cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7699489
title: Sentinel-1 InSAR unwrapped data of the 27 July 2022 Abra earthquake in Luzon, the Philippines

description: This is a supporting dataset for Tang et al. (2023), "Oblique blind faulting underneath the Luzon volcanic arc during the 2022 M<sub>w</sub> 7.0 Abra earthquake, the Philippines". The original and downsampled line-of-sight displacements for modeling are presented in this repository. The coseismic interferogram using the synthetic aperture radar images from Copernicus Sentinel-1A descending track 32 on 21 July and 2 August, 2022 (6 days before and after the mainshock). The flight direction is ~N190° with a westward look angle ranging from 36° to 45°. Details of processing and downsampling schemes can be found in the paper. The Sentinel-1 images were processed by European Space Agency (ESA) and downloaded from Alaska Satellite Facility (ASF) Data Search Vertex (https://search.asf.alaska.edu/).
DOI: 10.5281/zenodo.7699488
title: Sentinel-1 InSAR unwrapped data of the 27 July 2022 Abra earthquake in Luzon, the Philippines

description: This is a supporting dataset for Tang et al. (2023), "Oblique blind faulting underneath the Luzon volcanic arc during the 2022 M<sub>w</sub> 7.0 Abra earthquake, the Philippines". The original and downsampled line-of-sight displacements for modeling are presented in this repository. The coseismic interferogram using the synthetic aperture radar images from Copernicus Sentinel-1A descending track 32 on 21 July and 2 August, 2022 (6 days before and after the mainshock). The flight direction is ~N190° with a westward look angle ranging from 36° to 45°. Details of processing and downsampling schemes can be found in the paper. The Sentinel-1 images were processed by European Space Agency (ESA) and downloaded from Alaska Satellite Facility (ASF) Data Search Vertex (https://search.asf.alaska.edu/).
DOI: 10.6084/m9.figshare.22258444
title: Data for Evolution of the Practice of Software Testing in Java Projects

description: Contains the data and sample scripts for the paper titled "Evolution of the Practice of Software Testing in Java Projects"
DOI: 10.6084/m9.figshare.22258447.v1
title: Stochastic harmonic free energy

description: The geometries used and random sample data generated in our work https://arxiv.org/abs/2111.12200.
DOI: 10.25582/data.2022-03.2452294/1930300
title: Data for EMSL Project 51282 from March 2022
DOI: 10.25582/data.2022-03.2452503/1932993
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452769/1933004
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452803/1933009
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452715/1933022
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452571/1933961
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452624/1933990
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452221/1934014
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452270/1934032
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452855/1934057
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452754/1934232
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452237/1934260
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452609/1934284
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452602/1934301
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452591/1934303
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452275/1934315
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452823/1935207
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452786/1935301
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452533/1935351
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452273/1935408
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452229/1935537
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452269/1935602
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452797/1936106
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452581/1936116
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452233/1936117
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452549/1936215
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452224/1936254
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452238/1936296
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452593/1936331
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452264/1936344
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452831/1936349
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452841/1936382
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452537/1936395
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452523/1936417
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452632/1936437
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452828/1936439
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452460/1937199
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452597/1937209
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452249/1937214
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452226/1937253
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452258/1937258
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452252/1937272
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452524/1937275
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452244/1937300
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452279/1937356
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452645/1937392
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452521/1937472
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452257/1937537
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452248/1937979
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452230/1937996
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452628/1938009
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452223/1938042
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452765/1938105
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452239/1938137
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452612/1938156
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452654/1938204
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452559/1938258
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452277/1938311
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452256/1938316
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452262/1938377
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452497/1939051
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452234/1939087
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452647/1939111
title: Data for EMSL Project 51132 from March 2022
DOI: 10.5517/ccdc.csd.cc2dnmh6
title: CCDC 2224358: Experimental Crystal Structure Determination

description: Related Article: Qiannan Wang, Tao Fan, Jin Song|2023|Org.Lett.|25|1246|doi:10.1021/acs.orglett.2c04124
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25582/data.2022-03.2452744/1939136
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452516/1939190
title: Data for EMSL Project 51132 from March 2022
DOI: 10.5517/ccdc.csd.cc2dx6r9
title: CCDC 2231651: Experimental Crystal Structure Determination

description: Related Article: Kazutoshi Nishimura, Shibo Xu, Yuji Nishii, Koji Hirano|2023|Org.Lett.|25|1503|doi:10.1021/acs.orglett.3c00263
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25582/data.2022-03.2452489/1939242
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452527/1939264
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452600/1939279
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452639/1939298
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452485/1939332
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452836/1939349
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452790/1939392
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452255/1939466
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452554/1940163
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452809/1940240
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452276/1941079
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452259/1941107
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452246/1941129
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452232/1941173
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452742/1941178
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2452565/1941195
title: Data for EMSL Project 51132 from March 2022
DOI: 10.5281/zenodo.6383066
title: Artifact: "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices

description: Artifact for "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices
DOI: 10.5281/zenodo.6383065
title: Artifact: "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices

description: Artifact for "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices
DOI: 10.5517/ccdc.csd.cc28f1md
title: CCDC 2097913: Experimental Crystal Structure Determination

description: Related Article: Shanglin Chen, Hua Huang, Xin Li, Xingxing Ma, Jianke Su, Qiuling Song|2023|Org.Lett.|25|1178|doi:10.1021/acs.orglett.3c00150
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7512585
title: datasets of 2016 Nanjing extreme rainfall

description: The uploaded files are the datasets of radar, disdrometer, ERA5, gauges, lightning, radiosound, and city lights from 2016 Nanjing extreme rainfall event
DOI: 10.5281/zenodo.7212527
title: datasets of 2016 Nanjing extreme rainfall

description: The uploaded files are the datasets of radar, disdrometer, ERA5, gauges, lightning, radiosound, and city lights from 2016 Nanjing extreme rainfall event
DOI: 10.5281/zenodo.7724859
title: Data for: Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state

description: All code and data necessary to reproduce results and figures in the publication "Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state".
description: Instructions for reproducing results are in README.md.
DOI: 10.5281/zenodo.7724858
title: Data for: Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state

description: All code and data necessary to reproduce results and figures in the publication "Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state".
description: Instructions for reproducing results are in README.md.
DOI: 10.6084/m9.figshare.21929070.v1
title: Data GRL-MCL_MCR ratio of five sites in the Loess Plateau.xlsx

description: The dataset contains microcharcoal morphology records from 5 sites of the Chinese Loess Plateau
DOI: 10.5066/p9pki29c
title: Methane flux model for wetlands of the Prairie Pothole Region of North America: Model input data and programming code

description: This data release presents input data for plot- and landscape-scale models of Prairie Pothole Region wetland methane emissions as a function of explanatory variables and remotely sensed predictors. Field data for the plot- and landscape-scale models span the years 2003-2016 and 2005-2016, respectively. The data release also includes R programming code to run the generalized additive model (GAM; plot scale) and random forest (RF; landscape scale) model of methane flux rates. Input data were extracted and modified from existing sources, and combined to facilitate model development, as well as six scenario-based model runs (two historical, four future). Briefly, a bottom-up approach was used to develop a spatially explicit, temporally dynamic model of methane emissions from Prairie Pothole Region (PPR) wetlands. A dataset of greater than 18,000 static-chamber flux measurements along with environmental covariates was used to develop a chamber-based (plot) model of methane flux, which was then used to inform a landscape-model using remotely sensed predictors. Covariates for the chamber-based model included soil water-filled pore space, soil temperature, wetland size, hydroperiod, land cover, growing season interval, and normalized difference vegetation index (NDVI). Predictors for upscaling included the Dynamic Surface Water Extent based on Landsat 4, 5, 7, and 8 for the presence, permanence, and extent of surface water, ClimateNA for historical and future temperatures, and the North American Land Change Monitoring System for land cover. Model runs included historical dry (1991) and wet (2011) years, as well as future Socioeconomic Pathways emissions scenarios (SSP2-4.5, SSP5-8.5).
DOI: 10.5066/p9rktxct
title: Threat prioritization framework and input data for a multi-hazard risk analysis for the U.S. Department of the Interior

description: An integral part of disaster risk management is identifying and prioritizing hazards and their potential impacts in a meaningful way to support risk-reduction planning. There has been considerable use and subsequent criticism of threat prioritization efforts that simply compare likelihoods and consequences of plausible threats. This data supports an article that summarizes a new mixed-methods and scalable approach for prioritizing risks in a multi-hazard, multi-objective, and multi-criteria organizational context. This data describes (1) hazard characterizations using subject-matter-expert (SME) elicitation, (2) expressed preferences in planning priorities provided by emergency managers, and (3) quantitative estimates of asset exposure to hazards using geospatial data and geographic-information-systems (GIS) software. This data was derived from a case study designed to support multi-hazard mitigation and response planning done by the U.S. Department of the Interior (DOI) Office of Emergency Management, which required a national understanding of the risks posed by 75 different various natural, technological, and adversarial hazards to DOI lands, facilities, people, revenues, and resources.
DOI: 10.5066/p9kxairr
title: Input for assessing the impact of noisy data on earthquake magnitude estimates derived from peak ground displacement measured with real-time Global Navigation Satellite System data

description: This data release complements Murray et al. (2023) which presents a framework for incorporating earthquake magnitude estimates based on real-time Global Navigation Satellite System (GNSS) data into the ShakeAlert&reg; earthquake early warning system for the west coast of the United States. Murray et al. (2023) assess the impact of time-dependent noise in GNSS real-time position estimates on the reliability of earthquake magnitudes estimated using such data. To do so they derived peak ground displacement (PGD) estimates from time series of background noise in GNSS real-time positions. These noise-only PGD measurements were used as input to a published empirical relationship to compute magnitude for hypothetical earthquakes that are each defined by an epicentral location and origin time. The data files provided here give the locations of GNSS stations used in the study, the hypothetical epicenters and origin times, and the PGD for each GNSS station for four time windows following each hypothetical origin time. We also provide the epicenters and origin times used to simulate the impact of noisy PGD data in terms of the annual number of spuriously large magnitude estimates that would be generated in the geographic region spanned by California, Oregon, and Washington, United States, due to noise alone. Finally, we include the estimated magnitudes for the annual simulations along with the number of GNSS stations for which the measured PGD exceeding a threshold value that was defined empirically to eliminate unreliable magnitude estimates.
DOI: 10.5066/p92dhfo5
title: Mass mortality of collector urchins (Tripneustes gratilla) in Hawai`i

description: As grazers, sea urchins are keystone species in tropical marine ecosystems, and their loss can have important ecological ramifications. Die-offs of urchins are frequently described but their causesare often unclear, in part because systematic examinations of animal tissues at gross and microscopic level are not done. In some areas, urchins are being employed to control invasive marine algae. Here we describe the pathology of a mortality event in Tripneustes gratilla in Hawaii where urchins were being translocated to control invasive algae. Although we did not determine the cause of the mortality event, our investigation suggests that animals died from inflammation of the test and epidermal ulceration followed by inability to maintain coelomic fluid pressure, colonization of coelomic fluid by opportunists (diatom, algae), and inappetence. Parasites, bacteria, fungi, and viruses were not evident as a primary cause of death. Pathology was suggestive of an environmental cause such as food limitation or toxin, possibilities that could be more aggressively pursued in future investigations. These findings highlight the need for caution and additional tools to better assess health when translocating marine invertebrates to ensure maximal biosecurity.
DOI: 10.34740/kaggle/dsv/5149638
title: Loan Data Set
DOI: 10.5281/zenodo.7725024
title: Simulation dataset of "Wave activities throughout a low-Mach number quasi-parallel shock: 2-D hybrid simulations"

description: Simulation dataset of "Wave activities throughout a low-Mach number quasi-parallel shock: 2-D hybrid simulations", including magnetic fields, number density and average velocities of particles.
DOI: 10.6084/m9.figshare.22201006
title: Pyelonephritis data upload - repository.pdf

description: This is a dataset of patients referred for MR imaging for pyelonephritis in a population with renal dysfunction
DOI: 10.15151/esrf-es-1018306037
title: Structural Biology of Macromolecules and Macromolecular complexes from India

description: Structural biology research groups located in India constitute the INDIA-BAG and will utilize MX & SAXS to study different biological macromolecules. The non-proprietary agreement signed between ESRF and the Department of Biotechnology (DBT) enables studies that provide fundamental mechanistic insight regarding different biological processes. A brief description of over 50 research groups is provided in the global summary and a summary of their research contributions involving data collected at ESRF is provided in the 2 year BAG report. The ESRF-India partnership is supported by a single grant provided to the RCB by the DBT. Please note that a report listing no. of shifts utilized, details of users and the research output from these visits has to be sent to the DBT to obtain release of funds to pay ESRF membership dues. Consequently, all the structural biologists from India are grouped in a single BAG to enable monitoring of progress to collect the required statistics.
DOI: 10.15151/esrf-es-1018321878
title: ITABAG: 3D structures of macromolecules related to human health

description: The ITABAG includes structural biologists from Universities, the National Research Council and Hospital research centres in Italy. The laboratories take advantage of single crystal and small angle X-ray diffraction methods to study the structure-function relationships in various macromolecules. A unifying factor for the vast majority of the macromolecules under study is that they are related to human health, either directly, being human proteins involved in some disease, or indirectly, as proteins expressed by human pathogens and validated as potential pharmacological targets. Other projects investigate basic chemical processes, such as crystal growth, or aim at characterising macromolecules with potential biotechnological applications.
DOI: 10.15151/esrf-es-1041865156
title: IBS BAG

description: The IBS BAG includes in this proposal IBS teams and external groups from CEA-Cadarache (St Paul lez Durance) and IRBA (Brétigny-sur-Orge). We study structure-function relationships of proteins emphasizing in human health, biotechnology and methodology. Most of the projects are a continuation of the MX-2398 proposal and can be grouped into different topics as follows: host-pathogen interaction, new drug targets, fundamental cellular mechanisms, membrane proteins, biotechnological applications and methodology. Some of these projects use integrative approaches (X-ray crystallography, SAXS, NMR (and cryo-EM), together with other biophysical studies) with the aim to fully understand the complexity of the biological macromolecular systems on study.