cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/3tpp-j394
title: Multi-band Wireless Channel Measurements in High-mobility Environment

description: Future mobile communication systems include millimeter wave (mmWave) frequency bands and high mobility scenarios. To learn how wave propagation and scattering effects change from classical sub 6 GHz to mmWave frequencies, measurements in both bands have to be conducted. We perform wireless channel measurements at 2.55 GHz and 25.5 GHz center frequency at velocites of 40 km/h and 100 km/h. To ensure a fair comparison between these two frequency bands, we perform repeatable measurements in a controlled environment. Our measurement methodology enables measurements at the same transmitter and receiver positions and velocities, but at different center frequencies.
DOI: 10.5281/zenodo.6778094
title: Efficient Approximation Algorithms for the Diameter-Bounded Max-Coverage Group Steiner Tree Problem

description: It contains all the data used in our experiments, including 5 real graphs (<code>MONDIAL</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, and <code>DBpedia</code>) and 3 synthetic graphs (<code>LUBM-50K</code>, <code>LUBM-500K</code>, and <code>LUBM-5M</code>). Each real KG directory contains 8 files, including: <code>graph.txt</code>: The first value is the number of vertices. Then each line 'u v' means there is an undirected edge between 'u' and 'v'. <code>Weightgraph.txt</code>: The first value is the number of vertices. Then each line 'u v w' means there is an undirected edge between 'u' and 'v' weighted by 'w' which is computed by the Informativeness-based Weighting (IW) scheme. <code>nodeName.txt</code>: Mapping from vertex ID to vertex name (i.e., entity URI). <code>query.txt</code>: Each line is a keyword query containing a set of keyword names. <code>kwName.txt</code>: Mapping from keyword ID to keyword name. <code>kwMap.txt</code>: Mapping from keyword ID to vertex IDs. The first value of each line is keyword ID, and the rest are vertex IDs. <code>UWHBLL.txt</code>: The HBLL index file which was built based on the Unit Weighting. <code>IWHBLL.txt</code>: The HBLL index file which was built based on the Informativeness-based Weighting. Each synthetic directory contains 6 files, including: <code>graph.txt</code>: same as above. <code>Weightgraph.txt</code>: same as above. <code>nodeName.txt</code>: same as above. <code>queryList.txt</code>: Each line contains a set (separated by ',') of sets of vertex IDs. <code>UWHBLL.txt</code>: same as above. <code>IWHBLL.txt</code>: same as above. Apart from that, <code>Dbpedia</code> and <code>LUBM-5M</code> also contain a <code>PLLlabel.txt</code> file which was the supplementary file for the HBLL index.
DOI: 10.5281/zenodo.6778095
title: Efficient Approximation Algorithms for the Diameter-Bounded Max-Coverage Group Steiner Tree Problem

description: It contains all the data used in our experiments, including 5 real graphs (<code>MONDIAL</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, and <code>DBpedia</code>) and 3 synthetic graphs (<code>LUBM-50K</code>, <code>LUBM-500K</code>, and <code>LUBM-5M</code>). Each real KG directory contains 6 files, including: <code>graph.txt</code>: The first value is the number of vertices. Then each line 'u v' means there is an undirected edge between 'u' and 'v'. <code>Weightgraph.txt</code> The first value is the number of vertices. Then each line 'u v w' means there is an undirected edge between 'u' and 'v' weighted by 'w' which is computed by the Informativeness-based Weighting (IW) scheme. <code>nodeName.txt</code>: Mapping from vertex ID to vertex name (i.e., entity URI). <code>query.txt</code>: Each line is a keyword query containing a set of keyword names. <code>kwName.txt</code>: Mapping from keyword ID to keyword name. <code>kwMap.txt</code>: Mapping from keyword ID to vertex IDs. The first value of each line is keyword ID, and the rest are vertex IDs. Each synthetic directory contains 4 files, including: <code>graph.txt</code>: same as above. <code>Weightgraph.txt</code>: same as above. <code>nodeName.txt</code>: same as above. <code>queryList.txt</code>: Each line contains a set (separated by ',') of sets of vertex IDs. Generate HBLL and Hub Label
DOI: 10.5281/zenodo.7243772
title: Efficient Approximation Algorithms for the Diameter-Bounded Max-Coverage Group Steiner Tree Problem

description: It contains all the data used in our experiments, including 5 real graphs (<code>MONDIAL</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, and <code>DBpedia</code>) and 3 synthetic graphs (<code>LUBM-50K</code>, <code>LUBM-500K</code>, and <code>LUBM-5M</code>). Each real KG directory contains 8 files, including: <code>graph.txt</code>: The first value is the number of vertices. Then each line 'u v' means there is an undirected edge between 'u' and 'v'. <code>Weightgraph.txt</code>: The first value is the number of vertices. Then each line 'u v w' means there is an undirected edge between 'u' and 'v' weighted by 'w' which is computed by the Informativeness-based Weighting (IW) scheme. <code>nodeName.txt</code>: Mapping from vertex ID to vertex name (i.e., entity URI). <code>query.txt</code>: Each line is a keyword query containing a set of keyword names. <code>kwName.txt</code>: Mapping from keyword ID to keyword name. <code>kwMap.txt</code>: Mapping from keyword ID to vertex IDs. The first value of each line is keyword ID, and the rest are vertex IDs. <code>UWHBLL.txt</code>: The HBLL index file which was built based on the Unit Weighting. <code>IWHBLL.txt</code>: The HBLL index file which was built based on the Informativeness-based Weighting. Each synthetic directory contains 6 files, including: <code>graph.txt</code>: same as above. <code>Weightgraph.txt</code>: same as above. <code>nodeName.txt</code>: same as above. <code>queryList.txt</code>: Each line contains a set (separated by ',') of sets of vertex IDs. <code>UWHBLL.txt</code>: same as above. <code>IWHBLL.txt</code>: same as above. Apart from that, <code>Dbpedia</code> and <code>LUBM-5M</code> also contain a <code>PLLlabel.txt</code> file which was the supplementary file for the HBLL index.
DOI: 10.6084/m9.figshare.21432570
title: 科技论文颁奖数据.xlsx

description: 该数据是论文的科技论文颁奖相关数据
DOI: 10.6084/m9.figshare.21432570.v1
title: 科技论文颁奖数据.xlsx

description: 该数据是论文的科技论文颁奖相关数据
DOI: 10.6084/m9.figshare.21432579
title: Cloonmore archaeobotanical data.pdf

description: PDF document outlining archaeobotanical data from Cloonmore 21E0140 in Co. Kerry, Ireland. Associated with Johnston, Penny (2022): Cloonmore_Kerry_21E0140_Oct22.csv. figshare. Dataset. https://doi.org/10.6084/m9.figshare.21432564.v1
DOI: 10.6084/m9.figshare.21432564
title: Cloonmore_Kerry_21E0140_Oct22.csv

description: Archaeobotanical dataset from a site excavated at Cllonmore, Co. Kerry (21E0140) PDF outlining context and methodological details at https://doi.org/10.6084/m9.figshare.21432579.v1
DOI: 10.6084/m9.figshare.14552136.v5
title: Gun Violence

description: See the codebook for full details. A mass shooting is defined as four or more people injured or killed, because of firearms, excluding the shooter. The data on mass shootings is from https://www.gunviolencearchive.org/ .<br> <br> The curated datasets are included here along with a research question and guiding questions.<br> <br>
DOI: 10.6084/m9.figshare.21432606
title: 同行评议文本数据.xlsx

description: 这是关于论文的同行评议文本数据
DOI: 10.34740/kaggle/dsv/4421539
title: Ukraine Conflict Twitter Dataset (54.26M tweets)
DOI: 10.6084/m9.figshare.21432612
title: data zelezo OTS_LVAD_2021_anonymized.xlsx

description: Data set on patients with advanced heart failure. We focused on iron deficiency parameters.
DOI: 10.34740/kaggle/dsv/4422921
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.21378870
title: 颠覆性技术弱信号识别结果.xlsx

description: this is data of A article
DOI: 10.6084/m9.figshare.21378870.v2
title: 颠覆性技术弱信号识别结果.xlsx

description: this is data of A article
DOI: 10.6084/m9.figshare.21378870.v1
title: 论文数据.xlsx

description: this is data of A article
DOI: 10.34740/kaggle/dsv/4422958
title: Elon Musk Tweets
DOI: 10.5281/zenodo.7264573
title: A systematic assessment of deep learning methods for drug response prediction: From in vitro to clinical applications

description: ## GDSC dataset **GDSC_EXP.csv** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. **GDSC_MUT.csv** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0. **GDSC_CNV.csv** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1. **GDSC_DR.csv** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout. **GDSC_DrugAnnotation.csv** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage. ## TCGA dataset **TCGA_EXP.csv** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. **TCGA_MUT.csv** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0. **TCGA_CNV.csv** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1. **TCGA_DR.csv** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder. ## PMID17185464 (Bortezomib) dataset **PMID17185464_EXP.csv** Bortezomib clinical trial gene expression profiles, where each column represents a patient in the form of patient ID, and each row represents a gene in the form of the HGNC symbol. **PMID17185464_DR.csv** Bortezomib clinical trial clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, and the last column shows the clinical label as responder or non-responder (NR: Non-responder, R: Responder).
DOI: 10.5281/zenodo.7264572
title: A systematic assessment of deep learning methods for drug response prediction: From in vitro to clinical applications

description: ## GDSC dataset **GDSC_EXP.csv** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. **GDSC_MUT.csv** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0. **GDSC_CNV.csv** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1. **GDSC_DR.csv** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout. **GDSC_DrugAnnotation.csv** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage. ## TCGA dataset **TCGA_EXP.csv** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. **TCGA_MUT.csv** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0. **TCGA_CNV.csv** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1. **TCGA_DR.csv** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder. ## PMID17185464 (Bortezomib) dataset **PMID17185464_EXP.csv** Bortezomib clinical trial gene expression profiles, where each column represents a patient in the form of patient ID, and each row represents a gene in the form of the HGNC symbol. **PMID17185464_DR.csv** Bortezomib clinical trial clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, and the last column shows the clinical label as responder or non-responder (NR: Non-responder, R: Responder).
DOI: 10.5281/zenodo.7265088
title: Ariano-Sánchez et al-Sand temperatures on sea turtle nesting beaches

description: Dataset of sand temperatures on sea turtle nesting beaches recorded for 2018-2019 from two dark volcanic sand beaches in the Pacific coast of Guatemala, Central America. R Code for analysis of dataset.
DOI: 10.5281/zenodo.7265087
title: Ariano-Sánchez et al-Sand temperatures on sea turtle nesting beaches

description: Dataset of sand temperatures on sea turtle nesting beaches recorded for 2018-2019 from two dark volcanic sand beaches in the Pacific coast of Guatemala, Central America. R Code for analysis of dataset.
DOI: 10.21227/1xq3-te31
title: A Modular Structure Windings Variable Reluctance Resolver

description: FEA-RDC-simulation data and prototype data
DOI: 10.6084/m9.figshare.21432722
title: Metadata and references from A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks

description: Metadata of all data examples in csv format, and list of cited references
DOI: 10.6084/m9.figshare.21432725
title: Neural Network from A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks

description: The neural network used in this paper; python code to make predictions; and detailed instructions
DOI: 10.6084/m9.figshare.21432725.v1
title: Neural Network from A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks

description: The neural network used in this paper; python code to make predictions; and detailed instructions
DOI: 10.6084/m9.figshare.21432727
title: Dataset from A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks

description: The full dataset in the form of black-and-white png images
DOI: 10.6084/m9.figshare.21432727.v1
title: Dataset from A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks

description: The full dataset in the form of black-and-white png images
DOI: 10.6084/m9.figshare.21432750
title: TOTJ Appendix

description: This is the Appendix for the paper Energy-efficient Train Control and Integrated System Energy Management Considering PV Generation Forecast Information Jinpeng Li1, Minling Feng1, Chaoxian Wu2, Shaofeng Lu1,* <em>1Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 510000, China;</em> <em>2School of Systems Science and Engineering, Sun Yat-sen University, 510000, China</em>
DOI: 10.6084/m9.figshare.21432750.v1
title: TOTJ Appendix

description: This is the Appendix for the paper Energy-efficient Train Control and Integrated System Energy Management Considering PV Generation Forecast Information Jinpeng Li1, Minling Feng1, Chaoxian Wu2, Shaofeng Lu1,* <em>1Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 510000, China;</em> <em>2School of Systems Science and Engineering, Sun Yat-sen University, 510000, China</em>
DOI: 10.6084/m9.figshare.21345600.v1
title: Table S1 from Greater variability in rhesus macaque (<i>Macaca mulatta</i>) endocranial volume among males than females

description: Model results for relative ECV, absolute ECV, and body size
DOI: 10.6084/m9.figshare.21345600
title: Table S1 from Greater variability in rhesus macaque (<i>Macaca mulatta</i>) endocranial volume among males than females

description: Model results for relative ECV, absolute ECV, and body size
DOI: 10.6084/m9.figshare.21432768.v1
title: Original data and drawing files.rar

description: Conventional compost sludge takes a long time in the fermentation process and is not full of nutrients. Potassium-rich mining waste was used as an additive for aerobic composting of activated sludge to make a new sludge product. The effects of different feeding ratios of potassium-rich mining waste and activated sludge on physicochemical properties, thermophilic bacteria community structure during aerobic composting were investigated. The results showed that potassium-rich waste minerals contribute to the increase of mineral element content; although the addition of potassium-rich waste minerals affected the peak temperature and duration of compost, the more sufficient oxygen content promoted the growth of thermophilic bacteria, and thus shortened the overall compost period. Considering the requirements of composting temperature, it is recommended that the addition of potassium-rich waste minerals should be less than or equal to 20%.
DOI: 10.6084/m9.figshare.21432783
title: 52 razor clam ABC cds

description: The file contains the coding sequences of the total 52 ABC transporters identified in razor clam(<em>Sinonovacula constricta</em>).
DOI: 10.6084/m9.figshare.21432783.v1
title: 52 razor clam ABC cds

description: The file contains the coding sequences of the total 52 ABC transporters identified in razor clam(<em>Sinonovacula constricta</em>).
DOI: 10.6084/m9.figshare.21432789
title: Survey Package on Technical Debt in Uruguayan Software Organizations

description: Laboratory package that contains: - Row data - Survey questionnaire in Spanish - Survey questionnaire in English
DOI: 10.6084/m9.figshare.21432789.v1
title: Survey Package on Technical Debt in Uruguayan Software Organizations

description: Laboratory package that contains: - Row data - Survey questionnaire in Spanish - Survey questionnaire in English
DOI: 10.5281/zenodo.7265181
title: Raw time series of GNSS sites used in the manuscript 'South China plate motion modified by 2008 Mw7.9 great Wenchuan earthquake'

description: The *.pre.*.pos files are raw position time series before the Wenchuan earthquake (from July 2001 to December 2004) and the *.aft.*.pos files are the raw postition time sereis after the Wenchuan earthquake (from July 2014 to December 2017). The file 'breaks' contains the epoches of breaks due to equipment changes or that are indentified visually.
DOI: 10.5281/zenodo.7265180
title: Raw time series of GNSS sites used in the manuscript 'South China plate motion modified by 2008 Mw7.9 great Wenchuan earthquake'

description: The *.pre.*.pos files are raw position time series before the Wenchuan earthquake (from July 2001 to December 2004) and the *.aft.*.pos files are the raw postition time sereis after the Wenchuan earthquake (from July 2014 to December 2017). The file 'breaks' contains the epoches of breaks due to equipment changes or that are indentified visually.
DOI: 10.6084/m9.figshare.21432795
title: Potassium-rich mining waste addition can shorten the composting period by increasing the abundance of thermophilic bacteria at high temperature period

description: Conventional compost sludge takes a long time in the fermentation process and is not full of nutrients. Potassium-rich mining waste was used as an additive for aerobic composting of activated sludge to make a new sludge product. The effects of different feeding ratios of potassium-rich mining waste and activated sludge on physicochemical properties, thermophilic bacteria community structure during aerobic composting were investigated. The results showed that potassium-rich waste minerals contribute to the increase of mineral element content; although the addition of potassium-rich waste minerals affected the peak temperature and duration of compost, the more sufficient oxygen content promoted the growth of thermophilic bacteria, and thus shortened the overall compost period. Considering the requirements of composting temperature, it is recommended that the addition of potassium-rich waste minerals should be less than or equal to 20%.
DOI: 10.6084/m9.figshare.21432795.v1
title: Potassium-rich mining waste addition can shorten the composting period by increasing the abundance of thermophilic bacteria at high temperature period

description: Conventional compost sludge takes a long time in the fermentation process and is not full of nutrients. Potassium-rich mining waste was used as an additive for aerobic composting of activated sludge to make a new sludge product. The effects of different feeding ratios of potassium-rich mining waste and activated sludge on physicochemical properties, thermophilic bacteria community structure during aerobic composting were investigated. The results showed that potassium-rich waste minerals contribute to the increase of mineral element content; although the addition of potassium-rich waste minerals affected the peak temperature and duration of compost, the more sufficient oxygen content promoted the growth of thermophilic bacteria, and thus shortened the overall compost period. Considering the requirements of composting temperature, it is recommended that the addition of potassium-rich waste minerals should be less than or equal to 20%.
DOI: 10.34740/kaggle/dsv/4423384
title: Mahanagar Yatayat Dataset
DOI: 10.6084/m9.figshare.21432807.v1
title: Untitled Item

description: Supplementary materials
DOI: 10.6084/m9.figshare.21432830
title: Additional file 1 of Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT

description: Additional file 1. Individual radiomics features for the free-breathing and data-driven gating PET/CT data sets. Individual radiomics features values for the free-breathing (flow) and data-driven gating (DDG) PET/CT reconstruction of each pulmonary lesion. The radiomics features table is the output from the QuantImage platform (see Cid et al. (2017)).