cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4274088
title: Supplementary Material for "Kinetic Analysis of the Hydrolysis of Pentose-1-phosphates through Apparent Nucleoside Phosphorolysis Equilibrium Shifts"

description: This is the external Supplementary Information for our publication "Kinetic Analysis of the Hydrolysis of Pentose-1-phosphates through Apparent Nucleoside Phosphorolysis Equilibrium Shifts". The .pdf file contains the Supplementary Information: author contributions, experimental procedures, derivation of equations and supplementary items. The .zip file contains the raw data and metadata for all items (supplementary and main text) and the calculations. Like most publications, this one builds on previous work. The Python code used for spectral unmixing is available on this platform (10.5281/zenodo.3243376) and has been described in our previous publications (10.3390/mps2030060, 10.5281/zenodo.3333469, 10.1002/cbic.202000204, 10.5281/zenodo.3723806 ). Further applications of this method can be found in our recent article in <em>Adv. Synth. Catal.</em> (10.1002/adsc.201901230) and its supporting material (10.5281/zenodo.3568858), as well as a preprint (10.26434/chemrxiv.12993416.v1) and its supporting material (10.5281/zenodo.4043929).
DOI: 10.5281/zenodo.4088389
title: Supplementary Material for "Kinetic Analysis of the Hydrolysis of Pentose-1-phosphates through Apparent Nucleoside Phosphorolysis Equilibrium Shifts"

description: This is the external Supplementary Information for our publication "Kinetic Analysis of the Hydrolysis of Pentose-1-phosphates through Apparent Nucleoside Phosphorolysis Equilibrium Shifts". The .pdf file contains the Supplementary Information: author contributions, experimental procedures, derivation of equations and supplementary items. The .zip file contains the raw data and metadata for all items (supplementary and main text) and the calculations. Like most publications, this one builds on previous work. The Python code used for spectral unmixing is available on this platform (10.5281/zenodo.3243376) and has been described in our previous publications (10.3390/mps2030060, 10.5281/zenodo.3333469, 10.1002/cbic.202000204, 10.5281/zenodo.3723806 ). Further applications of this method can be found in our recent article in <em>Adv. Synth. Catal.</em> (10.1002/adsc.201901230) and its supporting material (10.5281/zenodo.3568858), as well as a preprint (10.26434/chemrxiv.12993416.v1) and its supporting material (10.5281/zenodo.4043929).
DOI: 10.21227/9crg-j792
title: submit 6

description: sub test 6
DOI: 10.21227/mm2p-6z98
title: submit 7

description: submit test 7
DOI: 10.21227/dnzy-4b75
title: submit 8

description: submit test 8
DOI: 10.21227/fg0j-4g44
title: submit 9

description: submit test 9
DOI: 10.5517/ccdc.csd.cc24lx6w
title: CCDC 1984378: Experimental Crystal Structure Determination

description: Related Article: Qian-Qian Hao, Xia-Lin Dai, Yong-Liang Huang, Jia-Mei Chen, Tong-Bu Lu|2020|Cryst.Growth Des.|20|7492|doi:10.1021/acs.cgd.0c01169
DOI: 10.5281/zenodo.4273921
title: Steamgen dataset

description: Dataset taken from https://www.cs.ucr.edu/~eamonn/iSAX/steamgen.dat who adopted it from https://ieeexplore.ieee.org/document/481767 Stored on Zenodo as backup for multiple tutorials found on https://stumpy.readthedocs.io/en/latest/tutorials.html
DOI: 10.5281/zenodo.4273920
title: Steamgen dataset

description: Dataset taken from https://www.cs.ucr.edu/~eamonn/iSAX/steamgen.dat who adopted it from https://ieeexplore.ieee.org/document/481767 Stored on Zenodo as backup for multiple tutorials found on https://stumpy.readthedocs.io/en/latest/tutorials.html
DOI: 10.21227/3rjt-rg09
title: opt expert

description: tuning lgp (without temperature) with GP optimization
DOI: 10.5519/qd.qgv991c1
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.21227/vjg3-em74
title: A dataset containing the table of contents of 56K ebook titles extracted from Springer

description: This dataset has been created from a collection of 56403 multidisciplinary book titles from Springer, available through the Hellenic Academic Libraries Link (https://www.heal-link.gr/en/home-2/) subscription. To obtain this dataset, a parser was created for extracting relevant information, such as the title, subtitle and ToC, from each book. The extracted information was stored in a database for further processing. Each book title in the database includes information regarding the bookid, title, and ToC. As a next step, a team of librarians who were working in the NTUA Digital Library manually added the subject field information. This dataset contains the primary subject field as each book’s label. In the 5 categories sub set there is also another field that contains the secondary labels for each book in the collectionThis dataset can serve as a basis for multiclass classification problems and/or content recommendation. The 5 categories subset can also be used for multilabel classification tasks. By utilizing information from the ToC, we can better capture the topics in each book, thereby facilitating the identification of similar books. The dataset contations 2 subsets: a. 26 categories, and b. 5 general categories as detailed below:26 categories: Anthropology, Art, Computer Science, Culture, Economics, Education, Engineering, Environment, Food, History, Humanities, Law, Life Sciences, Linguistics, Literature, Management, Mathematics, Medicine, Music, Organization, Physical Sciences, Popular works, Religion, Social Sciences, Science, Transportation5: categories: Computer Science, Engineering, Mathematics, Medicine, Physics
DOI: 10.21227/8asd-5k31
title: A dataset containing the table of contents of 56K ebook titles extracted from Springer

description: This dataset has been created from a collection of 56403 multidisciplinary book titles from Springer, available through the Hellenic Academic Libraries Link (https://www.heal-link.gr/en/home-2/) subscription. To obtain this dataset, a parser was created for extracting relevant information, such as the title, subtitle and ToC, from each book. The extracted information was stored in a database for further processing. Each book title in the database includes information regarding the bookid, title, and ToC. As a next step, a team of librarians who were working in the NTUA Digital Library manually added the subject field information. This dataset contains the primary subject field as each book’s label. In the 5 categories sub set there is also another field that contains the secondary labels for each book in the collectionThis dataset can serve as a basis for multiclass classification problems and/or content recommendation. The 5 categories subset can also be used for multilabel classification tasks. By utilizing information from the ToC, we can better capture the topics in each book, thereby facilitating the identification of similar books. The dataset contations 2 subsets: a. 26 categories, and b. 5 general categories as detailed below:26 categories: Anthropology, Art, Computer Science, Culture, Economics, Education, Engineering, Environment, Food, History, Humanities, Law, Life Sciences, Linguistics, Literature, Management, Mathematics, Medicine, Music, Organization, Physical Sciences, Popular works, Religion, Social Sciences, Science, Transportation5: categories: Computer Science, Engineering, Mathematics, Medicine, Physics
DOI: 10.5519/qd.ifn9xjxz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.21227/0qh7-qz77
title: grupo2_test

description: We used neural networks to make clusters, classify the data and to predict the consumption. The data is classidied by the daily parametres.
DOI: 10.21227/ggh2-ba93
title: grupo2_test2

description: Another try.
DOI: 10.6084/m9.figshare.12057957
title: Coursera project data

description: Illumina reads for Coursera course project
DOI: 10.6084/m9.figshare.12057957.v5
title: Coursera project data

description: Illumina reads for Coursera course project
DOI: 10.6084/m9.figshare.12057957.v4
title: Coursera project data

description: Illumina reads for Coursera course project
DOI: 10.5519/qd.ds7qpxa0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (594 records)
DOI: 10.21227/40y2-pj28
title: MATLAB code to test SPIM approach for SS

description: MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different threshold control values A and U. Compare the errors in detection for the SPIM method and the energy detector. Plot the values of the TPR FPR rates to calculate the ROC curve. Determines the MAD PDF of the instantaneous phase and the average instantaneous amplitude for comparison.
DOI: 10.21227/vgrh-6v88
title: MATLAB code to test SPIM approach for SS

description: MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different threshold control values A and U. Compare the errors in detection for the SPIM method and the energy detector. Plot the values of the TPR FPR rates to calculate the ROC curve. Determines the MAD PDF of the instantaneous phase and the average instantaneous amplitude for comparison.
DOI: 10.21227/ysxy-jw57
title: MATLAB code to test SPIM approach for SS

description: MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different threshold control values A and U. Compare the errors in detection for the SPIM method and the energy detector. Plot the values of the TPR FPR rates to calculate the ROC curve. Determines the MAD PDF of the instantaneous phase and the average instantaneous amplitude for comparison.
DOI: 10.21227/ppwq-w655
title: MATLAB code to test SPIM approach for SS

description: MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different threshold control values A and U. Compare the errors in detection for the SPIM method and the energy detector. Plot the values of the TPR FPR rates to calculate the ROC curve. Determines the MAD PDF of the instantaneous phase and the average instantaneous amplitude for comparison.
DOI: 10.21227/1fyg-qm86
title: 6.66 kVA Solar

description: This dataset is in support of my original Research Paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)Simulation is made to support the earlier proposed project (rejected) for funding.This is not connected to the grid but the other related dataset " " , where this will b conencted to the inverter and will be connected to the grid.This experiment is different from the exisiting approaches in the following ways(a) In the general approach, the solar panels are(b) The boost stage in general is(c) The mppt is(d) The(e)Abstract of the above submitted paper
DOI: 10.21227/nssr-fk07
title: 6.66 kVA Solar

description: This dataset is in support of my original Research Paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)Simulation is made to support the earlier proposed project (rejected) for funding.This is not connected to the grid but the other related dataset " " , where this will b conencted to the inverter and will be connected to the grid.This experiment is different from the exisiting approaches in the following ways(a) In the general approach, the solar panels are(b) The boost stage in general is(c) The mppt is(d) The(e)Abstract of the above submitted paper
DOI: 10.21227/drcz-j432
title: 6.66 kVA Solar

description: This dataset is in support of my original Research Paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)Simulation is made to support the earlier proposed project (rejected) for funding.This is not connected to the grid but the other related dataset " " , where this will b conencted to the inverter and will be connected to the grid.This experiment is different from the exisiting approaches in the following ways(a) In the general approach, the solar panels are(b) The boost stage in general is(c) The mppt is(d) The(e)Abstract of the above submitted paper
DOI: 10.21227/c9bk-kh14
title: 6.66 kVA Solar Inverter

description: This dataset is in support of my original Research Paper " 6.66 kVA Solar Inverter " submitted to "IEEE Transactions on Power Electronics" (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=63)This experiment is different from the exisiting approaches in the following ways(a)(b) Non-Linear(c)(d)(e)Abstract of the above submitted paper
DOI: 10.6084/m9.figshare.12338585
title: Guay J and Kopp SL PNBs for Hip Fractures in Adults RoB2 20 May 2020.pdf

description: Risks of Bias 2 applied to Cochrane Review Peripheral Nerve Blocks for Hip Fractures in Adults by Joanne Guay and Sandra L Kopp
DOI: 10.6084/m9.figshare.12338585.v7
title: Guay J and Kopp SL PNBs for Hip Fractures in Adults RoB2 20 May 2020.pdf

description: Risks of Bias 2 applied to Cochrane Review Peripheral Nerve Blocks for Hip Fractures in Adults by Joanne Guay and Sandra L Kopp
DOI: 10.21227/srcg-v227
title: 25 kVA PFC

description: This dataset is in support of my original Research Paper " Grid-Connected Wind " submitted to "IEEE Transactions on Power Electronics" (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=63) Abstract of the above submitted paper
DOI: 10.21227/fh8a-r755
title: 6.66 kVA Solar

description: This dataset is in support of my original Research Paper " 6.66kVA Solar " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60)This is not connected to the grid but a DC-DC . The other related dataset " " , where this will be conencted to the inverter which in turn will be connected to grid.This experiment is different from the exisiting approaches in the following ways(a) In the general approach, the solar panels are(b) The boost stage in general is(c) The mppt is(d) The(e)Abstract of the above submitted paper
DOI: 10.21227/yxjy-qc26
title: nn v2

description: ML model multi node processing
DOI: 10.25919/gg2j-mb92
title: Parkes observations for project P1067 semester 2020APRS_08

description: The Parkes Ultra-wide-bandwidth receiver enables, for the first time with the Parkes telescope, a commensal high time and high frequency resolution survey. We will observe Baade's window in which we are guaranteed detections of pulsars and spectral lines. Our science drivers are diverse and range from understanding scattering, to detecting new pulsars, to searching for galaxies in the zone of avoidance, probing the globular cluster at the centre of the window and studying OH circumstellar masers and CO-dark molecular gas. The observing strategy and processing software developed for this survey will be applicable to all future Parkes surveys with the wide-band receiver or cyrogenically-cooled phased array feed.
DOI: 10.6084/m9.figshare.3761562.v1678
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.21227/htmm-6488
title: Model 2.1.1

description: Model 2.1.1 Clustered-meter average monthly ratios Monthly (kWh) consumption clustering: 1. Twelve clustering models (one per month of signup): Spectral Clustering with nearest neighbors.2. Clustering using (daily kWh) consumption extracted features. Cluster count set to 2-4: final count is decided by maximum silhouette score of manhattan distances.3. Meters not in the training clustering data, are classified to a specific cluster using a Gaussian Process Classifier (GPC) on the same dataset (12 classifiers total). Features (170) from weekend/weekday and full series:- Statistical: median, variance, quantiles, ...- Time-series: autocorrelations, trends, seasonalities, ... Forecasting for a meter: Using the median montly consumption + in-cluster month-to-month ratios. Forecasting for a meter: Using the in-cluster month-to-month ratios * most probable value from a non-parametric Kernel Density Estimation.
DOI: 10.21227/we1h-p440
title: Model 2.1.3

description: Model 2.1.3 Clustered-meter average monthly ratios Monthly (kWh) consumption clustering: 1. Twelve clustering models (one per month of signup): Spectral Clustering with nearest neighbors.2. Clustering using (daily kWh) consumption extracted features. Cluster count set to 2-4: final count is decided by maximum silhouette score of manhattan distances.3. Meters not in the training clustering data, are classified to a specific cluster using a Gaussian Process Classifier (GPC) on the same dataset (12 classifiers total). Features (170) from weekend/weekday and full series:- Statistical: median, variance, quantiles, ...- Time-series: autocorrelations, trends, seasonalities, ... Forecasting for a meter: Using the median montly consumption + in-cluster month-to-month ratios. Forecasting for a meter: Using the in-cluster month-to-month ratios * most probable value from a non-parametric Kernel Density Estimation (fixed bug).
DOI: 10.5281/zenodo.4274295
title: Enhanced Artifical Model Metadata from Software Repositories

description: Replication set for evaluations performed in EMSE submission "Enhanced Artifical Model Metadata from Software Repositories"
DOI: 10.3886/e126581
title: Cash Transfers as a Response to COVID-19: A Randomized Experiment in Kenya

description: This archive contains the data to replicate the Brooks, et al. (2020) working paper "Cash Transfers as a Response to COVID-19: A Randomized Experiment in Kenya."
DOI: 10.3886/e126581v1
title: Cash Transfers as a Response to COVID-19: A Randomized Experiment in Kenya

description: This archive contains the data to replicate the Brooks, et al. (2020) working paper "Cash Transfers as a Response to COVID-19: A Randomized Experiment in Kenya."
DOI: 10.21227/kx9c-4564
title: attention-based convolutional gating fusion network

description: End-to-end Approach for Energy Prediction:Attention-based Convolutional Gating for Late Fusion of Meta, Weather, and Historical Consumption
DOI: 10.5281/zenodo.4273982
title: Wikidata Dump P279

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4273981
title: Wikidata Dump P279

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4273984
title: Wikidata Dump P279

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4273983
title: Wikidata Dump P279

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.6084/m9.figshare.13238534
title: Data Extraction Form - Factors Affecting Elective Cesarean Section in Women with Multiple Pregnancy at a Primary Referral Hospital in Indonesia

description: This file represents the form used for data extraction purposes from medical records<br>
DOI: 10.6084/m9.figshare.13238534.v1
title: Data Extraction Form - Factors Affecting Elective Cesarean Section in Women with Multiple Pregnancy at a Primary Referral Hospital in Indonesia

description: This file represents the form used for data extraction purposes from medical records<br>
DOI: 10.5281/zenodo.4118355
title: Saldo das operações de crédito por atividade econômica - Banco Central do Brasil - BCB

description: Base de dados consolidada pelo Calebe em arquivo CSV - Os arquivos em separados podem acessar a fonte no caso de desejar outros formatos. *Colhidos da origem em 22/10/2020 - Depositado pelo Calebe em 22/10/2020 Saldo das operações de crédito por atividade econômica - Indústria de mineração JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Indústria de metalur... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Indústria de petróle... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Indústria de papel e... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Indústria de têxtil,... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Indústria de açúcar JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Indústria de alimentos JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Serviços financeiros JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Demais serviços pres... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Demais serviços pres... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Serviços de informaç... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Serviços imobiliários JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Geral - bens de capital JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Geral - bens interme... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Geral - veículos aut... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Atacado - bens duráv... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Varejo - bens duráveis JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Varejo - bens não du... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Dutoviário Saldo das operações de crédito por atividade econômica - Meios aquaviário e a... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Via terrestre (carga... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Indústria de bens de... JSON CSV wsdl HTML Saldo das operações de crédito por atividade econômica - Indústria de embalagens
description: O arquivo consolidado foi acrescido de uma coluna indicando a origem dos valores para poder ser útil para criação de gráficos ou outras avaliações. Qualquer dúvida, crítica ou sugestão pode contatar caleberepositório@gmail.com