI measurements BAPTISe (vessel size, density, relative tissue perfusion)

description: Here we provide three datasets with VSI measurements (vessel size and vessel density) as well as relative tissue perfusion meassurements (rCBV and rCBF) in selected regions of interest (lesion, perilesional space, mirrored lesion) - these datesets were used for mixed-model analyses of variance in our manuscript entitled '<b>Changes in vascular morphology and cerebral perfusion in sub-acute ischemic stroke - a multiparametric MRI study</b>' <br>Patient demographics and related data are not publicly available as this may compromise patient privacy, this data however can be shared upon request.
DOI: 10.6084/m9.figshare.13176647.v1
title: MRI measurements BAPTISe (vessel size, density, relative tissue perfusion)

description: Here we provide three datasets with VSI measurements (vessel size and vessel density) as well as relative tissue perfusion meassurements (rCBV and rCBF) in selected regions of interest (lesion, perilesional space, mirrored lesion) - these datesets were used for mixed-model analyses of variance in our manuscript entitled '<b>Changes in vascular morphology and cerebral perfusion in sub-acute ischemic stroke - a multiparametric MRI study</b>' <br>Patient demographics and related data are not publicly available as this may compromise patient privacy, this data however can be shared upon request.
DOI: 10.5281/zenodo.4191747
title: Effect of glacial/interglacial recharge conditions on flow of meteoric water through deep orogenic faults: insights into the geothermal system at Grimsel Pass, Switzerland

description: This repository contains all files needed to run the base case simulation with PFLOTRAN. The input file pflotran.in provides information about the data sources as annotations. .
DOI: 10.5281/zenodo.4191746
title: Effect of glacial/interglacial recharge conditions on flow of meteoric water through deep orogenic faults: insights into the geothermal system at Grimsel Pass, Switzerland

description: This repository contains all files needed to run the base case simulation with PFLOTRAN. The input file pflotran.in provides information about the data sources as annotations. .
DOI: 10.11583/dtu.13161593.v1
title: Grid Frequency Measurements of DK2 and Bornholm during islanded event May 2019

description: Grid frequency of DK2 (Roskilde, Denmark) and Bornholm (Denmark) during an islanded event of Bornholm in May 2019. <br>Data provides UNIX timestamp and frequency measurements in Hz from the 7th May 2019 to the 17th May 2019, with sampling rate of 2 samples per second.<br>Two separate files, one for DK2 and one for Bornholm are provided. <br><br><br>
DOI: 10.5519/qd.48sgls3u
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5 records)
DOI: 10.5281/zenodo.4161028
title: UniBind 2.0 - LOLA databases for the robust and permissive collections

description: This repository contains all LOLA databases used in the UniBind 2.0 enrichment tool. For each organism, we provide the LOLA databases for all TFBS sets and the complete universe of TFBSs contained in the robust and permissive collections. The databases are provided as .RDS R objects, which can be directly loaded into R or used as inputs for the command-line UniBind enrichment tool.
DOI: 10.6084/m9.figshare.13175123.v1
title: R0219_20180228a_videos

description: Rat 0219, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13175123
title: R0219_20180228a_videos

description: Rat 0219, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.12493481
title: High three pro-nuclei (3PN) zygotes proportion associated with normal embryo multinucleation at the two-cell stage: two cases report

description: <b>Objective:</b> Blastomere multinucleation at the two-cell stage (MN2) is a common nuclear abnormality observed in early human embryos and known to decrease the implantation rate. The aim of this study is to explore whether or not there is a link between high 3PN zygotes proportion and MN2 incidence. <b>Methods:</b> For embryo culture in the conventional incubator, the evaluation of nuclear status of two-cell stage is usually not performed. Therefore, the MN2 phenomenon is easily ignored. The time-lapse monitoring system (TLS) offers a promising new method to evaluate embryo development. We reported two cases who had single 2PN zygote and more than four 3PN zygotes in the conventional in-vitro fertilization (c-IVF) cycle. <b>Results:</b> We observed the MN2 incidence in the single normal embryo by TLS which suggested that it might be resulted from high 3PN zygotes proportion incidence. No available embryo was obtained in the first c-IVF cycle and the intracytoplasmic sperm injection (ICSI) treatment was performed in the second cycle. In subsequent ICSI cycles, we observed no 3PN zygotes incidence and transferred two day 3 embryos for the patients. Finally, the two couples successfully obtained healthy babies. <b>Conclusions:</b> High 3PN zygotes proportion might be associated with the MN2 incidence.