cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6839154
title: RNA sequencing analysis of CAF treated by colon cancer cell-derived exosomes

description: In this study, we analyzed differentially expressed genes by obtaining gene expression values through transcriptome sequencing of Homo sapiens, and performed functional classification and gene annotation for significant genes based on gene ontology and pathway information. After the pre-processed trimmed reads were mapped to a known reference genome using the HISAT2 program, transcript assembly was performed through the StringTie program. As a result, expression profile values were obtained for each sample for the known transcript, and read count, based on transcript/gene Fragment per Kilobase of transcript per Million mapped reads (FPKM), Transcripts per Kilobase (TPM) Million) values have been summarized. This value was subjected to DEG (Differentially Expressed Genes) analysis using edgeR for comparison combinations (HT-29_Exo-CAF vs. CTL_PBS-CAF, LoVo_Exo-CAF vs. CTL_PBS-CAF, and SW480_Exo-CAF vs. CTL_PBS-CAF), and genes that satisfies the condition |fc|&gt;=2 & exactTest raw p-value&lt;0.05 in at least one comparison combination Dogs were extracted. Transcriptome resequencing data was used to compare expression profiles between comparable samples. Gene Ontology Enrichment analysis was performed using the g:Profiler tool (https://biit.cs.ut.ee/gprofiler/) for a list of genes with significant expression level differences. GO_stat is the result of organizing the associated gene and test stat based on term_id. GO_genes is the result of arranging the associated term_id and DEG analysis result stat based on the gene.
DOI: 10.5281/zenodo.6839129
title: Datasets for "Versatile Domain Mapping of Scanning Electron Nanobeam Diffraction datasets utilising Variational Auto Encoders and decoder-assisted latent clustering"

description: 20210925_152115_data.hdf5 is the P2 sample raw data. FinalMap-weights.hdf5 is the weights for the P2 model used for clustering SimulatedDSA-data.hdf5 is the simulated data set raw data. FullyTrainedModel.hdf5 is the weights for the Simulated Dataset model used for clustering <br>
DOI: 10.5281/zenodo.6839128
title: Datasets for "Versatile Domain Mapping of Scanning Electron Nanobeam Diffraction datasets utilising Variational Auto Encoders and decoder-assisted latent clustering"

description: 20210925_152115_data.hdf5 is the P2 sample raw data. FinalMap-weights.hdf5 is the weights for the P2 model used for clustering SimulatedDSA-data.hdf5 is the simulated data set raw data. FullyTrainedModel.hdf5 is the weights for the Simulated Dataset model used for clustering <br>
DOI: 10.6084/m9.figshare.20318340
title: Data.zip

description: The data used in this paper are obtained from the CMSAR database (China Stock Market & Accounting Research Database) of CSI 300 constituent stocks from January 1, 2016, to December 31, 2020. The stock news data used in this paper are from the CMSAR database and internet crawling, and the internet crawling was performed via aiqicha (https://aiqicha.baidu.com) and qichacha (https://www.qcc.com). The total number of texts between January 1, 2016 and December 31, 2020, for the 300 stocks is 1807207 (2016: 79091; 2017: 180868; 2018: 284679; 2019: 445356; 2020: 817213). The database news texts were classified as domestic finance, overseas finance, securities market, Asia-Pacific stock market, European and American market, individual stock news, and industry information (from Xinhua, 21st Century Business Herald, Economic Observer, Yangzi Evening News, and other media, the data is available at the following link. https://pan.baidu.com/s/1kLYSIitmcY_6GqBHh6HPPg, password: 9piq). Table 2 provides an overall description of the distribution of transaction data and news texts, and shows the number of days of trading and the number of news texts. Table 3 is the statistical description of stock market transaction data, Figure 6 is the distribution of news text data, and Figure 6(a) is the distribution of the number of news texts per year. The development of technology shows a trend of increasing year by year. Figure 1(b) shows the classification of news text data. The classification is based on the news classification of the CMSAR database (China Stock Market & Accounting Research Database). It is divided into 7 categories: domestic finance and economics (DFE), overseas finance and economics (OFE), securities market (SM), Asia-Pacific market (APM), European and American markets (EAM), individual stock news (ISN), and industry information (II). Figure 6(c) shows the distribution of news sources, with the top 10 news sources: 21st Century Business Herald (21st), Daily Business News (DBN), Securities Daily (SD), Securities Times (ST), Shanghai Securities News (SSN), Economic Information Daily (EID), Securities Times Website (STW), Beijing Business Today (BBT), First Financial Daily (FFD), and Wall Street News (WSN). According to the classification and sources of news texts in Figure 6(b)(c), it can be seen that the news text data, as the core experimental data of this paper, have a reasonable distribution, and the news coverage is relatively comprehensive.
DOI: 10.6084/m9.figshare.20318340.v1
title: Data.zip

description: The data used in this paper are obtained from the CMSAR database (China Stock Market & Accounting Research Database) of CSI 300 constituent stocks from January 1, 2016, to December 31, 2020. The stock news data used in this paper are from the CMSAR database and internet crawling, and the internet crawling was performed via aiqicha (https://aiqicha.baidu.com) and qichacha (https://www.qcc.com). The total number of texts between January 1, 2016 and December 31, 2020, for the 300 stocks is 1807207 (2016: 79091; 2017: 180868; 2018: 284679; 2019: 445356; 2020: 817213). The database news texts were classified as domestic finance, overseas finance, securities market, Asia-Pacific stock market, European and American market, individual stock news, and industry information (from Xinhua, 21st Century Business Herald, Economic Observer, Yangzi Evening News, and other media, the data is available at the following link. https://pan.baidu.com/s/1kLYSIitmcY_6GqBHh6HPPg, password: 9piq). Table 2 provides an overall description of the distribution of transaction data and news texts, and shows the number of days of trading and the number of news texts. Table 3 is the statistical description of stock market transaction data, Figure 6 is the distribution of news text data, and Figure 6(a) is the distribution of the number of news texts per year. The development of technology shows a trend of increasing year by year. Figure 1(b) shows the classification of news text data. The classification is based on the news classification of the CMSAR database (China Stock Market & Accounting Research Database). It is divided into 7 categories: domestic finance and economics (DFE), overseas finance and economics (OFE), securities market (SM), Asia-Pacific market (APM), European and American markets (EAM), individual stock news (ISN), and industry information (II). Figure 6(c) shows the distribution of news sources, with the top 10 news sources: 21st Century Business Herald (21st), Daily Business News (DBN), Securities Daily (SD), Securities Times (ST), Shanghai Securities News (SSN), Economic Information Daily (EID), Securities Times Website (STW), Beijing Business Today (BBT), First Financial Daily (FFD), and Wall Street News (WSN). According to the classification and sources of news texts in Figure 6(b)(c), it can be seen that the news text data, as the core experimental data of this paper, have a reasonable distribution, and the news coverage is relatively comprehensive.
DOI: 10.5519/qd.a66rt0dg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.19375331
title: DATASET Doctorat paper 2022 march 14 2.xlsx

description: The Dataset contains the refractive data and ultrasound biomicroscopy data of 2 groups of participants. One group is Smartphone abusers and other group is non user
DOI: 10.25625/sceqp6/l4w4l2
title: housemoney1.tab

description: :unav
DOI: 10.25625/sceqp6
title: Replication Data for: Reinstein and Riener (2011) Decomposing desert and tangibility effects in a charitable giving experiment

description: Several papers have documented that when subjects play with standard laboratory “endowments” they make less self-interested choices than when they use money they have either earned through a laboratory task or brought from outside the lab. In the context of a charitable giving experiment we decompose this into two common artifacts of the laboratory: the intangibility of money (or experimental currency units) promised on a computer screen relative to cash in hand, and the distinct treatment of random “windfall” gains relative to earned money. While both effects are found to be significant in non-parametric tests, the former effect, which has been neglected in previous studies, has a stronger impact on total donations, while the latter effect has a greater impact on the probability of donating. These results have clear implications for experimental design, and also suggest that the availability of more abstract payment methods may increase other-regarding behavior in the field.
DOI: 10.5517/ccdc.csd.cc20sm6n
title: CCDC 1870701: Experimental Crystal Structure Determination

description: Related Article: Shanshan Wang, Zhe Huang, Anyang Li, Yanxia Zhao, Wei Zuo, Yawen Li, Haohao Miao, Jiacheng Ma, Wei Sun, Xiaoqing Wang, Liping Cao, Biao Wu, Chuandong Jia|2021|Angew.Chem.,Int.Ed.|60|9573|doi:10.1002/anie.202100441
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: 10.15468/jxpwmd
title: A checklist dataset of the spider species from Kenya

description: Currently there are over 50,000 described spider species worldwide according to the world spider catalog (WSC). The study of spiders in Kenya is not well done and as of December 2018, 805 species were described from the region. Some of the species were considered nomen dubium and others described from juvenile species which are at best considered misidentified. The data presented here is a checklist of spider species described from Kenya up-to May of 2022. The species are described from Kenya and other regions of the world with some considered to be cosmopolitan. A total of 801 species from 58 families are represented in the data set. 320 out of the 801 species from this data set are considered endemic to Kenya. 84 species are genus types. The families Salticidae and Linyphiidae had the most species representatives i.e. 163 and 109 species respectively. The bracketed scientific name authorship represent species that have either been transferred to a new/different genus or have been synonymized with other species but maintain the original author. Letters in the scientific name authorship distinguish articles from authors who made multiple publications within the same year.
DOI: 10.34740/kaggle/dsv/3946548
title: Elon Musk Tweets
DOI: 10.34740/kaggle/dsv/3946578
title: Elon Musk Tweets
DOI: 10.25919/neb8-yy83
title: Parkes observations for project PX500 semester 2022APRS_17

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.6460723
title: Dataset for the manuscript "Crowding results from optimal integration of visual targets with contextual information"

description: There are seven experimental datasets, two program with which data are collected, two supplemetary programs needed to run the main code and one program to analyse data. Two .txt files are included, where we describe how to use the stimulation and analysis programs.
DOI: 10.5281/zenodo.6460722
title: Dataset for the manuscript "Crowding results from optimal integration of visual targets with contextual information"

description: There are seven experimental datasets, two program with which data are collected, two supplemetary programs needed to run the main code and one program to analyse data. Two .txt files are included, where we describe how to use the stimulation and analysis programs.
DOI: 10.5281/zenodo.6839257
title: Molcular Dynamics Data for Therapeutic High Affinity T Cell Receptor Targeting a KRAS G12D Cancer Neoantigen

description: This folder contains the starting structures and input scripts required to simulate the wild-type and G12D KRAS peptide bound TCR-pHLA complexes, as performed in this study. <br> Starting_Structures - This folder contains the amber parameter/topology files used to simulate each system (.prmtop) and the coordinates of the starting structure both as amber coordinate file (.rst) and PDB file (.pdb).<br> MD_Inputs - This folder contains the amber MD inputs used to run the md simulations. <br> MMPBSA_inputs - This folder contains the input files for running MMPBSA with the MMPBSA.py script in amber. The mmpbsa.in script was used for calculating overall binding energy whereas the mmpbsa_decomp.in script was used for calculating the per-residue contribution to binding energy.
DOI: 10.5281/zenodo.6818252
title: Molcular Dynamics Data for Therapeutic High Affinity T Cell Receptor Targeting a KRAS G12D Cancer Neoantigen

description: This folder contains the starting structures and input scripts required to simulate the wild-type and G12D KRAS peptide bound TCR-pHLA complexes, as performed in this study. <br> Starting_Structures - This folder contains the amber parameter/topology files used to simulate each system (.prmtop) and the coordinates of the starting structure both as amber coordinate file (.rst) and PDB file (.pdb).<br> MD_Inputs - This folder contains the amber MD inputs used to run the md simulations. <br> MMPBSA_inputs - This folder contains the input files for running MMPBSA with the MMPBSA.py script in amber. The mmpbsa.in script was used for calculating overall binding energy whereas the mmpbsa_decomp.in script was used for calculating the per-residue contribution to binding energy.
DOI: 10.5281/zenodo.6839250
title: Co-located Multi-device Audio Experiences Dataset

description: This dataset contains the survey responses obtained from the survey on co-located multi-device audio experiences, in the form of a .csv file.
DOI: 10.5281/zenodo.6801465
title: Co-located Multi-device Audio Experiences Dataset

description: This dataset contains the survey responses obtained from the survey on co-located multi-device audio experiences, in the form of a .csv file.
DOI: 10.5281/zenodo.6835641
title: Python Time Normalized Superposed Epoch Analysis (SEAnorm) Example Data Set

description: Solar Wind Omni and SAMPEX ( Solar Anomalous and Magnetospheric Particle Explorer) datasets used in examples for SEAnorm, a time normalized superposed epoch analysis package in python. Both data sets are stored as either a HDF5 or a compressed csv file (csv.bz2) which contain a Pandas DataFrame of either the Solar Wind Omni and SAMPEX data sets. The data sets where written with pandas.DataFrame.to_hdf() and pandas.DataFrame.to_csv() using a compression level of 9. The DataFrames can be read using pandas.DataFrame.read_hdf( ) or pandas.DataFrame.read_csv( ) depending on the file format. The Solar Wind Omni data sets contains solar wind velocity (V) and dynamic pressure (P), the southward interplanetary magnetic field in Geocentric Solar Ecliptic System (GSE) coordinates (B_Z_GSE), the auroral electrojet index (AE), and the Sym-H index all at 1 minute cadence. The SAMPEX data set contains electron flux from the Proton/Electron Telescope (PET) at two energy channels 1.5-6.0 MeV (ELO) and 2.5-14 MeV (EHI) at an approximate 6 second cadence.
DOI: 10.5281/zenodo.6835136
title: Python Time Normalized Superposed Epoch Analysis (SEAnorm) Example Data Set

description: Solar Wind Omni and SAMPEX ( Solar Anomalous and Magnetospheric Particle Explorer) datasets used in examples for SEAnorm, a time normalized superposed epoch analysis package in python. Both data sets are stored as either a HDF5 or a compressed csv file (csv.bz2) which contain a Pandas DataFrame of either the Solar Wind Omni and SAMPEX data sets. The data sets where written with pandas.DataFrame.to_hdf() and pandas.DataFrame.to_csv() using a compression level of 9. The DataFrames can be read using pandas.DataFrame.read_hdf( ) or pandas.DataFrame.read_csv( ) depending on the file format. The Solar Wind Omni data sets contains solar wind velocity (V) and dynamic pressure (P), the southward interplanetary magnetic field in Geocentric Solar Ecliptic System (GSE) coordinates (B_Z_GSE), the auroral electrojet index (AE), and the Sym-H index all at 1 minute cadence. The SAMPEX data set contains electron flux from the Proton/Electron Telescope (PET) at two energy channels 1.5-6.0 MeV (ELO) and 2.5-14 MeV (EHI) at an approximate 6 second cadence.
DOI: 10.5281/zenodo.6839280
title: TDMS data: arc_03

description: TDMS data: arc_03
DOI: 10.5281/zenodo.6839279
title: TDMS data: arc_03

description: TDMS data: arc_03
DOI: 10.5281/zenodo.6839282
title: Buteo nitidus decontaminated gx

description: .
DOI: 10.5281/zenodo.6839281
title: Buteo nitidus decontaminated gx

description: .
DOI: 10.5281/zenodo.6839385
title: Chelictinia riocourii decontaminated gx

description: .
DOI: 10.5281/zenodo.6839384
title: Chelictinia riocourii decontaminated gx

description: .
DOI: 10.5281/zenodo.6839433
title: Chologaster cornuta DAN 286 decontaminated gx

description: .
DOI: 10.5281/zenodo.6839432
title: Chologaster cornuta DAN 286 decontaminated gx

description: .
DOI: 10.5281/zenodo.6838757
title: Dataset for paper "Towards "Matter matters" in spin foam quantum gravity"

description: This is a dataset and Julia tools for evaluation / presentation corresponding to the article: https://arxiv.org/abs/2206.04076 The data correspond to samples of a coupled spin foam / matter system (via a Markov Chain Monte Carlo algorithm) for different lattice sizes. We also add an IJulia file and to generate the plots from the data and eventually modify them to study also other aspects. The code to generate these samples can be found at: https://github.com/amoosam/CuboidSpinfoamScalarField
description: {"references": ["arXiv 2206.04076"]}
DOI: 10.5281/zenodo.6838756
title: Dataset for paper "Towards "Matter matters" in spin foam quantum gravity"

description: This is a dataset and Julia tools for evaluation / presentation corresponding to the article: https://arxiv.org/abs/2206.04076 The data correspond to samples of a coupled spin foam / matter system (via a Markov Chain Monte Carlo algorithm) for different lattice sizes. We also add an IJulia file and to generate the plots from the data and eventually modify them to study also other aspects. The code to generate these samples can be found at: https://github.com/amoosam/CuboidSpinfoamScalarField
description: {"references": ["arXiv 2206.04076"]}
DOI: 10.5281/zenodo.6839499
title: Chromodoris orientalis decontaminated gx

description: .
DOI: 10.5281/zenodo.6839498
title: Chromodoris orientalis decontaminated gx

description: .
DOI: 10.34740/kaggle/dsv/3946664
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.6839514
title: Churabana kuroshioae decontaminated gx

description: .
DOI: 10.5281/zenodo.6839513
title: Churabana kuroshioae decontaminated gx

description: .
DOI: 10.5517/ccdc.csd.cc25wtpl
title: CCDC 2022740: Experimental Crystal Structure Determination

description: Related Article: Grigory Andreev, Nina Budantseva, Aleksander Fedoseev|2022|Polyhedron|224|116003|doi:10.1016/j.poly.2022.116003
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.cc260f9z
title: CCDC 2026200: Experimental Crystal Structure Determination

description: Related Article: Grigory Andreev, Nina Budantseva, Aleksander Fedoseev|2022|Polyhedron|224|116003|doi:10.1016/j.poly.2022.116003
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.cc25wtnk
title: CCDC 2022739: Experimental Crystal Structure Determination

description: Related Article: Grigory Andreev, Nina Budantseva, Aleksander Fedoseev|2022|Polyhedron|224|116003|doi:10.1016/j.poly.2022.116003
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.cc27z9l4
title: CCDC 2084706: Experimental Crystal Structure Determination

description: Related Article: Xiang Ma, Xueman Wei, Man Wang, Na Zhang, Pengju Chen, Jiai Hua|2022|J.Mol.Struct.|1267|133616|doi:10.1016/j.molstruc.2022.133616
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.6839568
title: Cichladusa arquata decontaminated gx

description: .
DOI: 10.5281/zenodo.6839567
title: Cichladusa arquata decontaminated gx

description: .
DOI: 10.6084/m9.figshare.20319060
title: TD-DFT

description: Green MR-TADF molecules <br>
DOI: 10.34740/kaggle/dsv/3946695
title: 2021 World Population (updated daily)