Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
TechnicalRemarks: This repository contains the supplementary data to our contribution "Particle Detection by means of Neural Networks and Synthetic Training Data Refinement in Defocusing Particle Tracking Velocimetry" to the 2022 Measurement Science and Technology special issue on the topic “Machine Learning and Data Assimilation techniques for fluid flow measurements”. This data includes annotated images used for the training of neural networks for particle detection on DPTV recordings as well as unannotated particle images used for training of the image-to-image translation networks for the generation of refined synthetic training data, as presented in the manuscript. The neural networks for particle detection trained on the aforementioned data are contained in this repository as well. An explanation on the use of this data and the trained neural networks, containing an example script can be found on GitHub (https://github.com/MaxDreisbach/DPTV_ML_Particle_detection)
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
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Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows the location and direction of flow of oil and gas pipelines along with the location of oil refineries. The daily crude oil capacity in barrels per day is indicated for refining centres by means of proportional circles. The source data for refineries is for the end of 1955 and pipeline source data is for the end of 1957. An additional map shows the location of coal, oil and gas fields. The coal fields shown are the major ones which were being worked in 1955 and the oil and gas fields shown were proven fields by the end of 1955. These maps are accompanied by three pie charts showing the provincial proportion of Canada's 1955 production of crude oil, natural gas and coal.
"When the shale oil program was begun in 1944, little work had been done with Colorado shale for a number of years, and little information was available on the composition of shale oil or its probable behavior when subjected o the newer processes used in petroleum refining. Also, at that time, there was considerable anxiety over the adequacy of our petroleum supply to meet continuing warfare, and great emphasis was placed on the investigation and development of processes which, although they might have recognized deficiencies, could be used to produce motor fuel from shale oil with a minimum of new construction. For these reasons, thermal cracking and acid treating, which are adaptable for use with a wide range of stocks, were the first processes investigated. When the Rifle, Colo., plant was placed in operation, efforts along these lines were reduced at Laramie, Wyo., and attention was shifted to the investigation of the newer catalytic processes. Unfortunately, many of the processes which have found extensive application in the petroleum industry do not produce equally satisfactory results when applied to shale oil, and attempts were made to improve the results by methods such as solvent extraction or other pretreatment of the charge stocks for the removal of nitrogen and sulfur. Although some improvement was made by these means, concurrent developments in the knowledge of the composition of shale oil and of the limitations of the various processes indicated that the maximum improvement possible in the application of conventional processes could produce only relatively low yields of high quality products. Hydrogenation offers a means of removing sulfur, nitrogen, and oxygen without loss of the hydrocarbon portion of the molecule, with simultaneous production of the valuable byproducts, sulfur and ammonia. Furthermore it can be used as a means of increasing the volatility of the oil without simultaneous production of coke or residuum of low market value. Attention was, therefore, shifted to the study of processes involving hydrogenation."
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
License information was derived automatically
Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows the location and direction of flow of oil and gas pipelines along with the location of oil refineries. The daily crude oil capacity in barrels per day is indicated for refining centres by means of proportional circles. The source data for refineries is for the end of 1955 and pipeline source data is for the end of 1957. An additional map shows the location of coal, oil and gas fields. The coal fields shown are the major ones which were being worked in 1955 and the oil and gas fields shown were proven fields by the end of 1955. These maps are accompanied by three pie charts showing the provincial proportion of Canada's 1955 production of crude oil, natural gas and coal.
To advance understanding of mangrove range dynamics in eastern North America, there is a need to refine temperature thresholds for mangrove freeze damage, mortality, and recovery. Here, We integrated data from 38 sites spread across the mangrove range edge in the Gulf of Mexico and Atlantic coasts of the southeastern United States, including data from a regional collaborative network called the Mangrove Migration Network (https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc/science/mangrove-migration-network). In 2018, an extreme freeze event affected 60 percent of these sites, with minimum temperatures ranging from 0 to -7 degrees Celsius. We used temperature data and vegetation measurements from before and after the freeze to quantify temperature thresholds for leaf damage, mortality, and biomass recovery of the black mangrove (Avicennia germinans) — the most freeze-tolerant mangrove species in North America. Note that the temperature logger data presented here are the site-level mean nightly minimum temperatures obtained from plot-level means of the raw temperature data collected from the loggers. For additional information, see the associated publication (Osland et al. 2019).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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aRsym = ∑h∑i |Ii(h) − | / ∑h∑i Ii(h) , where Ii(h) and are the ith and mean intensity over all symmetry-equivalent reflections h.bRmeas = ∑h (nh/nh−1)½ ∑i |Ii(h) − | / ∑h∑i Ii(h) , where Ii(h) and are the ith and mean intensity, and nh is the multiplicity over all symmetry-equivalent reflections h [42].cR = ∑||FO |− |FC|| / ∑|FO|, where |FC|is the calculated structure factor amplitude of the model, and |FO|is the observed structure factor amplitude; the Free R-factor was calculated against a random 5% test set of reflections that was not used during refinement.dRMSD, root-mean-square deviation from the parameter set for ideal stereochemistry [43].eValues in parentheses refer to the highest resolution shell.fThe model was only partially refined against this data set, but provided the search model for the refinement of the data set of crystal form 2 (see text for details).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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1Values for highest resolution shell given in parentheses.2Rmerge = 100 ×Σhkl|Ij(hkl) − j(hkl)>|/ΣhklΣjI(hkl), where Ij(hkl) and j(hkl)> are the intensity of measurement j and the mean intensity for the reflection with indices hkl, respectively.3Rwork = 100 ×Σhkl||Fobs| − |Fcalc||/Σhkl|Fobs|.4Rfree is the Rmodel calculated using a randomly selected 5% sample of reflection data that were omitted from the refinement.5RMS, root-mean-square; deviations are from the ideal geometry defined by the Engh and Huber parameters [45].
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
aThe values in parentheses refer to statistics in the highest bin.bRmerge = ∑hkl∑i|Ii(hkl)-|/∑hkl∑iIi(hkl), where Ii(hkl) is the intensity of an observation and is the mean value for its unique reflection; Summations are over all reflections.cR-factor = ∑h||Fo(h)|-|Fc(h)||/∑h|Fo(h)||, where |Fo| and |Fc| are the observed and calculated structure-factor amplitudes, respectively.dR-free was calculated with 5% of the data excluded from the refinement.eRoot-mean square-deviation from ideal values.fCategories were defined by Molprobity.Crystal parameters, data collection and structure refinement statistics.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
TechnicalRemarks: This repository contains the supplementary data to our contribution "Particle Detection by means of Neural Networks and Synthetic Training Data Refinement in Defocusing Particle Tracking Velocimetry" to the 2022 Measurement Science and Technology special issue on the topic “Machine Learning and Data Assimilation techniques for fluid flow measurements”. This data includes annotated images used for the training of neural networks for particle detection on DPTV recordings as well as unannotated particle images used for training of the image-to-image translation networks for the generation of refined synthetic training data, as presented in the manuscript. The neural networks for particle detection trained on the aforementioned data are contained in this repository as well. An explanation on the use of this data and the trained neural networks, containing an example script can be found on GitHub (https://github.com/MaxDreisbach/DPTV_ML_Particle_detection)