cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.18583934.v1
title: Similar magnetic enhancement mechanisms between Chinese loess and alluvial sediments from the Teruel Basin, NE Spain, and paleoclimate implications

description: This dataset includes paleomagnetic and environmental mangetism data from the Villalba Alta section of the Teruel Basin, NE Spain.
DOI: 10.5281/zenodo.5866986
title: Simulation dataset of "Deformation of electron distributions due to Landau trapping by the whistler-mode wave"

description: The file "Figure1a_data.dat" stores the data which is used to plot Figure1a. The file "Figure1cd_data.dat" stores the data which are used to plot Figure1c and Figure1c. And the other files are similar. The name and array of each parameter corresponding to that in Figures are stored in the files.
DOI: 10.5281/zenodo.5541032
title: Simulation dataset of "Deformation of electron distributions due to Landau trapping by the whistler-mode wave"

description: The file "Figure1a_data.dat" stores the data which is used to plot Figure1a. The file "Figure1cd_data.dat" stores the data which are used to plot Figure1c and Figure1c. And the other files are similar. The name and array of each parameter corresponding to that in Figures are stored in the files.
DOI: 10.6084/m9.figshare.16866790
title: Changes in the rodent gut microbiome following chronic restraint stress and low-intensity rTMS

description: Deposited 16S V3V4 amplicon sequencing data in unmapped BAM file format. The raw sequencing data was generated on the Illumina MiSeq platform using the paired-end 300 bp V3 chemistry.<br>
DOI: 10.6084/m9.figshare.16866790.v1
title: Changes in the rodent gut microbiome following chronic restraint stress and low-intensity rTMS

description: Deposited 16S V3V4 amplicon sequencing data in unmapped BAM file format. The raw sequencing data was generated on the Illumina MiSeq platform using the paired-end 300 bp V3 chemistry.<br>
DOI: 10.6084/m9.figshare.13489998
title: Metadata of sponge-associated culture bacteria.xlsx

description: Data of sponge-associated culture bacteria
DOI: 10.6084/m9.figshare.18584384
title: RawData_Montanhaetal2022

description: Raw Data from the manuscript entitled 'Physiological responses of plants to in vivo XRF radiation damage: insights from elemental, histochemical, anatomical and ultrastructural analyses', by Gabriel S. Montanha, Dr. João P. R. Marques, Eduardo S. Rodrigues, Dr. Michael W. M. Jones, and Dr. Hudson W. P. de Carvalho<br>
DOI: 10.6084/m9.figshare.18584384.v1
title: RawData_Montanhaetal2022

description: Raw Data from the manuscript entitled 'Physiological responses of plants to in vivo XRF radiation damage: insights from elemental, histochemical, anatomical and ultrastructural analyses', by Gabriel S. Montanha, Dr. João P. R. Marques, Eduardo S. Rodrigues, Dr. Michael W. M. Jones, and Dr. Hudson W. P. de Carvalho<br>
DOI: 10.6084/m9.figshare.18584531
title: Anxiety among youth in Bengaluru.sav

description: Anxiety among youth during Covid 19 in Bengaluru, India
DOI: 10.6084/m9.figshare.18584531.v1
title: Anxiety among youth in Bengaluru.sav

description: Anxiety among youth during Covid 19 in Bengaluru, India
DOI: 10.5281/zenodo.5866958
title: DepthTrack Test Set in one zip

description: The original link in here: https://zenodo.org/record/5792146 Here I zip them into one zip file and add a unzip script in it. It is also ready for vot-toolkit-python analysis.
DOI: 10.5281/zenodo.5866957
title: DepthTrack Test Set in one zip

description: The original link in here: https://zenodo.org/record/5792146 Here I zip them into one zip file and add a unzip script in it. It is also ready for vot-toolkit-python analysis.
DOI: 10.6084/m9.figshare.16727005.v1
title: Trajectory Trips Data

description: This data set contains weekly GPS Trajectory Trips
DOI: 10.6084/m9.figshare.18585089
title: December 2021 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>December, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16620238.v1
title: A public dataset on long-distance running training in 2019 and 2020

description: This dataset contains 10,703,690 records of running training during 2019 and 2020, from 36,412 athletes from around the world. The records were obtained through web scraping of a large social network for athletes on the internet.<br>The data with the athletes' activities are contained in dataframe objects (tabular data) and saved in the Parquet file format using the Pandas library, part of the Python ecosystem for data science. Each Pandas dataframe contains the following data (as different columns) for each athlete (as different rows), the first word identifies the name of the column in the dataframe:- datetime: date of the running activity;- athlete: a computer-generated ID for the athlete (integer);- distance: distance of running (floating-point number, in kilometers);- duration: duration of running (floating-point number, in minutes);- gender: gender (string 'M' of 'F');- age_group: age interval (one of the strings '18 - 34', '35 - 54', or '55 +');- country: country of origin of the athlete (string);- major: marathon(s) and year(s) the athlete ran (comma-separated list of strings).<br>For convenience, we created files with the athletes' activities data sampled at different frequencies: day 'd', week 'w', and month 'm' (i.e., there are files with the distance and duration of running accumulated at each day, week, and month) for each year, 2019 and 2020. Accordingly, the files are named 'run_ww_yyyy_f.parquet', where 'yyyy' is '2019' or '2020' and 'f' is 'd', 'w', or 'm' (without quotes). The dataset also contains data with different government’s stringency indexes for the COVID-19 pandemic. These data are saved as text files and were obtained from https://ourworldindata.org/covid-stringency-index. The Jupyter notebooks that we created and made available in the https://github.com/BMClab/covid19 repository exemplify the use of the data.
DOI: 10.6084/m9.figshare.16727005.v2
title: GPS Tracking Data

description: This data set contains GPS point coordinates corresponding to the clean GPS tracking of persons
DOI: 10.6084/m9.figshare.12401168.v8
title: The Brain Hierarchy Score

description: Here we provide data used in Nonaka, Majima, Aoki, & Kamitani (2021) Brain hierarchy score: Which deep neural networks are hierarchically brain-like? <i>iScience</i>.<br><br>See also https://github.com/KamitaniLab/BHscore.<br><br>History:<br><br>2021-12-24 Add encoding accuracy data (encoding_accuracy.zip)<br><br>2020-08-08 Add DNN unit activation data (features-*.zip)<br><br>2020-08-03 Update directory organization in decoding_accuracy.zip<br>
DOI: 10.6084/m9.figshare.16727011.v3
title: Trajectory Trips Summary Data

description: This data set contains summary measures of weekly trajectory trips GPS
DOI: 10.25345/c5p58g
title: MassIVE MSV000088694 - DeepSCP: utilizing deep learning to boost single-cell proteome coverage
title: We present a novel framework DeepSCP, a specifically utilizing deep learning to boost SCP coverage. The raw sample sets of TMT16-labeled for the quantification of single-cell from two cultured murine cell lines, GC-1 spg (GC-1) and GC-2spd (GC-2) and MaxQuant search result with both PSM and protein levels at 1. The raw bulk sets of TMT16-labeled and MaxQuant search result with both PSM and protein levels at 0.01. It provides a benchmark dataset for DeepSCP to improve single cell coverage.
DOI: 10.5281/zenodo.5867094
title: Sejarah Konsepsi Pemikiran Kewirausahaan

description: Sejarah Konsepsi Pemikiran Kewirausahaan (Respati, 2000) Secara garis besar, pemikiran tentang kewirausahaan dapat dimulai dari masa pra sejarah, kemudian masuk pada masa dimana pemikiran kewirausahaan dipengaruhi oleh ekonomi (pada masa gerakan klasik, neoklasik dan proses pasar Austria / Austrian Market Process (AMP)) hingga masa pemikiran kewirausahaan berdasar pada multidispliner. Unsur-unsur konsepsi ditempatkan secara kronologis dan digolongkan menurut 3 kategori basis yakni basis pra sejarah, basis ekonomi dan basis multidisipliner. Mengingat sejarah bersifat kronologis, unsur-unsur konsepsi lebih diutamakan dari pada temporal, hasilnya adalah pengertian tentang bagaimana teori masa lalu dapat memberikan informasi pada teori selanjutnya.
DOI: 10.5281/zenodo.5867093
title: Sejarah Konsepsi Pemikiran Kewirausahaan

description: Sejarah Konsepsi Pemikiran Kewirausahaan (Respati, 2000) Secara garis besar, pemikiran tentang kewirausahaan dapat dimulai dari masa pra sejarah, kemudian masuk pada masa dimana pemikiran kewirausahaan dipengaruhi oleh ekonomi (pada masa gerakan klasik, neoklasik dan proses pasar Austria / Austrian Market Process (AMP)) hingga masa pemikiran kewirausahaan berdasar pada multidispliner. Unsur-unsur konsepsi ditempatkan secara kronologis dan digolongkan menurut 3 kategori basis yakni basis pra sejarah, basis ekonomi dan basis multidisipliner. Mengingat sejarah bersifat kronologis, unsur-unsur konsepsi lebih diutamakan dari pada temporal, hasilnya adalah pengertian tentang bagaimana teori masa lalu dapat memberikan informasi pada teori selanjutnya.
DOI: 10.6084/m9.figshare.16727014.v1
title: Activity Point Location Data

description: This data set contains Activity Point Location GPS data and Clusters
DOI: 10.5281/zenodo.5867106
title: Self Efficacy and Risk Tolerance

description: This includes tally results for self efficacy and risk tolerance of stock investors in Baguio City
description: Correlation for Self Efficacy and Risk Tolerance
DOI: 10.5281/zenodo.5867105
title: Self Efficacy and Risk Tolerance

description: This includes tally results for self efficacy and risk tolerance of stock investors in Baguio City
description: Correlation for Self Efficacy and Risk Tolerance
DOI: 10.5281/zenodo.5867118
title: DASAR-DASAR CORPORATE PUBLIC RELATIONS

description: Dasar-Dasar Corporate Public Relations (Sitepu, 2011) <em>Public Relations </em>dalam makna yang sederhana adalah tatap muka (hubungan) antara kelompok-kelompok dalam suatu tatanan masyarakat. Hubungan ini terjadi antara kelompok-kelompok atau asosiasi dan para anggotanya, antara organisasi dan pihak-pihak terkait, antara pemerintah dengan para pemilih, antara perusahaan dengan para pemegang saham, dan antara satu organisasi dengan organisasi lainnya. Secara lebih khusus, menurut para professional di bidang <em>Public Relations, Public Relations </em>adalah manajemen dari berbagai hubunga antara suatu lembaga dengan publiknya.
DOI: 10.5281/zenodo.5867117
title: DASAR-DASAR CORPORATE PUBLIC RELATIONS

description: Dasar-Dasar Corporate Public Relations (Sitepu, 2011) <em>Public Relations </em>dalam makna yang sederhana adalah tatap muka (hubungan) antara kelompok-kelompok dalam suatu tatanan masyarakat. Hubungan ini terjadi antara kelompok-kelompok atau asosiasi dan para anggotanya, antara organisasi dan pihak-pihak terkait, antara pemerintah dengan para pemilih, antara perusahaan dengan para pemegang saham, dan antara satu organisasi dengan organisasi lainnya. Secara lebih khusus, menurut para professional di bidang <em>Public Relations, Public Relations </em>adalah manajemen dari berbagai hubunga antara suatu lembaga dengan publiknya.
DOI: 10.5519/qd.h31jt5yv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (22 records)
DOI: 10.5281/zenodo.5865157
title: DrCyZ: Techniques for analyzing and extracting useful information from CyZ.

description: DrCyZ: Techniques for analyzing and extracting useful information from CyZ. Samples from NASA Perseverance and set of GAN generated synthetic images from Neural Mars. Repository: https://github.com/decurtoidiaz/drcyz <br> Subset of samples from (includes tools to visualize and analyse the dataset): CyZ: MARS Space Exploration Dataset. [https://doi.org/10.5281/zenodo.5655473] Images from NASA missions of the celestial body. Repository: https://github.com/decurtoidiaz/cyz Authors: J. de Curtò c@decurto.be I. de Zarzà z@dezarza.be ------------------------------------------<br> File Information from DrCyZ-1.1<br> ------------------------------------------ • Subset of samples from Perseverance (drcyz/c).<br> ∙ png (drcyz/c/png).<br> PNG files (5025) selected from NASA Perseverance (CyZ-1.1) after t-SNE and K-means Clustering. <br> ∙ csv (drcyz/c/csv).<br> CSV file. • Resized samples from Perseverance (drcyz/c+).<br> ∙ png 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/drcyz_64-1024).<br> PNG files resized at the corresponding size. <br> ∙ TFRecords 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/tfr_drcyz_64-1024).<br> TFRecord resized at the corresponding size to import on Tensorflow. • Synthetic images from Neural Mars generated using Stylegan2-ada (drcyz/drcyz+).<br> ∙ png 100; 1000; 10000 (drcyz/drcyz+/drcyz_256_100-10000)<br> PNG files subset of 100, 1000 and 10000 at size 256x256. • Network Checkpoint from Stylegan2-ada trained at size 256x256 (drcyz/model_drcyz).<br> ∙ network-snapshot-000798-drcyz.pkl • Notebooks in python to analyse the original dataset and reproduce the experiments; K-means Clustering, t-SNE, PCA, synthetic generation using Stylegan2-ada and instance segmentation using Deeplab (https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+).<br> ∙ clustering_curiosity_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Curiosity.<br> ∙ clustering_perseverance_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Perseverance.<br> ∙ tsne_curiosity_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Curiosity.<br> ∙ tsne_perseverance_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Perseverance.<br> ∙ Stylegan2-ada_de_curto_and_de_zarza.ipynb<br> Stylegan2-ada trained on a subset of images from NASA Perseverance (DrCyZ).<br> ∙ statistics_perseverance_de_curto_and_de_zarza.ipynb<br> Compute statistics from synthetic samples generated by Stylegan2-ada (DrCyZ) and images from NASA Perseverance (CyZ).<br> ∙ DeepLab_TFLite_ADE20k_de_curto_and_de_zarza.ipynb<br> Example of instance segmentation using Deeplab with a sample from NASA Perseverance (DrCyZ).
description: {"references": ["de Curt\u00f2, J., & de Zarz\u00e0, I. (2021). CyZ: MARS Space Exploration Dataset. (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5655473"]}
DOI: 10.5281/zenodo.5816857
title: DrCyZ: Techniques for analyzing and extracting useful information from CyZ.

description: DrCyZ: Techniques for analyzing and extracting useful information from CyZ. Samples from NASA Perseverance and set of GAN generated synthetic images from Neural Mars. Repository: https://github.com/decurtoidiaz/drcyz <br> Subset of samples from (includes tools to visualize and analyse the dataset): CyZ: MARS Space Exploration Dataset. [https://doi.org/10.5281/zenodo.5655473] Images from NASA missions of the celestial body. Repository: https://github.com/decurtoidiaz/cyz Authors: J. de Curtò c@decurto.be I. de Zarzà z@dezarza.be ------------------------------------------<br> File Information from DrCyZ-1.1<br> ------------------------------------------ • Subset of samples from Perseverance (drcyz/c).<br> ∙ png (drcyz/c/png).<br> PNG files (5025) selected from NASA Perseverance (CyZ-1.1) after t-SNE and K-means Clustering. <br> ∙ csv (drcyz/c/csv).<br> CSV file. • Resized samples from Perseverance (drcyz/c+).<br> ∙ png 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/drcyz_64-1024).<br> PNG files resized at the corresponding size. <br> ∙ TFRecords 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/tfr_drcyz_64-1024).<br> TFRecord resized at the corresponding size to import on Tensorflow. • Synthetic images from Neural Mars generated using Stylegan2-ada (drcyz/drcyz+).<br> ∙ png 100; 1000; 10000 (drcyz/drcyz+/drcyz_256_100-10000)<br> PNG files subset of 100, 1000 and 10000 at size 256x256. • Network Checkpoint from Stylegan2-ada trained at size 256x256 (drcyz/model_drcyz).<br> ∙ network-snapshot-000798-drcyz.pkl • Notebooks in python to analyse the original dataset and reproduce the experiments; K-means Clustering, t-SNE, PCA, synthetic generation using Stylegan2-ada and instance segmentation using Deeplab (https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+).<br> ∙ clustering_curiosity_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Curiosity.<br> ∙ clustering_perseverance_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Perseverance.<br> ∙ tsne_curiosity_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Curiosity.<br> ∙ tsne_perseverance_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Perseverance.<br> ∙ Stylegan2-ada_de_curto_and_de_zarza.ipynb<br> Stylegan2-ada trained on a subset of images from NASA Perseverance (DrCyZ).<br> ∙ statistics_perseverance_de_curto_and_de_zarza.ipynb<br> Compute statistics from synthetic samples generated by Stylegan2-ada (DrCyZ) and images from NASA Perseverance (CyZ).<br> ∙ DeepLab_TFLite_ADE20k_de_curto_and_de_zarza.ipynb<br> Example of instance segmentation using Deeplab with a sample from NASA Perseverance (DrCyZ).
description: {"references": ["de Curt\u00f2, J., & de Zarz\u00e0, I. (2021). CyZ: MARS Space Exploration Dataset. (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5655473"]}
DOI: 10.6084/m9.figshare.18585419
title: Simulation Results

description: Simulation results of Zhang et al. (2022)
DOI: 10.6084/m9.figshare.18585365
title: Programs and code information

description: Programs and code of RNA-seq and MeRIP-seq.
DOI: 10.6084/m9.figshare.18585365.v1
title: Programs and code information

description: Programs and code of RNA-seq and MeRIP-seq.
DOI: 10.6084/m9.figshare.18550976
title: Conjoint analysis of MeRIP-seq and RNA-seq

description: The correlation analysis was designed to find overlaps between m6A difference genes identified by MeRIP-seq and the expression difference genes identified by RNA-seq
DOI: 10.6084/m9.figshare.18550943
title: MeRIP-seq result

description: Poplar 84K seedlings were treated with 100 mM NaCl for 48h. After uniform mixing, the processed tissue was analyzed with MeRIP-seq.
DOI: 10.6084/m9.figshare.18550943.v1
title: MeRIP-seq result

description: Poplar 84K seedlings were treated with 100 mM NaCl for 48h. After uniform mixing, the processed tissue was analyzed with MeRIP-seq.
DOI: 10.6084/m9.figshare.18550910
title: RNA-seq result

description: Poplar 84K seedlings were treated with 100 mM NaCl for 48h. After uniform mixing, the processed tissue was analyzed with RAN-seq.
DOI: 10.6084/m9.figshare.18550910.v1
title: RNA-seq result

description: Poplar 84K seedlings were treated with 100 mM NaCl for 48h. After uniform mixing, the processed tissue was analyzed with RAN-seq.
DOI: 10.6084/m9.figshare.18585434
title: Suppl. File

description: <b>The PSO-BP codes for modelling the relationship among the national official GDP and sum of DN values, which can be run by Matlab (R2017b).</b>
DOI: 10.21227/b7mg-yb75
title: Experimental dataset for the Performance Evaluation for Geographically Distributed Blockchain-based Services in a Cloud Computing Environment

description: This dataset is an experimental result of the paper “Performance Evaluation for Geographically Distributed Blockchain-based Services in a Cloud Computing Environment”. The Geographically Distributed Cloud Performance Evaluation Ambassador (GDCPEA) is deployed on each Go Ethereum (Geth) node to measure the elapsed time from the start to the end of the Geth main operations. When GDCPEA requests the GDCPE Log Server in the same region to record a log, the GDCPE Log Server records a timestamp log including the timestamp when the request was received, the name of the Geth node that sent the request, the name of the Geth operation to be measured, and so on. We conducted this experiment in AWS cloud computing services and deployed GDCPEA and GDCPE Log Server in up to three AWS regions (Seoul, Ohio, Singapore) depending on the purpose of the experiment. This dataset contains timestamp logs from each GDCPE Log Server, block and transaction information from the blockchain network, and resource usage data from each AWS EC2 instance. We attached the results of analyzing the performance changes when the number of nodes and clients of the blockchain network increases using these data to our paper.
DOI: 10.21227/2wac-fr85
title: Mood and calendar events information

description: This dataset maps mood to information about the events that influenced the mood. The dataset was obtained using a web-based data collection interface developed by us. The dataset consists of 5245 days of data from 134 participants in the experiment.
DOI: 10.6084/m9.figshare.18585461
title: RCC VF table data sharing_rev4.xlsx

description: <b>The Value Measurement of Emerging Therapeutics in Renal Cell Carcinoma: ASCO Value Framework and ESMO-MCBS</b>
DOI: 10.6084/m9.figshare.18585461.v1
title: RCC VF table data sharing_rev4.xlsx

description: <b>The Value Measurement of Emerging Therapeutics in Renal Cell Carcinoma: ASCO Value Framework and ESMO-MCBS</b>