cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25n8w0
title: CCDC 2015492: Experimental Crystal Structure Determination

description: Related Article: Benon P. Maliszewski, Nikolaos V. Tzouras, Sébastien G. Guillet, Marina Saab, Marek Beliš, Kristof Van Hecke, Fady Nahra, Steven P. Nolan|2020|Dalton Trans.|49|14673|doi:10.1039/D0DT03480K
DOI: 10.5517/ccdc.csd.cc25n8x1
title: CCDC 2015493: Experimental Crystal Structure Determination

description: Related Article: Benon P. Maliszewski, Nikolaos V. Tzouras, Sébastien G. Guillet, Marina Saab, Marek Beliš, Kristof Van Hecke, Fady Nahra, Steven P. Nolan|2020|Dalton Trans.|49|14673|doi:10.1039/D0DT03480K
DOI: 10.5517/ccdc.csd.cc25n8y2
title: CCDC 2015494: Experimental Crystal Structure Determination

description: Related Article: Benon P. Maliszewski, Nikolaos V. Tzouras, Sébastien G. Guillet, Marina Saab, Marek Beliš, Kristof Van Hecke, Fady Nahra, Steven P. Nolan|2020|Dalton Trans.|49|14673|doi:10.1039/D0DT03480K
DOI: 10.21227/vh2b-1803
title: Many and Multi-objective datasets used in MIP-DoM

description: This dataset brings some problem sets and results from some classical algorithms from the evolutionary computational community.We have used some tools: Pymoo, Platypus and Pagmo
DOI: 10.21227/egyr-tb44
title: Many and Multi-objective datasets used in MIP-DoM

description: This dataset brings some problem sets and results from some classical algorithms from the evolutionary computational community.We have used some tools: Pymoo, Platypus and Pagmo
DOI: 10.5517/ccdc.csd.cc239c82
title: CCDC 1945413: Experimental Crystal Structure Determination

description: Related Article: M. Naqi Ahamad, M. Shahnawaz Khan, M. Shahid, Musheer Ahmad|2020|Dalton Trans.|49|14690|doi:10.1039/D0DT02949A
DOI: 10.5517/ccdc.csd.cc259cql
title: CCDC 2005009: Experimental Crystal Structure Determination

description: Related Article: In-Hyeok Park, Ki Chang Kwon, Ziyu Zhu, Xiao Wu, Runlai Li, Qing-Hua Xu, Kian Ping Loh|2020|J.Am.Chem.Soc.|142|18592|doi:10.1021/jacs.0c08189
DOI: 10.5517/ccdc.csd.cc259crm
title: CCDC 2005010: Experimental Crystal Structure Determination

description: Related Article: In-Hyeok Park, Ki Chang Kwon, Ziyu Zhu, Xiao Wu, Runlai Li, Qing-Hua Xu, Kian Ping Loh|2020|J.Am.Chem.Soc.|142|18592|doi:10.1021/jacs.0c08189
DOI: 10.5517/ccdc.csd.cc26ft4m
title: CCDC 2039060: Experimental Crystal Structure Determination

description: Related Article: Hangcheng Ni, Zhenpin Lu, Zuowei Xie|2020|J.Am.Chem.Soc.|142|18661|doi:10.1021/jacs.0c08652
DOI: 10.6084/m9.figshare.12630581.v4
title: Agnostos-wf test dataset

description: Test dataset for the agnostos-wf (https://github.com/functional-dark-side/agnostos-wf).<br>The test dataset consists of 3 TARA Ocean surface samples from the Indian Ocean (IO), all within a size fraction of 0.1-0.22 micro-m (giant viruses, archaea and bacteria enriched fraction). For each sample we randomly selected 10K contigs.<br><br>20K contigs from two samples are used to test the DB-creation module:TARA_041_SRF_0.1-0.22 (originally 90,135 contigs)<br>TARA_039_SRF_0.1-0.22 (originally 118,883 contigs)<br><br>And 10K contigs from one sample are used to test the DB-update module:TARA_038_SRF_0.1-0.22 (originally 157,925 contigs)<br><br>Contextual data for the chosen samples can be found in the file: TARA_IO_samples_contextual_data.tsv.gz<br>
DOI: 10.21227/7fnx-x603
title: Many and Multi-objective datasets used in MIP-DoM

description: This dataset brings some problem sets and results from some classical algorithms from the evolutionary computational community.We have used some tools: Pymoo, Platypus and Pagmo
DOI: 10.6084/m9.figshare.13228658
title: VAMB paper figure source data

description: Source data for VAMB paper Figures 1-3
DOI: 10.21227/33ng-hz80
title: Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC based sensor nodes operating at 2.4 GHz Radio Frequency (RF)

description: Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation onreceived signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)".This database contains received signal strengthmeasurementscollected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed forPaddy Ricecrop monitoring from period 01/07/2020 to 03/11/2020.
DOI: 10.5281/zenodo.4269984
title: SOUTHTRAC-GW: An airborne field campaign to explore gravity wave 1 dynamics at the world's strongest hotspot

description: This contains the information on the zonal winds measured by the meteor radar at Tierra del Fuego in Argentina during July - December, 2019. Please contact the author for more information.
DOI: 10.5281/zenodo.4269983
title: SOUTHTRAC-GW: An airborne field campaign to explore gravity wave 1 dynamics at the world's strongest hotspot

description: This contains the information on the zonal winds measured by the meteor radar at Tierra del Fuego in Argentina during July - December, 2019. Please contact the author for more information.
DOI: 10.5281/zenodo.4269922
title: Imperial College London COVID-19 outputs Jan-Sep 20

description: Bibliographic records of Imperial College London COVID-19 research outputs. Includes journal articles, preprints, datasets, reports and software/code. Publication dates 01.01.2020 - 30.09.2020.
DOI: 10.5281/zenodo.4269921
title: Imperial College London COVID-19 outputs Jan-Sep 20

description: Bibliographic records of Imperial College London COVID-19 research outputs. Includes journal articles, preprints, datasets, reports and software/code. Publication dates 01.01.2020 - 30.09.2020.
DOI: 10.5281/zenodo.4269905
title: I see how you feel: facial expressions' recognition and distancing in the time of COVID-19

description: Data sets and R script for anlysis.
DOI: 10.5281/zenodo.4269904
title: I see how you feel: facial expressions' recognition and distancing in the time of COVID-19

description: Data sets and R script for anlysis.
DOI: 10.6084/m9.figshare.13228730.v1
title: Biotropica_McCulloch_2020

description: Data from a greenhouse experiment in Bahia, Brazil with seedlings from eight Neotropical legume species that examined the effect of drought and soil nutrients.
DOI: 10.5281/zenodo.4270057
title: Data/code for Interpretable, calibrated neural networks for analysis and understanding of neutron spectra

description: Simulated neutron spectra from inelastic neutron scattering on the double perovskite PCSMO. The dataset is related to the publication <em>Interpretable, calibrated neural networks for analysis and understanding of neutron spectra</em>. The paper is yet to be submitted, but the references will be added in due course. # Pre-trained model weights `model-weights.tgz` This file contains the weights for the models reported in the paper which can be downloaded and used to re-produce the results. Please download the `model-weights.tgz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github. # Data Generation `data_generation.tar.gz` Large data files for github repository associated with the publication _Interpretable, calibrated neural networks for analysis and understanding of neutron spectra_ These files relate to generation of the data in the paper. Please download the `data_generation.tar.gz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github. #Training data `pcsmo-simulated-spectra.tgz` Contains the data used to train the networks. Extract this file to a data directory and then point the `train.py` files for the various models to look for this `&lt;datadir&gt;` in the specified location in those files.
DOI: 10.5281/zenodo.4088239
title: Data/code for Interpretable, calibrated neural networks for analysis and understanding of neutron spectra

description: Simulated neutron spectra from inelastic neutron scattering on the double perovskite PCSMO. The dataset is related to the publication <em>Interpretable, calibrated neural networks for analysis and understanding of neutron spectra</em>. The paper is yet to be submitted, but the references will be added in due course. # Pre-trained model weights `model-weights.tgz` This file contains the weights for the models reported in the paper which can be downloaded and used to re-produce the results. Please download the `model-weights.tgz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github. # Data Generation `data_generation.tar.gz` Large data files for github repository associated with the publication _Interpretable, calibrated neural networks for analysis and understanding of neutron spectra_ These files relate to generation of the data in the paper. Please download the `data_generation.tar.gz` file and extract it to the root of the repository `interpretable-ml-neutron-spectroscopy` from github. #Training data `pcsmo-simulated-spectra.tgz` Contains the data used to train the networks. Extract this file to a data directory and then point the `train.py` files for the various models to look for this `&lt;datadir&gt;` in the specified location in those files.
DOI: 10.6084/m9.figshare.13228730.v2
title: Biotropica_McCulloch_2020

description: Data from a greenhouse experiment in Bahia, Brazil with seedlings from eight Neotropical legume species that examined the effect of drought and soil nutrients.
DOI: 10.5281/zenodo.4270084
title: snapdragon dataset & R script - Adaptive landscape fluctuates at an extremely fine spatial scale inside a wild population of snapdragon plants

description: - Dataset used in article "Adaptive landscape fluctuates at an extremely fine spatial scale inside a wild population of snapdragon plants" - R script used in article "Adaptive landscape fluctuates at an extremely fine spatial scale inside a wild population of snapdragon plants"
DOI: 10.5281/zenodo.4270094
title: Segmented lordotic angles to assess lumbosacral transitional vertebra on EOS

description: <strong>Purpose: </strong>To test the vertical posterior vertebral angles (VPVA) of the most caudal lumbar segments measured on EOS to identify and classify the lumbosacral transitional vertebra (LSTV). <strong>Methods: </strong>We reviewed the EOS examinations of 906 patients to measure the VPVA at the most caudal lumbar segment (cVPVA) and at the immediately proximal segment (pVPVA), with dVPVA being the result of their difference. Mann-Whitney, Chi-square, and ROC curve statistics were used. <strong>Results: </strong>172/906 patients (19%) had LSTV (112 females, mean age: 43 ± 21 years), and 89/172 had type I LSTV (52%), 42/172 type II (24%), 33/172 type III (19%), and 8/172 type IV (5%). The cVPVA and dVPVA in non-articulated patients were significantly higher than those of patients with LSTV, patients with only accessory articulations, and patients with only bony fusion (all p &lt; .001). The cVPVA and dVPVA in L5 sacralization were significantly higher than in S1 lumbarization (p &lt; .001). The following optimal cutoff was found: cVPVA of 28.2° (AUC = 0.797) and dVPVA of 11.1° (AUC = 0.782) to identify LSTV; cVPVA of 28.2° (AUC = 0.665) and dVPVA of 8° (AUC = 0.718) to identify type II LSTV; cVPVA of 25.5° (AUC = 0.797) and dVPVA of - 7.5° (AUC = 0.831) to identify type III-IV LSTV; cVPVA of 20.4° (AUC = 0.693) and dVPVA of - 1.8° (AUC = 0.665) to differentiate type II from III-IV LSTV; cVPVA of 17.9° (AUC = 0.741) and dVPVA of - 4.5° (AUC = 0.774) to differentiate L5 sacralization from S1 lumbarization. <strong>Conclusion: </strong>The cVPVA and dVPVA measured on EOS showed good diagnostic performance to identify LSTV, to correctly classify it, and to differentiate L5 sacralization from S1 lumbarization.
DOI: 10.5281/zenodo.4270093
title: Segmented lordotic angles to assess lumbosacral transitional vertebra on EOS

description: <strong>Purpose: </strong>To test the vertical posterior vertebral angles (VPVA) of the most caudal lumbar segments measured on EOS to identify and classify the lumbosacral transitional vertebra (LSTV). <strong>Methods: </strong>We reviewed the EOS examinations of 906 patients to measure the VPVA at the most caudal lumbar segment (cVPVA) and at the immediately proximal segment (pVPVA), with dVPVA being the result of their difference. Mann-Whitney, Chi-square, and ROC curve statistics were used. <strong>Results: </strong>172/906 patients (19%) had LSTV (112 females, mean age: 43 ± 21 years), and 89/172 had type I LSTV (52%), 42/172 type II (24%), 33/172 type III (19%), and 8/172 type IV (5%). The cVPVA and dVPVA in non-articulated patients were significantly higher than those of patients with LSTV, patients with only accessory articulations, and patients with only bony fusion (all p &lt; .001). The cVPVA and dVPVA in L5 sacralization were significantly higher than in S1 lumbarization (p &lt; .001). The following optimal cutoff was found: cVPVA of 28.2° (AUC = 0.797) and dVPVA of 11.1° (AUC = 0.782) to identify LSTV; cVPVA of 28.2° (AUC = 0.665) and dVPVA of 8° (AUC = 0.718) to identify type II LSTV; cVPVA of 25.5° (AUC = 0.797) and dVPVA of - 7.5° (AUC = 0.831) to identify type III-IV LSTV; cVPVA of 20.4° (AUC = 0.693) and dVPVA of - 1.8° (AUC = 0.665) to differentiate type II from III-IV LSTV; cVPVA of 17.9° (AUC = 0.741) and dVPVA of - 4.5° (AUC = 0.774) to differentiate L5 sacralization from S1 lumbarization. <strong>Conclusion: </strong>The cVPVA and dVPVA measured on EOS showed good diagnostic performance to identify LSTV, to correctly classify it, and to differentiate L5 sacralization from S1 lumbarization.
DOI: 10.5281/zenodo.4269967
title: Data Set htwddKogRob-InfDynSim for Localization in Highly Crowded Environments

description: This data set presents a major challenge for robot localization in highly crowded environments. The total distance travelled over all runs is 113.3 km. 50 dynamic obstacles (see htwddKogRob-InfDynSim_dynObstacles.png) were inserted into the map (see htwddKogRob-InfDynSim.png | 1px \(\widehat{=}\) 0.1m). The work was first presented in: A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme Date of Publication: 2016/10/6 Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics Publisher: ACTA Press Additionally, we present a video with the proposed algorithm and an insight of this dataset under: youtube.com/AugustDerSmarte https://www.youtube.com/watch?v=26NBFN_XeQg <strong>Instructions for use</strong> The zip archives contain ascii files, which hold the log files of the robot observations and robot poses. Since this data set was recorded in a simulated environment, the logfiles include both a changed starting position and a ground-truth pose. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets: <strong>Laser Range Measurements:</strong> <pre><code class="language-java"> List&lt;Double&gt; ranges = new ArrayList&lt;&gt;(numOfLaserRays); List&lt;Error&gt; errors = new ArrayList&lt;&gt;(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <strong>Poses:</strong> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre>
description: For further questions please contact frank.bahrmann@htw-dresden.de
DOI: 10.5281/zenodo.4269966
title: Data Set htwddKogRob-InfDynSim for Localization in Highly Crowded Environments

description: This data set presents a major challenge for robot localization in highly crowded environments. The total distance travelled over all runs is 113.3 km. 50 dynamic obstacles (see htwddKogRob-InfDynSim_dynObstacles.png) were inserted into the map (see htwddKogRob-InfDynSim.png | 1px \(\widehat{=}\) 0.1m). The work was first presented in: A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme Date of Publication: 2016/10/6 Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics Publisher: ACTA Press Additionally, we present a video with the proposed algorithm and an insight of this dataset under: youtube.com/AugustDerSmarte https://www.youtube.com/watch?v=26NBFN_XeQg <strong>Instructions for use</strong> The zip archives contain ascii files, which hold the log files of the robot observations and robot poses. Since this data set was recorded in a simulated environment, the logfiles include both a changed starting position and a ground-truth pose. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets: <strong>Laser Range Measurements:</strong> <pre><code class="language-java"> List&lt;Double&gt; ranges = new ArrayList&lt;&gt;(numOfLaserRays); List&lt;Error&gt; errors = new ArrayList&lt;&gt;(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <strong>Poses:</strong> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre>
description: For further questions please contact frank.bahrmann@htw-dresden.de
DOI: 10.5281/zenodo.4270112
title: Chromosome-scale assembly of winter oilseed rape Brassica napus

description: The files correspond to data and results referenced in research article titled "Chromosome-scale assembly of winter oilseed rape Brassica napus". Data files below were used in the scaffolding process of genome assembly: Genetic maps (csv) ExR53-DH_60kSNPmap ExV8-DH_60kSNPmap Result files below are assembled sequences of the genome and the predicted annotation: Genome assembly (Express617_v1.fa.gz) Predicted coding sequences (Express617_v1_cds.fa.gz) Predicted coding sequences (Express617_v1_gene.gff3.gz) Predicted protein sequences (Express617_v1_protein.fa.gz) Predicted repetitive elements (Express617_v1_repeats.gff.gz)
description: The following three files are being updated in this version to correct 26 naming discrepancies in the random scaffolds: Express617_v1_cds.fa.gz, Express617_v1_gene.gff3.gz and Express617_v1_protein.fa.gz. In specific, there are 25 random scaffolds named with an extra "c" in these files but not in the genome assembly fasta file (for example R211048c versus R211048) and there is 1 scaffold named as "R21141422" in these files but should be "R21141412" instead. In the updated files, the scaffold IDs are corrected to match the original genome assembly fasta file. Empty lines in the version 1 "Express617_v1_gene.gff3.gz" are also removed. There are no changes in the annotation or sequences.
DOI: 10.5281/zenodo.3524258
title: Chromosome-scale assembly of winter oilseed rape Brassica napus

description: The files correspond to data and results referenced in research article titled "Chromosome-scale assembly of winter oilseed rape Brassica napus". Data files below were used in the scaffolding process of genome assembly: Genetic maps (csv) ExR53-DH_60kSNPmap ExV8-DH_60kSNPmap Result files below are assembled sequences of the genome and the predicted annotation: Genome assembly (Express617_v1.fa.gz) Predicted coding sequences (Express617_v1_cds.fa.gz) Predicted coding sequences (Express617_v1_gene.gff3.gz) Predicted protein sequences (Express617_v1_protein.fa.gz) Predicted repetitive elements (Express617_v1_repeats.gff.gz)
description: The following three files are being updated in this version to correct 26 naming discrepancies in the random scaffolds: Express617_v1_cds.fa.gz, Express617_v1_gene.gff3.gz and Express617_v1_protein.fa.gz. In specific, there are 25 random scaffolds named with an extra "c" in these files but not in the genome assembly fasta file (for example R211048c versus R211048) and there is 1 scaffold named as "R21141422" in these files but should be "R21141412" instead. In the updated files, the scaffold IDs are corrected to match the original genome assembly fasta file. Empty lines in the version 1 "Express617_v1_gene.gff3.gz" are also removed. There are no changes in the annotation or sequences.
DOI: 10.5281/zenodo.4270150
title: Ultrasound-Guided Percutaneous Irrigation of Rotator Cuff Calcific Tendinopathy (US-PICT): Patient Experience

description: <strong>Purpose: </strong>To assess patients' experience of ultrasound-guided percutaneous irrigation of rotator cuff calcific tendinopathy (US-PICT). <strong>Methods: </strong>Ninety-one patients (58 females; mean age: 50.5 ± 8.3 years) treated by US-PICT (local anesthesia, single-needle lavage, and intrabursal steroid injection) answered to a list of questions regarding their experience of the procedure before treatment, immediately after treatment, and three months later. The Borg CR10 scale was used to evaluate perceived pain, discomfort during anesthetic injection, and anxiety. The Wilcoxon, Spearman's rho, linear regression, and chi-square statistics were used. <strong>Results: </strong>81/91 patients complained mild discomfort during the injection of anesthetics (2, 1-2). Pain scores during US-PICT were very low (0, 0-1), with 70% patients having not experienced pain. After treatment, we found a significant reduction of pain (before: 8, 7-8; 3-month: 3, 1-6; <em>p</em> &lt; .001) and anxiety (before: 5, 2-7; during treatment: 2, 1-7; <em>p</em> = 0.010), with high overall satisfaction (immediately after: 10, 9-10; 3-month: 9, 7-10) and confidence in the possibility of recovery (immediately after: 9, 8-10; 3-month: 10, 8-10), respectively. Treatments performed before US-PICT were not statistically associated with pain relief (<em>p</em> = 0.389) and clinical improvement (<em>p</em> = 0.937). We found a correlation between satisfaction immediately postprocedure and confidence in the possibility of recovery (<em>p</em> = 0.002) and between satisfaction three months after treatment and clinical improvement (<em>p</em> &lt; 0.001) and patients' reminds about the description of the procedure (<em>p</em> = 0.005) and of the potential complications (<em>p</em> = 0.035). <strong>Conclusions: </strong>US-PICT is a mildly painful, comfortable, and well-tolerated procedure, regardless of any previous treatments. Patients' satisfaction is correlated with clinical benefit and full explanation of the procedure and its complications.