cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13145/bacdive105714.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive105715.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106568.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106569.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106570.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106571.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106572.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106573.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive106574.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive107465.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive107466.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive107467.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive107468.20171208.2.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.5281/zenodo.4415731
title: chm13_asm_ctgs_2021_0104 / Jason Chin

description: Genome assembly of the CHM13 cell line. Done with a new assembler based on the SHIMMER indexing method. The run time is 1.6 hours with 64 core aarch CPUs. The wall clock time is about 1.6 hours. 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Total size: 3055717214<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Longest size: 140284871<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] N50: 82577644<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] N90: 17313701<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Number of contigs: 357<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Number of Contigs &gt; 100kb: 207 2021-01-04 08:48:22,512 INFO [pg_asm] pg_asm run end<br> 2021-01-04 08:48:22,512 INFO [pg_asm] total user cpu time: 272568 seconds = 75.71333 hours<br> 2021-01-04 08:48:22,512 INFO [pg_asm] total system cpu time: 920 seconds = 0.25555554 hours<br> 2021-01-04 08:48:22,512 INFO [pg_asm] total elapse time: 5773.4155 seconds = 1.6037266 hours Here is the result of the CHM13 BAC coverage with the assembly <br> Checking attempted with maximum distance from contig end of 1000 bp..Done<br> ******************* BAC SUMMARY ******************<br> TOTAL : 341<br> BP : 51532183<br> ************** Statistics for: _test9.fa ****************<br> BACs closed: 330 (96.7742); BACs attempted: 341 %good = 96.7742; BASES 49874385 (96.783)<br> Median: 99.99135<br> MedianQV: 40.62984<br> Mean: 99.94396<br> MeanQV: 32.51511<br> ***** STATS IGNORING INDELS ********************<br> Median: 100<br> MedianQV: Inf<br> Mean: 99.99418<br> MeanQV: 42.34828<br> **********************************************
DOI: 10.5281/zenodo.4415730
title: chm13_asm_ctgs_2021_0104 / Jason Chin

description: Genome assembly of the CHM13 cell line. Done with a new assembler based on the SHIMMER indexing method. The run time is 1.6 hours with 64 core aarch CPUs. The wall clock time is about 1.6 hours. 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Total size: 3055717214<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Longest size: 140284871<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] N50: 82577644<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] N90: 17313701<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Number of contigs: 357<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Number of Contigs &gt; 100kb: 207 2021-01-04 08:48:22,512 INFO [pg_asm] pg_asm run end<br> 2021-01-04 08:48:22,512 INFO [pg_asm] total user cpu time: 272568 seconds = 75.71333 hours<br> 2021-01-04 08:48:22,512 INFO [pg_asm] total system cpu time: 920 seconds = 0.25555554 hours<br> 2021-01-04 08:48:22,512 INFO [pg_asm] total elapse time: 5773.4155 seconds = 1.6037266 hours Here is the result of the CHM13 BAC coverage with the assembly <br> Checking attempted with maximum distance from contig end of 1000 bp..Done<br> ******************* BAC SUMMARY ******************<br> TOTAL : 341<br> BP : 51532183<br> ************** Statistics for: _test9.fa ****************<br> BACs closed: 330 (96.7742); BACs attempted: 341 %good = 96.7742; BASES 49874385 (96.783)<br> Median: 99.99135<br> MedianQV: 40.62984<br> Mean: 99.94396<br> MeanQV: 32.51511<br> ***** STATS IGNORING INDELS ********************<br> Median: 100<br> MedianQV: Inf<br> Mean: 99.99418<br> MeanQV: 42.34828<br> **********************************************
DOI: 10.21227/fk6v-9j19
title: Nurse Care Activities Datasets: In laboratory and in real field

description: Nurse Care Activity RecognitionActivity recognition is specially gaining attention in care facilities for remote monitoring of daily activities for elders living alone and automatic record creation for nurses in hospitals with a goal of providing better care to patients. One challenge for these applications is bridging the gap between models created within the lab and models for real life. In this challenge, participants will create methods for recognizing 12 activities conducted by nurses in both lab and in real-life settings. In real world, there is high chance of getting missing labels during experiments since users are busy at work. The main challenge is leveraging laboratory data to improve models in real-life. This dataset consists of data collected in both settings.
DOI: 10.34740/kaggle/dsv/1813900
title: 3500 Popular Creepypastas
DOI: 10.6073/pasta/241545f73a73e9d8b7b615e21e5cea2c
title: Roche Moutonnee Creek and Trevor Creek stream temperature and discharge measured each summer, Arctic LTER Toolik Field Station, Alaska, 2015-2019

description: Stream temperature and discharge measured each summer for Roche Moutonnee Creek and Trevor Creek. Dataloggers measured stream temperature and stream height at regular intervals. Stream height is converted into stream discharge based on a rating curve calculated from manual discharge measurements throughout the season. The principal investigator in charge of the temperature and discharge measurements is Dr. Breck Bowden.
DOI: 10.5517/ccdc.csd.cc25430g
title: CCDC 1999934: Experimental Crystal Structure Determination

description: Related Article: Guanyu Tao, Feichao Yang, Lujun Zhang, Yang Li, Zheng Duan, François Mathey|2021|Chin.Chem.Lett.|32|194|doi:10.1016/j.cclet.2020.11.018
DOI: 10.5517/ccdc.csd.cc25431h
title: CCDC 1999935: Experimental Crystal Structure Determination

description: Related Article: Guanyu Tao, Feichao Yang, Lujun Zhang, Yang Li, Zheng Duan, François Mathey|2021|Chin.Chem.Lett.|32|194|doi:10.1016/j.cclet.2020.11.018
DOI: 10.5517/ccdc.csd.cc25432j
title: CCDC 1999936: Experimental Crystal Structure Determination

description: Related Article: Guanyu Tao, Feichao Yang, Lujun Zhang, Yang Li, Zheng Duan, François Mathey|2021|Chin.Chem.Lett.|32|194|doi:10.1016/j.cclet.2020.11.018
DOI: 10.5517/ccdc.csd.cc25dvct
title: CCDC 2008346: Experimental Crystal Structure Determination

description: Related Article: Ryosuke Haraguchi, Tatsuro Yamazaki, Koki Torita, Tatsuki Ito, Shin-ichi Fukuzawa|2020|Dalton Trans.|49|17578|doi:10.1039/D0DT03213A
DOI: 10.5517/ccdc.csd.cc26gp2g
title: CCDC 2039895: Experimental Crystal Structure Determination

description: Related Article: Karlee L. Bamford, Douglas W. Stephan|2020|Dalton Trans.|49|17571|doi:10.1039/D0DT03665J
DOI: 10.5517/ccdc.csd.cc26gp3h
title: CCDC 2039896: Experimental Crystal Structure Determination

description: Related Article: Karlee L. Bamford, Douglas W. Stephan|2020|Dalton Trans.|49|17571|doi:10.1039/D0DT03665J
DOI: 10.5517/ccdc.csd.cc26gp4j
title: CCDC 2039897: Experimental Crystal Structure Determination

description: Related Article: Karlee L. Bamford, Douglas W. Stephan|2020|Dalton Trans.|49|17571|doi:10.1039/D0DT03665J
DOI: 10.5517/ccdc.csd.cc26gp5k
title: CCDC 2039898: Experimental Crystal Structure Determination

description: Related Article: Karlee L. Bamford, Douglas W. Stephan|2020|Dalton Trans.|49|17571|doi:10.1039/D0DT03665J
DOI: 10.5517/ccdc.csd.cc26gp6l
title: CCDC 2039899: Experimental Crystal Structure Determination

description: Related Article: Karlee L. Bamford, Douglas W. Stephan|2020|Dalton Trans.|49|17571|doi:10.1039/D0DT03665J
DOI: 10.5517/ccdc.csd.cc26ht0k
title: CCDC 2040978: Experimental Crystal Structure Determination

description: Related Article: S. Fwu Ming, L. Shie Fu|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1868|doi:10.1107/S2056989020014322
DOI: 10.5517/ccdc.csd.cc26hqxc
title: CCDC 2040913: Experimental Crystal Structure Determination

description: Related Article: S. Kotha, N.K. Gupta, S. Ansari|2020|IUCrData|5|x201464|doi:10.1107/S2414314620014649
DOI: 10.5517/ccdc.csd.cc24mrr9
title: CCDC 1985201: Experimental Crystal Structure Determination

description: Related Article: J. Quentin, E.W. Reinheimer, L.R. MacGillivray|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1859|doi:10.1107/S2056989020015303
DOI: 10.5517/ccdc.csd.cc1zd5m5
title: CCDC 1828957: Experimental Crystal Structure Determination

description: Related Article: T. Ayudhya, C. Raymond, N. Dingra|2020|Acta Crystallogr.,Sect.E:Cryst.Commun.|76|1854|doi:10.1107/S2056989020015327
DOI: 10.25345/c5t211
title: MassIVE MSV000086654 - Mouse cardiac proteomics + diabetes drugs
title: Proteomic analysis of mouse left ventricular wall post-exposure to diabetes drugs
DOI: 10.25345/c5pb6f
title: MassIVE MSV000086655 - The mechanism of action of apolipoprotein A5 - suppression of angiopoietin-like protein 3/8 complex-mediated inhibition of lipoprotein lipase activity
title: Global proteomics study of ANGPTL3/8 associated proteins
DOI: 10.5281/zenodo.4416033
title: Machine learning reactive mixing dataset-0

description: Reactive-mixing dataset-0 for machine learning analyses
description: For DIB article
DOI: 10.5281/zenodo.4416032
title: Machine learning reactive mixing dataset-0

description: Reactive-mixing dataset-0 for machine learning analyses
description: For DIB article
DOI: 10.5281/zenodo.3820026
title: OpenAIRE ScholeXplorer data dump in RDF

description: This dataset contains the RDF representation of OpenAIRE's ScholeXplore data dump from May 5th, 2019 (https://zenodo.org/record/2674330#.XrkfP6gzZPY).
DOI: 10.5281/zenodo.3820025
title: OpenAIRE ScholeXplorer data dump in RDF

description: This dataset contains the RDF representation of OpenAIRE's ScholeXplore data dump from May 5th, 2019 (https://zenodo.org/record/2674330#.XrkfP6gzZPY).
DOI: 10.6084/m9.figshare.13157585
title: Interactive Clustering of Cooking Recipe Instructions:Towards the Automatic Detection of Events Involving Kitchen Devices

description: Abstract: Cooking recipes are a rich source of semantic information. They contain instructions for food preparation tasks, specifying the actions that should be carried out which typically involve various ingredients and kitchen devices. In an IoT scenario, instructions in cooking recipes can form the basis for automatically controlling kitchen devices without any programming. However, as these instructions are written in natural language, they first need to be transformed or parsed into machine-interpretable commands. As a step towards this, we investigate methods for identifying the various types of actions (events) that kitchen devices are involved in. We cast this problem as a clustering task, whereby recipe instructions involving a given device of interest, are automatically grouped according to the type of event described. Each sentence in every instruction is represented by its embedding vector which is computed using a BERT-based model, specifically one pre-trained using a Roberta architecture. We cluster these sentence embeddings using our newly proposed interactive machine learning (IML)-based framework underpinned by the HDBScan clustering technique. We demonstrate that our IML framework can detect events in sentences with satisfactory accuracy, reaching almost the same level as human performance.
DOI: 10.6084/m9.figshare.13157585.v3
title: Interactive Clustering of Cooking Recipe Instructions:Towards the Automatic Detection of Events Involving Kitchen Devices

description: Abstract: Cooking recipes are a rich source of semantic information. They contain instructions for food preparation tasks, specifying the actions that should be carried out which typically involve various ingredients and kitchen devices. In an IoT scenario, instructions in cooking recipes can form the basis for automatically controlling kitchen devices without any programming. However, as these instructions are written in natural language, they first need to be transformed or parsed into machine-interpretable commands. As a step towards this, we investigate methods for identifying the various types of actions (events) that kitchen devices are involved in. We cast this problem as a clustering task, whereby recipe instructions involving a given device of interest, are automatically grouped according to the type of event described. Each sentence in every instruction is represented by its embedding vector which is computed using a BERT-based model, specifically one pre-trained using a Roberta architecture. We cluster these sentence embeddings using our newly proposed interactive machine learning (IML)-based framework underpinned by the HDBScan clustering technique. We demonstrate that our IML framework can detect events in sentences with satisfactory accuracy, reaching almost the same level as human performance.
DOI: 10.5281/zenodo.4416069
title: Automated prediction of visual complexity of web pages: Tools and evaluations

description: This dataset includes the screenshots of the web pages used for the evaluation of ViCRAM which is described in the following paper: Eleni Michailidou, Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2020. Automated Prediction of Visual Complexity of Web Pages: Tools and Evaluations. International Journal of Human-Computer Studies (SCI-E, SSCI), 145, 102523.
DOI: 10.5281/zenodo.4416068
title: Automated prediction of visual complexity of web pages: Tools and evaluations

description: This dataset includes the screenshots of the web pages used for the evaluation of ViCRAM which is described in the following paper: Eleni Michailidou, Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2020. Automated Prediction of Visual Complexity of Web Pages: Tools and Evaluations. International Journal of Human-Computer Studies (SCI-E, SSCI), 145, 102523.
DOI: 10.25387/g3.12637415.v1
title: Supplemental Material for Lee et al., 2020

description: File_S3 All annotated gene, repeat, CAZyme and secondary metabolite cluster features in GFF3 format, for <i>Ascochyta lentis</i> Al4<br>
DOI: 10.25387/g3.12637415
title: Supplemental Material for Lee et al., 2020

description: File_S3 All annotated gene, repeat, CAZyme and secondary metabolite cluster features in GFF3 format, for <i>Ascochyta lentis</i> Al4<br>
DOI: 10.18735/kb9v-6z16
title: Segmentation and genus classification of lactic acid bacteria from images

description: This comprises the full dataset used in the project "Segmentation and genus classification of lactic acid bacteria from images". It includes a training set, validation set and several test sets. Lastly it includes backgrounds which are needed for the custom image augmentation method described in the manuscript. The training and validation set are comprised of 1360 grayscale phase-contrast images containing >760,000 lactic acid bacteria which have been hand-labelled for segmentation (pixel classification) and classification of bacterial genus. Each image in the training and validation set contains only a single genus is named according to what genus it contains, e.g. "Lc" for Lactobacillus.
DOI: 10.7298/rekh-4w25
title: Supplemental data for: Identification, subtyping, and tracking of dairy spoilage-associated Pseudomonas by sequencing the ileS gene
DOI: 10.5281/zenodo.4405039
title: HIGH-REDSHIFT NARROW LINE SEYFERT 1 GALAXIES: A CANDIDATE SAMPLE

description: The study of Narrow line Seyfert 1 galaxies (NLS1s) is now mostly limited to low redshift (z &lt; 0.8). This is because their definition requires the presence of the Hβ emission line, which is red-shifted out of the spectral coverage of major ground-based spectroscopic surveys at z &gt; 0.8. We studied the correlation between the properties of Hβ and Mg II lines of a large sample of SDSS DR14 quasars to find high-z NLS1 candidates. Based on the strong correlation of FWHM(MgII)=(0.880±0.005)×FWHM(Hβ)+(0.438±0.018), we present a sample of high-z NLS1 candidates having FWHM of Mg II &lt; 2000 km s−1. The high-z sample contains 2684 NLS1s with redshift z = 0.8 − 2.5 with a median logarithmic bolometric luminosity of 46.16 ± 0.42 erg s−1, logarithmic black hole mass of 8.01 ± 0.35M⊙, and logarithmic Eddington ratio of 0.02 ± 0.27. The fraction of radio-detected high-z NLS1s is similar to that of the low-z NLS1s and SDSS DR14 quasars at a similar redshift range and their radio luminosity is found to be strongly correlated with their black hole mass. This page contains the catalog of high-z NLS1 candidates.
DOI: 10.5281/zenodo.4405038
title: HIGH-REDSHIFT NARROW LINE SEYFERT 1 GALAXIES: A CANDIDATE SAMPLE

description: The study of Narrow line Seyfert 1 galaxies (NLS1s) is now mostly limited to low redshift (z &lt; 0.8). This is because their definition requires the presence of the Hβ emission line, which is red-shifted out of the spectral coverage of major ground-based spectroscopic surveys at z &gt; 0.8. We studied the correlation between the properties of Hβ and Mg II lines of a large sample of SDSS DR14 quasars to find high-z NLS1 candidates. Based on the strong correlation of FWHM(MgII)=(0.880±0.005)×FWHM(Hβ)+(0.438±0.018), we present a sample of high-z NLS1 candidates having FWHM of Mg II &lt; 2000 km s−1. The high-z sample contains 2684 NLS1s with redshift z = 0.8 − 2.5 with a median logarithmic bolometric luminosity of 46.16 ± 0.42 erg s−1, logarithmic black hole mass of 8.01 ± 0.35M⊙, and logarithmic Eddington ratio of 0.02 ± 0.27. The fraction of radio-detected high-z NLS1s is similar to that of the low-z NLS1s and SDSS DR14 quasars at a similar redshift range and their radio luminosity is found to be strongly correlated with their black hole mass. This page contains the catalog of high-z NLS1 candidates.
DOI: 10.5281/zenodo.4361639
title: Observational Signatures of Tightly Wound Spirals Driven by Buoyancy Resonances in Protoplanetary Disks

description: These are simulated CO data cubes presented in "Observational Signatures of Tightly Wound Spirals Driven by Buoyancy Resonances in Protoplanetary Disks" by Bae, Teague and Zhu. They represent simulated 12CO and 13CO ALMA observations using the standard model (see Section 3.1.1 of the paper), with three different planet masses (0.5, 1, and 2 Jupiter-mass) and five different disk inclination values (5, 15, 30, 45, and 60 degrees).
DOI: 10.5281/zenodo.4361638
title: Observational Signatures of Tightly Wound Spirals Driven by Buoyancy Resonances in Protoplanetary Disks

description: These are simulated CO data cubes presented in "Observational Signatures of Tightly Wound Spirals Driven by Buoyancy Resonances in Protoplanetary Disks" by Bae, Teague and Zhu. They represent simulated 12CO and 13CO ALMA observations using the standard model (see Section 3.1.1 of the paper), with three different planet masses (0.5, 1, and 2 Jupiter-mass) and five different disk inclination values (5, 15, 30, 45, and 60 degrees).
DOI: 10.6084/m9.figshare.13247146.v1
title: MIP-BOOST: Efficient and Effective <i>L</i> <sub>0</sub> Feature Selection for Linear Regression

description: Recent advances in mathematical programming have made Mixed Integer Optimization a competitive alternative to popular regularization methods for selecting features in regression problems. The approach exhibits unquestionable foundational appeal and versatility, but also poses important challenges. Here we propose MIP-BOOST, a revision of standard Mixed Integer Programming feature selection that reduces the computational burden of tuning the critical sparsity bound parameter and improves performance in the presence of feature collinearity and of signals that vary in nature and strength. The final outcome is a more efficient and effective <i>L</i> <sub>0</sub> Feature Selection method for applications of realistic size and complexity, grounded on rigorous cross-validation tuning and exact optimization of the associated Mixed Integer Program. Computational viability and improved performance in realistic scenarios is achieved through three independent but synergistic proposals. Supplementary materials including additional results, pseudocode, and computer code are available online.
DOI: 10.5517/ccdc.csd.cc25ky7y
title: CCDC 2013240: Experimental Crystal Structure Determination

description: Related Article: Johannes Bayer, Jan Herberger, Lukas Holz, Rainer F. Winter, Thomas Huhn|2020|Chem.-Eur.J.|26|17546|doi:10.1002/chem.202003605
DOI: 10.5517/ccdc.csd.cc25ky8z
title: CCDC 2013241: Experimental Crystal Structure Determination

description: Related Article: Johannes Bayer, Jan Herberger, Lukas Holz, Rainer F. Winter, Thomas Huhn|2020|Chem.-Eur.J.|26|17546|doi:10.1002/chem.202003605
DOI: 10.5517/ccdc.csd.cc25ky90
title: CCDC 2013242: Experimental Crystal Structure Determination

description: Related Article: Johannes Bayer, Jan Herberger, Lukas Holz, Rainer F. Winter, Thomas Huhn|2020|Chem.-Eur.J.|26|17546|doi:10.1002/chem.202003605
DOI: 10.34740/kaggle/dsv/1814151
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.7906766.v1
title: Asymptotic efficient semiparametric empirical bayes estimation of multinomial responses

description: In this paper, Bayesian statistical inference for estimating of parameters in a Semi-parametric Multinomial Logit Models with repeated experiments is developed. Our method is based on empirical Bayes estimation. The results are shown, estimators are consistent and asymptotically efficient. Such approach allows us to take a new accurate theory using reliable and quick iteration algorithm to compute the empirical Bayes estimate. Results of real examples show that the proposed method is very competitive in terms of estimation accuracy and speed of computational estimation methods.