cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4171628
title: 4x and 10x Super Resolution Generator Models Trained With Planet CubeSat Satellite Imagery

description: Final resampling generator models produced from the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) (https://github.com/xinntao/ESRGAN). ESRGAN was trained at two different resampling factors, 4x and 10x, using a training data set of global Planet CubeSat satellite images. These generators can be used to resample Planet CubeSat satellite images from 30m and 12m to 3m resolution. Descriptions and results of training can be found at https://wandb.ai/elezine/pixelsmasher. In press at Canadian Journal of Remote Sensing: Super-resolution surface water mapping on the Canadian Shield using Planet CubeSat images and a Generative Adversarial Network, Ekaterina M. D. Lezine, Ethan D. Kyzivat, and Laurence C. Smith (2021).
DOI: 10.5281/zenodo.4171627
title: 4x and 10x Super Resolution Generator Models Trained With Planet CubeSat Satellite Imagery

description: Final resampling generator models produced from the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) (https://github.com/xinntao/ESRGAN). ESRGAN was trained at two different resampling factors, 4x and 10x, using a training data set of global Planet CubeSat satellite images. These generators can be used to resample Planet CubeSat satellite images from 30m and 12m to 3m resolution. Descriptions and results of training can be found at https://wandb.ai/elezine/pixelsmasher. In press at Canadian Journal of Remote Sensing: Super-resolution surface water mapping on the Canadian Shield using Planet CubeSat images and a Generative Adversarial Network, Ekaterina M. D. Lezine, Ethan D. Kyzivat, and Laurence C. Smith (2021).
DOI: 10.5517/ccdc.csd.cc20rgbl
title: CCDC 1869589: Experimental Crystal Structure Determination

description: Related Article: Haoran Hong, Hongyu Wang, Changwu Zheng, Gang Zhao, Yongjia Shang|2021|Chin.Chem.Lett.|32|708|doi:10.1016/j.cclet.2020.07.026
DOI: 10.5517/ccdc.csd.cc20rgdn
title: CCDC 1869591: Experimental Crystal Structure Determination

description: Related Article: Haoran Hong, Hongyu Wang, Changwu Zheng, Gang Zhao, Yongjia Shang|2021|Chin.Chem.Lett.|32|708|doi:10.1016/j.cclet.2020.07.026
DOI: 10.5517/ccdc.csd.cc23mhr0
title: CCDC 1955162: Experimental Crystal Structure Determination

description: Related Article: Hao Fu, Shuyu Zhou, Xi Fan, Lei Zhang, Jian Zhang|2021|Chin.Chem.Lett.|32|923|doi:10.1016/j.cclet.2020.04.004
DOI: 10.5517/ccdc.csd.cc23mhs1
title: CCDC 1955163: Experimental Crystal Structure Determination

description: Related Article: Hao Fu, Shuyu Zhou, Xi Fan, Lei Zhang, Jian Zhang|2021|Chin.Chem.Lett.|32|923|doi:10.1016/j.cclet.2020.04.004
DOI: 10.5517/ccdc.csd.cc23tl5q
title: CCDC 1961003: Experimental Crystal Structure Determination

description: Related Article: Xueguo Zhang, Peigen Wang, Liangwei Zhu, Baohua Chen|2021|Chin.Chem.Lett.|32|695|doi:10.1016/j.cclet.2020.06.011
DOI: 10.5517/ccdc.csd.cc2556p8
title: CCDC 2001009: Experimental Crystal Structure Determination

description: Related Article: Feng Zhang, Yujie Niu, Dacheng Hong, Yilin Ye, Yuhui Hua, Shihao Ding, Yandong Zhang|2021|Chin.Chem.Lett.|32|668|doi:10.1016/j.cclet.2020.06.008
DOI: 10.5517/ccdc.csd.cc27gggn
title: CCDC 2069481: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold and James A. Golen|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27gghp
title: CCDC 2069482: Experimental Crystal Structure Determination

description: Related Article: Arnold L. Rheingold|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27gnq3
title: CCDC 2069675: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27gpm1
title: CCDC 2069703: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc27gpyc
title: CCDC 2069713: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2021|CSD Communication|||
DOI: 10.6084/m9.figshare.14345504
title: Lasmiditan efficacy in migraine attacks with mild vs. moderate or severe pain

description: To evaluate the efficacy of lasmiditan (LTN) in treating migraine attacks of mild vs. moderate or severe pain intensity. Pooled data from two single-attack, placebo-controlled studies (SAMURAI [NCT02439320] and SPARTAN [NCT02605174]), and a prospective, randomized, open-label study (GLADIATOR [NCT02565186]) were assessed. Efficacy measures included the proportion of attacks with 2-h pain freedom (PF), 2-h most bothersome symptom (MBS) freedom, and 24-h sustained pain freedom (SPF). Fisher’s exact test was used to compare the proportion of PF, SPF, or MBS freedom outcomes among attacks treated at mild, moderate, or severe pain. In SAMURAI and SPARTAN, most treated attacks were of moderate (<i>N</i> = 2768) or severe (<i>N</i> = 1147) intensity, compared to mild (<i>N</i> = 65). Numerically greater 2-h PF and 24-h SPF response rates were observed in attacks treated at mild compared to moderate or severe pain. Analysis of GLADIATOR data included 273 (1.5%), 11,644 (65.1%), and 5948 (33.3%) attacks treated when pain was mild, moderate, and severe, respectively. In general, a significantly greater proportion of attacks treated at mild pain achieved 2-h PF and MBS freedom, as well as 24-h SPF. The incidence of treatment-emergent adverse events in LTN treatment groups were similar regardless of baseline head pain intensity. Data from two placebo-controlled, single-attack trials, and an open-label study including treatment of multiple attacks, suggested a tendency to relatively better efficacy outcomes when LTN treatment was initiated at mild vs. moderate to severe pain. Further research is needed to better understand the relationship of lasmiditan outcomes to the time of administration in the course of a migraine attack.
DOI: 10.6084/m9.figshare.14345504.v2
title: Lasmiditan efficacy in migraine attacks with mild vs. moderate or severe pain

description: To evaluate the efficacy of lasmiditan (LTN) in treating migraine attacks of mild vs. moderate or severe pain intensity. Pooled data from two single-attack, placebo-controlled studies (SAMURAI [NCT02439320] and SPARTAN [NCT02605174]), and a prospective, randomized, open-label study (GLADIATOR [NCT02565186]) were assessed. Efficacy measures included the proportion of attacks with 2-h pain freedom (PF), 2-h most bothersome symptom (MBS) freedom, and 24-h sustained pain freedom (SPF). Fisher’s exact test was used to compare the proportion of PF, SPF, or MBS freedom outcomes among attacks treated at mild, moderate, or severe pain. In SAMURAI and SPARTAN, most treated attacks were of moderate (<i>N</i> = 2768) or severe (<i>N</i> = 1147) intensity, compared to mild (<i>N</i> = 65). Numerically greater 2-h PF and 24-h SPF response rates were observed in attacks treated at mild compared to moderate or severe pain. Analysis of GLADIATOR data included 273 (1.5%), 11,644 (65.1%), and 5948 (33.3%) attacks treated when pain was mild, moderate, and severe, respectively. In general, a significantly greater proportion of attacks treated at mild pain achieved 2-h PF and MBS freedom, as well as 24-h SPF. The incidence of treatment-emergent adverse events in LTN treatment groups were similar regardless of baseline head pain intensity. Data from two placebo-controlled, single-attack trials, and an open-label study including treatment of multiple attacks, suggested a tendency to relatively better efficacy outcomes when LTN treatment was initiated at mild vs. moderate to severe pain. Further research is needed to better understand the relationship of lasmiditan outcomes to the time of administration in the course of a migraine attack.
DOI: 10.25387/g3.14370026
title: Supplemental Material for Dong et al., 2021

description: Figure S1 shows Δ (SNP-index) in QTL-seq on the other 18 chromosomes. Figure S2 shows the genotype based on a bin map of 200 individuals in the F<sub>2</sub> population in Yangluo in 2018. Figure S3 illustrates a matrix of pairwise recombination values for the F<sub>2</sub> linkage map using Checkmatrix. Figure S4 describes the QTL scanning for plant height on chromosome A10. Figure S5 shows Spearman’s correlation plot of the four samples based on the RNA-seq data in the study. Table S1 lists the primers used for RT-qPCR. Table S2 shows the log-max-likelihood values and AIC values of genetic models in SEA analysis. Table S3 shows the fitness test of candidate models in SEA analysis. Table S4 shows genetic parameters estimated in the MX1-AD-ADI model. Table S5 shows the quality of the sequencing data from the QTL-seq. Table S6 shows the statistics of comparison between the sequencing reads and reference genome in the two bulks. Table S7 Plant height of 200 individuals in the F<sub>2</sub> population in Yangluo in 2018. Table S8 the sequence data quality of 200 individuals in the F<sub>2</sub> population. Table S9 shows the bin marker information and the genotype of the 200 individuals in the F<sub>2</sub> population. Table S10 shows the genetic position of all the mapped Bin markers in each linkage group. Table S11 shows the physical position of all the Bin markers in each chromosome. Table S12 shows the R<sup>2</sup> value between the genetic map and the physical map in each linkage group. Table S13 shows the quality of the RNA-seq. Table S14 shows the DEGs information in the QTL region. Table S15 lists the variant information between the two parents in the QTL interval. Table S16 lists the candidate genes with missense and nonsense mutations.
DOI: 10.25387/g3.14370026.v1
title: Supplemental Material for Dong et al., 2021

description: Figure S1 shows Δ (SNP-index) in QTL-seq on the other 18 chromosomes. Figure S2 shows the genotype based on a bin map of 200 individuals in the F<sub>2</sub> population in Yangluo in 2018. Figure S3 illustrates a matrix of pairwise recombination values for the F<sub>2</sub> linkage map using Checkmatrix. Figure S4 describes the QTL scanning for plant height on chromosome A10. Figure S5 shows Spearman’s correlation plot of the four samples based on the RNA-seq data in the study. Table S1 lists the primers used for RT-qPCR. Table S2 shows the log-max-likelihood values and AIC values of genetic models in SEA analysis. Table S3 shows the fitness test of candidate models in SEA analysis. Table S4 shows genetic parameters estimated in the MX1-AD-ADI model. Table S5 shows the quality of the sequencing data from the QTL-seq. Table S6 shows the statistics of comparison between the sequencing reads and reference genome in the two bulks. Table S7 Plant height of 200 individuals in the F<sub>2</sub> population in Yangluo in 2018. Table S8 the sequence data quality of 200 individuals in the F<sub>2</sub> population. Table S9 shows the bin marker information and the genotype of the 200 individuals in the F<sub>2</sub> population. Table S10 shows the genetic position of all the mapped Bin markers in each linkage group. Table S11 shows the physical position of all the Bin markers in each chromosome. Table S12 shows the R<sup>2</sup> value between the genetic map and the physical map in each linkage group. Table S13 shows the quality of the RNA-seq. Table S14 shows the DEGs information in the QTL region. Table S15 lists the variant information between the two parents in the QTL interval. Table S16 lists the candidate genes with missense and nonsense mutations.
DOI: 10.6084/m9.figshare.13140182.v2
title: Impacts of Software Community Patterns on Process and Product:An Empirical Study

description: Software engineering projects are now more than ever a community effort. In the recent past, researchers have shownthat their success may not only depend on source code quality, but also on other aspects like the balance of distance,culture, global engineering practices, and more. In such a scenario, understanding the characteristics of the communityaround a project and foresee possible problems may be the key to develop successful systems. In this paper, we focuson this research problem and propose an exploratory study on the relation between community patterns, i.e., recurrentmixes of organizational or social structure types, and aspects related to the quality of software products and processes.We ?rst exploit association rule mining to discover frequent relations between community pattern and communitysmells, i.e., sub-optimal patterns across the organizational structure of a software development community that may beprecursors of some form of social debt. Further on, we use statistical analysis to understand their impact on softwaremaintainability and on the development process. Our ?ndings show that different organizational patterns are connectedto di?erent forms of socio-technical problems; further on, speci?c combinations are set in equally speci?c contextualconditions. Findings support two possible conclusions: (1) practitioners should put in place speci?c preventive actionsaimed at avoiding the emergence of community smells and (2) such actions should be drawn according to the contextualconditions of the organization and the project.
DOI: 10.5281/zenodo.4670229
title: Crowdsourced air traffic data from The OpenSky Network 2020 [CC-BY]

description: <strong>Motivation</strong> The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. <strong>License</strong> Creative Commons CC-BY The only difference with the original dataset comes from anonymised aircraft information. <strong>Disclaimer</strong> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found. Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database. <strong>Description of the dataset</strong> One file per month is provided as a csv file with the following features: <strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.) <strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API) <strong>aircraft_uid</strong>: a unique anonymised identifier for aircraft; <strong>typecode</strong>: the aircraft model type (when available); <strong>origin</strong>: a four letter code for the origin airport of the flight (when available); <strong>destination</strong>: a four letter code for the destination airport of the flight (when available); <strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network; <strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network; <strong>day</strong>: the UTC day of the last message received by the OpenSky Network; <strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft; <strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft. <strong>Examples</strong> Possible visualisations and a more detailed description of the data are available at the following page:<br> &lt;https://traffic-viz.github.io/scenarios/covid19.html&gt; <strong>Credit</strong> Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders<br> <strong>"</strong>Crowdsourced air traffic data from the OpenSky Network 2019–2020<strong>"</strong><br> <em>Earth System Science Data</em> 13(2), 2021<br> https://doi.org/10.5194/essd-13-357-2021
DOI: 10.5281/zenodo.4643784
title: Pupil responses to implied motion in figurative and abstract paintings

description: 24 folders, one for each subject participating in the experiment. Each “.mat” files contain: BUF2: Recording during fixation slide (column 1: time; column 2: right pupil width; column 3: right pupil height; column 4: left pupil width; column 5: left pupil height; column 6: glint data from right eye; column 7: glint data from right eye; column 8: image number) BUF: Recording during stimulus presentation (same columns of BUF2) c: block’s specifics (eye-tracker information, trials number and duration, start and end times of eye-tracker registrations) smile: images code Response: block’s information (first column: stimulus presentation time; variable number of other columns: smile variable; last column: trials number);
DOI: 10.5281/zenodo.4643783
title: Pupil responses to implied motion in figurative and abstract paintings

description: 24 folders, one for each subject participating in the experiment. Each “.mat” files contain: BUF2: Recording during fixation slide (column 1: time; column 2: right pupil width; column 3: right pupil height; column 4: left pupil width; column 5: left pupil height; column 6: glint data from right eye; column 7: glint data from right eye; column 8: image number) BUF: Recording during stimulus presentation (same columns of BUF2) c: block’s specifics (eye-tracker information, trials number and duration, start and end times of eye-tracker registrations) smile: images code Response: block’s information (first column: stimulus presentation time; variable number of other columns: smile variable; last column: trials number);
DOI: 10.5281/zenodo.4660164
title: CICE gx1 Grid and Initial Condition Data - 2021-04-02

description: Updated initial condition files with monthly initial states for 2005.
DOI: 10.3886/e119962
title: Data and Code for: Thy Neighbor’s Misfortune: Peer Effect on Consumption

description: Using a large, representative sample of credit and debit card transactions in Singapore, this paper studies the consumption response of individuals whose same-building neighbors experienced personal bankruptcy. The unique bankruptcy rules in Singapore suggest liquidity shocks drive personal bankruptcy decisions, leading to a substantial drop in consumption for the bankrupt. Peers’ monthly card consumption decreases by 3.4 percent over the one-year post-bankruptcy period. There exists no consumption decrease among individuals in immediately adjacent buildings, nor for consumers with diminished post-event social ties with the bankrupt. The findings imply a significant social multiplier effect of 2.8 times the original consumption shock.
DOI: 10.3886/e119962v2
title: Data and Code for: Thy Neighbor’s Misfortune: Peer Effect on Consumption

description: Using a large, representative sample of credit and debit card transactions in Singapore, this paper studies the consumption response of individuals whose same-building neighbors experienced personal bankruptcy. The unique bankruptcy rules in Singapore suggest liquidity shocks drive personal bankruptcy decisions, leading to a substantial drop in consumption for the bankrupt. Peers’ monthly card consumption decreases by 3.4 percent over the one-year post-bankruptcy period. There exists no consumption decrease among individuals in immediately adjacent buildings, nor for consumers with diminished post-event social ties with the bankrupt. The findings imply a significant social multiplier effect of 2.8 times the original consumption shock.
DOI: 10.5281/zenodo.4399993
title: Results from "The Significance of Neural Inter-Frequency Correlations", doi: 10.21203/rs.3.rs-329644/v1

description: Results from paper "The Significance of Neural Inter-Frequency Correlations", doi: 10.21203/rs.3.rs-329644/v1. This paper analysed the raw Broadband intracortical neural recordings from "Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology" (doi: 10.5281/zenodo.3854034), which are also publicly available. The code for reproducing and analysing the results is located in 'Code_for_IFC_Statistical_Significance_Test_Sabes_data.zip'. A _read_me word document is also located in 'Code_for_IFC_Statistical_Significance_Test_Sabes_data.zip', which gives detailed insight into the code and how to recreate and analyse the results. These results are in the form of '.mat' files, which can be opened in MATLAB, and '.jpg' files. They contain: The neural Inter-Scale Correlation Matrices (ISCM) for each of the analysed sessions, for all 96 Utah array channels. These are contained in the same files as the ISCMs derived from the phase-randomised neural Wavelet Power Spectrum (WPS) (bullet point below), as variable 'r_orig'. The null hypothesis ISCMs derived from the phase-randomised neural WPS. These are given for the FT phase randomisation method (Channels 1-96, Sessions 1-15), and for the IAAFT method (Channels 1-20, Sessions 1-15). The FT method nulls are given in batches of 5 channels, e.g. 'Channels_1_to_5_FT_distributions.zip'. The IAAFT method nulls are given for all 20 channels in the same file 'Channels_1_to_20_IAAFT_distributions.zip'. The null hypothesis ISCMs derived from white noise processes and the Morse Wavelet Continuous Wavelet Transform (CWT), used in the statistical test. These are for lengths of 350, 400, and 500 seconds at a sampling frequency of 24.414 kHz. Each file is named after the length in seconds, e.g. 'All_White_Noise_MCs_Length_350.mat'. Plots of the neural data: time series and Fourier domain plots. Plots of the statistically significant elements of the neural ISCMs, at a False Discovery Rate of 0.01.
DOI: 10.3886/e118525
title: Data and Code for: Unemployment Insurance Generosity and Aggregate Employment

description: This paper examines the impact of unemployment insurance (UI) on aggregate employment by exploiting cross-state variation in the maximum benefit duration during the Great Recession. Comparing adjacent counties located in neighboring states, there is no statistically significant impact of increasing UI generosity on aggregate employment. Point estimates are uniformly small in magnitude, and the most precise estimates rule out employment-to-population ratio reductions in excess of 0.35 percentage points from the UI extension. The results contrast with the negative effects implied by most micro-level labor supply studies and are consistent with both job rationing and aggregate demand channels.
DOI: 10.3886/e118525v1
title: Data and Code for: Unemployment Insurance Generosity and Aggregate Employment

description: This paper examines the impact of unemployment insurance (UI) on aggregate employment by exploiting cross-state variation in the maximum benefit duration during the Great Recession. Comparing adjacent counties located in neighboring states, there is no statistically significant impact of increasing UI generosity on aggregate employment. Point estimates are uniformly small in magnitude, and the most precise estimates rule out employment-to-population ratio reductions in excess of 0.35 percentage points from the UI extension. The results contrast with the negative effects implied by most micro-level labor supply studies and are consistent with both job rationing and aggregate demand channels.