cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5594045
title: Bias-corrected monthly air temperature data over South Siberia for 1979-2020 (CTSS 1.0)

description: Bias-<strong>C</strong>orrected Air <strong>T</strong>emperature data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CTSS 1.0</strong>) contains monthly air temperature at 2m for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2020. CTSS data were combined from monthly total air temperture data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and temperature data records from ground weather stations (Bulygina et al., 2014). The ERA5 data were scaled according to the derived correction coefficient. The additive coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CTSS reproduces the spatial variability of temperature more precisely than can be done from the weather station observation network. Data provided in NetCDF (Network Common Data Form) format. Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at: <em>https://cds.climate.copernicus.eu/cdsapp#!/home</em> <em>Bulygina O.N., Razuvaev V.N., Trofimenko L.T., Shvets N.V., 2014. Description of the monthly air temperature data at weather stations of Russia.</em> Certificate of state registration of the database No. 2014621485 Available at: <em>http://meteo.ru/data/156-temperature</em>
DOI: 10.5281/zenodo.5594044
title: Bias-corrected monthly air temperature data over South Siberia for 1979-2020 (CTSS 1.0)

description: Bias-<strong>C</strong>orrected Air <strong>T</strong>emperature data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CTSS 1.0</strong>) contains monthly air temperature at 2m for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2020. CTSS data were combined from monthly total air temperture data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and temperature data records from ground weather stations (Bulygina et al., 2014). The ERA5 data were scaled according to the derived correction coefficient. The additive coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CTSS reproduces the spatial variability of temperature more precisely than can be done from the weather station observation network. Data provided in NetCDF (Network Common Data Form) format. Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at: <em>https://cds.climate.copernicus.eu/cdsapp#!/home</em> <em>Bulygina O.N., Razuvaev V.N., Trofimenko L.T., Shvets N.V., 2014. Description of the monthly air temperature data at weather stations of Russia.</em> Certificate of state registration of the database No. 2014621485 Available at: <em>http://meteo.ru/data/156-temperature</em>
DOI: 10.5517/ccdc.csd.cc28fccg
title: CCDC 2098215: Experimental Crystal Structure Determination

description: Related Article: Michael S. M. Philipp, Mirjam J. Krahfuss, Krzysztof Radacki, Udo Radius|2021|Eur.J.Inorg.Chem.|2021|4007|doi:10.1002/ejic.202100632
DOI: 10.5517/ccdc.csd.cc28fcdh
title: CCDC 2098216: Experimental Crystal Structure Determination

description: Related Article: Michael S. M. Philipp, Mirjam J. Krahfuss, Krzysztof Radacki, Udo Radius|2021|Eur.J.Inorg.Chem.|2021|4007|doi:10.1002/ejic.202100632
DOI: 10.5517/ccdc.csd.cc28fcfj
title: CCDC 2098217: Experimental Crystal Structure Determination

description: Related Article: Michael S. M. Philipp, Mirjam J. Krahfuss, Krzysztof Radacki, Udo Radius|2021|Eur.J.Inorg.Chem.|2021|4007|doi:10.1002/ejic.202100632
DOI: 10.5517/ccdc.csd.cc28fcgk
title: CCDC 2098218: Experimental Crystal Structure Determination

description: Related Article: Michael S. M. Philipp, Mirjam J. Krahfuss, Krzysztof Radacki, Udo Radius|2021|Eur.J.Inorg.Chem.|2021|4007|doi:10.1002/ejic.202100632
DOI: 10.5517/ccdc.csd.cc28fchl
title: CCDC 2098219: Experimental Crystal Structure Determination

description: Related Article: Michael S. M. Philipp, Mirjam J. Krahfuss, Krzysztof Radacki, Udo Radius|2021|Eur.J.Inorg.Chem.|2021|4007|doi:10.1002/ejic.202100632
DOI: 10.5517/ccdc.csd.cc28fcjm
title: CCDC 2098220: Experimental Crystal Structure Determination

description: Related Article: Michael S. M. Philipp, Mirjam J. Krahfuss, Krzysztof Radacki, Udo Radius|2021|Eur.J.Inorg.Chem.|2021|4007|doi:10.1002/ejic.202100632
DOI: 10.5517/ccdc.csd.cc28gdrx
title: CCDC 2099219: Experimental Crystal Structure Determination

description: Related Article: Jia Li, Xujun Li, Lei Sun, Xiaoshuang Wang, Lixia Yuan, Lingang Wu, Xiang Liu, Yanlan Wang|2021|Eur.J.Inorg.Chem.|2021|4230|doi:10.1002/ejic.202100572
DOI: 10.5517/ccdc.csd.cc28kmn4
title: CCDC 2102316: Experimental Crystal Structure Determination

description: Related Article: Kai Ji, Ka Lu, Jie Huang, Zi-Hao Li, Hua Ke, Zhi-Min Chen|2021|Org.Lett.|23|8028|doi:10.1021/acs.orglett.1c03043
DOI: 10.5517/ccdc.csd.cc28nf6l
title: CCDC 2104999: Experimental Crystal Structure Determination

description: Related Article: Luan Du, Yiliang Gong, Jingpeng Han, Xiaolan Xin, Han Luo, Yi Tian, You Li, Baosheng Li|2021|Org.Lett.|23|7966|doi:10.1021/acs.orglett.1c02968
DOI: 10.6084/m9.figshare.16863125
title: Supplementary Data S1: simulated data for building growth models from Growth variability, dimensional scaling and the interpretation of osteohistological growth data

description: data and formulae for each growth model in figure 1 and the individual components of each
DOI: 10.6084/m9.figshare.16863125.v1
title: Supplementary Data S1: simulated data for building growth models from Growth variability, dimensional scaling and the interpretation of osteohistological growth data

description: data and formulae for each growth model in figure 1 and the individual components of each
DOI: 10.6084/m9.figshare.16863128
title: Supplementary Data S3: comparative data on LAG spacing in the femur of Raptorex from Growth variability, dimensional scaling and the interpretation of osteohistological growth data

description: LAG spacing measured at four transects and plotted in figure 2A
DOI: 10.6084/m9.figshare.16863128.v1
title: Supplementary Data S3: comparative data on LAG spacing in the femur of Raptorex from Growth variability, dimensional scaling and the interpretation of osteohistological growth data

description: LAG spacing measured at four transects and plotted in figure 2A
DOI: 10.6084/m9.figshare.16863131
title: Supplementary Data S2: empirical histological data for each sampled specimen from Growth variability, dimensional scaling and the interpretation of osteohistological growth data

description: data necessary to generate the comparative plots for each specimen in figure 1
DOI: 10.6084/m9.figshare.16863131.v1
title: Supplementary Data S2: empirical histological data for each sampled specimen from Growth variability, dimensional scaling and the interpretation of osteohistological growth data

description: data necessary to generate the comparative plots for each specimen in figure 1
DOI: 10.48512/xcv8466159
title: Dust Cave Molluscs (Parmalee)

description: Mollusk identifications from Dust Cave entrance trench 12 X 2 meters X 6 meters deep.
DOI: 10.6084/m9.figshare.16863149.v1
title: Demonstration of the Julia implementation from Symplectic decomposition from submatrix determinants

description: Example notebook showing how the Julia implementation can be used to find the diagonalising symplectic.
DOI: 10.6084/m9.figshare.16863149
title: Demonstration of the Julia implementation from Symplectic decomposition from submatrix determinants

description: Example notebook showing how the Julia implementation can be used to find the diagonalising symplectic.
DOI: 10.6084/m9.figshare.16863152.v1
title: Mathematica implementation from Symplectic decomposition from submatrix determinants

description: Mathematica implementation of the algorithm, calculations for the examples given in the main text, and comparisons of the new and old algorithms.
DOI: 10.6084/m9.figshare.16863152
title: Mathematica implementation from Symplectic decomposition from submatrix determinants

description: Mathematica implementation of the algorithm, calculations for the examples given in the main text, and comparisons of the new and old algorithms.
DOI: 10.6084/m9.figshare.16863155
title: Python implementation from Symplectic decomposition from submatrix determinants

description: Python implementation of the algorithm and demonstration of how to use the functions.
DOI: 10.6084/m9.figshare.16863155.v1
title: Python implementation from Symplectic decomposition from submatrix determinants

description: Python implementation of the algorithm and demonstration of how to use the functions.
DOI: 10.6084/m9.figshare.16863174.v1
title: figure_data from Development of a mesoscopic framework spanning nanoscale protofibril dynamics to macro-scale fibrin clot formation

description: Data files for the graphs in the manuscript
DOI: 10.6084/m9.figshare.16863174
title: figure_data from Development of a mesoscopic framework spanning nanoscale protofibril dynamics to macro-scale fibrin clot formation

description: Data files for the graphs in the manuscript
DOI: 10.21227/ht0r-wr68
title: Image and Video Aesthetics

description: Datasets for image and video aesthetics1. Video Dataset : 107 videosThis dataset has videos that can be framed into images.Color contrast,Depth of Field[DoF],Rule of Third[RoT] attributesthat affect aesthetics can be extracted from the video datasets.2.Slow videos and Fast videos can be assessed for motionaffecting aesthetics
DOI: 10.5281/zenodo.5588455
title: Replication Package for the Paper: "Understanding Code Smell Detection via Code Review: A Study of the OpenStack and Qt Communities"

description: This repository contains the data and results from the paper "Understanding Code Smell Detection via Code Review: A Study of the OpenStack and Qt Communities" submitted to the ICPC 2021 special issue of the Empirical Software Engineering Journal, 2021. The replication package contains the following two folders: <strong>1) data folder</strong> The data folder contains the following four folders, which is organized by research questions (RQs). RQ1: The RQ1 folder contains the retrieved 1,536 code reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL. RQ2: The RQ2 folder contains the coded data for RQ2, called <em>Data Labeling & Encoding for RQ2.mx18</em>. It is the results of data labeling and encoding for RQ2, which was analyzed by the MAXQDA tool. RQ3 and RQ5: <em>Extracted data for RQ3.1.xlsx</em>: this file contains the extracted data (i.e., specific refactoring actions suggested by reviewers) for RQ3.1. <em>Data Labeling & Encoding for RQ3 and RQ5.mx18</em>: this file contains the extracted data for RQ3 (excluding the specific refactoring actions in RQ3.1) and RQ5. RQ4: The RQ4 folder contains the extracted data for RQ4, called <em>Extracted data for RQ4.xlsx</em>. Note: The mx18 files can be opened by MAXQDA 18 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download. <strong>2) scripts folder</strong> The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms. <em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead". <em>get_changes.py</em> is used for getting code changes from OpenStack and Qt. <em>get_comments.py</em> is used for getting review comments for each code change. <em>keywords_search.py</em> is used for searching review comments that contain at least one keyword. <em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword. <em>keywords_improve.py</em> is used for improving the keyword-based mining approach. <em>tools.py</em> is used for supporting the process of keywords improving.
DOI: 10.5519/qd.k1tzjxo5
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (125 records)
DOI: 10.5519/qd.uxk56ldm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (140 records)
DOI: 10.21227/qkfd-2443
title: Stochastic Motion Planning Framework with Biomechanical Trajectory Optimization for Physically Assistive Robots

description: Biomechanics has predominantly relied upon the trajectory optimization method for the analysis and prediction of the movement of the limbs. Such approaches have paved the way for the motion planning of biped and quadruped robots as well. Most of these methods are deterministic, utilizing first-order iterative gradient-based algorithms incorporating the constrained differentiable objective functions. However, the limitation of prevailing methods concerning differentiability hinders the implementation of non-differentiable objective functions such as metabolic energy expenditure (MEE) function, which is highly relevant for physiological systems and can even be implemented across the muscular space. This paper consolidates the implementation of the prevalent direct collocation-based optimal control method with the stochastic trajectory optimization method based on Policy Improvement with Path Integral for comprehending the human sit-to-stand (STS) motion. This method utilizes reinforcement learning of Dynamic Movement Primitive (DMP) to learn a goal-based trajectory, which is appropriate for the implementation. This dataset includes the experimental and simulation data for the validation of the method and also for the advancement of this method into new applications.
DOI: 10.5519/qd.4ma4n2kv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (140 records)
DOI: 10.15161/oar.it/74206
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.25550/5-j8r6
title: Mouse Dspp frameshift model of human dentinogenesis imperfecta

description: Using CRISPR/Cas9, we generated a _Dspp_^−1fs^ mouse model by introducing a FLAG-tag followed by a single nucleotide deletion that translated 493 extraneous amino acids before termination. Developing incisors and/or molars from this mouse and a _Dspp_^P19L^ mouse were characterized by morphological assessment, bSEM, nanohardness testing, histological analysis, in situ hybridization and immunohistochemistry.
DOI: 10.6084/m9.figshare.16863259.v1
title: Data....xlsx

description: Data
DOI: 10.6084/m9.figshare.16863259
title: Data....xlsx

description: Data
DOI: 10.34740/kaggle/dsv/2733278
title: Covid-19 in Singapore : Latest Detailed Data
DOI: 10.5519/qd.0c9ox6zl
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (710 records)
DOI: 10.5281/zenodo.5594254
title: Dataset for the research paper "How and Why Developers Migrate Python Tests from unittest to pytest"

description: This is the dataset for the proposed paper "How and Why Developers Migrate Python Tests from unittest to pytest". <br> <br> <br> The `10_systems` zip file contains the aggregated and intermediate files for the systems used for precision and recall analysis. The `top_100_systems` zip file contains the aggregated and intermediate files for the top 100 python systems analyzes. The `reasons_theme` and `reasons_issues` contain data to assess advantages and disadvantages found in 61 issues.
DOI: 10.6084/m9.figshare.14235884
title: Effects of Rh-genotype on the physical and mental health of a non-clinical population

description: Data used in the paper "The importance of being heterozygote: The ffects of Rh-genotype-sex interaction on the physical and mental health of a non-clinical population"
DOI: 10.6084/m9.figshare.14235884.v1
title: Effects of Rh-genotype on the physical and mental health of a non-clinical population

description: Data used in the paper "The importance of being heterozygote: The ffects of Rh-genotype-sex interaction on the physical and mental health of a non-clinical population"
DOI: 10.25646/9170
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.48366/r147317
title: A review of lunar mineralogy using Hyperspectral imaging data from Moon Mineralogy Mapper (M3) of Chandrayaan-1 (1)

description: This comparison is focused on the identification and characterization of lunar minerals in the vicinity of several lunar craters. It includes various research papers that utilized the image data of the famous hyperspectral sensor, Moon Mineralogy Mapper (M3) onboard India's first lunar mission, Chandrayaan-1.
DOI: 10.5517/ccdc.csd.cc27m8v0
title: CCDC 2074112: Experimental Crystal Structure Determination

description: Related Article: Don M. Mayder, Christopher M. Tonge, Giang D. Nguyen, Michael V. Tran, Gary Tom, Ghinwa H. Darwish, Rupsa Gupta, Kelsi Lix, Saeid Kamal, W. Russ Algar, Sarah A. Burke, Zachary M. Hudson|2021|J.Am.Chem.Soc.|143|16976|doi:10.1021/jacs.1c06094
DOI: 10.5517/ccdc.csd.cc27m8w1
title: CCDC 2074113: Experimental Crystal Structure Determination

description: Related Article: Don M. Mayder, Christopher M. Tonge, Giang D. Nguyen, Michael V. Tran, Gary Tom, Ghinwa H. Darwish, Rupsa Gupta, Kelsi Lix, Saeid Kamal, W. Russ Algar, Sarah A. Burke, Zachary M. Hudson|2021|J.Am.Chem.Soc.|143|16976|doi:10.1021/jacs.1c06094
DOI: 10.5517/ccdc.csd.cc27m8x2
title: CCDC 2074114: Experimental Crystal Structure Determination

description: Related Article: Don M. Mayder, Christopher M. Tonge, Giang D. Nguyen, Michael V. Tran, Gary Tom, Ghinwa H. Darwish, Rupsa Gupta, Kelsi Lix, Saeid Kamal, W. Russ Algar, Sarah A. Burke, Zachary M. Hudson|2021|J.Am.Chem.Soc.|143|16976|doi:10.1021/jacs.1c06094
DOI: 10.5517/ccdc.csd.cc27m8y3
title: CCDC 2074115: Experimental Crystal Structure Determination

description: Related Article: Don M. Mayder, Christopher M. Tonge, Giang D. Nguyen, Michael V. Tran, Gary Tom, Ghinwa H. Darwish, Rupsa Gupta, Kelsi Lix, Saeid Kamal, W. Russ Algar, Sarah A. Burke, Zachary M. Hudson|2021|J.Am.Chem.Soc.|143|16976|doi:10.1021/jacs.1c06094
DOI: 10.6084/m9.figshare.16783438.v1
title: [Dataset]Hisataka Hasegawa et al., 2021,Diameter of human D6 blastocysts reflects genetic homogeneity of trophectoderm components, hum reprod

description: Patient background, inner diameter, culture time, and PGT-A results for human day 6 blastocysts.
DOI: 10.5281/zenodo.5594289
title: Vaccination and equity - overview of systematic reviews - data extraction

description: Data extracted from systematic reviews included in an overview of systematic review.
DOI: 10.5281/zenodo.5594288
title: Vaccination and equity - overview of systematic reviews - data extraction

description: Data extracted from systematic reviews included in an overview of systematic review.
DOI: 10.6084/m9.figshare.16863415.v1
title: Data of Dexterous Grasping Using GelStereo Tactile Gripper

description: Data of Dexterous Grasping Using GelStereo Tactile Gripper
DOI: 10.6084/m9.figshare.16863415
title: Data of Dexterous Grasping Using GelStereo Tactile Gripper

description: Data of Dexterous Grasping Using GelStereo Tactile Gripper
DOI: 10.6084/m9.figshare.16768842.v1
title: OHSU Employees’ Opinions of Receipt of Clinical Care and Participation in Clinical Research at Place of Employment

description: Academic medical centers conduct clinical research and provide patient care to the community and their workforce. Conflict may exist, as employees might expect benefits or feel pressured or coerced to participate in research studies or receive clinical care. Without evidence, some universities consider employees to be part of a vulnerable population for research consent at their institution, potentially restricting opportunities for employees to participate in clinical trials. At the same time, these universities encourage employees to receive health care at the same institution. We hypothesized that attitudes toward voluntary research participation and receipt of health care services at the site of employment are similar and favorable. To study this, we conducted a survey of employees at Oregon Health & Science University (OHSU) that asked parallel questions focusing on attitudes regarding concerns with participation in research and receipt of clinical care. We found the majority of respondents reported favorable and similar attitudes regarding employee participation in clinical care 596/688 (87%) or research 605/639 (95%) and personally comfortable with the idea (614/688 (90%) for clinical care, 582/639 (92%) for research participation). Our findings support efforts to remove barriers that restrict participation in clinical research by employees at academic medical centers.
DOI: 10.5519/qd.afk8k9uy
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (119 records)
DOI: 10.34740/kaggle/dsv/2733478
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.34740/kaggle/dsv/2733496
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.16863442.v1
title: Data of Kinematic analysis and motion planning simulation of cooperative robot

description: Data of Kinematic analysis and motion planning simulation of cooperative robot
DOI: 10.6084/m9.figshare.16863442
title: Data of Kinematic analysis and motion planning simulation of cooperative robot

description: Data of Kinematic analysis and motion planning simulation of cooperative robot
DOI: 10.5517/ccdc.csd.cc284kgg
title: CCDC 2089755: Experimental Crystal Structure Determination

description: Related Article: Rocío Uzal-Varela, Laura Valencia, Daniela Lalli, Marcelino Maneiro, David Esteban-Gómez, Carlos Platas-Iglesias, Mauro Botta, Aurora Rodríguez-Rodríguez|2021|Inorg.Chem.|60|15055|doi:10.1021/acs.inorgchem.1c02057
DOI: 10.5517/ccdc.csd.cc28m5bf
title: CCDC 2103794: Experimental Crystal Structure Determination

description: Related Article: Ushnish Mandal, Ion Ghiviriga, Khalil A. Abboud, Daniel W. Lester, Adam S. Veige|2021|J.Am.Chem.Soc.|143|17276|doi:10.1021/jacs.1c08806
DOI: 10.5281/zenodo.5594385
title: Ligand-induced unfolding mechanism of an RNA G-quadruplex

description: These are the trajectories for unbiased simulations of PQS-18-1 and TMPyP4.
DOI: 10.34740/kaggle/dsv/2733599
title: COVID Vaccination in World (updated daily)